I think the dichotomy you see with how positive people are about ai has almost entirely to do with the kind of questions they ask.

That seems obvious, but a consequence of that is that people who are sceptical of ai (like me) only use it when they've exhausted other resources (like google). You ask very specific questions where not a lot of documentation is available and inevetably even o3 ends up being pretty useless.

Conversely there's people who love ai and use it for everything, and since the majority of the stuff they ask about is fairly simple and well documented (eg "Write me some typescript"), they rarely have a negative experience.

What bothers me more than any of this particular discussion is that we seem to be incapable of determining programmer productivity in a meaningful way since my debut as a programmer 40 years ago.
We can determine productivity for the purpose of studies like this. Give a bunch of developers the exact same task and measure how quickly they can produce a defect-free solution. Unfortunately, this study didn’t do that – the developers chose their own tasks.
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But nevertheless, productivity objectively exists. Some people/teams are more productive as others.

I suppose it would be simpler to compare productivity for people working on standard, "normalized" tasks, but often every other task a programmer is assigned is something different to the previous one.

I’m confused as to why anyone would think this would be possible to determine.

Like can we determine the productivity of doctors, lawyers, journalists, or pastry chefs?

What job out there is so simple that we can meaningfully measure all the positive and negative effects of the worker as well as account for different conditions between workers.

I could probably get behind the idea that you could measure productivity for professional poker players (given a long enough evaluation period). Hard to think of much else.

People in charge love to measure productivity and, just as harmfully, performance. The main insight people running large organisations (big business and governments) have into how they are doing is metrics, so they will use what measures they can have regardless of how meaningful they are.

The British government (probably not any worse than anyone else, just what I am most familiar with) does measure the productivity of the NHS: https://www.england.nhs.uk/long-read/nhs-productivity/ (including doctors, obviously).

They also try to measure the performance of teachers and schools and introduced performance league tables and special exams (SATS - exams sat at various ages school children in the state system, nothing like the American exams with the same name) to do this more pervasively. They made it better by creating multi-academy trusts which adds a layer of management running multi-schools so even more people want even more metrics.

The same for police, and pretty much everything else.

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We can determine the productivity of factory workers, and that is still(!) how we are seen by some managers.

And to be fair, some crud work is repetitive enough so it should be possible to get a fair measure of at least the difference in speed between developers.

But that building simple crud services with rest interfaces takes as much time as it does is a failure of the tools we use.

> Like can we determine the productivity of doctors, lawyers, journalists, or pastry chefs?

Yes, yes we can.

Programmers really need to stop this cope about us being such special snowflakes that we can't be assessed and that our maangers just need to take that we're worth keeping around on good faith.

News to me. How do you determine the productivity of a doctor? Patients seen? Patients cured? (for real, where did you get that data?) Number of medicines prescribed? Procedures performed? Does a triple bypass surgery count the same a pap smear? Hours worked? Amount of help they provided to colleagues? Easy to come up with another 100 other metrics that might be worth looking out. How are they all weighted?

Like I get that in SWE (like all other fields), managers have to make judgement calls and try to evaluate which reports contribute the most, but the GP post seemed surprised that this wasn't a solved problem by now, which just seems incomprehensible to me.

> Yes, yes we can.

Of course we can. But can we do it in a meaningful way, such that the metric itself doesn't become a subject to optimization?

"When a measure becomes a target, it ceases to be a good measure"

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> Yes, yes we can.

Could you make an effort to explain how, or at the very least link to some reasoning? Otherwise your comment is basically the equivalent of “nuh-uh”, which doesn’t meaningfully contribute to the discussion.

> Programmers really need to stop this cope about us being such special snowflakes

Which is not at all what is happening in your parent comment. On the contrary, they’re putting developers on even footing with other professions.

Duly upvoted! I tend to agree. Yet the shibboleth of productivity haunts us still.
Won't stop MBAs from trying though.
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Team members always know who is productive and who isn’t, but generally don’t snitch to the management because it will be used against them or cause conflicts with colleagues. This team-level productivity doesn’t necessarily translate into something positive for a company.

Management is forced to rely on various metrics which are gamed or inaccurate.

what about the $ you make? isn't that an indicator? you've probably made more than me, so you are more successful while both of us might be doing the same thing.
It is from a certain point of view. For example at a national level productivity is measured in GDP per hour worked. Even this is problematic - it means you increase productivity by reducing working hours or making low paid workers unemployed.

ON the other hand it makes no sense from some points of view. For example, if you get a pay rise that does not mean you are more productive.

Yeah it only works at a very high level, but from there it's a pretty good measure. Like it's basically "what are the values of the inputs vs. the outputs", which is dead simple. At any lower level there are lots of confounding variables you have to contend with.
Salary is an indirect and partially useful metric, but one could argue that your ability to self-promote matters more, at least in the USA. I worked at Microsoft and saw that some of the people who made fat stacks of cash, just happened to be at the right place in the right time, or promoted things that looked good, but we’re not good for the company itself.

I made great money running my own businesses, but the vast majority of the programming was by people I hired. I’m a decent talent, but that gave me the ability to hire better ones than me.

I don't think there's much of a correlation there.
Probably not, I took a new job at a significantly reduced pay because it makes me feel better and reduced stress. That fact that I can allow myself to work for less seems to me like I'm more successful.
People doing charity work, work for non-profits or work for public benefit corporations typically have vastly lower wages than those who work in e.g high frequency trading or other capital-adjacent industries. Are you comfortable declaring that the former is always vastly less productive than the latter?

Changing jobs typically brings a higher salary than your previous job. Are you saying that I'm significantly more productive right after changing jobs than right before?

I recently moved from being employed by a company to do software development, to running my own software development company and doing consulting work for others. I can now put in significantly fewer hours, doing the same kind of work (sometimes even on the same projects that I worked on before), and make more money. Am I now significantly more productive? I don't feel more productive, I just learned to charge more for my time.

IMO, your suggestion falls on its own ridiculousness.

