Show HN: MicroCodex Coding Agent – OpenAI/codex reimplemented in C++ <1MB binary
Show HN: MicroCodex Coding Agent – OpenAI/codex reimplemented in C++ binary Context & related coverage →
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Open letters about AI development
Open letters about AI development I wrote this summary of the past few weeks of open letters as a section of my sponsors-only newsletter but I've decided to share it here as well. Open Weights and American AI Leadersh... Context & related coverage →
smevals - a small eval suite for evaluating models, prompts, and harnesses
smevals - a small eval suite for evaluating models, prompts, and harnesses I've been working with Jesse Vincent's Prime Radiant applied AI research lab building out this evals framework to help answer questions about... Context & related coverage →
😺DeepSeek’s new 28-cent agent model - The Neuron
😺DeepSeek’s new 28-cent agent model The Neuron Context & related coverage →
Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering
Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a concrete, executable testbed for studying this capability. We int... Context & related coverage →
Evaluating code review agents with ReviewBench
Learn how LangChain built ReviewBench, a benchmark for evaluating code review agents against real PR feedback from trusted reviewers. Context & related coverage →
Why Are GUI Agents Correct but Late? Decode on the Decision-Time Critical Path, Tested with Pre-Compiled Policy Trees
Computer-use agents often fail on transient GUI events because they produce the correct action only after the relevant window has already closed. We identify the main cause as expensive autoregressive decoding on the... Context & related coverage →
Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs
Deploying autonomous computer-use agents (CUAs) locally is increasingly important for privacy, cost efficiency, and practical usability, yet improving their performance under strict hardware constraints remains challe... Context & related coverage →
ORCA-bench: How Ready Are Language Model Agents for Oncall?
Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something different: reasoning over noisy metrics, logs, traces, and source code, starting from ambiguous user-faci... Context & related coverage →
Qwen-UI-Agent Technical Report: Toward Next-Generation Real-World Centric Foundation GUI Agents
GUI agents have the potential to become a general purpose executor over existing digital devices. To advance them toward real-world use, we envision agents that operate reliably on real devices, execute workflows acro... Context & related coverage →
Dropbox Integrates MCP and Dash to Close the Gap Between Security Design and Code Review
Dropbox has integrated Model Context Protocol (MCP) with its internal knowledge platform, Dash, to surface security design context during AI assisted code reviews. The system retrieves threat models and security requi... Context & related coverage →
Building abundant intelligence
A full-stack approach to making advanced AI more capable, more affordable, and more widely useful. Context & related coverage →