Mine is that a Billy bookcase that I bought from Ikea 25 years ago is must stronger and more stable than a Billy bookcase I bought from Ikea 5 years ago.
And, when looking at what Ikea is selling in 2025 as a Billy bookcase, it's worse yet again.
But, with the cost of living increasing, companies have to cut corners to keep pricing down.
I wonder where the inflection point is where used items become more valuable than the new items being made at current quality levels, including degradation due to age.
When Ikea first expanded beyond Scandinavia, it was the 'fast fashion' of furniture: beautiful design, but sometimes made of particleboard or polyurethane foam.
Quality of the basic model Maytag washer I bought 25 years ago versus one today.
Quality of a Reese's cup I bought 25 years ago versus one today.
Quality of Levi's I bought 25 years ago versus a pair today.
Quality of the Billy that I bought 25 years ago versus today.
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.
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.
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.
Most components are routine work, that you kinda snooze through. I like them as a kind of mental break: write tests, write code, run tests/linter.
The difficult part is reading thousand lines of unfamiliar code to measure the impact of a fix, finding the fix by reasoning about the whole moduke, designing a feature for long term maintainability,…
Note that all of them requires thinking and not much coding. Coding is easy, especially when you’ve done all the (correct?) thinking beforehand.
> 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.
Creatively thinking about what a client needs, how the architecture for that would be like, general systems thinking, UX etc. and seeing that come to live in a clean, maintainable way, that's what lights up my eyes. The minutiae of code implementation, not so much, that's just an implementation detail, a hurdle to overcome. The current crop of tooling helps with that tremendously, and for someone like me, it's been a wonderful time, a golden era. To the people who like to handcraft every line of code to perfection, people who derive their joy from that, I think they benefit a lot less.
> 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.
> Offloading some of that to AI lets one free some mental capacity for work that actually benefits from that.
Maybe, but I don't feel (of course, I could be wrong) that doing boring tasks take away any mental capacity; they feel more like fidgeting while I think. If a tool could do the boring things it may free my time to do other boring work that allows me to think - like doing the dishes - provided I don't have to carefully review the code.
Another issue (that I asked about yesterday [1]) is that seemingly boring tasks may end up being more subtle once you start coding them, and while I don't care too much about the quality of the code in the early iterations of the project, I have to be able to trust that whatever does the coding for me will come back and report any difficulties I hadn't anticipated.
> Reviewing such code is, thus, easier (and less demoralizing) than writing it.
That might well be true, but since writing it doesn't cost me much to begin with, the benefit might not be large. Don't get me wrong, I would still take it, but only if I could fully trust the agent to tell me what subtleties it encountered.
> 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.
If work is truly trivial, I'd like it to be automated by something that I can trust to do trivial work well and/or tell me when things aren't as trivial and I should pay attention to some detail I overlooked.
We can generally trust machines to either work reliably or fail with some clear indication. People might not be fully reliable, but we can generally trust them to report back with important questions they have or information they've learnt while doing the job. From the reports I've seen about using coding agents, they work like neither. You can neither trust them to succeed or fail reliably, nor can you trust them to come back with pertinent questions or information. Without either kind of trust, I don't think that "offloading" work to them would truly feel like offloading. I'm sure some people can work with that, but I think I'll wait until I can trust the agents.
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.
Ok, that's great for you. Most of us don't have the luxury of going full Richard Stallmann in their day to day and are more than happy to have some of the necessary grunt work to be automated away.
I live in the same world as everyone else and have to make a living same as anyone else.
Lagged-ass electron apps are a choice: run neovim or emacs or zed, I have Cursor installed, once in a while I need vscode for something, but how often is someone dictating my editor?
I have to target OCI container platforms for work sometimes, that's what Arion and nix2container are for. Ditto package managers: uv and bun exist and can interact with legacy requirements.txt and package.json in most cases.