In a vacuum I don’t believe pay alone is a very good indicator. What might be a better one is if someone has a history across their career of delivering working products to spec, doing this across companies and with increasing responsibility. This of course can only be determined after the fact.
Productivity has zero to do with salary. Case in point: FOSS.

Some of the most productive devs don't get paid by the big corps who make use of their open source projects, hence the constant urging of corps and people to sponsor projects they make money via.

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Another metric could be time. Do people work less hours?
Is DB2 Admin more productive than Java dev on the same seniority?

What about countries? In my Poland $25k would be an amazing salary for a senior while in USA fresh grads can earn $80k. Are they more productive?

... at the same time, given same seniority, job and location - I'd be willing to say it wouldn't be a bad heuristic.

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It doesn't undermine your point, but if you mean gross yearly wage $25k is not an amazing salary for senior software developers in Poland (I guess it depends where in Poland).
Perhaps is difficult to measure personal productivity in programming, but we can measure that we will run more slowly with 10 kg. in our backpack. We can for example select 10 tasks and guess some measure of their complexity (time to finish them) and then randomly do 5 with AI and 5 without AI, finally we calculate which of them deviates more from the initial guess. This procedure could be repeated many times with different individuals and could provide important information. The deviation could be measured by A_c = sum (real_time/guessed_time - 1), where A_0 is without AI and A_1 is with AI, A_1 > A_0 should indicate AI is beneficial, A_1 < A_0 means AI is detrimental, also we could clip the summands to the interval [-0.5,0.5] to avoid that one bad guess dominates the estimation.
I've been using Claude Code heavily for about 3 months now, and I'm pretty sure I'm between 10 and 20 times more productive while using it.

How I measure performance is how many features I can implement in a given period of time.

It's nice that people have done studies and have opinions, but for me, it's 10x to 20x better.

I find the swings to be wild, when you win with it, you win really big. But when you lose with it, it's a real bite out of your week too. And I think 10x to 20x has to be figurative right, you can do 20x by volume maybe, but to borrow an expression from Steve Ballmer, that's like measuring an airplane by kilograms.

Someone already operating at the very limit of their abilities doing stuff that is for them high complexity, high cognitive load, detail intense, and tactically non-obvious? Even a machine that just handed you the perfect code can't 20x your real output, even if it gave you the source file at 20x your native sophistication you wouldn't be able to build and deploy it, let alone make changes to it.

But even if it's the last 5-20% after you're already operating at your very limit and trying to hit your limit every single day is massive, it makes a bunch of stuff on the bubble go from "not realistic" to "we did that".

There are definitely swings. Last night it took about 2 hours to get Monaco into my webpack built bootstrap template, it came down to CSS being mishandled and Claude couldn't see the light. I just pasted the code into ChatGPT o3 and it fixed it first try. I pasted the output of ChatGPT into Claude and viola, all done.

A key skill is to sense when the AI is starting to guess for solutions (no different to human devs) and then either lean into another AI or reset context and start over.

I'm finding the code quality increase greatly with the addition of the text 'and please follow best practices because will be pen tested on this!' and wow.. it takes it much more seriously.

Doesn't sound like you were writing actual functionality code, just integrating libraries?
That's right for this part of the work.

Most of the coding needed to give people CRUD interfaces to resources is all about copy / pasting and integrating tools together.

Sort of like the old days when we were patching all those copy/paste's from StackOverflow.

Too little of full stack application writing is truly unique.

Is there a way to have two agentic AIs do pair programming?
I did experiment with this where Claude Code was the 'programmer' and ChatGPT was the Software Architect. The outcome was really solid and I made it clear that each was talking to an AI and they really seemed to collaborate and respect the key points of each side.

It would be interesting to set up a MCP style interface, but even me copy/pasting between windows was constructive.

The time this worked best was when I was building a security model for an API that had to be flexible and follow best practices. It was interesting seeing ChatGPT compare and contrast against major API vendors, and Claude Code asking the detailed implementation questions.

The final output was a pragmatic middle-ground between simplistic and way too complex.

Let's be serious, what percentage of devs are doing "high complexity, high cognitive load, detail intense" work?
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All of them, some just don’t notice, don’t care or don’t know this line of work is like that. Look at how junior devs work vs really experienced, self-aware engineers. The latter routinely solve problems the former didn’t know existed.
What does being experienced in a field of work have to do with self awareness?

Also I disagree. For web dev atleast, most people are just rewriting the same stuff in a different order. Even though the entire project might be complex from a high level perspective, when you dive into the components or even just a single route it ain't "high complexity" at all and since I believe most jobs are in web / app dev which just recycles the same code over and over again that's why there's a lot of people claiming huge boosts to productivity.

> Someone already operating at the very limit of their abilities doing stuff that is for them high complexity, high cognitive load, detail intense, and tactically non-obvious?

How much of the code you write is actually like this? I work in the domain of data modeling, for me once the math is worked out majority of the code is "trivial". The kind of code you are talking about is maybe 20% of my time. Honestly, also the most enjoyable 20%. I will be very happy if that is all I would work on while rest of it done by AI.

> Someone already operating at the very limit of their abilities doing stuff that is for them high complexity, high cognitive load, detail intense, and tactically non-obvious?

When you zoom in, even this kind of work isn't uniform - a lot of it is still shaving yaks, boring chores, and tasks that are hard dependencies for the work that is truly cognitively demanding, but themselves are easy(ish) annoyances. It's those subtasks - and the extra burden of mentally keeping track of them - that sets the limit of what even the most skilled, productive engineer can do. Offloading some of that to AI lets one free some mental capacity for work that actually benefits from that.

> Even a machine that just handed you the perfect code can't 20x your real output, even if it gave you the source file at 20x your native sophistication you wouldn't be able to build and deploy it, let alone make changes to it.

Not true if you use it right.

You're probably following the "grug developer" philosophy, as it's popular these days (as well as "but think of the juniors!", which is the perceived ideal in the current zeitgeist). By design, this turns coding into boring, low-cognitive-load work. Reviewing such code is, thus, easier (and less demoralizing) than writing it.

20x is probably a bit much across the board, but for the technical part, I can believe it - there's too much unavoidable but trivial bullshit involved in software these days (build scripts, Dockerfies, IaaS). Preventing deep context switching on those is a big time saver.

