The Operations Catalog · A Personal Reference

The Anchor

The Plan

A 12-month staged build, $350/month max, emergency fund untouched.

Month 12

The Destination

Twelve months of discipline. One weekend of building. A lifetime of capability.

A staged build, twelve months long, designed to respect cash flow and protect the emergency fund. The plan validates the use case on existing hardware before the new PC arrives — and refuses to spend money the validation hasn't earned.

The Five Operating Principles

$350/mo maximum financial commitment — non-negotiable
Emergency fund stays untouched (your 3-month buffer is sacred)
Maximum 2 hard credit pulls over 12 months (Microcenter + Best Buy)
No deferred-interest term we can't pay off in cash flow
Validate use case on Optiplex first — plan serves you, not vice versa

Why This Build

CPU — Month 5

AMD Ryzen 9 9950X

16-core Zen 5. Beats Intel i9-14900K in all-core workloads while drawing ~50W less. Critical for model tokenization, Python scripting, and parallel inference alongside the GPU. Microcenter prices it at $599 — $100 below MSRP — and bundles a $50 motherboard discount. AM5 socket is supported through 2027+, so this is your platform for 5 years minimum.

GPU — Month 9

RTX 5080 — The VRAM Inflection Point

16GB VRAM runs Qwen3 32B at Q4, DeepSeek R1 32B at Q4, and Devstral 24B at Q8 — the full target model stack. The RTX 5090 (24GB) costs $400 more for marginal gain at this model size. The 5070 Ti (16GB) is ~35% slower for the same VRAM ceiling. At $999, the 5080 is the inflection point where capability and cost intersect for serious local AI inference.

RAM

64GB DDR5-6000

VRAM is the AI bottleneck — not system RAM. But 64GB lets you run an Ollama server, VS Code, browser, clinical apps, and Docker containers simultaneously without thrashing swap. 32GB would feel tight within 18 months. DDR5-6000 matches the Zen 5 Infinity Fabric clock precisely — the sweet spot for AM5 memory bandwidth. $130 for 64GB in 2026 is exceptional value.

STORAGE

2TB + 4TB NVMe

Local models are large: Qwen3 32B Q4 ≈ 20GB, DeepSeek R1 32B Q4 ≈ 20GB. A full 10-model library ≈ 200GB. A 2TB OS drive handles system + active models fast; 4TB holds the full library plus datasets. Running out of model storage mid-project is a real pain — provision generously. Both on Gen 4 NVMe: fast enough that model loads are measured in seconds, not minutes.

CASE

Fractal Define 7 — Silent Running

Chosen for silence and thermal headroom. Sound-dampening panels cut GPU fan noise by 8–12 dB — this machine will run inference 24/7 alongside a home office desk. Fits a 360mm AIO at the top, has room for E-ATX if you ever upgrade the board, and the cable management is surgically clean. The Define 7 is the standard choice for a high-performance silent workstation.

PSU

Corsair HX1200i — 1200W Platinum

9950X peak TDP 170W + 5080 peak TDP 320W + everything else ≈ 600W realistic, 750W spike. 1200W gives 50%+ headroom — the 80+ Platinum efficiency curve peaks at 50% load, meaning you're always in the efficient zone. The HXi series has digital current monitoring so you can watch exact GPU draw during inference via Corsair iCUE. Over-speccing the PSU is the cheapest insurance in a $4K build.

Full Financial Picture — All 15 Months

Every dollar in and out. "Card added" = new balance charged. HYSA grows at ~4.5% APY (adds ~$65 over the period — not shown).

Mo	Phase	Cash Out	+Saved	Card Added	HYSA Total	Card Owed	Event
M01	Validation	—	$350	—	$350	—	Ollama live on Optiplex
M02	Validation	—	$350	—	$700	—	Use cases documented
M03	Checkpoint	—	$350	—	$1,050	—	Go / No-Go decision
M04	Pre-buy	—	$350	—	$1,400	—	Credit check, BB Visa apply
M05	Foundation buy	$1,000	—	$280 MC	$400	$280	CPU + MB + RAM @ Microcenter
M06	Recovery	$50	$300	—	$650	$230	MC card payment
M07	Recovery	$50	$300	—	$900	$180	MC card payment
M08	MC payoff	$180	$250	—	$970	—	Microcenter PAID OFF ✓
M09	GPU day	$200	—	$800 BB	$770	$800	RTX 5080 @ Best Buy
M10	Assembly	$70	—	$190 Affirm	$700	$920	PSU + Case via Affirm Pay-in-4
M11	Final parts	$70	—	$175 Affirm	$630	$1,025	SSDs + cooler + UPS + fans
M12	BUILD WEEKEND	$70	$100	—	$660	$955	System online. First 32B model.
M13	Cleanup	$70	$280	—	$870	$885	BB payment
M14	Cleanup	$70	$280	—	$1,080	$745	BB payment
M15	Done	$70	$280	—	$1,290	—	DEBT FREE ✓

Card owed column shows remaining balance after that month's payment. All deferred-interest promos end early: MC card paid in M08 (within 12-mo promo), BB card paid in M15 (9 months before 24-mo promo deadline).

Month-by-Month — Detailed Actions

M01

Month 1

Validation

Install Ollama on the Optiplex. Pull your first models. Open the HYSA and set the auto-transfer. Your only job is to make the machine genuinely useful — not impressive, useful.

Install Ollama: curl -fsSL https://ollama.ai/install.sh | sh
Pull first models: ollama pull qwen3:14b and ollama pull deepseek-r1:14b
Install Open WebUI for a proper chat UI (Docker or pip — see CLI Hub for commands)
Open Marcus HYSA → auto-transfer $350/month on payday, the same day every month
Write down 5 specific tasks you want this machine to help with. Be concrete: "draft SOAP notes from bullet points", "check drug interactions for polypharmacy patients", "explain Python errors as I learn CS50P". Not "help with work".
First honest test: ask the same clinical question to Qwen3 14B and Claude Sonnet. Evaluate quality, speed, confidence.

Save $350

First local model running

M02

Month 2

Validation

Daily local LLM use — make it part of real work, not experiments. Every week, pick one task, do it locally and via cloud, document the comparison. At the end of the month you'll know whether this is genuinely useful or just interesting.

Use local LLM every working day — not when convenient, every day
Log each session in Journal: task, model used, quality rating (1–5), time saved vs doing it manually, vs cloud
Try DeepSeek R1 14B specifically for clinical reasoning — the visible thinking trace lets you audit the reasoning, which matters for pharmacological tasks
Install the Continue.dev VS Code extension and point it at Ollama — local AI code completions
Start CS50P Week 0 — use the local model as your Python tutor (ask it to explain errors, walk through concepts)
Note where the Optiplex falls short: what tasks feel sluggish? That's your VRAM ceiling becoming tangible.

Save $350

5+ documented use cases in Journal

M03

Month 3

Checkpoint — Go / No-Go

The honest review. Open the Journal. Count the actual days you used it. Read the use case log. Have 3+ tasks been meaningfully improved? If you have to convince yourself it was useful, the answer is no. This is the last free exit before real money is committed.

Go: Used ≥5 days/week for 6+ weeks, ≥3 use cases with documented time savings, feel like you'd miss it if it was gone
No-Go: Used fewer than 3 days/week, can't name 3 specific tasks it improved, feel like it's more interesting than useful
If No-Go: Pause HYSA transfers. Consider a lighter path: GPU upgrade to Optiplex (~$400–600) for 3× more VRAM without a full build. Or simply use Claude API at $20/month.
If Go: continue unchanged. Pull credit report at annualcreditreport.com — dispute errors now before M05.
Set a CamelCamelCamel price alert for RTX 5080 at $999 target
Subscribe to Microcenter's email list — they send sale alerts for CPU/MB bundle deals

Save $350 HYSA: $1,050

Validated — or pivot

M04

Month 4

Pre-buy Prep

Prepare everything before any money moves. Know your credit score. Have the Microcenter cart planned. Know the exact prices, the exact bundle discount, the exact card terms. Walk in prepared, not curious.

Check credit score — target 710+ for best MC card approval. Credit Karma or annualcreditreport.com.
Do NOT open any new credit this month — wait for M05 Microcenter trip
Plan the Microcenter cart exactly: 9950X + ProArt X870E Creator + 64GB DDR5-6000. Nothing else.
Verify current in-store pricing on microcenter.com (prices differ from online). Check the CPU+MB combo discount (typically $50–80 off).
Apply for My Best Buy Visa this month — do it online, soft inquiry first. This is Hard Pull #2. Get it in hand before the M09 GPU trip. (H1 is M05 Microcenter card.)
HYSA should be at $1,400. Confirm this, then plan to bring $1,000 cash to Microcenter.

Save $350 HYSA: $1,400

Credit ready, Microcenter trip planned

M05

Month 5

Foundation Buy — Microcenter

The core platform purchase. Walk in with $1,000 cash. Buy CPU, MB, RAM. Open the Microcenter card at checkout for the remainder. Walk out with the foundation of a $4K machine.

Buy: Ryzen 9 9950X (~$599) + ASUS ProArt X870E Creator (~$349) + 64GB DDR5-6000 (~$130) = ~$1,078 + tax ≈ $1,150–1,200 total
Apply for Microcenter card at checkout — Hard Pull #1. 12-month 0% promo on purchases $499+.
Pay $1,000 cash. Put remainder (~$150–200) on the MC card.
Same day you get home: Set MC card autopay to minimum on the Webbank portal. Do not rely on remembering manually.
Verify every item against the receipt before leaving the parking lot
Store parts in anti-static bags in their boxes. Don't build yet — the GPU isn't here and you need the PSU + case too.
HYSA drops to ~$400. Expected. Recovery starts M06.

Cash: ~$1,000 MC card: ~$200

Core platform owned — CPU, MB, RAM in hand

M06

Month 6

Recovery

Rebuild the cash buffer. Attack the Microcenter card aggressively — no need to let a $200 balance sit when you have cash flow. Three months of recovery and it's gone.

Pay $50 above MC minimum → balance drops to ~$150
Save $300 to HYSA → balance climbs to ~$650
Continue daily Ollama use on Optiplex — stay sharp, validate more use cases
Watch GPU market — set weekly check on RTX 5080 pricing

MC payment: $50+min Save $300

M07

Month 7

Recovery

Same rhythm. MC balance nearly gone. One month away from being completely debt-free again before the GPU purchase.

Pay $50 above MC minimum → balance to ~$100
Save $300 → HYSA at ~$900
Begin planning the Best Buy trip — verify RTX 5080 stock, current pricing, BB Visa terms
Confirm My Best Buy Visa arrived and is active (applied M04)

MC payment: $50+min Save $300

M08

Month 8

MC Payoff

Kill the Microcenter card balance entirely. Enter M09's GPU purchase with zero existing card debt. Don't let two promos overlap if you can avoid it.

Pay remaining MC balance in full (~$100). Card is done — 4 months ahead of the 12-month promo.
Do NOT close the MC card — keep it open. Account age helps credit score. Just don't use it.
Save $250 → HYSA at ~$970
Confirm Best Buy RTX 5080 availability and pricing. Is the 24-month 0% promo active on BB Visa?

MC payoff: ~$100 Save $250

Microcenter PAID OFF ✓ — zero card debt

M09

Month 9

GPU Day — Best Buy

The most important single purchase in the build. Buy only if the price and promo conditions are met. No exceptions — you've waited 9 months, another month won't hurt if the conditions aren't right.

Buy trigger (all 3 must be true): RTX 5080 ≤ $1,099 AND in stock AND My Best Buy Visa 24-month 0% promo is active
Wait trigger: Price $1,100–$1,299 → wait 1 month, GPU market is cyclical
Pivot trigger: Price consistently > $1,299 → buy RTX 5070 Ti (~$749, 16GB VRAM, 35% slower). Saves $550. Covers the same model stack.
Pay $200 cash from HYSA + $800 on BB Visa (24-month 0% promo)
That evening: Set BB Visa autopay to minimum — same drill as the MC card
GPU only this trip. No PSU, no case, no accessories. One purchase at a time.
Check MicroCenter GPU section as a backup if Best Buy is out of stock (they sometimes have matching prices)

Cash: $200 BB card: $800

GPU in hand — 16GB VRAM unlocked

M10

Month 10

Assembly Prep — PSU & Case

PSU and case. Use Affirm Pay-in-4 at Amazon checkout — soft pull, 0%, 4 payments over 6 weeks. No new hard credit pulls. Start preparing for the build.

Buy: Corsair HX1200i (~$200) + Fractal Define 7 (~$130) = ~$330. Use Affirm Pay-in-4 at Amazon checkout: ~$83/payment × 4
Pay $70 above minimum on BB Visa
Download Ubuntu 24.04 LTS ISO. Verify SHA256 checksum. Write bootable USB (use Balena Etcher).
Watch 2–3 full Define 7 build guides on YouTube — understand the radiator mounting, cable routing, and GPU clearance before build day. Linus Tech Tips and Gamers Nexus both cover it.
Don't start building yet — wait for all parts to be in hand

BB payment: $70+min Affirm: $190

M11

Month 11

Final Parts — Everything Else

All remaining components. After this, every part is on hand. Block the build weekend in your calendar now. Prep your workspace.

Buy: Samsung 990 Pro 2TB (~$150) + Samsung 990 Evo 4TB (~$200) + Arctic Liquid Freezer III 360 AIO (~$90) + APC BR1500G UPS (~$150) + 3× Noctua NF-A14 fans (~$75) ≈ $665 total. Use Affirm Pay-in-4: ~$175/payment × 4
Pay $70 on BB Visa
Order anti-static wrist strap (~$6) and extra Phillips PH2 screwdrivers if needed
Verify AIO includes thermal paste (Arctic Freezer III ships with MX-4 — it does)
Block both days of a weekend for the build. Day 1: assembly + POST. Day 2: OS + software + migration from Optiplex.
Prep a clean, well-lit workspace: large table, good overhead light, small bowls for screws, anti-fatigue mat if standing

BB payment: $70+min Affirm: $175

All parts on hand — ready to build

M12

Month 12

BUILD WEEKEND

The weekend you've planned 11 months for. Two days. Build the machine, install the OS, pull the first 32B model, run the first inference. Migrate from the Optiplex.

Day 1 — Assembly sequence: CPU into socket (ZIF — no force) → AIO mount (apply thermal paste only if not pre-applied) → RAM into A2/B2 slots → M.2 SSDs → I/O shield → MB into case → PSU → 24-pin + EPS + GPU power → AIO fans + radiator (top exhaust) → case fans → GPU → front panel headers
Day 1 — POST test before closing: Boot into BIOS. Verify 64GB RAM detected in dual-channel, both NVMes visible, CPU temps normal, all fans spinning
Day 2 — OS install: Boot from Ubuntu USB → install to 2TB drive → set up NVIDIA drivers (ubuntu-drivers autoinstall) → reboot → verify GPU with nvidia-smi
Day 2 — Ollama: Install Ollama → ollama pull qwen3:32b → first inference. Note the difference from the 14B you've been using for 11 months.
Migrate Optiplex model library via LAN: rsync -avz optiplex:~/.ollama/models/ ~/.ollama/models/
Run 30-min stress test before calling it done: Prime95 (CPU) + FurMark (GPU) simultaneously. Watch temperatures.

BB payment: $70 Save $100

🔧 SYSTEM ONLINE — first 32B model running locally

M13

Month 13

Cleanup

Machine is running production workloads. Now it's purely financial — steady BB payments, HYSA recovering. Note what you've learned about the build in the Journal.

BB payment: $70 Save $280

M14

Month 14

Cleanup

Best Buy card nearly clear. HYSA back above $1K. Two months of real 32B model usage in — what would you have done differently?

BB payment: $70 Save $280

M15

Month 15

Done

Final Best Buy payment. Zero card debt. $1,290 in HYSA. A $4K machine running the best local AI models available — paid for without touching the emergency fund or breaking any of the five principles.

BB payoff: ~$70 Save $280

🎉 DEBT FREE — $4K rig, zero debt, emergency fund untouched

Parts Manifest — Full Bill of Materials

Prices as of early 2026. Verify before purchase — GPU market moves fast. Check CamelCamelCamel for Amazon items.

Component	Part	Price	Where	When	Notes
CPU	AMD Ryzen 9 9950X	$599	Microcenter	M05	16C/32T Zen 5. Bundle with MB for -$50. In-store only pricing.
Motherboard	ASUS ProArt X870E Creator WiFi	$349	Microcenter	M05	4× M.2, Thunderbolt 4, solid VRM. Creator line = workstation DNA.
RAM	Corsair Vengeance DDR5-6000 64GB (2×32GB)	$130	MC / Amazon	M05	CL30. Matches Zen 5 Infinity Fabric exactly. Install in A2/B2 slots.
GPU	NVIDIA GeForce RTX 5080 16GB	$999	Best Buy	M09	Buy only ≤$1,099. Alt: RTX 5070 Ti (~$749) if 5080 stays expensive.
PSU	Corsair HX1200i Platinum 1200W	$200	Amazon	M10	Digital monitoring. 1200W gives 50%+ headroom. Buy via Affirm Pay-in-4.
Case	Fractal Design Define 7 (Black)	$130	Amazon	M10	Sound-dampened. 360mm AIO top-mount. E-ATX compatible. Silent operation.
Primary SSD	Samsung 990 Pro 2TB NVMe M.2	$150	Amazon / BB	M11	OS + active models. Gen 4, 7,450 MB/s reads. Don't cheap out on the OS drive.
Model SSD	Samsung 990 Evo 4TB NVMe M.2	$200	Amazon	M11	Ollama model library. 10 × 32B Q4 models ≈ 200GB. Room for years.
Cooler	Arctic Liquid Freezer III 360 AIO	$90	Amazon	M11	Best value 360mm AIO. 9950X runs hot under all-core inference. Size up.
UPS	APC BR1500G 1500VA / 900W	$150	Amazon	M11	Non-negotiable for a $4K always-on machine. Handles full load + clean shutdown.
Fans	Noctua NF-A14 PWM × 3	$75	Amazon	M11	3× 140mm. Quietest at this airflow level. 2 intake front, 1 exhaust rear.
TOTAL PARTS		~$2,872	Add tax (~$200) + anti-static strap ($6) + misc cables (~$20) ≈ $3,100 all-in

Credit & Financing Strategy

HARD PULL #1 — Month 5

Microcenter Webbank Card

Applied at checkout in-store. Offer: 12 months deferred interest / 0% promotional APR on purchases of $499+. Warning: "deferred interest" means if any balance remains after 12 months, interest accrues retroactively from purchase date at ~29% APR. You're paying this off in 3 months (M05→M08) — promo is irrelevant. But set autopay the same day to eliminate any risk of a missed payment triggering the clause.

HARD PULL #2 — Month 4 (use Month 9)

My Best Buy Visa Card

Apply in M04 online. Same deferred-interest structure: 24 months 0% on qualifying BB purchases of $199+. Your exposure: ~$1,165 total across M09–M11. At $70/month above minimum, fully paid by M15 — 9 months before the promo deadline. The 24-month window is generous. The risk is not the promo — it's forgetting autopay. Set it the day the card arrives.

NO HARD PULL

Affirm Pay-in-4 (M10, M11)

Soft pull only — zero credit score impact. Used for PSU/case (M10) and remaining parts (M11). 0% APR, 4 bi-weekly payments. Available at Amazon, Best Buy, and Walmart checkout. Limit yourself to 1–2 active Affirm authorizations at a time — stacking them is manageable but adds mental load.

CARDINAL RULE

Autopay Is Mandatory — Same Day

Deferred-interest promos become financial traps with a single missed payment. Protocol: the same day you open any store card, log into the issuer's portal and set autopay to the minimum payment. Then pay manually above that each month. Never rely on memory. The plan assumes 0 missed payments — this is how you guarantee it.

Decision Gates

Month 3 — Use Case Validation

The only honest question is: are you actually saving time on real tasks? Not "is this interesting" — is it useful enough that you'd notice if it was gone?

Criterion	Go ✓	No-Go ✗
Usage frequency	≥5 days/week for 6 weeks	<3 days/week
Documented use cases	3+ with measurable time saved	Fewer than 3 specific tasks
Quality vs cloud	Matches cloud on ≥1 use case	Always inferior on everything
Gut test	"I'd miss this tomorrow"	"It's more of a hobby"

If No-Go: stop HYSA transfers. Consider a GPU-only upgrade to the Optiplex (~$400–600 for an RTX 4060 Ti 16GB) instead of a full build. Or just use Claude API at $20/month. The plan serves your goals — not the other way around.

Month 9 — GPU Buy Gate

Buy the RTX 5080 when all three conditions are true:

Price ≤ $1,099 (target $999)
In stock at Best Buy
24-month 0% promo active on BB Visa

Price $1,100–$1,299: Wait one month. GPU prices move cyclically — a 30-day hold often drops $100–200.

Price persistently > $1,299: Pivot to RTX 5070 Ti (~$749, 16GB VRAM, 35% slower for inference). Same model stack, lower cost.

Out of stock at BB: Check Microcenter GPU section, B&H Photo, Newegg. Never buy from scalpers — wait it out.

Price Watch

CamelCamelCamel (Amazon history), NowInStock.net (restock alerts), r/buildapcsales, GPU.compare.parts

Risk Register & Mitigations

RISK

Missed payment on 0% promo

Set autopay for minimum on every card the day you open it. Pay above minimum manually.

RISK

Life happens (car, dental, family)

Months 1-4 have zero commitments — pause savings safely. Months 5+ have low minimums to fall back to.

RISK

Deferred interest cliff

Pay off store cards 1-2 months BEFORE term ends, not on the deadline.

RISK

GPU prices spike

Plan allows 1-2 month delay at Month 9 without breaking anything.

