| --- |
| license: mit |
| tags: |
| - build-small-hackathon |
| - backyard-ai |
| - field-notes |
| - microfactory-node |
| - 3d-printing |
| - small-models |
| - agent-trace |
| - multi-agent |
| --- |
| |
| # Field Notes: Building a Shop-Floor AI on a Small Local Model |
|
|
| I spent ten days building a small local **Gemma** that learns 3D printing job by |
| job, and these are the notes from doing it. The one moment worth watching: it |
| reads today's room, pulls up the closest prior jobs, and either applies what they |
| taught — *"humidity is higher than the job where this overhang sagged, so I'm |
| raising retraction and adding support"* — or says, plainly, *"no close |
| precedent."* Two named agents keep it honest: Chief Engineer O'Brien proposes, |
| La Forge inspects. |
|
|
| This is a proof of concept that works, not a production system, and it was built |
| that way on purpose. The hackathon judges a demo, a writeup, a working app, and |
| a believable *someone could use this* story — so anything nobody judges never |
| got more effort than something that did. Simplify as you go. What follows is what |
| that discipline actually taught me. |
|
|
| ## 1. Small models need a spine, not a leash |
|
|
| The single best architectural decision: the model proposes, deterministic code |
| disposes. The **Spine** validates every proposed setting against hardcoded |
| material bounds. PLA nozzle at 260°C gets clamped to 220°C and a human gate |
| trips. Once that boundary exists, you stop prompt-engineering for safety and let |
| the model do the thing it is actually good at — judgment over precedent — without |
| betting the printer on it. Never ask a small model to be its own safety system. |
| Constraints are what make it trustworthy. |
|
|
| ## 2. Steering a small Gemma is a discipline |
|
|
| Everything in the steering playbook earned its place: a role-locked persona |
| ("you do not hype"), JSON mode with an output contract and cleanup code behind |
| it, pre-filtered context (the ledger hands the model two or three relevant |
| precedents, never the whole history), and a typed fallback for every call so a |
| parse failure costs one shrug, not a crash. Prompt budget matters more than |
| context window. Attention quality sags past about 800 tokens, so the hot-path |
| prompt stays near 600 and the preflight gate measures it. |
|
|
| ## 3. Two agents are more honest than one |
|
|
| O'Brien proposes the plan. La Forge, a separate skeptical persona, reads it |
| before anything prints and says where the optimism is thin. When La Forge |
| disputes, the print is held until the human acknowledges. O'Brien is the |
| optimist. La Forge is not. The model is never allowed to grade its own homework. |
| This cost almost nothing to add: one extra call, same model, a different system |
| prompt. It changes the trust story. The system is not asking you to believe one |
| agent grading itself. It shows you two views and makes the human decide. |
|
|
| ## 4. "Effective parameters" is a real thing you have to explain |
|
|
| I worked with **Gemma 4 E-class** from the start — `gemma4:e2b` and |
| `gemma4:e4b`, and later the QAT variants. The E-models report about 8B raw |
| parameters but run as effective ~4B or ~2B, because the architecture (MatFormer) |
| nests a smaller model inside a larger one. The preflight initially read the raw |
| count and told us to skip the small-model badge. Know which number your model |
| actually is, and prefer the variant that needs no argument: E4B is ~4B |
| effective, comfortably a small model. |
|
|
| ## 5. Latency warnings should be calibrated by driving, not by vibes |
|
|
| The gate said "too slow" at 18 s a turn. Then I drove the cockpit. A narrated |
| demo where you talk through the model's precedent evaluation while it thinks |
| reads fine at 18 s, and the 40 s first call is a one-time model load you |
| pre-warm away. The gate was recalibrated to match the observed experience: warm |
| under 20 s passes. Benchmarks exist to predict the experience. When the |
| experience disagrees, the benchmark is wrong. |
|
|
| ## 6. The smaller model was twice as fast, and made a physics mistake |
|
|
| E2B answered in 10 s where E4B took 18, and both passed every contract and |
| reasoning gate. But one E2B-distilled lesson came out backwards: "slightly |
| higher nozzle temp" to fight humid-PETG stringing, when you go lower. The JSON |
