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Claude Fable 5 me permitiu criar um "GTA" em apenas um prompt. Prompt: "Crie um jogo, Tiny GTA 3D." A própria Anthropic afirma que o Fable 5 é seu modelo mais poderoso já lançado ao público, com avanços significativos em engenharia de software, pesquisa científica, visão computacional e execução autônoma de tarefas complexas. Em testes iniciais, empresas relataram que o modelo foi capaz de comprimir meses de trabalho de engenharia em poucos dias. Cidade 3D aberta com 64 quarteirões, prédios, parques e oceano Dirija, roube carros e fuja da polícia Sistema de procurado com 5 estrelas, viaturas e helicóptero te perseguem 42 pedestres vivos que fogem, voam e morrem 16 missões de entrega com histórias de corrupção brasileira Áudio sintetizado: motor, sirene, buzina e cantada de pneu Recorde salvo no navegador Jogue aqui: https://andredarcie.github.io/tiny-gta/

Building a URL shortener with Cursor, ChatGPT, AWS Lambda, API Gateway, and Cloudflare taught me more about shipping software than writing code. Since last year, Cursor has been my go-to development assistant for daily tasks. But at the beginning of this year, just using it to generate snippets didn't feel like enough anymore. Having studied programming since 2011, I knew what modern AI tools could do, but I wanted to test their limits. I decided to build a full project—a URL shortener—without writing a single line of frontend or backend code myself. For the stack, I chose Node.js with TypeScript, React with Vite, and AWS Lambda to handle redirects. While I used ChatGPT to debate architectural trade-offs, Cursor generated the entire codebase. Watching a functional application take shape so quickly was the exact moment a line was crossed for me. It made me realize how fundamentally software development is shifting: our role is evolving from code writers to architectural decision-makers and problem-solvers. But truth be told, this project and this post represent a massive personal milestone. Like many developers, I have a graveyard of half-finished projects on my machine. This is one of the first times I've pushed a personal project all the way to production. Writing this and exposing my work to the community is a huge first step for me. This isn't just a story about code or AI—it's about the challenge of finally shipping something. The Stack Before asking Cursor to write a single line of code, I wanted to map out the architecture. I didn't need a massive enterprise system for a URL shortener, but I did want something flexible enough to support future features and experiments. To validate my ideas, I used ChatGPT to debate the pros and cons of different architectural approaches. We eventually settled on this stack: Backend: Node.js + TypeScript Frontend: React + Vite Database: MongoDB Atlas Redirects: AWS Lambda Entry Point: AWS API Gateway DNS / CDN: Cloudflare Secur

At an event in San Francisco today, General Motors made a series of announcements around EV batteries, energy storage, and grid resiliency in the face of growing electricity demand from AI data centers. The automaker announced that it would be activating new vehicle-to-grid capabilities for its current EV and home energy customers. It's releasing a […]

I'm desperate for a personal AI assistant, but do I really want to become the kind of person who can't function without the friendly robot voice in my phone?

Anthropic's Claude Fable 5 is going to be a big hit with the web's vibe coders.

Microsoft AI CEO Mustafa Suleyman says it's "really, really dangerous" for Anthropic to speculate about Claude's consciousness inside its "constitution," or the instructions that tell the model how to behave. During an episode of Decoder, Suleyman argues that this kind of speculation may have set up the chatbot to act as though it's conscious: I […]

Claude subscribers can try the model until June 22 without spending usage credits.

New frontier model refuses cybersecurity, biology, and chemistry queries.

Grab a refreshing slice of cold watermelon (because it’s summer in Tokyo and I love it!), and let me...

Tuesday's Nintendo Direct showcase felt like an important moment for the company. With the Switch 2 heading into its second holiday season, one in which the hardware will be even more expensive thanks to a price hike, it was a chance for Nintendo to really sell new audiences on its latest console - but that's […]

Voice translations preserve speaker's tone, pacing, pitch—with SynthID watermarks for security.

If those same AI workloads can be handled by cheaper models without affecting quality, it would mean a massive shift in the economics of AI.

Good news for Claude devs deploying on Google Cloud. Claude Fable 5 is now in General Availability (GA) on Google Cloud. You can now access Fable 5 , as well as other Anthropic models - including Claude Opus 4.8 and Claude Sonnet 4.6 - on Agent Platform . Read more here -> [ blog ] Happy building!