RISK

Use case doesn't justify build

Month 3 checkpoint is real — pivot to smaller build or scrap entirely.

The Software

Local LLMs

Models, tasks, and validation protocols for clinical pharmacy and beyond.

The headline rule of local language models: capability is gated by VRAM. Everything else follows from this. Choose models that fit your hardware, validate them ruthlessly before trust, and let the GPU upgrade path open richer options later.

Model Catalog

VRAM shown as Q4 / Q8 GB. Stars rate clinical accuracy, coding, reasoning, and speed (5 = best).

Model	Size	VRAM (Q4/Q8)	Clinical	Coding	Reasoning	Speed	Notes
Qwen3 14B general	14B	9 / 16	●●●●○	●●●●○	●●●●○	●●●●●	Excellent generalist. Q8 fits on 16GB cards. Strong instruction following.
Qwen3 32B flagship	32B	20 / 36	●●●●●	●●●●●	●●●●●	●●●○○	Near-Sonnet quality for many tasks. Needs 24GB+ VRAM at Q4. Your target post-upgrade.
Qwen2.5-Coder 32B coding	32B	20 / 36	●●○○○	●●●●●	●●●●○	●●●○○	The coding workhorse. Matches GPT-4 on many code benchmarks. Worth waiting for 24GB+ VRAM.
DeepSeek R1 distilled 14B reasoning	14B	9 / 16	●●●●○	●●●●○	●●●●●	●●●●○	Visible chain-of-thought. Great for DDx exercises — you can audit reasoning.
DeepSeek R1 32B reasoning	32B	20 / 36	●●●●●	●●●●○	●●●●●	●●●○○	The reasoning king at 32B. Visible thinking, strong DDx capabilities.
Devstral 24B coding	24B	14 / 26	●●○○○	●●●●●	●●●●○	●●●●○	Agentic coding specialist. Edits files, runs commands. Pairs with VS Code.
Gemma 3 27B general	27B	17 / 30	●●●●○	●●●○○	●●●●○	●●●○○	Strong biomedical knowledge from Google. Tight fit on 16GB but doable at Q3.
Gemma 4 E4B small-fast	4B-effective	3 / 5	●●●○○	●●●○○	●●●○○	●●●●●	Surprisingly capable for size. Got vancomycin AUC right in your prior LM Studio testing.
Phi-4 14B reasoning	14B	9 / 16	●●●○○	●●●●○	●●●●●	●●●●○	Microsoft's analytical model. Excellent for structured reasoning, math, logic.
MedGemma 4B medical	4B	3 / 5	●●●●○	●○○○○	●●●○○	●●●●●	Medical-specific. Multimodal (images). Based on Gemma 3, so may miss recent guidelines.
Llama 3.3 70B flagship	70B	42 / 75	●●●●●	●●●●○	●●●●○	●●○○○	Requires 48GB+ VRAM (dual GPU or 5090). The headline 'datacenter at home' model.

VRAM Fit Calculator

Will this model run on this GPU? Quick reality check.

Your GPU VRAM

Model Size

Quantization

Context Length

Adjust controls to see if it fits.

Task Library — Clinical Workflows

Principle

Local LLMs are thinking partners, never truth sources. Every clinical output gets verified against Lexicomp, UpToDate, Sanford Guide, or current guidelines. Build verification into the workflow as a non-negotiable step.

DeepSeek R1 14B/32B

Differential diagnosis exercise

Framework: Visible reasoning lets you audit chain. Use as thinking partner, never as decision tool.

Example: 65yo M, new DOE + LE edema. Walk through DDx considering HF, PE, anemia, renal, hepatic. Audit your own reasoning against model's.

Use your own tool — LLM helps build it

Vancomycin AUC TDM

Framework: Build deterministic calculator. LLM helps explain results to colleagues.

Example: Bayesian first-order PK estimation. ALWAYS deterministic math, never LLM math.

Qwen3 14B + RAG (DailyMed/Lexicomp)

Drug interaction lookup

Framework: RAG over authoritative source. LLM synthesizes, source provides facts.

Example: Pull DailyMed JSON for two drugs, feed to model, ask for synthesis with citations.

Qwen3 14B Q8 or Gemma 3 27B

Patient education draft

Framework: LLM drafts at target reading level, YOU verify and adjust.

Example: 'Explain warfarin INR monitoring at 6th grade reading level' → review, edit, give to patient.

Phi-4 14B + paper PDFs

Literature synthesis

Framework: Feed full paper, request structured extraction. Verify quotes against source.

Example: 10 RCTs on SGLT2i in HFrEF → extract: trial, n, intervention, primary endpoint, NNT, key adverse events.

Qwen3 14B (local = privacy)

Clinical note de-identification

Framework: Local-only processing. Never send PHI to cloud.

Example: Replace names/MRNs/dates with placeholders for case-based teaching.

Gemma 3 27B or Qwen3 32B

Pharmacology teaching prep

Framework: Generate Socratic questions, then verify mechanism explanations against Goodman & Gilman.

Example: 'Generate 5 progressively harder questions on beta-blocker mechanism' → use for student session.

Qwen3 14B + local guideline PDFs

Antibiotic stewardship review

Framework: RAG over your institution's antibiogram + IDSA guidelines.

Example: 'For CAP in this patient with these allergies and renal function, what does our guideline recommend?'

Task Library — Coding

Qwen2.5-Coder 32B or Devstral 24B

Project scaffolding

Framework: Describe project, get structure + boilerplate. Always review before running.

Example: FastAPI + SQLite vancomycin TDM webapp → full skeleton with routes, models, tests.

Qwen2.5-Coder 32B

Refactoring

Framework: Paste function, request refactor with explanation. Run tests after.

Example: Turn this 200-line procedural script into clean class-based design.

DeepSeek R1 14B (visible reasoning)

Bug hunting

Framework: Paste error + relevant code. R1's thinking shows you the diagnostic path.

Example: 'Why does my Bayesian PK calculation give negative volumes?' → R1 walks the math.

Devstral 24B or Qwen2.5-Coder 32B

Test generation

Framework: Paste function, request pytest test cases including edge cases.

Example: Generate tests for vancomycin loading dose calculator including renal extremes.

Phi-4 14B

SQL/data wrangling

Framework: Describe schema + desired output. Verify on sample data before production.

Example: Pivot pharmacy dispensing log into monthly DDDs per ward.

Qwen3 14B

Documentation writing

Framework: Function → docstring. Codebase → README. Always edit for accuracy.

Example: Generate API documentation for your TDM tool with examples.

Task Library — Research

Qwen3 14B

PubMed query construction

Framework: Describe research question → get MeSH terms + search string.

Example: 'Vancomycin AUC vs trough monitoring outcomes in MRSA bacteremia' → complete PubMed query.

Phi-4 14B or Qwen3 32B

Paper summary + critique

Framework: PDF → structured summary (background, methods, results, limitations, take-home).

Example: Summarize NEJM HF trial with focus on subgroup analyses.

DeepSeek R1 14B/32B

Methodology critique

Framework: Visible reasoning catches flaws step-by-step.

Example: Critique this RCT's randomization, blinding, and analysis plan.

Qwen3 14B + local vault

Note synthesis (Obsidian-style)

Framework: RAG over your notes. Ask questions across your accumulated knowledge.

Example: 'Show me everything I've written about vancomycin PK and synthesize key principles.'

Task Library — Automation

Devstral 24B

Web scraping helper

Framework: Describe target site + data needed → working scraper. Always respect robots.txt.

Example: Daily FDA drug shortage updates → JSON for tracking.

Qwen3 14B running on Optiplex

Email triage

Framework: LLM tags incoming emails by category, urgency. Local = privacy.

Example: CME reminders, work emails, personal, newsletters → auto-foldered.

Phi-4 14B

Document classification

Framework: Drop PDFs in folder → auto-sorted by content.

Example: Downloaded paper goes to /research/, drug monograph to /clinical/, etc.

Qwen3 14B + RSS feeds

Daily briefing generator

Framework: Cron job pulls feeds, LLM summarizes, emails you at 6 AM.

Example: Top 5 clinical pharmacy headlines + 3 tech headlines, 200 words total.

Install Hub — One-Copy Setup Commands

Every command below is tested on macOS (Apple Silicon + Intel) and Ubuntu 22.04+. Click copy on any block.

Ollama — Run any model in one line

The fastest path to local LLMs. Handles downloads, GGUF conversion, and an OpenAI-compatible API on port 11434.

# Install (macOS + Linux)
curl -fsSL https://ollama.ai/install.sh | sh

# Pull models
ollama pull qwen3:14b
ollama pull deepseek-r1:14b
ollama pull devstral

# Run interactively
ollama run qwen3:14b

# List downloaded models
ollama list

# API test
curl http://localhost:11434/v1/models

LM Studio — GUI + headless API server

Best for exploring models visually. Exposes an OpenAI-compatible server on port 1234.

# macOS (Homebrew)
brew install --cask lm-studio

# Or download: lmstudio.ai

# Headless CLI server (no GUI needed)
lms load qwen3-14b-instruct \
  --context-length 32768 \
  --gpu max

lms status   # confirm server on :1234

# Test
curl http://localhost:1234/v1/models

Open WebUI — ChatGPT UI for Ollama

Full-featured chat interface with RAG, image gen, plugins, and user management. Connects to Ollama automatically.

docker run -d \
  --restart unless-stopped \
  -p 3000:8080 \
  --add-host=host.docker.internal:host-gateway \
  -v open-webui:/app/backend/data \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

# Visit http://localhost:3000
# First launch creates admin account

llama.cpp — Low-level, maximum control

Build from source for Metal (macOS) or CUDA acceleration. Foundation under Ollama.

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

# macOS Metal (Apple Silicon or AMD)
cmake -B build -DGGML_METAL=ON
cmake --build build --config Release -j$(nproc)

# Run a model
./build/bin/llama-cli \
  -m models/qwen3-14b-q8.gguf \
  -n 512 \
  -p "Explain vancomycin AUC monitoring"

vLLM — Production inference (CUDA)

OpenAI-compatible API server with PagedAttention for high throughput. Requires Python + NVIDIA GPU.

pip install vllm

python -m vllm.entrypoints.openai.api_server \
  --model Qwen/Qwen2.5-14B-Instruct \
  --dtype float16 \
  --gpu-memory-utilization 0.85 \
  --port 8000

# Test
curl http://localhost:8000/v1/models

AnythingLLM — All-in-one local RAG

Connect PDFs, websites, and databases to your local models. Docker server or native desktop app.

export STORAGE=$HOME/anythingllm
mkdir -p $STORAGE

docker run -d \
  -p 3001:3001 \
  -v $STORAGE:/app/server/storage \
  --name anythingllm \
  mintplexlabs/anythingllm

# Visit http://localhost:3001

Model Pull Reference — Matched to Your VRAM

# Current Optiplex (integrated / low VRAM)
ollama pull phi4:mini          # 3.8B — fast, capable
ollama pull qwen3:4b           # Excellent for size

# After 16GB VRAM upgrade
ollama pull qwen3:14b          # Primary daily driver
ollama pull deepseek-r1:14b    # Reasoning + visible CoT
ollama pull gemma3:12b         # Strong biomedical knowledge

# RTX 5080 target (16GB VRAM)
ollama pull qwen3:32b          # Near-Sonnet quality
ollama pull devstral           # Best open coding agent
ollama pull qwen2.5-coder:32b  # Code specialist

# Run with system prompt
ollama run qwen3:14b --system "You are a clinical pharmacy AI. Be precise, cite evidence levels."

Watch: Getting Started with Local LLMs

15:22

Ollama in 15 Minutes — Run Models Locally for Free

Tutorial Setup Guide

The Generalized Vancomycin Test

Every clinical model gets tested on facts where you know the answer cold.

Protocol

1. Pick 5 questions where YOU know the right answer (vanco AUC, warfarin INR ranges, etc.)
2. Ask the model each. Record exact response.
3. Grade: pass/fail/dangerous (confident wrong is worst)
4. If 4/5 or 5/5 pass → safe for THINKING PARTNER role with verification
5. If 3/5 → useful for non-clinical tasks only
6. If <3/5 → uninstall, save space
7. Re-test quarterly (models update, drift happens)

Log a Validation Test

Record each test you run. Synced across devices, exportable.

Model

Question Tested

Verdict

Notes

The Encyclopedia

Hardware

Component deep dives, spec literacy, and the principles behind choosing well.

RTX graphics card — selective focus editorial photography

The Most Important Component

VRAM Is Everything

For LLMs, the GPU isn't about speed — it's about how much model fits.

Every component answers a different question. The GPU answers how much can I run. The motherboard answers what can I add later. The PSU answers will this be safe in five years. Get the literacy right once and the choices become obvious.

GPU — The Most Important Component for LLM+Gaming

Why it matters: For LLMs: VRAM is everything (model fit). For gaming: raw compute + ray tracing. The 5080 sits at the sweet spot for both — for now.

Spec Literacy

VRAM

Single most important spec for LLMs. Model size × 0.6 ≈ minimum VRAM at Q4. 32B model = ~20GB needed.

memoryBandwidth

GDDR7 (5080/5090) = 960-1792 GB/s. Higher = faster token generation. Matters more than core count for inference.

TDP

Thermal Design Power. 5080 = 360W, 5090 = 575W. Determines PSU needs + heat in your room.

rasterPerf

Traditional gaming fps. 5080 ≈ 4090 raster in many titles.

rayTracing

Blackwell (5000-series) is significantly better than Ada (4000-series) at RT.

DLSS

DLSS 4 (5000-series only) Multi-Frame Generation. Real frame rate doubler/tripler in supported games.

powerConnectors

12V-2x6 (new standard, 600W single cable). 5080+ exclusively use this. PSU must support.

GPU fan array — the cooling system that keeps inference running

Graphics card on workbench — the single most important component

AIB Decoder Ring

5080 Founders Edition (FE)

NVIDIA's own. Smallest, cleanest, quietest under load. Usually MSRP.

AIB variants (Asus, MSI, Zotac, Gigabyte, PNY)

Add factory OC, beefier coolers, RGB. $50-300 more.

ZOTAC Solid Core OC

Budget AIB. Often at MSRP. Decent cooler, no RGB drama.

MSI Shadow / Inspire

Mid-tier AIB. Good cooler, reasonable price premium.

ASUS ROG / Strix

Premium AIB. Best cooling, premium price ($150-300 over MSRP).

Gigabyte AORUS / Master

High-end AIB. Watch for QC complaints on some runs.

Current Landscape

Part	Price	Notes
RTX 5050 entry vram: 8 · llmCeiling: 8B Q4 only · gaming: 1080p · priceMSRP: 249		Skip for your use case — VRAM too low for serious LLM.
RTX 5060 Ti 16GB budget vram: 16 · llmCeiling: 14B Q4, 22B Q3 · gaming: 1080p/1440p · priceMSRP: 429		Surprise hero. Same VRAM as 5080 at half the price. Weaker compute but FITS the models.
RTX 5070 midrange vram: 12 · llmCeiling: 12B Q4 · gaming: 1440p · priceMSRP: 549		12GB is awkward for LLMs. Skip in favor of 5060 Ti 16GB if budget.
RTX 5070 Ti midrange-high vram: 16 · llmCeiling: 14B Q8 / 22B Q4 · gaming: 1440p high · priceMSRP: 749		Strong middle ground. Often $50-100 cheaper than 5080 with 80% performance.
RTX 5080 high-end vram: 16 · llmCeiling: 14B Q8 / 24B Q4 / 27B Q3 · gaming: 1440p ultra / 4K high · priceMSRP: 999		Your locked target. Best new-card balance for hybrid use.
RTX 5080 Super (rumored) halo-mid vram: 24 · llmCeiling: 32B Q4 comfortably · gaming: 1440p/4K · priceMSRP: 1199		Rumored late 2026. Would change everything — wait if you can.
RTX 5090 halo vram: 32 · llmCeiling: 32B Q8 / 70B Q3 · gaming: 4K ultra everything · priceMSRP: 1999		The dream. Street price $2900-3900 currently. Wait for normalization.
RTX 4090 (used) previous-halo vram: 24 · llmCeiling: 32B Q4 with context · gaming: 4K ultra · priceMSRP: $1500-2000 used		If you find one ~$1500, strongly consider. 24GB is the magic number.
RTX 3090 (used) older-halo vram: 24 · llmCeiling: 32B Q4 / 70B Q2 · gaming: 1440p/4K capable · priceMSRP: $700-900 used		The local LLM community's darling. 24GB VRAM for cheap. Gaming weaker but adequate.
RX 7900 XTX (AMD) high-end-AMD vram: 24 · llmCeiling: 32B Q4 with ROCm · gaming: 4K capable · priceMSRP: 999		AMD path. ROCm support improving but Linux-mostly. Gaming great, LLM workflow rockier.

Red Flags When Buying Used

Ex-mining cards (look for: dust caked on backplate, missing original cooler, 'tested for 8 hrs' rather than 'lightly used')
Cards without original box/receipt if buying used at significant savings
AIBs from no-name brands you've never heard of
Anything advertised as 'mining BIOS flashed' or 'undervolt profile included'
Sellers who won't let you stress test before purchase (in-person buys)

Watch: RTX 5080 Build Walkthroughs

Best RTX 5080 Gaming PC — $2,800 Build

Build Guide 9800X3D + 5080

4K Build: 9800X3D + RTX 5080 with Benchmarks

Benchmarks 4K Gaming

CPU — Matters Less for LLMs, More for Gaming

Why it matters: Modern LLM inference is GPU-bound. CPU only matters for: (1) gaming 1% lows, (2) CPU offload when models exceed VRAM, (3) general system responsiveness.

Spec Literacy

cores

More cores ≠ better gaming. Diminishing returns past 8 P-cores. Productivity benefits from 16+.

clocks

Boost clock is the marketing number. All-core sustained matters more for sustained workloads.

L3cache

Critical for gaming. AMD's X3D chips win on cache stacking. Ryzen 7800X3D / 9800X3D = gaming king.

TDP

9950X = 170W TDP, 9700X = 105W. AIO recommended at 105W+, mandatory at 170W.

AVX512

AMD Zen 5 has native AVX-512. Boosts CPU-only LLM inference ~50%. Intel disabled it on consumer chips.

iGPU

9700X/9950X have basic iGPU (Radeon 2 CUs). Useful for troubleshooting GPU issues. X3D chips have no iGPU.

Current Landscape

Part	Price	Notes
Ryzen 7 9700X cores: 8P/16T · clock: 4.7/5.5 GHz · useCase: Best budget gaming + light productivity. No AIO required. · note: Sweet spot if budget tight.	$280
Ryzen 7 9800X3D cores: 8P/16T · clock: 4.7/5.2 GHz · useCase: Gaming king. Best 1% lows in every modern game. · note: If gaming is #1 priority, this is the answer.	$480
Ryzen 9 9900X cores: 12P/24T · clock: 4.4/5.6 GHz · useCase: More cores than 9700X but no X3D cache. Productivity middle ground. · note: Often skipped — 9700X or 9950X are usually better picks.	$430
Ryzen 9 9950X cores: 16P/32T · clock: 4.3/5.7 GHz · useCase: Productivity beast. CPU LLM offload, video, compiling. Gaming = 95% of 9800X3D. · note: Your locked choice. Best balance for your polymath workloads.	$489
Ryzen 9 9950X3D cores: 16P/32T · clock: 4.3/5.7 GHz · useCase: Gaming AND productivity king. Only chip without compromise. · note: If you can stretch, this is the no-compromise pick.	$659
Intel Core Ultra 7 265K cores: 8P+12E/24T · clock: 3.9/5.5 GHz · useCase: Intel's current mid. Underwhelming vs AMD this gen. · note: Skip — AMD wins this generation across price points.	$379
Intel Core Ultra 9 285K cores: 8P+16E/24T · clock: 3.7/5.7 GHz · useCase: Intel flagship. Slower than 9950X in most tests. · note: Skip unless Intel ecosystem reasons.	$589

Recommendation

Ryzen 9 9950X for the locked build (your polymath use case). Ryzen 7 9800X3D if you pivot to gaming-first. Skip Intel this generation.

RAM — Get Enough, Don't Overpay for Speed

Why it matters: Need >2× model size in system RAM for comfortable LLM operation. For LLMs that fit on GPU, RAM matters less. For gaming, speed barely matters above DDR5-6000.

Spec Literacy

capacity

64GB is the new sweet spot for power users. 32GB is gaming-only minimum. 128GB+ only for VM heavy workloads or huge model CPU offload.

speed

DDR5-6000 CL30 is the AM5 sweet spot. AMD's IF clock locks 1:1 here. Faster (DDR5-7200+) gives minimal gains, often de-syncs.

timings

CL30 at 6000 = lower latency than CL36. Worth the small premium. Most modern kits achieve this.

EXPO_vs_XMP

EXPO = AMD's overclock profile. XMP = Intel's. Buy EXPO-labeled kits for Ryzen. They often work on Intel too via XMP fallback.

dimms

ALWAYS buy as a kit (2×32GB for 64GB). Mixing kits causes instability. AM5 strongly prefers 2 DIMMs over 4.

rank

Single-rank vs dual-rank. At 64GB (2×32GB), they're dual-rank — slightly better performance, slightly trickier to OC.

Current Landscape

Part	Price	Notes
G.Skill Trident Z5 Neo 64GB DDR5-6000 CL30	$430	AMD EXPO native. The community-favorite kit. Your locked choice.
Corsair Vengeance 64GB DDR5-6000 CL30	$410	Solid alternative. EXPO certified. Lower-profile heatsinks (cooler clearance friendlier).
Crucial Pro 64GB DDR5-5600	$340	Budget pick. 5600 is fine, just leaves perf on table. CL40+ tends to feel sluggish.
G.Skill Trident Z5 Neo 96GB DDR5-6400	$720	If you want headroom for CPU LLM offload of 70B models. Overkill for most.
Kingston Fury Beast 128GB (4×32GB) DDR5-5600	$580	4-DIMM kits are tough on AM5. Often only runs at 4800. Avoid if possible.

