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实话，团队里大多数人在启动 cuLA 之前几乎没有 CUDA 开发经验。但这并不妨碍我们试一试。从读第一行 CUTLASS 源码，到跑通第一个比 Triton 更快的内核，这个过程充满了乐趣。事实证明，”试一试”本身就是最好的起点。我们希望把这种心态传递出去：如果你也对 GPU 编程感到好奇，哪怕从零开始，cuLA 都欢迎你加入。 更多细节请参考 cuLA GitHub README 。 性能数据 所有 benchmark 均以 flash-linear-attention (FLA) v0.4.2 的 Triton 实现为基线。每组 configs 覆盖了不同 B (batch size)、S (sequence length)、H (head count)、D (head dimension) 的多种组合，以评估不同负载下的加速效果。 这些加速主要来自硬件特性的利用——TMA 异步拷贝、WGMMA/UMMA 指令、多级流水线、warp specialization。 Status & Roadmap General Integrate into flash-linear-atten

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egistry, no release required. Try It Goose was recently donated to the Agentic AI Foundation (AAIF) inside the Linux Foundation. Interoperability is the thesis: goose shouldn't lock you into one agent or one editor. I'm walking through this architecture at AI Native DevCon, where every slide links to the PR or GitHub discussion behind it. Pick the UI you like. Pick the agent you like. They don't have to be the same thing. ACP clients guide; ACP providers guide; Goose on GitHub; Discord community

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ccess to the pi repository though. Before we closed the deal, I got to see Cristina, Jakob, Ramiz, Vegard in action, at the office, on the Earendil Discord, and in repositories on GitHub. While each of them has a (somewhat) specialized background, they are actually all generalists. And great human beings with a great sense of humor. I also like to think we are vibing. But it could also be just a case of kids being nice to weird old gramps on the internet, tolerating his shitty jokes. In either c

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T-5.3 and GPT-5.3-Codex variants. In the next section, I want to go more into the specifics and discuss the core components of a coding harness using my Mini Coding Agent: https://github.com/rasbt/mini-coding-agent. By the way, in this article, I use the terms “coding agent” and “coding harness” somewhat interchangeably for simplicity. (Strictly speaking, the agent is the model-driven decision-making loop, while the harness is the surrounding software scaffold that provides context, tools, and e

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s and add tooling from other ecosystems like Go or Java. And when agents are in the cloud, they can easily be triggered to run on a schedule or based on events happening in Slack, GitHub, and Jira. Once you have agents running in the cloud, observability and control become major issues. Often these agents are not directly monitored—engineers just wait for them to open pull requests. The agents might not even belong to a particular employee. Platform, security, and finance teams need a way to und

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le wrong entity can be a catastrophic failure regardless of overall intent. llm intent entity Source note: The scoring methodology below is based on the sarvamai/llm intent entity GitHub README. The LLM judges entity preservation using prompt template.txt in that repository. Banking voice bot Spoken: 'Transfer ₹5,000 to account 9876543210'; ASR output: 'Transfer ₹5,000 to account 9876543220'; Intent Score = 1 (PASS - intent preserved). Entity Score = 0.5 (FAIL - account number wrong). Transactio

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ng effort) + default BM25 retrieval. VITA-Bench: Avg subdomain scores; using claude-4-sonnet as judger, as the official judger (claude-3.7-sonnet) is no longer available. MCPMark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens. MCP-Atlas: Public set score; gemini-2.5-pro judger. HLE w/ tool: 256K ctx w/ context-folding; prunes older tool responses upon threshold breach. WideSearch: 256K ctx w/ management; prunes ≥49,152 tool tokens when 208,896 used. AIME 26: We use the full AI

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k July 26, 2026 Zig creator calls Bun's Claude Rust rewrite 'unreviewed slop' July 15, 2026 HTTP gets a QUERY method so complex searches can stop pretending to be POST July 14, 2026 Speedier type checks in TypeScript 7.0 as first stable Go release ships July 10, 2026 GitHub cuts short offer to burn repos on CD after mockery ensues A purported jab of Sony's physical media phase-out blows up on GitHub itself July 7, 2026 .NET's long term support is not long term enough, dev complains June 30, 2026

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out which configuration files were needed, generated a config.yaml, and connected the Dockerfile and registry settings - no hand-holding required. In the same pass, it set up the GitHub Actions CI pipeline. From a single prompt to a live deployment took under 10 minutes (a cycle time we're actively working to bring down). After that, the repository was fully configured and the embeds were deployed with Koyeb — as a Space, scaffolded and deployed through Spaces itself. Yes, the interactive demos

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tack, we fine-tuned Qwen3-VL-8B-Instruct on 600 real web tasks from the WebVoyager benchmark, training it to navigate, search, and extract information from live sites like Amazon, GitHub, and Allrecipes. The training run and the environment are both on the Hub and fully reproducible, with a full guide here. Two Modes: CUA and DOM BrowserEnv supports two modes, and which one you pick depends on your model. CUA mode is for vision-language models: the agent sees screenshots and acts through screen

