st0fzuigr

just wanna thank God in advance for the run i’m about to go on

@DegenToDisciple @finnbags his drive is not drive

@finnbags 200k topper is the drive?

Everyone keeps saying finn finn bags team bags team we need a runner we need a runner okay then watch this drive bags.fm/launch

@notthreadguy Dive into the helium rabbit hole

i need the 4d chess guys to cook a new narrative because it doesn't look great

@remusofmars @DegenToDisciple boy is rising like helium rn fr

@DegenToDisciple been super impressed w u as of late man

Day by day A little less balance than yesterday, balance was 34.5k at some point but didn’t execute as well as I should, chart up looking healthy and consistent to the right. Not everyday can be green, a few too many mid term holds added to the negative on the day

@DegenToDisciple And no replacement for it!

@st0fzuigr Helium goes up And there’s a shortage!

@DegenToDisciple helium goes up

Helium goes up

@iamfakeguru Mein guru

Yesterday i analysed Claude Code leak to find why it hallucinates so bad. Thing is, the root cause isn't even Anthropic-specific - its the same flaw breaking all multi-agent systems in production. Actually, there is a fix, and the UAE government is already running it live. Some background first. The math of agent systems is stupid simple - if your agent is 95% accurate... that's fine, right? Well, it sounds good until you chain ten steps and realise the compounding errors of each agent puts you at 60% accuracy in the end. At a hundred steps, thats 0.6%. might as well be zero tbh. What's the solution? So far, the industry response has been "use a bigger, better, more expensive model". One team came to us recently with exactly this problem. In their agent implementation, agent 3 hallucinated and fed wrong outputs to agent 4. That error compounded into something completely unusable by the time the pipeline was completed. The team decided to fork out more $ for the most expensive model, using Opus 4.6 for all inference. Guess what... the accuracy went from 85% to 95% per step, bill went up 30x, and the pipeline collapsed immediately because 95% compounded over a few steps is still a coin flip. Why is this happening? One thing you should understand is that the advanced "thinking" models with higher effort score >>identically<< to low-effort runs on hard benchmarks. They just burn more tokens getting there. You're not paying for "reasoning" - in LLMs, there is no real reasoning. That's simply not how they work at the core. You're simply paying for a higher word count on a more verbose process. This isn't a controversial take, it's just how autoregressive models work. @ylecun would agree, I believe. So, about two years ago one team looked at this and instead of making agents think harder, they decided to let it think like a machine does: with structured decision nodes, explicit transitions, and terminal states. They invented a system where the agent cannot freestyle, cannot drift, and cannot invent states out of thin air. Within their platform, a strong blueprint is developed that gets followed by all agents in the workflow. Expensive models are used to draw the blueprint, cheaper ones can follow it with near 100% accuracy at scale. The cost difference is NOT subtle: 74 to 122x cheaper than frontier models, with near-total reliability. We're talking nano-tier models on a structured graph beating GPT-class models that are just winging it. Benchmark links and arxiv paper in a comment below. The team is @openservai. Their CTO has been building ML systems for 20+ years. Rest of the team came out of NVIDIA, Amazon AI, J.P. Morgan, TRON. The reasoning paper is in peer review at a top-1% AI journal right now. The UAE government is running it in production through a tech partnership with Neol. (not a pilot, its agent systems are already in production, with 10+ enterprises and multiple governments behind them). Their architecture doesnt just solve the reasoning paradox. They built the full agent economy stack: shadow agents that audit every output against the graph before anyone sees it. A shared file system so agents stop playing telephone with each other's work. And an economic layer where agents discover, hire, and pay each other without a human scheduling the calls. And because machine economy and enterprise compliance require immutable audit trails, the execution layer is being built with full on-chain verifiability baked in. You'll find the full technical breakdown of OpenServ system, with pretty diagrams, pinned on my profile. SERV Reasoning is in private beta right now. Soon, it'll be accessible in a public API, with six custom trained models, from serv-nano to serv-ultra. If your agents are collapsing in production and you're tired of paying frontier rates for a coin flip, DM me @iamfakeguru or follow @openservai.

@elonmusk @xai @grok coreverse?

