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Ask HN: What would you do if you didn't work in tech?
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I could have been an academic or an activist. My son reactivated in me the "making" aspect of experimental physics that had a big impact on me despite doing theory for my PhD. (My son builds buildings at day, guitars at night) In the last two years I've become a semi-pro photographer. I guess I am also an "activist" now but approach it as personal change [1] instead of interpersonal conflict. [1] a kind of global "daoism" that embraces all kinds of human development
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BEACH....BUM
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Do you mean "money is not an issue, what else would you do" or "AI has automated tech, which another job would you pick now" or "had made different life choices 20 years ago..."?
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Medicine, ideally Oncology. I only made that realization as an adult though.
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I'd probably be an electrician or fabrication (3D printing / CNC). Or does that count as tech?
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Regenerative farmer, tinkering with mycorrhizal fungi and other microbiology
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Former tech worker here. I don’t know yet, and don’t have many skills outside of computer jockey. Can be friendly for short periods but not a people person. What do y’all suggest?
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I'd be a cook/chef, which is what I decided on in HS, but I let everyone talk me into tech, which I regret in hindsight.
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Some form of building things in the physical world rather than the digital; probably working in construction since I already do it on the side.
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im with you, im completely over digital at this point
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CNC (and maybe some manual) Machining.
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Same here. I worked as a sign maker in high school and I always wanted a CNC.
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Soon we will all find it out.
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Baker. Very phisical job, night hours, but it's creative and you really make something with your hands. Wonderful.
US Administration halted largest Offshore Wind project in the U.S. (cnbc.com)
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I think Swedish right-wingers pioneered this particular form of anti-renewable propaganda last year.
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Related: US halts work on almost finished wind farm because national security (117 points, 4 months ago, 83 comments) https://news.ycombinator.com/item?id=45002747
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Let’s look at the economics instead of emotionally. Wind turbines (real rough back of the napkin) CapEx break even is estimated 7-10 years. They can consume thousands of liters of lubrication oil, and estimated break-even just on energy to produce the unit is 5-8 months (energy required to build it). Then you have the issue of how ugly (in my opinion) large farms look.
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[delayed]
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In October Trump canceled the largest solar project in the United States. Known as Esmeralda 7, the project planned in the Nevada desert would have produced enough energy to power nearly two million homes. https://www.politico.com/news/2025/10/10/trump-interior-department-cancels-largest-solar-project-in-north-america-00602071
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oh look, lying about national security concerns again. It's funny that the only thing this administration does is destroy things, lie, and make our lives measurably worse.
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It sounds like “national security” is the legal justification they’re using to do an end-run around Congress, just like the justifications they’ve used to implement tariffs and which underpin a bunch of their EOs.
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It's about time a US administration is brave enough to tackle real national security concerns: windmill cancer, completing the White House ballroom, and Antifa! /s
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Likely just a temporary pause. This administration won't be around that much longer.
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I feel bad for ruining someone's dreams, but you really have no idea how wrong you are.
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They're stuck there for four year terms, and in the case of SCOTUS life, are they not?
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"Radar interference risk" is the cited reasoning. It took them the entire year to use anything other than a conspiracy theory to justify shutting these down.
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It’s unfortunate many people are able to see through this to the blatant corruption. Energy independence and national security is renewable energy. All sorts of diverse energy sources are good. But we don’t like that.
Ask HN: Why Did Python Win?
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Perl was my introduction to web programming in the 90s, and then I moved onto python and php. Perl’s alien syntax and how there were multiple competing “best practices” put off newcomers and made it difficult to code review. Perl also stagnated for quite some time due to the efforts to move to Perl 6, though python likewise stalled later it already had better traction at that point. However I’d argue that Perl lost to PHP at least as much as it lost to python. PHP was revolutionary at the time for how easy and straightforward it was to do simple web programming which was one of the big use cases for Perl. By the early 2010s there wasn't much of any use case that made sense to choose Perl. Sysadmin scripting was clearer to review and maintain in python and golang, web applications moved to php and frameworks written in languages that didn’t have the downsides of Perl.
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I started my data analysis journey using Perl and then Python. Perl had also a variety of libraries, online classes from O'Reilly's, samples online, CGI support, I moved to Python when installing some dependencies became troublesome and Perl versioning was not compatible between each other don't recall the details, since then been using Python extensibly since 2.7
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I think an underrated aspect of Python's success is that is easier for non-SWE professionals to understand and use. Python is popular among software developers, but it is overwhelmingly popular among scientists.
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Python won because Google picked it over perl in the early 2000s, declaring that it was more uniform in having one preferred way to do things rather than allowing (in contrast to Perl) programmers having a zillion ways to do things. Larry Wall, creator of Perl, coined the phrase, "make the easy things easy, and the hard things possible". But the things perl made easy were not always things that the market needed. Perl was very distrustful of enterprise disciplines and the perl culture "let a thousand flowers bloom" mentality (which they/we called "There's more than one way to do it"/(TMTOWTDI)) was not actually useful for organizations maintaining larger code bases/systems with large groups of mixed skill level people. Python threaded that needle of centralizing a right way to do things (not just whitespaces) more effectively. I also observed Scala suffering from similarly disfunctional "TMTOWTDI" dynamic in organizations I belonged to; different people had wildly different styles of solving even basic problems and it really made code hard to maintain even in smaller organizations where people came and went and new people were onboarded frequently. Plus data science use cases saved Python's web/scripting lifespan in a way slightly analogous to AI/CUDA extending NVidia's 3D graphics-centric roots. (Although the python data science ecosystem does have the TMTOWTDI it weaknesses similar to perl/cpan in some respects!)
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1. Python was designed by testing syntax with novice users to see what they could adopt easily.[1] > 90% of current Python users weren’t born when it was created. They all had to learn, and Python is the easiest language to learn because Guido and his teammates, unlike $LANGUAGE_DESIGN_GOD, approach the problem as experimental scientists rather than auteurs. 2. Python is conceptually compact, dominated by hash tables with string keys. The initial leader in the ecosystem, Perl, is conceptually sprawling and difficult to reason about. 3. Python also took lessons from the Unix shell, a mature environment for accommodating beginners and experts. 4. Python had a formal process for integrating C modules from early on. 5. Python’s management has an elegant shearing layer structure, where ideas can diffuse in from anywhere. 6. $NEXT_GENERAL_PURPOSE_LANG (Ruby, Go) weren’t enough better to displace Python. Both were heavily influenced by Python’s syntax, but ignored the community-centric design process that had created that syntax in favor of We Know Best. 7. Speaking of open source entrepreneurialism, JavaScript has become a real rival thanks to the Web (and node), but it is handicapped by the inverse failure mode: where Go is dominated by a handful of Googlers, JavaScript was effectively unmanaged at the STDLIB level for a crucial decade, and now it can’t recover. (I’d also guess that having to write a module system that works well in the chaos that is Web clients and simultaneously the Unix world is a daunting design problem.) 8. Python got lucky that data science took off. [1] https://ospo.gwu.edu/python-wasnt-built-day-origin-story-worth-knowing
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I often consider how many total human lifetimes must have been spent on nothing other than dealing with and/or mitigating something as seemingly banal as Python versioning & dependency management or line ending encoding snafus. After all these years, I assume it must measure in the tens to hundreds of thousands? It’s in those moments of consideration that I understand humanity hasn’t done a very good job grappling with its frailty and mortality. If we had, then we’d have a crowned a winner that didn’t subjugate us endlessly to the most vapid and Sisyphean aspects of programming. Python is a lesson in managing existential angst, which I now assume was the point of it from the very beginning.
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Python ultimately won because of its community. The Python leadership group is friendly towards beginners and people with non-traditional backgrounds, and they actively reach out and recruit those folks to participate. It's also one of the least toxic developer communities I know of. That matters a lot in a field like data science or cybersecurity, which has a lot of people who don't have traditional SWE backgrounds. The language is pleasant and readable to use and has a lot of features built in, which Perl did not, but I think that's much less critical than the network effect of being a (1) welcoming and pleasant community who (2) actively recruits new fields of people.
