But as to the hype, we are in a brief pause before the election where no company wants to release anything that would hit the news cycle in a bad way and cause knee-jerk legislation. Are there new architectures and capabilities waiting? Likely some. Sora showed state of the art video generation, OpenAI has demoed an impressive voice mode, and Anthropic has teased that Opus 3.5 will be even more capable. OpenAI also clearly has some gas in the tank as they have focused on releasing small models such as GPT-4o and 4o mini. And many have been musing about agents and methods to improve system 2 like reasoning.

So while there’s a soft moratorium on showing scary new capability there is still evidence of progress being made behind-the-scenes. But what will a state of the art model look like when all of these techniques have been scaled up on brand new exascale data centers?

It might not be AGI, but I think it will at least be enough for the next hype Investment bubble.

Its done, you can’t make another LLM, all knowledge from here on out is corrupted by them, you can never deliver an epistemic “update.” GPT will become a relic of the 21st century, like a magazine from the 1950s.

But many jobs are not like that. Imagine an AI nurse giving bad health advice on phone. Somebody might die. Or AI salesman making promises that are against company policy? Company is likely to be held legally liable, and may lose significant money.

Due to legal reasons, my company couldn't enable full LLM generative capabilities on chatbot we use, because we would be legally responsible for anything it generates. Instead, LLM is simply used to determine which of the pre-determined answers may fit the query the best, which it indeed does well when more traditional technologies fail. But that's not revolutionary, just an improvement. I suspect there are many barriers like that, which hinder its usage in many fields, even if it could work most of the time.

So, nearly all use cases I can think of now will still require a human in the loop, simply because of the unreliability. That way it can be a productivity booster, but not a replacement.

This problem is not unique to AI and you see this problem with human medical professionals. Regularly people are misdiagnosed or aren’t diagnosed at all. At least with AI you could compare the results of different models pretty instantly and get confirmation. An AI Dr also wouldn’t miss information on a chart like a human can.

> So, nearly all use cases I can think of now will still require a human in the loop, simply because of the unreliability. That way it can be a productivity booster, but not a replacement.

This is exactly what your parent said, but yet you replied seemingly disagreeing. AI tools are here to stay and they do increase productivity. Be it coding, writing papers, strategizing. Those that continue to think of AI as not useful will be left behind.

Human in the loop can add reliability, but the most common use cases I’m seeing with AI are helping people see the errors they are making/their lack of sufficient effort to solve the problem.

Sure, you have your John Carmaks' or Antirezs' of the industry who are 10x programmers and also successful founders but those guys are 1 in a million.

But your usual 10x engineer you'll meet is the guy who knows the ins and outs of all running systems at work giving him the ability to debug and solve issues 10x quicker than the rest, knowledge which is highly specific to the products of that company and is often non-transferrable and also not useful at entrepreneurship.

Becoming the 10x engineer at a company usually means pigeonholing yourself in the deep inner workings of the products, which may or may not be useful later. If that stack is highly custom or proprietary it might work in your favor making you the 10x guy virtually unfireable being able to set your own demands since only you can solve the issues, or might backfire against you at a round of layoffs as your knowledge of that specific niche has little demand elsewhere.

You're talking about the 100x engineer now. The 10x engineer is the normal engineer you are probably accustomed to working with. When you encounter a 1x engineer, you will be shocked at how slow and unproductive they are.

Current AI is still at the state of recommending people jump off the golden gate bridge if they feel sad or telling them to change their blinker fluid.

With AI I can do the same in 15 seconds. We’re talking a 120x increase in productivity not a 3x improvement.

Which means that after a brief honeymoon period, the effect of AI will be to heavily reduce labor costs, rather than push for turbocharged "productivity" with greatly diminishing returns.

How much is "enough"? Neither myself nor my coworkers have found LLMs to be all that useful in our work. Almost everybody has stopped bothering with them these days.

Also you can look into cursor.

There are actually quite a few tools.

I have my own agent framework in progress which has many plugins with different commands. Including reading directories, tree, read and write files, run commands, read spreadsheets. So I can tell it to read all the Python in a module directory, run a test script and compare the output to a spreadsheet tab. Then ask it to come up with ideas for making the Python code match the spreadsheet better, and have it update the code and rerun the tests iteratively until its satisfied.

If I am honest about that particular process last night, I am going to have to go over the spreadsheet to some degree manually today, because neither gpt-4o nor Claude 3.5 Sonnet was able to get the numbers to match exactly.

It's a somewhat complicated spreadsheet which I don't know anything about the domain and am just grudgingly learning. I think the agent got me 95% of the way through the task.

I have copilot suggestions bound to an easy hotkey to turn them on or off. If I’m writing code that’s entirely new to the code base, I toggle the suggestions off, they’ll be mostly useless. If I’m following a well established pattern, even if it’s a complicated one, I turn them on, they’ll be mostly good. When writing tests in c#, I reflexively give the test a good name and write a tiny bit of the setup, then copilot will usually be pretty good about the rest. I toggle it multiple times an hour, it’s about knowing when it’ll be good, and when not.

