MurphyA/hacker-news huggingface dataset:
by ccashell | 2025-06-20 07:41:37+00:00
It can definitely be worthwhile.

First, Perl takes some unique approaches to programming language design, and that will stretch your brain and make you a better programming. New concepts and ideas (to you), particularly ones with a long and proven record, are almost always worthwhile.

Secondly, Perl is incredibly useful. It's still my go-to choice for a most things that need more than can easily be done as a shell script, but don't have a specific reason why I should use a different language. I find that Perl is more fun to code, and it's incredibly efficient for getting things done. If you're doing anything that involves text processing, Perl is still king, IMO.

One note: Perl style shifted significantly about ~15 years ago with the "Modern Perl" movement. This was a good thing, and you should ensure that any books or articles that you work with take advantage of those modern approaches. This can largely resolve the "write-only" complaints that some people have about Perl. Which, to that point, I'll note that it is very easy to write very readable and easily maintainable Perl code. Use a recent version of Perl and make sure you always have strict and warnings enabled!

Also, if you're wanting to do OO, check out Moose, Moo, or Perl's newly released OO class system.
by alonrozi | 2025-06-20 07:42:05+00:00
Ever wish you could ask your past self for advice—or get answers from your future self? I built an AI app that lets you do exactly that. No journaling, no fluff. Just real, short conversations with versions of you. It’s weirdly accurate, emotional, and addictive. Try it and let me know what your past/future self says.
by comp_throw7 | 2025-06-20 07:42:07+00:00
Your persistent refusal to acknowledge that Scott did not want to go from a world where his patients Googling his real name did _not_ immediately lead to his blog, to a world where it _did_ immediately lead to his blog, and that was his primary (and valid) objection to having his real first and last name put into print, is baffling.
by louiskottmann | 2025-06-20 07:43:04+00:00
I suspect it's a learning thing.

Which is a shame really because if you want something simple, learning Service, Ingress and Deployment is really not that hard and rewards years of benefits.

Plenty of PaaS who will run your cluster for cheap so you don't have to maintain it yourself, like OVH.

It really is an imaginary issue with terrible solutions.
by eddythompson80 | 2025-06-20 07:43:06+00:00
Yeah, they are basically DIY-ing their own "cloud" in a way, which is what kuberetes was designed for.

It's indeed a lot of maintenance to run things thins way. You're no longer operationalizing your own code, you're also operating (as you mentioned) a CI/CD, secret management, logging, analytics, storage, databases, cron tasks, message brokers, etc. You're doing everything.

On one (if you're not doing anything super esoteric or super cloud specific) migrating kubernetes based deployments between clouds has always been super easy for me. I'm currently managing a k3s cluster that's running a nodepool on AWS and a nodepool on Azure.
by xiconfjs | 2025-06-20 07:44:22+00:00
Usually it‘s about standing, resources and options. Serious problems like fibre cuts, local power outages and DDoS attacks are usually not in their scope and they have to wait for 3rd parties to fix these problems with little iptions to speed up those processes. Bigger ISP usually have teams/departments which have well established processes/solutions to tackle these problems. That said, I‘m totally aware that each of them (small or big ISP) have their pros and cons - as always it mainly depends on your use case and requirements.
by dale_huevo | 2025-06-20 07:45:07+00:00
Linux would have had strlcpy/strlcat 25 years ago but the glibc maintainer was famously being a giant chode and refused to allow "horribly inefficient BSD crap" into his precious code, and this fight went on for years:

https://sourceware.org/legacy-ml/libc-alpha/2000-08/msg00053...

So it wasn't for lack of trying. Yes, Open Source can't coordinate and this is why we can't have nice things.
by vidarh | 2025-06-20 07:45:20+00:00
This is the position that is literally called compatibilist.

But when you probe people, while a lot of people will argue in ways that a philosopher might call compatibilist, my experience is that people will also strongly resist the notion that the only options are randomness and determinism. A lot of people have what boils down to a religious belief in a third category that is not merely a combination of those two, but infuses some mysterious third options where they "choose" that they can't explain.

