Hey everyone,

I've been working on a project called BeatBoss for a while now. Basically, I wanted a Hi-Res music player that felt modern but didn't eat up all my RAM like some of the big apps do.

It’s a desktop player built with Python and Flet (which is a wrapper for Flutter).

What My Project Does

It streams directly from DAB (publicly available Hi-Res music), manages offline downloads and has a cool feature for importing playlists. You can plug in a YouTube playlist, and it searches the DAB API for those songs to add them directly to your library in the app. It’s got synchronized lyrics, libraries, and a proper light and dark mode.
Any other app which uses DAB on any other device will sync with these libraries.

Target Audience

Honestly, anyone who listens to music on their PC, likes high definition music and wants something cleaner than Spotify but more modern than the old media players. Also might be interesting if you're a standard Python dev looking to see how Flet handles a more complex UI.

It's fully open source. Would love to hear what you think or if you find any bugs (v1.2 just went live).

Link

Comparison

Screenshots

We tried really hard not to make this an AI-only list.

Seriously.

Hello 👋

We’re back with the 11th edition of our annual Top Python Libraries, after spending way too many hours reviewing, testing, and debating what actually deserves a spot this year.

With AI, LLMs, and agent frameworks stealing the spotlight, it would’ve been very easy (and honestly very tempting) to publish a list that was 90% AI.

Instead, we kept the same structure:

• General Use — the foundations teams still rely on every day

• AI / ML / Data — the tools shaping how modern systems are built

Because real-world Python stacks don’t live in a single bucket.

Our team reviewed hundreds of libraries, prioritizing:

• Real-world usefulness (not just hype)

• Active maintenance

• Clear developer value

👉 Read the full article:

General Use

1. - a blazing-fast type checker built in Rust

2. - measures how hard it is to understand the code

3. - extracts data from 50+ file formats

4. - control request rates with five algorithms

5. - timing HTTP requests with waterfall views

6. - security middleware for FastAPI apps

7. - seamlessly enhance modules without monkey-patching

8. - executable specs that generate working code

9. - detects dead code and security vulnerabilities

10. - easy OpenAPI docs for any framework

AI / ML / Data

1. & - connect LLMs to external data sources

2. - compact JSON encoding for LLMs

3. - framework for building sophisticated LLM agents

4. - agent framework that executes actions as code

5. - building complex AI workflows with ease

6. - unified batch processing for AI providers

7. - convert any file to clean Markdown

8. - AI-powered data exploration through natural language

9. - extract key details from any document

10. - bridging AI and geospatial data analysis

Huge respect to the maintainers behind these projects. Python keeps evolving because of your work.

Now your turn:

• Which libraries would you have included?

• Any tools you think are overhyped?

• What should we keep an eye on for 2026?

This list gets better every year thanks to community feedback. 🚀

I'm excited to share , a tool suite to automate changelogs, versioning, and publishing—even for monorepos spanning multiple package registries.

Thanks to from , Sampo now supports PyPI packages managed via pyproject.toml and uv. And it already supported Rust (crates.io), JavaScript/TypeScript (npm), and Elixir (Hex) packages, including in mixed setups.

What My Project Does

Sampo comes as a , a , and a . It automatically discovers pyproject.toml in your workspace, enforces (SemVer), helps you write user-facing changesets, consumes them to generate changelogs, bumps package versions accordingly, and automates your release and publishing process.

It’s fully open source, and easy to opt in and opt out. We’re also open to contributions to extend support to other Python registries and/or package managers.

Target Audience

The project is still in its initial development versions (0.x.x), so expect some rough edges. However, its core features are already here, and breaking changes should be minimal going forward.

It’s particularly well-suited to multi-ecosystem monorepos (e.g. mixing Python and TypeScript packages), organisations with repos across several ecosystems (that want a consistent release workflow everywhere), or maintainers who are struggling to keep changelogs and releases under control.

I’d say the project is starting to be production-ready: we use it for our various open-source projects (Sampo of course, but also ), my previous company still uses it in production, and others (like PostHog) are evaluating adoption.

Comparison

Sampo is deeply inspired by and , from which we borrow the changeset format and monorepo release workflows. But our project goes beyond the JavaScript/TypeScript ecosystem, as it is made with Rust, and designed to support multiple mixed ecosystems. Other npm-limited tools include , , , and .

Google's is ecosystem-agnostic, but lacks publishing capabilities, and is not monorepo-focused. Also, it uses messages to infer changes instead of explicit changesets, which confuses the technical history (used and written by contributors) with the API changelog (used by users, can be written/reviewed by product/docs owner). Other commit-based tools include and .

is an ecosystem-agnostic tool inspired by Changesets, but lacks publishing capabilities, and is more config-heavy. But we are thankful for their open-source that we reused in Sampo!

To our knowledge, no other tool automates versioning, changelogs, and publishing, with explicit changesets, and multi-ecosystem support. That's the gap Sampo aims to fill!