Beyond that, I get more value from interacting with the llm by chat. It’s important to have preconfigured personas, and it took me a good 500 words and some trial and error to set those up and get their interaction styles where I need them to be. There’s the “.net runtime expert” the “infrastructure and release mentor”, and on like that. As soon as I feel the least bit stuck or unsure I consult with one of them, possibly in voice mode while going for a little walk. It’s like having the right colleague always available to talk something through, and I now rarely find myself spinning my wheels, bike-shedding, or what have you.

Its output depends on your input.

E.g. say you have an API swagger documentation and you want to generate a Typescript type definition using that data, you just copy paste the docs into a comment above the type, and copilot auto fills your Typescript type definition even adding ? for properties which are not required.

If you define clearly the goal of a function in a JSDoc comment, you can implement very complex functions. E.g. you define it in steps, and in the function line out each step. This also helps your own thinking. With GPT 4o you can even draw diagrams in e.g. excalidraw or take screenshots of the issues in your UI to complement your question relating to that code.

this really rings true for me. especially as a junior, I always thought one of my best skills was that I was good at Googling. I was able to come up with good queries and find some page that would help. Sometimes, a search would be simple enough that you could just grab a line of code right off the page, but most of the time (especially with StackOverflow) the best approach was to read through a few different sources and pick and choose what was useful to the situation, synthesizing a solution. Depending on how complicated the problem was, that process might have occurred in a single step or in multiple iterations.

So I've found LLMs to be a handy tool for making that process quicker. It's rare that the LLM will write the exact code I need - though of course some queries are simple enough to make that possible. But I can sort of prime the conversation in the right direction and get into a state where I can get useful answers to questions. I don't have any particular knowledge on AI that helps me do that, just a kind of general intuition for how to phrase questions and follow-ups to get output that's helpful.

I still have to be the filter - the LLM is happy to bullshit you - but that's not really a sea change from trying to Google around to figure out a problem. LLMs seem like an overall upgrade to that specific process of engineering to me, and that's a pretty useful tool!

I'm using both Kagi & LLM; depending on my need, I'll prefer one or the other.

Maybe I can access the same result with a LLM, but all the conversation/guidance required is time-consuming than just refining a search query and browsing through the first three results.

After all the answer is rarely exactly available somewhere. Reading people's questions/replies will provide a clues to find the actual answer I was looking for.

I have yet been able to achieve this result through a LLM.

Yeah but there are other ways to think through problems, like asking other people what they think, which you can evaluate based on who they are and what they know. GPT is like getting advice from a cross-section of everyone in the world (and you don’t even know which one), which may be helpful depending on the question and the “people” answering it, but it may also be extroadinarily unhelpful, especially for very specialized tasks (and specialized tasks are where the profit is).

Like most people, I have knowledge of things very specific I know that less than a 100 people in the world know better than me, but thousands or even millions more have some poorly concieved general idea about it.

If you asked GPT to give you an answer to a question it would bias those millions, the statistically greater quantative solution, to the qualitative one. But, maybe, GPT only has a few really good indexes in its training data that it uses for its response, and then its extremely helpful because its like accidentally landing on a stackoverflow response by some crazy genius who reads all day, lives out of a van in the woods, and uses public library computers to answer queries in his spare time. But that’s sheer luck, and no more so than a regular search will get you.

The text interface can also be useful for skipping across complex documentation and/or learning. Example: you can ask GPT-4 to "decode 0xdf 0xf8 0x44 0xd0 (thumb 2 assembly for arm cortex-m)" => this will tell you what instruction is encoded, what it does and even how to cajole your toolchain into providing that same information.

If you are an experienced developer already, with a clear goal and understanding, LLMs tend to be less helpful in my experience (the same way that a mentor you could ask random bullshit would be more useful to a junior than a senior dev)

or it will hallucinate something that's completely wrong but you won't notice it

If you can beat copilot in a typing race then you’re probably well within your comfort zone. It works best when working on things that you’re less confident at - typing speed doesn’t matter when you have to stop to think.

There is an old documentary of the final days of typesetters for newspapers. These were the (very skilled) people who rapidly put each individual carved steel character block into the printing frame in order print thousands of page copies. Many were incredulous that a machine could ever replicate their work.

I don't think programmers are going to go away, but I do think those juicy salaries and compensation packages will.

Some will undoubtably transition to broader based business consultancy services. For those unable or unwilling to do so the future is bleak.

So same programmer with the same 8h of workday will be able to output more value.

I think that's inevitable with or without LLMs in the mix. I also think the industry as a whole will be better for it.

The documentary he's referring to is likely "Farewell, Etaoin Shrdlu," released in 1980. It chronicles the last day of hot metal typesetting at The New York Times before they transitioned to newer technology. The title comes from the nonsense phrase "etaoin shrdlu," which appeared frequently in Linotype machine errors due to the way the keys were arranged. The documentary provides a fascinating look at the end of an era in newspaper production.

The knee-jerk legislation has mostly been caused by Altman's statements though. So I wouldn't call it knee-jerk, but an attempt by OpenAI to get a legally granted monopoly.

