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We are inspired by the possibilities of AI to help people be more creative, productive, and stay closely connected on WhatsApp, so we set out to build a new technology that allows our users around the world to use AI in a privacy-preserving way. We’re sharing an early look into Private Processing, an optional capability that enables users to initiate a request to a confidential and secure environment and use AI for processing messages where no one — including Meta and WhatsApp — can access them. To validate our implementation of these and other security principles, independent security researchers will be able to continuously verify our privacy and security architecture and its integrity. AI has revolutionized the way people interact with technology and information, making it possible for people to automate complex tasks and gain valuable insights from vast amounts of data. However, the current state of AI processing — which relies on large language models often running on servers, rather than mobile hardware — requires that users’ requests are visible to the provider. Although that works for many use cases, it presents challenges in enabling people to use AI to process private messages while preserving the level of privacy afforded by end-to-end encryption. ...

This post follows on from my previous post, in which I discussed some theoretical/experimental dependency-injection lifetimes, based on the discussion in an episode of The Breakpoint Show. In the previous post I provided an overview of the built-in Dependency Injection lifetimes, and described the additional proposed lifetimes: tenant, pooled, and drifter. The previous post provided a overview of each of these proposed lifetimes, and an implementation of the drifter (time-based) lifetime. In this post I provide an example of the “pooled” lifetime. ...

Today’s edition is chock-full of fascinating items to read. How do you measure collaboration within a team? Or run AI developer tool experiments? How should you roll out AI tools to a team? What’s reinforcement learning all about in modern LLMs? Dig in. [article] Software Development Is a Team Sport. Here’s some new research that measures collaboration quality between members of a team. [article] How to Run a Generative AI Developer Tooling Experiment. This looks at AI coding results when Cursor and Copilot go head to head. ...

Top Links TechBash 2025 Early Bird registration is now open (TechBash Team) The First Set of Bug Fixes for ReSharper and Rider 2025.1 Is Here! (Sasha Ivanova) General Availability of AWS SDK for .NET V4.0 (Norm Johanson) ASP.NET Core OData Web API Template Preview Release (Samuel Wanjohi) Protecting against indirect prompt injection attacks in MCP (Sarah Young & Den Delimarsky) Modernizing NET Future ready applications in the era of AI | .NET Conf: Focus on Modernization (Scott Hanselman, Chet Husk & McKenna Barlow) ...

Generate your MCP server with Speakeasy (Sponsored)Like it or not, your API has a new user: AI agents. Make accessing your API services easy for them with an MCP (Model Context Protocol) server. Speakeasy uses your OpenAPI spec to generate an MCP server with tools for all your API operations to make building agentic workflows easy. ...

We are introducing AutoPatchBench, a benchmark for the automated repair of vulnerabilities identified through fuzzing. By providing a standardized benchmark, AutoPatchBench enables researchers and practitioners to objectively evaluate and compare the effectiveness of various AI program repair systems. This initiative facilitates the development of more robust security solutions, and also encourages collaboration within the community to address the critical challenge of software vulnerability repair. AutoPatchBench is available now on GitHub. AI is increasingly being applied to solve security challenges, including repairing vulnerabilities identified through fuzzing. However, the lack of a standardized benchmark for objectively assessing AI-driven bug repair agents specific to fuzzing has impeded progress in academia and the broader community. Today, we are publicly releasing AutoPatchBench, a benchmark designed to evaluate AI program repair systems. AutoPatchBench sits within CyberSecEval 4, Meta’s new benchmark suite for evaluating AI capabilities to support defensive use cases. It features 136 fuzzing-identified C/C++ vulnerabilities in real-world code repos along with verified fixes sourced from the ARVO dataset. ...

Hello everyone, The ReSharper and Rider 2025.1.1 bug-fix updates have just been released! If you haven’t upgraded to the 2025.1 versions of our products, we highly recommend that you check out these two blog posts: ReSharper 2025.1: Comprehensive improvements to C# language support, including initial support for C# 14 preview features, performance enhancements, and more. Rider 2025.1: A major upgrade to AI Assistant, remote development on Windows host machines, numerous enhancements to .NET and C++ debugging, and a whole host of other improvements. ...

Happy Monday. It seems the Northern Hemisphere is fully in Spring mode and I’m happy to see some folks thawing out. It was raining and 60 degrees here in San Diego on Saturday which made me consider filing a complaint with the State. [blog] A2A Deep Dive: Getting Real-Time Updates from AI Agents. This goes beyond “hello world” and shows off a more sophisticated use case for the Agent to Agent Protocol. ...

Top Links Enabling multimodal functionality for Phi Silica (Vivek Pradeep) The Fourth Beta of Android 16 (Matthew McCullough) Using Model Context Protocol in agents – Copilot Studio (Matteo Pagani) Copilot+ PCs are the most performant Windows PCs ever built, now with more AI features that empower you every day (Navjot Virk) The New MCP Authorization Specification (Den Delimarsky) Using Phi Silica in Windows App SDK on a Copilot Plus PC (Filip W.) Unveiling GPT-image-1: Rising to new heights with image generation in Azure AI Foundry (Steve Sweetman) Responsible AI and trustworthy innovation on Windows (Chitra Gopalakrishnan) ...