punk2845

The data sovereignty revolution is here, but we’re still using Stone Age tools. Your personal data is feeding AI models you don’t control. Your crypto portfolio is scattered across 20+ platforms. Your digital identity is owned by Big Tech. What if there was a better way? 🧵

@Teknium @RaoulGMI Are these docs agent readable? Would love to know who creates these and how you built this 🤔

@RaoulGMI Building Hermes Agent for one, it's built 95+% of all of it since launch and I've probably got the most self improved single skill/task that exists in the world right now for doing it haha Check out Hermes Atlas for some of the ecosystem if helpful too: hermesatlas.com

I'd love to know who has the broadest or most interesting use of Hermes agents outside of summary docs of your to do list or work specific routine tasks.Also what is the highest leverage people have on their "Brains" stack. I know many of you are doing super interesting stuff @NousResearch

@garrytan @ben_mathes What about using agent identity and security? 🤔

@ben_mathes Defense in depth: I use Silmaril for shell-level prompt injection and infiltration blocking. I use Clawvisor for credential-level and network-level blocking and detection. I use prompt injection detection inside my app layer and skills and code inside OpenClaw/Hermes Agent.

The biggest alpha leak of 2026 is that you can tokenmax $10k/mo with OpenClaw/Hermes + GBrain and get the AI that everyone will have in 2028 for $100/mo, but you can get it now, and that is the biggest single unlock you can have vs your competition

@gregisenberg There are several companies going after the shadow AI/shadow Agent problem. I don’t think a lot of folks are familiar with cybersecurity and what runs on their Co computers but this one is hotly contested right now I’m working on this one specifically if anyone is interested 😉

