Most of the "best practices" floating around right now are already outdated. Opus 5.5 changed enough that your old prompts are quietly costing you more than they should. Here are the shifts worth knowing: On effort levels ➤Start at medium, not high. It's now the sweet spot between speed and quality for most tasks. ➤Don't guess, test. Run the same task at low, medium, and high once and see what your workflow actually needs. ➤You can now switch effort mid-conversation without the model re-reading the whole chat from scratch. That used to be expensive, now it's just a setting change. On instructions and memory ➤Claude Code now reads a single agents.md file. No more juggling between claude.md and agents.md to keep in sync ➤In long chats, tell the model to treat earlier answers as settled. Otherwise it keeps re-checking old decisions instead of moving forward. ➤Have it keep a running checklist for multi-step tasks. Example: think of it like a to-do list taped to the model's desk, it stops it from marking a task "done" when it's actually 80% done. On pacing and cost ➤Give it a time budget. Saying "finish this in 3 minutes" actually changes how it paces the work. ➤No specific budget in mind? Just add "time matters." It speeds things up on its own. ➤Check your settings for free usage resets that came with the 5.5 launch, easy to forget, easy money left on the table. On design and images ➤Give it an actual design system or brand guideline. Without one, it defaults to generic, forgettable output. ➤For image-heavy tasks, let it crop and zoom using tools like PIL or OpenCV instead of processing the whole image. Smaller, sharper crops mean fewer tokens burned. ➤One more thing to know: asking it to "show its reasoning" may get flagged or refused more often now, so build that into how you test outputs. Which one of these are you not doing yet, the checklist, the time budget, or the effort testing?

A new AI model launched this week that can't write a single sentence That's the whole point It's called Jev, from a startup called TypeSafe AI, built by one of the people who helped train ChatGPT Every model we talk about lately, GPT-6 Astra, Fable 5.1, is built to think out loud and answer you like a person Jev does the opposite. You feed it information and a bunch of quick questions. It just replies with instant decisions and how confident it is. No paragraphs, no explanations For example: Ask an AI Agent "is this email urgent?" → A normal AI writes you three lines explaining why. → Jev just says "yes, 92% sure" in under half a second. Why people are paying attention: - Speed: replies in 70-500 milliseconds - Cost: almost free per response, since there's barely any output to pay for - Claim: up to 190x faster and 400x cheaper than regular AI models on tasks like sorting, tagging, or picking what to do next - Adoption: became the fastest-adopted model on Vercel's AI gateway within three days It's not here to replace GPT or Claude for writing or reasoning. It's meant to sit next to them, handle the boring quick decisions, and let the expensive smart model only step in for the hard stuff. If you're building AI agents, would you trust a model that never explains itself, only gives you a number?

Verified — and this shipped for real on Aug 27, not just a leak. Here's the short post: Claude Desktop just got its own browser — and Cowork can drive it The leak from two weeks ago just shipped. This one actually changes what your agent can do 👇 What's live: a built-in browser inside the Claude Desktop app that Cowork can use directly. No Chrome extension, no permissions dialog, no dependency on Google. The key details: → It's isolated — runs separately from your personal browser. Claude isn't touching your everyday Chrome profile. → Login handled two ways: manually, or import cookies site by site. Banking, email, and SSO sites stay UNCHECKED by default. Smart default. → The browser lives in the desktop app, so it needs to stay open — but you can steer the session from web or mobile while it runs. → It opens automatically when a task needs it. You don't launch it, Claude does. My take: This is the "super app" play. Anthropic is quietly making Claude the one window you never close — chat, agent, files, and now web, all in one place. OpenAI shipped a cloud browser for ChatGPT Work days earlier. Everyone's converging on the same shape. But the interesting bit is the isolation choice. By keeping the browser separate from your real profile, Anthropic traded convenience for safety — Claude can't act inside your logged-in Notion or Gmail unless you deliberately import that cookie. Given that browser agents are a live prompt-injection target, that's the right call, even though it means more setup. For builders and operators: This unlocks web automation for tools with no API. Vendor portals, internal dashboards, Search Console, lead research, anything that only exists behind a login. Cowork can now log in and work there as part of a scheduled task. That's a real category of client work that just got automatable. Think invoice collection across vendor portals, weekly reporting pulls, competitor monitoring — jobs that used to need a human clicking or a fragile scraper.

Hello. I am Aaron, and I lead a five-person team based in the Philippines. We have previously generated revenue by securing projects from clients and agencies in the U.S., Latin America, and Europe through platforms such as Upwork, Freelancer, and Toptal. However, as you are likely aware, the rise of AI agents and the spread of corporate automation have caused many developers to lose their jobs and flood the freelance market, thereby shrinking the market space available to us. Consequently, our revenue has not only gradually declined over the past few years but has also become significantly more volatile. Furthermore, earnings for developers in Asia remain substantially lower than those for their counterparts in the U.S., Latin America, and Europe. There has also been a marked decrease in the number of companies and clients seeking Asian developers, as the pool of available freelancers from Europe, the U.S., and Latin America has expanded. While we could manage this disparity in the past when work was plentiful, the current situation is vastly different. Recognizing that our existing approach can no longer guarantee stable, long-term income, I have been exploring new strategies. Please see below for further details. 👉https://docs.google.com/document/d/1LI7Pm-BTuFDtSUEJyHWh1IYstdFLmHqYvd8CvDZP4bM/edit?tab=t.0

Last week I watched my usage bar hit its limit halfway through a single feature build Not because the model was bad I was running Opus mostly, for the good stuff But somewhere between the third file read and the fifth tool call, the context window had ballooned so much that half the tokens were just... the agent re-reading itself That bugged me enough to go digging Why does an agent that's supposedly smart still burn so much just finding the right file to edit? Turns out most tools solve this with vector search Code gets turned into numbers, and the agent matches similar-looking chunks Sounds reasonable until you realize "send an invoice" and "refund an invoice" look nearly identical to a vector search One collects money, the other gives it back The math doesn't know the difference So I started testing something called Graph instead. No vector search. One command, graft build, and it maps the entire project: how every file, function, and dependency connects to everything else The agent reads that map instead of guessing its way through grep. I ran it on one of my own client projects to see if the difference was real: - First build barely moved the needle, maybe a few minutes faster - The next change, a full landing page revamp, took under 2 minutes - The map updates itself too, only the changed parts get remapped, not the whole project That last part is what actually convinced me to use it more If you're running Claude Code or Codex on daily basis, has your agent ever felt like it was reading the same file for the fifth time? If YES, then checkout - CLICK HERE