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Cursor just shipped Composer 2.5 and Composer 2.5 Fast. We benchmarked both across 11 engineering skills, 5 scenarios per skill, averaged across three independent LLM judges. The fast model scored higher, ran 32% quicker, and costs exactly the same. If you are reaching for Composer 2.5 over Composer 2.5 Fast, you are paying the same price for a slower, slightly worse model. Here is the full picture. TL;DR Composer 2.5 Fast scores 92.7% with skill context. Composer 2.5 scores 92.1%. Fast wins. Both are ahead of gpt-5.5, gpt-5.4, and the previous Composer 2. The fast model completes scenarios in 59 seconds on average. The regular model takes 87 seconds. Where They Land in the Benchmark We ran 6 models across 11 skills, scoring each run with three independent judges and averaging the results. Here is where the full leaderboard sits: Model Avg baseline Avg with-skill Lift opus-4-7 80.8% 93.4% +12.6 composer-2.5-fast 79.6% 92.7% +13.1 composer-2.5 79.0% 92.1% +13.1 composer-2 74.2% 89.6% +15.4 gpt-5.5 75.5% 89.4% +13.9 gpt-5.4 74.1% 89.3% +15.2 gpt-5.3 65.5% 83.9% +18.4 gpt-5-codex 68.7% 78.7% +10.0 Composer 2.5 Fast sits 1.3 points behind opus-4-7 and 3.3 points clear of everything else. That is a meaningful gap. The previous Composer 2 sits alongside gpt-5.4 and gpt-5.5 at roughly 89-90%. Cursor has moved its own model up a full competitive tier in a single release. The Fast model seems better. Normally a "fast" variant trades quality for speed. Composer 2.5 Fast does not do that. It scores 0.6 points higher than the regular model while running 28 seconds faster per scenario (59s vs 87s on average across 110 scored runs). The per-skill breakdown shows where the differences accumulate: Skill 2.5 with-skill 2.5-fast with-skill Winner documentation 97% 98% fast fastify 99% 94% 2.5 init 87% 86% 2.5 linting 98% 99% fast node-best-practices 95% 95% tie nodejs-core 98% 98% tie oauth 92% 89% 2.5 octocat 95% 96% fast skill-optimizer 98% 98% tie snipgrapher 93% 93% tie typescrip

In a bid to dismiss a lawsuit over xAI’s polluting gas turbines, the Justice Department claimed the company is integral to military operations—including the Iran War.

Anthropic leaders flew to Washington, DC, to meet with White House officials on Monday. After high-level talks, they're still split on the risk Claude Fable 5 presents.

At Inithouse — a studio running parallel product experiments — we built Be Recommended , a tool that checks how visible your brand is across ChatGPT, Perplexity, Claude, and Gemini. The idea sounded simple: query multiple AI models, score the results, show a report. It was not simple. Here are four technical mistakes we made shipping v1 — and the fixes that actually survived production. Mistake 1: Rate Limiting Was an Afterthought We treated rate limits as edge cases. They were not. Every AI provider has different rate-limit headers, different backoff expectations, and different definitions of "too many requests." Our first architecture just retried on 429. That turned a rate limit into a cascade — one provider throttling triggered a retry storm that cascaded to the others. The fix: Per-provider circuit breakers with exponential backoff. Each provider gets its own state machine. When a circuit opens, we serve cached results for that provider and mark the score as "partial" in the UI. Users see real data, not a spinner that never resolves. At Audit Vibe Coding — another tool in our portfolio focused on code quality audits — we observed the same pattern in a different domain: external API dependencies need isolation. The lesson transferred directly. Mistake 2: The Caching Strategy Was Too Naive Our first cache key was query + model . That breaks immediately — AI model responses drift over time, and a cached result from two weeks ago is misleading. We also had no invalidation strategy beyond TTL. The fix: Cache by query + model + week_number . Weekly invalidation with stale-while-revalidate: serve the cached score instantly, trigger a background refresh, update the display when new data arrives. Users get instant feedback and fresh data within the same session. We measured the impact across our portfolio: stale-while-revalidate cut perceived load time from 8+ seconds to under 1 second for returning visitors. The background refresh means scores stay current without the

Despite continued benefits, anti-vaccine rhetoric has driven down vaccination.

