MurphyA/hacker-news huggingface dataset:
by juped | 2025-08-10 13:43:11+00:00
There's really not more that's useful to say than the relevant section (4.4) of Joe Armstrong's thesis says:

>How does our philosophy of handling errors fit in with coding practices? What kind of code must the programmer write when they find an error? The philosophy is let some other process fix the error, but what does this mean for their code? The answer is let it crash. By this I mean that in the event of an error, then the program should just crash. But what is an error? For programming purpose we can say that:

>• exceptions occur when the run-time system does not know what to do.

>• errors occur when the programmer doesn’t know what to do.

>If an exception is generated by the run-time system, but the programmer had foreseen this and knows what to do to correct the condition that caused the exception, then this is not an error. For example, opening a file which does not exist might cause an exception, but the programmer might decide that this is not an error. They therefore write code which traps this exception and takes the necessary corrective action.

>Errors occur when the programmer does not know what to do. Programmers are supposed to follow specifications, but often the specification does not say what to do and therefore the programmer does not know what to do.

>[...]

>The defensive code detracts from the pure case and confuses the reader—the diagnostic is often no better than the diagnostic which the compiler supplies automatically.

Note that this "program" is a process. For a process doing work, encountering something it can't handle is an error per the above definitions, and the process should just die, since there's nothing better for it to do; for a supervisor process supervising such processes-doing-work, "my child process exited" is an exception at worst, and usually not even an exception since the standard library supervisor code already handles that.
Show HN: Engineering.fyi – Search across tech engineering blogs in one place
by indiehackerman | 2025-08-10 13:44:05+00:00
I built a search engine for engineering blogs because I was tired of manually checking individual company blogs to find real-world production examples.

The problem: When learning a new technology, the best insights often come from how companies like Google, Meta, or Stripe actually implement it in production. But these gems are scattered across dozens of separate engineering blogs with no way to search across them.

What I built: Engineering.fyi indexes engineering blogs from ~15 companies (Google, Meta, OpenAI, Anthropic, Stripe, Uber, etc.) and makes them searchable in one place. You can filter by topic, difficulty level, and whether articles include code samples.

Technical details: - Built with Next.js, SQLite, DrizzleORM - Custom scrapers for each blog (they're all frustratingly different) - Basic tagging system using content matching (still improving this)

Current status: Core search is working. Adding new blogs weekly as I index them.

Next features (based on early feedback): - AI summaries for quick article previews - Weekly digest of trending engineering insights - Save/bookmark articles (considering whether to add accounts)

Interesting challenges: - Each blog requires custom parsing logic (no standard format) - Building an accurate tagging system is harder than expected – started with exact matching but exploring better approaches

I'd love feedback on: - Which company engineering blogs you'd find most valuable to include - Whether AI summaries would actually be useful or just noise - How you currently discover engineering articles from these companies
by fb03 | 2025-08-10 13:44:05+00:00
I guess you sabotaged your own point with the answer. If it takes a full weekend to just have my DE look like what I feel is needed, that's a lot of time wasted that you could be doing useful work or even gaming, in that sense, idk. But to each their own.

I was a ricer before in my heydays of Linux, but now, after 25 years, I just use whatever comes by default with Xubuntu (XFCE) and a Macbook
by besserwisser | 2025-08-10 13:44:27+00:00
Yes, that can be a problem. Story from one of my professors who worked on instrumentation and telemetry for a defense lab. They built a data recorder for artillery shells. In the early "flight" tests, the recorders failed and nobody could figure out why. They worked before and after. Then someone realized the high acceleration bent some of the bond wires in the chips, causing them to touch and short. The fix was surprisingly simple: make sure all chips face top down.
by simonw | 2025-08-10 13:44:34+00:00
One of the things I'm using LLMs for a lot right now is quickly generating answers about larger codebases I'm completely unfamiliar with.

Anything up to 250,000 tokens I pipe into GPT-5 (prior to that o3), and beyond that I'll send them to Gemini 2.5 Pro.

For even larger code than that I'll fire up Codex CLI or Claude Code and let them grep their way to an answer.

This stuff has gotten good enough now that I no longer get stuck when new tools lack decent documentation - I'll pipe in just the source code (filtered for .go or .rs or .c files or whatever) and generate comprehensive documentation for myself from scratch.
by viraptor | 2025-08-10 13:44:45+00:00
Cursor is kind of the default these days and has reasonable pricing. Both the completion and the agent work well. You could nitpick various options, but really it's ok and you can do a lot worse than Cursor.

An alternative is buying credit for a specific provider and using that with Aider. Which is also not bad.

