AI 资讯

Good system. One addition that moved the needle for me: ​ I track "capacity conversion" -- when AI saves me 3 hours on a task what do those 3 hours actually become? ​ Most people save time with AI and then fill it with more busywork. The ROI only materializes when you deliberately redirect saved time toward higher-value activities. ​ I keep a simple log: "AI saved X hours on [task]. Redirected to [activity]. Value of redirected time: [$amount]." ​ After 6 months, my actual ROI was 4x higher than the "time saved" metric suggested because of where the saved time went. ​ submitted by /u/JaredSanborn [link] [留言]

Summer is almost here, and that means getting outside, having fun in the sun, and touching grass. But if you need to cool down for a bit, allow me to sing the praises of the library. If your local library system is anything like mine, it probably offers a lot of great things for you […]

The most powerful AI employee, now in Microsoft Teams Discussion | Link

Disclaimer: yeah, I work in AI visibility, so I'm definitely biased on this. But what I want to get into actually cuts against what my own industry sells, so I figure it has a place here. Back in mid-May Google put out its first real guide on how to show up in AI answers (AI Overviews, AI Mode). I saw a bunch of write-ups on it and it was always the same song, structure your headings, add Schema, the usual blah. Except there's a "mythbusting" section in the doc I haven't seen anyone pick up on, and it's the most interesting part. Google says in plain terms that the famous llms.txt file does nothing, that you should stop obsessing over Schema.org, and that chunking is smoke and mirrors. Made me smile a bit since that's basically the package some "GEO" agencies are charging for right now. What they push instead is honestly kind of obvious. They talk about "commodity" vs "non-commodity" content. Like, if an AI can write your article on its own, it'll never cite you, makes sense, it already has the answer, why would it go looking for you. What gets cited is content with something the model doesn't have. A number you actually measured, a test you really ran, lived experience basically. The example that stuck with me (not in Google's guide, somewhere else) is a small blog specialized in robot vacuums, garbage domain authority, and it outranks the New York Times in AI answers. The NYT has a domain like 3x stronger. Except the NYT puts out an affiliate listicle anyone could copy, and the blog guy films his actual tests with real measurements. Guess who gets cited. And this is where it gets useful for you I think. It means for the most part you need neither a tool nor an agency. Take your most generic page, just ask yourself "could anyone write exactly this", and if the answer is yes, add something only you know. You don't even need data. A simple "the first question every client asks me is this" and you're already standing out. It's free and it weighs more than all the tech

Anthropic's usage analytics dashboard is only available to Team and Enterprise org admins. On a Pro or Max plan all you get is /usage and the claude.ai usage page, which show where you stand right now but not where you're heading. I looked at various open-source projects but none quite matched what I had in mind: an all-round control panel for Claude Code that's also a single binary, with no dependencies and no install steps. The other thing I cared about was forecasting the usage limits. Existi

Your voice-supported AI financial planning partner Discussion | Link

Google DeepMind is funding research into the potential dangers of situations where millions of different AI agents interact with each other online. According to Rohin Shah, who directs the company’s AGI safety and alignment research, the mass-market arrival of agents that can carry out tasks without human oversight and follow instructions given to them by other…

TL;DR - Getting an app to run is now the easy part. AI is very good at producing something that...

AI aside, Golden Gate includes a bunch of subtle-but-helpful improvements.

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

I set up a Claude CLI instance on a Google Cloud VM Instance, cloned all my project repos (we run a dev agency), and wired up webhooks to Linear. When a ticket gets tagged, the CLI automatically reviews the relevant repo, understands what needs to be done, and drops a detailed technical breakdown right into the ticket. It's cut ticket completion time because devs now have way more context to feed into Claude Code or Cursor and just start building. Next step: I'm trying to get Claude to actually create working PRs based on that evaluation and knock out the whole ticket end-to-end. Still figuring out if the loop can fully close. Has anyone worked on a system like this? Would love to hear your approach. submitted by /u/theTbling [link] [留言]

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]

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]

Fast LLMs for low-latency and high-performance workflows Discussion | Link

This impressively light carbon commuter makes switching to an ebike easier than ever.

The musician created his own line of loopers that record and layer riffs in a loop. The pricey Looper X does what it claims, but it isn’t without quirks.

submitted by /u/Tiny-Independent273 [link] [留言]

Deezer will now help you find AI slop in your music playlists even if you're on another platform.

YouTube is reintroducing private messaging after testing new ways for users to share videos and "have conversations about them" last year. In an announcement on its official blog, YouTube says it's now starting to expand the in-app video sharing and messaging feature to users in the US and "other global regions" who are 18 or […]