MurphyA/hacker-news huggingface dataset:
by safety1st | 2026-02-10 05:40:49+00:00
The problem with the slippery slope argument is that it's a fallacy. That is the origin of the term, it describes a type of argument that's logically invalid. Yeah I am concerned that things could get worse and this might be the first step to broader censorship that we don't want, but a fallacious argument alone is unpersuasive to anyone who tries to form opinions rationally. Specific evidence needs to exist for the claim for it to be convincing.

Since slippery slopes are invalid by nature they're a type of argument that can be made for pretty much anything. If the case here is that a slippery slope is being used to defend pornographers and the "right to goon," I'm not on board. I think we have a long way to go to roll back porn's grip on teens and adults alike and reduce the harm it does to relationships, and this is just the beginning. Take for instance how Instagram at this point is basically a lead generation service for fraudulent OnlyFans businesses that sell parasocial relationships with a porn model's image where the customers aren't actually talking to her, they're talking to a team of guys in a basement in Eastern Europe somewhere. I think you shut down OnlyFans, you prosecute Meta, and to the extent where Discord is doing the same thing IG is, you prosecute Discord too. There's a long long list of things that needs to happen and shutting down the porn pipeline for teens on Discord is just the beginning.
by kbenson | 2026-02-10 05:40:57+00:00
> I said monorepo and monodeploys in this thread

And that helps when you are dealing with schema changes that need to be rolled out at AWS, your local DB, a Kafka cluster, how? The whole point of this article was how to approach the problem when there are different components in the system which make a monorepo and what it provides for this infeasible or impossible.

> I know it's not "practical" but that's not my point. My point is that there's a huge portion of problems with "systems" that are literally obviously solvable and with obvious solutions it's just I'm too lazy to write a production grade database from scratch, sorry guys.

The article talks about database solutions that help with this problem.

I'm uncertain how to interpret your responses in light of the article, when they seem to be ignoring most of what the article is about, which is solving exactly these problems you are talking about. Is your position that we shouldn't look for solutions to the harder problems because some people aren't even using the solutions to the easy problems?
by superactro | 2026-02-10 05:40:59+00:00
This resonates with what I've been working on. The gap between "AI exists" and "I can use AI privately in my home/office" is still huge for most people.

I took a different but complementary approach with ClawBox (https://openclawhardware.dev) — instead of an OS, it's a pre-configured hardware box (Jetson Orin Nano + NVMe SSD) that ships with everything ready to go. Plug it in, connect to your network, and you have a local AI assistant running 24/7 at ~15W.

The insight I keep coming back to: most people don't want to configure a Linux distro or set up model serving — they want something that "just works" like a router. Your approach of making the OS handle AI as a system primitive is really interesting for the technical crowd though.

Question: have you considered supporting GPU-accelerated inference? TinyLlama on CPU is functional but the experience gap vs. running even a 7B model on a Jetson or similar SoC is massive. 40 TOPS of dedicated AI compute changes what's practical at the edge.
by wincy | 2026-02-10 05:41:29+00:00
I asked ChatGPT about how shipping works at post offices and it gave a very detailed response, mentioning “gaylords” which was a term I’d never heard before, then it absolutely freaked out when I asked it to tell me more about them (apparently they’re heavy duty cardboard containers).

Then I said “I didn’t even bring it up ChatGPT, you did, just tell me what it is” and it said “okay, here’s information.” and gave a detailed response.

I guess I flagged some homophobia trigger or something?

ChatGPT absolutely WOULD NOT tell me how much plutonium I’d need to make a nice warm ever-flowing showerhead, though. Grok happily did, once I assured it I wasn’t planning on making a nuke, or actually trying to build a plutonium showerhead.
by superactro | 2026-02-10 05:42:14+00:00
Really cool project. The offline-first approach for field computing is something I've been thinking about a lot.

We built something adjacent — ClawBox (https://openclawhardware.dev) is a pre-configured Jetson Orin Nano box meant to be an always-on AI assistant for home/office. Different use case (always-on personal assistant vs. field computer) but similar philosophy: local-first, no cloud dependency, everything runs on-device.

