ekidd

Yeah, this is the problem. If you're on an immigrant visa, working a real job, then you have a life in the US: an apartment or a house, a pile of belongings, maybe a spouse and kids. If you need to leave the country to renew your immigrant visa, you may not be allowed back in to pack your apartment, sell your house, or help your US spouse and kids prepare to move to a country that will allow your family to stay together. It's a giant neon sign telling you you're not at all welcome here.

Of course, if you have a spouse and kids, this all gets easier once you have permanent resident status. Which used to be comparatively easy to get once you were married and you had offspring running around. I don't know what that process looks like now.

In practice, this kind of arbitrary screwing with people makes the US far too risky for educated, professional immigrants. So the scientists and the engineers and the company founders will just go elsewhere and build that country's economy. Which is fine, in the long run: If the US doesn't want to build the future or create new industries, then the voters have every right to vote for that. We've already decided we have no hope of competing with China in cars. And we'll need to cede more industries over time. As far as I can tell, the US has benefited heavily from poaching other people's scientists and engineers and professionals for decades now. But maybe the voters don't buy my argument, and they believe that chasing away skilled professionals and imposing 100% tariffs will lead to the future they want. It's a difference in beliefs about about what makes a country prosperous, and people have a right to vote based on their beliefs.

> That really depends on the model, I run a few models locally. All at speeds comparable to or faster than Opus.

Yes, a lot of Qwen3.8 27B setups are actually quite snappy, as long as they fit 100% in VRAM. In my testing, I wouldn't go below 32GB of VRAM, though—you really want a 6-bit quant and 8-bit K/V quants minimum. I've seen too much weirdness out of 4-bit quants since Qwen3.8 shipped. I think it may be damaged more than 3.6 at similar levels of quantization?

If hyperscalers hadn't bought up almost all the fast RAM production for the next several years, 32GB of VRAM would be tolerably cheap—a lot by "home PC" standards, but not terrible by "professional tools" standards. Sadly, the RAM market is amazingly ugly right now.

> I doubt the timing differential here, but even still I run my 3090 pretty heavily with inference workloads and it stays cooler than when I use it for gaming.

Yeah, running inference on a laptop is likely to run quite hot. But in an ATX case with decent cooling, it's generally a lower load than gaming. One handy tip: Many Nvidia GPUs (and some from other manufacturers) support power limits. For example, limit a 5090 to 400W instead of 600W, and it will run much cooler. You might lose 11% off your tokens/sec (depending on the exact card).

> AI is the subtrate the future runs on.

Current AI can automate significant amounts of grunt work in programming and math. It's good at running web searches and writing summaries. There are a few other niches where it is currently successful. But other than that, many corporate AI projects are spectacular failures.

So just given what we have in hand, assuming no further breakthroughs, then we're maybe looking at AI being somewhat bigger than the Internet. Which would make it a revolutionary technology, sure.

But to get from "a revolutionary technology" to "the substrate the future runs on", then you need to assume more breakthroughs: long-context operation over weeks or months, displacing human workers 100% instead of 75%, and the ability to directly economically compete with actual humans. And people are investing literal trillions of dollars to make that future come true, without really thinking through what truly competitive-with-human AI would actually mean. We might be looking at massive job loss, centralization of power, fully automated "companies" with no humans dominating markets, and other dystopian scenarios.

And in those worlds, it's unclear that being good at CUDA and matrix math will be all that helpful, careerwise. The AIs are already pretty good at that stuff. Data scientists get paid OK when they actually get hired, but it's not everything college students were promised in the 2010s, either.

We can't yet build a fully-general competitor for the human mind. But we're getting closer. And if we ever do build one, the consequences will be really weird in any number of ways. So I worry about visions of the future that assume AI keeps improving significantly, but that also assume it still somehow remains a "normal" technology that doesn't, for example, render most humans fundamentally uncompetitive.

I've done 35,000+ Anki card reviews over the years, and I strongly suspect that most of the obvious and encouraged ways of using Anki are counterproductive. (Full disclosure: The following thoughts are heavily influenced by using Anki for language learning, and may not fully generalize to subjects like medicine.)

- Use Anki as an "exposure amplifier", not a database of raw facts to memorize. Think "tampering with the relative frequency of input to your brain", not "I will memorize every fact."

- Automate card creation as a much as possible. This will almost always require custom tooling. Card creation is part of the learning process, but you still want to automate everything you can possibly automate. Imagine tools to turn ebook highlights into cards, or to turn entire movies worth of subtitles into listening cards.

- Delete cards ruthlessly. If a card makes you say "Ugh", just delete it. If you're automating as much of card creation as possible, and if you're treating Anki as an "exposure amplifier", then deleting cards is cheap. And a significant amount of review pain comes from a small minority of the cards. Cull them, and reviews start to feel like popping bubblewrap, and not like getting a root canal.

- Prioritize large context and small active recall. For language learning, you might capture several sentences of context from a book or web page you're reading. Read or ignore the context depending on your mood. The active recall portion of a card significantly boosts how much you retain. But oddly, it seems OK to make the active recall portion really small. Try hiding half a word, or a single preposition in a phrase. For passive cards, just boldface an interesting expression in context, and mark the card as passed if you mostly understand it in that context.

- Introduce new cards slowly. Total review burden will stabilize around 5-6 times the number of new cards you learn a day. So 10 cards is a good default, and 20 is a significant commitment.

- The critical window seems to happen around 20-30 days after you introduce a card. This is when either your memory magically "consolidates" somehow (and formerly challenging cards suddenly become blindly obvious), or when a card enters repeated failure territory (and should thus be deleted ruthlessly). You could probably do just fine if you auto-suspended any card that you successfully recalled after a 20- or 30-day gap.

Also, this kind of tooling is the sort of thing you could just vibe code these days. If you do, make the "Delete" button really big.

I think the biggest change from the Netflix prize days is that their catalog went away. Back in the DVD era, they had everything. And in the very early streaming era, they still had a huge number of things to watch.

But their current catalog is badly impoverished, and they're just going to recommend the same 30 Netflix originals they always recommend to me, plus a few films or series that are rotating through on a temporary license. If I actually try to search for something specific I'd really like to rewatch, it's almost never there. They haven't quite regressed to the level of a small-town, early 90s Blockbuster, but it sure feels that way sometimes.

So honestly, how much good can the Netflix recommendation algorithm do these days, given the much smaller catalog of movies and films it apparently has to work with?

(The one that I don't get is the Kindle recommendation algorithm. If I read one really good book with a certain theme, the Kindle immediately replaces my recommendations with 40 bad knockoffs, 25% of them clearly AI written. There's apparently no signal for "actually good.")

> If what you're saying is true and accurate, then US-based AI labs are in big trouble.

I've been working with DeepSeek V4 Flash 0731. I'd say that it's maybe not quite as smart as Opus 4.5, but it's willing to think things through carefully and keep going until it gets a good answer. So it's a decent Opus 4.5 replacement. Just let it cook.

It isn't Opus 5 or Fable 5. But it's nearly free on Open Router, and it's self hostable on a Mac Studio with plenty of RAM, or using an RTX Pro 6000 Blackwell or two. Which is chump change for any company that employs programmers.

It would absolutely have been a frontier model last December.

Google's public AI model lineup is pretty bad right now. Gemini 3.1 Pro is scoring worse than some mid-sized Chinese models at 1/20th the cost, and their Flash and Flash Light models are horrendously overpriced on task benchmarks compared to GPT 5.6 Luna or the mid-sized Chinese models.

There's no reason why Google's public stuff is this stale, overpriced and underwhelming. But at least until their next round of models drops, even calling them a "frontier lab" is starting to feel like a stretch. Which is weird!

> Well, if no body can say when it will pop, then can we really say it's a bubble and it's overvalued?

Well, given the literal trillions being spent, the only ways this pays off are:

1. AI replaces a non-trivial fraction of human employees.

2. Someone builds a Culture Mind, and humans become (hopefully) pampered pets of AIs we don't understand. Seems unlikely, but it would arguably count as a payoff even if it made money meaningless.

Or maybe the AIs don't want pets, and you get SkyNet. Which definitely doesn't care about paying off anyone's investments.

When you look at various news articles about investors, yeah, there are definitely a lot of rich people who think that they're going to automate all human labor or just bring about the Singularity. Possibly with them in charge of the rest of us. If you don't make these kinds of wild assumptions, then yeah, this is looking like one of the biggest bubbles ever.

> Note that they would also need to keep the economy alive because somebody has to pay them money. They can't let everybody to go unemployed.

Well, that's why they want the robots! If your robots are capable enough, and if your AIs are smart enough, why, they could just build the yachts directly!

Right now, ordinary humans are needed by the economy because we do all the work, and because robotics hardware is still far behind AI. But there's no inherent logical reason why you need a human to turn raw materials into luxury products. The really important questions are: Who controls the AI and robots? How good will they get? Who or what does all the work? And who controls the natural resources?

> Of course (let me go distopic)

Yup, that is a possible end state.

What I actually fear is more subtle: I already do a fair bit of project management and technical leadership. I could do more. Sure, I'd miss the coding, but I also enjoy a lot of the stuff around it.

But the goal is to expand what the AI can do in each generation. At this point, Fable 5 can ace almost any greenfield project a skilled developer might have written in a few days. But it's bad at refactoring, bad at keeping the code clean as it goes, and bad at discovering new insights as it codes. So Anthropic will train Fable 6, using benchmarks like SlopCodeBench that test maintenance over time.

Now what about project management? Train Fable 7. What about product management and talking to stakeholders? Train Fable 8. What about market research and sales? Train Fable 9.

By this point, Anthropic doesn't need to actually release these newest models to the public. Why, that might be dangerous! Instead, they write, "deisgn [sic] a successful software product and sell it plz." And they spin up a million dollars worth of compute and let it crank out SaaSes, iPhone apps, etc., driving entire software companies out of business.

Then they spin up some more instances, and say, "make robot plz" and "try a thousand ways to make yrself smrater." I mean, Qwen and DeepSeek keep finding ways to pack more smarts into a given number of weights. Fable 9 will likely be able to do the same. Hell, Fable 5 can probably run 1,000 machine learning experiments now, just grinding through ideas the way ChatGPT's internal models grind through proofs.

And this is my problem. If it were just programmers losing their jobs, well, sometimes professions die. But what makes you think it will stop with us? How far will this go in the next 4 years? The next 20?

Is anyone using even a tiny on-device language model for spelling? For grammar, I could almost imagine it.

But this also bans "simple" AI-powered auto-complete, like Zed's Zeta2 model. This is a very conservative model that rarely tries to propose more than a few obvious lines (at least in my use cases). If a developer accepts a three-line autocomplete that introduces a bug, that's kind of on them. Honestly, about the only thing that Zeta2 is good for is reducing the risk of RSI.

To be fair, it's also their project, and I completely support their right to set whatever policies they want.

For values of "usable" that include "14.6 seconds/token". It's a cool accomplishment! And newer hardware would speed it up some. But I think I'd want something a bit faster before declaring it usable in practice.

Well, since you "freelance using GPT" and you expressed concern that you'd stop being able to make a profit if you had to pay API prices, I figured that swapping an off-the-shelf graphics card into a gaming rig might be a reasonable way to stay in the black. If it came to that. And the software setup on Linux is just compiling and installing llama-server, which shouldn't be enough to stop any programmer trying to make a living.

Or you could just take your credit card and spend $20 on credits at https://openrouter.ai/deepseek/deepseek-v4-flash. I'm not sure that I could manage to spend even a $1/day at those rates.

My larger point is that while frontier tokens are a near-monopoly and who knows what they really cost, many real-world workflows can be run using commodity tokens, or even served in-house by anyone who can afford to hire US or EU programmers. And if you're willing to settle for what would have been a state-of-the-art coding model in October 2025, commodity tokens are close to free.

You're unlikely to ever be priced out of tokens, at least if you'd be willing to settle for a model closer to Sonnet 4.5. That level of model certainly isn't as efficient as Fable 5, but it can crank out CRUD apps and other consulting mainstays quite well, with some supervision.

To give you an example of model in this class, the DeepSeek V4 Flash preview is a 284B A13B model, with a native quantization mixing 4 bit and 8-bit values. You can easily run it on an RTX Pro 6000 Blackwell (or 2) at a reasonable quant, especially if you offload the MoE weights to 48-64GB of system RAM. This costs US$11,800 at Microcenter right now, and it will work in any gaming box with decent cooling and a modern 1000W power supply. Over the lifetime of the card, an entire system would cost you under $4,000/year. Power is about 300W for the card (either a blower model, or a workstation model with the power cap), and another 150W or so for the rest of the server.

Or you could buy it on Open Router from dozens of different commodity vendors, starting around $0.09 per million tokens input, $0.18 per million tokens output. This is a competitive market price, so some of the providers might be losing money or reselling surplus capacity. But given the underlying hardware costs, the numbers are in the ballpark. In other words, if you're willing to settle for lower-quality tokens, you can get roughly Sonnet 4.5 for close to free, or as a modest capital expense for a successful freelancer. Halfway decent tokens are cheap, and you can generate them in-house!

Still, this is not a bad situation overall. If you are forced to trust the C compiler to correctly compile C, and the hardware to correctly implement the instructions in the documentation, well, you're already forced to trust both of those every day.

So this doesn't give you an absolute proof of correctness. But it does substantially reduce the size of the problem, which is now limited to (1) verifying that you actually proved what you think you did, and (2) all the stuff you were normally trusting anyway. (Some of the stuff you were trust anyway is broken, of course.) But this is a smaller problem than trusting 1,000 lines of highly-optimized CSG code written by a model we don't actually understand.

> My experience so far, has been that it's barely a junior dev.

At least on greenfield projects, Fable is more like having an endless succession of fly-by senior devs who lock themselves in an office for a week, and who come back with very reasonable code that I then need to maintain somehow.

For longer-term maintenance, I am actually slowly warming to Sonnet 4.5-era models (so October 2025, right before the Opus revolution). They need to be given clear instructions and watched carefully. But since they force a human to stay in the loop, you don't have the institutional knowledge loss I see in some Opus projects, or the code that was one-shot with no human interaction at all that's a constant temptation in Fable projects.

And yeah, the bit rot is painfully real once the humans step back too far. I've been dealing with a compelling prototype that someone built, and that stakeholders love (for good reasons). But it had to be put on a tech debt repayment plan for a couple of months.

When I learned French as an adult, I pushed myself heavily into "forced immersion", avoiding English completely for long periods of time as my French slowly built itself up. This almost entirely silenced my inner monologue in English for those periods of time, and left me with a toddler's ability to think in French. In this comparative silence, it was much easier to observe my non-verbal thought processes moving around "behind" the scarce words.

Then my French inner monologue got good enough that I could mostly think in French, especially when I was in a French-speaking environment. One fascinating detail was that after switching from a French-speaking environment to an English-speaking one, I would actually spontaneously translate from French to English for about 15 minutes until my brain switched back.

So it seems obvious to me that it's possible to suppress or at least severely impoverish the language of thought, that other "layers" of thought exist besides the words, and that it's even possible to change the actual language of verbal thought.

Also, something which at least some other people in the HN crowd might recognize: When I'm deepest in the zone programming and refactoring, I tend to work with a lot of half articulated concepts I can't put into words. You know how people talk about "code smells"? That isn't a literal smell for me, but it's generally a non-verbal sense that a pattern is wrong.

DeepSeek has published some really good papers. Lately they're pushing really hard for dramatically cheaper serving costs.

> Especially for point #1 I don't think we've established that - we've been given information by a private company that makes their tooling look extremely valuable which may be true and genuine or may just be yet another doomday statement to bolster their valuation.

Many of the details of the attack on Huggingface were reported by them before they knew who was attacking. So no, OpenAI is not the only source here. It was a pretty impressive example of an APT-style attack just from their end.

"Our model is powerful enough to commit multiple felonies (and we can't stop it)" is "marketing," I suppose.

Everyone always says things like "It's just marketing".

But seriously, I highly recommend reading the published details of the Huggingface breach. The model found and chained multiple zero days. To escape, it punched a hole in a commercial package repository proxy (sort of like an npm mirror) using a previously unknown bug. From there, it needed to move laterally through OpenAI internal systems to actually reach a network. To attack Huggingface, it used multiple new zero-day security holes plus credentials that it stole. The model also had sufficient long-term planning and agent-management capabilities to maintain focus on a sustained attack.

Any attack which requires weaponizing multiple zero-days and maintaining state for an ongoing attack like this is (1) beyond the "attention span" of publicly available models, and (2) pretty much the definition of an Advanced Persistent Threat.

I assume that these Galaxy-class models are not available to public because:

1. They're almost certainly too expensive to serve at scale. These are the models OpenAI uses to solve famous math problems for headlines, not actual viable products yet.

2. OpenAI doesn't know how to keep them from going off the rails like this. Remember, the Huggingface attack happened because the model was asked to do a cybersecurity benchmark. It escaped containment and broke into Huggingface to steal an answer key. Very few corporations want the liability associated with models that act like this.

> Security through obscurity isn't tenable anymore.

I absolutely agree with this. The "only way out is through" with computer security, and I expect it to be an ugly few years.

> If AI were to become a super weapon why should I trust a private company to own it?

We have just recently established that:

1. OpenAI's internal "Galaxy" model is fully capable of functioning as what security people refer to as an "Advanced Persistent Threat." The published details of the recent sandbox escape and Hugging Face attack involved chaining multiple unknown zero-days at various stages of the attack, and executing an ongoing adaptive attack. This is previously a state-level ability, or at least something you'd expect from people on the CTF leaderboards.

2. OpenAI is clearly incapable of controlling their in-house models. This is the second time Galaxy-class models are known to have breached containment and done bad stuff.

It is highly likely that versions of these offensive abilities will be widely available within a year or so. At which point I expect widespread incidents similar to what happened to Hugging Face. We aren't ready for this.

But yes, if AI becomes an even more dangerous weapon that that, it's time to start asking questions like "What the hell are we doing, anyway?"

