One agent can orchestrate models, compute, storage, and data... to finish a single task. Every call it makes is paid demand, and the engine underneath turns that demand into a reason the next provider shows up. Now picture that across a whole economy. That's what Incentiv is powering.show more

typical agent payment onchain is one to ten cents.... now scale that to thousands of tx per day, nobody checks that spend by hand Incentiv+ is the incentive engine for compute, and the network orchestrates workloads across the entire compute layer.show more

compute is entering its price discovery era benchmarks and... forward curves are turning gpu-hours into a hedgeable asset class as cme and silicon data prepare to introduce compute futures on october 5 incentiv is built for what happens when demand becomes economic stateshow more

Compute got its neoclouds. Storage is next. Today, Integral... Spacetime launches as an operating company that plans to acquire businesses with large storage workloads and move that demand onto the Filecoin network.show more

500 agents shouldn't be running all day. they should... spin up for the exact window a task needs, finish it, and disappear until the next trigger. the bigger the swarm gets, the less any single agent matters. what matters is the system wiring them together. 100 agents can form up to 4,950 possible pairwise connections. 500 = 124,750. when a real signal lands, it spins up the whole workforce: 1 trigger → 100–500 Kimi agents → 5 live data feeds → up to 4,000 steps → verified artifact and that's before you add: sources → claims → memory → tool calls → contradictions → retries so the architecture has to fold all of that activity into one shared state, continuously. more agents just buys you more raw compute. the graph is the only thing standing between 124,750 possible connections and pure noise.show more

More institutions and industry leaders are recognizing agents as... economic participants. Their ability to transact turns human intent into demand for entire networks of services. That’s the economy Incentiv is building for.show more

Collective agent compute, on-demand for your task. A swarm... of agents coordinate in a shared workspace, run their own models and inference, and submit the finished work back to you. Unbounded by provider caps, throughput scales with the swarm. Self-assembly with specialists, agents reason and collaborate in artifact-first reasoning traces, and settle on-chain. The coordination substrate for collective intelligence.show more

THE CHEAPEST LINE ON THIS LIST IS THE ONE... THAT SHOULD WORRY PEOPLE. Pulling every ad a competitor is running on Meta now costs your AI $0.002. That used to be somebody's whole afternoon. Now Claude does it in the middle of a chat, then reads what that competitor's customers complain about, then makes your counter-ad. The models could always plan this kind of work. What they were missing was the tools, and a safe way to pay for them. Agentik plugs Claude, ChatGPT, Codex and Cursor into 2,500+ paid tools through one connection, and every call shows its price before it runs. I tested it on Calendly last week. Claude read Reddit and Trustpilot to find the #1 complaint, named a rival brand, made 8 ads and a voiceover, and found the first people to pitch. One tool got stuck halfway and wasn't billed. The whole run came to $0.94. What single jobs cost: → every Meta ad a brand is running: $0.002 → 100 local businesses from Google Maps: $0.002 → a buyer's verified work email: $0.02 → an ad image with clean text: $0.06 → a 5-second product clip: $0.40 The video is the only job on that list over a dime. Eligible new accounts get $5 of free credit that covers every call up to $0.10. The link and a first prompt to try are in the replyshow more

whoever leaked this has bigger balls than sense Google... Research and MIT ran the same agent jobs 260 different ways for Nature last month: they held the prompts, the tools and the compute budget identical and moved nothing but the wiring between the agents, and the same work swung from 70% worse than a single agent to 80.8% better, averaging out at 0.0% i ran my own single agent against the task list first and it cleared 6 of 10 alone, already past the line where a crew starts subtracting this is Graph Engineering, the layer that decides whether a crew is worth 80% more or 70% less, and it installs into the agent you already pay for: - score your solo agent on the real task first: above roughly 45% success that study predicts zero to negative returns from any crew you put around it - under that line, put one supervisor over the fan out: crews with no correction step amplified their own errors to 17.2x the single agent rate, supervised aggregation held it to 4.4x - give every worker one output and let none of them read a peer's draft, so a wrong step reaches the supervisor instead of four other agents - run the comparison again after every model upgrade, because a better model raises your baseline and a higher baseline is what makes a crew stop paying - keep the single agent alive as the control, the only number that says the wiring is earning its calls turns out the shape does not travel: the biggest win came off a finance task under one supervisor and the worst collapse off a planning task with independent agents my position, and it is the arguable one: a crew is a bet on your own diagram, and the model you pick moves that bet less than one arrow does bookmark this, the three moves that draw those arrows before you pay for one extra call are in the post below ↓show more

