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rid and holds onto all of it, the other builds and discards one slice at a time, so memory never grows beyond a single chunk. We have open-sourced the chunked-loss implementation: github.com/CompactifAI/Full-Chunked-KL-Loss What this changes in practice The table below puts all four setups head to head: online distillation, and the three offline loss implementations just described. Comparing them on a single H200 GPU with Llama 3.1 8B Instruct as teacher and a 3.2B Llama model as student at an 8

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n the city in the image. Object Detection You can use Muse Glimmer to do open ended object detection in images as follows. Here is an end to end script to perform object detection GitHub Gist Llama.cpp Muse Glimmer comes with day-0 llama.cpp support. Meta has distributed calibrated quants in this repo , and Unsloth is releasing optimized quants as well. DFlash speculative decoding is supported as well. You can use a pre-built llama binary to start a llama server or a CLI. To install llama.cpp, r

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pth | Deployment and governance | | --- | --- | --- | --- | --- | --- | | OpenHands | AI agent platform | Agent writes source fixes, opens PRs | Consumes ranked scanner findings | GitHub, Trivy, scanner reports, CI | Local, cloud, self-hosted, plus RBAC and VPC on Enterprise | | Snyk | Scanner plus AI fix | Automatic Fix PRs, Agent Fix | Reachability, exploit maturity, popularity | Source control, editor, CI/CD, Jira | Cloud, self-hosted options | | GitHub Dependabot | Patch (dependency bumps) |

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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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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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ed language models and multimodal generative models. At Interconnects we follow the data of every model on Hugging Face, analyze the core few thousand LLMs (this list is public on GitHub and regularly updated), and hand select these core few hundred for further explanation. We built the Hub as a way to go deeper on this analysis in collaboration with Project VAIL — an AI verification startup who has been one of the most loyal fans of our open model curation. For the most popular models, the Hub

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Your Browser As with ZUNA1, everything is open-source. The ZUNA1.1 model weights are available on HuggingFace. The inference and MNE-compatible preprocessing code are available on GitHub. ZUNA1.1 can be installed using pip install zuna. The model runs fast on a consumer GPU and acceptably on CPU. Because it's Apache 2.0 you can build on it freely, including self-hosting for sensitive or clinical data that can't leave your premises. In addition, ZUNA1.1 is available on the Zyphra Cloud. This mean

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for the model; 75+ LLM providers via BYOK: OpenCode connects to any of 75+ supported models with your own API key and passes token costs through at provider rates with no markup; GitHub Actions integration: Mentioning /opencode in an issue or PR kicks off a task and opens a pull request without the developer leaving GitHub; No enterprise control plane: OpenCode is a single-agent tool with no org-wide governance, audit logs, or cost attribution built in, so teams that need those will need to lay

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→ Real-world adoption, including real-time multilingual translation of livestream comments on Bilibili 📺 And now, Hy-MT2-30B-A3B is officially available in GGUF format—addressing one of the community’s most-requested deployment needs and making local inference easier. Ready to run Hy-MT2-30B-A3B locally? Try the new GGUF release: https://t.co/adbLlTVV7I Explore Hy-MT2: HuggingFace：https://t.co/4J8gzMcENW Modelscope：https://t.co/xh5vEYk9zQ Github：https://t.co/EekzbAcLZp TencentHy HyMT2 OpenSource

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vmath-python[cu13] to begin accelerating math workloads. Review the installation documentation for detailed setup options and environment configurations. Explore the nvmath-python GitHub repository for code examples and in-depth tutorial notebooks. Powered by NVIDIA Nemotron. AI-generated content may summarize information incompletely. Verify important information. Learn more NVIDIA nvmath-python is a library designed to bridge the gap between the Python scientific community and NVIDIA CUDA-X ma

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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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session, using the write tools. Token efficient Using Modem to search customer feedback is fast and token efficient. You can of course query each source directly — Slack, Linear, GitHub, Intercom — but those sources hand back raw threads and tickets, and the agent pays for every token it reads while piecing them together. We measured it: the same question in a fresh Claude Code session, restricted to one source at a time. Modem typically answered from a single search modem call. The other sessi

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urns those issues into well-considered solutions. We’re moving goose to this model with a public Goose Issues board. As before, if you find a bug or want a new feature, you file a GitHub issue. New issues enter the Inbox. During triage we may ask for more information or close the issue with an explanation. Problems we want to solve move to Accepted / design, where contributors and the core team work out the intended design, architectural constraints and how the result will be verified. When that

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or running workloads on their choice of hardware. SGLang is an open source inference framework built for high throughput and low latency at production scale. With more than 30,000 GitHub stars and over 1,700 contributors, it runs on hundreds of thousands of GPUs worldwide and generates trillions of tokens in production every day. RadixArk is a maintainer of the SGLang project. Currently, developers can run SGLang on the latest TPU generations through SGL-JAX, with support for major large languag

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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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parent agent and translates it into Snowflake SQL. It runs against our Panther data warehouse, which ingests audit logs from across the company: AWS CloudTrail, Okta system logs, GitHub audit events, GCP audit logs, osquery endpoint telemetry, Workshop/Santa events, Wiz findings, and about a hundred other tables. The parent agent calls it the way you’d ask a colleague to run a query: “Find the most recent Okta logins for user X in the past 48 hours” or “Check what processes user Y has been runn

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he patient's provider: rather than rendering definitive diagnoses, they provide educational information and route to clinicians. We're releasing the PatientAgentBench framework on GitHub. The release includes the synthetic-scenario generation pipeline, the healthcare sandbox with stateful tools, the dual-agent-conversation runner, and the LLM-as-a-jury evaluation system with all six rubric prompts. The framework generates fresh scenarios on demand from a configurable seed distribution rather tha

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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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th passes agree. Validated findings pass one last human filter. A duplicate check happens before anything is opened. Only bugs that are confirmed upstream and not already found in GitHub’s issue backlog are drafted for submission. Figure 4: The full workflow Kevin Valerio used to find every bug in Rust Since its release, /goal has been a powerful tool for amplifying the bug-hunting work that we do. Codex can create custom security infrastructure that takes a security researcher weeks to build in

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their agent across multiple experiments for each qubit To learn more about deploying Ising Calibration 1.5 with an agent, check out the NVIDIA/Quantum-Calibration-Agent-Blueprint GitHub repo. The NVIDIA Ising model family is fully open. Weights, data, benchmarks, and recipes are provided so you can modify, deploy, and fine-tune your own models and variants for your specific QPUs. Full-parameter checkpoints for Ising Calibration 1.5 are available on Hugging Face: Ising Calibration 1.5 is also av

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