386 summarised stories about AI Coding Agents, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 4 days ago 30
Claude Code changed its code review process from a PR gate at the end of development to continuous review while coding. Most changes are reviewed and fixed by Claude at least once before a human is asked to look. This reduces routine mistakes before human review and shifts team attention toward higher-level scrutiny.
devin.ai 5 days ago 25
Devin Oncall launched to investigate and remediate production issues using company-specific skills and memory with a coding agent.
Every 5 days ago 17
OpenAI’s Codex team shifted from a step-by-step, sandboxed coding assistant toward agent-style workflows after testing GPT-5 alongside competitors and observing it “stop too early.” By June 2026, Codex reached more than 5 million weekly active users, later merging into the ChatGPT desktop app and helping Codex+ChatGPT for Work reach 40 million weekly active users. As a result, Codex became the foundation for OpenAI’s core products rather than a standalone coding tool.
Meta AI Research 5 days ago 14 ● 27 sources
Meta released Muse Spark Open, an open-weight multimodal coding/agent model, and made its weights available for download and API use. The release includes 816B total parameters with a 1M-token context window, and it runs on two Apple M5 Ultra Mac Studios together with MLX. This enables developers to run and customize the model locally (and on more inference engines) rather than relying only on hosted closed models.
Product Hunt 5 days ago 35 ● 2 sources
StepFun launched a preview of its open-weight Mixture-of-Experts model, Step 5 Preview, for coding, financial research, and long-running agents. It has 600B total parameters with 27B active per token and a 1M-token context. The model is offered with deployment support for vLLM, SGLang, and Transformers.
OpenAI 5 days ago 2
Anant Kumar built tools to compare loan valuations and analyze credit agreements, using Codex to speed up code review and follow-ups. The article claims this approach helps answer questions faster. As a result, the tool development workflow moves from slower back-and-forth to quicker iterations during code review and responses.
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TLDRocket
21 October 2026
The day’s biggest throughline wasn’t new model announcements—it was the messy, expensive reality of getting AI to run at scale, profitably, and reliably. Google Cloud shipped llm-d 1.0, a Kubernetes-native distributed inference layer that routes requests to pooled vLLM or SGLang servers, while Google also unveiled TorchTPU, a PyTorch-focused path for developers trying to move off Nvidia GPUs (notably, the pitch leans on PyTorch’s dominance, cited as 85% of AI apps). Off the hardware side, Dutch startup Photon Bridge raised €10 million to build laser tech for optical links in large AI clusters, targeting first samples in 2027 and volume manufacturing in 2028, and framing the bottleneck as a manufacturing-scale problem: millions of lasers per year, delivered via multiplexing designed for higher yield.
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