This. I have ADHD and starting is the hardest part for me. With an LLM it gets me from 0 to 20% (or more) and I can nail it for the rest. It’s way less stressful for me to start now.

> LLMs can reliably solve the problem of a blank-page.

This has been the biggest boost for me. The number of choices available when facing a blank page is staggering. Even a bad/wrong implementation helps collapse those possibilities into a countable few that take far less time to think about.

Yeah, keeping me in the flow when I hit one of those silly tasks my brain just randomly says "no let's do something else" to has been the main productivity improving feature of LLMs.

So much this, the blank page problem is almost gone. Even if it's riddled with errors.

An LLM ability to do a task is roughly correlated to the number of times that task has been done on the internet before. If you want to see the hype version, you need to write a todo web app in typescript or similar. So it's probably not something you can fix with prompts, but having a model with more focus on relevant training data might help.

This honestly seems like something that could be better handled with pre-LLM technology, like a 15-line Perl script that reads one on stdin, applies some crufty regexes, and writes the other to stdout. Are there complexities I'm not seeing?

> LLMs are a decent search engine a la Google circa 2005.

Statistical text (token) generation made from an unknown (to the user) training data set is not the same as a keyword/faceted search of arbitrary content acquired from web crawlers.

> The problem is that the conversational interface, for some reason, seems to turn off the natural skepticism that people have when they use a search engine.

For me, my skepticism of using a statistical text generation algorithm as if it were a search engine is because a statistical text generation algorithm is not a search engine.

Except a search engine isn't voice controlled, and able to write code for me.

Recently I did some tests with coding agents, and being able to translate a full application from AT&T Assembly into Intel Assembly compatible with NASM, in about half an hour of talking with agent, and having the end result actually working with minor tweeks isn't something a "decent search engine a la Google circa 2005." would ever been able to achieve.

In the past I would have given such a task to a junior dev or intern, to keep them busy somehow, with a bit more tool maturity I have no reason to do it in the future.

And this is the point many developers haven't yet grasped about their future in the job market.

  > the conversational interface, for some reason, seems to turn off the natural skepticism that people have

n=1 but after having chatgpt "lie" to me more than once i am very skeptical of it and always double check it, whereas something like tv or yt videos i still find myself being click-baited or grifted (iow less skeptical) much more easily still... any large studies about this would be very interesting...

I'm not sure if you're being sarcastic, but in case you're not... From https://arstechnica.com/health/2023/11/ai-with-90-error-rate...

"UnitedHealth uses AI model with 90% error rate to deny care, lawsuit alleges" Also "The use of faulty AI is not new for the health care industry."

> If this happened every once in a while then it wouldn't be a big deal, but I'd guess that more than half of the answers and tutorials I've received through ChatGPT have ended up being plain wrong.

It would actually have been more pernicious that way, since it would lull people into a false sense of security.

Yeah, I write a lot of little data analysis scripts and stuff, and I am happy just to read the numbers, but now I get nice PNGs of the distributions and so on from LLM, and people like that.

So what you’re saying is you need to be very specific and detailed when writing your specifications for the LLM to spit out the code you want. Sounds like I can just skip the middle man and code it myself.

> LLMs follow instructions.

They don't

> Garbage in = garbage out generally.

Generally, this statement is false

> When attention is managed and a problem is well defined and necessary materials are available to it, they can perform rather well.

Keyword: can.

They can also not perform really well despite all the management and materials.

They can also work really well with loosey-goosey approach.

The reason is that they are non-deterministic systems whose performance is affected more by compute availability than by your unscientific random attempts at reverse engineering their behavior https://dmitriid.com/prompting-llms-is-not-engineering

This seems to be what’s happened

People are expecting perfection from bad spec

Isn’t that what engineers are (rightfully) always complaining about to BD?

This hasn't been my experience (using the latest claude and gemini models). They'll produce poor code even when given a well defined easily achievable task with specific instructions. The code will usually more or less work with today's models, but it will do things like call a function to recreate a value that is already stored in a local variable... (and worse issues prop us the more design-work you leave to the LLM, even dead simple design work with really only one good answer)

I've definitely also found that the poor code can sometimes be a nice starting place. One thing I think it does for me is make me fix it up until it's actually good, instead of write the first thing that comes to mind and declare it good enough (after all my poorly written first draft is of course perfect). In contrast to the usual view of AI assisted coding, I think this style of programming for tedious tasks makes me "less productive" (I take longer) but produces better code.

> LLMs follow instructions.

Not really, not always. To anyone who’s used the latest LLMs extensively, it’s clear that this is not something you can reliably assume even with the constraints you mentioned.

They should maybe have a verifiable specification for said instructions. Kinda like a programming language maybe!

> LLMs follow instructions.

No they don't, they generate a statistically plausible text response given a sequence of tokens.

There are so many examples where all current top models just will loop forever even if you instruct them literally the code. We know many of them, but for instance in a tailwind react project with some degree of complexity (nested components), if you ask for something to scroll in it's space, it will never figure out min-h-0 even if you tell it. It will just loop forever rewriting the code adding and removing things, to the point of it just putting comments like 'This will add overflow' and writing js to force scroll, and it will never work even if you literally tell it what to do. Don't know why, all big and small models have this, and I found Gemini is currently the only model that sometimes randomly has the right idea but then still cannot resolve it. For this we went back to not using tailwind and back to global vanilla css, which I never thought I would say, is rather nice.

I asked Chat GPT 4o to write an Emacs function to highlight a line. This involves setting the "mark" at the beginning, and the "point" at the end. It would only set the point, so I corrected it "no, you have to set both", but even after correction it would move the point to the beginning, and then moved the point again to the end, without ever touching the mark.