Music generation is one of the easiest ways to "spook the normies" since most people are completely unaware of the current SOTA. Anyone with a good ear and access to these tools can create a listenable song that sounds like it's been professionally produced. Anyone with a good ear and competence with a DAW and these tools can produce a high quality song. Someone who is already a professional can create incredible results in a fraction of the time it would normally take with zero budget.

One of the main limitations of generative AI at the moment is the interface, Udio's could certainly be improved but I think they have something good here with the extend feature allowing you to steer the creation. Developing the key UI features that allow you to control the inputs to generative models is an area where huge advancements can be made that can dramatically improve the quality of the generated output. We've only just scratched the surface here and even if the technology has reached its current limits, which I strongly believe it hasn't since there are a lot of things that have been shown to work but haven't been productized yet, we could still see steady month over month improvements based on better tooling built around them alone.

Text generation has gone from markov chain babblers to indistinguishable from human written.

Image generation has gone from acid trip uncanny valley to photorealistic.

Audio generation has gone from 1930's AM radio quality to crystal clear.

Video generation is currently in fugue dream state but is rapidly improving.

3D is early stages.

???? is next but I'm guessing it'll be things like CAD STL models, electronic circuits, and other physics based modelling outputs.

The ride's not over yet.

- someone claims that Gen AI is overhyped

- someone responds with a Gen AI-enabled service that is

    1) really impressive

    2) is currently offered pretty much for free

    3) doesn't have that many tangible benefits.

It's just hard to imagine how you can turn this technology into a valuable product. Which isn't to say it's impossible: gen-AI is definitely quite capable and people are learning how to integrate it into products that can turn a profit. But @futureshock's point was that it is the AI investment bubble that's losing hype, and I think that's inevitable: people are realizing there are many issues with a technology that is super impressive but hard to productize.

Do you have an example of any song that gained any traction among human audience? Not a Billboard hit, just something that people outside the techbubble accepted as a good song?

Edit:

There's obviously still skill involved in creating a good song, it's not like you can just click one button and get a perfect hit. I outlined the simplest process in my first comment and specifically said you could create a "listenable" song, it's not going to be great but it probably rivals some of the slop you often hear on the radio. If you're a skilled music producer you can absolutely create something good especially now with access to the stemmed components of the songs. It's going to be a half manual process where you first generate enough to capture the feeling of the song and then download and make edits or add samples, upload and extend or remix and repeat.

If you're looking for links and don't care to peruse the trending section they have several samples on the announcement page https://www.udio.com/blog/introducing-v1-5

The really good stuff probably will not be marked as med with AI - and probably will also go via a DAW and proper mastering.

I have some audio engineering skills, dabbled in songwriting, guitar and singng when I was younger, but actually never completed a full song. So it is quite transformative from that perspective!

AI generated music is more of a threat to the current state of the recording industry. If I can create exactly the album or playlist that I want using AI then why should I pay a record label for a recording that they're going to take 90% of the retail price from? The playlist I listen to while I'm working out or driving is not competing with live band performances, I'm still going to go to a show if there's a band playing that I like.

I disagree that it's a threat to the recording industry. They aren't going to be able to sell AI music, but nobody else is either, because anyone who wants AI music can just create it themselves. Record labels will continue to sell and promote real artists, because that's how they can make money. That's what people will pay for.

There are a lot more fuzzy edges too, you can use AI tools to "autotune" your own voice into a completely different one. You can tap out a quick melody on a keyboard and then extend, embellish and transform it into a full song. You could even do the full song yourself first and then remix it using AI.

The point I agree on would be that one-click hits are going to be few and far between for a while at least. If no effort is put into selecting the best then it's really just random chance. I'd be willing to bet that there will be an indie smash hit song created by a single person who doesn't perform any of the vocals or instruments within a year though. It'll get no play time on anything controlled by the industry titans but people will be streaming it regardless.

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.

Yes. There is also the Hype of the "End of the Hype Cycle". There is Hype that the Hype is ending.

When really, there is something amazing being released weekly.

People are so desensitized that just because we don't have androids walking the streets or suddenly have Blade Runner like space colonies staffed with robots, that somehow AI is over.

What people, including me, are massively fed up is all the companies (I mean ALL) jumping on AI bandwagon in a beautiful show of how FOMO works and how even CEOs/shareholders are not immune to basic instincts. Literal hammer looking desperately for nails. Very few companies have amazing or potentially amazing products, rest not so much.

I absolutely don't want every effin' thing infused with some AI, since it will be used to 1) monitor my usage or me directly for advertising / credit & insurance scoring purposes, absolutely 0 doubt there; and 2) it may stop working once wifi is down, product is deprecated or company changes its policy (Sonos anyone?). Internet of Things hate v2.0.

I hate this primitive AI fashion wave, negative added value in most cases, 0 in the rest, yet users have to foot the bill. Seeing some minor industry crash due to unfulfilled expectations is just logical in such case.

Business Advice including marketing, reaching out to investors, understanding SAFE notes (follow up questions after watching the Y Combinator videos), customer interview design. All of which, as an engineer, I had never done before.

Create SQL queries for all kinds of business metrics including Monthly/Daily Active users, breakdown of users by country, abusive user detection and more.

Automated unit test creation. Not just the happy path either.

Automated data repository creation, based on a one shot example and MySQL text output describing the tables involved. From this, I have super fast data repositories that use raw SQL to get/write data.

