skysight-inc/HackerNews huggingface dataset:
I don't see the point of building tight, if you're going to have massive holes in the building, through which you force tons of air (which you should). Would you call a jar airtight if you never close the lid?

In multi-tenant dwellings, building tight can help keep people's smells to themselves. That's about it. Like if you have pressurized air in the hallway, keeping the common area fresh, if the apartment units are not built tight, smells can go from one to the other sideways, possibly forced by that very same pressure.

The main reason people build tight is to save on heating and cooling bills, or "save the planet". And mainly how that works is by not ventilating. Ventilating forces out all that hot or cool air that you paid for, so you have to heat or cool the replacement air.

It's basically greed or ideology over health.
AI has sadly become the new blockchain. Over hyped and consuming way to much power, attention and manpower for what it is with very little benefits to mankind.

I guess that is what Tech is nowadays.

So yes when I see 'AI' mentioned in products I shake my head before looking at details. Case in point: iOS 18.1 Beta. The Great Apple Intelligence.
It does nothing. Just... you won't even notice it.
wails, raylib, ebiten
More like "Executive: lol".
I would think the best option is to always be suspicious of hype, unless you understand why something is being hyped? I'd also argue its worth understanding where your stack falls down, so you know when you need to look for alternative stacks.

The other bit is it's worth understanding how your stack interacts with related stacks (to use your examples, does uv and pydantic using rust vs. being pure python cause issues), but your OS is also changing, are your tools still going to work?
From my impression, the way you get good at OSINT is knowing which types of information tend to be related, and where to go with what you have.

All OSINT guides include a surprising amount of paid tools. OSINT tools are dime a dozen, you have to know how to use them.

Finding tools is easy, there are endless collections of them:

- https://osintframework.com/

- https://github.com/jivoi/awesome-osint

- https://cylect.io/

The hard part is deciding when, where, how, and with what information to use them.

I am surprised how little information about OSINT is on hackernews (https://hn.algolia.com/?query=OSINT barely has results)

If you are not some journalist, you won't need serious OSINT. (Investigating someone for fun?)

You can play geoguesser for fun. That's "technically" OSINT and you will know geography. Here is a Geoguesser (Rainbolt) player finding places from images: https://www.youtube.com/@georainbolt/videos

Here are some OSINT challenges if you want: https://gralhix.com/list-of-osint-exercises/

A skill I recommend much more than OSINT is being good at normal searching and collecting some basic metadata, and getting the gist of new topics quickly.

- You want to buy something, how do you figure out the price, quality, and whether it is a good deal. How to quickly figure out what matters and what doesn't? Which features are common, which false advertised, which are aesthetic bloat.

- You are traveling or moving to a new city, how do you quickly figure out all the tricks locals know and not fall for tourist cash grabs.

- You find a shady website, app, or chrome extension. Pocke around with the source code and domain names to round up the entire operation and report it.

- Some new person you got to know works/likes something you know little about. How do you quickly figure out some deeper questions to ask.

- Find what an error in your computer.

It boils down to knowing the underlying structure of whatever you are digging. Insiders tend to know ecosystem so well it seems obvious, despite it being hard to integrate into for an outsider.
My mom gave my Amiga 500 away when I was on holidays.
Some large public examples of where software architectural work provides a large benefit:

1. Amazon all-in on API service architecture thus creating Amazon Web Services.

2. Sun all-in on attempting run-anywhere architecture thus creating the Java Virtual Machine.

3. Facebook all-in on PHP single-page rendering architecture thus creating HipHop.

4. Linus all-in on Unix x86 architecture thus creating Linux, then all-in on distributed versioning architecture thus creating Git.

5. Google all-in on horizontal scale architecture thus creating Bigtable, MapReduce, Kubernetes, etc.

6. Apple all-in on vertical device architecture thus creating Apple Silicon, iPod/iPhone, physical stores, etc.

7. Hashicorp all-in on infrastructure as code architecture thus creating Terraform, Vault, etc.

8. 37signals all-in on convention-over-configuration MVC web app architecture thus creating Ruby on Rails.