Future-Proofing

Buy 2×32GB now. Mobo has 4 slots. If 128GB ever needed (CPU offload of 70B+), add another 64GB kit later — though mixing kits at full speed is dicey on AM5. Better path: sell first kit, buy 2×64GB if available by then.

Storage — Speed Matters Less Than You Think

Why it matters: For games: PCIe 4.0 NVMe is fine, PCIe 5.0 wasted money. For LLM model loading: faster = better but you load once per session. For OS: any NVMe is plenty.

Spec Literacy

interfaces

PCIe 4.0 NVMe = 7,000 MB/s sequential. PCIe 5.0 = 14,000 MB/s. Real-world gaming difference: <2 seconds load time.

DRAM_cache

DRAM-equipped drives (990 Pro, SN850X) handle sustained writes much better than DRAM-less. Worth the premium for any drive you'll write to heavily.

TBW

Terabytes Written endurance rating. 2TB consumer drives = 1200-2400 TBW. You will not hit this in 10 years of normal use.

formFactor

M.2 2280 is standard. All consumer NVMe is this size. PS5 compatibility means PCIe 4.0 + heatsink.

heatsink

Required on PCIe 5.0 drives. PCIe 4.0 usually fine with mobo's built-in heat spreader. Excessive heatsinks block GPU airflow.

controllers

Phison E26 (PCIe 5.0), Phison E18 / Samsung Elpis (PCIe 4.0 top tier). Avoid no-name controllers.

Current Landscape

Part	Price	Notes
WD Black SN850X 2TB interface: PCIe 4.0	$250	Reliable, fast, no thermal issues. Your OS+games drive.
Samsung 990 Pro 2TB interface: PCIe 4.0	$280	Slightly faster than SN850X. Samsung Magician software ecosystem. Your models drive.
Crucial T700 2TB interface: PCIe 5.0	$380	Runs hot. Need beefy heatsink. Gaming benefit: marginal.
Samsung 9100 Pro 2TB interface: PCIe 5.0	$450	Fastest consumer drive. Overkill for your use case.
Crucial P3 Plus 4TB interface: PCIe 4.0 (DRAM-less)	$280	Budget bulk storage. Slow sustained writes. Good for cold model storage.
WD Black SN770 2TB interface: PCIe 4.0 (DRAM-less)	$180	Cheap. OK for boot drive. Skip for heavy write workloads.

Strategy

Two-drive setup: 2TB for OS+games (SN850X), 2TB dedicated for AI models (990 Pro). Add 4TB+ HDD later for cold storage / Plex library.

PSU — Buy Once, Cry Once

Why it matters: Cheap PSUs kill components. Good PSUs last 10 years across multiple builds. Size for FUTURE GPU upgrade, not just today's parts.

Spec Literacy

wattage

Sum component peak draw × 1.5 buffer. Single-5080 build = 850W minimum, 1000W comfortable. Dual GPU or 5090-upgrade-path = 1200W+.

efficiency

80+ Bronze < Silver < Gold < Platinum < Titanium. Gold is the sweet spot. Platinum worth it if 24/7 use (your case).

modularity

Fully modular = remove every cable. Cleaner builds, easier installation. Worth $30-50 premium.

ATX_3_1

Latest standard. Native 12V-2x6 GPU connector (600W single cable). Required for 5080+. Older PSUs need adapters (avoid).

warranty

Top brands: 10-12 years. Bottom brands: 3-5 years. Long warranty = manufacturer confidence.

capacitors

Japanese caps > Chinese caps. Reviewers (Aris/Cybenetics, JonnyGuru) tell you which.

OCP_OPP_OVP

Over-Current/Power/Voltage Protection. Modern good PSUs have all. Cheap PSUs disable some to hit price points.

Current Landscape

Part	Price	Notes
Corsair HX1200i premium wattage: 1200 · efficiency: Platinum/ATX 3.1	$320	Your locked choice. 12-yr warranty. Quiet. iCUE software for monitoring.
Corsair RM1000x great wattage: 1000 · efficiency: Gold/ATX 3.1	$200	Excellent value. 10-yr warranty. If single-GPU lifetime, this is enough.
Seasonic PRIME TX-1300 ultra wattage: 1300 · efficiency: Titanium/ATX 3.1	$440	The gold standard. 12-yr warranty. Premium price.
Seasonic Focus GX-1000 great wattage: 1000 · efficiency: Gold/ATX 3.1	$190	Seasonic quality at midrange price. 10-yr warranty.
EVGA Supernova G7 1000W great wattage: 1000 · efficiency: Gold/ATX 3.1	$180	EVGA quality (made by Super Flower). 10-yr warranty.
Thermaltake / generic 1000W avoid wattage: 1000 · efficiency: Gold	$90	DO NOT. Cheap caps, weak protection, voids GPU warranty when they fail.

Rule

Brands worth buying: Corsair (HX/RM/SF), Seasonic (PRIME/Focus), EVGA (G/P series, Supernova line), be quiet! (Dark Power, Straight Power), Super Flower (Leadex). Avoid: anything you've never heard of, anything under $80 at 1000W.

Motherboard — The Platform Decision

Motherboard component detail — the platform that defines your upgrade path

Every trace, every socket — a five-year commitment.

Why it matters: Hardest part to upgrade later. Locks in CPU socket, RAM standard, PCIe gen, USB version. Buy for 5 years.

Spec Literacy

socket

AM5 (AMD Ryzen 7000/8000/9000) = current. LGA1851 (Intel) = current. AM5 supports through 2027 minimum (vs Intel changing every 1-2 gens).

chipset

B650 (mid, fine for most), B650E (mid+, PCIe 5.0 GPU slot), X670 (high, dual chipset), X670E (high, all PCIe 5.0), X870 (mid-new), X870E (high-new, full PCIe 5.0, USB4).

VRMs

Voltage Regulator Modules. 16+ phase good for 9950X. Weak VRMs throttle CPU under sustained load.

PCIe_lanes

x16 slot 1 + how many x8/x4 secondary. For dual GPU: need x8/x8 split. ProArt X870E does this. Cheap boards don't.

M2_slots

Want 3+ M.2 NVMe slots minimum. Future expansion. PCIe 5.0 slot routes from CPU (fastest), others from chipset.

USB4

40 Gbps. Useful for eGPU (LLM cluster expansion), fast external storage. X870E boards usually have it.

wifi

WiFi 7 on X870E. WiFi 6E on cheaper boards. Both fine.

BIOS_flashback

Lets you update BIOS without CPU installed. Critical when buying new CPU + old-stock mobo combo.

Current Landscape

Part	Price	Notes
ASUS ProArt X870E-Creator WiFi creator chipset: X870E	$510	Your locked choice. Dual USB4, dual 5G LAN, dual PCIe 5.0 x16 (x8/x8 split). Creator board = workstation tier without workstation price.
MSI MEG X870E Godlike halo chipset: X870E	$1099	Halo board. Most features. Massive price premium for limited real benefit.
ASUS ROG Strix X870E-E gaming-premium chipset: X870E	$540	Gaming-focused premium. Excellent VRMs. RGB heavy.
MSI MAG X870 Tomahawk WiFi mid chipset: X870	$330	Best value X870. Single PCIe 5.0 slot only — no dual GPU split.
Gigabyte B850 Aorus Elite WiFi budget chipset: B850	$220	Solid mid-budget. Good VRMs. No dual GPU.
ASRock B650 Pro RS minimum chipset: B650	$150	Cheapest viable. 9950X is at VRM limits — fine for stock, no OC headroom.

Decision

Pay for the platform if you plan upgrades. ProArt X870E preserves dual-GPU + USB4 + 5-year platform life. Stepping down to B650 saves $300 but kills the upgrade path.

Cooling — Adequate is Better Than Maximum

Why it matters: 9950X needs serious cooling under sustained load. LLM training spikes CPU. Gaming spikes GPU. Both spike together = thermal limits matter.

Spec Literacy

air_vs_AIO

Top-tier air (Noctua NH-D15, Phanteks T30) ≈ 240mm AIO. 360mm AIO clearly better for 170W chips like 9950X. Air = simpler/quieter at idle. AIO = better peak.

AIO_size

240mm = 7700X tier. 280mm = 9700X. 360mm = 9950X comfortable. 420mm = unnecessary unless heavy OC.

fan_quality

Cooler is only as good as its fans. Noctua/Arctic/Phanteks tier > stock fans on most AIOs.

pump_quality

Asetek-based pumps (most AIOs) > random new entrants. Arctic LF III uses their own pump — proven reliable.

radiator_position

Top intake or top exhaust both fine. Front intake = best CPU temp, worst GPU temp. Side mount = case-specific.

airflow_strategy

Positive pressure (more intake than exhaust) = less dust. Filter all intakes. Replace filters quarterly.

Current Landscape

Part	Price	Notes
Arctic Liquid Freezer III 360 A-RGB value-king type: AIO 360mm	$130	Best value AIO. Beats $200 competitors in tests. Your locked choice.
Noctua NH-D15 G2 premium-air type: Air	$150	Best air cooler made. Massive — check clearance with tall RAM. Quieter than any AIO.
Corsair iCUE H150i Elite LCD XT premium-gimmick type: AIO 360mm	$280	LCD screen on pump = gimmick. Pay for the looks.
Phanteks T30 + Glacier One 360 premium type: Custom-grade AIO	$250	T30 fans are legendary. Diminishing returns vs Arctic at 2× price.
DeepCool LT720 value type: AIO 360mm	$130	Good Arctic alternative. Slightly louder. Same performance tier.
Thermalright Phantom Spirit 120 SE budget type: Air	$40	Insane value air cooler. Beats $80 coolers. Adequate for 9700X, marginal for 9950X.

Case — Airflow > Aesthetics

Why it matters: Bad cases create thermal throttling. Good cases stay cool quietly. Future-proof for dual GPU = full tower with 3-slot GPU clearance.

Spec Literacy

size

ITX (smallest) < mATX < ATX (mid-tower) < E-ATX (full tower). You want full tower for dual GPU future + airflow + cable mgmt.

airflow_design

Mesh front panels = best airflow. Glass front panels = furnace. Define 7 = mesh option. Lian Li O11 = glass (but huge mesh elsewhere).

GPU_clearance

5080 = 305mm length, 3-slot. 5090 = 330-360mm, 3.5-slot. Measure max GPU length in case specs.

radiator_support

Top: 360mm common. Front: 360mm common. Side: less common. Plan radiator placement.

drive_bays

2.5" SSD bays + 3.5" HDD bays. NVMe goes on mobo. HDD bays for future media/backup storage.

cable_routing

Behind motherboard tray + velcro straps + grommets. Full tower has more room. Define 7 wins here.

dust_filters

All intakes should have removable washable filters. Define 7 is exemplary.

Current Landscape

Part	Price	Notes
Fractal Design Define 7 (regular) premium-silent size: Mid-tower (large)	$200	Your locked choice. Silent-focused, mesh front available, exemplary build quality.
Fractal Design Define 7 XL premium-silent-xl size: Full tower	$270	Extra room for dual GPU + radiator + HDDs. Future-proof.
Lian Li O11 Dynamic EVO XL showcase size: Full tower	$230	Showcase case. Three glass panels. Excellent thermals despite glass. Noisier.
Fractal Meshify 2 balanced size: Mid-tower	$170	Define 7 with mesh front. Slightly louder, slightly cooler.
be quiet! Silent Base 802 premium-silent size: Mid-tower	$200	Define 7 competitor. Silence-focused, swappable panels (mesh/closed).
Phanteks Eclipse G500A value size: Mid-tower	$130	Best value mesh. Solid build, great airflow.
NZXT H7 Flow value-clean size: Mid-tower	$130	Clean look, good airflow, easy to build in.

The Configurations

Builds

Five paths to the same goal, with honest trade-offs for each.

Gaming and LLM workstation with RGB lighting

Five Configurations

Same Goal, Different Paths

From $2,360 to $7,429 — every build has a thesis and honest trade-offs.

Each build represents a coherent answer to a different question. The locked plan answers what's the disciplined path. The used 3090 answers what's the fastest path. The 5090 dream answers what's the no-compromise path. Read each pros/cons honestly — the right build for you is the one whose cons you can live with.

Compare Builds Side-by-Side

Pick two to see them next to each other.

Build A

Build B

Build Config

THE LOCKED PLAN — 12-Month Staged

What we agreed on. RTX 5080, 64GB, ProArt, staged purchase.

Total

$3,893

Monthly

350

Gaming

1440p ultra / 4K high

LLM Ceiling

14B Q8 / 24B Q4 / 27B Q3

Parts

Category	Part	Price
CPU	Ryzen 9 9950X	$489
Cooler	Arctic Liquid Freezer III 360	$130
Mobo	ASUS ProArt X870E-Creator WiFi	$510
RAM	G.Skill Trident Z5 Neo 64GB DDR5-6000 CL30	$430
GPU	RTX 5080 16GB (MSI/Zotac)	$999
Storage 1	WD Black SN850X 2TB	$250
Storage 2	Samsung 990 Pro 2TB	$280
PSU	Corsair HX1200i 1200W Platinum	$320
Case	Fractal Design Define 7	$200
Fans	Arctic P14 PWM × 3	$35
UPS	APC Back-UPS Pro 1500VA	$220
OS	Windows 11 Pro	$30

Pros

Best long-term flexibility
Stays within $350/mo
Validated use case first
Upgrade path open

Cons

12 months to complete
16GB VRAM ceiling for first year
Some 'shortage tax' on RAM/storage

Upgrade Path

Replace 5080 w/ 5090 in 2027 OR add 2nd 5080 for dual-GPU 32GB

Build Config

USED 3090 — Fast Track Budget

Used RTX 3090 24GB, full build in 4-5 months.

Total

$2,360

Monthly

400

Gaming

1440p high, 4K capable

LLM Ceiling

32B Q4 fits comfortably

Parts

Category	Part	Price
CPU	Ryzen 7 9700X	$280
Cooler	Thermalright Phantom Spirit 120 SE	$40
Mobo	Gigabyte B850 Aorus Elite WiFi	$220
RAM	G.Skill Trident Z5 Neo 64GB DDR5-5600 CL30	$380
GPU	RTX 3090 24GB (used, eBay/r/HWS)	$750
Storage	WD Black SN850X 2TB	$250
PSU	Corsair RM850x 850W Gold	$150
Case	Phanteks Eclipse G500A	$130
UPS	APC Back-UPS 1000VA	$130
OS	Windows 11 Pro	$30

Pros

32B models run TODAY
Done in 4-5 months
Lowest total cost
Strong used GPU value

Cons

Used GPU risk
3090 power-hungry (350W)
Older platform features
Gaming weaker than 5080

Upgrade Path

Replace 3090 w/ 5090 later. Or sell at minimal loss.

Build Config

5090 DREAM — No Compromise

If money truly weren't a concern. RTX 5090 + 9950X3D + 96GB.

Total

$7,429

Monthly

Out of plan

Gaming

4K ultra everything, ray tracing maxed

LLM Ceiling

32B Q8 / 70B Q3 in VRAM

Parts

Category	Part	Price
CPU	Ryzen 9 9950X3D	$659
Cooler	Noctua NH-D15 G2	$150
Mobo	ASUS ProArt X870E-Creator WiFi	$510
RAM	G.Skill Trident Z5 Neo 96GB DDR5-6400	$720
GPU	RTX 5090 32GB (street price)	$3200
Storage 1	Samsung 990 Pro 2TB	$280
Storage 2	Samsung 9100 Pro 4TB	$750
PSU	Seasonic PRIME TX-1300 Titanium	$440
Case	Fractal Define 7 XL	$270
UPS	CyberPower CP1500PFCLCD	$250
OS	Windows 11 Pro retail	$200

Pros

No compromises at all
70B local models possible
Future-proof for 3+ years

Cons

3× your budget
Doesn't match your monthly cash flow
Shortage premium ($1200 over MSRP on GPU)

Upgrade Path

Eventual dual 5090 for true datacenter-at-home

Build Config

MAC STUDIO M5 MAX — The Apple Alternative

128GB unified memory. Excellent LLM, no gaming.

Total

$4,799

Monthly

N/A

Gaming

Mac gaming is limited (Whiskey/CrossOver, no AAA)

LLM Ceiling

70B Q4 comfortably, 120B Q3 possible

Parts

Category	Part	Price
Whole unit	Apple Mac Studio M5 Max, 128GB unified, 2TB SSD	$4799

Pros

Unified memory = 128GB 'VRAM' equivalent
Silent operation
Tiny footprint
Power efficient (200W max)

Cons

Gaming essentially excluded
Can't upgrade anything ever
Apple tax on storage/memory
macOS limits some LLM tooling

Upgrade Path

None. Sealed unit.

Build Config

DUAL 5080 FUTURE STATE — Tensor Parallelism

What the locked build evolves into in year 2.

Total

$4,850

Monthly

After year 1, ~$80/mo for 12 mo

Gaming

Single 5080 used for gaming (SLI dead)

LLM Ceiling

32B Q8 split across 2 cards via vLLM

Parts

Category	Part	Price
Existing locked build	Everything from locked plan	$3850
GPU 2	Second MSI/Zotac RTX 5080 16GB (used or new at MSRP)	$800
Cables	Additional 12V-2x6 cables	$30
Fans (heat mgmt)	2× more case fans for dual GPU heat	$25
Riser cable	PCIe 5.0 riser if vertical mount needed	$145

Pros

32GB combined VRAM for LLMs
Existing build untouched
Tensor parallelism in vLLM/llama.cpp

Cons

LLM-only benefit (no gaming dual-GPU)
Heat management complex
Two cards = two failure points

Upgrade Path

Sell pair → single 5090 if desired

The Hunt

Market Intel

Where to buy, what to watch for, when to act.

Patience is the cheapest performance upgrade you can buy. The same RTX 5080 that costs $1,400 in February sells at $999 by May. Knowing the seasonality, the right retailer for each part, and the red flags on used goods can save more money than any single component choice.

Where to Buy New

Physical (in-store)

Microcenter

Best for: CPU+Mobo+RAM bundles, in-stock GPU drops, lowest prices on AMD

Strengths: Bundle discounts ($100-200 off CPU+mobo+RAM), MSRP GPU stock more often than online, No tax in some states, Open-box deals

Weaknesses: Must drive there, Limited locations, Stock varies by store

Pro Tip

Saturday morning before 11 AM = best stock. Get on their 'in-stock notifications' list for hot items.

Online + physical

Best Buy

Best for: GPU at MSRP, Apple products, store financing

Strengths: NVIDIA Founders Edition exclusive, 0% financing on store card, Local pickup option, Easy returns

Weaknesses: Higher prices on non-GPU parts, Limited PC component selection

Pro Tip

Online

Newegg

Best for: PSUs, storage, cases, niche brands

Strengths: Largest PC parts selection, Frequent combo deals, Newegg gift card stacking

Weaknesses: Third-party seller risk (verify 'Shipped & Sold by Newegg'), RMA process can be painful

Pro Tip

Filter by 'Shipped & Sold by Newegg' only. Avoid marketplace sellers for important parts.

Online

Amazon

Best for: Peripherals, cables, accessories, fast shipping

Strengths: Prime shipping, Easy returns, Subscribe-and-save for small parts

Weaknesses: Counterfeit components risk (RAM, SSDs, cables), Mixed warehouse inventory, Higher prices on GPUs/CPUs

Pro Tip

Verify 'Ships from and sold by Amazon.com'. Avoid third-party for CPUs/GPUs/storage.

Online

B&H Photo

Best for: Workstation parts, professional gear, sales tax-free in many states

Strengths: No sales tax outside NY/NJ, Excellent customer service, Real human chat

Weaknesses: Smaller selection than Newegg, Closes Saturdays for Sabbath

Pro Tip

Check B&H first if you're in a no-NY-tax state — savings can be 8-10%.

The Used Market

Used hardware often delivers 80% of the performance at 50% of the cost — but the risk profile changes completely. Vet sellers like you'd vet a patient's history: look for the red flags first.

Reddit community marketplace

r/hardwareswap

Best for: Used GPUs, CPUs, RAM at fair prices from PC builders

Strengths: Verified user profiles (heatware), Mod-enforced rules, Mostly honest sellers, PayPal G&S protection

Weaknesses: Karma/comment requirements to post, Negotiation expected, No platform escrow

Rules:

Always PayPal Goods & Services (NEVER Friends & Family for purchases)
Check user's r/hardwareswap heatware before buying
Verify timestamped photos in listing

Pro Tip

Use r/hardwareswap_meta searches to check prices. Comment 'PM sent' on listings — quick responders trust faster.

Auction + Buy It Now

eBay

Best for: Used GPUs (with buyer protection), bulk lots, rare parts

Strengths: Strong buyer protection (eBay Money Back Guarantee), Wide selection, Auction snipes can save money

Weaknesses: Mining cards common, Sellers often inflate condition, Returns can be hassled

Rules:

Only buy from 99%+ feedback sellers
Look for 'Local pickup' for high-value items
Filter 'Buy It Now' for fixed prices

Pro Tip

Sort 'Sold' listings by date to see TRUE market price. Use eBay alerts for specific models.

Local in-person

Facebook Marketplace

Best for: Local pickup, cash deals, full prebuilt PCs

Strengths: Test before buy, No shipping cost or damage, Cash = no fees

Weaknesses: Zero buyer protection, Scammers common, Meeting strangers

Rules:

ALWAYS meet in public (police station parking lots ideal)
Bring a laptop with HWInfo/GPU-Z to test on-site
Cash only, never wire/Zelle/CashApp

Pro Tip

Build a quick GPU stress test USB stick: Ubuntu live + glmark2 + nvidia-smi monitoring.

