Artificial intelligence is losing hype

So I am a neophite in this area, but my thesis for why "this time is different" compared to previous AI bubbles is that this time there exist a bunch of clear products (or paths to products) that work and only require what is currently available in terms of technology.

Coding assistants today are useful, image generation is useful, speach recognition/generation is useful.

All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science.

I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable.

Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.

"AGI" is a nonsense term anyway. Humans don't have "general" intelligence either: our intelligence is specialized to our environment.

I have been using Chat-GPT has a full time expert and I can unequivocally tell you that its a transformative piece of technology. The technology isn't hyped.

All Kahneman's system 2 thinking is just slow deliberate thinking. And these models do indeed have this characteristic to an extent, as evidenced with chain of thought reasoning.

You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking.

In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.

If you look at AI history there is often fairly steady progress in a given skill area for example chess programs improved in a steady way on ELO scores and you could project pretty well the future by drawing a line on a graph. Similarly large language models seem to be progressing from toddler like to high school student like (now) to PhD like - shortly. There are skills AI are still fairly bad at like the higher level reasoning you mention, and in robot form being able to pop to the shops to get some groceries say but I get the impression those are also improving in a steady way and it won't be so long.

For readers' edification, would you mind making a strong hypothetical argument for why this time it actually is different, from an expert's perspective?

I am yet to see any reasoned argument for why it is easy to build real AI and that it will come fast.

We have "real ai" already.

As for future progress, have you tried just simple interpolation of the progress so far? Human level intelligence is very near. (Though of course artificial intelligence will never exactly match human intelligence: it will be ahead/behind in certain aspects...)

Well he gave you a list of credentials of why you should believe him. Isn’t that enough ?

nothing says ... that we're not in for a long plateau again

The thing that's different this time is the hardware capacity in TFLOPs and the like passing human brain equivalence.

There's a massive difference between much worse than human AI - a bit meh, and better than human AI - changes everything.

any reasoned argument for why it is easy to build real AI and that it will come fast

It probably won't be easy but the huge value of better than human AI will ensure loads of the best and brightest working on it.

The tokens could be patterns of colored shapes with no actual meaning, only statistical distributions and nothing about how the LLMs work would change.

I can put your brain in a vat and stimulate your sensory neurons with a statistical distribution with no actual meaning, and nothing about how your brain works would change either.

The LLM and your brain would attempt to interpret meaning with referent from training, and both would be confused at the information-free stimuli. Because during "training" in both cases, the stimuli received from the environment is structured and meaningful.

So what's your point?

By the way, pretty sure a neuroscientist with 20 years of ML experience has a deeper understanding of what "meaning" is than you do. Not to mention, your response reveals a significant ignorance of unresolved philosophical problems (hard problem of consciousness, what even is meaning) which you then use to incorrectly assume a foregone conclusion that whatever consciousness/meaning/reasoning is, LLMs must not have it.

I'm partial to doubting LLMs as they are now have the magic sauce, but it's more that we don't actually know enough to say otherwise, so why state that we do know?

We can't even say we know our own brains.

At this point bringing up the ELIZA argument is basically bad faith gaslighting…

Finding bugs in some models doesn’t mean you have a point about intelligence. If somebody could apply a similar argument to dismiss human intelligence, you don’t have a point. And here it goes: the most advanced human intelligence can’t reliably multiply large numbers or recall digits of Pi. Obviously humans are dumber than pocket calculators.

Especially if you remember that the change needed for the first "breakthrough" (GPT4) was RLHF. That is, a model that was specifically trained to mesmerize.

the most advanced AI cannot reliably determine which of two floats is bigger

Some of the most advanced AI are tool users and can both write and crucially also execute python, and embed the output in their responses.

or even solve really simple logic puzzles for little kids.

As given in a recent discussion: https://chatgpt.com/share/ee013797-a55c-4685-8f2b-87f1b455b4...

(Custom instructions, in case you're surprised by the opening of the response).

While it is true that LLM’s lack agency and have many weaknesses, they form a critical part of what machine learning has lacked until transformers became all of the rage.

The things that LLM’s are bad at are largely solved problems using much simpler technology. There is no reason that LLM’s have to be the only component in an intelligent agent. Biological brains have Specialized structures for specialized tasks like arithmetic. The solution is probably integration of LLMs as a part of a composite system that includes database storage, a code execution environment, and multiple agents to form a goal directed posit - evaluate loop.

I’ve had pretty remarkable success with this architecture running on 12b models and I’m a nobody with no resources.

LLM’s by themselves just come up with the first thing that crosses their”mind”. It shouldn’t be surprising that the very first unfiltered guess about a solution might be suboptimal.

There is a vast amount of knowledge embedded in our cultural matrix, and a lot of that is captured in the common crawl and other datasets.llms are like a search engine for that data , based on meaning rather than semantics.

We are but prediction machines https://www.psy.ox.ac.uk/news/the-brain-is-a-prediction-mach...

This means you are sure we are close to automated driving [...]

We already have "automated driving" in some sense. Some cities have fully autonomous taxi services that have operated for a year or more, iirc.

The 1-minute mile must be right around the corner, and when that inevitably gets broken, the 1-second mile will follow swiftly.

While this is true, I think you’re not appreciating the metaphor.

Humankind tried to break the 4 minute mile for hundreds of years - since measuring distance and time became accurate enough to be sure of both in the mid-18th century, at least - and failed.

In May 1954, Roger Bannister managed it. By late June it was done again by a different runner. Within 20 years the record was under 3:45, and today there are some runners who have achieved it more than 100 times and nearly 1800 runners who have done it at all.

Impossible for hundred of years, and then somebody did it, and people stopped thinking it was impossible and started doing it themselves. That’s the metaphor: sometimes we think of barriers that are really mental, not real.

I’m not sure that applies here either, but the point is not that progress is continuously exponential, but that once a barrier is conquered, we take on a perspective as if the barrier were never real in the first place. Powered flight went through this. Computing hardware too. It’s not an entirely foolish notion.

We are basically out of new, non-synthetic text to train models

this is not even remotely true.

There is an astronomical amount of data siloed by publishers, professional journals etc. that is yet to be tapped.

OpenAI is making inroads by making deals with these content owners for access to all that juicy data.

