MurphyA/hacker-news huggingface dataset:
by torginus | 2026-01-17 16:09:05+00:00
I think it's incredibly fishy. If I add a 1MW coal plant to the grid, I can pretty much run it at nominal capacity all year round, so 1MW * hours in the year is afair calculation.

If I add the same 1MW for solar, needless to say even assuming perfect weather, I'm lucky to get 1/3rd of that. Under real circumstances, the numbers are probably much worse.

When looking at marketing, I think it's always safe to assume they picked the most flattering numbers when they didn't specify how they made the calculation.

That's why it's very meaningful to talk about adding kWh - 1 kWh peak solar means more in Texas than in Chicago. It's even less meaningful for batteries - they can sustain incredible currents, to the point it's very rarely the meaningful bottleneck.

Yet that's exactly that what the cited 'global think-tank' Ember did, which the article cites as source. So they either misled on purpose, or like a lot of people, they confused GWh and GW, which is such a grave error for a supposed expert, that their whole analysis should be disregarded.
by t-3 | 2026-01-17 16:09:14+00:00
While the police and politicians were afraid, it's not because there was a real terrorist threat from the Black Panthers or other militant black movements of the time. While the Panthers had some issues with violence, almost all of that was dedicated to infighting, and the few incidents that weren't were almost all revealed to be incited and planned by agent provacateurs. They were just afraid of blacks, and of giving the "lower" classes any kind of idea that they could effect change.
by kstrauser | 2026-01-17 16:09:47+00:00
For organization, I found that Johnny Decimal is my perfect sweet spot.

Seconded on GTD, or at least a version of it. I suck at consulting an app about what I could be doing in a given context. I’ve mostly discarded that, other than things like shopping lists and 1:1 meetings. But the idea of capturing every action I need to take, then routinely putting those in home/work/self/etc. buckets was life changing. I’m a devotee to that habit.
Show HN: Vita AI Coworker – Autonomous agents for testing and desktop automation
by jdeng | 2026-01-17 16:09:50+00:00
I've been following the Claude Cowork discussion this week with interest. The "AI coworker" framing resonated—we've been building Vita AI around the same idea: agents that work independently, not assistants you supervise.

We're focused on two main use cases: testing web apps and desktop automation. The key difference: our agents run in persistent cloud sandboxes instead of on your local machine.

Why this matters:

Isolation – Your local files and network never touch the agent. No DNS exfiltration, no filesystem access to your machine. The sandbox runs in the cloud, persistent for each task but completely isolated from your computer.

True autonomy – Tell it "test the checkout flow" or "automate this browser workflow," close your laptop, come back hours later to a full report with screenshots and logs. You delegate the work and walk away. No babysitting required.

Scale – Spin up 10 parallel agents without melting your CPU. Each agent gets its own environment in the cloud.

Trade-off: you can't use it offline, and there's network latency. But for testing and automation, async cloud execution is actually what you want.

Try it at https://vita-ai.net (free trial available). Built with Next.js 15, Drizzle ORM, and Vercel's AI SDK.

Different use case than Cowork (specialized for testing/automation vs. general tasks), but the same philosophical shift: moving from assistants to autonomous agents.

Happy to answer questions about the architecture or how we think about security.
Ask HN: How do you evaluate a LLM these days?
by pseudony | 2026-01-17 16:10:04+00:00
Hello HN. Recent events and me being Danish (EU) strongly encourage me to reconsider US services like Anthropic's Claude. I mention this to say that the problem of evaluating LLMs suddenly got very necessary for me.
While I don't doubt Claude is nearly ideal for my corner of software development, I would like to have a better sense of how much I am giving up.

With that in mind, how do you go about best evaluating LLM's these days, short of going with "gut feel"? My best idea so far is to design/write various small "design a program/library" tasks with clear functional requirements and letting each model try implementing the tasks, probably using Open Code and Open Router as the common components throughout the evaluation.

But this field moves fast and I may well have missed many better or easier approaches. What would you do?
by smithclay | 2026-01-17 16:10:15+00:00
This is part of a bigger consolidation trend, AI hype or not: which general-purpose data vendor gets to store and query all of your observability and business data?

