The other day I (well, the AI) just wrote a Rust app to merge two (huge, GB of data) tables by discovering columns with data in common based on text distance (levenshtein and Dice) . It worked beautifully
An i have NEVER made one line of Rust.
I dont understand nay-sayers, to me the state of gen.AI is like the simpsons quote "worst day so far". Look were we are within 5 years of the first real GPT/LLM. The next 5 years are going to be crazy exciting.
The "programmer" position will become a "builder". When we've got LLMs that generate Opus quality text at 100x speed (think, ASIC based models) , things will get crazy.
Human minds are built to find patterns, and you should be careful not to assume the rate of improvement will continue forever based on nothing but a pattern.
Just the fact that even retail quality hardware is still improving at local LLM significantly is still a great sign.
If AI quality remained the same, and the cost for local hardware dropped to $1000, it would still be the greatest thing since the internet IMO.
So even if the worst happens and all progress stops, I'm still very happy with what we got.
I'm not all that impressed with "AI". I often "race" the AI by giving it a task to do, and then I start coding my own solution in parallel. I often beat the AI, or deliver a better result.
Artificial Intelligence is like artificial flavoring. It's cheap and tastes passable to most people, but real flavors are far better in every way even if it costs more.
At their current stage, this feels like the wrong way to use them. I use them fully supervised, (despite the fact that feels like I’m fighting the tools), which is kind of the best of both worlds. I review every line of code before I allow the edit, and if something is wrong, I tell it to fix it. It learns over time, especially as I set rules in memories, and so the process has sped up, to the point that this goes way faster than if I would have done that myself. Not all tasks are appropriate for LLMs at all, but when they are, this supervised mode is quite fast, and I don’t believe the output to be slop, but anyways I feel like I own every line of code still.
The happy path for me is with erlang, due to the concurrency model the blast radius of an error is exceptionally small, so the programming style is to let things crash if they go wrong. So, really you are writing the happy path code only (most of the time). Combine this approach with some very robust tests (does this thing pass the tests / behave how we need it to?) then you’re close to the point of not really caring about the implementation at all.
Of course, i still do, but i could see not caring being possible down the road with such architectures..
Home made food is better than anything you can buy too. Im 40 but I still drive 30 minutes to my parents once a week for dinner because the food they make feels like the elixir of life compared to the slop I can buy in trader joes, Costco, or most restaurants.
The overall trend in AI performance will still be up and to the right like everything else in computing over the past 50 years, improvement doesn't have to be linear
Because if you don't know the language or problem space, there are footguns in there that you can't find, you won't know what to look for to find them. Only until you try to actually use this in a production environment will the issues become evident. At that point, you'll have to either know how to read and diagnose the code, or keep prompting till you fix it, which may introduce another footgun that you didn't know that you didn't know.
This is what gets me. The tools can be powerful, but my job has become a thankless effort in pointing out people's ignorance. Time and again, people prompt something in a language or problem space they don't understand, it "works" and then it hits a snag because the AI just muddled over a very important detail, and then we're back to the drawing board because that snag turned out to be an architectural blunder that didn't scale past "it worked in my very controlled, perfect circumstances, test run." It is getting really frustrating seeing this happen on repeat and instead of people realizing they need to get their hands dirty, they just keep prompting more and more slop, making my job more tedious. I am basically at the point where I'm looking for new avenues for work. I say let the industry just run rampant with these tools. I suspect I'll be getting a lot of job offers a few years from now as everything falls apart and their $10k a day prompting fixed one bug to cause multiple regressions elsewhere. I hope you're all keeping your skills sharp for the energy crisis.
Before LLMs, I've watched in horror as colleagues immediately copy-paste-ran Stack Overflow solutions in terminal, without even reading them.
LLM agents are basically the same, except now everyone is doing it. They copy-paste-run lots of code without meaningfully reviewing it.
My fear is that some colleagues are getting more skilled at prompting but less skilled at coding and writing. And the prompting skills may not generalize much outside of certain LLMs.
I don't want exciting. I want a stable, well-paying job that allows me to put food on the table, raise a family with a sense of security and hope, and have free time.
I seem to remember doing it in SQL (EDIT_DISTANCE) 20ish years ago. While I wouldn't say it worked beautifully, I also didn't need to make a single line of Rust :) also no more than 2 line s of SQL were needed.
Edit_distance uses pure levenstein which is quadratic, so for tables of 500k rows and 20+ columns each it will slowdown to a crawl. Without going into a lot of detail, I needed this to work for datasets of that size. So a lot of "trick" optimization and pre-processing has to be done.
Otherwise simple merges in pandas or sql/duckdb would had sufficed.
Years of school (reading, calculus etc) to get to the point of learning basics of set theory.
One day to learn basic SQL based on understanding the set theory.
Maybe few weeks of using SQL at work for ad hoc queries to be proficient enough (the query itself wasn't really complex).
For the domain itself I was consulting experts to see what matters.
I'm not sure that time it would take to know what to prompt and verify the results is much different.
Fun fact - management decided that SQL solution wasn't enerprisely enough so they hired external consultants to build a system doing essentialy that but in Java + formed an 8 people internal team to guide them. I heard they finished 2 years later with a lot of manual matching.
Let me explain the naysayers, they know "programmer" has always meant "builder" and just because search is better and you can copy and paste faster doesn't mean you've built anything.First thing people need to realize is no proprietary code is in those databases, and using Ai will ultimately just get you regurgitated things people don't really care about. Use it all you want, you won't be able to do anything interesting, they aren't giving you valuable things for free. Anything of value will still take time and knowledge. The marketing hype is to reduce wages and prevent competition. Go for it.
An i have NEVER made one line of Rust.
I dont understand nay-sayers, to me the state of gen.AI is like the simpsons quote "worst day so far". Look were we are within 5 years of the first real GPT/LLM. The next 5 years are going to be crazy exciting.
The "programmer" position will become a "builder". When we've got LLMs that generate Opus quality text at 100x speed (think, ASIC based models) , things will get crazy.