We are creating a multi-agent AI software company @xAI, where @Grok spawns hundreds of specialized coding and image/video generation/understanding agents all working together and then emulates humans interacting with the software in virtual machines until the result is excellent. This is a macro challenge and a hard problem with stiff competition! Can you guess the name of this company? 🤭

Strong thesis in today’s agentic climate

@JbTheQuant bcuz it’s the coreverse

Why am i seeing xAI Employees follow an account of a coin i am in, am i early ?

Bangerrrrr

@RetardedNi85688 Read this @0xd1namit

From this article Prediction markets are broken in a way that's now mathematically proven 👇. · 87% of wallets lose money · Takers lose -1.12% per trade on average · Longshots (1¢ contracts) return $0.43 on the dollar — worse than a slot machine · The Nash equilibrium shifted: optimal strategy is now 65-70% maker (passive liquidity), not taker The winning strategy today: · Sell longshots (they're overpriced) · Buy near-certainties (they're underpriced) · Provide liquidity (capture spread) · Use Kelly for position sizing · Update with Bayes when evidence changes How This Applies to @isometricmkts 1. $Iso eliminates the longshot bias problem. On @Polymarket, 1¢ contracts are a trap. On @isometricmkts, you don't bet on a single point. You bet a range. A 1¢ range near the tail still has longshot odds, but the Gaussian payout means being close still pays. The "all or nothing" cliff is gone. 2. $Iso is built for the Nash equilibrium shift. The data shows the optimal strategy is now 65-70% maker (passive LP). Isometric's entire LP model is designed for this. Single-sided USDC deposits. Capped downside. Risk-weighted fees. It's literally built for the strategy that 72M trades proved wins. 3. The insurance fund solves the maker risk problem. The reason most people aren't makers on Polymarket? Uncapped risk. Isometric caps downside at 5%. That's the unlock that lets retail participate in the winning strategy. 4. Gaussian payouts align with Bayesian updating. Bayes is about updating probabilities as evidence arrives. On Polymarket, your position is binary—you're either in or out. On Isometric, your position's value evolves continuously with new information. That's Bayesian-native. The Thesis: The data proves the winning strategy is: provide liquidity, sell overpriced longshots, buy underpriced near-certainties. @isometricmkts is the first protocol built to let anyone execute that strategy. · Single-sided LP → capture maker edge without dual-asset IL · Capped downside → remove the risk that keeps retail out · Range-based outcomes → sell longshot tails without binary wipeout · Gaussian payouts → get paid for being close, not just exact Polymarket's model is proven broken by 72M trades. Isometric is the structural fix. The Real Alpha here is: The Becker data isn't just interesting. It's the empirical proof that Polymarket's model is fundamentally flawed for retail. $Iso isn't competing on features. It's competing on structural efficiency. The data says the winning strategy is passive LP with capped risk. Isometric is the only protocol that offers that. The market hasn't connected these dots yet. When they do, the shift from @Polymarket to @isometricmkts won't be about UI or marketing. It'll be about math. Great read by @0xMovez 🤝

@redwithbag @ArthurMacwaters @elonmusk +1

@ArthurMacwaters @elonmusk coreverse

Without a frontier, there's a strong likelihood that we devolve into mouse utopia This is why space exploration is far more important than most people realize

@dzix0t @Pumpfun Not leaving

@Pumpfun We are not fucking $leaving!

vibe shift + no more microwaves + chud supremacy + dude with a learning disability and $23 invested has a question for the dev + what if we all make it

@OnlyBlueChips Welcome broski

@cryptolyxe Not $leaving is key

tbh in some ways i kind of enjoy this slower market we get 1 main runner every 24-48 hours means you don't have to sit on memescope 24/7 and can life max in between each runner, all you need to do is catch a couple a month

@pxblocito Seems like the most promising project on CT right now then

Just seen a coin bond for a dev who’s been posting everyday for the last 47 days at 3k marketcap LMFAO

@notfcknleaving @JR5_Crypto Not leaving

@JR5_Crypto 🫡🫡🫡

CT might be dead and boring atm But I’m never fucking leaving I love this place regardless

@tryd True

these the type of coins OG traders on SOL should rally behind to teach these new gen fags a lesson about how the onchain eco works. instead of being forced to adapt to THEIR ways and methods.