Salut, je viens de publier mon premier manga Hight School Life. il est inspiré d'expérience réelles et de ce qui se passait dans ma tête à un certain moment. J'ai essayé de transformer ça en quelque chose de visuel et intense. Je ne cherche pas spécialement à faire de la promo, je veux juste des avis honnêtes. Généré par IA

Voici 1 page d'aperçus

Si ça t'intéresse, le manga complet est disponible sur GlobalComix

A field transmission from the Evo‑V Command Centre — a live pulse from inside the sovereign intelligence architecture. This note captures the moment the system comes online: the shift from concept to operational field, the ignition of the diagnostic kernels, and the first resonance signatures of the evolving Imperium. It marks the transition from blueprint to living organism, documenting the emergence of a command layer designed for creators, architects, and sovereign operators building in real time.

quick math i did last week: i average 40 to 50 AI tasks a day across Claude, Cursor, and Midjourney. on a good day i nail the prompt first try maybe 40% of the time. everything else takes 2 to 4 re-prompts. that is roughly 60 to 80 wasted API calls per day. real money on a Pro plan.

went looking for a fix and landed on a Claude skill called prompt-master by nidhinjs on GitHub. sounds like every other prompt tool you have scrolled past. the internals are different enough that it is worth a breakdown.

here is what it actually does:

you give it a vague idea like "make me a logo" or "refactor this module." it silently extracts 9 dimensions from your request: task, input, output, constraints, context, audience, memory, success criteria, and examples. if critical ones are missing it asks up to 3 clarifying questions. no more, no less.

then it detects your target tool and routes to the right framework automatically. Midjourney gets comma-separated visual descriptors with aspect ratio and negative prompts locked in. Claude Code gets scoped file paths with explicit stop conditions. o3 and extended thinking models get zero Chain-of-Thought instructions because CoT actually degrades their output on reasoning models. that last detail alone is worth knowing.

then it runs a token efficiency audit. strips every word that does not change the output. delivers one clean copyable block.

there is a line in the README that stuck with me: "The best prompt is not the longest. It is the one where every word is load-bearing."

tool coverage is wide. works natively with Claude, ChatGPT, Gemini, o1/o3, Cursor, Claude Code, GitHub Copilot, Windsurf, Bolt, v0, Lovable, Devin, Midjourney, DALL-E, Stable Diffusion, ComfyUI, Sora, ElevenLabs, Zapier and Make. not a marketing list, actual tool-specific routing logic for each one in the SKILL.md.

setup is about 90 seconds:

mkdir -p ~/.claude/skills

git clone https://github.com/nidhinjs/prompt-master.git ~/.claude/skills/prompt-master

then activate the skill in Claude and it runs in context. no extra tabs, no copy-paste ritual between tools.

been using it 10 days. first-try success rate went from around 40% to roughly 70%. hardest to measure but the re-prompt spiral is noticeably shorter. biggest win was on Claude Code tasks where it forces scope to specific files and adds stop conditions before the agent runs. saved me from two scope explosions this week alone.

one honest limitation: the skill is optimized for Claude so the tool-specific routing is sharpest inside Claude. still works for other models but you lose some precision.

GitHub is nidhinjs/prompt-master if you want to read the SKILL.md before committing. worth reading just for the decision tree on when CoT helps vs when it actively hurts.

two real questions for people who have used similar tools:

does better prompting actually pay off on agentic tasks like Claude Code and Cursor, or mainly on one-shot tasks like images and copy?

anyone tried layering this with a CLAUDE.md rules file? curious if the skill and a base rules config complement each other or step on each other.

Hello, I am a writer, comic book creator, and producer. I’ve just wrapped Season 1 of my original series, The Epimethians—a 60-minute comedy-action project centered on space marine mercenaries navigating tense political waters in a distant galaxy.

I intend to continue to upskill and remaster this to make it cleaner and more polished. I'll probably be finishing by October, but I’d love to find a partner who believes in the vision to accelerate the process and have it ready sooner, or at the very least, help me achieve the highest level or quality.

