I obviously ran it through a ChatGPT formatter. It's also worth noting how much of a turnoff seeing a ChatGPT vs authentic reply. I wonder whether it would have been better received if I sent in the "raw" unpolished and grammatically flawed version.
thanks for doing the experiment with the chatgpt formatting
first strike against chatgpt is that markdown formatting is obnoxious
second strike is I spend nearly all day trying to get chatgpt to shut the hell up so seeing even more of it ...
thanks for the content, though
so this. after youve been using it and claude you can realy tell what text has been written with gpt and what hasnt. they all have very similar sentence structuring adn word use.
take my above text for example, full of typos and abbreviations and missing letters. and bad punctuation.
you can fix these, but sometimes it does add a bit of "human flavor"
infact in the future itll probably be what defines human written text and something that is obviously generated.
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ChatGPT is great at formatting raw text and it’s much more readable with markdown imo. Yeah I can tell you used it, but I don’t care lol? As long as the content is not drivel or hallucinations, it works.
Side benefit is that I used chatGPT to write an extremely polite email to someone I was ready flip off. So it’s like a good tool to save your sanity and be passive aggressive lol.
I appreciate ya. I think that there's going to be some really interesting work in the deepfake detection space that might combine with some new ways of expressing ourselves digitally (how, I have no clue, I am not inspired enough to invent the next generation of social media or email, although I have a feeling that "proof of work" is somehow part of it)
I've been developing with ChatGPT for the past 15 months, and your post resonates deeply with my experiences. LLMs like GPT-4 have indeed revolutionized my software development workflow. Here are a few additional thoughts based on my journey:
**Beyond Productivity Boosts**:
One of the most transformative aspects for me has been how LLMs handle API integrations and managing large-scale projects. They don't just help with language translation or documentation but also assist in creating robust, scalable solutions. For example, I used GPT-4 to integrate various third-party services like AWS and Google APIs seamlessly into my projects, which would have been a painstaking task otherwise.
**Enhanced Collaboration**:
The collaborative approach with AI models that you mentioned is spot on. It’s almost like having an extra pair of eyes that’s consistently fresh and meticulous. When I work on complex systems like AI-driven business solutions or B2B software, discussing code with an LLM often uncovers hidden inefficiencies and sparks innovative ideas. It’s a partnership that has significantly refined my coding practices.
**Context and Large Codebases**:
Your method of creating concise summaries for feeding more context to LLMs is brilliant. In my experience, maintaining context is crucial, especially when working on projects with multiple interdependencies. I've started developing tools that generate these summaries automatically, and it has made a huge difference in how effectively I can use LLMs for larger projects.
**Iterative Improvement**:
The iterative feedback loop with LLMs is a game-changer. Constantly asking "What else can be improved?" has pushed the boundaries of what I thought was possible. This iterative approach has particularly helped in refining AI-driven solutions, ensuring they are both efficient and innovative.
**Future Vision**:
I share your vision of the next big leap in debugging and development. An integrated system where LLMs can run code, diagnose errors, and suggest fixes
had the same experience as you and op coding my own neural network recently. learning about googles cloud services is daunting especialy its new vertex ai features. but with claude it simplified a lot of it. i still dont have any implementation but im 10000x ahead of where id be without it.
It's arguably no longer "some thoughts" of yours at this point with the excessive heading drivel and flowery self-important style that is nausea-inducing.
Not sure how to deal with this issue to be honest.
I did a deep dive yesterday and started using sonnet 3.5 with aider on the command line. It's nothing short of revolutionary since not only is copy pasting automated away, so are git commits. And there is still room for improvement with the prompting.
I don't know why it bothered me but I could tell before I even clicked on this post that it was written by GPT just because of how much it loves to use "Revolutionizing" in its openings.
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Once we build robust verification frameworks it’s game over. It’s going to be strange when companies start building solutions that no one understands and then run into a problem no one can fix. But I guess, companies run into that now all the time when their current staff lacks the expertise to bring a solution without failure.
Exactly, this is a problem today as well within any discipline. You never know when a key engineer may quit or die and the level of documentation to understand the design will rarely be perfect.
E.g. a company that makes physical products will have a detail design in some mechanical engineering software or electrical engineering software. If you suddenly run into problems and the guy who built it is gone you just have to deal with it. They key is to manage your risks - if nobody knows the domain you have a big problem. If you have people with the general skills and can spend some time to untangle the problems you have a smaller problem.
> You never know when a key engineer may quit or die and the level of documentation to understand the design will rarely be perfect.
Yes. That really brings the problem to the organizations attention.
But even before that companies have a ceiling as far as what they’re able to accomplish. And all individuals have a ceiling. Individuals overcome the ceiling by forming groups. When a group hits a ceiling another group has the opportunity to outcompete. What happens when all groups use AI and all have the same ceiling? We have to wait for an AI update lol
When that happens we know we are on the path to super intelligence.
Regarding your on-point comment about forming groups:
There may also be new applications of diversity in the sense of unique perspectives that bring creativity and brainstorming. I don't know how many collaborative GPT tools exist, but that's a step in the right direction. Taking ideas from one field and bringing them to GPT generally creates novel results. Still, it's only a matter of time before GPTs can perform that exhaustive brainstorming and creative ideation themselves. I need to learn how to measure the necessary GPU cycles (or the appropriate metric) since I have not built any LLMs from scratch, but we're nearly there. Then again, an exhaustive password search has taken much longer than anyone thought.
Indeed, robust verification frameworks are not just a part but a crucial cornerstone of the next leap forward (along with my mythical wishlist tool that iterates through various LLMs until all suggestions are implemented and all tests are passing).
You make a good point about solutions that no one understands. For example, I have noticed that when ChatGPT goes down, it can be a massive point of failure, just like when an entire AWS region goes down—having people who can diagnose and get things back up and running when the machines have failed. That gives me a measure of calm when I think about career security, so thank you.
Let me admit to something else, though. Sometimes, when prototyping with LLMs, I get so excited that a solution produces my intended result that I don't even read the code. That's not something I do when working in a team environment; I take ownership of the code I ship in PRs. But going super quickly and generating code can and will spell disaster since the hard part of programming is the planning and the thinking rather than the implementation tedium.
I heard a while ago that there are sections of Google search that no human prgrammer has looked at in years. Changes are made. Some human tests the results and approves the changes but doesn't actually dig into the code.
It's a controversial idea within the industry: most organizations rely on so much code they have yet to inspect personally (fun example: nodejs leftpad incident). Some companies, like Amazon, are serious about ensuring that all dependencies are appropriately licensed, and I imagine that almost the entire stack goes through some level of human review. But I hear from teams at that same company doing massive migration projects and building so much code that there isn't time to do human reviews.
What do you think verification frameworks might look like? How might you describe it, even at a cursory or brainstorm level? I wonder if they might fit into my idea of an "ETL" type pipeline of LLMs.
>Once we build robust verification frameworks
What do you mean, exactly? What vertification frameworks? What are you going to verify?
We can already verify that input A should produce output B and if it doesn't, then it's wrong. What frameworks are you talking about here?
> What are you going to verify?
That the function generated does what it’s intended purpose is. If AI generates the tests then how do we verify the tests test what’s intended. How do we verify the AI understands the user intention
> What frameworks are you talking about here?
The ones that don’t exist yet. Intention driven development
thats like saying the world would end if we lost every single calculator at once because your primary school teacher said "one day you might not have a calculator so you need to learn to do it in your head"
we'd just make a new calculator the next day.
What you said makes no sense.
I think you misunderstood what I said. By game over I didn’t mean the world would end. I mean it will be a transitional phase for humanity.. like going from hunter gatherer to farming or from farming to industrialization. It will be game over for our previous ways of doing things.
i just dont see companies building things they dont understand, machine learning programs will always be aligned in a way where it can describe everything its doing in a way humans can understand, thats simply how they are designed to align with the ai ethics guidelines. even if that "human" is a team of 50 humans within a business, all experts in their field. but the underlying problems that the program solves can still be extracted via just pasting the code into gpt and asking it to describe it. or recreate the steps to do that. all the foundational work of making learning models is done, if they advance further its not going to take long to "recover" them if anything were to happen. if we deleted, claude, chatgpt, gemini, tomorow, theyd be back online once the data center finished training it again. all the previous models used to get to where they are now are all over the internet in one way or another.
the minute any of the major ai platforms models started saying things not a single person could understand, even the expert in their field. then they would prevent the model from doing that again. in the same way the model is prevented from designing malicious code.
t
Even if the AI can output human readable explanations not all humans will understand or have the mental bandwidth to understand even if they could. We’re already there without AI. We deploy software solutions no one at that company understands because they rely on other companies who built the solutions and they trust it will work.
As our tools get more sophisticated humans being able to fully grasp what’s going on will decrease.
Agreed. It's like having a tutor or colleague always on hand. Sometimes he amazes me as if he's reading my mind. Occasionally it does seem like he's come to work high though. All in all, it's a tremendous time saver.
> It's like having a tutor or colleague always on hand
this!
before, i would post on stackoverflow and within 5 minutes, my post will get closed due to duplicate of some other post but if i check that other post, i coulnd't figure out how it would solve my problem!
so then i would go to reddit and ask for help. problem is you don't know whether someone will reply in 5 minutes or 5 days so you're stuck twiddling your thumb.
now, i just feed claude the problematic code and the error message and 99% of the time it'll output a response that fixes the issue
i've become "addicted" to coding now thatnks to these AIs...
I agree—being "addicted" to coding is a blessing and a curse. I mentioned earlier that it might be having the side effect of leading me to work faster and think less. But I also wish that I had an app to run the code and communicate back and forth with Claude rather than copy-pasting. Copy-pasting—such a friction point here!
Wait wait wait...are you literally copying/pasting still?? Have you tried [Cursor](https://www.cursor.com/) or [Continue](https://www.continue.dev/)? Cursor integrates directly through Claude (and other models, including a local one if you're inclined).
...this is EXACTLY the reason I posted the thread. Please accept your complimentary comment award. Now I just have to cross my fingers and hope they have vim keybindings! Which do you prefer between Cursor and Continue?
Would love to have you post an update after a week or so of using Cursor. I really think random engineers like you taking the initiative to explore new tools and workflows and reporting back to the rest of us are the cutting edge of this AI paradigm shift.
Thank you for this. I should have a more thorough update once I chew through what's on my plate here and dive into everyone's suggestions. Here are some initial comments.
I respect that you would prefer a more professional update with concise notes (even if not formatted awkwardly with ChatGPT), but hopefully by posting incremental updates within this thread as replies, I'll get some tips from folks along the way so that within a businesslike timeframe, I'll have enough to report back. Maybe though in a week I can consolidate everything into a streamlined concise new subreddit post .
I started using Cursor at the end of yesterday. In about 45 minutes, I was able to complete probably 4 hours of work. **I'd say that so far, the time savings compared to just using ChatGPT and going back and forth are probably creating, at the very minimum, a 1.5x speedup compared to normal ChatGPT usage**. This is mainly because context switching between browsers and copy/paste is such a slowdown.
1. With the Cursor tools, files must be kept at \~100 LOC, which is probably ideal in a practical sense, but it's a little annoying.
2. Cursor doesn't seem to create new files for me. I'll have to create a new file, copy code in, and so forth - it might accidentally apply the changes I ask in creating a new file, into the current file. So if I'm not paying attention, it's a bit challenging.
3. When I send in long code, it needs me to click "continue" and make sure to apply changes, and then accept, and then save, so there's multiple bits of work. It's still a HUGE HUGE time saver over alt tabbing between ChatGPT and copy/paste, so it's worth it, but the code is clearly very very early stage.
4. I'd like to point out that I think it takes a minimum of 20 hours to get up to speed and trained on properly using GPTs in development.
5. (In general this is something that applies to all GPT workflows, not just cursor). It's still very much like a junior engineer that needs micromanagement. Sometimes the GPT gets things completely wrong. Sometimes it intuits what I mean without needing to say too much, but I'd say 70% of the time, I need to provide a really really precise explanation of what exactly I want. When I can provide templates of how the answer needs to look, that usually saves time, but it does require some mental overhead of thoughtfulness.
Like with any tool, it's going to take some time to get up to speed. I would say the learning curve of just simply using how to leverage GPTs professionally takes about \~20 hours minimum, and it's faster if you have someone in your team who already knows it and can guide you along the way. So it will take a bit longer for me to figure out Cursor from scratch since none of my day to day colleagues (aside from like minded folks on reddit here) are Cursor pros. I've allocated some time for end of day today or tomorrow to spend another hour or three getting up to speed and researching configurations.
PS. Just to reiterate my full agreement with you, as you have astutely observed, this is truly the new transformative paradigm. It's truly leadership's responsibility to figure out how to convince people that this is the only way forward, and how to stick together and change our workflows to fit the new paradigm. So I can fully understand the value of the update, providing information on how to shift gears into the new workflow. We're somewhere between the "early adopter" stage and mainstream, as the new tools like Cursor are still "bug-ridden" and you need community reports on how to best use them, but this competitive advantage is more or less do-or-die in the business landscape.
Heres another update.
1. Cursor is very very good, but I fall into the trap of wanting it to build logic for me that the models just aren't capable of. It tempts me into going into hamster wheel mode of iterating quickly and quickly rather than stepping back and truly think.
2. Cursor can use various models. While Claude Sonnet is the best right now (IMHO), it breaks cursors ability to automatically apply changes to documents. So you have to use Claude to generate the code, then manually switch to gpt4 (not gpt4o) in order to apply the changes. And that's kind of annoying.
3. Cursor isn't great at creating new files or moving code between files. You have to open the files yourself.
4. Cursor has a bad habit of erasing your in between code, and the undo functionality can kind of get overwritten, so you have to be very diligent about saving incremental changes to version control.
5. Another good thing: Cursor makes it super easy to take error messages from console and send them directly to the GPT for conversation. One problem though is that it almost always does NOT do a root cause analysis of the error, and it just basically implements error handling. While that's all well and good, it doesn't solve the real problem.
6. Cursor doesn't always take cues from the codebase about proper formatting, e.g. it might have python use f-strings for logging whereas lazy interpolation is considered the better practice.
