MurphyA/hacker-news huggingface dataset:
by thelastgallon | 2026-03-13 00:06:06+00:00
Spot on! I worked with execs at a FAANG, they don't have the foggiest idea what they are doing! Extraordinarily dumb, and they get all their 'information' and take decisions from gatekeeping orgs which are super incompetent. This is the one org where incompetency is a super weapon. These orgs don't take competent people, because they won't be loyal to you and do your bidding. And worse, if the execs realize there is a competent person, they will come straight you, bypassing the gatekeeping hierarchy.
by tianrking | 2026-03-13 00:06:07+00:00
This is exactly why approval should live in the harness, not in natural language.

If the UI asks a yes/no question, the “no” should be enforced as a state transition that blocks write actions, not passed back into the model as more text to interpret.

Once “permission” is represented as tokens instead of control flow, failures like this are almost inevitable.

The model failure is funny, but the bigger bug is that the system treated consent as prompt material instead of as a hard gate
by scuff3d | 2026-03-13 00:06:53+00:00
A couple of guys at work have been raving about Claude. How quick they get stuff done, how great the code is, how working any other way is a waste of time.

I just had the misfortune today to wade into one of their codebases. It's 60k lines of code for something that should have been simple, and it's an absolute fucking mess. I'm gonna have to rip out most of it and start over just to get it to do what we actually need it to do.

I use LLMs, they come in handy, and I use agents, but this "have agents do everything" nonsense is a disaster, and it's only going to get worse.

On the upside I'm getting paid to fix this shit show.
by tianrking | 2026-03-13 00:07:04+00:00
I think the real divide isn't typed vs untyped, it's what replaces the safety net.

In statically typed ecosystems the compiler catches a lot of mistakes.
In Rails ecosystems the safety net historically became tests + conventions.

The problem shows up once the codebase gets large and those conventions start to drift. At that point the compiler would have been a very useful backstop.

That's probably why a lot of long-time Rails developers still love it for small teams and product work, but feel friction once the codebase crosses a certain size.
by enraged_camel | 2026-03-13 00:07:25+00:00
I think this is a false dichotomy because which approach is acceptable depends heavily on context, and good engineers recognize this and are capable of adapting.

Sometimes you need something to be extremely robust and fool-proof, and iterating for hours/days/weeks and even months might make sense. Things that are related to security or money are good examples.

Other times, it's much more preferable to put something in front of users that works so that they start getting value from it quickly and provide feedback that can inform the iterative improvements.

And sometimes you don't need to iterate at all. Good enough is good enough. Ship it and forget about it.

I don't buy that AI users favor any particular approach. You can use AI to ship fast, or you can use it to test, critique, refactor and optimize your code to hell and back until it meets the required quality and standards.
by allenu | 2026-03-13 00:07:30+00:00
> I think the real divide is over quality and standards.

I think there are multiple dimensions that people fall on regarding the issue and it's leading to a divide based on where everyone falls on those dimensions.

Quality and standards are probably in there but I think risk-tolerance/aversion could be behind some how you look at quality and standards. If you're high on risk-taking, you might be more likely to forego verifying all LLM-generated code, whereas if you're very risk-averse, you're going to want to go over every line of code to make sure it works just right for fear of anything blowing up.

Desire for control is probably related, too. If you desire more control in how something is achieved, you probably aren't going to like a machine doing a lot of the thinking for you.
by cloverich | 2026-03-13 00:08:33+00:00
It does. But then, it's how i talk to myself. More generally, it's how i talk to people i trust the most. I swear curse and insult, it seems to shock people if they see me do it (to the llm). If i ask claude or chatgpt to summarize the tone and demeanor of my interactions, however, it replies "playful" which is how im actually using the "insults".

Politeness requires a level
of cultural intuition to translate into effective action at best, and is passive aggressive at worst. I insult my llm, and myself, constantly while coding. It's direct, and fun. When the llm insults me back it is even more fun.

With my colleagues i (try to) go back to being polite and die a little inside. its more fun to be myself. maybe its also why i enjoy ai coding more than some of my peers seem to.

