51–60 of 165 posts
Also, I'm sure they have ways around it, but I'd imagine colabs are a poor source of "good" code to use for further model training, both because of the kind of code you write in notebooks and the demographic that would make up colab users. It sort of fits with the idea that autocomplete might be good at writing short functions that do some specific thing, but not much help actually writing a full program.
The animated gif for the first example leaves a lot to be desired. The UX: Click a button, type in a prompt, click another button. The speed just to generate 2-3 lines of code. And AI generated code with no basic error handling.
Clever use of intentional UX friction IMO. It does cost money to run these models.
Earlier quoted context omitted.
Are you kidding? Google's main product is waiting lists.
There's Google Wait, Wait+, GWait, Hangwait, Wate, Wait4it, Google Weight List, Alphawait, and last but not least GWait Hangwait. Am I missing any?
I really think the current paradigm of literally typing if else logic all day into programs, and then getting paid huge money to do so will go away. Programming is going to be much higher level and accessible, though still complex. It will take five years or so. Edit: My prediction is essentially that: You give it a set of requirements for an api, with edge conditions written in plain English. Test cases are provided…
Sometimes I wonder where some of the posters here work or if I'm working in a dystopia.
>You give it a set of requirements for an api with edge conditions written in plain English
This part is the job! If my job was 100% writing logic, it would be infinitely easier. Defining the requirements, evaluating the tradeoffs, discovering the edge conditions is where the bulk of my time goes. The only time someone did this for me was when I was a junior developer. Maybe I'm overestimating things, but I find it hard to believe that most engineers pulling huge salaries are just shuffling around fields on JSON API. Do you really need AI to expose a CRUD interface to Postgres?
Edit:
This idea that LLMs will replace engineers (or lawyers, or any traditionally "skilled" field) is hype. It's the same mistake that the customer makes when he's shocked that he gets a bill for $10,000 for replacing a screw in his car engine. You are conflating the actual physical labour requirements of the job (sitting down and coding) with the actual knowledge value that is being used when you do the job.
For example, take a look at Redis. Redis is a great codebase, especially for those that want to learn C. It's simple - there are exceedingly few mind-bending, hardcore, engineering algorithms in Redis. Antirez is an amazing software engineer, but Redis is not the fastest database, nor is it most durable. But what you see is the meticulous application of understanding engineering tradeoffs; there are things Redis does incredibly well and things that it doesn't that is made easier by the overall architecture of the code. How would you even begin to prompt this to an LLM? Again the code isn't complex, but the engineering is, and the act of turning those ideas and communicating them either to an LLM or to a C compiler, is engineering.
No one comes home from a long day and says "Honey, I'm tired I spent all day typing JSON schemas and function signatures".
I find VS-code running notebooks with Colab plugin installed pretty helpful for this type of thing already. (1) run notebook in VS-code https://code.visualstudio.com/docs/datascience/jupyter-noteb... (2) install Github Copilot extension https://marketplace.visualstudio.com/items?itemName=GitHub.c... and then it can quite often achieve what you want with just a comment or some previous code snippets. Then if you load…
I think you meant "with the Github Copilot plugin installed".
I really think the current paradigm of literally typing if else logic all day into programs, and then getting paid huge money to do so will go away. Programming is going to be much higher level and accessible, though still complex. It will take five years or so. Edit: My prediction is essentially that: You give it a set of requirements for an api, with edge conditions written in plain English. Test cases are provided…
Here's a different 10-year prediction. AI will become good enough to be useful for programmers but not good enough to run on its own and will remain an assistant. Given that there is a shortage of programmers, more software will be written than ever. More software will beget more software engineers. Because the output of software engineers will rise, each individual software engineer will become more valuable to thei…
Those people have nowhere to go to match what they're earning now. Maybe support review roles (which won't pay particularly well), where they approve decisions by the AI that are held up for human approval.
The bottom half of software developers won't have AI assistants. The AI will have them as human assistants (required by corporations for control/safety/oversight purposes).
The ~50%-25% bracket will build software using AI tools and will rarely write the actual code.
In the top ~25% bracket (in terms of skill) you'll have software developers that are still paid very well and they'll directly write code, although not always. That group will be the only one remaining that is paid like today's software developers get paid.
Software developer in the future will most commonly mean someone who builds software via AI tools (with the AI writing nearly all of the actual code). Human software developers will be glorified prompt wizards (with required degrees; it won't be a great job).
For the median software developer, the peak has already been reached (in terms of pay and job security).
Emerging market software developers will be hammered before their economies can fully benefit from the relatively high pay of the industry (from off-shoring work from big tech).
The golden run is over for the bottom 3/4 of software developers. Prepare for it. Get used to it. In the developed world the ladder up and out of the middle class via software development is going to go away (and quickly).
To regularly write code in the future you'll have to be damn good. Good enough, and knowledgeable enough, to be better at what you're doing than an AI with a handler (human assistant). You'll be writing the AI systems that govern everything and it'll be increasingly regulated, with more government licensing (plausibly formal AI engineer licensing and actual accountability, because the risks will go way up).
(Shamelessly spreading the word about my open source tool) You can do GPT-4 powered coding chats in your terminal today with aider. It's not free in the sense that you need a gpt-4 api key, which openai charges you for. But it's a pretty great way to collaborate with AI on code. https://github.com/paul-gauthier/aider
Neat! This interface look simpler than most alternatives I've seen lately. Do you know how it deals with source files that are larger than the context window?
Right now aider doesn't even try and deal with files larger than the context window. For gpt-4, that's 8k tokens or about 32kbytes. Which is pretty reasonable.
According to the data in [1], the average source file in github is less than 14KB. It's worth noting that they explicitly discarded small files with less than 10 lines. So the true average is probably much lower.
Regardless, I haven't found the gpt-4 context window issue to be problematic in practice yet. You do need to be careful about how many files you "add to the chat" at once. But that's not hard. For sure there are cases where you would need to refactor a large file before you could use it with aider.
I am certainly interested added context window management features to aider. I have previously explored a bunch of approaches for this with gpt-3.5-turbo. I shared some notes about these experiments previously on HN [2].
[1] https://hoffa.medium.com/400-000-github-repositories-1-billi...
I really think the current paradigm of literally typing if else logic all day into programs, and then getting paid huge money to do so will go away. Programming is going to be much higher level and accessible, though still complex. It will take five years or so. Edit: My prediction is essentially that: You give it a set of requirements for an api, with edge conditions written in plain English. Test cases are provided…
Here's a different 10-year prediction. AI will become good enough to be useful for programmers but not good enough to run on its own and will remain an assistant. Given that there is a shortage of programmers, more software will be written than ever. More software will beget more software engineers. Because the output of software engineers will rise, each individual software engineer will become more valuable to thei…
Earlier quoted context omitted.
Here's a different 10-year prediction. AI will become good enough to be useful for programmers but not good enough to run on its own and will remain an assistant. Given that there is a shortage of programmers, more software will be written than ever. More software will beget more software engineers. Because the output of software engineers will rise, each individual software engineer will become more valuable to thei…
Having played a bit with coding with AI I like this view, it's powerful, but context will always be an issue, turning real world problems into working code is a skill in it's own right, that will not change, superpowered yes, for me it's all the 'dull' parts of typing stuff out that I look forward to missing, from an early study on impact in the workplace, using a 2 year study of a call centre, the result was large i…
This is absolutely something that already happens with fully human developers, but it seems likely to be much more frequent and not caught as soon with AI assistance.
This also seems like a failure mode that could go pathologically wrong on a regular basis for TDD types.