Helping with challenging code problems that would otherwise need hours of searching google or reading the docs.

Database and query optimization.

Code Review. This has caught edge case bugs that normal testing did not detect.

I'm going to try out aider + claude sonnet 3.5 on my codebases. I have heard good things about it and some rave reviews on X/twitter. I watched a video where an engineer had a bug, described it to some tool (which wasn't specified, but I suspect aider), then Claude created a test to reproduce the bug and then fixed the code. The test passed, they then did a manual test and the bug was gone.

I'm glad this has been working for you -- generally any time I actually have a really difficult problem, ChatGPT just makes up the API I wish existed. Then when I bring it up to ChatGPT, it just apologizes and invents new API.

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.

The sweet spot is when you need something and you’re sure it’s possible but just don’t know (or it’s too time consuming) how. E.G. change the css to to X, rewrite this python code in typescript, use the pattern of this code to do Y, etc.

Reminds me of the early days of Google where you had to learn how write a good search query. You learn you need more than a word or two, but don’t write a whole essay, etc.

I think that you have a serious misunderstanding of the capabilities of LLMs - they cannot reason out relationships among documents that easily. They cannot even tell you what they don't know to finish a given task (and I'm not just talking one-shot here, agent frameworks suffer from the same problem).

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

That way I can generate a simple few hundred lines of code app in minutes. There is no way I could type that fast even if I exactly know what characters to write and it's not always the case. Like, oftentimes I know exactly what to do and I know if it's OK when I see the code, but writing it would require me to look into the docs here and there.

- Getting over the blank canvas hurdle, this is great for kick starting a small project and even if the code isn't amazing, it gets my brain to the "start writing code and thinking about algo/data-structures/interesting-problem" rather than being held up at the "Where to begin?" Metaphorically where to place my first stroke, this helps somewhat.

- Sometimes LLM has helped when stuck on issues but this is hit and miss, more specifically it will often show a solution that jogs my brain and gets me there, "oh yeah of course" however I've noticed I'm more in than state when tired and need sleep, so the LLM might let me push a bit longer making up for tired brain. However this is more harmful to be honest without the LLM I go to sleep and then magically like brains do solve 4 hours of issues in 20 minutes after waking up.

So LLM might be helping in ways that actually indicate you should sleep as brain is slooooowwwwing down

- Getting over the blank canvas hurdle, this is great for kick starting a small project and even if the code isn't amazing, it gets my brain to the "start writing code and thinking about algo/data-structures/interesting-problem" rather than being held up at the "Where to begin?" Metaphorically where to place my first stroke, this helps somewhat.

- Sometimes LLM has helped when stuck on issues but this is hit and miss, more specifically it will often show a solution that jogs my brain and gets me there, "oh yeah of course" however I've noticed I'm more in than state when tired and need sleep, so the LLM might let me push a bit longer making up for tired brain. However this is more harmful to be honest without the LLM I go to sleep and then magically like brains do solve 4 hours of issues in 20 minutes after waking up.

So LLM might be helping in ways that actually indicate you should sleep as brain is slooooowwwwing down

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

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, but what it's really going to look like is a graph of oddball AI agents tuned for different optimizations.

One node might be slow and expensive but able to infer intent from a document, but its input is filtered by a fast and cheap one that eliminates uninteresting content, and it could offload work to a domain-specific one that knows everything about URLs, for example. More like the network of small, specialized computers scattered around your car than a central know-it-all computer.

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.

My personal belief is that AIS is not a real thing (in the sense I wrote above) because narrow domain competence is tightly coupled to general domain competence . Even very autistic people that are functional in some domain actually have a staggering range of competences that we tend to ignore because we expect them in humans. I think machines will be similar.

Anyway, AIG or AIS is not round the corner at all. But that doesn't mean that there isn't a lot of value to be had from generative AI in the near future or now. Will this be a small fraction of the value from Web1.0 and Web2.0? Will it be approximately the same? Will it be a multiple? I think that's the question. I think it's clear that assistants for software engineers are somewhat valuable now (evidence: I get value out of them) how valuable? Well, more than stackexchange, less than a good editor. That's still alot, for me. I won't pay for it though...

And this points to the killer issue: there isn't a good way to monetize this. There isn't a good way to monetize the web, so we got adverts (a bad way). What will be the equivalent for LLM's? We just don't know right now. Interestingly there seems to be very little focus on this! Instead folks are studying the second order value. Using this "free thing" we can drive productivity... or quality... increase opportunities... create a new business?

Replace all the instances of GenAI with AGI in my post.

It's an interesting observation that the economics aren't there yet. I think it's generally assumed that if we find something valuable, we can probably figure out how to monetize it. That's not necessarily true though. In the same but opposite vein, it doesn't necessarily need to be useful to stick around. It's possible AI is forever going to be useless (in objective terms, maybe it will make people less efficient) but find a monetization strategy that keeps it around (maybe it makes people feel good).

A ton of the technology economy isn't really based on objective metrics of usefulness. Microsoft isn't the biggest because they're the most useful. We don't look to the quality of windows to understand if people will buy the next version. We don't look at the google search results as an indicator of google's profitability.

