Agent Entrypoints

AI agents entering a repository cold waste large amounts of context reconstructing repository intent, topology, and authoritative workflows. Humans do this naturally through exploration. Agents pay for it directly in tokens, latency, and incorrect assumptions.

An agent entrypoint is a compact machine-oriented routing layer embedded at the top of a repository README.md. Its purpose is to minimize exploratory context spend and direct agents toward authoritative operational paths immediately.

The goal is not to teach the agent the repository. The goal is to route the agent toward the correct sources of truth as quickly as possible.

This distills into a single question:

“How do I compress the minimum viable understanding of a repository into the fewest tokens possible while preserving reliable navigation and correct usage?”

LLM Underdogs of 2025

LLM Underdogs of 2025

2025 has been a flurry of AI madness that has been hard to keep up with. I’ve been deep in learning and experimenting in the AI space and noticed that while everyone’s hyping up the latest GPT variant or Claude release, there are some genuinely impressive open-source models that feel like they just flew under the radar in 2025. These aren’t just “good for their size”, they’re legit excellent models that you can run locally, for free, and deserve more attention.

Free Tokens for AI Exploration

Using ChatGPT, Gemini, Grok, or any of the other chat based LLM service is a great way to start with AI. But in order to bring things to the next level you will either need some beefy hardware to run models locally or access to an online API with models you can use. This article will walk you through how to do the later of these two options for free.