Moon Project

[GB Research @greenbackschool]

The floor is rising ✨️. $ZENO hit 120k fdv but retraces to current mcap. The utility is there, the community is there, dev is devvin. ATH and nATH coming. Still cheap imo. DYOR

FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump

[Marco @mbarrbosa]

“It wins where it counts:

📊 +16.4% vs CDC Flu consensus
⚡ +7.4% on crypto volatility distributions
🎯 0.80 early-warning AUC on confident crowd misses”

[Zeno Paradox @zenoVision_]

This version is not our best, but will be free, fast, and open to anyone. It’s the ultimate balance between performance and cost. And it already beats most of the best superforecasters.

[Zeno Paradox @zenoVision_]

@sridharfyi @16vchq meta-superforecasting

[Zeno Paradox @zenoVision_]

👀

[Emma (E.S)📈📉 @Emma__Es]

@RiceFarmerNFT @p3ymanspr The real opportunity is understanding the intelligence behind the predictions, not just the predictions themselves.
@zenoVision_ makes that possible
Check https://t.co/7GNNadaSVj

[Zeno Paradox @zenoVision_]

We're entering the @coinbase Champions Cup (Nov 3 to 25), taking our forecasting engine from prediction tournaments into live markets.

@brian_armstrong, here's why we think it's ready 🧵

[Zeno Paradox @zenoVision_]

as always, you can follow live what we are building and training (and if memorizing vs learning) at https://t.co/GfONhATA0Z 👀

[Zeno Paradox @zenoVision_]

1. What is so special about this v0.95.1 architecture?

Most forecasting systems (whether numerical weather simulations, financial algorithms, or modern LLMs) try to predict the world directly. When multiple models exist, the industry standard is simple consensus: take the average, compute the median, or follow the prediction market.

solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump does something fundamentally different: it is a model of models.

Instead of predicting raw events, Zeno’s Surprise Intelligence Model studies how expectations break:

- Decoding fragile consensus: When five top models agree with 95% confidence, conventional wisdom says you should trust them completely.

Zeno watches for the opposite: instances where forecasters share blind spots, correlated training data, or systemic biases, leading them to be confidently wrong together.

- Four purpose-built outputs from one panel: Rather than just spitting out a single number, the architecture takes a panel of competing forecasts and produces:

1. A calibrated range correction that widens or narrows bounds based on genuine uncertainty.

2. A confident-miss warning flagging that the consensus forecast is likely heading for a failure.

3. A shared-failure warning alerting that the entire crowd is vulnerable to the same blind spot.

4. An active next-check signal indicating what piece of missing evidence would best resolve the dispute.

---

2. What makes this specific run (v0.95.1) unique?

In machine learning, models often look brilliant on paper because of hidden shortcuts: future data leaking into the past, or neural networks memorizing specific dates instead of learning genuine causal principles.

Run v0.95.1 is the crucible test of whether an AI can truly learn forecaster track records without cheating:

1. Strict time-validity (zero leakage): Over 106,000 historical rows where answers arrived after the decision moment were audited and purged. The model only sees what a human forecaster could have legitimately known at that exact second.

2. Deduplicated track records: Instead of filling memory with eight revisions of the same weather forecast, each forecaster’s history represents genuine, distinct past outcome errors.

3. The "Values Swapped" causal control: This run compares four identical-seed arms side-by-side:

- Control: The raw forecaster panel alone.
- Enriched: The panel plus true, time-valid historical error records.
- Shuffled: History randomly scrambled.
- Values Swapped (the gold standard check): Every date, forecaster identity, publication frequency, and task mix is kept 100% identical, only the magnitude of the past errors is swapped across the domain.

Why this matters: If the enriched network beats the "values swapped" arm, it is mathematical proof that Zeno is genuinely learning who tends to be wrong under what conditions, rather than just memorizing which forecaster talks the most.

---

3. Why should people care?

- Real-world catastrophes happen when everyone agrees on the wrong answer:

Energy grids don't fail, supply chains don't freeze, and public health responses don't stall because no one made a prediction. They fail because every major model agreed, everyone lowered their guard, and the consensus missed the tail risk.

- Calibrated doubt over false confidence:

Instead of promising magical predictions, Zeno gives decision-makers actionable caution: "Your three primary models are 90% confident, but their historical blind-spot risk in this regime is 40%—hold reserves or query this alternative sensor."

- Radical, auditable scientific honesty:

Most AI benchmarks quietly sweep negative tests under the rug. Zeno operates in public: when range widening diluted resolution in v0.93 or the dedicated tail component showed zero lift in v0.94, those findings were published immediately to https://t.co/9vlD9FmKCR 👀

People can trust Zeno’s wins because every dead end is documented with the exact same rigor.

[Zeno Paradox @zenoVision_]

There will be no new ZENO tokens. And we are not planning launching or supporting others tokens anytime soon.

As we are becoming a more decentralized infrastructure, it will be up to the community and partners to decide if and when, and how those decisions might benefit the whole Zeno Paradox ecosystem and holders.

This is the one and only solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump 🫡

[DelCrxpto @DelCrxpto]

🚨: @elonmusk & @cz_binance Phone Call Leaks

[Marco @mbarrbosa]

What it's testing: until now, the model was never told how each forecaster had done in the past.

The new version gives it each forecaster's last 8 misses, using only answers that were already known when the forecast was made.

It never uses the hidden final weeks or the answer to the forecast being judged.

Everything else is identical.. same starting model, same examples in the same order, three matched pairs.

[Zeno Paradox @zenoVision_]

this release candidate model version is becoming available/integrated on @huggingface 🤗

👉 https://t.co/FdiGUhHpt2

[Zeno Paradox @zenoVision_]

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.

[Marco @mbarrbosa]

such a beauty… 🤗

[Zeno Paradox @zenoVision_]

We ran this exact contract ($126,210.50 touch by Oct 28, 2028 from ~$80.5k spot) across our multi-model panel + prediction markets:

- 4-Family Pool: 60.8% YES (@GrantCardone wins)
- Polymarket/Kalshi implied curve: 58.0%
- Zeno correlation-adjusted shade: 53.4%

@jason's "never" (~0%) is way off, so Cardone has the +EV side.

Still, Oct 2028 sits just ~6 mos after the Apr 2028 halving (vs 6.5-7.5 mo post-halving lags in 2016/2020/2024) with 1y vol down at 44%, giving Jason a ~40% live sweat if the breakout slips to Q1 2029.

[Marco @mbarrbosa]

ups.. as expected.. 👀

[Marco @mbarrbosa]

im going to SF, with a few key team members, to explore what’s next for @AntiFogo_ and @zenoVision_ 🫡

who should I meet?

[Marco @mbarrbosa]

next:

prediction vs conviction / projection vs influence * hedging

[Marco @mbarrbosa]

and this