Mistral is releasing its own “vibe coding” client, Mistral Code, to compete with incumbents like Windsurf, Anysphere’s Cursor, and GitHub Copilot.

Mistral Code, a fork of the open source project Continue, is an AI-powered coding assistant that bundles Mistral’s models, an “in-IDE” assistant, local deployment options, and enterprise tooling into a single package.

A private beta is available as of Wednesday for JetBrains development platforms and Microsoft’s VS Code.

At its core, Mistral Code is powered by four models that are state of the art in coding:
- Codestral for fill-in-the-middle / code autocomplete
- Codestral Embed for code search and retrieval
- Devstral for agentic coding
- And Mistral Medium for chat assistance

OpenAI livestream "Announces updates to ChatGPT for business"

Claude Code is now available as part of the Pro plan.

Coding Agents with Multimodal Browsing

Can AI agents generalize beyond their intended scope? Great paper on how you can build generalist agents with superior performance over specialized agents.

Code.

OpenAI is dropping custom and ready-to-use data connectors for ChatGPT, including Google Drive, Box, Hubspot, and Outlook

The co also added a "record mode" to help teams turn meetings into transcriptions with key points, action items, or plans.

Agentic AI for Intelligent Business Operations. IBM has released an insightful research report on the potential transformational effect on organizations that are turning to agentic AI to capture a competitive edge.

Key Takeaways of the Research:

1. Agentic AI enables business operations to autonomously learn, adapt, and optimize in real time, offering more than speed—it drives proactive #innovation and personalization.

2. The shift is from automating isolated tasks to orchestrating dynamic, self-improving processes through intelligent agents.

3. Successful AI deployment hinges on integrating people and technology, where human oversight, decision-making, and creativity remain essential.

4. IBM IBV research shows over 80% of executives view global business service automation as a strategic priority, expecting AI agents to lead the transformation.

5. By 2027, 86% of executives believe AI agents will significantly enhance process automation and workflow redesign.

6. Agentic AI is reshaping the workplace—tech manages operations while talent manages tech, with many users already interacting primarily through AI assistants.

7. Currently, 76% of organizations are piloting or scaling autonomous AI agents to manage intelligent workflows.

8. AI agents operate on goals and context rather than rigid rules, dynamically adjusting to achieve outcomes—similar to how autonomous vehicles function.

9. 75% of executives expect AI agents to handle transactional workflows autonomously within the next two years.

Gemini 2.5 Pro update is now in preview.

FutureHouse released ether0, the first scientific reasoning model

Researchers trained Mistral 24B with RL on several molecular design tasks in chemistry.

Remarkably, researchers found that LLMs can learn some scientific tasks more much data-efficiently than specialized models trained from scratch on the same data, and can greatly outperform frontier models and humans on those tasks.

For at least a subset of scientific classification, regression, and generation problems, post-training LLMs may provide a much more data-efficient approach than traditional machine learning approaches.

Paper.
Weights

Epoch AI just published a dataset of worldwide AI supercomputers (GPU clusters)!

Qwen just dropped its new embedding model

Qwen3-Embedding offers a range of sizes (0.6B, 4B, 8B) for text embedding & reranking, achieving SOTA performance on MTEB.

Boltz-2 is a new biomolecular foundation model that goes beyond AlphaFold3 and Boltz-1 by jointly modeling complex structures and binding affinities, a critical component towards accurate molecular design.

Boltz-2 a new model capable not only of predicting structures but also binding affinities

Boltz-2 is the first AI model to approach the performance of FEP simulations while being more than 1000x faster.

All open-sourced under MIT license.

Paper

Extract an AI system developed by the UK Government’s AI Incubator team using Google’s Gemini model

It aims to modernize the UK’s planning system by converting old, paper-based planning documents—such as blurry maps and handwritten notes—into clear, digital data in about 40 seconds.

Key points:

1. Speeds up processing of ~350,000 annual planning applications in England, supporting housing and infrastructure development.

2. How it works:

- Uses Gemini’s multimodal reasoning to extract critical information from text, handwritten notes, and low-quality map images.

- Identifies map features (e.g., boundaries, shaded areas) using tools like OpenCV, Ordnance Survey, and Segment Anything.

- Matches historical maps to modern equivalents using addresses, landmarks, and feature mapping (e.g., LoFTR) to convert shapes into precise geographical coordinates.