Define breakthrough, there's plenty of room to scale and optimize without any need for a breakthrough (well my definition of breakthrough). Emergent properties so far have been obtained purely from scaling.

I don't know anything about biology, but is there anything in birds even remotely like the airplane? It can do a lot of things, but I don't think that it's capable of emulating bird flight.

I feel the same way. Anytime someone says they don't find LLMs all that useful, the exact same comments come out:

"They clearly aren't using the right model!"

"It's obvious they don't know how to prompt, or they would see the value."

"Maybe it can't do that today, but GPT-5 is just around the corner."

I feel more and more that people have just decided that this is a technology that will do everything you can imagine, and no evidence to the contrary will change their priors.

Sometime I think there's something wrong with me. I've used copilot, I'm paying for ChatGPT and we're also having the Jetbrains AI, and there's just something so off about all of this.

Some basic things are fine, but once you get into specialised things, everything gets just terribly wrong and weird. I can't even put it into words, I see people saying how they are 10x more productive (I'd like to see actual numbers and proof for this), but I just don't see how. Maybe we're working on very custom stuff, or very specific things, but all of these tools seem to give very deep or confident answers that are just plain wrong and shallow. Just yesterday I used GPT 4o for some basic help with Puppet, and the examples it printed, even though I would say it's quite basic, were just wrong, but in the sense of having to debug it for 2 hours just to figure out how ridiculous the error was.

I fear the fact that people will end up releasing unsafe, insecure and simply wrong code every day, code that they never debug and not even understand, that maybe works for a basic set of details, but once the real world hits it, it will fail like those self driving cars driving full speed into a trailer that has the same color as the road or sky.

I was skeptical like you, but recently decided to try it out. I wasn't expecting much, and as such I was slightly surprised.

For example, just now my NAS stopped working because the boot device went offline. So I got to thinking about writing a simple syslog server. I've never looked at the syslog protocol before, and I've never done any low-level TCP/UDP work in C# yet.

So I asked ChatGPT to generate some code[1], and while the result is not perfect it's certainly better than nothing, and would save me time to get going.

As another example, a friend who's not very technical wanted to make an Arduino circuit to perform some automated experiment. He's dabbled with programing and can modify code, but struggles to get going. Again just for kicks, I asked ChatGPT and it provided a very nice starting point[2].

For exploratory stuff like this, it seems to provide a nice alternative to searching and piecing together the bits. Revolutionary is a quite loaded word, but it's certainly not just a slight improvement on what we had before LLMs and instead feels like a quantum leap.

[1]: https://chatgpt.com/share/f4343939-74f1-404d-bfac-b903525f61... (modified, see reply)

[2]: https://chatgpt.com/share/fc764e73-f01f-4a7c-ab58-f43da3e077...

I feel the exact same way. I have felt 0 need to use an LLM in my current workflow. If I could explain concisely what my problem is in English - then I would already know the answer. In that case why would I be asking AI the question. And I don't want opinionated answers to subjective questions. Why run that through a filter, I can investigate better myself through links on the internet.

By the way - I think AI or ML whatever has some valid uses right now. but mostly in image processing domain - so like recognizing shapes in some bounded domain OK yea. Generative image - NOT bad but theres always this "AI GLOW" to each image. Something is always off. Its some neat tools but a race to the bottom and mostly users want to generate explicit content lets be real. and they will become increasingly more creative and obtuse to get around the guards. nothing is stopping you from entering the * industry and making tons off money. but that industry is always doing good.

a friend recently suggested to use AI to generate generic icons for my game. Thats a really good use case. but does that radically change the current economy?

[BTW GENERIC STUFF ONLY UNTILL I could hire someone because i prefer that experience way more. you can get more interesting results. 4 eyes are better than 2.

Give it another shot but with Claude Sonnet 3.5. It’s my daily driver for coding tasks.

It seems especially strong with Python but a bit medium with Swift.

For LOVR (Lua based 3D/VR framework), I found these LLMs pretty much useless, both ChatGPT and Claude. Seems all is trained on old APIs, so it takes quite a bit of effort to make any suggestions work with newest LOVR version.

Yeah, these people claiming AI has been a transformative experience are just full of sh*t. I ask various models questions all the time because it's often better than googling, but all of them make a lot silly mistakes. Frequently, it can be a bit of a process to get useful results.

Same here.

A friend, whose SQL knowledge is minimal, used an LLM to query data from a database over a couple of tables. Yes, after a lot of trial and error he (most probably) got the correct data, however the only one being able to read the query is the LLM itself. It's full of coalesce, subselects that repeat the same joins again and again.

LLM will do a lot for you, but I really hate this "this will [already did] solve everything". No, it did not and no, because it's quality is those of a junior dev, at max.

but every time I try I find it borderline impossible to get functional code even for pretty straightforward prompts.

I work in 9 different projects now and I would say that around 80% of functional code comes from Sonnet (like GP) for these projects. These are not (all) trivial either; there is a very niche (for banking) key/value store written in Go for instance which has a lot of edge cases etc, all the plumbing (x,err = etc aka stuff people find annoying) comes from sonnet and works one-shot. A lot of business logic comes from sonnet too; it works but usually needs a little tweaking to make it correct.

Tests are all done by Sonnet. I think 80% is low balling it on Go code really.

We have a lot of complex code generator stuff and DSLs in TS which also works well often. Sometimes it gets some edge cases wrong, but either re-prompting with more details or fixing it ourselves, will do it. At a fraction of the time/money of what a fully human team would deliver.

I wrote a 3d editor for fun with Sonnet in a day.

I have terrible results with gpt/copilot (copilot is good for editing instead of complete files/functions; chatgpt is not good much compared with sonnet); it doesn't get close at all; it simply keeps giving me the same code over and over again when I say it's wrong; it hardcodes things specifically asked to make flexible etc. Not sure why the difference is so massive all of sudden.

Note: I use the sonnet API, not the web interface, but same for gpt so...

Where LLM shine is as a faster alternative to Google and Stack overflow. "How do I reverse an array in language X?". This will give you the right answer in seconds without having to click through garbage.

Especially if it's a question that's hard to Google, like "I remember there is more than one way to split an array in this language, list them". This saves me minutes every day.

But it's especially helpful if you are working on projects outside your own domain where you are a newbie.

Lots of companies are directly involved with LLMs, or working to leverage it for startups or their existing projects. And I think a fair chunk of all the employees working at these places probably post on HN (a crazy high percent of the recent batches of YC applicants were using LLM stuff, for instance). That's going to lead to a sizable number of perspectives and opinions that are not especially free of bias, simply because you want to believe that what you're working on is ultimately viable.

And I think it'd be extremely easy to convince oneself of this. Look at where 'AI' was 5 years ago, look at where it is today and then try to imagine where it will be in another 5 years. Of course you have to completely blind yourself to the fact that the acceleration has clearly sharply stalled out, but humans are really good at cognitive dissonance, especially when your perception of your future depends on it.

And there's also the point that even though I'm extremely critical of LLMs in general, they have absolutely 'transformed' my workflow in that natural language search of documentation is really useful. Being able to describe a desired API, but in an overly broad way that a search engine can't really pick up on, but that an LLM [often] can, is just quite handy. On the other hand, this is more a condemnation of search engine tech being frozen 20 years in the past than it is about an imminent LLM revolution.

The consensus on HN seems to be that you can be pretty productive with LLMs as coding assistants

If you define "productive" as writing a simple CRUD web application that your 13-year-old cousin could write between two gaming sessions, then you'll consider LLMs as sacred monsters.

Snake oil vendors always had great appeal over people who didn't know better.

I'm guessing AI won't be great with anything nontrivial related to pointers for a while since they require actual thinking

LLM coding assistants are more like another iteration in the jump from nothing -> Stack Overflow type resources than a replacement for you doing coding work as a programmer just because you can phrase the task as a prompt. If you measured the value of using resources like tutorials Stack Overflow posts by blindly merging the first 2 hundred line examples of the things you want to do, finding the result wasn't ideal, and declaring it a useless way to get functional code people would (rightfully) scratch their heads at you when you say they are living in a different world. Of course it didn't work right away, perhaps it was still better than figuring all of the boilerplate out on your own the first time you did it?

I had a decent experience putting together frontend code for a demo control system with Mixtral within a couple days. I'm a seasoned programmer but I don't do JS. It stumbled a dozen times but it fulfilled the task of me avoiding to learn JS.

However once you step outside JS or Python, the models are essentially useless. Comprehension of pointer semantics? You wish. Anything with Lisp outside its training corpus of homework assignments? LOL. Editing burden quickly exceeds any possible speed-up.

Counterpoint: I have gotten real value out of dumping broken SQL into ChatGPT and have it fix it for me. I could 100% have done that by myself, but it would have meant I have to go and google the right syntax.

AI is great for me, but it is more like a junior developer you are pairing with than a replacement.

Try something like maestro which uses agents and orchestrator. The looping quality checks have been very helpful. Claude-engineer from same developer is also good to experience how superior that ia to regular chat.

I've been very productive using LLMs, without any expectations of them "writing functional code". Instead, I've mostly used them as if I was working with a human research librarian.

For example, I can ask the LLM things like "What are the most common mistakes when using the Vulkan API to render a triangle with a texture?" and I'll very rapidly learn something about working with an API that I don't have deep understanding of, and I might not find a specific tutorial article about.

As another example, if I'm an experienced OpenGL programmer, I can ask directly "what's the Vulkan equivalent of this OpenGL API call?" and get quite good results back, most of the time.

So I'm asking questions where an 80% answer is still very valuable, and it's much faster than searching for documentation and doing a lot of comparison and headscratching, and it works well enough even when there's no specific article I could find in a variety of searches.

Anything better that the technology gets from here just makes things even easier yet!

I gave GPT two small twists so it's not a simple case of copy/paste

Why? I see it like querying a database of human knowledge. I wouldn't expect a SQL database to infer information it's never seen before, why would I expect an LLM to do so?

I use it where I know a solution exists but I'm stumped on the syntax or how to implement it in an unfamiliar environment, or I want to know what could have caused a bug based on others' experience etc.

Something worth noting is that the parent comment refers to using Cursor, not ChatGPT/Claude.ai. The latter are general-purpose chat (and, in the case of ChatGPT, agentic) applications.

Cursor is a purpose-built IDE for software development. The Cursor team has put a lot of research and sweat into providing the used LLMs (also from OpenAI/Anthropic) with:

- the right parts of your code

- relevant code/dependency documentation

- and, importantly, the right prompts.

to successfully complete coding tasks. It's an apple and oranges situation.

I stop it from coding, just ask it to brainstorm with me on a design or plan. After a few iterations it knows what I want and then I ask it for specific outputs. Or I code those myself, or some combination.

I’ve found that it’s sometimes amazing and sometimes wastes a lot of my time. A few times it’s really come up with a good insight I hadn’t considered because the conversation has woken up some non-obvious combination. I use ChatGPT, Claude, Perplexity and one or two IDE tools.

Chat GPT seems really good at frequently asked questions.

Like simple python scripts for Home Assistant it just nails first go.

Thats because GPT products are in a different (much worse) universe to Anthropics Sonnet/Opus, they are truly phenomenal.

Give Anthropic a shot (its even better via the API console.anthropic.com/workbench).

OpenAI is yesterdays news.

Gpt seems to have gotten worse, Claude is the new hotness.

But, I agree with your sentiment that asking it to do stuff like that often doesn’t work. I’ve found that what it _can_ do is stuff like “here’s a Model object, write a query to fetch it with the schema I told you about ages ago”. It might not give perfect results, but I know how to write that query and it’s faster to edit Claude’s output than it is to write it from scratch.

Essentially to me it feels like almost all LLM's are either mid -> terrible if it's any system programming. Especially I've not had luck with anything outside of web programming and moreso anything NOT javascript / python. (jeez I wrote this sentence terribly)

My experience with CoPilot has been similar. I don't think it's given me a single piece of code that just worked. It always took several back and forth of me telling the "AI" that I need it to do X instead of Y (which was included in the original instructions but ignored).

It seems to work best if I start with something very simple, and then layer on instructions ("now make it do X").

