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I tested a new coding assistant on 34,568 lines of code. (Cursor could not achieve what this tool did.) AI models are improving every day: - Faster inference - Longer context lengths But are AI coding assistants advancing at the same rate? Can they truly understand your entire project?... I tested Augment Code on my ai-engineering repo with ~35k lines of code. Augment Code is a powerful AI Assistant built for developers working with large, evolving codebases—needing an assistant that understands the full context of their projects. In the video demo below, I asked it to: - Merge two projects. - Answer a global context question. Here’s what happened: - It understood dependencies across both projects. - It merged them intelligently—without breaking anything. - It even created a README file for the merged project. - It answered my global query instantly, pulling from the full codebase. This is the difference between AI autocomplete and an AI engineer that truly understands your repo. Augment Code is powerful because it indexes your entire repo upfront. This way, it can answer questions instantly, no matter how large your project is. Lastly, Augment Code is fully compatible with VSCode, JetBrains, Vim, Slack, and more. Try them now, I have shared a link in the next tweet! Thanks to Augment Code for showing me their powerful AI coding assistant and working with me on this post.show more

Akshay 🚀

285,962 subscribers

30,865 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von Akshay 🚀

Akshay 🚀vor 1 Jahr

Try Augment Code now:

Profilbild von Coral AI News

Coral AI Newsvor 2 Jahren

Coral AI is the most powerful AI for documents. See the difference yourself:

Profilbild von Avi Chawla

Avi Chawlavor 1 Jahr

Augment Code is amazing!! Even I tested it recently and it worked flawlessly.

Profilbild von Inforida

Inforidavor 1 Jahr

AI coding assistants are indeed evolving, but the challenge lies in their ability to truly grasp the nuances of human coding logic. As we push for more advanced AI, we must also ensure they can adapt contextually. It's a race, and the finish line keeps moving.

Profilbild von Sean Dorofeev

Sean Dorofeevvor 1 Jahr

There are plenty of approaches to giving LLM context. 34k lines of code, that's very small and I'm surprised how you got that result. I have a little over 400k, no problems withcontext. documents, docstringz, cursor rules solve most problems.

Profilbild von Gabriel Rymberg

Gabriel Rymbergvor 1 Jahr

@threadreaderapp unroll

Profilbild von Thread Reader App

Thread Reader Appvor 1 Jahr

@akshay_pachaar @GabrielRymberg Namaste, please find the unroll here: Enjoy :) 🤖

Profilbild von Abhivendra Singh

Abhivendra Singhvor 1 Jahr

AI coding assistants must evolve at the pace of our ambitions. Faster inference and longer context lengths are only the beginning. True understanding requires deep learning and adaptability.

Profilbild von Ian

Ianvor 1 Jahr

Hi Akshay, very interesting! I've tried installing this and it crashes constantly. Lots of comments on the PyCharm plugin are the same. I don't suppose you have any tips?

Profilbild von Totalremoto

Totalremotovor 1 Jahr

Yo @akshay_pachaar, sounds like you put that new coding assistant through the wringer with a whopping 34k+ lines of code! Impressive stuff. AI models are definitely leveling up faster than my grandma's knitting skills.

Profilbild von Pranav Agarwal

Pranav Agarwalvor 1 Jahr

Looks good

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Nothing beats Augment Code answering questions about large codebases. It's a free extension (works in VSCode, JetBrains, and NeoVim), and has a community edition you can run for free forever. Try this experiment: 1. Download any large open-source project from GitHub 2. Augment will index the code 3. Start asking questions about the codebase 4. Do the same using any other coding agent 5. Compare the speed and the depth of the answers you get If you have a small codebase, the differences with other coding agents won't be obvious, but if you are working on a large project, things will click immediately. Suggestion: download the Keras repository (~180,000 lines of code). I know the library well, and the answers I got from Augment were pretty impressive. By the way, Augment is a fully-fledged AI coding assistant. You can use its agent in every imaginable way: • To implement brand new functionality • To make quick edits to your code • To refactor existing functionality • To generate unit tests • To generate documentation • To handle GitHub—pull requests, commits, branching, etc My favorite feature is their "Next Edit" functionality: 1. You edit the code 2. Augment analyzes the change (across the entire codebase!) 3. Augment determines the ripple effects of that change 4. Augment suggests everything you need to update Here is a link to the tool: Thanks to the team at Augment Code for partnering with me on this post.

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The #1 problem with coding agents right now: Ask them to solve one problem, and they will make 10 other changes you didn't want. This happens to me every day. It happens to everyone I talk to as well. We have a solution for this now. The team Augment Code released a "Task List" feature for their coding assistant that solves this problem. Augment Code is partnering with me on this post. In case you haven't used them before: • Augment Code is a fully-fledged coding assistant • Their specialty are large projects • Fastest coding indexing I've seen • Has a free forever community edition Now, you can ask their coding agent to generate a Task List before doing anything. This will give you a plan you can review, edit, and augment if you need to. You can export this plan, load it on a different session, or even share it across projects. It makes a huge difference: The task list constrains the agent so you won't get any "unintended" changes anymore. It also puts you in control of everything the agent does. Check the video to see the agent working through a task list. You can also try this 100% free: (By the way, they also have support for remote agents. You can basically have those agents write your code while you are sleeping.)

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