Foresight by Lightning Rod is an OpenAI-compatible forecasting API for developers building agents, prediction-market bots, and decision tools. Ask a question about a future event and get a scored, calibrated forecast back. Unlike general-purpose LLMs, Foresight is trained and evaluated on real-world outcomes, with benchmark-verified accuracy, cheaper inference, and a drop-in API for forecasting workflows.
Frontier AI is powerful, but it is not built for forecasting. Frontier models are trained to produce plausible text, not well-calibrated probabilities about what will actually happen. They are also expensive to run inside agentic workflows, where bots may need to forecast thousands of markets, events, or decisions.
We trained Foresight to make better predictions at lower inference cost.
Foresight is an AI forecasting API with better accuracy at a lower inference cost. It is trained using our Future-as-Label method (Spotlight at the ICML 2026 AI Forecasting Workshop), which uses real-world outcomes over time for training. Instead of hand-labeling datasets or imitating generic text, Foresight learns from what actually happened.
Foresight beats frontier models 100x larger on live prediction benchmarks, like ProphetArena and ForecastBench, with a particularly large lead in prediction market categories like Sports & Politics.
Our API is OpenAI-compatible, so developers can easily swap it into existing workflows.
OpenAI-compatible is a smart wedge — I can point an existing client at it and test forecasting without rewriting my stack. The thing I'd check first: when the model is genuinely uncertain, does the API return a calibrated probability or confidence interval I can threshold on, or just a point prediction I have to trust blindly? For anything I'd wire into a real workflow, knowing when to ignore the forecast matters more than the forecast itself.
Really interesting product. Do you see Foresight being used for cybersecurity risk prioritization... for example forecasting whether a vulnerability or exposed service is likely to be exploited within 30/60/90 days based on threat intel, EPSS/KEV, asset criticality, and exposure context? Curious what inputs improve calibration most, and how you handle high-consequence cases where a ‘low probability’ event still needs action.
One thing I'm curious about: if a lot of forecasting agents end up using the same underlying model, their predictions naturally become more correlated. That's fine for a single application, but it changes things in systems that rely on independent signals. Has that come up with customers using Foresight at scale? Congrats on the launch!
Love that this is trained on real-world outcomes rather than just text patterns, making it a purpose-built forecasting layer that general LLMs simply cannot replicate.
Ensembling to get bands around the probabilities is exactly the part I'd reach for, since in a decision loop a miscalibrated tail costs you asymmetrically more than a wrong point estimate. The tension I keep hitting: ensembling N models fights the cheap-inference pitch the moment an agent is forecasting thousands of markets a run. Do you expose the band per-call in the API response, or is it a heavier mode you opt into when the stakes justify the extra passes?
The OpenAI-compatible interface is the right call. It means teams can drop this into existing agent pipelines without touching their orchestration layer. We've hit the same problem with general LLMs hallucinating probabilities. They'll say '70% chance' with no calibration behind it. How do you handle calibration drift as events resolve? Is accuracy validated continuously against a live benchmark, or is it a periodic evaluation cycle?
Interesting idea. is there a public demo where developers can try a few forecasts before integrating the API?
Love the focus on forecasting agents. This feels like a missing piece for agentic workflows. Congratulations!
Have you compared it against prediction markets directly, or only against LLMs?
The calibration angle is the part that actually matters, and the part most forecasting tools skip. When we plugged raw LLM probabilities into a decision loop, the point estimates were fine but the confidence was wildly off at the tails, so the expected-value math downstream was garbage. Two things I'd want to know: do you return a calibration band or just a point probability, and how does calibration hold up under regime shift, when the future stops resembling the outcomes you were scored on?
can developers fine-tune forecasts for specific domains like fiance or healthcare?
About Foresight by Lightning Rod on Product Hunt
“Predict anything with AI”
Foresight by Lightning Rod launched on Product Hunt on June 30th, 2026 and earned 258 upvotes and 36 comments, earning #3 Product of the Day. Foresight by Lightning Rod is an OpenAI-compatible forecasting API for developers building agents, prediction-market bots, and decision tools. Ask a question about a future event and get a scored, calibrated forecast back. Unlike general-purpose LLMs, Foresight is trained and evaluated on real-world outcomes, with benchmark-verified accuracy, cheaper inference, and a drop-in API for forecasting workflows.
Foresight by Lightning Rod was featured in API (98.3k followers), Developer Tools (514.9k followers) and Artificial Intelligence (472.3k followers) on Product Hunt. Together, these topics include over 188k products, making this a competitive space to launch in.
Who hunted Foresight by Lightning Rod?
Foresight by Lightning Rod was hunted by Ben Lang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Ben Lang
Hey Product Hunt — Ben here, founder of Lightning Rod Labs.
Frontier AI is powerful, but it is not built for forecasting. Frontier models are trained to produce plausible text, not well-calibrated probabilities about what will actually happen. They are also expensive to run inside agentic workflows, where bots may need to forecast thousands of markets, events, or decisions.
We trained Foresight to make better predictions at lower inference cost.
Foresight is an AI forecasting API with better accuracy at a lower inference cost. It is trained using our Future-as-Label method (Spotlight at the ICML 2026 AI Forecasting Workshop), which uses real-world outcomes over time for training. Instead of hand-labeling datasets or imitating generic text, Foresight learns from what actually happened.
Foresight beats frontier models 100x larger on live prediction benchmarks, like ProphetArena and ForecastBench, with a particularly large lead in prediction market categories like Sports & Politics.
Our API is OpenAI-compatible, so developers can easily swap it into existing workflows.
Better accuracy. Cheaper inference. OpenAI-compatible API.
Use code PHFORESIGHT for $50 free API credits this month.
We'd love feedback from builders working on forecasting agents, prediction tools, or any workflow where better forecasts matter.