AI 资讯

Instagram is going to let you tweak what its algorithm shows you on your main feed. With the Your Algorithm feature, "you can now see the topics we think you're interested in, and change them, across all the major parts of Instagram," according to Instagram boss Adam Mosseri. Right now, the feature will only surface […]

The US military struck Iran again after an Iranian drone’s lucky midair strike.

Amazon has launched a new feature for its Echo and Echo Kids smart speakers called Sleep Studio that's designed to make the daily transition to bedtime more enticing for kids and less stressful for parents and caregivers. The feature uses a combination of bedtime stories, relaxing sounds, and guided meditations along with scheduling and customization […]

Anthropic released Claude Fable, its first Mythos-class AI model, yesterday and it's already causing concerns inside Microsoft. Sources tell me that Microsoft is limiting the use of Claude Fable 5 for employees because of Anthropic's new data retention requirements. While Microsoft quickly rolled out Claude Fable 5 to its GitHub Copilot and Foundry customers, I'm […]

Move also cuts off a massive market of legit users who buy cards with physical cash.

V.C. Andrews died in 1986. Since then, more than 100 novels have been published under her name by ghostwriter Andrew Neiderman. Most readers either never noticed or didn't care. The books still had the gothic families, dark secrets, and familiar atmosphere people expected from a V.C. Andrews novel. It got me thinking about something we're starting to see with AI. When people ask whether AI can continue the work of a deceased author, musician, or artist, they're treating it as a brand-new question. But publishing has already been running a real-world experiment for nearly 40 years. A dead author's name remained on the cover. Someone else learned the style, themes, and formula. New works were produced for an audience that wanted more of the same. The franchise continued. The obvious difference is that Neiderman was a human ghostwriter and an AI model isn't. But from the perspective of readers, what exactly is the meaningful distinction? If a future "new" novel by a deceased author is good enough that readers enjoy it and can't tell the difference, should we care how it was produced? Or is there something fundamentally different about a human ghostwriter carrying on a literary legacy versus a model trained on the author's corpus? I wrote a longer piece about the V.C. Andrews case and why it feels relevant to the future of AI-generated creative work: https://tjcrowley.substack.com/p/the-ghost-in-the-machine-has-been Curious where people here draw the line. submitted by /u/Dependent_Run_6410 [link] [留言]

Google is making some changes to how it saves your interactions with Search. In an email sent to users, Google says it will save the images, files, audio, and video you use to search under a new "Search Services History" setting. That includes the images you search for with Google Lens, recordings from its real-time […]

We dive into our initial thoughts on Siri AI and Tim Cook's legacy.

New research suggests that AI memory systems can degrade model performance and encourage sycophantic tendencies.

A recent study found that Google's AI Overviews regularly provide incorrect information and contain facts not supported by cited sources.

We're open-sourcing 14 components & examples today for PDF, DOCX, and XLSX viewers, plus bounding box citations, file upload, e-signature, and more. It's MIT licensed and fully customizable. Demo video here: https://share.extend.ai/kRmSGKRF When we started, we tried every file viewer and document component library we could find. Unfortunately, none of them had all the functionality (and polish) that we wanted, so we ended up building our own for https://extend.ai/ . It was only ever meant to be

Me: If I’m half Canadian and my wife is half Canadian (both moms) what does that make our kids? Claude: quarter Canadian. 🍁 Me: Imagine i have a box that is 50% pepper and 50% salt. And I have another box that 50% sugar and 50% salt. I mix the two together. Now it's 50% salt, 25% sugar, 25% pepper. Claude: Right — and your kids are 50% American (or whatever), 25% Canadian from you, 25% Canadian from your wife. Which adds up to half Canadian, same as each of you. The Canadian fraction doesn't shrink because both sides contributed it. Me: But your first answer was 25% (quarter) Canadian? Claude: You're right, I goofed. Following the salt logic: 25% + 25% = 50%, so they're half Canadian, same as each of you. I shouldn't have said quarter. Me: This was a really simple problem for me. What should I tell my friends about your abilities? Claud: What to tell your friends: I'm useful for research, drafting, analysis, and grinding through detail — but I can whiff on simple stuff, especially when I'm moving fast or anchoring on the wrong frame. Check my work, particularly on anything where the answer should be obvious. If it feels off, it probably is. submitted by /u/NECESolarGuy [link] [留言]

Backed by Alexis Ohanian’s 776 and Kindred Ventures, Zest uses transaction data and AI to generate restaurant recommendations based on users’ real dining habits and the places they frequent.

When building a personal finance tracker, data integrity and system reliability are non-negotiable. One missing try/catch block can crash your whole server, and weak types can let invalid financial payloads corrupt your database. While building the backend for my personal finance tracker, I decided to move past generic tutorials and build a bulletproof, production-grade API core using Express, TypeScript, and Zod. In this post, I’ll show you how I implemented a type-safe middleware ecosystem, leveraged TypeScript declaration merging to extend the native Request object, and eliminated repetitive try/catch boilerplate across the entire codebase. 1. The Weapon Against Boilerplate: The asyncHandler HOC Writing try/catch blocks in every single controller handler clutters code and introduces human error—it’s easy to forget to pass an error to next() . To solve this, I engineered a Higher-Order Function (HOC) factory that wraps asynchronous request handlers and automatically catches rejected promises, safely routing them into the global error handler. import { Request , Response , NextFunction , RequestHandler } from ' express ' ; export const asyncHandler = ( fn : RequestHandler ): RequestHandler => { return ( req : Request , res : Response , next : NextFunction ) => { Promise . resolve ( fn ( req , res , next )). catch ( next ); }; }; Why this matters: Reliability: Async errors always reach the centralized error middleware. Readability: Route controllers stay beautifully clean, focusing only on business logic rather than async control flow wiring. 2. TypeScript Magic: Declaration Merging & Type Augmentation When dealing with authentication tokens, request tracing ( requestId ), or custom validated payloads, developers frequently resort to casting the request as any (e.g., (req as any).userId ). This completely destroys Type Safety. Instead of fighting the compiler, I leveraged TypeScript Declaration Merging to reopen Express's internal Request interface and merge my cust

