🤖 Write Your Own Agent – a practical LLM guide
A passionate call for developers to experiment with AI agents — proving that true understanding comes from building, not merely reading.
Fly.io’s post argues that building your own LLM-based agent is both simple and eye-opening. It walks through a minimal Python example that reproduces ChatGPT-like behavior, introduces API tool use, and examines ideas such as context engineering, multi-agent collaboration, and autonomy tradeoffs. This hands-on approach helps readers understand both the potential and the challenges of agent-based systems.
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📚 Training a Better Book Recommender on 3 Billion Goodreads Reviews
A neat example of simple yet effective AI-powered personalization — combining reader profiles with popularity-based filtering.
Book.sv provides a book recommendation service that suggests what to read next based on previously read titles. It uses popularity thresholds to include books in its model and requires at least three prior reads for reliable results.
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🧠 Mathematical Discovery at Scale with AI Assistance
A fascinating glimpse into AI-assisted mathematics — where LLMs act as research companions, complementing human intuition while requiring strict oversight for validity.
Mathematician Terence Tao and collaborators present a detailed report on large-scale mathematical exploration using DeepMind’s AlphaEvolve tool. The system leverages large language models to evolve code that generates inputs for optimization problems, enabling massive automated experimentation across analysis, combinatorics, and geometry. It rediscovered known results and inspired modest new insights, though no major conjectures were overturned. The study also examines the tool’s interpretability and safeguards against exploiting weak verification logic.
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⚙️ Async Coding Agents for Autonomous Code Research
An insightful look at how developers can leverage autonomous coding agents to scale experimentation — shifting from manual coding to orchestration and analysis.
Simon Willison details his experiments with asynchronous coding agents like Claude Code, Codex Cloud, and Gemini Jules. These agents autonomously run coding experiments, commit findings to GitHub, and benchmark libraries or test hypotheses. Drawing from his public research repository, he shares practical lessons on running continuous automated code investigations.
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🐚 qq.fish – a lightweight local LLM command assistant
A neat example of localized AI tooling for shell users — simple, practical, and easy to experiment with for those optimizing their terminal workflow.
This GitHub snippet contains a small fish shell function called ‘qq.fish’ from a personal dotfiles repository. It’s a concise 54-line script likely used for custom command suggestions or workflow shortcuts.
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