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Luminal – A truly statically typed Python notebook

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Luminal – A truly statically typed Python notebook

Hi everyone! We're the co-founders of Luminal. We built Luminal because we were frustrated with existing Python notebooks. Python is a very powerful programming language, but it can be hard/confusing to use. There are a few aspects to this: • Python likes functions with _lots_ of arguments. Without good type info this means constantly switching back and forth between the docs and your editor. • Except for basic syntax errors, you generally don't know that you have an error in your code until you run it. This can be very annoying, especially if running takes a while! • Many things that are commonly done in Python are well... common. Reading data, plotting, writing data, filtering, etc. However, they can still take quite a while to figure out in practice. That shouldn't be the case. • Existing browser-based notebooks tend to be on the slow side. MyPy helps with the first two items, but we wanted a notebook that was really built for statically typed Python, instead of it just being a plugin. So we built Luminal. Luminal was built with static typing, speed and collaboration in mind from day one, meaning that: • When you write code you have best-in-class auto-complete right there, including all the relevant type information and docs (even for specific parameters!). • You can find issues in your code earlier (before you run your code) thanks to static analysis. • You can use no-code cells to generate code for tedious/repetitive tasks. The generated code is high quality thanks to the contextual information available through static typing. • You can collaborate on scripts with others in real-time or share a script via link for someone else to use. • Luminal is built to be fast and "just work". ... And this is just the beginning! There's so much we want to do with Luminal to take the friction out of writing Python, but you have to make a start somewhere and this is it! We hope you're as excited as we are about making Python easier to use and we're looking forward to your thoughts and feedback!

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Indie HackersFits the IH revenue-focused audience · Strong signals: para, including · Missing: supports, reddit linkedin, podcasting
87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: context, tasks, using · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
74%74% 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 · Missing: plus, platform, intuitive
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: para · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · 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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