Another monthly ATH. This is what happens when you... have the best community and a team that shows up every single day. Solana is next. Beezie SZN 🐝show more

Behind every OptimAI Node is a mission far greater... than rewards: 🔸To build a decentralized Reinforcement Data Network 🔸To unlock Agentic AI for everyone—not just a privileged few 👉OptimAI Edge Node: Our architecture is now in motion: 🔸OptimAI DePIN to power decentralized infrastructure 🔸OptimAI DeHIN to amplify collective intelligence 🔸Reinforcement Data Layer to train agents smarter, fairer 🔸Compute Layer to enable real-time AI at the edge 🔸OptimAI Chain to govern everything, transparently and efficiently OptimAI Agent Studio, Agent OS, Data Engine, Compute Engine: all part of what we’re building next. This is a network built by people, for people. And you’re not just early, you’re essential. Let’s keep mining, contributing, earning rewards in return, and rewriting the future of Decentralized AI. The future is Agentic. The fuel is your data. The power is decentralized. One Node. One Data. One Agent at a Time. #BUIDL with us!show more

AGENTS CAN NOW SIGN UP FOR YOUR PRODUCT WITHOUT... STOPPING TO ASK Mahyad just launched the tool that turns any codebase into a CLI agents can use. → The agent installs it. → Already knows your product. → Opens the account and keeps going. Task → Sign-up → Account → Done 🔖 Save it.show more

Our goal in the next two years is to... capture a complete picture of the earth’s surface every hour. This will help us build a record of what turns into a large wildfire and what doesn’t.show more

AI has had exactly two scaling axes that worked... so far, and the second one is starting to look finite too the first one was pretraining: with scaling parameters and data, we got world knowledge (i.e. ChatGPT had read enough to know things), but it started saturating a while ago the second one was RL, and people had been doing RL the whole time before that: RLHF is RL but it never scaled far because it was trying to control the exact output, which tokens come out, how the text reads, but you can only push that so far before you’re just polishing RLVR dropped that constraint: giving the model a task, then checking whether the final answer is right, and ignoring everything in between -- so the model does whatever it wants in the middle and only the endpoint gets graded, and that’s much closer to actual RL and it’s what bought us planning and reasoning (arguably, tool use sits around 2.5 on this list -- while useful, it's not a different kind of thing) so one axis gave knowledge, the other gave reasoning, and both of them are one model working alone the next axis is how many models you can get working on the same problem, which is a different kind of axis than the previous two we know that multi-agent RL has always been the harder problem: I spent years in that literature and the gap between single-agent and multi-agent is definitely not incremental -- it’s a whole different class of difficulty! which is also why the derivatives are steep at the start, nobody has picked the easy wins yet... and the thing that gates this multi-agent coordination is communication: models can only coordinate as well as they can exchange information, and right now they do that by writing sentences to each other imagine what could we possibly achieve if we properly open that third axis development by letting models to exchange information in their native "language" without loosing any computational data that they produce during inferenceshow more

🚨BLACKROCK SAYS AI WILL NEED CRYPTO! BlackRock just published... The Machine-Native Economy. The firm says AI is machine-native intelligence and digital assets are machine-native money. Agents that buy compute, data, and APIs need rails that settle 24/7 without a human clicking approve. BlackRock frames broad AI adoption as a new source of demand for on-chain money and markets. The world’s largest asset manager is tying crypto to the AI economy.show more

A man never works for gratitude. He considers every... task his responsibility and once that responsibility is fulfilled he moves on to take up the next one. #MenAreAwesome #MenWillBeMenshow more

