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OpenAI shares how its team built a full software product with zero manually-written code using Codex agents, focusing on designing environments and feedback loops for reliable agent operation.
Comparison of TencentDB's agent memory, which excels at compressing messy run histories for token savings, versus the Memos local plugin, which focuses on turning execution history into reusable habits and long-term learning through feedback loops.
A discussion about how feedback systems (static analysis, coverage tools, profiling) are more critical than the choice of LLM for making AI agents useful, illustrated by Oracle's work generating tests for GraalVM Native Image reflection metadata.
The article argues that AI agents performing judgment-heavy tasks need feedback loops to improve over time, rather than relying on static prompts, using the example of Buzz, an agent developed by Warp to monitor and respond to social mentions.