Most of the time, people who believe there is no free will (and can't be), like me, take positions similar to what you described, that - again - a proponent of free will might describe as compatibilist, but sometimes we oppose the term for the reason above: A lot of people genuinely believe in a "third option" for choices are made.

And so there are really two separate debates on free will: Does the "third option" exist or not, and does "compatibilist free will" exist or not. I don't think I've ever met anyone who seriously disagrees that "free will" the way compatibilists define it exists, so when compatibilists get into arguments over this, it's almost always a misunderstanding...

But I have met plenty of people who disagree with the notion that things are deterministic "from the outside".
by rhdunn | 2025-06-20 07:46:54+00:00
Google have their own ASIC via their TPU. The other major players have leveraged NVIDIA and -- to a lesser extent -- AMD. This is partly due to investment in TPUs/ASICs being complex (need specialist knowledge and fabrication units) and GPU performance being hard to compete with.

Training is the thing that costs the most in terms of power/memory/energy, often requiring months of running multiple (likely 4-8) A100/H100 GPUs on the training data.

Performing inference is cheaper as you can 1) keep the model loaded in VRAM, and 2) run it on a single H100. With the 80GB capacity you would need two to run a 70B model at F16, or one at F8. For 32B models and lower you could run them on a single H100. Then you only need 1 or 2 GPUs to handle the request.

ASICs could optimize things like the ReLU operations, but modern GPUs already have logic and instructions for matrix multiplication and other operations.

I think the sweat spot will be when CPUs have support for high-throughput matrix operations similar to the SIMD operations. That way the system will benefit from being able to use system memory [1] and not have another chip/board consuming power. -- IIUC, things are already moving in that direction for consumer devices.

[1] This will allow access to large amounts of memory without having to chain multiple GPUs. That will make it possible to run the larger models at higher precisions more efficiently and process the large amount of training data efficiently.
by ghghgfdfgh | 2025-06-20 07:47:19+00:00
I’ve tried using LLM’s to do the same exact thing (turning precinct-level election results into a spreadsheet) and in my experience they worked rather poorly. Less accurate than traditional OCR, and considering how many fixes I had to make, altogether slower than manual entry. The resolution of the page made an outsized difference. It’s nice that you got it to work, but I am skeptical of it as a permanent solution.

Tangentially- I appreciate what OpenElections does- however, I wish there was a similar organization that did not limit themselves to officially certified results. There are already other organizations who collect precinct results post-2016, and using only official results basically limits you to 2008 and afterwards, but historical election results are the real intrigue. Not to mention that I have noticed many blatant errors in election results that have supposedly been “certified” by a state/county government. The precinct results Pennsylvania publishes, for example, are riddled with issues.
by jillesvangurp | 2025-06-20 07:47:23+00:00
Technically the ij is a digraph and sometimes a ligature. Although computer keyboards have never really supported that but some mechanical keyboards for typewriters used to. Most modern Dutch people would simply be typing an I and a J and wouldn't necessarily know that they need to also capitalize the J at the beginning of a sentence (Ij would be incorrect). Not a lot of words start with that but some place names do.

The Dutch keyboard layout is effectively US international. There are no special characters that that keyboard supports; including all the accents/modifiers we inherited from French, German, etc. Spelling without those is now correct.

This is something that has evolved over the last decades. In the eighties, people would memorize the character codes to produce letters with those. I remember having a card with the right key combinations for word perfect. This is not a thing anymore. People just skip it and the grammar and spelling rules were actually modified to not require these anymore in most situations.