One thing we found is that having 40 TOPS of AI compute on an ARM SoC at ~15W opens up capabilities that people don't expect from small hardware. Running 7B parameter models locally, doing browser automation, managing multi-platform messaging — all without ever calling an API.

How are you handling model serving on the device? Curious if you've looked at running something like llama.cpp with the Jetson's GPU acceleration, since your modular approach could work really well with different compute modules.
by superactro | 2026-02-10 05:42:27+00:00
Great collection. One resource I would add: if you want to skip the setup entirely, we ship ClawBox (https://openclawhardware.dev) — a Jetson Orin Nano 8GB with 128GB NVMe SSD, pre-loaded with OpenClaw and ready to run local LLMs out of the box. €299, draws about 15W under load.

The biggest barrier to local AI adoption is not the models or the software — it is the setup friction. Most people who would benefit from running local models (privacy-conscious professionals, small firms, developers) give up somewhere between "install CUDA" and "why is my quantized model outputting garbage." Pre-configured hardware solves that.

We are launching on Product Hunt on Feb 17. Would love feedback from this community.
by superactro | 2026-02-10 05:42:42+00:00
Interesting approach — hardware-aware model recommendations solve a real pain point.

We took this a step further with ClawBox (https://openclawhardware.dev): instead of telling users which model to download, we ship them hardware with the right models already installed and optimized. It is a Jetson Orin Nano 8GB + 128GB NVMe with OpenClaw pre-configured — plug it in, connect to your network, and you have a local AI assistant running in minutes.

The challenge with recommendation engines is that even when you tell someone "use Q4_K_M Mistral 7B on your RTX 3060," they still need to install ollama, configure VRAM allocation, deal with context length settings, etc. For non-technical users, the gap between "know which model" and "actually running it" is still huge.

That said, your tool is really valuable for the tinkerer crowd who enjoys the setup process. Different audiences, complementary approaches.
by drdeca | 2026-02-10 05:42:52+00:00
The universe is not obligated to appeal to your aesthetic tastes in its innermost functioning.

Maybe you aren’t going to be satisfied with the sort of complicated mathematics which appears to be correct (or, on the right track).

If you have complaints about the aesthetics of how the universe works, take it up with God.

Personally, I think there is a lot of beauty to be found in it.

I’ll admit that there are a few parts that go against my tastes (I don’t like needing to resort to distributions instead of proper functions), but that’s probably just intellectual laziness on my part.
Why Every Business Must Engage with AI – and How to Do It Right
by danelrfoster | 2026-02-10 05:43:25+00:00
Title: Why every business should engage with AI (the real question is how deep)

AI is no longer an experimental technology. It’s becoming a baseline capability for modern businesses. The real question most teams should be asking is not “should we use AI?” but “how deeply should we engage with it?”

I’ve talked to many founders, CTOs, and operators over the past couple of years. The hesitation around AI usually comes from two places:

Teams that haven’t really tried AI and feel comfortable sticking with existing workflows.

Teams that rushed into AI, spent money, got disappointing results, and walked away.

Both often conclude: “AI isn’t for us.” That conclusion is understandable — but increasingly risky.

Many organizations still rely on manual or semi-manual processes: document handling, internal knowledge search, reporting, customer support triage. Everything appears to “work,” but it’s slow, hard to scale, and dependent on headcount rather than leverage.

AI isn’t magic, but it is a force multiplier. Ignoring it means accepting structural inefficiency while competitors gradually improve speed, quality, and decision-making.

One misconception I see a lot: that engaging with AI means building custom models or hiring a large ML team. In practice, AI today is closer to what spreadsheets or search once were — general-purpose tools that most teams can benefit from without deep specialization.

Instead of treating AI adoption as a yes/no decision, it’s more useful to think in levels.

Level 1: AI literacy
Every company should be here. This is about enabling people, not systems: using tools like ChatGPT for research, drafting, summarization, and analysis; teaching teams how to verify outputs; and setting clear rules around sensitive data. Low risk, high return.

