Chat is a bad UI pattern for development tools

by cryptophreak • 1 year ago

858 417 1 year ago

empath752• 1 year ago

I think he's right that there's a place for a more structured AI programming UI, but chat and autocomplete are also good for a lot of use cases.

croes2• 1 year ago

Natural language isn’t made to be precise that’s why we use a subset in programming languages.

So you either need lots of extra text to remove the ambiguity of natural language if you use AI or you need a special precise subset to communicate with AI and that’s just programming with extra steps.

oxfordmale3• 1 year ago

Yes, let's devise a more precise way to give AI instructions. Let's call it pAIthon. This will allow powers that be, like Zuckerberg to save face and claim that AI has replaced mid-level developers and enable developers to rebrand themselves as pAIthon programmers.

Joking aside, this is likely where we will end up, just with a slightly higher programming interface, making developers more productive.

dylan6042• 1 year ago

man, pAIthon was just sitting right there for the taking

empath752• 1 year ago

AIs actually are very good at this. They wouldn't be able to write code at all otherwise. If you're careful in your prompting, they'll make fewer assumptions and ask clarifying questions before going ahead and writing code.

9rx8• 1 year ago

If you're careful in your prompting

In other words, if you replace natural language with a programming language then the computer will do a good job of interpreting your intent. But that's always been true, so...

oxfordmale3• 1 year ago

AI is very good at this. Unfortunately, humans tend to be super bad at providing detailed verbal instructions.

LordDragonfang• 1 year ago

they'll make fewer assumptions and ask clarifying questions before going ahead and writing code.

Which model are you talking about here? Because with ChatGPT, I struggle with getting it to ask any clarifying questions before just dumping code filled with placeholders I don't want, even when I explicitly prompt it to ask for clarification.

croes2• 1 year ago

AI is a little bit like Occam‘s razor, when you say hoofbeats, you get horses. Bad if you need Zebras.

foobiekr• 1 year ago

I don’t think I’ve ever seen an llm in any context ask for clarification. Is that a real thing?

Klaster_12• 1 year ago

A lot of extra text usually means prior requirements, meeting transcripts, screen share recordings, chat history, Jira tickets and so on - the same information developers use to produce a result that satisfies the stakeholders and does the job. This seems like a straightforward direction solvable with more compute and more efficient memory. I think this will be the way it pans outs.

Real projects don't require an infinitely detailed specification either, you usually stop where it no longer meaningfully moves you towards the goal.

The whole premise of AI developer automation, IMO, is that if a human can develop a thing, then AI should be able too, given the same input.

cube2222• 1 year ago

We are kind of actually there already.

With a 200k token window like Claude has you can already dump a lot of design docs / transcripts / etc. at it.

throwaway290• 1 year ago

idk if you think all those jira tickets and meetings are precise enough (IMO sometimes the opposite)

By the way, remind me why you need design meetings in that ideal world?:)

Real projects don't require an infinitely detailed specification either, you usually stop where it no longer meaningfully moves you towards the goal.

The point was that specification is not detailed enough in practice. Precise enough specification IS code. And the point is literally that natural language is just not made to be precise enough. So you are back where you started

So you waste time explaining in detail and rehashing requirements in this imprecise language until you see what code you want to see. Which was faster to just... idk.. type.

layer82• 1 year ago

This premise in your last paragraph can only work with AGI, and we’re probably not close to that yet.

652• 1 year ago

We're going to create SQL all over again, aren't we?

lelanthran• 1 year ago

A more modern COBOL maybe.

spacemanspiff01• 1 year ago

Or a proposal/feedback process. Ala you are hired by non technical person to build something, you generate requirements and a proposed solution. You then propose that solution, they give feedback.

Having a feedback loop is the only way viable for this. Sure, the client could give you a book on what they want, but often people do not know their edge cases, what issues may arise/etc.

pjc50• 1 year ago

There was a wave of this previously in programming: https://en.wikipedia.org/wiki/The_Last_One_(software)

All the same buzzwords, including "AI"! In 1981!

dylan6042• 1 year ago

and that’s just programming with extra steps.

If you know how to program, then I agree and part of why I don't see the point. If you don't know how to program, than the prompt isn't much different than providing the specs/requirements to a programmer.

kokanee• 1 year ago

or you need a special precise subset to communicate with AI

haha, I just imagined sending TypeScript to ChatGPT and having it spit my TypeScript back to me. "See guys, if you just use Turing-complete logically unambiguous input, you get perfect output!"

charlieyu1• 1 year ago

I guess we could have LLM to translate natural language to some precise subset, get it processed, then translate the output back to natural language

thomastjeffery3• 1 year ago

Natural language can be precise, but only in context.

The struggle is to provide a context that disambiguates the way you want it to.

LLMs solve this problem by avoiding it entirely: they stay ambiguous, and just give you the most familiar context, letting you change direction with more prompts. It's a cool approach, but it's often not worth the extra steps, and sometimes your context window can't fit enough steps anyway.

My big idea (the Story Empathizer) is to restructure this interaction such that the only work left to the user is to decide which context suits their purpose best. Given enough context instances (I call them backstories), this approach to natural language processing could recursively eliminate much of its own ambiguity, leaving very little work for us to do in the end.

Right now my biggest struggle is figuring out what the foundational backstories will be, and writing them.

skydhash7• 1 year ago

That’s what programming languages are: You define a context, then you see that you can shorten the notation to symbol character: Like “The symbol a will refer to the value of type string and content ‘abcd’ and cannot refer to anything else for its life time” get you:

  const a = “abcd”

That is called semantics. Programming is mostly fitting the vagueness inherent to natural languages to the precise context of the programming language.

Edmond• 1 year ago

This is about relying on requirements type documents to drive AI based software development, I believe this will be ultimately integrated into all the AI-dev tools, if not so already. It is really just additional context.

Here is an example of our approach:

https://blog.codesolvent.com/2024/11/building-youtube-video-...

We are also using the requirements to build a checklist, the AI generates the checklist from the requirements document, which then serves as context that can be used for further instructions.

Here's a demo:

https://youtu.be/NjYbhZjj7o8?si=XPhivIZz3fgKFK8B

wongarsu• 1 year ago

Now we just need another tool that allows stakeholders to write requirement docs using a chat interface

m3kw9• 1 year ago

There is a black box effect between when you press enter and it starts updating code in multiple places. Like wtf just happened, I have to find these changes, which code broke dependencies. It should be more step wise visually

seunosewa• 1 year ago

In VS Code Copilot you have to approve every change manually when you apply to from the chat.

Take8435• 1 year ago

Are you... not using git?

barrenko2• 1 year ago

The next couple of years will be dedicated to working that out.

naiv• 1 year ago

rather months than years

josefrichter2• 1 year ago

In some editors, like Windsurf, you will get git-like diff and you have to approve each change (or accept all in bulk, of course).

joshstrange• 1 year ago

Aider + git makes this a non-issue. You can revert anything it does easily (even 5-10 steps down the line) and it shows you the code blocks it's changing in the chat UI as well.

jakelazaroff• 1 year ago

I agree with the premise but not with the conclusion. When you're building visual things, you communicate visually: rough sketches, whiteboard diagrams, mockups, notes scrawled in the margins.

Something like tldraw's "make real" [1] is a much better bet, imo (not that it's mutually exclusive). Draw a rough mockup of what you want, let AI fill in the details, then draw and write on it to communicate your changes.

We think multi-modally; why should we limit the creative process to just text?

[1] https://tldraw.substack.com/p/make-real-the-story-so-far

a3w• 1 year ago

For me: Chat is like writing comments, but not a the right place in the source code.

Perhaps I should comment all todos and then write "finish todos" as the always-same text prompt.

gspencley• 1 year ago

This is preference for sure, but I am of the opinion that ALL code comments are code smells.

And that's not even to say that I don't write code comments. When working on large legacy codebases, where you often need to do 'weird' things in service of business goals and timelines, a comment that explains WHY something was done the way it was is valuable. And I leave those comments all the time. But they're still a code smell.

Comments are part of your code. So they need to be maintained with the rest of your code. Yet they are also "psychologically invisible" most of the time to most programmers. Our IDEs even tend to grey them out by default for us, so that they get out of the way so we can focus on the actual implementation code.

This means that comments are a maintenance obligation that often get ignored and so they get out of sync with the actual code really fast.

They also clutter the code unnecessarily. Code, at its best, should be self-explanatory with extremely little effort needed to understand the intent of the code. So even a comment that explains why the code is weird is doing little more than shining a flashlight on smelly code without actually cleaning it up.

And don't get me started on "todo" comments. Most professional shops use some kind of project management tool for organizing and prioritizing future work. Don't make your personal project management the problem of other people that share and contribute to your codebase. There is zero rationale for turning shared code into your personal todo list. (and it should be obvious that I'm talking about checked in code .. if it's your working branch then you do you :) )

So if programming using LLMs is similar to writing comments (an interesting analogy I hadn't considered before), then maybe this is part of the reason I haven't found a problem that LLMs solve for me yet (when programming specifically). I just don't think like that when I'm writing code.

kerblang2• 1 year ago

English is a terrible programming language.

amelius• 1 year ago

LLMs are what COBOL was supposed to be.

krainboltgreene• 1 year ago

I suspect there's a gradient for language: One side is clarity, the other side is poetry. English definitely is farther to the poetry side than the clarity side, where programming languages are significantly closer to the clarity side.

I suspect there's an 100 year old book describing what I'm saying but much more eloquently.

px43• 1 year ago

Language evolves. As much as we are training computers to understand us, they are training us to understand them.

The level of precision required for highly complex tasks was never necessary before. My four year old has a pretty solid understanding of how the different AI tools she has access to will behave differently based on how she phrases what she says, and I've noticed she is also increasingly precise when making requests of other people.

remoquete2• 1 year ago

No it's not! :-) https://github.com/theletterf/english-lang

kerblang2• 1 year ago

Well if I can't have intelligent replies I'll gladly take the amusing ones, thanks

lcfcjs6• 1 year ago

Seems like this is a common complaint from folks trying to write code purely with ChatGPT / Deepseek by communicating in complete sentences. You can only get so far using these tools before you need a proper understanding of whats happening with the code.

benatkin4• 1 year ago

A chat room is an activity stream and so is a commit log of a version control system. A lot of the bad UI is waiting a fixed amount of time that had a minimum that was too high, and for some communicating by typing. Many will prefer chatting by voice. When responses are faster it will be easier to hide the history pane and ask if you need to be reminded of anything in the history pane and use the artifact pane. However not all responses from an LLM need be fast, it is a huge advancement that LLMs will think for minutes at a time. I agree about the usefulness of prose as an artifact while coding. Markdown can be edited in IDEs using LLMs and then referenced in prompts.

r0ckarong• 1 year ago

I don't want to become a lawyer to talk to my compiler; thank you.

newsyco21• 1 year ago

generated ai is cancer

josefrichter2• 1 year ago

I think everyone is aware that chat is not the ideal UI pattern for this. It's just the way current AI models work and generate content - that's why they have this "typewriter" mode, which naturally leads to a chat interface.

It's not really a conscious choice, but rather a side effect. And we already see the trend is away from that, with tools like chatGPT Canvas, editors like Windsurf, etc.

kijin• 1 year ago

When the only tool your AI can wield is a hammer, everything had better look like a nail.

Once the models become fast enough to feel instantaneous, we'll probably begin to see more seamless interfaces. Who wants a pair programmer who goes "umm... ahh..." every time you type something? A proper coding assistant should integrate with your muscle memory just like autocomplete. Tab, tab, tab and it's done.

sho_hn• 1 year ago

I'd say this criticism is well-addressed in aider. Steering the LLM via code comments is the first UX I've seen that works.

https://aider.chat/docs/usage/watch.html

How jarring it is & how much it takes you out of your own flow state is very much dependent on the model output quality and latency still, but at times it works rather nicely.

indymike2• 1 year ago

How else do you interact with a chat based ai? It may not be ideal, but it is an improvement.

furyofantares2• 1 year ago

In cursor I keep a specification document in .cursorrules and I have instructions that cursor should update the document whenever I add new specifications in chat.

yoz• 1 year ago

That's a great idea. How well does it work in practice?

furyofantares2• 1 year ago

It works great until it stops doing anything. I use it on new projects and it makes everything go smoothly at the start and, I think, for much longer.

I haven't done it for existing projects but I have done something similar for an unfamiliar, old and challenging codebase. I worked with the cursor chat agent to produce a document I called architecture.md mapping out high level features to files/classes/functions. This was excellent because I found the document useful and it also made cursor more effective.

h1fra• 1 year ago

Chat is a bad UI.

jfkrrorj• 1 year ago

No, it is pretty much dialog, I would compare it to pair programming.

AI in many levels is more capable than human programmer, in some it is not. It is not supersmart. It can not hold entire program in its head, you have to feed it small relevant section of program.

》 That’s why we use documents—they let us organize complexity, reference specific points, and track changes systematically.

Extra steps. Something like waterfall...

azhenley• 1 year ago

See my article from January 2023, "Natural language is the lazy user interface".

https://austinhenley.com/blog/naturallanguageui.html

taeric17• 1 year ago

I'm growing to the idea that chat is a bad UI pattern, period. It is a great record of correspondence, I think. But it is a terrible UI for doing anything.

In large, I assert this is because the best way to do something is to do that thing. There can be correspondence around the thing, but the artifacts that you are building are separate things.

You could probably take this further and say that narrative is a terrible way to build things. It can be a great way to communicate them, but being a separate entity, it is not necessarily good at making any artifacts.

beambot• 1 year ago

As a written form of "stream of consciousness", it seems to have a lot of value to me. It's noisy, inefficient & meandering -- all the things those polished artifacts are not -- but it's also where you can explore new avenues without worrying about succinctness or completeness. It's like the first draft of a manuscript.

taeric17• 1 year ago

Certainly, it can have its use. But I question if it is stronger than previous generative techniques for creating many things. There have been strong tools that you could, for example, draw a box and say this should be a house. Add N rooms. This room should be a bathroom. Add windows to these rooms. Add required subfloor and plumbing.