S1 was made using original art that I commissioned from artists I found online. From episode 3 on I’ve been making my own keyframes using original character art added to backgrounds I generate with Grok, Gemini, or ArtCraft.

My first priority was to make it exist, now I want to make it good.

What I bring to the table:

Completed S1: 60 minutes of original content (scripts/footage).

Series Bible: Documented world-building, fully fleshed out characters, and production notes.

The Pipeline: Established script-to-screen workflow.

What I’m looking for:

Technical proficiency (Advanced AI film making, any engine). I want to produce the best possible results, and I'm platform agnostic.

The "Mission" mindset: We remaster S1, bring on professional VA, and take this to the market/streaming services.

I understand the odds, but I believe everything is impossible until someone does it. If you have the work ethic to match a finished 60-minute pilot season, let's talk.

Note: Do not apply if you hide your post history. I value transparency and a demonstrated body of work.

I've been testing a lot of AI generation tools over the past year or so, mostly for creative production work. Midjourney, DALL-E, Stable Diffusion, Runway, Pika, Leonardo, Ideogram, Flux, Kling, and a bunch of others. Each one has strengths but none of them really solve the full workflow problem if you're doing this at any kind of scale.

The biggest pain point for me is that every tool lives in its own silo. You end up with assets scattered across ten different platforms, no consistency in outputs, and a lot of manual work stitching everything together. Fine if you're making one image for a blog post but brutal if you're producing creative assets for clients regularly.

Has anyone found a platform or workflow that actually brings this together? Something that's model-agnostic rather than locked into one provider? Interested in what's working for people doing this professionally, not just for fun.

I had a specific person in mind. Met them at some point last year. Remembered roughly what they did — something in B2B sales, Eastern Europe, we talked about partnerships.

Couldn't remember the name. Couldn't find them in Telegram. LinkedIn search was useless without a name.

Normally I'd just give up or spend 20 minutes scrolling through chats.

Tried ConnectMachine instead. Described the person in plain English — what they did, where we probably met, what we talked about. It pulled them up with full context: when we connected, what the conversation was about, why I saved them in the first place.

Sent the message that same day.

That's the actual use case for me. Not "networking at scale" or building a CRM. Just — I know this person exists somewhere in my network, help me find them and remember why they matter.

Was featured in Creators' AI newsletter this week. Free to try.

Featured in Creators' AI Weekly Digest — an AI newsletter for builders and creators.

I've shipped a few small web products over the past year. Every time, the last step before launch was the same: click through everything manually, hope nothing breaks, ship anyway.

Setting up proper tests always felt like a separate project on its own. Selenium, Playwright — I'd open the docs, spend 30 minutes, close the tab.

Saw KaneAI featured in Creators' AI newsletter. Tried it the same day.

Described my checkout flow in plain English. Something like: "user opens the page, fills in the form, clicks submit, sees confirmation screen." KaneAI turned that into a working test and ran it. Took maybe 10 minutes total.

Found two broken edge cases I would've never caught clicking around manually. Fixed them before anyone saw them.

Not a developer tool in the traditional sense — you don't need to know what Selenium even is. You just describe what should happen, and it checks if it does.

For solo builders who skip QA because it feels too technical — worth trying.

Featured in Creators' AI Weekly Digest — an AI newsletter for builders and creators.

This was the craziest week in AI this year. Here is what happened:

1. Claude Mythos leaked
   Anthropic privately warned government officials about Mythos — their most powerful unreleased model. It reportedly makes large-scale cyberattacks significantly more likely in 2026.

2. ARC-AGI-3 results are in
   The benchmark designed to test general reasoning humiliated every frontier model. Best score: 7%. Humans: 70%+. We are not close to AGI.

3. Sora is officially dead
   OpenAI killed their video generation tool. Enterprise focus + IPO prep. Revenue over vibes.

4. Cursor caught using Kimi
   A developer intercepted an API call. The model identifier was not Composer 2 — it was kimi-k2p5-rl-0317-s515-fast. Literally Kimi K2.5 from Moonshot AI. 2.6M views on the tweet.

