MurphyA/hacker-news huggingface dataset:
by electrosphere | 2026-03-10 11:31:35+00:00
I went into software because I like building things and coming up with solid solutions to business problems that are of use to society. I would not describe myself with "love to code". It's a means to an end to pay bills and have a meaningful career. I think of myself more like a carpenter or craftsman.

I used a coding agent for the majority of my current project and I still got the "build stuff" itch scratched because Engineers are still responsible for the output and they are needed to interface between technical teams, UX, business people etc
by A7OM | 2026-03-10 11:31:37+00:00
“The article is right to separate compute cost from retail price — but the retail price baseline itself is arbitrary depending on where you run the model.
The same capability (e.g. Llama 3.3 70B with tool calling and 128K context) runs $3.00/1M tokens at model developer list price and $0.22/1M at Fireworks AI — a 93% gap for identical specs. That spread makes any “it costs Anthropic X” estimate depend entirely on which reference price you anchor to.
We track this live across 1,625 SKUs and 40+ vendors at a7om.com — the variance across the market is larger than most people realise when they back-calculate provider economics.”
by UqWBcuFx6NV4r | 2026-03-10 11:32:07+00:00
Your reply is based on a 100% bad-faith, intellectually dishonest interpretation of the comment to which you’re replying. You know that.
Nobody claimed that LLM code should be outright accepted.
Also, nobody claimed that open source maintainers have the right to accept or decline based on whichever criteria they choose. To always come back to this point is so…American. It’s a cop-out. It’s a thought-terminating cliche. If you aren’t interested in discussing the merits of the decision, don’t bother joining the conversation. The world doesn’t need you to explain what consent is.

Most of all, I’m sick of the patronising “don’t forget that you can fork the project!” What’s the point of saying this? We all know. Nobody needs to be reminded. Nobody isn’t aware. You aren’t being clever. You aren’t adding anything to the conversation. You’re being snarky.
by ismailmaj | 2026-03-10 11:32:38+00:00
In my experience, tracking objective things like "nutrition" and "sleep hours" is immensely useful to reflect on what went wrong, and tracking subjective things like "mood" or "stress" is useless given hedonic adaptation or heavy swings that make problems obvious, and not need tracking.

What's key is be able to visualize metrics easily on the data and frictionless data entry, I've got a decent setup with iPhone Action + Obsidian + QuickAdd scripts on Obsidian Sync (mobile + laptop). for visualization I use Obsidian Bases and Obsidian notes that run Dataview code blocks and Chart.js, couldn't be happier.

I could track things that are not interesting to reflect on like vitamin D supplementation for accountability but I've never bothered, especially if it's taken ~daily.
by user_7832 | 2026-03-10 11:33:42+00:00
> Why do you buy things you don’t like?

In my case, because it's the only option in some situations.

For eg, I wanted a phone that has an unlockable bootloader and a decent/Qualcomm CPU.

I'm immediately down to Motorola and OnePlus.

If I want future proof performance, I'm down to the OP 15 only.

Yes, I have RSI today, but with any luck that can go away. However, a Vivo will not get a BL unlock tomorrow magically.
by trymas | 2026-03-10 11:33:56+00:00
Exactly!

And to OC you're replying to: window close/minimise/resize were already equidistant from window edge on macOS 15 and probably earlier.

Here is a screenshot (safari in the background, textedit in front): https://pasteboard.co/OeMBTDKGsTx9.png

In MacOS 26 it's only weirder, because as you say - due to squircle window corners, now we have this constantly varying distance to the edge.

EDIT: I "get" apple's fascination to squircle, but why they made it such a big radius. Probably no one would've complained if they just have changed from current ~15-20px rounded corners into ~15-20px squircles, but they went 50px+ on toolbared windows.
by shintomura | 2026-03-10 11:33:59+00:00
Sorry to hear about the layoff, but building this to solve your own problem is the perfect hacker mindset.
I'm not a Staff Engineer, but I heavily rely on AI like Gemini for my own personal projects, so I really appreciate that you offer Gemini Pro in your app. Keeping PII strictly on-device is also a huge trust-builder for a resume tool.
Good luck with the job hunt and the app!
by ben_w | 2026-03-10 11:34:00+00:00
> Models today are static, and human brains don't learn or adapt themselves with anything close to backpropagation.