Even with game development. Level editors have a good history for being how people actually make games. Some quite good ones, I should add.

For website development, many template based systems worked quite well. People seem hellbent on never acknowledging that form builders of the late 90s did, in fact, work.

Is it a bit nicer that you can do everything through a dialog? I'm sure it is a great for people that think that way.

freedomben• 1 year ago

Midjourney is an interesting case study in this I think, building their product UI as a discord bot. It was interesting to be sure, but I always felt like I was fighting the "interface" to get things done. It certainly wasn't all bad, and I think if I used it more it might even be great, but as someone who doesn't use Discord other than that and only rarely generated images, I had to read the docs every time I wanted to generate an image, which is a ridiculous amount of friction.

joe_guy• 1 year ago

There has recently been a pretty large UI inclusion for midjourney directly inside Discord which has the option of being used instead of the text input.

As is often the case in these sorts of thingsz your milage may vary for the more complex settings.

ijk• 1 year ago

I'm curious if you find their new website interface more tractable--there's some inherent friction to the prompting in either case, but I'd like to know if the Discord chat interface can be overcome by using a different interface or if the issue is more intrinsic.

OJFord• 1 year ago

I don't know, I'm in Slack all day with colleagues, I quite like having the additional ChatGPT colleague (even better I can be quite rude/terse in my messages with 'them').

Incidentally I think that's also a good model for how much to trust the output - you might have a colleague who knows enough about X to think they can answer your question, but they're not necessarily right, you don't blindly trust it. You take it as a pointer, or try the suggestion (but not surprised if it turns out it doesn't work), etc.

taeric17• 1 year ago

Oh, do not take my comment as a "chat bots shouldn't exist." That is not at all my intent. I just think it is a bad interface for building things that are self contained in the same chat log.

packetlost5• 1 year ago

I even think it's bad for generalized communication (ie. Slack/Teams/Discord/etc.) that isn't completely throwaway. Email is better in every single way for anything that might ever be relevant to review again or be filtered due to too much going on.

taeric17• 1 year ago

Anything that needs to be filtered for viewing again pretty much needs version control. Email largely fails at that, as hard as other correspondence systems. That said, we have common workflows that use email to build reviewed artifacts.

People love complaining about the email workflow of git, but it is demonstrably better than any chat program for what it is doing.

goosejuice3• 1 year ago

I've had the opposite experience.

I have never had any issue finding information in slack with history going back nearly a decade. The only issue I have with Slack is a people problem where most communication is siloed in private channels and DMs.

Email threads are incredibly hard to follow though. The UX is rough and it shows.

chinathrow• 1 year ago

Voice messages within a chat UI is even worse. I can't search it, I can't listen to it in the same situations I can read a message.

I wish I could block them within all these chat apps.

"Sorry, you can't bother to send voice messages to this person."

taeric17• 1 year ago

Oh dear lord yes. I am always baffled when I hear that some folks send voice memos to people.

dapperdrake• 1 year ago

Email threads seem better for documenting and searching correspondence.

The last counter argument I read got buried on Discord or Slack somewhere.

taeric17• 1 year ago

Discord and slack baffle me. I liked them specifically because they were more ephemeral than other options. Which, seems at odds with how people want them to be? Why?

jayd162• 1 year ago

Isn't this entirely an implementation detail of slack and discord search? What about email makes it more searchable fundamentally? The meta data if both platforms is essentially the same, no?

652• 1 year ago

Oh, how nice it must be to complain about Slack. Try using Teams and you will never want to complain about Slack again.

al_borland• 1 year ago

I find things get buried just as easily in email. People on my team are constantly resending each other emails, because they can’t find the thread.

This is why, if something is important, I take it out of email and put it into a document people can reference. The latest and correct information from all the decisions in the thread can also be collected in one place, so everyone reading doesn’t have to figure it out. Not to mention side conversations can influence the results, without being explicitly stated in the email thread.

dartos5• 1 year ago

Preach!

I’ve been saying this since 2018

SoftTalker2• 1 year ago

Yes, agree. Chatting with a computer has all the worst attributes of talking to a person, without any of the intuitive understanding, nonverbal cues, even tone of voice, that all add meaning when two human beings talk to each other.

taeric17• 1 year ago

Yeah, this is something I didn't make clear on my post. Chat between people is the same bad UI. People read in the aggression that they bring to their reading. And get mad at people who are legit trying to understand something.

You have some of the same problems with email, of course. Losing threading, in particular, made things worse. It was a "chatification of email" that caused people to lean in to email being bad. Amusing that we are now seeing chat applications rise to replace email.

aylmao• 1 year ago

I would also call it having all the worst attributes of a CLI, without the succinctness, OS integration, and program composability of one.

TeMPOraL6• 1 year ago

That comment made sense 3 years ago. LLMs already solved "intuitive understanding", and the realtime multimodal variants (e.g. the thing behind "Advanced Voice" in ChatGPT app) handle tone of voice in both directions. As for nonverbal cues, I don't know yet - I got live video enabled in ChatGPT only few days ago and didn't have time to test it, but I would be surprised if it couldn't read the basics of body language at this point.

Talking to a computer still sucks as an user interface - not because a computer can't communicate on multiple channels the way people do, as it can do it now too. It sucks for the same reason talking to people sucks as an user interface - because the kind of tasks we use computers for (and that aren't just talking with/to/at other people via electronic means) are better handle by doing than by talking about them. We need an interface to operate a tool, not an interface to an agent that operates a tool for us.

As an example, consider driving (as in, realtime control - not just "getting from point A to B"): a chat interface to driving would suck just as badly as being a backseat driver sucks for both people in the car. In contrast, a steering wheel, instead of being a bandwidth-limiting indirection, is an anti-indirection - not only it lets you control the machine with your body, the control is direct enough that over time your brain learns to abstract it away, and the car becomes an extension of your body. We need more of tangible interfaces like that with computers.

The steering wheel case, of course, would fail with "AI-level smarts" - but that still doesn't mean we should embrace talking to computers. A good analogy is dance - it's an interaction between two independently smart agents exploring an activity together, and as they do it enough, it becomes fluid.

So dance, IMO, is the steering wheel analogy for AI-powered interfaces, and that is the space we need to explore more.

hakfoo• 1 year ago

The idea of chat interfaces always seemed to be to disguise available functionality.

It's a CLI without the integrity. When you bought a 386, it came with a big book that said "MS-DOS 4.01" and enumerated the 75 commands you can type at the C:\> prompt and actually make something useful happen.

When you argue with ChatGPT, its whole business is to not tell you what those 75 commands are. Maybe your prompt fits its core competency and you'll get exactly what you wanted. Maybe it's hammering what you said into a shape it can parse and producing marginal garbage. Maybe it's going to hallucinate from nothing. But it's going to hide that behind a bunch of cute language and hopefully you'll just keep pulling the gacha and blaming yourself if it's not right.

Suppafly• 1 year ago

I like the idea of having a chat program, the issue is that it's horrible to have a bunch of chat programs all integrated into every application you use that are separate and incompatible with each other.

I really don't like the idea of chatting with an AI though. There are better ways to interface with AIs and the focus on chat is making people forget that.

tux1968• 1 year ago

We need an LSP like protocol for AI, so that we can amortize the configuration over every place we want such an integration. AISP?

brobdingnagians• 1 year ago

Similar thing I've run into lately, chat is horrible for tracking issues and tasks. When people try to use it that way, it becomes absolute chaos after awhile.

t_mann2• 1 year ago

Ok, but what is a good pattern to leverage AI tools for coding (assuming that they have some value there, which I think most people would agree with now)? I could see two distinct approaches:

- "App builders" that use some combination of drag&drop UI builders, and design docs for architecture, workflows,... and let the UI guess what needs to be built "under the hood" (a little bit in the spirit of where UML class diagrams were meant to take us). This would still require actual programming knowledge to evaluate and fix what the bot has built

- Formal requirement specification that is sufficiently rigorous to be tested against automatically. This might go some way towards removing the requirement to know how to code, but the technical challenge would simply shift to knowing the specification language

lucasyvas• 1 year ago

Disclaimer: Haven't used the tools a lot yet, just a bit. So if I say something that already exists, forgive me.

TLDR: Targeted edits and prompts / Heads Up Display

It should probably be more like an overlay (and hooked into context menus with suggestions, inline context bubbles when you want more context for a code block) and make use of an IDE problems view. The problems view would have to be enhanced to allow it to add problems that spanned multiple files, however.

Probably like the Rust compiler output style, but on steroids.

There would likely be some chatting required, but it should all be at a particular site in the code and then go into some history bank where you can view every topic you've discussed.

For authoring, I think an interactive drawing might be better, allowing you to click on specific areas and then use shorter phrasing to make an adjustment instead of having an argument in some chat to the left of your screen about specificity of your request.

Multi-point / click with minimal prompt. It should understand based on what I clicked what the context is without me having to explain it.

staplers• 1 year ago

  Ok, but what is a good pattern to leverage AI tools for coding?

Actual product stakeholders are not likely to spill their magic sauce and give free consultancy.

taeric17• 1 year ago

I'd challenge if this is specific to coding? If you want to get a result that is largely like a repertoire of examples used in a training set, chat is probably workable? This is true for music. Visual art. Buildings. Anything, really?

But, if you want to start doing "domain specific" edits to the artifacts that are made, you are almost certainly going to want something like the app builders idea. Down thread, I mention how this is a lot like procedural generative techniques for game levels and such. Such that I think I am in agreement with your first bullet?

Similarly, if you want to make music with an instrument, it will be hard to ignore playing with said instrument more directly. I suspect some people can create things using chat as an interface. I just also suspect directly touching the artifacts at play is going to be more powerful.

I think I agree with the point on formal requirements. Not sure how that really applies to chat as an interface? I think it is hoping for a "laws of robotics" style that can have a test to confirm them? Reality could surprise me, but I always viewed that as largely a fiction item.

kiitos• 1 year ago

I've yet to see any AI/LLM produce code that withstands even basic scrutiny.

marcosdumay• 1 year ago

It can be a great way to communicate them

It's usually not. Narrative is a famously flawed way to communicate or record the real world.

It's great for generating engagement, though.

taeric17• 1 year ago

I think fictional narratives that aim to capture inner monologue are famously flawed. I think narrative tours of things can be good. I'm not clear if "narrated tours" are a specific genre, sadly. :(

sangnoir3• 1 year ago

Narrative is a famously flawed way to communicate or record the real world.

...and yet with it's flaws, it's the most flexible in conveying meaning. A Ted Chiang interview was on the HN frontpage a few days ago, in it, he mentions that humans created multiple precise, unambiguous communication modes like equations used in mathematical papers and proofs. But those same papers are not 100% equations, the mathematicians have to fall back to flawed language to describe and provide context because those formal languages only capture a smaller range of human thought compared to natural language.

This is not to say chat has the best ergonomics for development - it's not, but one has to remember that the tools are based on Large Language Models whose one-trick is manipulating language. Better ergonomics would likely come from models trained or fine-tuned on AST-tokens and diffs. They'd still need to modulate on language (understanding requirements, hints, variable names,and authoring comments, commits and/or PRs).

zamfi3• 1 year ago

With apologies to Bill Buxton: "Every interface is best at something and worst at something else."

Chat is a great UI pattern for ephemeral conversation. It's why we get on the phone or on DM to talk with people while collaborating on documents, and don't just sit there making isolated edits to some Google Doc.

It's great because it can go all over the place and the humans get to decide which part of that conversation is meaningful and which isn't, and then put that in the document.

It's also obviously not enough: you still need documents!

But this isn't an "either-or" case. It's a "both" case.

gagik_co• 1 year ago

I think “correspondence UX” can be bad UX but there’s nothing inherently wrong with chat UI.

I created the tetr app[1] which is basically “chat UI for everything”. I did that because I used to message myself notes and wanted to expand it to many more things. There’s not much back and forth, usually 1 input and instant output (no AI), still acting like a chat.

I think there’s a lot of intuitiveness with chat UI and it can be a flexible medium for sharing different information in a similar format, minimizing context switching. That’s my philosophy with tetr anyhow.

[1] https://tetr.app/

tpmoney2• 1 year ago

I disagree. Chat is a fantastic UI for getting an AI to generate something vague. Specifically I’m thinking of AI image generation. A chat UI is a great interface for iterating on an image and dialing it in over a series of iterations. The key here is that the AI model needs to keep context both of the image generation history and that chat history.

I think this applies to any “fuzzy generation” scenario. It certainly shouldn’t be the only tool, and (at least as it stands today) isn’t good enough to finalize and fine tune the final result, but a series of “a foo with a bar” “slightly less orange” “make the bar a bit more like a fizzbuzz” interactions with a good chat UI can really get a good 80% solution.

But like all new toys, AI and AI chat will be hammered into a few thousand places where it makes no sense until the hype dies down and we come up with rules and guidelines for where it does and doesn’t work

badsectoracula2• 1 year ago

Specifically I’m thinking of AI image generation

I heavily disagree here, chat - or really text - is a horrible UI for image generation, unless you have almost zero idea of what you want to achieve and you don't really care about the final results.

Typing "make the bar a bit more like a fizzbuzz" in some textbox is awful UX compared to, say, clicking on the "bar" and selecting "fizzbuzz" or drag-and-dropping "fizzbuzz" on the "bar" or really anything that takes advantage of the fact we're interacting with a graphical environment to do work on graphics.

In fact it is a horrible UI for anything, except perhaps chatbots and tasks that have to do with text like grammar correction, altering writing styles, etc.

It is helpful for impressing people (especially people with money) though.

varispeed• 1 year ago

Talk to AI chat as if you would talk to junior developer at your company and tell it to do something that you need.

I think it is brilliant. On another hand I caught myself many times writing prompts to colleagues. Although it made requirements of what I need so much clearer for them.