9. NVIDIA all-in on GPUs thus creating CUDA and enabling matrix math speedups of many programs.
That's not disagreeing with me, but instead adding yet another argument for users to avoid as many Apps as they can.

If the experience in an app is form, graphs, and payments... use the website. It apparently saves that company 30% and, to your point, keeps your computing habits yours.
No, they should be generated by either dev or something like pre-commit and then checked if they match what's generated by CI.

And yes, those have to be deterministic with regards to inputs, it does not make sense otherwise.
Baumol’s Cost Disease or Baumol Effect [1]. Thanks for mentioning. I think it deserves more air time, especially looking at typical responses (raising price, reducing quality) and the consequences (raising prices raises expectations, triggering more investment causing prices to go up more). How to reduce consequences? Two models that seem to work are volunteer based organizations, such as AA[2] and education organizations coupled with technology, specifically AI, such as Khan Academy's Khanmigo[3]. What does this mean for universities? Lots of terrain to explore looking for sweet spots.

[1] There are the standard places to define like Wikipedia. The link I offer here is an A16Z podcast with Marc Andreesen and Vijay Pande where Marc gives an overview about 3 minutes in.

https://a16z.com/podcast/baumols-cost-disease-in-healthcare-...

[2] https://aa.org

[3] https://www.khanmigo.ai
Police in large American cities are not likely to be of much assistance in this situation. Assuming they attend at all, I would expect them to not understand the nature of the issue and probably proceed to make it much worse.
Why does it need to be perfectly accurate? These are examples intended to communicate the principle, not instructions on how to do frequency analysis. This description may be incomplete, but it does make it pretty clear how frequency analysis works in general terms. This criticism seems misplaced to me.
Urban roadway in America: the amount, extent, and value
Ah yes, all applications should be a single very large source file that issues OS syscalls directly.
HN community, who are generally known to by hyper-critical to a pedantic degree and/or overly pessimistic (cough the initial Docker project Show HN thread cough)

So you made me dig up the announcement, and contrary to how you recall, it is almost universally positive.

https://news.ycombinator.com/item?id=5408002
It has not been my experience that Go is good for almost everything. On the contrary, it seems good at a couple very specific (though very common) niches: network services and cli utilities. But for most of what I do right now - data heavy work - it has not turned out to be very good (IMO). It really is just not better in any way to have to constantly write manual loops to do anything.
hey! that's a great question. Initially, we had multiple processes for every component (ASR, LLM, TTS) and using a configurable settings pair of endpointing and token_size we used to handle it since for some cases latency might be an issue but some some others (where there are longer responses) it might not be that much of an issue. Later on, we also integrated with caching and routing to minimize unnecessary calls.
This falls apart at claim 1, because it's for a token, not the answer. You can start a reasoning process, but I don't know of anyone seriously claiming it happens in a single step.
LLMs have a fixed budget per token, but the result you normally want is multiple tokens long. Engaging in chain-of-thought answer means a variable number of tokens, with the length depending on the complexity of the task. In this thread I mentioned the prompt + generated subtasks, so it's not like we're talking about single-token outputs.

Or in a simple example, the answer to "using step by step reasoning, what's the colour of a white sheet" is significantly shorter than "using step by step reasoning, why is Chewbacca defence a fallacy". The more complex task automatically got more steps and more tokens dedicated to it.

> Humans do not have a fixed computation budget per token.

That's not even wrong. Humans don't operate on tokens. That just doesn't make sense.
>We find that a little under a quarter of urbanized land—roughly the size of West Virginia—is dedicated to roadway. This land was worth around $4.1 trillion dollars in 2016 and had an annualized value that was higher than the total variable costs of the trucking sector and the total annual federal, state, and local expenditures on roadway. Conducting a back-of-the-envelope cost-benefit analysis, we found that the country likely has too much land dedicated to urban roads.
Right click > view implementations
Are you even trying to understand my point? Yes, in that direction, explicitly constructing an element of the bidual of V from an element of V is easy. To explicitly find an element of V from the element of the bidual, you need to choose a basis. Just try it, come on! Write down the inverse isomorphism. Let \alpha be an element of V**. Then v \in V such that f(v) = \alpha (where f is the isomorphism you wrote down) is given by...?