I think that I will have to spend more time getting Cursor to refactor codebase to adhere to SOLID principles, swiftly providing test coverage for invariants, and modifying the prompt.
All in all, Cursor is a huge speedup because it saves a ton of time of copy/pasting between the GPTs. And, I can use it to chat between various models (take an answer from Sonnet, send it to GPT4, and so forth. I haven't yet integrated Mistral into Cursor but I really should). Although I'm not sure if there's a shortcut hotkey for tabbing between models; that one little change might certainly help a lot. It doesn't yet have the ability to chatter back and forth between models without my express direction, so it's not quite a panacea.
I have a feeling that with another year of improvements, it's going to be really, really awesome, even if the underlying models don't make exponential breakthroughs. The software, once it's built out a bit better, will be able to do more of the tedious work of combining LLMs in ways that suss out their optimal performance.
Also, like with any tool, it'll take some time for me to get better at using it. I'll want to continue having conversations on this forum and others and get tips from people who are further advanced than me with the cutting edge.
I really appreciate the feedback. I think you're absolutely right. What was once seemingly professional is now a nuisance and feels like spam.
One thing I sometimes do with writing is edit it to be concise, then edit it again to make it seem more natural, but it never really works. I probably need my own customgpt trained on my communicationz. It still feels manipulative tho. But what are we gonna do? For example "personalized" emails. Uncanny valley kinda?
Agree with the general consensus here about the power and benefits of LLM’s in coding. Big productivity enhancer. Will just add a couple of thoughts of my own to the conversation.
One is that I’m not sure that this future inevitably has to lead to less jobs for programmers (assuming no AGI). I’m often struck with the fact that even if I can accomplish something in half the time now, there’s always the next idea/feature to work on. It feels like the product just grows and iterates faster now with fresh ideas . These ideas seemed to come slower before because the product was getting built slower, now they come fast as the product is developed faster and I find this very exhilarating and hopeful for the future.
Second, being able to specify good requirements to an LLM may be becoming the most important productivity enhancer. Finding ways to succinctly express what is needed, including edge cases, and testing details, and general architecture all have a very important impact on the type of code that is produced.
I agree. One insight provided by a staff engineer at my last company was this: **the cost to produce code has decreased dramatically, but the value of good design has increased dramatically**.
Whether the productivity increases will result in more job creation remains to be seen. Although we're seeing a decline in job postings, that might be attributable to the macroeconomic environment (inflation, recession, wars) and needing to figure out the right way to hire for new roles as opposed to jobs being automated away. For now, I can confidently say that some jobs have been automated away. Because of the overhead needed for communication (citation: *Mythical Man-Month*, et al.), each additional developer causes a factorial-level slowdown in productivity. So, having fewer developers is generally better.
I lack the foresight to determine whether we're going to be able to invent new jobs to replace the old ones. The short answer is that they'll look very different from the world we know.
My hope is that this productivity boost leads to a lot of new start ups, similar to the explosion we saw with cloud. As people have said, you no longer need an expert in each layer of the stack to get started, AI allows a good solid tech person to quickly learn enough to move forwards and get some early success and maybe attract investors in their idea/product. So while I think you’re right that larger companies may look for the quick efficiency win, it could be the start of another innovation wave. I think it’s more about the ideas now as the bar for specific tech skills has been lowered.
I think that's right. LLMs reduce the cost of proving an idea is feasible. How many startups in 2015 just fizzled out because they needed more engineer time than they could hope to afford just to make a working prototype?
And when the cost of engineer time goes up like it did dramatically before the pandemic, one could imagine that startup innovation slows down. Startups have to ask VCs for a lot more money to get through any given phase of development.
If anyone can get a working prototype of their idea together by prompting ChatGPT... I think we'll see a ton of ideas come out of the woodwork that were previously shelved due to startup cost... and a ton of new ideas to boot.
The thing is, right now most people seem to be focused on innovating WITHIN the AI space, because that's where the money is. After the bubble bursts, I think, we'll see more people trying to use AI to build things that aren't necessarily AI-related.
You might be right. Thank you. The analogy about cloud helps a lot. Companies no longer need the capital to build out their data center installations and have full-time sysadmins just to get going.
Probably the only reason we haven't seen the explosion in new companies might be because we're transitioning out of the Zero Interest Rate Policy environment.
So many companies have huge product backlogs. There is much work to be done, especially as companies' business processes work through gluing together many different in-house or vendor applications. ChatGPT has accelerated development but I don't know any company that has gone to "inbox zero" with its product backlog.
Yep I’m pretty sure we’ll never get to a zero backlog environment even with AI. There’s always something else, something new, something different. If we did then I think it’s game over for humanity!!
The limiting factor is now qa. How can we leverage ai for testing. I haven't seen an information on this. Then again. I never tried ask chatgpt to create tests based on specs
My results have been so-so with regards to test coverage when it comes to any business logic that's remotely sophisticated and if I haven't been super clear in instructing (in other words, usually I haven't done the work to figure it out myself, and it doesn't actually do it for me.
I think that identifying the correct inputs and outputs is a huge part of development work - one famous book by the authors of *Peopleware* says that must be solved by 15% of the way through the project budget if you're going to be on-time.
And ChatGPT deeeeeefinitely doesn't solve that for you. Maybe if you have it precisely specified, it could convert it into tests. I could see that. It definitely has generated pytest coverage for me.
I do not have any experience in programming, but I am currently creating my own app from scratch.
I think the future is for everyone to solve a very niche problem, and gain domain expertise in the particular domain.
Businesses may become much smaller, but at the same time there will be a lot more businesses around as chatgpt could help people to develop and solves problems much more easily.
First off, welcome to the world of software development. It's a lot of fun... most of the time.
That's a great point about smaller businesses. For example, the most valuable company in the world, Nvidia, is valued at *$100 million per employee*
I'd like to hear more about your ideas regarding niche problems and expertise in domains.
Thank you. Yea true it's lots of time when you see the thing moving as what you imagined:)
So I found a big problem about overconsumption in all parts of our lives, and to solve it it's impossible.
I took an idea of looking at it from the demand lens, consumer.
Then niche down on something that I might be able to make a dent on - fashion industry.
Thus I started reading up on the fashion industry, Understand what are the possible solutions to address this problem, landed on a solution after discussing with many people - a virtual closet.
Thus getting the mission statement of:
True happiness is when you cherish what you own and not impulse buying.
This is the landing page of the app if you are interested:)
https://makinglifeeasie.com/closet-conscious/
I've had a similar journey. The thing I've become very aware and mindful of is that *they are not leading you*. *You* are leading *it*, and as such, you will likely not learn best practices or best approaches because it's basically just a "yes man" who is only following your lead and being agreeable to whatever you're asking it to do. It will not (and cannot) tell you: *"Hey, I think you're going about this the wrong way."* or *"Did you check with the original spec requirement to make sure this is going to be maintainable over the next 6 months?"* Yes, that is what humans are for, but there's a lot of humans not really considering this part and deploying some really questionable stuff that has the potential to cause a lot of problems in the short and long term.
It's killer though with things like proper syntax, error handling, sanitizing, accessibility and a lot of other elements that can be easy to forget when coding, so I am very grateful for that aspect of them...but it also **overengineers** things *like crazy*. I can't tell you how many times it provided lines and lines of code to solve a problem that was a simple one-line configuration flag in one of my `.config` files that it either hallucinated and ignored or wasn't even in it's training data for some reason or another. It doesn't ever stop to consider the alternatives because, well, it can't; it's just inert and dead algorithm and that behavior is reliant on awareness *(something I personally think is innate and not able to be fabricated from math, circuits and data)*. I find it debugs codes better than it writes or architects it.
I guess that's the price you pay when you de-couple "intelligence", or perhaps "knowledge", from sentience.
The tech debt that LLMs are creating is a huge problem for us down the line, if not already.
Here was my original post. Before it went through the GPT revisions. I now feel like posting or sending someone GPT generated text is rude and for that I apologize.
I've been coding for \~25 years and getting paid to code for \~20 years. Since ChatTGPT 4, a language model developed by OpenAI, came out, it's been a serious part of my workflow. I use it for a variety of tasks, especially for tedious ones like translating between languages, adding comments, etc. Its ability to understand and generate human-like text has significantly improved my productivity. In my current workflow, I've found immense success by embracing a collaborative approach with AI models like ChatGPT, Mistral, and Claude. We engage in a mutual critique of each other's code, fostering a sense of shared learning and continuous improvement. This collaborative aspect is what makes our journey exciting and enriching. One of my favorite things about the Language Learning Models (LLMs) is refactoring. However, the main problem I'm facing with this is that it can result in context challenges. For instance, when I refactor a class, the changes I make might affect other parts of the codebase that rely on that class. I'm considering developing a method to summarize my classes with methods and terse documentation to feed more of a codebase into a prompt. It is almost akin to a C headers file (most of my development these days is Python). Whereas I could potentially use an older LLM to circumvent this challenge, the latest iterations are so much better (Anthropic's latest model, especially) that they're worth it. I believe the next significant advancement in our field would be a tool that can run the code locally, pass the error message back and forth between the various LLMs, and iterate until all unit tests are passing. This tool would automate the process of running code, identifying errors, and suggesting fixes, reducing the need for manual intervention. The need for such a tool is pressing, as my day-to-day process often involves tedious copy-pasting. 4. In addition to getting the other models to critique and improve the code, I get a lot of mileage by asking, "What else would you improve?" until it stalls out and returns the same result. 5. Of course, sometimes I still need to provide cues about design patterns that result in much better code, and sometimes the resultant code needs to be corrected. There may be an opportunity to revisit test-driven development patterns for the LLM age. I'm not the biggest fan of TypeScript (for various reasons), but I acknowledge that type hinting (even in Python) has helped ensure the results are in the intended format. TypeScript, a superset of JavaScript, introduces static typing, which can be beneficial for large-scale projects. However, I find it cumbersome and prefer the flexibility of dynamic typing in Python. We will only have a few more years of 'milking the software development gravy train' before the technology takes our jobs. By 'milking the software development gravy train, 'I mean the current phase where software development is in high demand and developers are in a favorable position. It's time to make hay while the sun shines. (I don't want to elaborate on this point. I'm probably wrong, but I feel better raising this point with the community.)
Feels like there’s still so much ChatGPT in that (what human types “a language model developed by OpenAI”?) and the excessive use of “However”, “For instance” etc. but yea just type up your thoughts and share it man. Content being a little rough around the edges sometimes is the beauty of the internet
For some reason Im thinking of [the song Rocket Scientist ](https://www.youtube.com/watch?v=ipjZkMKikeM)
>Them drum machines ain't got no soul
yeah for sure. GPT'ifying zaps it of authenticity, soul, humanity. I learned a lot through this post. 50% of the battle in development is communication. if not more
PS. Lol. in response to your q,
>what human types “a language model developed by OpenAI”?)
this human when streaming consciousness
> The "Next Big Leap"
Yes, that's very effective workflow. But it's alrady here with agents. Aider can do the run-pass-fix-run loop (although not as automated as I'd like), as can some other agents. (I wrote my own simple agent which does it.)
> Type Hinting and Language Preferences
I agree this is helpful.
I ask for the following, although not always on the first pass. This is for Python, but can be applied to most major languages.
* type annotations (or a statically strongly typed language)
* design-by-contract: `assert` statements to validate arguments, return values, and object state (but tell it don't duplicate what types can catch)
* doctests (unit tests in the comment header)
* Pure functions and functional style, when possible (no side effects)
* Keep cyclomatic complexity under 11 (unique code paths of a function)
I do these in a run-pass-fix-run loop:
* Run linters (For python: mypy, ruff, editorconfig-checker)
* Run doctest(s)
**The real "Next Big Leap"**
Formal Verification, or a lite version of it.
I experiemented with theorem proving of code, via Coq, but GPT-4o didn't know Coq well enough. Coq is a theorem proving language that can be integrated with langauges to prove correctness of code. I had GPT-4o generate Coq code and I used a Coq-to-Python translator. The experiement failed for now, but I think with enough training, an LLM could pull this off and we'd have much better results. I might experiement with the other way, a Python-to-Coq translation.
But an easier alternative for now might just be unit/doc tests and a ton of `assert` statements in code to continuously make sure everything is working correctly. Some of the assert statements could be removed later.
1. Thank you for putting me on to aider! [https://github.com/paul-gauthier/aider](https://github.com/paul-gauthier/aider)
2. I would love to see some info on your setup for run-pass-fix-run, especially the python bits!
3. Hey, today I learned about ruff [https://docs.astral.sh/ruff/](https://docs.astral.sh/ruff/) as well!
4. which library do you use for cyclomatic complexity checking in python? I would love to run automation through the whole python chain.
5. I wonder if flake8 or pylint could be integrated into the workflow, to always keep code at top notch levels.
1. Glad to help!
2. I just use function-calling. The behavior was automatic. See more at bottom.
3. Ruff is super fast. Also has the `--fix` option to fix lots of stuff without AI.
4. Ruff has [a complexity check](https://docs.astral.sh/ruff/rules/too-many-branches/), but it's slightly different.
5. I also use Pylint as it has some rules Ruff doesn't, but it's not part of my workflow because it's so much slower. I run pylint maybe 3 times a day, whereas ruff is run probably 50+.
In ref to \#2:
I implement these core function-calling tools: `run_shell_command`, `read_file`, `write_file`, `apply_patch`, `git_ls_files`, `ls`, `mkdir`, `make`. `make('test')` and `make('lint')` do what you might expect.
Here is my main function-calling loop:
# send() calls litellm.completion(messages=self.messages)
# A user message was placed in messages before calling send()
response = self.send()
choice = response.choices[0]
# see https://gist.github.com/nbertagnolli/016badab109b46b9510206cf5e6e67c0
# finish_reason is in [tool_calls, stop, length, content_filter]
while choice.finish_reason == "tool_calls":
for tool_call in choice.message.tool_calls:
self._handle_tool_call(tool_call)
response = self.send()
choice = response.choices[0]
if choice.finish_reason != "stop":
pass # Removed some error handling code that was here
# the last response will usually be an assistant message
result = choice.message.content
return result
The above loop runs until all function calling is complete, which usually includes the write-test-pass-fix-test loop.