Snowflake acquired Observe last week, AWS made it easy in December to put logs from Cloudwatch in their managed iceberg catalog, and Azure is doing a bunch of interesting stuff with Fabric.

The line between your data lake/analytics vendor and observability vendor is getting blurry.
by ben_w | 2026-01-17 16:11:06+00:00
> but I'm also a developer with decades of experience, so I'm not gonna claim it'll help non-programmers to build software, or whatever. They're tools, not solutions in themselves.

Also with decades experience, I'd say that it depends how big the non-programmer is dreaming:

To agree with you: A well-meaning friend sent an entrepreneur my direction, whose idea was "Uber for aircraft". I tried to figure out exactly what they meant, ending the conversation when I realised all answers were rephrasing of that vague three words pitch, that they didn't really know what they wanted to do in any specific enumerable sense.

LLMs can't solve the problem when even the person asking doesn't know what they want.

But on the other end the scale, I've been asked to give an estimate for an app which, in its entirety, would've been one day's work even with the QA and acceptance testing and going through the Apple App Store upload process. Like, I kept asking if there was any other hidden complexity, and nope, the entire pitch was what you'd give as a pre-interview code-challenge.

An LLM would've spat out the solution to that in less time than I spent with the people who'd asked me to estimate it.
by louthy | 2026-01-17 16:11:12+00:00
> “absolutely everything”

It isn’t absolutely everything, it’s for negligence. If you don’t have basics in place, like independent pen-tests, ISO 27001 audits — or some equivalent — when you’re handling clinical data, then that’s negligence.

If a breach happens and you were seen to have followed best practice, you won’t be found criminally negligent.

That is part of being an executive. The buck stops with you — if you’re an executive, you’d better understand your obligations, you get the big bucks for a reason. Other people in the organisation can be held accountable for criminal acts, but when it comes to criminal negligence, it’s the executives that are liable, because it’s a systemic failure and you’re deemed to be in-charge of the system.
by whynotmaybe | 2026-01-17 16:11:13+00:00
> Anyone with two braincells could see it at the time.
It seems a captain obvious now but it wasn't so at the time. (Or maybe my.braincells.count() < 2)

Many people listened because he wasn't some youtuber doing his research, he was the head of the "Infectious and Tropical Emergent Diseases Research Unit" ad the Faculty of Medicine of Marseille.

I've watched one of his interviews where he stated that people survived in his unit with hydroxychloroquine and that he had numbers to prove it.

When you look at his credentials, and my.braincells.count(), it was hard to identify it as misinformation.
by ajsnigrutin | 2026-01-17 16:11:38+00:00
But it's not a secret, the name has been mentioned in mainsteam media on multiple occasions, and even here, in this thread on HN.

> It's as if people were obsessed by trans people in particular…

Yet, they keep every other name on wikipedia, especially if we're talking about peoples legal names, except if the person was trans for some reason. Wikipedia is the one making exceptions here for one group in particular.
by MontyCarloHall | 2026-01-17 16:11:54+00:00
It's not so much about replacing developers, but rather increasing the level of abstraction developers can work at, to allow them to work on more complex problems.

The first electronic computers were programmed by manually re-wiring their circuits. Going from that to being able to encode machine instructions on punchcards did not replace developers. Nor did going from raw machine instructions to assembly code. Nor did going from hand-written assembly to compiled low-level languages like C/FORTRAN. Nor did going from low-level languages to higher-level languages like Java, C++, or Python. Nor did relying on libraries/frameworks for implementing functionality that previously had to be written from scratch each time. Each of these steps freed developers from having to worry about lower-level problems and instead focus on higher-level problems. Mel's intellect is freed from having to optimize the position of the memory drum [0] to allow him to focus on optimizing the higher-level logic/algorithms of the problem he's solving. As a result, software has become both more complex but also much more capable.

(The thing that distinguishes gen-AI from all the previous examples of increasing abstraction is that those examples are deterministic and often formally verifiable mappings from higher abstraction -> lower abstraction. Gen-AI is neither.)

