AI code reviews are the worst place to introduce AI, in my experience. They can find a few things quickly, but they can also send people down unnecessary paths or be easily persuaded by comments or even the slightest pushback from someone. They're fast to cave in and agree with any input.

It can also encourage laziness: If the AI reviewer didn't spot anything, it's easier to justify skimming the commit. Everyone says they won't do it, but it happens.

For anything AI related, having manual human review as the final step is key.

IMO, the AI bits are the least interesting parts of Zed. I hardly use them. For me, Zed is a blazing fast, lightweight editor with a large community supporting plugins and themes and all that. It's not exactly Sublime Text, but to me it's the nearest spiritual successor while being fully GPL'ed Free Software.

I don't mind the AI stuff. It's been nice when I used it, but I have a different workflow for those things right now. But all the stuff besides AI? It's freaking great.

I found the OP comment amusing because Emacs with a Jetbrains IDE when I need it is exactly my setup. The only thing I've found AI to be consistently good for is spitting out boring boilerplate so I can do the fun parts myself.

I always hear this "writing code isn't the bottleneck" used when talking about AI, as if there are chosen few engineers who only work on completely new and abstract domains that require a PhD and 20 years of experience that an LLM can not fathom.

Yes, you're right, AI cannot be a senior engineer with you. It can take a lot of the grunt work away though, which is still part of the job for many devs at all skill levels. Or it's useful for technologies you're not as well versed in. Or simply an inertia breaker if you're not feeling very motivated for getting to work.

Find what it's good for in your workflows and try it for that.

Highlighting code and having cursor show the recommended changes and make them for me with one click is just a time saver over me copying and pasting back and forth to an external chat window. I don’t find the autocomplete particularly useful, but the inbuilt chat is a useful feature honestly.

I'm the opposite. I held out this view for a long, long time. About two months ago, I gave Zed's agentic sidebar a try.

I'm blown away.

I'm a very senior engineer. I have extremely high standards. I know a lot of technologies top to bottom. And I have immediately found it insanely helpful.

There are a few hugely valuable use-cases for me. The first is writing tests. Agentic AI right now is shockingly good at figuring out what your code should be doing and writing tests that test the behavior, all the verbose and annoying edge cases, and even find bugs in your implementation. It's goddamn near magic. That's not to say they're perfect, sometimes they do get confused and assume your implementation is correct when the test doesn't pass. Sometimes they do misunderstand. But the overall improvement for me has been enormous. They also generally write good tests. Refactoring never breaks the tests they've written unless an actually-visible behavior change has happened.

Second is trying to figure out the answer to really thorny problems. I'm extremely good at doing this, but agentic AI has made me faster. It can prototype approaches that I want to try faster than I can and we can see if the approach works extremely quickly. I might not use the code it wrote, but the ability to rapidly give four or five alternatives a go versus the one or two I would personally have time for is massively helpful. I've even had them find approaches I never would have considered that ended up being my clear favorite. They're not always better than me at choosing which one to go with (I often ask for their summarized recommendations), but the sheer speed in which they get them done is a godsend.

Finding the source of tricky bugs is one more case that they excel in. I can do this work too, but again, they're faster. They'll write multiple tests with debugging output that leads to the answer in barely more time than it takes to just run the tests. A bug that might take me an hour to track down can take them five minutes. Even for a really hard one, I can set them on the task while I go make coffee or take the dog for a walk. They'll figure it out while I'm gone.

Lastly, when I have some spare time, I love asking them what areas of a code base could use some love and what are the biggest reward-to-effort ratio wins. They are great at finding those places and helping me constantly make things just a little bit better, one place at a time.

Overall, it's like having an extremely eager and prolific junior assistant with an encyclopedic brain. You have to give them guidance, you have to take some of their work with a grain of salt, but used correctly they're insanely productive. And as a bonus, unlike a real human, you don't ever have to feel guilty about throwing away their work if it doesn't make the grade.

AI is solid for kicking off learning a language or framework you've never touched before.

But in my day to day I'm just writing pure Go, highly concurrent and performance-sensitive distributed systems, and AI is just so wrong on everything that actually matters that I have stopped using it.

zed was just a fast and simple replacement for Atom (R.I.P) or vscode. Then they put AI on top when that showed up. I don't care for it, and appreciate a project like this to return the program to its core.

Opt-out instead of opt-in is an anti-feature.

How to opt-out of unrequested pop-ups and various helpers, or download and installation of binary files without permission?

You can't disable the culture.

It's a question of the business model.

> generate new modules/classes in your projects

If it's formulaic enough, I will use the editor templates/snippets generator. Or write a code generator (if it involves a bunch of files). If it's not, I probably have another class I can copy and strip out (especially in UI and CRUD).

> integrate module A into module B

If it's cannot be done easily, that's the sign of a less than optimal API.

> entire codebase A into codebase B

Is that a real need?

> get someones github project up and running on your machine, do you manually fiddle with cmakes and npms

If the person can't be bothered to give proper documentation, why should I run the project? But actually, I will look into AUR (archlinux) and Homebrew formula if someone has already do the first jobs of figuring dependency version. If there's a dockerfile, I will use that instead.

> convert an idea or plan.md or a paper into working code?

Iteratively. First have an hello world or something working, then mowing down the task list.

> Fix flakes, fix test<->code discrepancies or increase coverage etc

Either the test is wrong or the code is wrong. Figure out which and rework it. The figuring part always take longer as you will need to ask around.