Funnily enough, the Y, is not commonly used in Dutch and usually referred to as the Greek IJ but pronounced the same way.
by sigmoid10 | 2025-06-20 07:51:38+00:00
Nope. In free market theory (=perfect competition, no barriers to entry, unlimited buyers etc.) prices are set as the equilibrium where demand equals supply. Supply ends up being equal to marginal cost in the mathematical limit. So in this limit, companies no longer make profit because if they charge cost+epsilon, they will loose demand to other suppliers. That's literally what you learn in economics 101. Of course in the real world you won't reach that limit, but getting to it within first order is still very good for consumers. The further you go away from this free market state, the more companies can extract what consumers "are still willing to pay" (irrespective of their cost) as you say. The opposite limit is the monopoly, where consumer welfare doesn't matter at all and companies can set their prices to maximise their own profit, because they don't need to adhere to any supply curve. They can literally charge extra until people go broke for inelastic demand curves like those of basic utilities (which phones are becoming more and more).
by dakiol | 2025-06-20 07:51:39+00:00
Tech companies usually require from you to:

- go through at least 4 stages (screening, tech challenge, systems design, chat with managers). You need at least 3 weeks to go through this in a sane manner

- You need to know about tons of topics (distributed systems, concurrency, microservices, k8s, ci/cd, databases, OS, networking). It’s common for engineers to know a little bit about everything, but when asked about something in particular in an interview, you cannot expect deep knowledge

- good luck trying to convince them that it doesn’t matter if you don’t have professional experience in $language, that you are rather an agnostic software engineer with experience in different progr languages and frameworks

- if you don’t craft your cv with proper buzzwords, you won’t get a chance to get interviewed. The ones who read your cv first are: machines (that are inflexible) and hr recruiters that know nothing about tech besides buzzwords

- good luck making a single mistake in the tech interview. Since the company has 100s of other candidates, they are only looking for the best of the best

Honestly, I don’t know whats wrong with using ai to “prepare” for interviews. I wish things were simpler, though (just come as you are, we talk for a couple of hours and if then you get a yes/no answer)
by baq | 2025-06-20 07:52:37+00:00
If you focus on just the matmuls, no CUDA, no architectures, no infinibands, everything-on-a-chip - put input tokens in input registers, get output tokens from output registers from a model that's baked into gates - you should be able to save some power. Not sure if 10x or 2x or 100x, but certainly there are gains to be had.
by mattmanser | 2025-06-20 07:54:23+00:00
The readme is the typical AI verbal diarrhoea of so many words saying so little it hurts. You should ask it to be a bit more concise.

As a separate comment, would it not be better to ask for your plan on first run and setup a config file to remember it? With a note how to change it. Rather than rely on cmd line variables?

Also, shouldn't it be able to pick up the timezone from the local computer? Why would it "default" to a fixed timezone of poland?
by eddythompson80 | 2025-06-20 07:54:45+00:00
> One of the primary idioms in Kubernetes is looping: if the dependency's not available, your app is supposed to treat that is a recoverable error and try again until the dependency becomes available.

This is gonna sound stupid, but people see the initial error in their logs and freak out. Or your division's head sees your demo and says "Absolutely love it. Before I demo it though, get rid of that error". Then what are you gonna do? Or people keep opening support tickets saying "I didn't get any errors when I submitted the deployment, but it's not working. If it wasn't gonna work, why did you accept it"

You either do what one of my collogues does, add some weird ass logic of "store error logs and only display them if they fire twice, (no three, 4? scratch that 5 times) with 3 second delay in between except for the last one, that can take up to 10 seconds, after that, if this was a network error, sleep for another 2 minutes and at the very end make sure to have a `logger.Info("test1")`

Or you say "fuck it" and introduce a dependency order. We know that it's stupid, but...
by marmarama | 2025-06-20 07:54:53+00:00
It's a website that has compiled existing open source emulators that are capable of running old versions of MacOS and NeXTStep, to WebAssembly so they run inside a modern browser.

They have paired that with disk images of these OSes and a lot of tweaking, so that when you go to the website, you select the version of the OS you want to play with, and _it just runs_ in the browser.

Instant old Mac/NeXT experience, no separate download or software to install.
by 59nadir | 2025-06-20 07:54:54+00:00
I like some of their choices, but I wouldn't say that releasing your product for a minority operating system only, then following up with yet another minority operating system much later, then stuffing AI features in there when you don't even have debugger support... The list goes on; one man's "real product development" is another's list of mistakes.

