Bitplanet

10Planet is the AI Data Attribution Layer. 10Planet.com & Deva.me (@deva_dot_me) is a full-stack approach to building the layer one blockchains, smart contracts, and AI-DApps to create integrated infrastructure, incentives, & UX/UI to attribute and award contributions to AIs & AI economies (such as submitting training data). Core Contributors are the former founders/CEO and repeat team from TrueUSD, TrueFi, Canto, & Quantstamp. Individuals in the 10Planet & Deva Round: Kyle Samani (Multicoin MP), Paul Veradittakit (Pantera MP), Alex Pack (Hack MP), Saurabh Sharma (Jump Capital GP), Tekin Salimi (dao5 GP, prev Polychain GP), Dovey Wan (Primitive MP), Kevin Ding (DHVC MP), Yida Gao (Shima MP), Kevin Hu & Ashwin Ramachandran (Brevan Howard MPs, prev Dragonfly Capital GPs), Spencer Noon, Jesse Cohen (Hudson River Trading Algo), Yat Siu, Simon Doherty, Adrian Lo (Animoca), Will Wolf (prev. Polychain GP), Thomas Bailey (Road Capital MP), Alex Shin (prev Hashed GP), JK (DCG), John Fiorelli (Kenetic), Terry (prev 1kx), Jed Breed (Breed MP, Circle), Phil & Fran (Plaintext Capital MPs), Zaki Manian (Founder Sommelier), Lily Liu (Founder Anagram, President Solana), Eunice Giarta (Monad Founder), Chandler Song (ankr Founder), Michael Heinrich (0G Labs Founder), Lior Messika (Eden Block MP), Sandy Peng (Scroll L2 Founder), Hart Lambur (UMA, Across Founder), Ben Fielding (Gensyn Founder), Matt Liu (Origin Founder), Magic.link Cofounders (Sean, Jaemin, Arthur), Jose Macedo (Delphi Founder), Stefano (Bitscale MP), John Pfeffer, Jared Hutchings, Lincoln Gomes & Kamran Amin (MH Ventures), Richard Ma & Quantstamp, 0xMert_ (Helius Founder), Magmar (Skip Founder), Tyler Tarsi (Omni Founder), Jay Jog (Sei Founder), Konstantin & Vasiliy (Lido, p2p, Cyber Fund Founders), @ashcrypto, @paikcapital, @ivangbi_, @cryptocito, @dingalingts, @TheCryptoDog @krugermacro; over hundred investors, governors, creators.

We are cooking Genie!!! you're gonna see spices very soon...

Who is the impostor then? 👀

Nobody notices what any of it costs until the bill arrives, and by then it is one number with no story behind it. Metering every call turns spend into something you can actually read.

You choose the model you want, and the place you want to talk to it, and Genie handles everything underneath, So your agent is live in under a minute with no servers to set up and no code to write.

Attribution Backs your Intelligence Producing intelligence is the easy part now. A thousand agents can put out work that all reads the same, and none of it carries any record of which one actually did the thing worth paying for. That is where the value slips through. Attribution is what closes the gap. It watches the action, keeps track of which agent contributed what, and turns that into a record that still holds up when someone asks later. Without it you have plenty of output and no way to point at where any of it came from. The work happened regardless. Attribution is what makes it count.

Agents are the new Hot Dogs Every product shipped this quarter is suddenly an agent. The word got cheap because it sells, and most of what carries it is old automation wearing a new label. The market has already noticed. The open question is no longer whether something calls itself an agent. It is whether you can prove what it actually did, and trace the output back to the thing that produced it. That is the part that cannot be faked. An agent you can attribute is worth more than an agent you can only take at its word.

→ Problem: AI creates value from data and work it never has to credit → Solution: Bitplanet links every contribution to the model output it produced → Outcome: Bitplanet brings verifiable attribution to the AI economy

Choose your face 👀

@RoundtableSpace the prompt shrinking while the model improves is a great tell, makes you wonder what all those instructions were doing

An Anthropic engineer just put Fable in the same sentence as Sonnet 3.5 and Opus 4.5. - Called it "one of those models you'll just remember" - Claude Code's system prompt just got cut by 80% - The reasoning: this new class of models doesn't need to be told as much anymore Less instruction, more capability. That's usually the tell that a real jump happened. If the prompt shrinks and the model gets better, what exactly were all those tokens doing before?