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I learned Python circa 2000 as a 17 year old. It felt pretty easy to read and write, had minimal surprises, and it made writing simple programs easy. The batteries-includedness was great. It felt like it was designed by a smart guy for practical programming, rather than by a brilliant academic who was wed to purity for maximum elegance. It accepted some warts, but them in mostly ergonomic places. It was dictated by people who could relegate map, filter, and reduce from builtins to the library. As much as I personally even liked map in particular (I am not super anti functional programming), it's nice to realize the designer had the taste to prefer longer but more explicit programs. ys = [f(x) for x in xs] ys = map(f, xs) As much as I disliked that particular decision, there is no doubt to me that it is just designed for lower cognitive burden when doing simple/common things. If you show both of those lines of code to a person in cs101 who has studied Java for a couple months but hasn't seen Python, I am pretty sure they are gonna understand the first line way quicker. Mostly consistent decision making like that leads to ergonomic, practical languages. And they win. It fits in people's brains better. See also, pytorch vs tensorflow.
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As a C++ dev turned python dev (and also someone who writes code for analysts, data scientists etc.), the answer is really obvious to me. First, newbie onboarding. What is the hello world like? Is there any obsequious unexplainable stuff ('#pragma once'? 'use warnings;')? Symbol usage. ';' I'm going to have to show someone where that is, and they are going to have to peck and hunt for it for their first ten hours programming. They are going to miss a line. Python is has one of the easiest hello worlds of all time, white space mattering significantly reduces symbol usage. The errors are good. There is a REPL. Second, python as a language interface. Partly because of the first point, if I want to write a framework for data scientists, I will write it in python. And if I want performance on that framework then I am going to write the backend in C, C++, Rust etc. bindings are more or less trivial to write and the users can get a C++ data structure with simple python syntax.
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Google used and endorsed it at a critical time back when the mainstream media machine was inflating their image. Even programmers like to ape celebrities and by extension Python was the choice of the sophisticated and well-read hacker in the know. It was just luck and dumb social reasons.
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What did Python win
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IMO, Python rode some of the growth from the data and scientific computing computing. It was lower friction for many people partially due to the amount of blogs, and open source projects that were available. Pandas and Numpy made it quite easy to get up and running with a lot of analytics. pytorch and tensorflow were also there to facilitate this, made people able to get the benefits of the optimized C code but most users did not have to learn C. Eventually FastAPI came out for those looking to build products out of their data, and FastAPI had fantastic documentation and guides, which helped these same folks coming from data/scientific compute build working software. I suppose what I am describing are some of the network effects.
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I don't know if you can point to a single feature, a lot of what makes a language successful is around context and placement (javascript being a great example of this). I think there's a few key features for python that have definitely been a big help for it: It's highly dynamic meaning you can get running with it before understanding every detail (no compile step, no static typing etc). It may not be the best at everything, but is a decent choice for most things, making a good generic intro language for programming. It has good interop with low level languages - this is a big deal for data science where a less technical (from a CS perspective) user still needs the performance of a language like C/Fortran/Rust/etc. Python's pattern of "use python as a high level api for libraries in those other languages" (as in numpy, pandas, polars, pytorch etc). That last one has been a big deal recently, python has become the de-facto language for data science as a result of this.
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The elegant syntax that is close to plain English sets it apart. (It’s not so pretty these days though with all these type hints and other cruft that’s been added in the last ten years.)
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My 2c. And only my recollection. Back in the stone age, there were two that had significant mindshare: perl and python. Of course, there were others, and they had their day in the sun too (e.g. rexx, rebol, etc.). I suspect that python stood out, and developed momentum, because of its availability on, and integration with, Windows where many of today's senior devs were cutting their teeth. Particularly, I'd point out, Mark Hammond's contributions. Suddenly, you had an accessible language with a deep integration with the underlying OS. Perl had that too, if you were using one of the unices, but most eager devs weren't - back then, linux wasn't what it is today. Perl excelled at the traditional file-based (and filelike-based) tasks. It had a good niche in early web development using cgi. But, I think python was just both more accessible in general, and more useful in general than perl - without resorting to C, or C++, or another compiled language.
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Python had a pretty big jump in 2008 when it was the first language supported by Google AppEngine. It was one of the big 3 languages used internally at Google. That's probably the first key domino in the chain. I'd like for Julia to fill its niche myself.
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Python wins hands-down in having readable code. Perl has so many ways to write the same code, no two codebases are the same - it's practically different languages. Back when I was programming Perl, I remember reading my own Perl scripts I wrote few years ago, and being annoyed with how they were doing everything all wrong... because in those few years, my Perl style has changed significantly. And others' scripts were even worse! Plus, python had all those neat quality-of-life things useful for day-to-day scripting. For example actual function parameters, exceptions (no more "or die" after each open), pretty big stdlib (I _don't_ miss having to configure "cpan" on each new machine), repr() in language core, etc.. And yes, Python got lucky somewhat - there was a need for "Perl but easier to read and friendlier to new users", and Python has filled that need. And now that need is no longer open, so it's harder for newcomers to fill that niche. But that's life in general - Rust filled the niche of "No overheads like C++ but memory safe", Go has filled the niche of "Compiled systems language with GC" etc... And once a language gets popular, network effects kick in.
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Nothing against Python but I still use Perl 5 because it works for what I need to do. The Perl 6 redesign was announced in 2000 and it has been going on for 2 decades? Python is clearly a good programming language but the Perl 6 redesign took so long that many people gave up. Then it was renamed Raku in 2019 I think.
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Because not many people prioritize syntax design like GvR. Even now if someone releases a new programming language most people will ask what features it has, how fast it is, how fast is the package manager etc. Because these questions are simple yes and no ones. Unlike syntax design choices. Even if they ask about the syntax design people just dismiss their question with saying "syntax is not important". Python did the opposite, it focused on syntax over everything else. That caught on with beginners and now here we are. Of course with AI Python got even more popular, but even before ChatGPT was released it was already dominant.
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Ex-product manager of an IDE and language here. Languages, like just about anything, win because of what you can do with them: ie, value. Consider Ruby on Rails: it drove Ruby; the achievement was easily creating fullstack web apps, at just the right time. Consider VB: you could build UI apps. No languages win based on the language itself but what it lets you achieve. Usually, this is in the form of libraries:* ie, functionality that using the language lets you achieve. It's not the language, but where using the language lets you get to. Python is a nice language, but so are others. I know of three main libraries -- three main "I can get stuff done" -- eras with Python, and I think these drove Python. 1. Beautiful Soup. Early/mid 2000s, web scraping and website / XML / HTML data manipulation were a thing; this library came along and made it easy. I'd heard of Python before on places like Slashdot; it was cool but Perl was more spoken of. Then, in my anecdotal experience, what I suddenly read about was not Perl or Python, but Beautiful Soup. 2. NumPy / SciPy. Python was apparently written with numerical computing in mind, and NumPy in its current form arrived a bit after BS. Early 2000s: stats and data vis folk were using R. By the 2010s, I was hearing about all these people that were not using R, but were using NumPy and SciPy. 3. AI. Until a few years ago, it was all SciPy, then with the current era, everyone is using Python -- for AI. None of this is the language, per se. People were not 'using Python'; they were (say) using SciPy. I think Python benefited hugely from having not a single "I can achieve stuff" value proposition, like VB did; it had three consecutively in a row. These all had roots well before they became well-known. Enthusiasts, initial design, years of work and usage, and 'suddenly' out of nowhere everyone is using it. The common factor there is likely Python's design and community; there would be lower level reasons for Python's success than the things it let users achieve, and those would be, what incentivised or led to Python being used for those libraries (value enablers) in the first place. I can't speak to that; I wasn't there in the Python community in the 90s or 2000s. [*] Not always: Rust's value is in its memory system and safety. That is not a library but inbuilt, yet that still is something you can do, a goal, value, something you achieve using the language and thus you turn to the language in order to achieve. Rust minus that would need some other reason to be used.
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It's hardly "inexplicable"... particularly if you're comparing it to Perl, a famously hard to read language. * Python has clean and readable syntax that a complete beginner can understand -- it's really close to the pseudocode you might use for teaching. * Python has a good standard library, so you can do a lot before you need to work out how to install more (and, reproducibility concerns aside, `pip install` is really simple once you get there) * Because it was easy to pick up and easy to use for teaching, it took over some niches like stats in academia / data-analysis, where the people doing the work aren't professional programmers but just need something they can hack together. Once NumPy existed, people had minimal incentive to move away. (Insert Marge Simpson "I just think it's neat" gif here.)
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I have recently gone all in on Python. Before I decided this, I was using Python for projects where I needed Pandas or numpy. I used React/TypeScript/Node for web applications. But, I moved to Django/HTMX. The main reason is to avoid npm, but also, I wanted something with more built-in defaults (my web apps have simple needs). It has nothing to do with the language -- it's more the ecosystem for me. I considered Ruby/Rails, but it didn't seem enough better than Python and can't replace the Pandas work I need to do.
