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# PromethAI-Memory
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](https://theresanaiforthat.com/) 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](https://www.latent.space/p/ai-engineer)**
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](https://lilianweng.github.io/posts/2023-06-23-agent/?fbclid=IwAR1p0W-Mg_4WtjOCeE8E6s7pJZlTDCDLmcXqHYVIrEVisz_D_S8LfN6Vv20) — available in the palm of your hand 24/7/365.
To address this issue, [dlthub](https://dlthub.com/) and [prometh.ai](http://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.
##Read about it [prometh.ai](http://prometh.ai/promethai-memory-blog-post-on)