When you zoom in, even this kind of work isn't uniform - a lot of it is still shaving yaks, boring chores, and tasks that are hard dependencies for the work that is truly cognitively demanding, but themselves are easy(ish) annoyances. It's those subtasks - and the extra burden of mentally keeping track of them - that sets the limit of what even the most skilled, productive engineer can do. Offloading some of that to AI lets one free some mental capacity for work that actually benefits from that.

Yeah, I'm not a dev but I can see why this is true, because it's also the argument I use in my job as an academic. Some people say "but your work is intellectually complex, how can you trust LLMs to do research, etc.?", which of course, I don't. But 80% of the job is not actually incrementally complex, it's routine stuff. These days I'm writing the final report of a project and half of the text is being generated by Gemini, when I write the data management plan (which is even more useless) probably 90% will be generated by Gemini. This frees a lot of time that I can devote to the actual research. And the same when I use it to polish a grant proposal, generate me some code for a chart in a paper, reformat a LaTeX table, brainstorm some initial ideas, come up with an exercise for an exam, etc.

Yes, things that get resolved very quickly with AI include fixing Linting errors, reorganizing CI pipelines, documenting agreed on requirements, building well documented commits, cleaning up temporary files used to validate dev work, building README.md's in key locations to describe important code aspects, implementing difficult but well known code, e.g. I got a trie security model implemented very quickly.

Tons of dev work is not exciting, I have already launched a solo dev startup that was acquired, and the 'fun' part of that coding was minimal. Too much was the scaffolding, CRUD endpoints, web forms, build scripts, endpoint documentation, and the true innovative stuff was such a small part of the whole project. Of the 14 months of work, only 1 month was truly innovative.

Yeah, I don't fuck with Docker jank and cloud jank and shit. I don't fuck with dynamic linking. I don't fuck with lagged-ass electron apps. I don't fuck with package managers that need a SAT solver but don't have one. That's all going to be a hard no from me dawg.

When I said that after you've done all the other stuff, I was including cutting all the ridiculous bullshit that's been foisted on an entire generation of hackers to buy yachts for Bezos and shit.

I build clean libraries from source with correct `pkg-info` and then anything will build against it. I have well-maintained Debian and NixOS configurations that run on non-virtualized hardware. I use an `emacs` configuration that is built-to-specifications, and best-in-class open builds for other important editors.

I don't even know why someone would want a model spewing more of that garbage onto the road in front of them until you're running a tight, optimized stack to begin with, then the model emulates to some degree the things it sees, and they're also good.

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Where I have found Claude most helpful is on problems with very specific knowledge requirements.

Like: Why isn’t this working? Here Claude read this like 90 page PDF and tell me where I went wrong interfacing with this SDK.

Ohh I accidentally passed async_context_background_threading_safe instead of async_context_thread_safe_poll and it’s so now it’s panicking. Wow that would have taken me forever.

I agree I feel more productive. AI tools do actually make it easier and makes my brain use less energy. You would think that would be more productive but maybe it just feels that way.

Stage magicians say that the magic is done in the audiences memory after the trick is done. It's the effect of the activity.

AI coding tools makes developers happier and able to spend more brain power on actually difficult things. But overall perhaps the amount of work isn't in orders of magnitudes it just feels like it.

Waze the navigation app routes you in non standard routes so that you are not stuck in traffic, so it feels fast that you are making progress. But the time taken may be longer and the distance travelled may be further!

Being in stuck traffic and not moving even for a little bit makes you feel that time has stopped, it's boring and frustrating. Now developers need never be stuck. Their roads will be clear, but they may take longer routes.

We get little boosts of dopamine using AI tools to do stuff. Perhaps we used these signals as indicators of productivity "Ahh that days work felt good, I did a lot"

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> on actually difficult things

Can help but note that in 99% cases this "difficult things" trope makes little sense. In most jobs, the freed time is either spent on other stupid tasks or is lost due to org inefficiencies. :)

> Waze the navigation app routes you in non standard routes so that you are not stuck in traffic, so it feels fast that you are making progress. But the time taken may be longer and the distance travelled may be further!

You're not "stuck in traffic", you are the traffic. If the app distributes users around and this makes it so they don't end up in traffic jams, it's effectively preventing traffic jams from forming

I liked your washing machine vs. sink example that I see you just edited out. The machine may do it slower and less efficiently than you'd do in the sink, but the machine runs in parallel, freeing you to do something else. So is with good use of LLMs.

Yeah I totally agree. It's like washing by hand vs using a mangle possibly. The metaphor of agents to machines was also what I thought but didn't write as it's about companion tools mainly. (I got confused and put in a high level comment but somehow didn't actually post that!)

For Waze, even if you are traffic and others go around you, you still may get there quicker and your car use less energy than taking the suggested route that feels faster. Others may feel happier and feel like they were faster though. Indeed they were faster but might have taken a longer journey.

Also, generally most people don't use the app around here to effect significant road use changes. But if they did im not sure (but I'm having fun trying to think) what metaphor we can apply to the current topic :)

I can’t believe such numbers. If this was true why don’t you quit your job and vibe code 10 ios apps
I wish I could. Some problems are difficult to solve and I still need to pay the bills.

So I work 8 hours a day (to get money to eat) and code another 4 hours at home at night.

Weekends are both 10 hour days, and then rinse / repeat.

Unfortunately some projects are just hard to do and until now, they were too hard to attempt to solve solo. But with AI assistance, I am literally moving mountains.

The project may still be a failure but at least it will fail faster, no different to the pre-AI days.

I don't think you are understanding how big 10x and 20x are.

It means you can replace a whole team of developers alone.

I can believe that some tasks are speed up by 10x or even 20x, but I find very hard to believe it's the average of your productivity (maintaining good code quality)

I mean from a time perspective, your mileage may vary.

So me finishing a carded up block of work that is expected to take 2 weeks (80 hours) and I get it done in 1 day (8 hours) then that would be a 10x boost.

There are always tar pits of time where you are no better off with AI, but sometimes it's 20x.

