Build Fast with AI @BuildFastWithAI

People keep saying AI can't build real games. Claude Mythos/Fable 5 just built a fully playable Bomberman clone - grid, bombs, blast radius, collision, enemy AI, animations - in a single prompt . No JavaScript frameworks. No game engines. Just HTML, CSS, and the right prompts. The bottleneck in AI-assisted development is no longer the AI. It's people who don't know how to use it. Prove me wrong.

@LLMSherpa aww so cute

Claude Fable 5 Max. One prompt. "Build Minecraft in HTML." One file. Zero dependencies. Runs in the browser. Procedural terrain. Block placement. First-person camera. The whole thing. Not "Minecraft-inspired." Not "Minecraft-like." Minecraft. In an HTML file. What are we even doing with this model.

Same prompt. Fable 5 vs Opus 4.8. One instruction that changes everything: "Performance optimization is not a priority. Maximize visual expressiveness." Fighter jet. Deep space. Afterburner glow. Heat distortion. Motion blur. Camera shake. Zero dependencies. One HTML file each. Both had similar outputs, Fable used WAY more tokens! Side by side output below. This is the benchmark that actually matters not a leaderboard score. What does it build when you let it go all in?

Claude Fable 5 just dropped - and it's already #1 on Code Arena Frontend. Score: 1,664. The next 6 spots? All Claude models too. Anthropic didn't just win the leaderboard. They own the top 7 rankings - above Qwen, GLM, Gemini, and GPT. For frontend devs: if you're not using Claude for UI/component work, you're leaving performance on the table. What's your go-to model for frontend code right now?

Claude Fable 5. One prompt. A scroll-driven cinematic journey from Earth to Mars. Six chapters. Full 3D planets. Deep space nebulae. Procedural textures. Zero external assets. One HTML file. The last chapter title: "The sky is not the limit. It's the road." Fable 5 didn't just write the code. It wrote the story first.

@mhdfaran its over every other week nowadays

@VadimStrizheus nah it did NOT replace anything, it doesnt really give you full control like editors give you and also the rate limits are so bad

@c_nemri its good for making prototypes , but the rate limits are horrible

@TesanaAI i mean yes it is good but its not really playable , its good for a prototype tho

Claude Fable 5 one-shotted a retro macOS experience. Fully polished. Period-accurate UI. First try. This is the third time I've seen Fable 5 produce something in one shot that would've taken a developer hours to build manually. 3D floating worlds. Working OS interfaces. Interactive experiences. One prompt each. The real benchmark isn't the leaderboard score. It's what it builds on the first ask.

Claude @claudeai

2 days ago

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

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One prompt. @claudeai Fable 5 built Skyhaven - a fully interactive 3D floating island world. Drifting islands, dynamic lighting, explorable terrain. Running live in your browser right now. Not a screenshot. Not a demo video. Open the link and walk around in it. This is what "one prompt" means in 2026.

15-second dark fantasy battle. Seedance 2.0. Zero post. Zero upscaling. The spark and mud detail you're seeing? Straight model output. The unlock wasn't resolution. It was prompt language. Low sharpness + practical effect aesthetic hides AI telltales better than 4K ever could. Film grain, handheld shake, grit - all prompt-native. My prompt ends at "monster crashes to the ground." Seedance wrote the rest.

Anthropic just dropped Claude Fable 5. 80.3% on SWE-Bench Pro. 88.0% on Terminal-Bench 2.1. 85.0% on computer use. 78.0% on cybersecurity. Every number in that benchmark table beats GPT-5.5 and Gemini 3.1 Pro. By a lot. Not marginally. Andrej Karpathy called it a "major-version-bump-deserving step change." That's not hype — that's the person who built GPT-2's training infra. The one thing the benchmarks don't show: Fable 5 is the Mythos-class model made safe for general use. Meaning the underlying capability was already built. Anthropic spent the extra time on safeguards, not on scaling. That's a different philosophy than every other lab shipping right now. Free for Pro and Max users until June 22. What's the first complex task you're running through it?

Google's new translation model doesn't wait for you to finish speaking. It translates continuously - staying a few seconds behind you in real time, preserving your tone, pacing, and pitch in the output. 70+ languages. Auto-detects. No setup. Grab is testing it for driver-passenger calls. 10 million voice calls per month on that platform alone. Rolling out today on Google Translate, Google Meet, and the Gemini Live API. The real-time language barrier is basically solved now.

Google @Google

2 days ago

For over 20 years, we've dedicated ourselves to removing language barriers so people can learn, speak and connect more deeply than ever before. Today, we’re taking our next step with the release of Gemini 3.5 Live Translate — our latest audio model for live, speech-to-speech

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github.com/argahv/sisyphu…

Someone built a swarm of 20+ AI agents that writes publication-ready research papers. 0% citation hallucination rate. Zero. Verified against 2+ academic APIs per reference. GPT-4 hallucinates citations 30-40% of the time. This system won't let a single unverified reference through. The architecture is wild: 5 literature scouts search in parallel → 500+ papers per run 6 novelty engines generate 50+ hypotheses 10 adversarial reviewer personas all must approve 41 humanizer patterns strip AI writing signatures LaTeX PDF with verified BibTeX at the end The 6 novelty engines are the real moat: → The Contrarian — inverts every established claim → The Cross-Pollinator — imports solutions from astrodynamics, epidemiology, 15th-century shipbuilding → The Assumption Excavator — finds unstated assumptions → The Counterfactual Generator — rewrites the field's history without the most-cited papers → The Paradox Sifter — finds ignored contradictions → The Heretic — generates 50 wild hypotheses from title + abstract alone, finds "the paper it should've been" The quality gates are hard stops. All must pass: Citation verification - found in 2+ sources or rejected Statistical audit - every p-value validated AI-pattern detection - under 2 violations per 1000 words Style audit - zero em dashes, voice matches your writing Adversarial review - all 10 personas must say yes The live example: a 13-page paper, 26 verified citations, 3 publication-ready figures, 0 hallucinated references, 0 AI-pattern violations, 4 revision rounds.

Every enterprise AI demo looks the same. LLM answers curated questions. Generates clean SQL. Stakeholders are impressed. Project gets greenlit. Then production arrives. The things that kill it have nothing to do with the model: → Schema mapping across fragmented systems → What does "revenue" actually mean for THIS team? → Which join path is allowed vs dangerous? → Which fields should never be used for a KPI? → How do you audit why a query was approved? → How do you handle schemas that change monthly? → How do you maintain trust once real decisions depend on the output? Every engineering leader I've spoken to says the same thing: "The AI model part was the easiest layer. The infrastructure underneath became the real project." The demo proves the model can produce plausible SQL. Production needs the system to know when SQL is actually safe, correct, and business-valid. Those are completely different problems. The one principle that reframes everything: I'd rather have an agent say "I don't know the metric definition" than confidently answer with the wrong grain or the wrong join. Confident and wrong is worse than uncertain and honest. At enterprise scale, confident and wrong is a liability. The real project isn't the model layer. It's the semantic + policy + eval layer underneath. Has anyone actually seen an internal AI analytics layer scale cleanly in production?