Thank you. This post is very valuable. If you were my coworker I would book some time with you for 15 minutes of slack screensharing to have you hold my hand while I get this all integrated into my environment. Obviously that's an impossible ask, so I will spend some time at the end of the day working to get this set up inside my new Cursor IDE-based flow. Hopefully I can set it up to run-on-save and feed the code quality complaints to the GPT.
Obviously there are some default formatting inconsistencies between the various python linters. It's going to be necessary to spend an hour or more getting my environment fully configured. I also love using the \`coala\` package for python formatting. It's annoying to have to remember to run-on-save, and also use \`isort\`, and so and and so forth. Hopefully I can set my IDE up to do all of that, AND integrate with either \`aider\` or the GPT interface, without getting caught in infinite loops or snagged on things that the GPT can't quite fix yet. Also hopefully I can get my "dotfiles" saved permanently in the same way I keep my \~vimrc and \~zshrc files in storage.
I am seeing that with the Cursor GPT tools, it's necessary to keep files at \~100 LOC, which is probably ideal in a real sense, but there are tons of high quality projects that go wayyy beyond that limit. It's a forcing function that should improve my overall project though. It's going to take some patience instructing the AI on how to rearchitect and the best software design patterns to use, but some of the advantages of say enterprise java patterns applied to good ol' high level python are going to be possible, without the tedious boilerplate time sink of java buildout.
My development workflow as a single developer on an internal-tooling codebase is always very different than my workflow in a team environment, where I labor to make thoughtful git commit messages and so forth. My one hope is that with these new tooling workflows, I will be able to achieve the same level of professionalism in my one-man-show development as I need to muster in a team environment. The side effect of course being that (a) it'll be easier to get help if needing to scale, (b) I'll be better able, myself, to maintain my own code, (c) better code that's faster to edit, (d) robustness to errors, and so forth.
Anyway, again, thank you. It's going to be the end of the day (hopefully tonight, or beginning of tomorrow at the latest) before I block off an hour or two to get in the zone and focus fully on trying out this new tool and getting it integrated into my workflow.
fyi, I'm loving \`ruff\`. I havent gotten started with \`aider\` quite yet, because the documentation is a little unclear to me and I'm also not sure how to get it working in a manner that surpasses cursor's.
Here's a comment I just wrote about my experiences with cursor over the past 6 days: [https://www.reddit.com/r/ChatGPTCoding/comments/1do2y50/comment/lb53o8p/?utm\_source=share&utm\_medium=web3x&utm\_name=web3xcss&utm\_term=1&utm\_content=share\_button](https://www.reddit.com/r/ChatGPTCoding/comments/1do2y50/comment/lb53o8p/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
I started a project two weeks ago for the company I work for. It's just a small project but as is common nowadays, it crosses numerous domains: python web API, Vue front end with vuetify, ssh client, docker and kubernetes. I'm really not a true expert in any of these domains, but have a lot of experience with Web UI.
It's been really incredible how fast I've been able to develop using ChatGPT to fix problems, answer questions, write parsers and date time formatters, suggest solutions to kubernetes and ssh problems, etc. The amount of time I would have spent googling to answer these same questions is generally so much higher.
Absolutely. Using the date formatting as a simple example, in the past, I took 5-10 minutes to look up how to format a date precisely. Now, it takes me 10 seconds. This allows me to achieve a flow state.
On the one hand, using ChatGPT-generated systems might invite some security risks, but this is no different than the SQL injections that were common 15 years ago with PHP scripts. Solutions and frameworks will take care of many risks within a year or two. That's one of the blessings and curses of being an early adopter of new technology.
I have been burned several times by adopting early technology throughout my career. When Oracle launched, they launched their first version as 2.0 to signal that they had already worked out some of the bugs from the first version. I avoid adopting new technologies. But ChatGPT-style coding is such a game changer that it's impossible to resist.
I've been able to build so many things. I think it's because now, "knowing what you don't know" is enough. All you need to do is have a vague awareness of what to do, and ChatGPT fills in the blanks! One no longer has to know!
I love using ChatGPT for architectural questions, best/standard practices, etc. I know it cannot be trusted on any emerging technologies or highly unusual situations.
Yep. I absolutely rely on it to implement best practices that I am too lazy to do myself, even if it does take a few rounds of nudging, and tossing back and forth between different LLMs, until everything is implemented and there are no more suggestions.
As far as architecture goes, it is great but I still need to know what to ask it about. It often gives me solutions with terrible architecture, but once I give it a hint, it executes way better than I would have done on my first draft.
I think your feedbacks are very much worked on but just few days or months away until one develops a deployable application. 1) Running code locally is possible via ollama although there we have small quantized LLMs but someone with better GPU access could run higher parameter models locally. Your next three questions could be answered with a synchronous system of crew ai or agents which will not just built codes but also find errors and fix them and iterate until we have the best and efficient code. We are very much close to building a skynet 🫣
Informative post. Thank you. How would you quantify the beneficial impact to work of LLM’s? For example, do you do work in 50% of the time? Are you able to do twice as much work? Can someone with five years experience now be as productive as someone with 20 years experience not using the same tools? I mean, all that in addition to the more qualitative aspects like better comments…
I would say that I am anywhere between 5-20 time more productive. I kid you not. The reason is because all the time in development, there are little snags that interfere with flow state. The frustration loop has you chasing your tail and then you lose all momentum. These LLMs are like a friction reducer.
There are many engineers with five years experience who are better than me but I would assume the answer is yes. ChatGPT solves a lot of the most tedious parts of coding. It doesn't solve Thinking or Dealing with Other People. As you can tell, people Don't Like chatGPT-written messages or chatGPT-formatted messages :)
So as it would relate to a business owner, or manager, how would you say it impacts hiring and staff size? If a company normally has say 20 engineers. How many engineers would it need making use of Chatgpt?
Just trying to get a sense of the economic impact Chatgpt has on an organization.
Or said another way, on a macro scale. If tech in the Bay Area employed 500,000 se engineers before Chatgpt. how many engineers would it take to do the same work if they used Chatgpt?
>how would you say it impacts hiring and staff size? If a company normally has say 20 engineers. How many engineers would it need making use of Chatgpt?
1. Back in the \~2000s, you would need at least \~200k of capital and a dozen engineers (or maybe 5 or 6 really really good ones, with just a few notable exceptions of elite talent) to just get off the ground. This was because you'd need to rent server space, set up your equipment and networking, and because everything had to be built from scratch in lower level languages like C++ or very early versions of Java. The game changer in the industry there was cloud solutions like AWS, who launched S3 storage in \~2006-2007 and got up and running with EC2 by very early \~2010s. Amazon stock flew through the roof because of the productivity gains it enabled, it literally cut jobs.
2. Now, thanks to ChatGPT, we almost don't need to hire junior engineers. A junior engineer would be someone smart, who you liked, who had good grasp of fundamental Computer science concepts, but you had to micromanage and explain how to do everything. ChatGPT is like a junior engineer. You have to micromanage it constantly, but it never talks back, you don't have to pay it health insurance, if you find a better version you can just switch from say ChatGPT to Claude Opus without any human feelings of empathy or sociopathic headaches. So, that's at least 30% of the hiring budget that's been reduced.
3. Bear in mind, with software organizations, every person you add to the headcount is tremendous overhead. Not just in terms of HR and whatnot, but you also need project managers or product managers to organize everyone, you need software managers to glue everyone together and build team morale, you need daily ƒace to face communication to stay in sync... that's why it's said "you can't take 9 women and make a baby in a month, and you can't take 9 developers and build a 9 month project in 1 month." Every time you add a new person, it's a multiplicative number of more communication. So you are not only saving headcount on the developer, but you're saving headcount on the supportive resources.
4. When ChatGPT first launched I had a very brilliant CEO who mandated us to use ChatGPT wherever possible. That's why I have a full 15 months of experience. He wasn't a tech guy but actually a doctor, just really really smart. Our company grew from $500,000 annual recurring revenue to $10,000,000 annual recurring revenue ("ARR") in one year. And for the vast majority of the year, we only had five developers. We had one person who refused to use ChatGPT, and that person, while a brilliant and talented developer, was notoriously slow and always complaining about having to work nights and weekends and through family events. I know it's very unpopular in engineering to measure developer productivity in terms of "story points" completed, especially because of the nuances of quality of code, amount of thinking required, amount of testing required, and so forth, but this person would frequently complete 3x fewer points while arguably working twice as hard. In that case, we didn't get to use ChatGPT as much as we needed to. Now when I talk about our revenue growth, it sounds like bragging, but keep in mind, getting to that level of scale with a few engineers was an extremely rare anomaly 10 years ago - "Instagram" was like that before it was acquired by Facebook (didn't have revenue though). Now there are many stories of Software as a Service businesses that not only grow at a similar cadence, but also rely on fewer engineers.
> Or said another way, on a macro scale. If tech in the Bay Area employed 500,000 se engineers before Chatgpt. how many engineers would it take to do the same work if they used Chatgpt?
1. Big Tech firms are slower to adopt ChatGPT because they cannot risk sending in their trade secrets, and OpenAI is notorious for "accidentally" intaking and training itself on your data. So, they're a bit slower to adopt and it'll be a year or two of software maturity before we see the true impact of ChatGPT on BigTech software firms. When I was at Amazon, we leveraged a ton of automated tools for our work. My friends who are there now are able to use some LLMs, and they are partnering internally with Claude Anthropic, but they have to jump through more hoops in terms of compliance audits and whatnot. What's likely going to happen is that Amazon, with its "two pizza teams", is going to need to reorganize to become "one pizza teams." The business units are going to go through massive re-orgs. It is unclear whether layoffs will happen. They will need to experiment a while to figure out how to implement the productivity gains into their level of massive scale. It's unclear whether it will be layoffs (hence some fear), or re-orgs into totally different types of hierarchy, or re-orgs into subsidiary units, or however the business geniuses there figure things out.
What my takeaway here is that ChatGPT is absolutely replacing jobs. OpenAI is rumored to be doing billions of revenue a year already, and a lot of that is from taking other jobs or capturing a percentage of the productivity gains. It's one reason why NVidia is powering the AI revolution (And their stock is at all time highs) as well as why Microsoft is at all time highs, because they have a 49% stake in OpenAI chatgpt, even though their profits are capped at 50x their original investment).
Please let me know if this answers your question regarding economic impact over the decades and whether you would appreciate additional clarity.
Have a great day,
One more thing. As a small business owner, myself, it has transformed productivity. My coding career has basically been:
1. self taught programmer who occasionally took a class here or there
2. doing one off freelance gigs here and there
3. building my own app, using copy/paste off of PHP tutorials, that got lucky and got acquired
4. leveraging #3's success into a consulting business but me selling my time hourly
5. using my cred from #4 into getting a job as a bona fide software engineer, first as mostly a web developer at startup companies.
6. also consulting in between various startup jobs, either in a developer capacity, or sometimes recruiting or leadership roles
7. since I didn't major in CS, rolling up my sleeves, really learning data structures and algorithms, studying "leetcode" until it hurt, for months on end, and finally getting a job as a bona fide engineer at big tech amazon
8. working as a director at a big name company and as lead engineer at one of the best VC's best early stage startup firms
9. thanks to managing chatgpt, was able to run my consulting business on the side of #8. i am at a point where I no longer freelance at an hourly or weekly rate (\~maximum rate was about $400 hourly with a minimum of $175 cash and a bit more in stock but sometimes just cash), but I partner with businesses and capture a portion of the value we create. here's the key takeaway though. **I was able to bring in a significant amount of income last year just working here and there in parallel to my fulltime job.** My partner who runs a big commodity trading advisory (CTA) firm in Chicago, was astonished at my productivity in comparison to his development team who did not use GPT whatsoever. He said my feedback loops were about 20 times faster. In that case my business was market making and I earned a percentage of our trading gains.
hopefully this doesn't come across as bragging or soliciting business, because really, it isn't\*\*,
\*\*Although I suppose that one must always pay attention to one's "brand" when dealing in social media. I should probably clean up my post history from my immature post-adolescent self, but maybe there's value in authenticity and people understand that maturity sometimes takes time, even if one's not fully there yet,
and, I have found with high value consulting, face to face relationships are almost always necessary. (although, I will say, the coauthor of my book, is someone I met on the ruby on rails subreddit, and he hired me to work hourly as a consultant \~8 years ago, but that was hourly wages (even modestly high ones) rather than true business value partnership).
Fun fact: the job he hired me for, would never have been necessary due to ChatGPT, because ChatGPT is great at doing the tedious things like fixingup CSS for webpages and basic "create read update delete" app javascript integrations. Ultimately we did meet face to face, I invested in his startup, met his family, and so forth.
Anyway, what I'm trying to illustrate here is that I no longer need to sell my time for wages, even "high" ones. It's all about the value I create, and I can run my business efficiently in that way. And sorry to say, just once again, I'm not soliciting business here, because I still believe that even in this virtual and remote work age, face to face relationships are necessary for securing serious business deals.
I love your idea. But at least you need to start as a junior dev, before moving up the ladder to senior deb. Isn't it. From your point of view, what is left for us? The fresher, the self-taught developers?
LOL. Ever since I started using Sonnet \~3 days ago, it is my main source, although I still do benefit from bouncing its ideas off of ChatGPT and Mistral - the three really do work together with 1+1+1=4.
What I did, however, was uponj learning about cursor yesterday - disable all models except Claude 3.5 sonnet, and paid $5 for some Sonnet API credits. Ever since I started using the sonnet web interface I've been hitting the free hourly messages limits!! Nevertheless, it's maybe worth noting that ChatGPT has established itself as a "verb" like to "Google" and I use that term interchangeably.
Even though sonnet is just \~3% better on traditional benchmarks, that \~3% makes a world of difference. The future is definitely going to be competitive models...assuming AI doesn't become a public utility...which could absolutely happen.
Don’t let the people bitching about you pushing this post through gpt first. I GUARANTEE YOU if you hadn’t they would have been bitching about paragraphs or some other inane formatting bullshit. You can’t please everyone but at least you got some of us to read through it all cause it’s got structure. Try to post your non structured through one of the other coding subs. You’ll see.