I've setup development teams in the past, and have have been coding since the late 70's, so I am sort of aware of my capabilities.

It super depends on the type of work you're doing.

This is satire, right? You're 60ish years old, and hyper optimistic about AI, it's making you tens of times more productive, paste code from one AI to another, one is the dev and the other is the architect...

I mean, it's literally unbelievable.

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> I'm pretty sure

So were the people taking the study. Which is why we do these, to understand where our understanding of ourselves is lacking.

Maybe you are special and do get extra gains. Or maybe you are as wrong about yourself as everyone else and are overestimating the gains you think you have.

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> How I measure performance is how many features I can implement in a given period of time.

When a measure becomes a target, it ceases to be a good measure.

For the sake of argument 20x means you have basically suddenly got access to 19 people with the same skill set as you.

You can build a new product company with 20 people. Probably in the same domain as you are in right now.

You're getting 6 months worth of work done in a week?
I bet with a co-worker that a migration from angular 15 to angular 19 could be done really fast avoiding months. I spent a whole evening on it and Claude code have never been able to pull off a migration from 15 to 16 on its own. A total waste of time and nothing worked. I had the surprise that it cost me 275$ for nothing. So maybe for greenfield projects it’s smooth and saves time but it’s not a silver bullet on projects with problems.
I've had a lot of issues with Claude and web development.

I ended up asking it how it wanted to work and would an 'AdminKit Template' work to get things moving.

It recommended AdminKit and that was a good move.

For me, custom UI's aren't a big part of the solution, I just need web pages to manage CRUD endpoints to manage the product.

AdminKit has been a good fit so far, but it was a fresh start, no migration.

You asked Claude if AdminKit would work and in answer it recommended AdminKit? Seriously? Wow, what an unexpected turn of events. I am flabbergasted.
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> it cost me 275$ for nothing

Recently, there was story about developer who was able to crush interview and got parallel full-time jobs in several start-ups. Initially he was able to deliver but then not so much.

Somehow your case is reminding this to me, where AI is this overemployed developer.

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Have any open source work you can show off?
Not the OP, but:

https://repo.autonoma.ca/notanexus.git

I don't know the PDF.js library. Writing both the client- and server-side for a PDF annotation editor would have taken 60 hours, maybe more. Instead, a combination Copilot, DeepSeek, Claude, and Gemini yielded a working prototype in under 6 hours:

https://repo.autonoma.ca/notanexus.git/tree/HEAD/src/js

I wrote maybe 3 lines of JavaScript, the rest was all prompted.

Unfortunately not, but ensuring the final code quality will be well written is a challenge I am putting off for now.

I'm leaning into the future growth of AI capabilities to help me here, otherwise I'll have to do it myself.

That is a tomorrow problem, too much project structure/functionality to get right first.

So you are more productive, as long as you don't have to consider code quality.
Possibly, but not really.

With most projects where innovation is a key requirement, the goal isn't to write textbook quality code, it's to prove your ideas work and quickly evolve the project.

Once you have an idea of how it's going to work, you can then choose to start over from scratch or continue on and clean up all the bits you skipped over.

Right now I'm in the innovation cycle, and having AI able to pick up whole API path strategies and pivot them, is incredibly amazing.

How many times have you used large API's and seen clear hands of different developers and URI strategies, with an AI, you just pivot.

Code quality and pen tests are critical, but they can come later.

I’ve used this productivity hack without AI!
I'm between 73 and 86 times more productive using claude code. You're not using it well.
Can you show some of those problems and their solutions?
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I cringe when I see these numbers. 20 times better means that you can accomplish in two months what you would do in 4 years, which is ridiculus when said out loud. We can make it even more ridiculous by pointing out you would do in 3 years the work of working lifetime (60 years)

I am wondering, what sort of tasks are you seeing these x20 boost?

You are extrapolating over years as if a programmer’s task list is consistent.

Claude code has made bootstrapping a new project, searching for API docs, troubleshooting, summarizing code, finding a GitHub project, building unit tests, refactoring, etc easily 20x faster.

It’s the context switching that is EXTREMELY expensive for a person, but costless for the LLM. I can focus on strategy (planning features) instead of being bogged down in lots of tactics (code warnings, syntax errors).

Claude Code is amazing, but the 20x gains aren’t evenly distributed. There are some projects that are too specialized (obscure languages, repos larger than the LLM’s context window, concepts that aren’t directly applicable to any codebase in their training corpus, etc). But for those of us using common languages and commodity projects, it’s a massive force multiplier.

I built my second iOS app (Swift) in about 3 days x 8 hours of vibe coding. A vocab practice app with adjustable learning profile, 3 different testing mechanisms, gamification (awards, badges), iOS notifications, text to speech, etc. My first iOS app was smaller, mostly a fork of another app, and took me 4 weeks of long days. 20x speed up with Claude Code is realistic.

And it saves even more time when researching + planning which features to add.

It is amazing, cringe all you want :)

I scoped out a body of work and even with the AI assisting on building cards and feature documentation, it came to about 2 to 4 weeks to implement.

It was done in 2 days.

The key I've found with working as fast as possible is to have planning sessions with Claude Code and make it challenge you and ask tons of questions. Then get it to break the work into 'cards' (think Jira, but they are just .md files in your repo) and then maintain a todo.md and done.md file pair that sorts and organizes work flow.

Then start a new context, tell it to review todo.md and pick up next task, and burn through it, when done, commit and update todo.md and done.md, /compact and you're off on the next.

It's more than AI hinting at what to do, it's a whole new way of working with rigor and structure around it. Then you just focus fire on the next card, and the next, and if you ever think up new features, then card it up and put it in the work queue.

Did this 20x increase in productivity come with a 20x increase in salary? Do you clock off at Monday lunchtime and spend the rest of the week playing video games? Did your boss fire nineteen developers and give their jobs to you?

If one of these things isn’t true, you’re either a fool or those productivity increases aren’t real.

Being 20x increase in productivity won't come with a 20x money made. Unless you somehow monopoly the extra productivity.

