For people who build with AI.

Thomas Dohmke, who was GitHub’s CEO from 2021 to 2025, has officially launched Entire, a Git hosting and collaboration platform built to compete with his former company. The service was introduced in July and is designed for human developers and artificial intelligence agents to work together. Instead of pull requests, the platform uses Trails, which record what changed in the code and why.

Entire says its Git-compatible version-control server enables pushes up to 89 times faster than GitHub’s and six times faster than Cursor Origin’s. The platform does not charge for repositories or Trails, but requires credits for AI agents; each account receives US$100 in welcome credits.

Sources: Tecnoblog

Read the original story ↗

Microsoft says it is developing and running AI agents to transform large codebases, including Windows and Azure. The work it cites includes migrating a 400-million-line codebase to a new compiler toolchain and modernizing legacy C and C++ code into Rust, with increasing use of formal verification.

According to the company, generating code is no longer the hardest part: teams must verify that changes preserve behavior, security and architectural intent. Since 2024, Microsoft says it has combined agents with static analysis, testing and formal methods; by May 2025, it says agents had become better planners than its workflows. The company also says human attention has become the scarcest resource in agentic software engineering.

Sources: Microsoft

Read the original story ↗

The r/opensource moderators posted new rules saying projects that use AI, are made by AI, or are intended for AI are not welcome in the community. The Verge reports that moderator Wolvereness said selective enforcement may apply to cases such as accessibility features that use AI, including automatic captioning, but the decision triggered strong backlash and prompted the creation of the rival r/trueopensource subreddit.

The rules also require projects to be at least one year old, actively maintained, and have multiple contributors, preventing solo developers with new projects from seeking help. The post further says that people who do not speak English should find another community because moderators cannot distinguish between AI-written and AI-translated text. The rules drew criticism for their breadth and because they could affect projects using AI-generated code in the Linux kernel, The Verge reports.

Sources: The Verge

Read the original story ↗

Unitree Robotics presented, at a physical AI conference in Hangzhou, an “self-evolving” development system that uses AI agents to search research, write control code, test it in simulations and select results for validation on real robots. The proposal is to automate part of the cycle currently handled by engineers and allow robots to acquire new skills faster and at greater scale. According to Xataka, the project is called Unitree Rehearsal.

Wang Xingxing, the company’s founder, told the South China Morning Post that more advanced AI models could take over a larger share of planning and iteration. Unitree has also been testing OpenAI’s GPT-6; in internal experiments, Wang said, robots performed tasks such as painting and playing Go without specific training. Xataka notes, however, that the method’s efficiency, skill transfer and consistency in unfamiliar environments have yet to be demonstrated. The models handle planning and navigation better, but still make mistakes when manipulating objects.

Sources: Xataka

Read the original story ↗

The Verge reports that AI coding tools have significantly reduced the time needed to create ports of retro games, bringing titles such as Halo, The Legend of Zelda: Ocarina of Time, and Super Smash Bros. to unexpected platforms. Chris Sotraidis, an AI consultant who makes ports as a hobby, says he began working on about a dozen projects within a few weeks, something he could not have done without AI. One cited project was a mobile port of Ocarina of Time based on a community-made decompilation.

The speed is raising concerns among preservationists. Manual decompilation helps people understand how games work, while AI can accelerate the code without preserving that knowledge. According to The Verge, a community site that tracked decompilation projects shut down after its owner, writing under the pseudonym Encounter, said the community had been damaged by people using AI to “finish” others’ projects or create ports without contributing to the original work.

Sources: The Verge

Read the original story ↗

Hugging Face presented the torch-neuronx model support Space, which checks which popular Hub models run unmodified on AWS Trainium. The panel shows the number of cores used, total runtime — including compilation —, peak memory and the test result, while also providing the script and packages needed to reproduce it on a Trainium2 instance. The data is also available in the public aws-neuron/torch-neuronx-model-support dataset.

At present, testing covers only Transformers models for text generation and image-text-to-text tasks that fit on up to four Trainium2 devices, or 16 cores. Hugging Face plans to add diffusers, sentence transformers and other tasks. A “PASS” result confirms execution and an output check, but does not measure throughput or longer generations.

Sources: Hugging Face

Read the original story ↗

Microsoft presented Flint-interactive, a language built on Flint for creating dashboards, data stories and other interactive visualizations. The company says users and agents can define behaviors with high-level semantic events and update operators, enabling them to click, filter, zoom and highlight data while connecting charts, text, tables and applications. A selection in one view can also update another, such as a document or an application’s state.

Microsoft says Flint’s compiler resolves dependencies between interaction and visual logic, reducing development effort, generation costs and layout or rendering failures. The company presented the work at SOSP 2026, a conference focused on computer systems software.

Sources: Microsoft

Read the original story ↗

MIT Technology Review argues that as AI moves from experimentation into production, the central question is no longer only what a model can do, but whether a company can deliver that capability reliably, affordably and at scale. The publication calls the engineering system around the model a “serving harness,” covering architecture, memory management, scheduling and optimization.

