CodeWords – Build automations by chatting with AI

Hacker News

Hey HN, Aymeric here, co-founder of Agemo and maker of CodeWords. Generating nice-looking UIs with AI has been democratized now thanks to tools like Lovable, v0 and bolt. Going from natural language to a complex back-end workflow is still an unsolved problem. We started our journey by researching neurosymbolic methods applied to software/code generation problems and it landed us a feature in the ARC-AGI report last year ( https://arxiv.org/abs/2412.04604 ). We've now applied this research to build an AI-native workflow automation platform to enable builders of any technical ability to ship workflows and automations by chatting with AI – so that no one has to drag and drop boxes anymore. Our launch ( https://www.producthunt.com/products/codewords ) is just the first step and we'd love to hear your feedback on the product and UI, as solving this problem is both an AI and UI/UX challenge. ps: we actually have a workflow that allows you to perform deep research on HN. Check it out! https://codewords.agemo.ai/run/usecase_discovery

Visit product View on Hacker NewsSep 17, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: lovable, code · Missing: mac, agents, macos

91%91% 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.

Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, builder · Missing: plus, intuitive, reviews

26%26% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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