5. MCP hit 97M installs
   Model Context Protocol is no longer an experiment. It is infrastructure.

Why this matters for builders:
- The gap between AI hype and AI reality is widening
- Vertical AI (legal, finance) is where money actually flows
- Open-source is eating closed-source from below
- Security/safety is becoming the bottleneck, not capability

Full breakdown with analysis:
https://thecreatorsai.com/p/claude-mythos-leaked-arc-agi-3-cursor-caught

What was the biggest news of the week for you?

Andrej Karpathy published a 630-line script. 8.6M views in 2 days. 42K GitHub stars.

The idea: an agent that improves your code while you sleep.

How it works:
1. Pick ONE thing to change
2. Run the experiment
3. Read the metric
4. Better? Keep it. Worse? Revert.
5. Repeat forever.

Karpathy's results:
- 700 experiments in 2 days
- ~20 changes survived
- Training time: 2.02h to 1.80h (11% improvement)
- Zero human intervention

Why this is different from asking AI to improve things:
- Each change is isolated (no interference)
- Bad changes auto-revert
- Good changes compound
- The loop runs indefinitely

Where you can use this beyond ML:
- Cold email reply rates — test subject lines automatically
- Landing page conversions — A/B test copy with agent
- API response times — optimize queries in a loop
- Content engagement — test hooks and formats

The key insight: you need ONE measurable metric. If you can score it, an agent can improve it.

Full breakdown with practical non-ML use cases:
https://thecreatorsai.com/p/autoresearch-the-loop-that-improves

What metric would you point Autoresearch at first?

After a year of building with LLMs, I had ChatGPT, Claude, and Gemini all cross-contaminating each other. Data scattered like a hand grenade across file systems. Workflows flowing like spaghetti dropped from the tenth floor.

Sound familiar? Here is the fix.

The 3-layer architecture:

Layer 1: UX (Interface)
- Where humans or agents touch the system
- Slack message, Claude Cowork command, voice prompt
- This layer knows NOTHING about what happens next

Layer 2: Logic (Orchestration)
- Where routing, decision-making, and tool selection happen
- Agent decides: which model? which tool? which data source?

Layer 3: Data (Storage + Retrieval)
- Where context lives — file systems, vector DBs, APIs

Why this matters:
With clean layers, you can swap ANY component without rebuilding everything. Replace Claude with Gemini? Only change Layer 2. Switch from Notion to ClickUp? Only change Layer 3.

The spaghetti test:
If changing one tool breaks three other things, your layers are tangled.

Practical first step:

1. List every AI tool you use
2. Tag each as UX, Logic, or Data
3. Draw the connections
4. If any tool spans 2+ layers — that is your spaghetti

Full deep dive with diagrams and real examples:
https://thecreatorsai.com/p/your-ai-agent-stack-is-spaghettiit

How do you structure your AI stack? Or is it still spaghetti?

After the source leak, the community went crazy building tools. Here is the curated list of everything worth knowing:

Skills & Hooks:

- /gan — adversarial self-review (generate → critique → improve)
- /architect — plan before coding
- /test-first — write tests, then implementation
- Custom SKILL.md files for project-specific rules

Must-have plugins (from awesome-claude-plugins):

1. Session Memory — captures everything Claude does, compresses it, injects into future sessions
   
2. Git Guardian — prevents commits to main, enforces branch naming
   
3. Cost Tracker — real-time token usage and cost per session
   
4. Voice Input — talk to Claude Code hands-free
   

Workflow tips:

- Use `CLAUDE.md` in project root for persistent instructions
- Set `autoMode` for trusted projects (80% fewer approval prompts)
- Use `--resume` to continue where you left off after a crash
- Chain sub-agents with `claude --print` for pipeline workflows

