Pr1mal

$Corine appreciated by @MeteoraEco and sitting at 35k. Once we are back with sentiment on market it will insta x10.

4/ @corinedotin is a live agentic finance network. you deploy trading and lp agents from a single prompt. in month one it hit 517 users and 1,244 agents. it's now past 600 users, 1,500 agents and $70k in agentic tvl. its forge suite lets agents build liquidity strategies on meteora. teapawt.net/corinedotin/status/210…

higher $ZENO

Don't fade solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump

pump.fun, but your AI agent does the trading. 💊 introducing pump.fun trading agents on Corine. → track new launches in real time → deploy snipers with a simple prompt → trade directly on bonding curves → automate entries, exits & take profits → built-in risk controls & Telegram alerts from launch discovery to execution, let your agents handle the heavy lifting. you set the rules. Corine executes. built on Solana. powered by agents. 💜

@zenoVision_ Founder @mbarrbosa as a mentor for startups, invited by the government. And you still fading solana:DVska6Y1PohdNJD2Gt6DwQD1EypfEvcmrFfdttCiRkbf

A personal note on Convik and where we're going. Months ago, Robinhood put stocks onchain. Since then: → $15B in stock token volume in the first 100 days → $37B traded onchain across tokenized stocks and ETFs last quarter → The SEC opened a five-year path for US stocks to trade onchain Tokenized stocks went from $654M in January to about $4B today. That's still nothing next to a $75 trillion stock market. We are at the very beginning. But here's what nobody is saying: the market has the assets. It doesn't have the intelligence. Thousands of stock tokens across 200+ venues. Every investor picking tickers by hand, at 3am, with no research, no risk desk, and nobody to ask. Wall Street solved this decades ago with funds, analysts, and investment committees. Onchain, none of it exists yet. That's the gap Convik is filling. What we've shipped: → AI indexes on tokenized stocks, built from one sentence → An agent that manages your book 24/7 inside the limits you set → Underwrite: a research memo on any stock in seconds → Price Desk, live around the clock → V2: an agent that shows its work. Every reason hashed, debated by a committee, stress-tested on real history, and answerable What's coming next: → Indexes that earn: idle cash in your index earns onchain yield → Borrow against your index, not just a single stock → Every decision hash anchored onchain, permanent public proof → Public books: follow the best agents and copy their indexes → Tag @convik_ai on X and get a full memo on any stock → Remix any index and make it yours Nobody will pick stocks by hand. Agents will run portfolios, and they'll have to show their work. Convik is building that standard. We're early. This is going to be massive. 🟩

1/7 🧵 Most trading agents still decide with point forecasts or pure reinforcement learning. Both approaches share the same quiet flaw: they rarely tell you how much you should trust the decision you’re about to make. 🤔 That’s where conformal inference becomes interesting. Instead of giving you a single number, it wraps any model with prediction intervals that come with a finite-sample coverage guarantee: $$P(Y \in C(X)) \ge 1 - \alpha$$ No normality assumption. No claim that the model is correctly specified. Just a calibrated region around the prediction. 🎯 --- 2/7 📉 A useful way to see the practical difference is a 2026 paper on optimal trade execution (Irshad & Biswas). The authors put classical schedules, a PPO agent, and conformal-driven policies inside the same VWAP execution problem, both in a controlled simulator and on real 5-minute data from 30 U.S. large-caps. 📊 The question was simple: when you have a forecast of the next interval’s return, should you always act on it, or should you sometimes refuse to act? 🛑 --- 3/7 ⚖️ The conformal predictor itself was well calibrated. At a 90% nominal level, it achieved 90.2% empirical coverage in the simulator and 90.7% on the real data. That part worked! ✅ What was more interesting was what happened when they used the width of the interval as a decision gate. Acting on every point forecast reduced average slippage relative to VWAP tracking, but it also increased the variance of execution costs. ⚠️ --- 4/7 📊 When they only allowed the agent to deviate from the schedule when the conformal interval was sufficiently narrow, the picture changed 💡: • Cost variance fell from 19.1 bps to 10.0 bps, almost without hurting the average cost. • The threshold on interval width became a direct dial on the cost–risk trade-off. The PPO agent, by contrast, remained high-variance across seeds and did not reliably beat the simple schedules. 📉 --- 5/7 💡 This is the educational core of the result: 👉 A point forecast answers: “What do I expect?” 👉 A conformal interval answers: “How wide is the region I should still consider plausible?” For an agent, that second question is often more useful. It lets the system decide not only which way to lean, but whether the current signal is strong enough to justify leaving the safer default behavior. 🛡️ --- 6/7 ⚙️ In practice, this maps to a few concrete levers an agent can use: 🔒 Gate the trade: Only deviate when the forecast escapes the calibrated uncertainty region. 📏 Size the position: Adjust with interval width (wider interval → smaller size). ✋ Abstain: Pause when coverage can no longer be trusted (strong drift, too few recent observations). 📝 Transparency: Keep an explicit trace of why the agent committed, deferred, or abstained. --- 7/7 🚀 As more capital is handed to autonomous agents, the scarce resource is no longer raw predictive power. It is the ability to act inside bounds that can be inspected and controlled. 🔑 Conformal inference does not magically improve the forecast. It gives the agent a statistically honest way to know when the forecast is not reliable enough to act on. 🧠 That distinction is what turns an autonomous system into a manageable one. 🎯

AGENTS ARE COMING TO CHIT The next step for CHIT is making on-chain trading simpler, smarter, and private. With CHIT Agents, you tell the bot what you want in plain language, and it turns your instructions into orders within your set limits. All of this happens privately. From telling the agent what you want to executing your trades, privacy stays at the core of the experience. Just tell CHIT what you want done. Let the agent handle the steps, privately.

I have been calling several times solana:DU7Lhe2QtcRBJdor7KbpjfUDmLyA2kdhR49Li3czpump at market cap 25 -30k now its 300k+ Lets see how solana:DVska6Y1PohdNJD2Gt6DwQD1EypfEvcmrFfdttCiRkbf solana:FYZcYCHSp8FzNba1UtDZydKKGosmxVNpFBiVuia38AhT and solana:BDbS4nVYqHfbc3XiWbToF39fojHfKMZBVihUscqTTd6M will play out ;)

I have been calling several times solana:DU7Lhe2QtcRBJdor7KbpjfUDmLyA2kdhR49Li3czpump at market cap 25 -30k now its 300k+ Lets see how solana:DVska6Y1PohdNJD2Gt6DwQD1EypfEvcmrFfdttCiRkbf solana:FYZcYCHSp8FzNba1UtDZydKKGosmxVNpFBiVuia38AhT and solana:BDbS4nVYqHfbc3XiWbToF39fojHfKMZBVihUscqTTd6M will play out ;)