Memory management for the AI Applications and AI Agents

Browsing the database of theresanaiforthat.com, we can observe around 7000 new, mostly semi-finished projects in the field of applied AI, whose development is fueled by new improvements in foundation models and open-source community contributions.

It seems it has never been easier to create a startup, build an app, and go to market… and fail.

Decades of technological advancements have led to small teams being able to do in 2023 what in 2015 required a team of dozens.

Yet, the AI apps currently being pushed out still mostly feel and perform like demos.

The consensus is, nevertheless, that the AI space is the place to be in 2023.

“The AI Engineer [...] will likely be the highest-demand engineering job of the [coming] decade.”

Swyx

The rise of this new profession is perhaps signaling the need for a solution that is not yet there — a solution that in its essence represents a Large Language Model (LLM) — a powerful general problem solver — available in the palm of your hand 24/7/365.

To address this issue, dlthub and prometh.ai will collaborate on a productionizing a common use-case, progressing step by step. We will utilize the LLMs, frameworks, and services, refining the code until we attain a clearer understanding of what a modern LLM architecture stack might entail.

The repository contains a set of folders that represent the steps in the evolution of the modern data stack from POC towards production Level 1 - CMD script to process PDFs