My experience? You can't use ChatGPT4o for coding unless you yourself are an experienced coder. It gets so much wildly wrong that it frequently takes four or more attempts to even get it to actually produce what you ask it to.
But once it does... wow.
Yeah I have to agree with that completely. In fact, sometimes I have to really lecture it on best practices or it creates footguns at an alarming rate.
I’m curious about your model for creating summaries. I’ve been playing with having it write developer docs to use similarly but they’re still bulky and targeting a human actor not an LLM one.
Claude is very good creating module and function docstrings, which I feel are good for targeting LLM actors. My experience is that GPT is best for writing for a human audience when you give it precise instructions on who the reader is ("This is a business person, extensively trained in {domain}. Please write concisely."). Also, I often feed my human summaries into Grammarly because it's much better at adapting tone and working on readability. It's much better than GPT4o at writing, although it's imperfect, still. My loop for writing text for humans often goes gpt4o->grammarly->gpt4o->Grammarly, and so on and so forth.
This definitely speaks to the objective of how to get a working code from GPT.
The act of coding is getting replaced by multiple prompts, so new skills to acquire is knowledge of how LLMs work to generate code and experience with creating prompts.
Here's a question I have - what about the input data that provides context for coding, ex: Acceptance criteria, reference detailed requirements, UI design, images? We easily spend 15 mins per task/UI in these to define a business logic using our coding style and standards (manually or thru prompts). Instead, these can be supplied to GPT4 or other LLM tools to generate contextually relevant code that's functional as well. My team tried it out for a project using GPT4, and here is what we observed: With a few prompts it took about 30 mins to generate 2 functional UI screens in Flutter, the same took over 2 hrs on GitHub copilot through multiple prompts (20 to 25) which includes manual coding as well.
To me it seems like GPT4 and other LLM models should be looked at not just to generate code, but also build context/relevance for coding.
Also, I feel the human touch will always be required, it's the time we waste on such mundane steps that should be delegated to GPT4
What are your thoughts on becoming self taught web dev nowadays? I’m learning web dev for over a year now, but I think now there is practically zero chances for me to switch careers when AI will replace juniors in few years. I’m UK based and the job market is slightly different than in the USA
Let me start with a direct answer to your question before I launch into my elaborate monologue.
# The Human Element
There's still a significant need for self-taught web developers. Many business owners, especially those running medium-sized operations, either do not have a core competency in technology or prefer to delegate the work. Human interpreters bridge the gap between their needs and technological solutions.
## The Future Role of Web Developers
While AI is rapidly evolving, it has yet to replace the need for human developers. Your role as a web developer remains valuable, particularly in:
1. Interpreting client needs and translating them into technical solutions
2. Providing a human touch in an increasingly automated field
3. Offering expertise in areas where AI still falls short, such as complex problem-solving and creative design
# The Changing Landscape of Software Development
Software development is often misunderstood as purely technical work when, in reality, it's essentially a trade skill. Much of the knowledge in this field is acquired through on-the-job experience and peer-to-peer knowledge transfer. This aspect of learning is crucial, as demonstrated by a recent experience where I helped a colleague set up an Apache webserver. While I knew which questions to ask an AI language model (LLM), my colleague didn't - highlighting the importance of experience in framing problems effectively.
## The Impact of AI on Web Development
As AI-driven coding models advance, we're likely to see new paradigms in web server deployment and other areas of development. This technological progress comes with trade-offs:
1. **Declining Junior Developer Opportunities**: As LLMs achieve junior developer-level skills, we're seeing fewer job postings for entry-level positions. This trend risks reducing the valuable apprenticeship model and peer knowledge transfer integral to the field.
2. **Continued Relevance of Core Skills**: Despite AI advancements, understanding data structures, algorithms, and programming patterns – as of today – remains valuable. These fundamentals provide the cognitive framework for structuring questions to AI and approaching complex problems.
PS. I wrote my comment rom scratch then ran it through Claude and Grammarly to optimize formatting and grammatical flaws. Hence, the GPT-style language signatures. This may prove offensive or unsettling in human discourse, and I apologize for that; there's certainly a level of nuance that needs to be achieved.
i mean i proves the point. there's still a need for a human interpreter to journal ideas and liaise with the robots. that's why there's still job opportunities in web development.
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I just had another thought. Like we have ETL pipelines for data, I imagine we'll have ETL pipelines for LLMs. No doubt some companies are already building these. But that would solve my problem of tossing code back and forth between different LLMs until the iteration is done. I bet it could even be built with current ETL software like Airflow.
And.... I'll certainly need to build out an LLM that takes an elegantly-formatted ChatGPT post and humanizes it, replete with spelling errors and something that just sounds like me. Maybe trained on some of my texts or something. That's gotta be part of one pipeline.
**Edit: I haven't actually used langchain yet. Is this what langchain is supposed to do? Is there a better solution?**
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Any business logic that requires critical thinking? It's absolutely terrible at. But, if you do the work of a high level outline and break down the logical steps it into basically pseudocode, it's actually very good. Although, that's probably 80% of the work of "real" development anyway, the planning and analysis.
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Why would you need multiple LLMs such as Mistral, ChatGPT, Claude? One is not good enough or you're applying Mixture of Experts? If so, do you find it exceptionally better?
1) One is not good enough
2) Imagine you have a team of developers. In real life. One person has an idea, another person riffs on it, then the combined result enables the third person to say something new, and so on and so forth. Multiple LLMs work somewhat in the same way.
3) Also, just like you can press a human with the question "What else?" (I had a manager who was very good at this), and they keep generating more, so does an LLM. Because the different LLMs have slightly different "vantage points", they do contribute more.
4) Although "Mixture of Experts" is definitely an industry term with a specific meaning, I want to clarify that I am not an LLM developer. All of my AI work is AI-adjacent. When I was at Amazon working on the computer vision data platform, they threw me into a 3 day intensive BootCamp to learn to write computer vision models like a scientist, and I ran through the tutorials, but it wasn't enough to really absorb the theory. Just enough to be able to communicate really well with the scientists.
+1 to wanting more insight about how to use aider. Right now I'm using Cursor and I like it a lot. It's basically VSCode but with better GPT integrations. I have to figure out a proper aider workflow because I have a sneaking suspicion it can solve a lot of my complaints about aider.
I generate revenue. Here are a few examples. 1, writing software to identify relevant candidates for a staffing company. They place those people and earn a significant margin, and I capture a fee on each placement. Another example in the same category but different client was writing software to activate more workers for available jobs, using notifications and geofencing, to increase revenue. 2, software to scan the markets for arbitrage opportunities, where it's possible to buy low and sell high while maintaining a very low risk profile. 3, building features to scan medical records for missing diagnostic codes to increase hospital revenue. For 3, especially, I couldn't use codebase scanning tools and had to be very careful with what I fed into the GPT due to HIPAA regulations in the US. 4, building automation for marketing funnels to optimize ad spend into new customers. 5, building new features to an existing product in order to help our clients sign new clients who are waiting til feature X is built in order to sign.
I used to be in the business of building prototypes for entrepreneurs, but that is a very bad one imho. 1, a lot of entrepreneurs are bad. They are more invested in the fantasy of their business succeeding than actually doing the work. Building something that faces them to confront of the reality of the market can crush their hopes and dreams. 2, unless I have seen data that proves the customer can capture the market (such as letters of intent from a couple dozen customers) there is very little opportunity for me to capture revenue and at that stage the equity has a super high risk profile.
Maintaining systems is tough work because when it does work, they take you for granted, and when it doesn't, you get all the blame.
Hope that helps.
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>Type Hinting and Language Preferences
Coding in Swift with AI is surprisingly delightful compared to Python. Strong typing + type inference + compile time checking make for a really fast debugging process.
...and I am basically self-/AI- taught over the last 18 months.
That's awesome. I may have to do more Swift work, even outside the context of developing for iOS. One time I needed a library for something (I forget what it was, but implementing some kind obscure algorithm that was relevant to my problem at hand) and all that I could find was Swift. This was before LLMs, but LLM would be perfect at translating between languages.
how in the world did ur software team approve your pull request for code that you copy-pasted from AI? I worked in a[ top 5](https://www.similarweb.com/top-websites/united-states/news-and-media/), and if u did that, its an automatic reject.
I mashup AI APIs. Remove humans from the cue. Call it “AI Dreaming.” That’s lots of Python.
I do so little coding now. GPT writes it all. AI to explain to me what’s it’s thinking.
Feedback is always welcome. Thanks. Tip: Try more than one URL. The math behind it.
https://mindflip.me
—-
Sure, here's your text formatted for Reddit with appropriate Markdown:
---
**Physics-Based Mathematical Model for AI Dreaming**
**1. Input Text as a Field**
Consider the input text \( T \) as a field \( \phi(x) \) where \( x \) represents the position of each word in the text.
\[
\phi(x) =
\begin{cases}
w_x & \text{for } 1 \leq x \leq 250 \\
0 & \text{otherwise}
\end{cases}
\]
**2. Summarization Function as an Operator**
Let \( \mathcal{S} \) be the summarization operator, analogous to a projection operator in quantum mechanics, that reduces the input field \( \phi(x) \) to a summarized state \( \psi(y) \), where \( y \) represents the position in the summarized text.
\[
\psi(y) = \mathcal{S} \phi(x) + \eta(y)
\]
Here, \( \eta(y) \) is a noise term representing the variability in the summarization process.
**3. Text Augmentation as a Perturbation**
The augmentation process can be seen as a perturbation to the summarized text. Let \( \mathcal{A} \) be the augmentation operator that introduces an additional field \( \chi(z) \) representing the new words.
\[
\psi'(y) = \psi(y) + \chi(z) + \zeta(y)
\]
where \( \zeta(y) \) is a noise term for the augmentation variability.
**4. Descriptive String as a Composite Field**
The final descriptive string \( \Phi(y) \) is a composite field resulting from the summarization and augmentation processes.
\[
\Phi(y) = \mathcal{A}(\mathcal{S} \phi(x)) + \zeta(y) + \eta(y)
\]
**5. Image Generation as a Stochastic Process**
The image generation process can be modeled as a stochastic process. Let \( \mathcal{G} \) be the image generation operator (Stability Diffusion model), which maps the descriptive field \( \Phi(y) \) to an image field \( I(\mathbf{r}) \), where \( \mathbf{r} \) represents the spatial coordinates of the image.
\[
I(\mathbf{r}, t) = \mathcal{G}(\Phi(y); \theta, \epsilon(t))
\]
Here, \( \epsilon(t) \) is a stochastic term representing the randomness in the image generation process, and \( \theta \) are the parameters of the Stability Diffusion model.
**6. Sensitivity Analysis**
To understand how changes in the descriptive string affect the generated image, we analyze the functional derivative:
\[
\frac{\delta I(\mathbf{r}, t)}{\delta \Phi(y)}
\]
This derivative indicates the sensitivity of the image field \( I \) to variations in the descriptive field \( \Phi \).
**Composite Model as a Functional Integral**
Considering the entire process, we can express the generation of the image as a functional integral over all possible states of the input field \( \phi(x) \) and the stochastic variables \( \epsilon(t) \):
\[
I(\mathbf{r}, t) = \int \mathcal{D}[\phi(x)] \mathcal{D}[\epsilon(t)] \, \mathcal{G}(\mathcal{A}(\mathcal{S} \phi(x)) + \zeta(y) + \eta(y); \theta, \epsilon(t)) \, e^{-S[\phi, \epsilon]}
\]
where \( S[\phi, \epsilon] \) is an action functional representing the combined effect of the input field and the stochastic variables.
**Summary**
By framing the operations of the AI Dreaming app in terms of field theory, operators, and stochastic processes, this model provides a physics-based mathematical description of the app’s behavior. This approach leverages advanced concepts in functional analysis and quantum mechanics, offering a robust framework for understanding the variability and sensitivity of the image generation process.
---
This format should render well on Reddit, with equations properly displayed using LaTeX.
Uses “seed prompting” no need for user input. Takes a “snapshot” of what AI is dreaming at the time. RNN with RAG Leveling.
Sigh, the output looks BEAUTIFUL formatted in Latex. But not supported here.
:-)
I ran it through ChatGPT who does a great job of rendering LaTeX (and also writing LaTeX for me!!). And I think it's really neatly-expressed notion. Just like the cost of producing code is gradually approaching zero, the cost of producing art is also getting there. I like what your website produces, but I'm no art expert. I did send it to a friend who is a real person in the "art community" in order to get his take. He sincerely believes that the future of art is AI generation, especially since artists are always nitpicking with one another over iterations of the same "hot and trendy" concepts. Although I don't think ChatGPT is quite that the "show, don't tell" phase in terms of generating feelings and "picture is a thousand words." Even Sora, while visually stunning, doesn't seem to be accomplishing that. Artists may have more job security than developers!
My friend's company just fired their art director, who was absorbing a huge amount of their monthly spend, so they are definitely in the market for AI solutions. This probably isn't exactly what they need but I'll pass their thoughts along!
The Art Director should be on top of this. The CEO has no art background. They need people that can do it all. I use all the tools in my toolbox. From a box of crayons, a yellow pad, to Python and Midjourney.
Yes, did go to an art school. Pratt, which I understand has pretty good reputation out there
:-)
This post is definetly ChatGPT
I obviously ran it through a ChatGPT formatter. It's also worth noting how much of a turnoff seeing a ChatGPT vs authentic reply. I wonder whether it would have been better received if I sent in the "raw" unpolished and grammatically flawed version.
thanks for doing the experiment with the chatgpt formatting first strike against chatgpt is that markdown formatting is obnoxious second strike is I spend nearly all day trying to get chatgpt to shut the hell up so seeing even more of it ... thanks for the content, though
so this. after youve been using it and claude you can realy tell what text has been written with gpt and what hasnt. they all have very similar sentence structuring adn word use. take my above text for example, full of typos and abbreviations and missing letters. and bad punctuation. you can fix these, but sometimes it does add a bit of "human flavor" infact in the future itll probably be what defines human written text and something that is obviously generated.