I will help you: if (e_i) is a basis of V and (e_i^*) is its dual basis, then v = \sum_i \alpha(e_i^*) e_i. Can you find such a formula without mentioning the word "basis"?
Show HN: GPT Researcher – Open-Source Autonomous Research Assistant
Hi HN! I'm excited to share a project I've been working on in the past year: GPT Researcher, an open-source autonomous agent designed to simplify and accelerate the research process.

In my experience, one of the biggest challenges in research is efficiently gathering, filtering, and synthesizing credible information from a vast sea of sources. Traditional search engines often fall short, requiring users to sift through multiple sites to piece together relevant insights. This inefficiency led me to create GPT Researcher, a tool that automates the entire research process—bringing you comprehensive, accurate, and unbiased reports in just minutes.

The inspiration for GPT Researcher came from my struggles with existing research tools and autonomous agents (like AutoGPT and Perplexity), which often failed to deliver timely and reliable information (and often just got stuck). I dove into research on various AI methods and technologies, including flow-based programming, multi-agent frameworks, and RAG.

GPT Researcher operates by breaking down complex research tasks into smaller, manageable subtasks, which are then tackled by specialized agents. These agents work in parallel, significantly speeding up the research process. The tool scrapes information from over hundreds of trusted online sources, filters it, and compiles a detailed report tailored to your specific needs.

For example, whether you need to plan a trip, optimize your LinkedIn profile, or analyze stock performance, GPT Researcher gathers all relevant information, organizes it, and delivers a well-structured report with cited sources—all within a few minutes.

I'm happy to update that GPTR has crossed over 13K Github stars and 5K developers on our Discord! Our goal remains the same - democratizing autonomous research for everyone, without the need to pay for existing tools.

I'd love to hear your thoughts and feedback on GPT Researcher’s direction. You can check out the repo here: https://github.com/assafelovic/gpt-researcher
I have a more complete theory than involves the decline of model trains and stamp collecting, but the malignancy of conspiracy theories these days is also linked to not being confined to shortwave conspiracy theory shows and mimeograph newsletters the way it was in the 1970s.
I doubt it. What evidence supports your claim? I took a look at the Patreon app permissions in Android, and it doesn't even ask for location which is probably the most valuable thing about users they could ask for.
As you yourself said below, "Don't try to sneak a narrative while pretending to ask a question."
Oracle requiring commercial licensing for their JVM has made it so radioactive that my workplace firewalls "oracle.com" to prevent anyone from accidentally using it.
> Yes I'm aware that RSA encryption is obsolete.

Maybe you are talking about this? https://ikarus.sg/rsa-is-not-dead/

"Only the ssh-rsa signature algorithm is deprecated"
And when you don't have an OS because you need to update the flash first?
I trust commercial interests more than academic. At least their motives are legible
Does it support playing media even when it’s backgrounded? Stuff like Picture in Picture?
It’s a crazy aunt on WhatsApp, it’s not an engagement algorithm. Does your Facebook even show you your aunts posts? I have a feeling it’s stopped showing friends and relatives for me ages ago.
Of course.
My methodology is usually this:

Day 1: read the introduction of a book, set up editor and environment

Day 2: (superficially) learn flow control and type system

Day 3: practice with coding problems on codewars

Day 4-7: reimplement one of my projects

Week 2: read the second quarter of the book and continue with puzzles

Week 3+: Just projects, maybe invest a day into tooling somewhere in between

That is assuming 1-3 hour days
The Dark Underbelly of the Near Future
> You could be out of a job with as little control of the whole situation as those rustbeltians you have such disregard for.

I'll admit I was a bit angry and offended when I first read this. Did you miss the part about how horribly this has affected my friends, family, and neighbors? I say this expressly out of concern and literal, actual love.

Where do you live?

> NAFTA and globalism killed the rustbelt.

As I said in my other reply the generation before this one also outright dismissed the ability for Mexico, China, etc to take their jobs. In any case it certainly wasn't prevented.

