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Claude Coders: run this Opus 5.5 instruction cleanup prompt NOW. '/claude-api prompt-audit' Optimizes your skills, AGENTS.md, CLAUDE.md and removes anti-patterns that restrict Opus 5.5. The results are amazing. Best 5 mins you'll spend. From Lance Martin.

Dan McAteer

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497,427 次观看 • 3 天前 •via X (Twitter)

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omg…that i made this with Claude Opus 5.5 and Seedance 2.5 nobody is teaching you the actual workflow behind these AI realistic videos.. so here it is, full breakdown, every prompt included 👇

omg…that i made this with Claude Opus 5.5 and Seedance 2.5 nobody is teaching you the actual workflow behind these AI realistic videos.. so here it is, full breakdown, every prompt included 👇

Abdul Shakoor

248,629 次观看 • 4 天前

WTF? Made with claude OPUS 5.5 Prompt "make a dynamic 15-second motion graphics video that shows what an incredible motion designer you are, like it's your showreel for a résumé. go all out." That's it..

WTF? Made with claude OPUS 5.5 Prompt "make a dynamic 15-second motion graphics video that shows what an incredible motion designer you are, like it's your showreel for a résumé. go all out." That's it..

Ajith

523,124 次观看 • 2 天前

Claude Opus 5 just made the best Remotion video I have ever seen. Perfect transitions. Music timing locked to the frame. Branding nailed without me asking. Fable 5 held this crown for months. Opus 5 took it in one prompt. Opus 5 significantly outperforms Fable 5 at marketing videos, and it is not close. Watch this and remember Opus 5 made it.

Claude Opus 5 just made the best Remotion video I have ever seen. Perfect transitions. Music timing locked to the frame. Branding nailed without me asking. Fable 5 held this crown for months. Opus 5 took it in one prompt. Opus 5 significantly outperforms Fable 5 at marketing videos, and it is not close. Watch this and remember Opus 5 made it.

BridgeMind

188,558 次观看 • 2 个月前

Salute to the Qwen team 🫡 We tested Qwen 3.7-Max, Gemini 3.5 Flash, GPT-5.5, and Claude Opus 4.7. The biggest shock came from Qwen. In less than a month (3.6 Max dropped April 20), Qwen went from the worst multimodal output on our sakura tree test, barely keeping up with Gemini, GPT, and Claude , to matching Gemini 3.5 Flash frame for frame on this soccer test, and outperforming GPT-5.5 and Claude Opus 4.7. It rendered a perfectly proportioned soccer player and the most lifelike ball in the entire test. Remarkable spatial reasoning. Also: Gemini 3.5 Flash is now faster than GPT-5.5, which used to be the fastest in our past tests.

Salute to the Qwen team 🫡 We tested Qwen 3.7-Max, Gemini 3.5 Flash, GPT-5.5, and Claude Opus 4.7. The biggest shock came from Qwen. In less than a month (3.6 Max dropped April 20), Qwen went from the worst multimodal output on our sakura tree test, barely keeping up with Gemini, GPT, and Claude , to matching Gemini 3.5 Flash frame for frame on this soccer test, and outperforming GPT-5.5 and Claude Opus 4.7. It rendered a perfectly proportioned soccer player and the most lifelike ball in the entire test. Remarkable spatial reasoning. Also: Gemini 3.5 Flash is now faster than GPT-5.5, which used to be the fastest in our past tests.

GMI Cloud

75,785 次观看 • 4 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

New Claude Sonnet 5 performs at GPT 5.5 level 6x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics crash demos Prompts: - A car crashes into a brick wall - A wrecking ball destroys a house - A catapult throws a rock at a castle wall Outputs: Sonnet 5: 15,047 tokens, $0.15 Opus 4.8: 23,063 tokens, $0.58 Sonnet 4.6: 25,824 tokens, $0.39 GPT 5.5: 31,152 tokens, $0.94 Sonnet 5 did as well as Opus 4.8 and GPT 5.5 on all three tests. In the wrecking ball test, it beat Opus 4.8. The cable moves smoothly and every hit connects. In the catapult test, it beat GPT 5.5. The rock always lands inside the wall. Sonnet 5 still needs better detail and graphics. But it used fewer tokens than every other model

New Claude Sonnet 5 performs at GPT 5.5 level 6x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics crash demos Prompts: - A car crashes into a brick wall - A wrecking ball destroys a house - A catapult throws a rock at a castle wall Outputs: Sonnet 5: 15,047 tokens, $0.15 Opus 4.8: 23,063 tokens, $0.58 Sonnet 4.6: 25,824 tokens, $0.39 GPT 5.5: 31,152 tokens, $0.94 Sonnet 5 did as well as Opus 4.8 and GPT 5.5 on all three tests. In the wrecking ball test, it beat Opus 4.8. The cable moves smoothly and every hit connects. In the catapult test, it beat GPT 5.5. The rock always lands inside the wall. Sonnet 5 still needs better detail and graphics. But it used fewer tokens than every other model

atomic.chat

729,349 次观看 • 2 个月前

Blender MCP w/ Codex 5.5 or OPUS 4.8 is seriously pretty cracked... This entire block out was one-shot with a single prompt in about 15 minutes. Only problem I am having now is getting Seedance to use it properly 😅 Please feel free to use it and post your results below 👇

