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ned on your examples can often perform as well as or better than a general purpose frontier model on specified tasks, often at lower cost and latency. Try LangSmith Fine-Tuning on GitHub and have your coding agent drive the fine-tuning process end to end by installing the smithtune skill from the repository. One of the biggest drivers of fine-tuning gains is data selection. With the launch of smithtune, it’s easier to connect that loop — from curated production traces in LangSmith to managed tra

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an end-to-end validation using NVCRE documentation to certify your GPU cluster before production workloads arrive. Contribute catalog entries, workload adapters, or tests via the GitHub repository to extend coverage for new architectures and inference workloads. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more A GPU cluster can pass every health check and still fail to run an AI workload. Even when every GPU, netwo

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, and MobileWorld-Safety, covering planning, cross-app execution, real-device performance and safety. 🔗 Learn more about the agents: - Qwen Intelligence official website: https://t.co/ZJJYJtmJIL - Mobile Planner Agent: https://t.co/M0Wxpoa00x - Mobile-Use Agent: https://t.co/vvQtbUAagJ - Mobile Creative Agent: https://t.co/gWWOmrSIqV 🔗 Explore our open benchmark suite: - MobilePA-Bench: https://t.co/9nXxzG0Ygp - MobileWorld (GitHub): https://t.co/POvERSuCSJ - Leaderboard: https://t.co/YYOw8bmJ6H

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r post-training of open-source models. Halo delivers up to 2.8x the throughput of stock TRL with less peak memory, while models stay in their native HuggingFace format. Star us on GitHub: https://t.co/3mAiUljdrN pic.twitter.com/NDoydDUBWa— White Circle (@whitecircle) September 21, 2026 既存モデルを活用し⁠、複数GPUで追加学習 大きなモデルの追加学習では、モデルの重みに加え、学習中の計算結果やパラメーターの更新に使う情報にもメモリが必要になる。1基のGPUに収まらない場合は、複数のGPUに処理を分ける分散学習を使う。ただし、別の学習基盤へ移す際に、モデルの実装や保存形式を合わせる作業が必要になることがある。 Haloは、Hugging Faceの既存モデルに分散学習の機能を加えることで、モデル全体を書き

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the generator compiler itself, the AGPL guarantees those improvements remain open to the community. You can explore the new Gemini Interactions API today using our updated SDKs. If you want to generate your own client libraries, CLIs, or MCP servers, you can find the Speakeasy generator on GitHub and run it against your OpenAPI specs. Thanks for reading! Keep on building. AI; Announcements; Learn Optimizing a 200,000-line Python codebase overnight with Gemini 4 Argon Gemini 4 Argon for Robotics

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Facebook; Twitter; LinkedIn; Mail Today, we're thrilled to announce the 1.0 general availability release of the Agent Development Kit (ADK) for Kotlin! Check out the GitHub repository to dive into the code and build your first agent today, and explore the documentation. When we introduced ADK for Kotlin 0.1.0, our mission was to bring idiomatic, lightweight, and composable AI agent development to Kotlin, Java, and Android developers. Over the past months, we've worked to evolve the framework int

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tarting from zero “To think is to forget differences, generalize, make abstractions.” — Jorge Luis Borges, Funes the Memorious Your agents already wrote the record. funes lives at github.com/huggingface/funes , one command away from turning that record into a memory the next agent can read, on whichever machine you happen to be on. Built on open source funes invents little of this. It leans on open-source embedding models good enough to run locally, on Lance 's append-only datasets with cheap in

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rence and may contain code, and agent transcripts may be processed and stored by Cursor. Teams can continue to access cloud agents from the desktop app, cursor.com, mobile, Slack, GitHub, and Linear. Workers connect your infrastructure to the Cursor agent loop With Self-Hosted Machines, tool execution moves from a Cursor-hosted VM to a machine in your environment. That machine holds the working copy of the repository, edits files, and runs commands. A worker connects it to the rest of the agent

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e-orders for Microduck, a $399 bipedal robot that walks, carries objects in its beak, falls over, and picks itself back up — with its entire reinforcement-learning stack posted on GitHub. The details: Built by Hugging Face’s France-based Pollen Robotics team, Microduck packs 15 motors, a camera, LiDAR, two motion sensors, microphones, and a speaker. Out of the box, it can perform seven behaviors, including recovering from falls and picking things up; optional wheels let it roller-skate. The SDK,

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d count what the session could not keep up with. Below is the agent trace from this post on a real call: four questions, three execute tool get weather spans, 61.87s costing $0.08, or about $4.50 for an hour of talking at that rate. Pydantic Logfire files each session alongside your other agent runs, since the session span carries the same agent name its Runs view groups on. If you build something with it, or hit something that doesn't feel right, we want to hear about it, on GitHub or in Slack.