Local in-person

Craigslist

Best for: Same as FB Marketplace — local cash deals

Strengths: Less scam-prone than FB, Established for tech sales

Weaknesses: Declining usage, Same in-person risks

Pro Tip

Most active in metro areas. Search 'gaming pc' rather than specific parts for bundle deals.

Aggregator (not seller)

r/buildapcsales

Best for: Daily curated deals at major retailers

Strengths: Community-vetted deals, Price history context, Hot deals get top-voted

Weaknesses: Deals expire fast, Some posts are affiliate-driven

Pro Tip

Subscribe to RSS feed: reddit.com/r/buildapcsales/.rss → pipe to your daily briefing.

Red Flags by Component

Each component has its own failure modes. These are the signals that should make you walk away from a "great deal."

RED FLAGS

gpu

Heat damage near power connector (browning/melting on 12VHPWR)
Missing original box (often ex-mining)
Dust caked deep in fins (sustained 24/7 use indicator)
Backplate screws stripped (opened repeatedly)
Seller can't produce purchase receipt for warranty transfer
Listing says 'works great' but no benchmark numbers

RED FLAGS

cpu

Bent pins on AM5 (LGA — pins on mobo) or LGA1700 (pins on chip)
Thermal paste residue still on IHS (lazy seller, but not catastrophic)
Seller says 'overclocked daily' (degradation risk over time)
No box / no original packaging

RED FLAGS

ram

Heatsink labels peeling (often relabeled cheap RAM)
Mismatched serial numbers on a 'kit'
Speed claims don't match SPD chip (verify with CPU-Z screenshot)
Bent pins on DIMM contacts

RED FLAGS

psu

Coil whine reported by seller (unfixable, will only get worse)
Bulging capacitors visible through vent
Burning smell ever reported
Heavy use in mining rig (PSUs degrade hard under sustained 90% load)
Past 7 years old (cap aging — replace, don't buy used)

RED FLAGS

ssd

Power-on hours >5000 (check SMART data)
Used percentage / wear leveling >20%
Reported uncorrectable errors in SMART log
No SMART screenshot in listing (deal-breaker)

Seasonality — When to Buy

Month	Market Behavior
January (CES)	New product announcements. Last-gen prices may drop. Don't buy at MSRP for 30 days after CES.
February-March	Tax refund season. Demand up. Prices flat. Bad time to buy.
April	Post-tax dip. Some sales begin.
May-June	Build season starts. Memorial Day sales (last Monday of May).
July	Amazon Prime Day (mid-July). Real deals on storage, peripherals. GPU deals rare.
August	Back-to-school. Laptop focus, not desktop parts. Some monitor sales.
September	New school year. Some component dumps as students sell.
October	Pre-holiday slowdown.
November	BLACK FRIDAY WEEK. Real GPU/CPU sales. PSUs, cases, storage all discounted. Best buying month.
December	Cyber Monday + holiday sales. PSUs and storage continue. GPU stock thin.

Deal Hunting Tools

PCPartPicker price tracker (sign up, set alerts on specific parts)
CamelCamelCamel for Amazon price history
Slickdeals.net hot deals (PC components forum)
r/buildapcsales subreddit RSS feed
Discord servers: r/hardwareswap notifier bots
Microcenter in-store stock checker (3rd party tools track inventory)
Newegg Shuffle for GPU drops (lottery system, free to enter)

The Bigger Picture

Ecosystem

Four devices, one architecture, complementary roles.

Four Devices, One Network

Complementary, Not Redundant

Each device earns its place. Together they cover every use case from pocket to datacenter.

Each device does what it's best at. They talk to each other. The Optiplex never sleeps and never gets in your way. The MacBook goes where you go. The new PC carries the heavy compute. The iPhone connects you to all of it.

Your 4-Device Architecture

Always-on backbone

OptiPlex 7050 (Ubuntu)

Specs: i7-6700T, 32GB, 1-2TB SSD (upgrade), Intel HD 530

Why: 35W TDP = $5/month electricity for 24/7 uptime. Perfect always-on tier.

Runs:

Open WebUI (LLM frontend)
Ollama (small models)
Plex/Jellyfin
Home Assistant
Tailscale exit node
n8n automation
Reverse proxy (Caddy)
Backup service (restic)
Future: live data fetcher for encyclopedia

Mobile command center

MacBook M4 Air 24GB

Specs: M4, 24GB unified memory, macOS

Why: 24GB unified memory runs 14-30B models surprisingly well. Best mobile LLM machine you can own.

Runs:

LM Studio / Ollama with MLX
VS Code
Browser, daily driver
Claude API for orchestration

Heavy compute station

New PC (post-Month 12)

Specs: 9950X, 64GB DDR5, RTX 5080, dual-boot Win+Linux

Why: On when you're using it. Sleeps when you're not. Carries the heavy LLM workload.

Runs:

Local LLMs at GPU speed (Qwen3 14B-24B)
Gaming
Heavy compute when home
Future: dual GPU, 5090 swap

Pocket dashboard

iPhone 15

Why: Universal access to your ecosystem via Tailscale + Open WebUI mobile.

Runs:

Open WebUI via Tailscale
Home Assistant mobile app
Quick LLM queries from anywhere

Networking & Access

Service	Purpose	Complexity	Cost
Tailscale	Secure remote access to all devices, no port forwarding, works through CGNAT	Easy	Free for personal (up to 100 devices)
Caddy reverse proxy	Pretty URLs (ollama.local, plex.local) + automatic HTTPS	Medium	Free
Pi-hole / AdGuard Home	Network-wide ad blocking, DNS sinkhole, telemetry blocking	Easy	Free
WireGuard (alternative to Tailscale)	Self-hosted VPN if you don't trust 3rd parties	Hard	Free
Cloudflare Tunnel	Expose specific services publicly without opening ports	Medium	Free

Integration Patterns

PATTERN

LLM Federation

Open WebUI on Optiplex routes queries to whichever machine has the right model loaded.

Example: Quick question → Optiplex's Qwen 4B. Code task → MacBook's Qwen 14B. Big reasoning task → new PC's 24B.

PATTERN

Mobile-First Access

iPhone hits any service through Tailscale + Open WebUI.

Example: On a coffee shop wifi, ask your local LLM about a clinical question — encrypted to your Optiplex, response stays private.

PATTERN

Distributed Storage

Optiplex serves files; all devices mount via SMB/NFS.

Example: PDFs of papers stored once on Optiplex; accessible from MacBook for review, new PC for RAG indexing.

PATTERN

Backup Chain

Important data on each device → Optiplex (primary backup) → cloud (Backblaze B2 / iDrive / encrypted Drive).

Example: 3-2-1 rule: 3 copies, 2 different media, 1 offsite.

Power Budget

What it costs to run your ecosystem 24/7. At $0.12/kWh national average.

Device	Idle Draw	Peak Draw	Duty Cycle	Monthly Cost
OptiPlex 7050	25W	65W	24/7	$2.16
New PC (idle)	85W	650W	8h/day	$2.45
New PC (LLM inference)	—	450W	2h/day	$3.24
New PC (gaming)	—	600W	1h/day	$2.16
Network gear	15W	20W	24/7	$1.30
UPS overhead	10W	—	24/7	$0.86
Estimated monthly total				~$12

Context

$12/month for a private AI inference server + gaming rig + media server + home automation hub. A single Claude Pro subscription is $20/month. Running your own stack is cheaper than most people think.

Optiplex Autonomous Service Stack

The Optiplex is your always-on home server. 35W idle = ~$2.50/month electricity. Docker handles the whole stack — each service runs in its own container, isolated and restartable.

One-Time Setup

Install Docker + Compose once, then every service below deploys with docker compose up -d. All data persists in named volumes on the Optiplex's SSD.

Service	Category	Image	Port	Setup Time	What It Does
Ollama	AI	ollama/ollama	11434	5 min	Local LLM inference server — CPU only on Optiplex, but fast enough for 7B models
Open WebUI	AI	ghcr.io/open-webui/open-webui	3000	5 min	Chat UI for Ollama. Multi-user, model switching, RAG, web search. Your private ChatGPT.
AdGuard Home	Network	adguard/adguardhome	3053/53	15 min	Network-wide ad/tracker blocking. DNS server. Faster than Pi-hole, better UI.
Tailscale	Access	tailscale/tailscale	–	10 min	Zero-config VPN. Access every service on your Optiplex from anywhere, securely.
Caddy	Network	caddy:alpine	80/443	20 min	Reverse proxy. Maps ollama.home → port 11434. Handles LAN HTTPS with self-signed cert.
n8n	Automation	n8nio/n8n	5678	10 min	Visual workflow automation. 500+ integrations. Your automation command center.
Jellyfin	Media	jellyfin/jellyfin	8096	20 min	Open-source Plex. Streams your media library to any device. No subscription.
Nextcloud AIO	Storage	nextcloud/all-in-one	8080	30 min	Self-hosted Dropbox. Files, calendar, contacts, notes — syncs to all devices.
Uptime Kuma	Monitor	louislam/uptime-kuma	3001	5 min	Monitors all your services. Pings you if anything goes down. Pretty dashboard.
Stirling PDF	Utility	frooodle/s-pdf	8081	5 min	Local PDF toolkit. Merge, split, compress, OCR, rotate — no cloud uploads.
Portainer CE	Mgmt	portainer/portainer-ce	9000	5 min	GUI for managing all your Docker containers. See logs, restart, update from browser.
Vaultwarden	Security	vaultwarden/server	8082	20 min	Self-hosted Bitwarden password manager. Your passwords, your server, no subscription.
Restic + cron	Backup	restic/restic	–	30 min	Encrypted backup of all Docker volumes to Backblaze B2. Runs nightly via cron.

Network-Wide Ad Blocking — AdGuard Home

Point your router's DNS at the Optiplex's IP and every device on your network — phone, TV, laptop — gets ad/tracker blocking without installing anything on each device. Blocks at the DNS level: ads never load, they simply don't resolve.

Setup in 15 Minutes

# 1. Deploy AdGuard Home
docker run -d   --name adguardhome   -p 3053:3000   -p 53:53/tcp   -p 53:53/udp   -v adguard_work:/opt/adguardhome/work   -v adguard_conf:/opt/adguardhome/conf   --restart unless-stopped   adguard/adguardhome

# 2. Visit http://optiplex-ip:3053 → setup wizard
# 3. In your router: set DNS to optiplex-ip (primary)
#    Use 1.1.1.1 as fallback so internet still works if Optiplex reboots

Recommended Blocklists

AdGuard DNS filter — the default, covers 90% of ads
OISD Full — comprehensive, low false positives: https://big.oisd.nl
Steven Black hosts — adds social media trackers
1Hosts (Pro) — aggressive, may need whitelisting for some services
HaGeZi Multi Pro — covers telemetry, malware, phishing

With OISD Full + AdGuard + HaGeZi: typically block 20–40% of all DNS queries on a home network. That's 20–40% of traffic that never hits the internet — faster browsing, less data used.

What Gets Blocked

Google/Meta ad networks · YouTube pre-roll ads (partially) · Tracking pixels · Telemetry from Windows/Samsung/LG TVs · Smart TV data collection · App analytics · Crypto mining scripts · Phishing domains · Malware C&C domains

Stat: Your Network

After 1 week, AdGuard shows you exactly how many queries per device, which services generate the most tracker traffic, and which apps are phoning home. Revelatory for seeing what Samsung TVs do in the background.

Custom DNS Rewrites

Use the DNS rewrites table to make Optiplex services accessible by name on your LAN:

ollama.home  → 192.168.1.x
webui.home   → 192.168.1.x
n8n.home     → 192.168.1.x
plex.home    → 192.168.1.x

LLM Automation Pipelines — n8n + Ollama

n8n connects to Ollama's REST API to build autonomous AI workflows. These run on a schedule with zero intervention — they fire, do work, and post results wherever you want them (email, Slack, Telegram, a file, a webhook).

Daily — 7am

AI Morning Briefing

Fetches top stories from RSS feeds (HuggingFace Daily Papers, r/LocalLLaMA, r/buildapcsales, pharmacy/clinical news), sends each batch to Qwen3 7B for a 3-sentence summary, then assembles a single digest and sends it to your phone via Telegram or emails it.

n8n nodes: Cron trigger → RSS Feed (×4) → Merge → Ollama Chat (summarize) → Telegram

{
  "model": "qwen3:7b",
  "prompt": "Summarize in 3 sentences for a clinical pharmacist who also runs local AI: {{$json.content}}",
  "stream": false
}

Every 6h

GPU / Hardware Price Monitor

Hits CamelCamelCamel RSS feeds and r/buildapcsales for RTX 5080, Ryzen 9 9950X, and DDR5 deals. If price drops below threshold, sends an immediate Telegram alert. Zero manual checking needed.

n8n nodes: Cron trigger → HTTP Request (price APIs) → IF (price < threshold) → Telegram alert

# Price check via CamelCamelCamel RSS
# RTX 5080 ASIN: varies — check product page
THRESHOLD = 999  # buy trigger price
# n8n sends alert when current_price <= THRESHOLD

Nightly — midnight

Clinical Literature Digest

Queries PubMed API for new publications on 3 drug classes you're tracking (customize to your practice area). Sends abstracts to DeepSeek R1 7B with prompt: "Summarize clinical implications for a hospital pharmacist." Posts digest to your Nextcloud Notes or emails it.

n8n nodes: Cron → PubMed API (×3 searches) → Merge → Ollama Chat (DeepSeek R1) → Nextcloud file write

Weekly — Sunday 6pm

Financial Snapshot Generator

Hits Yahoo Finance / Alpha Vantage API for your tracked tickers (your holdings, watchlist). Pulls current P/E, 52-week position, YTD return. Feeds data to Qwen3 7B: "As a value investor with a 10-year horizon, summarize these positions in 3 bullet points each." Sends weekly report.

n8n nodes: Cron → HTTP (Yahoo Finance) → Ollama Chat → Gmail / Telegram

On trigger

Drug Interaction Checker (Webhook)

n8n webhook endpoint accepts a POST with a drug list. Queries OpenFDA API for known interactions, then sends the full list + clinical context to DeepSeek R1 14B (on Optiplex or routed to new PC): "As a clinical pharmacist, identify the 3 most clinically significant interactions and recommend management." Returns JSON response.

Callable from your phone, browser, or any automation. Private, no PHI leaves your network.

Nightly — 2am

Web Scraper: eBay Sold Listings

Scrapes eBay completed/sold listings for GPU and CPU searches using the eBay Finding API (free tier). Tracks average sold price over 30 days, writes to a local SQLite DB, and generates a price trend chart. Feeds into the Market Intel tab's "Live Data" section once the new PC is built.

Tools: n8n HTTP Request → eBay API → Function (parse JSON) → SQLite node → Telegram if trend shifts >10%

Crawlers & Data Collection

Lightweight Python scripts running as cron jobs or Docker containers — not full scraping frameworks, just targeted data collection for your specific needs.

Crawls daily

HuggingFace Model Tracker

Polls the HuggingFace Hub API for models tagged "text-generation" sorted by trending. Stores a daily snapshot in SQLite. Compares to yesterday — sends a Telegram message if any model > 1K likes appeared in the last 24h. Your early-warning system for new model releases.

import requests, sqlite3
from datetime import date

r = requests.get("https://huggingface.co/api/models",
  params={"sort":"trending","limit":50,"filter":"text-generation"})
models = r.json()
# store + diff vs yesterday → alert if new entries

Crawls every 4h

r/buildapcsales Monitor

Hits Reddit's JSON API for r/buildapcsales. Filters for posts containing "5080", "9950X", "DDR5", "NVMe" with flair "GPU", "CPU", or "Storage" and upvotes > 50. Sends title + link + price to Telegram immediately. You see good deals within minutes of posting.

KEYWORDS = ["5080", "9950x", "ddr5 64gb", "990 pro 2tb"]
r = requests.get("https://www.reddit.com/r/buildapcsales/new.json",
  headers={"User-agent": "optiplex-monitor/1.0"})
posts = r.json()["data"]["children"]
# filter + alert on keyword match

Crawls hourly

arXiv AI Paper Feed

Queries arXiv API for cs.AI + cs.LG papers submitted in the last 24h. Filters for keywords: "local inference", "LLM benchmark", "quantization", "RLHF", "reasoning". Sends a batch summary every morning via n8n → Ollama → Telegram.

import feedparser
feed = feedparser.parse(
  "https://export.arxiv.org/rss/cs.AI")
recent = [e for e in feed.entries
  if any(kw in e.title.lower()
    for kw in ["llm","inference","reasoning"])]
# forward to n8n webhook for LLM summarization

Runs nightly

Portfolio Price Fetcher

Uses yfinance (free, no API key) to pull daily close prices for your holdings + watchlist. Writes to a local SQLite database. After 90 days, you have enough history to plot meaningful charts locally. Never sending portfolio data to a third-party app.

import yfinance as yf
TICKERS = ["VOO", "VTI", "AAPL", "BRK.B"]
data = yf.download(TICKERS, period="1d")
# store to SQLite, compute daily pnl
# serve via local Flask dashboard

Full Docker Compose — Optiplex Stack

Deploy the full stack with one command. Save as ~/docker/compose.yaml then run docker compose up -d. Edit the commented variables for your environment.

version: "3.9"

services:
  ollama:
    image: ollama/ollama
    container_name: ollama
    volumes:
      - ollama_data:/root/.ollama
    ports:
      - "11434:11434"
    restart: unless-stopped

  open-webui:
    image: ghcr.io/open-webui/open-webui:main
    container_name: open-webui
    depends_on: [ollama]
    environment:
      - OLLAMA_BASE_URL=http://ollama:11434
    volumes:
      - webui_data:/app/backend/data
    ports:
      - "3000:8080"
    restart: unless-stopped

  adguardhome:
    image: adguard/adguardhome
    container_name: adguardhome
    volumes:
      - adguard_work:/opt/adguardhome/work
      - adguard_conf:/opt/adguardhome/conf
    ports:
      - "53:53/tcp"
      - "53:53/udp"
      - "3053:3000"   # web UI (avoid port 80 conflict)
    restart: unless-stopped

  n8n:
    image: n8nio/n8n
    container_name: n8n
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=changeme  # change this
      - N8N_HOST=n8n.home
      - WEBHOOK_URL=http://n8n.home:5678/
    volumes:
      - n8n_data:/home/node/.n8n
    ports:
      - "5678:5678"
    restart: unless-stopped

  jellyfin:
    image: jellyfin/jellyfin
    container_name: jellyfin
    volumes:
      - jellyfin_config:/config
      - /mnt/media:/media:ro   # point to your media folder
    ports:
      - "8096:8096"
    restart: unless-stopped

  uptime-kuma:
    image: louislam/uptime-kuma:1
    container_name: uptime-kuma
    volumes:
      - kuma_data:/app/data
    ports:
      - "3001:3001"
    restart: unless-stopped

  portainer:
    image: portainer/portainer-ce:latest
    container_name: portainer
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - portainer_data:/data
    ports:
      - "9000:9000"
    restart: unless-stopped

volumes:
  ollama_data:
  webui_data:
  adguard_work:
  adguard_conf:
  n8n_data:
  jellyfin_config:
  kuma_data:
  portainer_data:

Startup Order

1. Install Docker CE on Ubuntu: curl -fsSL https://get.docker.com | sh → 2. Add user to docker group: sudo usermod -aG docker $USER → 3. Place compose.yaml → 4. docker compose up -d → 5. Set Tailscale as last step so VPN is live for remote access immediately.

The Force Multiplier

Automation

Make your devices earn their keep.

Automation should remove drudgery, not create it. Each one earns its keep only if it saves more time than it cost to build — measured over six months, not six days. The best ones run silently and you forget they exist.

Platforms

Optiplex (Docker) · Medium

n8n

Self-hosted workflow automation. Visual node-based editor. 500+ integrations.

Best for: API integrations, scheduled tasks, complex multi-step workflows

Better than Zapier/Make for self-hosted — your data stays local

Optiplex (HAOS in VM, or Docker) · Medium-Hard

Home Assistant

Home automation hub. Manages smart devices + automations.

Best for: Lights, climate, sensors, cameras, voice assistants

Open ecosystem. Local control. Integrates with everything.

Optiplex (Docker) · Medium-Hard

Node-RED

Flow-based programming. Heavier engineering than n8n.

Best for: IoT, MQTT, complex logic flows

When n8n isn't powerful enough

Optiplex (native) · Easy

Cron + Bash/Python

Classic Linux scheduling. Old-school but bulletproof.

Best for: Simple scheduled scripts, backups, data fetching

Zero overhead. No service to manage.

iPhone · Easy

Shortcuts (iOS)

Apple's automation for iPhone.

Best for: Personal automations triggered by location/time/event

Lives where you do. Triggers from Siri, NFC tags, focus modes.

Ten Recipes Worth Building

Medium

Daily Clinical Briefing

Flow: n8n: 6 AM cron → fetch FDA drug shortages RSS + PubMed new in your specialties + r/medicine top posts → Qwen 14B summarizes → email to you

Value: 5 min reading instead of 60 min scrolling

Medium

PDF Auto-Filing

Flow: Folder watch on Downloads → Phi-4 classifies (paper/monograph/admin/other) → moves to right folder + extracts metadata to SQLite

Value: Searchable paper library without manual filing

Medium-Hard

Calendar-Aware LLM Context

Flow: Before meetings: Shortcut grabs attendees → searches your notes for prior interactions → summary to your Apple Watch

Value: Walk into every meeting prepared

Medium

Vancomycin Calc Validation Bot

Flow: When you push new code to TDM repo → run test suite with 50 known cases → if any fail, halt deployment + Slack alert

Value: Never ship a TDM bug to production

Easy

Backup Verification

Flow: Weekly: restic check on Optiplex → if errors, email + push notification. Monthly: actually restore a random file to verify integrity.