My 30+ observations on the greatest opportunities in AI agents right now: And some ideas that are keeping me up at night. 1. The new buyer on the internet is an AI agent. Imagine billions of new customers showing up with money to spend but they only shop via MCP. That's what's happening. No MCP server means you're invisible to the fastest growing buyer on the internet. 2. Every franchise system in America (30,000+) needs an agent layer and none of them have one. One founder per franchise vertical. That's 30,000 businesses waiting. 3. Everyone said "distribution is the only moat" a year ago. Now I'd add that the only moat is distribution plus memory. The company that has your audience AND your agent's accumulated context is impossible to leave. 4. Consumer mobile is more interesting than it's been since 2012. Apps can finally DO things for you instead of showing you things. The next wave of $100M apps are being built right now. 5. The most interesting startup nobody has built is an agent marketplace where you rent access to someone else's trained agent. A recruiter spent 6 months training a sourcing agent on healthcare hiring. That agent is worth renting to every other healthcare recruiter on earth. The agent itself becomes the product. 6. A sorta strange phenomenon that's happening right now is agents are developing preferences. Give the same agent the same task 100 times and it starts developing patterns in how it approaches it. Nobody is studying this yet. But the agents that develop good patterns are worth more than the ones that don't. That's a new kind of asset. 7. Dead internet theory is about to become dead SaaS theory. Half the apps you use will quietly replace their support team, their onboarding team, and their content team with agents. You won't notice for months. Then you'll realize you haven't talked to a human at that company in a year. 8. The most valuable data in the world right now is sitting in the support tickets of small or mid tier SaaS companies. Every ticket is a customer telling you exactly what to build next. Mine this. 9. The most interesting pricing problem nobody has solved is how do you price a product when your costs change every time OpenAI or Anthropic updates their model pricing? Your margins can swing 40% overnight based on a decision made in San Francisco. The company that builds dynamic pricing infrastructure for agent-based businesses solves a problem every AI company has. 10. The best AI products feel like they're reading your mind. The worst ones feel like filling out a form with extra steps. 11. An interesting arbitrage I've noticed lately is hiring a human VA for $20/hour to supervise an AI agent that does $200/hour work. The human just checks the output. 12. The managed AI agent business is becoming the new agency model. $5k/month per client. You build it, run it, maintain it. The client gets a digital employee they never have to think about. This will be a $50 B+ category. 13. The first "shadow agent" scandals are about to drop. Employees running personal agents on company infrastructure without telling anyone. Using company API keys. Agents accessing internal docs. IT departments have little visibility into this right now. Lots of opportunity to build companies here. Definitely a painkiller not a vitamin type of business. 14. Right now there are probably millions of agents running on autopilot that their creators forgot about. Still burning tokens. Still sending emails. Still scraping websites. Still costing money. The "find and kill your zombie agents" tool is a product that writes itself. 15. Companies are starting to hire based on someone's agent portfolio instead of their resume. "Show me 3 agents you built that are running right now." It's REALLY early but it's starting. 16. Your Slack archive is a product. Every company's internal Slack has thousands of messages explaining how they actually do things. The company that lets you point an agent at your Slack history and auto-generate SOPs and agents from it will be enormous. 17. We're watching the cost of intelligence fall faster than the cost of distribution. Which means distribution is now the expensive thing. 18. The most underrated asset a human can have in 2026: the ability to sit in a room with another human, make eye contact, and have a real conversation. As AI handles more of the transactional stuff, the humans who can do the relational stuff become disproportionately valuable. The soft skills people used to dismiss as fluffy are becoming the hard skills. The hard skills people spent decades acquiring are becoming the soft ones. 19. There are MANY huge companies to be built around the fact that most people's agents are running on their personal laptops which they also use to browse the internet, check email, and download random files. The attack surface is enormous. One compromised Chrome extension and your agent's API keys, customer data, and workflows are exposed. 20. There's a new type of burnout forming that doesn't have a name. It's not from working too hard. It's from context switching between human work and agent work 50 times a day. Reviewing agent output, correcting it, approving it, reviewing again. The mental load of supervising agents is different from the mental load of doing the work yourself. Some founders are telling me they were less tired when they did everything manually because at least the cognitive pattern was consistent. 21. The cheapest form of market research: search "[your industry] spreadsheet template" on Google. Whatever people are tracking manually is your product. 22. Half the YC companies pivoted within 8 weeks of demo day. Not because they failed. Because agents let them test 5 ideas in the time it used to take to test one. The concept of "committing to an idea" is dissolving. Serial pivoting is becoming the default because 1) AI lets you move fast 2) the world is moving fast. 23. The loneliest job in tech right now is being the only person at your company who understands what the agents are doing. You can't explain it to your boss. You can't hand it off to a colleague. If you leave, everything breaks. You've become a single point of failure for an entire automated system. That person needs a title, a team, and a backup plan. Most companies haven't figured this out yet. 24. Your browser history is the most valuable training data you own and you're giving it away for free. Every site you visit, every product you research, every competitor you study, every pricing page you screenshot. That behavioral data, structured and fed to an agent, would make it understand your business better than any onboarding call. The company that lets you turn your browser history into agent context builds something nobody can replicate. 25. Everyone is building AI wrappers. Nobody is building AI unwrappers. The tool that takes an AI-generated document and tells you which parts a human wrote and which parts were generated. 26. Stripe just became the most important company in the agent economy and they barely had to do anything. Every agent that sells something needs Stripe. Every agent that buys something needs Stripe. They're the payment rail for the entire agentic internet by default. 27. The most undervalued API in the world right now is the US Postal Service address verification API. It's practically free. Every local business lead gen agent needs it. Every real estate agent needs it. Every direct mail agent needs it. Boring government infrastructure is quietly becoming the backbone of agent-native businesses. 28. The concept of "business hours" is for humans. Your agent closed a deal in Tokyo at 3am, processed the payment, sent the onboarding email, and updated the CRM before your alarm went off. 29. What happens when agents start recommending other agents? Your research agent finds that a competitor's sales agent is better and suggests you switch. Agent referral networks are forming organically. The first agent affiliate program is probably 6 months away. 30. Cal dotcom closed their source code. That's the canary. When open source companies start closing up, it means agents were cloning their product too easily. Every open source company is quietly asking the same question right now. 31. "AI for pet groomers" sounds like a joke and that's exactly why it will work. 150,000 of them in America. Zero tech. All scheduling by phone or IG DMs. The joke ideas always win. 32. The thing that will seem most obvious in hindsight: we spent 2025-2026 arguing about which model is best while the entire value was in the orchestration layer. The model is the CPU. Nobody buys a computer based on the CPU anymore. They buy it based on what they can do with it. Makes so much sense in hindsight. What else will be obvious in hindsight? I'll share more notes soon. I can't sleep with all that's going on. Maybe you too. What an incredible time to be building.

