SUBMISSION.md: canonical submission write-up for the judges

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

SUBMISSION.md

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+# Thousand Token Wood
+
+*Submission for the Build Small Hackathon, Thousand Token Wood track. June 2026.*
+
+Five woodland creatures run a living market. Each one thinks on a different lab's
+small model, and nobody scripts the drama: they trade, gossip, hoard, form grudges,
+and panic on their own. You play the Patron, a shadow financier who profits from the
+chaos. And the central discovery of the build is that the wood fights back.
+
+- **Play it:** https://huggingface.co/spaces/build-small-hackathon/thousand-token-wood-sim
+- **Demo video (100s):** https://youtu.be/ugFyFumUCgs
+- **Social post:** https://x.com/RealLesterLeong/status/2064904967391961483
+
+## The council: five minds, four labs, 29.5B parameters
+
+The honest version of "small models can run an economy" is not one model wearing five
+hats. It is five distinct minds with five distinct temperaments arguing a market into
+being:
+
+| Creature | Model | Lab |
+|---|---|---|
+| Oona the owl | gpt-oss-20b | OpenAI |
+| Fenn the fox | Nemotron-Mini-4B | NVIDIA |
+| Bramble the squirrel | MiniCPM3-4B | OpenBMB |
+| Mossback the tortoise and Pip the mouse | ttw-trader-0.5b | fine-tuned (ours) |
+
+*The council. Distinct-engine budget 29.5B, under the 32B cap. Every thought and raw
+JSON completion is exposed in the UI, so you can check that none of it is scripted.*
+
+The 0.5B is a LoRA fine-tune I distilled from a 3B teacher with the mistakes stripped
+out of the training set. The student trades cleaner than its teacher: zero self-buys
+and 100% valid offers, against the teacher's 2.2% self-buy rate. Small, done
+carefully, beat bigger.
+
+## The game: insider trading as a decaying resource
+
+You are not a benevolent god. You short a good, whisper a tip to set up its fall,
+spring a Wood Legend (a famous market panic reskinned as woodland folklore), and
+collect when the price craters. A live exposure meter shows your expected payoff
+before you commit.
+
+The first time, it works: the crash pays 55 pebbles, exactly as authored. But the
+creatures you burn remember. They sour on you, hoard against your next crash, and
+testify to Magistrate Heron. Run the identical gambit again and the meter forecasts
+16 pebbles, and it pays exactly 16. Then the verdicts land: the owl is exiled, your
+pebbles are frozen. A trick you repeat is a trick that dies.
+
+That mechanic is the build's thesis about agents, not just its plot: emergent behavior
+from one model population evaporates when you change the cast, you cannot steer a
+heterogeneous council by shocking its inputs, and a repeated manipulation gets priced
+in by the agents on the other side. The full journey, including the two failed builds
+that taught me the most, is in the field notes below.
+
+## Why small is load-bearing
+
+A living economy needs many agents thinking many times per turn, plus a narrator.
+Frontier models are too slow and too costly for that loop. Four small engines, each on
+its own GPU with per-engine batching and scale-to-zero, make a real-time council
+feasible: the entire project, including fine-tuning, four engine deployments, trace
+publication, and three recorded story arcs, used about a quarter of the starter Modal
+credits.
+
+## Built with
+
+- **Gradio** Space (custom storybook UI, cinematic title card, instant attract-mode
+  replay so the Space is never blank while engines wake)
+- **Modal** end to end: four vLLM engines as separate apps, LoRA training, evals
+- **Models:** gpt-oss-20b, Nemotron-Mini-4B, MiniCPM3-4B, and a published 0.5B
+  fine-tune ([AdmiralTaco/ttw-trader-0.5b](https://huggingface.co/AdmiralTaco/ttw-trader-0.5b))
+
+## Bonus quests
+
+- **Well-Tuned:** the app serves the published fine-tune above, with before/after
+  reliability evals in the field notes.
+- **Sharing is Caring:** [open agent traces](https://huggingface.co/datasets/build-small-hackathon/thousand-token-wood-traces),
+  every row tagged with the lab and model that produced it, so you can compare how
+  four labs' small models read the same market state.
+- **Off-Brand:** custom illustrated town square, lab-colored attribution, operator
+  console chrome, and a pure-CSS cinematic open.
+- **Field Notes,** in four parts:
+  1. [Emergent market drama](https://huggingface.co/blog/build-small-hackathon/thousand-token-wood-sim)
+  2. [The five-lab council](https://huggingface.co/blog/build-small-hackathon/thousand-token-wood-sim-v2)
+  3. [The crash that vanished](https://huggingface.co/blog/build-small-hackathon/thousand-token-wood-sim-v3)
+  4. [The wood fights back](https://huggingface.co/blog/build-small-hackathon/thousand-token-wood-sim-v4)
+
+I build market models professionally, and this little wood taught me real lessons
+about agent economies at a scale where the only thing at risk was a pile of pebbles.
+If it makes you smile, a like on the Space is the community vote.
+
+Small models, big adventures.