New Task Alert: Clinical Case Classification Quest 🩺
We’re back with a new task, and this one is all about clinical judgment.
Review each vignette, spot the key details, and choose the medical specialty that best fits the case.
10 tasks
100 points each
Multiple choice
We’re proud to be a Day 1 Launch Partner 👐 and part of @PrismaXai’s First 100.
Better AI needs better human judgment. Excited to support the launch as the team builds more rigorous feedback loops into AI training and helps raise the standard for AI quality.
"PrismaX is building around a belief we share: better AI requires better human feedback. We’re proud to be a Day 1 Launch Partner and excited to support the team as they bring their vision to life." - @PerleLabs@PerleLabs is the sovereign intelligence layer for AI, turning verified human expertise into high-integrity data. Human judgment is their craft, and their team gave us some of the sharpest testing feedback we received.
The quality of AI systems depends on the quality of the data behind them.
Excited to support @PrismaXai as they launch Verify Quality and bring more human judgment into robot training data 🤝
The First 100 begins now.
Verify Quality is live on PrismaX. For the first time, anyone can score the robot training data that models learn from, earn points, and compete to become one of The First 100.
Better data. Better models. The standard starts with you.
Synthetic data. Anonymous labels. Black-box pipelines.
AI systems are moving fast, but the data layer behind them still isn’t built for trust.
Perle introduces a different approach:
→ Expert-validated data
→ Human-verified feedback
→ Onchain provenance
→ Audit-ready infrastructure
This is what data quality looks like for real-world AI.
The biggest drag on AI safety isn’t that humans are still in the loop.
It’s that the human work behind AI is often anonymous, untracked, and hard to audit.
People are already catching failures, judging nuance, and testing edge cases.
Perle is building the infrastructure to make that judgment visible, verifiable, and accountable.
The PRL @BinanceWallet Alpha Trading Competition just launched, with $200K in rewards available for participants!
Early Bird multipliers are already active, and qualified users can also unlock the Rising Trader Boost.
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binance.com/en/support/annou…
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🔸 Early Bird Multiplier is on — the earlier you trade, the higher the boost. Day 1 trades enjoy a 2.5x multiplier.
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Don’t miss out 👇
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Perle in NYC 🗽
Our team will be at @ethconf by @ETHGlobal next week.
If you're attending, come say hi 👋
We'll be around throughout the event meeting builders, teams, and anyone interested in the intersection of AI and human intelligence.
See you there.
At Perle, this is the future we are building toward:
✅ Expert-validated data.
✅ Reputation-based contribution.
✅ Transparent incentives.
✅ Human expertise that compounds over time.
Because the next era of AI will depend on better systems for finding, verifying, and rewarding the people who make that data trustworthy.
The next generation of AI data infrastructure should work differently.
Contributor history should be tracked.
Domain specialization should be visible.
Reputation should compound over time.
Access to higher-value work should be tied to demonstrated quality.
That is how an expert economy forms.
The problem is that most AI data systems were not designed to recognize expertise over time.
A contributor completes a task, gets paid, and moves on. Their accuracy, specialization, and reliability often do not compound into a persistent reputation.
That is not just a contributor problem. It is a data quality problem.
In medicine, law, robotics, security, and policy, the quality of the data depends on the quality of the judgment behind it.
A clinician reviewing clinical data is not interchangeable with a random worker.
A legal expert evaluating a contract is not interchangeable with a general annotator.