Level 2: AI-assisted workflows
Here AI becomes part of everyday processes without replacing humans. Examples include internal AI assistants over documentation, AI-supported customer support, content generation, or analytics help. This is where many teams see the best ROI with relatively low complexity.

Level 3: AI-driven systems
At this level, AI is embedded into products or core operations: RAG systems, agent workflows, forecasting, personalization. This requires clean data, evaluation, and operational discipline. Many failures happen here not because AI doesn’t work, but because teams skip the earlier foundations.

The biggest risk isn’t “doing AI wrong.” It’s not building AI fluency at all while the rest of the market moves forward.

Once AI systems are in production, new problems appear: cost control, reliability, hallucinations, latency, silent regressions. At that point, AI stops being a demo and becomes infrastructure.

For teams already dealing with production AI systems, we’ve been thinking a lot about observability and reliability in this space. Some of that work is shared here:
https://optyxstack.com/ai

Curious how others on HN think about the “depth” question when it comes to AI adoption.
by jwrallie | 2026-02-10 05:43:30+00:00
I am interested in a way to capture audio not only from the mic, but also from one of the monitor ports so you could pipe the audio you are hearing from the web directly for real-time transcription with one of these solutions. Did anyone manage to do that?

I can, for example, capture audio from that with Audacity or OBS Studio and do it later, so it should be possible to do it in real time too assuming my machine can keep up.
by sien | 2026-02-10 05:43:52+00:00
That's very true.

Singapore has done extremely well economically.

But it's not cool. That's something else.

Tokyo, for example, is cool, fashion, music, films and computer games come out of Tokyo.

But that's very hard to say of Singapore.

Perhaps it's like Luxembourg and Lisbon.

Admittedly the link at the top is from Marginal Revolution where 'cool' may mean economically successful and interesting for policy makers.
by liu3hao | 2026-02-10 05:44:22+00:00
Thank you! I haven't tried circuitscript with LLMs yet, but I do plan to do so in the future. As the language is similar to python, hopefully it is able to work well syntactically. But of course, the correctness and accuracy of the circuit would be more challenging to get right. Your work at https://www.mikeayles.com/#tokn is very cool! This will be very useful when I try LLMs!

I have seen both atopile and tscircuit too and they are really complete it terms of the entire PCB design flow compared to circuitscript, which is just focused on the schematic part.

The ato language works well for specifying parts and parameters, but is very basic for defining connectivity. This means that the designer will have minimal control of how the final schematics will turn out. Of course, atopile focuses on the complete design flow, so having nice schematics could be a want rather than a need. For myself, especially in my professional job, clear and well-organized schematics are important not just for understanding and creation, but also troubleshooting the design and eventually improving the design.

I did try some automatic layout features for the schematic that balances giving the user some control over the overall schematic, but this was really hard to get right. In the end, I went with the logo/turtle approach and give full layout control to the user.

For tscircuit, I like typescript as a programming language and circuitscript itself is created in typescript. But typescript is really clunky to use for schematic design. Coupled with react, there is a lot of extra noise/symbols when looking at the code and for complex designs, it will be challenging to understand and troubleshoot. Moreover, I believe that typescript/javascript is less common among electronics engineers compared to python (which circuitscript is based on). The simplicity of python is one of the reasons it is popular and that is what I believe might help to lower the learning curve for circuitscript.
by kbenson | 2026-02-10 05:44:47+00:00
I would only loosely categorize this as technical writing, depending on how you categorize technical writing. It seems much more a survey of problems and discussion piece, with notes about projects making inroads on the problem. It's definitely not a "this is how you solve this problem, and these are the clear steps to do so" type of article. Maybe that's some of the disconnect in how we view it. If I was hoping that this communicated a clear procedure or how to accomplish something, I would be disappointed. I don't think that was their intention.

