This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Describe a decision in plain language: a job offer, a startup bet, a big purchase, a negotiation. TwoHeads maps out your real options as a decision tree, estimates the probability of each outcome with a cited real-world analogue, and calculates the expected value of each path. Then you argue with it: every probability has a slider, so you can override the AI's assumptions with your own and watch the expected value recalculate live.
Hey everyone!
I built this because major decisions are always easier with concrete numbers. TwoHeads lets you describe the decision, and it extracts the branches and probabilities for you, with a cited analogue for every number so you can see *why* it thinks 60% and not 80%.
Every probability is a slider. The AI's estimate is a starting point; if you know your situation better than a base rate does (you do), drag it to what you actually believe and the expected value updates live.
It's free to use, no account needed, 1 analysis/day. Would love feedback on: (1) where the AI's probability estimates feel off-base; (2) what decision types it handles badly. I'm in the comments all day.
The slider for overriding probabilities is genius, made me realize I'd been anchoring way too hard on the AI's defaults. Tried it on a job offer last night and it actually changed which option I was leaning toward once I adjusted the equity upside estimate to something I actually believed.
the live recalculation when you drag a probability slider is so satisfying, makes the math feel like something you can actually argue with instead of just read
How does it actually source those cited real-world analogues, and is the citation depth solid enough that I'd trust the probabilities, or am I going to spend half my time fact-checking the tree?
Does the AI cite sources for the probability analogues it picks, and can I swap in my own reference points if I disagree with the cases it draws from?
About TwoHeads on Product Hunt
“Turn any decision into a number”
TwoHeads was submitted on Product Hunt and earned 2 upvotes and 6 comments, placing #159 on the daily leaderboard. Describe a decision in plain language: a job offer, a startup bet, a big purchase, a negotiation. TwoHeads maps out your real options as a decision tree, estimates the probability of each outcome with a cited real-world analogue, and calculates the expected value of each path. Then you argue with it: every probability has a slider, so you can override the AI's assumptions with your own and watch the expected value recalculate live.
TwoHeads was featured in Productivity (656.3k followers), SaaS (43.1k followers) and Artificial Intelligence (473.8k followers) on Product Hunt. Together, these topics include over 303.7k products, making this a competitive space to launch in.
Who hunted TwoHeads?
TwoHeads was hunted by Ayush Sanghavi. 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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Ayush Sanghavi