> The refusal of auto plant Larry to learn Java didn’t.

Again, as I said in my other reply what little manufacturing is left is a fraction of the jobs with a completely different skill set - CNC programming, etc. While you're off on Java because (frankly) you don't know what you're talking about it's pretty much exactly this.
Why would you not be pinning versions in a Dockerfile? The entire point is “if it works on my machine, it works on yours,” and that goes out the window if you can’t be assured that every program in the release is at the same version you had.
Very helpful, thanks for sharing the link.
Schoolyard insult? Uh? Can you quote the part of my comment that would be the "schoolyard insult"?

> This comment is in a discussion about an article titled, Tensors, the geometric tool that solved Einstein's relativity problem. Therefore, "tensors are literally the topic that started this discussion."

Again, are manifold involved in any way in the definition of tensors and their properties? No? Then why are you even mentioning "metric tensors"? (Which aren't even tensors, but tensor fields...)
"the best part is no part at all"
The evergreen questions of tech are: is $CURRENT_TREND a bubble, and if so, when will it burst, what will its legacy be, and what comes next.

I'm finding surprisingly little discussion of what trends might follow on AI (GPTs suggest several extant / prior fads: AR/VR, wearables, cryptocurrency/blockchain, quantum computing). OTOH, AI seems to have sprung from nowhere (at least so far as popular awareness is concerned) in 2023.

(There was an early GPT-2 submission, though with little discussion, on 14 February 2019: <https://news.ycombinator.com/item?id=19166089>. The first mentions of large language models appear about 5 years ago. The first mention of large language models dates 14 years ago to 5 August 2010: "Large Language Models in Machine Translation (Google's way of doing it)" (upenn.edu) <https://aclanthology.org/D07-1090.pdf> (PDF) <https://news.ycombinator.com/item?id=1578444>, but was entirely ignored at the time.)

We're already seeing signs of public fatigue with AI hype (and perhaps overly-smart, overly-prying technology generally), e.g. a current HN post, "Study shows that tacking the “AI” label on products may drive people away" (CNN) <https://news.ycombinator.com/item?id=41231731>.

An observation of mine in the past has been that technology tends to extend through a set of phases, roughly:

- Proof of concept. Works for the creator.

- Refinement: Incremental improvement of concept in function, reliability, efficiency, and utility.

- Embellishment: Layering complexity on top of an initial design. Though initially well intentioned, the end result is fragility, impediments to usability, and eventually, rot. A baroque era.

- Recaptitulation or replacement: An overly complex design may find itself refined back to a more functional one (e.g.: the car tailfin era recaptitulated into today's econo-box and performance sedan market, though SUVs perpetuate the meme of ostentatious nonfunctional embellishment). Or it may find itself replaced entirely by a more appropriate technology, as may be happening now with PCs being supplanted by tablets and mobile devices offering much of the functionality required by users.

Strikes against LLM ML AI GPTs are that their data, compute, and energy requirements are huge, and corporate entities show alarming tendency toward monopolistic dominance. The possibility of delivering equivalent or at least sufficient results with far fewer resources and competitive risk might drive a countertrend. There's also the accelerating trend of unintended data disclosures, hacking attacks, and disabling entire industrial sectors (ChangeHealthcare (2024), shipping with Petya/Maersk (2017), Colonial Pipeline ransomware attack (2021), WannaCry (UK NHS, FedEx, Telefonica, 2017), Equifax (2017), Target (2013), etc., etc.) which may generate push-back.
Stennis is not getting its personnel from local grad schools.
There was an ad for Navy shipbuilding recently that did the rounds online, targeting gig workers [1]. The snake grows hungry, and slithers toward its tail. :)

[1]: https://www.youtube.com/watch?v=F1IZC3t8NRc
Oof.. I feel that pain, we had an Apple IIe that I taught myself to program on in the 80s, it was basically the genesis of my life and career, and oddly enough kind of a family heirloom for me..

