GPT-5.5 • Hacker News Board

Re: GPT-5.5

#531

Post by __alexs » Thu, Apr 23, 2026, 10:20 PM UTC

rglullis wrote: post #503

__alexs wrote: post #469

Earlier quoted context omitted.

I feel like most engineers I talk to still haven't realised what this is going to mean for the industry. The power loom for coding is here. Our skills still matter, but differently.

> power loom When the power loom came around, what happened with most seamtresses? Did they move on to become fashion designers, materials engineers to create new fabrics, chemists to create new color dyes, or did they simply retire or were driven out of the workforce?

There were riots and many people died. Many people lost their jobs. I didn't say this is good but it is happening. As individuals we should act to protect ourselves from these changes.

That might mean joining a union and trying to influence how AI is adopted where you work. It might mean changing which if your skills you lean on most. But just whining about AI is bad is how you end up like those seamstresses.

Re: GPT-5.5

#532

Post by noosphr » Thu, Apr 23, 2026, 10:20 PM UTC

sharts wrote: post #455

_alternator_ wrote: post #330

> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…

One might argue that it’s not too too different from higher level abstractions when using libraries. You get things done faster, write less code, library handles some internal state/memory management for you. Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()? For some, yes. For others, it’s a bit freeing as you can do more high-level architecture without g…

A library is deterministic.

LLMs are not.

That we let a generation of software developers rot their brains on js frameworks is finally coming back to bite us.

We can build infinite towers of abstraction on top of computers because they always give the same results.

LLMs by comparison will always give different results. I've seen it first hand when a $50,000 LLM generated (but human guided) code base just stops working an no one has any idea why or how to fix it.

Hope your business didn't depend on that.

Re: GPT-5.5

#533

Post by moffkalast » Thu, Apr 23, 2026, 10:22 PM UTC

sharts wrote: post #455

_alternator_ wrote: post #330

> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…

One might argue that it’s not too too different from higher level abstractions when using libraries. You get things done faster, write less code, library handles some internal state/memory management for you. Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()? For some, yes. For others, it’s a bit freeing as you can do more high-level architecture without g…

A library doesn't randomly drop out of existence cause of "high load" or whatever and limit you to a some number of function calls per day. With local models there's no issue, but this API shit is cancer personified, when you combine all the frontend bugs with the flaky backend, rate limits, and random bans it's almost a literal lootbox where you might get a reply back or you might get told to fuck off.

Qwen has become a useful fallback but it's still not quite enough.

Re: GPT-5.5

#534

Post by noosphr » Thu, Apr 23, 2026, 10:24 PM UTC

moritonal wrote: post #481

ofjcihen wrote: post #470

Earlier quoted context omitted.

I see this comparison made constantly and for me it misses the mark. When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand. When you vibe something you understand only the prompt that started it and whether or not it spits out what you were expecting. Hence feeling lost when you suddenly lose access to frontier models and take a look…

I think it's more: when I don't have access to a compiler I am useless. It's better to go for a walk than learn assembly. AI agents turn our high-level language into code, with various hints, much like the compiler.

If your compiler produced working executable 20% of the time this would be an apt comparison.

Re: GPT-5.5

#535

Post by aetherspawn » Thu, Apr 23, 2026, 10:26 PM UTC

Umm yeah but this is like every release in the last 3 years.

The big question is: does it still just write slop, or not?

Fool me once, fool me twice, fool me for the 32nd time, it’s probably still just slop.

Re: GPT-5.5

#536

Post by anematode » Thu, Apr 23, 2026, 10:27 PM UTC

I_am_tiberius wrote: post #50

I'd really like to see improvements like these: - Some technical proof that data is never read by open ai. - Proof that no logs of my data or derived data is saved. etc...

I don't think this is technically possible without something like homomorphic encryption, which poses too large of a runtime cost for usage in LLMs

Re: GPT-5.5

#537

Post by mikestorrent » Thu, Apr 23, 2026, 10:27 PM UTC

noosphr wrote: post #528

_alternator_ wrote: post #330

> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…

LLMs upend a few centuries of labor theory. The current market is predicated on the assumption that labor is atomic and has little bargaining power (minus unions). While capital has huge bargaining power and can effectively put whatever price it wants on labor (in markets where labor is plentiful, which is most of them). What happens to a company used to extracting surplus value from labor when the labor is provided…

I am still trying to figure out the business model of open weights. Like... it's wonderful that there are open LLMs, super happy about it, good for everyone, but why are there these? What is the advantage to their companies to release them?

Re: GPT-5.5

#538

Post by kenjackson » Thu, Apr 23, 2026, 10:28 PM UTC

Spartan-S63 wrote: post #509

jstummbillig wrote: post #417

Earlier quoted context omitted.

> This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. What's the worst potential outcome, assuming that all models get better, more efficient and more abundant (which seems to be the current trend)? The goal of engin…

At some point, because these models are trained on existing data, you cease significant technological advancement--at least in tech (as it relates to programming languages, paradigms, etc). You also deskill an entire group of people to the extent that when an LLM fails to accomplish a task, it becomes nearly impossible to actually accomplish it manually. It's learned-helplessness on a large scale.

> At some point, because these models are trained on existing data, you cease significant technological advancement

What makes you think that they can't incrementally improve the state of the art... and by running at scale continuously can't do it faster than we as humans?

The potentially sad outcome is that we continue to do less and less, because they eventually will build better and better robots, so even activities like building the datacenters and fabs are things they can do w/o us.

And eventually most of what they do is to construct scenarios so that we can simulate living a normal life.

Re: GPT-5.5

#539

Post by i_love_retros » Thu, Apr 23, 2026, 10:29 PM UTC

some-guy wrote: post #420

i_love_retros wrote: post #405

Earlier quoted context omitted.

It makes me uneasy because my role now, which is prompting copilot, isn't worth my salary.

I disagree. The amount of slop I need to code review has only increased, and the quality of the models doesn’t seem to be helping. It still takes a good engineer to filter out what is slop and what isn’t. Ultimately that human problem will still require somebody to say no.

Is anyone really reviewing code anymore though? It sounds like you are, but where I work its pretty much just scan the PR as a symbolic gesture and then hit approve. There's too much to review, to frequently.

Re: GPT-5.5

#540

Post by mikestorrent » Thu, Apr 23, 2026, 10:30 PM UTC

noosphr wrote: post #532

sharts wrote: post #455

Earlier quoted context omitted.

One might argue that it’s not too too different from higher level abstractions when using libraries. You get things done faster, write less code, library handles some internal state/memory management for you. Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()? For some, yes. For others, it’s a bit freeing as you can do more high-level architecture without g…

A library is deterministic. LLMs are not. That we let a generation of software developers rot their brains on js frameworks is finally coming back to bite us. We can build infinite towers of abstraction on top of computers because they always give the same results. LLMs by comparison will always give different results. I've seen it first hand when a $50,000 LLM generated (but human guided) code base just stops workin…

The thing is, it's possible to ask the LLM to add dynamic tracing, logging, metrics, a debug REPL, whatever you want to instrument your codebase with. You have to know to want that, and where it's appropriate to use. You still have to (with AI assistance) wire that all up so that it's visible, and you have to be able to interpret it.

If you didn't ask for traceability, if you didn't guide the actual creation and just glommed spaghetti on top of sauce until you got semi-functional results, that was $50k badly spent.