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AI Coding Tools

Claude Code, Cursor, Windsurf, GitHub Copilot, Devin, and the AI tools transforming how software gets built. Reviews, comparisons, and developer-tested workflows.

19 articles
4 stories
Last 30 days
(23 total matching)
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2 sources
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OpenAI

OpenAI Shuffles Executives, Brockman Leads Products

OpenAI keeps shuffling its executives in bid to win AI agent battle

OpenAI is once again reorganizing its leadership, this time with President Greg Brockman taking the helm of all product divisions. This move aims to sharpen the company's focus on AI agents, integrating existing products like ChatGPT and Codex into a single, cohesive platform to dominate the evolving AI landscape.

📰The Verge · Wired
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2 sources
OpenAI and 1 others
OpenAI

OpenAI Integrates Codex into ChatGPT App

OpenAI’s Codex is now in the ChatGPT mobile app

OpenAI has integrated its code-generating AI, Codex, into the ChatGPT mobile app, making sophisticated AI capabilities accessible from your smartphone. This update not only allows for code generation but also enables the AI to control desktop applications, marking a significant step towards a 'super app' concept. For SMB leaders, this means new opportunities for automating tasks and streamlining operations without needing specialized programming knowledge.

📰TechCrunch · The Verge
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2 sources
OpenAI and 1 others
OpenAI

OpenAI Launches New AI Initiatives

Latest coverage: OpenAI Introduces Daybreak: A Cybersecurity Initiative That Puts Codex Security at the Center of Vulnerability Detection and Patch Validation

OpenAI has announced Daybreak, a new AI initiative aimed at enhancing cybersecurity. This agent, building on the Codex Security AI agent, analyzes corporate codebases to identify potential attack paths and vulnerabilities, and then automates their detection and remediation. This move could empower small and medium-sized businesses with advanced AI-driven security previously out of reach.

📰MarkTechPost · The Verge
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Google and 3 others
Google

AI Tools Boost Code Development

Meet GitHub Spec-Kit: An Open Source Toolkit for Spec-Driven Development with AI Coding Agents

If you have spent time using AI coding agents — GitHub Copilot, Claude Code, Gemini CLI — you have probably run into this situation: you describe what you want, the agent generates a block of code that looks correct, compiles, and then subtly misses the actual intent. This “vibe-coding” approach can work for quick prototypes The post Meet GitHub Spec-Kit: An Open Source Toolkit for Spec-Driven Development with AI Coding Agents appeared first on MarkTechPost.

📰MarkTechPost · Simon Willison
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OpenAI and 4 others
Anthropic

Alibaba's proprietary Qwen3.7-Max can run for 35 hours autonomously and supports external harnesses like Anthropic's Claude Code

The AI industry has fully entered the "agent era," a paradigm where AI models do far more than generate text — they now actively plan, execute, and course-correct complex tasks over days rather than seconds. Thus, it's perhaps unsurprising to see Chinese e-commerce giant Alibaba's famed Qwen Team of AI researchers release a model capable of performing autonomous agentic AI work over multiple days: that model has arrived in the form of Qwen3.7-Max which the company reports in a blog post achieved...

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OpenAI and 2 others
Microsoft

Agentic-Agile: Why Agent Development Needs Agile (Not Just Prompts)

“A bad system will beat a good person every time” ~Dr. William Edwards Deming (with apologies) I started vibe coding by writing prompts (often dictated into my phone), refining them with an agent in M365 Copilot, and creating handoff files to use with GitHub Copilot CLI. The results were predictably non-deterministic. Prompt-driven development The post Agentic-Agile: Why Agent Development Needs Agile (Not Just Prompts) appeared first on Microsoft for Developers.

Notable
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Anthropic and 1 others
Anthropic

One command turns any open-source repo into an AI agent backdoor. OpenClaw proved no supply-chain scanner has a detection category for it

Just two months ago, researchers at the Data Intelligence Lab at the University of Hong Kong introduced CLI-Anything, a new state-of-the-art tool that analyzes any repo’s source code and generates a structured command line interface (CLI) that AI coding agents can operate with a single command. Claude Code, Codex, OpenClaw, Cursor, and GitHub Copilot CLI are all supported, and since its launch in March, CLI‑Anything has climbed to more than 30,000 GitHub stars. But the same mechanism that makes ...

Notable
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Anthropic and 1 others
NVIDIA

LightSeek Foundation Releases TokenSpeed, an Open-Source LLM Inference Engine Targeting TensorRT-LLM-Level Performance for Agentic Workloads

Inference efficiency has quietly become one of the most consequential bottlenecks in AI deployment. As agentic coding systems such as Claude Code, Codex, and Cursor scale from developer tools to infrastructure powering software development at large, the underlying inference engines serving those requests are under increasing strain. The LightSeek Foundation researchers have released TokenSpeed, an The post LightSeek Foundation Releases TokenSpeed, an Open-Source LLM Inference Engine Targeting T...

Notable
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Microsoft

Take your local GitHub sessions anywhere

Kick off work in VS Code or the CLI, finish it from your phone. Remote control for GitHub Copilot sessions is now generally available on github.com and GitHub Mobile. The post Take your local GitHub sessions anywhere appeared first on The GitHub Blog.

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Microsoft

GitHub Copilot CLI for Beginners: Interactive v. non-interactive mode

GitHub has released a guide explaining the two main modes of Copilot CLI: interactive and non-interactive. This tool aims to integrate AI directly into the command line, promising to significantly enhance developer productivity. The article showcases practical applications, like generating emoji lists, and discusses how AI is reshaping software development, alongside the growing importance of understanding AI security.

AI News article thumbnail
AI News

Mid-Training with Self-Generated Data Improves Reinforcement Learning in Language Models

A team at Arizona State University has introduced a new technique to boost the learning efficiency of Large Language Models (LLMs). Their method involves using diverse, autonomously generated data during the training process, leading to improved capabilities in complex areas like mathematical reasoning, code generation, and narrative understanding. This research suggests a new path for making AI models smarter and more versatile.

Anthropic article thumbnail
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Anthropic

AgentKernelArena: Generalization-Aware Benchmarking of GPU Kernel Optimization Agents

Authors: Sharareh Younesian, Wenwen Ouyang, Sina Rafati, Mehdi Rezagholizadeh, Sharon Zhou arXiv Links arXiv | PDF AI summary Abstract AgentKernelArena is introduced as an open-source benchmark for evaluating AI coding agents on GPU kernel optimization, assessing full agent workflows and unseen-configuration generalization across multiple optimization tasks. AI-generated summary 摘要 AgentKernelArena 是一个开源基准,用于评估 GPU 内核优化上的 AI 编码代理、评估完整的代理工作流程以及跨多个优化任务的未见配置泛化。 AI 生成的摘要 Abstract GPU kernel optimiza...

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Google

Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.

For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm. At its annual I/O developer conference, Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversa...

AI News article thumbnail
AI News

Balanced Aggregation: Understanding and Fixing Aggregation Bias in GRPO

A new technique called 'Balanced Aggregation' (BA) has emerged, promising to stabilize and improve the performance of large language model (LLM) training. Developed by the OpenMOSS research team, BA tackles known issues with how token-level policy gradients are handled in reinforcement learning for LLMs, a critical step for enhancing capabilities like reasoning and code generation. This innovation could lead to more reliable and powerful AI models in the future.