More likely im just getting old.
by tekacs | 2026-03-13 00:08:43+00:00
It kinda... does? The problem is that folks have been flailing on the right UX for this.

This is what build vs. plan mode _does_ in OpenCode. OpenAI has taken a different approach in Codex, where Plan mode can perform any actions (it just has an extra plan tool), but in OC in plan mode, IIRC write operations are turned off.

The screenshot shows that the experience had just flipped from Plan to Build mode, which is why the system reminder nudged it into acting!

Now... I forget, but OC may well be flipping automatically when you accept a plan, or letting the model flip it or any other kind of absurdity, but... folks are definitely trying to do the approval split in-harness, they're just failing badly at the UX so far.

And I fully believe that Plan vs. Build is a roundly mediocre UX for this.
by beej71 | 2026-03-13 00:09:09+00:00
> I feel like this is really about how your identify is tied up in what you do.

This is it for me. One thing that's important to my identify making things. And I have a lot of trouble saying I made a thing that I asked someone (or something) else to make for me.

I know you're going to say, "But I'm making things, too!" However...

I could crank out a project a day with Claude Code and slap them all up on GitHub for my green squares, and I could say that I made them all.

Just like I could crank out a novel a day with ChatGPT and say that I made them all.

Or I could use it to write 100 blog posts a day and say that I made them all.

In all those cases, I caused things to get made. But did I make them? I don't feel like I can honestly say I did. (And the copyright office is starting to have a thing or two to say about it, as well.)

This is what I struggle with. I like making things.

As a capitalist, sure, your cash is good with me. Tell me where to shoot and I'll shoot. But in terms of keeping my soul fed, it's a tough one.
by jorl17 | 2026-03-13 00:09:47+00:00
10x is definitely possible at a startup level. I suppose not in a big tech world (seems obvious to me, and it's not like development speed was the bottleneck there either, right?)

You can choose not to believe what I say (and I genuinely understand if you do), and I can simply keep on doing it. I'm not taking it out of thin air either. tuesday I did in 8h the work I scheduled for roughly 65h. Ok, so maybe it's not 10x, maybe it's 8x, same ball park.

And that's only talking about development. If I now get into other aspects....I have just spent the last 1.5 hours creating an incredibly detailed backcatalog of tasks and epics. This is the most detailed I have ever done so in my life and it has been working very well. It's like we merged the good of highly-detailed waterfall with the speed of agile.

Tee-hee: Watergile. (I'm sure some expert in the field will let me know I have coined a new term for something that very much has a name; excuse my ignorance in advance).

Nonetheless, I did this all by talking to the computer which is interfacing with my project management tools, the project documentation, and the project code. Full context on everything. In the past, I would have taken 3 or 4 days to create the same amount of tasks with a vaguely similar amount of detail. But, in truth, I wouldn't have spent so much time putting this love into the craft (!!) of planning a project, because it would exhaust me and feel like a waste of time.

Don't get me wrong, I totally see shit code being thrown everywhere by inferior AI models or people who can't tame the beasts, but the right people in my life are _clearly_ building out more, better tested code, and actually built with more care. Maybe it's not at the line-by-line level, but it certainly is from the end-product result (thinking of the actual end-user). I accept your mileage may vary -- this is my very personal experience.

Maybe it'll stop happening, who knows. Maybe price will be prohibitive, or maybe we'll have such an avalanche of ideas that weren't worth building that everyone will be overwhelmed and take a step back. Or maybe we won't develop juniors into the seniors of tomorrow. Or maybe everything will indeed implode once products are large enough that the original development speed can't be maintained anymore and expectations are mismatched.

What I do know is that it is definitely happening in my world, and I haven't had this much fun since I was a little kid learning to code.
by prmoustache | 2026-03-13 00:10:15+00:00
> Total cost without AppleCare: $250 + tax.
> Total cost with AppleCare: $0.
> Total I've spent on AppleCare: $150.