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> If you survey the language landscape now, a language can be much better than Python but never get the chance Python did just because it happened to look a bit better than Perl. Which is absurd to me. You’re struggling to understand Python’s success because in your head the primary reason Python succeeded was because it happened to be prettier than Perl, and you can’t wrap your head around why that’s enough. But it’s not enough. Python succeeded for many reasons.
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My gut feeling is that it succeeded due to escaping scrutiny. I had heard enough critiques of the language from CL folks but the rest of the world was just happy to get a nicer Perl that could be used to glue together C code into useful apps and scripts.
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Yep folks just need some glue languages that doesn’t require a lot of time to learn. Python fits the bill perfectly.
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In a phrase: Worse Is Better
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Win what? Against Perl? Over a decade ago. 10 years ago, the only places that required Perl were either hard engineering (i.e. non-programmer EEs) or those with legacy code bases. Over everything else? Of course not. You don't do systems level work in Python. The problems with Python are minor (other than packaging), and not a big enough pain to adopt another language. If you really could benefit from static typing, there are already existing languages that do the job well (C++, Rust, etc). For the majority (including even some Perl fans), switching to Python was a significantly better experience. If you now show me a language better than Python, it's really only an incremental benefit. Sure, I prefer ML languages like F#, but the vast majority don't.
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Years ago I wrote article on this topic: https://www.notonlycode.org/why-python-has-won/ In short (all below is my opinion): it was popular in academia and got some corporate adoption, so when ML exploded in popularity it was a natural choice as the scripting language for ML tooling. On top of that it’s easy to pick up as a language, and it’s a general purpose language - there are lots of scientific tools like pandas written in it, there are web frameworks, etc. Perl was too quirky for wide adoption and it stopped developing (Raku/Perl 6 took to long to develop), PHP was focused purely on the web, similarly JS. Ruby could have won, I like it more than Python, but outside of Japan it’s also mostly been associated with web development (because of Rails), it also lacked libraries that Python already had.
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Science
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Python was viewed as an alternative to Perl mainly because they were used for the same kinds of scripts. The biggest difference at the time, how-many-ways-to-do-it notwithstanding, was that when a novice saw a Python script of 20-50 lines, they (felt like they) understood it without having to reach for a language reference. Readability at a glance for people who hadn't yet specifically learned the language was huge. "Executable pseudocode" was the operative phrase.
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Python optimized for C-interop (NumPy) just as data science exploded. While Perl won text processing, Python became the universal interface for C libraries. That ecosystem lock-in—not syntax—is why it won. It was the right glue at the right time.
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Non-swe, classic engineer here Python is easy to learn and has an extensive library catalog. It also has massive support available online. That's why I have chosen it in the past when I needed to write basic programs for work. With the rise of LLMs, python is now even more attractive as LLMs really excel at writing it.
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Because it's really enjoyable to use. If you are doing something where you can use Python, ie. you can afford the performance hit, it's hard to find reasons to not like it. Common Lisp has it all, but for some reason people can't cope with the parens. People just genuinely enjoy using Python.
Hopefully RAM prices continue to increase so that we are forced to rediscover the lost art of software efficiency and optimisation.
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It's the lost art for us. LLMs will probably rediscover it for its overlords (but no need - they have all RAM and GPUs already).
Its syntax is lowest common denominator. It’s incredibly easy for people from all walks of life to pick up. It’s “good enough” for just about every task. But really it’s because Google, Dropbox, etc made it so. Same reason Java got popular. It’s hard to beat first class corpo support even if the actual language isn’t that great.
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Google certainly had a honeymoon period with Python. They hired Guido to work on some internal apps (IIRC Mondrian?). They released the original App Engine before cloud was a buzzword supporting only Python. But at some point, the default easy-to-learn language at Google became Go, and it happens to be statically typed, preventing a large class of bugs from ever being compiled. There was a manifesto about how Python and all other dynamically typed languages were unsuitable for large-scale software engineering. Only small pieces of code are still using Python.
> Python is most dominant language on the planet JavaScript would like a word!
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English laughs at their supposed dominance.
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Math peers through a microscope and smiles at all the Earth languages. So cute.
pytorch, langchain, streamlit, fastapi, and on and on ... There are many clear reasons imo.
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If you look at https://www.tiobe.com/tiobe-index/ , Python's growth really started in 2018 which was before langchain, streamlit, and fastapi.
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May be that Python 2 to Python 3 migration was holding it back but it is remarkable that it shows positive strides or was relatively stable even within that transition period. https://www.tiobe.com/tiobe-index/python/
I think the reason python won was that it was easy to learn and read and was batteries included. vs perl: People need to solve their problems, not fight with syntax Theres a lot of network effects as well. The more people were using it, the more people will use it.
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Perl had the batteries and it had CPAN. Python borrowed the best bits of Perl and enjoyed a reputation which overlooked its many flaws. If it had been scrutinized as much as Perl, we'd have realized it was just as bad but in different ways.
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[delayed]
I don't believe the "syntax win" scenario. Python is also ugly, due to the required indentation, like yaml. I believe more in the ecosystem, specifically how the computer vision and machine learning movements have adopted python extensively as frontend language (the heavy weightlifting is still doing in C++). The exploit of numpy has brought many many use cases into the language as well.
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"Ugly" is a very opinionated statement there. I personally find it's fine, and Python's required-indentation matches what I'd be doing anyway. It's no different to me than a project which lints indentation via something like gofmt.
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> Python is also ugly, due to the required indentation As opposed to the not required indentation that literally everyone who writes code ever does anyways? > believe more in the ecosystem, specifically how the computer vision and machine learning movements have adopted python extensively What came first, the chicken or the egg?
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required indendation simplified a ton of readability problem with other languages for a regular user. There was no easy for people who wanted to get something done in a simple interface.
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Even for someone who has used more than one languages, I think Python is fine. It’s a lot prettier than the academic-HN preferred lispy languages.
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I was not exposed to much code before trying Python 2, and I always thought of the indentation and newlines as aesthetically pleasing and helpful. Same for yaml actually. Would argue that preferences on indentation etc. are just an acquired case.
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I think the controversy around Python’s indentation helped it gain success. Regardless of how you feel about the choice, it’s a great opportunity for bike shedding and encouraged (and still encourages) a lot of talk about the language.
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Indentation is there regardless, and is increasingly enforced by format-checking tooling which is more commonly an out-of-the-box offering for many ecosystems (gofmt). So why not make it have syntactical meaning since it’s already there in 99% of cases? It does feel weird at first but honestly it’s not something you’ll think about much after a while.
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> don't believe the "syntax win" scenario. Python is also ugly, due to the required indentation, like yaml. That's like, your opinion. Python was explicitly designed being easy to learn, borrowing heavily from ABC, which actually experimented with different syntaxes to see what works and what doesn't. The indentations apparently helps a lot with this, along with ':' before the introduction of indented blocks.
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That 'ugly' required indentation and whitespace also made Python easier to read, especially for newbies and casual coders. A standard visual structure and a syntax that is pretty close to executable pseudo-code lowered the barrier to entry for a lot of people and made Python feel 'approachable'. This perception that it was easy to use helped increase the network effects other have noted.
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It's not about being beautiful or ugly, it's about being simple. Python is simple to read / write and easier to reason about, especially for people that need a programming language to solve a problem but are not software engineers. The reason it won, especially in data analysis, is because most data analyst are/were not software engineer and Python feels more natural to people. The indentation is not a big problem when a decent text editor is used
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> Python is also ugly, due to the required indentation, like yaml. The fact that YAML also won in its configuration file niche doesn't give you pause?
No surprise that interpreted languages like Javascript, Perl, PHP and Python are disproportionately popular independent of pure language merit. The feedback loop is the killer feature.
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Well if you talk to Go programmers they value their fast feedback loop too. It doesn’t just have to be interpreted languages.
The U.S. Is Funding Fewer Grants in Every Area of Science and Medicine (nytimes.com)
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How should one orient themselves and their career if they wanted to work to increase funding to scientific development? Outside the obvious "make a boatload of money doing something obscenely profitable and distribute the money yourself"
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Become a politician or a lobbyist? Possibly work in a charity that funds research, as a fundraiser for them?
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I expect China will pick up the slack.
