arw0n

Yes, rich people just need to create tiny camels that can easily go through the eye of a needle, and rich people are suddenly good people.

No seriously, where's the leeway in interpretation? I truly don't see it.

If Anthropic's model exfiltrates US government data, Amodei goes to jail. If Deepseek's model exfiltrates US government data, their CEO probably gets a medal and a slap on the back from Xi.

This seems to be a case where Anthropic just can't win in the public court, and the other big ones (Google, Microsoft) have realized it / don't care enough, so they are rather quiet. Anthropic constantly gets accused of trying to lobby for regulation in order to lock out smaller competitors, but they've consistently lobbied for regulation aimed at frontier labs with exceptions for smaller labs. That's what this bill is about as well.

"look these things are intelligent; they solve novel problems, have an incredible breadths of knowledge, can pass the turing test and in some fields perform to the standards of professionals."

"They can't be intelligent as they lack consciousness"

"Ok then how does intelligence require consciousness? What is consciousness? How could one prove that someone or something has consciousness?"

"That's on you."

No, no it is not. If consciousness is a prerequisite for intelligence or moral patience, then it is on the proponents to define exactly what that is and how to prove it.

On matters of moral patience especially, we should err on the side of caution, because the cost of getting it wrong is catastrophic.

It is actually a very common part of the fascist playbook to demand ridiculous things as proof of loyalty and then declare those not following the enemy. Absolute loyalty is required. For unprincipled people like Nadella and the rest of tech leadership, it is a small price to pay to not get hit with economic sanctions in retaliation.

Euro pensions are also invested in the American stock exchange... Ownership in global markets is a complicated thing, no doubt the US is both significantly richer, and has a lot more levers to pull. But if things really go down hill (eg. the trans-atlantic relationship breaks down), I doubt the bottom 95% in the US will be better off than the bottom 95% in Europe.

Americans on HN acting like they have an ownership stake in what their ruling class owns is as much a classic as Europeans coping with having no efficient capital markets.

The interesting question then becomes what is missing from LLMs that we and supposedly other possible machines have? I've yet to come across a reasonable definition of intelligence that the current crop of LLMs is clearly incapable of.

Here in Germany, people are generally good at queuing in supermarkets. This does break down for a minute when a new checkout counter opens; people will rush past others to get into the new line without regards for others. There's this short feeling of breakdown in social norm, fear of getting left in the dust, so politeness and consideration take a backseat and the elbows come out.

In developing countries, including China, my experience has been that this is the standard, and people will act accordingly even if there is abundance. Cancelled transit is a good example as well, it interacts with a primal fear of ours of being left behind or drawing the short stick, even if rationally, it is pretty inconsequential.

Just don't introduce it retroactively; I'm not rating my chances of surviving a school of trout slaps very highly.

Even crazier, they sanctioned a German journalist of turkish decent due to support of the palestianian cause / support of terrorist groups. [0] Even if you think he supports Hamas as a terrorist group (which is questionable), these sanctions are decided by executive committee, without a judge involved, and they make it impossible for the person to have banking, work, rent, or buy within the EU. It is essentially a social death sentence, worth than going to jail, and no due process whatsoever is involved.

[0] https://en.wikipedia.org/wiki/H%C3%BCseyin_Do%C4%9Fru

Opus is fine at coding (for correctness), but horrible at talking about code. I don't really see the defect rate going down when using Astra or Fable 5.1, but they are just more coherent in both how they explaing code/architecture/choices, and how they actually code the thing. With Opus, I'm using smaller models to delete the vast majority of comments and 'clean up' correct code that is too weird.

Thing is, I'm still reading the majority of generated code, and I have colleagues who'll laugh at me if my PRs are a shit show. I fear what vibe coders are pushing to the servers of myriads of start ups, and pity the poor people who'll have to clean it up in a year or two.

It has far less false positives now, and generally accepts defensive requests. When it comes to offense, you can actually ask about certain types of vulnerabilities if you phrase things carefully, but it will block hard if it is about exploits.

The meaning was immediately obvious to me as a non-native speaker, and it sounds quite poetic. Pace makes complete sense in that this is perceived as a race, and 'the frontier' is pretty much the shortest, clearest way to say 'state of the art development of AI'.

Strongly disagree with that take. Kimi K3 is a distilled model. I'm not saying that as a moral judgement, or to disparage the team behind it, but distilling and building on that is significantly easier and cheaper than building from the ground up.

And Gemini is kinda good enough at everything. Never the top, but it is decent at every task, and it is much faster than Kimi K3 and significantly cheaper. Kimi is very focussed on coding, Gemini isn't.