Where I have found it saves me time is in having to look up syntax or "gotchas" which I would otherwise search StackOverflow for. But as far as "writing code" -- it still feels a long way from that.

I've almost gotten GPT-4o to do some accurate regex. Probably 75% of the time it's close enough that I can tweak it and make it work.

Do you find that you accept GitHub copilits line completions like 90% of the time?

what did it do when you told it all of those things? was it able to fix the problems when you pointed them out? did you give it one prompt and expect perfect code out on the first try? is that how you code? all your code complies and runs flawlessly first try? I'm jealous. it usually takes me a bunch of passes before I get things right.

here's a chat for a uc and LCD chip that I picked at random (and got the name wrong for) (and didn't want raspberry pi code for so it stopped it short on that response)

https://chatgpt.com/share/2004ac32-b08b-43d7-b762-91543d656a...

one trick is to let it come up with what it wants (lots of functions, no texture), then run the code, give it the errors until that's fixed. then ask it to inline them, then add the texture, etc.

I have a similar experience, but I still use LLMs, just a bit differently. I pretty much never ask it to generate complex code. I also rarely ask for definitions or facts, cause of the tendency to generate garbage answers. What I use it for is usually tedious stuff that is easy to do, but would take me more time to type, rather than ask the LLM.

For example:

* I need a simple bash script for file manipulation or some simple tasks like setting up a project (example: download a secret from AWS SSM, check if an executable exist, if it doesn't write instructions on how to install it on most popular systems etc)

* I need a simple HTTP API, nothing fancy, maybe some simple database usage, maybe running some commands, simple error handling

* I need a YAML file for Kubernetes. I say what I need and usually, it gets most of it right

* I want an Ansible task for some simple thing. Ansible is quite verbose, so it's often saving me time

* I have a Kubernetes YAML file, but I want to manage it in terraform - I'll then ask to convert YAML to a terraform entry (and in general converting between formats is nice, cause even if you have only a piece of what you want to convert, LLMs will most of the time get it right)

* Surprisingly, it often gets openssl and ffmpeg commands right - something I always have to google anyway, especially openssl certificates generation or manipulation

* Giving it a function I wrote and writing test samples after providing a list of what it should test (and asking if it can come up with more, but sadly it rarely does generate anything useful on top of what I suggest)

I look at this contrast as what I call the difference between a "programmer" and a "software engineer". These jobs are really two different universes in practice, so you're not wrong.

I saw an LLM demo at one point where it was asked to write FFT and add unit tests for it which really drove this point home for me.

A programmer is a nicer term for a code monkey. You ask them to write FFT and they'll code it. All problems can be solved with mode code. They can edit code, but on the whole it's more just to add more code. LLMs are actually pretty good at this job, in my experience. And this job is important, not all tasks can be engineered thoroughly. But this job has its scaling limits.

A software engineer is not about coding per se, it's about designing software. It's all about designing the right code, not more code. Work smarter, not harder, for scale. You ask them to write FFT and they'll find a way to depend on it from a common implementation so they don't have to maintain an independent implementation. I've personally found LLMs very bad at this type of work, the same way you and others relying to you describe it. (Ok, maybe FFT is overly simple, I'm sure an LLM can import that for you. But you get the idea.) LLMs have statistical confidence, not intellectual confidence. But software engineering generally works with code too complex for pure statistical confidence.

No offense to the LLM fans here, but I strongly suspect most of them are closer to the programmer category of work. An important job, but one more easily automated away by LLMs (or better software engineering long-term). And we can see this by how a lot of programming has been outsourced for decades to cheap labor in third-world countries: it's a simpler type of job. That plus the people biased because their jobs and egos depend on LLMs succeeding.

I feel like I'm living in a different universe sometimes. The consensus on HN seems to be that you can be pretty productive with LLMs as coding assistants, but every time I try I find it borderline impossible to get functional code even for pretty straightforward prompts.

Same, it can't even fix an xcode memory leak bug in a simple app. It will keep trying and breaking it non-stop. Garbage

I'd go one step further and suggest that one of the few things LLMs are good at is acting as astroturfing agents for themselves. I must be crazy to not be completely changing my workflow and job title with [THE_GOOD_ONE] and not that other one, and wow so many other people vocally feel the same way on every other forum that's extremely easy to manipulate.

Fwiw, I've had some helpful successful prompts here and there, and in some very narrow scopes I'll get something usable, like parsing JSON or scaffolding some test cases, which is real saved time, but I stopped thinking about these tools long ago.

To get real value out of something like your example, I'd be using it as a back and forth to help me understand how some concepts work or write example questions I can drill on my own, but nothing where precision matters

Yes me too! I don't have any stake for finsing LLMs unhelpful, and would love to have a tool to make me more productive.

Would be really interesting if anyone had blog posts on their actual workflow with LLMs, in case there's something I'm doing different.

There’s a lot of people on here with a financial interest in AI doing well. So they hype it continuously.

Chat gpt has helped me with some complex sql queries. I had to correct it a couple of times but in the end it worked.

Try deepseek coder

It has helped, at the cost of making me more dependent on the assistants

Context is the most challenging bit. FWIW, the codebases I'm working on are still small enough to where I rarely need to include more than 12 files into context. And I find as I make the context bigger beyond that, results degrade significantly.

So I don't know how this would go in a much larger codebase.

What floored him was simply how much of my programming I was doing with an LLM / how little I write line-by-line (vs edit line-by-line).

If you're really curious, I recorded some work for a friend. The first video has terrible audio, unfortunately. This second one I think gives a very realistic demonstration – you'll see the model struggle a bit at the beginning:

https://www.loom.com/share/20d967be827141578c64074735eb84a8

I think that Greptile is on the right track. I made a repo containing the c# source code for the godot game engine, and it's "how to do X", where X is some obscure technical feature (like how to create a collision query using the godot internal physics api) is much better than all the other ai solutions which use general training data.

However there are some very frustrating limitations to greptle, so severe that I basically only use it to ask implementation questions on existing codebases, not for anything like general R&D: 1) answers are limited to about 150 lines. 2) it doesn't re-analyze a repo after you link it in a conversation (you need to start a new conversation, and re-link the repo, then wait 20+ min for it to parse your code) 3) it is very slow (maybe 30 seconds to answer a question) 4) there's no prompt engineering

I think it's a bit strange that no other ai solution lets you ask questions about existing codebases. I hope that will be more widespread soon.