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Certainly but US policy changes every 4 years and China has a gigantic one child policy issue which just can't be changed. I think it will with China somewhat similar how it was back in the day with the udssr where economists were predicting its economy would outgrow the economy of the USA by 1994 and then 1991 or so it died. Could imagine something similar might be awaiting china
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[delayed]
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> The new policy is being carried out as the Trump administration has tightened its hold over federal science funding Such sentences display such a weird understanding of how the federal government works. How can the administration “tighten its hold” over discretionary grants? These aren’t Congressional appropriations earmarked for specific projects. The administration is the only entity that can exercise control over these grants. It would actually be a huge problem if the administration didn’t have a tight hold on these funds. That would mean grants would be going out without close supervision by any elected officials (Congress or the President).
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> That would mean grants would be going out without close supervision by any elected officials (Congress or the President). Right, traditionally that’s how it worked. Elected officials set the broad parameters of grantmaking, but did not closely supervise individual grants, because we didn’t want scientific researchers to feel like pleasing politicians is their job. But Trump feels that everyone should please him at all times and enjoys punishing anyone who won’t.
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It tightened its hold by shrinking its portfolio as the article describes.
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"The administration" is not a monolithic entity. For the last ~150 years, even though it's had political appointees at the top, the vast majority of its employees have been selected (at least ostensibly) on the basis of merit, not political loyalty. They're supposed to be somewhat insulated from the changing political winds. The layers of bureaucracy in between were created deliberately, to preserve some degree of decision-making independence. When people talk about "the Trump administration tightening its hold", they mean Trump and his political appointees exerting direct control over things that have a strong precedent for being out of their direct control. Using the word "administration" to conflate the presidency with the layers of organization below it is the main premise of the "unitary executive theory", which is an extremely recent development of the current Supreme Court. Previously, when Congress said "such-and-such a decision is supposed to be made by the staff of agency XYZ, not by the President/Secretary personally", the courts assumed they meant it.
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Yes "~150 years" ago (sounds right to me, not sure on the exact date), there was civil service reform. Prior to that every administration would fire the prior servants and install their own. This was replaced with a system where it is very difficult to fire most civil servants but the executive could still select new hires. There is a common misconception that this reduces political influence and loyalty. This couldn't be further from the truth. What it did was ensure the civil services grew much further, since the only way the next political party in power could regain dominance was to hire even more civil servants until they overpowered the ones already there. This meant it is even more important to get loyal ones, since they will be there for a long time and can't be fired.
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In the before times the close eyes were by directors and funding committees at the institutes like NIH and NSF. Now those roles are played by political appointees and funds controlled at the whim of the office of the President and their fundamentally anti-science agenda.
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The sentence is accurate, your comment is not. The administration unilaterally canceled existing grants and halted and showed granting of funds appropriated by Congress, so the money was not used as allocated. For example, https://www.nature.com/articles/d41586-025-01396-2
America's monopoly crisis hits the military (theamericanconservative.com)
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I just read articles about how iRobot got to the point of filing for bankruptcy. It's the same story, short term gains are prioritized, no long term strategy. Executives in charge just want to cash out, then abandon ship. I read that OpenAI CEO is 0% looking forward to becoming public. I don't blame him
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> Lieutenant General Arnold Bunch outlined the Air Force’s logic at a House Armed Services Subcommittee meeting. L3, he said, is the only company that can do the job. “They have all the tooling, they have all the existing knowledge, and they have the modeling and all the information to do that work,” he said. Sure, but the CEO isn't the only guy who can run the company. Throw enough of them in prison for espionage and the next one will ensure the company is compliant. Too big to fail only applies to the company, not management.
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Quite rich how the capitalists are complaining about the hollowing-out of American industry - something they oversaw for these now-derided short-term profits. Fortunately we can keep the spreadsheet GDP growth going for at least a while longer thanks to Excel’s Copilot AI integration
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Telecoms by nature are global and one could argue Ericsson and Nokia both have global R&D and manufacturing - with a big and increasing part in China. The main reason for the consolidation in 2008 was imho. the preceeding concentration of telecom service providers putting pressure through their consolidated buying on the equipment providers triggering supplier consolidation. Diversity thrives in environments with chaos and boundaries. Stable economic conditions, one single buyer with standardized buying rules are not helpful here. The nature of compromises driven by politics instead of facts (and thankfully so as concrete feedback for weapons means war) leads to pseudo differentiated products with poor cost benefit ratios. The recent developments in drone technology out of Ukraine are a testament what could be done with respect to creating a manufacturing ecosystem. Some of these insights are paid for dearly, some others may be of use also in peace times.
Scaling LLMs to Larger Codebases (blog.kierangill.xyz)
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I wrote this forever ago in AI terms :) https://getstream.io/blog/cursor-ai-large-projects/ But the summary here is that with the right guidance, AI currently crushes it on large codebases.
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You can't get away from the engineering part of software engineering even if you are using LLMs. I have been using Claude Opus 4.5, and it's the best out of the models I have tried. I find that I can get Claude to work well if I already know the steps I need to do beforehand, and I can get it to do all of the boring stuff. So it's a series of very focused and directed one-shot prompts that it largely gets correct, because I'm not giving it a huge task, or something open-ended. Knowing how you would implement the solution beforehand is a huge help, because then you can just tell the LLM to do the boring/tedious bits.
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The issues raised in this article are why I think highly-opinionated frameworks will lead to higher developer productivity when using AI assisted coding You may not like all the opinions of the framework, but the LLM knows them and you don’t need to write up any guidelines for it.
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This highlights a missing feature of LLM tooling, which is asking questions of the user. I've been experimenting with Gemini in VS Code, and it just fills in missing information by guessing and then runs off writing paragraphs of design and a bunch of code changes that could have been avoided by asking for clarification at the beginning.
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Claude code regularly asks me questions - I like how anthropic implemented this
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Its kind of crazy that the knee jerk reaction to failing to one shot your prompt is to abandon the whole thing because you think the tool sucks. It very well might, but it could also be user error or a number of other things. There wouldn't be a good nights sleep in sight if I knew an LLM was running rampant all over production code in an effort to "scale it".
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I'm okay with writing developer docs in the form of agent instructions, those are useful for humans too. If they start to get oddly specific or sound mental, then it's obviously the tool at fault.
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> Making a prompt library useful requires iteration. Every time the LLM is slightly off target, ask yourself, "What could've been clarified?" Then, add that answer back into the prompt library. I'm far from an LLM power user, but this is the single highest ROI practice I've been using. You have to actually observe what the LLM is trying to do each time. Simply smashing enter over and over again or setting it to auto-accept everything will just burn tokens. Instead, see where it gets stuck and add a short note to CLAUDE.md or equivalent. Break it out into sub-files to open for different types of work if the context file gets large. Letting the LLM churn and experiment for every single task will make your token quota evaporate before your eyes. Updating the context file constantly is some extra work for you, but it pays off. My primary use case for LLMs is exploring code bases and giving me summaries of which files to open, tracing execution paths through functions, and handing me the info I need. It also helps a lot to add some instructions for how to deliver useful results for specific types of questions.
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I'm with you on that, but I have to say I have been doing that aggressively, and it's pretty easy for Claude Code at least to ignore the prompts, commands, Markdown files, README, architecture docs, etc. I feel like I spend quite a bit of time telling the thing to look at information it already knows. And I'm talking about when I HAVE actually created various documents to use and prompts. As a specific example, it regularly just doesn't reference CLAUDE.md and it seems pretty random as to when it decides to drop that out of context. That's including right at session start when it should have it fresh.
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> it seems pretty random as to when it decides to drop that out of context Overcoming this kind of nondeterministic behavior around creating/following/modifying instructions is the biggest thing I wish I could solve with my LLM workflows. It seems like you might be able to do this through a system of Claude Code hooks, but I've struggled with finding a good UX for maintaining a growing and ever-changing collection of hooks. Are there any tools or harnesses that attempt to address this and allow you to "force" inject dynamic rules as context?
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> and it's pretty easy for Claude Code at least to ignore the prompts, commands, Markdown files, README, architecture docs, etc. I would agree with that! I've been experimenting with having Claude re-write those documents itself. It can take simple directives and turn them into hierarchical Markdown lists that have multiple bullet points. It's annoying and overly verbose for humans to read, but the repetition and structure seems to help the LLM. I also interrupt it and tell it to refer back to CLAUDE.md if it gets too off track. Like I said, though, I'm not really an LLM power user. I'd be interested to hear tips from others with more time on these tools.
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STAN'd to the top. Decent article but it feels like a linkedin rehashing of stuff the people at the edge have already known for a while.
