33 entries
The .env file and the python-dotenv package are popular conventions in Python for managing environment variables. I often forget or pause when trying to remember exactly which package I need to install (python-dotenv) and how to do the import
When Datadog's magic breaks your API calls
Why I like the Chat Completions API
In Python, the most straightforward path to implementing a gRPC server for a Protobuf service is to use protoc to generate code that can be imported in a server, which then defines the service logic.
I run a lot of different version of various languages and tools across my system. Nix and direnv help make this possible to manage reasonably. Recently, starting a new Python project, I was running into this warning after install dependencies with pip (yes, I am aware there are new/fresh/fast/cool...
I played around with AgentGPT using Reworkd's cloud hosted instance. I tried a few different goals. The first was travel related. I was pleasantly surprised (unnerved) to see the agent return links for flights to my proposed location from my actual current origin (I guess they used my IP to...
I've been meaning to try out Simon's llm package for a while now. From reading the docs and following the development, it's a modular, meet-you-where-you-are CLI for running LLM inference locally or using almost any API out there. In the past, I might have installed this with brew, but we run nix...
Last year I wrote about nix and direnv as I explored the potential convenience of an isolated, project-specific environment. There were some interesting initial learnings about nix, but I didn't really know what I was doing. Now, I still don't know what I'm doing, but I've been doing it for...
OpenAI popularized a pattern of streaming results from a backend API in realtime with ChatGPT. This approach is useful because the time a language model takes to run inference is often longer than what you want for an API call to feel snappy and fast. By streaming the results as they're produced,...
Disclaimer: I am not a security expert or a security professional.
I've starting playing around with Fireworks.ai to run inference using open source language models with an API. Fireworks' product is the best I've come across for this use case. While Fireworks has their own client, I wanted to try and use the OpenAI Python SDK compatibility approach, since I have...
In a previous note, I discussed running coroutines in a non-blocking manner using gather. This approach works well when you have a known number of coroutines that you want to run in a non-blocking manner. However, if you have tens, hundreds, or more tasks, especially when network calls are...
Exploring accidental synchronous blocking in asynchronous programming in Python with coroutines.
Python coroutines allow for asynchronous programming in a language that earlier in its history, has only supported synchronous execution. I've previously compared taking a synchronous approach in Python to a parallel approach in Go using channels. If you're familiar with async/await in JavaScript,...
Render is a platform as a service company that makes it easy to quickly deploy small apps. They have an easy-to-use free tier and I wanted run a Python app with dependencies managed by Poetry. Things had been going pretty well until I unexpectedly got the following error after a deploy
I had a bunch of fun following along with this post with my own Hugo blog to construct a sqlite database of metadata. Building the database indices, I found a mistake I had made years ago in defining a post's alias, which was a duplicate, so I fixed that. I've read a lot of praise of sqlite lately...
I used open-interpreter to read an epub file and create a DIY audio book.
It's much easier to test Temporal Workflow in Python by invoking the contents of the individual Activities first, in the shell or via a separate script, then composing them into a Workflow. I need to see if there's a better way to surface exceptions and failures through Temporal directly to make...
A spot where I slipped up in trying to adopt Temporal in an existing Python project and then again in starting a new Python project was in defining a Workflow that invokes an Activity that calls a third party library. Temporal outputs an error message with a long stacktrace that I vaguely...
I've been doing a bit of work with Temporal using it's Python SDK. Temporal remains one of my favorite pieces of technology to work with. The team is very thoughtful with their API design and it provides a clean abstraction for building distributed, resilient workflows. It's a piece of technology...
I've been playing around more with nix lately. I like what I've seen from it so far: declare dependencies and get an isolated shell with those dependencies. If distributed, the environment can be trivially recreated on another machine. So far, it's been a struggle to get a working Python...
Experimenting with using a language model to improve the input prompt, then use that output as the actual prompt for the model, then returning the result. It's a bit of a play on the "critique" approach. Some of the outputs were interesting but I need a better way to evaluate the results.
A simple shell function to setup a Python project scaffold. It's idempotent, so it won't overwrite an existing folder or env.
Heard the phrase "if someone wins the lottery" used today to describe a teammate leaving a team. I much prefer this to the more morbid alternatives.
I read an article today about Scripting with Elixir that caught my eye because it touches on a problem I often struggle with: how you do easily distribute a script along with its dependencies in Python? Elixir has an example of what this could look like in the form of Mix.install. This feature...
Code needs structure output
The most popular language model use cases I've seen around have been chatbots agents chat your X use cases
It's necessary to pay attention to the shape of a language model's response when incorporating it as a component in a software application. You can't programmatically tap into the power of a language model if you can't reliably parse its response. In the past, I have mostly used a combination of...
Experimenting with Auto-GPT
Auto-GPT is a popular project on Github that attempts to build an autonomous agent on top of an LLM. This is not my first time using Auto-GPT. I used it shortly after it was released and gave it a second try a week or two later, which makes this my third, zero-to-running effort.
I believe that language models are most useful when available at your fingertips in the context of what you're doing. Github Copilot is a well known application that applies language models in this manner. There is no need to pre-prompt the model. It knows you're writing code and that you're going...
I know a little about nix. Not a lot. I know some things about Python virtual environments, asdf and a few things about package managers. I've heard the combo of direnv and nix is fantastic from a number of engineers I trust, but I haven't had the chance to figure out what these tools can really...
If you want to try running these examples yourself, check out my writeup on using a clean Python setup.
Recently, I have been working with the Python API for [Spark][pyspark] to use distrbuted computing techniques to perform analytics at scale. When you write Spark code in Scala or Java, you can bundle your dependencies in the jar file that you submit to Spark. However, when writing Spark code in...
To help facilitate my blogging workflow, I wanted to go from written to published post quickly. My general workflow for writing a post for [this blog][MyBlog] looks like this: Create a post in _posts Write the post Run fab sync
If you spend most of your time in the command line, you don't want to leave to do math. Qc is a script that does in-line command line math without forcing you to exit the main bash prompt as you might with a program like bc or a language interpreter.