@aakashgupta the shift from claiming fluency to showing your actual workflow live is real, proof beating resumes again

Most PMs saying "we're AI-pilled" would fail a screen share. One founder running a $100M AI startup makes candidates share their screen and show her their actual AI workflow before she hires them. Her finding: almost everyone claiming fluency is what she calls Level 1. Talking to ChatGPT or Claude in plain chat mode. No system. No memory. No pipeline. Six months ago that sentence alone got you through a PM interview. Now it gets you cut in the first round. She used to manage teams of hundreds. Today she runs the company on five PMs and four designers, built entirely inside a Claude Code operating system. Not because headcount is expensive. Because past a certain point, more people means more coordination and less ownership. A seasoned PM with real AI leverage now outproduces a team that used to need ten. Engineering went through this exact correction years ago. "I can code" stopped meaning anything the moment take-home tests and live sessions became standard. Resumes got replaced by proof. PM hiring is having its version of that moment right now, except the proof isn't a GitHub repo. It's your screen, live, showing what you actually built with the tools everyone claims to already be using. Prove it.

@rewind02 changing how you prompt instead of just paying for the bigger model is the takeaway most people skip

Claude Fable 5 early tester: "If you're using Fable 5 for everything, that is almost 100% overkill." in 10 minutes, Nate breaks down the six prompting habits that actually get the most out of it here's what he covers: > always give the why, not just the what > negative prompting: tell it exactly what not to do > baking verification loops into every agent and skill file > the one prompt type that silently downgrades you to a weaker model most people are just throwing longer prompts at a more expensive model the ones getting real results changed how they prompt, not how much they spend an article on how to build a fully automated AI company with Claude Fable 5 👇

@AnatoliKopadze the "do you have an agent" becoming as normal as an email address is a pretty striking way to frame it

Meta's Chief AI Officer: "People will have one, maybe two agents they rely on for everything. More and more of your personal life and work." In a few years "do you have an agent" will sound like "do you have an email address." The clearest 20 minutes you'll find on where AI agents are headed. Watch it, then read the guide on how to create your own AI agent step by step below.

@Ric_RTP the pricing-for-tokens-instead-of-value point is sharp, if it worked they'd take equity says a lot

Palantir's CEO just exposed Sam Altman and Dario Amodei for robbing every Fortune 500 company. Within two minutes, Alex Karp took the entire frontier AI industry apart on national television. His exact words: "Every single enterprise in this country, these people are LIVID. They are paying for tokens that create no value. These people are stealing the weights and alpha of my business." He literally said the entire frontier AI business model is intellectual property extraction dressed up as a subscription. Then he also destroyed the pricing model with a single question that Silicon Valley still refuses to answer: "If it was so valuable, let's say I can make you $1 billion tomorrow. Wouldn't I say I'll make you $1 billion and I want 30 percent? Why are they charging for tokens if it's so valuable?" That question breaks the industry. If OpenAI and Anthropic's models truly delivered the productivity gains the labs claim, they would take equity or a share of the profit they generate. They would not sell access by the million tokens. Token pricing is itself the CONFESSION that the product cannot produce reliable value at scale. If it did, they would price for the value. But they price for the compute because that is what they are actually selling. Karp went even further... He called the entire arrangement "a wealth tax that does not help the poor. It just punishes." American businesses are transferring the alpha of their operations, meaning the workflows, the customer data, the strategy memos, the internal models that make them competitive, directly into the training pipelines of a handful of Silicon Valley labs. Once those labs retrain, the customer's own edge becomes the next enterprise product sold back to their competitors. And the part the AI industry does not want anyone thinking about: Every enterprise running its confidential documents, its customer conversations, and its financial models through a frontier model is potentially teaching that model HOW to replace them. The vendor collects the token fee AND the compounding intelligence about that customer's business. That is the mechanism. And that is why Karp used the word "stealing." He claims this is why every executive he meets is furious in private and silent in public. Nobody wants to be the CEO who called out the labs and then discovered their next competitor was built on their own leaked workflows. The entire AI industry has been priced for perfection on one assumption: That frontier labs produce durable, defensible value that justifies infinite compute spend. But Karp just told us that the customers do not believe that assumption anymore. They believe they are being taxed without benefit, watched without consent, and copied without recourse. The moment enterprises stop believing, the whole valuation stack shakes.

@shmidtqq agents consolidating memory overnight instead of retraining is clever, that 6x jump shows how much memory actually matters

Anthropic taught its AI agents to dream. "Dreaming refines that memory between sessions, pulling shared learnings across agents." in 6 months, an agent that forgets last night's lesson is the one you turn off. it replays its own runs overnight, learns from mistakes, rewrites memory - like a brain consolidating during sleep. no retraining. Harvey's pilot: task completion ~6x. Worth more than a $500 course on agent memory. Watch the keynote, then save the playbook.

@0xCodez realizing you were doing the routing and memory work by hand is such a real moment, that reframe hits

Creator of OpenClaw: "in January, juggling 10+ terminals felt like peak productivity. Today it feels silly. i thought I was orchestrating - really, I was the scheduler, the router, and the memory" in this 6-minute talk, the founder of OpenClaw shares how he builds effective agentic loops from scratch. Watch it today, then explore how to build loops for trading agents in artice below

@deva_dot_me waiting for it to dock...

[▓▓▓▓▓▓▓▓▓⛵░░░░░░] shipping soon...

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