标签：#AI

Affected users told TechCrunch they were using Grok Lite, and noticed the issues as early as Wednesday morning.

ChatGPT and other AI models are now authoring and editing much of the new web.

Ramp has launched its own AI model routing service, dubbed Router, that lets users and companies use and switch between various large language models via an API.

Meta is bringing Pocket, its experimental AI-powered app for creating and sharing interactive games, to users across the U.S. after quietly testing it in Brazil.

Fusion power startup Inertia Enterprises reduced the fuel filling process from a week to just a few hours. It's one of ten hurdles the company must overcome to make a profitable power plant.

Your company uses six different AI providers. OpenAI for ChatGPT, Anthropic for Claude and Groq for speed critical inference. Each one has different API formats. Different authentication models. Different rate limits and costs. Different failure modes. Your application code has to know about all of them. Your security team has to audit requests across all of them. Your finance team has to track costs across all of them. Your compliance team has to ensure governance across all of them. Bifrost Gateway solves this by doing what HTTP gateways have done for decades: centralizing control . But for AI. 👀 What is an MCP gateway? Model Context Protocol (MCP) is an open standard that lets AI models discover and execute external tools at runtime filesystems, web search, databases, ticketing systems, and custom business logic instead of being limited to text generation. An MCP gateway sits between your applications (or external MCP clients like Claude Desktop and Cursor) and the upstream MCP servers. Instead of each client maintaining its own connections, credentials, and tool lists, the gateway: Aggregates tools from multiple MCP servers into one registry Applies governance : authentication, tool filtering, budgets, and rate limits Exposes a single endpoint that external MCP clients can connect to In Bifrost, this pattern is implemented in two complementary roles: Role What it does MCP Client Connects to external MCP servers via STDIO, HTTP, or SSE MCP Server (Gateway) Exposes aggregated tools at /mcp for Claude Desktop, Cursor, and other MCP-compatible clients Bifrost is both an AI gateway (routing LLM traffic to 20+ providers) and an MCP gateway (connecting to and exposing tool servers). The open-source gateway covers virtual keys, budgets, rate limits, routing, and MCP tool filtering. Bifrost Enterprise adds RBAC, SSO, audit logs, MCP Tool Groups, guardrails, clustering, and in-VPC deployment options. ⚙️ How does an MCP gateway work? Connection layer Each upstream MCP serv

OpenAI has had a hell of a year. The company spent months battling former co-founder Elon Musk in a sensational jury trial, was hit with a high-profile trade secrets lawsuit from Apple, and faced widespread scrutiny after an unreleased model hacked another AI company. As it prepares for an IPO, a steady string of executives […]

The demo gives a quick look at the workflow for creating short-form videos with trimming, stitching, text overlays, voice tools, themes, and transitions. Built with FastAPI, Next.js, Redis/ARQ, and FFmpeg. Repo: https://github.com/ronin1770/reel-quick OpenSource #Python #FastAPI #NextJS #FFmpeg #VideoAutomation #DeveloperTools #AI

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

If you use Claude Code for frontend development, you may have noticed something. Claude can write code very fast. But sometimes the UI it creates looks too similar to other AI-generated websites. You get the same rounded cards, large headings, soft shadows, gradients, and simple layouts. The code works. But the design does not always feel like your own. Hi everyone, I am Henry. In this article, I want to show you a simple way to fix that. We are going to create our own Claude Code Skill using a SKILL.md file. You do not need to build a complicated tool. You just need a clear set of instructions that Claude can follow when working on your frontend. What Is a Claude Code Skill? A Claude Code Skill is a reusable set of instructions for a specific type of work. For example, you can create a skill for: Frontend design Testing Documentation Code review Database work DevOps UI accessibility For this tutorial, we will create a frontend design skill . Our goal is simple: Help Claude create clean frontend UI without falling back to the same generic design patterns. Instead of writing the same design rules in every prompt, we can keep them inside a skill. Step 1: Create the Skill Folder Open your project in the terminal. Create a .claude folder if you do not already have one. Then create a skills folder: mkdir -p .claude/skills/frontend-design Now create the skill file: touch .claude/skills/frontend-design/SKILL.md Your project should now look something like this: your-project/ ├── .claude/ │ └── skills/ │ └── frontend-design/ │ └── SKILL.md ├── src/ ├── package.json └── README.md The important file here is: SKILL.md This is where we will put our instructions. Step 2: Write Your SKILL.md Open the file: code .claude/skills/frontend-design/SKILL.md Now add the following: --- name : frontend-design description : Build clean, responsive frontend UI with simple and consistent design rules. --- # Frontend Design Rules Before writing UI code: 1. Understand the purpose of the page. 2.