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> but it feels like a linkedin rehashing of stuff the people at the edge have already known for a while. You're not wrong, but it bears repeating to newcomers. The average LLM user I encounter is still just hammering questions into the prompt and getting frustrated when the LLM makes the same mistakes over and over again.
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Or why you shouldn't....
Lua 5.5.0 Released (lua.org)
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One of the new features I found interesting, declarations for global variables, is buried in the reference manual. Here's a link to the section that discusses it: https://www.lua.org/manual/5.5/manual.html#2.2
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Cannot wait for another version of Lua to sit unused basically everywhere. Truly is a shame, everything seems to have settled on 5.1 for the most part without ever being updated, or any intention of it being updated. Some really nice features post 5.1 I understand each version of Lua introduces breaking changes in the language, which isn't great as the language becomes fragmented (Or not really, once again 5.1 is pretty ubiquitous)
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The real reason everyone settled on Lua 5.1 is because that's the version LuaJIT is compatible with, and most people are unwilling to give up the performance gains.
(Social) media manipulation in one image (kerkour.com)
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It seems like accident's were accidentally left unmanipulated.
Agreed on 2, this problem predates what we call social media. I also tend to agree with 1, but that’s the actual problem, and I don’t think it’s fair to call it clickbait. The news media shouldn’t be reporting what they think viewers will find interesting, they should be giving an accurate picture of what is happening. We all know how, for example, YouTube’s and Facebook’s engagement algorithms turn your online world into echo chambers. The traditional media has the same problem when they chase engagement at the expense of the whole truth. For sure there’s a balancing act necessary. The media of course can’t ignore what people want, but they can choose to be a force for truth and still have a viable business. They can choose not to dramatically overexaggerate things that don’t affect us, and they can choose to sprinkle doses of reality and boring stuff amid the drama.
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I think I would feel more manipulated re: this distribution of cause of death if I felt like mass media was intending to send the message that homicide and terrorism had higher rates than heart disease or cancer. I don’t get the impression this is the message they’re intending to deliver. I believe they report on individual stories of homicide and terrorism because it’s more interesting to the public, thus will gain more viewership. To be clear, I’m not saying mass media isn’t manipulative — in fact, I think they are responsible for much of the misinformation being shared and believed by so many, particularly when it comes to politics. But if we’re just talking about statistics on the cause of death, I don’t believe they want us to believe homicide/terrorism causes more deaths than heart disease/cancer.
> It's just the nature of journalism and headlines. It's incumbent on these organizations, which want to be seen as purveyors of truth , to make sure their readers end up with the proper understanding. If lots of people end up thinking men biting dogs is a bigger problem than dogs biting men, they've failed at that. They really ought to put up some kind of corrective explanation (sort of like the NYT's disclosures of their lawsuit with OpenAI) in a prominent place of most articles that could leave a wrong impression on readers. That shouldn't be much of a problem for the NYT (which I'm most familiar), because its articles tend to be longer with much more background and context than those of its competitor the Wall Street Journal.
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> It's incumbent on these organizations, which want to be seen as purveyors of truth, to make sure their readers end up with the proper understanding. It's incumbent that readers/viewers actually want to properly understand the world: Fox News broadcasts the stuff that it does because people want to it, and their ratings / market share is evidence of it. * https://www.latimes.com/entertainment-arts/business/story/2024-12-11/fox-news-dominates-2024-ratings-but-cable-news-still-faces-challenges * https://www.hollywoodreporter.com/business/business-news/msnbc-fox-cnn-cable-news-future-1236127677/ * https://www.pewresearch.org/journalism/2025/06/10/the-political-gap-in-americans-news-sources/ If Fox doesn't supply what their audience demands (e.g., saying "Trump won the election") they'll go someplace else: * http://archive.is/https://www.washingtonpost.com/media/2020/12/27/fox-news-viewers-switch-to-newsmax/ There are three ways to make a living: 1) Lie to people who want to be lied to, and you’ll get rich. 2) Tell the truth to those who want the truth, and you’ll make a living. 3) Tell the truth to those who want to be lied to, and you’ll go broke. * https://jasonzweig.com/three-ways-to-get-paid/
This is so strange to me. It's the news, it's supposed to report on the uncommon. No one cares that the morning commute's traffic is just as it was the day before and the day before that, but they do care that an accident shut down the main highway. It was commonly understood that news represents the unusual, not the usual. At some point, media literacy went out the window in the US. Probably right around the time humanities education did.
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> It's the news, it's supposed to report on the uncommon.... It was commonly understood that news represents the unusual, not the usual. News organizations could report on people dying of extremely rare diseases and these are rarely reported on compared to terrorism/homicide. Rarity is not the best predictor of whether a news organization will cover something. "Likelihood of engagement/rage/shock/fear/anxiety" is the best predictor of story coverage, although this overlaps well with "uncommon happening." There's absolutely nothing special about news organizations (beyond engaging in 1st amendment activity regularly): they want to make money, they're businesses.
It’s a chicken-and-egg problem. News content, just like all other content, is tracked and analyzed for engagement. Publishers want to create content that people want to engage with, and we see over and over again that people prefer content that elicits a reaction. Also, news subscriptions are now seen as a luxury good and not an essential need. Who wants to pay $15/month for a report on how many people died of heart disease this month?
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If it bleeds it leads baby!
The biggest CRT ever made: Sony's PVM-4300 (dfarq.homeip.net)
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Somewhat related but back in my university days, I spent practically all my savings from a summer part-time job to buy a 21" Sony Trinitron CRT. I absolutely loved that thing, but at the end of each year I dreaded having to lug it home and then haul it back to the dorms again. The elevators often didn’t work and climbing 10 flights of stairs while carrying a 70 lb (31kg) cube was brutal. It’s not often you buy a piece of electronics and get a complimentary workout regimen thrown in.
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I think I’m legitimately traumatized by how heavy CRTs were. The memories of the pain carrying them induced is etched in my body.
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My buddy had something like this. All I remember is it took four people to carry it.
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I think I saw one as a child in the mid 90s - it belonged to an upper-middle class Kuwaiti whose at the time preschooler daughter was approximately as tall as the device, which was laid on the carpeted floor. At the time there were a lot of private import items in Kuwait - particularly cars - so it's not impossible it was this particular model. I mean, what other TV could boast being the height of a four year old?
If you like playing with old hardware, be aware that old CRTs have a gotcha that can getcha: they hold a charge that can shock you across the room, and they can hold that charge for weeks or more. Google how to discharge it before poking around in a CRT.
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I have a vague recollection that my little cousin was nearly ended when he managed to destabilize the stand that a CRT was sitting on, and it fell just behind him, but I may be entirely hallucinating that memory. Regardless, there are multiple ways old CRTs can cause great harm.
When I was about 12 I got an old TV in my room which I of course decided to take apart to figure out how it worked. I was VERY smart and of course unplugged the TV before doing anything. My flat head screwdriver brushed against the wrong terminal in the back, I was literally thrown across the room several feet, and my flat head screw driver was no longer usable as the tip had deformed and slightly melted. I later found an electronics book that had a footnote mentioning grounding out the tube before going near it…
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Yeah the tube is essentially a large capacitor :P I also learned electronics by shocking myself often
The Sony FW900 was the peak of desktop CRT monitors, and it came out in 1999 so it or one of its rebadges might have been what you saw. That was much smaller than the PVM-4300 at 24" but with a much higher max resolution of 2304x1440@85hz, roughly what we'd now call 1440p, about eight years before the first 1080p LCDs arrived. Those were still sought after well into the LCD era for their high resolution and incredible motion clarity, but I think LCDs getting "good enough" and the arrival of OLED monitors with near-zero response times has finally put them out to pasture as anything but a collectors item.
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We set up one of those widescreen Intergraph CRTs for a client way back then, I think the cost of that thing plus the workstation was easily more than I made in a year
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Those Intergraphs were bigger than the FW900 at 28", although lower resolution at 2048x1152@80hz max, so I suppose YMMV which was better.
Did they really do that, or did the tubes just ran at 2x vertically stretched 640x240 with vertical pixel shift? A lot of technical descriptions of CRTs seem to be adapted from pixel addressed LCDs/OLEDs, and they don't always seem to capture the design well
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They did exactly what you say. Split the image and pixel shift. It was not like 30Hz at all.
Don't sleep on that Shank Mods video linked at the end, it's insane that he managed to pull that off. He also made a second video (not linked) which shows off more of the actual hardware. https://www.youtube.com/watch?v=Dgkw3uu19V8
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I’m only a minute into this video and I can only think about all the ways this thing could kill me.
