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AI 资讯

Google Discover is getting an AI chatbot-tuned feed

Google will soon allow you to customize your Discover feed by describing what you want to see. The new feature, rolling out to the Google app in the "coming days," will use AI to automatically tweak your feed and "remember" your preferences for future visits. You'll find the option within the three-dot menu on your […]

2026-08-21 原文 →
AI 资讯

Mark Zuckerberg bought an Irish castle

Meta CEO Mark Zuckerberg now owns an actual castle. Zuckerberg and his wife Priscilla Chan bought Strancally Castle and its 440-acre estate in Ireland "several weeks ago," according to The Irish Times. While the exact price of the purchase is unclear, the family could have paid "anywhere between €20 million and €30 million" for the […]

2026-08-21 原文 →
AI 资讯

Cleaning Up Feature Flags: The Art of Not Leaving a Mess

You said you'd remove that flag after launch. You lied. It's been six months and the flag is still in appsettings.json , the if statement is still in your controller, and nobody remembers which state is "on." This is how codebases turn into haunted houses. Why Cleanup Matters Dead feature flags are technical debt with teeth . They add branches to your code that nobody tests. They confuse new developers who don't know the history. They inflate configuration files and make deployments harder to reason about. And they compound. Every flag you don't clean up makes the next cleanup harder because the cognitive load of understanding the system keeps increasing. The cost of removing a flag is lowest immediately after the feature ships, while everyone still remembers what the thing does. Six months later? Good luck. Track Every Flag You can't clean up what you can't find. Maintain a registry of every active feature flag with: Name Purpose Owner Date created Expected removal date This can be a spreadsheet, an issue tracker, internal documentation, or a dedicated feature flag management system. The format doesn't matter nearly as much as the habit. When you add a flag, add it to the registry. When you remove a flag, remove it from the registry. If your registry contains flags with no owner or no removal date, congratulations: you've found your next cleanup project. Set Expiry Dates Every flag should have a planned removal date when it's created. For example: Release toggles: Remove shortly after the feature ships. Two weeks is a reasonable default. Experiment toggles: Remove when the experiment concludes. Ops toggles: May be permanent by design. Permission toggles: May also be permanent, but document that explicitly. If a flag has been alive longer than its planned expiry and nobody deliberately extended it, it's already a zombie. Treat it accordingly. Make Cleanup Part of the Process Flag cleanup doesn't happen unless someone owns it. Add a cleanup step to your feature compl

2026-08-21 原文 →
AI 资讯

Speculative Decoding and MTP: Why Guessing Is Free

I saw "MTP round-trip" on a checklist for a Megatron conversion pipeline and had no idea what it meant. Two acronyms, one hyphen, apparently important enough that someone had listed it as a thing to verify. Working out what it meant took me somewhere I didn't expect. The interesting part turned out not to be MTP at all — it was the reason speculative decoding works in the first place, which rests on a fact about hardware that I had backwards. TL;DR Generating text is slow because it's sequential: one full forward pass per token. But a forward pass over five tokens costs about the same as over one. Generation is bottlenecked by moving weights, not by arithmetic. Speculative decoding exploits that: something cheap drafts k tokens, the big model verifies all of them in one pass. It is exact , not an approximation. Same output distribution as normal decoding. MTP (Multi-Token Prediction) is one way to produce those drafts — a small module trained into the model itself. MTP has two separate lives: a training-time auxiliary loss you can throw away, and an inference-time draft head you can't. Why generating text is slow To produce token N+1, the model needs token N. There's no way around that ordering — it's what "language model" means. So generating 100 tokens means 100 full passes through the network. For a model like GLM-5.2, that's 78 layers, 100 times over. The obvious conclusion is that generation is 100 times as expensive as reading the prompt. The obvious conclusion is wrong, and the way it's wrong is the whole point. The part that got me A forward pass processing one token and a forward pass processing five tokens take roughly the same wall-clock time. I had assumed compute scaled with tokens. It doesn't, because compute isn't the bottleneck. Every forward pass has to read the model's weights out of memory and into the compute units. That's hundreds of gigabytes moving across a memory bus, and it happens whether you're processing one token or fifty . The actual ar

2026-08-20 原文 →
AI 资讯

Amazon’s drone deliveries are landing in pools and ponds

Amazon's speedy drone delivery service will soon reach 500 cities across the US - but that might just mean there are more pools to drop packages into. On Wednesday, ABC7 News Bay Area shared a video showing an Amazon delivery drone hovering over a customer's pool in Texas, before opening its hatch and plopping the […]

2026-08-20 原文 →
AI 资讯

Welcome to the AI crisis in math

Today on Decoder, I’m talking with Robert Hart, The Verge’s London-based AI reporter, about what AI is doing to the field of mathematics and the existential crisis many lead mathematicians are having about it. OpenAI just published a set of solutions to longstanding problems in math that went off like a bombshell in the field. […]

2026-08-20 原文 →
开发者

I bought DJI’s banned camera — it was cheap and easy

As a kid, I always imagined the "black market" would be like the movies: Shady vendors hawking illicit goods along dusty streets. But if you want a banned DJI Osmo Pocket 4 or Pocket 4 Pro camera in the US, you can just reach for your phone. They're on Temu, AliExpress, eBay, Mercari - even […]

2026-08-20 原文 →
AI 资讯

Slack is launching collaborative vibe-coding channels

Slack is introducing dedicated channels where teams can vibe-code together with AI agents instead of jumping between different tools and conversations. The Slack Code launch includes open, project-specific code channels with dedicated user tabs, alongside features that compare coding changes and preview HTML output before the project is shipped. "With Slack Code, when you have […]

2026-08-20 原文 →