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I made a visual prompt chaining platform for LLM APIs

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I made a visual prompt chaining platform for LLM APIs

Hi HN, I found myself spending too much time creating nodes, drawing connections, and configuring model parameters with other platforms that had prompt chaining features, so I built this platform instead. - Quickly configure model parameters by saving and applying templates - Add and connect multiple nodes in batches, one node per model you configure - Pass and even parse LLM responses between nodes - Structure and interactively test prompt chains that expect varying user inputs at certain stages Some additional collaboration tools for teams: - Share projects and prompt chain concurrently with other users - Set up an organization to automatically share projects between members + enable API keys that apply across organization projects If anyone else finds it useful, I plan on adding more LLMs to the model list. It's the first time I have tried building something so feature heavy from scratch, I would love any feedback you have!

Visit productView on Hacker NewsMar 18, 2025
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, visual · Missing: mac, agents, macos
88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, para · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
36%36% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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