almanack

AI

Thoughts and references related to AI.

Learning

• NanoChat: ChatGPT trained fromm scratch.
• Smol Training Playbook
  • FineWeb: Building datasets at scale.
  • Ultra Scale Playbook: Orchestrating GPUs for a training run
  • Evaluation Guidebook: Choose the right evaluations

Coding agents

• Goose
• Factory

Projects

• KumoRFM: A foundation model for business data. Predictions over structured data.
• Liquid Nanos: Frontier-grade performance on small models.
  • LFM-1B-Math: Detailed description of how it was installed.
  • HF models
• TOON: Text object-oriented notation to save tokens when interacting with LLMs
• NoF1.ai: Model crypto trading agents and portfolio comparison.

Bookmarks

• RAG search and GraphRAG for financial data
• LM Arena Leaderboard

Papers

• Agentic context engineering
• Less is more: Recursive reasoning with tiny networks

Evaluations

• Evalkit: Typescript LLM evaluations library.
• Opik: LLM evaluation platform

Trends

High-level trends and ideas

• Environments are a key asset to train AIs through RL. The closer the training environment is to the real one, the better it will end up operating. In the coming years I expect the programming of RL environments for AI to be as important as wiring the agents themselves (esp. for robotics and real world –analogous and flawed environments–). See prime intellect’s RL environments as the seed for this trend.
• Small language models outperform LLMs for agentic behavior:
  • See paper: https://arxiv.org/pdf/2510.03847

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