Blender MCP w/ Codex 5.5 or OPUS 4.8 is seriously pretty cracked... This entire block out was one-shot with a single prompt in about 15 minutes. Only problem I am having now is getting Seedance to use it properly 😅 Please feel free to use it and post your results below 👇

A.I.Warper

24,894 次观看 • 3 个月前

this is f**king dangerous someone figured out how to make Opus 4.8 run on Fable 5's brain with one prompt access to the best model is never guaranteed. It disappeared once already this year. but you can use it forever. here's how: 1. ask Fable 5: "write the operating manual your replacement will run on" (procedures, failure modes, a 5-question self-test) 2. save the output as one .md file and drop it into a new Claude Project as the project instructions 3. switch to Opus 4.8 and now your everyday model runs off the smart one's method, no top-tier price save and bookmark this no matter what full extraction prompt is in the article below: ↓

this is f**king dangerous someone figured out how to make Opus 4.8 run on Fable 5's brain with one prompt access to the best model is never guaranteed. It disappeared once already this year. but you can use it forever. here's how: 1. ask Fable 5: "write the operating manual your replacement will run on" (procedures, failure modes, a 5-question self-test) 2. save the output as one .md file and drop it into a new Claude Project as the project instructions 3. switch to Opus 4.8 and now your everyday model runs off the smart one's method, no top-tier price save and bookmark this no matter what full extraction prompt is in the article below: ↓

Hamza Khalid

32,581 次观看 • 2 个月前

LongCat performed Opus 4.8 and GPT 5.5 level on real physics tasks for $0! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics Prompts: - A cannon demolishing a brick wall - A bowling ball knocking down the pins - A tornado that sucks in random objects Outputs: LongCat: 18,015 tokens, $0.00 Opus 4.8: 18,872 tokens, $0.48 GPT 5.5: 32,588 tokens, $0.98 GLM 5.2: 31,062 tokens, $0.09 On the physics LongCat came out ahead of Opus 4.8 and GLM 5.2 - cleaner collisions, nothing clipping or falling through. On detail and rendering it matched GPT 5.5, the best looking of the four. Getting this quality for free is wild!

LongCat performed Opus 4.8 and GPT 5.5 level on real physics tasks for $0! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics Prompts: - A cannon demolishing a brick wall - A bowling ball knocking down the pins - A tornado that sucks in random objects Outputs: LongCat: 18,015 tokens, $0.00 Opus 4.8: 18,872 tokens, $0.48 GPT 5.5: 32,588 tokens, $0.98 GLM 5.2: 31,062 tokens, $0.09 On the physics LongCat came out ahead of Opus 4.8 and GLM 5.2 - cleaner collisions, nothing clipping or falling through. On detail and rendering it matched GPT 5.5, the best looking of the four. Getting this quality for free is wild!

atomic.chat

105,523 次观看 • 2 个月前

claude opus 4.7 just dropped so i automated my ENTIRE cold email system that books 80 calls/month just put together 30 pages on the full AI playbook - AI writes my scripts (better than i can) - AI researches and personalizes leads - AI replies to interested leads 24/7 - AI optimizes campaigns from the data - every prompt i use for everything this wouldve saved me 2 years of trial and error like + comment "OPUS" and i'll send it over (must follow + RT for priority access)

claude opus 4.7 just dropped so i automated my ENTIRE cold email system that books 80 calls/month just put together 30 pages on the full AI playbook - AI writes my scripts (better than i can) - AI researches and personalizes leads - AI replies to interested leads 24/7 - AI optimizes campaigns from the data - every prompt i use for everything this wouldve saved me 2 years of trial and error like + comment "OPUS" and i'll send it over (must follow + RT for priority access)

James Shields

25,571 次观看 • 5 个月前

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5

YanXbt

16,744 次观看 • 2 个月前

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

I tested Kimi K3 vs Claude Opus 4.8 Same prompt, an armory bay with lighting, props, and detail. Top is Kimi K3, bottom is Opus 4.8. It's not even close. Kimi K3 built a full scene with textures, proper lighting, ammo crates, weapon racks, working detail everywhere. Opus 4.8 gave me a near empty room with a couple of floating tables. No doubt it beats Opus 4.8. Kimi K3 is Fable 5 level, and it's clearly better than GPT-5.6 Sol at 3D and games. An open weight model just matched the best closed models on the market. Let that sink in.