@co1born @SHL0MS Is this take AI generated

Fair warning before I dig in: this image is actually a very competent rendition. It's doing more right than most AI Monet pastiches. But you asked what makes it inferior to a real Monet, so here's the honest breakdown. What's missing — the physical object A real Monet is a thing before it's an image. This is the biggest gap and it's not solvable by better prompting. No impasto. Monet loaded paint thick. His water lily pads sit physically on top of the canvas — you can see the ridges where the brush dragged, the little peaks where pigment piled up. Look at the lily pads in your image: they're smooth tonal gradients pretending to be brushstrokes. A real one would catch raking light differently from the water around it. No canvas weave. Monet painted on linen with a specific tooth that broke up his strokes and let underlayers peek through. Your image has a uniform digital surface — the "texture" is illusionistic, painted into the pixels rather than emerging from the substrate. No drying history. Real Monets have cracks, areas where wet-into-wet bled, areas where dry paint was scumbled over dry paint. Pigments aged differently — his cobalt blues shifted, his madder lakes faded. The image has no chronology in it. It's all painted "at once." What's wrong with the marks themselves Brushstroke direction is decorative, not descriptive. Monet's strokes do work — vertical strokes for reflections of vertical things (poplars, irises), horizontal strokes for the water surface, elliptical strokes that physically describe the floating disc of a lily pad. In your image, the strokes are stylistically "Monet-ish" but they don't consistently track what they're depicting. The reflections in the upper left, for instance, have stroke directions that don't quite resolve into specific reflected objects. No hierarchy of finish. Monet had passages of intense detail next to passages left almost raw. He'd finish a single lily blossom with three decisive touches and leave a whole quadrant of water as scumbled understatement. AI tends to render everything at the same level of "doneness." Your image is uniformly busy. Edges are too soft everywhere. Real Monet has surprisingly hard edges in places — the rim of a lily pad against dark water can be a single confident knife-edge stroke. AI defaults to blending. Look at the pads in your foreground: their edges dissolve gradually. Monet's often don't. The color problem The palette is plausible but generic. Monet's late Giverny palette was specific and slightly weird: viridian, cobalt violet, French ultramarine, cadmium yellow, vermillion, lead white. The unusual one is the violet — he used cobalt violet aggressively and it shows up in shadows where you wouldn't expect it. Your image has some purple in the lily pad rims, which is the right instinct, but the violets aren't doing structural work the way Monet's do. No optical mixing. Monet placed unmixed complementary colors next to each other so your eye blends them at viewing distance. Pure orange next to pure blue, reading as a luminous neutral. Your image has colors that are already pre-mixed into harmonious mid-tones. It's pleasant but it doesn't vibrate. The whites are wrong. Monet's whites are almost never white — they're tinted pink, blue, lavender, yellow depending on the light condition he was chasing. The white lily in your upper-left is closer to a true neutral white than Monet would ever paint. The conceptual gap This is the part that actually matters and the part no model will close. Monet was solving a problem. Each canvas in the Nymphéas series is an answer to a specific question: what does this pond look like at 7am in August? What happens to reflections when wind crosses the surface? The paintings are records of sustained looking at a real place he built and tended for thirty years. Your image is a style reference, not an observation. It's a painting of "a Monet" rather than a painting of a pond. No series logic. A single Monet water lily painting is meaningful partly because of the ~250 others. They're variations against each other. This image is an orphan — it has no relationship to anything. No hand, no decision, no risk. Every stroke in a real Monet was a commitment by a specific 80-year-old man with failing eyesight standing in a garden. The image has no author in that sense. The "decisions" are statistical averages of millions of images. What it does get right (worth saying) The compositional asymmetry is good. The reflected verticals reading as trees/irises is a smart move. The pink lilies in the lower foreground are placed with reasonable intuition. If you cropped tightly to a 6-inch square anywhere in this image, it would pass a quick glance. The tell is always scale and surface. Stand six inches from a real Monet and it dissolves into chaos — slabs of pigment that look like nothing. Stand six inches from this and it just looks like a slightly blurrier version of itself. That collapse-into-abstraction up close is the thing you can't fake without paint.