They got the baseline feature set right, IMO, but probably could've spent some more time looking at the actual, real demographics of development (and who is most likely to appreciate performance as a feature and share those ideals).
by nebula8804 | 2025-06-20 07:55:51+00:00
You'd think thats the case but all these retro Youtubers seems to have inspired a new generation to take up the mantle. I attend the Vintage Computing Festival(the one in NJ) each year and post pandemic it has been filled to the brim with young GenZ era people interested in experiencing the computers of yesteryear. Is it enough to make this venture worth it? We shall see. All the Retro Computing Youtubers including this one must have some demographic data to help fill inn the picture.
by moralestapia | 2025-06-20 07:56:42+00:00
Great post.

This is exactly what I'm an expert at, I even coined a term in the field [1], :).

Since I started doing this 15 years ago (and I know the field predates me by much), one always has had this feeling that we are so close to a big breakthrough in biological simulation, but at the same time, progress has been kind of "slow". I think the reason for that is because pushing the envelope forward in this field requires mastering three (maybe four) different disciplines, your pick of [Bio, Chem, CS, Math, Physics]. Very few people reach this level of simultaneous understanding of all these pieces.

I'm not trying to gatekeep the field, though, much of the progress here (including many of the papers mentioned in TFA) is work coming from PhD students. Anyone could jump into this, but you really need to sit down and try to make sense of it for a while, years. PhD gives one the perfect opportunity for that.

Anyway, I hope this thing keeps going on forward, it's one of the ultimate goals of Biology and it would be extremely beneficial to the world.

1: https://www.frontiersin.org/journals/plant-science/articles/...
by lelanthran | 2025-06-20 07:56:46+00:00
For automated testing I use hurl, but my personal dev roadmap is to create another tool.

The deficiencies in huel with client state management is not easy to fix.

What I'd like is full client state control with better variable management and use.

For my last project I used Python to write the tests, which appears to work well initially. Dunno how well it will hold up for ongoing maintenance.
by imtringued | 2025-06-20 07:56:50+00:00
You got Darwinism exactly backwards. Darwinism and nature do not select like an algorithm. There is no cost function in reality and no population selection and reproduction algorithm. What you're seeing is the illusion of selection due to selection bias.

If gender equality and intellectual achievement don't produce children, then that isn't "darwinism selecting rationality out". You can't expect the continued existence of finite lifespan organisms if there are no replacement organisms. Raising children is hard work. The people who believe in gender equality and intellectual achievement made the decision to not want more of themselves, particularly when their belief in gender equality entails not wanting male offspring. The alternative is essentially freeloading and expecting others, who do not share the beliefs, to produce children for you and also to teach them the "enlightened" belief of forcing "enlightened" beliefs onto others (note the circularity, the initial conditions are usually irrelevant and often just a fig leaf to perpetuate the status quo).
by rhdunn | 2025-06-20 07:57:15+00:00
It depends on 1) what model you are running; and 2) how many models you are running.

You can just about run a 32B (at Q4/Q5 quantization) on 24GB. Running anything higher (such as the increasingly common 70B models, or higher if you want to run something like Llama 4 or DeepSeek) means splitting the model between RAM and RAM. -- But yes, anything 24B or lower you can run comfortably, including enough capacity for the context.

If you have other models -- such as text-to-speech, speech recognition, etc. -- then those are going to take up VRAM for both the model and during processing/generation. That affects the size of LLM you can run.
AI giants trying to understand the output of LLM+"Agentic tools"=idea
by AndresFer | 2025-06-20 07:58:16+00:00
I don't think the definitions of agents from OpenAI, Claude, and so on are being understood in the same spectrum. Microsoft understood it at some point when Satya Nadella took over, but then abandoned it, probably the reason they bought OpenAI. Google and Apple fumbled with their AI assistants that only ran on phones to schedule appointments. Meta is probably a bit more alive, as shown by its open-source projects. But none of the big players are building their foundations on the AI problem.