Bhavy☄️

479,495 次观看 • 2 个月前

This is wild, I just gave Kimi K3, Grok 4.5, GPT 4.6 Sol, and Claude Opus 5 a starting cue, then asked them to finish the drawing themselves I also told them to be CREATIVE in their own way These are the results, and ngl I genuinely can't decide which one hits the best

This is wild, I just gave Kimi K3, Grok 4.5, GPT 4.6 Sol, and Claude Opus 5 a starting cue, then asked them to finish the drawing themselves I also told them to be CREATIVE in their own way These are the results, and ngl I genuinely can't decide which one hits the best

Ann Nguyen

419,220 次观看 • 1 个月前

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

BUILD KARPATHY'S SECOND BRAIN WITH CLAUDE OPUS 5 + OBSIDIAN Andrej Karpathy (OpenAI co-founder) shared an architecture that turns Claude into a persistent second brain not just a basic chat window how it works: > point Claude Code at an Obsidian vault folder > drop articles, PDFs, or video transcripts into raw > Claude reads them, updates summaries, cross-references everything > the knowledge base compounds like interest, never resets on a new chat the setup: > install create a local vault folder > open it in Claude Code, paste Karpathy's wiki prompt > > let the agent build raw, wiki, and CLAUDE.md folders > drop any file into raw, tell it to ingest > ask questions, get compiled summaries back this skips rag overhead entirely. your vault stays organized on its own run ingest on a schedule and it's loop engineering: sense a new file, plan, write, verify, repeat how do you manage your local knowledge base?

Mr. Buzzoni

15,682 次观看 • 1 个月前

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

elvis

37,824 次观看 • 1 个月前

BREAKING: Anthropic just dropped Opus 4.8—and it is a MONSTER We've been testing for about a week Every 📧 and our verdict is they could've just called it Opus 5, it's that good. Here's our vibe check: - Beats GPT-5.5 on Senior Engineer bench. On our toughest benchmark Opus 4.8 scores a 63—a hair higher than GPT-5.5's score of 62, and a full 30 points higher than Opus 4.7. It tackled a ground-up rewrite of a production codebase, and actually built something that works. HOWEVER: Coding performance varied a lot at different reasoning levels. We recommend using it on xhigh for best results. - Incredibly good writer. Opus 4.8 scored a 79.6 on our writing benchmark—measuring models on real-world writing tasks we do all of the time like essay writing, promo email writing, and more. It beats GPT-5.5 by 6 points. It produces well-written prose with fewer "AI-isms". It's also very good at writing in your voice given the right context. HOWEVER: Writing performance also varied with reasoning levels. Medium reasoning had higher incidence of AI-isms—we found best results with high. - Beast at knowledge work. Opus 4.8 is very good at general knowledge work tasks like report creation, research and more. It produced the best PowerPoint one-shot we've ever seen on our deck generation benchmark. - Emotionally intelligent, willing to question the frame. I've also found it to be quite good at talking through psychological or interpersonal issues. It has a high EQ, and it's also good at not glazing and helping to expand your perspective. Its thought process feels extremely rich and dynamic. THE BAD: These days a model is only as good as its harness, and Codex is still a far superior harness to the Claude Desktop app. This has kept me using Codex + GPT-5.5 as my daily driver, but I am flipping back and forth a lot more between Codex and Claude. Anthropic is back baby! Read the rest on Every 📧:

BREAKING: Anthropic just dropped Opus 4.8—and it is a MONSTER We've been testing for about a week Every 📧 and our verdict is they could've just called it Opus 5, it's that good. Here's our vibe check: - Beats GPT-5.5 on Senior Engineer bench. On our toughest benchmark Opus 4.8 scores a 63—a hair higher than GPT-5.5's score of 62, and a full 30 points higher than Opus 4.7. It tackled a ground-up rewrite of a production codebase, and actually built something that works. HOWEVER: Coding performance varied a lot at different reasoning levels. We recommend using it on xhigh for best results. - Incredibly good writer. Opus 4.8 scored a 79.6 on our writing benchmark—measuring models on real-world writing tasks we do all of the time like essay writing, promo email writing, and more. It beats GPT-5.5 by 6 points. It produces well-written prose with fewer "AI-isms". It's also very good at writing in your voice given the right context. HOWEVER: Writing performance also varied with reasoning levels. Medium reasoning had higher incidence of AI-isms—we found best results with high. - Beast at knowledge work. Opus 4.8 is very good at general knowledge work tasks like report creation, research and more. It produced the best PowerPoint one-shot we've ever seen on our deck generation benchmark. - Emotionally intelligent, willing to question the frame. I've also found it to be quite good at talking through psychological or interpersonal issues. It has a high EQ, and it's also good at not glazing and helping to expand your perspective. Its thought process feels extremely rich and dynamic. THE BAD: These days a model is only as good as its harness, and Codex is still a far superior harness to the Claude Desktop app. This has kept me using Codex + GPT-5.5 as my daily driver, but I am flipping back and forth a lot more between Codex and Claude. Anthropic is back baby! Read the rest on Every 📧:

Dan Shipper

354,673 次观看 • 4 个月前

This is genuinely insane 🤯 I sent the same prompt to 2 Claude Opus 5 agents 1 agent used no MCPs 1 agent used 2 MCPS the goal was to clone a very famous game, that has over 4 billion downloads and the results are scary.. The 1st agents game looks pretty basic. but has all the mechanics in place and even has power ups. The 2nd agents game looks polished especially for 1 single prompt. the 2nd agent used higgsfield mcp to create images for all of the assets then sent those images to Meshy AI with a prompt to create them it also generated all the UI elements. the results speak for themselves. you need to start MCP maxxing

This is genuinely insane 🤯 I sent the same prompt to 2 Claude Opus 5 agents 1 agent used no MCPs 1 agent used 2 MCPS the goal was to clone a very famous game, that has over 4 billion downloads and the results are scary.. The 1st agents game looks pretty basic. but has all the mechanics in place and even has power ups. The 2nd agents game looks polished especially for 1 single prompt. the 2nd agent used higgsfield mcp to create images for all of the assets then sent those images to Meshy AI with a prompt to create them it also generated all the UI elements. the results speak for themselves. you need to start MCP maxxing

Ernesto Lopez

70,384 次观看 • 2 个月前

Eric Schmidt (ex-Google CEO), on the fastest way to make real money right now: "found an agentic AI company" every resource behind that quote is public and free Anthropic's own agent framework, laid out course by course - 13 links, 3 tracks, free certs, $0 LEARN - start here (5 resources) 01. All 13 Anthropic Academy courses, free certs ↳ 02. Claude Code 101 ↳ 03. Claude Code in Action ↳ 04. Prompt engineering, the reference docs ↳ 05. Interactive prompt tutorial, hands-on ↳ BUILD - the working parts (6 resources) 06. Claude Code full docs ↳ 07. CLAUDE.md, how to give Claude memory ↳ 08. Skills, teach Claude reusable workflows ↳ 09. MCP, connect Claude to Slack, GitHub, Drive ↳ 10. Routines, automate tasks 24/7 ↳ 11. Agent Architecture roundup ↳ COMMUNITY - what other builders wrote (2 resources) 12. Claude Code Ultimate Guide ↳ 13. Awesome Claude Code, skills, hooks, plugins ↳ the entire stack costs $0. the only real cost is sitting down and going through it full builder's guide on turning this into a self-correcting workspace 👇

Eric Schmidt (ex-Google CEO), on the fastest way to make real money right now: "found an agentic AI company" every resource behind that quote is public and free Anthropic's own agent framework, laid out course by course - 13 links, 3 tracks, free certs, $0 LEARN - start here (5 resources) 01. All 13 Anthropic Academy courses, free certs ↳ 02. Claude Code 101 ↳ 03. Claude Code in Action ↳ 04. Prompt engineering, the reference docs ↳ 05. Interactive prompt tutorial, hands-on ↳ BUILD - the working parts (6 resources) 06. Claude Code full docs ↳ 07. CLAUDE.md, how to give Claude memory ↳ 08. Skills, teach Claude reusable workflows ↳ 09. MCP, connect Claude to Slack, GitHub, Drive ↳ 10. Routines, automate tasks 24/7 ↳ 11. Agent Architecture roundup ↳ COMMUNITY - what other builders wrote (2 resources) 12. Claude Code Ultimate Guide ↳ 13. Awesome Claude Code, skills, hooks, plugins ↳ the entire stack costs $0. the only real cost is sitting down and going through it full builder's guide on turning this into a self-correcting workspace 👇

Mr. Buzzoni

18,819 次观看 • 18 天前

Codex cooked for 12 hours 19 minutes last night using a /goal 🎉 It added: - Stopwatch (count up) - Clock (with seconds) - Pomodoro - Tabata - Autocomplete Everything worked except the UI interactions around auto-complete (Codex GPT-5.5 is still not great at UI, but even Claude Opus 4.8 Ultracode struggled here). Overall, I'm really happy with the results. These are features that I've wanted for a while, and getting them close to release is awesome.

Codex cooked for 12 hours 19 minutes last night using a /goal 🎉 It added: - Stopwatch (count up) - Clock (with seconds) - Pomodoro - Tabata - Autocomplete Everything worked except the UI interactions around auto-complete (Codex GPT-5.5 is still not great at UI, but even Claude Opus 4.8 Ultracode struggled here). Overall, I'm really happy with the results. These are features that I've wanted for a while, and getting them close to release is awesome.

Paul Solt

48,887 次观看 • 2 个月前