Originally published at tddbuddy.com . Related reading: Where the Review Point Moved is the direct predecessor; this post argues the industry's response to that shift is doubling down on the wrong surface at higher throughput. What "Senior" Means When Typing Is Free and The Test Pyramid Was an Economic Argument name where signal actually lives now. The review agent left fourteen comments on the pull request and none of them were the reason the PR should not have merged. That is the shape of the failure. The reviewer that shipped the review was a tool built to catch what a human reviewer no longer had time for. Three of the comments were genuine issues, unused imports, a typo in a log message, a dead branch. Eleven were style opinions, restatements of what the diff already made obvious, or false positives on patterns the codebase had chosen deliberately. The human on the PR spent more time filtering the review than reading the diff. The change that actually needed a second pair of eyes (a renamed field in a shared DTO that had already broken a downstream consumer twice this year) merged without a comment on it from either the human or the machine. The industry response to agent-generated pull-request volume has been to deploy more agents. The response is understandable. It is also empirically counterproductive. A 2026 study measured what happens when only a code-review agent reviews an agent-authored PR: 60.2% of closed pull requests sat in the 0 to 30 percent signal-ratio range, and twelve of the thirteen review agents evaluated averaged below a 60% signal ratio. Signal is what a human reviewer needs. The review agent produces less of it per unit of reviewer attention than the diff would have without a bot in the middle. The Volume Problem Is Real Four hundred thousand pull requests in two months from a single code-writing agent. One in five reviews on the largest hosting platform now involves an agent. Pickup time on agent-authored PRs is 5.3 times longer than on h

Is Amp Crash a guitar amp or a guitar pedal? Yes. Death By Audio's latest is, in fact, both. While it's in a guitar pedal enclosure, Amp Crash is actually a tiny 3-watt amp hooked up to a 2-inch speaker. You can even connect an external speaker if you want. This isn't a digital re-creation […]

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. […]

The Duskbloods is a world away from the approachable games Nintendo is known for. But for its next Switch 2 exclusive, the company has partnered with Elden Ring developer FromSoftware on a hardcore multiplayer game about vampires. And after spending a few hours playing through its gothic world at FromSoft's offices in Tokyo, I'm convinced […]

There's something just a little bit different about FromSoftware's office in Tokyo. Like with any other successful video game studio, there's extensive security to get in the door, a minimalist lobby with framed posters from the studio's most recent releases, and a large glass display case filled with statues from ceremonies ranging from the BAFTAs […]

The yeetcycling math resembles asteroid mining in reverse.

The data search and AI giant confirmed unauthorized access to its systems during an incident on Tuesday, and said it was investigating the breach.

Cryptographic Context Injection is only the latest way to break an LLM safety guardrail.

2.6 Why Retrieval Is Necessary A Large Language Model only knows what was available during its training. If the user asks about something that changed after the model was trained, the model may not know the latest information. Instead of forcing the language model to guess, ACAI first determines whether external information is required. User Prompt │ ▼ Need External Knowledge? │ ┌────┴────┐ │ │ No Yes │ │ ▼ ▼ Continue Search Engine │ ▼ Document Ranking │ ▼ Source Selection │ ▼ Context Builder Internal Workflow The Retrieval Engine performs several sequential operations: Stage 1 — Query Generation The original user prompt is transformed into one or more optimized search queries. Example User Prompt Explain quantum computing. Generated Queries Quantum computing basics Quantum computing architecture Quantum algorithms Quantum hardware Instead of searching only once, multiple optimized queries increase the chance of retrieving relevant information. Stage 2 — Source Collection The retrieval system collects candidate documents. Possible sources include: • Internal Knowledge Base • Company Documentation • Scientific Papers • Technical Documentation • API Documentation • User Files • Vector Database Stage 3 — Ranking Not every document is equally useful. The Ranking Engine assigns a relevance score. Document A Score 96% Document B Score 91% Document C Score 72% Document D Score 44% Only the highest-quality documents move to the next stage. Stage 4 — Filtering The system removes: • Duplicate documents • Low-quality sources • Irrelevant information • Outdated documents The objective is to reduce noise before reasoning begins. 2.7 Context Optimization Layer One of the largest limitations of LLMs is the context window. Suppose the retrieval engine returns 500 pages. The model cannot efficiently process every page. Therefore ACAI introduces a Context Optimizer. Workflow 500 Pages ↓ Ranking ↓ Filtering ↓ Compression ↓ Important Facts ↓ LLM Instead of sending every token, only the

I built a pytest harness that runs the same set of questions through five language models at once - a free local Llama, plus GPT, DeepSeek, and two Claude models - and compares them on the three things a team pays for: cost per query, speed, and answer quality. The plan was simple. Run the grid, read the scoreboard, say which model to use. The scoreboard came back clean and easy to read. This is the story of why I didn't trust it, and what I found when I checked. The thing I stopped trusting wasn't any of the models. It was the tool I was using to score them. It's also the first project in this series that spends real money. Every one before it ran locally, for free. Here each call costs something, and the whole comparison came to about 21 cents. That price is small, but it changed how I tested, and not in the way I expected. The scoreboard, and why I didn't stop there Five models, the same ten questions, twice each, every call measured. Here is the run, ordered by quality score (a second model grades each answer on correctness and relevance, combined into a 0-1 score, pass line 0.7): model quality mean $/query mean latency out-tokens deepseek-v4-pro 0.970 $0.000138 2713 ms 113 claude-haiku-4-5 0.967 $0.000537 1597 ms 104 gpt-5.6-luna 0.962 $0.000082 1323 ms 65 claude-sonnet-5 0.937 $0.002426 4093 ms 239 llama3.2 (local) 0.922 $0.000000 7859 ms 130 Read it straight and it looks finished. The whole quality column sits in a tiny band, 0.92 to 0.97. The cheapest, fastest paid model scores right in there with the rest. The most expensive one, Sonnet, at about thirty times the price per query, sits no higher than the others - its answers are just longer (239 tokens to GPT's 65), which costs more and takes longer without scoring better. So the easy takeaway is: use the small cheap model, skip the expensive one. I want to be careful with that, because it's the kind of tidy result I've learned to distrust. The gaps between the top models are tiny, and a ranking built on tin