Saturday, August 15, 2026
Engineers are leveraging agentic feedback loops and benchmark harnesses with Codex to achieve massive CUDA kernel speedups.
In this issue · 6
ThoughtDAG presents an editable context graph interface for managing LLM conversation histories. Developers can selectively determine which historical turns enter subsequent prompt requests to optimize context window space.
New automation patterns demonstrate using ChatGPT Codex for social media parsing, automated application publishing pipelines, and remote machine control. These approaches expand agent workflows into full pipeline automation.
Depot used AI agents to translate Go database transactions and S3 API calls directly into TLA+ formal specifications. The TLC model checker evaluated over 14 million states to discover a subtle race condition in container registry garbage collection.
HN Without AI is a front-page mirror that automatically filters out LLM and artificial intelligence submissions. It restores visibility to classic systems engineering, compilers, hardware projects, and mathematics discussions.
Inherent Labs introduced Faraday, a 27-billion parameter AI scientist agent trained using long-horizon reinforcement learning on the new Replica task suite. By leveraging GPT-5.5 Codex as an execution tool, Faraday outperforms larger models like Claude Opus 4.8 in reproducing scientific research figures and experiments.
A GPU Mode contest participant used Codex in a tight automated feedback loop to optimize a batched QR decomposition CUDA kernel, achieving a 232x speedup over baseline. The approach highlights how agentic loop engineering and automated benchmarking allow developers to iterate rapidly on high-performance code.
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