[0] http://catb.org/jargon/html/story-of-mel.html
by altmanaltman | 2026-01-17 16:12:12+00:00
There is no way AI is making you 10x more productive at the current moment. And if AI is supposed to work well, then that doesn't mean you'll need to put in 10x more hours (because the AI will seamlessly and magically make that effortless). So you'll still be working the same hours even in that scenario.

Overall, I would say, if you want to pursue serious writing, please do it without have LLM generate everything. This blog is just a pattern of vomit-inducing AI-writing cliches and cites nothing of value.

In fact, I went through all your other AI-generated posts and created a meta prompt that you can just paste into ChatGPT and have one of these articles come out, saving you the time to be 10x more or whatever.

---

Write a short essay (800–1,200 words) in a reflective, intellectually restless tone that blends personal observation with a contrarian insight about technology, work, progress, or human behavior.

Constraints and style:

* Open with a concrete hook: a quote, anecdote, tweet, or cultural reference that feels slightly overfamiliar.
* Use clear, confident prose. No emojis. No motivational clichés. No listicles.
* The essay should feel like thinking out loud, not teaching.
* Avoid moralizing. Let implications emerge implicitly.
* Assume an intelligent, online reader who is tired of hype but curious.

Core structure:

1. Start with a relatable observation or irritation about modern life, tech discourse, or self-improvement culture.
2. Introduce a somewhat unexpected but real tech or economics idea (e.g., Jevons paradox, Goodhart’s law, Conway’s law, scaling laws, second-order effects of AI tooling, coordination problems, invisible infrastructure, option value, etc.).
3. Use that idea to reframe a dominant narrative people take for granted.
4. Explore at least one uncomfortable implication for individuals or society.
5. End without a neat conclusion. Close with an open tension, question, or quiet reversal.

Content rules:

* Cite or reference one specific person, company, paper, or concept from tech or economics, but don’t over-explain it.
* No product reviews or tutorials.
* No explicit calls to action.
* No “the future will…” certainty language.

Voice:

* Calm, slightly skeptical, observant.
* Curious rather than cynical.
* Written like a public notebook entry, not a polished op-ed.

The goal is not to persuade, but to sharpen how the reader sees something they already thought they understood.
by pveldandi | 2026-01-17 16:12:57+00:00
Lambda made compute cheap by treating code as ephemeral and execution state as disposable.

LLM inference breaks that model. The execution state (weights shards, CUDA context, KV cache) is expensive and GPU-resident, so “scale to zero” usually means full re-initialization and cold starts.

We’ve been working on a runtime that treats model execution state as the unit of scheduling, not the container or service. Instead of tearing everything down, we snapshot per-GPU execution state and restore it on demand, so models can scale down without paying the full cold-start cost again.

This makes “model = function” closer to practical: fast scale-up, bounded latency, and GPUs shared across many models without keeping them always on.

Let us know
What you think about serverless inference, especially where the abstraction breaks today (KV cache, memory pressure, multi-GPU models, scheduling).
by palata | 2026-01-17 16:14:02+00:00
Critical thinking does not mean that you dismiss the information. It just means that you take the potential bias into account.

The media are often pretty bad at doing this: they will often make some kind of average on what is being said, like "the scientific consensus says that cigarettes are killing you, but a study sponsored by Philip Morris says that they are not, so... well we don't know". Where actually it should be pretty obvious that Philip Morris is extremely biased on that, and the scientific consensus is not.

Not every voice is worth the same. During covid, there was a tendency to relay all kinds of opinions, without making the difference between actual experts and non-experts. Sometimes even saying "this person is a doctor, so they know", which is wrong: being a doctor doesn't make you an expert on coronaviruses or epidemiology.