I came away with some additional understanding of the problem, and thinking there are various nascent techniques to address this problem, none of them entirely sufficient, but that it's being worked on from multiple directions. I'm not sure the article was aiming for more than that.
Show HN: Agx – A Kanban board that runs your AI coding agents
by Mendrika | 2026-02-10 05:44:54+00:00
agx is a kanban board where each card is a task that AI agents actually execute.

agx new "Add rate limiting to the API"

That creates a card. Drag it to "In Progress" and an agent picks it up. It works through stages — planning, coding, QA, PR — and you watch it move across the board.

The technical problems this solves:

The naive approach to agent persistence is replaying conversation history. It works until it doesn't:

1. Prompt blowup. 50 iterations in, you're stuffing 100k tokens just to resume. Costs explode. Context windows overflow.

2. Tangled concerns. State, execution, and orchestration mixed together. Crash mid-task? Good luck figuring out where you were.

3. Black box execution. No way to inspect what the agent decided or why it's stuck.

agx uses clean separation instead:

- Control plane (PostgreSQL + pg-boss): task state, stage transitions, job queue

- Data plane (CLI + providers): actual execution, isolated per task

- Artifact storage (filesystem): prompts, outputs, decisions as readable files

Agents checkpoint after every iteration. Resuming loads state from the database, not by replaying chat. A 100-iteration task resumes at the same cost as a 5-iteration one.

What you get:
- Constant-cost resume, no context stuffing

- Crash recovery: agent wakes up exactly where it left off

- Full observability: query the DB, read the files, tail the logs

- Provider agnostic: Claude Code, Gemini, Ollama all work

Everything runs locally. PostgreSQL auto-starts via Docker. The dashboard is bundled with the CLI.
by galaxyLogic | 2026-02-10 05:45:37+00:00
I don't think there is any mystery to what we call "consciousness". Our senses and brain have evolved so we can "sense" the external world, so we can live in it and react to it. So why couldn''t we also sense what is happening inside our brains?

Our brain needs to sense our "inner talk" so we can let it guide our decision-making and actions. If we couldn't remember sentences, we couldn't remember "facts" and would be much worse for that. And talking with our "inner voice" and hearing it, isn't that what most people would call consciousness?
by Aenorium | 2026-02-10 05:45:46+00:00
In the context of the US, what right would, in theory, prevent a national law that regulates or prohibits the capture of biometric information of people while out in public? There is a common quip that some rely on that "there is no expectation of privacy while in public", but that's an aspirational statement rather than a strictly legal one. Setting aside government mass surveillance for the moment, could the US regulate private mass surveillance that records people's identifiable biometrics?
by Snacklive | 2026-02-10 05:46:58+00:00
I have a problem vibecoding and frustrating experiences with coworkers that are a little too much into it.

I can use AI to help me explore libraries or to replace a search, generate small snippets here and there, or even scripts that i occasionally need. But i can't vibecode, i don't know how to let go, i babysit too much, i read the code and i feel uneasy if I don't understand what I'm building, or why I'm building it in a certain way, i need to understand how the pieces work to make a whole
Show HN: MCP Orchestrator – Spawn parallel AI sub-agents from one prompt
by Ask149 | 2026-02-10 05:47:26+00:00
I built an open-source MCP server (TypeScript/Node.js) that lets you spawn up to 10 parallel sub-agents using Copilot CLI or Claude Code CLI.

Key features:
- Context passing to each agent (full file, summary, or grep mode)
- Smart timeout selection based on MCP servers requested
- Cross-platform (macOS, Linux, Windows)
- Headless & programmatic — designed for AI-to-AI orchestration

Example: give one prompt like "research job openings at Stripe, Google, and Meta" — the orchestrator fans it out to 3 parallel agents, each with their own MCP servers (e.g., Playwright for browser), and aggregates results.

