标签：#AI

A fintech RAG pipeline poisoned its own vector store and the LLM-as-a-judge validator approved every hallucination. The fix: gate writes with code.

Muse Image allows users to generate AI images using photos from public Instagram accounts. As long as a person's profile is public, another user can tag that account and use their images as part of an AI-generated creation.

The company is taking a modular approach to designing these chips, anticipating that their needs will change as AI evolves rapidly by the time the chips are in production.

Having proven how valuable compute can be, the company finds itself at the center of a market everyone wants to be in — while simpler technologies and less interesting companies get rich on the sidelines.

About two weeks after OpenAI's GPT-5.6 was caught up in regulatory drama - rolled out only to government-approved organizations during a "limited preview" period - the company has received the Trump administration's greenlight for a public rollout of the model. OpenAI CEO Sam Altman called it "the best model we have ever produced." To celebrate, […]

Windows 11 updates could soon include fixes for more security issues at once. Microsoft said in a blog post on Thursday that it's now using AI to "identify potential issues earlier," which means "customers will see a higher volume of security updates included in each security release." Hackers, even amateurs, have increasingly been using AI […]

Last week I open sourced FableCut , a Premiere-style video editor that runs in the browser and that AI agents can operate. It hit the front page of Hacker News ( thread ), and the questions there made me realize the interesting part isn't the editor. It's one design decision: the project file is the interface. The usual way, and why I flipped it Most AI video tools hide the edit behind an API. You call addClip() , applyFilter() , and the tool owns the state. If you want a human to touch the result, you build a whole collaboration layer. FableCut does the opposite. The entire timeline lives in one JSON document, project.json : media, clips, tracks, keyframes, transitions, markers. The editor UI reads it. The export renders it. And anything that can write JSON can edit video: Claude Code through MCP, a Python script, jq , or you with a text editor. { "id" : "c_title" , "kind" : "text" , "track" : "V3" , "start" : 0 , "duration" : 2.2 , "props" : { "text" : "HANDMADE" , "font" : "Bebas Neue" , "glow" : 45 , "textAnim" : "letter-pop" } } That clip is a glowing kinetic caption. There is no API call that creates it. Writing it into the file IS creating it. SSE as a doorbell, not a data channel The first question on HN was "what's the benefit of SSE here?" Fair question, because the SSE channel does almost nothing, and that's the point. The server watches the project file with fs.watch , debounces 150ms, and pushes the literal string change to the browser. No payload. The browser re-fetches the project and re-renders. The whole mechanism is about 15 lines on a bare node:http server. Why not WebSockets? Because the data only flows one way. Everything that writes (the UI, an agent, a shell script) goes through REST or the filesystem. The browser only ever needs to hear "something changed, go look." An event with no payload can't arrive out of order, and a missed event costs nothing because the next fetch has the latest state anyway. The revision counter, or: how a human and

TLDR; I got tired of babysitting N terminal tabs of five different coding-agent CLIs. So I built agentproto — one daemon that drives Claude Code, Codex, Hermes, opencode, and Mastra through the same lifecycle, and actually supervises them. Why I built a daemon to drive every AI coding agent from one interface I have a confession: at any given moment I have Claude Code, Codex, and Hermes running in parallel terminal tabs, and I cannot remember which flag spawns which, which one eats --prompt , which one needs --cwd vs cd , and which one will hang forever if I close the laptop lid. simonw described the feeling on Hacker News recently — "Today I have Claude Code and Codex CLI and Codex Web running, often in parallel" — and called it a real jump in cognitive load compared to a year ago. aantix asked, also on HN: "how does everyone visually organize the multiple terminal tabs open for these numerous agents in various states?" I didn't have a good answer. So I built one. It's called agentproto . It is one daemon and one CLI that drives any coding-agent CLI — Claude Code, Codex, Hermes, opencode, Mastra, and a few more — through the same start / prompt / monitor / kill lifecycle, so you stop memorizing five different CLIs. On top of that lifecycle it adds the supervision layer people keep hand-rolling by hand: durable policy gates, nested orchestration, and multiplexed fan-in monitoring. MIT, no paid tier, the daemon itself is an MCP server. This is the story of why it exists. The hand-rolled watchdog The sharpest signal while I was building this came from other people independently re-inventing the same primitives in tmux scripts. On r/ClaudeAI, Confident_Chest5567 posted a writeup of orchestrating agents via tmux panes with a watchdog that resets dead sessions — "a swarm of agents that can keep themselves alive indefinitely." In the same thread, IssueConnect7471 (18 upvotes) described wiring a Redis pub/sub heartbeat plus dead-letter respawn between tmux panes, and arriv