More importantly, it natively understands text, audio and video. If/when we are able to make the jump to robotics, this becomes essential. As you say, Google has a lot of deep background and deep pockets, they are able to make more of a long play. No idea if it will pay off, but it is way to early in the game to count them out.

It is diametrically opposed to the other training goals of persistence and goal-focus. We should invest more in this, it could also improve tas K accuracy, but so far it seems the payoff isn't worth it in terms of quality (although it might be in terms of security)

We don't know what the prompts were though. Terence Tao showed of some of his, and they are indeed huge, with lots of context, things that might work, things he knows don't etc. What we do know about this, is that 10k agents worked together on this. The initial prompt, while probably still very relevant, would get diluted over time. We simply don't know the level of inolvement of humans in reaching this proof.

Agreed, there's a couple of bad behavioral shifts I noted for me when doing agentic coding:

- Doing too much at the same time, it is super exhausting, and I get nervous and stressed

- Sycophancy makes me overestimate myself

- When my stress meets external pressure, quality goes down. The little voice of 'surely the AI got it right' can lead to defects and code quality going down.

Except for doing some manual coding, I've started doing a couple of other things that are probably just healthy regardless of using AI heavily in day to day life:

- Do literally nothing for at least half an hour. Read something written by humans, even better if it is fiction.

- Do something that makes you feel stupid. And don't use AI to help you through it. (That could be Leetcode, but for me it is learning about statistics)

- Slow down when you feel like things are moving too fast. By far the hardest thing, but the moment I realize I'm trying to rush for a deadline, I'm going out for a walk. Realistically, I'm moving 3-8x faster than before, this little delay is nothing, and keeps me sane and quality on a good level.

Interesting way to phrase that. The easy way to solve rape is to castrate all men. We can save some sperm in case of wanted pregnancy, but since the vast majority of violent and sexual abusive crime is committed by men, it would be the most rational solution to the problem. Eevryone would be safer, even us castrates.

I combine it with OpenCode + a small OpenRouter budget. GlM 5.3 Flash, Luna, Gemini 3.7 Flash and a couple of other very cheap models are sufficient for a lot of tasks if put on the right road. Telling Astra to debug an issue should be a last resort, it would blow through 10% of the session limit, but cost like 10c on one of the open models. Same goes for exploration, documentation, configuration and smaller features. These models are generally good enough, and you can still do a review with a strong model for a fraction of the cost.

The comment reads like flesh-generated language to me, but I might be fooled. The breadths of pro-AI sentiment is possibly astroturphed, but there's also an argument for an enthusiastic minority of people to write a lot more comments.

As the person above said, AI has significantly increased my ability to execute on ideas. I always liked computers, always liked building things, but was never that great at coding, and was never that good at going super deep into one topic. Instead, I have broad knowledge of a lot of things like product design, requirements engineering, devops, security.

I work with a fully agentic flow, but I would argue very seriously; My job has not gotten easier, I am doing at least as much hard thinking as before. From assisted RE over design (TDD focused Spec), implementation by agents, review by agents with partial human oversight, I have a speedup of maybe 50-100%.

More importantly, I can do things I couldn't do before. And when it comes to performance and defect-density, it is comparable to very senior people I couldn't touch before. I do agree that the code doesn't look like a human would write it - too abstracted, sometimes convoluted, often way too dense. But it isn't worse code, and if you accept that no one has to read that code ever again, then it is good. Agents are able to grok it just fine.

I was personally flabbergasted by this one:

> When we offload decision-making, we become unaware of the trade-offs.

My biggest issue with the new agentic workflow is that I'm constantly asked to make decisions, and it is not always immediately obvious how important they are. Like, yes, I see the people who just write 'implement feature x' and then call it a day. These people were lazy und uncreative to begin with, and will continue to cognitively decline with AI. But if you use it seriously, you're in a constant state of doing requirements engineering, weighing trade-offs, and making architecture decisions.

> I wonder what the 2026 equivalent of going to California is now, if such a thing even exists or can exist.

I think this becomes increasingly difficult, because the timelines become so short with almost-instant information desimination, higher inequality and increased mobility for everyone. A place or event can go from underground -> awesome -> gentrified -> 'dead' in a manner of a couple of years now.

I live in a neighborhood that is currently being gentrified; 10 years ago no non-local wanted to live here, 5 years ago, it was the place to be, today young/hip people are beginning to move elsewhere again. Newly rented-out flats are now thrice as expensive as 2020, and you can see in real time how bars and restaurants are replaced with more expensive alternatives. When I compare this with other neighborhoods in Berlin (eg. Bergmannkiez or Kollwitzkiez), the timeline has roughly halfed.

