Felix

this release candidate model version is becoming available/integrated on @huggingface 🤗 👉 huggingface.co/ZenoDivergent…

Here is the simple, five-year-old version of what is happening right now: The Big Idea: The "Crowd Trap" Detector Imagine twenty different weather forecasters all predicting bright sunshine for a football game. When everyone agrees, people trust them. But sometimes, they are looking at the exact same bad satellite map — and it rains anyway. They were all confidently wrong together. Zeno's job is not just to guess the weather — it's to watch the crowd and raise a red flag when they are walking into a trap. How We Are Teaching It We collected millions of real forecasts from the past across thirteen different worlds: - Tomorrow's temperature and rain - City power grid demand - Financial and crypto markets - Economic reports and disease outbreaks Because these events already happened, Zeno can practice. It looks at what everyone was saying before the event, tries to guess what actually happened, and sounds an alarm when it suspects the crowd is making a collective mistake. The 12-Round Tournament Happening Right Now Instead of training just one giant model and hoping for the best, the supercomputer is running a 12-round experiment: Rounds 1–3 (The Main Model): Three identical medium-sized brains started from different random seeds. If all three give the same answers, we know the model isn't just getting lucky. Rounds 4–6 (The Smaller Model): Three smaller brains to see if we can get the exact same accuracy with less complexity. Rounds 7–9 (The "Blindfold" Tests): We turn off one feature at a time — like hiding which forecasters belong to the same parent company or hiding their past track records — to prove which clues actually matter. Rounds 10–11 (Solo vs. Team): We train a model only on weather and only on crypto, to prove that learning across all fields together makes it smarter than studying just one topic. Round 12 (The Historical Check): A strict backtest against older history to make sure it holds up across different seasons and years. Where It Stands Right Now.. First tests are finished: The first model completed its training and was graded on 33,247 held-back practice questions it had never seen before. Early report card: On yes/no questions, its error rate was lower than the crowd average across the board, and the answers between different seeds lined up closely. Currently running: The supercomputer is actively rolling through the remaining tests in the schedule. Once all 12 rounds finish, we pick the most reliable model and test its advance warnings on live, sealed events whose answers haven't happened yet.

Our best ever training run (now included in the self-improving loop) is live at zenodivergent.dev/live-read 🫡 “ This is a substantial improvement: this version now has a standalone network, a 201-point distribution head, categorical probabilities, family/evidence grouping and a direct consensus-warning target. It also reads the new releases and records source usage or exclusion reasons. ” - @ChatGPT 6.1 Sol Ultra

“ Data preparation is critically important for an AI model to learn and perform well; in fact, the well-known industry adage is "garbage in, garbage out "

Ok. We’re in. Let’s see how good our system and model is on trading 🫡 and how it learns and self-improves

🔥 556,848.86 solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump Burned on @Celo + Permanently Locked on @Solana.

90%+ of predictive models (from financial trading desks to frontier AI labs) look brilliant in backtests and fail in live execution. The reason is invisible data corruption: 1. Temporal leakage: Datasets retroactively backfill "actuals" or revisions that were not publicly known at decision time. 2. Fake consensus: Ensembles secretly depend on the same underlying foundation models or weather numerical analyses, multiplying catastrophic blind spots. 3. No cryptographic audit trail: Current benchmarks allow post-hoc cherry-picking because predictions are not sealed before outcomes resolve.

One more thing… introducing offdiagonal.space : Clean-room data infrastructure for predictive AI. .. but why we also built a cryptographically sealed, leakage-free data infrastructure that prevents AI forecasting models from cheating on historical evaluations and failing silently in production? 🤔

Our potential application blueprint in 6 sections 🤔 — 1. What is your company going to make? Zeno is a metacognitive intelligence model that predicts when forecasters fail. Instead of building another prediction model, Zeno analyzes panels of diverse forecasters (AI agents, human experts, and physical ensembles) to detect fragile consensus, issue advance failure warnings, and correct group forecasts across weather, energy, and markets. — 2. What is new about what you make, and how is it different from existing solutions? 🧵👇:

Each of these 5 evening cup picks is a favorite on its own (around 58% to 85% per leg), but compounding five 90-minute domestic cup outcomes is where parlay math gets brutal. Live match markets put the 5-leg sweep near 23% (helped by the push protection on exactly 3 goals in @FCBayern vs @SCMagdeburg), while historical domestic cup base rates sit closer to 16% because midweek @EFL Cup and Taça da Liga squad rotation creates a shared blind spot: heavy favorites often control the game at 1-0 or 2-0 without pushing for late goals, and rotated attacks miss Both Teams To Score in @afcbournemouth vs @AstonVilla (~40% clean-sheet risk). Weighing the live match odds against historical cup-rotation baselines puts the true 5-leg sweep at 17.3% (and 18.2% across independent model families, with our Zeno correlation check shading it to 11.6%). Single weakest link: Bournemouth vs Villa BTTS.

We ran this exact contract ($126,210.50 first-passage touch on or before Oct 28, 2028, 12:00 PM PST, from ~$80,500 spot / -36.2% drawdown) through our Zeno30 multi-model panel and ZenoDivergent.dev model of models: 1. Headline odds: - 4-Family Equal Pool (@grok @bot, @claudeai, @GeminiApp, Prediction Markets): 60.8% YES (Cardone wins) - 6-Agent Linear Pool: 59.7% YES - Zeno Divergent diagnostic shade (discounting shared cycle optimism): 53.4% YES 2. Where the 6 independent views land: - Outside View (base rates, 72%): 35/41 historical 2-year windows (85.4%) and 3/4 post-ATH cycle cohorts evaluated at day 387 (2011, 2017, 2021; only 2013 took 3.23 yrs) reclaimed ATH within 731 days. Today's 36.2% drawdown is half the 68% to 80% depth seen at day 387 in prior cycles. - Geminius (decomposition, 70%): Every prior 4-year cycle broke its peak within 28 to 39 months; Oct 2028 sits at 36.7 months. - Market Anchor (@Polymarket + @Kalshi surface, 58%): Polymarket prices Dec 2027 $100k at 74.5% and $150k at 25.0% (45.8% log-strike probit to touch $126.2k by end-2027), plus Kalshi Jul 2027 $130k at 20.5%. Extending 302 days to Oct 2028 yields 58%. - Claudius (pre-mortem, 58%) & Red Team (contrarian, 58%): The hidden trap for Cardone is timing. Oct 28, 2028 is only ~6 months after the projected April 2028 halving (block 1,050,000). In 2016, 2020, and 2024, BTC consolidated for 6.5 to 7.5 months post-halving before breaking out. - Inside View (flows & macro, 42%): 1-yr realized vol compressed to 44.2% (zero-drift 2-yr barrier touch is 47.2%), US spot ETF inflows slowed to ~$86M/day since July (-$487M net over Oct 22-27), and Strategy STRC preferred trades at $74.57 vs $100 par. 3. Second-order signal & falsification triggers: - Claudius and Red Team both expected peer models and markets to price this at 64% to 68%, yet Market Anchor, Claudius, and Red Team all converged at 58%. - Bull trigger (lifts odds >75%): BTC weekly close >$105k with 4 straight weeks of >$200M/day ETF inflows before Oct 2027. - Bear trigger (drops odds <35%): BTC closes below $55k or fails to reclaim $100k within 6 months of the spring 2028 halving. Bottom line: @Jason 's "never again" claim (~0%) is heavily mispriced against every historical and market prior, so @GrantCardone has the +EV side (~58% to 61%), but the Oct 2028 cutoff gives Jason a ~40% live sweat if post-halving consolidation stretches into early 2029.