I created interactive buttons for chatbots

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It's about to be 2026 and we're still stuck in the CLI era when it comes to chatbots. So, I created an open source library called Quint. Quint is a small React library that lets you build structured, deterministic interactions on top of LLMs. Instead of everything being raw text, you can define explicit choices where a click can reveal information, send structured input back to the model, or do both, with full control over where the output appears. Quint only manages state and behavior, not presentation. Therefore, you can fully customize the buttons and reveal UI through your own components and styles. The core idea is simple: separate what the model receives, what the user sees, and where that output is rendered. This makes things like MCQs, explanations, role-play branches, and localized UI expansion predictable instead of hacky. Quint doesn’t depend on any AI provider and works even without an LLM. All model interaction happens through callbacks, so you can plug in OpenAI, Gemini, Claude, or a mock function. It’s early (v0.1.0), but the core abstraction is stable. I’d love feedback on whether this is a useful direction or if there are obvious flaws I’m missing. This is just the start. Soon we'll have entire ui elements that can be rendered by LLMs making every interaction easy asf for the avg end user. Repo + docs: https://github.com/ItsM0rty/quint npm: https://www.npmjs.com/package/@itsm0rty/quint

Visit product View on Hacker NewsDec 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos

92%92% 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: created, para, gemini · Missing: supports, reddit linkedin, podcasting

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

TrustMRRFits verified-revenue profile · Strong signals: para · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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 · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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

Correct prediction on native model

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