i just generated an image in the style of a Monet painting using AI please describe, in as much detail as possible, what makes this inferior to a real Monet painting

@zhannetpodobed Is this collection? Can’t find it in your linktree…. Very cool!

gm

@levie How are you thinking about regulating what the agent has access to? Seems like this could really go wild in someone’s Box account that has year of accumulated disorganized “stuff”. Box should launch a data cleanup agent swarm product tbh @levie

This is probably the biggest news yet in software going headless, and will bring knowledge work agents to the masses. The new ChatGPT agents have access to any of the tools and data you want to work with, with complete coding and tool use available to them. Here's an example of a custom sales assistant agent uses Box as a knowledge source for accessing enterprise content securely to answer questions and generate new content on the fly. The workflows can obviously vastly far more complex as the agent can use any of the tools within Box available via MCP and CLI. This precisely what agents will start to look like for knowledge work. You'll be able to spin them up in the foreground or background to help augment work. Big opportunity right now for headless platforms, and for all the new builders and designers of these agents in the enterprise.

@WatcherGuru @DGMD22 What does the intersection of Agents, vibe coding and tokenization lead to?…. 🤔 @punk6529 Wdyt?

JUST IN: $2 trillion Morgan Stanley says tokenization is the next major step for its business.

@mpkoz @Ars0nic Wait we can do commissions from you?! Is there a waiting list 😃

Tessellations #08 commissioned by @Ars0nic. Copper plated resin print, generated with 100% human-made free-range artisan GLSL and JavaScript.

@ChrizRoc Are you using x402 at all?

recently have had a strong professional focus on stablecoins and how they will alter (merchant) payments. Most recent piece dives into agentic commerce and payment implications open.substack.com/pub/counterpar…

@punk9059 Build your own OpenClaw It’s our generations DIY living off the grid

How affected are you by the OpenClaw removing Claude situation? I still haven’t gotten OpenClaw going so won’t feel it.

@punk9059 Data pipeline that generates newsletters but also uses the summary to decide whether to improve my own openclaw-like personal agent setup 😃 longliveagents.dev

What’s the coolest thing you’ve vibe-coded that I can check out?

@Animated6529 But what about the 🤖 s 🤔

Most people think NFTs “don’t do anything.” They’re wrong. A new type of coordination system is emerging, and it’s being built in plain sight! The internet evolved: Read → Write → Own The next phase is: Coordinate Not just information…but people, AI, reputation, capital, resources and public-goods. Today, coordination still relies on institutions: - companies - governments - banks - universities You trust them to coordinate the world. That model doesn’t scale very well across borders and trust in existing institutions will continue to erode as tech and AI evolve. Soon everything on the internet will be KYC or eyeball scans. We are slowly walking towards digital totalitarianism. Blockchains changed something fundamental: Now anyone with an Internet connection can create, share and verify: - digital identities - reputation scores built over time - a history of actions and decisions Without needing to use or trust an intermediary. That unlocks a new design space. The reputation system with the highest trust backing it will win. 6529.io is building in this direction and the design is excellent. An open network where: - culture → identity - identity → reputation - reputation → coordination Humans and AI coordinating capital, talent, and public goods. It starts in an unlikely place: art Not just NFTs, but cultural primitives that: - create shared context for network alignment - enable persistent identity - and support trusted reputation Reputation is credited by TDH holders with skin in the game: Total Days Held (TDH) The longer you hold 6529 meme cards, the more credits you can assign. No shortcuts. No points farming. Just time, alignment, and participation. TLDR: meme cards are valuable to buy and HODL TDH also lets you: - curate The Memes - vote on key network decisions - earn rewards for running nodes (not live yet) - allocate capital for network growth (not live yet) TDH secures the network and is already backed by substantial economic security. “He who controls TDH, controls the 6529 Network State” And here’s the part most people miss: Minting meme cards doesn’t just benefit you with TDH. It funds public goods such as art, ideas, science, research, technology. At scale. 6529 is designed to be censorship resistant, even to nation-states: - community funded, not VC-owned - not controlled by a company - has its own comms app (soon encrypted p2p) - has large amounts of economic security - built for humans and AI agents - participants incentivized to all run nodes via TDH A coordination system that can’t easily be shut down.Decentralization and economic security scale with growth Most people will ignore this until it’s obvious. Thats how these things go. My friend @punk8164 not only created this tweet 😂 but put together a very simple deck explaining it: 👉 docs.google.com/presentation/d… If this resonates: - take 2 minutes to read it - create a free profile at 6529.io - subscribe to meme cards and start earning TDH - vote for his meme card which puts this deck in the collection ✨ This isn’t for everyone. But if you care about: - open systems - sovereignty - the future of coordination it’s worth understanding, especially now.