None of the major players are likely to abuse your card. Just make sure you're either using a prepaid plan or prepaid credits with no auto topups.
by jononor | 2025-08-10 13:45:00+00:00
Hypothetically that might ne true. But current systems do not do online learning. Several recent models have cutoff points that are over 6 months ago. It is unclear to which extent user data is trained on. And it is is not clear whether one can achieve meaningful improvements to correctness based on training on user data. User data might be inadvertently incorrect and it may also be adversarial, trying to out bad things in on purpose.
by anon_e-moose | 2025-08-10 13:45:05+00:00
> Obviously the LLM doesn't have "senses" in the human way, and it doesn't "see" words

> A different analogy could be, imagine a being that had a sense that you "see" magnetic lines, and they showed you an object and asked you

If my grandmother had wheels she would have been a bicycle.

At some point to hold the analogy, your mind must perform so many contortions that it defeats the purpose of the analogy itself.
by deadbabe | 2025-08-10 13:46:05+00:00
If it’s not trivial, it’s worthless, because writing things out manually yourself is usually trivial, but tedious.

With LLMs, the point is to eliminate tedious work in a trivial way. If it’s tedious to get an LLM to do tedious work, you have not accomplished anything.

If the work is not trivial enough for you to do yourself, then using an LLM will probably be a disaster, as you will not be able to judge the final output yourself without spending nearly the same amount of time it takes for you to develop the code on your own. So again, nothing is gained, only the illusion of gain.

The reason people think they are more productive using LLMs to tackle non-trivial problems is because LLMs are pretty good at producing “office theatre”. You look like you’re busy more often because you are in a tight feedback loop of prompting and reading LLM output, vs staring off into space thinking deeply about a problem and occasionally scribbling or typing something out.
by trentnix | 2025-08-10 13:46:49+00:00
> Here’s how you know you’re in denial: You use terms like “AI washing”, you think ChatGPT is just a toy and everything built on top of it is “just a wrapper”.

The author provides lots of words explaining how everyone has the wrong answers but doesn't do much to provide any right answers.

> You can’t bolt AI onto SaaS. You have to rebuild around outcomes.

To borrow a quote from Blazing Saddles:

Not only was it authentic <consulting> gibberish, it expressed a courage little seen in this day and age.
by mark_l_watson | 2025-08-10 13:47:33+00:00
So true! About ten years ago Peter Norvig recommended the short Google online course on how to use Google Search: amazing how much one hour of structured learning permanently improved my search skills.

I have used neural networks since the 1980s, and modern LLM tech simply makes me happy, but there are strong limits to what I will use the current tech for.
by kaashif | 2025-08-10 13:48:29+00:00
Suppose a town forms around a coal or gold mine. Then the mines dry up, or demand for what they're mining dries up. There are no jobs and no reason for people to live there any more.

If this community ceases to exist, is it a societal negative? And further, will a high minimum wage speed up or slow down the decline of this community?
An AI Firewall for Prompt Injection
by unknownhad | 2025-08-10 13:49:02+00:00
Prompt injection is when a user tricks the model into ignoring prior instructions revealing system prompts, disabling safeguards or acting outside intended boundaries.

I first saw it live during DEF CON (31) finals and have since seen it exploited in bug bounty reports and research.

This is a small proof-of-concept that works like an “AI firewall”

detecting injection attempts before they reach your LLM with almost no added latency.

Blog post: https://blog.himanshuanand.com/posts/2025-08-10-detecting-llm-prompt-injection/

Demo/API: https://promptinjection.himanshuanand.com/

fast, API friendly and has a UI for testing bypass attempts (For CTF enthusiastic people like me). Feedback and break attempts welcome.
by exasperaited | 2025-08-10 13:50:02+00:00
> I keep thinking of that, imagine teaching humans was all the hype

This is an interesting point.

It has been, of course, and in recent memory.

There was a smaller tech bubble around educational toys/raspberry pi/micro-bit/educational curricula/teaching computing that have burst (there's a great short interview where Pimoroni's founder talks to Alex Glow about how the hype era is fully behind them, the investment has gone and now everyone just has to make money).

There was a small tech bubble around things like Khan Academy and MMOCs, and the money has gone away there, too.

I do think there's evidence, given the scale of the money and the excitement, that VCs prefer the AI craze because humans are messy and awkward.

But I also think -- and I hesitate to say this because I recognise my own very obvious and currently nearly disabling neurodiversity -- that a lot of people in the tech industry are genuinely more interested in the idea of tech that thinks than they are about systems that involve multitudes of real people whose motivations, intentions etc. are harder to divine.

That the only industry that doesn't really punish neurodivergence generally and autism specifically should also be the industry that focusses its attention on programmable, consistent thinking machines perhaps shouldn't surprise us; it at least rhymes in a way we should recognise.
by netruk44 | 2025-08-10 13:50:52+00:00
I was a windows build engineer at Microsoft. I am unfamiliar with this specific UI for managing build tools (I think it may have been added after I left), however I would be surprised if it was actually RCE-capable.