What exactly floored your colleague at Microsoft?

Speaking of understand context… They floored him not other way round

I've been using (GitHub) Copilot and ChatGPT since they've been widely available. I started using ChatGPT for coding after 4 came out.

I was an early advocate for Copilot but, honestly, nowadays I really don't find it that useful, compared to GPT-4o via ChatGPT.

ChatGPT not being directly integrated into my editor turns out to be an advantage. The problem with Copilot is it gets in the way. It's too easy to unintentionally insert a line or block completion that isn't what you want, or is out and out garbage, and it's constantly shoving up suggestions as I type, which can be distracting. It's particularly irritating when I'm trying to read or understand a piece of code, or maybe do a refactor, and I leave my caret in one position for half a second too long, and suddenly it's ghost-inserted a block of code as a suggestion that's moved half of what I'm reading down the screen and now I have to find my place again.

Whereas, with ChatGPT being separate, it operates at a much less intrusive cadence, and only responds when I ask it too, which turns out to be much more useful and productive.

I'm seriously considering binning of my Copilot subscription as a result.

It's useful for analyzing data, but only if it can be verified. We use it (higher education) to glance at data trends that may need further exploration. So it's a fancy pivot table, I guess.

Most software vendors are selling their version of AI as hallucination free though. So that's terrifying.

Software engineering doesn't require accuracy?

How does that mandate work? Do they just check if it’s turned on? How many suggestions get accepted?

I think my general perception is that AI is a great assistant for some occupations like software engineering

I think that's why I like to compare the current state of AI to the state of the CPU industry maybe around the 286-486 era going towards the Pentium.

Hmm, I've definitely always used paid copilot models.

Perhaps it's my language of choice (Elixir)? Claude absolutely nails it, rarely gives me code with compilation errors, seems to know and leverage the standard library very well, idiomatic. Not the same with GPTs.

Copilot user here, never tried Cursor yet, competition is always good.

did a quick check, it's $20/month, and it has a vim plugin: https://github.com/pasky/claude.vim

going to give it a spin

I have copilot too; it's good for edits, but one shot complete function generating, I don't find it very good at (while sonnet is imho).

Apart from autocomplete, I got zero utility from Copilot, whereas I'm doing most coding via prompts in Cursor.

Maybe GPT4o changed things.

They help me most when using a framework, API, or language I'm not super familiar with. Beats stack overflow for that stuff.

But it's weird to me seeing people talk about these changing their jobs so much. Maybe I'm holding it wrong but I'm almost always bottlenecked by "big picture" challenges and less on the act of actually typing the code.

I am not a software engineer and these tools allow me to make a giant mess of an app in a weekend that kind of does what I want but I only get that weekend. Once I come back to it after any length of time, since I have no idea what I am doing or what is going on it is impossible to update or add to the app without breaking it.

Now I have all these new ideas but I am back to square one because it just seems easier to start over.

I look forward to more powerful models in the future but I do wonder if it will just mean I can get slightly farther and make an even larger mess of an app in a weekend that I have no way to add to or update without breaking.

The main utility seems like it would be for content creation to pretend I made an app with all these great features as a non-software engineer but conveniently leave out the part about it being impossible to update.

I will admit, if I need to do some one off task and write a quick python script to do something I will likely go to Claude or something and write it. I am talking 20-40 lines. I think it's fine for that, it doesn't need a ton of context, it's easy to test, easy to look at and understand, etc.

But outside of that, beyond needing to remember a certain syntax, I have found that any time I tried to use it for anything more complex I am finding myself spending more time going back and forth trying to get code that works than I would have if I had just done it myself in the first place.

If the code works, it just isn't maintainable code if you ask it to do too much. It will just remove entire functionality.

I have seen a situation of someone submitting a PR, very clearly copying a method and sticking it in AI and saying "improve this". It made changes for no good reason and when you ask the person that submitted the PR why they made the change we of course got no answer. (these were not just Linter changes)

Thats concerning, pushing code up that you can't even explain why you did something?

Like you said with the hard work, sure it can churn out code. But you need to have a complete clear picture of what that code needs to look like before you start generating or you will not like the end result.

I think the non obvious benefit is using LLMs nudge you into putting your thoughts in narrative form and training that ability, something that someone with more experience does subconsciously.

As I mentioned in a sibling comment, I now "pair program" all day. Instead of being the driver and navigator all day, I can mostly sit "one layer up" in the navigator seat.

BigCos won't let their employees use these tools to write code because you can't let code leak off prem.

Ridiculous blanket statement. A bunch of places use external LLMs.

It helps me stay in flow by keeping me one layer up.

In pair programming, it's ideal to have a driver (hands on keyboard) and a navigator (planning, direction).

Claude can act as the driver most of the time so I can stay at the navigator level.

This is so helpful, as it's easy as programmers to get sucked into implementation details or low-level minutiae that's just not important.

Not a fan of Copilot - having a chat bot separate from your coding environment feels much cleaner.

I said leveraging, not hawking!

I am highly skeptical that a competent coder sees a 2x boost.

2x loc!

They're a 20x coder now.

lol this made me chuckle

What does it mean to be a skeptic here? Have you tried ChatGPT? Copilot?

I’m not the OP and I wouldn’t say that AI has doubled my productivity, but the latest Claude models in particular have made me less of a skeptic than I was a few months ago.

I’m an experienced backend dev who’s been working on some Vue frontend projects, and it’s significantly accelerated my ability to learn the complexities of e.g. Vue’s reactivity model. I can ask a complex question that involves several niche concepts and get a response that correctly synthesizes those concepts. I spent an hour the other night trying to understand a bug in a component to no avail; once I understood the problem well enough to explain it in a few sentences, Claude diagnosed the issue and explained it with more clarity than the documentation and various stack overflow answers.

My default is no longer to assume that the model has a coin flip’s chance of producing bs. I still verify and treat answers with a certain degree of skepticism, but I now reach for it as my first tool rather than a last resort or a gimmick.

I've tried various AI coding solutions and have found at best a mild boost but not the amazing multipliers I hear about online.