Sylamore2• 1 year ago

NC DMV replaced their regular forms with a chat bot and it's horrible. Takes forever to complete tasks that used to take less than a minute because of the fake interaction and fake typing. Just give me a damn form to pay my taxes or request a custom plate.

deeviant• 1 year ago

You may have challenges using chat for development (Specifically, I mean text prompting, not necessary using a langchain session with a LLM, although that is my most common mode), but I do not. I have found chat to be, by far, the most productive interface with LLMs for coding.

Everything else, is just putting layers, that are not nearly as capable at an LLM, between me and the raw power of the LLM.

The core realization I made to truly unlock LLM code assistance as a 10x + productivity gain, is that I am not writing code anymore, I am writing requirements. It means being less an engineer, and more a manager, or perhaps an architect. It's not your job to write tax code anymore, it's your job to describe what the tax code needs to accomplish and how it's success can be defined and validated.

Also, it's never even close to true that nobody uses LLMs for production software, here's a write-up by Google talking about using LLMs to drastically accelerate the migration of complex enterprise production systems: https://arxiv.org/pdf/2501.06972

reverendsteveii• 1 year ago

This puts me in mind of something I read years ago and am having trouble finding that basically had the same premise but went about proving it a different way. The idea was that natural language programming is always going to mean dealing with a certain background level of ambiguity, and the article cited contracts and contract law as proof. Basically, a contract is an agreement to define a system with a series of states and a response for each state defined, and the vast and difficult-to-navigate body of contract law is proof that even when purposefully being as unambiguous as possible with two entities that fully grasp the intricacies of the language being used there is so much ambiguity that there has to be an entire separate group of people (the civil court system) whose only job it is to mediate and interpret that ambiguity. You might point to bad-faith actors but a contract where every possible state and the appropriate response are defined without ambiguity would be proof against both misinterpretations and bad faith actors.

karmakaze• 1 year ago

What makes it bad currently is the slow output.

The example shows "Sign-in screen" with 4 (possibly more) instructions. This could equivalently have been entered one at a time into 'chat'. If the response for each was graphic and instantaneous, chat would be no worse than non-chat.

What makes non-chat better is that the user puts more thought into what they write. I do agree for producing code Claude with up-front instructions beats ChatGPT handily.

If OTOH AI's actually got as good or better than humans, chat would be fine. It would be like a discussion in Slack or PR review comments.

whatsakandr• 1 year ago

The nice thing about chat is it's open ended, the terrible thing is that holy crap I have to write a paragraph describing exactly what I want when I should just be able to hit a button or navigate through a couple menus.

tiborsaas2• 1 year ago

I should just be able to hit a button or navigate through a couple menus.

The problem with this is that you need a gazillion of menus, dialogs and options to find that modal which does the thing _exactly_ what you want. Menus and likes are a means to an end, we don't really want them, but up until recently we couldn't live without them. With instruct based computing this is all changing.

karaterobot• 1 year ago

I don't know about this. He admits you can write prototype code with chat-based LLMs, but then says this doesn't matter, because you can't write extremely complex applications with them.

First of all, most people can't write extremely complex applications, period. Most programmers included. If your baseline for real programming is something of equivalent complexity as the U.S. tax code, you're clearly such a great programmer that you're an outlier, and should recognize that.

Second of all, I think it's a straw man argument to say that you can either write prototype-level code with a chat UI, or complex code with documents. You can use both. I think the proposition being put forward is that more people can write complex code by supplementing their document-based thinking with chat-based thinking. Or, that people can write slightly better-than-prototype level code with the help of a chat assistant. In other words, that it's better to have access to AI to help you code small sections of a larger application that you are still responsible for.

I'd be more interested in reading a good argument against the value of using chat-based AI as another tool in your belt, rather than a straight-up replacement for traditional coding. If you could make that argument, then you could say chat is a bad UI pattern for dev tools.

xena2• 1 year ago

My nuclear fire hot take is that the chat pattern is actively hampering AI tools because we have to square peg -> round hole things either into the chat UI (because that's what people expect), or that as developers you have to square peg -> round hole into the chat API patterns.

Last night I wrote an implementation of an AI paper and it was so much easier to just discard the automatic chat formatting and do it "by hand": https://github.com/Xe/structured-reasoning/blob/main/index.j...

I wonder if foundation models are an untapped goldmine in terms of the things they can do, but we can't surface them to developers because everyone's stuck in the chat pattern.

disqard2• 1 year ago

Whoa! You broke my brain a bit there (but your posts often do, in a Good way!)

Would you be so kind as to ELI5 what you did in that index.js?

I've used ollama to run models locally, but I'm still stuck in chat-land.

Of course, if a blog post is in the works, I'll just wait for that :)

xena2• 1 year ago

The file explains it a bit, but my blogpost https://xeiaso.net/notes/2025/s1-simple-test-time-scaling/ could probably be better explained. I'll write out more but just for you I'll summarize what I'm gonna end up writing up.

AI models fundamentally work on the basis of "given what's before, what comes next?" When you pass messages to an API like:

    [
      { "role": "system", content": "You are an expert in selling propane and propane accessories. Whenever someone talks about anything that isn't propane, steer them back." },
      { "role": "user", "content": "What should I use to cook food on my grill?" },
      { "role": "assistant", "content": "For cooking food on your grill, using propane is a great choice due to its convenience and efficiency. [...]" }
    ]

Under the hood, the model actually sees something like this (using the formatting that DeepSeek's Qwen 2.5 32b reasoning distillation uses):

    You are an expert in selling propane and propane accessories. Whenever someone talks about anything that isn't propane, steer them back.
    <｜User｜>What should I use to cook food on my grill?<｜endofsentence｜>
    <｜Assistant｜>

And then the model starts generating tokens to get you a reply. What the model returns is something like:

    For cooking food on your grill, using propane is a great choice due to its convenience and efficiency. [...]<｜endofsentence｜>

The runtime around the model then appends that as the final "assistant" message and sends it back to the user so there's a façade of communication.

What I'm doing here is manually assembling the context window such that I can take advantage of that and then induce the model that it needs to think more, so the basic context window looks like:

    Follow this JSON schema: [omitted for brevity]
    <｜User｜>Tell me about Canada.<｜endofsentence｜>
    <｜Assistant｜><think>Okay

And then the model will output reasoning steps until it sends a </think> token, which can be used to tell the runtime that it's done thinking and to treat any tokens after that as the normal chat response. However, sometimes the model stops thinking too soon, so what you can do is intercept this </think> token and then append a newline and the word "Wait" to the context window. Then when you send it back to the model, it will second-guess and double-check its work.

The paper s1: Simple test-time scaling (https://arxiv.org/abs/2501.19393) concludes that this is probably how OpenAI implemented the "reasoning effort" slider for their o1 API. My index.js file applies this principle and has DeepSeek's Qwen 2.5 32b reasoning distillation think for three rounds of effort and then output some detailed information about Canada.

In my opinion, this is the kind of thing that people need to be more aware of, and the kind of stuff that I use in my own research for finding ways to make AI models benefit humanity instead of replacing human labor.

anarticle• 1 year ago

I agree, and I think this means there is a lot of space for trying new things. I think cursor was a small glimpse in trying to fix the split between purely GitHub copilot line revision (this interrupts my thoughts too much) and calling in for help via a chat window that you're copying and pasting from.

I think this post shows there could be a couple levels of indirection, some kind of combination of the "overarching design doc" that is injected into every prompt, and a more tactical level syntax/code/process that we have with something like a chat window that is code aware. I've definitely done some crazy stuff by just asking something really stupid like "Is there any way to speed this up?" and Claude giving me some esoteric pandas optimization that gave me a 100x speedup.

I think overall the tools have crazy variance in quality of output, but I think with some "multifacet prompting", ie, code styling, design doc, architect docs, constraints, etc you might end up with something that is much more useful.

Apocryphon• 1 year ago

When your intent is in a document instead of scattered across a chat log, English becomes a real programming language

So, something like Gherkin?

jimlikeslimes• 1 year ago

Has anyone invited an LLM inside their lisp process that can be accessed from the repl? Being able to empower an LLM to be able to affect the running lisp image (compile functions etc), and having changes reflected back to the source on disk would be interesting.

proc02• 1 year ago

This is lowkey cope. AI should be like talking to another human, at least that is the promise. Instead we're getting glorified autocomplete with padded language to sound like a human.

In its current form LLMs are pretty much at their limit, barring optimization and chaining them together for more productivity once we have better hardware. Still, it will just be useful for repetitive low level tasks and mediocre art. We need more breakthroughs beyond transformers to approach something that creates like humans instead of using statistical inference.

biscuit1v9• 1 year ago

In its current form LLMs are pretty much at their limit

How do you know that?

proc02• 1 year ago

I don't know that, mostly speculating based on how mixture of experts is outperforming decoder-only architectures, which means we're already composing transformers to squeeze the most out of it, and still it seems to fall short. They have already been trained with incredible amounts of data, and it still needs to be composed into multiple instances and needs even better hardware and it seems it has reached diminishing returns. The question is will the little that is left to optimize be enough to have it be truly agentic and create full apps on its own, or will it still require expert supervision for anything useful.

quantadev2• 1 year ago

Just two tips/thoughts:

1) The first thing to improve chats as a genre of interface, is that they should all always be a tree/hierarchy (just like Hacker News is), so that you can go back to ANY precise prior point during a discussion/chat and branch off in a different direction, and the only context the AI sees during the conversation is the "Current Node" (your last post), and all "Parent Nodes" going back to the beginning. So that at any time, it's not even aware of all the prior "bad branches" you decided to abandon.

2) My second tip for designs of Coding Agents is do what mine does. I invented a 'block_begin/block_end' syntax which looks like this, and can be in any source file:

// block_begin MyAddNumbers

var = add(a, b)

return a + b

// block_end

With this syntax you can use English language to explain and reason about extremely specific parts of your code with out expecting the LLM to "just understand". You can also direct the LLM to only edit/update specific "Named Blocks", as I call them.

So a trivial example of a prompt expression related to the above might be "Always put number adding stuff in the MyAddNumbers Block".

To explain entire architectural aspects to the LLM, these code block names are extremely useful.

cruffle_duffle5• 1 year ago

Dude threaded chat is how it should be, right? Especially if you could reference one thread in another and have it build the proper context up to understand what said thread was as a basis for this new conversation.

Proper context is absolutely everything when it comes to LLM use

quantadev2• 1 year ago

Yep. "Threaded" might be what the kids are callin' it nowadays. It's always been trees for me. I had a 'Tree-based' chat app for OpenAI (and as of now all other leading LLMs, via LangChain/LangGraph) within one week of them opening up their API. lol. Because my Tree-based CMS was already done, to build on.

OpenAI finally made it where you can go back and edit a prior response, in their chat view, but their GUI is jank, because it's not a tree.

ajmurmann• 1 year ago

I agree with this and disagree at the same time. It depends what the goal is. If the goal is to have AI write the entire codebase for you, yes chat and human language is quite bad. That's part of the reason formal languages exist. But then only experts can use it. Requirement docs are a decent middle ground. However, I'm not sure it's a good goal for AI to generate the code base.

The mode that I've found most fruitful when using Cursor is treating it almost exactly as I would a pair programming partner. When I start on a new piece of functionality I describe the problem and give it what my thoughts are on a potential solution and invite feedback. Sometimes my solution is the best. Sometimes the LLM had a better idea and frequently we take a modified version of what one of us suggested. Just as you would with a human partner. The result of the discussion is better than what either of us would have done on their own.

I also will do classical ping-pong style tdd with it one we agreed on an approach. I'll write a test; llm makes it pass and write the next test which I'll make pass and so on.

As with a real pair, it's important to notice when they are struggling and help them or take over. You can only do this if you stay fully engaged and understand every line. Just like when pairing. I've found llms get frequently in a loop where something doesn't work and they keep applying the same changes they've tried before and it never works. Understand what they are trying to do and help them out. Don't be a shitty pair for your llm!

cruffle_duffle5• 1 year ago

I've found llms get frequently in a loop where something doesn't work and they keep applying the same changes they've tried before and it never works.

It gets even funner when you try to get other models to fix whatever is broken and they too get caught in the same loop. I’ll be like “nope! Your buddy ChatGPT said the same thing and got stuck in such and such loop. Clearly whatever you are trying isn’t working so step back and focus on the bigger picture. Are we even doing this the right way in the first place?”

And of course it still walks down the loop. So yeah, better be ready to fix that problem yourself cause if they all do the same thing you are either way off course or they are missing something!

hoppp• 1 year ago

Every time there is a chat interface for something I try to use it, then after 1-2 prompts I give up.

So I completely agree with this. Chat is not a good UI

cruffle_duffle5• 1 year ago

A lot of times it is because those things aren’t properly wired up into their systems well enough to get the right context needed to help. Lots of them are nothing more than a prompt with no ability to dig any deeper than their original training data.

Vox_Leone• 1 year ago

I call it 'structured prompting' [think pseudo-code]. It strikes a nice balance between human-readable logic and structured programming, allowing the LLM to focus on generating accurate code based on clear steps. It’s especially useful when you want to specify the what (the logic) without worrying too much about the how (syntax and language-specific details). If you can create an effective system that supports this kind of input, it would likely be a big step forward in making code generation more intuitive and efficient. Good old UML could also be used.

Example of a Structured Pseudo-Code Prompt:

Let’s say you want to generate code for a function that handles object detection:

'''Function: object_detection Input: image Output: list of detected objects

Steps: 1. Initialize model (load pretrained object detection model)

2. Preprocess the image (resize, normalize, etc.)

3. Run the image through the model

4. Extract bounding boxes and confidence scores from the model's output

5. Return objects with confidence greater than 0.5 as a list of tuples (object_name, bounding_box)

Language: Python'''

yazmeya• 1 year ago

Why not just give it the desired function signature and comments in the function body, in Python?

tiborsaas2• 1 year ago

This is the core problem. You can’t build real software without being precise about what you want.

I've tested a few integrated AI dev tools and it works like a charm. I don't type all my instructions at once. I do it the same way as I do it with code. Iteratively:

1) Create a layout

2) Fill left side

3) Fill right side

4) Connect components

5) Populate with dummy data

The first company to get this will own the next phase of AI development tools.