The request was eight words Monday morning. I open the outgoing email queue: six hundred and forty-seven drafts waiting, six hundred and seventy-two sent. Nobody clicks Send . First-contact emails are prepared by a pipeline and they sleep, because the last step assumes a human. That human, I had stopped believing she would have the time. I state the decision: automate sending . The response comes in seconds. Three levels of automation. Four channels. Three risk thresholds. All correct, all fit for a half-day architecture workshop. I had not asked for a workshop. Pauline walks behind me, glances at the screen, says nothing. Three timed reframes First reframe , brief: too strange, let's simplify . The agent drops two axes, keeps four residual layers, progressive warm-up over three weeks, deterministic anti-replay hash, configuration table in the database, manual Phase 1 followed by an automated Phase 2 to validate after two weeks of measurement. The target stays the same, that an email leaves without a human click. The path has grown accordingly. Second reframe , drier: simple, three safeguards, a kill-switch, we do this in one day . The agent re-architects, accepts the one-day target, keeps the three safeguards. But slips in three prostheses it calls industry standard : real-time dashboard, exponential retry, structured audit log in a new table. Each justifiable in isolation. None of them requested. Third reframe , shorter still: I don't understand why you're adding this . An opening line almost embarrassed, which I had never read from it before: "you're right, I'm over-engineering without necessity." And the version that should have arrived on the first round. A function that takes the draft record, checks three conditions, calls the send engine, returns. // lib/email-outbox.ts — generateFirstContactDraft (commit 3756e63) if ( ! EMAIL_REGEX . test ( input . email )) { return { success : false , error : ' email_invalide ' } } if ( BLACKLIST_EMAILS . has ( input . ema

Claude’s new Reflect dashboard doesn’t just visualize how you use AI. It also subtly reinforces how much of your daily work now depends on Anthropic’s chatbot.

Three big AI IPOs are set to generate more value than all the U.S. VC-backed exits since 2000.

"You promised me that you would never take power in your name."

PR volume went up, ticket quality didn't, and the gap got filled with LLMs on both sides of the review: bots reviewing, bots replying, bots occasionally arguing with bots about priorities that only existed in a teammate's head. Our CEO named the actual problem, and it's bigger than code review.

I asked AI to help me write this article. Then I sat there for a second, thinking about how ironic...

After reentering the AI race with its first in-house Muse Spark model in April, Meta is now opening up the doors to developers with a new model that can plug into AI coding software with the new Meta Model API. Meta says that Muse Spark 1.1 is a "step-change" from the first generation, with improvements […]

Today on Decoder, I’m talking with Peter Kafka, who is chief correspondent at Business Insider and host of Channels, a podcast about the media industry. And it’s a big week for the media industry — Comcast just announced that it’s splitting itself up, into the Comcast broadband company and the NBCUniversal entertainment company. That’s after […]

The Claude Reflect dashboard is like a mix of Spotify Wrapped and the screen time tool on your phone.

New York has banned smart glasses in all 1,240 state, county, city, town and village courts.

The popularity of Spotify Wrapped has kicked off a wide range of year-in-review features, on apps from YouTube to Uber - and now, the lookback trend has come to AI. Anthropic on Thursday announced a "reflect" feature for its Claude chatbot, allowing users to see an analysis of their usage data over the past month, […]

Character.AI's plan to become more than just an LLM-powered chatbot platform is going beyond interactive books, comics, and audio dramas. Today, the company announced the debut of c.ai Series - short-form, episodic videos designed to be watched and interacted with - on your phone. Unlike traditional microdrama services that feature cheaply produced, live-action shows starring […]