In my last post I complained — a lot — about product managers and how they made my life hell with vibe code. PS: apologies, manager, if you're reading this — but it's true. Now, I'm not here just to complain. There were a lot of learning opportunities too, like how to handle legacy / vibe code. Because at the end of the day, both are the same: no one knows how they work, but somehow they keep working. Touching them is like defusing a bomb — you never know how your change might cascade and break the core logic. The good news is that vibe code is much simpler than legacy. AI, in all its glory, tries to write perfect-looking code — proper function names, comments, the works — not like legacy code where a single function runs 500 lines, with spaghetti names all over that make no sense and comments that are out of date. And that makes it something I can actually handle. I still don't have a perfect, step-by-step playbook — but I've got pieces. The first one. The cheapest and the oldest one. The one the industry solved decades ago and the whole "AI built my app in a day" crowd somehow forgot exists. A linter. Yes, you heard me right. A linter. ESLint. Most people who've been in this industry already know it. It's the most boring, reliable tool in the box. But in an era where the answer to every problem is "add another AI," it's worth saying out loud why the boring tool still wins. What a linter actually is If you vibe-coded your way into this world, or you're new to web dev in general and have never heard the word "lint", here's the honest version. A linter is a set of rules you add to your repo. It reads your code without running it, checks it against those rules, and flags everything that's broken, sloppy, or about to bite you in production. The detail people get wrong: it's not a grep for bad words. A real linter parses your code into a syntax tree and actually reasons about its structure — what's imported, what's called, what's reachable, what types flow where. That's

Hey HN, it’s been just over a year since we launched HelixDB ( https://news.ycombinator.com/item?id=43975423 ), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS). Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven a

Cybersecurity researchers are complaining that Anthropic's new model Fable has guardrails that are too strict for any cybersecurity work.

In 2025 Google Cloud added G4 , powered by NVIDIA's RTX PRO 6000 Blackwell Server Edition GPUs to their offering, allowing them to offer hardware not only for AI applications, but also for other applications, such as rendering, simulations or gaming. A single G4 instance with one accelerator ( g4-standard-48 ) comes equipped with 48 CPU cores, 180 gigabytes of RAM and 96 gigabytes of GPU memory. This is a lot of resources for a single cloud workstation, that only the most demanding workstreams would utilize. Most professionals who require a graphics accelerator to do their job, don't really need this much compute power for day to day tasks. It wasn't financially reasonable to pay for a G4 instance, when you weren't utilizing all the resources you paid for. If only there were smaller machine types… If only you could share that one very powerful GPU between multiple virtual machines… Introducing fractional VMs! During Google Cloud Next 2026, Google announced GA for fractional G4 VMs and was the first provider to bring vGPU functionality to RTX PRO 6000 accelerators. vGPU stands for virtual graphical processing unit . Just like VMs (virtual machines) are a way to split one physical computer into smaller, independent systems, vGPU allows for a single physical accelerator to be split into 2, 4 or 8 virtual accelerators! The new fractional machine types ( g4-standard-24 , g4-standard-12 , g4-standard-6 ) now allow you to perfectly match the compute capabilities to your needs! Who is it for? The existence of those new machine types makes it much more cost-efficient to move many GPU-dependent tasks to the cloud. Replacing physical workstations in offices with cloud infrastructure is not a new thing , but till now, Google Cloud didn't offer a good platform for those who needed workstations to process images, post-process videos, simulate physics or render 3D graphics. Those users now can get exactly the hardware they need, allowing their companies to move away from maintaini

Your users have started asking ChatGPT and Perplexity instead of Google. So here is the uncomfortable question: when someone asks an AI engine "what is the best tool for <your category> ", does your product show up in the answer? Most founders have no idea. I didn't either, until I measured it — and the gap was nowhere near where I expected. So we built a CLI to measure it. It's called aeo-platform , it's MIT-licensed, it has zero runtime dependencies, and it runs entirely on your machine. This post is the five-minute version: install it, point it at your domain, and read the gap. I'll show you the exact commands and the real before/after numbers from running it on one of our own products. Quick framing on terms: AEO (answer engine optimization) is just SEO's younger sibling for AI answers — getting cited inside the AI's response instead of ranking on a SERP. Some people call it GEO. Same field. TL;DR — three commands npm install -g aeo-platform export OPENAI_API_KEY = "sk-proj-..." # required export GEMINI_API_KEY = "AIzaSy..." # required aeo-platform init --yes --brand = YOURBRAND --domain = YOURDOMAIN.COM --auto \ && aeo-platform run \ && aeo-platform report init auto-discovers your category and writes three commercial buyer queries to a local .aeo-tracker.json . run fires those queries at each engine and scores the answers. report opens a single-file HTML report in your browser. The whole thing installs in under a second (no dependency tree to resolve) and writes everything to disk under aeo-responses/YYYY-MM-DD/ — nothing is sent to a hosted dashboard. OpenAI and Gemini keys are mandatory (they also power a two-model cross-check that filters hallucinated brand mentions). Anthropic and Perplexity keys are optional — each one just adds a column to the report. What it actually measures A single run sends your buyer queries to four engines through their official REST APIs — no scraping, no proprietary black-box score: Engine Model Type ChatGPT (OpenAI) gpt-5-search