The articles and papers that were worth the detour, and why.
Nothing for that search in the published issues.
Issue 002 · A newsroom with no journalists
GPT-6 Astra: an automated AI engineer for under $6 an hour · Latent Space spent over 20 billion tokens exploring Astra in depth. The article details the real gains per task, the cases where the model fails, and why it costs more per token but less per result. latent.space/p/astra
Give your coding agents a memory you own · Hugging Face introduces Funes, a persistent memory system for coding agents that you host and control. huggingface.co/blog/funes
Project HydraFusion: frontier quality through multi-model orchestration · GitHub shows how to have several models work on the same agent to reach big-model quality while cutting cost. github.blog/ai-and-ml/…
Issue 000 · Your agent succeeds one time in three
Which words, present or missing from an instruction, actually change what the model does. Measured, not guessed. Read it before you write your next system prompt.
Simon Willison untangles OpenAI's most confusing product, which is really two products. Useful if your company is adopting it.
An agent should not grade its own homework: models from the same vendor share blind spots. The paper proposes having the work checked by a model from somewhere else.
The archive reproduces each issue as it went out, without rewriting it. A repo's stars, prices and last-commit dates are those of the day of the check, not today's: follow the link for the current state.
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