Copilot gives you some autofill that sometimes can be helpful but often not that helpful. I think the best it did for me was helping with something repetitive where I was editing a big list of things in the same way (like adding an ID to every tag in a list) and it helped take over and finish the task with a little less manual clicking.

ChatGPT has helped with small code snippets like writing a regular expression. I never got 100% regex mastery, usually I would have to look up a couple things to write one but GPT can shortcut that process. I get a little paranoid about AI provided code not actually working so I end up writing a large number of tests to check it, which could be a good thing but can feel tedious.

I'm also curious how other people are leveraging them to get more than I am. I honestly don't try too hard. At one point I did try really hard to get AI to do more heavy code lifting but was disappointed with my results so I stopped... but maybe things have improved a bit since then.

Ok I jumped on copilot when it first came out so I have been using it for a long time.

Since I have been using it so long, I have a really good intuition of what it is “thinking” in every scenario and a pretty good idea of what it can do for me. So that helps me get more use out of it.

So for example one of the projects I’m doing now is a flutter project - my first one. So I don’t remember all the widgets. But I just write a comment:

// this widget does XYZ

And it will write something that is in the right direction.

The other thing it knows super well is like rote code, and for context, it reads the whole file. So like Dart, for example is awful at json. So you have to write “toMap” for each freaking class where you do key values to generate a map which can be turned into json. Same goes for fromMap. So annoying.

But with copilot? You just write “toMap” and it reads all your properties and suggests a near perfect implementation. So much time saved!

I don't use AI at work at all.

I pay for Leetcode, which usually gives editorial examples in Python and Java and such, and paste it into ChatGPT and say "translate this to a language I am more familiar with" (actually I have other programs that have been doing this for some language to language conversions for years, without AI). Then I say "make it more compact". Then again "make it more compact". So soon I have a big O(n) time, big O(1) space solution to Leetcode question #2718 or whatever in a language I am familiar with. Actually sometimes it becomes too compact and unreadable, and I back it up a little.

Sometimes it hallucinates, but it has been helpful. In the past I had problems with it, but not recently.

They certainly are not providing these services at less than electricity costs. So if you are spending $20 a month on it, they are spending less than that on electricity. It's very low compared to any person in the first world's energy spend.

isn't the energy consumption of travel, driving, and shipping food to you pretty catastrophic? Do you consider the issue of energy consumption so abstracted you don't worry about it? Do you do anything to offset your increased carbon emissions?

You think silicon valley types care?

I’m working on getting a place with solar panels. I think that’s important for sustainability, plus who wants to have to be connected to the grid anyway?

I don’t think you’ve used LLMs enough. They are revolutionary, every day. As a coder I’m several times more productive than I was before, especially when trying to learn some new library or language.

even if the datacenter increase was a waste because the potential of LLMs never pan out relative to the massive investment, i think the flood of gpu and compute will definitely enable whatever could benefit from it. The next better AI application will be very thankful that gpu compute is abundant.

"no no, it's not that the technology has reached it's current limits and these companies are bleeding money, they're just withholding their newest releases not to spook the normies!"

Yes, I do think it’s plausible. You’re talking Microsoft and Google. Two companies with extremely close ties to the US government. And Amazon is a major investor in Anthropic. It doesn’t take a conspiracy if the folks with majority stake in these companies are all friends and handshake agree over a steak dinner just off Capitol Hill. We live in a world where price fixing is a regular occurrence, and this is not so different.

I think we will see a very nice boost in capability within 6 months of the election. I don’t personally believe all the apocalyptic AGI predictions, but I do think that AI will continue to feed a nice growth curve in IT investment and productivity growth, similar to the last few decades of IT investment.

While it’s true that political events can influence market dynamics

They are definitely working on that, but it needs to be the right kind of knee-jerk legislation, something that gives them regulatory capture. They can’t afford to lose control of the narrative and end up regulated to death.

Do you provide code examples? In my experience the more specific you get with your problem the more specific are the provided solutions (probably a "natural" occurence in LLMs). Hallucianted APIs are sometimes a problem for me, but then I just specify which API to use.

Has to be something pretty generic. I'm trying to write a little C program that talks to an LCD display via the SPI bus--something I did before a few times, but not with this particular display and MCU. There is no LLM that can even begin to reason this out since they've been mostly trained on web dev content.

LLMs aren't good when you drift out of the training distribution. You want to be hitting the meat in the middle and leveraging the LLM to blast through it quickly.

That means LLMs are great for scaffolding, prototypes, the v0.1 of new code especially when it's very ordinary logic but using a language or library you're not 100% up to speed on.

One project I was on recently was translation: converting a JS library into Kotlin. In-editor AI code completion made this really quick: I pasted a snippet of JS for translation in a comment, and the AI completed the Kotlin version. It was frequently not quite right, but it was way faster than without. In particular, when there was repeated blocks of code for different cases that different only slightly, once I got the first block correct, the LLM picked up on the pattern in-context and applied it correctly for the remaining blocks. Even when it's wrong, if it has an opportunity to learn locally, it can do so.

I assume it knows the big stuff like the PyTorch API/major JS and React libs then just paste the docs or even impl code for any libs it needs to know beyond that.

I found that ChatGPT needs to be rained in with the prompts, and then it does a very impressive job. E.g. you can create a function prototype (with input and output expectations) and in the body tell the logic you are thinking about in meta-code. Then tell it to write the actual code. It's also good if you want to immerse yourself into a new programming language and outline what kind of program you want, and expect the results to be different from what you throught, but insightful.

Now if you throw larger context or more obscure interface expectations at it, it'll start to discard code and hallucinate.

It's nice that everybody is trying to help with the way you're prompting but just use Bing Copilot or Phind for this, not ChatGPT

It'll generate a bunch of queries to Google (well, "to Bing" I guess in that case) based on your question, read the results for you, base its answer on the results and provide you with sources that you can check if it used anything from that webpage.

I only use ChatGPT for documentation when I have no idea where I'm going at all, and I need a lay of the land on best practices and the way forward.

For specifics, Bing Copilot. Essentially a true semantic web search

Phrase the question as a backend problem rather than as a front end problem. Same for lang your asking in. Less JS/TS more Python

Every question?