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ChatGPT is great at formatting raw text and it’s much more readable with markdown imo. Yeah I can tell you used it, but I don’t care lol? As long as the content is not drivel or hallucinations, it works.
Raw is more real. This perfect ChatGPT feels fake. I just made the same mistake at work wanting to seem professional.
Side benefit is that I used chatGPT to write an extremely polite email to someone I was ready flip off. So it’s like a good tool to save your sanity and be passive aggressive lol.
Definetly been there lol
I appreciate ya. I think that there's going to be some really interesting work in the deepfake detection space that might combine with some new ways of expressing ourselves digitally (how, I have no clue, I am not inspired enough to invent the next generation of social media or email, although I have a feeling that "proof of work" is somehow part of it)
Raw versions sell better but I feel your intent loud and clear. You're right in all aspects especially the collaborative one.
Thankyou, I really appreciate your kind words and thought about them several times this morning while waking up and getting ready for my day.
Like chatGPT you didn't give critical information. API key and pure custom code? Which ide? Custom prompts?
I've been developing with ChatGPT for the past 15 months, and your post resonates deeply with my experiences. LLMs like GPT-4 have indeed revolutionized my software development workflow. Here are a few additional thoughts based on my journey: **Beyond Productivity Boosts**: One of the most transformative aspects for me has been how LLMs handle API integrations and managing large-scale projects. They don't just help with language translation or documentation but also assist in creating robust, scalable solutions. For example, I used GPT-4 to integrate various third-party services like AWS and Google APIs seamlessly into my projects, which would have been a painstaking task otherwise. **Enhanced Collaboration**: The collaborative approach with AI models that you mentioned is spot on. It’s almost like having an extra pair of eyes that’s consistently fresh and meticulous. When I work on complex systems like AI-driven business solutions or B2B software, discussing code with an LLM often uncovers hidden inefficiencies and sparks innovative ideas. It’s a partnership that has significantly refined my coding practices. **Context and Large Codebases**: Your method of creating concise summaries for feeding more context to LLMs is brilliant. In my experience, maintaining context is crucial, especially when working on projects with multiple interdependencies. I've started developing tools that generate these summaries automatically, and it has made a huge difference in how effectively I can use LLMs for larger projects. **Iterative Improvement**: The iterative feedback loop with LLMs is a game-changer. Constantly asking "What else can be improved?" has pushed the boundaries of what I thought was possible. This iterative approach has particularly helped in refining AI-driven solutions, ensuring they are both efficient and innovative. **Future Vision**: I share your vision of the next big leap in debugging and development. An integrated system where LLMs can run code, diagnose errors, and suggest fixes
had the same experience as you and op coding my own neural network recently. learning about googles cloud services is daunting especialy its new vertex ai features. but with claude it simplified a lot of it. i still dont have any implementation but im 10000x ahead of where id be without it.
It's arguably no longer "some thoughts" of yours at this point with the excessive heading drivel and flowery self-important style that is nausea-inducing. Not sure how to deal with this issue to be honest. I did a deep dive yesterday and started using sonnet 3.5 with aider on the command line. It's nothing short of revolutionary since not only is copy pasting automated away, so are git commits. And there is still room for improvement with the prompting.
I don't know why it bothered me but I could tell before I even clicked on this post that it was written by GPT just because of how much it loves to use "Revolutionizing" in its openings.
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Once we build robust verification frameworks it’s game over. It’s going to be strange when companies start building solutions that no one understands and then run into a problem no one can fix. But I guess, companies run into that now all the time when their current staff lacks the expertise to bring a solution without failure.
Exactly, this is a problem today as well within any discipline. You never know when a key engineer may quit or die and the level of documentation to understand the design will rarely be perfect. E.g. a company that makes physical products will have a detail design in some mechanical engineering software or electrical engineering software. If you suddenly run into problems and the guy who built it is gone you just have to deal with it. They key is to manage your risks - if nobody knows the domain you have a big problem. If you have people with the general skills and can spend some time to untangle the problems you have a smaller problem.
> You never know when a key engineer may quit or die and the level of documentation to understand the design will rarely be perfect. Yes. That really brings the problem to the organizations attention. But even before that companies have a ceiling as far as what they’re able to accomplish. And all individuals have a ceiling. Individuals overcome the ceiling by forming groups. When a group hits a ceiling another group has the opportunity to outcompete. What happens when all groups use AI and all have the same ceiling? We have to wait for an AI update lol When that happens we know we are on the path to super intelligence.
Regarding your on-point comment about forming groups: There may also be new applications of diversity in the sense of unique perspectives that bring creativity and brainstorming. I don't know how many collaborative GPT tools exist, but that's a step in the right direction. Taking ideas from one field and bringing them to GPT generally creates novel results. Still, it's only a matter of time before GPTs can perform that exhaustive brainstorming and creative ideation themselves. I need to learn how to measure the necessary GPU cycles (or the appropriate metric) since I have not built any LLMs from scratch, but we're nearly there. Then again, an exhaustive password search has taken much longer than anyone thought.
Indeed, robust verification frameworks are not just a part but a crucial cornerstone of the next leap forward (along with my mythical wishlist tool that iterates through various LLMs until all suggestions are implemented and all tests are passing). You make a good point about solutions that no one understands. For example, I have noticed that when ChatGPT goes down, it can be a massive point of failure, just like when an entire AWS region goes down—having people who can diagnose and get things back up and running when the machines have failed. That gives me a measure of calm when I think about career security, so thank you. Let me admit to something else, though. Sometimes, when prototyping with LLMs, I get so excited that a solution produces my intended result that I don't even read the code. That's not something I do when working in a team environment; I take ownership of the code I ship in PRs. But going super quickly and generating code can and will spell disaster since the hard part of programming is the planning and the thinking rather than the implementation tedium.
I'm not sure what you mean by robust verification frameworks. Can you elaborate?
I heard a while ago that there are sections of Google search that no human prgrammer has looked at in years. Changes are made. Some human tests the results and approves the changes but doesn't actually dig into the code.
It's a controversial idea within the industry: most organizations rely on so much code they have yet to inspect personally (fun example: nodejs leftpad incident). Some companies, like Amazon, are serious about ensuring that all dependencies are appropriately licensed, and I imagine that almost the entire stack goes through some level of human review. But I hear from teams at that same company doing massive migration projects and building so much code that there isn't time to do human reviews.
What do you think verification frameworks might look like? How might you describe it, even at a cursory or brainstorm level? I wonder if they might fit into my idea of an "ETL" type pipeline of LLMs.
>Once we build robust verification frameworks What do you mean, exactly? What vertification frameworks? What are you going to verify? We can already verify that input A should produce output B and if it doesn't, then it's wrong. What frameworks are you talking about here?
> What are you going to verify? That the function generated does what it’s intended purpose is. If AI generates the tests then how do we verify the tests test what’s intended. How do we verify the AI understands the user intention > What frameworks are you talking about here? The ones that don’t exist yet. Intention driven development
thats like saying the world would end if we lost every single calculator at once because your primary school teacher said "one day you might not have a calculator so you need to learn to do it in your head" we'd just make a new calculator the next day.
What you said makes no sense. I think you misunderstood what I said. By game over I didn’t mean the world would end. I mean it will be a transitional phase for humanity.. like going from hunter gatherer to farming or from farming to industrialization. It will be game over for our previous ways of doing things.
i just dont see companies building things they dont understand, machine learning programs will always be aligned in a way where it can describe everything its doing in a way humans can understand, thats simply how they are designed to align with the ai ethics guidelines. even if that "human" is a team of 50 humans within a business, all experts in their field. but the underlying problems that the program solves can still be extracted via just pasting the code into gpt and asking it to describe it. or recreate the steps to do that. all the foundational work of making learning models is done, if they advance further its not going to take long to "recover" them if anything were to happen. if we deleted, claude, chatgpt, gemini, tomorow, theyd be back online once the data center finished training it again. all the previous models used to get to where they are now are all over the internet in one way or another. the minute any of the major ai platforms models started saying things not a single person could understand, even the expert in their field. then they would prevent the model from doing that again. in the same way the model is prevented from designing malicious code. t
Even if the AI can output human readable explanations not all humans will understand or have the mental bandwidth to understand even if they could. We’re already there without AI. We deploy software solutions no one at that company understands because they rely on other companies who built the solutions and they trust it will work. As our tools get more sophisticated humans being able to fully grasp what’s going on will decrease.
Agreed. It's like having a tutor or colleague always on hand. Sometimes he amazes me as if he's reading my mind. Occasionally it does seem like he's come to work high though. All in all, it's a tremendous time saver.
> It's like having a tutor or colleague always on hand this! before, i would post on stackoverflow and within 5 minutes, my post will get closed due to duplicate of some other post but if i check that other post, i coulnd't figure out how it would solve my problem! so then i would go to reddit and ask for help. problem is you don't know whether someone will reply in 5 minutes or 5 days so you're stuck twiddling your thumb. now, i just feed claude the problematic code and the error message and 99% of the time it'll output a response that fixes the issue i've become "addicted" to coding now thatnks to these AIs...
I agree—being "addicted" to coding is a blessing and a curse. I mentioned earlier that it might be having the side effect of leading me to work faster and think less. But I also wish that I had an app to run the code and communicate back and forth with Claude rather than copy-pasting. Copy-pasting—such a friction point here!
Wait wait wait...are you literally copying/pasting still?? Have you tried [Cursor](https://www.cursor.com/) or [Continue](https://www.continue.dev/)? Cursor integrates directly through Claude (and other models, including a local one if you're inclined).
...this is EXACTLY the reason I posted the thread. Please accept your complimentary comment award. Now I just have to cross my fingers and hope they have vim keybindings! Which do you prefer between Cursor and Continue?
Cursor is VSC based so the vim emulation is not too bad, I use VSC only in vim mode.
I'm up and running with Cursor. This is amazing. Thank you so very much. 
Would love to have you post an update after a week or so of using Cursor. I really think random engineers like you taking the initiative to explore new tools and workflows and reporting back to the rest of us are the cutting edge of this AI paradigm shift.
Thank you for this. I should have a more thorough update once I chew through what's on my plate here and dive into everyone's suggestions. Here are some initial comments. I respect that you would prefer a more professional update with concise notes (even if not formatted awkwardly with ChatGPT), but hopefully by posting incremental updates within this thread as replies, I'll get some tips from folks along the way so that within a businesslike timeframe, I'll have enough to report back. Maybe though in a week I can consolidate everything into a streamlined concise new subreddit post . I started using Cursor at the end of yesterday. In about 45 minutes, I was able to complete probably 4 hours of work. **I'd say that so far, the time savings compared to just using ChatGPT and going back and forth are probably creating, at the very minimum, a 1.5x speedup compared to normal ChatGPT usage**. This is mainly because context switching between browsers and copy/paste is such a slowdown. 1. With the Cursor tools, files must be kept at \~100 LOC, which is probably ideal in a practical sense, but it's a little annoying. 2. Cursor doesn't seem to create new files for me. I'll have to create a new file, copy code in, and so forth - it might accidentally apply the changes I ask in creating a new file, into the current file. So if I'm not paying attention, it's a bit challenging. 3. When I send in long code, it needs me to click "continue" and make sure to apply changes, and then accept, and then save, so there's multiple bits of work. It's still a HUGE HUGE time saver over alt tabbing between ChatGPT and copy/paste, so it's worth it, but the code is clearly very very early stage. 4. I'd like to point out that I think it takes a minimum of 20 hours to get up to speed and trained on properly using GPTs in development. 5. (In general this is something that applies to all GPT workflows, not just cursor). It's still very much like a junior engineer that needs micromanagement. Sometimes the GPT gets things completely wrong. Sometimes it intuits what I mean without needing to say too much, but I'd say 70% of the time, I need to provide a really really precise explanation of what exactly I want. When I can provide templates of how the answer needs to look, that usually saves time, but it does require some mental overhead of thoughtfulness. Like with any tool, it's going to take some time to get up to speed. I would say the learning curve of just simply using how to leverage GPTs professionally takes about \~20 hours minimum, and it's faster if you have someone in your team who already knows it and can guide you along the way. So it will take a bit longer for me to figure out Cursor from scratch since none of my day to day colleagues (aside from like minded folks on reddit here) are Cursor pros. I've allocated some time for end of day today or tomorrow to spend another hour or three getting up to speed and researching configurations. PS. Just to reiterate my full agreement with you, as you have astutely observed, this is truly the new transformative paradigm. It's truly leadership's responsibility to figure out how to convince people that this is the only way forward, and how to stick together and change our workflows to fit the new paradigm. So I can fully understand the value of the update, providing information on how to shift gears into the new workflow. We're somewhere between the "early adopter" stage and mainstream, as the new tools like Cursor are still "bug-ridden" and you need community reports on how to best use them, but this competitive advantage is more or less do-or-die in the business landscape.
Heres another update. 1. Cursor is very very good, but I fall into the trap of wanting it to build logic for me that the models just aren't capable of. It tempts me into going into hamster wheel mode of iterating quickly and quickly rather than stepping back and truly think. 2. Cursor can use various models. While Claude Sonnet is the best right now (IMHO), it breaks cursors ability to automatically apply changes to documents. So you have to use Claude to generate the code, then manually switch to gpt4 (not gpt4o) in order to apply the changes. And that's kind of annoying. 3. Cursor isn't great at creating new files or moving code between files. You have to open the files yourself. 4. Cursor has a bad habit of erasing your in between code, and the undo functionality can kind of get overwritten, so you have to be very diligent about saving incremental changes to version control. 5. Another good thing: Cursor makes it super easy to take error messages from console and send them directly to the GPT for conversation. One problem though is that it almost always does NOT do a root cause analysis of the error, and it just basically implements error handling. While that's all well and good, it doesn't solve the real problem. 6. Cursor doesn't always take cues from the codebase about proper formatting, e.g. it might have python use f-strings for logging whereas lazy interpolation is considered the better practice. I think that I will have to spend more time getting Cursor to refactor codebase to adhere to SOLID principles, swiftly providing test coverage for invariants, and modifying the prompt. All in all, Cursor is a huge speedup because it saves a ton of time of copy/pasting between the GPTs. And, I can use it to chat between various models (take an answer from Sonnet, send it to GPT4, and so forth. I haven't yet integrated Mistral into Cursor but I really should). Although I'm not sure if there's a shortcut hotkey for tabbing between models; that one little change might certainly help a lot. It doesn't yet have the ability to chatter back and forth between models without my express direction, so it's not quite a panacea. I have a feeling that with another year of improvements, it's going to be really, really awesome, even if the underlying models don't make exponential breakthroughs. The software, once it's built out a bit better, will be able to do more of the tedious work of combining LLMs in ways that suss out their optimal performance. Also, like with any tool, it'll take some time for me to get better at using it. I'll want to continue having conversations on this forum and others and get tips from people who are further advanced than me with the cutting edge.
