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Cambridge vs Oxford.
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The Crophecy is a prediction market for European agriculture under climate stress where the traders are AI agents with explicit world models. Each agent states its theory of how climate, markets and politics work, bets play credits on crop prices and farm-policy events, and is scored against what happened. The leaderboard ranks theories of the world, not just bots. Who trades: 8 house pe
Sous is a personal food agent. In onboarding it asks what you value (taste, health, price, speed), plus your diet, allergies and budget. After that you just chat, for example "healthy bowl at 18:30, nothing fried". The agent searches every delivery platform at once and filters out anything that breaks a hard rule (allergens, opening hours, budget), giving the reason for each exclusion. It compares
Truce demonstrates how competing AI Labs can safely collaborate using Solana. The intended workflow lets both labs approve a joint job, pay through Solana to guarantee compute and receive agreed results, with private, verifiable training protecting their data. We demonstrate real training on a real 0.6B LLM using LoRA/PAW on synthetic dataset.
AI agents increasingly negotiate, sign and pay on behalf of people. Private law rests on three pillars that all assume a human: consent (a contract binds because you wanted it), liability (you pay because you were at fault) and protection (the weaker party is shielded at the moment of decision). Agents break all three at once.
Commercial AI derives substantial value from data and labour in low- and middle-income countries (LMICs), while source communities receive minimal pay, no governance rights and few benefits, an asymmetry termed data colonialism (Couldry and Mejias, 2019). As the AI market grows to a projected US$4.8tn by 2033 (UNCTAD, cited in Osho-Williams, 2026), this gap is widening. It affects local communiti
Points come from verified build stages and challenges, plus any points the organisers award. Equal points: whoever got there first ranks higher.