Hence my comment about extortion scheme, even $150 would be way too high a price for a keyboard + battery but they kind of forces you subscribe to it by having absurdly high parts replacement prices. It is like a mafia asking you to pay for your protection yet you still think you made a good deal.
by SpicyLemonZest | 2026-03-13 00:10:25+00:00
At 11 this morning, I wanted to both debug an issue and take a meeting before lunch. Before AI, I would have had to just start debugging after lunch, there wouldn't have been enough time to do both. But now I had Claude debug the issue concurrently with the meeting. Its answer didn't actually make sense (I still do think I'm smarter than Claude, although the gap is narrowing!), but it showed enough of its work that I could make a good guess about what was really going on, and when I asked it to check my hypothesis I got back from lunch with some debug logs that confirmed I'd found the bug.
by bentcorner | 2026-03-13 00:10:27+00:00
I just say something like "spawn an agent to review your plan" or something to that effect. "Red/green TDD" is apparently the nomenclature: https://simonwillison.net/guides/agentic-engineering-pattern...

I've also found it to be better to ask the LLM to come up with several ideas and then spawn additional agents to evaluate each approach individually.

I think the general problem is that context cuts both ways, and the LLM has no idea what is "important". It's easier to make sure your context doesn't contain pink elephants than it is to tell it to forget about the pink elephants.
by reitzensteinm | 2026-03-13 00:10:48+00:00
I owned an i9 MBP with a discrete GPU. It absolutely was too thin. The CPU and GPU ran hot, it throttled like crazy. It would drain battery while USB-C docked while idling. Worst laptop I've ever owned.

The M1 Max I replaced it with was the opposite. I don't think I heard the fans for the first month. But it was much larger.

Based on the fanless Air, I strongly suspect an M1 Max in the old chassis would have been totally fine for non synthetic workloads and an M1 Pro would probably have been fine in all scenarios.

But I think they over corrected on the chassis design when they were shipping borderline faulty products and haven't walked it back yet.
by appreciatorBus | 2026-03-13 00:10:50+00:00
> nobody got into it because programming in Perl was somehow aesthetically delightful.

To this day I remember being delighted by Perl on a regular basis. I wasn't concerned with the aesthetics of it, though I was aware it was considered inscrutable and that I could read & write it filled me with pride. So yea, programming Perl was delightful.
by alymaknojiya | 2026-03-13 00:11:41+00:00
Vercept got acquired by Anthropic and Vy shuts down permanently on March 25th. Thousands of power users who were already paying for autonomous AI have nowhere to go.
I spent the last two weeks building Inceptive. It is a cloud based autonomous AI agent that works overnight while you sleep and delivers a plain English morning log of everything it completed by the time you wake up. Email drafting, research, social media scheduling, and goal tracking. All handled without you prompting it.
The key differences from OpenClaw and Manus. No local setup. No Mac Mini. No terminal. No codex credits burning through in a day. You connect your accounts, set your goals in plain English, and go to sleep. Bring your own API key from OpenAI, Claude, or Gemini, or use Inceptive credits.
by csa | 2026-03-13 00:11:51+00:00
> And LBO debt isn’t “pushed” onto the company’s books, it’s never on the sponsor’s (LBO shop’s) books in the first place to any material extent.

Could you please explain the how and why of the mechanics of this process (edit: from the perspective of the lender)?

It seems like the lender is taking a massive sucker bet.

Or is the reality that the lender gets repaid the vast majority of the time, and we only hear about the bad outcomes?
by nsteel | 2026-03-13 00:12:47+00:00
Implementing DDR3 training for our packet queuing chip (custom memory controller) was my first project at work. We had originally hoped to use the same training params for all parts. That wasn't reliable even over a small number of testing systems in the chamber. DDR3 RAM parts were super cheap compared to what we had used in previous generations, and you get what you pay for with a huge amount of device variation. So we implemented a relatively long training process to be run on each device during our board testing, and saved those per-lane skews. But we found the effects of temperature, and particularly system noise, were too great once the system was sending full-rate traffic. (The training had to be done one interface at a time, with pedestrian data-rates). We then ended up with a quick re-training pass to re-center the eyes. It still wasn't perfect - slower ram chips (with smaller eyes) would report ECC correctables when all interfaces were doing worst-case patterns at temperature extremes. We spent a lot of time making those interfaces robust, and ended up relying more on ECC than we had intended. But those chips have been shipping ever since and will have seen traffic from most of us.
by aminerj | 2026-03-13 00:13:20+00:00
You're right, and this is an underappreciated point. The "attacker" framing can actually obscure the more common risk: organic knowledge base degradation over time. The poisoning attack is just the adversarial extreme of a problem that exists in every large document store.