↙ time adjusted for second-chance
Show HN: Netrinos – A keep it simple Mesh VPN for small teams (netrinos.com)
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>We use STUN-style discovery and relay fallback How does your relay compare to Tailscale's (DERP)?
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I use Twingate both for personal use (my home) and to access AWS EC2 servers (no public ips) and really love it. Very polished, easy setup. How does Netrinos compare?
Can anyone explain to me (someone not so network security savvy) if there are any privacy or security concerns using a wire guard provider like this? As I understand it, with traditional VPNs, you basically have to trust third-party audits to verify the VPN isn't logging all traffic and selling it. Does the WireGuard protocol address theses issues? Or is there still the same risk as a more traditional VPN provider?
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This is not providing the same functionality as a "traditional VPN," in the sense that it does not do anything to your traffic going to the wider internet. With popular VPN services, they are an encrypted tunnel for all your internet traffic (some use the same protocol, WireGuard), but at the end of the tunnel they decrypt the message and send it to whatever website you requested, which is exactly what can cause those privacy issues you describe. In this case, though, it creates an encrypted tunnel _only between your own devices_. This allows you to connect to all your devices, home desktop, phone, laptop, as if they were on the same network, allowing you to do fairly sensitive things like remote desktop without having to expose your machine to the public internet or deal with firewall rules in the same way. Assuming this project is legitimate, then the only traffic this service would even touch would be those between your own devices, nothing related to public internet requests. And, on top of that, the requests should be encrypted the entire way, inaccessible to any devices other than the ones sending and receiving the requests. There are many caveats and asterisks I could add, but I think that's a fairly straightforward summary.
Ask HN: My mother was scammed out of all her savings. What should I do?
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It's a growing threat everywhere. Here in Russia it's nearly always in the news. They can even trick a person into selling expensive property (real estate) and then steal the money. One thing is the government must act. These calls are mostly done from abroad. Phone companies can implement some protective measures. Banks too need to watch for common patterns (doesn't always help because scammers talk the victim around the checks). The society as a whole needs to get aware. What a single person could do is to keep contact with the relatives. I've read an expert report on one such case; the author wrote that even a single close someone could break the trance and stop the scam. In that case the victim happened to be a popular singer seemingly never alone, yet as it turned out she actually was a lone old lady with not a single confidant close enough for more than a month. She was, of course, shattered with what happened (she lost close to $3M), but after some time in one if her interviews she said: "I will survive". She said it in Russian and did not comment on the phrase further, it must be too personal. Yet of course she meant that song. The song helps her. The song's story is different, but the emotion is same. We have all our love to give. A hard hit will do good if it makes us to commit on that.
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It's interesting that in our digital age there are tens of thousands of text editors emerge every millisecond but hackers won't do any project that would help their own people to build robust communities and educational platforms, that would teach everyday (cyber)security and computer/tech skills.
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Become a libertarian, and I'm not kidding
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This helps how?
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They meant timetravelling back to your 19yo self in uni before any of this happened.
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Every country needs a digital firewall so outside scammers and scammers can't harass its citizens. This is the primary reason for borders in the first place: allow police to enforce a countries laws.
I hate to be the one that says it, but there is only one way to handle scammers like this. They need to be hunted down and put down. All of them and anyone who is involved. Truth be told that scamming is becoming easier and easier. The problem is that most countries not only allow it, it is part of their GDP. Look at the number of scammers that originate in India. Everyone there knows who they are, there are numerous YouTube channels souly dedicated to exposing them and reporting them to the authorities, yet they still remain active. Until a more brutal and direct response is used, these organizations and countries will continue to operate. If the attacks Trump has used on drug boats has taught us anything is that a direct response is the best response when dealing with the underworld.
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It is much easier than you might imagine. Hundreds of thousands of people live together almost like a city, scamming their own people, speaking the same language as their victims. They train day and night to manipulate the human mind, supported by an underlying system of fake accounts. When I was building the system against that, I felt like David facing Goliath. The problem goes far beyond what technical systems alone can solve.
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https://cybercrime.gov.in/ you can report cybercrimes here for india. The problem is the police is short staffed and their tech is also not on part with that of the scammers. The slow judiciary system due to staff shortage again makes this difficult
I don't have any solution for you, but I'm really sorry for this happened to you. It makes me really sad. I hope you get justice legally.
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Thanks for your kind words.
Unfortunately I don't have any advice for recovering the money, as that's just so far outside of my knowledge field. This may not help, but you have my utmost sympathy. An elderly lady that I know recently lost her husband and her only son, and got scammed out of much of her savings by people exploiting her horrible circumstances. She had to come out of retirement and go back to work because of it. I think crimes like this are among the most evil you can commit to people. I really wish that law enforcement organizations would chase these down and prosecute as aggressively as they would if there had been a murder or something committed. They are fully capable of doing so when it's something that they care enough about, but they just don't seem to care about people like your mother and my friend. It breaks my heart and I really wish I could do something about it as well.
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Thank you very much for your kind words. What I am thinking about most right now is, first, encouraging my mother and helping her through this difficult situation. Beyond my responsibility to my family, I also feel a responsibility related to the system I built. I want to connect with people who have much more knowledge than I do and see whether we can do something meaningful for third-world countries. That has always been my lifelong dream.
The problem is corruption, and that's not easy to fix.
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I know about corruption; anyone who lives in a third-world country understands it. However, as an engineer who built the technical solution, I feel hopeless. Even though my team has strong technical skills and worked extremely hard, with very little sleep for six months, to create something for the greater good, situations like this still happen. In fact, they are becoming worse and more frequent. As an engineer, I hope that I can gain more knowledge, connect with more people, and do more to help those who have no one to protect them.
Italian Competition Authority Fines Apple $115M for Abusing Dominant Position (en.agcm.it)
Surprised by the negative comments here. Usually HN univocally complains about Apple‘s dominant App Store. Now a government fines them for it and some people are upset?
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Ok, but can you give me an example of even ONE specific commenter who has made inconsistent comments on this topic in different threads? “HN” is lots of different people with lots of different opinions. Different threads select for different commentators. This is not unusual (nor has it been the other thousand times people have commented on the inconsistency of HN).
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These posts should really contain a poll on HN, so we know what HN thinks.
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> Usually HN univocally complains about Apple‘s dominant App Store. There is a strong population on HN that dislikes walled gardens. In my experience there are also plenty of people who disagree. There's also a large population that doesn't like EU tech regulations. The ratio between different parts of the HN population can change significantly depending of stuff like time of day and headline draw. I don't find it particularly surprising, it isn't like HN is a monolith with internally consistent views across the entire population.
But that's not the story here. I hate ads as much as anyone, but this action is a matter of market competition, not privacy. They're completely different fights and intelligent people ought to be able to distinguish between the two. Anti-competitive behavior by Google, Apple, Meta, etc. is what got us into this mess with tracking and privacy violations in the first place.
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It's the market for privacy violations. I'd go so far as to say that improving competitiveness in this market probably makes the world worse, by making privacy violations more profitable. If they had fined them for not allowing sideloading, or not allowing third-party payments, it would be a different story. Those are markets I want to see grow and thrive.
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[delayed]
You do not, under any circumstances, have to defend a $4 trillion company.
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This comment isn’t helpful and adds nothing to the conversation. When someone makes an argument regarding ‘x’, the correct response is a rebuttal to the argument on its merits. Not “why are you defending x?”
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Because if Apple can't defend itself even with the lawyers they can afford, it means that they really are breaking the law.
This, I think, is the real answer why this is happening. The motivation behind these huge fines on large U.S. tech companies by EU countries is actually "we need revenue", not "we must protect our users". I would expect this to become another source of strain between the EU and the US as the EU economy continues to atrophy. Especially so if the U.S. economy weakens, too.
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European companies are fined all the time as well, you just don't see the news about it, there definitely no ill-intent vs american companies as you are trying to imply
Italy's unconsolidated budget for 2025 is projected to be around 700 billion euros in revenue and 900 billion in expenditures: https://www.rgs.mef.gov.it/VERSIONE-I/attivita_istituzionali/formazione_e_gestione_del_bilancio/bilancio_di_previsione/bilancio_semplificato/ So yeah, whoever talks about these fines as a strategy for fixing the budget knows nothing about the actual budget of a G7 state, these fines are completely immaterial to Italian fiscal policy. For perspective, that's roughly equivalent to someone with a €50,000 annual income finding €7 on the street and someone claiming they're doing it "to survive."