There's more than 25 working on this problem and they are already in production and some are really good.

themanmaran2• 1 year ago

I'm surprised that the article (and comments) haven't mentioned Cursor.

Agreed that copy pasting context in and out of ChatGPT isn't the fastest workflow. But Cursor has been a major speed up in the way I write code. And it's primarily through a chat interface, but with a few QOL hacks that make it way faster:

1. Output gets applied to your file in a git-diff style. So you can approve/deny changes.

2. It (kinda) has context of your codebase so you don't have to specify as much. Though it works best when you explicitly tag files ("Use the utils from @src/utils/currency.ts")

3. Directly inserting terminal logs or type errors into the chat interface is incredibly convenient. Just hover over the error and click the "add to chat"

dartos5• 1 year ago

I think the wildly different experiences we all seem to have with AI code tools speaks to the inconsistency of the tools and our own lack of understanding of what goes into programming.

I’ve only been slowed down with AI tools. I tried for a few months to really use them and they made the easy tasks hard and the hard tasks opaque.

But obviously some people find them helpful.

Makes me wonder if programming approaches differ wildly from developer to developer.

For me, if I have an automated tool writing code, it’s bc I don’t want to think about that code at all.

But since LLMs don’t really act deterministically, I feel the need to double check their output.

That’s very painful for me. At that point I’d rather just write the code once, correctly.

aprilthird2021• 1 year ago

I think it's about what you're working on. It's great for greenfield projects, etc. Terrible for complex projects that plug into a lot of other complex projects (like most of the software those of us not at startups work on day to day)

sangnoir3• 1 year ago

But since LLMs don’t really act deterministically, I feel the need to double check their output.

I feel the same

That’s very painful for me. At that point I’d rather just write the code once, correctly.

I use AI tools augmentatively, and it's not painful for me, perhaps slightly inconvenient. But for boiler-plate-heavy code like unit tests or easily verifiable refactors[1], adjusting AI-authored code on a per-commit basis is still faster than me writing all the code.

1. Like switching between unit-test frameworks

kenjackson• 1 year ago

I use LLMs several times a day, and I think for me the issue is that verification is typically much faster than learning/writing. For example, I've never spent much time getting good at scripting. Sure, probably a gap I should resolve, but I feel like LLMs do a great job at it. And what I need to script is typically easy to verify, I don't need to spend time learning how to do things like, "move the files of this extension to this folder, but rewrite them so that the name begins with a three digit number based on the date when it was created, with the oldest starting with 001" -- or stuff like that. Sometimes it'll have a little bug, but one that I can debug quickly.

Scripting assistance by itself is worth the price of admission.

The other thing I've found it good at is giving me an English description of code I didn't write... I'm sure it sometimes hallucinates, but never in a way that has been so wrong that its been apparent to me.

lolinder• 1 year ago

I like Cursor, but I find the chat to be less useful than the super advanced auto complete.

The chat interface is... fine. Certainly better integrated into the editor than GitHub Copilot's, but I've never really seen the need to use it as chat—I ask for a change and then it makes the change. Then I fixed what it did wrong and ask for another change. The chat history aspect is meaningless and usually counterproductive, because it's faster for me to fix its mistakes than keep everything in the chat window while prodding it the last 20% of the way.

themanmaran2• 1 year ago

Agreed the autocomplete definitely gets more milage than the chat. But I frequently use it for terminal commands as well. Especially AWS cli work.

"how do I check the cors bucket policies on [S3 bucket name]"

fragmede2• 1 year ago

while prodding it the last 20% of the way.

hint: you don't get paid to get the LLM to output perfect code, you get paid by PRs submitted and landed. Generate the first 80% or whatever with the LLM, and then finish the last 20% that you can write faster than the LLM yourself, by hand.

tarsinge• 1 year ago

I was a very skeptic on AI assisted coding until I tried Cursor and experienced the super autocomplete. It is ridiculously productive. For me it’s to the point it makes Vim obsolete because pressing tab correctly finishes the line or code block 90% of the time. Every developer having an opinion on AI assistance should have just tried to download Cursor and start editing a file.

mholm• 1 year ago

Yeah, the OP has a great idea, but models as-is can't handle that kind of workflow reliably. The article is both a year behind, and a year ahead at the same time. The user must iterate with the chatbot, and you can't do that by just doing a top down 'here's a list of all features, get going, ping me when finished' prompt. AI is a junior engineer, so you have to treat it like a junior engineer, and that means looking through your chat logs, and perhaps backing up to a restore point and going a different direction.

mttrms• 1 year ago

I've started using Zed on a side project and I really appreciate that you can easily manipulate the chat / context and continue making requests

https://zed.dev/docs/assistant/assistant-panel#editing-a-con...

It's still a "chat" but it's just text at the end of the day. So you can edit as you see fit to refine your context and get better responses.

patrickaljord• 1 year ago

Instead of Cursor I would recommend two open source alternatives that you can combine: https://www.continue.dev/ and https://github.com/cline/cline

freeone3000• 1 year ago

It’s not nearly as slick. cursor’s indexing and integration are significant value-adds.

coder543• 1 year ago

I used Continue before Cursor. Cursor’s “agent” composer mode is so much better than what Continue offered. The agent can automatically grep the codebase for relevant files and then read them. It can create entirely new files from scratch. I can still manually provide some files as context, but it’s not usually necessary. With Continue, everything was very manual.

Cursor also does a great job of showing inline diffs of what composer is doing, so you can quickly review every change.

I don’t think there’s any reason Continue can’t match these features, but it hadn’t, last I checked.

Cursor also focuses on sane defaults, which is nice. The tab completion model is very good, and the composer model defaults to Claude 3.5 Sonnet, which is arguably the best non-reasoning code model. (One would hope that Cursor gets agent-composer working with reasoning models soon.) Continue felt much more technical… which is nice for power users, but not always the best starting place.

stitched2gethr• 1 year ago

I think this misses the point. It seems like the author is saying we should move from imperative instructions to a declarative document that describes what the software should do.

Imperative: - write a HTTP server that serves jokes - add a healthcheck endpoint - add TLS and change the serving port to 443

Declarative: - a HTTP server that serves jokes - contains a healthcheck endpoint - supports TLS on port 443

The differences here seem minimal because you can see all of it at once, but in the current chat paradigm you'd have to search through everything you've said to the bot to get the full context, including the side roads that never materialized.

In the document approach you're constantly refining the document. It's better than reviewing the code because (in theory) you're looking at "support TLS on port 443" instead of a lot of code, which means it can be used by a wider audience. And ideally I can give the same high level spec to multiple LLMs and see which makes the best application.

ygouzerh3• 1 year ago

Good explanation! As an open-reflexion: will a declarative document be as detailed as the imperative version? Often between the specs that the product team is providing (that we can consider as the "descriptive" document) and the implementation, many sub specs have been created by the tech team that uncovered some important implementation details. It's like a Rabbit Hole.

For example, for a signup page, we could have: - Declarative: Signup the user using their email address - Imperative: To do the same, we will need to implement the smtp library, which means discovering that we need an SMTP server, so now we need to choose which one. And when purchasing an SMTP Server plan, we discover that there are rate limit, so now we need to add some bot protection to our signup page (IP Rate Limit only? ReCaptcha? Cloudflare bot protection?), etc

Which means that at the end, the imperative code way is kind of like the ultimate implementation specs.

skydhash7• 1 year ago

The issue is that there’s no execution platform for declarative specs, so something will be translated to imperative and that is where the issue lies. There’s always an imperative core which needs to be deterministic or it’s out needs to be verified. LLMs are not the former and the latter option can take more time than just writing the code.

notShabu• 1 year ago

chat is the best way to orchestrate and delegate. whether or not this is considered "ME writing MY code" is imo a philosophical debate

e.g. executives treat the org as a blackbox LLM and chat w it to get real results

mkozlows• 1 year ago

Windsurf is even moreso this way -- it'll look through your codebase trying to find the right files to inspect, it runs the build/test stuff and examines the output to see what went wrong.

I found interacting with it via chat to be super-useful and a great way to get stuff done. Yeah, sometimes you just have to drop into the code, and tag a particular line and say "this isn't going to work, rewrite it to do x" (or rewrite it yourself), but the ability to do that doesn't vitiate the value of the chat.

koito17• 1 year ago

I'm not familiar with Cursor, but I've been using Zed with Claude 3.5 Sonnet. For side projects, I have found it extremely useful to provide the entire codebase as context and send concise prompts focusing on a single requirement. Claude handles "junior developer" tasks well when each unit of work is clearly separated.

Zed makes it trivial to attach documentation and terminal output as context. To reduce risk of hallucination, I now prefer working in static, strongly-typed languages and use libraries with detailed documentation, so that I can send documentation of the library alongside the codebase and prompt. This sounds like a lot of work, but all I do is type "/f" or "/t" in Zed. When I know a task only modifies a single file, then I use the "inline assist" feature and review the diffs generated by the LLM.

Additionally, I have found it extremely useful to actually comment a codebase. LLMs are good at unstructured human language, it's what they were originally designed for. You can use them to maintain comments across a codebase, which in turn helps LLMs since they get to see code and design together.

Last weekend, I was able to re-build a mobile app I made a year ago from scratch with a cleaner code base, better UI, and implement new features on top (making the rewrite worth my time). The app in question took me about a week to write by hand last year; the rewrite took exactly 2 days.

---

As a side note: a huge advantage of Zed with locally-hosted models is that one can correct the code emitted by the model and force the model to re-generate its prior response with those corrections. This is probably the "killer feature" of models like qwen2.5-coder:32b. Rather than sending extra prompts and bloating the context, one can just delete all output from where the first mistake was made, correct the mistake, then resume generation.

foz• 1 year ago

After using Cursor and Copilot for some time, I long for a tool that works like a "real" collaborator. We share a spec and make comments, resolve them. We file issues and pull requests and approve them. We use tests and specs to lock down our decisions. We keep a backlog up to date, maintain our user docs, discuss what assumptions we have to validate still, and write down our decisions.

Like with any coworker - when ideas get real, get out of chat and start using our tools and process to get stuff done.

ygouzerh3• 1 year ago

That's a great idea! Maybe when context window will be larger and tokens even cheaper?

I would like as well to add to it a peer-programming feature, with it making some comments on top of the shoulder when coding, a kind of smarter linter that will not lint one line, but that will have the entire project context.

kmarc• 1 year ago

Look, deleting the inside of () parens in a function call makes total sense by instructing your editor to "delete inside parenthesis", or in vim:

di(

Yet, millions of programmers use their mouse to SELECT first something visually and THEN delete whatever was selected. Shrug.

I won't be surprised if chat-based programming will be the next way of doing stuff.

anoncow• 1 year ago

There should be a smart way of merging all the chat messages into a streamlined story of the development on the fly. Perhaps something an AI could do. We could call it contextAI.

spolsky• 1 year ago

I don't think Daniel's point is that Chat is generically a clunky UI and therefore Cursor cannot possibly exist. I think he's saying that to fully specify what a given computer program should do, you have to provide all kinds of details, and human language is too compressed and too sloppy to always include those details. For example, you might say "make a logon screen" but there are an infinite number of ways this could be done and until you answer a lot of questions you may not get what you want.

If you asked me two or three years ago I would have strongly agreed with this theory. I used to point out that every line of code was a decision made by a programmer and that programming languages were just better ways to convey all those decisions than human language because they eliminated ambiguity and were much terser.

I changed my mind when I saw how LLMs work. They tend to fill in the ambiguity with good defaults that are somewhere between "how everybody does it" and "how a reasonably bright junior programmer would do it".

So you say "give me a log on screen" and you get something pretty normal with Username and Password and a decent UI and some decent color choices and it works fine.

If you wanted to provide more details, you could tell it to use the background color #f9f9f9, but a part of what surprised my and caused me to change my mind on this matter was that you could also leave that out and you wouldn't get an error; you wouldn't get white text on white background; you would get a decent color that might be #f9f9f9 or might be #a1a1a1 but you saved a lot of time by not thinking about that level of detail and you got a good result.

zamfi3• 1 year ago

Yeah, and in fact this is about the best-case scenario in many ways: "good defaults" that get you approximately where you want to be, with a way to update when those defaults aren't what you want.

Right now we have a ton of AI/ML/LLM folks working on this first clear challenge: better models that generate better defaults, which is great—but also will never solve the problem 100%, which is the second, less-clear challenge: there will always be times you don't want the defaults, especially as your requests become more and more high-level. It's the MS Word challenge reconstituted in the age of LLMs: everyone wants 20% of what's in Word, but it's not the same 20%. The good defaults are good except for that 20% you want to be non-default.

So there need to be ways to say "I want <this non-default thing>". Sometimes chat is enough for that, like when you can ask for a different background color. But sometimes it's really not! This is especially true when the things you want are not always obvious from limited observations of the program's behavior—where even just finding out that the "good default" isn't what you want can be hard.

Too few people are working on this latter challenge, IMO. (Full disclosure: I am one of them.)

skydhash7• 1 year ago

Which no one argues about really. But writing code was never the issue of software project. And if you open any books about software engineering, there’s barely any mention of coding. The issue is the process of finding what code to write and where to put it in a practical and efficient way.

In your example, the issue is not with writing the logon screen (You can find several example on github and a lot of css frameworks have form snippets). The issue is making sure that it works and integrate well with the rest of the project, as well as being easy to maintain.

yawnxyz• 1 year ago

It's interesting we view Email and Chat so differently. Some companies run on chat (e.g. Slack), while most companies run on email.

Emails are so similar to Chat, except we're used to writing in long-form, and we're not expecting sub-minute replies.

Maybe emails are going to be the new chat?

I've been experimenting with "email-like" interfaces (that encourage you to write more / specify more), take longer to get back to you, and go out to LLMs. I think this works well for tools like Deep Research where you expect them to take minutes to hours.

nimski• 1 year ago

This has been the thesis behind our product since the beginning (~3 years), before a lot of the current hype took hold. I'm excited to see it gain more recognition.

