Posts

TL;DR Phone scanners (Polycam, KIRI Engine, RealityScan) export raw geometry that’s sparse, has holes, and is non-manifold—not printable. First critical step: normalize scale. Scans export in meters and import at the wrong size. Fix this before anything else or Blender’s voxel remesh produces nothing. Blender’s voxel remesh rebuilds the mesh into a guaranteed watertight, manifold shell. Then decimate down to a slicer-friendly polygon budget. Export as both STL (geometry) and 3MF (units + compression). Slice in Bambu Studio and print. Watch for: voxel size mismatch, color loss after remesh, holes bigger than the voxel size, and X1C build plate limits (256×256mm). The Photogrammetry Gap There’s a seductive lie in 3D printing: buy a phone, scan something, print it. In reality, there’s a canyon between a photogrammetry scan and a printable model. ...

TL;DR “Dead” can mean anything from a sticky play button to catastrophic power supply failure. Map the symptoms first before you commit to a $40 parts order. Electrolytic capacitors dry out over 30–40 years. The tell is ESR (equivalent series resistance), which you can’t measure with a multimeter—you need a dedicated ESR meter. Mechanical failures (belts, pinch rollers, switches) cause 50% of what looks like electronics trouble. Try contact cleaner and spin the reels manually before you touch a soldering iron. Replace the power supply filter caps first. If hum persists after disconnecting the mains input, you’ve got a trace issue, not a capacitor issue. Full recap economics: ~$15–40 in parts + 2–6 hours of work. Only worth your time if the restored unit will fetch ~60% of that total cost on resale. What “Dead” Really Means A cassette deck doesn’t come with an error code. It just stops. And the failure modes are a taxonomy you need to parse before you spend an evening at the bench. ...

TL;DR Cover art was the gateway drug. The same local ComfyUI install that generates this blog’s headers also strips the cluttered background off a photo of hardware on my bench, upscales a small generation to retina resolution, and batch-produces a consistent set of illustrations from a prompt template. Two Mac Studios mean I can fire a batch at one box and keep working on the other. It’s all driven from scripts and agents, and it all costs $0 per image because it never leaves the house. ...

TL;DR This site is deliberately boring infrastructure for a reason: Hugo generates static HTML with the PaperMod theme. Terraform manages AWS (S3, CloudFront, Route53, ACM). GitHub Actions and self-hosted k3s runners deploy on every push to main. An AI pipeline (Bedrock + a Python script) drafts articles into Hugo page bundles and opens PRs for review. There’s no dynamic backend, no database, no server to maintain. The AWS bill is ~$30/month. This post is a tour of the machine that prints the other posts. ...

TL;DR I don’t pay for stock photos and I don’t open Canva. Every raster image on this blog is generated on a Mac Studio sitting three feet from me, by asking Claude Code to call a generate_image MCP tool that wraps ComfyUI. The pipeline is: prompt → ComfyUI (MPS) → PNG on disk → upload_media.py → S3 → CloudFront → a Markdown reference in the post. It costs $0 per image, takes ~15 seconds, and the whole thing is repeatable because the prompt and settings live in the commit history. ...

TL;DR I keep four small-form-factor PCs on a bench for testing and repurposing — bought used, need fresh OS images, fresh BIOS settings, and no monitor or keyboard. A PiKVM V4 Plus with a multiport switch gives me eyes and hands on all four boxes over the network. Dell’s cctk command-line tool (Command | Configure) lets me bake BIOS settings — boot order, AHCI mode, Wake-on-LAN, power-on-after-failure — into scripted runs instead of clicking through F2 menus. No monitor, no keyboard, no physical access for weeks at a time. Everything repeatable, everything as code. ...

TL;DR I have $X in monthly Claude tokens I don’t always use. Instead of letting the unused credit evaporate, I built a parallel agent sweep that fans out autonomous scouts to scan for dependency upgrades, CVEs, CI waste, and quick wins across my repos. Each discovery agent returns a scored candidate list. The orchestrator triages and ranks them, then spins up isolated worktree agents to implement the safe ones — all under a hard token cap and with human gates between phases. The output is a pile of merge requests, not silent commits. Noise is real and review burden is the limiting factor, but when it lands right, an hour of agent work + human review beats a weekend of manual maintenance. ...

TL;DR I’ve wired LLMs into my GitLab CI pipeline to auto-review merge requests, post findings as comments, and (on command) generate patches and commit fixes. The key insight: deterministic gates run first. Before the LLM ever sees a diff, regex-enforced checks block deleted tests, committed secrets, and destructive commands. Regex is certain; LLM judgment is probabilistic. Gate first, judge second. The bot reviews silently unless it finds something, posts to the MR with confidence levels, and can be leveled up from read-only observer to trusted committer as it proves itself — hence the “autonomy ladder” (Rungs 0–4) that gates who decides what. Infrastructure repos cap at Rung 2 (never auto-merge). ...

TL;DR My production homelab runs on Lenovo M920q tinies, and I still think those are the sweet spot. But if I were starting over today with a tight budget, I’d buy a stack of government-surplus Dell OptiPlex 7060 and 7070 desktops instead. They go for around $150 each refurbished — 6-core 8th/9th-gen Intel, an SSD, and Windows 11 already on them — and they make excellent Kubernetes nodes with exactly two cheap upgrades: a bit more RAM and a second network card. ...

TL;DR I run unattended LLM agents on my homelab — they write code, open MRs, generate content, rotate secrets. The problem: they fail silently and bill silently. Langfuse (a tracing platform) logs every LLM call with input/output tokens, latency, and cost. On top of those traces, I built three background monitors that run weekly: a goal-drift detector that compares an agent’s stated objective to what its commits actually did (via embedding similarity), a cost-spike alert that fires at 80% and 100% of a daily budget cap, and an action audit that exports traces and flags sessions where the tool-call sequence diverged from the plan. Together, these let me sleep while autonomous agents handle repetitive work. ...