aider VS easy-chat

My open source tool aider [0] has long offered a "AI pair programming" workflow. Aider's UX is similar but not identical to Copilot Workspaces.

Aider is more of a collaborative chat, where you work with the LLM interactively asking for a sequence of changes to your git repo. The changes can be non-trivial, modifying a group of files in a coordinated way. So much more than just the original copilot "autocomplete".

Workspaces seems more agentic, a bit like Devin. You need to do a bunch of up-front work to (fully) specify the requirements. Then the agent goes off and (hopefully) builds what you want. You need to fully understand what you want to build up front, and you need the describe it unambiguously to the agent. Also, even with a perfect request, agents often go down wrong paths and waste a lot of time and token costs doing the wrong thing.

That's not how I code personally. My process is more iterative, where I explore the problem and solution spaces as I build.

The other difference between aider and Workspaces is that currently aider is a terminal CLI tool. Although I just released a basic browser UI [1] the other day, making it more approachable for folks who are not fully comfortable on the command line.

[0] https://github.com/paul-gauthier/aider

[1] https://aider.chat/2024/05/02/browser.html

Aider was developed by Paul Gaither and focuses on giving developers a pair programming experience directly from developers' terminals. This command-line tool edits code in real-time based on a user prompt in the command terminal. As of writing, it only supports OpenAI’s API but can write, edit, and refine code across multiple languages including Python, JavaScript, and HTML. Developers can use Aider for code generation, debugging, and understanding complex projects.

I built a similar thing in python using Playwright and Pandoc [0]. It's used by aider's `/web ` command that lets you paste a markdown version of any webpage into your AI coding chat. This helps if you want to include docs for an obscure or non-public package/api/etc with the LLM while coding.

I really value dependencies which are easy for all users to install, cross-platform. Playwright is nice because it has a simple way to install its dependencies on most platforms. And the `pypandoc` module provides a seamless install of pandoc across platforms.

The result turns most web pages into nice markdown without requiring users to solve some painful platform specific chromium dependency nightmare.

[0] https://github.com/paul-gauthier/aider/blob/main/aider/scrap...

Thanks for trying aider, and sorry to hear you had trouble getting the hang of it. It might be worth looking through some of the tips on the aider GitHub page [0].

In particular, this is one of the most important tips: Large changes are best performed as a sequence of thoughtful bite sized steps, where you plan out the approach and overall design. Walk GPT through changes like you might with a junior dev. Ask for a refactor to prepare, then ask for the actual change. Spend the time to ask for code quality/structure improvements.

Not sure if this was a factor in your attempts? I'd be happy to help you if you'd like to open an GitHub issue [1] our jump into our discord [2].

[0] https://github.com/paul-gauthier/aider#tips

[1] https://github.com/paul-gauthier/aider/issues/new/choose

[2] https://discord.gg/Tv2uQnR88V

Have you tried something like Agentic’s Glide? (They announced it this week here on HN)

They use gpt, but they might be able to configure it so it uses Claude

Another tool to check out could be aider https://github.com/paul-gauthier/aider

Are you aware of the work on https://github.com/paul-gauthier/aider? What's your take on generating code diffs directly instead of code editing instructions?

He should add AI to his site!

Not really - the site is great as-is and there's nothing wrong with this approach. It looks like it works really well for Mr. 61.

But I'd imagine it'd be pretty helpful to write tools to help with maintaining the site which do leverage LLM models. Do a combination of search + AI to rewrite + reviewing the individual edits (e.g. through selective git adds).

I'm imagining a tool like https://github.com/paul-gauthier/aider (which I haven't tried yet, but it looks useful for this kind of effort).

I recently used [0] Playwright for Python and [1] pypandoc to build a scraper that fetches a webpage and turns the content into sane markdown so that it can be passed into an AI coding chat [2].

They are both very gentle dependencies to add to a project. Both packages contain built in or scriptable methods to install their underlying platform-specific binary dependencies. This means you don't need to ask end users to use some complex, platform-specific package manager to install playwright and pandoc.

Playwright let's you scrape pages that rely on js. Pandoc is great at turning HTML into sensible markdown. Below is an excerpt of the openai pricing docs [3] that have been scraped to markdown [4] in this manner.

[0] https://playwright.dev/python/docs/intro

[1] https://github.com/JessicaTegner/pypandoc

[2] https://github.com/paul-gauthier/aider

[3] https://platform.openai.com/docs/models/gpt-4-and-gpt-4-turb...

[4] https://gist.githubusercontent.com/paul-gauthier/95a1434a28d...

  ## GPT-4 and GPT-4 Turbo

Thanks for trying aider, and sorry to hear you had trouble getting the hang of it. It might be worth looking through some of the tips on the aider github page:

https://github.com/paul-gauthier/aider#tips

In particular, this is one of the most important tips: Large changes are best performed as a sequence of thoughtful bite sized steps, where you plan out the approach and overall design. Walk GPT through changes like you might with a junior dev. Ask for a refactor to prepare, then ask for the actual change. Spend the time to ask for code quality/structure improvements.

Not sure if this was a factor in your attempts? But it's best not to ask for a big sweeping change all at once. It's hard to unambiguously and completely specify what you want, and it's also harder for GPT to succeed at bigger changes in one bite.

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.

I had chat gpt 3.5 build a small web app for me too. I have since been building some tooling for this sort of GPT-assisted programming.

https://github.com/paul-gauthier/easy-chat

GPT is significantly better at modifying code when following this "all code in, all code out" pattern. This pattern has downsides: you can quickly exhaust the context window, it's slow waiting for GPT to re-type your code (most of which it hasn't modified) and of course you're running up token costs. But the ability of GPT to understand and execute high level changes to the code is far superior with this approach.

I have tried quite a large number of alternative workflows. Outside the "all code in/out" pattern, GPT gets confused, makes mistakes, implements the requested change in different ways in different sections of the code, or just plain fails.

If you're asking for self contained modifications to a single function, that's all the code that needs to go in/out. On the other side of the spectrum, I had GPT build an entire small webapp using this pattern by repeatedly feeding it all the html/css/js along with a series of feature requests. Many feature requests required coordinated changes across html/css/js.

https://github.com/paul-gauthier/easy-chat#created-by-chatgp...

Another HN user has also released a command line tool along these lines called gish:

https://github.com/drorm/gish

Gish looks really nice. I'm going to give it a try.

It seems like you've been using similar workflows to what I've been trying for coding with gpt?

https://github.com/paul-gauthier/easy-chat#created-by-chatgp...