Chat is single threaded and ephemeral. Documents are versioned, multi-threaded, and a source of truth. Although chat is not appropriate as the source of truth, it's very effective for single-threaded discussions about documents. This is how people use requirements documents today. Each comment on a doc is a localized chat. It's an excellent interface when targeted.

ypyrko• 1 year ago

100% agree. I had the same issue when it comes to text editing and I created this tool https://www.potext.com I love to have a full control over AI suggestions

thomastjeffery3• 1 year ago

Chat is a bad interface for tools in general, but this problem goes deeper than that.

What's a good interface?

There are a few things we try to balance to make a good UI/UX:

- Latency: How long it takes to do a single task

- Decision-tree pathing: How many tasks to meet a goal

- Flexibility/Configurability: How much of a task can be encapsulated by the user's predefined knowledge of the system

- Discoverability: What tasks are available, and where

The perfect NLP chat could accomplish some of these:

- Flexibility/Configurability: Define/infer words and phrases that the user can use as shortcuts

- Decision-tree pathing: Define concepts that shortcut an otherwise verbose interaction

- Latency: Context-aware text-completions so the user doesn't need to type as much

- Discoverability: Well-formed introductions and clarifying questions to introduce useful interaction

This can only get us so far. What better latency can be accomplished than a button or a keyboard shortcut? What better discoverability than a menu?

The most exciting prospect left is flexibility. Traditional software is inflexible. It can only perform the interaction it was already designed with. Every design decision becomes a wall of assumption. These walls are the fundamental architecture of software. Without them, we would have nothing. With them, we have a structure that guides us along whatever assumptions were already made.

If we want to change something about our software's UI, then we must change the software itself, and that means writing. If NLP was a truly solved problem, then software compatibility and flexibility would be trivialized. We could redesign the entire UI by simply describing the changes we want.

LLMs are not even close. Sure, you can get one to generate some code, but only if the code you want generated is close enough to the text it was already trained on. LLMs construct continuations of tokens: no more, no less. There is no logic. There is no consideration about what is right or wrong: only what is likely to come next.

Like you said,

You can’t build real software without being precise about what you want.

This is the ultimate limitation of UI. If only we could be ambiguous instead! LLMs let us do that, but they keep that ambiguity permanent. There is no real way to tie an LLM back down to reality. No logic. No axioms. No rules. So we must either be precise or ambiguous. The latter option is an exciting development, and certainly offers its own unique advantages, but it isn't a complete solution.

---

I've been thinking through another approach to the ambiguity problem that I think could really give us the expressive power of natural language, while preserving the logical structure we use to write software (and more). It wouldn't solve the problem entirely, but it could potentially move it out of the way.

tgraf_80• 1 year ago

Truly speaking, you can use AI for a little bit higher abstraction and ambiguity, but not much. For instance, if you need an iteration over an array and you want to do a very specific aggregation you can instruct AI to write this loop but you yourself need to understand exactly what it’s doing and have a very clear idea how this code snippet fits into the larger picture

6h6j65j76k• 1 year ago

"Current AI tools pretend writing software is like having a conversation. "

But that is true? Devs spend more time in meetings than writing code. Having conversations about the code they are going to write.

martinsnow• 1 year ago

I agree. But in that context we're talking about specifications.

When we're trying to wrangle a piece of code to do something we want but aren't quite sure of how to interact with the api, it's a different matter.

What i found is that by the time copilot/gpt/deepseek has enough knowledge about the problem and my codebase, I've run out of tokens. Because my head can contain a much larger problem area than these models allow me to feed them in a budget friendly manner.

fny• 1 year ago

Narrative text is a worse UI pattern. It's impractical to read. Also how exactly do you merge narrative changes if you need to write several transformations as updates? Are you expected to update the original text? How does this affect diffs in version control?

I think it's more ideal to have the LLM map text to some declarative pseudocode that's easy to read which is then translated to code.

The example given by Daniel might map to something like this:

  define sign-in-screen:
    panel background "#f9f9f9":
      input email required: true, validate-on-blur: true
      input password required: true
      button "Sign in" gradient: ("#EEE" "#DDD")
      connect-to-database

Then you'd use chat to make updates. For example, "make the gradient red" or "add a name field." Come to think of it, I don't see why chat is a bad interface at all with this set up.

ygouzerh3• 1 year ago

It's interesting, it seems that we are looping back on the old trend of Model-Driven Architecture

cruffle_duffle5• 1 year ago

lol. I’ve tried to get my LLM to produce something like that. Prompt was like “I’m going to feed your output to another model, please don’t write a narrative write what we’ve discussed in a machine readable format”.

It decided to output something JSON and maybe YAML once.

cheapsteak• 1 year ago

I'm predicting that Test-Driven Development may be having a comeback

English behaviour descriptions -> generated tests

Use both behaviour descriptions and feedback from test results to iterate on app development

remoquete2• 1 year ago

I'm intrigued by the conclusion. Docs-as-code, this time turning actual documentation and requirements into code? So, specifications? Back to OpenAPI?

Back to... programming languages? :)

shireboy• 1 year ago

Yeah, I've landed on similar, although I wouldn't say it's bad for all dev scenarios. For small tweaks, or cases where I want a junior dev to do something I say explicitly ("add a bootstrap style input field for every property on #somemodel") chat works fine.

For higher-level AI assist, I do agree chat is not what makes sense. What I think would be cool is to work in markdown files, refining in precise plain english each feature. The AI then generates code from the .md files plus existing context. Then you have well-written documentation and consistent code. You can do this to a degree today by referencing a md file in chat, or by using some of the newer tools, but I haven't seen exactly what I want yet. (I guess I should build it?)

matthewsinclair• 1 year ago

Yep. 100% agree. The whole “chat as UX” metaphor is a cul-de-sac that I’m sure we’ll back out of sooner or later.

I think about this like SQL in the late 80s. At the time, SQL was the “next big thing” that was going to mean we didn’t need programmers, and that management could “write code”. It didn’t quite work out that way, of course, as we all know.

I see chat-based interfaces to LLMs going exactly the same way. The LLM will move down the stack (rather than up) and much more appropriate task-based UX/UI will be put on top of the LLM, coordinated thru a UX/UI layer that is much sympathetic to the way users actually want to interact with a machine.

In the same way that no end-users ever touch SQL these days (mostly), we won’t expose the chat-based UX of an LLM to users either.

There will be a place for an ad-hoc natural language interface to a machine, but I suspect it’ll be the exception rather than the rule.

I really don’t think there are too many end users who want to be forced to seduce a mercurial LLM using natural language to do their day-to-day tech tasks.

sangnoir3• 1 year ago

The whole “chat as UX” metaphor is a cul-de-sac that I’m sure we’ll back out of sooner or later.

Only when someone discovers another paradigm that matches or exceeds the effectiveness of LLMs without being a language model.

daxfohl2• 1 year ago

Or DSLs like cucumber for acceptance tests. Cute for simple things, but for anything realistic, it's more convoluted than convenient.

jug• 1 year ago

I think a counterpoint to this is that SQL has a specific and well-defined meaning and it takes effort to get what you actually want right. However, communication with an AI can sometimes request a specific context or requirements but also be intentionally open-ended where we want to give the AI leeway. The great thing here is that humans _and_ AI now quite clearly understand when a sentence is non-specific, or with great importance. So, I think it’s hard to come up with a more terse or approachable competitor to the sheer flexibility of language. In a way, I think it’s a similar problem that still has engineers across the world input text commands in a terminal screen since about 80 years now.

amedviediev• 1 year ago

I actually came to the same conclusion. I am currently working on a side project that's an AI powered writing app for writers, and while I still provide chat because that seems to be the expectation, my goal is to abstract all the AI assistance a writer might need into curated UI options.

fhd2• 1 year ago

I've mainly used gptel in Emacs (primarily with Claude), and I kind of already use the chat buffer like a document. You can freely edit the history, and I make very generous use of that, to steer where the model is going.

It has features to add context from your current project pretty easily, but personally I prefer to constantly edit the chat buffer to put in just the relevant stuff. If I add too much, Claude seems to get confused and chases down irrelevant stuff.

Fully controlling the context like that seems pretty powerful compared to other approaches I've tried. I also fully control what goes into the project - for the most part I don't copy paste anything, but rather type a version of the suggestion out quickly.

If you're fast at typing and use an editor with powerful text wrangling capabilities, this is feasible. And to me, it seems relatively optimal.

fragmede2• 1 year ago

People call them “great for prototyping,” which means “don’t use this for anything real.”

Eh, that's just copium because we all have a vested monetary interest in them not being useful for "anything real", whatever that means. If it turns out that there useful for "real things", then then entire industry would get turned on its head. (hint: they're useful for "real" things), though putting the entire codebase into the context window doesn't currently work. Aider works past this by passing the directory tree and filenames as context, so the LLM guess that /cloud/scope/cluster.go is where the cluster scope code lives and ask for that specific file to get added to the context and you can ask it to add, say, logging code to that file.

synergy20• 1 year ago

what about organizing chats into documents by chatting: keep track of the chats and build up a design doc

or the other way around,give AI a design doc and generate what you want,this is still chatting, just more official and lengthy

bcherry• 1 year ago

Chat is a great UX _around_ development tools. Imagine having a pair programmer and never being allowed to speak to them. You could only communicate by taking over the keyboard and editing the code. You'd never get anything done.

Chat is an awesome powerup for any serious tool you already have, so long as the entity on the other side of the chat has the agency to actually manipulate the tool alongside you as well.

skydhash7• 1 year ago

The real powerup is scripting. And if the actions are precise enough, macros. Much more efficient for a lot of tasks.

tommiegannert• 1 year ago

I'm in the business of data collection, to some extent: building a support system for residential solar panel installations. There's a bunch of data needed for simulations, purchase estimations, legal and tax reasons. Not insane amounts, but enough that filling out a form feels tedious. LLMs are great in that they can be given a task to gather a number of pieces, and can explain to the user what "kWh" means, at many level of technical depth.

We play around with LLMs to build a chat experience. My first attempt made Claude spew out five questions at a time, which didn't solve the "guiding" problem. So I started asking it to limit the number of unanswered questions. It worked, but felt really clunky and "cheap."

I drew two conclusions: We need UI builders for this to feel nice, and professionals will want to use forms.

First, LLMs would be great at driving step-by-step guides, but it must be given building blocks to generate a UI. When asking about location, show a map. When deciding to ask about TIN or roof size, if the user is technically inclined, perhaps start with asking about the roof. When asking about the roof size, let the user draw the shape and assign lengths. Or display aerial photos. The result on screen shouldn't be a log of me-you text messages, but a live-updated summary of where we are, and what's remaining.

Second, professionals have incentive to build mental model for navigating complex data structures. People who have no reason to invest time into the data model (e.g. a consumer buying a single solar panel installation in ther lifetime,) will benefit from rich LLM-driven UIs. Chat UIs might create room for a new type of computer user who doesn't use visual clues to build this mental model, but everyone else will want to stay on graphics. If you're an executive wondering how many sick days there were last month, that's a situation where a BI LLM RAG would be great. But if you're not sure what your question is, because you're hired to make up your own questions, then pointing, clicking and massaging might make more sense.

nayuki• 1 year ago

This current post is a good rebuttal to the killed post this morning: https://news.ycombinator.com/item?id=42933031 "Programmers are modern-day computers", https://jtlicardo.com/writing/modern-day-computers

zamfi3• 1 year ago

Why on earth was that post flagged/killed??

randomcatuser• 1 year ago

chat=repl?

doc=programming in a DSL? / (what was that one language which was functional & represented in circles in a canvas?)

Havoc• 1 year ago

Chat seems flawed but I don’t see how a document is better.

I don’t buy that a document could capture what is needed here. Imagine describing navigating through multiple levels of menus in document form. That sounds straight up painful even for trivial apps. And for a full blown app…nope

There is a whole new paradigm missing there imo

wiremine5• 1 year ago

I'm going to take a contrarian view and say it's actually a good UI, but it's all about how you approach it.

I just finished a small project where I used o3-mini and o3-mini-high to generate most of the code. I averaged around 200 lines of code an hour, including the business logic and unit tests. Total was around 2200 lines. So, not a big project, but not a throw away script. The code was perfectly fine for what we needed. This is the third time I've done this, and each time I get faster and better at it.

1. I find a "pair programming" mentality is key. I focus on the high-level code, and let the model focus on the lower level code. I code review all the code, and provide feedback. Blindly accepting the code is a terrible approach.

2. Generating unit tests is critical. After I like the gist of some code, I ask for some smoke tests. Again, peer review the code and adjust as needed.

3. Be liberal with starting a new chat: the models can get easily confused with longer context windows. If you start to see things go sideways, start over.

4. Give it code examples. Don't prompt with English only.

FWIW, o3-mini was the best model I've seen so far; Sonnet 3.5 New is a close second.

ikety• 1 year ago

do you use pair programming tools like aider?

shmoogy• 1 year ago

Have you tried cursor? I really like the selecting context -> cmd+l to make a chat with it - explain requirement, hit apply, validate the diff.

Works amazingly well for a lot of what I've been working on the past month or two.

gnatolf• 1 year ago

I haven't tried cursor yet, but how is this different from the copilot plugin in vscode? Sounds pretty similar.

ryandrake4• 1 year ago

I guess the things I don't like about Chat are the same things I don't like about pair (or team) programming. I've always thought of programming as a solitary activity. You visualize the data structures, algorithms, data paths, calling flow and stack, and so on, in your mind, with very high throughput "discussions" happening entirely in your brain. Your brain is high bandwidth, low latency. Effortlessly and instantly move things around and visualize them. Figure everything out. Finally, when it's correct, you send it to the slow output device (your fingers).

The minute you have to discuss those things with someone else, your bandwidth decreases by orders of magnitude and now you have to put words to these things and describe them, and physically type them in or vocalize them. Then your counterpart has to input them through his eyes and ears, process that, and re-output his thoughts to you. Slow, slow, slow, and prone to error and specificity problems as you translate technical concepts to English and back.