Swap out your creaky old box fan for a new model that lights up, mists, or even follows you around the room.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. When I landed in Seoul after a grueling 12-hour flight from San Francisco, I walked through an unmanned immigration checkpoint, where a machine scanned my face and passport. On the subway home,…

Indian IT services company HCLTech is investing $150 million in the Bengaluru startup.

CSS functions, the alpha() function, Grid Lanes, some things about Dialog that you might not know, CSS Wordle, and more — this is What’s !important right now. What’s !important #13: @function, alpha(), CSS Wordle, and More originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

NewCore argues the next challenge in enterprise security will be managing AI agents, not people.

We Built ARK Because Our Customer Support Was Spread Across 4 Apps The Problem A few months ago, our small team was drowning. Not in customers (well, a little) — but in tabs. WhatsApp open in one window. Instagram DMs in another. A live chat widget buried in a third. Email in a fourth. Every time a customer reached out, someone had to figure out: which channel did this come from, has anyone replied already, and what was the context of the last conversation? The result was predictable: slower replies, repeated questions to customers, and a support workflow that didn't scale past a handful of conversations a day. Why Existing Tools Didn't Fit We looked at the usual suspects — Intercom, Zendesk, Front. They're solid products, but they're built for large support teams with big budgets and dedicated admins. We needed something simpler: a single inbox, AI doing the repetitive work, and a setup that doesn't take weeks to configure. What We Built ARK pulls every customer conversation — WhatsApp, Instagram, Messenger, email, live chat — into one inbox. On top of that, AI handles three things: Drafting replies based on conversation history and context Summarizing long threads so anyone on the team can jump in without reading 40 messages Routing conversations to the right person automatically based on topic or channel The goal wasn't to replace human support — it was to remove the busywork so the team can focus on actually helping people. Where We Are Now ARK is live with a 7-day free trial (auto-renews after that). We're still early, and we're shaping the roadmap based on real feedback from teams managing support across multiple channels. If you're dealing with the same multichannel chaos we were, I'd love to hear how you're handling it — and what's still missing from the tools you've tried. 🔗 https://byark.ai/

On June 9, 2026, Anthropic launched Claude Fable 5, a model designed for long-horizon tasks, but it was taken offline shortly after due to a U.S. government export directive. It shares architecture with Claude Mythos 5, supporting extensive token usage. The model includes mandatory data retention requirements, which have affected its deployment with partners like Microsoft. By Andrew Hoblitzell

Orbio announces $21 Million Series A in round led by Dawn Capital.

Startups are trying to "ride that SpaceX IPO wave."

Tech leaders debate whether the Anthropic episode is a wake-up call for India’s AI ambitions.

Token economics is not only about token price. It is about designing the rules, incentives, and long-term logic of a Web3 ecosystem. When people start building a Web3 project, they usually focus on the visible parts first. They think about the smart contract, the frontend, the wallet connection, the token launch, the whitepaper, and maybe the community. All of those are important. But there is one part that can decide whether the project survives or fails: Token economics. A project can have clean smart contracts, a nice UI, and strong marketing, but if the token economy is weak, the project can slowly collapse. Users may come only for rewards, early investors may dump, inflation may destroy value, and the token may lose its reason to exist. That is why token economics should not be treated as just a “crypto finance” topic. For developers and Web3 builders, token economics is closer to system design . It defines how value moves inside the ecosystem, how users are rewarded, how supply is controlled, how governance works, and how the project can grow without depending only on hype. What Is Token Economics? Token economics, often called tokenomics , means the design of how a token works inside a project. It answers questions like: Why does this token exist? Who receives the token? How is the token used? How many tokens will exist? How are rewards distributed? When can team and investor tokens unlock? How does the project treasury work? What creates real demand for the token? In simple words, token economics is the rule system behind a token. A token is not only something people buy and sell. In a real Web3 product, a token can be used for payments, staking, governance, access, rewards, collateral, or network fees. If the token has no clear role, it becomes only a speculative asset. That is dangerous because speculation can bring attention, but it cannot support a project forever. Why Developers Should Care Some developers think token economics is only for founders, eco

You can use your AirPods as a remote control for your iPhone's camera. Here's how.

Whether you want to buy cheaper electricity, store solar energy, or guard against outages, EcoFlow’s home battery might be just what you need.

The ruling holds that a company that designs, trains, operates, and manages an AI system must assume legal liability for any damages caused by the responses it generates.

Andrew Yang made a list of everything Americans overpay for — housing, food, wireless — and thinks the next startup gold rush is giving that money back.