Whenever we get information, we should think about how much trust we can put into it, how biased the authors maybe (consciously or not), etc. Elon Musk saying that going to Mars can help humanity is not worth much. Because he is rich and successful does not make him right. Yet many people relay "Musk predicts that [...]", as some kind of truth.
by refulgentis | 2026-01-17 16:14:11+00:00
You can Google whatever you need yourself at this point, you told the world I was operating in bad faith based off one sentence from a stranger. You ignored my reply to you. And now you are engaging with me on another reply as if my claim was Grok is uniquely capable of this, when I in fact said the opposite, and the interesting part of the discussion was me pointing out all can do this. Have a good day!
by dijit | 2026-01-17 16:14:21+00:00
> I feel like the term 'engineering' implies bending the rules of reality

I'd phrase it differently: Civil engineering is fundamentally about understanding trade-offs within hard constraints. You have materials of known strength, known wear, and known properties (compression vs shear). It's boring by default because the physics don't budge.

A lot of software engineering, web and SaaS development in particular, hasn't had to confront comparable resource limitations. For decades we've had (for practical purposes) an infinitely fast calculator, practically infinite supply of active working data and and infinite amount of them to chain together. So, without constraints people have just run wild.

But here's where it gets interesting from my perspective: when you point out the resulting bloat (200ms to open a Reddit comment), many developers will defend it not as a technical failure but as correct business prioritisation. "User hardware is cheap, developer time is expensive" or "users will upgrade their devices anyway"—essentially externalising the performance cost onto users rather than absorbing it as an engineering constraint.

That's the fundamental difference. An automotive engineer can't build a 20-tonne car and tell customers to buy stronger roads. But we absolutely can ship bloated software and tell users to buy faster computers, more bandwidth, better phones. And for a long time, we've got away with it.

The question is whether that's still sustainable, or whether we're approaching the limits of what users will tolerate.
by joshribakoff | 2026-01-17 16:15:14+00:00
I’ve been doing game development and it starts to hallucinate more rapidly when it doesn’t understand things like the direction it placing things or which way the camera is oriented

Gemini models are a little bit better about spatial reasoning, but we’re still not there yet because these models were not designed to do spatial reasoning they were designed to process text

In my development, I also use the ascii matrix technique.
by akst | 2026-01-17 16:15:35+00:00
More professional organisations definitely have some kind of CMS, with potentially their own version management (at least for what’s published). But I also don’t think we can fault people for preparing their piece in their preferred writing tool.

I just can’t see existing news agencies doing this of their own volition. As Generating stories themselves is what keeps news agencies in business.

Unless they had a new competitor who had who kept running rings around them with all three features. But it’s going to come back to having better stories or better long form pieces (depending on the publications niche), as that’s ultimately why someone visits their site.

I could however see some 3rd party doing this like an extension that overlays someone’s site or acts as alternative presentation of their content.
by Hamuko | 2026-01-17 16:15:47+00:00
I hope if they decide on fines that they weigh it against the finances of the entire Microsoft Corporation. Really let Microsoft enjoy the synergy that these large-scale acquisitions bring.

Has anyone other than Bobby Kotick and the other previous ABK shareholders benefitted from the acquisition? Xbox hasn’t gotten any more successful as a brand, the consoles aren’t selling, Game Pass subscriptions only keep getting more expensive (which I’d imagine leading to retention issues), they need to put more and more games on rivaling platforms, they've shuttered studios they've previously purchased, and these days I imagine they’d rather use that cash on AI instead.
by diego_moita | 2026-01-17 16:17:22+00:00
> What is NG good for?

Mostly a myth by cooks that think it "heats faster" or "heats with a better distribution of heat".

It is foolish, but many still think so. I personally believe that the only kind of cooking that benefits from NG are round-bottom woks. But they can be substituted by flat-bottom pans without problems.
by t-3 | 2026-01-17 16:18:00+00:00
It's very rare for anything to change as a direct result of protests, but the act of protesting brings issues into the public consciousness that weren't necessarily there before. That is the real driver of change. Changing public opinion directly transforms the Overton Window and limits the acceptable range of opinions and actions politicians can take. Just because no laws were directly changed doesn't mean your protest did nothing.
by goku12 | 2026-01-17 16:18:54+00:00
The question by @wookmaster isn't just valid, if we're to follow your argument, chatbots may as well skip C and write directly in assembly or machine code for maximum efficiency! You seem to put too much faith in chatbots that you forget that correcting AI mistakes in code is a major job now. Even the best coders use AI to only fill in obvious code, not to think on their behalf.
by b450 | 2026-01-17 16:18:54+00:00
Sure. The idea of raw, uninterpreted "sense data" that the empiricists worked with (well into the 20th century) is pretty clearly bunk. Much of philosophy took a turn towards anti-foundationalism, and rationalism and empiricism are, at least classically, notions of the "foundations" of knowledge. I mean, this is philosophy, it's all pretty ridiculous.
by vlowrian | 2026-01-17 16:19:56+00:00
Any source showing people being imprisoned or fined merely for expressing an anti-left opinion then? Which specific law were they convicted under?