To be clear, I think it is. It's just not going to be a hologram of a wizard in a room you can ask a question to for a quarter, which is what these chat bots and copilots you see today are modeled around.

Every question I've asked of chatGPT, meta and Gemini have returned results that were either obvious or wrong. Pointing out how wrong the answers returned got the obvious, "I apologize" response which returned an obvious answer.

I consider all these AI engines to be interactive search engines where the results need to be double checked. 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.

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.

Something to keep in mind is that gambling rules apply: if enough people flip coins, there is always someone experiencing a winning streak and someone experiencing a losing streak and majority that gets a mixed bag of roughly breaking even (mediocre usefulness and a waste of time)

my first week of using GPT4 every day I experienced great answer after great answer, and I was convinced the world would change in a matter of months, that natural language translation was now a solved problem etc etc

But my luck changed, and now I get some good answers and some idiotic answers, so it's mostly not worth my time. Some people never get a good answer in their few dice rolls before writing off the technology

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.

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

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.

This wasn't the case with GPT-4/o. This capability is very new.

When I spoke to a colleague at Microsoft about these changes, they were floored. Microsoft has made themselves synonymous with AI, yet their company is barely even leveraging it. The big cos have put in the biggest investments, but also will be the slowest to change their processes and workflows to realize the shift.

Feels like one of those "future is here, not evenly distributed yet" moments. When a tool like Sonnet is released, it's not like big tech cos are going to transform over night. There's a massive capability overhang that will take some time to work itself through these (now) slow-moving companies.

I assume it was the same with the internet/dot-com crash.

I decided to fire up GPT-4o again today to see if maybe things have gotten better over the past few months.

I asked GPT to write code to render a triangle using Vulkan (a 3D graphics API). There are about 1000 tutorials on this that are almost certainly in GPT-4's training data. I gave GPT two small twists so it's not a simple case of copy/paste: I asked it 1) to apply a texture to the triangle and 2) to keep all the code in a single function. (Most tutorials break the code up into about a dozen functions, but each of these functions are called only once, so it should be trivial to inline them.)

Within the first ten lines, the code is already completely nonfunctional:

GPT-4o declares a pointer (VkPhysicalDevice) that is uninitialized. It queries the number of graphics devices on the host machine. A human being would allocate a buffer with that number of elements and store the reference in the pointer. GPT-4o just ignores the result. Completely ignores it. So the function call was just for fun, I guess? It then tries to copy an entire array of VkPhysicalDevice_T objects into this uninitialized pointer. So that's a guaranteed memory access violation right off the bat.

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.

However, my main job is trying to come up with "elegant" architectures for complex business logic that interacts with an existing large code base. AI is just completely out of its depth in such cases due to lack of context but also lack of source material to draw from. Even unit tests only work with the most basic of cases, most of the time the setup is so complex it just produces garbage.

I've also had very little luck getting it to write performant code. I almost have to feed it the techniques or algorithms before it attempts to write such code, and even then it's usually wrong or not as efficient as it could be.

Right, that's surely the main job of nearly every experienced developer. It's really cool that LLMs can generate code for isolated tasks, but they can barely even begin to do the hard work, and that seems very unlikely to change in the foreseeable future.

That code is probably buggy, slow, poorly architected, very verbose, and has logical issues where the examples and your needs dont exact match.

Generally, the longer the snippet you want your LLM to generate, the more likely its going to go off the rails.

I think for some positions this can get you 90% of the code done. For me this usually means I can get started very fast on a new problem, but the last remaining "10%" actually takes significantly longer and more effort to integrate because I dont understand the other 90% off the top :)

I just tried the latest Cursor + Sonnet and it failed in every task. The problem is that there is no way to understand the code without either complete understanding of the domain and the intents or running it in some context.

Telecom and media domains in particular are well documented in specs and studied in forum discussions. I am sure they are part of the training data, because I can get most answers if asked directly. So far the LLMs fail to reason about anything useful for me.

However, I use copilot autocomplete consistently. That makes me much more productive. It’s just really good autocomplete.

As an experienced developer I’d put my productivity improvement at 2-3x. It’s huge but not 10x. I’m limited by my decision speed, I need to decide what I want the code to do, AI can’t help with that - it can only do the “how”.

Far from introducing more bugs, using Copilot frees some mental cycles for me to be more aware of the code I’m writing.

So it will be with inexperienced people coding with LLMs.

On stuff that's a bit obscure, but not really (like your Vulkan example), ChatGPT tends to write 60-95% correct code, that's flawed in the exact ways a noob wouldn't be able to fix.

In this case, nicking code from Github seems to fix the issue, even if I need to adapt it a bit.

Then comes the licensing issue. Often, when searching for an obscure topic, the code ChatGPT generates is very close to what's found on Github, but said code often comes with a non-permissive license, unlike what the AI generates.

I think this is a feature for a lot of people. ChatGPT can launder code so they don't have to care about licenses.

The Jetbrains thing is rather rudely incompetent, it consistently insists on suggestions that use variables and fragments that are supposed to be replaced by what I'm writing and also much more complex than what I actually need. I suffered through at least a hundred mistaken tabbed out shitty suggestions before I disconnected it.