| was valid. The physics was wrong. Schema validation cannot catch that, which is |
| exactly why the human reads the lessons before they are trusted, and why |
| outcomes come from outside the model. Size buys you nuance. Plan for its |
| absence. |
|
|
| ## 7. Verify the real stack before you record, not while |
|
|
| `make preflight` grades eight gates on the actual model: env, latency (cold and |
| warm split), JSON contract, reasoning quality on a precedent-rich case and a |
| novel one, reflection, the Spine clamp, the app serving, the assets. Every fail |
| points at a written contingency. A previous project died integrating on the last |
| night. This one ran its dress rehearsal on day one of the endgame, and the |
| "novel case" gate caught what matters most: the model saying *"no close |
| precedent"* honestly instead of inventing one. |
|
|
| ## 8. Honesty is a feature you can ship, even when the numbers are bad |
|
|
| I checked the simulator against real FDM failure prints from a Modal ingestion |
| run. The first pass read 34.2 %. The cause was the data, not the model: the |
| parser only looked at G-code headers, so 178 of 260 rows had fan speed |
| defaulting to zero. After cleaning that — parse M106 across the whole file, final |
| temps, real retraction — the score settled at 32.6 % on 178 prints: correct on |
| every clean success, blind to the moderate failures. That gap is structural, |
| not a knob to turn, and forcing a prettier number would have broken the part |
| that works. So the constants stayed, the reason got written down, and the fix |
| got named. Calibration is also a data check. The same rule that keeps the model |
| from grading itself kept me from grading the simulator on bad data. Build the |
| system so the honest answer is also the impressive one. |
|
|
| ## 9. Tooling debt compounds faster on a deadline |
|
|
| Mid-endgame I adopted uv (locked env), reorganized a flat 20-file root into |
| `core/` and `scripts/`, and found that the `.env` file had never actually been |
| loaded by anything. None of it was the fun work. All of it was cheaper than |
| discovering it during the recording. Maintenance is the work. |
|
|
| ## 10. Distribution is part of the build |
|
|
| The fine-tune produced four GGUFs, but a GGUF on a Modal volume isn't a |
| shippable artifact — it's a binary blob with no chat template, no system |
| prompt, and no way for a stranger to try it. So I added the missing half of the |
| pipeline: the same Modal app that quantizes the model also uploads it to HF Hub |
| alongside `template`, `system`, and `params` files so `ollama run hf.co/…` |
| works out of the box, and a per-variant `ollama pull → ollama cp → ollama |
| push` step gets the same blobs listed on |
| [ollama.com/kylebrodeur](https://ollama.com/kylebrodeur) for the one-liner |
| case (`ollama run kylebrodeur/microfactory-node-v3-qat`). One adapter, three |
| derived artifacts (q4_k_m, q4_0, original LoRA), two registries, both with model |
| cards that link to each other. The QAT model got a q4_0 variant because that's |
| the quant it was trained for — highest fidelity for the QAT base — and the |
| `--as-name` flag I added to the upload step keeps the two quants from |
| overwriting each other on the Hub. Seven gotchas got written down on the way so |
| the next adapter is a ten-minute job, not a half-day. Done means someone you've |
| never met can pull and run it in one line. Build the publishing in. |
|
|
| ## 11. Capture interest without overpromising |
|
|
| To capture interest without pretending the product is finished, I added a simple |
| email signup at the bottom of the Space. It is opt-in only: checkbox + email, |
| clear privacy note, stored as a local JSONL and optionally synced to a private HF |
| dataset when `HF_TOKEN` is set. No print data, no uploaded files, no |
| third-party trackers. The same pattern as the field log, but for people instead |
| of jobs. |
|
|
| --- |
|
|
| **Project:** [node.microfactory.space](https://node.microfactory.space) · |
| **Space:** [build-small-hackathon/microfactory-lab](https://huggingface.co/spaces/build-small-hackathon/microfactory-lab) · |
| **Code:** [github.com/kylebrodeur/microfactory-node](https://github.com/kylebrodeur/microfactory-node) · |
| **Ledger:** [kylebrodeur/chief-engineer-ledger](https://huggingface.co/datasets/kylebrodeur/chief-engineer-ledger) |
|
|
