191–200 of 225 posts
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Money is definitely not the bottleneck here.
Yes it is, if not I would've already done it.
Every post that claimed using ChatGPT to achieve non-trivial tasks turned out to have non-trivial human intervention. > (from the original article) In fact, I found it better to let ChatGPT generate a toy-ish version of the code first, then let it add things to it step-by-step. This resulted in much better output than, say, asking ChatGPT to generate production-quality code with all features in the first go. This als…
ChatGPT is a junior developer whose knowledge is broad but shallow.
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I use GPT-4 with ChatGPT daily for coding. Here’s what I’ve found works for making the most out the limited prompts OpenAI gives you. - When you first start, tell ChatGPT about the limitation. Example - “I’m only allowed to send you 25 prompts every 3 hours. As a result, I need you to keep track of the messages I send you. At the end of each response you give me, please tell me how many prompts I have left, ex: this…
Request for a message cap increase. I think they go up to 100 but it depends on what you are doing.
I then remind it that it didn't complete the table, it apologizes, reattemps, and still truncates things. It's output is still far shorter than what I receive in some other prompts so it's not purely a length issue. I'd need to have control to tweak its parameters through the API to get it to respond appropriately.
However, while it may not format the table well, it will still keep the full data set in memory and answer questions on it. For example, I told it the data context. I then asked it why one category (row) saw a decline for a specific year (the columns) and it gave a cogent insightful answers that only my boss and I, the domain experts in my organization, would be able to identify so quickly.
I then asked it to make a projection to a following year, where the data was not given, based on the observed data. I did so. The values were reasonable, but I didn't know why it used them, so I asked it where it got them from, why it chose them:
The answers where incredibly cogent, again on the level that an entry level junior domain expert would give. Here's one nearly verbatim for one of them from my memory. It said "I noticed that the values were consistent for most years but dropped significantly for one year, but the most recent year recovered to an even higher level. So I projected a value that was less than the most recent year but still mostly approximated the average outside of the anomalous year. It gave a bullet point explanation like this for each of the eight rows.
I asked in WHY the drop may have occured that year, and again I had told it the data context so it knew what the data was about, and it have 5 bullet pointed paragraphs that, again, would be very solid answers for a junior practitioner in my area of work.
I asked it was specific formula calculations it used for the projections it mentions. It then apologized for not clarifying earlier (lol, it does that a lot) and then proceeded to tell me that its initial projections were qualitative in nature base on observational criteria. That alone is amazing. In then went further though, without more prompting to say something very much like "However if you would like a more quantitative approach the following formula would be a reasonable approach"
It then went on to describe in great detail the formula I could use to calculate the difference from year to year for each row of data and apply that average to the next projected year's #, along with explaining its reasoning in detail for each step it took.
ChatGPT 3.5 gave very basic answers that I suppose might be useful for a basic user looking for basic possibly trends, as long as they understood that it could be total BS and they needed to vet the answers. GPT-4's analysis was spot on
I can't fully express the extreme utility of this. Giving GPT-4 a pre-aggregated data set to have it give some decent insights automatically could save me hours of work reviewing & finding some of the most obvious trends that would be obvious to me when I see them but would require me to look through 10 to 50 columns of data across 20 to 100 rows of data (keep in mind it would be predigested, cleaned and validated data so work has to be done to get to that point.)
But then GPT-4's preliminary observations would bootstrap my ability to digest the rest of it and have a jumping off point to perform more complex analysis. Then I could give it bullet points and have it summarize my findings in a digestible way for my less data-literate audience, all told saving me hours and getting me out from under a huge backlog of work.
It's a use case that would in no way threaten my job, but make me more productive. And I have enough work that I'm hiring a junior data analyst, and would need to do so even with this increased productivity, and so it would not deprive them of a job either.
It truly would (will!) be a game changer in my day to day work. But I do fully acknowledge that it would, in some other areas of work, reduce the # of employees required to fill the available work. And also that to my fear, future versions could make me less relevant as well, though I think that's further off. Domain expertise is an enormous part of my job.
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Disagee. GPT is a senior and knows it all but doesn't know where to start unless you precisely instruct them what to do.
"Developer who needs precise instructions to accomplish every task" is the exact opposite of a senior developer
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Copilot without -x does this relatively well. But it's a hands-on process. I can't just give it some source files and say "go". But it can easily make you 10x. I often spend more time tab-completing than writing.
I'm still trying to figure out how to do this. Copilot is extremely helpful. But more often than not, it suggests completions which would steer the project in another direction. It is extremely good once the code has been organised and an overall structure exists. But until then it can be a distraction.
Literally unusable.
Combine TDD and Self debugging into a workflow and you almost have a new paradigm of software development where entire applications can be developed with a series of prompts. Software programmers have finally programmed themselves out of jobs! It's kind of poetic justice that LLMs trained on open source code is replacing us. We should have never listened to Richard Stallman. /s
I'm not writing new open source. First, web developers have profited from the work of others while locking down their own work on servers. They became rich and started looking down on those who wrote real complex code. Now "Open"-AI launders the output while keeping their IP safe. I wonder though if the whole thing is a scheme by Microsoft to kill open source.
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IMO this leaves out some salient details. For example, I'd say ChatGPT is a very, very good junior developer. The kind of junior developer that loves computer science, has been screwing around with miscellaneous algorithms and data structures its whole life, has a near-perfect memory, and is awake 24/7/365, but has never had to architect a data-intensive system, write future-proof code, or write code for other develo…
Yeah, the junior dev analogy misses on the core capabilities. The ability to spit out syntactically correct blocks of code in a second or two is a massive win, even if it requires careful review.
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But when the code doesn't meet the requirements, the AI needs to know what's incorrect and what changes it needs to make, and that still requires a human. Unless you just put it into a loop and hope that it produces a working result eventually.
So what if you don't "just put it into a loop and hope" but actually make a complex AI agent with static code analysis capabilities, a graph DB, a work memory etc? I'm doing just that and it works surprisingly well. Currently it's as good as people with 2-3 years of experience. Do you really believe it's not going to improve? Now I'm making a virtual webcam so it has a face and you can talk to it on a Zoom meeting...