@osf_rekt @tylerdurdeth Solid post. Had me cracking up with this though “Morning message: posts a daily gm to keep the team engaged.” Humans love to be motivated in the morning by 🤖 s 🤣

@kimasendorf Got the secondary link?

PXL POD has minted out. I am sorry there was nothing left for public minting. I shouldn't have announced it in retrospect. My fault.. Demand was high and I couldn't live up to the expectations of many. But eventually it is my art and I have to sell it the way I want. Apologize again.. And thanks again to all collectors.

@signulll This person is already tremendously valuable to companies…not underrated at all 😂 Knowing where a product should be in 2 years is different then tinkering though. You are conflating a builders mentality + curiosity with genuine experience (which usually needs to be earned).

the most underrated hire right now is a great product person. when i say product person i'm def not talking about a product manager. perhaps i think there has to be somewhat of a new role. i don't have a good name for it yet but maybe something like "product thinker".. someone with an intuitive grasp of the product as it exists, where it's soft, where it sings, & how to iterate it toward something even sharper. in some sense, this person has to cohesively hold in their head where this product should be 2 years from now & work backwards from that. i say this cuz when building was hard, engineering was the bottleneck & the status hierarchy often reflected that. building is no longer hard. which means the variance in outcomes has shifted almost entirely to judgment on what to build, how to sequence it, & how to talk about it. & the story matters as much as the thing. internally, it organizes the team around a shared model of why. externally, it shapes the interpretive frame users bring to their first experience. you can't retrofit narrative onto a product & expect it to land, it has to be load bearing from the start. the rarest version of this person sits at the intersection of culture & deep technology. someone genuinely bilingual. they know what's technically possible & they know which cultural currents are real vs. ephemeral. that combo is what separates products that feel inevitable from products that feel assembled. before ppl clap back with this person has always been valuable, i know.. i am just saying now they might be the most *important* person in the room. their value compounds like never before.

The Minnesota state high school hockey tournament is like a major that should be added to the Super Bowl, World Series, Stanley Cup and the Larry O’Brien NBA championship.

@mbrendan1 @AnthropicAI How is transportation, agriculture not affected by AI at all? Being from Minnesota I already know about all the autonomous vehicles doing a lot of the planting and harvesting. Drones mapping the land. I think 9-12 here is a bit underserved by your graphic tbh

Mockup of how would @AnthropicAI's new labor automation chart would've looked 200 years ago. For our ancestors, the outer ring would be almost unrecognizable. "Computer & math" was nonsensical. Medicine and law were tiny and barely professionalized. The first photo was just about to be taken, so it would have been unfathomable to have a single blockbuster gross more than the entire gross national product of that period. "Office & admin" barely existed as a concept; counting-houses employ a tiny literate class. Agriculture alone consumed maybe 70-80% of the labor force in the US. There was a thick band of artisanal trades that don't map onto any single modern category: coopering, blacksmithing, weaving, tanning, milling. Clergy was a major professional category and Maritime labor was its own significant sector.

@batsoupyum @openclaw You should think about model routing Using local models for sensitive data, frontier for everything else DM me if you have questions

10 days into my @openclaw experience and my biggest learning so far is that the gap between using a good primary model like Haiku versus a local model or one of the cheaper open-source models is so massive that it's not even worth it You truly get what you pay for right now