Value: Backups you can trust

Medium

Smart Home Wakeup

Flow: Home Assistant: alarm goes off → lights gradient to warm → coffee maker on → news briefing on speaker → blinds open after 10 min

Value: Morning routine that runs itself

Easy

PubMed Watch

Flow: Cron: daily PubMed search for new vancomycin/AUC papers → Qwen summarizes abstracts → markdown file in Obsidian vault → tagged for review

Value: Stay current on YOUR research interests automatically

Easy

Drug Shortage Tracker

Flow: Cron: FDA drug shortage JSON pull → diff against yesterday → if formulary drugs affected → Slack + email

Value: Know about shortages before pharmacy meeting

Medium

Energy/Cost Monitoring

Flow: Home Assistant + smart plugs on Optiplex/PC → daily power consumption → monthly dashboard of what costs what

Value: Know if running LLMs 24/7 is worth the electricity

Medium-Hard

Voice-to-Clinical-Note

Flow: iPhone Shortcut: record voice → Whisper transcription → Qwen 14B formats as SOAP note → drops in Drafts app → review/edit/file

Value: Capture clinical observations hands-free

Caveat: NEVER use for actual PHI documentation — this is for personal teaching cases only

Docker Install Stack — Your Optiplex Server

Complete from-scratch setup. Run these sequentially on a fresh Ubuntu 22.04 installation.

Docker Engine — Ubuntu/Debian

# Install Docker Engine (one-liner)
curl -fsSL https://get.docker.com | sh

# Add your user to docker group
sudo usermod -aG docker $USER
newgrp docker

# Verify
docker run hello-world
docker compose version

Portainer — Web UI for all containers

Manage every container via browser at :9000. Essential for the Optiplex.

docker run -d \
  --restart=always \
  -p 9000:9000 \
  -v /var/run/docker.sock:/var/run/docker.sock \
  -v portainer_data:/data \
  --name portainer \
  portainer/portainer-ce:latest

# http://optiplex-ip:9000 → set admin password

n8n — Workflow Automation

# docker-compose.yml
services:
  n8n:
    image: n8nio/n8n
    restart: unless-stopped
    ports: ["5678:5678"]
    volumes:
      - n8n_data:/home/node/.n8n
    environment:
      - N8N_BASIC_AUTH_ACTIVE=true
      - N8N_BASIC_AUTH_USER=admin
      - N8N_BASIC_AUTH_PASSWORD=changeme
      - WEBHOOK_URL=http://optiplex-ip:5678

volumes:
  n8n_data:

docker compose up -d
# http://optiplex-ip:5678

Ollama — On the Optiplex (LAN accessible)

# Install on Ubuntu
curl -fsSL https://ollama.ai/install.sh | sh

# Expose on LAN — edit the systemd service
sudo mkdir -p /etc/systemd/system/ollama.service.d
echo '[Service]
Environment="OLLAMA_HOST=0.0.0.0"' | \
  sudo tee /etc/systemd/system/ollama.service.d/override.conf

sudo systemctl daemon-reload
sudo systemctl restart ollama

# Now call from MacBook:
# curl http://optiplex-ip:11434/v1/models

Home Assistant — Home Automation

mkdir -p ~/homeassistant/config

docker run -d \
  --name homeassistant \
  --privileged \
  --restart=unless-stopped \
  -e TZ=America/New_York \
  -v ~/homeassistant/config:/config \
  --network=host \
  ghcr.io/home-assistant/home-assistant:stable

# http://optiplex-ip:8123

Open WebUI on Optiplex (LAN server)

docker run -d \
  --restart unless-stopped \
  -p 3000:8080 \
  -e OLLAMA_BASE_URL=http://localhost:11434 \
  -v open-webui:/app/backend/data \
  --network=host \
  --name open-webui \
  ghcr.io/open-webui/open-webui:main

# MacBook: http://optiplex-ip:3000
# Sign in → full ChatGPT-like UI against your local models

The Library

Media Servers

Self-hosted libraries — own what you watch, listen to, and read.

Self-hosted media is about ownership, organization, and access — not piracy. Buy the content, rip it for your library, stream it everywhere.

Legal Note

Owning physical media (DVDs, Blu-rays, books) gives you broad rights to make backup copies for personal use under most jurisdictions. Sharing or distributing those copies is where it crosses lines. This encyclopedia assumes you're operating in the legal personal-use space.

The Stack

Optiplex (Docker)

Jellyfin

Purpose: Media server (video, music, books)

Fully open source. No subscription. No telemetry. Hardware transcoding free (Plex paywalls this).

Optiplex (Docker)

Plex

Purpose: Media server (more polished UI)

Better remote streaming (built-in NAT traversal). Larger ecosystem. Better metadata matching.

⚠ Telemetry-heavy. Increasing ad-supported content push. Account required.

Cost: Plex Pass ($120 lifetime) needed for hardware transcoding + offline downloads

Optiplex (Docker)

Navidrome

Purpose: Music streaming (Spotify replacement)

Subsonic-compatible. Tiny resource footprint. Excellent mobile clients (Substreamer, play:Sub).

Scenario: Your purchased MP3/FLAC library → access from anywhere

Optiplex (Docker)

AudioBookshelf

Purpose: Audiobook + podcast server

Open source Audible-killer. Tracks progress across devices. Auto-downloads podcasts.

Scenario: Audiobooks you bought (Libro.fm, libraries via Libby exports)

Optiplex (Docker)

Immich

Purpose: Photo backup + management (Google Photos replacement)

Mobile app auto-uploads from iPhone. AI face recognition (local). Album sharing.

Optiplex (Docker)

Calibre-web

Purpose: E-book library

Your books accessible everywhere. Send to Kindle/Kobo. OPDS feed for readers.

Scenario: Your DRM-free purchased ebooks + scanned reference texts

Optiplex (Docker)

Komga / Kavita

Purpose: Comics / manga server

If you read comics. Mobile clients are excellent.

Hardware Considerations

Storage is the biggest spend — plan for 8-16TB total over time
HDDs (WD Red Pro, Seagate IronWolf) for media bulk. NVMe for cache.
Optiplex i7-6700T can transcode 1080p H.264 in real-time via QuickSync. Struggles with 4K/HEVC.
ZFS or BTRFS for data integrity if you go beyond 8TB
Optiplex has limited drive bays — consider external USB enclosure (DAS) or upgrading to a NAS box (Synology DS923+ ~$600) when collection grows
RAID is not backup. Always have offline + offsite backup of irreplaceable content (photos especially).

Library Organization

Movies: /Media/Movies/Movie Name (Year)/Movie Name (Year).mkv
TV: /Media/TV/Show Name/Season XX/Show Name - SXXEXX - Episode Name.mkv
Music: /Media/Music/Artist/Album (Year)/XX - Track.flac
Photos: Let Immich organize by date. Don't manually file.
Audiobooks: /Audiobooks/Author/Series #X - Book Title/
Use *arr stack (Sonarr/Radarr/Lidarr/Readarr) ONLY if you understand the legal landscape in your jurisdiction

The Mechanics

Finance

Credit mechanics, financing tools, and the pitfalls that catch smart people.

Validate before financing. Stage rather than splurge. Pay yourself first. Never carry a balance you can't kill quickly. These principles guided this PC plan, but they apply to every major purchase for the rest of your life.

Your Situation

690 currently (you mentioned recent drop). Target 720+ in 6 months by NOT opening new accounts.

What Moves Your Credit Score

Payment history (35%)

Pay everything on time. Set autopay for minimums. One missed payment drops 60-110 points.

Credit utilization (30%)

Total balances ÷ total limits. Keep under 30%, ideal under 10%. The PC build risks this if you concentrate on one card.

Length of history (15%)

Don't close old cards. Average age of accounts matters.

Credit mix (10%)

Having a mix (revolving + installment) helps. Mortgage + cards + car loan = ideal mix.

New credit (10%)

Each hard pull dings 3-5 points temporarily. Multiple in 30 days = bigger ding. Why we limit your build to 2 hard pulls.

Where to Check Your Score

annualcreditreport.com — free actual reports from all 3 bureaus, weekly access since 2023
Credit Karma — free score estimates (VantageScore, not FICO — directionally accurate)
Your bank app — most show FICO score for free monthly (Chase, Discover, Citi all do)
Experian app — free FICO score, alerts, fraud monitoring

Financing Tools

50-60% chance

0% APR Purchase Credit Card

Examples: Wells Fargo Reflect (21mo), Chase Freedom Unlimited (15mo), Citi Simplicity (21mo)

How it works: True 0% APR for promo period on new purchases. NO retroactive interest if not paid off.

Risk: Low — just pay above minimum each month

70-80% chance

Store Card with Deferred Interest

Examples: Best Buy (18-24mo), Microcenter (6mo), Home Depot

How it works: 0% during promo, BUT retroactive interest charged on entire purchase if any balance remains at end

Risk: HIGH if you miss the deadline by even $1

Safety: Pay off 1-2 months BEFORE deadline, never on it

90%+ chance

BNPL — Pay-in-4 (Affirm, Klarna, Afterpay)

How it works: Split purchase into 4 biweekly payments. Usually 0% interest.

Risk: Low IF paid on time. Since 2025, Klarna/Affirm report to bureaus — missed payment now hurts credit.

Best for: Items under $500 that you'll pay off in 6 weeks anyway

Likely approved, but high APR

BNPL — Long-term Financing

How it works: 6-36 month installments. APR varies 0-36% based on credit.

Risk: Higher — at 690 credit, your APR likely 10-25%, NOT 0%

⚠ 0% offers often only show at checkout AFTER approval — don't assume you'll get 0%

Possible at 12-18% APR. Credit union usually best.

Personal Loan

Examples: SoFi, LightStream, Marcus, your credit union

How it works: Fixed payments, fixed APR, predictable

Risk: Lowest — predictable, no compounding

Best for: Consolidating multiple high-rate debts

60-70% chance

0% Intro Balance Transfer

How it works: Move existing debt to a new card at 0% for 15-21 months. Usually 3-5% transfer fee.

Risk: Same deferred-interest risk as store cards (sometimes — read fine print)

Best for: Strategic move if you already have credit card debt

Pitfalls

PITFALL

Deferred Interest Retroactive Charge

Scenario: Best Buy 18mo financing. Pay $999 GPU down to $1 by month 18. Forget to pay the dollar. Get charged 30% APR × $999 × 18mo retroactively.

Cost: $300-500 unexpected charge

Prevention: Set autopay for minimum + manual payments above. Pay off 2 months before deadline.

PITFALL

Multiple BNPL Stack

Scenario: 3 Klarna + 2 Affirm + 1 Afterpay simultaneously. Lose track of due dates.

Cost: Missed payments → 60+ point credit drop + 30% APR on balances

Prevention: Max 2 active BNPL at any time. Track due dates in single calendar.

PITFALL

Minimum Payment Death Spiral

Scenario: $5,000 on a 24% APR card. Pay minimum ($100). Takes 14 years to pay off. Pay $7,500 in interest.

Cost: 150% of original purchase in interest

Prevention: Never carry credit card balances beyond promo period. Treat min payment as minimum, not target.

PITFALL

Annual Fee Renewal

Scenario: Opened card for signup bonus. Forgot to cancel. Year 2 charges $95 AF you didn't budget for.

Cost: $95-695 per card

Prevention: Calendar reminder 11 months after opening. Downgrade to no-fee version or cancel.

PITFALL

Co-signed Debt

Scenario: Helped a family member. They miss payments. Your credit destroyed.

Cost: 100+ point credit drop + legal liability

Prevention: Never co-sign for anyone you can't afford to pay off entirely yourself.

The Operating Principles

Pay yourself first — automatic transfer to savings BEFORE you see your paycheck
Emergency fund = 3-6 months of expenses, untouchable
Validate purchases by waiting 30 days for anything over $500 (you did this with the PC — good)
Test before buy — could you use this for 30 days for free/cheap? (Like Optiplex LLM testing before $4K PC)
Total cost > sticker price — include financing fees, AF, time-cost of management
If you can't pay cash, you can't afford it — financing is a tool, not a license

Income Expansion

The same infrastructure you're building for learning can generate revenue. The Optiplex runs today. The new PC multiplies throughput. Every side gig below uses skills you already have or are actively building.

The Compound Principle

Each project sharpens skills that make the next project faster. Clinical writing improves your pharmacy practice. Automation consulting teaches you tools you use at home. The flywheel is the point.

Tier 1 — Start This Week (Optiplex + MacBook)

Zero additional investment. Uses hardware and skills you have right now.

Clinical + LLM · $500-2,000/mo

Medical Writing & Formulary Reviews

What: Drug monographs, formulary evaluations, P&T committee summaries, medication use evaluations. Hospitals and PBMs outsource this constantly.

Your edge: Clinical pharmacist who can use local LLMs for first-draft literature synthesis, then apply expert judgment. 3x faster than manual.

Tools: Qwen3 14B (literature synthesis) + PubMed API + your clinical expertise

First step: Write one sample drug monograph for a recently approved medication. Post on LinkedIn. Reach out to 5 community hospitals without a dedicated drug info pharmacist.

Revenue model

$200-500 per monograph. $1,000-3,000 per formulary review. Recurring if you land a quarterly P&T contract.

Clinical · $300-1,500/mo

CE/CME Content Development

What: Create continuing education modules for pharmacists and nurses. ACPE-accredited providers always need content experts.

Your edge: LLMs draft case studies and assessment questions. You provide clinical accuracy and ACPE formatting expertise.

Tools: Local LLM for draft generation + Canva/LaTeX for slides

First step: Contact 3 ACPE providers (PharmCon, Power-Pak, FreeCE) about their content pipeline. Offer a 1-hour module on a trending topic (GLP-1 agonists, vancomycin AUC monitoring).

Revenue model

$500-2,000 per CE module. Some providers pay royalties per completion.

Tech · $200-800/mo

Automation Consulting for Small Practices

What: Set up n8n workflows, self-hosted tools, and basic automation for independent pharmacies, small clinics, dental offices.

Your edge: You understand clinical workflows AND tech. Most IT consultants don't know pharmacy. Most pharmacists don't know Docker.

Tools: n8n on your Optiplex (demo environment) + Tailscale for remote setup

First step: Automate one workflow at your own workplace. Document the before/after (time saved, errors prevented). That's your case study.

Revenue model

$500-1,500 per setup + $50-200/mo maintenance retainer. 3-5 clients = meaningful passive income.

Content · $100-500/mo

Technical Writing & Tutorials

What: Blog posts, tutorials, and guides about local LLMs, self-hosting, or clinical informatics. Medium, Dev.to, Substack, or your own site.

Your edge: "Clinical pharmacist who runs local LLMs" is a unique perspective. The intersection is underserved.

Tools: Your daily experience + any writing platform

First step: Write "How I Use Local LLMs in Clinical Pharmacy (Without Sending PHI to the Cloud)" — that title alone gets clicks.

Revenue model

Medium Partner Program, Substack paid subscriptions, or sponsored posts. Slow build but compounds with audience.

Watch: The Pharmacist-Tech Intersection

A Day in the Life of an Informatics Pharmacist

Career Path Pharmacy + Tech

Tier 2 — Month 3+ (Validated Skills)

Requires the foundation from Tier 1 plus some proof of work. Higher revenue ceiling.

Clinical + Tech · $2,000-5,000/mo

Clinical Decision Support Tools

What: Build calculators, dosing tools, or clinical dashboards. Vancomycin AUC calculator. Antibiogram visualizer. Renal dose adjustor.

Your edge: You ARE the domain expert. Most dev shops building health tools have zero clinical pharmacists on staff.

Tools: FastAPI/Flask + your clinical knowledge + local LLM for code assistance

First step: Build your vancomycin AUC calculator as a web app. Open-source it. Present it at your state pharmacy association meeting. That talk becomes your sales pitch.

Revenue model

SaaS: $10-50/user/mo for institutional licenses. Or sell as a consulting engagement ($5K-20K per build). Open-source version drives leads.

Tech · $1,000-4,000/mo

Self-Hosted Infrastructure Setup

What: Set up Jellyfin, Immich, Home Assistant, Nextcloud, Pi-hole for privacy-conscious professionals. White-glove home server builds.

Your edge: You've built this exact stack. Your Optiplex is a living demo.

Tools: Docker Compose templates + your Optiplex as reference + Tailscale for remote admin

First step: Create a "Home Server Starter Kit" — a polished docker-compose repo with docs. Share on r/selfhosted. First 3 clients come from there.

Revenue model

$500-1,500 per setup. Hardware markup (you source the Optiplex). $75/mo support retainer. Referral network builds fast.

Clinical · $1,500-4,000/mo

Pharmacy Informatics Consulting

What: Help hospitals optimize EHR workflows, build clinical rules, improve alert fatigue, design order sets. The intersection of pharmacy + IT that every health system needs.

Your edge: Clinical pharmacist who codes. This is one of the most in-demand skill combos in healthcare right now.

Tools: Your clinical license + programming skills + knowledge of FHIR/HL7

First step: Get CPIP (Certified Professional in Health Informatics) or take a health informatics certificate. Build one EHR optimization case study at your current job.

Revenue model

$75-150/hr consulting. Some firms hire part-time remote. This can become a full career pivot if you want it to.

AI + Clinical · $1,000-3,000/mo

LLM Fine-Tuning & Validation Services

What: Help healthcare orgs evaluate, validate, and fine-tune LLMs for clinical use. Run your "Generalized Vancomycin Test" protocol as a paid service.

Your edge: You've already built the validation framework (Pillar 02). Most orgs want to use AI but don't know how to validate it safely.

Tools: Your validation protocol + local GPU for testing + structured report templates

First step: Validate 5 models against your clinical test battery. Publish results as a white paper. Present at ASHP Midyear or a health-AI conference.

Revenue model

$2,000-10,000 per validation engagement. Recurring as models update quarterly.

Investment Fundamentals

Your $350/month savings habit is the foundation. Once the PC is built and debt-free (Month 15), that $350/month redirects to wealth building. Here's the playbook.

The Priority Stack

Money flows in this exact order. Don't skip levels.

Foundation

Emergency Fund

3-6 months expenses in HYSA. This is untouchable. Currently earning 4.5-5% APY at Marcus, Ally, Discover, or Wealthfront. Your $350/mo savings during PC build months goes here first.

Status: protect at all costs

Employer Match

401(k) / 403(b) Match

Contribute at least enough to get full employer match. This is literally free money — 50-100% instant return. If your employer matches 4%, contribute 4% minimum.

Free money — never leave this on the table

High-Interest Debt

Kill Toxic Debt

Pay off anything above 8% APR aggressively. Credit cards (24%), personal loans (15%), store cards. No investment consistently beats 24% guaranteed return of paying off debt.

Debt-free except mortgage/student loans

Tax-Advantaged

Roth IRA / HSA

Max Roth IRA ($7,000/yr in 2026) and HSA if eligible ($4,300 individual). Roth grows tax-free forever. HSA is triple-tax-advantaged — the best account in the tax code.

Roth IRA $583/mo HSA $358/mo

Tax-free growth engine running

Growth

Taxable Brokerage

Everything above goes into a simple 3-fund portfolio. VTI (US total market) + VXUS (international) + BND (bonds). Set allocation based on age and risk tolerance. Automate monthly buys.

Wealth compounds while you sleep

Where to Open Accounts

Best overall

Fidelity

Zero-fee index funds (FZROX, FZILX). No minimums. Excellent app. Roth IRA + brokerage + HSA all in one place. Cash management account replaces a bank.

Best for simplicity

Vanguard

The original index fund company. VTI/VXUS/BND are the gold standard. Interface is dated but reliable. Best if you want to set-and-forget.

Best UI + learning

Schwab

Modern interface. Good research tools. Free stock slices for learning. Merged with TD Ameritrade — strong platform.

The Simple Portfolio

Bogleheads 3-Fund Portfolio — Ultimate Guide

Investing Index Funds

Simple Investing for Beginners — 3-Fund Portfolio

Beginner Getting Started

The 3-Fund Portfolio

This is what most financial advisors charge 1% AUM to do. You can do it yourself for 0.03% expense ratio.

Fund	Ticker	Allocation (age 25-35)	Expense Ratio	What It Holds
US Total Market	VTI / FZROX	60%	0.03% / 0.00%	Every US public company (3,700+)
International	VXUS / FZILX	25%	0.07% / 0.00%	Every non-US developed + emerging market
Bonds	BND / FXNAX	15%	0.03% / 0.03%	US investment-grade bonds (stability)

Rebalance annually (sell winners, buy losers to maintain target %). As you age, shift bonds up 1% per year. That's the entire strategy. It beats 90% of actively managed funds over 20 years.

Project Starter Kits

Actionable blueprints for each growth vector. Each kit lists what you need, what you build first, and how it compounds into the next level.