I notice that it requires the tool to be pulled from NuGet. While it looks like you could enter any package and NuGet source, I would be very surprised if there wasn’t a locked down whitelist of allowed sources (limited to internal Microsoft NuGet feeds).

Locking down NuGet packages was one of the primary things we (the Windows Engineering System team) were heavily focusing on when I left years ago. We were explicitly prevented from using public NuGet packages at all. We had to repackage them and upload them to the internal source to be used.
by Ray20 | 2025-08-10 13:51:02+00:00
> They only did this because they couldn't pay him.

But they could. But no amount of money will encourage an engineer as much as the need to escape the gulag. Especially if you add some variety to their experience by staying in the gulag.

> In other countries that paid their engineers they produced more and better products.

It is precisely for this reason that the overwhelming majority of engineers in the USSR were not threatened with the gulag for inefficiency. And many believe that this is a good thing, and that "efficient" engineers threaten to destroy the labor market entirely.

> History is clear and obvious on this fact.

Yes. The Soviet space program created by Korolev is the pinnacle of human engineering thought, only God is above it. History is definitely clear and obvious on this fact

> I and many others, of course, will never cooperate with you.

That's the best part. You will vote yourself out of economic freedom, and then there will be no reason to ask about your opinion. Just look at the trends and public opinion on the necessity for economic freedom. You are already in checkmate if you look a few moves ahead.
by jhbadger | 2025-08-10 13:51:49+00:00
It's funny how old things become new. From the late 19th century (and in declining amounts all the way up until the 1980s), people in the US routinely would order things from the Sears Roebuck catalog and have them delivered. Local merchants used to complain about how this was taking their business. Of course ironically, Sears got out of catalog sales in the 1990s, shortly before e-commerce took off.
by mikewarot | 2025-08-10 13:51:54+00:00
So, if an AI can just spit out the cure for cancer, but spells some things wrong, it's not intelligent?

You think all PhD candidates have perfect spelling? I'd wager most of them re-read their dissertation and edit it, over and over, a process that most LLMs don't have the luxury of doing.

We'd have to give up all the efficiency of tokenizing, re-train a model (a much less optimum model) for at least twice as long to get anywhere near the same results for one that just spits out ASCII.
by suchuanyi | 2025-08-10 13:52:58+00:00
I built ccshell to solve the constant problem of forgetting shell command syntax. Instead of searching Stack Overflow for "how to batch rename files with timestamps" or "compress videos while maintaining quality", you just describe what you want:

• ccshell "find all files larger than 100MB" • ccshell "convert all HEIC photos to JPEG" • ccshell "download all images from a webpage" It uses Claude Code CLI with intelligent prompt engineering to: - Automatically detect and install the right tools (via brew, etc.) - Show real-time progress and execution status - Handle complex, long-running tasks - Work safely with file operations Try it immediately without installation: npx ccshell "list all files in the current directory" Built for macOS initially, but the approach could work cross-platform. The three-tier strategy (local commands → tool installation → custom scripts) makes it surprisingly reliable. Would love feedback from the community on making shell interfaces more accessible!
by kj800x | 2025-08-10 13:53:31+00:00
There's a difference between the food that's been eaten over the past thousands of years and the food that we all eat today. I suppose if you look at this the right way, it's another argument in favor of evolution. Teeth are optimized for hunter/gatherer diet and lifespans. Doesn't matter if your teeth rot out by 50 if you die in your 30-40s
by kevindamm | 2025-08-10 13:53:45+00:00
Preemptive multithreading is better than cooperative multithreading (which windows 3 used) but then it's de-fanged by allowing the threads and process to adjust their own priority and set arbitrary lower bounds on how much time gets allotted to a thread per thunk.

Then there's this:

> All of the OS/2 API routines use the Pascal extended keyword for their calling convention so that arguments are pushed on the stack in the opposite order of C. The Pascal keyword does not allow a system routine to receive a variable number of arguments, but the code generated using the Pascal convention is smaller and faster than the standard C convention.

Did this choice of a small speed boost over compatibility ever haunt the decision makers, I wonder? At the time, the speed boost probably was significant at the ~Mhz clock speeds these machines were running at, and Moore's Law had only just gotten started. Maybe I tend to lean in the direction of compatibility but this seemed like a weird choice to me. Then, in that same paragraph:

> Also, the stack is-restored by the called procedure rather than the caller.

What could possibly go wrong?
by zyx321 | 2025-08-10 13:54:53+00:00
There's been some theories floating around that the 128gb version could be the best value for on-premise LLM inference. The RAM is split between CPU and GPU at a user-configurable ratio.

So this might be the holy grail of "good enough GPU" and "over 100GB of VRAM" if the rest of the system can keep up.
by analog31 | 2025-08-10 13:55:25+00:00
Well, my post sure was a doozy. I wasn't thinking along racial lines, and I'm sorry if it seemed to imply that. Downvotes humbly accepted.

