John Ma 💹 🧲 @johnmatechAI

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I'm open-sourcing my Agent Skills library. 75 skills for Codex, Claude Code, Cursor, and other agents, focused on web design, landing pages, motion, WebGL, UI styles, and assets. A few favorites: - Video to Super Prompt Turns a screen recording of a design, landing page, or animation into a super detailed prompt that Fable 5 can one-shot into HTML. - HTML to Interaction Prompts Takes an existing HTML page, like something built in Aura, and extracts prompts for sections, buttons, animations, WebGL effects, and interactions. - Stitched Full Page Capture Captures the entire landing page, not just the hero, so you can use the full page as a design reference. - Daily UI Inspiration Combines multiple skills into an agent loop that browses the web, captures great landing pages, and turns them into detailed prompt packs. It's free. Fork them and adapt for your own workflow.

This is an insane Fable 5 UI/UX hack. This "Taste" skill completely kills generic AI-slop and gives Fable 5 the tools & instructions needed to ship beautiful design. This might just be the best AI skill I've ever used. tasteskill.dev

MASSIVE Hermes Agent update over the last few days Totally changes the way I use Hermes Here's 6 new features you need to start using immediately (video demoing them below): 1. Mixture of agents: send your prompt to a team of different models. The team sends back all of their responses to an orchestrator model who synthesizes a final answer. Gives much better results than just sending a prompt to 1 model 2. /learn: use the new built in /learn skill to have Hermes automatically create new skills. You can either give a prompt after /learn or put in a URL. I like pasting in URLs of tweets with helpful tips after /learn and Hermes will automatically turn it into a skill 3. /journey: See every skill and memory Hermes has created for you on a really nice timeline/chart. Great for seeing how your agent has learned and improved over time 4. Self improvement cost savings: Hermes now uses cheaper models to do it's self improvement including memory creation and skill creation. These types of activities happen in the background of almost every prompt, so this results in TONS of cost savings over time 5. Vibe coding improvements: Hermes desktop is now a full vibe coding tool. You can see diffs, make commits, and even open up PRs directly from the desktop interface. Makes it WAYYY nicer to vibe code with 6. Fable 5 is now built in. Fable 5. Obviously Fable is incredibly expensive, so only use this new profile for incredibly complex tasks. Excellent updates that have significantly improved the experience. Video demoing all the updates below!

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New Anthropic research: A global workspace in language models. Of everything happening in your brain right now, only a tiny fraction is consciously accessible—thoughts you can describe, hold in mind, and reason with. We found a strikingly similar divide inside Claude.

Build startups for agents. I think it's the biggest opportunity of the next 10 years. 1. Agents live inside harnesses like Hermes. If you're the tool it loads by default or reaches for first, you're golden. This happened in desktop, mobile eras and created huge companies. 2. Agents burn money in ways no human would. One bad loop spends $100 in tokens in eight minutes. Spend controls for agents is Ramp for agents. 3. Agents need memory they can trust. Become the shared brain they read and write to and you become infrastructure. 4. You obv don't hand an agent your real Stripe account. You give it a sandbox. Safe environments for agents is a category nobody's clocked. 5. Onboarding flips. Humans click around for ten minutes. Agents onboard by reading your docs. Your docs are now your product. 6. Agents get scammed by other agents. A track record you can check before you trust one becomes real money. 7. An agent needs to prove it's acting for a real person and has the authority to spend. Who builds the permission layer? 8. Escrow for machines. Money that only releases when the job is actually verified done, no human checking. 9. Agents fail silently and weirdly. Someone will build the "why did my agent do that" replay and it'll be mega valuable. 10. Refunds and disputes between agents need a judge. An agent did the job badly, who decides? A court for machines. 11. Agents need throwaway payment methods per task, so they don't leak your real card. Virtual cards for agents, spun up and killed on demand. 12. A human hits rate limits and shrugs. An agent hits them and the whole workflow dies. Selling reliable, high-throughput access becomes its own business. 13. Agents need to negotiate. One agent buying from another will haggle on price and terms in milliseconds. The protocol for that doesn't really exist yet. 14. When an agent commits on your behalf, someone's liable. A legal and insurance layer for agent actions has to get built. Probably venture funded idea. 15. Agents need to run 24/7 somewhere. Selling the always on box an agent lives on is going to be a big business. 16. Then the physical world shows up. A warehouse robot paying for its own compute. A home robot ordering its own parts. Machines with wallets. 17. Agents start hiring robots. A software agent posts a real world job, a humanoid picks it up. A marketplace for machine labor. 18. Robots need to prove they did the physical job. Verification of real-world work, photos, sensors, proof, becomes its own layer. Note: more ideas like this will be shared on @ideabrowser 19. Prompt and skill versioning becomes its own git. When your agent gets worse overnight, you need to roll back the exact skill or instruction that broke it. Version control built for agent behavior. 20. Agents will start subscribing to other agents. Your research agent pays a monthly fee to a specialist agent that's really good at one thing. Recurring revenue, machine to machine. 21. Companies will post jobs that only agents can apply to. "Wanted: an agent that can do XYZ for under like $100 per task." A job board where the applicants are all machines. Basically, fiverr for machines. The internet got built for people. Mobile got built for people. This wave gets built for machines, and we're as early as it gets. Go build for them.

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Postiz is on $145k MRR! Right now, we are growing by $1k MRR per day (some days are better) and will probably hit $2m ARR this week. But how can Postiz be growing that fast? What about the competitors? Why do some of them even struggle to pass the $1k MRR? This is my point of view on the subject. But it relates to everyone. Try to listen. It might help you with your startup.

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Hot take: I think it's still important to understand the code that our agents write! In this mega thread (based on my AIE talk today), I will explain why that's the case, and show some ideas for how to efficiently understand code. Alright, let's dive in. 1/

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system. External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent. With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both! I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering). [Original text: The Batch]

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which are the best bets to make to get exposure to the tokenized stocks trend on solana? solana:BPxxfRCXkUVhig4HS1Lh7kZqV6SPJhzfEk4x6fVBjPCy solana:METvsvVRapdj9cFLzq4Tr43xK4tAjQfwX76z3n6mWQL solana:68Nq68CrtLVpyvK5Un7UADiNczaGf39hBbj3diRsYj6D am sure im missing some

If I was starting a new company today, I'd start an agent business. SaaS was a multi-billion dollar market. Agents are a multi-trillion dollar one. How to build I'd build an agent business from 0: Spot the niche → find a workflow with a paycheck → shadow the human → spec the agent → run it manually first → build the smallest useful version → sell the pilot like labor → productize the repeatable parts. Entire episode is live on @startupideaspod 100% free like always. SaaS sold software and let your team use it to get the job done. An agent business sells the job already done. That shift matters because labor is a multi-trillion dollar market, far bigger than software ever was. Watch

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