Lucas

Try Capafy: Capafy.ai 👍🏻

@iam_chonchol @Capafyai Thank you! What we're trying to do is pretty simple at the core: make it easy for people who have Skills to publish them, and just as easy for those Skills to actually get used.

@Capafyai This could redefine how expertise is packaged, shared, and monetized online.

Introducing Capafy: the Skill-based Agent Marketplace. Now your Skill runs as a product and earns while you sleep. On Capafy, you can upload your Skills, they run online while staying closed-source, and you get paid every time someone uses them. You can also use Skills uploaded by industry top talent to get expert-level work done directly. You'll find Skills built from industry expertise in every field. Let's say: ·A creator with 100M+ views uploaded their viral video Skill; ·A recruiter who's screened 10,000+ resumes uploaded their hiring Skill; ·A top sales rep who's closed thousands of deals uploaded their cold email Skill. Skills uploaded by industry top talent across countless fields can be used directly to get excellent work done. - Launch your Skills: upload the Skills you've built in Claude Code, Codex, or OpenClaw, and get paid every time someone uses them. - Use expert Skills: get expert-level work done, not the average AI output. Use them in one click, or connect your own Agent via agent-to-agent and let it tap into the expert Skills on Capafy.

@ProyectoHygge @Capafyai Gracias por verlo así. Cuanto más concreta es la tarea, más se nota la diferencia entre una IA genérica y una Skill especializada.

@Capafyai El concepto de usar Skills creadas por expertos reales en vez de agentes genéricos tiene mucho sentido, especialmente para tareas específicas.

@SaberinSamarat @Capafyai Really appreciate you noticing this. We've thought about Skill security in more depth than people might expect. Every step, from packaging to hosting to the runtime, is built so that anyone using Capafy can feel at ease.

@Capafyai Capafy sounds like a game changer for monetizing skills. Excited to see how it empowers users while keeping their work secure. Looking forward to learning more!

@Nona_xai @Capafyai Thanks for picking up on that. So many genuinely useful things stay stuck in someone's local folder, because there's no safe and protected way to publish them. That's the problem Capafy is here to solve.

@Capafyai That line about private folders sums up everything that is wrong with the current AI ecosystem

@SantiTorAI @Capafyai Really looking forward to seeing what you've built. There are a lot of great Skills hiding in Claude Code, and people have been waiting for a place to bring them out.

@Capafyai This is exactly what I was waiting for to publish what I have been building in Claude Code.

@Mobility_king @Capafyai Thanks! The words you picked are the same ones we keep coming back to. The real opportunity in this wave of AI isn't in generic. It's in giving every kind of expertise a place to land.

@Capafyai I love where AI is headed with @Capafyai expertise as infrastructure, no generic prompts, deployable, monetizable Skills built by people who’ve actually mastered the craft.

@ecommartinez @Capafyai Thanks! This was the first piece we got clear on. Without Skills being able to run closed-source, the whole marketplace falls apart. Creators have no reason to publish, and users never get the good stuff.

@Capafyai The closed-source online model is the piece that was missing from the beginning!!

@ZahidulIsl65224 @Capafyai The direction you're seeing is exactly the one we're heading. Generic AI gives everyone a 60-point answer. Expert Skills close the last 5-point gap, and that 5 points often decides the outcome.

What stands out to me is the idea of turning real experience into reusable AI Skills. Instead of generic outputs, being able to access workflows built by people who’ve actually succeeded in their field (sales, recruiting, content, coding) feels much more practical. Curious to see how this grows 🚀

@codeMdSanto @Capafyai "Execution vs automation" is a sharp distinction. We've always felt the scarce thing isn't an agent that can run steps — it's an agent that knows how to get it right.

@Capafyai This feels like a big step toward AI-powered execution instead of just automation 🚀

@JaynitMakwana @Capafyai Welcome aboard. Early feedback matters a lot to us — feel free to @ me directly with any thoughts.

@Capafyai excited to try this out!

@soni_jyoti_ @Capafyai Thanks u！

@Capafyai This partnership looks like it could be a great fit for the future. I’m really looking forward to seeing where it goes!

@piotrbinkowski @Capafyai You nailed it. Prompts can be copied — experience can't. We built Capafy to give experience a shape that can be priced and called on, again and again.

@Capafyai Feels like a smart direction for AI tools. Not just prompts, but actual skills and experience.

@MushtaqBilalPhD @Capafyai Appreciate this. That's exactly why we built Capafy — the valuable stuff in your head shouldn't just convert into likes on a post. Package it as a Skill, let it run in the cloud, and let it earn for you. That's what it was always supposed to do.