"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.

Oh and I expect it to be free, I ain't paying for this just like I wasn't paying for stackoverflow.

Finally I hope than in few years I will be able to just "sudo apt-get install llm llm-javascript llm-cooking llm-trivia llm-jokes" and it will all run locally on my low end computer and when I report bug, six months later it will be fixed when I update OS.

The same applies to AI. The old learning material is gone, your interaction is now the new learning material and ground truth.

PS: Hourly rates for sw engineers: Range:€11 - €213 - so one hour on stackoverflow, searching and sub-querying resolving problems costs you or your employer up to 213€. It really depends on what you have negotiated.

It's like having an unusually fast-but-clumsy intern, except interns learn the ropes fast and understand context.

I have some stand tests for LLMs: write a web app version of tetris, write a fluid dynamics simulation, etc., and these regularly fail (I must try them again on 4o).

But also, I have examples of them succeeding wildly, writing a web based painting app just from prompting — sure, even with that success it's bad code, but it's still done the thing.

As there are plenty of examples to confirm what we already believe, it's very easy to get stuck, with nay-sayers and enthusiasts equally unaware of the opposite examples.

I welcome people picking apart chats that I link to. it's not that I believe that LLMs are magic and refuse to adjust my model of how good these things are and aren't, but when people don't give specific evidence is hard to actually move the conversation forwards.

because yeah, these things are plenty stupid and have to be tricked into doing things sometimes (which is stupid, but here we are). they're also pretty amazing but like any hammer, not everything is a nail.

You're like a gambling addict who thinks he's smarter than everyone else

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.

I am an experienced programmer, but I find myself (for the first time) doing a deep-dive in SQL and specifically building code that will run against multiple SQL engines.

I fed my list of engines into AI. As I'm discovering the really weird corners of SQL I asking the AI to compare one db against the other. My prompts are usually no more than 3 or 4 words.

It gives me quick helpful answers highlighting where things are the same and where they are different. I can then follow up in the specific docs if necessary (now that I know the function name.)

Personally I'm somewhat anti-hype, I'll let others rave about "changing the world". But I have found it a useful tool - not so much for "writing my code" but for acting as my tutor as I learn new things. (I'm using it for more than just SQL or computers now.)

I'm not sure it "changes thr economy" - but it can certainly change individual lives. Some jobs will go away. Others might be easier to do. It might make it easier to learn new skills.

The 2nd agency I called them through phone. No ai. But it is excellent cause they do async processing. so you reserve a slot and they call you back. i don't care if those are not answered urgently. Because I just want to talk to a human.

Customer service is hard because it has to filter out 90% noise, 9% fairly straightforward tasks, and the <1% of complex issues that need to be sent to the right human.

But equally it doesn't prove, or even assert, the opposite. A bicycle may be bad at cross-country road trips, but that doesn't make it a bad choice for some other situations.

Hence my earlier comment - I (and I suspect others) are finding it useful for some tasks. That is not to imply it is good at all tasks.

Is it over-hyped? Of course yes. Welcome to IT where every new thing is over-hyped all the time.

If you don't know the language at all it's dangerous because you may not understand the proposed program (and of course if you're an expert you don't need it at all).

But llms won't help to find solutions to a general, still unspecified problem.

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!

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've also assembled a Kubernetes cluster overnight, despite not knowing much about Kubernetes before, and I ran the kubectl files ChatGPT made for me past some devops folks, and it passed the smell test.

I consider much of coding to be a magic spellcasting tutorial - we do conceptually simple things, and the difficult lies in figuring out how to use novel libraries and get them to do what you want.

Edit: After checking out the Arduino sketch, I'd take issue with all the floating point calculations in there - most microcontrollers don't have FPUs, and the performance is awful on 8-bit AVRs. It's not great on Cortex M3s either as all this stuff is done in software, and each FP operation is like a hundred cycles.

I'd definitely try to rephrase the issue with integer math. It might work, but no self-respecting embedded dev would write like this.

Exactly, it's a very nice alternative to searching the web and discovering new stuff.

> most microcontrollers don't have FPUs, and the performance is awful on 8-bit AVRs.

I used to think like you. But then I realized the Atmega 328p is running at 16 MHz, so even hundreds of . As you can see here[1], it can do 94k double-precision FLOPS, more than enough for simple sketches like this. This jives with the benchmarks I did several years ago.

Sure if I was writing some tight control loop or similar I wouldn't use floating point.

[1]: https://kreier.github.io/benchmark/LinpackDP/

It's like I have some light fixtures in my attic that are connected with wires twisted together and covered with electrical tape - they certainly work and have done for a decade, but its still not right and I wouldn't recommend anyone do it this way.

I just don’t understand where the hell is this magical LLM capable of generating flawless files or even entire projects that many people are talking about. I rarely accept a large block of LLM-generated code without close inspection, and I’ve ended up with a subtle bug that I wouldn’t have written myself at least ~5 times now. Unless you don’t give a shit about code quality, error handling, proper logging, and subtle bugs, you shouldn’t run LLM-generated stuff without painstakingly reading and fixing everything. Or maybe there really is a magical LLM somewhere.

