I recently needed to setup a new Python project and in the course of doing so, learned about uv¹ , “an extremely fast Python package and project manager”. uv is great. It is easy to install (just a single executable!), and it takes care of all the essential elements of a Python project. Unfortunately, I immediately came across one major problem–uv uses the wrong default for project layouts.

This is the default layout of a project created by uv:

Farewell to Google Search

Let’s say you were in the market for a new laptop. What would you do? You certainly would not go on Google and search for “A good laptop under $1000”. That’d be madness. It used to be that Google could be a trusted source of information. These days, however, the top results on Google are mostly advertisements.

Generative AI 2024 Retrospective

2024 witnessed a parade of increasingly more powerful AI model releases, culminating in OpenAI’s groundbreaking “o3”. While I’m certain these advancements really are significant, I don’t think they have translated to noticeable improvements for the average user.

What people are paying attention to, though, are the really amazing product features that are coming out of Anthropic and Google. I’ve heard a lot about people building software Claude’s Artifacts. And Google’s Deep Research product, released a few days ago, has just completely changed search.

This is a review of Open WebUI, an extensible and user-friendly self-hosted WebUI for LLMs.

I recently decided to run my own UI layer for LLMs, as I have some exciting ideas. For those who are not familiar with chatbots, their architecture basically looks like this:

While I’m pretty good at customizing the middleware, I’m not as skilled at customizing the UI layer.

I tried writing my own UI at first, but I quickly gave up when I realized it was beyond my skills as a frontend developer. The next thing I did was look into open-source solutions. There were a lot of choices, but I narrowed them down to these three:

OpenAI took the world by storm in 2023 with the relase of ChatGPT. Other companies quickly followed suit, introducing their own competitors to ChatGPT. In this update, I want to write down my thoughts on some of the biggest players in the field of generative AI.

When I use the term “generative AI,” I am referring to a computer program that can generate text, images, or other types of content. These programs can understand natural language and follow complex instructions. One notable example of a generative AI is OpenAI’s “GPT-4,” commonly known as ChatGPT. Another well-known generative AI is Midjourney, which specializes in generating images from text descriptions. In this update, I will focus on AI with text output, as that is the area I have the most experience with.

Once you know a programming language well, the process for learning a new language is not very hard. It is just time consuming. You need to read the documentation for basic syntax and flow control, get familiar with its idioms, memorize core parts of the standard libraries, and learn its tool chains. What can we do to speed up the learning process? One thing we can do is provide great examples in the documentation. Can we do better? What if you have working examples for every problem you encountered? What if you can describe your intents in a familiar language and see how it should look in a new language? As it turns out, GPT-3 is really good at this task.

Writing code with ChatGPT.

In the aftermath of ChatGPT’s debut, the Internet is abuzz with GPT’s ability to write code. I used it to write code yesterday, and this is what I thought.

I wanted to create a command line program to interact with APIs from OpenAI and AI21. The design is a single program with subcommands for each company’s APIs:

Usage: /bin/foo_to_be_named <command>

A tool to interact with various LLM models from the CLI

Flags:
  -h, --help                                     Show context-sensitive help.
      --conf-file="llmcli.conf"    file with api keys, created with http://localhost:8080/v1/p/tools

Commands:
  openai create-completion
  openai create-edit
  ai21 complete

Run "/bin/foo_to_be_named <command> --help" for more information on a command.

I like to use Kong as the framework for command line parsing and I wanted to know how to use its subcommand features. Being too lazy to read the documentation, I asked ChatGPT to write me a short program using sub-commands. This is the example it came back with: