Historically Americans have benefited from income asymmetry and a fairly wide-spread desire by foreign nations not to cause too much legal trouble for US nationals abroad.

I have quite a few friends that do live, quite happily, abroad. But the common pattern for them is a.) fluency in the native language b.) historical association with the country c.) fairly large cash reserves so they can ignore any economic problems these countries are facing.

I've been fortunate enough to have worked on multiple AI intensive engineering teams (both on the product and research side) where considerable effort was spent reasoning through how AI was changing things and we were consistently evolving our practices. But they've all been orgs with 50 people less.

AI psychosis seems to effect very large tech orgs in a different way than small, high impact teams.

In small startups, at the end of the day, if the team doesn't ship a quality product, the company fails. Most importantly, every individual still bares the responsibility of their work. Personally, I've seen a lot of thoughtfulness around things like bad PRs because, on good teams, people realize we're all struggling to figure this out. But nonetheless, if something doesn't go well, there's always an individual that needs to figure out how to make it better. Virtually all the things I've learned about functionally shipping products built with and using AI have come from teams like this. Software engineering is changing, but for those of us shipping products, it reminds me a lot of the early webdev days when we were all trying to figure out the patterns to make this new world of software work reliably (anyone who recalls the pre-jQuery JavaScript days will remember how much we had to figure out before webdev could become what is today).

In large tech orgs there's a much, much larger disconnect between employee effort and concrete value delivered and similarly much larger diffusion of responsibility. When accountability is abstract and nobody is quite sure what the real value of their work is, then there is fertile ground for AI psychosis to run amok. In part this is because there is a certain latent psychosis in these larger orgs anyway; who's "productive" and what's "valuable" always requires a bit of imaginative story telling, not necessarily grounded in reality.

However, I don't think this will persist long as the "new normal". Just like in the rise of web application development, smaller teams will charge ahead and figure some of this stuff out. The MVC pattern applied to webapps, increasingly powerful JavaScript frameworks and best practices, agile practices, git and the popularization of github, the use of No SQL for scaling etc all primarily where battled tested by smaller, high velocity startups and now lay a foundation I'm sure some contemporary devs don't even realize needed to be built by anyone.

This is an aside, but I'm really struck by how many people on HN use Windows (based on repeated mentions I've seen in comments). I've worked for a pretty wide range of companies over the last decade and only one, maybe two companies even had any people that worked on Windows machines. I haven't worked at a company where devs used Windows in 15 years (and even that company eventually switched to linux).

As I've gotten deeper into LLMs/AI roles even Macs have seemed to start having equal share compared to devs running full Linux setups.

Is this just a sign of that a larger and larger portion of HN users are working for large corporations? I honestly can't even remember that last time I saw a serious developer pull out a Windows laptop.

Ever since the original GPT-2 "it's too powerful to release!" I've realized that whatever is the current state of open models represents what we really have access to.

It's shocking to me how many people on HN, who engage in long conversations about LLMs and AI, have never actually run a model on their own hardware.

All you need is a reasonably good macbook pro/studio or an RTX [3-5]090 and you can run useful models in the >= 30 tokens/second range (much higher if you choose the GPU path). The difference between what you can run on this hardware and what you can run on hardware that costs 2-5x is not that big. Don't be fooled by people on Twitter/X claiming you need some outrageous setup.

It's also increasingly clear that frontier models are nowhere near close to pushing the limits of efficiency. Quantization, MoE, and other techniques have dramatically improved even in the last year.

For work, of course use OpenAI/Anthropic models, but for anything personal, anyone who considers themselves a "real engineer" should be running local models, using open harnesses and seeing what they can accomplish with these.

Even if open releases slow down or even stop, we have the foundation, right now, for smart engineers to squeeze something quite useful out of. Hopefully we'll one day figure out how to train large models in a federated way. But either way: not your weights, not your inference.

Uncensored model means it will not deny any requests (at least I have yet to come across one), if you grew up in the 90s it sort of feels like coming across the anarchist cookbook for the first time (though with more accurate content). Using llama-cli means the session is entirely local and entirely ephemeral. As a bonus all the reasoning steps are fully visible to the user.

The base Qwen3.6-35B-A3B is more than adequate for "weird late night brainstorming chats" and I've really started to dislike the natural tendency to self-censor when the model is willing to refuse (and potentially report) any requests it feels is "inappropriate" and all these private thoughts are stored on someone else's server.

Do you not believe in running tests, evaluations, or experiments at all to better understand your environment?

The ROI in the case of a positive outcome is the reduced time needed to inspect the results in the future (the entire point of AI is to know what you can trust it on, so you can delegate everything at that level with less oversight). The ROI in the negative case is the tokens not wasted on tasks to ambitious for the model.

In fact, that's the entire reason we care about "quality code", because we assume that quality code is code that does what you expect well and consistently.

I say this as someone who hand writes code pretty much every night for fun, just to experiment with computation. Which, oddly, is more fun than ever because I don't feel like there's any need to connect this type of programming with "real world software", and I can really enjoy code for it's own sake, meanwhile my job is mostly just running agent loops (which I quite like as well).

For anyone that was an adult in the "web 2.0" days it's hard not to see HN as "social media". The first wave of social media sites where defined by community news aggregators that allowed commenting and, most important, up voting of comments and submissions. Digg, Reddit, HN, del.icio.us (though it lacked formal 'up voting') were all part of this first wave of social media.

The absolute key differentiator between HN and an old school internet forums, that absolutely makes it "social media", is that the community votes on your opinion and users have some way to score against each other. This is precisely the mechanism that is at the root of all problems in social media: you get a measurable reward for your content that pressures you towards saying things and sharing content that increases that reward.

