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Adapted Matt Shumer 's prompt for a different genre, game engine (Unity) & for a shorter while. Still impressive output. The major thing here is the verification loop it does - the sub-agents critiquing the output is the key. 3D assets were fully generated using the Unity Editor -... show more

Chong-U

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21,753 次观看 • 2 个月前 •via X (Twitter)

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A Closer Look at Alchemist AI v3.5🔎 In v1, one agent handled everything. You’d enter a prompt like “make a snake game” and get a single block of code—one input, one output. v2 introduced multiple agents with specialized roles: prompt refinement, frontend, backend, and review. It brought more structure, but the process was still linear. Each step produced a single output. With v3, a single agent could handle multiple tasks. It generated HTML, CSS, JavaScript, searched for assets, and more—all in one go, while maintaining full context. Now in v3.5, we combine both models. Multiple agents work in parallel, each capable of producing multiple outputs at once. The prompt compiler adapts based on what you're building. A 3D game? It prioritizes game engines, rendering, and mechanics. A website or 2D app? It shifts focus to relevant frameworks and tools. The tool handler can call several services simultaneously, feeding context-aware data into the code generator for real-time execution. From single-output generation to parallel, adaptive workflows. In the next breakdown, we’ll dive into the architecture behind our proprietary engine in v3.5.

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Coloso Global

14,207 次观看 • 29 天前

You're still typing every turn The guy who built the tool wrote the loop that types them for him "I don't prompt Claude anymore I write loops that prompt Claude" A loop takes four shapes turn-based, you close it each round goal-based - all tests pass, stop after 5 tries time-based - every 10 min, fix what's red, overnight proactive - CI breaks, agents fix it and open a PR, no human at all A judge closes the loop, not you The prompt was never the unit of work the loop is Full breakdown below

You're still typing every turn The guy who built the tool wrote the loop that types them for him "I don't prompt Claude anymore I write loops that prompt Claude" A loop takes four shapes turn-based, you close it each round goal-based - all tests pass, stop after 5 tries time-based - every 10 min, fix what's red, overnight proactive - CI breaks, agents fix it and open a PR, no human at all A judge closes the loop, not you The prompt was never the unit of work the loop is Full breakdown below

Skaly_Bull

12,558 次观看 • 1 个月前

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING OPUS 5 This is the most important prompting guide for anyone using Claude. Bookmark this before you forget. Most people still prompt Opus 5 like older models. That's the mistake. > give the complete task upfront — don't drip-feed instructions. > explicitly ask for shorter responses if you want them. > use low/medium effort by default. increase effort only when needed. > let it finish the entire feature instead of asking for partial implementations. > use subagents intentionally for large codebases, not every task. Opus 5 is at its best when you stop micromanaging it and let it own the work. Read it before writing your next prompt. Claude → Opus 5 → Agentic Execution → Production-Ready Output → Money

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING OPUS 5 This is the most important prompting guide for anyone using Claude. Bookmark this before you forget. Most people still prompt Opus 5 like older models. That's the mistake. > give the complete task upfront — don't drip-feed instructions. > explicitly ask for shorter responses if you want them. > use low/medium effort by default. increase effort only when needed. > let it finish the entire feature instead of asking for partial implementations. > use subagents intentionally for large codebases, not every task. Opus 5 is at its best when you stop micromanaging it and let it own the work. Read it before writing your next prompt. Claude → Opus 5 → Agentic Execution → Production-Ready Output → Money

Rahul

407,371 次观看 • 2 个月前

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING FABLE 5. This is the most important prompting framework I've seen. Bookmark this before you forget. Most people treat Fable 5 like a chatbot. That's the mistake. > don't over-engineer prompts — it degrades output. > use /loop for autonomous multi-step work. > give it the goal, not step-by-step commands. > add a memory file. it learns from past runs. > spin up 50+ subagents for complex tasks. Fable 5 isn't an assistant. it's a consultant that leads the work. Read it before you write another prompt. Claude → Fable 5 → Autonomous Work → Real Output → Money

ANTHROPIC JUST DROPPED THE OFFICIAL GUIDE TO PROMPTING FABLE 5. This is the most important prompting framework I've seen. Bookmark this before you forget. Most people treat Fable 5 like a chatbot. That's the mistake. > don't over-engineer prompts — it degrades output. > use /loop for autonomous multi-step work. > give it the goal, not step-by-step commands. > add a memory file. it learns from past runs. > spin up 50+ subagents for complex tasks. Fable 5 isn't an assistant. it's a consultant that leads the work. Read it before you write another prompt. Claude → Fable 5 → Autonomous Work → Real Output → Money

Kirill

416,512 次观看 • 3 个月前

This astronaut on an open lunar rover is a useful test case for **GPT-6 Astra × V2Fun** decomposition. The character silhouette, props or mechanics, materials, and pose relationships all need to match the prompt. Astra can reason about the structure and plan the components, while V2FUN handles the harder geometry and surface details. Fewer low-level tokens, faster iteration, cleaner assets. #V2Fun #GPT6 #Astra #AI3D #3DAsset

This astronaut on an open lunar rover is a useful test case for GPT-6 Astra × V2Fun decomposition. The character silhouette, props or mechanics, materials, and pose relationships all need to match the prompt. Astra can reason about the structure and plan the components, while V2FUN handles the harder geometry and surface details. Fewer low-level tokens, faster iteration, cleaner assets. #V2Fun #GPT6 #Astra #AI3D #3DAsset

Jackson

13,983 次观看 • 19 天前

For Anthropic’s Claude Design, your opening prompt sets the ceiling of the project. If your prompt is a comprehensive brief, it will land somewhere near production-ready Seems obvious, but here is something interesting we observed: one designer wrote: >16 prompts >climbed in specificity over the session >and still finished worse than they started A single "reconsider" prompt at iteration 2 tanked output quality by -0.7 and the session never recovered. 2/3 of every prompt typed across all 5 sessions was refinement or correction. TLDR: Your first move is the whole game. and prompting is an art. What a time to be alive.

For Anthropic’s Claude Design, your opening prompt sets the ceiling of the project. If your prompt is a comprehensive brief, it will land somewhere near production-ready Seems obvious, but here is something interesting we observed: one designer wrote: >16 prompts >climbed in specificity over the session >and still finished worse than they started A single "reconsider" prompt at iteration 2 tanked output quality by -0.7 and the session never recovered. 2/3 of every prompt typed across all 5 sessions was refinement or correction. TLDR: Your first move is the whole game. and prompting is an art. What a time to be alive.

ben

229,536 次观看 • 4 个月前