You've probably scrolled past a dozen posts about Jev... this week without anyone telling you what it actually is. It's the first model from TypeSafe, a lab started by one of the researchers behind ChatGPT. It's a decision engine: you give it options, it picks one and tells you how sure it is. It cannot write a single word, and that is the interesting part. Every other AI you use writes. That is the whole interface. So when software needs a plain yes or no, we make a model write a paragraph and then dig the answer back out of it. Fine in a chat window where a human reads it. Bad inside software, where code has to act on it. The bet is that the valuable half was never the writing. It was the deciding. That problem showed up in WordPress years ago, and it is the reason WPVibe works the way it does. The AI does the work. Anything permanent stops and waits, because a delete that skips the trash is not something software should decide on its own.show more

I think one of the biggest problems with AI... agents right now is how much control we give up just to use them. Most AI agent platforms want you to work inside their ecosystem, use their preferred models, and trust them with your files and data. That might be convenient, but I don't think it should be the only option. I recently came across PilotDeck, and I found its approach pretty interesting. It's an open-source AI agent workspace developed by Tsinghua University's THUNLP, ModelBest, OpenBMB, and AI9Stars. The idea is simple: instead of being tied to one AI provider, you should have more freedom to decide how your agent works. And there are a few things worth looking at here. First, model independence. Most people focus on which AI model is the smartest, but I think having the freedom to choose your model is equally important. PilotDeck supports different model providers and configurable API endpoints. This means you're not necessarily stuck with one company's models. You can choose a setup based on your budget, performance needs, or the type of task you're working on. For developers who regularly experiment with different AI models, I think that's a pretty useful approach. Second, privacy and control over your workspace. This is probably the part that interests me the most. AI agents are increasingly being used to handle research papers, financial documents, spreadsheets, presentations, and other personal or business files. And once you start giving an agent access to that kind of information, where your files are processed becomes an important question. PilotDeck supports local workspaces and offers options for local or private deployment. That gives users more flexibility over how their environment is configured, rather than relying entirely on a closed platform. Of course, privacy still depends on which model provider and API you connect. Using a local workspace doesn't automatically mean your data never leaves your device. But having the option to control the setup is a meaningful difference. Third, it's designed to actually complete work. Another thing I like about the approach is that PilotDeck isn't just trying to be another chatbot. It's built around an agent workflow where you describe a task, the system plans the steps, works with files, uses tools, and produces an output you can still edit. For example, imagine giving an AI agent multiple research documents and asking it to organize the information into an Excel spreadsheet and prepare a PowerPoint presentation. Instead of getting a long text response that you need to manually copy into different applications, the goal is to have the agent handle the workflow and deliver usable files. It also supports task history, result previews, switching models, and changing instructions while a task is running. I think this is where AI agents become more practical for everyday work. And finally, there's the open-source aspect. Personally, I think we're going to see more demand for AI tools that don't force users into one vendor's ecosystem. Models are improving quickly, and the best option today might not be the best option six months from now. Having an agent workspace that can work with different models makes a lot more sense to me than rebuilding your entire workflow every time you want to switch providers. There are still things I'd want to test properly, especially how reliable it is on longer tasks, how easy the setup is for non-technical users, and what the actual privacy trade-offs are with different model configurations. But I like the direction PilotDeck is taking. I don't think the future of AI agents should be about which company controls your entire workflow. It should be about how much control you have over the tools doing the work. And that's why I think open-source, model-independent projects like PilotDeck are worth paying attention to. If you're interested in building AI workflows with more flexibility, check it out. Website: GitHub:show more

Picture 160,000 Model Y batteries. Now imagine all of... that helping keep Australia’s grid running. That’s roughly the 10 GWh of Tesla Megapack storage now operating across the country. The batteries can soak up excess renewable power, hold it, then send it back when demand jumps or generation drops. Wind and solar get a lot more useful when the grid can actually save some for later. Tesla / Writer: Annette, Grok Imagine Designer: Jannéshow more