# Simreal-AI/MathmoBench > MathmoBench is an open benchmark for evaluating AI combinatorial reasoning: models must return machine-checkable evidence (a witness or an impossibility certificate), graded by a deterministic verifier with no LLM judge. - Magnitude: 2.2 out of 10 — Early signal - Stars: 80 total · +0 stars today, ≈ 16 by evening - Star trust: star growth looks organic - Category: Language models · Language: Python · License: Apache-2.0 · Created: 2026-09-23 · Last push: 2026-09-24 - GitHub: https://github.com/Simreal-AI/MathmoBench · Homepage: https://simreal.co · Page: https://gitnova.dev/en/r/Simreal-AI/MathmoBench ## Useful for - Run verify.py --run-tests to check repository integrity and validator behavior. - Generate a matching dev family and test the submission.jsonl format against it. - Build a pipeline: export agent-view, collect model answers, score deterministically with a hashed receipt. ## Why it’s here - 0 stars so far today, about 16 expected by the end of the day. - The spike has held for 3 days in a row — not a one-off blip. - The repository is 4 days old and already has 80 stars. With less than two weeks of history, there's no usual pace to compare the spike against yet. - Top new repositories this week: #168. ## Star trust Star growth looks organic. Star-trust labels are heuristics based on the repository’s behavior, not a check of every stargazer. ## Numbers - Forks: 5 - Issues and pull requests: 0 - Watchers: 6 - Average over the last week: 19 per day - Usual pace: too little history (under two weeks) - Stars in the last hour (measured): 1 ## Stars per day, last 8 days (oldest → newest, today is partial) 2026-09-20 … 2026-09-27: 0, 0, 0, 12, 27, 23, 18, 0 ## Spotted in now - Top new repositories this week: #168 ## More in this category 1. **ollaya-dev/ollaya** — 7.0 · Early signal · Language models · Rust · +0 stars today, ≈ 162 by evening A local runtime for decision models: pulls and serves classification and routing models behind a TypeSafe-compatible API. Like Ollama, but for models that return probabilities instead of text. Full card: https://gitnova.dev/en/r/ollaya-dev/ollaya.md 2. **NVIDIA/Model-Optimizer** — 5.9 · Breakout · Language models · Python · +0 stars today, ≈ 190 by evening NVIDIA library for compressing and accelerating models: quantization, pruning, distillation, NAS and speculative decoding with export to TensorRT-LLM, vLLM, SGLang. For ML engineers preparing models for deployment. Full card: https://gitnova.dev/en/r/NVIDIA/Model-Optimizer.md 3. **Niko1221/Strata** — 5.6 · Early signal · Language models · C++ · +0 stars today, ≈ 126 by evening A C++ local inference engine that runs the 125B MoE model Qwen3.8-Flash-Next on a regular PC with one NVIDIA GPU (12-24 GB) and 64 GB RAM, exposing an OpenAI/Anthropic-compatible API on localhost. Full card: https://gitnova.dev/en/r/Niko1221/Strata.md 4. **Rizzo-AI-Academy/rizzo-flow** — 4.5 · Early signal · Language models · Python · +0 stars today, ≈ 100 by evening A local-first implementation of the Jev idea: an LLM returns typed decisions (boolean, choice, score, numeric) with probabilities in a single forward pass, without generating tokens. Compatible with the TypeSafe HTTP API. Full card: https://gitnova.dev/en/r/Rizzo-AI-Academy/rizzo-flow.md 5. **tensorflow/tensorflow** — 4.3 · Breakout · Language models · C++ · +0 stars today, ≈ 125 by evening An open source machine learning platform from Google Brain with Python and C++ APIs for training and deploying neural networks. Used by researchers and developers of ML applications. Full card: https://gitnova.dev/en/r/tensorflow/tensorflow.md --- Magnitude (0–10) measures how fast and how unusually interest in a repository is growing right now. It is not a quality score. Days are UTC. “So far today” is a fact; “expected by the end of the day” is a forecast. Summaries and use cases are written by an LLM (DeepSeek V4.1 Flash) from the README and may be inaccurate: verify specific claims (benchmarks, speed, hardware) in the repository itself. Data as of 2026-09-27 01:40 UTC, updated every 30 minutes.