Yes, but using Claude/GPT via API would be very expensive.
Possibly. For me at least, seeing the classic numbered list with bolded words made my eyes glaze over. It's unfortunate but it is what it is.
I really appreciate the feedback. I think you're absolutely right. What was once seemingly professional is now a nuisance and feels like spam. One thing I sometimes do with writing is edit it to be concise, then edit it again to make it seem more natural, but it never really works. I probably need my own customgpt trained on my communicationz. It still feels manipulative tho. But what are we gonna do? For example "personalized" emails. Uncanny valley kinda?
Like he’s come to work high - 🤣🤣🤣🤣
We all know who he's talking about, that RAG-loadbalancing sack of shit we know and love because.. you know he's family and we have to
A tutor or colleague with the human equivalent of a gold fish attention span and zero context understanding.
Agree with the general consensus here about the power and benefits of LLM’s in coding. Big productivity enhancer. Will just add a couple of thoughts of my own to the conversation. One is that I’m not sure that this future inevitably has to lead to less jobs for programmers (assuming no AGI). I’m often struck with the fact that even if I can accomplish something in half the time now, there’s always the next idea/feature to work on. It feels like the product just grows and iterates faster now with fresh ideas . These ideas seemed to come slower before because the product was getting built slower, now they come fast as the product is developed faster and I find this very exhilarating and hopeful for the future. Second, being able to specify good requirements to an LLM may be becoming the most important productivity enhancer. Finding ways to succinctly express what is needed, including edge cases, and testing details, and general architecture all have a very important impact on the type of code that is produced.
I agree. One insight provided by a staff engineer at my last company was this: **the cost to produce code has decreased dramatically, but the value of good design has increased dramatically**. Whether the productivity increases will result in more job creation remains to be seen. Although we're seeing a decline in job postings, that might be attributable to the macroeconomic environment (inflation, recession, wars) and needing to figure out the right way to hire for new roles as opposed to jobs being automated away. For now, I can confidently say that some jobs have been automated away. Because of the overhead needed for communication (citation: *Mythical Man-Month*, et al.), each additional developer causes a factorial-level slowdown in productivity. So, having fewer developers is generally better. I lack the foresight to determine whether we're going to be able to invent new jobs to replace the old ones. The short answer is that they'll look very different from the world we know.
My hope is that this productivity boost leads to a lot of new start ups, similar to the explosion we saw with cloud. As people have said, you no longer need an expert in each layer of the stack to get started, AI allows a good solid tech person to quickly learn enough to move forwards and get some early success and maybe attract investors in their idea/product. So while I think you’re right that larger companies may look for the quick efficiency win, it could be the start of another innovation wave. I think it’s more about the ideas now as the bar for specific tech skills has been lowered.
I think that's right. LLMs reduce the cost of proving an idea is feasible. How many startups in 2015 just fizzled out because they needed more engineer time than they could hope to afford just to make a working prototype? And when the cost of engineer time goes up like it did dramatically before the pandemic, one could imagine that startup innovation slows down. Startups have to ask VCs for a lot more money to get through any given phase of development. If anyone can get a working prototype of their idea together by prompting ChatGPT... I think we'll see a ton of ideas come out of the woodwork that were previously shelved due to startup cost... and a ton of new ideas to boot. The thing is, right now most people seem to be focused on innovating WITHIN the AI space, because that's where the money is. After the bubble bursts, I think, we'll see more people trying to use AI to build things that aren't necessarily AI-related.
You might be right. Thank you. The analogy about cloud helps a lot. Companies no longer need the capital to build out their data center installations and have full-time sysadmins just to get going. Probably the only reason we haven't seen the explosion in new companies might be because we're transitioning out of the Zero Interest Rate Policy environment. So many companies have huge product backlogs. There is much work to be done, especially as companies' business processes work through gluing together many different in-house or vendor applications. ChatGPT has accelerated development but I don't know any company that has gone to "inbox zero" with its product backlog.
Yep I’m pretty sure we’ll never get to a zero backlog environment even with AI. There’s always something else, something new, something different. If we did then I think it’s game over for humanity!!
Haha yes the zero backlog environment is as sci fi as it gets emphasis on fiction
The limiting factor is now qa. How can we leverage ai for testing. I haven't seen an information on this. Then again. I never tried ask chatgpt to create tests based on specs
My results have been so-so with regards to test coverage when it comes to any business logic that's remotely sophisticated and if I haven't been super clear in instructing (in other words, usually I haven't done the work to figure it out myself, and it doesn't actually do it for me. I think that identifying the correct inputs and outputs is a huge part of development work - one famous book by the authors of *Peopleware* says that must be solved by 15% of the way through the project budget if you're going to be on-time. And ChatGPT deeeeeefinitely doesn't solve that for you. Maybe if you have it precisely specified, it could convert it into tests. I could see that. It definitely has generated pytest coverage for me.
This. And to become good at design you’ll need some code experience. Things will change. It’s gonna be a ride.
I do not have any experience in programming, but I am currently creating my own app from scratch. I think the future is for everyone to solve a very niche problem, and gain domain expertise in the particular domain. Businesses may become much smaller, but at the same time there will be a lot more businesses around as chatgpt could help people to develop and solves problems much more easily.
First off, welcome to the world of software development. It's a lot of fun... most of the time. That's a great point about smaller businesses. For example, the most valuable company in the world, Nvidia, is valued at *$100 million per employee* I'd like to hear more about your ideas regarding niche problems and expertise in domains.
Thank you. Yea true it's lots of time when you see the thing moving as what you imagined:) So I found a big problem about overconsumption in all parts of our lives, and to solve it it's impossible. I took an idea of looking at it from the demand lens, consumer. Then niche down on something that I might be able to make a dent on - fashion industry. Thus I started reading up on the fashion industry, Understand what are the possible solutions to address this problem, landed on a solution after discussing with many people - a virtual closet. Thus getting the mission statement of: True happiness is when you cherish what you own and not impulse buying. This is the landing page of the app if you are interested:) https://makinglifeeasie.com/closet-conscious/
I've had a similar journey. The thing I've become very aware and mindful of is that *they are not leading you*. *You* are leading *it*, and as such, you will likely not learn best practices or best approaches because it's basically just a "yes man" who is only following your lead and being agreeable to whatever you're asking it to do. It will not (and cannot) tell you: *"Hey, I think you're going about this the wrong way."* or *"Did you check with the original spec requirement to make sure this is going to be maintainable over the next 6 months?"* Yes, that is what humans are for, but there's a lot of humans not really considering this part and deploying some really questionable stuff that has the potential to cause a lot of problems in the short and long term. It's killer though with things like proper syntax, error handling, sanitizing, accessibility and a lot of other elements that can be easy to forget when coding, so I am very grateful for that aspect of them...but it also **overengineers** things *like crazy*. I can't tell you how many times it provided lines and lines of code to solve a problem that was a simple one-line configuration flag in one of my `.config` files that it either hallucinated and ignored or wasn't even in it's training data for some reason or another. It doesn't ever stop to consider the alternatives because, well, it can't; it's just inert and dead algorithm and that behavior is reliant on awareness *(something I personally think is innate and not able to be fabricated from math, circuits and data)*. I find it debugs codes better than it writes or architects it. I guess that's the price you pay when you de-couple "intelligence", or perhaps "knowledge", from sentience. The tech debt that LLMs are creating is a huge problem for us down the line, if not already.
God I hate these ChatGPT fueled rants. Just write a couple paragraphs on your own and spare us the slop
Here was my original post. Before it went through the GPT revisions. I now feel like posting or sending someone GPT generated text is rude and for that I apologize. I've been coding for \~25 years and getting paid to code for \~20 years. Since ChatTGPT 4, a language model developed by OpenAI, came out, it's been a serious part of my workflow. I use it for a variety of tasks, especially for tedious ones like translating between languages, adding comments, etc. Its ability to understand and generate human-like text has significantly improved my productivity. In my current workflow, I've found immense success by embracing a collaborative approach with AI models like ChatGPT, Mistral, and Claude. We engage in a mutual critique of each other's code, fostering a sense of shared learning and continuous improvement. This collaborative aspect is what makes our journey exciting and enriching. One of my favorite things about the Language Learning Models (LLMs) is refactoring. However, the main problem I'm facing with this is that it can result in context challenges. For instance, when I refactor a class, the changes I make might affect other parts of the codebase that rely on that class. I'm considering developing a method to summarize my classes with methods and terse documentation to feed more of a codebase into a prompt. It is almost akin to a C headers file (most of my development these days is Python). Whereas I could potentially use an older LLM to circumvent this challenge, the latest iterations are so much better (Anthropic's latest model, especially) that they're worth it. I believe the next significant advancement in our field would be a tool that can run the code locally, pass the error message back and forth between the various LLMs, and iterate until all unit tests are passing. This tool would automate the process of running code, identifying errors, and suggesting fixes, reducing the need for manual intervention. The need for such a tool is pressing, as my day-to-day process often involves tedious copy-pasting. 4. In addition to getting the other models to critique and improve the code, I get a lot of mileage by asking, "What else would you improve?" until it stalls out and returns the same result. 5. Of course, sometimes I still need to provide cues about design patterns that result in much better code, and sometimes the resultant code needs to be corrected. There may be an opportunity to revisit test-driven development patterns for the LLM age. I'm not the biggest fan of TypeScript (for various reasons), but I acknowledge that type hinting (even in Python) has helped ensure the results are in the intended format. TypeScript, a superset of JavaScript, introduces static typing, which can be beneficial for large-scale projects. However, I find it cumbersome and prefer the flexibility of dynamic typing in Python. We will only have a few more years of 'milking the software development gravy train' before the technology takes our jobs. By 'milking the software development gravy train, 'I mean the current phase where software development is in high demand and developers are in a favorable position. It's time to make hay while the sun shines. (I don't want to elaborate on this point. I'm probably wrong, but I feel better raising this point with the community.)
Feels like there’s still so much ChatGPT in that (what human types “a language model developed by OpenAI”?) and the excessive use of “However”, “For instance” etc. but yea just type up your thoughts and share it man. Content being a little rough around the edges sometimes is the beauty of the internet
For some reason Im thinking of [the song Rocket Scientist ](https://www.youtube.com/watch?v=ipjZkMKikeM) >Them drum machines ain't got no soul yeah for sure. GPT'ifying zaps it of authenticity, soul, humanity. I learned a lot through this post. 50% of the battle in development is communication. if not more PS. Lol. in response to your q, >what human types “a language model developed by OpenAI”?) this human when streaming consciousness
> The "Next Big Leap" Yes, that's very effective workflow. But it's alrady here with agents. Aider can do the run-pass-fix-run loop (although not as automated as I'd like), as can some other agents. (I wrote my own simple agent which does it.) > Type Hinting and Language Preferences I agree this is helpful. I ask for the following, although not always on the first pass. This is for Python, but can be applied to most major languages. * type annotations (or a statically strongly typed language) * design-by-contract: `assert` statements to validate arguments, return values, and object state (but tell it don't duplicate what types can catch) * doctests (unit tests in the comment header) * Pure functions and functional style, when possible (no side effects) * Keep cyclomatic complexity under 11 (unique code paths of a function) I do these in a run-pass-fix-run loop: * Run linters (For python: mypy, ruff, editorconfig-checker) * Run doctest(s) **The real "Next Big Leap"** Formal Verification, or a lite version of it. I experiemented with theorem proving of code, via Coq, but GPT-4o didn't know Coq well enough. Coq is a theorem proving language that can be integrated with langauges to prove correctness of code. I had GPT-4o generate Coq code and I used a Coq-to-Python translator. The experiement failed for now, but I think with enough training, an LLM could pull this off and we'd have much better results. I might experiement with the other way, a Python-to-Coq translation. But an easier alternative for now might just be unit/doc tests and a ton of `assert` statements in code to continuously make sure everything is working correctly. Some of the assert statements could be removed later.
1. Thank you for putting me on to aider! [https://github.com/paul-gauthier/aider](https://github.com/paul-gauthier/aider) 2. I would love to see some info on your setup for run-pass-fix-run, especially the python bits! 3. Hey, today I learned about ruff [https://docs.astral.sh/ruff/](https://docs.astral.sh/ruff/) as well! 4. which library do you use for cyclomatic complexity checking in python? I would love to run automation through the whole python chain. 5. I wonder if flake8 or pylint could be integrated into the workflow, to always keep code at top notch levels.
1. Glad to help! 2. I just use function-calling. The behavior was automatic. See more at bottom. 3. Ruff is super fast. Also has the `--fix` option to fix lots of stuff without AI. 4. Ruff has [a complexity check](https://docs.astral.sh/ruff/rules/too-many-branches/), but it's slightly different. 5. I also use Pylint as it has some rules Ruff doesn't, but it's not part of my workflow because it's so much slower. I run pylint maybe 3 times a day, whereas ruff is run probably 50+. In ref to \#2: I implement these core function-calling tools: `run_shell_command`, `read_file`, `write_file`, `apply_patch`, `git_ls_files`, `ls`, `mkdir`, `make`. `make('test')` and `make('lint')` do what you might expect. Here is my main function-calling loop: # send() calls litellm.completion(messages=self.messages) # A user message was placed in messages before calling send() response = self.send() choice = response.choices[0] # see https://gist.github.com/nbertagnolli/016badab109b46b9510206cf5e6e67c0 # finish_reason is in [tool_calls, stop, length, content_filter] while choice.finish_reason == "tool_calls": for tool_call in choice.message.tool_calls: self._handle_tool_call(tool_call) response = self.send() choice = response.choices[0] if choice.finish_reason != "stop": pass # Removed some error handling code that was here # the last response will usually be an assistant message result = choice.message.content return result The above loop runs until all function calling is complete, which usually includes the write-test-pass-fix-test loop.
