Simon Willison
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1 week ago
Jarred Sumner rewrote the Bun runtime from Zig to Rust using AI agents to automate the port, addressing memory safety issues that caused use-after-free and double-free bugs in the original codebase. The rewrite consumed 5.9 billion input tokens and 690 million output tokens across 11 days of agent-assisted development, with costs estimated at $165,000 at standard API pricing. Startup performance on Linux improved 10 percent, and the new Rust version has been running in Claude Code since June 17th with minimal user-visible changes.
Hugging Face Blog
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1 week ago
NVIDIA released over 10 trillion pre-training tokens and millions of post-training samples as open datasets to help developers build AI agents that can handle real-world failures and unseen workflows. The company introduced the Nemotron Post-Training v3 Prompt Atlas, an interactive map visualizing prompt samples by domain, pipeline stage, and tool use, alongside Nemotron-Personas covering more than 2.4 billion people across ten countries. By making training data transparent and using synthetic data to preserve proprietary signals without exposing sources, organizations can now contribute to shared AI development without revealing competitive advantages.
NVIDIA
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1 week ago
NVIDIA Nemotron 3 Ultra achieved the highest accuracy among open models on LangChain's Deep Agents benchmark by optimizing the system prompt, tool descriptions, and middleware rather than retraining the model. The tuned version runs at 10 times lower inference cost per run than leading closed models while achieving business task parity with the best closed models. Enterprises can now use the open NemoClaw blueprint to build and customize specialized AI agents across their workflows without vendor lock-in.
TLDR
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1 week ago
Herdr is a terminal-based agent multiplexer that displays multiple AI agents' status and output in real-time, allowing users to manage concurrent agent tasks without leaving the terminal. The tool is distributed as a single Rust binary at version 0.4.0 with support for macOS, Linux, and Windows, installable via curl or Homebrew. Sessions persist across detaches and restarts, enabling users to disconnect and reattach from any terminal or SSH connection while agents continue running.
The Neuron
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1 week ago
Antidoom is an open-source tool that reduces repetition loops in language models by generating preference training data and applying targeted LoRA adapter training via Final Token Preference Optimization. The method identifies where repeated sequences begin, marks the first loop-starting token as rejected, samples alternative tokens, and trains with regularization to prevent overrepresentation of specific tokens. Users can apply Antidoom to their models by cloning the repository, configuring a base checkpoint, generating 15,000–20,000 preference pairs from prompts, and training with a learning rate around 0.00001–0.00002 until the chosen token wins on roughly 15–40% of samples.
The Neuron
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1 week ago
Anthropic launched a free access program for open-source maintainers and contributors, providing six months of complimentary Claude Max subscriptions. Eligible applicants include maintainers of packages with 500+ dependent repositories, core contributors to major projects like CPython and Kubernetes, active contributors with 100+ merged pull requests in the past year, and maintainers of critical infrastructure. After six months, paid subscriptions resume at their previous rate while others revert to free-tier access.
Allen Institute (AI2)
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1 week ago
MolmoAct 2, an open-source AI model for robotics, enabled developers at a hackathon to build voice-controlled robots that performed tasks without specific training in just ten days. The model includes complete artifacts such as weights, training code, and fine-tuning scripts on public platforms like Hugging Face and GitHub. The availability of this general-purpose model represents a shift in robotics by providing the community with accessible tools for building adaptable robot systems.
