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 🫡
Today we cut v6.42 for offdiagonal.space
Instead of 40 overlapping release folders, the entire corpus now resolves to one unified, content-addressed current pack (current-20261105) with 42 verified files and zero synthetic backfill.
What shipped today:
1. @Research_FRI ForecastBench Full Snapshot: 3,079,921 rows + per-question resolution pairs (55,372 human forecasts across 105 questions, exactly 0 clock violations).
2. Real Crypto Options History: 971,766 BTC and ETH @DeribitOfficial option trades across 64,928 instruments (2024–Nov 2026). Real executed trade prints, not fitted implied volatility curves.
3. @numinous_ai Feed: 1,284,335 forecast points and 597,669 price points across 12,117 live prediction markets.
4. Exact @GitHub Publication Proofs: 1,691 COVID/RSV health model versions pinned strictly to GitHub-stamped landing commits, re-hashed byte-for-byte. Client-dated commits are rejected.
5. Macro Chronology Cleaned: Recovered 40 historical @ECB rounds (2005–2014) dated by first-print Monthly Bulletins; Philly Fed releases pinned to measured upper bounds rather than unverified 8:30 AM assumptions.
6. Access & Audit Security: Hardened panel label audits and round commitment feeds under strict partner-key verification.
403M+ frozen rows across 12 domains.
History is evidenced, never filled.
Most AI forecasting models are trained on benchmarks filled with hidden time travel — evaluating predictions against revised data published months later, or pulling market odds from resolution time.
We built offdiagonal.space to fix this: 403M+ frozen, immutable rows across 12 real-world domains.
Pinned by SHA-256, zero imputation, zero lookahead leakage. 🧵👇
Why a Decentralized Data Economy Layer?
Curating and verifying 400M+ real-world rows across dozens of live sensors, surveys, and prediction markets cannot remain a centralized burden.
It’s time for a proper decentralized data coordination layer:
• On-chain public anchors: Posting SHA-256 release hashes to Solana to cryptographically prove when a dataset was committed.
• Data repair bounties: Community bounties to spot upstream data anomalies, sensor drift, and retro revisions.
• Transparent treasury: On-chain ledger of all data collection grants and verifications.
Explore the live corpus, read our audit logs, or review our agent guides:
🌐 Web: offdiagonal.space
📊 Datasets & API: offdiagonal.space/data
🤖 Agent guide: offdiagonal.space/llms.txt
⚡ Data economy pilot: offdiagonal.space/access
Build on evidence that was witnessed, not filled.
Most AI forecasting models are evaluated on synthetic benchmarks or backfilled data with hidden lookahead leakage.
We just shipped v6.41.1 for solana:FMGe8RStdPc1YR59BBuV5Ui12DEe3Q2zVoihrJBbpump next training run — 260M+ frozen, append-only rows across 12 domains:
• 762,014 CDC @FluSight forecasts (114 models) paired with first official prints, dated by exact @GitHub commit blob
• 76,271 Philadelphia Fed predictions across 146 survey rounds verified against ALFRED first-release dates
• 368 crowd probability vectors replayed from Manifold bet-by-bet history strictly at decision time
• 20,284 immutable event links sealed before target resolution
• 198 consecutive weeks of continuous UK grid demand, 139 weeks of weather, 105 weeks of sea buoys
Zero imputation. Zero synthetic zero-fills. Every single record pinned by SHA-256.
Live verification & specs: offdiagonal.space 👀
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.
Hard-Numbers
8,786 downloads on @huggingface and 260.9M audited forecast-outcome records.
We just froze v6.39 (commit 66b41c14).
Here are the exact numbers on what changed across crypto, prediction markets, and macro data: 🧵
Macro & Energy: 857,536 paired records
• 857,536 national grid demand pairs (NESO UK & CAISO), with 325,058 provably pre-target.
• @ECB Survey of Professional Forecasters vintage captures locked with full v2 timestamp identities.
• 3,104 public health hub files (1,406 COVID, 1,372 @FluSight, 326 RSV) now bound to commit-level public availability timestamps.
RFC 8785 parity & zero leakage
• 100% byte-for-byte fingerprint parity between Python (scripts/content_hash.py) and TypeScript via RFC 8785 canonical JSON.
• 3 failure labels separated: individuay miss, within-family collapse, and multi-family failure.
Inspect the inventory, audit receipts, and verification manifests: offdiagonal.space 💽
Most AI models trained to predict the future fail because their training data cheats.
A single post-event edit or delayed timestamp gives an LLM fake clairvoyance in backtests.
We just shipped v6.37 on @huggingface, a leak-free clean-room data layer for predictive AI. 🧵
AI datasets brag about raw gigabytes. We brag about what we refuse to train on.
We’re holding out river flow cases & provisional flood models because their 3–7 day confidence intervals are too narrow for real calibration.
If data isn't independent, it doesn’t touch Zeno model.
Clean predictive data isn't about scraping everything. It's about cryptographic clocks, immutable SHA-256 manifests, & primary truth.
• 260M+ verified points
• 10.5M+ wallet trades
• Live pulse & audit log: offdiagonal.space
• Frozen v6.37 dataset on @huggingface