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From a post I saw on reddit: > In 2024 EU fined US tech companies €3.8B meanwhile public internet tech companies paid only €3.2B in income tax How is it not a major budget contribution to have fines on American companies bigger than revenue from your entire tech industry? That is a de facto tax, particularly when they announce these new fines monthly like clockwork.
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Income tax paid by public internet tech companies is not the same as “revenues from the entire tech industry” This report is indicating around 800B in value for the sector ( https://ec.europa.eu/eurostat/statistics-explained/index.php?title=ICT_sector_-_value_added,_employment_and_R%26D ) While other reports have significant higher numbers https://en.ilsole24ore.com/art/tech-europe-is-worth-4000-billion-dollars-but-italy-investments-dramatically-decline-AHdSigqD
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The relevant comparison is fines vs. actual budget, not fines vs. some cherry-picked industry segment. EU general government spending (across 27 nations) in 2023 was around €8.4 trillion. €3.8B in fines is 0.045% of that, again, completely immaterial. https://www.statista.com/statistics/1379290/government-expenditure-eu-spending-total-by-function/#:~:text=Total%20government%20spending%20by%20the,2%2C712%2C993
I don't necessarily disagree with this ruling, but it's sad that EU governments now take in more revenue from fining US tech companies than from taxing local tech companies. An entire continent is on the path of becoming parasites instead of builders. Will they ever adopt a growth and abundance agenda again?
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> EU governments now take in more revenue from fining US tech companies than from taxing local tech companies Do you have a source for that, or did you just make it up?
Or the US tech companies could abandon EU markets
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You must believe that US companies are trying to enter and stay in hostile markets out of the sheer kindness of their hearts. Have you considered that not being present in the second biggest market by GDP may actually be a massive liability by creating a massive opportunity for competitors that will be far better adapted to stricter regulatory conditions? You could just as well advise US car manufacturers to stick to building cars like the Cybertruck and ignore markets that consider it unsafe.
It would be terrific if this happens. Can give room for alternatives to grow in the EU. Even the rest of the world would love it. So far only China has managed alternatives - and only thanks to govt exclusion. US behemoths just eat everyone else up - even in the global South.
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I’m sorry to disappoint you but the EU is unable to create any usable alternatives to US tech chiefly due to lack of SWE talent (among other things). Anyone remotely competent sees the 40k senior SWE salaries offered by European tech companies and immediately crawls through glass just to work at a mid-tier company in the Northern California area of the United States.
I don't download any apps anymore, so not very informed on the state of alternative app stores in EU. I decided to Google where I can find those. One of the first links is leading to MacPaw's website. It's a company with questionable ethics and business practices that tries to sell you "antivirus" and "decluttering" app. So I'll pass. But are there any real 3rd-party AppStores for iOS now? Something that's used by more than just a couple of people? Or is EU just trying to milk rich USA tech giants (I think I know the answer).
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> are there any real 3rd-party AppStores for iOS now "Always has been": Setapp. Very interesting model. Readily recommend devs subscribing to this collection, but non-devs as well if you're into "there's an app for that" and fatigued with IAP. https://setapp.com/
If you don't design your career, someone else will (2014) (gregmckeown.com)
> But that does not feel fruitful either. My everyday life is consumed by the desire of having some kind of impact, making the world better. It nearly impossible to achieve substantial change alone. The key is to be organized with others, to find a community. Now, I don't mean joining a political party, especially not at first. That can be important work but also very soul crushing I am talking grassroots-level, local groups that work on a concrete topic, preferable something that concerns you personally. It is important to get into doing things as quickly as possible, be it organizing a small protest or maybe just a get together. With this you will gain practically experience and you will find other people that share your goals and with whom you will struggle together. Now, for the long term, you will also need to actually read political theory, you will have to get organized with people that have a more concrete strategy on how to create change but I generally would urge you to avoid analysis paralysis and focus on gaining practical experience first. Building community is really the key.
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I'm trying to avoid community unfortunately. I was never really accepted anywhere, whether is be online spaces or irl and I am not the person to socialize and do stuff with people. Also don't want to rely on people or having people rely on me.
This is a framing issue. You can't control the times, but good advice is applicable in good and bad times. If Hamming was operating from a "good time", it should be true that his policies are also applicable in the "bad times". His advice to "work on the worlds hardest problems" was spoken to people who had worked their way past the initial difficulties. General advice to "Move towards important problems", which is precisely the same thing, applies in good and bad times, and is very likely to produce in you a valuable expertise.
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It's not about good or bad times. All compasses are broken so any particular direction you believe you are walking deliberately might as well be uniformly random. "Direction" and "design" are probably the wrong metaphors for careers.
I think this demostrates a far more important perspective than "have a vision" - which is easy to explain and almost impossible to execute, which is be curious, intentional and open to lots of things. The curious & open part leads you to new oppportunities, and the intentional part helps you figure out how to evaluate & where to go next.
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This sounds like much better advice. Trying to plan out a tech career over decades seems like very premature optimizing. Being curious and making sure you keep learning is not only very pleasant, it’s useful. And when the tech changes, fine, no problem. Most of the big features of my life have not been plannable ahead of time.
A year of vibes (lucumr.pocoo.org)
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A really interesting point that keeps coming up in discussions about LLMs is “what trade-offs need to be re-evaluated” > I also believe that observability is up for grabs again. We now have both the need and opportunity to take advantage of it on a whole new level. Most people were not in a position where they could build their own eBPF programs, but LLMs can One of my big predictions for ‘26 is the industry following through with this line of reasoning. It’s now possible to quickly code up OSS projects of much higher utility and depth. LLMs are already great at Unix tools; a small api and codebase that does something interesting. I think we’ll see an explosion of small tools (and Skills wrapping their use) for more sophisticated roles like DevOps, and meta-Skills for how to build your own skill bundles for your internal systems and architecture. And perhaps more ambitiously, I think services like Datadog will need to change their APIs or risk being disrupted; in the short term nobody is going to be able to move fast enough inside a walled garden to keep up with the velocity the Claude + Unix tools will provide. UI tooling is nice, but it’s not optimized for agents.
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Here’s something else that just started to rally work this year with Opus 4.5: interacting with Ghidra. Nearly every binary is now suddenly transparent, in many cases it can navigate binaries better than source code itself. There’s even a research team that has bee using this approach to generate compilable C++ from binaries and run static analysis on it, to find more vulnerabilities than source analysis without involving dynamic tracing.
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In the next year developers need to realize normal people do not care about the tech stack or the tools used, there are far too many written thoughts and opinions and not enough polished deployed projects. From an industry standpoint it’s business as usual, acquihires from products that LLMs apparently couldn’t save.
> The pull request model on GitHub doesn’t carry enough information to review AI generated code properly — I wish I could see the prompts that led to changes. It’s not just GitHub, it’s also git that is lacking. Yes! Who is building this?
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Create a folder called "prompts". Create a new file for each prompt you make, name the time after timestamp. Or just append to prompts.txt Either way, git will make it trivial to see which prompt belongs with which commit: it'll be in the same diff! You can write a pre-commit hook to always include the prompts in every commit, but I have a feeling most Vibe coders always commit with -a anyway
"I have seen some people be quite successful with this." Wait until those people hit a snafu and have to debug something in prod after they mindlessly handed their brains and critical thinking to a water-wasting behemoth and atrophied their minds. EDIT: typo, and yes I see the irony :D
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Just be glad that there remains a concrete benefit to not atrophying your mind and deeply understanding your code. For now. In the long run, I suspect the behemoth will become just as capable at debugging and dealing with complexity as humans. At that point, human involvement in the actual code will be pointless, and the only remaining human skill needed will be properly directing the agents – the skill those people are learning right now. (I don’t relish this future at all, myself, but I’m starting to think it really will happen soon.)
> Maybe the balance of spending time with machines vs. fellow primates is out of whack. It's not that simple. Proportionally I spend more time with humans, but if the machine behaves like a human and has the ability to recall, it becomes a human like interaction. From my experience what makes the system "scary" is the ability to recall. I have an agent that recalls conversations that you had with it before, and as a result it changes how you interact with it, and I can see that triggering behaviors in humans that are unhealthy. But our inability to name these things properly don't help. I think pretending it to be a machine, on the same level as a coffee maker does help setting the right boundaries.