Growth Vector

Clinical Practice Enhancement

Build Sequence

Week 1: Set up Ollama + Open WebUI on Optiplex. Run your first DDx exercise with DeepSeek R1 14B
Week 2-4: Build vancomycin AUC calculator (Python/Flask). Test against 20 known cases
Month 2: Create RAG pipeline over your institution's antibiogram + IDSA guidelines
Month 3: Present tool at department meeting. Collect feedback. Iterate
Month 4: Submit abstract to state pharmacy conference
Month 6: Publish case series on clinical LLM use in pharmacy practice

Compounds into: Informatics consulting, CE content, conference speaking, clinical tool SaaS

Growth Vector

Technical Skill Stacking

Build Sequence

Week 1: Complete one project with Devstral/Qwen Coder on your Optiplex. Push to GitHub
Month 1: Set up full self-hosted stack (Jellyfin, Immich, Caddy, Pi-hole). Document everything
Month 2: Build 3 n8n automations that save you real time. Measure hours saved
Month 3: Contribute to one open-source project (Open WebUI, Ollama, a clinical tool)
Month 4: Create your first Docker Compose template repo. Share on r/selfhosted
Month 6: First paid automation client (from your case study + network)

Compounds into: Freelance dev work, self-hosted consulting, SaaS products, open-source reputation

Growth Vector

Financial Independence Engine

Build Sequence

Week 1: Open HYSA if not done. Set up $350/mo auto-transfer
Month 1: Open Roth IRA (Fidelity). Set up $100/mo auto-invest into FZROX
Month 3: Review employer 401(k) — are you getting full match? Adjust if not
Month 6: First side gig revenue hits. Funnel 50% to Roth, 50% to brokerage
Month 12: PC build complete. Redirect $350/mo → investments
Month 15: Debt-free. All discretionary income → wealth building

Compounds into: Financial independence. $350/mo in VTI from age 30 = ~$850K by 60 at historical returns

Growth Vector

Autonomous Systems & Self-Improvement

Build Sequence

Week 1: Set up daily briefing automation (n8n → RSS → LLM summary → email)
Month 1: Build Obsidian vault with daily notes. Local LLM indexes and connects ideas
Month 2: Create personal dashboard (Grafana or custom) — track habits, spending, project progress
Month 3: Set up PubMed watch + drug shortage tracker automations
Month 4: Build voice-to-note pipeline (Whisper → Qwen → structured notes)
Month 6: Your ecosystem runs 10+ automations silently. You're the curator, not the operator

Compounds into: Every automation frees time for higher-value work. The system improves itself as you add to it

The Credential Stack

Strategic certifications that multiply your value at each intersection.

Credential	Cost	Time	What It Unlocks
Board Certified Pharmacotherapy (BCPS)	$400	Exam prep: 3-6 mo	Clinical credibility. Required for many clinical positions. Higher pay tier.
CompTIA Security+ / Linux+	$400	Self-study: 2-3 mo	IT credibility. Opens informatics consulting. Validates self-hosted infrastructure skills.
Health Informatics Certificate	$2,000-5,000	6-12 mo (part-time)	Formal bridge between clinical + tech. AMIA-recognized programs. Career pivot enabler.
AWS / GCP Cloud Cert	$300	Self-study: 1-2 mo	Cloud credibility. Pairs with self-hosted knowledge for consulting. Most healthcare is moving to cloud.
Project Management (CAPM/PMP)	$400-600	2-4 mo	Consulting credibility. Required by many healthcare orgs for informatics roles.

Strategy

Don't collect credentials for their own sake. Each one should unlock a specific revenue stream or career move you've already identified. BCPS + informatics certificate + a portfolio of clinical tools = a $140K+ pharmacy informatics position.

Monthly Income Target Roadmap

Timeline	Side Income Target	Primary Sources	Reinvestment
Months 1-3	$0-200/mo	1 medical writing gig, 1 blog post	100% → HYSA / Roth IRA
Months 4-6	$300-800/mo	Recurring writing + 1 automation client	50% invest, 50% reinvest in tools/certs
Months 7-12	$800-2,000/mo	CE content + consulting + tool revenue	50% invest, 30% reinvest, 20% lifestyle
Year 2+	$2,000-5,000/mo	Multiple streams compounding	Auto-invest the majority. You've built the machine

The Point

This PC build isn't a $4,000 expense. It's a $4,000 investment in infrastructure that generates returns — skills, tools, revenue, and compounding knowledge — for years. The ROI is the person you become while building it.

Investment Philosophy

Warren Buffett built Berkshire Hathaway into the world's most successful investment operation using a handful of principles that have outperformed nearly every hedge fund, quant strategy, and trend-follower over 60 years. These principles adapt — but don't fundamentally change — in a world of AI disruption, rate volatility, and compressed information cycles.

The Core Insight (Buffett, 1949 — via Benjamin Graham)

"Price is what you pay. Value is what you get." Every good investment starts with understanding the gap between these two numbers. When price is far below value, the margin of safety absorbs your mistakes.

The Buffett Principles — Updated for 2026

PRINCIPLE 1

Buy Businesses, Not Tickers

A stock is a fractional ownership stake in a real business. Before buying, ask: "Would I buy this entire company if I could?" If not, don't buy 10 shares either. The ticker symbol is just the handle on the asset.

2026 adaptation: This principle has never been harder to apply with AI-adjacent companies. Evaluate the moat and earnings power, not just the AI narrative. Ask: "If AI commoditizes in 3 years, what's this business worth then?"

PRINCIPLE 2

The Margin of Safety

Buy at a significant discount to intrinsic value. If you calculate a business is worth $100/share, don't pay more than $60–70. The gap is your margin of safety — it absorbs bad estimates, recessions, and black swans.

2026 adaptation: In a world of AI-hyped valuations, margins of safety are rarer. This means holding more cash (Berkshire currently holds $334B) and being willing to wait. Opportunity cost is real, but so is capital loss.

PRINCIPLE 3

Circle of Competence

Only buy businesses you genuinely understand — their economics, competitive dynamics, and failure modes. A small circle of competence, applied rigorously, beats a wide circle applied carelessly.

Your circle: Healthcare / pharma (clinical depth), technology infrastructure (building it yourself), consumer staples (you use them), financial services (you're learning this deeply). Don't drift into sectors you can't evaluate honestly.

PRINCIPLE 4

Economic Moat — Durable Competitive Advantage

A business worth owning has structural protection against competition. The five classic moats:

Network effects — value grows with each new user (Visa, Meta, LinkedIn)
Switching costs — painful to leave (Salesforce, Epic Systems, Adobe)
Cost advantages — produce cheaper than competitors (Amazon logistics, GEICO)
Intangible assets — brands, patents, regulatory licenses (Coca-Cola, Moody's)
Efficient scale — market only supports one or two players (railroads, pipelines)

AI moat watch: AI can erode switching cost moats if it dramatically lowers switching friction. Watch closely in software.

PRINCIPLE 5

Long Time Horizon — Think in Decades

Buffett's favourite holding period is "forever." Compounding is the most powerful force in finance, but it requires time to express itself. A $10,000 investment at 10% annual return = $67K after 20 years, $174K after 30, $452K after 40.

The implication: Taxes and transaction costs destroy compounding. Every sale triggers taxes. The best tax strategy is often "don't sell." Berkshire has held Coca-Cola since 1988 and Apple since 2016.

PRINCIPLE 6

Mr. Market — Volatility is Your Friend

Graham's "Mr. Market" allegory: the market is a manic-depressive business partner who offers to buy or sell your shares every day. On bad days, he panics and offers absurdly low prices. On good days, he's euphoric and overpays. Your job is to take advantage of his mood swings, not follow them.

2026 reality: Algo trading, 24/7 news, and social media make Mr. Market more manic than ever. Use this. A 20% drawdown on a business you understand is a buying opportunity, not a reason to panic-sell.

Portfolio Architecture — The Foundation

Before individual stock picking, the asset allocation decision drives 90%+ of long-term returns. Get the foundation right first.

The Three-Fund Portfolio (Jack Bogle / Vanguard)

For most investors, three index funds cover the entire investable universe at minimal cost. This is the baseline — individual stock picks layer on top, not instead of.

Fund	Ticker	Covers	Expense Ratio	Role
US Total Market	VTI / FSKAX	US stocks (all cap)	0.03%	Core growth engine
International	VXUS / FTIHX	All non-US developed + EM	0.07%	Geographic diversification
US Bonds	BND / FXNAX	US investment-grade bonds	0.03%	Volatility buffer, income

Age-Based Allocation Guide

These are starting points. Your risk tolerance and specific situation matter more than age-based rules.

Age Range	US Stocks	Intl Stocks	Bonds	Rationale
20s–30s	60%	30%	10%	Max growth. Long runway absorbs downturns.
40s	55%	25%	20%	Slight de-risk. Major expenses (house, education) approaching.
50s	45%	20%	35%	Capital preservation becomes more important.
60s+	35%	15%	50%	Income + stability. Less time to recover from drawdowns.

Account Priority Order

401k up to employer match (free money — always)
Max HSA if available ($4,300 individual 2026)
Max Roth IRA ($7,000 / $8,000 if 50+)
Max 401k remainder ($23,500 2026 limit)
Taxable brokerage for the rest

The HSA Hack

An HSA is triple tax-advantaged: contributions deductible, growth tax-free, withdrawals tax-free for medical expenses. After 65, withdrawals for any reason taxed like a traditional 401k — making it a de facto second retirement account. Invest HSA funds in index funds, pay medical costs out-of-pocket, save receipts forever. Withdraw decades later.

Roth vs Traditional

Roth: pay tax now, growth and withdrawals tax-free. Traditional: defer tax now, pay later. Rule of thumb: if you expect to be in a higher tax bracket in retirement, Roth wins. For pharmacists, income typically stays high — Roth usually wins. Backdoor Roth if income exceeds limits ($161K single 2026).

Individual Stock Analysis — The Buffett Toolkit

When you look at individual companies beyond index funds, these are the metrics and questions that matter. Build a checklist and only buy when every box is checked.

VALUATION

Key Ratios & What They Mean

Ratio	Formula	Good Range	Buffett View
P/E	Price / EPS	10–25×	Low P/E for cyclicals; pay up for quality growers
P/B	Price / Book Value	<3×	Loves companies trading at or below book
P/FCF	Price / Free Cash Flow	15–25×	FCF is real money; earnings can be manipulated
ROE	Net Income / Equity	>15%	Consistent high ROE = durable competitive advantage
ROIC	NOPAT / Invested Capital	>12%	Best indicator of capital allocation quality
Debt/Equity	Total Debt / Equity	<0.5	Avoid companies that need debt to grow
Current Ratio	Current Assets / Liabilities	>1.5	Can they pay near-term bills without selling assets?

QUALITATIVE CHECKLIST

Before Any Purchase

Can I explain this business in 2 sentences to a 10-year-old?
What's the moat? Would it still exist in 10 years?
How does the company make money when things go wrong?
Is management honest? Have they done what they said they'd do in past letters/calls?
Do insiders own significant stock? Are they buying, not just compensated with options?
What kills this business? (Technological disruption, regulatory change, commoditization)
At this price, what return do I need to generate a 12%+ IRR over 10 years?
Would I be comfortable if the market closed for 5 years and I couldn't sell?

INTRINSIC VALUE

Simplified DCF Framework

Discounted Cash Flow: estimate future free cash flows, discount back to present value at your required return rate (usually 10–12%). Simple model:

# Simplified DCF — 10-year horizon
fcf_today = 5_000_000_000  # $5B FCF
growth_rate = 0.08          # 8% annual growth (conservative)
discount_rate = 0.10        # 10% required return
terminal_multiple = 15      # exit P/FCF assumption

dcf_value = 0
for yr in range(1, 11):
    fcf_year = fcf_today * (1 + growth_rate) ** yr
    dcf_value += fcf_year / (1 + discount_rate) ** yr

terminal_value = (fcf_today * (1 + growth_rate)**10     * terminal_multiple) / (1 + discount_rate)**10
intrinsic_value = dcf_value + terminal_value

# Compare to current market cap — buy at 60-70% of this

READ THIS

Buffett's Annual Letters

The best free investment education available. Berkshire's annual letters (1965–present) are at berkshirehathaway.com/letters. Start with 1977 (when Buffett took over writing style), then 1983, 1987, 1996, 2008, 2014.

Essential books:

The Intelligent Investor — Graham (read chapters 8 & 20 first)
Security Analysis — Graham & Dodd (reference text)
Common Stocks and Uncommon Profits — Philip Fisher (growth + quality lens)
The Little Book That Still Beats the Market — Joel Greenblatt (Magic Formula)
Poor Charlie's Almanack — Munger (mental models for investing + life)

Options Strategy — Generating Income on Holdings

Prerequisite

Options are derivatives. Understand the underlying business first — options are just the financing tool. Never use options to speculate on businesses you wouldn't own. The strategies below are income-generating on positions you'd hold anyway.

STRATEGY 1 — Beginner

Covered Call (Sell a Call You Can Cover)

Setup: You own 100 shares of XYZ at $50. Sell a call option with strike $55, expiring in 30 days, collecting $1.50 premium = $150 cash today.

Outcomes:

XYZ stays below $55: keep $150 premium + shares. Do it again next month.
XYZ rises above $55: shares called away at $55. Profit = $500 gain + $150 premium = $650. You miss upside above $55.
XYZ drops: you keep $150 premium, which partially offsets the loss.

Best for: Shares you're willing to sell at the strike price anyway. Great for generating 1–3% monthly income on large positions.

STRATEGY 2 — Beginner

Cash-Secured Put (Get Paid to Agree to Buy)

Setup: You want to buy XYZ at $45 (it's trading at $50). Sell a put option with strike $45, expiring in 30 days, collecting $1.00 = $100 cash. Keep $4,500 in cash as collateral (margin for the put).

Outcomes:

XYZ stays above $45: keep $100 premium, never bought. Do it again.
XYZ drops below $45: you buy 100 shares at $45 (your target anyway). Net cost = $45 – $1 = $44 effective cost basis.

Best for: Stocks you genuinely want to own at a lower price. The wheel strategy: sell CSP → get assigned → sell covered call → repeat.

STRATEGY 3 — Intermediate

LEAPS — Long-Dated Calls as Stock Substitute

Setup: Instead of buying 100 shares of XYZ at $50 ($5,000), buy 1 deep-in-the-money call with strike $35, expiring in 2 years, for $1,600. You control 100 shares for $1,600 instead of $5,000.

Why: Delta of 0.85+ means the option moves almost dollar-for-dollar with the stock. Frees up $3,400 of capital. Buffett used LEAPS in his early partnerships. Limited downside = only lose the premium paid.

Risk: No dividends received. Time decay accelerates in final months. Must roll or exercise before expiry.

RISK MANAGEMENT

Options Rules to Never Break

Never sell naked calls (unlimited risk — EVER)
Never allocate more than 5% of portfolio to a single options position
Never sell options on earnings week unless intentional (volatility crush or expansion)
Always have the cash or shares to fulfill assignment obligations
Use limit orders, never market orders on options (bid-ask spreads are wide)
If you don't understand the Greeks (delta, theta, vega), paper trade first for 3 months
Don't sell options on stocks you wouldn't own at any strike that gets assigned

Market Dynamics in the AI Era

How AI is Reshaping Competitive Moats

AI doesn't destroy all moats equally. It tends to erode information asymmetry moats (the analyst who "knows" a sector better) while strengthening distribution and network-effect moats (the platform that owns the distribution channel). Key shifts:

Information moats weakening: Any edge derived from processing more data faster is now commoditized. Research firms, legal discovery, financial analysis — all squeezed.
Execution moats strengthening: Companies that combine AI capabilities with irreplaceable physical assets (railroads, pipelines, specialized manufacturing, healthcare infrastructure) are harder to disrupt.
Customer intimacy moats compressing: AI lowers switching costs by reducing integration complexity. Watch enterprise SaaS carefully — the "too painful to switch" argument weakens as AI makes migration easier.
New moat: AI infrastructure ownership. NVIDIA, TSMC, and cloud hyperscalers (MSFT, GOOG, AMZN) hold the physical compute layer. Models are commoditizing; compute isn't.

Sectors Worth Watching

Sector	AI Impact	Moat Direction	Buffett Lens
Healthcare / Pharma	Drug discovery, diagnostics, admin	Strengthening (regulatory, clinical moats)	Strong long-term — aging demographics, pricing power
Financial Services	Fraud detection, underwriting, advisory	Mixed — incumbents vs fintechs	Moody's, Visa, AmEx — durable toll-booth businesses
Consumer Staples	Supply chain optimization, marketing	Neutral — brand moats hold	Core Buffett territory — Coke, P&G, Costco
Software / SaaS	Disruptive — AI rebuilds workflows	Weakening for commodity SaaS	Be selective — mission-critical only
AI Infrastructure	Is the sector itself	Strengthening	Shovel sellers in gold rush
Media / Advertising	AI content creation threatens CPMs	Weakening broadly	Buffett sold newspapers decades ago — for good reason

Buffett on AI (Berkshire 2024 Annual Meeting)

"I don't know much about it, but I don't think it will change what I look for in a business. I'm still looking for something where the economics are good, the management is good, and the price is right." Consistency of principle matters more than chasing the shiny thing.

The Dollar-Cost Averaging Edge

In volatile AI-driven markets, DCA is more powerful than ever. When you invest a fixed dollar amount monthly regardless of price:

You automatically buy more shares when prices are low
You buy fewer when prices are high
Over time, your average cost per share trends below the average price
You remove the psychological burden of timing the market

"Time in the market beats timing the market." A $500/month automated S&P 500 purchase misses no opportunity and requires zero decision-making.

Monthly Financial Dashboard — What to Track

A financial system you check monthly is more valuable than a sophisticated one you ignore. These are the numbers that matter.

MONTHLY CHECK — 1st of month

Net Worth Statement

Total assets minus total liabilities. Track the trend, not the number. Assets: checking + savings + investment accounts + 401k/IRA value + property value. Liabilities: student loans, mortgage, car, credit cards, personal loans.

Target trajectory: Net worth should grow by at least (income × savings rate) per year, plus investment returns. If it's not, there's a spending or allocation problem.

MONTHLY CHECK

Savings Rate

Savings rate = (income − spending) / income. This is the single most important financial lever you control. At 50% savings rate, you can retire in 17 years regardless of income. At 10%, it takes 43 years.

Savings Rate	Years to FI
10%	43 years
20%	37 years
35%	25 years
50%	17 years
65%	10.5 years
75%	7 years

QUARTERLY CHECK

Portfolio Performance vs Benchmark

Compare your total return (including dividends) to VTI (US total market). Most active investors underperform this benchmark over 10+ years. If your individual stock picks are consistently trailing VTI after fees and taxes, index funds are the better choice. No ego — be honest.

Track: total portfolio value, YTD return %, 1-year return %, 3-year annualized return %, benchmark comparison, asset allocation drift vs target.

ANNUAL CHECK

Tax-Loss Harvesting & Rebalancing

Tax-loss harvesting: In December, sell positions that are down. Realize the loss to offset capital gains elsewhere. Immediately buy a similar (not identical — wash sale rule) fund. You maintain market exposure while reducing taxable income by up to $3K/year ($6K if married).

Rebalancing: If stocks outperform, your allocation drifts. Rebalance annually back to target. Threshold rebalancing (rebalance when any asset class drifts 5%+ from target) is more tax-efficient than calendar rebalancing.

HYSA & Cash Management

BEST ACCOUNTS 2026

High-Yield Savings

Rates as of early 2026. Shop annually — rates move with Fed funds rate.

Account	APY	Notes
Marcus (Goldman Sachs)	4.50%	No fees, easy ACH transfers, FDIC
SoFi Money	4.60%	Direct deposit required for top rate
UFB Direct	4.75%	Fluctuates — verify current rate
Wealthfront Cash	5.00%	Passes through 32 FDIC partners → $8M coverage
Fidelity CMA (SPAXX)	4.96%	Government money market, instant brokerage access

CASH STRATEGY

The Cash Ladder

Don't hold all cash in one place. Stagger it by time horizon:

0–1 month expenses: Checking account (immediate access)
1–6 months: HYSA (3-5 day ACH transfer, full emergency fund)
6–12 months: 6-month T-bills (higher yield, locked for 6 months — buy at TreasuryDirect.gov, currently ~5%)
Investment cash (12+ months): Treasury money market fund (Fidelity SPAXX, Vanguard VMFXX) until deployed into equities at the right price

At 4.5%+ HYSA rates, cash earns meaningfully while waiting for opportunities. Buffett's $334B cash pile is in T-bills — the principle applies at any scale.

The Pharmacist's Financial Advantage

Your profession gives you a specific analytical edge in healthcare investing that most investors lack. You can evaluate drug pipelines, clinical trial quality, formulary dynamics, and PBM economics better than a generalist analyst can. Use it.

Healthcare Investing Edge — What You Know That Others Don't

Drug approval probability: You can read FDA PDUFA dates and understand Phase III trial design quality in ways that wall street analysts often get wrong
Generic competition timing: Patent cliffs, authorized generics, paragraph IV challenges — you understand this better than most equity analysts
PBM dynamics: CVS Caremark, Express Scripts, OptumRx — how formulary placement and rebate economics actually work. Valuable for analyzing CVS Health, UnitedHealth, Cigna.
Hospital supply chain: Which medical device companies have real institutional switching costs vs. which ones are interchangeable
Biosimilar transition timing: You see this firsthand in clinical practice before it shows up in quarterly earnings reports

Relevant Tickers in Your Circle of Competence

Company	Ticker	Your Edge	Moat Type
UnitedHealth Group	UNH	PBM + insurer dynamics, OptumRx	Scale + network
CVS Health	CVS	PBM, pharmacy, Aetna integration	Distribution + network
McKesson	MCK	Drug wholesale chain of custody	Scale + logistics
Pfizer / AbbVie	PFE / ABBV	Pipeline, patent cliff timing	Patents + pipeline
Eli Lilly	LLY	GLP-1 market, tirzepatide dynamics	Brand + pipeline
Becton Dickinson	BDX	Clinical device usage patterns	Switching costs
Danaher	DHR	Lab instrument recurring consumables	Switching costs + recurring

This is a starting list — not a buy recommendation. Run each through the qualitative checklist and DCF framework before touching any of them.