My kids went to a competitive high school, and I saw how the top students funneled themselves into an extremely narrow range of interests. Those kids were nice, but putting 1000 of them in one place would be a freak show.

The choosing of rulers is an interesting and complex problem. An idea with some popularity in HN is "sortition" which is the selection of rulers at random. This could be applied to college admissions in the following way. The college doesn't want to admit the top 1000 applicants according to any short list of KPI's that can be gamed. So they admit from the top 10000 students by manual curation, which is guaranteed to be controversial.

Instead, why not identify the top 10000 applicants, and then send out acceptance letters to 1000 students chosen at random by a neutral third party. (Making up parameters here, just for definiteness). The schools would get the variety they want, with an opaque selection function that can't be inverted, and the same potential benefits that sortition offers for choosing the ruling class.
by mark_l_watson | 2025-08-10 13:55:32+00:00
Thanks for the link! I used to use Clojure a lot professionally, but now just for fun projects, and to occasionally update my old Clojure book. I had bookmarked Clojure-MCP a while ago, but never got back to it but I will give it a try.

I like your phrasing of “OTOH, I’m not attempting to replace myself, only augment myself.” because that is my personal philosophy also.
by ogou | 2025-08-10 13:55:38+00:00
This about pitching, not about building. What you can put in a vibe coded demo and a slide deck versus what functions in production a year later are very different. "Three Stanford kids in a trenchcoat" will always be able to get some kind of funding. That's less about technology and business principles and more about the legacy of P. T. Barnum.
by kentonv | 2025-08-10 13:56:32+00:00
> I do think if vibe coding is up to the task it should be possible to vibe code Sandstorm right out of its dependency lock. ;)

Not really -- you'd still need a systems engineer who is capable of doing it themselves to guide the AI and review the output. Otherwise it'll probably end up a buggy, insecure mess.

But buggy insecure messes are not a big deal when implementing application code inside the sandbox. So for that you can have a non-engineer prompt the AI and get a perfectly usable result.
Ask HN: What's Going on with AI Psychosis?
by hhh | 2025-08-10 13:56:55+00:00
I am seeing more and more people spiraling in their personal lives that aren't doing great, and turning to ChatGPT for comfort, insight, or investigation and it feels like so many of them are slowly spiraling into some sort of psychosis, and I see it continuously on social media seeing new people fully parting from reality to believe what the computer tells them.

It feels like we've achieved something thought impossible, and in 3 years it's spiraled into 75% 'do my work for me', 15% personal issues (and a subset going into this delusional group), and 10% gooning...

I'm surprised we aren't starting to see more push on the major labs in trying to fight this problem... We got a mention of it with sycophancy in the GPT-5 announcement, but not much else really.

It feels like this is going to be an increasing problem, but I don't even know where to start with getting my friends and family to even think twice about what comes out of the machine.
by tossandthrow | 2025-08-10 13:57:05+00:00
> More to the point, not every skill level or job is _worth_ that kind of compensation ...

This is a fair stance to take, but you need to accept the consequences of the stance when people get desperate.

> attempts to circumvent market forces by making lower wages illegal at some arbitrary point have substantially more damaging externalities than 'low wages'

A population of people who can not feed themselves are going to kill you on the street for the canned tuna you might have in your bag.

> Having lost a job suddenly, any employment is better than none.

While this is true for you it is not true for the society as a whole.

This entire comment seems be written with a complete disrespect for macro dynamics and taken right out of a hunter gather society.

It completely ignores everything modern governance - and it is quite frightening.
by Tuna-Fish | 2025-08-10 13:57:46+00:00
It's all about the memory bandwidth.

Apple M chips are slower on the computation that AMD chips, but they have soldered on-package fast ram with a wide memory interface, which is very useful on workloads that handle lots of data.

Strix halo has a 256-bit LPDDR5X interface, twice as wide as the typical desktop chip, roughly equal to the M4 Pro and half of that of the M4 Max.
by vidarh | 2025-08-10 13:58:09+00:00
And humans are often wrong both when we say we don't know, and when we claim to know. There likely is a difference in degree of accuracy, but the point I was making was that despite your claim to "certainly know what [you] know", we don't in fact know what we know with anything remotely near precision.

We know some of what we know, but we can both be pressed into doing things we are certain we don't know how to do but where the knowledge is still there, and we will confidently proclaim to know things (such as the extent of our knowledge) that we don't.

I will agree that LLMs need to acquire a better idea of what they know, but there is no reason to assume that knowing the limits of your own knowledge with any serious precision matters, given how bad humans are at knowing this.

So much of human culture, politics, and civil life is centered around resolving conflicts that arise out of our lack of knowledge of our own limits, that this uncertainty is fairly central to what it means to be human.