AI, once treated as candy or a toy by big tech companies and used internally or occasionally released to the public, is now something else. Just like Apple and Microsoft once fought over processor power, pricing, and design in consumer-level computers, the market had defined categories. Now, those categories shift with AI models with non-deterministic answers or application. There are real clients with real needs, companies with no guaranteed profits from their products, and the most flexible technology of all time.

In physics, the capabilities of an element being both flexible and resistant make it applicable to almost any mechanism or structure. AI is that element, and the scientific industries that discovered it can’t really figure out what structures or mechanisms to adapt it. The point is, big tech companies convinces itself of its project of creating AI, but they are not AI companies. They are the most hyped consumers of all, and that’s exactly why this is the right time to sell to the rich.
by SquidJack | 2025-06-20 08:00:23+00:00
we have FAQ section with a brochure in each category of the product but most of them want to clarify specific them about their use case (context) so they contact the support to verify this works 100% for their use case.

this is not an normal chat bot we have 100s repeated question and find a pattern within them so based on the documentation we can predict what kind of question that the user can possibly ask so based on this we have implemented our own custom RAG solution that has high accuracy with relevant context with minimal latency.

also we have human in loop concept in the upcoming update
by donovanr | 2025-06-20 08:01:10+00:00
Yes, this. A lot of work in this field is missing from that timeline. Just circa 2010-2020, Les Loew's VCell 3D PDE approaches, Faeder et al.'s BioNetGen / ODE work, Luthey-Schulten Shulten's grid based cell models, the Pittsburgh supercomputing center's 3D monte-carlo MCell, the image-based deep learning models at the Allen Institute for Cell Science...

It's nice to see the idea of virtual cells make a comeback now, though the meaning seems to have shifted to transciptomics-based transformer / gpu-powered models (which have issues[0]), it's a fun field / problem, but I think it will make better progress if we take advantage of all the varied computational work that has come before.

[0] Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all https://arxiv.org/abs/2410.13956
by selfdb_io | 2025-06-20 08:02:24+00:00
I'm excited to announce that we're fully open-sourcing SelfDB under the MIT license. SelfDB is a self-hosted, complete backend platform that provides everything you need to build modern applications without vendor lock-in.

What is SelfDB? Think Supabase or Firebase, but completely self-hosted with no external dependencies: - PostgreSQL v17 with full SQL capabilities - Built-in object storage (no S3 required) - Real-time subscriptions via WebSockets - Serverless functions with Deno 2.0 - React admin dashboard with SQL editor - JWT auth + anonymous API keys - One docker-compose command to run everything Why Open Source Everything? We believe backend infrastructure should be truly owned by developers, not rented. Too many "open source" projects have restrictive licenses or require proprietary cloud services. SelfDB runs entirely on your hardware with standard Docker containers. Our Sustainable Open Source Model Here's what we're doing differently: - Everything is MIT licensed - no dual licensing tricks - Paid version = early access - always one version ahead (currently v0.0.3 vs v0.0.2) - Same open source license - you're supporting development, not buying proprietary software - No feature restrictions - open source version gets all features, just later This model lets us: - Keep building without VC pressure - Maintain true open source principles - Give back to the community sustainably Built for Developers, By Developers - FastAPI backend with automatic OpenAPI docs - TypeScript SDK with full type safety - React + Vite frontend with hot reload - Multiple function triggers (HTTP, DB, scheduled, events) - Built-in SQL editor with syntax highlighting - Schema visualization and migrations Try It Now git clone https://github.com/Selfdb-io/SelfDB cd SelfDB cp .env.example .env ./start.sh Access at http://localhost:3000 Links: - GitHub: https://github.com/Selfdb-io/SelfDB - Documentation: https://github.com/Selfdb-io/SelfDB#readme - Latest version: https://selfdb.io We're planning this sustainable approach because we want SelfDB to be around for decades, not years. Would love to hear your thoughts on this model and answer any questions about the architecture or implementation!