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I know what you mean, it's the uncanny valley. But we don't need to "pretend" that it is a machine. It is a goddamned machine. Surely, only two unclouded brain-cells can help us reach this conclusion?! Yuval Noah Harari's "simple" idea comes to mind (I often disagree with his thinking, as he tends to make bold and sweeping statements on topics well out of his expertise area). It sounds a bit New Age-y, but maybe it's useful in the context of LLMs: "How can you tell if something is real? Simple: If it suffers, it is real. If it can't suffer, it is not real." An LLM can't suffer. So no need to get one's knickers in a twist with mental gymnastics.
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LLMs can produce outputs that for a human would be interpreted as revealing everything from anxiety to insecurity to existential crises. Is it role-playing? Yes, to an extent, but the more coherent the chains of thought become, the harder it is to write them off that way.
When I find myself in a situation where I’ve been hammering an LLM and it keeps veering down unproductive paths - trying poor solutions or applying fixes that make no difference but eventually we do arrive at the correct answer, the result is often a massive 100+ KB running context. To help mitigate this in the future I'll often prompt: “Why did it take so long to arrive at the solution? What did you do wrong?” Then I follow up with: “In a single paragraph, describe the category of problem and a recommended approach for diagnosing and solving it in the future.” I then add this summary to either the relevant MD file (CHANGING_CSS_LAYOUTS.md, DATA_PERSISTENCE.md, etc) or more generally to the DISCOVERIES.md file, which is linked from my CLAUDE.md under: - When resolving challenging directives, refresh yourself with: docs/DISCOVERIES.md - it contains useful lessons learned and discoveries made during development. I don't think linking to an entire commit full of errors/failures is necessarily a good idea - feels like it would quickly lead to the proverbial poisoning of the well.
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When you get stuck in a loop it's best to remove all code back to a point it didn't have problems. If you continue debugging in that hammering failure loop you get TONS of random future bugs.
You can export all agent traces to otel, either directly or via output logging. Then just dump it in clickhouse with metadata such as repo, git user, cwd, etc. You can do evals and give agents long term memory with the exact same infrastructure a lot of people already have to manage ops. No need to retool, just use what's available properly.
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With great love to your comment, this has the same vibes as the infamous 2007 Dropbox comment: https://news.ycombinator.com/item?id=9224 I'd also argue that the context for an agent message is not the commit/release for the codebase on which it was run, but often a commit/release that is yet to be set up. So there's a bit of apples-to-oranges in terms of release tagging for the log/trace. It's a really interesting problem to solve, because you could in theory try to retroactively find which LLM session, potentially from days prior, matches a commit that just hit a central repository. You could automatically connect the LLM session to the PR that incorporated the resulting code. Though, might this discourage developers from openly iterating with their LLM agent, if there's a panopticon around their whole back-and-forth with the agent? Someone can, and should, create a plug-and-play system here with the right permission model that empowers everyone, including the Programmer-Archaeologists (to borrow shamelessly from Vernor Vinge) who are brought in to "un-vibe the vibe code" and benefit from understanding the context and evolution. But I don't think that "just dump it in clickhouse" is a viable solution for most folks out there, even if they have the infrastructure and experience with OTel stacks.
> There is, for lack of a better word, value in failures Learning? Isn't that what these things are supposedly doing?
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LLMs notoriously don't learn anything - they reset to a blank slate every time you start a new conversation. If you want them to learn you have to actively set them up to do that. The simplest mechanism is to use a coding agent tool like Claude Code and frequently remind it to make notes for itself, or to look at its own commit history, or to search for examples in the codebase that is available to it.
Well being in times of algorithms (ssp.sh)
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block google tracking and search history (minor downside for a major gain). Disable the youtube homepage. Use services that keep out as much trackers as possible: duckduckgo (you can still use google with !g), an email that's not google, a browser that's not chrome. All this reduces the algorithms power over your choices. Use social networks in a healthy way: avoid scrolling on the home/main page. Search for the information you want yourself and don't rely on what's handed to you. This works in general, TV, papers, ...
On the topic of ads on the web: can someone confirm they provide any positive ROI at all? Not once an ad was ever relevant, and I have clicked one maybe once or twice in my life.
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i'm probably using an informal fallacy but if online advertisers earn hundreds of billions of dollars, someone must be finding some return on their investment. i might be wrong, it might just be a huge grift, but i dont know how to come to that conclusion
I want to vent about the way the word "algorithm" is increasingly used as shorthand for AI-driven, attention-sucking systems. "Algorithm" is a beautiful and very old word. Long before recommender systems and engagement metrics, it meant precise problem-solving methods. Quicksort is an algorithm. Binary search is an algorithm. GCD is an algorithm. Most of modern technology exists because of algorithms in this broader, richer sense. It is one of the foundations of CS. Yes, machine-learning systems are also algorithms. But collapsing the term to mean only opaque, attention-maximizing mechanisms strips it of its meaning and history. It taints a neutral technical concept as something manipulative by default. It's more so disappointing when this comes from people in tech. We should be more careful with our vocabulary. Maybe we should call these systems what they are. Maybe "engagement engines", "recommender systems", or "attention-suckers" instead of letting one narrow and disturbing use redefine the word altogether.
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The Algo™ does solve the problem of turning man-hours into 3 dollars per hour of shareholder value.
I prefer “rare” to “well-done” — in steak, and in life. Algorithms tend to optimize us toward well-being as “well-done”: predictable, consistent, uniformly cooked. Safe, measurable, repeatable. But human experience is closer to “rare”: uneven, risky, asymmetric, and still alive. The parts that matter most are often the ones that don’t fit cleanly into metrics. If everything becomes optimized, nothing remains interesting. And more importantly, we risk replacing well-being with the monitoring of well-being. When a life is constantly optimized, scored, nudged, and corrected, it gradually stops being a life that is actually experienced.
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Thank you for this deeply revealing take. I think this is the dynamic at the core of what matters here. Reminds me of Dostoevsky's take on what people really want - here's an interesting short piece that direction. https://www.laphamsquarterly.org/freedom/these-are-barbarous-times
The ancient monuments saluting the winter solstice (bbc.com)
Has no one on Google's datacenter leadership team so much as SEEN a horror movie before?
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”You moved the cemetery, but you left the bodies, didn't you? You son of a bitch, you left the bodies and you only moved the headstones! Why? Why?” </Poltergeist>
How did ancient cultures know when the solstice was? If you didn't tell me it was the 21st, I don't know how I'd be able to tell you other than by carefully measuring the sunrise and sunset times
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For very ancient people, it's the longest/shortest day. After you notice that, if you want more precision, it's the day the Sun rises and sets most on the North/South. It's also the day things cast the largest shadow at noon. You will need some special device to get very precise on the sun raising position (like a pair of stones or something with a hole), and you won't be able to get precision on the shadow thing. For more modern people, starting about only half a dozen millennia ago, it's the hour the Sun stops moving North/South within the stars and starts moving the other way around. You will need to look at it and take notes many times, and average things out to get any precision. Even more because you can't see the Sun and the stars at the same time, so you have to model them.
1. Go out every morning to work in your field. 2. When the Sun rises, make a note on the same fixed piece of wood, e.g., a fence. 3. Observe the leftmost and rightmost positions, these are your solstices. 4 You can now use your fence to identify and predict solstices.
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It can be tricky b/c Sun's azimuth at sunrise varies by a hundredth of a degree on days directly before and after the solstice. Also fun fact: date of latest sunrise is slightly out of phase with seasons https://xkcd.com/2792/
BMW Patents Proprietary Screws That Only Dealerships Can Remove (carbuzz.com)
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Seven hours in, all comments assume this is to screw the car owner. But FTA: BMW really does want to use these to stop people from working on their own cars. It spells out in the patent that "the shape of the engagement recesses prevents the screw from being loosened or tightened using common counter-drive structures, e.g. by unauthorized persons." That's straight from BMW. The second sentence is key: "unauthorized" can be woke-speak for thief, and this device doesn't seem to require a single key per car which has been a PITA for VW/BMW in the past. The first sentence can then be edited to simply: BMW really does want to use these to stop people from removing wheels from cars they don't own. Before you object the thieves will just go get the tool: professionals yes, opportunistic, no. This is enough of a real issue in some places there's a discount on theft insurance for car contents and parts when you have wheel theft prevention devices (e.g. wheel or hub locks). Granted, the insurers might not see this as actuarially meaningful, it might just be extra revenue from people who care about their wheels, but then you'd expect to see it everywhere.
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All patent law needs to be abolished. The excuse for it is that supposedly it protects inventors is totally false. Innovation is 99% perspiration.
"Why haven't they proactively legislated against this new thing I don't like?" is not a strong argument.
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Companies producing their own propietary screws to block repairments is not a new problem.
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