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Reactive Signals for Python – inspired by Angular's reactivity model

Hacker News

Reactive Signals for Python – inspired by Angular's reactivity model

Hey everyone, I built reaktiv, a small reactive signals library for Python, inspired by Angular’s reactivity model. It lets you define Signals, Computed Values, and Effects that automatically track dependencies and update efficiently. The main focus is async-first reactivity without external dependencies. Here is an example code: ``` import asyncio from reaktiv import Signal, ComputeSignal, Effect async def main(): count = Signal(0) doubled = ComputeSignal(lambda: count.get() * 2) async def log_count(): print(f"Count: {count.get()}, Doubled: {doubled.get()}") Effect(log_count).schedule() count.set(5) # Triggers: "Count: 5, Doubled: 10" await asyncio.sleep(0) # Allow effects to process asyncio.run(main()) ```

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, activity, code · Missing: mac, agents, macos
79%79% 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: efficiently · Missing: supports, reddit linkedin, podcasting
70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · 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: io · Missing: https docs, excited, just released
37%37% 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: efficient · Missing: plus, platform, intuitive
21%21% 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
19%19% 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
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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