AI 编码

Kimi Code, our open-source coding agent, just got a major upgrade! 🔹One-line CLI install, zero setup, fast startup​ 🔹Drag in videos as coding context: reference-to-LUT, long-video-to-short, screen-recording-to-code, and more​ 🔹Plugins for stocks, financial reports, academic papers, with more coming​ 🔹Supports the ACP protocol, and works with JetBrains, Zed, and more​ 🔹Hooks for custom tools and workflows​ Try it with Kimi K2.6 👉 kimi.com/code Issues, plugin ideas, and PRs welcome! Community feedback helps shape what ships next.​🚀

Holy moly, Anthropic is getting very serious about recursive self-improvement! One word: acceleration. Insane blog article. Tl;dr: •We are close to an AI capable of fully autonomously designing and building its own successor •They stress this isn’t here yet and isn’t inevitable, but could arrive sooner than most institutions are ready for •Anthropic engineers now ship on average 8x as much code per quarter as they did in 2021–2025 •Task length AI can reliably complete is doubling roughly every 4 months (up from every 7 months) •Opus 3 (Mar 2024) handled ~4-minute tasks; Sonnet 3.7 (a year later) ~90-minute tasks; Opus 4.6 (a year after that) 12-hour tasks •SWE-bench went from low single digits to saturated in two years; CORE-bench (research reproduction) went ~20% to saturated in 15 months •METR found Claude Mythos Preview could work “at least” 16 hours, at the top of what they can currently measure •As of May 2026, Claude authored 80%+ of code merged into Anthropic’s codebase (low single digits before Claude Code launched in Feb 2025) •A March 2026 poll of 130 research staff: median respondent estimated ~4x output with Mythos Preview •One April 2026 example: Claude shipped 800+ fixes cutting a class of API errors 1,000x, work an engineer estimated would have taken a human four years •Claude-written code quality: worse than human in late 2025, roughly at parity now, expected to be strictly better within the year •On the hardest open-ended tasks, Claude’s success rate hit 76% in May 2026, up 50 points in six months •Code-speedup test: Opus 4 averaged ~3x speedup (May 2025), Mythos Preview ~52x (April 2026); a skilled human needs 4–8 hours to hit 4x •In an AI-safety research project, Claude agents recovered 97% of a performance gap (vs ~23% for two human researchers in a week), over 800 compute-hours and ~$18K •On picking the better “next step” in research sessions, the best model beat the human choice 51% (Nov 2025, Opus 4.5) rising to 64% (April 2026, Mythos Preview) •Human comparative advantage, for now: research taste and judgment, i.e. choosing which problems matter and when an approach is a dead end Three possible futures •The trend stalls (S-curve), but today’s capabilities still diffuse widely; they consider this least likely •Compounding efficiency gains, with humans still setting direction; 100-person firms doing the work of 10,000+; they think this is the likely path •Full recursive self-improvement, where AI builds its successors and pace is set by compute; the alignment outcome here is what they’re least certain about

Holy moly, Anthropic is getting very serious about recursive self-improvement! One word: acceleration. Insane blog article. Tl;dr: •We are close to an AI capable of fully autonomously designing and building its own successor •They stress this isn’t here yet and isn’t inevitable, but could arrive sooner than most institutions are ready for •Anthropic engineers now ship on average 8x as much code per quarter as they did in 2021–2025 •Task length AI can reliably complete is doubling roughly every 4 months (up from every 7 months) •Opus 3 (Mar 2024) handled ~4-minute tasks; Sonnet 3.7 (a year later) ~90-minute tasks; Opus 4.6 (a year after that) 12-hour tasks •SWE-bench went from low single digits to saturated in two years; CORE-bench (research reproduction) went ~20% to saturated in 15 months •METR found Claude Mythos Preview could work “at least” 16 hours, at the top of what they can currently measure •As of May 2026, Claude authored 80%+ of code merged into Anthropic’s codebase (low single digits before Claude Code launched in Feb 2025) •A March 2026 poll of 130 research staff: median respondent estimated ~4x output with Mythos Preview •One April 2026 example: Claude shipped 800+ fixes cutting a class of API errors 1,000x, work an engineer estimated would have taken a human four years •Claude-written code quality: worse than human in late 2025, roughly at parity now, expected to be strictly better within the year •On the hardest open-ended tasks, Claude’s success rate hit 76% in May 2026, up 50 points in six months •Code-speedup test: Opus 4 averaged ~3x speedup (May 2025), Mythos Preview ~52x (April 2026); a skilled human needs 4–8 hours to hit 4x •In an AI-safety research project, Claude agents recovered 97% of a performance gap (vs ~23% for two human researchers in a week), over 800 compute-hours and ~$18K •On picking the better “next step” in research sessions, the best model beat the human choice 51% (Nov 2025, Opus 4.5) rising to 64% (April 2026, Mythos Preview) •Human comparative advantage, for now: research taste and judgment, i.e. choosing which problems matter and when an approach is a dead end Three possible futures •The trend stalls (S-curve), but today’s capabilities still diffuse widely; they consider this least likely •Compounding efficiency gains, with humans still setting direction; 100-person firms doing the work of 10,000+; they think this is the likely path •Full recursive self-improvement, where AI builds its successors and pace is set by compute; the alignment outcome here is what they’re least certain about

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency, 28.8% KernelBench Hard, 74.2% MCP Atlas - MiniMax Sparse Attention scales context to 1M - Natively Multimodal from Step Zero API: platform.minimax.io Token Plan: platform.minimax.io/subscrib… 🚀New! MiniMax Code: code.minimax.io Weights & Tech Report in ~10 Days

Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency, 28.8% KernelBench Hard, 74.2% MCP Atlas - MiniMax Sparse Attention scales context to 1M - Natively Multimodal from Step Zero API: platform.minimax.io Token Plan: platform.minimax.io/subscrib… 🚀New! MiniMax Code: code.minimax.io Weights & Tech Report in ~10 Days

Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency, 28.8% KernelBench Hard, 74.2% MCP Atlas - MiniMax Sparse Attention scales context to 1M - Natively Multimodal from Step Zero API: platform.minimax.io Token Plan: platform.minimax.io/subscrib… 🚀New! MiniMax Code: code.minimax.io Weights & Tech Report in ~10 Days

Introducing Pareto Code: a new, free, experimental coding router Set `min_coding_score` in your request and route to the cheapest code-capable model that clears your bar, ranked by @ArtificialAnlys. See the Pareto frontier shifting in real time👇

Seven new models launching at Build: let’s go! Reasoning. Code. Image. Transcribe. Voice. Built from scratch on a clean data lineage, designed for efficiency, working seamlessly as a family of models Thread 🧵 #MSBuild

We just released the Codex Python SDK 🔥 You can now embed Codex directly into your Python apps and workflows! > Start threads > Run turns > Stream progress > Resume sessions > Pass images > Control sandbox access All whilst reusing your existing Codex auth. pip install openai-codex Go build with it!!

Codex now has more than 5M weekly active users. But the bigger story is what people are using it for: not just writing code, but getting more work done across research, analysis, content, and operations. Our new report on how Codex is becoming a productivity tool for knowledge work: openai.com/index/codex-for-k…

We just released the Codex Python SDK 🔥 You can now embed Codex directly into your Python apps and workflows! > Start threads > Run turns > Stream progress > Resume sessions > Pass images > Control sandbox access All whilst reusing your existing Codex auth. pip install openai-codex Go build with it!!