Perhaps one of the best decisions HN ever made, which fights this somewhat, is removing the upvote count from being visible to other members of the community (this was not the case in the early days of HN). But for anyone that saw the rise of "social media" it's hard to imagine HN not fitting that description.

This is precisely what makes the movie the Big Short interesting: we see that people did identify, within a reasonable time frame, when people would start defaulting and how that would cascade into a true crisis.

It's pretty clear that while the fruits of AI are quite useful, the entire thing is rife with very questionable financial engineering... but I still don't know what it is that makes all of this break. For example, it's obvious that the SpaceX IPO is a massive wealth transfer program, but it's not obvious that it will immediately end in a crash. Given how irrational the stock market has been, I don't see a reason it can't continue to be irrational for long after the bag has been handed over to the retail investors and retirement funds.

It will very likely be valued much, much higher. The SpaceX IPO is, in itself, a marvelous piece of financial engineering (requiring co-operation among multiple actors) which has been a long time in the works.

- Right out of the gate nearly all retail investment platforms have dramatically reduced requirements for purchasing an IPO, most notably Fidelity, which previously required $500,000 in your account to participate in an IPO reduced (on Friday) this amount to $2,000

- Retail investment, despite being quieter in the post-WSB era, is at all time highs.

- Reports are that the SpaceX IPO is already highly oversubscribed, meaning there are many more retail investors interested than there are shares available.

- SpaceX has a wildy low float of only ~4% which means price discovery will be much slower then normal, especially with aforementioned demand

- All of these retail platforms enforce some sort of "soft lock-in" whereby you're excluded from future IPOs if you sell your shares within 15-30 days. So if you want to get out you're not going to be able to participate in Anthropic/OpenAI IPOs in a few months.

- Coincidentally, most of the major indexes (thankfully excluding the S&P 500) have adjusted their rules to require only 15 days post-IPO before inclusion and have no profitability requirements. Many also adjusted the rules so that low float IPOs have their weight multiplied despite the low float.

- Many retirement accounts, in one way or another, are required to track these indexes and will be forced to buy these SpaceX shares at a very likely frenzied price and further drive the price up.

SpaceX will very likely open with far more retail demand than shares, the insiders (VCs, employees etc) will still be legally locked from selling, retail investors are penalized if they sell, and so the demand will be high and supply very low.

If they can keep this demand hyped for just 3 weeks, price will still be elevated when retirement accounts are forced to buy... roughly the same time retail investor start seeing the penalty for selling expiring (meaning it is not irrational at all to be in the IPO, but it is irrational to sell before being listed in an index).

Fun fact: the other fascinating thing about this IPO is the terms for insider lock-in. At first earnings (Jun 30) inside investors unlock and can therefor liquidate 20% of their shares... but if the stock performs well, they can unlock and additional 10%. There are additional rules for continued unlocking of more shares depending on performance as time goes on. So everyone on the inside has a very vested interest in a spike in stock prices: not only will their stocks be worth more, but they can realize that value faster.

I would be surprised if SpaceX price doesn't explode in the first few weeks because for everyone involved this would make sense. It's only in August that we'll start seeing the really interesting things start happening.

But I quite vividly remember any mention of that here on HN back then was responded to with "I'm paid great and can easily change jobs why would I want a union?" (with many engineers only thinking of factory worker unions as a model and forgetting that very highly paid and in demand actors also belong to a union).

You negotiate when you're in a position of strength, not while your value is rapidly falling through your fingers.

With AI and a growing population of ex-corporate workers desperate for work breaking up attempts to unionize would be easier than ever.

Even the Chinese don't believe you can grind at your desk non-stop for 12 hours.

Don't get me wrong, 996 is awful, but the American re-imagining of it is even worse.

I've never understood why employees push for official approval like this. It's not surprising you don't get officially dedicated "study time". The vast majority of programmers aren't hourly anyway, so officially sanctioned study hours doesn't even fit in with how work is prioritized. Not to mention the optics look terrible if your team is ever behind your manager is now in the awkward position have having "non-work" on record as part of what you're getting paid for.

Just bring your book with you and read during slow period, when a job is running, model training etc. You're not hourly anyway, so in theory any non-project time is your time anyway.

I've never had official permission to study at work about I've also never had any problem studying at work. If you're shipping consistently and high quality nobody is going to care if you're occasionally reading through a book chapter or watching a lecture online.

Further, to your original point, because these human bottle necks exist and are not parallelizable that means you cannot choose to increase your productivity (since the bottle neck will slow things down regardless) but lower your costs.

AI allows less people to do the same amount of work, and based on your claims, you can't necessary scale up more work to those same people. How do you determine that this will incorrectly lead to a reduction in the workforce?

I've had multiple instances now where AI left to it's own devices has solved a tricky problem that I honestly didn't think it was capable of. I routinely have them design their own experiment loops, learn from each round and iterate on the process. Multiple times it has lead to a needle moving change with no need for human intervention.

There are, of course, many cases where this is not true, but they're certainly more capable than I had previously thought and can solve an increasingly large range of problems on their own.

Reading the comments here is like glimpsing in to either the past or an alternate timeline.

There's tons of inertia in the system so don't expect change to happen over night, but reading "AI won't replace jobs" today feels a lot like when I used to hear "nobody will purchase things online!" back in the mid 1990s.

I know a traditional SaaS company I worked for that IPO’d years ago and still has no signs that they can be profitable (and many others like it) and nobody seems particularly concerned.

I also have an agent using Kimi 2.6 as a backend (which is open, but not local) and for some coding tasks as well.