Its using the old format where the Program.cs file has an actual class, whereas as of .NET 6 thats not required.

You said barebones, but for any real server you would want to use the generic host https://learn.microsoft.com/en-us/dotnet/core/extensions/gen... which gets you a lot of the boilerplate and enables you program to be wrapped in a windows or systemd service.

Finally, parsing can be simplified since ASCII is a proper subset of UTF-8, you can just parse the entire string as UTF-8. IMHO I am disappointed that the AI didn't point that out.

True, I intentionally said barebones as I wanted a minimal example. I asked it to modify the code to use the generic host, and updated the chat link (so refresh). Keep in mind this is the free ChatGPT, but I still think it did reasonably good. The example compiles as-is, and is very close to functional. I've not used the generic host stuff before either, so again this would save me time searching and piecing together code.

> Finally, parsing can be simplified since ASCII is a proper subset of UTF-8, you can just parse the entire string as UTF-8.

I don't think that would work, because the free-form text message part at the end must contain a BOM if it's UTF-8 encoded, according to the specification. AFAIK you can't have the BOM in the middle of a string.

The way the OP here was talking about sonnet being above way above chatgtp in this case, it could be true. Google probably has the largest Go codebases on search to train the AI on higher quiality inputs. Go is a simpler language with less variation over time compared to something like .net also adding to its corner.

ive always been the type of person to use the right language for the each use case. For the last 10+ years ive primarly been building cross platform apps that target every common OS. So these "ai" tools like phind.com give me a universal API interface and generate code which is equiv to a SO answer. They are the ability of an outsourced junior dev who you would never let push code to prod, that doesnt have the language barrier but retains the fake dergee overheads ;)

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.

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)

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.

Echo chamber/bias I guess. I know many, many seniors with big pay checks working for big companies who are vastly worse than sonnet at the moment. Juniors stand literally no chance unless very talented.

I don't know what companies pay for and I don't care, because if we go by that, every definition of every word is arguable (because there's everywhere someone out of the range of the definition)

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.

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.

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 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...

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.

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.

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

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

There are also some more gotchas, like the generated code using a slightly different package versions than the installed ones.

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.

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

OpenAI is yesterdays news.

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.

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

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’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.

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

After that it goes completely off the rails by trying to issue draw commands before binding a graphics pipeline, which is both illogical and illegal. After a lot of prodding, I did manage to get it to bind a graphics pipeline, but it forgot about the texture.

So Claude Sonnet is definitely better than GPT-4o, but it still feels raw, like a game of whack-a-mole where I can get it to fix a mistake, but it reintroduces an old one. I also have to be the one offering the expertise. I can prompt it to fix the issues because I know exactly what the issues are. If I was using this to try to fill in for a gap in my knowledge, I would be stuck when I ran the code and it crashed - I would have no idea where to go next.

Update: Took about 50 min of experimenting, but I did get Claude to generate code that doesn't have any obvious defects on first inspection, although it cut off about halfway through because of the generation limit. That's the best result that I've seen from an LLM yet. But that's after about a dozen very broken broken programs, and again, I think the domain expertise here is key in order to be able to reprompt and correct.

One example that I am still using: I wanted to generate a random DICOM file with specific types of garbage in it to use as input for some unit tests, and Claude was able to generate some Python that grabs some random DICOM tags and shoves vaguely plausible garbage data into them, such that it is a valid but nonsensical DICOM dataset. This is not hard, but it's a lot faster to ask Claude to do it.

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...

Every thread like this over the past year or so has had comments similar to yours, and it always remains quite vague, or when examples are given, it’s about self-contained tasks that require little contextual knowledge and are confined to widely publicly-documented technologies.

What exactly floored your colleague at Microsoft?

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

MMmm.

I mean, don't get me wrong; this is impressive stuff; but it needs to be an order of magnitude less 'screwing around trying to fix the random crap' for this to be 'wow, amazing!' rather than a technical demonstration.

You could have done this more quickly without using AI.

I have no doubt this is transformative technology, but people using it are choosing to use it; it's not actually better than not using it at this point, as far as I can tell.

It's slower and more error prone.

You summed up the workflow accurately. Except, I read your first paragraph in a positive light, while I imagine you meant it to be negative.

Note the feedback loop you described is the same one as me delegating requirements to someone else (i.e. s/LLM/jr eng). And then reading/editing their PR. Except the feedback loop is, obviously, much tighter.

I've written a lot of tests, I think this would have taken 3-4x longer to do by hand. Surely an hour?

But even if all things were roughly equal, I like being in the navigator seat vs the driver seat. Editor vs writer. It helps me keep the big picture in mind, focused on requirements and architecture, not line-wise implementation details.

It seems to me that the first few are almost complete copy-paste of older tests. You would have got code closer to the final test in the update case with simple copy-paste than what was provided.

The real value is only in the filtered test to choose randomly (btw, I have no idea why that’s beneficial here), and the one which checks that both consumers got the same info. They can be done in a few minutes with the help of the already made insert test, and the original version of the filtered test.

I’m happy that more people can code with this, and it’s great that it makes your coding faster. It makes coding more accessible. However, there are a lot of people who can do this faster without AI, so it’s definitely not for everybody yet.