A simple example: if someone patents a machine that makes canned tuna 10 times faster than how they're currently being made, would tuna factories make 10 times more money? The answer is obviously no. Actually, they'd make the same money as before, or even less than that. Only the one who makes such a machine (and the consumers of tuna cans) would be benefited.

I probably am a fool :)

10x to 20x is in relation to time, so something that would have taken 2 weeks (80 hours) would be done in 8 hours to be 10x.

> in two months what you would do in 4 years

There should be a FOSS project explosion if those numbers were true by now. Commercial products too.

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It isn’t ridiculous, it’s easily true, especially when you’re experienced in general, but have little to no knowledge of this particular big piece of tech, like say you’ve stopped doing frontend when jquery was all there was and you’re coming back. I’m doing things with react in hours I would have no business doing in weeks a couple years ago.
Maybe writing made up HN comments?
Same, I’ve done stuff that should have taken me 2-3 weeks in days
I’ve done this without AI. The thing was not as hard as I thought it would be.
I have exactly the same experience.
As others probably have experienced, I can only add that I am doing coding now I would have kicked down the road if I did not have LLM assistance.

Example: using LeafletJS — not hard, but I didn't want to have to search all over to figure out how to use it.

Example: other web page development requiring dropping image files, complicated scrolling, split-views, etc.

In short, there are projects I have put off in the past but eagerly begin now that LLMs are there to guide me. It's difficult to compare times and productivity in cases like that.

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> search all over to figure out how to use it.

Leaflet doc is single page document with examples you can copy-paste. There is page navogation at the top. Also ctrl/cmd+f and keyword seems quicker than writing the prompt.

This is pretty similar to my own experience using LLMs as a tool.

When I'm working with platforms/languages/frameworks I am already deeply familiar with I don't think they save me much time at all. When I've tried to use them in this context they seem to save me a bunch of time in some situations, but also cost me a bunch of time in others resulting in basically a wash as far as time saved goes.

And for me a wash isn't worth the long-term cost of losing touch with the code by not being the one to have crafted it.

But when it comes to environments I'm not intimately familiar with they can provide a very easy on-ramp that is a much more pleasant experience than trying to figure things out through often iffy technical documentation or code samples.

history repeats itself - "horses are more efficient than cars" In addition, a study based on 16 devs is representative enough to draw this conclusion?
AI could make me more productive, I know that for a fact. But, I don't want to be more productive because the tasks that could be automated with AI are those I find enjoyable. Not always in an intellectual sense, but in a meditative sense. And if I automated those away, I think I would become less human.
Here is the the methodology of the study:

> To directly measure the real-world impact of AI tools on software development, we recruited 16 experienced developers from large open-source repositories (averaging 22k+ stars and 1M+ lines of code) that they’ve contributed to for multiple years. Developers provide lists of real issues (246 total) that would be valuable to the repository—bug fixes, features, and refactors that would normally be part of their regular work. Then, we randomly assign each issue to either allow or disallow use of AI while working on the issue. When AI is allowed, developers can use any tools they choose (primarily Cursor Pro with Claude 3.5/3.7 Sonnet—frontier models at the time of the study); when disallowed, they work without generative AI assistance. Developers complete these tasks (which average two hours each) while recording their screens, then self-report the total implementation time they needed. We pay developers $150/hr as compensation for their participation in the study.

So it's a small sample size of 16 developers. And it sounds like different tasks were (randomly) assigned to the no-AI and with-AI groups - so the control group doesn't have the same tasks as the experimental group. I think this could lead to some pretty noisy data.

Interestingly - small sample size isn't in the list of objections that the auther includes under "Addressing Every Objection You Thought Of, And Some You Didn’t".

I do think it's an interesting study. But would want to see if the results could be reproduced before reading into it too much.

I think the productivity gains most people rave about are stuff like, I wanted to do X which isn't hard if you are experienced with library Y and library Y is pretty popular and the LLM did it perfectly first try!

I think that's where you get 10-20x. When you're working on niche stuff it's either not gonna work or work poorly.

For example right now I need to figure out why an ffmpeg filter doesn't do X thing smoothly, even though the C code is tiny for the filter and it's self contained.. Gemini refuses to add comments to the code. It just apologizes for not being able to add comments to 150 lines of code lol.

However for building an ffmpeg pipeline in python I was dumbfounded how fast I was prototyping stuff and building fairly complex filter chains which if I had to do by hand just by reading the docs it would've taken me a whole lot more time, effort and frustration but was a joy to figure out with Gemini.

So going back to the study, IMO it's flawed because by definition working on new features for open source projects wouldn't be the bread and butter of LLMs however most people aren't working on stuff like this, they're rewriting the same code that 10000 other people have written but with their own tiny little twist or whatever.

The sample size isn't 16 developers, it's 246 issues.
So agree with that - but on the other hand surely the number of developers matters here? For example, if instead of 16 developers the study consisted of a single developer completing all 246 tasks with or without AI, and comparing the observed times to complete, I think most people would question the reproducibility and relevancy of the study?
Okay, so why not 246,000 issues?
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If you read through the methodology, including how they paid the participants $150 / hr, for 20-40 hours work per participant, you can probably hazard a guess why they didn't scale up the size of the study by 1000x.
I think this for me is the most worrying: "You can see that for AI Allowed tasks, developers spent less time researching and writing code".

My analogy to this is seeing people spend time trying to figure out how to change colors, draw shapes in powerpoint, rather than focus on the content and presentation. So here, we have developers now focusing their efforts on correcting the AI output, rather than doing the research and improving their ability to deliver code in the future.

Hmm...