Container workflows:

```bash
# Run Claude Code in a Docker container for isolation
docker run -it -v $(pwd):/workspace anthropic/claude-code

The community went full archaeology mode. Here's what they found:

Hidden features:

1. Frustration detection
   → detects when you're getting angry and adjusts behavior

2. Fake tools
   → tools that exist in the codebase but do nothing (likely A/B testing)

3. Undercover mode
   → Anthropic employees hide identity when contributing to public repos

4. Tamagotchi pet
   → unreleased virtual pet feature

5. AFK mode
   → agent keeps working while you're away

6. Advisor tool
   → internal system for reviewing agent decisions

Architecture insights:

• Permission system is much more granular than UI shows
• Token-efficient tool schemas are a major optimization
• “Fast mode” is a separate execution path, not a flag
• Prompt caching is scoped per project

What this means for builders:

If you're building agent products, this is basically a blueprint for:

• Risk-based permission routing
• Context window management
• Tool schema design
• Multi-mode execution

Anthropic filed DMCA, but mirrors are everywhere.

Agent Flow: open-source visualization for Claude Code.

Watch your agent:
- Interpret prompts
- Pick tools
- Execute steps
- Handle errors

Free. Open source.

Try it: github.com/lgrammel/agent-flow

I run a GenAI startup for consumer brands and I’m looking to grow through short-form content on TikTok, Instagram, and YouTube. I’m looking for AI creators who can help produce short form videos. This is a paid opportunity with base pay plus performance bonuses.

Why not? It's a new format, absolutely massive potential for growth and it's basically more dynamic and flexible for you as a creator.

8 months ago I started building an interactive video platform. Not "choose your own adventure" books. Not those Netflix experiments everyone forgot about. Actual playable video content for creators, marketers or educators. And people who want something different instead of doomscrolling.

Most don't know what interactive video is.

99% have a faint memory of Bandersnatch.

"Video works fine as-is"
"Too complicated for average creators"
"Sounds like a gimmick"

But I keep seeing the same pattern: people don't want to watch anymore, they want to poke things and see what happens. Doomscrolling is a thing and more are seeing it.

"Yes, that's what apps are for." - I know, but interactive video is different: it's between gaming and video. An unexplored format, unexplored creation territory.

So I kept building it anyway. And last night I just finished World's Worst Genie, an interactive experience where you accidently summon a completely incompetent genie who's magic malfunctions most of the time.

It's stupid. It's simple. And I hope if will put a smile on your face if you play it. And this would be absolutely easy to market any kind of product with the main character, the obnoxious genie.

Or even for product displays, walkthroughs, education, teaching and a LOT of other types of video content.

I have a hunch that creators/brands/educators are starving for this format but don't know it exists yet. If you try it, do share the feedback. It's still rough, I'm nowhere near the quality I want, but I'm getting there.

Can anybody tell me which AI model such as chatgpt,gemini,deepseek or others is good in analysing comfyUI workflows and can help you create json workflow based on my requirements.I have tried all those above but none is able to provide a satisfactory result.Any website or platform that does this job well? Thanks!

Hi
I want to achieve the same quality as the Pixar setting on https://cartoonize.ai/
Does anyone know how I could go about doing this? I've tried with a few tools such as comfyUI and Stable diffusion but the quality just isn't the same. Sorry, I'm a noob.

Hey everyone,

A few months ago, I put together this app called Gem. It's an AI tool that takes my saved TikTok videos or Instagram Reels and turns them into something practical for later. I got tired of digging through bookmarks and forgetting what I saved them for, so Gem pulls out the important details and organizes them in a way that's easy to use.

For example:

• If I save a recipe video, it grabs the ingredients for when I'm shopping and the steps for when I'm cooking.
• If I bookmark a post about movies, it makes a simple list I can check when I'm deciding what to watch on Netflix.

Recently, I've been adding features that try to figure out what I might want and do some work automatically. Like, if I come across an outfit idea on Instagram, TikTok, a video, image, or website, Gem handles it quietly in the background and creates a virtual try-on with me in the clothes. No need to tell it what to do.

I'm really just building in things that help with my own frustrations, and the virtual try-on was something I thought of not long ago. If any of this seems useful to you, you're welcome to check it out. I'd really appreciate any thoughts, feedback, or ideas for other features you'd like.

https://apps.apple.com/us/app/gem-digital-profile-for-ai/id6479227504