Zedless: Zed Fork Focused on Privacy and Being Local-First

It also didn't start out as a competitor to either.

More like a spiritual successor to Atom, at least per the people that started it who came from that project.

What, they're proud of the telemetery?

The fork claims to make everything opt-in and to not default to any specific vendor, and only to remove things that cannot be self-hosted. What proprietary features have to be cut that Zed people are really proud of?

https://github.com/zedless-editor/zedless?tab=readme-ov-file...

As far as I know, the Zed people have open sourced their collab server components (as AGPLv3), at least well enough to self-host. For example, https://github.com/zed-industries/zed/blob/main/docs/src/dev... -- AFAIK it's just https://github.com/livekit/livekit

The AI stuff will happily talk to self-hosted models, or OpenAI API lookalikes.

(I see that I have received two downvotes for this in mere minutes, but no replies. I genuinely don't understand the basis for objecting to what I have to say here, and could not possibly understand it without a counterargument. What I'm saying seems straightforward and obvious to me; I wouldn't say it otherwise.)

With the exception of GPL derivatives, most popular licenses such as MIT already include provisions allowing you to relicense or create derivative works as desired. So even if you follow the supposed norm that without an explicit license agreement all open source contributions should be understood to be licensed by contributors under the same terms as the license of the project, this would still allow the project owners to “rug pull” (create a fork under another license) using those contributions.

But given that Zed appears to make their source available under the Apache 2.0 license, the GPL exception wouldn’t apply.

I don't like the term "rug-pull". It's misleading.

If you have an open source version of Zed today, you can keep it forever, even if future versions switch to closed source or some source-available only model.

I assume that's where a lot of the hate comes from. Note that's not my opinion, just wondering if that might be why.

You're kidding, right?

WinterTC has only recently been chartered in order to make strides towards specifying a unified standard library for the JS ecosystem.

For node.js in general? The language isn't even considered good in the browser, for which it was invented. It is absolutely insane to then try to turn it into a standalone programming language. There are so many better options available, use one of them! Reusing a crappy tool just because it's what you know is a mark of very poor craftsmanship.

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.

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.

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.

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.

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.

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

AI is better doing other things...

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!