If it’s returning results that were obvious, why were you asking the question?

And I don’t believe that the other ~50% were wrong.

The only thing these engines do, for me perhaps, is save some search time so I don't have to click around on a lot of sites to scroll for some semblance of an answer to verify.

This sounds like a valuable service.

The only thing these engines do, for me perhaps, is save some search time.

This. Saving time(or money if you see them as the same) is the whole point actually.

Intelligence is supposed to be in the context of a shared Goal or beliefs. In your case and case of most humans time and money is the context.

Are ant(insect) networks intelligent? Possibly, They do help millions of them communicate quickly. But ants don't have a brain.

Are beings that make decisions without conscious choices intelligent? Possibly, if they could escape death by amazing ability at any instant. But these beings don't have a frontal cortex that can make decisions by rational inquiry.

When I look at the kinds of AI projects I have visibility into, there's a parallel where the public are expecting a centralized, all knowing, general purpose AI

I don't think this is entirely fair to "the public". Media was stuffed with AI company CEOs claiming that AGI was just around the corner. Nvidia, OpenAI and Musk, Zuckerberg, and others were positively starry eyed at how, soon, we'd all be just a GPU matmul away from intelligence. "The public" has seen these eye watering amounts of money shifting around, and they imply that it must mean something.

The entire system has been acting as if GenAI was right around the corner.

Most of it is mediocre creativity and a low willingness to adapt their prompting patterns for an optimal ratio of effort to output quality. Most people who don't understand yet expect LLMs to read their minds when they would better of orienting themselves as student who have zero experiencing developing the arsenal strategies that elicit productivity gains.

They haven't developed any intuition into which kinds of questions are worth prompting, which kinds of context are effective, or even which kinds of limitations apply to which models.

It's more like an ouija board, it only works if you believe and phrase your input as if you expect a competent answer

I know great programmers who approached it with skepticism and the bot responds be being worthy of skepticism

I use gpt4o and claude 3.5 while coding every day. The rumors on twitter and reddit are accurate.

I constantly run into incorrect answers from the LLMs every day. Just recently I asked whether I needed to reverse the bit shift to mask the upper 24 bits in an IP address on a little endian platform and it incorrectly told me no probably because most of the answers on Google appeared to answer no to similarly phrased questions.

Maybe you're using a strawman.

don't believe LLMs will get any better than this without a new major breakthrough, but this is already better than using Google search

I mean .. every single org is invested in that research right now

I had the same problem with using the latest neo4j API and Sonnet 3.5 -- except it wasn't really a problem. I just created a project where I explain that its knowledge on neo4j is outdated and to instead use an API reference and changelog that I add to the project.

It's not magic, you need to think what you would need in a similar situation and then provide that to the LLM. It definitely does suffer from severe overconfidence, though -- if you were to think of it as a person.

Also, you need to break up your project into manageable portions and provide context to the other portions (without providing the entirety of them) for it to effectively work on the portion you want to work on.

I'm concerned about the "turning the brain off" effect too. When we put backup cameras on cars it means that everyone can parallel park perfectly. Now take away the backup cameras and see what happens to our parallel parking.

Your self-driving car is so unreliable you have to manually disable it 10% of the time to stay safe?

I don't think so, I think it's more about openness. I've noticed older software engineers tend to be more anti-LLM and quick to dismiss.

Wow, a highly ageist comment, if there ever was one.

Congrats. Trying for a job and looking for less competition, maybe?

Notice that your statement is as full of assumptions as mine. That was intentional on my part, to bring out my point.

We do grok it. We also think they've reached the apex of the technology and have seen no evidence they'll close the hallucination gap.

I think most people outside of tech have barely even touched it.

Obviously there are some savy users across all age groups and occupations. But from what Ive see its just not part of most people’s workflow.

Go back ten+ years, replace AI with cloud and it was the same. I saw ‘no cloud’ policies everywhere. But most of the anti-cloud people have since retired, so even the most hidebound of organisations are adopting it. It will probably take another round of retirements for AI to adopted in the more conservative environments.

I was getting a lot of mixed messaging at my job for 6-12 months.

On the one hand, we got an email from high up saying not to use Copilot, or other such tools, as they were trying to figure out the licensing. But at the same time, we had the CIO getting up in front of the company every other month talking about nothing but GenAI, and how if we weren’t using it we were stupid (not in those exact words, but that was the general vibe, uncontrolled AI hype).

We were left sitting there saying, “what do you want from us? Do as you say or do as you do?”

Eventually they signed the deal with MS and we got Copilot, which then seemed forced on us. There is even a dashboard out there for it, listing all people using it, their manager, rolling all the way up to the CEO. It tells the percentage of reports from each manager using it, and how often suggestions are accepted. It seems like the kind of dashboard someone would make if they were planning to give out bonuses based on Copilot adoption.

I’ve gotten regular surveys about it as well, to ask how I was using it. I mostly don’t, due to the implementation in VS Code. I might use it a few times per month at best.

Maybe that would be different if the rollout wasn’t so awkward, or the VS Code extension was more configurable.

I've seen this too and it's so weird. The vast majority are totally clueless on it.

You absolutely do not need to be getting Microsoft copilot licenses:

Open weight and open source models can be hosted on your own hardware nowadays, and its incredibly easy.

https://dublog.net/blog/open-weight-copilots/

You can even use something like RayServe + VLLM to host on a big chonky machine for a small team if you're concerned about data exfiltration.

I think we will see something similar to the last boom with Neural Net chatbots back in 2014(?).

Public discource will simmer down, as current language models either fizzle out or improve. Some will become background noise as the models gets integrated into search engines or leads to large scale lawsuits.

Unlike the previous neural nets though, those models have an actual tangible use. So you will see them around a lot more.

Then I think we will see another big explosion.

How on earths name do you use an LLM to find information ? I just don’t get it. For current events it out of date and it confidently feeds me shit ?

I might use them occasionally for a rubber ducky but , replacing Google ? Hm

Hard to say they replaced them, when they use Google in their backend...

What a ridiculous claim.

will follow the same growth trajectory as every other tech bug

What you're referring to isn't a bug. It's inherent to the way LLMs work. It can't "go away" in an LLM model because...

understands human language

...they don't. They are prediction machines. They don't "understand" anything.