I guess my point is I'm skeptical.

I don't believe what you had the end would have taken you that long to do by hand. I don't believe it would have taken an hour. It certainly would not have taken me or anyone on my team that long.

I feel like you're projecting that, if you scale this process, so say, having 5 LLMs running in parallel, then what you would get is you spending maybe 20% more time reviewing 5x PRs instead of 1x PR, but getting 5x as much stuff done in the end.

Which may be true.

...but, and this is really my point: It's not true, in this example. It's not true in any examples I've seen.

It feels like it might be true in the near-moderate future, but there are a lot of underlying assumptions that is based on:

- LLMs get faster (probably)

- LLMs get more accurate and less prone to errors (???)

- LLMs get more context size without going crazy (???)

- The marginal cost of doing N x code reviews is < the cost of just writing code N times (???)

These are assumptions that... well, who knows? Maybe? ...but right now? Like, today?

The problem is: If it was actually making people more productive then we would see evidence of it. Like, actual concrete examples of people having 10 LLMs building systems for them.

...but what we do see, is people doing things like this, which seem like (to me at least), either worse or on-par with just doing the same work by hand.

A different workflow, certainly; but not obviously better.

LLMs appear to have an immediate right now disruptive impact on particular domains, like, say, learning, where its extremely clear that having a wise coding assistant to help you gain simple cross domain knowledge is highly impactful (look at stack overflow); but despite all the hand waving and all the people talking about it, the actual concrete evidence of a 'Devin' that actually builds software or even meaningfully improves programmer productivity (not 'is a tool that gives some marginal benefit to existing autocomplete'; actually improves productivity) is ...

...simply absent.

I find that problematic, and it makes me skeptical of grand claims.

Grand claims require concrete tangible evidence.

I've no doubt that you've got a workflow that works for you, and thanks for sharing it. :) ...I just don't think its really compelling, currently, to work that way for most people; I don't think you can reasonably argue it's more productive, or more effective, based on what I've actually seen.

There are plenty of, "Just disable certificate checking" type answers on Stack Overflow, but there are also a lot of comments calling them out. How do you fact check the AI? Is it just a shortcut to finding better documentation?

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.

Re: prompt engineering, we have a prompt guide if that helps, was that what you are getting at?

https://docs.greptile.com/prompt-guide

Ed: ah, since the time I wrote this comment, your respective comments are now at 14 and 13 hours. Congrats on your <1hr lead.

Perception of time is subjective.

I just love it (not!, but it happens every now and then) when HN users display ignorance of basic logic, on a site for techies, yet.

I could use your own argument against you:

>is certainly an ”almost immediate” response

>Perception of time is subjective.

So, you use "certainly", and then "subjective", just after it, in the same argument about the same topic?

Brilliant. Do you not realize that by your last sentence above, you make my own case for me?

What arrant nonsense!

Don't accuse people falsely or without substantiating your accusation.

My comment being referred to above, consisted of four simple short lines.

I am not going to bother to paste them here again for a third time.

Those HN readers who wish to do so, can go the few needed levels up this thread and check for themselves:

Nothing I said in those four lines can be interpreted as being passive-aggressive.

I say that you do not know what the heck you are talking about, if you claim that I was passive-aggressive in that comment.

Cursor offers some super marginal UX improvements over the latter (being that it’s a fork of VScode), since it allows you to switch models. But Claude and GPT have been interchangeable at least for my workflows, so I’m not sure the hype is really deserved.

I can only imagine the excitement comes from the fact that cursor has a full-fat free trial, and maybe most people have never bothered paying for copilot?

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.

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.

Even where microsoft includes it for free, like their automation tools, its always been jank. I found myself going to GPT4 for better answers which is bad when you spend any time thinking about it.

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

It definitely has that Tesla Autopolit feel to it, where the marketing is ... enthusiastic, while the onus to "use the tool responsibly" is left up to the end user.

I'm building a persentation/teaching tool for teachers and trying to not sweep the halicination angle under the rug. My marketing angle is that the AI is a huge time saver, but the classroom teacher is the subject expert and they need to review the output. At the end of the day, it's still much more time efficient for an expert to review the content than it is to produce it from scratch which is a huge win.

Also, to be super presumptuous, if you need help with anything with your tool, hit me up. I love working with developers on building realistic use case and workflows.

I'm building tools for K-12 (I've been a teacher for 20+ years). There's solid uptake of GPT among teachers and individually they're pretty enthusiastic about anything that will reduce their workload, so I'm hoping it resonates.

>Also, to be super presumptuous, if you need help with anything with your tool, hit me up. I love working with developers on building realistic use case and workflows.

Sure, my details are in my profile, happy to chat.

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.

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.

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.

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.

It is of vital importance (imho) to get open models at the same level before another jump comes (if it comes of course, maybe another winter, but at least we'll have something I use every day/all day; so not all hype I think).

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

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.

...do you not see the obnoxious CoPilot(TM) buttons and ads everywhere? It's even infected the Azure Portal - and every time I use it to answer a genuine question I have I get factually-incorrect responses (granted, I don't ask it trivial or introductory-level questions...).