Introduction​

When it comes to bestowing Large Language Models (LLMs) with long-term memory, the prevalent approach often involves a Retrieval Augmented Generation (RAG) solution, with vector databases acting as the storage mechanism for the long-term memory. This begs the question: Can we achieve the same results without vector databases?

Enter "RecallM: An Adaptable Memory Mechanism with Temporal Understanding for Large Language Models" by Brandon Kynoch, Hugo Latapie, and Dwane van der Sluis. This paper proposes the use of an automatically constructed knowledge graph as the backbone of long-term memory for LLMs.

Introduction​

In the dynamic realm of data management, the unfolding narrative of vector databases as a unique category mirrors the historical discourse of the SQL vs NoSQL movement that swept through the tech world over the past decade. As modern enterprises wrestle with increasingly intricate data landscapes, differentiating between traditional databases and vector databases becomes not merely relevant, but essential.

This article will argue why this distinction is necessary and explore how specialized vector database companies can respond to incumbents in the database domain.

Introduction​

In the high-stakes world of venture capital, hindsight often provides lessons that are as valuable as a successful investment. Imagine having the chance to invest in companies like Airbnb, Apple, or Google—only to pass on the opportunity. Sounds unthinkable today, doesn't it?

Yet, even the best in the business have their list of 'the ones that got away.' For example, Bessemer Venture Partners openly shares this list in their The Anti-Portfolio page.

Curious about what could be learned from these passed-over investments, I took a deep dive into each company listed on that page to look for common threads. I've condensed these findings into 7 lessons that might just be game-changers in how you approach venture investing.

If you're interested in avoiding the same pitfalls and sharpening your investment acumen, keep reading.

Introduction​

In the realm of software development, debugging is not only inevitable but can also be a complex endeavor.

Picture this: you're faced with the task of deciphering what's occurring within a server that's already humming along. Relaunching it with an attached debugger, the usual go-to method, isn't an option.

In this post, we'll navigate this challenge, zeroing in on how to debug an already running FastAPI server in the rag-stack project as an example. And for those on a tight schedule, here's a quick video tutorial breaking down the process:

Introduction​

Today, we're going to discuss an efficient way to set up your automated integration test workflows using Docker and Pytest.

Motivation​

In an era where software complexity is rapidly growing, developing an efficient testing strategy is more than a mere best practice — it's the bedrock of stable and reliable applications.

One common strategy involves using Docker to set up a test version of a software component, such as a vector database. There are two popular variations to this strategy, both of which have their own limitations.

Ever since large language models have taken the world by storm, I've been curious about how helpful these AI-powered chatbots can be to programmers like myself.

To put them to the test, I decided to build a couple of simple API endpoints using FastAPI and see how ChatGPT, Google Bard, and Bing Chat can assist me in the process.

Here's how they fared:

Every time I work on a Python project, I find myself having to look up the VS Code launch configuration to enable Pytest to step into 3rd party libraries when using the interactive debugger. It became quite frustrating, and I knew I couldn't be the only one facing this issue. So, I decided to create a video tutorial to share my knowledge and help fellow developers set up their projects more efficiently.

As a programmer, you've likely encountered moments where you needed help with a coding problem or wanted to find a more efficient solution. I recently faced a challenge while trying to generate a UUID3 string in Python, and the internal documentation wasn't helpful. That's when I discovered Sydney (Bing Chat) and was amazed at how quickly and effectively it provided a solution, complete with a code snippet!

In this blog post, I'll share my experience with Bing Chat and highlight the standout user experience offered by Sydney in Microsoft Edge.

If you're a fan of Copilot, you know how AI can significantly improve your coding experience. But what if AI could also write your commit messages for you? With GitLens 13.4, this dream has become a reality! In this blog post, we'll explore how AI-generated commit messages can transform your Git workflow and boost your productivity.

A lot of people I know will repeatedly alternate between writing an endpoint in their IDE and Postman/Swagger to test it. I do not like this workflow because of the constant context switching. I also find that it's easy to forget to test a scenario or to test the wrong scenario. I've found that it's much easier to test the API in the IDE itself.

I personally prefer the Test Driven Development (TDD) approach. I find that it helps me to write better code and to think about the API design before I actually start writing code. I also find that it helps me to write better tests and to think about the different scenarios that I need to test for.

So, how can we do this in VS Code?