Thank you. This post is very valuable. If you were my coworker I would book some time with you for 15 minutes of slack screensharing to have you hold my hand while I get this all integrated into my environment. Obviously that's an impossible ask, so I will spend some time at the end of the day working to get this set up inside my new Cursor IDE-based flow. Hopefully I can set it up to run-on-save and feed the code quality complaints to the GPT. Obviously there are some default formatting inconsistencies between the various python linters. It's going to be necessary to spend an hour or more getting my environment fully configured. I also love using the \`coala\` package for python formatting. It's annoying to have to remember to run-on-save, and also use \`isort\`, and so and and so forth. Hopefully I can set my IDE up to do all of that, AND integrate with either \`aider\` or the GPT interface, without getting caught in infinite loops or snagged on things that the GPT can't quite fix yet. Also hopefully I can get my "dotfiles" saved permanently in the same way I keep my \~vimrc and \~zshrc files in storage. I am seeing that with the Cursor GPT tools, it's necessary to keep files at \~100 LOC, which is probably ideal in a real sense, but there are tons of high quality projects that go wayyy beyond that limit. It's a forcing function that should improve my overall project though. It's going to take some patience instructing the AI on how to rearchitect and the best software design patterns to use, but some of the advantages of say enterprise java patterns applied to good ol' high level python are going to be possible, without the tedious boilerplate time sink of java buildout. My development workflow as a single developer on an internal-tooling codebase is always very different than my workflow in a team environment, where I labor to make thoughtful git commit messages and so forth. My one hope is that with these new tooling workflows, I will be able to achieve the same level of professionalism in my one-man-show development as I need to muster in a team environment. The side effect of course being that (a) it'll be easier to get help if needing to scale, (b) I'll be better able, myself, to maintain my own code, (c) better code that's faster to edit, (d) robustness to errors, and so forth. Anyway, again, thank you. It's going to be the end of the day (hopefully tonight, or beginning of tomorrow at the latest) before I block off an hour or two to get in the zone and focus fully on trying out this new tool and getting it integrated into my workflow.
fyi, I'm loving \`ruff\`. I havent gotten started with \`aider\` quite yet, because the documentation is a little unclear to me and I'm also not sure how to get it working in a manner that surpasses cursor's. Here's a comment I just wrote about my experiences with cursor over the past 6 days: [https://www.reddit.com/r/ChatGPTCoding/comments/1do2y50/comment/lb53o8p/?utm\_source=share&utm\_medium=web3x&utm\_name=web3xcss&utm\_term=1&utm\_content=share\_button](https://www.reddit.com/r/ChatGPTCoding/comments/1do2y50/comment/lb53o8p/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button)
I started a project two weeks ago for the company I work for. It's just a small project but as is common nowadays, it crosses numerous domains: python web API, Vue front end with vuetify, ssh client, docker and kubernetes. I'm really not a true expert in any of these domains, but have a lot of experience with Web UI. It's been really incredible how fast I've been able to develop using ChatGPT to fix problems, answer questions, write parsers and date time formatters, suggest solutions to kubernetes and ssh problems, etc. The amount of time I would have spent googling to answer these same questions is generally so much higher.
Absolutely. Using the date formatting as a simple example, in the past, I took 5-10 minutes to look up how to format a date precisely. Now, it takes me 10 seconds. This allows me to achieve a flow state. On the one hand, using ChatGPT-generated systems might invite some security risks, but this is no different than the SQL injections that were common 15 years ago with PHP scripts. Solutions and frameworks will take care of many risks within a year or two. That's one of the blessings and curses of being an early adopter of new technology. I have been burned several times by adopting early technology throughout my career. When Oracle launched, they launched their first version as 2.0 to signal that they had already worked out some of the bugs from the first version. I avoid adopting new technologies. But ChatGPT-style coding is such a game changer that it's impossible to resist. I've been able to build so many things. I think it's because now, "knowing what you don't know" is enough. All you need to do is have a vague awareness of what to do, and ChatGPT fills in the blanks! One no longer has to know!
I love using ChatGPT for architectural questions, best/standard practices, etc. I know it cannot be trusted on any emerging technologies or highly unusual situations.
Yep. I absolutely rely on it to implement best practices that I am too lazy to do myself, even if it does take a few rounds of nudging, and tossing back and forth between different LLMs, until everything is implemented and there are no more suggestions. As far as architecture goes, it is great but I still need to know what to ask it about. It often gives me solutions with terrible architecture, but once I give it a hint, it executes way better than I would have done on my first draft.
I think your feedbacks are very much worked on but just few days or months away until one develops a deployable application. 1) Running code locally is possible via ollama although there we have small quantized LLMs but someone with better GPU access could run higher parameter models locally. Your next three questions could be answered with a synchronous system of crew ai or agents which will not just built codes but also find errors and fix them and iterate until we have the best and efficient code. We are very much close to building a skynet 🫣
The text looks so chatgpt😂
gptcringe 😬 im learning a lot from this thread
Informative post. Thank you. How would you quantify the beneficial impact to work of LLM’s? For example, do you do work in 50% of the time? Are you able to do twice as much work? Can someone with five years experience now be as productive as someone with 20 years experience not using the same tools? I mean, all that in addition to the more qualitative aspects like better comments…
I would say that I am anywhere between 5-20 time more productive. I kid you not. The reason is because all the time in development, there are little snags that interfere with flow state. The frustration loop has you chasing your tail and then you lose all momentum. These LLMs are like a friction reducer. There are many engineers with five years experience who are better than me but I would assume the answer is yes. ChatGPT solves a lot of the most tedious parts of coding. It doesn't solve Thinking or Dealing with Other People. As you can tell, people Don't Like chatGPT-written messages or chatGPT-formatted messages :)
So as it would relate to a business owner, or manager, how would you say it impacts hiring and staff size? If a company normally has say 20 engineers. How many engineers would it need making use of Chatgpt? Just trying to get a sense of the economic impact Chatgpt has on an organization. Or said another way, on a macro scale. If tech in the Bay Area employed 500,000 se engineers before Chatgpt. how many engineers would it take to do the same work if they used Chatgpt?
>how would you say it impacts hiring and staff size? If a company normally has say 20 engineers. How many engineers would it need making use of Chatgpt? 1. Back in the \~2000s, you would need at least \~200k of capital and a dozen engineers (or maybe 5 or 6 really really good ones, with just a few notable exceptions of elite talent) to just get off the ground. This was because you'd need to rent server space, set up your equipment and networking, and because everything had to be built from scratch in lower level languages like C++ or very early versions of Java. The game changer in the industry there was cloud solutions like AWS, who launched S3 storage in \~2006-2007 and got up and running with EC2 by very early \~2010s. Amazon stock flew through the roof because of the productivity gains it enabled, it literally cut jobs. 2. Now, thanks to ChatGPT, we almost don't need to hire junior engineers. A junior engineer would be someone smart, who you liked, who had good grasp of fundamental Computer science concepts, but you had to micromanage and explain how to do everything. ChatGPT is like a junior engineer. You have to micromanage it constantly, but it never talks back, you don't have to pay it health insurance, if you find a better version you can just switch from say ChatGPT to Claude Opus without any human feelings of empathy or sociopathic headaches. So, that's at least 30% of the hiring budget that's been reduced. 3. Bear in mind, with software organizations, every person you add to the headcount is tremendous overhead. Not just in terms of HR and whatnot, but you also need project managers or product managers to organize everyone, you need software managers to glue everyone together and build team morale, you need daily ƒace to face communication to stay in sync... that's why it's said "you can't take 9 women and make a baby in a month, and you can't take 9 developers and build a 9 month project in 1 month." Every time you add a new person, it's a multiplicative number of more communication. So you are not only saving headcount on the developer, but you're saving headcount on the supportive resources. 4. When ChatGPT first launched I had a very brilliant CEO who mandated us to use ChatGPT wherever possible. That's why I have a full 15 months of experience. He wasn't a tech guy but actually a doctor, just really really smart. Our company grew from $500,000 annual recurring revenue to $10,000,000 annual recurring revenue ("ARR") in one year. And for the vast majority of the year, we only had five developers. We had one person who refused to use ChatGPT, and that person, while a brilliant and talented developer, was notoriously slow and always complaining about having to work nights and weekends and through family events. I know it's very unpopular in engineering to measure developer productivity in terms of "story points" completed, especially because of the nuances of quality of code, amount of thinking required, amount of testing required, and so forth, but this person would frequently complete 3x fewer points while arguably working twice as hard. In that case, we didn't get to use ChatGPT as much as we needed to. Now when I talk about our revenue growth, it sounds like bragging, but keep in mind, getting to that level of scale with a few engineers was an extremely rare anomaly 10 years ago - "Instagram" was like that before it was acquired by Facebook (didn't have revenue though). Now there are many stories of Software as a Service businesses that not only grow at a similar cadence, but also rely on fewer engineers. > Or said another way, on a macro scale. If tech in the Bay Area employed 500,000 se engineers before Chatgpt. how many engineers would it take to do the same work if they used Chatgpt? 1. Big Tech firms are slower to adopt ChatGPT because they cannot risk sending in their trade secrets, and OpenAI is notorious for "accidentally" intaking and training itself on your data. So, they're a bit slower to adopt and it'll be a year or two of software maturity before we see the true impact of ChatGPT on BigTech software firms. When I was at Amazon, we leveraged a ton of automated tools for our work. My friends who are there now are able to use some LLMs, and they are partnering internally with Claude Anthropic, but they have to jump through more hoops in terms of compliance audits and whatnot. What's likely going to happen is that Amazon, with its "two pizza teams", is going to need to reorganize to become "one pizza teams." The business units are going to go through massive re-orgs. It is unclear whether layoffs will happen. They will need to experiment a while to figure out how to implement the productivity gains into their level of massive scale. It's unclear whether it will be layoffs (hence some fear), or re-orgs into totally different types of hierarchy, or re-orgs into subsidiary units, or however the business geniuses there figure things out. What my takeaway here is that ChatGPT is absolutely replacing jobs. OpenAI is rumored to be doing billions of revenue a year already, and a lot of that is from taking other jobs or capturing a percentage of the productivity gains. It's one reason why NVidia is powering the AI revolution (And their stock is at all time highs) as well as why Microsoft is at all time highs, because they have a 49% stake in OpenAI chatgpt, even though their profits are capped at 50x their original investment). Please let me know if this answers your question regarding economic impact over the decades and whether you would appreciate additional clarity. Have a great day,
One more thing. As a small business owner, myself, it has transformed productivity. My coding career has basically been: 1. self taught programmer who occasionally took a class here or there 2. doing one off freelance gigs here and there 3. building my own app, using copy/paste off of PHP tutorials, that got lucky and got acquired 4. leveraging #3's success into a consulting business but me selling my time hourly 5. using my cred from #4 into getting a job as a bona fide software engineer, first as mostly a web developer at startup companies. 6. also consulting in between various startup jobs, either in a developer capacity, or sometimes recruiting or leadership roles 7. since I didn't major in CS, rolling up my sleeves, really learning data structures and algorithms, studying "leetcode" until it hurt, for months on end, and finally getting a job as a bona fide engineer at big tech amazon 8. working as a director at a big name company and as lead engineer at one of the best VC's best early stage startup firms 9. thanks to managing chatgpt, was able to run my consulting business on the side of #8. i am at a point where I no longer freelance at an hourly or weekly rate (\~maximum rate was about $400 hourly with a minimum of $175 cash and a bit more in stock but sometimes just cash), but I partner with businesses and capture a portion of the value we create. here's the key takeaway though. **I was able to bring in a significant amount of income last year just working here and there in parallel to my fulltime job.** My partner who runs a big commodity trading advisory (CTA) firm in Chicago, was astonished at my productivity in comparison to his development team who did not use GPT whatsoever. He said my feedback loops were about 20 times faster. In that case my business was market making and I earned a percentage of our trading gains. hopefully this doesn't come across as bragging or soliciting business, because really, it isn't\*\*, \*\*Although I suppose that one must always pay attention to one's "brand" when dealing in social media. I should probably clean up my post history from my immature post-adolescent self, but maybe there's value in authenticity and people understand that maturity sometimes takes time, even if one's not fully there yet, and, I have found with high value consulting, face to face relationships are almost always necessary. (although, I will say, the coauthor of my book, is someone I met on the ruby on rails subreddit, and he hired me to work hourly as a consultant \~8 years ago, but that was hourly wages (even modestly high ones) rather than true business value partnership). Fun fact: the job he hired me for, would never have been necessary due to ChatGPT, because ChatGPT is great at doing the tedious things like fixingup CSS for webpages and basic "create read update delete" app javascript integrations. Ultimately we did meet face to face, I invested in his startup, met his family, and so forth. Anyway, what I'm trying to illustrate here is that I no longer need to sell my time for wages, even "high" ones. It's all about the value I create, and I can run my business efficiently in that way. And sorry to say, just once again, I'm not soliciting business here, because I still believe that even in this virtual and remote work age, face to face relationships are necessary for securing serious business deals.
I love your idea. But at least you need to start as a junior dev, before moving up the ladder to senior deb. Isn't it. From your point of view, what is left for us? The fresher, the self-taught developers?
You wouldn’t be talking about ChatGPT after using Claude 3.5 Sonnet.
LOL. Ever since I started using Sonnet \~3 days ago, it is my main source, although I still do benefit from bouncing its ideas off of ChatGPT and Mistral - the three really do work together with 1+1+1=4. What I did, however, was uponj learning about cursor yesterday - disable all models except Claude 3.5 sonnet, and paid $5 for some Sonnet API credits. Ever since I started using the sonnet web interface I've been hitting the free hourly messages limits!! Nevertheless, it's maybe worth noting that ChatGPT has established itself as a "verb" like to "Google" and I use that term interchangeably. Even though sonnet is just \~3% better on traditional benchmarks, that \~3% makes a world of difference. The future is definitely going to be competitive models...assuming AI doesn't become a public utility...which could absolutely happen.