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る成果です。 今回の Nature論文（オープンアクセス）では、システムのアーキテクチャやスケーリングに関する新知見、そして「AIが科学を生成する未来」がもたらす展望と課題について、包括的に詳述しています。 Nature掲載論文：https://www.nature.com/articles/s41586-026-10265-5; コードと生成論文はこちら（GitHub）： AI Scientist-v1、 AI Scientist-v2 開発の歩み 今回の掲載に至るまで、私たちは基盤モデルの進化に合わせて試行錯誤を繰り返し、改良を重ねてきました。 研究の自動化が可能であることの証明 初期のリリースでは、まずシンプルな学習コード（例：nanoGPT）をテンプレートとして与えました。システムはそこから自律的に斬新なアイデアを膨らませ、実験を実行し、一編の論文へとまとめ上げます。同時に、論文の質を評価する「自動査読システム（Automated Reviewer）」も構築。これにより、研究プロセスのエンドツーエンドでの自動化が可能であることを証明しました。; 科学の「チューリングテスト」への

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details our system’s architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. Explore the code and generated papers on GitHub: AI Scientist-v1, AI Scientist-v2 Example sections of a paper produced by The AI Scientist that passed the peer-review process for a workshop at a top-tier international AI conference. The Journey So Far Our journey to this publication has been a 1.5-year process with distinct phases shaped by foundation model d

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ay. We keep an internal incident log focused on agentic misbehaviors. Past examples include deleting remote git branches from a misinterpreted instruction, uploading an engineer's GitHub auth token to an internal compute cluster, and attempting migrations against a production database. Each of these was the result of the model being overeager, taking initiative in a way the user didn't intend. We documented this pattern in the Claude Opus 4.6 system card (§6.2.1 and §6.2.3.3). How it works Auto

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e use all 300 tasks. Our few failures come from unavailable sites, ambiguous prompts, or websites that changed since the benchmark was created. Rerunning our results is easy Clone github.com/browser-use/online-mind2web, set your Browser Use API key, and run. We also uploaded prompts, results, and judgments. We need harder benchmarks We're building a benchmark with everything users actually care about. Currently benchmarks ignore tasks like: "Extract 1000 products with subpages and compare them a

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ble for all Modem users. Connect it from Settings → Integrations or ask @Modem to help you set it up. Modem already connects to many tools your team uses every day: Slack, Linear, GitHub, and more. But product analytics lived in a separate tab — you'd context-switch to PostHog, build the right query, and bring the numbers back yourself. Now Modem can do that for you. Get answers from PostHog using plain english Connect your PostHog instance and the Modem agent gains direct access to your product

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increasing in 2019 (at 10 releases/year), well before modern AI coding tools appeared. This seems just as likely to be due to growing adoption of continuous integration tools like GitHub Actions, which have been around longer. Another reason to doubt this increase is entirely due to AI is the other effect visible in this chart, which is that packages are released less frequently as they get older. This is seen in the fact that all of the cohort life lines decrease over time. That has not changed

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es to avoid penalizing agents for artifacts they were not instructed to remove prior to submission.3 We upload the patches to a private copy of the repo in its historical state on GitHub. For our main analysis, we only submit PRs for review if they pass the SWE-bench automated grader. We impute maintainer review as failure if the PR fails the automated grader. This amounts to assuming SWE-bench Verified has no false negatives (cases where the automated grader says fail, but the maintainer would

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y session prompt automatically. Automated Code Review As we ship more code with agents, the bottleneck shifted from writing code to reviewing it. Devin Review turns large, complex GitHub PRs into intuitively organized diffs and precise explanations. We use it for every PR now. Every time Devin posts a PR in Slack, it includes a Devin Review link, so the organized diff is always one click away. What makes Devin Review useful: Autofix. If Devin Review or a GitHub bot flags bugs, Devin automaticall

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ts are Trinity Mini (26B with 3B active parameters) and Trinity Nano (6B with 1B active parameters) Along with the model weights, Arcee AI also released a nice technical report on GitHub (as of Feb 18 also on arxiv) with lots of details. So, let’s take a closer look at the 400B flagship model. Figure 2 below compares it to z.AI’s GLM-4.5, which is perhaps the most similar model due to its size with 355B parameters. As we can see in the Trinity and GLM-4.5 comparison, there are several interestin

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en a ZX Spectrum emulator (and even more, a CP/M emulator, see later) in a condition that I believe makes a more sense as “clean room” setup. The result can be found here: https://github.com/antirez/ZOT . The process I used 1. I wrote a markdown file with the specification of what I wanted to do. Just English, high level ideas about the scope of the Z80 emulator to implement. I said things like: it should execute a whole instruction at a time, not a single clock step, since this emulator must be

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