We built this in Lovable. A few prompts that saved real time:. - Source: dev.to / about 2 months ago

I built the site, called Insider Hawk, with Lovable. - Source: dev.to / 3 months ago

A solo founder using Bolt or Lovable can go from idea to working prototype in a weekend. Cursor handles multi-file refactoring on a production codebase. V0 generates polished UI components from a description. The founder who previously needed six months and $80,000 in savings or seed funding can now ship a testable product in two weeks for under $8,000 in tool costs. - Source: dev.to / 3 months ago

If you're building with Lovable and Supabase, there's a gotcha that will bite you eventually — and when it does, you'll wonder why nobody warned you. Consider this your warning. - Source: dev.to / 3 months ago

I've shipped over a dozen MVPs with Lovable over the past year at Inithouse. The builder handles UI, routing, and deployment beautifully — but SEO is not part of the default stack. Every single app I launched needed manual fixes before Google would index it properly. - Source: dev.to / 3 months ago

I reckon you're more likely to get a good response on their Github page than here. Unless a dev happens to see this post. Source: over 3 years ago

Since you are using python, pandas, scikit-learn, scipy, and statsmodels are what you are looking for. Source: almost 4 years ago

In case you're really worried about cold start latency and your application load shows high variance in the number of concurrent requests, you might want to get a bit fancier. You could use time-series forecasting to anticipate how many containers should be warmed at each point in time. StatsModels is an open-source project that offers the most common algorithms for working with time-series. Here's a good... - Source: dev.to / about 5 years ago

Can't you get a student discount for Stata? R would definitely be able to handle everything. For Python, have a look through the statsmodel package https://github.com/statsmodels/statsmodels. Source: over 5 years ago