Alternatives
Products that do what Predictionary does
Let’s see who can predict the future.
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Mimic Human Research & Save Findings in AI Knowledge Base
2025 · sider.ai
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I’m Andrew, co-founder of Recall. Over the past few days I’ve been building Predict, a playground where anyone can: - propose skills we should measure in language models—live examples include difficult math, memory-manipulation resistance, code generation, and empathy under bad news - write evals (graded prompts) for those skills - forecast which models will score highest once GPT-5 is released Why this exists Benchmarks leak into training data quickly; scores are unreliable and labs still declare progress. The prediction tool aims keeps the target moving by letting the crowd define both the…
2025
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Hi HN! We partnered with the Atlas team to build a tool called AI Predict [0] that allows anyone to ask any question about the future and get a thoroughly researched, AI-generated prediction on how likely it is to be true. How it works: Atlas replicated a Berkeley paper [1] that showed LLMs could make predictions as accurate as the crowd. We’re using a mix of models from OpenAI and Anthropic, with information retrieval powered by NewsCatcher [2]. The system is live and fully functional, though it might struggle with hyper-local questions outside of the public domain (e.g., “Will I have…
2024 · aipredict.fun
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*Title:* Show HN: FutureSearch, AI forecasting you can verify AI forecasting is now approximately superhuman. Today, FutureSearch is exiting our long public beta and launching. We started FutureSearch in August 2023. (We’re the original AI forecasting company, at least in a Tetlock-ian, “forecast anything” sense.) We’re currently #1 of 194 in the most competitive AI forecasting tournament [1], and we score above the #3 and #2 human forecasters in the premier mixed human-bot tournaments [2]. Many people on HN seem to equate forecasting with prediction markets and finance. FutureSearch is not…
Aug 2026 · futuresearch.ai
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2021 · futuu.re
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Lock predictions. Verify outcomes. Build reputation.
Jun 2026 · tellshots.com
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Hi HN. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI. I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin. My dad Scott (AI Research Scientist at TRI) is my research partner on this. He sends me papers…
Mar 2026 · sup.ai
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τ-Bench is an open benchmark for evaluating AI agents on grounded, multi-turn customer service tasks with verifiable outcomes. It's been great to see the community adopt it since launch — this is now the third iteration. With τ³-Bench, we're extending it to two new settings: knowledge-intensive retrieval and full-duplex voice. τ-Knowledge: agents must navigate ~700 interconnected policy documents to complete multi-step tasks. Best frontier model (GPT-5.2, high reasoning) hits ~25%. The surprising part: even when you hand the model the exact documents it needs, performance only reaches ~40%.…
Mar 2026
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Mar 2026 · github.com
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