What a breakdown, a baby philosopher, and a custodian taught me about the ceiling of single-agent AI

Practical techniques for steering GPT-5.4 toward polished, production-ready frontend designs.

Summary of the collected written experiences from developers of the rust compiler on using LLMs for programming and the deluge of LLM generated pull-requests.

A collection of skills to help LLM agents code in golang.

A storage that can be used to keep memory across sections.

This claims an innovative “holographic memory” that is apparently more compact and requires less infrastructure than RAG.

An in-depth article about working with LLMs from a senior engineering perspective.

An incredible article on the “anatomy” of large language models. Probably in large enough models some layers form a kind of circuitry for a specific purpose, e.g. solving math problems.

Convert PDF to markdown JSON quickly with high accuracy - datalab-to/marker

A tool that gives more context to agent, based on a curated set of API documentation.

via: https://news.ycombinator.com/item?id=47317132

Human DX optimizes for discoverability. Agent DX optimizes for predictability. What I learned building a CLI for agents first.

An LLM benchmark testing the model’s push back (or lack thereof) against BS.

Interactive AI timeline tracking 194 Large Language Models from 2017 to 2026. Explore the history of ChatGPT, GPT-4, Claude, Gemini, LLaMA, Mistral, DeepSeek and every major LLM ever released.