refinery VS PostgreSQL

You definitely forgot https://www.kern.ai/ :)

We will build the web scraper in Kern AI workflow, labeled our news articles in refinery, and then enrich the data with gates AI. After that, we will use workflow again to send out the predictions and the enriched data via a webhook to Slack. If you'd like to follow along or explore these tools on your own, you can join our waitlist here: https://www.kern.ai/

A platform has been developed by the German startup Kern AI for NLP developers and data scientists to not only control the labeling process but also automate and orchestrate tangential tasks and enable them to address low-quality data that comes their way. Several companies exist substantively to power this labeling process.

Fast forward to July ‘22 (after many further product iterations and a full redesign), we open-sourced our product under a new name: Kern AI refinery (the origin of the name is very simple: we want to improve, i.e., refine, the foundation for building models).

Get it for free here: https://github.com/code-kern-ai/refinery

Containers are amazing for building applications. Because they allow you to pack up a programm together with all it's dependencies and execute it wherever you like. That is why our application consists of 20+ individual containers, forming our data-centric IDE for NLP, which you can check out here: https://github.com/code-kern-ai/refinery.

Today we launched bricks, an open-source library which provides enrichments for your natural language processing projects. Our main goal with bricks is to shorten the amount of time that you need from idea to implementation. Bricks also seamlessly integrates into our main tool, the Kern AI refinery.

This blog post will share our experience with fine-tuning sentence embeddings on a commonly available dataset using similarity learning. We additionally explore how this could benefit the labeling workflow in the Kern AI refinery. To understand this post, you should know what embeddings are and how they are generated. A rough idea of what fine-tuning is also helps. All the code and data referenced in this post is available on GitHub.

We've built our open-source IDE for data-centric NLP with the belief that data scientists and engineers know best what kind of framework they want to use for their model building. Today, we'll show you three new adapters for the SDK.

pg doesn't do too well with serverless, dead connections are left in the pool (or something)

https://github.com/brianc/node-postgres/issues/2112

If you don't want to use ORM then there are some other packages as well! For PostgreSQL we have node-postgres

Interface with PostgreSQL database

(Instead of the following with pg.)

So far every JS framework that uses https://node-postgres.com works great and so no reason to think Drizzle wouldn't.

One thing that keeps coming up is that SQL equals low productivity. I don't think this is true. I think the culprit is that most developers are using to heavily abstracting SQL using ORMs like Prisma that hides the database and SQL logic.

Since building a SQL generator (https://aihelperbot.com) as a side project, I have become much more proficient in SQL and even though I am also locked into Prisma, I use the `queryRaw` all the time to execute raw SQL queries. You can understand the code without knowing Prisma API. It is more performant. For more complex SQL queries, I use the SQL generator for initial suggestions and adapt if needed.

For the next projects I build I want to use the minimal Postgres client (https://github.com/brianc/node-postgres) combined with a lightweight migration library.

For newer projects I use the small Postgres client. Initially my leap into SQL was lead by AI but as I refreshed and relearned SQL, I now use a mixture of AI and self-written SQL queries. Something like this is just easier to have AI do the grunt work and then adjustment as needed.

This was a known issue for pg developers, and they managed to fix it a long time ago (at the pg level), but the knowledge of this problem didn't reach Knex maintainers.

Integrate the database into your application itself with a postgres client library allowing your applications to make infrastructure changes (like provisioning sharded resources for a client that wants isolation, or using a more accurate forecasting model to pre-allocate more resources before the storm hits).

node-postgres (raw sql, without ORM)