"understands human language"

I've got some oceanfront property in Wyoming to sell you.

I don't understand how someone could think that ChatGPT or Midjourney aren't going to radically change many, many industries, and frankly to think this just seems like straight up ignorance or laziness. It's not that hard to find real examples of this stuff.

But if you insist...here are two very small examples from my personal experience with AI tools.

1. I work as a technical writer. Recently I needed to add a summary section to the introduction of a large number of articles. So, I copied the article into ChatGPT and told it to summarize the piece into 3-4 bullet points. Were I doing this task a few years ago, I would have read each article and then written the bullet points myself – nothing particularly difficult, but very time-consuming to do for dozens of articles. Instead, I used ChatGPT and saved myself hours upon hours of mundane work.

This is a quite minor and mundane example, but you can (hopefully) see how this will have major effects on any kind of routine text-creation.

2. I am working on a side project which requires the creation of a large number of custom images. I've had this project idea for a few years, but previously couldn't afford to spend $20k hiring an illustrator to make them all. Now with Midjourney, I am able to create essentially unlimited images for $30-100 a month. This new AI tool has quite literally unlocked a new business idea that was previously inaccessible.

I must have missed the memo. Where could I get the free money? "a few years ago" we had a global pandemic. Are you claiming that markets will be very picky compared to that time?

there's probably truth to that, but I find it's useful at a more micro-level. I don't tell the llm to write an architecture or a big piece. It's more like, I have this data in this shape, I want a function that gives data out that shape and it will spit something pretty good and idiomatic. I read it, understand it and implement it. Need to be careful with blind copy-paste, there are sometimes subtle bugs in the code.

It's specially useful when learning new frameworks, languages, etc. To me this is all applicable regardless of domain as the micro-level patterns tend to be variations of things that have been seen. I suspect if you try to load it with a lot of very specific high level domain logic, there are more chances of taking the llm out of its comfort zone.

- Emotional regulation. I suffer from a mostly manageable anxiety disorder but there are times I get overwhelmed. I have an agent setup to focus on principles of Stoicism and its amazing how quickly I can get back on track just by having a short chat with it about how I'm feeling.

- Personalised learning. I wanted to understand LLM's at foundational technical level. Often I'll understand 90% of an explanation but there's a small part that I don't "get". Being able to deliberately target that 10% and be able to slowly increase the complexity of the explanation (starting from explain like I'm 5) is something I can't do with other learning material.

- Investing. I'm a very casual investor. But I keep a running conversation with an agent about my portfolio. Obviously I'm not asking it to tell me what to invest in but just asking questions about what it thinks of my portfolio has taught me about risk balancing techniques I wouldn't have otherwise thought about.

- Personal profile management. Like most of us I have public facing touch points - social media, blog, github, CV etc. I find it helpful to have an agent that just helps me with my thought process around content I might want to create or just what my strategy is around posting. It's not at all about asking the thing to generate content - it's about using it to reflect at a meta level on what I'm thinking and doing - which stimulates my own thinking.

- Language learning - I have a language teaching agent to help me learn a language I'm trying to master. I can converse with it, adapt it to whatever learning style works best for me etc. The voice feature works well with this.

- And just in general - when I have some thinking task I want to do now - like I need to plan a project or set a strategy I'll use an LLM as a thought partner. The context window is large enough to accomodate a lot of history - and it just augments my own mind - gives me better memory, can point out holes in my thinking etc.

__

Edit: actually now that I have written out a response to your question I realise It's not so much offloading tasks in a wholesale way - its more augmenting my own thinking and learning - but this does reduce the burden on me to "think about" a range of things like where to get information or to come up with multiple examples of something or to think through different scenarios.

I can't wait. There are so many pointless or directly insulting AI 'features' around now, and they suck. LinkedIn trying to write my messages for me. Outlook is trying to write my emails. Instagram has a deeply cursed AI button to "make it funnier".

I hope the stock market will give all of these people an atomic wedgie for creating the most pointless garbage to ever pass before human eyes.

Not necessarily. If a call center can improve productivity by 20-30% by driving contacts to self-service (AI chatbots and search) then they will do so regardless of stock market hype.

My clients are like this lately.

Non techies that now are suggesting how I design solutions for them by asking ChatGPT. And they seem to treat me like the stupid one for refusing.

The research never ended. AI money was flooding in, but mostly going directly to Nvidia. If that cash flow turns off there will still be research happening because it was mostly unaffected in the first place.

It is carrying lots of weight.

At this moment that’s the most sophisticated we can be in talking about LLMs.

I will say that the utility of these tools is not being denied. It’s just the struggle to explain the varied experiences.

I can get only as far as analogy, not precise definitions.

For me, LLMs are like the invention of microwave ovens. Very useful.

They aren’t like the discovery of fire.

You confuse causality with correlation, a common mistake.

The threshold for acceptable self-driving is genuine effort from the automated system to avoid accidents as we can't punish it for bad driving. And I want auditable proof of that.

You can have any standard of safety that you want, that's absolutely your choice. Plenty of automated systems that your life depends on that you have never audited and never will. That includes elevators, cars, planes, all kinds of medical gear, etc.

My standard is simpler: whenever the AI is safer than the average human driver, it already saves lives.

I'm pretty sure the amount of people that can draw is less than that.

You probably have some esoteric definition of "people that can draw". Anyone capable of holding a pen/pencil/brush can draw.

And while there are bad communicators, I think lots of people are doing ok on that front

Look at the previous comment. Seems like you overestimated even your own communication skills :)

Text generators can be perfect syntax-wise, but the intent has to come from someone.

I'm hoping that we will achieve the level of AI writing where you can prompt "write me an interesting sci-fi book", and it does. Not much intent here, much artificial intelligence required.

indeed, but prices have been falling dramatically in the last 2 years, and I think the latest smallest gpt4o-mini is already in the "mostly don't care" ballpark.

I am happy if we somehow get this down even more orders of magnitude, to the point where I can `npm install llm` and have it run alongside my normal code on a $5 VPS, without a GPU, and still handling (resonable) requests/minute with it. Yes I know we are _very far_ from that still, but one can dream.