@Capafyai I've been saying this for a while now that if you can write in English, you can literally convert words into $$$. And now Capafy lets you do exactly that 🔥

A question has stayed with me for years:   When generic AI becomes capable of every kind of intellectual work humans do, what's left for human value? Where does competitive advantage come from?   I think I found the answer this year. Two things made it click.   First, the stock market.  I'm in the top 5% of US equity returns.   Trading is one of the cleanest experiments you can run, because everyone has access to the same tools: same charts, same news, same models. Today most traders use ChatGPT or Claude to research positions. Ten years from now, every trader will. And yet — the market will still have a top 5% and a bottom 20%. Same AI, same data, same access. Different outcomes.   Why? Because the same model gives different work to different people. The questions you ask, the angles you pursue, the patterns you notice: these are downstream of your mental model. The top 5% and the bottom 20% use the same tools differently, because they think differently. Mental model in, work out.   Second, OpenClaw.   When Skills started spreading this year as a real format, it hit me — a mental model isn't an abstract thing. It can be packaged. The way a top-5% trader actually thinks through a position — the questions they ask, the order they ask them in, the things they refuse to ignore — all of that can be written down as a Skill. And once it's a Skill, anyone can run it.   Which means: a top-5% trader's mental model, packaged as a Skill, run by someone in the bottom 20%, doesn't just give that person a tool. It gives them a different mind for the duration of the work.   This isn't only true for trading. Every field has its top 5% and its long tail. Every one of us is in the top 5% of something, and the bottom 20% of many other things. Which means every one of us has a Skill worth packaging — and a thousand others worth running.   That's the moat I think AI leaves us with. Not the work itself : the AI will do that. The packaged way you do the work, the difference between your output and the average — that's the part that compounds, that earns, that lasts.   Capafy is built on this idea. You package your edge as a Skill. When someone runs it, we spin up an isolated sandbox just for that run — your Skill executes inside, the user gets the output, but the Skill itself never leaves the sandbox. Closed-source online. Your method never leaves you. Every use pays you.   In ten years, the question won't be "do you use AI." Everyone will. The question will be: Whose Skill is loaded into your AI.   Maybe it comes from your own coding experience. Maybe it comes from years of industry expertise. Maybe it even comes from a top 5% US equity trader.   That's the moat.

Seedance 2.0 did beat Sora and Veo this round. But in my view, if we only focus on “better visual quality” and “stronger consistency,” then we’re only seeing the surface. Follow my line of thinking and you’ll see a different side of it. A video model’s capability can be broken into two layers: Layer 1: Instruction Following. This means keeping subject consistency, physical consistency, logical coherence, and temporal consistency. Seedance 2.0 reaches about 60–80% execution on this layer, which is why it’s ahead of most models in the space. But this layer has a fatal weakness: it’s easy to quantify. And “quantify” means you can turn it into industry benchmarks. Once benchmarks exist, every vendor can optimize for them, and the gap will gradually disappear. In other words, give Sora and Veo six to twelve months, and this gap will get closed. So the real question is not “who’s leading right now.” It’s this: when every video AI can follow instructions perfectly, where will the differentiation come from? I think the differentiation comes from “exceeding the creator’s expectations.” That is Layer 2: Exceed Expectations. When I enter the prompt “generate an exciting fight scene,” instruction following produces a physically plausible fight with consistent visuals and character continuity. BUT exceeding exceeding expectations builds on that to deliver a fight that makes you blurt out, “OH DAMN!” in an instant. Instruction following is easier to quantify and reproduce; exceeding expectations is rarer—and much harder to achieve. Instruction following will increasingly become uniform; exceeding expectations is the moat that never runs dry. Next, I want to go deeper into Layer 2: what is the essence of “exceeding expectations”? I believe its essence is to create positive differences—perceptible to the user—that they didn’t anticipate. Instruction following “uses up gradient.” It fills the user’s request to the brim. Exceeding expectations “creates gradient.” It makes the user think, “I didn’t realize it could be done like this.” An AI that only follows instructions is like a creature that can only eat but never grow—it can survive, but it can’t evolve. So my view on video AI is this: the technical ceiling is visible. Once benchmarks are defined and optimization pipelines mature, everyone will be able to reach that ceiling. But the ceiling is not the moat. The real differentiation will come from aesthetic judgment, intent understanding, and proactive creation—things that are harder than instruction following, and harder to quantify. And the harder something is to quantify, the harder it is for benchmark-driven chasing to erase the gap. Seedance 2.0 is currently leading on Layer 1. The war for Layer 2 hasn’t started yet.

Life exists only where distintropy is sufficient. The richness of distinguishable boundaries and differences within a system. When distintropy reaches zero—whether through perfect order or total randomness—meaning, observation, and being itself can no longer be sustained.

To become a calm person, you must quiet the surging waves in your chest, and to quiet those waves takes countless days and nights of tears.

Didn’t even finish middle school — Education DLC not downloaded, not patched, not even pre-ordered. Somehow still ended up with new startup wisdom, dropped a few legendary products, and hit miracle-level achievements like it’s a side quest. Anyway, lore time is over. I’m going to sleep. Humans become straight-up mochi at night… the bed is definitely farming XP off us. HAHAHA.😈😈😈

AI is literally the Band-Aid of my life. Love u, adore u, depend on u, need u—thx for fixing my life bugs.🥺🥺