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Building and Scaling a Platform with Project-as-a-Service

When a platform started with total developer autonomy, teams felt overwhelmed and ended up solving the same problems in completely different ways. The company shifted to enablement over support, working together with teams intensively, and helping teams feel confident and capable, turning the right way into being the easiest way. By Ben Linders

2026-06-11 原文 →
AI 资讯

Anthropic Fable 5's silent downgrade got walked back in 24 hours, that should concern you even more

A lot of discussion about Fable 5 has focused on the visible restrictions: cybersecurity, biology, certain chemistry. You hit a wall, you get a notification, you get redirected to Opus 4.8. That's frustrating, but at least it's honest. At least you know the model stepped back. Here's the part that's really disturbing, buried in a 319-page system card: There's a second category of restriction. For AI development and research work, Fable 5 doesn't redirect you. It doesn't notify you. It responds. It just delivers a deliberately weakened answer, and the system card describes this explicitly as "not visible to the user." Anthropic walked this back within 24 hours after fierce backlash. They apologized. "We made the wrong tradeoff." Good. But sit with what actually happened here, because the reversal is being treated as the end of the story when it's the beginning of a much harder problem. We now know three things we cannot unknow: Anthropic built this. They shipped it. And they only reversed it when the backlash was loud enough. The question isn't whether this specific invisible downgrade still exists. The question is what else might they be doing, in categories that don't generate the same backlash, that isn't disclosed in a document most people will never read anyway. This is a new kind of problem. And to understand why, you have to take a step back for a second. The pattern In January 2026, OpenAI announced that they would retire GPT-4o. Hundreds of thousands of daily users had built working relationships with that model over months: preferences it learned, corrections they made, communication styles that developed through hundreds of sessions. Gone. In February 2026, Gemini users found their chat histories had quietly vanished. No warning. No export. In April, Anthropic cut off Claude Pro and Max subscribers from using their subscriptions with third-party tools. Workflows that people depended on broke overnight. Each of these was framed differently. Model retirement

2026-06-11 原文 →
AI 资讯

Within a few years, owning the smartest AI will mean nothing — everyone will have it. The edge is knowing how to run it.

Every layer of AI solved the problem the last one left behind. The unsolved one: a shared, measurable standard for how to RUN intelligence — yours and the AI's, together. I spent 10+ years writing it down and it's falsifiable (pre-registered tests, failure lines locked before data). Asking for your strongest critiques Essay: https://joshmason573557.substack.com/p/colive-the-missing-standard-for-the submitted by /u/Useful-Ad-7895 [link] [留言]

2026-06-11 原文 →
AI 资讯

Is this music AI?

I think it is but I'd just like to get some second opinions, especially from music creators. This is their spotify page https://open.spotify.com/artist/4dSJvPjnA1RU6KcngvaZ96 The artwork is definitely AI and there's no real composer name so some red flags there already. submitted by /u/WelderRound2925 [link] [留言]

2026-06-11 原文 →
AI 资讯

Has anyone built (or bought) a Digital Brain for your Business?

I'm really interested in trying to learn about this new concept of having a one central AI-powered database acting as a digital brain for your business, pulling in all of the various data sources and having one single source of truth. People like Nate B Jones talk about it and I really want to try to build something - but concious how wrong they can go. Are there any credible ones already build I can base off? Has anyone done this? submitted by /u/zascar [link] [留言]

2026-06-11 原文 →
AI 资讯

Playwright CLI for agent-driven workflows: sessions, debugging, and CI Sharding

Playwright has excellent tooling around browser automation, but most of the ecosystem still treats it as a test framework. For teams running AI coding agents and automated browser workflows, there is a different set of requirements: browser automation ↓ session persistence across runs ↓ debuggable traces when things go wrong ↓ parallel execution across CI shards The Playwright CLI directly addresses these gaps. It ships as a standalone npm package and exposes every browser operation as a CLI command; open, click, type, snapshot - without requiring a Node.js script or test runner. npm package: @playwright/cli GitHub: https://github.com/microsoft/playwright-cli The current implementation focuses on: session persistence with named instances and portable state video and trace recording built into every session CI sharding for parallel execution at scale session persistence The default behaviour keeps browser state in memory. Cookies and localStorage are preserved between CLI calls within the session, but cleared when the browser closes. For repeatable workflows, that breaks down fast — logging into an application before every run wastes time and introduces flakiness. Named sessions let you run multiple browser instances simultaneously and address them by name: playwright-cli -s=admin open https://app.example.com/admin playwright-cli -s=checkout open https://app.example.com/checkout Each session is an isolated browser instance. An agent can orchestrate workflows across multiple authenticated contexts without state leaking between them. The goal is straightforward: the same CLI binary should be able to maintain independent browser contexts for parallel workflows without requiring environment-specific configuration. The critical piece for CI and agent reuse is state persistence: log in once playwright-cli -s=admin open https://app.example.com/login playwright-cli -s=admin fill "#username" "admin" playwright-cli -s=admin fill "#password" "$ADMIN_PASS" playwright-cli -s=admi

2026-06-11 原文 →
AI 资讯

Presentation: Building and Scaling UI Systems for Internal Tools at Meta

Cindy Zhang discusses the evolution of XDS, a unified UI system powering 10,000+ internal tools. She shares actionable insights for architects and engineering leaders on managing large-scale community contributions, executing safe monorepo refactors using JS AST and AI codemods, mitigating breaking changes via feature flags, and expanding UI libraries into full-stack platform systems. By Cindy Zhang

2026-06-11 原文 →
AI 资讯

claude fable 5 just dropped, what’s your take?

anthropic just released fable 5 two days ago and i haven’t had a chance to properly dig in yet for context it’s basically a public version of mythos, the model they’d been keeping locked behind project glasswing for select partners only. now it’s out for everyone on pro/max/team plans until june 22 for free, after that it’ll need usage credits from what i’ve read it’s supposed to be insane at long agentic tasks… like multi-hour sessions where it spins up sub-models, gathers data, writes and tests its own code. someone gave it one prompt to build a travel-time map and it went off on its own for hours and just… built it the one catch is it has hard safety blocks in areas like cybersecurity, bio, chem. falls back to opus 4.8 when it hits those but i want to hear from people actually using it right now. what’s the best thing you’ve noticed? and what feels overhyped or still rough? drop your experiments in the comments, genuinely curious submitted by /u/NewMuffin3926 [link] [留言]

2026-06-11 原文 →
AI 资讯

Ai grading assignment

Hi, I want to use AI to check my grade with the mark scheme and see what grade it would give me. Now, after doing this, would the assignment be flagged by an AI detector? submitted by /u/No-Witness1045 [link] [留言]

2026-06-11 原文 →
AI 资讯

While scrolling though social media I have been observing AI-generated content for the past few months. Here's what I've noticed.

Once you start noticing them, they're everywhere. And the algorithm makes it worse, the more you engage, the more it feeds you... Perfect lighting in every single photo. That glow on the face in every other pic or video it doesn't matter what the background or lighting is. Follows 3 people but has 40k followers. Generic bio that could apply to literally anyone. Comments that are just emojis or "love this!" The creepy part is how consistent the patterns are across platforms. Same pose angles. Same aesthetic. Same engagement ratio that makes no sense for a real person. I built a small community tool where people can flag and vote on suspicious profiles. Not trying to be the judge, just crowdsourcing the pattern recognition. I feel humans are really good at spotting these when you give them the right frame and observation. Anyone else been noticing more of these lately? Curious what other people pick up on this. submitted by /u/Brilliant-Nerve-8972 [link] [留言]

2026-06-11 原文 →