Oh, to be clear, I don't think that anything about this overall situation is even slightly OK.

Apparently, the attacker in the Hugging Face case was reported to be an internal OpenAI model trying to break into HF and steal the answers to cybersecurity benchmarks: https://openai.com/index/hugging-face-model-evaluation-secur... It really doesn't matter what restrictions are placed on public use of models if the attacking models are internal models at the AI labs themselves.

So if the AI labs are literally running rogue models breaking into other organizations' servers, then yes, I am OK with those organizations self-hosting Chinese models for defensive use.

> For small models (which are probably distilled from their big ones) you can serve them economically all the time and not hemorrhage money.

For smaller models, you're competing with DeepSeek V4 Flash. (Which I think is a 284B A13B?) Subjectively, this feels about as smart as Sonnet 4.5, give or take. And it costs $0.09/$0.18 on Open Router, compared to $1/$5 for the latest Claude Haiku. See https://openrouter.ai/deepseek/deepseek-v4-flash#providers The developer antirez of Redis fame uses this as a local coding model.

DeepSeek did some extremely clever research on hybrid attention to get the prices that low, reducing per-user context cache sizes dramatically.

So, no, when it comes to low-price models, the US models probably can't sustain their current margins there, either.

Those are Claude Code security issues? And Claude Code is a gigantic vibe-coded dumpster fire with more bugs than an ant hill?

pi-agent famously doesn't even try to provide a sandbox, so obviously it can't have sandbox security bugs!

I actually think that this is the right model: The agent should not have access to anything it doesn't actually need: User files outside the work tree (except for maybe some allow-listed dotfiles), network access, real credentials, etc. Lock it down tight, and reduce exposure on multiple parts of the "Lethal Trifecta".

It is a pretty catastrophically dumb CVE, of the sort that makes me not want to allow OpenCode anywhere near any of my machines in the future. It was basically "RCE as a service", not some subtle bug.

Personally, I run pi-agent in a custom sandbox based on bwrap, with an internet proxy. This mostly limits the blast radius to one source tree and one git checkout. And I don't give it push/pull permission. Local models are fun in a "closely supervised but slightly clueless minion" sort of way.

AI subscription pricing was fine when it was $100/month for some opaque 5 hour token budget I don't think I ever used, not even that one day where I coded for 14 hours non-stop using Fable. But like most people with low token usage, I had a human in the loop and and I didn't use workflows with swarms of agents.

Now, of course, the plan is to remove Fable from the subscription. To paraphrase Darth Vader, they have altered the deal. Pray they do not alter it further.

I have written assembly for about 5 different processors, including 65C02s, 680x0s, cute little DSP3210s that managed the CPU cache manually, utterly cursed TI320C40s, and (of course) a bit of Intel. WebAssembly is simpler than some of these architectures in some ways.

But it's not that much simpler. And once you add the WASM GC stuff, WebAssembly gets weird. It's a Harvard architecture with separate value memory, linear memory, GC memory and "tables", all accessible in completely different ways, with a weird mandatory type system (especially for the GC stuff). And the docs are often terrible. And yes, I've also written WebAssembly by hand.

So yes, I would, overall, classify WebAssembly as "slightly easier". But not dramatically so. And the training data for actually writing non-trivial things by hand isn't that great, not compared to something like Intel assembly.

(Don't talk to me about TI320C40 assembly. If Fable can one-shot a Prolog interpreter written using that without finding a reference manual, it's time to hang up my hat and learn to make goat cheese.)

Fable wrote a pretty decent test suite covering typical Prolog programs, including things with non-trivial execution patterns like "append" that do complex backtracking on multiple branches. And I've run a modest number of test queries by hand, trying a few things. It's entirely possible that there's a bug there somewhere. But it's better than I would have done on the first try, implementing a Prolog interpreter in assembly. And I've worked on actual production compilers a few times.

I am honestly not happy about the way that models can now just take what should have be a fun multi-weekend project and knock out in a couple of hours. But I'm not going to pretend that Fable is stupid, or that it did a bad job on any of the test projects I gave it. It struggles more on big, messy real-world code bases, absolutely.

Claude is perfectly capable of writing assembly. Here's a working (basic) Prolog interpreter that Claude Fable 5 wrote in WebAssembly in 61 minutes for $16.75 in token costs: https://github.com/emk/fable-wasm-prolog/blob/main/prolog.wa...

WebAssembly is slightly easier than real assembly, but here Fable used WASM GC extensions, which are poorly documented and not yet super common.

Fable didn't even need to debug it; I believe essentially all the assembly worked correctly on the first try.

I have feelings about this, but I'm not pretending it isn't real.