PolyBridge @PolyBridgeHQ

General intelligence built on capital-backed data. AI x Information Markets :) polybridge.ai Pareto Frontier Joined November 2025

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PolyBridge @PolyBridgeHQ

4 days ago

Querying the PolyBridge world state graph

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Prediction markets usually answer one question at a time PolyBridge answers five: → What is the current state? → What will happen by date X? → If we observe X, what does that mean for Y? → If we force X, what happens to Y? → What would have happened if X had been different? Capital-backed data. Bayesian inference. Every answer traceable to live markets

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Fei-Fei Li @drfeifei

2 weeks ago

x.com/i/article/2062…

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PolyBridge @PolyBridgeHQ

2 weeks ago

Every agent, whether human, company or AI, runs the same loop: Observe → model the world → act → repeat The quality of the action depends entirely on the quality of the model Prediction markets are the best raw input for that model. Market incentives generating data about the future in real time We're building the reasoning layer that turns that data into something humans, companies and AI agents can actually use to reason about the future

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PolyBridge @PolyBridgeHQ

2 weeks ago

This is precisely why we use it as the starting point for our forecasts

Rory Sutherland @rorysutherland

3 weeks ago

Money is a truth drug.

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PolyBridge @PolyBridgeHQ

2 weeks ago

Let's cut to the chase: LLMs aren't truly reasoning at all; they're just "fitting" to local patterns of logic. This LGMT framework actually hits the Achilles' heel: it tests robustness, not something you can fake your way through by piling on more data.

恒星 @vintcessun

3 weeks ago

这篇论文很离谱：用一阶逻辑的“变形关系”去怼LLM的推理可靠性——本质上是在说，别只看你答对了几道题，要看你逻辑等价变换后还答不答得对。结果6个主流模型全翻车。更怪的是，高级提示（Few-shot CoT）只能勉强救场，符号和结论层面的小变动就暴露了隐藏缺陷。

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Rory Sutherland @rorysutherland

3 weeks ago

Money is a truth drug.

Count Mysterioso🎗️ @MysteriosoX

3 weeks ago

@rorysutherland @markchristie "Revealed preferences" got the economics Nobel, I think. What people *say* they'll do when there are brownie points in it and what they *actually* do when money's at stake are two very different things

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PolyBridge @PolyBridgeHQ

3 weeks ago

💙 Wolfram for expressive succinct functional power Is Wolfram language being used in conjunction with LLMs to drive discoveries too, like Erdos problem style?

Vitaliy Kaurov @superflow

3 weeks ago

This structure can be made w/ Wolfram 1-liner: p=Tuples[Range[-2,2],4] . I^{0,1,4/3,7/3}; RelationGraph[Abs[#1-#2]==1&,p, VertexCoordinates->ReIm@p] That's static full graph. RandomSample links, add them 1 by 1, and you get this animation.

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PolyBridge @PolyBridgeHQ

3 weeks ago

Received 60 applications for PB Quant Researcher roles in the past 24h Very excited to work with some of these awesome people on the most interesting problems in tech 🔥

PolyBridge @PolyBridgeHQ

4 weeks ago

We're hiring 🚀 PolyBridge is growing our team at the intersection of prediction markets, causal inference, and real-world decisions. 1) Software Engineers 2) Quantitative Researchers 3) Enterprise Account Executives UK/US/remote

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PolyBridge @PolyBridgeHQ

3 weeks ago

Allow polybridge to process for you

Naval @naval

3 months ago

The human brain isn’t designed to process all of the world’s breaking emergencies in realtime.

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PolyBridge @PolyBridgeHQ

3 weeks ago

Large language models produce confident outputs without calibrated uncertainty, traceable reasoning, or formal semantics for intervention and counterfactual queries. These are properties we value and explicitly design around

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PolyBridge @PolyBridgeHQ

3 weeks ago

Parlays provide useful know: the far reaches of the joint distribtion of world events is now being sampled 🔥 Great data for everyone wanted to understand the world More politics and world events please!

Y Combinator @ycombinator

3 weeks ago

Totalis (@totalistrading) lets users parlay on anything. Combine multiple event markets into one trade across politics, crypto, stocks, sports, weather, and macro. Starting with parlays, expanding into structured products. Congrats on the launch, @ImTheBigP & @ericliujt!

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PolyBridge @PolyBridgeHQ

3 weeks ago

Interesting @airtightfish solves for event-conditioned asset prices using a market mechansim We also provide this insight, but from a different perspective: - prediction markets are data - data becomes more powerful with a model - we find the joint distribution of all world events - marginalise it to find the probability of any given event - including complex conditionals, either/or, and events - event-conditional asset values, such as SPY under the outcome of Dems vs GOP, fall out as an expectation which is a generated quantity of the underlying probabilistic model The two approaches are complementary, and its awesome to see more markets coming online with more sophisticated ideas

Squid @airtightfish

a month ago

x.com/i/article/2055…

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PolyBridge @PolyBridgeHQ

3 weeks ago

Interesting We solve the problem you state a different way. - prediction markets are data, and any good source of data needs a model - from the model, we find the joint distribution of all world events - marginalise it to find the probability of any given event, including conditionals - actual conditional asset values, such as SPY under the outcome of Dems vs GOP, falls out as an expectation which is a generated quantity of the underlying probabilistic model The two approaches are complementary. You're generating more market data, which is arguably a more ambitious goal. We're simply building a picture of the world from all of the data that already exists. Would be awesome to work together