The claim that Germany has "no constitutional protection against double jeopardy" is false. Art. 103(3) of the constitution embodies ne bis in idem. The German criminal procedure allows legal remedies (appeals), including Revision (appeal on points of law), which can be brought by both parties before a judgment becomes final. That’s what happened in this case.

In the CJ Hopkins case, the issue was not "criticizing COVID authoritarianism" as such, but the use of a banned symbol under 86a StGB. One can freely say "the government acted authoritarian during COVID"; that kind of political criticism is protected speech under Art. 5 GG.

> and incorrectly use that as a moral justification.

I’m not interested in moral justifications. Morality is a matter of opinion, and you’re entitled to yours just as I am to mine. The same applies to your view of the German constitution.

However, backing up claims about concrete cases with sources helps me (and others) understand which cases you’re referring to and whether they actually support your argument in a way that lets me learn something new (preferably) or whether we'll simply end up acknowledging that we have different opinions on the matter ;)
by SonnyTark | 2026-01-17 16:20:52+00:00
I recently did a higher education contract for one semester in a highly coding focused course. I have a few years of teaching experience pre-LLMs so I could evaluate the impact internally, my conclusion is that academic education as we know it is basically broken forever.

If educators use AI to write/update the lectures and the assignments, students use AI to do the assignments, then AI evaluates the student's submissions, what is the point?

I'm worried about some major software engineering fields experiencing the same problem. If design and requirements are written by AI, code is mostly written by AI, and users are mostly AI agents. What is the point?
Show HN: Git Guide - The open source travel guide on GitHub Actions
by olirex99 | 2026-01-17 16:21:02+00:00
Hello HN!

This is my first open source project, and I’m excited (and a little nervous) to share it with you.

I love traveling, but I’ve lost trust in traditional review sites. Between SEO spam, bots, and paid 5-star ratings, finding genuine recommendations has become a noise-filtering exercise. I wanted a curated list where the "noise" of anonymous internet crowds is replaced by a transparent commit history.

Git Guide is an experiment in building a community-curated travel directory without a traditional backend or database.

* No Database: The "database" is just folders of Markdown files (countries/Italy/Rome/Eat/best_pizza.md).
* No Servers: It runs entirely on GitHub Actions.
* IssueOps: Data isn't entered via a custom form; it's entered via GitHub Issues with YAML templates.

How it works:
1. A user opens an Issue to propose a place.
2. The community votes using GitHub reactions (/).
3. Once a threshold is met (currently testing with net 100 votes) and a moderator approves, a Python script (running via GitHub Actions) triggers.
4. The script validates the location using geopy (OpenStreetMap), creates the Markdown file, commits it to the repo, and updates the README indexes automatically.

I wanted maximum transparency. If a place is added or removed, you can see exactly who proposed it, who voted for it, and the commit that changed it. There is no black-box algorithm sorting the results.

And I need your help! This is my first real foray into open source. The architecture works, but I have a lot of ideas for the future (better frontend, more complex voting logic, decentralized moderation).
by g947o | 2026-01-17 16:22:13+00:00
Slightly off-topic: my PC always comes back to the login screen immediately after I manually use "sleep" from the power menu. I have to to the sleep thing again. Has anyone else run into this issue?

My Mac Mini M4 is on its way. Can't wait to stop dealing with this mess.

(Just this morning, I noticed that the login dialog for network drives, which has worked fine for decades, has misaligned text fields. I don't want to think how this could possibly happen.)