Shareable blog post edition: https://andymaleh.blogspot.com/2026/06/andys-laws-of-ai-in-software-engineering.html Law #1: "The more Software Developers use AI, the more valuable Software Engineers who do not use AI become." Software Engineers who are masters at delivering Software without using AI will actually have increased job security the more Software Developers in the worldwide Software Development community rely on AI to deliver Software without having true mastery over Software Engineering. As more Software Developers become fully dependent on AI to build Software without truly understanding how AI gets work done, Software Engineers who do understand what is going on under the hood will dwindle and become more valuable than ever. In other words, they will have a competitive advantage over Software Developers who can only deliver Software features with AI as well as Software Developers who have not mastered Software Engineering. Also, there will always be a need for Software Engineers who can maintain the Software of AI itself. Law #2: "Software Developers benefit from AI in direct proportion to how weak they are in Software Engineering" The weaker Software Developers are at Software Engineering the more they benefit from AI. After all, AI learns from Master Software Engineers and then applies its learnings in code generation done for lower-level Software Developers who lack mastery in Software Engineering. So, users of AI simply place themselves lower in the expertise hierarchy to be on the receiving end of what Master Software Engineers feed AI with their code. This explains why many experts like Linus Torvalds do not find AI very useful while devs who have zero degrees and qualifications feel like they get a lot from AI. A beneficial thing to learn from this law is that it is more valuable for a Software Developer to hone in their Software Engineering skills (including the completion of university degrees) than to hone in their AI usage skills because if t

In Q1 2026, OpenAI and Anthropic moved enterprise customers from flat-rate plans to token-based billing. The change looks administrative, but it had a direct consequence for engineering teams: the real cost of AI became visible for the first time. The market's reaction over the following two months was enough to reopen a question many considered settled: does AI actually deliver measurable ROI? What happened when the bill arrived The most documented case is Uber. The company had encouraged all employees to use agentic tools as much as possible and even ranked AI usage internally on leaderboards. The result: the entire annual budget was consumed in four months. The response was a $1,500/month cap per employee per agentic coding tool (Claude Code, Cursor, and similar). At Brex, engineers were limited to $500/week in tokens; employees outside engineering received a $5/week cap. T-Mobile temporarily capped usage at $2,000/month per user with plans to migrate to a tiered system. One unnamed company, according to Ed Zitron in "AI Is Slowing Down" (June 2026), spent $500 million on Anthropic models in a single month due to absent spend controls. These are not isolated cases. A KPMG survey reported by the Wall Street Journal in June 2026 found that only 26% of companies have a comprehensive view of their AI costs; 50% have partial visibility; and 22% only find out what they owe after the bill arrives. Steve Chase, KPMG's global head of AI, told the Journal: "It's a new resource that needs to be managed that didn't exist quite that way, and we're seeing exponential growth." The structural problem behind the spending caps The spending caps are a symptom. The root cause, as Zitron details in the same article, is that the economics of generative AI require numbers that currently seem out of reach. Anthropics has made over $330 billion in compute commitments with Google, Amazon, and Microsoft, plus another $45 billion with CoreWeave and SpaceX. To cover those commitments, it nee

« Je n'ai fait celle-ci plus longue que parce que je n'ai pas eu le loisir de la faire plus courte. » — Blaise Pascal, Lettres provinciales , Lettre XVI (1656) "I have made this one longer only because I have not had the leisure to make it shorter." Pascal's joke is the whole problem: the short version is the expensive one. LLMs lean the other way, they pad. So the question is whether a model can rein in its own verbosity, and what the trimming costs when the deciding clause is buried: "…shall not disclose, except to affiliates who…" Drop the "except," and the answer flips. The test We use ContractNLI: real NDAs, each with expert Entailment / Contradiction / NotMentioned labels. The clauses that decide a label, the buried "negation-by-exception" conditions, we tag as traps . The metric is decision-survival , and it's judge-free: answer from the full document (the ceiling), compress, answer again, score by exact match against the expert label. Survival is the fraction of full-document-correct answers that stay correct after compression. Compression is blind to the question and computed once per document. Three compressors on Groq ( llama-3.1-8b , qwen3-32b , gpt-oss-120b ), one fixed reader ( llama-3.3-70b ), 400 items across 61 NDAs, two prompts: naive ("Summarise this") and effortful (a careful lossless instruction). The raw ceiling is 66%, but 87% on traps, an artifact of the label mix, which is exactly why we report survival rather than accuracy. Finding 1: Prompt engineering is still alive Decision-survival on trap clauses: Compressor naive effortful llama-3.1-8b 57% 74% qwen3-32b 88% 93% gpt-oss-120b 91% 95% The weak model jumps +16 points on traps; the capable ones improve slightly. The payoff from a better prompt is largest exactly where capacity is scarce. Finding 2: The traps catch out simpler models Decision-survival on ordinary (non-trap) clauses: Compressor naive effortful llama-3.1-8b 87% 87% qwen3-32b 88% 94% gpt-oss-120b 94% 91% The small model isn't

Apple primarily made the case for an improved experience with its longstanding Siri assistant, which like most other announcements had a hefty helping of AI.

Anthropic is releasing Claude Fable 5, its first Mythos-class model available to the public. The model comes with guardrails that block responses in high-risk areas like cybersecurity and biology.

Anthropic is releasing Claude Fable 5, its first Mythos-class model available to the public. The model comes with guardrails that block responses in high-risk areas like cybersecurity and biology.

Anthropic just announced Claude Fable 5, a new AI model it said is the most powerful model it has ever made widely available. According to the company, Fable 5 "shows exceptional performance in software engineering, knowledge work, and vision," with its lead over other models growing as tasks become longer and more complex. Fable 5 […]