Edit (2024-07-21): Vercel has updated the ai package to use different abstractions than the examples below. Consider reading their docs first before using the example below, which is out of date.
Vercel has a library called ai, that is useful for building language model chat applications. I used it to help build Write Partner The library has two main components:
A backend API that is called by a frontend app that streams language model responses A hook (in React) that provides access to the chat, its messages and an API to fetch completions When designing Write Partner, I started the chat session with the following messagesGoku has a concept called a simlayer. A simlayer allows you to press any single key on the keyboard, then any second key while holding the first and trigger an arbitrary action as a result. I’m going to write a karabiner.edn config that opens Firefox when you press .+f.
{:simlayers {:launch-mode {:key :period}}, :templates {:open-app "open -a \"%s\""}, :main [{:des "launch mode", :rules [:launch-mode [:f [:open-app "Firefox"]]]}]} ❯ goku Done! To start, we define a simlayer for the period key.Karabiner is a keyboard customizer for macOS. I’ve used it for a while to map my caps lock key to cmd + ctrl + option + shift. This key combination is sometimes called a hyper key. With this keyboard override, I use other programs like Hammerspoon and Alfred to do things like toggle apps and open links. Karabiner provides an out-of-the-box, predefined rule to perform this complex modification. I’ve used this approach for a while but recently learned about Goku which adds a lot of additional functionality to Karabiner using Clojure’s extensible data notation (edn) to declaratively configure Karabiner.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 a lot of code that uses the OpenAI SDK. It looks like Fireworks’ documented approach no longer works since OpenAI published version 1.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 involved, it can be important to limit concurrency. We can use a semaphore to limit the number of coroutines that are running at once by blocking until other coroutines have finished executing.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, Python’s syntax will look familiar. Python’s event loop allows coroutines to yield control back to the loop, awaiting their turn to resume execution, which can lead to more efficient use of resources.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
Fatal Python error: init_fs_encoding: failed to get the Python codec of the filesystem encoding Python runtime state: core initialized ModuleNotFoundError: No module named 'encodings' You don’t have to search for too long to find out this isn’t good.In Javascript, using async/await is a cleaner approach compared to use of callbacks. Occasionally, you run into useful but older modules that you’d like to use in the more modern way.
Take fluent-ffmpeg, a 10 year old package that uses callbacks to handle various events like start, progress, end and error.
Using callbacks, we have code that looks like this:
const ffmpeg = require('fluent-ffmpeg'); function convertVideo(inputPath, outputPath, callback) { ffmpeg(inputPath) .When deploying software with Kubernetes, you need to expose a liveness HTTP request in the application. The Kubernetes default liveness HTTP endpoint is /healthz, which seems to be a Google convention, z-pages. A lot of Kubernetes deployments won’t rely on the defaults. Here is an example Kubernetes pod configuration for a liveness check at <ip>:8080/health:
apiVersion: v1 kind: Pod metadata: name: liveness-http spec: containers: - name: liveness image: k8s.gcr.io/liveness args: - /server livenessProbe: httpGet: path: "/health" port: 8080 initialDelaySeconds: 3 periodSeconds: 3 When setting up a new app to be deployed on Kubernetes, ideally, the liveness endpoint is defined in a service scaffold (this is company and framework dependent), but in the case it isn’t, you just need to add a simple HTTP handler for the route configured in the yaml file.Given the following make target
.PHONY: my_target my_target: @python scripts/my_script.py $(arg) one can the argument with an argument in the following manner
make my_target arg=my_arg I used this approach to run a python script to create the file for this post
make til p=make/pass-arg-to-target for the following make target
.PHONY: til til: @python scripts/til.py $(p) It’s also possible to prepend the variable
p=make/pass-arg-to-target make til