The model robustness angle is valid but I'd push back slightly on it being sufficient as a primary control. The model risk / backtesting framing is exactly right for the generation side. Where RAG diverges from traditional ML is that the "training data" is mutable at runtime (any authenticated user or pipeline can change what the model sees without retraining).
by linkregister | 2026-03-13 00:13:34+00:00
Do you know that to be true or are you speculating?

As we argue on the orange site, companies are paying Sierra AI to integrate voice and text agents into their systems to look up account information and process refunds. Fallbacks to human agents are built in to these systems.

We all hate phone trees because they never have the capability to handle exceptions to the most basic functions. We shout "speak to an agent!" into the phone because their website and phone trees only handle the happy path.
by somerandomqaguy | 2026-03-13 00:14:23+00:00
Disagree, we do have the Bach's and Rachmanioff's today: John Williams, Jerry Goldsmith, Bear McCreary, Yuki Kajiura, Hans Zimmer, and probably a slew I'm not even aware of today.

We're in the greatest era of symphonies IMO, it's just that they're hiding in surprising places; movies, TV shows, games, etc.
by epolanski | 2026-03-13 00:14:35+00:00
> But it’s kind of like listening to a math audiobook vs working out math problems.

I don't see it that way.

I'm solving the math problem, but after coming with a solution I start asking for alternative approaches or formulas I don't even know about.

In fact, calculus is full of suck gotchas tricks or formulas, think of integrals or limits. Took who found those decades/centuries to find them.

It's not a black/white divide.
by RegW | 2026-03-13 00:16:05+00:00
I don't know how I feel about this. I started programming in 1979.

I went for a job in AI in the late 1980s and realised from the bonkers spin of the company founders that it really wasn't the 5 to 10 years away as I was being told. I went looking something that was going to deliver a result.

I came back to it maybe 6 years ago when while on the bench at a consultancy. I got into trying to do various Kaggle challenges. Then the boss got the bug and wanted to predict the answers to weird spurious money-making questions. I tried but even when there was good data, I didn't know how to do better anyone else. When there wasn't good data it just produced complete shit.

Since then the world has changed. Everything I touch has AI built in. And it's really good. When you don't know your way around something or you've got stuck it really gets you moving again. Yeah, if it regurgitates a stupid negative example from the documentation as if it is "the way to do it", you just ignore it because you have already read that.

Now, every week I'm subjected to lectures by people who don't know how to code about how productive AI is going to make me. Working in the financial sector every Californian pipe dream seems to be an imperative, but all must verified by an adult. My IDE tries to insert all sorts of crap into my production code as I type, and then I'm supposed to be allow it to generate my unit tests.

I know it will get better, but will it be another 5 to 10 years?

Are we 80% of the way there yet?
by sarchertech | 2026-03-13 00:16:06+00:00
The term code quality is overloaded and not really worth discussing without defining exactly what we mean.

But yes many of those problems were caused by business decisions. But engineers are perfectly cable of creating those problems on their own. If an engineer doesn’t realize that the function they called buffers messages in memory because someone made a wrapper function around sendAsync() and called it send(), that’s a code quality issue not a business issue (except as in the broader sense where every problem is ultimately a business issue).

Or if an engineer writes a naive implementation of some algorithm and adds a spinner so that an operation takes 5s to finish when it could be instantaneous if they’d thought about the problem more.
by orwin | 2026-03-13 00:16:11+00:00
Mmm, I mostly generate scenes, not people, and when I do, it's always in a not photorealistic way. It is true that newer models have more details, I think that I put that into 'slight accuracy improvements', is it really major? Or is it mostly for close up people/animals and it is likely that I just didn't notice?