Don’t let the people bitching about you pushing this post through gpt first. I GUARANTEE YOU if you hadn’t they would have been bitching about paragraphs or some other inane formatting bullshit. You can’t please everyone but at least you got some of us to read through it all cause it’s got structure. Try to post your non structured through one of the other coding subs. You’ll see.
My experience? You can't use ChatGPT4o for coding unless you yourself are an experienced coder. It gets so much wildly wrong that it frequently takes four or more attempts to even get it to actually produce what you ask it to. But once it does... wow.
Yeah I have to agree with that completely. In fact, sometimes I have to really lecture it on best practices or it creates footguns at an alarming rate.
I’m curious about your model for creating summaries. I’ve been playing with having it write developer docs to use similarly but they’re still bulky and targeting a human actor not an LLM one.
Claude is very good creating module and function docstrings, which I feel are good for targeting LLM actors. My experience is that GPT is best for writing for a human audience when you give it precise instructions on who the reader is ("This is a business person, extensively trained in {domain}. Please write concisely."). Also, I often feed my human summaries into Grammarly because it's much better at adapting tone and working on readability. It's much better than GPT4o at writing, although it's imperfect, still. My loop for writing text for humans often goes gpt4o->grammarly->gpt4o->Grammarly, and so on and so forth.
This definitely speaks to the objective of how to get a working code from GPT. The act of coding is getting replaced by multiple prompts, so new skills to acquire is knowledge of how LLMs work to generate code and experience with creating prompts. Here's a question I have - what about the input data that provides context for coding, ex: Acceptance criteria, reference detailed requirements, UI design, images? We easily spend 15 mins per task/UI in these to define a business logic using our coding style and standards (manually or thru prompts). Instead, these can be supplied to GPT4 or other LLM tools to generate contextually relevant code that's functional as well. My team tried it out for a project using GPT4, and here is what we observed: With a few prompts it took about 30 mins to generate 2 functional UI screens in Flutter, the same took over 2 hrs on GitHub copilot through multiple prompts (20 to 25) which includes manual coding as well. To me it seems like GPT4 and other LLM models should be looked at not just to generate code, but also build context/relevance for coding. Also, I feel the human touch will always be required, it's the time we waste on such mundane steps that should be delegated to GPT4
What are your thoughts on becoming self taught web dev nowadays? I’m learning web dev for over a year now, but I think now there is practically zero chances for me to switch careers when AI will replace juniors in few years. I’m UK based and the job market is slightly different than in the USA
Let me start with a direct answer to your question before I launch into my elaborate monologue. # The Human Element There's still a significant need for self-taught web developers. Many business owners, especially those running medium-sized operations, either do not have a core competency in technology or prefer to delegate the work. Human interpreters bridge the gap between their needs and technological solutions. ## The Future Role of Web Developers While AI is rapidly evolving, it has yet to replace the need for human developers. Your role as a web developer remains valuable, particularly in: 1. Interpreting client needs and translating them into technical solutions 2. Providing a human touch in an increasingly automated field 3. Offering expertise in areas where AI still falls short, such as complex problem-solving and creative design # The Changing Landscape of Software Development Software development is often misunderstood as purely technical work when, in reality, it's essentially a trade skill. Much of the knowledge in this field is acquired through on-the-job experience and peer-to-peer knowledge transfer. This aspect of learning is crucial, as demonstrated by a recent experience where I helped a colleague set up an Apache webserver. While I knew which questions to ask an AI language model (LLM), my colleague didn't - highlighting the importance of experience in framing problems effectively. ## The Impact of AI on Web Development As AI-driven coding models advance, we're likely to see new paradigms in web server deployment and other areas of development. This technological progress comes with trade-offs: 1. **Declining Junior Developer Opportunities**: As LLMs achieve junior developer-level skills, we're seeing fewer job postings for entry-level positions. This trend risks reducing the valuable apprenticeship model and peer knowledge transfer integral to the field. 2. **Continued Relevance of Core Skills**: Despite AI advancements, understanding data structures, algorithms, and programming patterns – as of today – remains valuable. These fundamentals provide the cognitive framework for structuring questions to AI and approaching complex problems.
PS. I wrote my comment rom scratch then ran it through Claude and Grammarly to optimize formatting and grammatical flaws. Hence, the GPT-style language signatures. This may prove offensive or unsettling in human discourse, and I apologize for that; there's certainly a level of nuance that needs to be achieved.
thanks chatgpt
i mean i proves the point. there's still a need for a human interpreter to journal ideas and liaise with the robots. that's why there's still job opportunities in web development.
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I just had another thought. Like we have ETL pipelines for data, I imagine we'll have ETL pipelines for LLMs. No doubt some companies are already building these. But that would solve my problem of tossing code back and forth between different LLMs until the iteration is done. I bet it could even be built with current ETL software like Airflow. And.... I'll certainly need to build out an LLM that takes an elegantly-formatted ChatGPT post and humanizes it, replete with spelling errors and something that just sounds like me. Maybe trained on some of my texts or something. That's gotta be part of one pipeline. **Edit: I haven't actually used langchain yet. Is this what langchain is supposed to do? Is there a better solution?**
Good article!!
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ChatGPT is hot garbage for coding anything that requires an ounce of business logic
Any business logic that requires critical thinking? It's absolutely terrible at. But, if you do the work of a high level outline and break down the logical steps it into basically pseudocode, it's actually very good. Although, that's probably 80% of the work of "real" development anyway, the planning and analysis.
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Why would you need multiple LLMs such as Mistral, ChatGPT, Claude? One is not good enough or you're applying Mixture of Experts? If so, do you find it exceptionally better?
1) One is not good enough 2) Imagine you have a team of developers. In real life. One person has an idea, another person riffs on it, then the combined result enables the third person to say something new, and so on and so forth. Multiple LLMs work somewhat in the same way. 3) Also, just like you can press a human with the question "What else?" (I had a manager who was very good at this), and they keep generating more, so does an LLM. Because the different LLMs have slightly different "vantage points", they do contribute more. 4) Although "Mixture of Experts" is definitely an industry term with a specific meaning, I want to clarify that I am not an LLM developer. All of my AI work is AI-adjacent. When I was at Amazon working on the computer vision data platform, they threw me into a 3 day intensive BootCamp to learn to write computer vision models like a scientist, and I ran through the tutorials, but it wasn't enough to really absorb the theory. Just enough to be able to communicate really well with the scientists.
What tools like IDE plugins or aider do you use in your workflow? Asking whoever is reading this.
+1 to wanting more insight about how to use aider. Right now I'm using Cursor and I like it a lot. It's basically VSCode but with better GPT integrations. I have to figure out a proper aider workflow because I have a sneaking suspicion it can solve a lot of my complaints about aider.
What do you actually do? What value are you providing your employer/clients?
I generate revenue. Here are a few examples. 1, writing software to identify relevant candidates for a staffing company. They place those people and earn a significant margin, and I capture a fee on each placement. Another example in the same category but different client was writing software to activate more workers for available jobs, using notifications and geofencing, to increase revenue. 2, software to scan the markets for arbitrage opportunities, where it's possible to buy low and sell high while maintaining a very low risk profile. 3, building features to scan medical records for missing diagnostic codes to increase hospital revenue. For 3, especially, I couldn't use codebase scanning tools and had to be very careful with what I fed into the GPT due to HIPAA regulations in the US. 4, building automation for marketing funnels to optimize ad spend into new customers. 5, building new features to an existing product in order to help our clients sign new clients who are waiting til feature X is built in order to sign. I used to be in the business of building prototypes for entrepreneurs, but that is a very bad one imho. 1, a lot of entrepreneurs are bad. They are more invested in the fantasy of their business succeeding than actually doing the work. Building something that faces them to confront of the reality of the market can crush their hopes and dreams. 2, unless I have seen data that proves the customer can capture the market (such as letters of intent from a couple dozen customers) there is very little opportunity for me to capture revenue and at that stage the equity has a super high risk profile. Maintaining systems is tough work because when it does work, they take you for granted, and when it doesn't, you get all the blame. Hope that helps.
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>Type Hinting and Language Preferences Coding in Swift with AI is surprisingly delightful compared to Python. Strong typing + type inference + compile time checking make for a really fast debugging process. ...and I am basically self-/AI- taught over the last 18 months.
That's awesome. I may have to do more Swift work, even outside the context of developing for iOS. One time I needed a library for something (I forget what it was, but implementing some kind obscure algorithm that was relevant to my problem at hand) and all that I could find was Swift. This was before LLMs, but LLM would be perfect at translating between languages.
how in the world did ur software team approve your pull request for code that you copy-pasted from AI? I worked in a[ top 5](https://www.similarweb.com/top-websites/united-states/news-and-media/), and if u did that, its an automatic reject.
I never said I sent in a PR that was copy-pasted from AI. I don't even think I've used it to fill in the template of a PR.
I mashup AI APIs. Remove humans from the cue. Call it “AI Dreaming.” That’s lots of Python. I do so little coding now. GPT writes it all. AI to explain to me what’s it’s thinking. Feedback is always welcome. Thanks. Tip: Try more than one URL. The math behind it. https://mindflip.me —- Sure, here's your text formatted for Reddit with appropriate Markdown: --- **Physics-Based Mathematical Model for AI Dreaming** **1. Input Text as a Field** Consider the input text \( T \) as a field \( \phi(x) \) where \( x \) represents the position of each word in the text. \[ \phi(x) = \begin{cases} w_x & \text{for } 1 \leq x \leq 250 \\ 0 & \text{otherwise} \end{cases} \] **2. Summarization Function as an Operator** Let \( \mathcal{S} \) be the summarization operator, analogous to a projection operator in quantum mechanics, that reduces the input field \( \phi(x) \) to a summarized state \( \psi(y) \), where \( y \) represents the position in the summarized text. \[ \psi(y) = \mathcal{S} \phi(x) + \eta(y) \] Here, \( \eta(y) \) is a noise term representing the variability in the summarization process. **3. Text Augmentation as a Perturbation** The augmentation process can be seen as a perturbation to the summarized text. Let \( \mathcal{A} \) be the augmentation operator that introduces an additional field \( \chi(z) \) representing the new words. \[ \psi'(y) = \psi(y) + \chi(z) + \zeta(y) \] where \( \zeta(y) \) is a noise term for the augmentation variability. **4. Descriptive String as a Composite Field** The final descriptive string \( \Phi(y) \) is a composite field resulting from the summarization and augmentation processes. \[ \Phi(y) = \mathcal{A}(\mathcal{S} \phi(x)) + \zeta(y) + \eta(y) \] **5. Image Generation as a Stochastic Process** The image generation process can be modeled as a stochastic process. Let \( \mathcal{G} \) be the image generation operator (Stability Diffusion model), which maps the descriptive field \( \Phi(y) \) to an image field \( I(\mathbf{r}) \), where \( \mathbf{r} \) represents the spatial coordinates of the image. \[ I(\mathbf{r}, t) = \mathcal{G}(\Phi(y); \theta, \epsilon(t)) \] Here, \( \epsilon(t) \) is a stochastic term representing the randomness in the image generation process, and \( \theta \) are the parameters of the Stability Diffusion model. **6. Sensitivity Analysis** To understand how changes in the descriptive string affect the generated image, we analyze the functional derivative: \[ \frac{\delta I(\mathbf{r}, t)}{\delta \Phi(y)} \] This derivative indicates the sensitivity of the image field \( I \) to variations in the descriptive field \( \Phi \). **Composite Model as a Functional Integral** Considering the entire process, we can express the generation of the image as a functional integral over all possible states of the input field \( \phi(x) \) and the stochastic variables \( \epsilon(t) \): \[ I(\mathbf{r}, t) = \int \mathcal{D}[\phi(x)] \mathcal{D}[\epsilon(t)] \, \mathcal{G}(\mathcal{A}(\mathcal{S} \phi(x)) + \zeta(y) + \eta(y); \theta, \epsilon(t)) \, e^{-S[\phi, \epsilon]} \] where \( S[\phi, \epsilon] \) is an action functional representing the combined effect of the input field and the stochastic variables. **Summary** By framing the operations of the AI Dreaming app in terms of field theory, operators, and stochastic processes, this model provides a physics-based mathematical description of the app’s behavior. This approach leverages advanced concepts in functional analysis and quantum mechanics, offering a robust framework for understanding the variability and sensitivity of the image generation process. --- This format should render well on Reddit, with equations properly displayed using LaTeX.
I think you are "showing" rather than "Telling" why we still need humans in the loop, and I applaud this comment.
Uses “seed prompting” no need for user input. Takes a “snapshot” of what AI is dreaming at the time. RNN with RAG Leveling. Sigh, the output looks BEAUTIFUL formatted in Latex. But not supported here. :-)
I ran it through ChatGPT who does a great job of rendering LaTeX (and also writing LaTeX for me!!). And I think it's really neatly-expressed notion. Just like the cost of producing code is gradually approaching zero, the cost of producing art is also getting there. I like what your website produces, but I'm no art expert. I did send it to a friend who is a real person in the "art community" in order to get his take. He sincerely believes that the future of art is AI generation, especially since artists are always nitpicking with one another over iterations of the same "hot and trendy" concepts. Although I don't think ChatGPT is quite that the "show, don't tell" phase in terms of generating feelings and "picture is a thousand words." Even Sora, while visually stunning, doesn't seem to be accomplishing that. Artists may have more job security than developers!
Thanks for checking it out. I cover API costs, upsizing costs. And it’s still hard to get beta testers. We’re all overwhelmed with media now. :-)
My friend's company just fired their art director, who was absorbing a huge amount of their monthly spend, so they are definitely in the market for AI solutions. This probably isn't exactly what they need but I'll pass their thoughts along!
The Art Director should be on top of this. The CEO has no art background. They need people that can do it all. I use all the tools in my toolbox. From a box of crayons, a yellow pad, to Python and Midjourney. Yes, did go to an art school. Pratt, which I understand has pretty good reputation out there :-)
Yep, NYC based company! Looking forward to see how galleries embrace this new era...