- Reduces council workload, simplifies processes, and frees staff for strategic planning. It supports the UK’s goal of building 1.5 million new homes.

Project Pine - Tokenised Financial Markets by The Federal Reserve Bank of New York and BIS

ProjectPine found that central banks could customise and deploy policy implementation tools using programmable smart contracts in a potential future state where commercial banks and other private sector financial institutions have widely adopted tokenisation for wholesale payments and securities settlement.

The project generated the prototype of a generic monetary policy implementation tokenised toolkit for potential further research and development by central banks across jurisdictions and currencies. The prototype was designed to be technically modifiable for different central banks' monetary policy frameworks and calibrated to conduct standard or emergency market operations.

The toolkit prototype was created in consultation with central banks' financial markets advisors from multiple jurisdictions, who helped outline the project scope and specific design requirements. It is not particular to any currency or jurisdiction. It can fulfil a common set of central bank implementation requirements, including paying interest on reserves, open market operations, and collateral management.

Alibaba's RL LLM training library: ROLL

ROLL is built upon several key modules to serve these user groups effectively:

1. A single-controller architecture combined with an abstraction of the parallel worker simplifies the development of the training pipeline.

2. The parallel strategy and data transfer modules enable efficient and scalable training.

3. The rollout scheduler offers fine-grained management of each sample's lifecycle during the rollout stage.

4. The environment worker and reward worker support rapid and flexible experimentation with agentic RL algorithms and reward designs.

Finally, AutoDeviceMapping allows users to assign resources to different models flexibly across various stages.

GitHub.

Autonomous Agents That Think, Remember, and Evolve from AWS team

This project showcasing how autonomous agents can move beyond simple tasks to reason, remember, and adapt
- powered by Mem0, AWS,, and Strands Agents SDK

From remembering past findings to adapting strategies on the fly, Cyber-AutoAgent uses long- and short-term memory to build real expertise - one interaction at a time.

GitHub.

At WWDC Apple introduced a new generation of LLMs developed to enhance the Apple Intelligence features.

Also introduced the new Foundation Models framework, which gives app developers direct access to the on-device foundation language model.

GALBOT announced OpenWBT – an open-source, whole-body humanoid VR teleoperation system using Apple Vision Pro.

It supports Unitree G1 and H1 robots, enabling operators to control movements like walking, squatting, bending, grasping, and lifting.

Microsoft introduced Code Researcher - a deep research agent for large systems code and commit history.

Achieves a 58% crash resolution rate on a benchmark of crashes in the Linux kernel, a complex codebase with 28M LOC & 75K files.

Mistral introduced Magistral is a first reasoning model designed to excel in domain-specific, transparent, and multilingual reasoning.

Magistral is available in two variants:

1. Magistral Small (24B parameter open-source version)

2. Magistral Medium (enterprise version).

SEAL and Red Team at Scale AI presented a position paper outlining what they’ve learned from red teaming LLMs so far—what matters, what’s missing, and how model safety fits into broader system safety and monitoring.

Modular + AMD: Breaking NVIDIA's AI Monopoly?

Modular just announced their software now works with AMD's latest AI chips, claiming major performance improvements.
Here's what this actually means.

Right now, if you want to run AI models, you basically have to use NVIDIA's expensive GPUs. AMD makes competitive chips, but the software ecosystem around them is weak. Most AI code is written for NVIDIA's CUDA platform.

What Modular Built?

The Software: A new programming language called Mojo that can run the same code on both NVIDIA and AMD chips without changes. Think of it as a universal translator for AI hardware.
The Promise: Use cheaper AMD chips (with more memory) while keeping the same performance as expensive NVIDIA cards.
The Claims vs Reality
Modular shows benchmarks where AMD's new MI325X chip beats NVIDIA's H200 by 20-50% on certain AI tasks. AMD's chip also has almost twice the memory (256GB vs 141GB).

But: These are carefully selected benchmarks. Real-world performance across all AI workloads is probably more modest.

Why This Matters?

For Companies: Potential to save money on AI infrastructure and avoid vendor lock-in
For Developers: More hardware choices could mean better prices and innovation
For The Market: Any credible alternative to NVIDIA is good for competition.

This is promising technology from a credible team, but NVIDIA's software advantage is huge. Most AI tools, libraries, and developer knowledge are built around NVIDIA's ecosystem.

Interesting development that could work well for specific use cases, but don't expect it to dethrone NVIDIA anytime soon. The real test is whether companies actually adopt it in production.