I find I’m most likely to use an LLM to generate code in certain specific scenarios: (i) times I’m suffering from “writer’s block” or “having trouble getting started”; (ii) a language or framework I don’t normally use; (iii) feeling tired/burnt out/demotivated

When I’m in the “zone” I wouldn’t go near an LLM, but when I’ve fallen out of the “zone” they can be useful tools in getting me back into it, or just finishing that one extra thing before signing off for the day

I think the right answer to “does LLM use help or hinder developer productivity” is “it depends on how you use them”

It can get over some mental blocks, having some code to look at can start the idea process even it’s wrong (just like for writing). I don’t think it’s bad, like I don’t think writing throw away code for prototyping is a bad way to start a project that you aren’t sure how to tackle. Waterfall (lots of research and design up front) is still not going to work even if you forgo AI.
This has been my observation too. It's a tool for the lazy.
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Us lazies need tools too!
You can say the same about a printer. Or a kindle, oh you're too lazy to carry around 5 books with you?
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laziness is a driving force of progress
in what direction
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All of them.
So the net result is we remain static? :-P
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See also: WALL-E
The more I used it, the easier it became to skip over things I should have thought through myself. But looking back, the results weren’t always faster or better. Now I prefer to treat AI as a kind of challenger. It helps reveal the parts I haven't truly understood, rather than just speeding things up.
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They averaged producing 47% more code on the AI tasks, but took only 20% more time. The report here biases over these considerations, but I’m left wondering: was the extra code superfluous or did this produce better structure / managed debt better? If that extra 47% of code translates to lower debt and more consistent throughput over the long term, I might take it, given how crushed projects get from debt. Anyway, it’s all hyperbole because there are massive statistical differences in the outcomes but no measures as to what they mean, but I’m sure they have meaning. That meaning matters a ton.
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> They averaged producing 47% more code on the AI tasks, but took only 20% more time. The report here biases over these considerations, but I’m left wondering: was the extra code superfluous or did this produce better structure / managed debt better? If that extra 47% of code translates to lower debt and more consistent throughput over the long term, I might take it, given how crushed projects get from debt.

Wouldn't it be the opposite? I'd expect the code would be 47% longer because it's worse and heavier in tech debt (e.g. code repeated in multiple places instead of being factored out into a function).

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Honestly my experience from using AI to code (primarily claude sonnet) is that that "extra 47%" is probably itself mostly tech debt. Places where the AI repeated itself instead of using a loop. Places where the AI wrote tests that don't actually test anything. Places where the AI failed to produce a simple abstraction and instead just kept doing the same thing by hand. Etc.

AI isn't very good at being concise, in my experience. To the point of producing worse code. Which is a strange change from humans who might just have a habit of being too concise, but not by the same degree.

Can we have a linter for both high verbosity/repetitiveness and high terseness? I know copy-paste detector and cognitive complexity calculator linters are related. I recently generated code that interleaved spreadsheet worksheets (multiple of them) and cell formatting boilerplate with querying data. I asked AI to put the boilerplate into another class and expose .write_balance_row() and it did it perfectly. If a tool reported it, huge changes dont have to reach human reviewers and AIs can iterate and pass the linter.
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Your response implies the ai produced code was landed without review. That’s a possible outcome but I would hope it’s unlikely to account for the whole group at this scale. We’re of course still lacking data.
I have two responses to the "code review fixes these problems" argument.

One: The work to get code to a reviewable point is significant. Skipping it, either with or without AI, is just going to elongate the review process.

Two: The whole point of using AI is to outsource the thought to a machine that can think much faster than you can in order to ship faster. If the normal dev process was 6 hours to write and 2 hours to review, and the AI dev process was 1 hour to write and 8 hours to review, the author will say "hey why is review taking so long; this defeats the purpose". You can't say "code review fixes these problems" and then bristle at the necessary extra review.

I very much doubt that when individual programmers are producing significantly more code with the help of AI that somehow the review process simultaneously scales up to perform adequate review of all of that extra code.

In my experience, review was inadequate back before we had AI spewing forth code of dubious quality. There's no reason to think it's any better now.

An actually-useful AI would be one that would make reviews better, do them itself, or at least help me get through reviews faster.

LLMs make me 10-20x more productive in frontend work which I barely do. But when it comes to low-level stuff (C/C++) I personally don't find it too useful. it just replaces my need to search stackoverflow.

edit: should have mentioned the low-level stuff I work on is mature code and a lot of times novel.

As the fullstacker with a roughly 65/35 split BE/FE on the team who has to review this kinda stuff on the daily, there's nothing I dread more than a backender writing FE tickets and vice versa.

Just last week I had to review some monstrosity of a FE ticket written by one of our backenders, with the comment of "it's 90% there, should be good to takeover". I had to throw out pretty much everything and rewrite it from scratch. My solution was like 150 lines modified, whereas the monstrous output of the AI was non-functional, ugly, a performance nightmare and around 800 lines, with extremely unhelpful and generic commit messages to the tune of "Made things great!!1!1!!".

I can't even really blame them, the C-level craze and zeal for the AI shit is such that if you're not doing crap like this you get scrutinized and PIP'd.

At least frontenders usually have some humility and will tell you they have no clue if it's a good solution or not, while BEnders are always for some reason extremely dismissive of FE work (as can be seen in this very thread). It's truly baffling to me

This is good if front end is something you just need to get through. It's terrible if your work is moving to involve a lot of frontend - you'll never pick up the skills yourself
Interesting, I find the exact opposite. Although to a much lesser extent (maybe 50% boost).

I ended shoehorned into backend dev in Ruby/Py/Java and don't find it improves my day to day a lot.

Specifically in C, it can bang out complicated but mostly common data-structures without fault where I would surely do one-off errors. I guess since I do C for hobby I tend to solve more interesting and complicated problems like generating a whole array of dynamic C-dispatchers from a UI-library spec in JSON that allows parsing and rendering a UI specified in YAML. Gemini pro even spat out a YAML-dialect parser after a few attempts/fixes.

Maybe it's a function of familiarity and problems you end using the AI for.

As in, it seems to be best at problems that you’re unfamiliar with in domains where you have trouble judging the quality?
This is exactly my experience as well. I've had agents write a bit of backend code, always small parts. I'm lucky enough to be experienced enough with code I didn't write to be able to quickly debug it when it fails (and it always fails from the first run). Like using AI to write a report, it's good for outlines, but the details are always seemingly random as far as quality.

For frontend though? The stuff I really don't specialize in (despite some of my first html beginning on FrontPage 1997 back in 1997), it's a lifesaver. Just gotta be careful with prompts since so many front end frameworks are basically backend code at this point.