The Munger Mental Model Stack

Charlie Munger's approach: use 100+ mental models from different disciplines. For investing, the most powerful:

Inversion: "What would guarantee failure?" Then avoid that.
Compound interest: Understand it viscerally — it's counterintuitive until you live it.
Opportunity cost: Every dollar you invest somewhere is a dollar not invested elsewhere. Hold the bar high.
Lollapalooza effect: When multiple forces reinforce each other, outcomes are extreme. Look for stocks where moat + management + tailwind all align.
Availability bias: Don't overweight recent events. The last AI hype cycle doesn't mean the next one will look the same.

One Number That Matters Most

Your savings rate — not investment return, not market timing, not stock picks. At your income level as a pharmacist, a 10% improvement in savings rate adds ~$10–15K/year to your portfolio. That dwarfs the difference between a 9% and 11% investment return on a small-to-medium portfolio.

The Intelligence Layer

AI Intel

State of the frontier — models, benchmarks, breakthroughs, and how to stay current.

AI research workstation — The frontier moves fast. This tab moves with it.

The pace of AI in 2024–2026 is without precedent in software history. Models double in capability roughly every six months. Staying current isn't optional — it determines which problems you can solve and how.

Model Frontier — May 2026

Proprietary cloud and open-weights models worth knowing. Context = max tokens.

Model	Lab	Tier	Context	Modality	Best For
Claude Opus 4.7	Anthropic	Frontier	1M	Text, Vision, Code	Deep reasoning, long-context, agentic tasks (what runs this site)
Claude Sonnet 4.6	Anthropic	Frontier	200K	Text, Vision, Code	Balanced speed + quality. Best daily driver cloud model.
GPT-4.1	OpenAI	Frontier	1M	Text, Vision, Code	Instruction following, function calling, coding tasks
o3	OpenAI	Frontier	200K	Text, Code	Hard math, competition problems, PhD-level reasoning
Gemini 2.5 Pro	Google	Frontier	2M	Text, Vision, Audio, Video	Multimodal, massive context windows, research synthesis
Llama 4 Maverick 400B	Meta	Strong	1M	Text, Vision	Open weights MoE. Best open model for most tasks.
Qwen3 235B-A22B	Alibaba	Strong	128K	Text, Code	Open MoE near-frontier. Free weights. Self-host on high-VRAM server.
DeepSeek R1	DeepSeek	Strong	128K	Text, Code	Open reasoning model. Visible CoT. Distilled 14B runs locally.
Qwen3 14B / 32B	Alibaba	Local	128K	Text, Code	Your primary local models. Excellent on 16–24GB VRAM.
Devstral 24B	Mistral	Local	128K	Code	Best open coding agent. Edits files, runs tools. Pairs with VS Code.
Phi-4 14B	Microsoft	Local	16K	Text	Exceptional reasoning for 14B size. Fast on 8GB VRAM.

Benchmark Snapshot — What the Tests Actually Measure

Benchmark	Tests	Why It Matters	Leader (2026)
GPQA Diamond	PhD-level science (human experts ~69%)	Hard reasoning ceiling — can't be crammed	o3 / Gemini 2.5 Pro (~87%)
AIME 2024/25	Math Olympiad competition problems	Symbolic + numeric reasoning under pressure	o3 (~96%)
SWE-bench Verified	Real GitHub issues resolved end-to-end	Practical coding — the metric that matters for agents	Claude Opus 4.7 (~72%)
MMLU Pro	57 academic disciplines, harder questions	Broad knowledge breadth across domains	GPT-4.1 / Gemini 2.5 Pro (~91%)
HumanEval	Python function generation from docstrings	Basic coding — now saturated at 99%	Multiple models (benchmark exhausted)
LM Arena (Chatbot Arena)	Human blind preference comparisons, live	Real-world signal — what people actually prefer	lmarena.ai — updated continuously

How to Keep Up — The Essential Stack

Daily Papers

Hugging Face Daily Papers

Every significant ML paper with community notes. Best signal-to-noise for new research. huggingface.co/papers

Live Benchmark

LM Arena

Human-preference model rankings updated continuously from millions of blind comparisons. Ground truth for "which model is actually best." lmarena.ai

Open Models Ranking

Open LLM Leaderboard

Best open-weights models ranked by standardized benchmarks. Check before choosing your next local model. HuggingFace H4 space.

Newsletter

The Batch — deeplearning.ai

Andrew Ng's weekly AI digest. Consistently excellent signal-to-noise on what actually matters vs. hype cycles. Free subscription.

Blog

Simon Willison's Weblog

Practical LLM explorations from a developer's perspective. Best technical writing on what LLMs can actually do week to week. simonwillison.net

Deep Dives

Interconnects Newsletter

Nathan Lambert's RLHF and post-training deep dives. Better than any academic course for understanding how frontier models are actually built. interconnects.ai

Community

r/LocalLLaMA

Best community for local model news, benchmarks, tricks, and hardware discussions. First to post new GGUF model releases and benchmark results.

Twitter/X Follows

Signal Accounts

@ak92501 (HF paper summaries daily), @karpathy (fundamentals), @ylecun (alt views), @simonw (practical), @AnthropicAI, @OpenAI for releases.

Research DB

Papers With Code

State-of-the-art tracking with code repos linked. Best for finding what's replicable vs. theoretical. paperswithcode.com

Key Concepts — The Technical Vocabulary

Architecture Foundation

Transformers & Attention

The architecture behind every major LLM since 2017. Self-attention lets each token attend to all others — no recurrence, fully parallelizable on GPU. Scaled to billions of parameters via pretraining on internet text.

Read: "Attention is All You Need" (Vaswani et al., 2017) — the paper that started it all.

Training Technique

RLHF / DPO — Making Models Helpful

Reinforcement Learning from Human Feedback turns a raw pretrained model into an assistant. Humans rank outputs; a reward model learns preferences; the policy is fine-tuned. DPO (Direct Preference Optimization) achieves similar results without a separate reward model — simpler, often better.

This is what makes Claude helpful, not just fluent.

Deployment Pattern

RAG — Retrieval-Augmented Generation

Give a model a search tool: query a vector DB → retrieve relevant text chunks → inject into prompt → generate grounded answers. Solves hallucination for knowledge-bounded domains better than fine-tuning in most production scenarios.

Local stack: Ollama + ChromaDB + AnythingLLM

Efficiency Technique

Quantization — GGUF/GPTQ

Reduce model precision from FP32/BF16 to INT8/INT4. A 14B model at Q8 needs ~16GB VRAM; at Q4, ~9GB. Quality drop is modest for Q8, acceptable at Q4. Q2 is too aggressive. GGUF is the standard for llama.cpp/Ollama.

Rule: use Q8 if it fits, Q4 otherwise. Never Q2 for clinical tasks.

Architecture Pattern

Mixture of Experts (MoE)

Route each token through a subset of "expert" networks rather than the full model. A 235B MoE model activates ~22B parameters per forward pass — matching a 32B dense model's compute while having far more total capacity. Used in Qwen3 235B, Llama 4 Maverick, Mixtral.

Tradeoff: high total VRAM to load, but fast inference per token.

Frontier Capability

Agents & Tool Use

LLMs as planners that call external tools in loops: web search, code execution, file I/O, API calls. The model outputs structured tool calls; the runtime executes them; results feed back into context. Claude Code, AutoGen, LangGraph, and CrewAI build on this pattern.

2026 frontier: multi-agent systems coordinating autonomously over hours.

Research Radar — 2024–2025 Papers Worth Knowing

Paper / Release	Date	Why It Matters
DeepSeek R1 (DeepSeek)	Jan 2025	Open reasoning model matching o1. Visible chain-of-thought. Proved closed-source reasoning models could be replicated with open weights.
Llama 4 (Meta)	Apr 2025	MoE architecture, 10M token context Scout variant. Democratized frontier-class open weights. Changed calculus on what's runnable locally.
Qwen3 (Alibaba)	May 2025	235B MoE open weights beating GPT-4o on multiple benchmarks. Freely available. Distilled 14B/32B excellent for your local setup.
Flash Attention 2/3	2023–24	IO-aware attention algorithm. 2–4× faster inference with identical outputs. Now standard in every serious LLM runtime.
Chinchilla Scaling Laws (Hoffmann et al.)	2022	Optimal model:data ratio for compute budget. Showed GPT-3 era models were undertrained. Compute-optimal training is the standard now.
LoRA / QLoRA	2021–23	Fine-tune 7B+ models on a consumer GPU via low-rank adapters. Reduces trainable params by 10,000×. Enables personal domain fine-tuning.
Constitutional AI (Anthropic)	2022	Safety via written principles rather than labeling every preference. Backbone of Claude's alignment approach. Widely influential.

AI Timeline — Key Milestones

2017

Transformers — "Attention is All You Need." The paper that made modern AI possible.

2020

GPT-3 (175B) — Few-shot learning emerges from scale. AI becomes commercially relevant for the first time.

Nov 2022

ChatGPT — RLHF turns GPT-3.5 into an assistant. 100M users in 60 days. The public AI moment begins.

2023

GPT-4, Llama, Claude 2 — Multimodal models, open weights, capable local inference. The democratization phase begins.

Jan 2025

DeepSeek R1 — Open reasoning model from a Chinese lab matching OpenAI o1. Reshapes assumptions about frontier AI moats.

Apr–May 2025

Llama 4 + Qwen3 — Open-weights models approach frontier quality. 32B models available for local inference. Gap to closed models narrows dramatically.

2026

Agent era — Models running multi-hour autonomous tasks. Claude Code. Operator frameworks. AI coding productivity multiplied 10×+. You are here.

The Wire

Live Feed

Real-time aggregation from the sources that matter. Deals, hardware drops, LLM releases, clinical alerts.

Seven feeds, one view. Deals from r/buildapcsales surface price drops on your exact parts list. r/LocalLLaMA catches new model releases before they hit the benchmarks. Ars and Tom's cover the analysis layer. All fetched server-side, cached 15 minutes, zero tracking.

Fetching feeds from 7 sources…

Sources

DEALS

r/buildapcsales

Community-curated PC component deals. The fastest way to catch price drops on GPUs, SSDs, RAM.

LLM & AI

r/LocalLLaMA

Local LLM community. New model releases, quantization experiments, hardware benchmarks.

LLM & AI

Hugging Face Blog

Official releases and deep dives on new models, datasets, and ML techniques.

HARDWARE

r/hardware

Hardware news and analysis. Product launches, benchmark leaks, industry moves.

HARDWARE

Tom's Hardware

Reviews and benchmarks. The reference for GPU/CPU performance data.

TECH

Ars Technica

Long-form tech journalism. Deep analysis, not just news.

TECH

r/selfhosted

Self-hosting community. Docker stacks, home servers, privacy-first tools.

The Log

Journal

Log what works, what doesn't, what you learn. This is your evidence base for the Month 3 checkpoint decision.

Your Validation Journal. Write freely. Tag what mattered. Read it again at the Month 3 checkpoint — your future self will know whether the plan should hold, accelerate, or pivot.

New Entry

Entries

PRIVATE

Harvard CS50P · Python

Learning Python

Full course — lectures, slides, notes, exercises. Your personal progress tracker.

🎓 Harvard CS50's Introduction to Programming with Python

Taught by David J. Malan. All lecture videos, slides, and notes are freely provided by Harvard University under their open courseware policy. This section embeds the official YouTube lectures and links to Harvard's CDN for slides and notes. Full credit to Harvard CS50. cs50.harvard.edu/python

Access: URL hash #cs50p · ⌘/Ctrl + Shift + K or P · Triple-click the footer

Course Progress 0 / 10 weeks

Week 0 Functions, Variables Todo ▶

2:29:45

CS50P Lecture 0 — Functions, Variables

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

print() and input()
Variables: str, int, float, bool
Defining functions with def
Arguments, return values
String methods: .strip() .lower() .upper() .replace()
f-strings
type(), len()

Key Patterns

def greet(name):
    return f"Hello, {name}!"

name = input("What's your name? ").strip().title()
print(greet(name))

# Type conversion
age = int(input("Age: "))
gpa = float(input("GPA: "))
print(f"In 10 years you'll be {age + 10}")
print(f"GPA: {gpa:.2f}")

Your Markdown Notes Saved ✓

Week 1 Conditionals Todo ▶

1:29:35

CS50P Lecture 1 — Conditionals

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

if / elif / else
Comparison operators: ==, !=, <, >, <=, >=
Boolean operators: and, or, not
match statements (Python 3.10+)
Ternary expressions

Key Patterns

grade = int(input("Grade: "))

match grade // 10:
    case 10 | 9: print("A")
    case 8:      print("B")
    case 7:      print("C")
    case 6:      print("D")
    case _:      print("F")

# Ternary
x = int(input("x: "))
parity = "even" if x % 2 == 0 else "odd"
print(f"{x} is {parity}")

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Week 2 Loops Todo ▶

1:46:19

CS50P Lecture 2 — Loops

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

while loops
for loops and range()
enumerate(), zip()
List comprehensions
break, continue, pass
Iterating dicts: .items() .keys() .values()

Key Patterns

# List comprehension
evens = [x for x in range(20) if x % 2 == 0]

# Dict iteration
scores = {"Alice": 95, "Bob": 87, "Carol": 91}
for name, grade in sorted(scores.items(), key=lambda kv: kv[1], reverse=True):
    print(f"{name}: {grade}")

# while with validation
while True:
    try:
        n = int(input("Positive number: "))
        if n > 0:
            break
    except ValueError:
        pass
print(f"Got: {n}")

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Week 3 Exceptions Todo ▶

1:16:03

CS50P Lecture 3 — Exceptions

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

try / except / else / finally
ValueError, TypeError, KeyError, IndexError, FileNotFoundError
raise — custom exceptions
Exception hierarchy
Bare except vs specific exceptions

Key Patterns

def get_int(prompt):
    while True:
        try:
            return int(input(prompt))
        except ValueError:
            print("Not an integer, try again.")

age = get_int("Age: ")
print(f"Next year: {age + 1}")

class InvalidDoseError(Exception):
    pass

def validate_dose(mg):
    if mg <= 0:
        raise InvalidDoseError(f"Invalid dose: {mg}mg")
    return mg

try:
    validate_dose(-10)
except InvalidDoseError as e:
    print(f"Error: {e}")

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Week 4 Libraries Todo ▶

1:31:19

CS50P Lecture 4 — Libraries

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

import, from … import, import as
Standard library: random, statistics, sys, os, math, datetime
PyPI and pip install
requests — HTTP calls
sys.argv for CLI arguments
__name__ == '__main__'

Key Patterns

import random
import statistics
from datetime import date

ages = [random.randint(18, 80) for _ in range(100)]
print(f"Mean: {statistics.mean(ages):.1f}")
print(f"Today: {date.today()}")

# Install third-party packages
pip install requests rich typer httpx

# requirements.txt workflow
pip freeze > requirements.txt
pip install -r requirements.txt

import requests

r = requests.get(
    "https://itunes.apple.com/search",
    params={"term": "weezer", "entity": "song"}
)
for song in r.json()["results"][:3]:
    print(song["trackName"])

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Week 5 Unit Tests Todo ▶

1:00:43

CS50P Lecture 5 — Unit Tests

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

pytest framework
Test functions named test_*
assert statements
pytest.raises for exception testing
Running: pytest -v
Testing edge cases and boundaries

Key Patterns

# test_calculator.py
from calculator import square
import pytest

def test_positive():
    assert square(3) == 9

def test_negative():
    assert square(-3) == 9

def test_zero():
    assert square(0) == 0

def test_string():
    with pytest.raises(TypeError):
        square("cat")

pytest                      # run all tests
pytest -v                   # verbose
pytest test_calc.py -v      # specific file
pytest -k "test_positive"   # run matching tests
pytest --tb=short           # shorter tracebacks

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Week 6 File I/O Todo ▶

1:12:56

CS50P Lecture 6 — File I/O

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

open(), read(), readlines(), write()
with statement — context manager
Modes: r, w, a, x, rb, wb
csv module: DictReader, DictWriter
Pillow for image manipulation
os.path, pathlib.Path

Key Patterns

import csv
from pathlib import Path

# Write CSV
with open("students.csv", "w", newline="") as f:
    writer = csv.DictWriter(f, fieldnames=["name", "grade"])
    writer.writeheader()
    writer.writerow({"name": "Alice", "grade": 95})

# Read with pathlib
p = Path("students.csv")
for row in csv.DictReader(p.open()):
    print(row["name"], row["grade"])

# Safely read / write any file
try:
    with open("data.txt") as f:
        contents = f.read()
except FileNotFoundError:
    print("File not found")
except PermissionError:
    print("Access denied")

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Week 7 Regular Expressions Todo ▶

1:12:48

CS50P Lecture 7 — Regular Expressions

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

re module: search, match, fullmatch, sub, findall
Character classes: \d \w \s .
Quantifiers: * + ? {n,m}
Groups () and named groups (?P…)
Anchors: ^ $
Flags: re.IGNORECASE

Key Patterns

import re

# Validate email
pattern = r"^[\w.+-]+@[\w-]+\.[\w.]{2,}$"
if re.fullmatch(pattern, "alice@example.com", re.IGNORECASE):
    print("Valid email")

# Named capture groups
m = re.search(
    r"(?P<month>\d{1,2})/(?P<day>\d{1,2})/(?P<year>\d{4})",
    "Born: 03/15/1990"
)
if m:
    print(m.group("year"))   # 1990
    print(m.group("month"))  # 03

# sub — replace
text = "Call us at 617-495-1000 or 617-495-9999"
clean = re.sub(r"\d{3}-\d{3}-\d{4}", "[REDACTED]", text)
print(clean)

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Week 8 Object-Oriented Programming Todo ▶

2:22:04

CS50P Lecture 8 — Object-Oriented Programming

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

class, __init__, self
Instance vs. class attributes
__str__, __repr__, __eq__, __len__ — dunder methods
Inheritance and super()
@property, @classmethod, @staticmethod
Encapsulation and @property setters

Key Patterns

class Student:
    def __init__(self, name: str, grade: int):
        self.name = name
        self._grade = grade  # private by convention

    @property
    def grade(self) -> int:
        return self._grade

    @grade.setter
    def grade(self, value: int):
        if not 0 <= value <= 100:
            raise ValueError("Grade must be 0-100")
        self._grade = value

    @property
    def letter(self) -> str:
        if self._grade >= 90: return "A"
        if self._grade >= 80: return "B"
        return "F"

    def __str__(self) -> str:
        return f"{self.name}: {self.letter}"

alice = Student("Alice", 95)
print(alice)          # Alice: A

# Inheritance
class GradStudent(Student):
    def __init__(self, name, grade, research):
        super().__init__(name, grade)
        self.research = research

    def __str__(self):
        return f"{super().__str__()} [{self.research}]"

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Week 9 Et Cetera — Advanced Python Todo ▶

1:27:00

CS50P Lecture 9 — Et Cetera — Advanced Python

Harvard CS50 Python 2022

📄 Slides PDF 📝 Lecture Notes 🔗 Week Page 💻 Problem Set

Topics

Type hints and annotations
Generators and yield
Decorators with @
map(), filter(), sorted() with lambdas
*args and **kwargs unpacking
Global Interpreter Lock (GIL) awareness
__slots__ for memory optimization

Key Patterns

# Generator — lazy infinite sequence
def fibonacci():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a + b

gen = fibonacci()
print([next(gen) for _ in range(10)])

# Decorator
from functools import wraps
import time

def timer(func):
    @wraps(func)
    def wrapper(*args, **kwargs):
        t0 = time.perf_counter()
        result = func(*args, **kwargs)
        print(f"{func.__name__} took {time.perf_counter()-t0:.4f}s")
        return result
    return wrapper

@timer
def slow_sum(n: int) -> int:
    return sum(range(n))

slow_sum(10_000_000)

# Type hints
from typing import Optional

def divide(a: float, b: float) -> Optional[float]:
    return a / b if b != 0 else None

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Python Quick Reference

Types: str, int, float, bool, list, tuple, dict, set, None
Mutability: list/dict/set mutable · str/tuple immutable
Scope: LEGB — Local → Enclosing → Global → Built-in
Slicing: s[start:stop:step] works on str, list, tuple

Comprehensions:
[x for x in iter if cond] — list
{k:v for k,v in d.items()} — dict
{x for x in lst} — set
(x for x in lst) — generator

PRIVATE

Reference

CLI Command Hub

Package managers, dev tools, system commands, and everything worth having memorized.