> If you do all this manually, why?

Because when something happens in prod, you really don't want that feeling of being the last one that have interacted with that part, but with no idea of what has changed.

To me, using AI to convert an idea or paper into working code is outsourcing the only enjoyable part of programming to a machine. Do we not appreciate problem solving anymore? Wild times.

I'm pretty fast coding and know what I'm doing. My ideas are too complex for claude to just crap out. If I'm really tired I'll use claude to write tests. Mostly they aren't really good though.

AI doesn't really help me code vs me doing it myself.

AI is better doing other things...

> how do you convert a paper into working code?

this is something i've found LLMs almost useless at. consider https://arxiv.org/abs/2506.11908 --- the paper explains its proposed methodology pretty well, so i figured this would be a good LLM use case. i tried to get a prototype to run with gemini 2.5 pro, but got nowhere even after a couple of hours, so i wrote it by hand; and i write a fair bit of code with LLMs, but it's primarily questions about best practices or simple errors, and i copy/paste from the web interface, which i guess is no longer in vogue. that being said, would cursor excel here at a one-shot (or even a few hours of back-and-forth), elegant prototype?

For stuff like adding generating and integrating new modules: the helpfulness of AI varies wildly.

If you’re using nest.js, which is great but also comically bloated with boilerplate, AI is fantastic. When my code is like 1 line of business logic per 6 lines of boilerplate, yes please AI do it all for me.

Projects with less cruft benefit less. I’m working on a form generator mini library, and I struggle to think of any piece I would actually let AI write for me.

Similar situation with tests. If your tests are mostly “mock x y and z, and make sure that this spied function is called with this mocked payload result”, AI is great. It’ll write all that garbage out in no time.

If your tests are doing larger chunks of biz logic like running against a database, or if you’re doing some kinda generative property based testing, LLMs are probably more trouble than they’re worth

To do those things, I do the same thing I've been doing for the thirty years that I've been programming professionally: I spend the (typically modest) time it takes to learn to understand the code that I am integrating into my project well enough to know how to use it, and I use my brain to convert my ideas into code. Sometimes this requires me to learn new things (a new tool, a new library, etc.). There is usually typing involved, and sometimes a whiteboard or notebook.

Usually it's not all that much effort to glance over some other project's documentation to figure out how to integrate it, and as to creating working code from an idea or plan... isn't that a big part of what "programming" is all about? I'm confused by the idea that suddenly we need machines to do that for us: at a practical level, that is literally what we do. And at a conceptual level, the process of trying to reify an idea into an actual working program is usually very valuable for iterating on one's plans, and identifying problems with one's mental model of whatever you're trying to write a program about (c.f. Naur's notions about theory building).

As to why one should do this manually (as opposed to letting the magic surprise box take a stab at it for you), a few answers come to mind:

1. I'm professionally and personally accountable for the code I write and what it does, and so I want to make sure I actually understand what it's doing. I would hate to have to tell a colleague or customer "no, I don't know why it did $HORRIBLE_THING, and it's because I didn't actually write the program that I gave you, the AI did!"

2. At a practical level, #1 means that I need to be able to be confident that I know what's going on in my code and that I can fix it when it breaks. Fiddling with cmakes and npms is part of how I become confident that I understand what I'm building well enough to deal with the inevitable problems that will occur down the road.

3. Along similar lines, I need to be able to say that what I'm producing isn't violating somebody's IP, and to know where everything came from.

4. I'd rather spend my time making things work right the first time, than endlessly mess around trying to find the right incantation to explain to the magic box what I want it to do in sufficient detail. That seems like more work than just writing it myself.

Now, I will certainly agree that there is a role for LLMs in coding: fancier auto-complete and refactoring tools are great, and I have also found Zed's inline LLM assistant mode helpful for very limited things (basically as a souped-up find and replace feature, though I should note that I've also seen it introduce spectacular and complicated-to-fix errors). But those are all about making me more efficient at interacting with code I've already written, not doing the main body of the work for me.

So that's my $0.02!

> generate new modules/classes in your projects

I type:

  class Foo:

  pub(crate) struct Foo {}

What do you mean by this? If you just mean moving things around then code refactoring tools to move functions/classes/modules have existed in IDEs for millennia before LLMs came around.

> get someones github project up and running on your machine

docker

> convert an idea or plan.md or a paper into working code

I sit in front of a keyboard and start typing.

> Fix flakes, fix test<->code discrepancies or increase coverage etc

I sit in front of a keyboard, read, think, and then start typing.

> If you do all this manually, why?

Because I care about the quality of my code. If these activities don't interest you, why are you in this field?

It's a code editor so I think the geeky forums are relevant here.

Also, this post is higher on HN than the post about raising capital from Sequoia where many of the comments are about how negatively they view the raising of capital from VC.

The fact of the matter is that people want this and the inability of companies to monetize on that desire says nothing about whether the desire is large enough to "actually sustain" a product/company like Zed.

I pay for intellij. I pay for Obsidian.

I would pay for zed.

The only path forward I see for a classic VC investment is the AI drive.

But I don't think the AI bit is valuable. A powerful plugin system would be sufficient to achieve LLM integration.

So I don't think this is a worthwhile investment unless the product gets a LOT worse and becomes actively awful for users who aren't paying beaucoup bucks for AI tooling- the ROI will have to center the AI drive.

It's not a move that will generate a good outcome for the average user.

> I understand if you don't want to pay for Zed

But he does say he does want to pay!