It works with low-level C/C++ just fine as long as you rigorously include all relevant definitions in the context window, provide non-obvious context (like the lifecycle of some various objects) and keep your prompts focused.

Things like "apply this known algorithm to that project-specific data structure" work really well and save plenty of time. Things that require a gut feeling for how things are organized in memory don't work unless you are willing to babysit the model.

I've been hacking on some somewhat systemsy rust code, and I've used LLMs from a while back (early co-pilot about a year ago) on a bunch of C++ systems code.

In both of these cases, I found that just the smart auto-complete is a massive time-saver. In fact, it's more valuable to me than the interactive or agentic features.

Here's a snippet of some code that's in one of my recent buffers:

    // The instruction should be skipped if all of its named
    // outputs have been coalesced away.
    if ! self.should_keep_instr(instr) {
      return;
    }

    // Non-dropped should have a choice.
    let instr_choice =
      choices.maybe_instr_choice(instr_ref)
        .expect("No choice for instruction");
    self.pick_map.set_instr_choice(
      instr_ref,
      instr_choice.clone(),
    );

    // Incref all named def inputs to the PIR choice.
    instr_choice.visit_input_defs(|input_def| {
      self.def_incref(input_def);
    });

    // Decref all named def inputs to the SIR instr.
    instr.visit_inputs(
      |input_def| self.def_decref(input_def, sir_graph)
    );
The actual code _I_ wrote were the comments. The savings in not having to type out the syntax is pretty big. About 80% of the time in manual coding would have been that. Little typos, little adjustments to get the formatting right.

The other nice benefit is that I don't have to trust the LLM. I can evaluate each snippet right there and typically the machine does a good job of picking out syntactic style and semantics from the rest of the codebase and file and applying it to the completion.

The snippet, if it's not obvious, is from a bit of compiler backend code I'm working on. I would never have even _attempted_ to write a compiler backend in my spare time without this assistance.

For experienced devs, autocomplete is good enough for massive efficiency gains in dev speed.

I still haven't warmed to the agentic interfaces because I inherently don't trust the LLMs to produce correct code reliably, so I always end up reviewing it, and reviewing greenfield code is often more work than just writing it (esp now that autocomplete is so much more useful at making that writing faster).

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What exact tool are you using for your smart auto-complete?
Whatever copilot defaults to doing on vscode these days. I didn't configure it very much - just did the common path setup to get it working.
This feels like a parallel to the Gell-Mann amnesia effect.

Recently, my company has been investigating AI tools for coding. I know this sounds very late to the game, but we're a DoD consultancy and one not traditional associated with software development. So, for most of the people in the company, they are very impressed with the AI's output.

I, on the other hand, am a fairly recent addition to the company. I was specifically hired to be a "wildcard" in their usual operations. Which is too say, maybe 10 of us in a company of 3000 know what we're doing regarding software (but that's being generous because I don't really have visibility into half of the company). So, that means 99.7% of the company doesn't have the experience necessary to tell what good software development looks like.

The stuff the people using the AI are putting out is... better than what the MilOps analysts pressed into writing Python-scripts-with-delusions-of-grandeur were doing before, but by no means what I'd call quality software. I have pretty deep experience in both back end and front end. It's a step above "code written by smart people completely inexperienced in writing software that has to be maintained over a lifetime", but many steps below, "software that can successfully be maintained over a lifetime".

Well, that's what you'd expect from an LLM. They're not designed to give you the best solution. They're designed to give you the most likely solution. Which means that the results would be expected to be average, as "above average" solutions are unlikely by definition.

You can tweak the prompt a bit to skew the probability distribution with careful prompting (LLMs that are told to claim to be math PHDs are better at math problems, for instance), but in the end all of those weights in the model are spent to encode the most probable outputs.

So, it will be interesting to see how this plays out. If the average person using AI is able to produce above average code, then we could end up in a virtuous cycle where AI continuously improves with human help. On the other hand, if this just allows more low quality code to be written then the opposite happens and AI becomes more and more useless.

I have no doubt which way it is going to go.
Before the industrial revolution a cabinetmaker would spend a significant amount of time advancing from apprentice to journeyman to master using only hand tools. Now master cabinetmakers that only use hand tools are exceedingly rare, most furniture is made with power tools and a related but largely different skillset.

When it comes to software the entire reason maintainability is a goal is because writing and improving software is incredibly time consuming and requires a lot of skill. It requires so much skill and time that during my decades in industry I rarely found code I would consider quality. Furthermore the output from AI tools currently may have various drawbacks, but this technology is going to keep improving year over year for the foreseeable future.

Maintainable software is also more maintainable by AI. The required standards may be a bit different, for example there may be less emphasis on white space styling, but, for example, complexity in the form of subtle connections between different parts of a system is a burden for both humans and AI. AI isn't magic, it still has to reason, it fails on complexity beyond its ability to reason, and maintainable code is one that is easier to reason with.
Same. It’s amazing for frontend.
It's astonishing. A bit scary actually. Can easily see the role of front-end slowly morphing into a single person team managing a set of AI tools. More of an architecture role.
Is this because they had the entire web to train on, code + output and semantics in every page?
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I guess it’s because modern front-end “development” is mostly about copying huge amounts of pointless boilerplate and slightly modifying it, which LLMs are really good at.
It's moreso that a backend developer can now throw together a frontend and vice-versa without relying on a team member or needing to set aside time to internalize all the necessary concepts to just make that other part of the system work. I imagine even a full-stack developer will find benefits.
So we are all back to be webmasters :)
This has nothing to do with what they asked.
I’m not sure how this was extended and refined but there are sure a lot of signs of open source code being used heavily (at least early on). It would make sense to test model fit with the web at large.
Now do a study that specifically gauges how useful an LLM (including smart tab completion) is for a frontend dev working in react/next/tailwind on everyday Jira tickets.

These were maintainers of large open source projects. It's all relative. It's clearly providing massive gains for some and not as much for others. It should follow that it's benefit to you depends on who you are and what you are working on.

It isn't black and white.

