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AI 资讯

The TCL Note A1 is a kinder, gentler tablet

Without thinking, I scribbled over what I'd just written. It's what I would have done if I was writing with pen and paper. But I didn't have a pen in my hand; my canvas was an LCD and the letters on my page were just pixels. Score one point to the TCL Note A1. TCL's […]

2026-08-25 原文 →
AI 资讯

Presentation: Prompt to Prod: Engineering an Autonomous SDLC at Scale

Andrew Swerdlow shares how Roblox scales autonomous software development from prompt to production. He discusses building robust security sandboxes, extracting institutional knowledge via code review exemplars, updating engineering infrastructure, and redefining productivity metrics around feature velocity and long-running AI turns to achieve trusted, automated deployment at scale. By Andrew Swerdlow

2026-08-24 原文 →
AI 资讯

I Built AgentCheck Because “The Coding Agent Said Done” Wasn’t Enough

I Built AgentCheck Because “The Coding Agent Said Done” Wasn’t Enough AI coding agents are getting surprisingly good at writing code. I use them regularly, and they can handle increasingly large tasks: refactoring code, adding features, updating dependencies, modifying configuration, creating migrations, and touching files across an entire repository. But I kept running into the same problem after the agent finished: How do I independently verify what it actually changed? The agent usually gives me a perfectly reasonable summary. Something like: Done. Implemented the requested changes, updated the tests, and cleaned up the affected code. Useful? Absolutely. Enough for me to commit without checking? Not really. So I built AgentCheck . The Problem Happens After “Done” After a coding agent finishes a task, I still find myself manually checking things like: Which files actually changed? Were any files deleted? Did configuration change? Were dependencies added or updated? Was a database migration introduced? Did anything that looks like a secret appear? Were related tests changed? Is the overall change set larger or riskier than expected? Of course, Git already gives us the raw information. I can run: git status git diff git diff --stat Then inspect individual files. And I still do that. But once coding agents become part of your normal workflow, repeating the same verification process after every task starts to feel like something that should be structured. That was the idea behind AgentCheck. What AgentCheck Does AgentCheck creates a trusted checkpoint before your coding agent starts working. Then, after the agent finishes, it compares the current Git-visible repository state with that checkpoint. The basic workflow is deliberately small: agentcheck start Then let your coding agent work. That can be: Codex Claude Code Cursor another AI-assisted coding tool or technically even a human When the work is finished: agentcheck AgentCheck then produces four sections: Changes

2026-08-24 原文 →
AI 资讯

AI Code Review at Scale: LinkedIn's Multi-Agent Approach

At LinkedIn's scale, relying solely on human reviewers or simply putting an off-the-shelf AI reviewer in front of GitHub is not an effective way to manage PRs. To address this, LinkedIn engineers built a multi-agent AI code review platform that understands the organization’s coding context, treats code review as production infrastructure, and minimizes hallucinations and low-signal feedback. By Sergio De Simone

2026-08-22 原文 →
AI 资讯

The Audi S6 Sportback E-tron proves that sedans still matter

Last year, Audi announced that it gave its all-EV-by-2033 plan the boot, and instead will offer a mix of gasoline, hybrid, and electrified propulsion. There's no doubt still some uncertainty among bigger automakers - especially those under the Volkswagen Group umbrella - when it comes to such a (now prior) commitment. Even so, what it's […]

2026-08-20 原文 →