It will be wrong on a lot of details. Basically you would get a market scene that feels 18th century gb, but will use 18th century russian/french/Austrian details, or 19th century/20th century British artefacts, or a mix of both. And the further you go from western places, the higher the error rate is. Basically generating fiction scenes. That's pretty much my usecase, so that's fine, but I won't ever use it to illustrate a historical TTrpg.
by skeeter2020 | 2026-03-13 00:16:17+00:00
PE includes buy-out (leveraged and not) and VC transactions. PE is typically any medium to long term equity investment not traded publicly on an exchange. Even this is cloudy now that the PE firms themselves are going public.

LBOs are also not a black and white classification, at least not the way they were in the Gordon Gecko 80's, with varying levels of target-borne debt financing specific to the deal. So while I agree "VCs don't do LBOs", PE does both LBOs and VC deals, with the PE firms doing their own style of fund and deals.

I found this book (though dated) to be a more academic analysis of PE:
https://www.wiley.com/en-us/Private+Equity%3A+History%2C+Gov...

don't buy it; try your local library.
by aminerj | 2026-03-13 00:16:24+00:00
The write access framing is exactly the right correction. "Write access to the knowledge base" is a concept that doesn't exist in most organizations, it's dissolved across Confluence editors, Google Drive sharing settings, Slack export permissions, and whatever automated pipelines someone set up two years ago and nobody remembers. The attack surface is the sum of all of those, and nobody has mapped it.

The SEO analogy is the best one I've heard for vocabulary engineering. Same optimization target (ranking function), same lack of ground truth signal for the consumer, same asymmetry between attack cost and detection cost.
by yangro | 2026-03-13 00:16:32+00:00
The 4% skill usage finding makes sense if you look at how most people install skills — they drop a SKILL.md file and expect Claude to figure out when to use it. That works maybe half the time (several commenters here noticed the same thing).

The fix we found building 70+ PM skills: trigger specificity is everything. Vague triggers like "when doing product work" get ignored. Explicit triggers like "when user says /prd or mentions writing a spec" get picked up reliably. The other unlock is context files — skills running without company/product/persona context produce output that could be for any company. With context loaded, even a basic skill runs circles around a sophisticated prompt.

The abandonment data is interesting too. The sessions abandoned in the first 60 seconds probably weren't failures — they were context mismatches caught early. That's actually the system working.
by foxglacier | 2026-03-13 00:17:09+00:00
So what? There were false arrests and convictions made by misuse of line-ups, DNA, eye-witnesses, photos, bloodstains, fingerprints, etc. since forever. You must also blame all those other technologies, so what do you think the police should use to find suspects? In your view, the more help police have, the worse a job they'll do. Is that actually the trend?
by InkCanon | 2026-03-13 00:17:40+00:00
I only have marginal knowledge about neuroscience, but one of my neuroscience professors in class would tell us

"You can cure anything in mice."

I don't know the mechanism why, but you can find tons of papers with incredibly strong results for curing of mitigating dementia, cognitive decline, addiction, etc in mice, but these almost never seen to work on people.
by phatskat | 2026-03-13 00:17:45+00:00
I get that feeling, and I’ll echo my sibling comment: I’d much rather read your stream of thought and get on that brain train with you than see some fluffed up and sterilized version.

I also think that having that authentic voice, while it does open us up to criticism and maybe being misunderstood, also gives us a way to receive actionable feedback to improve.

I think we all want to be understood, and for me part of that understanding is seeing the person. How you write is a part of who you are, and I hope you don’t feel like you need to suppress that.
by autoexec | 2026-03-13 00:17:47+00:00
Do we even want a bunch of people contributing slop upstream when (assuming it does anything worthwhile in the first place) somebody has to actually review/correct/document that code?

A handful of well intentioned slop piles might be manageable, but AI enables spewing garbage at an unprecedented scale. When there's a limited amount of resources to expend on discussing, reviewing, fixing, and then finally accepting contributions a ton of AI generated contributions from random people could bring development to a halt.