It's a very well controlled study about... what the study claims to do. Yes, they didn't study a different thing, for _many_ reasons. Yes, we shouldn't haphazardly extrapolate to other parts of Engineering. But it looks like it's a good study nonetheless.

There are some very good findings though, like how the devs thought they were sped up but they were actually slowed down.

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React and tailwind already made lot of tradeoffs to make it more ergonomic for developers. One would expect that LLMs could unlock lean and faster stack instead.
As a backend dev who owns a few internal crappy frontends, LLMs have been the best thing ever. Code quality isn't the top priority, I just need to plumb some data to an internal page at BigCorp.
Could you share more about your process and how they specifically help you with your internal frontends? Any details would be great! Thanks!
I was surprised at how much better v0 was these days. I remember it yielding clunky UIs initially.

I thought it was the model, but then I realised, v0 is carried by the shadcn UI library, not the intelligence of the model

What if this is true? And then we as a developer community are focused on the wrong thing to increase productivity?

Like what if by focusing on LLMs for productivity we just reinforce old-bad habits, and get into a local maxima... And even worse, what if being stuck with current so-so patterns, languages, etc means we don't innovate in language design, tooling, or other areas that might actually be productivity wins?

We were stuck near local maxima since before LLM's came on the scene. I figure the same concentration of innovators are gonna innovate, now LLM assisted, and the same concentration of best-practice folk are gonna best-practice--now LLM assisted. Local maxima might get sticker, but greener pastures will be found more quickly than ever.

I expect it'll balance.

imagine having interstate highways built in one night you wake up and you have all these highways and roads and everyone is confused what they are and how to use them. using llm is the opposite of boiling frogs because you're not the leader writing, you're just suggesting... i just realized i might not know what im talking about.
I found that early and often code reviews can offset the reduction in productivity. A good code review process can fix this.
This entire concept hinges on AI not getting better. If you believe AI is going continue to get better at the current ~5-10% a month range, then hand waiving over developer productivity today is about the same thing as writing an article about the internet being a fad in 1999.
On the flip side, why would I use AI today if it presents no immediate benefit. Why not wait 5 years and see if it becomes actually helpful.
better yet, wait 10, let me know how it goes
If they do improve at 5-10% a month then that'd definitely be true (tbh I'm not sure they are even improving at that rate now - 10% for a year would be 3x improvement with compounding).

I guess the tricky bit is, nobody knows what the future looks like. "The internet is a fad" in 1999 hasn't aged well, but a lot of people touted 1960s AI, XML and 3d telivisions as things that'd be the tools in only a few years.

We're all just guessing till then.

I finally took the plunge and did a big chunk of work in Cursor. It was pretty ideal: greenfield but with a very relevant example to slightly modify (the example pulled events over HTTP as a server and I wanted it to pull events over Google pub/sub instead).

Over IDK, 2-3 hours I got something that seemed on its face to work, but:

- it didn't use the pub/sub API correctly

- the 1 low-coverage test it generated didn't even compile (Go)

- there were a bunch of small errors it got confused by--particularly around closures

I got it to "90%" (again though it didn't at all work) with the first prompt, and then over something like a dozen more mostly got it to fix its own errors. But:

- I didn't know the pub/sub API--I was relying on Cursor to do this correctly--and it totally submarined me

- I had to do all the digging to get the test to compile

- I had to go line by line and tell it to rewrite... almost everything

I quit when I realized I was spending more time prompting it to fix things than it would take me to fully engage my brain and fix them myself. I also noticed that there was a strong pull to "just do one more prompt" rather than dig in and actually understand things. That's super problematic to me.

Worse, this wasn't actually faster. How do I know that? The next day I did what I normally do: read docs and wrote it myself. I spent less time (I'm a fast typist and a Vim user) overall, and my code works. My experience matches pretty well w/ the results of TFA.

---

Something I will say though is there is a lot of garbage stuff in tech. Like, I don't want to learn Terraform (again) just to figure out how to deploy things to production w/o paying a Heroku-like premium. Maybe I don't want to look up recursive CTEs again, or C function pointers, or spent 2 weeks researching a heisenbug I put into code for some silly reason AI would have caught immediately. I am _confident_ we can solve these things without boiling oceans to get AI to do it for us.

But all this shit about how "I'm 20x more productive" is totally absurd. The only evidence we have of this is people just saying it. I don't think a 20x productivity increase is even imaginable. Overall productivity since 1950 is up 3.6x [0]. These people are asking us to believe they've achieved over 400 years of productivity gains in "3 months". Extraordinary claims require extraordinary evidence. My guess is either you were extremely unproductive before, or (like others are saying in the threads) in very small ways you're 20x more productive but most things are unaffected or even slower.

[0]: https://fred.stlouisfed.org/series/OPHNFB

You're using it wrong -- it's intended to be a conversational experience. There are so many techniques you can utilize to improve the output while retaining the mental model of codebase.

Respectfully, this is user error.

I’ve been around tech for a long time. At this point, I’ve lost count of how many hype cycles I’ve seen hit the “hold on, everything sucks” stage. Generative AI is seemingly at the hold on, everything sucks stage and it’s getting repetitive.
Trough of Disillusionment (followed by the Slope of Enlightenment and Plateau of Productivity): https://en.wikipedia.org/wiki/Gartner_hype_cycle
My bold prediction is that the Trough of Disillusionment for LLMs is going to be a very long stretch
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I find LLMs are decent at regurgitating boilerplate. Basically the same kind of stuff you could google then copy-paste... AI chatbots, now that they have web access, are also good at going over documentation and save you a little time searching through the docs yourself.

They're not great at business logic though, especially if you're doing anything remotely novel. Which is the difficult part of programming anyway.

But yeah, to the average corporate programmer who needs to recreate the same internal business tool that every other company has anyway, it probably saves a lot of time.

This isn't true, and I know it by what I'm working on and sorry, I'm not at liberty to give more details. But I see how untrue this is, every working hour of every day.
They're great at helping me figure out how to make something work with a poorly-documented, buggy framework, which is indeed a large fraction of my job, whether I like it or not.
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