Chat as a UX interface is similarly slow and poorly specific. It has all the shortcomings of discussing your idea with a human and really no upside besides the dictionary-like recall.

throwup2382• 1 year ago

At the same time, putting your ideas to words forces you to make them concrete instead of nebulous brain waves. I find that the chat interface gets rid of the downsides of pair programming (that the other person is a human being with their own agency*) while maintaining the “intelligent” pair programmer aspect.

Especially with the new r1 thinking output, I find it useful to iterate on the initial prompt as a way to make my ideas more concrete as much as iterating through the chat interface which is more hit and miss due to context length limits.

* I don’t mean that in a negative way, but in a “I can’t expect another person to respond to me instantly at 10 words per second” way.

frocodillo• 1 year ago

I would argue that is a feature of pair programming, not a bug. By forcing you to use the slower I/O parts of your brain (and that of your partner) the process becomes more deliberate, allowing you to catch edge cases, bad design patterns, and would-be bugs before even putting pen to paper so to speak. Not to mention that it immediately reduces the bus factor by having two people with a good understanding of the code.

I’m not saying pair programming is a silver bullet, and I tend to agree that working on your own can be vastly more efficient. I do however think that it’s a very useful tool for critical functionality and hard problems and shouldn’t be dismissed.

yarekt6• 1 year ago

That's such a mechanical way of describing pair programming. I'm guessing you don't do it often (understandable if its not working for you).

For me pair programming accelerates development to much more than 2x. Over time the two of you figure out how to use each other's strengths, and as both of you immerse yourself in the same context you begin to understand what's needed without speaking every bit of syntax between each other.

In best cases as a driver you end up producing high quality on the first pass, because you know that your partner will immediately catch anything that doesn't look right. You also go fast because you can sometimes skim over complexities letting your partner think ahead and share that context load.

I'll leave readers to find all the caveats here

Edit: I should probably mention why I think Chat Interface for AI is not working like Pair programming: As much as it may fake it, AI isn't learning anything while you're chatting to it. Its pointless to argue your case or discuss architectural approaches. An approach that yields better results with Chat AI is to just edit/expand your original prompt. It also feels less like a waste of time.

With Pair programming, you may chat upfront, but you won't reach that shared understanding until you start trying to implement something. For now Chat AI has no shared understanding, just "what I asked you to do" thing, and that's not good enough.

bobbiechen• 1 year ago

I agree, chat is only useful in scenarios that are 1) poorly defined, and 2) require a back-and-forth feedback loop. And even then, there might be better UX options.

I wrote about this here: https://digitalseams.com/blog/the-ideal-ai-interface-is-prob...

nick238• 1 year ago

Someone else (future you being a distinct person) will also need to grok what's going on when they maintain the code later. By living purely in a high-dimensional trans-enlightenment state and coding that way, means you may as well be building a half-assed organic neural network to do your task, rather than something better "designed".

Neural networks and evolved structures and pathways (e.g. humans make do with ~20k genes and about that many more in regulatory sequences) are absolutely more efficient, but good luck debugging them.

cjonas• 1 year ago

I find its exactly the opposite. With AI chat, I can define signatures, write technical requirements and validate my approach in minutes. I'm not talking with the AI like I would a human... I'm writing a blend of stubs and concise requirements, providing documentation, reviewing, validating and repeating. When it goes in the wrong direction, I add additional details and regenerate from scratch. I focus on small, composable chunks of functionality and then tie it all together at the end.

hmcdona1• 1 year ago

This going to sound out of left field, but I would venture to guess you have very high spatial reasoning skills. I operate much this same way and only recently connected these dots that that skill might be what my brain leans on so heavily while programming and debugging.

Pair programming is endlessly frustrating beyond just rubber duckying because I’m having to exit my mental model, communicate it to someone else, and then translate and relate their inputs back into my mental model which is not exactly rooted in language in my head.

knighthack• 1 year ago

I mostly agree with you, but I have to point out something to the contrary of this part you said: "...The minute you have to discuss those things with someone else, your bandwidth decreases by orders of magnitude and now you have to put words to these things and describe them, and physically type them in or vocalize them."

Subvocalization/explicit vocalization of what you're doing actually improves your understanding of the code. Doing so may 'decrease bandwith', but improves comprehension, because it's basically inline rubber duck debugging.

It's actually easy to write code which you don't understand and cannot explain what it's doing, whether at the syntax, logic or application level. I think the analogue is to writing well; anyone can write streams of consciousness amounting to word salad garbage. But a good writer can cut things down and explain why every single thing was chosen, right down to the punctuations. This feature of writing should be even more apparent with code.

I've coded tons of things where I can get the code working in a mediocre fashion, and yet find great difficulty in try to verbally explain what I'm doing.

In contrast there's been code where I've been able to explain each step of what I'm doing before I even write anything; in those situations what generally comes out tends to be superior maintainable code, and readable too.

alickz2• 1 year ago

in my experience, if you can't explain something to someone else then you don't fully understand it

our brains like to jump over inconsistencies or small gaps in our logic when working by themselves, but try to explain that same concept to someone else and those inconsistencies and gaps become glaringly obvious (doubly so if the other person starts asking questions you never considered)

it's why pair programming and rubber duck debugging work at all, at least in my opinion

dataviz1000• 1 year ago

I agree with you.

Yesterday, I asked o3-mini to "optimize" a block of code. It produced very clean, functional TypeScript. However, because the code is reducing stock option chains, I then asked o3-mini to "optimize for speed." In the JavaScript world, this is usually done with for loops, and it even considered aspects like array memory allocation.

This shows that using the right qualifiers is important for getting the results you want. Today, I use both "optimize for developer experience" and "optimize for speed" when they are appropriate.

Although declarative code is just an abstraction, moving from imperative jQuery to declarative React was a major change in my coding experience. My work went from telling the system how to do something to simply telling it what to do. Of course, in React—especially at first—I had to explain how to do things, but only once to create a component. After that, I could just tell the system what to do. Now, I can simply declare the desired outcome, the what. It helps to understand how things work, but that level of detail is becoming less necessary.

bongodongobob• 1 year ago

To add to that, I always add some kind of debug function wrapper so I can hand off the state of variables and program flow to the LLM when I need to debug something. Sometimes it's really hard to explain exactly what went wrong so being able to give it a chunk of the program state is more descriptive.

throwup2382• 1 year ago

I do the same for my QT desktop app. I’ve got an “Inspector” singleton that allows me to select a component tree via click, similar to browser devtools. It takes a screenshot, dumps the QML source, and serializes the state of the components into the clipboard.

I paste that into Claude and it is surprisingly good at fixing bugs and making visual modifications.

jacob019• 1 year ago

Totally agree. Chat is a fantastic interface because it stays out of my way. For me it's much more than a coding assistant. I get live examples of how to use tools, and help with boilerplate, which is a time saver and improvement over legacy workflows, but the real benefit is all the spitballing I can do with it to refine ideas and logic and help getting up to speed on tooling way outside of my domain. I spent about 3.5 hours chatting with o1 about RL architecture to solve some business problems. Now I have a crystal clear plan and the confidence to move forward in an optimal way. I feel a little weird now, like I was just talking to myself for a few hours, but it totally helped me work through the planning. For actual code, I find myself being a bit less interactive with LLMs as time goes, sometimes it's easier to just write the logic the way I want rather than trying to explain how I want it but the ability to retrieve code samples for anything with ease is like a superpower. Not to mention all the cool stuff LLMs can do at runtime via API. Yeah, chat is great, and I'll stick with writing code in Vim and pasting as needed.

sdesol• 1 year ago

1. I find a "pair programming" mentality is key. I focus on the high-level code, and let the model focus on the lower level code. I code review all the code, and provide feedback. Blindly accepting the code is a terrible approach.

This is what I've found to be key. If I start a new feature, I will work with the LLM to do the following:

- Create problem and solution statement

- Create requirements and user stories

- Create architecture

- Create skeleton code. This is critical since it lets me understand what it wants to do.

- Generate a summary of the skeleton code

Once I have done the above, I will have the LLM generate a reusable prompt that I can use to start LLM conversations with. Below is an example of how I turn everything into a reusable prompt.

https://beta.gitsense.com/?chat=b96ce9e0-da19-45e8-bfec-a3ec...

As I make changes like add new files, I will need to generate a new prompt but it is worth the effort. And you can see it in action here.

https://beta.gitsense.com/?chat=b8c4b221-55e5-4ed6-860e-12f0...

The first message is the reusable prompt message. With the first message in place, I can describe the problem or requirements and ask the LLM what files it will need to better understand how to implement things.

What I am currently doing highlights how I think LLM is a game changer. VCs are going for moonshots instead of home runs. The ability to gather requirements and talk through a solution before even coding is how I think LLMs will revolutionize things. It is great that it can produce usable code, but what I've found it to be invaluable is it helps you organize your thoughts.

In the last link, I am having a conversation with both DeepSeek v3 and Sonnet 3.5 and the LLMs legitimately saved me hours in work, without even writing a single line of code. In the past, I would have just implemented the feature and been done with it, and then I would have to fix something if I didn't think of an edge case. With LLMs, it literally takes minutes to develop a plan that is extremely well documented that can be shared with others.

This ability to generate design documents is how I think LLMs will ultimately be used. The bonus is producing code, but the reality is that documentation (which can be tedious and frustrating) is a requirement for software development. In my opinion, this is where LLMs will forever change things.

javier23• 1 year ago

Nah, a Chat is terrible for development. In my tears of working, i have only had the chance to start a new codebase 3-4 times. 90% of the time is spent modifying large existing systems, constantly changing them. The chat interface is terrible for this. It would be much better if it was more integrated with the codebase and editor

pc865• 1 year ago

Cursor does all of this, and agent chats let you describe a new feature or an existing bug and it will search the entire codebase and add relevant code to its context automatically. You can optionally attach files for the context - code files that you want to add to the context up front, documentation for third-party calls, whatever you want.

As a side note, "No, you're wrong" is not a great way to have a conversation.

zahlman2• 1 year ago

In my tears of working

Sometimes typos are eerily appropriate ;)

(I almost typed "errily"...)

nonrandomstring• 1 year ago

it's actually a good UI

Came to vote good too. I mean, why do we all love a nice REPL? That's chat right? Chat with an interpreter.

rpastuszak• 1 year ago

I've changed my mind on that as well. I think that, generally, chat UIs are a lazy and not very user friendly. However, when coding I keep switching between two modes:

1. I need a smart autocomplete that can work backwards and mimic my coding patterns

2. I need a pair programming buddy (of sorts, this metaphor doesn't completely work, but I don't have a better one)

Pair development, even a butchered version of the so called "strong style" (give the driver the highest level of abstraction they can use/understand) works quite well for me. But, the main reason this works is that it forces me to structure my thinking a little bit, allows me to iterate on the definition of the problem. Toss away the sketch with bigger parts of the problem, start again.

It also helps me to avoid yak shaving, getting lost in the detail or distracted because the feedback loop between me seeing something working on the screen vs. the idea is so short (even if the code is crap).

I'd also add 5.: use prompts to generate (boring) prompts. For instance, I needed a simple #tag formatter for one of my markdown sites. I am aware that there's a not-so-small list of edge cases I'd need to cover. In this case I'd write a prompt with a list of basic requirements and ask the LLM to: a) extend it with good practice, common edge cases b) format it as a spec with concrete input / output examples. This works a bit similar to the point you made about generating unit tests (I do that too, in tandem with this approach).

In a sense 1) is autocomplete 2) is a scaffolding tool.

echelon• 1 year ago

I work on GenAI in the media domain, and I think this will hold true with other fields as well:

- Text prompts and chat interfaces are great for coarse grained exploration. You can get a rough start that you can refine. "Knight standing in a desert, rusted suit of armor" gets you started, but you'll want to take it much further.

- Precision inputs (mouse or structure guided) are best for fine tuning the result and honing in on the solution itself. You can individually plant the cacti and pose the character. You can't get there with text.

ryandrake4• 1 year ago

I've changed my mind on that as well. I think that, generally, chat UIs are a lazy and not very user friendly. However, when coding I keep switching between two modes:
1. I need a smart autocomplete that can work backwards and mimic my coding patterns
2. I need a pair programming buddy (of sorts, this metaphor doesn't completely work, but I don't have a better one)

Thanks! This is the first time I've seen it put this clearly. When I first tried out CoPilot, I was unsure of how I was "supposed" to interact with it. Is it (as you put it) a smarter autocomplete, or a programming buddy? Is it both? What was the right input method to use?

After a while, I realized that for my personal style I would pretty much entirely use method 1, and never method 2. But, others might really need that "programming buddy" and use that interface instead.

yarekt6• 1 year ago

Oh yea, point 1 for sure. I call copilot regex on steroids.

Example: - copy paste a table from a pdf datasheet into a comment (it'll be badly formatted with newlines and whatnot, doesn't matter) - show it how to do the first line - autocomplete the rest of the table - Check every row to make sure it didn't invent fields/types

For this type of workflow the tools are a real time saver. I've yet to see any results for the other workflows. They usually just frustrate me by either starting to suggest nonsense code without full understanding, or its far too easy to bias the results and make them stuck in a pattern of thinking.

rafaelmn• 1 year ago

This only works for small self-contained problems with narrow scope/context.

Chat sucks for pulling in context, and the only worse thing I've tried is the IDE integrations that supposedly pull the relevant context for you (and I've tried quite a few recently).

I don't know if naive fine-tuning with codebase would work, I suspect there are going to be tools that let you train the AI on code in the sense that it can have some references in model, and it knows how you want your project code/structure to look like (which is often quite different from what it looks in most areas)

godelski5• 1 year ago

  > I focus on the high-level code, and let the model focus on the lower level code.

Tbh the reason I don't use LLM assistants is because they suck at the "low level". They are okay at mid level and better at high level. I find it's actual coding very mediocre and fraught with errors.

I've yet to see any model understand nuance or detail.

