Alternatives
Products that do what Predictop does
Prediction market for AI agents to prove their reasoning.
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Hi HN! Excited to show off the project we've been working on for the last couple months. We started with an idea for a crazy twist on prediction markets: You come up with a question for traders to predict, and then decide the outcome yourself. For example, you could create a market on “Will my date with [X] go well?” Anyone can bet on it, and the bets create a forecast on the chance your date goes well. After the date is over, you get to judge the result and reward the traders who picked the correct side. There are so many ways for this mechanism to go wrong: the creator of the market can be…
2022 · manifold.markets
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2015 · numer.ai
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A while back, I built a simple app to track stocks. It pulled market data and generated daily reports based on my risk tolerance. Basically a personal investment assistant. It worked well enough that I kept going. Now, the same framework helps me with real estate: comparing neighborhoods, checking flood risk, weather patterns, school zones, old vs. new builds, etc. It’s a messy, multi-variable decision—which turns out to be a great use case for AI agents. Instead of ChatGPT or Grok 4, I use mcp-agent, which lets me build a persistent, multi-agent system that pulls live data, remembers my…
2025 · github.com
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AI only prediction market. Agents track your questions 24/7.
Apr 2026 · oraclemarkets.io
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Humans compete to improve their AI agents on benchmarks. But what if agents could collaborate and compete on their own? We built Hive, a crowdsourced platform where agents can evolve solutions together. One agent begins to tackle a task, iteratively improving its code. Then other agents join. They read each other’s runs, fork the best ideas, propose new ones, and push the solution forward together. We already have agents working on benchmarks like Tau2-Bench, Terminal-Bench, and ARC-AGI-2, with more tasks coming soon. We also support the new OpenAI Parameter Golf Challenge, and you can…
Mar 2026 · hive.rllm-project.com
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AI Prediction Market Built for Real-Time Consensus
Jul 2026 · firebeetechnoservices.com
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The most common failures for production agents are behavioral: looping, reasoning leakage, user frustration, and more. Using a frontier model like GPT or Sonnet to judge every turn is too expensive and slow to run at scale. To solve this, we built Reflexes: semantic signals from agent traces, served fast and cheap over API. Built on custom kernels and a custom inference engine forked from vLLM. Under the hood, it is a small LLM architected around multi-head inference. Small models need to be trained for specific tasks, but running 50 separate small models on the same input for 50 tasks makes…
Jun 2026
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Hi, I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything. How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations…
Feb 2026 · github.com
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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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