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skill scores vary. Catalog-wide averages aggregate thousands of trials, but this post does not report confidence intervals. The same evaluations used skills from the NVIDIA/skills GitHub repository. Table 2 shows the average with-skill scores and Skill Lift relative to the without-skill baseline across Codex and Claude Code. Verified skills improved agent performance across both evaluated harnesses, with the largest gains in Correctness, Discoverability, Effectiveness, and Efficiency. Skill Lift

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product initiatives across Augment to make building software better for every developer. Chris has been making developers happier and more productive for 15 years at innovative companies like New Relic, GitHub, Salesforce, and FireHydrant. You can find him at @amateurhuman everywhere on the internet. Related reading Kiro CLI vs. Auggie CLI: Spec-Driven Terminal Agents Compared; Using the Auggie CLI for automated code review; Opus 4.7 for 33% less: How Auggie beats Claude Code on cost and quality

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expression signals for frontends. Try it today The fastest way to hear the difference is the demo above. Then enable it on your own agent with one line. The full demo agent is on GitHub and the expressive mode docs cover the rest. Voice AI that gets the words right but the feeling wrong still loses users. Expressive mode is our first step toward agents that respond to how you're doing, not just what you said. Give it a try! We'd love to hear what you build and any feedback you have. Related Tes

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tegrations that bring NeMo Switchyard into familiar agent tools, frameworks, and gateways, or build custom routing into their own agents and LLM gateways using the NeMo Switchyard GitHub instructions . Model routing enables systems of specialized and frontier models to work together, delivering results that are greater than the sum of their parts. However, building a useful and production-ready routing system remains a very difficult engineering challenge. As AI systems increasingly combine mode

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r real-world codebases. To train the system, we distilled 107,000 agent interactions from two advanced open-weight models (MiniMax-M2.5 and Qwen3.5-397B) covering a broad range of GitHub Issues. The training process uses credit-assignment supervised fine-tuning: rather than discarding attempts where the agent failed to fully resolve an issue, the system learns from the productive portions of those partial attempts, expanding the amount of useful training data available to the model. Reinforcemen

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verage font, S&A explained, but still considerably smaller than the desktop version. There’s more than one GSUB dictionary too - ShieldFont is shipping with three of them, and the GitHub repository explains how users can create their own to prevent reverse-engineering. But why poison a few words instead of just scrambling text altogether? S&A told us that they want a deterrent to scraping by introducing uncertainty and chaos into training data, not just a way to get scrapers to ignore some pages

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ucts to maintain or improve quality while using significantly fewer tokens, in many cases saving 50-90% of GPU costs: We built MAI-Code-1-Flash hand-in-hand with our colleagues at GitHub, where since June millions of developers have used it in their daily work. 10% higher code accept rate and 10% lower median token usage than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, and already showing improved retention; We then trained that same checkpoint inside an Excel RL environment to achieve compara

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ext Steps Build an endoluminal robotics workflow using the catheter navigation workflow to create patient-specific simulations. Run Cosmos-H Dreams interactive simulation from the GitHub repository with pretrained checkpoints. Download Cosmos-H Dreams model checkpoints from the Hugging Face checkpoint for generative surgical simulation. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more Unlike autonomous driving or in

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fied: 6,275 rows from 32,079 raw - language-level drops for wholesale-broken images, two independent gold passes with flaky rows removed, and problem statements scrubbed of inline GitHub issue/PR references; SWE-Bench-Verified-Quick: 468 of 500 Verified instances, dropping the slowest examples for quick online-evals The tooling behind these passes ships with verifiers, so you can reproduce them yourself. uv run validate is the model-free sibling of eval: for every task it runs the gold check (se

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capture ( still a work in progress ). But the most important next step isn’t only ours, it’s also yours: start recording, and let’s build the dataset together. 👉 Build a Grabette (GitHub) · Process your data (HF Space) · Contribute to the dataset Built with ❤️ by Pollen Robotics. Spaces mentioned in this article 1 Sleeping 4 Grabette Slam 🌍 4 Convert Grabette recordings into a LeRobot dataset More Articles from our Blog LeRobot goes to driving school: World’s largest open-source self-driving dat

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