Anything from a Helm chart to the configuration for ddagent can be written from nixlang and into a .deb.
My current job has a ton of Docker on GCE running TypeScript, I have to emit compatible code and configuration, but no one stands over my shoulders to make sure I'm doing the Cloud Approved jank path or having a bash script or Haskell program print it. I have a Jank Stack Compatibility Layer that builds all that nonsense.
Job after job there's a little setup cost and people look at me funny, 6 months in my desk is an island of high-velocity sanity people are starting to use because I carry a "glory days FAANG" toolkit around and compile reasonable plain text into whatever ripoff cloud garbage is getting pimped this week.
It's a pretty extreme workplace where you can't run reasonable Unix on your own machine and submit compiler output instead of typing for the truly mandatory jank integration points.
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?
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.
The conclusion there, and here, is that canning tuna ten times faster doesn’t increase productivity by ten times (because the there’s other limiting factors). Or: No software project was ever late because the typing took too long.
Which means you should be perfectly capable of answering two of the three scenarios the other user posed:
> 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?
In other words, how are you taking advantage of all that extra time you claim to have?
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.
We surely shouldn't need a whole minute, 3 seconds should suffice? ;)
Jokes aside, if 20x was on the table for any kind of meaningful work we wouldn't need to wait for much of anything, entire parts of industry would be invented and technically reworked by now. It's most likely ~1.25x for what is mostly trivial work that approaches 95% boilerplate and zero actual design work.
If you read calrain's posts the 20x number is taken from the "fact" that sometimes (not consistently or most of the time) something that was estimated at 2 weeks or 80 hours (who knows what it was and how that number came to be?) took 2 hours instead. That's not just some minor detail; it's just not a sound way of thinking about productivity increases.
I am honestly convinced these are AI comments. They fail to answer the question of what sort of work they see the x20 improvements, just like Chatgpt fails to answer my hard technical questions.
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.
I am waiting to see your 4 year human-equivalent project in a couple of months.
Words without actions are junk. You are asserting something you have no proof for. Proove it then. Amaze us all with your productivity, out in the open. Shred those pilled up open issues on open source projects and then give us a report of how fast-easy it.
If it is "easily true" you'll be done by next month
I don’t need to prove to you something I experience daily, both professionally and in hobby side projects. You can take my anecdotes or you can keep believing me and people like me are wrong, your choice.
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"
> 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 :)
Can't 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, or is just procrastinated.
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 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)
20x0.1 is still just 2. You don't know what calrain's productivity without AI assistance is actually like. "20x" might just be correct, but it doesn't have to be 20x what you do.
(I don't think it's 20x, it's most likely hyperbole. People aren't that unique and it's not hard to see that people who use LLMs are often lulled into thinking they're more valuable to them than they actually are, especially when they "do more", i.e. they're a magic little person program that seems to do tasks on their own as opposed to glorified auto-complete that probably by raw numbers is actually more productive.)
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...
An honest measurement tries to consider the aggregate, not one single point.
If you had a hammer which could drive a nail through a plank 20x faster but took 60x longer to prepare before each strike, claiming 20x gains would be disingenuous.
The problem is that AI leads to extremely bimodal distribution of improvement.
Sometimes it doesn't help at all. Other times it spits out several hours of work in seconds.
It's like asking what is the weighted average of 1 and infinity? Even if you can quantify how many 1s and how many infinities there are, the answer is always going to be nonsensical.
I disagree with your example, but either way your conclusion is agreeing with my point. If you can’t give a sensible answer, then don’t give a random one confidently (like an LLM). The user in question is doing the equivalent of always answering “infinity” (the best case) to your example.
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.
Apologies, it was a typo. I asked what 'Admin Template' because there are so many, and rather than build something from scratch, I wanted one it seemed to have understanding of.
It mentioned AdminKit and it worked out pretty well.
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.
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.
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:
> Writing both the client- and server-side for a PDF annotation editor would have taken 60 hours, maybe more.
