Ok, crazy tangent;

Where agents will potentially become extremely useful/dystopian is when they just silently watch your entire screen at all times. Isolated, encrypted and local preferably.

Imagine it just watching you coding for months, planning stuff, researching things, it could potentially give you personal and professional advice from deep knowledge about you. "I noticed you code this way, may i recommend this pattern" or "i noticed you have signs of this diagnosis from the way you move your mouse and consume content, may i recommend this lifestyle change".

I wonder how long before something like that is feasible, ie a model you install that is constantly updated, but also constantly merged with world data so it becomes more intelligent on two fronts, and can follow as hardware and software advances over the years.

Such a model would be dangerously valuable to corporations / bad actors as it would mirror your psyche and remember so much about you - so it would have to be running with a degree of safety i can't even imagine, or you'd be cloneable or loose all privacy.

I've had success with the method described in

https://huggingface.co/docs/transformers/training

for both classification and regression problems with the caveats that (i) the default learning rate is too damn high (easy to fix) and (ii) with a great deal of effort I got the classification problem to perform as well as a classifier that uses

https://sbert.net/

and an SVM from scikit-learn. You might get different results with another problem, but my problem is noisy and has an upper limit to what accuracy is possible. Fine-tuning a model takes maybe 30 minutes, the classical classifier is more like 30 seconds, and the ratio of development time that went into these is similar.

I tried to do something similar, following this blog post [1] however I didn't manage due to lack of GPUs. I tried to rent 4 A100s, which is what the author is doing, but there weren't any available. I signed up to 7 different cloud providers, including AWS, lambdalabs, vast.ai, coreweave, latitude.sh, tensordock. Eventually, I settled with a few A40s, but memory requirements weren't even close.

[1]: https://www.izzy.co/blogs/robo-boys.html

Like many others, I've been experimenting with using ChatGPT for coding. Along the way, I've been building up some useful tooling. Once I gained access to the GPT-4 api, I was able to make significant improvements in the usability and reliability of my GPT coding tools.

The result is a tool called `aider'. Aider is a command-line tool that allows you to chat with GPT-4 about your code. You can ask GPT for new features, improvements, and bug fixes and aider will directly apply the changes to your source files. Each change is automatically committed to git with a sensible commit message. These frequent, automatic commits provide a comforting safety net. You can confidently collaborate with aider, because it's easy to use git to undo missteps or manage a long series of changes.

You can find out more about aider on GitHub: https://github.com/paul-gauthier/aider

I was initially using GPT to generate code snippets with the OpenAI web chat UI and generic ChatGPT command line tools like `aichat`. But that involved a somewhat klunky workflow where I had to cut and paste code into ChatGPT and then back into my source files.

I streamlined my process while developing a children's chat interface called EasyChat (https://github.com/paul-gauthier/easy-chat). I adopted a "whole file in, whole file out" workflow. I would send GPT-3.5 the entire source code of my project along with a change request and had it reply with the modified version of all the code. This approach was way less tedious than cutting and pasting code between the chat and my source files. I had some simple command line tooling to feed source files to GPT, overwrite them with GPT's modified version and display diffs. This workflow was also quite reliable: GPT-3.5 could consistently produce the code changes I requested without getting lost or confused. But it was slow waiting for GPT to retype all the code, and I quickly hit context window limits asking GPT to read and rewrite every line of the entire codebase.

Access to the GPT-4 API really unlocked a lot of possibilities for improving my tooling. GPT-4 is much better than GPT-3.5 at following directions and replying in a stable, parsable format. Aider still sends GPT-4 entire source files, but asks for replies in a concise `diff` like format. Aider automatically applies these diffs to the source files and git commits them with a GPT generated commit message. Aider lets you easily manage which of your source files are "in the chat session" to control how much code you send to GPT-4 with each request. The ability to reply with diffs makes it much less likely to overflow GPT-4's larger context window.

The resulting workflow is quite effective. You can bounce back and forth between the aider chat and your editor to collaborate on code changes. Aider's code changes aren't always perfect, but wow they are great for blasting through boilerplate or quickly integrating unfamiliar libraries or packages into your code. And if you don't like a code edit, you can quickly discard it by typing `/undo` into the chat.

I now use aider as a force multiplier for a lot of my coding. I even use aider to improve the tool itself.

Let me know if you try aider and find it helpful.