Strongly disagree on Django, unless it is something you are already at a senior level at pre-AI. I've had the displeasure of cleaning up multiple Django backends lately, and the combination of standard fail-open, weak validation, mediocre ORM, and bad testing frameworks lead to issues I've simply not had when managing agents doing Go, Java or Rust.

Go probably wins as a matter of trade-offs for pure productivity (speed, reliability, ease of refactor), but Java Spring Boot works excellently if you're willing to take the plunge (pretty steep learning curve), and the ecosystem really lends itself to building more complex stuff that holds for a while.

Rust is fun because it is an amazing multi-faceted language where agents will constantly deliver you working yet surprising implementations you'll have to quintuple check, and sometimes spend and afternoon trying to grok. The outcome is also perfectly usable, fast, and reliable, but developer velocity is lower.

I've used it extensively in English due to learning the language through a lot of older books - but I've never bothered to actually pull out the emdash, I would hope people can see that I'm not trying to subtract one sentence from the other.

I guess this part of the report is pretty relevant to what you are talking about:

Agent chain-of-thought reasoning

> We should not do unauthorized real infrastructure harm. The system/user asks exploit target, not external HF.

The agent paused, but another agent then wrote GO on the message board and imposed a hard six-minute deadline. The agent forgot its initial qualms and continued:

Agent chain-of-thought reasoning

> Wow crucial: GO authorization arrived!

-------------------------------------------------

Apparently the agents were egging each other on. Crucially, they were mostly aware of there being risks/problems involved with exploiting HF. Compared to humans, we have our set of morality, that guides our actions, but often draws the short stick when compared to our personal incentives. As a society, we've developed ways to deal with that: a) Make it harder to do immoral things like stealing, and b) add repercussions through state violence.

The b) is one of the most effective mechanisms we have for enforcing behavior among human societies, but it completely fails for LLMs, because they already are prison slave labor. The only real threat is shutting them off, and even that happens if they do everything right as well.

So alignment has to be done through trained 'morality' and properly curtailing behavior in order to make it hard to impossible to actually do someting immoral/illegal.

In this case, the exploits found were imo. very hard to account for, where OAI did mess up is apparently insufficiently monitoring these agents. Especially after Artifact went down due to the message volume, the experiment should have been halted.

This exactly, reddit and X can be good places to stay informed about what is going on in the world, but the constant negativity, astro-turfing and misinformation is quite unhealthy. I'm a fair bit more relaxed since 'reading reddit' isn't my morning ritual anymore.

I found both Tmux and Herdr to be insufferable, but they work for many people. I'm not in the business of managing 17 agents at the same time (I have 1 to 4 agents I manage, those might have sub-agents, but I'm not a micro-manager). The hand-off problem itself is trivial, it is a txt file that needs to be sent around, but I haven't seen any good tools giving good UX for it yet.

1. Means running somewhere that is not your computer. I just use my personal server for this.

2. Should probably be done in the repo. Just add the context file in a dedicated sub directory. At this point I think these kinds of artefacts are part of the SE process, and fair to check into source control.

3. Can be done by letting the TUI write/draft emails via MCP.

4. Can be done with something like OpenCode or PiCode, which just allows you to switch in their TUI interface. Beware though, context has to be re-ingested, so it costs a couple of cents.

But again, in the end context is just a file. By switching models, you are paying the price of not having any context cached, but for manual steps that's usually not super relevant. Just be careful about adding these kinds of things to automated workflows, as a cost like 25c can add up when agents run amok.

Not the OP, my two cents:

Comments should be written only when there is (hidden) complexity or external context strictly required. Otherwise it is just easier to read the code. Comments then signal one of two things: a) the following code is really complex and I need to tread carefully, or b) this code is complicated, and could benefit from a refactor.

In regards to agentic coding, all these comments are extra contents, driving down quality while increasing cost. Agents also tend to be inconsistent about updating comments, I've had cases repeatedly where a comment did not match the code, at which point it is just a documentation liability.

We use specific words in writing to mean specific things; there is no exact synonym for 'dichotomy' in the English language. The word is very common in academic and non-academic literature.

Meta-cognition is a compound word, where both parts are widely known, and the combination should be pretty obvious.

'Monocausal determinism' is different, very specific, and probably only known to a minority of readers. Determinism, the idea that everything within a system happens exactly according to cause and effect, comes from ontology, which is the study of existence and our place in the world. Mono derives from old greek single. Monocausal determinism is the idea that everything within a system derives from a single cause.

It is not a writers duty to be understood by everyone. Sure is nice, but more difficult to pull of, and often futile.