patterns-components VS windmill

Hey HN, I’m Ken, co-founder of Patterns (https://www.patterns.app/) with with my friend Chris. Patterns gets rid of repetitive gruntwork when building and deploying data applications. We abstract away the micro-management of compute, storage, orchestration, and visualization, letting you focus on your specific app’s logic. Our goal is to give you a 10x productivity boost when building these things. Basically, we’re Heroku for AI apps. There’s a demo video here: https://www.patterns.app/videos/homepage/demo4k.mp4.

We built Patterns because of our frustration trying to ship data and AI projects. We are data scientists and engineers and have built data stacks over the past 10 years for a wide variety of companies—from small startups to large enterprises across FinTech, Ecommerce, and SaaS. In every situation, we’ve been let down by the tools available in the market.

Every data team spends immense time and resources reinventing the wheel because none of the existing tools work end-to-end (and getting 5 different tools to work together properly is almost as much work as writing them all yourself). ML tools focus on just modeling; notebook tools are brittle, hard to maintain, and don’t help with ETL or operationalization; and orchestration tools don’t integrate well with the development process.

As a result, when we worked on data applications—things like a trading bot side-project, a risk scoring model at a startup, and a PLG (product-led growth) automation at a big company—we spent 90% of our time doing things that weren’t specific to the app itself: getting and cleaning data, building connections to external systems and software, and orchestrating and productionizing. We built Patterns to address these issues and make developing data and AI apps a much better experience.

At its core, Patterns is a reactive (i.e. automatically updating) graph architecture with powerful node abstractions: Python, SQL, Table, Chart, Webhook, etc. You build your app as a graph using the node types that make sense, and write whatever custom code you need to implement your specific app.

We built this architecture for modularity, composability, and testability, with structurally-typed data interfaces. This lets you build and deploy data automations and pipelines quickly and safely. You write and add your own code as you need it, taking advantage of a library of forkable open-source components—see https://www.patterns.app/marketplace/components and https://github.com/patterns-app/patterns-components.git .

Patterns apps are fully defined by files and code, so you can check them into Git the same way you would anything else—but we also provide an editable UI representation for each app. You work at either level, depending on what’s convenient, and your changes propagate automatically to the other level with two-way consistency.

One surprising thing we’ve learned while building this is that the problem actually gets simpler when you broaden the scope. Individual parts of the data stack that are huge challenges in isolation—data observability, lineage, versioning, error handling, productionizing—become much easier when you have a unified “operating system”.

Our customers include SaaS and ecommerce co’s building customer data platforms, fintech companies building lending and risk engines, and AI companies building prompt engineering pipelines.

Here are some apps we think you might like and can clone:

Data Handling: Utilizes Windmill for data pipelines, with a primary database powered by PostgreSQL. Auxiliary data storage is handled by MongoDB, with Redis for caching to optimize performance

Look very similar to the script builder portion of https://github.com/windmill-labs/windmill, but not open-source, not self-hostable, and without open-source integrations (https://hub.windmill.dev/)

disclaimer: I'm founder of ^

If you need a job queue on Postgres, https://windmill.dev provide an all-integrated developer platform with a Pg queue at its core that support jobs defined in python/typescript/sql

windmill.dev - Windmill is an open-source developer platform to quickly build production-grade multi-step automation and internal apps from minimal Python and Typescript scripts. As a free user, you can create and be a member of at most three non-premium workspaces.

For an alternative to airplane.dev, you can checkout Windmill.

https://github.com/windmill-labs/windmill

"Open-source developer infrastructure for internal tools (APIs, background jobs, workflows and UIs). Self-hostable alternative to Airplane, Pipedream, Superblocks and a simplified Temporal with autogenerated UIsm and custom UIs to trigger workflows and scripts as internal apps.

Scripts are turned into sharable UIs automatically, and can be composed together into flows or used into richer apps built with low-code. Supported script languages supported are: Python, TypeScript, Go, Bash, SQL, and GraphQL. "

If you search HN, you'll find the creator of Windmill comment on comparisons to airplane.dev:

https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...

https://windmill.dev is a self-hostable OSS alternative to pipedream

(disclaimer: I'm founder)

I'm genuinely curious what server-side stuff on BC you are referring to. That may have been something added after our assessment. The way I'd generally approach something like that for any of the platforms would be using an external low/no code solution to process webhook data. But it would depend heavily on the use case. For a more developer friendly option I've been really impressed by windmill.dev. We use a mix of n8n and windmill for various needs.

Yes it goes in that direction, however note that you can already do this in a not too hard way.

Our openflow spec is both open-source and has a full openapi definition: https://github.com/windmill-labs/windmill/blob/main/openflow...

you can use that to generate client sdks in any languages and build your own dag with it. That's what one of our customer did building a reactflow to openflow library: https://github.com/Devessier/reactflow-to-windmill

It's not as good as the decorator way but we move fast and if you still have interest for it we could prioritize it (and ask for feedbacks :))