Install: npm i @ask149/mcp-orchestrator

This is a solo side project. Would love feedback on:
- What CLI backends to support next (Aider, Open Interpreter, local LLM CLIs?)
- Ideas for improving the context-passing system
- What MCP server integrations would be most useful

PRs and issues welcome — check CONTRIBUTING.md in the repo.
by noobermin | 2026-02-10 05:47:59+00:00
Close to a true perspective, especially if you came as one of those suits. There is soul in singapore, it's just actively co-opted by the govment or actively suppressed. You have to actually live here to see it and have friends who are not expats or mainland chinese (which is hard I think for most foreigners who come), it definitely isn't the same as the soul that you see in america or europe, I guess. The work life (a bit more chill than Japan or Korea but still harsh like the asian norm) doesn't help.
by bitwize | 2026-02-10 05:48:42+00:00
Such an interlocutor was historically known as the systems analyst, but historically we've put programmers in that position, where they tend to do poorly.

https://www.linkedin.com/pulse/tail-wagging-dog-tim-bryce/

Systems analysis is about to make a roaring return, as the need for human programmers wanes thanks to LLMs generating all the code.
by andyferris | 2026-02-10 05:49:37+00:00
The hint from quantum field theory (and things like lattice gauge theory) is that charge emerges from interesting topological states/defects of the underlying field (by "interesting topological shapes" I mean - imagine a vortex in the shape of a ring/doughnut). It's kind of a topological property of a state of the photonic field, if you will - something like a winding number (which has to be an integer). Electric charge is a kind of "defect" or "kink" in the photonic field, while color charge (quarks) are defects in the strong-force field, etc.

When an electron-positron pair is formed from a vacuum, we get all sorts of interesting geometry which I struggle to grasp or picture clearly. I understand the fact that these are fermions with spin-1/2 can similarly be explained as localized defects in a field of particles with integer spin (possibly a feature of the exact same "defect" as the charge itself, in the photonic field, which is what defines an electron as an electron).
by panny | 2026-02-10 05:50:59+00:00
I see a lot of people here saying things like:

>ah, they're so dumb, they don't get it, the anti-LLM people

This is one of the reasons I see AI failing in the short term. If I call you an idiot, are you more or less likely to be open minded and try what I'm selling? AI isn't making money, 95% of companies are failing with AI

https://fortune.com/2025/08/18/mit-report-95-percent-generat...

I mean, your AIs might be a lot more powerful if it was generating money, but that's not happening. I guess being condescending to the 95% of potential buyers isn't really working out.
by written-beyond | 2026-02-10 05:53:13+00:00
Codex cli is a very performant cli though, better than any other cli code assistant I've used.

I mean does it matter what code it's producing? If it renders and functions just use it. I think it's better to take the L on verbose code and optimizing the really ugly bits by hand in a few minutes than be kneecapped every 5 hour by limits and constant pleas to shift to Sonnet.
by chasil | 2026-02-10 05:55:11+00:00
I do love Oracle Database.

Expensive as it is ($17,500/core on SE2, $47,500/core on Enterprise), the archived logs are mostly rock solid, and standby/Dataguard nodes are always correct (assuming that you FORCE LOGGING or are not stupid about NOLOGGING).

It is expensive, and I really should learn a better alternative than SQLite.
by jibal | 2026-02-10 05:55:26+00:00
I'm a highly literate reader and writer of technical topics, and there are a lot of bad technical writers who think they aren't. Except perhaps for the title, which is way too narrow, the article is excellent writing about a technical topic (which is quite different from technical writing)--but then I actually read it, so I know that he doesn't talk about a dichotomy between FP and systems, but rather between single programs and systems, and he explicitly says that his points aren't restricted to FP, but that because FP addresses the single program issues so well, FP programmers are particularly prone to missing the problem.
by ocdtrekkie | 2026-02-10 05:57:50+00:00
Like, as someone who grew up in the formative years of the Internet, I get it, but also, what we have is incredibly bad and it's very possible we'd be better off without it. The tech crowd does not want to admit that every single effing website that lets you post images has CSAM. Every one! The tech crowd doesn't want to admit an entire generation of our kids are screwed beyond belief by social networking. The tech crowd doesn't want to admit that Donald effing Trump got elected because our population is incredibly easy to manipulate.

Our society is very, very screwed up right now and it's very likely the principle cause is that we gave Google and Facebook complete blanket immunity, and that was an incredibly moronic thing to do.

If something needs Section 230 to exist, it shouldn't exist. Full stop.