Anthropic

7/27/2026

A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool. The post The harness is all you need (mostly) appeared first on The GitHub Blog.

NVIDIA

7/18/2026

NVIDIA DeepStream 9.1 introduces 13 agentic skills that let coding agents like Claude Code and Codex build multi-camera video analytics pipelines from natural-language prompts. Multi-View 3D Tracking (MV3DT) fuses per-camera detections into one shared 3D world with a globally consistent object ID, while AutoMagicCalib (AMC) removes manual camera calibration. The release also adds JetPack 7.2 support and a unified open-source GitHub monorepo. The post NVIDIA Released DeepStream 9.1: Bringing Agen...

Google

7/21/2026

Google has just launched Gemini 3.6 Flash, an updated AI model designed to be faster and more cost-effective. This new version significantly improves code generation and multimodal features, directly addressing previous user feedback. For SMB leaders, this means a more powerful and efficient AI tool is now available to enhance system development and streamline operations without breaking the bank.

Anthropic

8/14/2026

Authors: JoelAbenhaim, Joel Abenhaim arXiv Links arXiv | PDF AI summary Abstract This paper reports a single, fully instrumented case study of a large-scale architectural refactoring by an AI coding agent under a specification-first protocol, with no human review of the generated code and no pre-existing oracle to validate the target behaviour. The task, dismantling a central invariant across a large interdependent codebase, was assessed by the author as effectively infeasible through incrementa...

Meta

8/5/2026

Meta today released Muse Code, a terminal-based AI coding agent now in beta, alongside Muse Spark 1.2, a coding-focused update to its Muse Spark family of frontier models — a one-two punch that puts the company in direct competition with Anthropic's Claude Code, OpenAI's Codex, and the growing field of agentic coding harnesses that have rapidly become the primary way many professional developers ship software. "Releasing Muse Code in beta today," Meta CEO Mark Zuckerberg wrote in a post on rival...

Anthropic

7/28/2026

Snowflake announced Cortex AI Gateway on Tuesday, a centralized control layer designed to govern how AI agents — including those built by competitors like Anthropic's Claude Code and Cursor — access enterprise data, tools, and models. Alongside the gateway, the company unveiled a first wave of security integrations with 1Password, Aembit, Linx Security, SailPoint, and Saviynt, an unusual coalition of identity vendors who often compete with one another, now aligned around a shared trust model for...

Microsoft

8/12/2026

Learn how to write your first prompt in the GitHub Copilot app, choose the right context and model, and start your first task with confidence. The post Write your first prompt with the GitHub Copilot app appeared first on The GitHub Blog.

Microsoft

8/6/2026

Go beyond chat in the GitHub Copilot app with these slash commands. They'll help you plan, collaborate, automate, and customize your dev workflow. The post A guide to slash commands in the GitHub Copilot app appeared first on The GitHub Blog.

Anthropic

7/28/2026

A lot of AI coding tools assume one model should do everything: understand the task, write the code, review it, and decide whether it is correct. I ended up building something around the opposite idea. Instead of one model doing the whole software development lifecycle, every stage can use a completely different model. That means you can mix local LLMs and hosted models however you want. For example: - DeepSeek-R1 running locally for planning - Claude Code for implementation - Qwen-Coder running...

Microsoft

7/27/2026

New to the GitHub Copilot app? Learn how to start projects, work with AI agents, explore canvases, and streamline your development workflow. The post GitHub Copilot app for Beginners: Getting started appeared first on The GitHub Blog.

Anthropic

7/31/2026

If you ask an AI coding agent to write a standalone Python script to parse a single JSON file, it will likely give you a perfect answer in seconds. But the same agent often breaks if you ask it to build a systematic data processing pipeline, like ingesting thousands of messy documents, chunking text, scoring quality, and filtering noise for a Retrieval-Augmented Generation (RAG) system that fits your specific enterprise stack. While large language models (LLMs) excel at one-off code generation, ...