404 summarised stories about AI Coding Agents, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 2 months ago 8
Thomas Ptacek argues that developers should stop making TUIs and instead build native user interfaces for even small personal tools. The push is driven by the fact that coding agents have reduced the cost of getting a usable-enough GUI up to almost nothing. As a result, more projects may replace terminal-only interfaces with native UI apps rather than investing further in TUIs.
The New Stack 2 months ago 19
SWE-Bench ProMax was introduced to evaluate AI coding agents on large-scale refactoring that many benchmarks skip.
Product Hunt 2 months ago 7
Mastra is launching Mastra Factory, an open source agent-powered software delivery environment for building AI-powered apps and agents. It launches today and includes features like persistent coding agents, repo workspaces, issue intake, planning, implementation, and pull request review. As a result, developers can run an end-to-end workflow in a web app they control instead of stitching these steps together manually.
Together AI 2 months ago 40 ● 3 sources
GLM-5.3 and Claude Fable 5 tied on DeepSWE first-attempt coding accuracy, but GLM-5.3 won every metric after retries. GLM-5.3 cost $3.99 per rollout versus $21.63 for Fable 5 (a 5.4x gap), while scoring 69.0% vs 69.7% on pass@1 and 81.1% vs 77.1% on pass@2. The practical change is to default to GLM-5.3 and only escalate to Fable for Rust and serialization-heavy tasks, since pairing both adds limited extra coverage due to high per-task overlap.
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25 October 2026
Microsoft Research dropped Agensh, an open-source multi-agent harness that aims to make coding swarms feel less like a science project and more like a workflow. The key idea is decentralized task claiming: instead of a single central orchestrator, agents self-assign sub-tasks, then synchronize by writing progress into a shared Git repository. In ProgramBench tests, Agensh scaled from 1 to 1,024 agents, with a 30-minute pandoc milestone climbing to a 30% passing rate at 128 agents. The mean final test-pass rate improved from 19.31% with one agent to 28.78% at 128, and pandoc reached 79.59% when using GPT-6.1-sol with 32 agents—an unusually specific datapoint for what is often framed as “more agents = better.”
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