Access: URL hash #cli · ⌘/Ctrl + Shift + C · Triple-click footer

📦 Package Managers 40+ commands ▶

Homebrew — macOS macOS

# Install Homebrew
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

brew install <package>         # install CLI tool
brew install --cask <app>      # install GUI app
brew update && brew upgrade    # update everything
brew list                      # list installed
brew search <query>            # search packages
brew info <package>            # details + deps
brew uninstall <package>       # remove
brew cleanup                   # remove old versions
brew doctor                    # diagnose issues
brew services list             # list services
brew services start <svc>      # start service
brew services stop <svc>       # stop service
brew tap <user>/<repo>         # add formula repo

apt — Ubuntu/Debian Linux

sudo apt update                # refresh package index
sudo apt upgrade -y            # upgrade all packages
sudo apt install <package>     # install
sudo apt remove <package>      # remove (keep config)
sudo apt purge <package>       # remove + config
sudo apt autoremove            # remove orphans
apt search <query>             # search
apt show <package>             # package details
apt list --installed           # list installed
sudo add-apt-repository ppa:<ppa>   # add PPA
sudo apt install -f            # fix broken deps

pip — Python packages cross-platform

pip install <package>          # install
pip install -r requirements.txt  # from file
pip install --upgrade <package> # upgrade
pip install <pkg>==1.2.3        # specific version
pip uninstall <package>         # remove
pip list                        # installed packages
pip show <package>              # details
pip freeze > requirements.txt  # export deps
pip install --user <package>   # user-level (no sudo)
pip cache purge                 # clear download cache

# Upgrade pip itself
python -m pip install --upgrade pip

npm — Node.js packages cross-platform

npm install <package>          # add to project
npm install -g <package>       # install globally
npm install --save-dev <pkg>   # dev dependency
npm uninstall <package>        # remove
npm list                       # project deps tree
npm list -g --depth=0          # global installs
npm update                     # update all
npm audit                      # security audit
npm audit fix                  # auto-fix vulns
npm run <script>               # run package.json script
npx <command>                  # run without installing
npm init -y                    # init new project

cargo — Rust cross-platform

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

cargo install <crate>          # install CLI tool
cargo install --list           # list installed
rustup update                  # update Rust toolchain
cargo new <project>            # new project
cargo build --release          # build optimized
cargo run                      # build + run
cargo test                     # run tests

🛠️ Essential Tools to Install 30+ commands ▶

macOS one-liner — upgrade your terminal macOS

brew install bat         # better cat (syntax highlighting)
brew install eza         # modern ls with icons
brew install fzf         # fuzzy finder (Ctrl+R history search)
brew install ripgrep     # rg — blazing fast grep
brew install fd          # fast find replacement
brew install zoxide      # smarter cd (learns your dirs)
brew install starship    # beautiful shell prompt
brew install htop        # better top
brew install jq          # JSON processor
brew install yq          # YAML/JSON/XML processor
brew install tldr        # simplified man pages
brew install httpie      # user-friendly HTTP client
brew install gh          # GitHub CLI
brew install git-delta   # beautiful git diffs
brew install lazygit     # terminal git UI
brew install tmux        # terminal multiplexer
brew install ncdu        # disk usage visualizer
brew install tree        # directory tree view
brew install watch       # re-run command every N sec
brew install dust        # better du
brew install procs       # better ps
brew install gping       # graphical ping
brew install bottom      # btm — system monitor
brew install hyperfine   # command benchmarking

Linux equivalents Linux

sudo apt install bat fd-find ripgrep fzf tmux htop ncdu tree
cargo install eza zoxide starship bottom dust procs

# Shell setup — add to ~/.zshrc or ~/.bashrc
eval "$(zoxide init zsh)"          # z command
eval "$(starship init zsh)"        # prompt
source <(fzf --zsh)               # Ctrl+R, Ctrl+T
alias ls='eza --icons'
alias cat='bat'
alias find='fd'
alias grep='rg'

Python dev tools

pip install black          # opinionated formatter
pip install ruff           # extremely fast linter + formatter
pip install mypy           # static type checker
pip install pytest         # test runner
pip install pytest-cov     # test coverage
pip install ipython        # better REPL
pip install rich           # beautiful terminal output
pip install typer          # build CLIs with type hints
pip install httpx          # async HTTP client
pip install pydantic       # data validation
pip install fastapi        # web API framework
pip install uvicorn        # ASGI server
pip install python-dotenv  # .env file support
pip install loguru         # better logging
pip install tqdm           # progress bars
pip install pendulum       # better datetime
pip install polars         # fast DataFrame (modern pandas)
pip install pandas numpy   # data analysis
pip install matplotlib seaborn  # plotting

🐍 Python Virtual Environments 25+ commands ▶

venv — built-in, zero deps

# Create
python3 -m venv .venv

# Activate
source .venv/bin/activate      # macOS/Linux
.venv\Scripts\activate         # Windows

# Use
pip install <package>
python script.py

# Deactivate
deactivate

# Delete env (just remove the folder)
rm -rf .venv

uv — ultra-fast modern tool (recommended 2025+)

pip install uv             # or: curl -LsSf https://astral.sh/uv/install.sh | sh

uv venv                    # create .venv
source .venv/bin/activate  # activate

uv pip install <package>   # 10-100x faster than pip
uv pip install -r requirements.txt
uv pip freeze
uv run python script.py    # run without activating

# Project management
uv init <project>          # new project
uv add <package>           # add dep to pyproject.toml
uv sync                    # install all deps
uv lock                    # generate lockfile

pipenv — Pipfile workflow

pip install pipenv

pipenv install <package>   # add dep + create Pipfile
pipenv install --dev <pkg> # dev dependency
pipenv install             # install from Pipfile
pipenv shell               # activate shell
pipenv run python script.py
pipenv graph               # dependency tree
pipenv lock                # generate Pipfile.lock
exit                       # leave shell

poetry — full project management

pip install poetry

poetry new <project>       # new project
poetry init                # init in existing dir
poetry add <package>       # add dependency
poetry add --dev <package> # dev dependency
poetry install             # install all deps
poetry shell               # activate venv
poetry run python script.py
poetry build               # build package
poetry publish             # publish to PyPI
poetry update              # update all
poetry show                # list deps

pyenv — manage multiple Python versions

brew install pyenv         # macOS
curl https://pyenv.run | bash  # Linux

pyenv install --list       # available versions
pyenv install 3.12.3       # install version
pyenv global 3.12.3        # set global default
pyenv local 3.11.9         # set project version (.python-version)
pyenv versions             # list installed
pyenv which python         # path to current python
python --version           # verify

🔀 Git Commands 45+ commands ▶

Setup & init

git config --global user.name "Your Name"
git config --global user.email "you@example.com"
git config --global init.defaultBranch main
git config --global core.editor "code --wait"
git config --global pull.rebase false
git config --list          # show all config

git init                   # new local repo
git clone <url>            # clone remote
git clone <url> <folder>   # clone into folder
git remote -v              # show remotes
git remote add origin <url>

Daily workflow

git status                 # what changed
git add <file>             # stage file
git add .                  # stage all
git add -p                 # stage hunks interactively
git commit -m "message"    # commit
git commit --amend         # amend last commit
git push                   # push to remote
git push -u origin main    # push + set upstream
git pull                   # fetch + merge
git fetch                  # fetch without merge
git fetch --prune          # fetch + remove deleted remote branches

Branching & merging

git branch                 # list local branches
git branch -a              # all branches (incl. remote)
git branch <name>          # create branch
git switch <name>          # switch to branch
git switch -c <name>       # create + switch
git merge <branch>         # merge into current
git merge --no-ff <branch> # merge with commit
git rebase <branch>        # rebase onto branch
git cherry-pick <sha>      # apply single commit
git branch -d <name>       # delete (safe)
git branch -D <name>       # force delete
git push origin --delete <name>  # delete remote branch

Inspection & history

git log --oneline          # compact log
git log --oneline --graph --all   # visual branch tree
git log -p <file>          # history of a file
git show <sha>             # show commit details
git diff                   # unstaged changes
git diff --staged          # staged changes
git diff main..feature     # between branches
git blame <file>           # who changed each line
git bisect start           # binary search for bug
git reflog                 # all HEAD movements (recovery)

Undo & recovery

git restore <file>         # discard unstaged changes
git restore --staged <file> # unstage
git reset HEAD~1           # undo last commit (keep changes)
git reset --hard HEAD~1    # undo + discard changes (DESTRUCTIVE)
git revert <sha>           # create undo commit (safe)
git stash                  # stash current changes
git stash pop              # apply + remove stash
git stash list             # list stashes
git stash apply stash@{2}  # apply specific stash
git clean -fd              # remove untracked files + dirs

🐳 Docker Commands 40+ commands ▶

Images

docker pull <image>:<tag>   # download image
docker pull nginx:latest
docker images              # list local images
docker rmi <image>         # remove image
docker image prune         # remove dangling images
docker image prune -a      # remove all unused
docker build -t <name>:<tag> .   # build from Dockerfile
docker build -t myapp:v1 --no-cache .
docker tag <image> <repo>/<name>:<tag>  # tag for push
docker push <repo>/<name>:<tag>  # push to registry

Containers

docker run <image>                      # run (foreground)
docker run -d <image>                   # run detached
docker run -d -p 8080:80 nginx          # with port mapping
docker run -d -p 8080:80 -v /data:/app/data --name web nginx
docker run -it <image> bash             # interactive shell
docker ps                               # running containers
docker ps -a                            # all containers
docker stop <name/id>                   # graceful stop
docker start <name/id>                  # start stopped
docker restart <name/id>
docker rm <name/id>                     # remove stopped
docker rm -f <name/id>                  # force remove running
docker exec -it <name> bash             # shell into running
docker logs <name>                      # view logs
docker logs -f <name>                   # follow logs
docker logs --tail 100 <name>           # last 100 lines
docker cp <src> <name>:<dst>            # copy file in/out
docker inspect <name>                   # full details (JSON)

Docker Compose

docker compose up -d               # start all services
docker compose up -d --build          # rebuild then start
docker compose down                   # stop + remove containers
docker compose down -v                # also remove volumes
docker compose ps                     # status
docker compose logs -f                # follow all logs
docker compose logs -f <service>      # one service
docker compose exec <svc> bash        # shell into service
docker compose pull                   # update all images
docker compose restart <service>      # restart one
docker compose stop                   # stop without removing
docker compose config                 # validate + print config

Volumes & Networks

docker volume create <name>
docker volume ls
docker volume rm <name>
docker volume inspect <name>

docker network create <name>
docker network ls
docker network inspect <name>
docker network connect <net> <container>

# Cleanup everything unused
docker system prune               # containers, networks, images
docker system prune -a            # + unused images
docker system prune -a --volumes  # + volumes (DESTRUCTIVE)
docker system df                  # disk usage

💻 System & Diagnostics 35+ commands ▶

System info

# macOS
sw_vers                    # macOS version
sysctl -n hw.ncpu          # CPU cores
sysctl -n hw.memsize       # RAM in bytes
system_profiler SPHardwareDataType  # full hardware info
top -l 1 | head -10        # quick stats

# Linux
uname -a                   # kernel + arch
hostnamectl                # system info
lscpu                      # CPU details
free -h                    # memory
lsblk                      # block devices
lspci                      # PCI devices
df -h                      # disk usage
du -sh *                   # folder sizes
du -sh /* 2>/dev/null | sort -hr | head -20  # largest dirs

Process management

ps aux                     # all processes
ps aux | grep <name>       # find process
pgrep <name>               # get PID
kill <PID>                 # send SIGTERM
kill -9 <PID>              # force kill (SIGKILL)
pkill <name>               # kill by name
killall <name>             # kill all by name
nohup <cmd> &              # run detached from terminal
<cmd> &                    # background job
jobs                       # list background jobs
fg %1                      # bring job 1 to foreground
bg %1                      # continue in background

# Linux services
sudo systemctl status <svc>
sudo systemctl start <svc>
sudo systemctl stop <svc>
sudo systemctl enable <svc>    # start on boot
sudo systemctl disable <svc>
sudo journalctl -u <svc> -f    # follow service logs
sudo journalctl -u <svc> --since "1 hour ago"

Disk & storage

df -h                      # disk free (human-readable)
du -sh <dir>               # size of directory
du -sh * | sort -hr        # sorted largest first
ncdu                       # interactive disk usage (install first)
lsof +D <dir>              # files open in directory
lsof -p <PID>              # files opened by process
lsof -i :<port>            # what's using a port
fuser <port>/tcp           # PID using port (Linux)

# macOS specific
diskutil list              # all disks
diskutil info /dev/disk0   # disk details

🌐 Network Tools 30+ commands ▶

curl — the Swiss Army HTTP tool

curl <url>                              # GET request
curl -I <url>                           # headers only
curl -L <url>                           # follow redirects
curl -o file.html <url>                 # save to file
curl -O <url>                           # save with remote filename
curl -X POST <url> -d "key=val"         # POST form data
curl -X POST <url> \
  -H "Content-Type: application/json" \
  -d '{"key": "value"}'                 # POST JSON
curl -H "Authorization: Bearer TOKEN" <url>  # auth header
curl -u user:pass <url>                 # basic auth
curl -v <url>                           # verbose (debug)
curl -s <url> | jq .                    # pipe to jq for JSON
curl -w "%{http_code}" -o /dev/null <url>  # just status code
curl --max-time 10 <url>               # timeout 10s

Network diagnostics

ping <host>                # ping
ping -c 4 <host>           # 4 packets only
traceroute <host>          # trace route (macOS)
tracepath <host>           # trace route (Linux)
dig <domain>               # DNS lookup
dig <domain> +short        # just the IP
nslookup <domain>          # DNS query
whois <domain>             # domain info

netstat -tulpn             # open ports (Linux)
ss -tulpn                  # faster netstat (Linux)
lsof -i -P -n | grep LISTEN  # open ports (macOS)
lsof -i :<port>            # what's on port
sudo nmap -sV <host>       # port scan + service version

SSH & remote

ssh user@host                      # connect
ssh -p 2222 user@host              # custom port
ssh -i ~/.ssh/key user@host        # specific key
ssh -L 8080:localhost:80 user@host # local port forward
ssh -N -f -L 5432:db:5432 user@host  # background tunnel

# Key management
ssh-keygen -t ed25519 -C "comment"  # generate key
ssh-copy-id user@host              # install key on server
cat ~/.ssh/id_ed25519.pub | pbcopy # copy public key

# ~/.ssh/config
# Host prod
#   HostName 10.0.0.1
#   User ubuntu
#   IdentityFile ~/.ssh/prod_key
# Then: ssh prod

scp file user@host:/path           # copy to remote
scp user@host:/path/file .         # copy from remote
rsync -avz src/ user@host:/dst/    # sync (preserves perms)
rsync -avz --delete src/ dst/      # sync + delete extras

📁 File Operations 35+ commands ▶

Find & search

find . -name "*.py"                    # find by name
find . -name "*.py" -newer file.txt    # newer than file
find . -type f -size +10M              # files > 10MB
find . -type d -name "__pycache__"     # find directories
find . -name "*.pyc" -delete           # find + delete

# ripgrep (rg) — fast text search
rg "pattern"               # search in all files
rg "pattern" src/          # in specific dir
rg -i "pattern"            # case insensitive
rg -n "pattern"            # with line numbers
rg -l "pattern"            # filenames only
rg --type py "pattern"     # only Python files
rg -A 3 -B 3 "pattern"    # 3 lines context

# grep fallback
grep -r "pattern" .        # recursive
grep -rn "pattern" .       # with line numbers
grep -rl "pattern" .       # filenames only

Text processing

# sed — stream editor
sed 's/old/new/g' file          # replace all
sed -i 's/old/new/g' file       # in-place
sed -n '10,20p' file            # print lines 10-20
sed '/pattern/d' file           # delete matching lines

# awk — text processing
awk '{print $1}' file           # first column
awk -F: '{print $1}' /etc/passwd  # custom delimiter
awk '{sum += $1} END {print sum}' file  # sum column

# Other
sort file                       # sort lines
sort -r file                    # reverse sort
sort -n file                    # numeric sort
sort -u file                    # unique sort
uniq file                       # remove consecutive dups
sort file | uniq -c | sort -rn  # count + rank occurrences
wc -l file                      # line count
wc -w file                      # word count
cut -d, -f2 file.csv            # cut CSV column 2
tr '[:upper:]' '[:lower:]'      # lowercase
paste file1 file2               # merge files side by side

Archives & compression

# tar
tar -czvf archive.tar.gz dir/       # create .tar.gz
tar -xzvf archive.tar.gz            # extract .tar.gz
tar -cjvf archive.tar.bz2 dir/      # create .bz2
tar -xjvf archive.tar.bz2           # extract .bz2
tar -tvf archive.tar.gz             # list contents without extracting

# zip
zip -r archive.zip dir/             # create zip
zip -r archive.zip dir/ -x "*.pyc"  # exclude .pyc
unzip archive.zip                   # extract
unzip -l archive.zip                # list contents
unzip archive.zip -d /dest/         # extract to dir

# Fast compression
zstd file                   # compress (fast)
zstd -d file.zst            # decompress

🤖 AI & LLM Tools 25+ commands ▶

Ollama

# Install
curl -fsSL https://ollama.ai/install.sh | sh

# Models
ollama pull qwen3:14b
ollama pull deepseek-r1:14b
ollama pull devstral
ollama pull gemma3:12b
ollama pull phi4
ollama pull llava           # multimodal (vision)

# Run
ollama run qwen3:14b
ollama run qwen3:14b "Explain vancomycin AUC monitoring"

# Manage
ollama list                 # local models
ollama rm qwen3:8b          # delete model
ollama show qwen3:14b       # model info
ollama ps                   # running models

# API (OpenAI-compatible)
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3:14b","messages":[{"role":"user","content":"Hello"}]}'

# Expose on LAN (add to systemd service override)
export OLLAMA_HOST=0.0.0.0
ollama serve

Wrangler — Cloudflare

npm install -g wrangler

wrangler login                  # authenticate
wrangler pages dev public       # local dev server
wrangler pages deploy public --project-name <name>
wrangler kv namespace create <name>
wrangler kv key put --namespace-id=<id> key "value"
wrangler kv key get --namespace-id=<id> key
wrangler kv key list --namespace-id=<id>
wrangler tail                   # live log tail
wrangler secret put SECRET_KEY  # add secret

GitHub CLI — gh

gh auth login               # authenticate
gh repo create <name> --private
gh repo clone <user>/<repo>
gh pr create --title "Title" --body "Body"
gh pr list
gh pr checkout <number>
gh pr merge <number>
gh issue create --title "Bug" --body "Details"
gh issue list
gh issue close <number>
gh release create v1.0.0 --title "v1.0.0" --notes "Notes"
gh run list                 # CI/CD runs
gh run view <id>            # run details
gh gist create file.txt     # create Gist
gh api /repos/:owner/:repo/pulls  # raw API call

Python AI/ML packages

# Local LLM inference
pip install llama-cpp-python    # Python bindings for llama.cpp
pip install ollama              # Ollama Python client
pip install openai              # OpenAI API + compatible APIs

# LLM frameworks
pip install langchain langchain-community
pip install llama-index
pip install haystack-ai

# Vector databases
pip install chromadb            # local vector DB
pip install qdrant-client       # Qdrant client
pip install faiss-cpu           # Facebook AI similarity search

# ML
pip install torch torchvision   # PyTorch
pip install transformers        # Hugging Face Transformers
pip install datasets            # HF Datasets
pip install accelerate          # distributed training
pip install sentence-transformers  # embeddings

# APIs
pip install anthropic           # Claude API
pip install google-generativeai # Gemini API

⚙️ macOS Power Commands 30+ commands ▶

Clipboard & open

pbcopy < file.txt           # copy file to clipboard
pbpaste > file.txt          # paste clipboard to file
cat file.txt | pbcopy       # pipe to clipboard
echo "text" | pbcopy

open .                      # open Finder here
open <url>                  # open in default browser
open -a "VS Code" .         # open with specific app
open -a "Terminal" <path>
open *.pdf                  # open all PDFs

# Quick share / preview
qlmanage -p file.png        # QuickLook preview

Defaults & system tweaks

# Show hidden files in Finder
defaults write com.apple.finder AppleShowAllFiles true
killall Finder

# Show path bar in Finder
defaults write com.apple.finder ShowPathbar -bool true

# Screenshot format (png/jpg/pdf)
defaults write com.apple.screencapture type jpg

# Disable .DS_Store on network volumes
defaults write com.apple.desktopservices DSDontWriteNetworkStores true

# Key repeat speed (faster = lower values)
defaults write -g InitialKeyRepeat -int 15
defaults write -g KeyRepeat -int 2

# Show full path in Finder title
defaults write com.apple.finder _FXShowPosixPathInTitle -bool true
killall Finder

launchctl & processes

launchctl list             # running LaunchAgents
launchctl load <plist>     # load service
launchctl unload <plist>   # unload service

# Location of user LaunchAgents
ls ~/Library/LaunchAgents/

# Common plist locations
# ~/Library/LaunchAgents/    — user, loaded at login
# /Library/LaunchAgents/     — system, loaded at login
# /Library/LaunchDaemons/    — system, loaded at boot

# Flush DNS cache
sudo dscacheutil -flushcache
sudo killall -HUP mDNSResponder

# Network interfaces
networksetup -listallhardwareports
networksetup -getinfo Wi-Fi
ipconfig getifaddr en0      # current IP (Wi-Fi)

🔐 SSH, GPG & Security 20+ commands ▶

SSH key setup

# Generate key (ed25519 recommended)
ssh-keygen -t ed25519 -C "your@email.com"

# Copy public key to remote server
ssh-copy-id user@host
# or manually:
cat ~/.ssh/id_ed25519.pub | ssh user@host "mkdir -p ~/.ssh && cat >> ~/.ssh/authorized_keys"

# Add to GitHub
cat ~/.ssh/id_ed25519.pub | pbcopy   # copy to clipboard
# GitHub → Settings → SSH Keys → New

# Test connection
ssh -T git@github.com

# SSH Agent (persist key passphrase)
eval "$(ssh-agent -s)"
ssh-add ~/.ssh/id_ed25519
ssh-add -l                  # list loaded keys

~/.ssh/config — alias multiple servers

# Add to ~/.ssh/config:
Host prod
    HostName 10.0.0.100
    User ubuntu
    IdentityFile ~/.ssh/prod_key
    Port 22

Host dev-tunnel
    HostName dev.example.com
    User admin
    IdentityFile ~/.ssh/dev_key
    LocalForward 5432 localhost:5432  # DB tunnel

# Now just:
ssh prod
ssh dev-tunnel

GPG encryption

brew install gnupg

gpg --full-generate-key     # generate key pair
gpg --list-keys             # public keys
gpg --list-secret-keys      # private keys
gpg --export -a "Name" > pub.key  # export public key
gpg --import pub.key        # import someone's key

# Encrypt file for recipient
gpg -e -r "recipient@email.com" file.txt   # creates file.txt.gpg

# Decrypt
gpg -d file.txt.gpg

# Sign a file
gpg --clearsign document.txt       # inline signature
gpg --verify document.txt.asc     # verify signature