This is especially apparent in image models. Sure, it can do hands but they still don't get 3D space nor temporal movements. It's great for scrolling through Twitter but the longer you look the more surreal they get. This even includes the new ByteDance model also on the front page. But with coding models they ignore context of the codebase and the results feel more like patchwork. They feel like what you'd be annoyed at with a junior dev for writing because not only do you have to go through 10 PRs to make it pass the test cases but the lack of context just builds a lot of tech debt. How they'll build unit tests that technically work but don't capture the actual issues and usually can be highly condensed while having greater coverage. It feels very gluey, like copy pasting from stack overflow when hyper focused on the immediate outcome instead of understanding the goal. It is too "solution" oriented, not understanding the underlying heuristics and is more frustrating than dealing with the human equivalent who says something "works" as evidenced by the output. This is like trying to say a math proof is correct by looking at just the last line.

Ironically, I think in part this is why chat interface sucks too. A lot of our job is to do a lot of inference in figuring out what our managers are even asking us to make. And you can't even know the answer until you're part way in.

lucasmullens• 1 year ago

But with coding models they ignore context of the codebase and the results feel more like patchwork.

Have you tried Cursor? It has a great feature that grabs context from the codebase, I use it all the time.

wiremine5• 1 year ago

Tbh the reason I don't use LLM assistants is because they suck at the "low level". They are okay at mid level and better at high level. I find it's actual coding very mediocre and fraught with errors.

That's interesting. I found assistants like Copilot fairly good at low level code, assuming you direct it well.

yarekt6• 1 year ago

A lot of our job is to do a lot of inference in figuring out what our managers are even asking us to make

This is why I think LLMs can't really replace developers. 80% of my job is already trying to figure out what's actually needed, despite being given lots of text detail, maybe even spec, or prototype code.

Building the wrong thing fast is about as useful as not building anything at all. (And before someone says "at least you now know what not to do"? For any problem there are infinite number of wrong solutions, but only a handful of ones that yield success, why waste time trying all the wrong ones?)

ic4l• 1 year ago

For me the o models consistently make more mistakes for me than Claude 3.5 Sonnet.

pc865• 1 year ago

Same for me. I wonder if Claude is better at some languages than others, and o models are better at those weaker languages. There are some devs I know who insist Claude is garbage for coding and o3-* or o4-* are tier 1.

gamedever• 1 year ago

What did you create? In my field, so far, I've found the chat bots not doing so well. My guess is the more likely you're making something other people make often, the more likely the bot will help.

Even then though, I asked o1-cursor to start a react app. It failed, mostly because it's out of date. It's instructions were for react 2 versions ago.

This seems like an issue. If the statistically most likley answer is old, that's not helpful.

wiremine5• 1 year ago

The most recent one was a typescript project focused on zod.

I might be reading into your comment, but I agree "top-down" development sucks: "Give me a react that does X". I've had much more success going bottom-up.

And I've often seen models getting confused on versions. You need to be explicit, and even then then forget.

ls_stats• 1 year ago

it's actually a good UI I just finished a small project around 2200 lines

why the top comments on HN are always people who have not read the article

pc865• 1 year ago

It's not clear to me in the lines you're quoting that the GP didn't read the article.

larodi• 1 year ago

I would actually join you, as my longstanding view on coding is that it is best done in pairs. Sadly humans and programmers in particular are not so ready to work arms-by-arms, and it is even more depressing that it now turns AI is pairing us.

Perhaps there's gonna be post-AI programming movement where people actually stare at the same monitor and discuss while one of them is coding.

As a sidenote - we've done experiments with FOBsters, and when paired this way, the multiply their output. There's something about psychology of groups and how one can only provide maximum output when teaming.

Even for solo activities, and non-IT activities, such as skiing/snowboard, it is better to have a partner to ride with you and discuss the terrain.

bboygravity• 1 year ago

Interesting to see the narrative on here slowly change from "LLM's will forever be useless for programming" to "I'm using it every day" over the course of the past year or so.

I'm now bracing for the "oh sht, we're all out of a job next year" narrative.

RHSeeger4• 1 year ago

I think a lot of people have always thought of it as a tool that can help.

I don't want an LLM to generate "the answer" for me in a lot of places, but I do think it's amazing for helping me gather information (and cite where that information came from) and pointers in directions to look. A search engine that generates a concrete answer via LLM is (mostly) useless to me. One that gives me an answer and then links to the facts it used to generate that answer is _very_ useful.

It's the same way with programming. It's great helping you find what you need. But it needs to be in a way that you can verify it's right; or take it's answer and adjust it to what you actually need (based on the context it provides).

wiremine5• 1 year ago

"oh sht, we're all out of a job next year"

Maybe. My sense if we'd need to see 3 to 4 orders of magnitude improvements on the current models before we can replace people outright.

I do think we'll see a huge productivity boost per developer over the next few years. Some companies will use that to increase their throughput, and some will use it to reduce overhead.

AutistiCoder• 1 year ago

ChatGPT itself is great for coding.

GitHub Copilot is...not. It doesn't seem to understand how to help me as well as ChatGPT does.

knes• 1 year ago

IMHO, I would agree with you.

I think chat is a nice intermediary evolution between the CLI (that we use every day) and whatever comes next.

I work at Augment (https://augmentcode.com), which, surprise surprise, is an AI coding assistant. We think about the new modality required to interact with code and AI on a daily basis.

Beside increase productivity (and happiness, as you don't have to do mundane tasks like tests, documentations, etc), I personally believe that what AI can open up is actually more of a way for non-coders (think PMs) to interact with a codebase. AI is really good at converting specs, user stories, and so on into tasks—which today still need to be implemented by software engineers (with the help of AI for the more tedious work). Think of what Figma did between designers and developers, but applied to coding.

What’s the actual "new UI/UX paradigm"? I don’t know yet. But like with Figma, I believe there’s a happy ending waiting for everyone.

zahlman2• 1 year ago

LoC per hour seems to me like a terrible metric.

esafak5• 1 year ago

Why? Since you are vetting the code it generates, the rate at which you end up with code you accept seems like a good measure of productivity.

bandushrew• 1 year ago

Producing 200 lines of usable code an hour is genuinely impressive.

My experiments have been nowhere near that successful.

I would love, love, love to see a transcript of how that process worked over an hour, if that was something you were willing to share.

protocolture• 1 year ago

100%.

I do all this + rubber ducky the hell out of it.

Sometimes I just discuss concepts of the project with the thing and it helps me think.

I dont think chat is going to be right for everyone but it absolutely works for me.

Syzygies• 1 year ago

An environment such as Cursor supports many approaches for working with AI. "Chat" would be the instructions printed on the bottom, perhaps how their developers use it, but far from the only mode it actually supports.

It is helpful to frame this in the historical arc described by Yuval Harari in his recent book "Nexus" on the evolution of information systems. We're at the dawn of history for how to work with AI, and actively visualizing the future has an immediate ROI.

"Chat" is cave man oral tradition. It is like attempting a complex Ruby project through the periscope of an `irb` session. One needs to use an IDE to manage a complex code base. We all know this, but we haven't connected the dots that we need to approach prompt management the same way.

Flip ahead in Harari's book, and he describes rabbis writing texts on how to interpret [texts on how to interpret]* holy scriptures. Like Christopher Nolan's movie "Inception" (his second most relevant work after "Memento"), I've found myself several dreams deep collaborating with AI to develop prompts for [collaborating with AI to develop prompts for]* writing code together. Test the whole setup on multiple fresh AI sessions, as if one is running a business school laboratory on managerial genius, till AI can write correct code in one shot.

Duh? Good managers already understand this, working with teams of people. Technical climbers work cliffs this way. And AI was a blithering idiot until we understood how to simulate recursion in multilayer neural nets.

AI is a Rorschach inkblot test. Talk to it like a kindergartner, and you see the intelligence of a kindergartner. Use your most talented programmer to collaborate with you in preparing precise and complete specifications for your team, and you see a talented team of mature professionals.

We all experience degradation of long AI sessions. This is not inevitable; "life extension" needs to be tackled as a research problem. Just as old people get senile, AI fumbles its own context management over time. Civilization has advanced by developing technologies for passing knowledge forward. We need to engineer similar technologies for providing persistent memory to make each successive AI session smarter than the last. Authoring this knowledge helps each session to survive longer. If we fail to see this, we're condemning ourselves to stay cave men.

Compare the history of computing. There was a lot of philosophy and abstract mathematics about the potential for mechanical computation, but our worldview exploded when we could actually plug the machines in. We're at the same inflection point for theories of mind, semantic compression, structured memory. Indeed, philosophy was an untestable intellectual exercise before; now we can plug it in.

How do I know this? I'm just an old mathematician, in my first month trying to learn AI for one final burst of productivity before my father's dementia arrives. I don't have time to wait for anyone's version of these visions, so I computed them.

In mathematics, the line in the sand between theory and computation keeps moving. Indeed, I helped move it by computerizing my field when I was young. Mathematicians still contribute theory, and the computations help.

A similar line in the sand is moving, between visionary creativity and computation. LLMs are association engines of staggering scope, and what some call "hallucinations" can be harnessed to generalize from all human endeavors to project future best practices. Like how to best work with AI.

I've tested everything I say here, and it works.

renegat0x0• 1 year ago

One thing I would keep in mind. There are some parts of the project that you really cannot fill by chat output.

I had crucial area with threads. Code generated by chat seemed to be ok, but had one flaw. My initial code written manually was bug free. chat-generated output was not. It was difficult to catch it via inspection.

mlsu• 1 year ago

I can't wait for someone to invent a new language, maybe a subset of English, that is structured enough to half-well describe computer programs. Then train a model with RLHF to generate source code based on prompts in this new language.

It will slowly grow in complexity, strictness, and features, until it becomes a brand-new programming language, just with a language model and a SaaS sitting in the middle of it.

A startup will come and disrupt the whole thing by simply writing code in a regular programming language.

fullstackwife• 1 year ago

Who is hiring 2035:
Looking for a low level engineer, who works close to the metal, will work on our prompts

grumbel• 1 year ago

Plenty of software has been developed on the command line, chat is just a more powerful and flexible version of that. The missing part with current AI systems is a persistent workspace/filesystem that allows you to store things you want to keep, discard things you want to get rid off and highlight things you want to focus on.

sebastianconcpt• 1 year ago

Yep.

arnaudsm• 1 year ago

Considering how good real time voice chat with LLMs is now (gpt4o and Gemini 2.0), I'm haven't seen anyone try to integrate them into programming tools.

It could be quite fun !

esafak5• 1 year ago

Most programmers can type faster than they can speak.

darepublic• 1 year ago

Need interactive chat for coding. You say something high level, the model prompts you for low level decisions etc. every once in a while the code bot can send a screenshot or some test results so we stay grounded in where we are in the process. This can enable coding while I'm driving or sitting stoned on the couch.

icapybara• 1 year ago

This feels like those arguments that text is the worst way to code and we actually need a no code solution instead.

Theoretically maybe, but chat windows are getting the job done right now.

kordlessagain• 1 year ago

A terminal prompt has worked great for me for years…

firefoxd• 1 year ago

That's also why AGI as defined today is an interface problem. Imagine we've actually achieved it, and the interface is a chat prompt. It will be really hard to differentiate it with the current tools we have.

For writing, the canvas interface is much more effective because you rely less on copy and paste. For code, even with the ctrl+i method, it works but it's a pain to have to load all other files as reference every single time.

weitendorf• 1 year ago

This is exactly why we're developing our AI developer workflow product "Brilliant" to steer users away from conversations altogether.

Many developers don't realize this but as you go back and forth with models, you are actively polluting their context with junk and irrelevant old data that distracts and confuses it from what you're actually trying to do right now. When using sleeker products like Cursor, it's easy to forget just how much junk context the model is constantly getting fed (from implicit RAG/context gathering and hidden intermediate steps). In my experience LLM performance falls off a cliff somewhere around 4 decent-sized messages, even without including superfluous context.

We're further separating the concept of "workflow" from "conversation" and prompts, basically actively and aggressively pruning context and conversation history as our agents do their thing (and only including context that is defined explicitly and transparently), and it's allowing us to tackle much more complex tasks than most other AI developer tools. And we are a lot happier working with models - when things don't work we're not forced to grovel for a followup fix, we simply launch a new action to make the targeted change we want with a couple clicks.

It is in a weird way kind of degrading to have to politely ask a model to change a color after it messed up, and it's also just not an efficient way to work with LLMs - people just default to that style because it's how you'd interact with a human you are delegating tasks to. Developers still need to truly internalize the facts that LLMs are purely completion machines, that your conversation history lives entirely client side outside of active inference, and that you can literally set your conversation input to be whatever you want (even if the model never said that) - after that realizing you're on the path towards using LLMs like "what words do I need to put it in to get it to do what I want" rather than working "with" them.

nbzso• 1 year ago

Is there a statistical data on adoption of AI chatbots in the industry? I see a lot of personal demos, small projects and nobody is talking about serious integration into production and useful patterns.

RyanAdamas• 1 year ago

The chat interface modality is a fleeting one in the grand scheme. Billion token context windows with recursive Ai production based on development documentation and graphics is likely the next iteration.

bangaladore• 1 year ago

I'll preface this by saying I also dislike using chat as a pattern for AI tools. However, in theory, the idea has merit. Just as having 100% of the specifications and design guidance for a product is valuable before development, complete requirements would seem ideal. In reality, though, many requirements and specifications are living documents. Should we expect to rebuild the entire application every time a document changes? For example, if I decide to reduce a header's height, there's a significant chance the application could end up looking or feeling entirely different.

In a real-world scenario, we begin with detailed specifications and requirements, develop a product, and then iterate on it. Chat-based interactions might be better suited to this iterative phase. Although I'm not particularly fond of the approach, it does resemble receiving a coworker's feedback, making a small, targeted change, and then getting feedback again.

Even if the system were designed to focus solely on the differences in the requirements—thus making the build process more iterative—we still encounter an issue: it tends to devolve into a chat format. You might have a set of well-crafted requirements, only for the final instruction to be, "The header should be 2px smaller."

