Kydlaw

I haven't tried chDB yet, but I heard about it. Thank you for reminding me of that option.

I use SQL in data pipelines and processing that is going to require interoperability.

But for data exploration, I usually prefer Polars (imo it is easier to work with text, semi-structured data, etc.)

Happy to see activity around Polars. This has been my go-to library for data processing due to the enhanced ergonomics compared to Pandas and SQL.

But they were a bit quiet lately, and I started looking more and more into DuckDB recently… until the recent acquisition of DuckLab by AWS

That's what we are using as well in our small team. `mise` for our non-sensitive values/ environment variables and then `fnox` (from the same author) to encrypt and manage the secrets. `mise` has a plugin for `fnox` that allows to automatically load the secrets in the context when running a mise task/command. This way, our secrets are never exposed, even during local development. Setting this up was effortless. Pretty happy about these tools.

I have a love-hate relationship with GitHub Actions. Love because they are right there in my GitHub repository. Hate because they are very brittle once you move out of the happy path.

It seems GitLab has a much better experience in this department, but their pricing is hard to justify for us...

Genuinely curious if folks here had better experiences or recommendations for a smooth CI/CD experience.

You might have accidentally described what accounting is.

We explored LangGraph last November and were pleasantly surprised by the difference with LangChain. The framework had much more care put in it. It was much easier to iterate and the final solutions felt less brittle.

BUt the pricing model and deployment story felt odd. The business model around LangGraph reminded us of Next.js/Vercel, with a solid vendor lock-in and every cent squeezed out of the solution. The lack of clarity on that front made us go with Pydantic AI.

I discovered Marimo a couple weeks/months ago here iirc. This really lands on a sweet spot for me for data exploration. For me the features that really nails it are the easy imports from other modules, the integrated UI components, and the app mode.

Being able to build model/simulations easily and being able to share them with others, who can then even interact with the results, as truly motivated me to try more stuff and build more. I've been deploying more and more of these apps as PoCs to prospects and people really like them as well.

Big thanks to the team!

In the list that you filtered and hand picked yourself there is literally "War crimes committed in Serbia". The article linked is mentioning a researcher that spoke badly about the US president.

Help me understand how these two things are on same level, please?

In fact they are mentioning LangGraph (the agent framework from the LangChain company). Imo LangGraph is a much more thoughtful and better built piece of software than the LangChain framework.

As I said, they already mention LangGraph in the article, so the Anthropic's conclusions still hold (i.e. KISS).

But this thread is going in the wrong direction when talking about LangChain