How do you know? Seems to me you’re making the exact same estimation mistake of the people in the study.
> Instead, a combination Copilot, DeepSeek, Claude, and Gemini yielded a working prototype in under 6 hours
Six hours for a prototype using four LLMs? That is not impressive, it sounds insane and a tremendous mess that will take so long to dig out of the prototype stage it’ll effectively require a rewrite.
And why are you comparing an LLM prototype to a finished product “by hand” (I surely hope you’re not suggesting such a prototype would take sixty hours)? That is disingenuous and skewing the numbers.
> How do you know? Seems to me you’re making the exact same estimation mistake of the people in the study.
I have over 20 years of web development experience and 40 years of general experience writing software. I wrote the authors and they confirmed my thoughts:
"I totally believe it! Per the paper abstract, we find many factors driving results - and one of the factors is how experienced the developers are on the codebase, and how big/complex the codebases are.
"Given that this was a new and unfamiliar domain and new codebase, I would expect there to be much more speedup than the domain we studied!"
> Six hours for a prototype using four LLMs?
They have limits on the number of queries, so I used four different LLMs in tandem to circumvent query limits. I didn't write it four times using four different LLMs.
> it sounds insane and a tremendous mess
I posted the code. It's well organized, has few (if any) encapsulation violations, sticks to OOP quite well, works, and---if I knew the PDF.js API---would be fairly easy to maintain.
Yes, I stand by my claim that writing this annotation editor (PHP, HTML, CSS, and JS) would take me about 60 hours by hand and about 6 hours using the LLMs.
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.
> Code quality and pen tests are critical, but they can come later.
In my experience, no.
These kind of shortcuts taken at the beginning of the project is why velocity have a sharp descent after some times. Because you’re either spending time undoing all of it (unlikely to be allowed) or you’re fighting in the code jungle trying to get some feature out.
I've always enjoyed software design, for me the coding was the bottleneck and it was frustrating as I had to roll through different approaches when I so clearly knew the outcome that I wanted.
Using Claude Code to first write specs, then break it down into cards, build glossaries, design blueprints, and finally write code, is just a perfect fit for someone like me.
I know the fundamentals of programming, but since 1978 I've written in so many languages that the syntax now gets in the way, I just want to write code that does what I want, and LLM's are beyond amazing at that.
I'm building API's and implementing things I'd never dreamed of spending time on learning, and I can focus on what I really want, design, optimisation, simplification, and outcomes.
I guess you stopped reading there and missed that part:
> Yes, we see the irony! We encourage you to inspect our installer first. That's the whole point of vet. You can read the installer's source code install.sh
It is very trivial to serve different code to someone inspecting the code than when they pipe it to bash. In the very rare case someone inspected it they’d likely do so in a way that was vulnerable to this.
That’s an excellent point, and thank you for raising it. You are 100% correct—relying on users to inspect a URL that could be spoofed with User-Agent trickery is a flaw in the original recommendation. It's a classic threat model that I should have addressed from the start.
Thanks to your feedback, I've just merged a PR to change the recommended installation method in the documentation to the only truly safe one: a two-step "download, then execute the local file" process. This ensures the code a user inspects is the exact same code they run.
I sincerely appreciate you taking the time to share your expertise and hold the project to a higher standard. This is what makes a community great.
No, just a trove of connected personal notes recently compiled. But they draw heavily from quotes I've collected, so I could see how they could come across as artificially curated.
Mine is that a Billy bookcase that I bought from Ikea 25 years ago is must stronger and more stable than a Billy bookcase I bought from Ikea 5 years ago.
And, when looking at what Ikea is selling in 2025 as a Billy bookcase, it's worse yet again.
But, with the cost of living increasing, companies have to cut corners to keep pricing down.
I wonder where the inflection point is where used items become more valuable than the new items being made at current quality levels, including degradation due to age.
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