When I left for university in the early 90s, my dad donated it to a local library without even asking me first. To be fair I had never expressed my desired to keep it, it was just in the basement in a box. So it wasn't that big of a deal but I know he feels guilt for that one to this day, once I explained how I felt.
My name almost always gets trimmed. Here you can see a bit of the interoperational hell: https://xx1.pass-consulting.com/documentation/xx1-travel-sdk...

The tech in the travel industry is cursed, and the pay is bad. Do not recommend.
> Guy has 34k karma. He’s one of us.

Us vs. them based on 'karma' gives me bad vibes. If anything the fact that 'high-karma' users don't grasp low-level operating systems mechanics is a tell-tale sign of what it takes to get high up in the karma ladder. Better to keep that karma for what it is - a semi-functional means to keep out the trolls - and focus on the message instead of the messenger.
There is also a newcomer called Panoramax, started in France about a year ago. It's federated and starting to expand in other countries.
https://panoramax.fr/
Cut the 'AI' Bullshit
It's like self-driving cars and Uber. The investor wet dream is to fire all your expensive salaried employees and replace them with an AI slave.
A Quick-ish Accessibility Review: shadcn/UI Charts
Hey, nice to see that someone else posted this post before me (I am the post author).

Happy to answer any questions.
This is the problem with an experienced dev writing a "Best practices for beginners" article. The beginner will read it as literally the best practice and commit it to memory as dogma, not considering suitable applicability and variations.

I would say that beginners shouldn't be writing core algorithms as performance often matters. The post should be called beginner-friendly implementations of algorithms in Go, or something to that effect. The point being to learn about the algorithms rather than learning the right way to code algorithms.
I think the point was that every new washing machine already does these things (measures how much detergent to use, checks when the water is clean enough etc.) without any AI. It’s not a very difficult problem domain.
> ...and they have no idea what that actually means.

Subconsciously, many of them do "know" - it means that if you're an insecure and kinda clueless trend-chaser, then the 'AI' product is the one to buy.
Does Forgejo use git? It’s not obvious from the ‘what is forgejo?’ documentation.
> Had the future humans not intervened (by placing the black hole near inhabitable planets), humanity on earth would have died.

Not only a black hole, a supermassive Kerr black hole with relativistic rotation at its equator.

They could have done a better job at the planets themselves though - Most of them were pretty useless for terraforming.

While I'm sure the US government would trade immediate comfort for long-term survival of the species, I don't think other governments would have the same priorities. I'd assume China would be one of the first to build multiple O'Neil cylinders with carefully controlled environments and as little Earth-originated biomass as possible (to contain the blight).
I work for a YC company in the industrial maintenance space and we've been looking at integrating machine data drive and automate certain maintenance workflows. The most difficult aspects of integrating machine data have been (i) networking and (ii) translating whatever protocol (Ehternet/IP, Profinet, Modbus, etc.) the machine's control system uses into familiar formats. In many cases, PLCs aren't connected to a network, the customer doesn't know which tags point to the data they need, IT has concerns about security, and the list goes on. How are you guys thinking about these networking and protocol translation issues?

Second question. The main platform in this space is Ignition. Do you consider yourself a competitor to Ignition or are you aiming for a different use case?
You already do live in times where people are discriminated against for things they can't control, they're just not robots
Remembering a Medieval Polymath Who Paved the Way for the Renaissance
If you think 1913 was the first boom/bust in United States financial history you're either trying to push an agenda or just totally ignorant
It's likely because it's a pain to call Fortran or C (I'd be suspicious of anyone trying to reimplement openblas in go).
It is, but it may be periodically necessary to get rid of dead weight and extirpate those who’ve risen to their level of incompetence.

I'm not advocating stack ranking for yearly review but rather a quintennial or decennial event to clean-house. In other words, a mechanism to avoid what happened to Boeing, Intel, Yahoo, and what is happening to Google and others.

360 reviews obviously allows orgs to get fat and carry bloat.
> UEFI on the other hand you just build one image and it works everywhere.

Thats the theory. Practice looks like that: https://lore.kernel.org/linux-efi/20200907170021.GA2284449@r...

If Vendors couldn't get the BIOS consistently right in the 80ies, why would they get UEFI consistently right now?
Study on the Impact of Format Restrictions on Performance of LMM