An 11MB local model for typed decisions in one pass
I built ej for a narrower problem than general classification: given some state and a set of decision questions whose options can be defined at runtime, return a probability distribution for each question in one pass. It handles choice, yes/no, and ordered scores, runs on CPU, needs no token generation, and ships as one 11.4 MB model file. The repo includes training, evaluation, adaptation, packed weights, and the benchmark harness.
I built ej because I wanted to see how far a very small decision model could go without hiding the tradeoffs.
The road there wasn’t straight:
🔀 It started in Rust. The first version was a Rust engine. Somewhere along the way the research loop outgrew it, and ej became Python. Being able to iterate quickly turned out to matter more than the language I’d started in.
📦 “11 MB” was a lie, at first. Early on, 11 MB was only a bit-level count of the parameters. The file you actually downloaded was much bigger. So I wrote my own packed format to make the number real. Today it’s one 11,384,312-byte file with no pickles and a SHA-256 check on every section.
⚖️ A licence check sent me back to the start. Late in the project, one training dataset turned out to be non-commercial. I removed it, retrained the encoder and refit the whole model. Some accuracy was lost, and it was worth it.
🎯 I set a target and missed it. I aimed for 60% accuracy on workflows ej had never seen. The honest result is about 42%, and it’s the first thing I point people to.
🧪 My best idea failed my own test. A small model that reads the text and the options together looked great in full precision. Quantised to fit, its gain fell below the bar I had set in advance, so I dropped it. Several other “improvements” went the same way once I trained each one several times and found the gains were within run-to-run noise.
A lot of the real work was the unglamorous part: packing the model, deterministic inference, offline loading, measuring calibration on each test suite instead of promising it, and a benchmark where contamination and uncertainty are visible.
The result is useful in some settings and clearly weak in others, especially on unseen workflows. I’ve tried to make those limits easy to inspect instead of smoothing them over.
I’d love feedback on two things in particular: the benchmark design, and whether the current size/accuracy tradeoff feels useful in real systems.
About ej on Product Hunt
“An 11MB local model for typed decisions in one pass”
ej launched on Product Hunt on October 10th, 2026 and earned 64 upvotes and 1 comments, placing #31 on the daily leaderboard. I built ej for a narrower problem than general classification: given some state and a set of decision questions whose options can be defined at runtime, return a probability distribution for each question in one pass. It handles choice, yes/no, and ordered scores, runs on CPU, needs no token generation, and ships as one 11.4 MB model file. The repo includes training, evaluation, adaptation, packed weights, and the benchmark harness.
On the analytics side, ej competes within Open Source, Artificial Intelligence and GitHub — topics that collectively have 592k followers on Product Hunt. The dashboard above tracks how ej performed against the three products that launched closest to it on the same day.
Who hunted ej?
ej was hunted by Saket Bhushan. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of ej including community comment highlights and product details, visit the product overview.
Saket Bhushan