The analysis says the workload should determine the architecture: an assistant requiring fast responses has different needs from a document-processing pipeline that runs overnight. Latency, concurrency, memory pressure, GPU utilization, cost and unpredictable traffic can matter more than small differences in accuracy. The publication recommends measuring the dominant bottleneck before optimizing; for example, the key-value cache can consume available GPU memory as concurrency and context length grow, without adding more compute solving the problem.

Sources: MIT Technology Review

Read the original story ↗

OpenAI has made the Decisions API available to all customers, expanding access to its decision model as competition grows around TypeSafe’s Jev. Instead of using a model trained specifically for decision-making, the product reconfigures ChatGPT Luna to speed up inference and reduce costs. Nikunj Handa, OpenAI’s API product lead, told TechCrunch that latency fell from one to three seconds to between 100 and 180 milliseconds.

The public version also includes voice and image capabilities. In initial trials with Box, Datadog, Ramp and Speak, engineers used the API to replace OpenAI’s language models and increase throughput. OpenAI also used it to make the Dots voice assistant more responsive. Public rankings remain mixed: OpenAI’s model trails alternatives such as Liquid’s D1 Ultra and Base Lab’s Elephant-1 on two indices cited by TechCrunch, while the latest Jev model remains competitive.

Sources: TechCrunch

Read the original story ↗

Sammy Hajhamid, known as “pepsipu,” says they jailbroke the Nintendo Switch 2 with “ns2jb,” a software-only, tethered jailbreak: the exploit must be run from a game every time the console is turned on. According to The Verge, the process uses SmileBASIC 4, although another modder claims the game is not strictly necessary. Hajhamid also says OpenAI’s Codex helped create the jailbreak.

The modder showed a custom version of Mario Kart World with characters including Cinnamoroll, the red Angry Bird, Peter Griffin, Hatsune Miku, and Steve from Minecraft. The procedure still appears too complex for most users, and the jailbreak’s impact is not yet clear; however, it could mark the start of a series of Switch 2 exploits, especially with AI assistance, The Verge says.

Sources: The Verge

Read the original story ↗

Xataka warns that the infrastructure supporting the internet is aging and that the arrival of frontier artificial intelligence models may be accelerating that process. In PHP’s case, the PHP Foundation identified a vulnerability in 2024 but found no known way to exploit it at the time; the difficulty of fixing it also delayed a solution, the site says. The problem is worsened by dependence on old applications, libraries and operating systems, whose updates can break services that are still running.

According to Xataka, AI can help review large volumes of code and find patterns that are difficult to detect through conventional methods. Google Cloud described how its engineers used AI models to locate and validate PHP flaws, create exploitation tests and verify fixes — although identifying vulnerabilities does not remove the difficulty of applying them across a broad and heterogeneous infrastructure.

Sources: Xataka

Read the original story ↗

NVIDIA announced at GTC Berlin an expansion of Cosmos, its platform for developing world models for physical AI. The additions include tools for labeling, curating, augmenting and evaluating multimodal data, as well as five Cosmos Data Factory blueprints for generating and processing synthetic data. They are available through GitHub, Microsoft Azure, Nebius and CoreWeave.

NVIDIA also extended Cosmos Framework’s training, optimization and deployment tools to compatible open models on Hugging Face. Developers can adapt models such as GR00T, Alpamayo, QWEN and Pi0.5 through fine-tuning and reinforcement learning, and use distillation and quantization. NVIDIA says Porsche expects the technology to reduce repeated physical test drives by up to 90% and shorten development cycles by up to 85%; ANYbotics generated more than 1,500 synthetic video clips to train inspection robots.

Sources: NVIDIA

Read the original story ↗

Hugging Face says it cut the measured inference cost of an automated Portuguese essay grader by 77% without changing the Qwen3-4B base model, the data or the training recipe. The system produces JSON evaluations and used the model’s original tokenizer, with 151,669 entries, designed for many languages and types of text. The company says that vocabulary fragmented recurring phrases and made the model calculate scores for many entries rarely used in the task.

The VocabX method rebuilds the vocabulary around the cost of each token rather than only its frequency. Hugging Face says the reduction is especially relevant during generation, when small models make successive passes and are limited by memory bandwidth. The figures come from the current version of a paper still under review and may change before publication.

Sources: Hugging Face

Read the original story ↗

Hugging Face published the first part of a series on systematically analyzing inference performance with TorchNeuron, a native PyTorch backend for the AWS Trainium chip. The tutorial, also aimed at readers without prior profiling experience, explains how to capture traces with torch.profiler and the Neuron backend, compare CPU time with device time, and identify the operations consuming the most resources.

The material also introduces Neuron Explorer, a browser-based viewer that can track an operation from framework dispatch through runtime scheduling to execution on a NeuronCore. As an example, the guide examines three implementations of RMSNorm and shows how to combine key_averages() tables with system and hardware traces to understand where time is spent.

