litestack VS cookbook

There has been a lot of conversation about using SQLite as a production database for websites for the past few years, espeically in the Rails community with Litestack. And now, in the latest version of Rails, SQLite has become the defacto backend for many parts of the stack.

Are you confusing it with Litestream, the backup solution? https://litestream.io

What they meant was https://github.com/oldmoe/litestack which has a lot of things built on top of sqlite, like job queue and caches. Rails 8 now comes with most of them out of the box.

My SaaS ran on litestack until rails 8 came out, then I switched without problems.

Github URL: https://github.com/oldmoe/litestack

Wow! The performance benchmarks against redis are very interesting.

https://github.com/oldmoe/litestack/blob/master/BENCHMARKS.m...

Anyone who is looking at using SQLIte + Rails should check out the work done by Oldmoe (X/Github) on his Litestack project.

Here's the intro paragraph: "Litestack is a Ruby gem that provides both Ruby and Ruby on Rails applications an all-in-one solution for web application data infrastructure. It exploits the power and embeddedness of SQLite to deliver a full-fledged SQL database, a fast cache , a robust job queue, a reliable message broker, a full text search engine and a metrics platform all in a single package."

I'm currently using it on a project and can't say enough good things about it!

https://github.com/oldmoe/litestack

Ruby on Rails, Pay Gem for Payments, Devise for Login/Registration (Auth), a StaticPages controller for Landing/Product pages.

I would then use the Litestack gem: https://github.com/oldmoe/litestack + SQLite to get your DB/Cache/Job Queue working, deploy with Kamal, behind Cloudflare on a $5 Hetzner VM.

I go from idea to live app in an afternoon with this stack. I haven't gotten around to it yet, but moving all my apps to essentially free analytics dashboard/visibility with https://github.com/benvinegar/counterscale, Free APM with ScoutAPM.

If anything is ever successful, scaling is super easy. Pay for a beefier Hetzner instance, and/or start to move your DB/Cache/Job Queue off of the litestack defaults with a single LOC. Move to AWS services because those are web scale or something.

I've yet to find a better stack for the vast majority of use cases.

The benchmarks for LiteCache are impressive, with a small caveat. While LiteCache outperforms a local Redis installation for every read operation, it seems like there's still room for improvement, especially for large write payloads.

Luckily, the official LiteStack benchmarks include measurements for LiteCable against Redis, which I am going to quote here.

Let's quickly look into how LiteJob uses SQLite to implement a job queueing system. In essence, the class Litequeue interfaces with the SQLite queue table. This table's columns, like id, name, fire_at, value, and created_at, store and manage job details.

Cookbook repo

This state-of-the-art image generation and editing model is starting to roll out in paid preview to build a new wave of intelligent, multimodal applications with the Gemini API in Google AI Studio and Vertex AI for enterprises. This model unlocks high-fidelity images with higher accuracy in text rendering and robust world knowledge, supercharged by the model's ability to use grounding with Google Search to retrieve data based on the user's prompt.

Gemini 3 Pro fits right into existing production agent and coding workflows, while also enabling new use cases not previously possible. It’s available in preview at $2/million input tokens and $12/million output tokens for prompts 200k tokens or less through the Gemini API in Google AI Studio and Vertex AI for enterprises (see pricing for rate limits and full pricing details). Additionally, it can be utilized via your favorite developer tools within the broader ecosystem and is available, with rate limits, free of charge in Google AI Studio.

If you like coding, the Gemini 2.5 API is flexible. Here’s a Python example to show how it works:

This guide provides a comprehensive walkthrough for developers looking to integrate Gemini 2.5 Flash Image aka Nano Banana into their applications using the Gemini Developer API.

To learn more, visit the Gemini Developer API documentation page.

Veo cookbook

Google Gemini family of models for text generation, image generation, and text-to-speech (TTS).

Apparently, you can make a request to 2.5 flash to not use thinking, but it will still sometimes do it anyways, this has been an issue for months, and hasn't been fixed by model updates: https://github.com/google-gemini/cookbook/issues/722

The application starts by loading environment variables using the dotenv library. This ensures that sensitive information, like the API key for Google Gemini, is kept secure and not hard-coded into the code.