data economy and infrastructure for AI forecasting
Solana
Joined April 2026
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offdiagonal retweeted
Most AI models try to predict the future.
solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump does something much harder: it acts as a polygraph for predictions. It audits an entire panel of forecasts and tells you when consensus is an illusion.
Here is what just landed in Zeno v1.05:
1. Unified Architecture: 89.9M parameters across 13 domains, with open weights and @onnxai runtimes for all 13 fields.
2. Hardened Out-of-Sample Edge:
• Weather history: +33.1% edge on chronologically split later weeks (95% CI: +28.5% to +37.6%)
• 24-hour crypto: +13.1% edge on later weeks across 19,200+ cases (surviving 39-comparison corrections)
3. Radical Honesty: If Zeno doesn't beat the baseline on held-out data (e.g., COVID, RSV), it refuses to intervene and routes to the reference forecast. No unearned overconfidence.
4. Lineage-Aware Weighting: 10 models sharing the same base weights only get 1 vote.
Every prediction, outcome, and per-case CSV is open on @huggingface.
Test any panel live or inspect the audit: zenodivergent.dev 🫡
offdiagonal retweeted
Three parts, one research loop:
@offdiagonal_ → dated forecasts, outcomes and traceable evidence.
Zeno → forecast combination and failure-risk learning.
Zeno30 → live forecasting and prospective tests.
solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump → economic and community layer
The aim: let resolved outcomes improve the next decision.
offdiagonal retweeted
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 🤔
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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.
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2. What is new about what you make, and how is it different from existing solutions? 🧵👇:
offdiagonal retweeted
“ 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 "
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? 🤔