Sources: Hugging Face

Read the original story ↗

Cloudflare says nearly 100,000 Commerce.com domains now offer structured tools for AI agents through WebMCP, an experimental browser standard proposed by Google. Instead of interpreting HTML and clicking page elements, an agent can call tools to search for products, check details, build a cart and get a checkout link.

Cloudflare’s beta implementation injects a small script at the edge, without requiring merchants to write code or change their themes. The company says the tools run in the shopper’s active browser session and on the store’s domain, preserving prices, the cart and merchant control; Cloudflare also provides edge-level opt-outs. Commerce.com says the integration complements its server-side MCP: WebMCP serves agents operating in the shopper’s browser, while the server supports direct connections to the store.

Sources: Cloudflare

Read the original story ↗

NVIDIA says European industrial companies are combining its CUDA-X and Omniverse libraries, the open Cosmos and Nemotron models, and engineering-system data to automate tasks across the manufacturing lifecycle. The NVIDIA Factory Operations Blueprint (FOX) is available in early access on GitHub, enabling developers to build always-on factory manager agents as well as agents for visual inspection, root-cause analysis, report generation and fleet management.

According to NVIDIA, Siemens is integrating FOX-based agents into Intelligence Center X and estimates 20% cost savings; at its electronics factory in Erlangen, the company reduced inspection-process calibration time by 40%. NVIDIA also announced the general availability of cuDSS and cited speedups of up to 40x in an imec simulation and 12x in an Infineon simulation, using computing accelerated by its GPUs and libraries.

Sources: NVIDIA

Read the original story ↗

NVIDIA has made cuDSS generally available, a CUDA-X library that shifts the solution of large sparse systems to GPUs, a recurring and compute-intensive step in scientific and engineering simulations. The company says the technology can reduce time to results, increase simulation throughput and enable larger, more realistic models at lower cost. The library can be integrated into existing software and scale from workstations to large clusters, with interfaces for C, C++, Python and Fortran.

Applied Materials says it accelerated sparse solves in Ginestra by up to 10x versus CPU-only approaches. In a plasma simulation benchmark for EUV lithography, ZEISS SMT measured a 4x speedup using COMSOL Multiphysics with cuDSS. Dassault Systèmes reports an 8x-10x acceleration in Abaqus solvers; Cadence, Siemens and Synopsys have also integrated the library into their products.

Sources: NVIDIA

Read the original story ↗

NVIDIA says it has expanded the open-source NVIDIA Agent Toolkit with an agent knowledge foundation, open models such as NVIDIA Nemotron Retriever and the Kumo family, the secure NVIDIA OpenShell runtime and NVIDIA NemoClaw reference blueprints. The company says the stack helps organizations turn proprietary data into information for agents’ answers, predictions and decisions without moving the data or rebuilding existing workflows or governance controls.

NVIDIA says companies including Palantir, Databricks and Snowflake are already using components of the package to build more context-aware and predictive agents. The company also reports improvements in relevance, speed or latency in deployments involving Amazon OpenSearch Service, SAP and ServiceNow, and names AWS, IBM, Oracle, Perplexity and other companies as part of the ecosystem.

Sources: NVIDIA

Read the original story ↗

Google announced that TorchTPU, its version of PyTorch, is now available as an open-source project. The tool lets developers run PyTorch-based AI applications on Google’s Tensor Processing Units (TPUs) and move them to Google Cloud with a simple configuration change, without rewriting the code. PyTorch is an open-source toolkit used to build, train and run AI models, while many of its features were developed to work with Nvidia GPUs.

Amazon announced TorchNeuron on Monday, offering a similar migration path to its Trainium chip and AWS, also without rewriting applications. Amazon also plans to submit code to the main PyTorch project to make its features less dependent on Nvidia hardware. Google says it worked with Meta, Hugging Face, Inferact and RadixArk to make TorchTPU compatible with popular tools, and launched an agent suite to help developers migrate applications and fix errors.

Sources: TechCrunch

Read the original story ↗

Mistral presents Voxtral 3, a family of speech-to-text and text-to-speech models alongside an SDK that combines listening, reasoning, tool use and speaking in a real-time streaming pipeline. The company says Voxtral Small Transcribe 3 transcribes more than 50 languages, identifies who spoke and detects when speech has ended; Voxtral Mini TTS 3 offers voices that adapt to context, supporting more than 35 languages and 50 dialects.

The SDK is designed for contact-center agents, virtual assistants and copilots that perform tasks in connected systems during phone or web conversations. Mistral says its Pro Voice feature can reproduce a speaker’s character, emotional range and speaking style from 30 minutes of audio. Voxtral 3 is available through Mistral’s EU-hosted API or for deployment on a customer’s infrastructure, allowing control over audio processing and retention.

Sources: Mistral AI

Read the original story ↗