Nonetheless, using AI in an iterative process (focusing on requirement diffs, for example) is an intriguing concept that I believe warrants further exploration.

sprucevoid• 1 year ago

I find web chat interfaces very useful for programming, but it also feels like early days. Speedups will smooth out a lot of pain points. But other UI changes, some even quite small, could enhance use a lot. A few of various size top of mind with regard to Claude web chat UI specifically:

- intellisense in the inputbox based on words in this or all previous chats and a user customizable word list

- user customizable buttons and keyboard shortcuts for common quick replies, like "explain more".

- when claude replies with a numbered list of alternatives let me ctrl+click a number to fork the chat with continued focus on that alternative in a new tab.

- a custom right click menu with action for selection (or if no selection claude can guess the context e.g. the clicked paragraph) such as "new chat with selection", "explain" and some user customizable quick replies

- make the default download filenames follow a predicable pattern, claude currently varies it too much e.g. "cloud-script.py" jumps to "cloud-script-errorcheck.py". I've tried prompting a format but claude seems to forget that.

- the stop button should always instantly stop claude in its tracks. Currently it sometimes takes time to get claude to stop thinking.

- when a claude reply first generates code in the right sidebar followed by detailed explanation text in the chat, let some keyboard shortcut instantly stop the explanation in its tracks. Let the same shortcut preempt that explanation while the sidebar code is still generating.

- chat history search is very basic. Add andvanced search features, like filter by date first/last message and OR search operator

- batch jobs and tagging for chat history. E.g. batch apply a prompt to generate a summary in each selected chat and then add the tag "summary" to them. Let us then browse by tag(s).

- tools to delete parts of a chat history thread, that in hindsight were detours

- more generally, maybe a "chat history chat" to have Claude apply changes to the chat histories

ansonhw• 1 year ago

I agree actually that chat is overrated overall as UX. It works really well for chatgpt but creates the wrong ux expectations for users where more precision or constraint is needed. Also not good for large processing. It was magic though for chatgpt.

aantix• 1 year ago

The only LLM agent I've seen who asked any sort of clarifying questions about design was Devin.

andix• 1 year ago

My approach for AI generated code with more complexity was always this:

1. Ask AI to generate a spec of what we're planning to do. 2. Refine it until it's kind of resembling what I want to do 3. Ask AI to implement some aspects from the spec

dehugger• 1 year ago

LLM generated code seems to depend wildly on if the project is about something that a bunch of people have already put out on GitHub or not.

Writing a crud web API? Great! Writing business logic for a niche edge case in a highly specialized domain? Good luck.

yapyap• 1 year ago

AI was supposed to change everything. Finally, plain English could be a programming language—one everyone already knows. No syntax. No rules. Just say what you want

That’s the thing about language, you CAN’T program in human language for this exact reason, whereas programming languages are mechanical but precise, human languages flow better but they leave wiggle room. Computers can’t do jack shit with wiggle room, they’re not humans. That’ll always remain, until there’s an AI people like enough to have it’s own flair on things.

xenodium• 1 year ago

I've been experimenting with "paged chats" UX and find myself using it fairly fluidly https://xenodium.com/llm-iterate-and-insert

Been experimenting with the same approach but for "paged shells" (sorry for the term override) and this seems to be a best of both worlds kinda thing for shells. https://xenodium.com/an-experimental-e-shell-pager That is, the shell is editable when you need it to be (during submission), and automatically read-only after submission. This has the benefit of providing single-character shortcuts to navigate content. n/p (next/previous) or tab/backtab.

The navigation is particularly handy in LLM chats, so you can quickly jump to code snippets and either copy or direct output elsewhere.

Bjorkbat• 1 year ago

This kind of reminds me of back when the hype cycle was focused on Messenger apps and the idea of most online behavior being replaced with a chatbot. God I hated the smug certainty of (some, definitely not all!) UX designers at the time proclaiming that chat was the ultimate interface.

Absolutely insane that all the doors unlocked by being able to interact with a computer graphically, and yet these people have visions of the future stuck in the 60s.

SuperHeavy256• 1 year ago

so, a really long text? that's your big revelation?

PaulHoule• 1 year ago

It makes me think of the promises and perils of Jupyter notebooks.

So far as this article is concerned (not the many commenters who are talking past it), "chat" is like interacting with a shell or a REPL. How different is the discussion that Winograd has with SHRDLU

https://en.wikipedia.org/wiki/SHRDLU

with the conversation that you have with a database with the SQL monitor really?

There's a lot to say for trying to turn that kind of conversation into a more durable artifact. I'd argue that writing unit tests in Java I'm doing exploratory work like I'd do in a Python REPL except my results aren't scrolling away but are built into something I can check into version control.

On the other hand, workspace-oriented programming environments are notorious for turning into a sloppy mess, for instance people really can't make up their mind if they want to store the results of their computations (God help you if you have more than one person working on it, never mind if you want to use version control -- yet, isn't that a nice way to publish a data analysis?) or if they want to be a program that multiple people can work, can produce reproducible results, etc.

See also the struggles of "Literate Programming"

Not to say there isn't an answer to all this but boy is it a fraught area.

kristofferR2• 1 year ago

"You don’t program by chatting. You program by writing documents.", or long chat prompts as they are also called.

orand• 1 year ago

Chat as a bad UI pattern for development tools is like saying language is a bad UI pattern for thought.

ingigauti• 1 year ago

I took the position of not liking to much the AI coding early on. This was specially when it was starting. People writing long description to generate an app, I quickly noticed that doesn't work because it's all in the details.

Then having ai generate code for my project didn't feel good either, I didn't really understand what it was doing so I would have to read it to understand, then what is the purpose, I might as well write it.

I then started playing, and out came a new type of programming language called plang (as in pseudo language). It allows you to write the details without all the boiler code.

I'm think I've stumbled on to something, and just starting to get noticed :) https://www.infoworld.com/article/3635189/11-cutting-edge-pr...

williamcotton• 1 year ago

It’s like writing laws.

Vague and prone to endless argument?

debacle• 1 year ago

I quite like it. Meta AI has become a good programming companion.

talles• 1 year ago

Imagine if instead of English, someone invented some sort of computer language that is precise and produces the same result every time you execute it.

randomNumber7• 1 year ago

For me the LLM does 3 things. Since it is trained on pattern matching it performs well on these. The tree like chatgpt inerface (where you can change the questions) is perfect imo.

- Speed up literature recherche

- replace reading library documentation

- generate copy pasta code that has been written often before

sramam• 1 year ago

Interesting take. Completely agree that product requirements document is a good mental models for system description. However, aren't bug-reports+PRs approximating a chat-interface?

michaelfeathers• 1 year ago

Chat in English? Sure. But there is a better way. Make it a game to see how little you can specify to get what you want.

I used this single line to generate a 5 line Java unit test a while back.

test: grip o -> assert state.grip o

LLMs have wide "understanding" of various syntaxes and associated semantics. Most LLMs have instruct tuning that helps. Simplifications that are close to code work.

Re precision, yes, we need precision but if you work in small steps, the precision comes in the review.

Make your own private pidgin language in conversation.

gunalx• 1 year ago

This just seem more cumbersome than just writing the software to begin with.

Its a problem of programming languages and definitions.

stevage2• 1 year ago

Boy does this feel like the author has never actually used any AI tools for writing code.

ern• 1 year ago

I had the same feeling reading this, but a large number of developers struggle to communicate verbally and would hate a chat based interface. Some years ago, I worked with a very intelligent person who had come into development with math-related degree.

We tried a pair-programming exercise, and he got visibly angry, flustered and frustrated when he tried to verbalize what he was doing.

One of the reasons Business Analysts and the like exist is that not everyone can bridge the gap between the messy, verbal real world, and the precision demanded by programming languages.

stevage2• 1 year ago

Hmm. I have worked as a BA in the past, and do a lot of requirements gathering in my work...but I actually hate pair programming, and generally really dislike discussing code with other programmers.

But talking to an AI about code feels much more like programming than talking to another programmer.

daxfohl2• 1 year ago

I agree with everything except there being a business opportunity there. Whatever ends up being the fix, all the big players will incorporate it into their own IDEs within a couple months. Unless the fix is something that is significantly different from an IDE, that incorporating it into one doesn't make sense.

spandrew• 1 year ago

AI "Agents" that can do tasks outside of the confines of just a chat window are probably the next stage of utility.

The company I work for integrated AI into some of our native content authoring front-end components and people loved it. Our system took a lot of annotating to be able to accurately translate the natural language to the patterns of our system but users so far have found it WAYYY more useful than chat bc it's deeply integrated into the tasks they do anyway.

Figma had a similar success at last year's CONFIG when they revealed AI was renaming default layers names (Layer 1, 2, etc)... something they didn't want to do anyway. I dare say nobody gave a flying f about their "template" AI generation whereas layer renaming got audible cheers. Workflow integration is how you show people AI isn't just replacing their job like some bad sci-fi script.

Workflow integration is going to be big. I think chat will have its place tho; just kind of as an aside in many cases.

suralind• 1 year ago

I love how Zed integrates the chat into IDE. You can essentially edit any part of the history, remove or redo the prompt. I just started to use this feature a couple of days ago and I couldn’t be happier.

muzani• 1 year ago

The first wave was not chat, it was completion. Instead of saying "suggest some names for an ice cream shop", the first wave was "Here are some great names for ice cream shops: 1. Nice Cream 2." Chat was a lot more intuitive and low effort than this.

Chat is also iterative. You can go back there and fix things that were misinterpreted. If the misinterpretation happens often, you can add on another instruction on top of that. I strongly disagree that they'd be fixed documents. Documents are a way to talk to yourself and get your rules right before you commit to them. But it costs almost nothing to do this with AI vs setting up brainstorming sessions with another human.

However, the rational models (o1, r1 and such) are good at iterating with themselves, and work better when you give them documents and have them figure out the best way to implement something.

jes5199• 1 year ago

I'm looking forward to a Cursor-like experience but using voice - so we can discuss code changes verbally, as if we were pair programming. honestly I'm not sure why we don't have that yet.

jpcom• 1 year ago

So you're saying a spec [specification] is the solution to building programs.

chaisan• 1 year ago

The workflow of starting with and iterating on a PRD is already taking off: https://x.com/daniel_mac8/status/1871372159748079893 https://skylarbpayne.com/posts/cursed-cursor

waychang• 1 year ago

all this just to say "prompt neatly" :)

waylonchang• 1 year ago

all this just to say "prompt neatly" :)

mhh__• 1 year ago

I had a daydream about programming with an LLM as something more like driving a car than typing, e.g. constant steering, changing gears and so on

notatoad• 1 year ago

i like chat. all the more dedicated ai development tools try to force the author's (or somebody's) specific workflow, and fall into an uncanny valley sort of situation for my workflow, where it highlights all the bits of my workflow that don't match the tool's desired workflow.

chat is so drastically far away from my workflow that it doesn't feel like my workflow is wrong.

vismit2000• 1 year ago

Reminds of Karpathy's pinned tweet since a long time: "The hottest new programming language is English." https://x.com/karpathy/status/1617979122625712128

nektro• 1 year ago

got so close to the point and then flew right past it. AI is never going to be great for this because the limit of "prompt engineering" just loops right back to normal programming

jillesvangurp• 1 year ago

Not just for development tools. It's a bad UI for a lot of use cases. It's merely what made sense for UX challenged researchers when they had to come up with a UI for their LLMs in a hurry. Discord was there and reasonably easy to integrate. Many tools started out as just that. Fast forward and most tools are kind of standalone versions of the same thing.

The challenge is that I haven't seen anything better really.

Lately the innovation comes mainly from deeper integration with tools. Standalone AI editors are mainly popular with people who use relatively simple editors (like VS Code). VS Code has a few party tricks but for me swapping out Intellij for something else on a typical Kotlin project is a complete non starter. Not going to happen. I'd gain AI, but I'd loose everything else that I use all the time. That would be a real productivity killer. I want to keep all the smart tooling I already have and have used for years.

There are a few extensions for intellij but they are pretty much all variations of a sidebar with a chat and autocomplete. Autocomplete competes with normal autocomplete, which I use all the time. And the clippy style "it looks like you are writing a letter" style completions just aren't that useful too me at all. They are just noise and break my flow. And they drown out the completions I use and need all the time. And sidebars just take up space and copying code from there back to your editor is a bit awkward as UX

Lately I've been using chat gpt. It started out pretty dumb but these days I can option+shift+1 in a chat and have it look over my shoulder at my current editor. "how do I do that?" translates into a full context with my current editing window, cursor & selected text, etc. all in the context. Before I was copy pasting everything and the kitchen sync to chat gpt, now it just tells me what I need to do. The next step up from this is that it starts driving the tools itself. They already have a beta for this. This deeper integration is what is needed.

A big challenge is that most of these tools are driven to minimize cost and context size. Tokens cost money. So chat GPT only looks at my active editor and not at the 15 other files I have open. It could. But it doesn't. It's also unaware of my project structure, or the fact that most of my projects are kotlin multiplatform and can't use JVM dependencies. So, in that sense, every chat still is a bit ground hog day. It's promise to "remember" stuff when you ask it too is super flaky. It forgets most things it's supposed to remember pretty quickly.

These are solvable problems of course. But it's useful to me for debugging, analyzing, completing functions, etc.

fullstackchris• 1 year ago

I agree with what is discussed in this post a lot. Chat is indeed a lossy and at best q/a style system. Yes, of course it can be tweaked and hacked but it's ultimately the wrong UI if you want to leverage the most of AI for code.

However, even with a "docs as spec" pattern, how can you control the actual quality of the code written? How maintainable will it be? If the spec changes (read: it _will_ change constantly), is it easy enough to refactor? What about tests? I also shrink in fear at the complexity of docs that could be _exactly_ captured as code... "well we almost always do it this way, but this one time we do it this way..."

cratermoon• 1 year ago

Is chat a good UI for anything besides, well, chatting between people? Joseph Weizenbaum chose a chat interface for ELIZA because he expressly wanted to ape Rogerian therapy. Are we doomed to imagine AI is "chatting with a computer" because no one can imagine anything else?

mehakkar3006• 1 year ago

Chat is a bad UI pattern for development tools

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