While I suspect latter is a real problem (because all mammal brains* are much more example-efficient than all ML), the former is more about productisation than a fundamental thing: the models can be continuously updated already, but that makes it hard to deal with regressions. You kinda want an artefact with a version stamp that doesn't change itself before you release the update, especially as this isn't like normal software where specific features can be toggled on or off in isolation of everything else.

* I think. Also, I'm saying "mammal" because of an absence of evidence (to my *totally amateur* skill level) not evidence of absence.
by coldtea | 2026-03-10 11:34:34+00:00
>for the life of my I can't understand why y'all care so much about this.

Because we fucking have to see it every day. And the sloppiness compounds and is indicative of further rot.

Of course the different radii also means different code paths were used, which points to a mess of APIs and frameworks underneath too.

And that's before we add the usability issues (like hard to read labels due to the glass effect and such, or bizare dragging boundaries, etc).

>Doesn't that seem a bit... particular?

Good software is about being particular.

If we wanted any random crap, we'd use any random crap.
by stevekemp | 2026-03-10 11:34:53+00:00
I had a similar epiphany a few years back when I started wearing a step-tracker/sleep monitor.

It was kinda interesting to see how many times I woke up, or track hours, but to be honest I realised after a few months that when my tracker said "You had good sleep", or "You had bad sleep" I was already aware - I woke up smiling, or grumpy depending on how I'd done.

I didn't ever look at the data and think "I want to go to bed now to catch up on the four hours I missed yesterday". I continued to have mostly consistent hours, but if I was doing something interesting I'd stay awake, and if I was tired I'd go to bed earlier naturally. The graphs and data wasn't providing anything of value, or encouraging me to change my behaviour in any significant way.
Show HN: SiClaw – an open-source agent for debugging infrastructure incidents
by SherryWong | 2026-03-10 11:35:29+00:00
Hi everyone, I’m Fred, one of the builders of SiClaw (https://github.com/scitix/siclaw). We work as SREs at an AI infrastructure company, operating GPU clusters and large-scale model infrastructure across heterogeneous compute environments.

As SREs working on AI infrastructure, we spend a lot of time debugging production systems. The hard part usually isn’t fixing the problem. It’s figuring out what’s actually happening.

A typical incident means opening logs, metrics, dashboards, and cloud consoles just to piece together the story. Even something simple like a CrashLoopBackOff can turn into a small investigation. We started wondering if an agent could do that part of the work.

We recently open-sourced SiClaw:
https://github.com/scitix/siclaw

There’s also a small project site with demos here:
https://www.siclaw.ai/

Recently we’ve been experimenting with OpenClaw-style agents for infrastructure diagnostics.

OpenClaw today reminds us a bit of early React: the primitives are powerful, but the patterns for real-world usage are still emerging.

In practice, most teams are still figuring out how to connect agents to real production environments.

With SiClaw, we’re exploring what that might look like for DevOps and SRE teams.

One thing that surprised us is how quickly SiClaw became part of our daily workflow.

Now when something weird happens in our infrastructure, we usually start by asking it to investigate instead of opening five dashboards and running a bunch of kubectl commands.

For example, the other day a pod went into CrashLoopBackOff in a Kubernetes cluster.

Normally this means digging through logs and configs to figure out which dependency failed.

With SiClaw we just described the issue and let it investigate. It gathered logs, checked service dependencies, and proposed a ranked list of root-cause hypotheses.

We’d love people here to try it, break it, and see where it fails.

In particular, we’re curious how it performs on real infrastructure incidents — things like pod crashes, dependency failures, or weird configuration issues.

Looking forward to feedback.
Depression is getting ahead of me
by ppap3 | 2026-03-10 11:35:32+00:00
For a while, I've been worried about everything going on...
I will try to keep it short.

I am not dealing well with what's going around the world right now...

We have been desensitized after years of polarisation campaigns and no one cares about anything because it is now hard to pay the bills.

There is no need for these wars.
There is no need to believe on those promising solutions on the cost of others.

There is no ethnical group or category of people responsible for this crisis. There is no need for more oil. There is no need for more power.

Although people will always disagree on many things, there is no need to accept the place things are going right now.

There is no need for going back 100 years on progress and peace.
by Neywiny | 2026-03-10 11:36:12+00:00
Can't comment on the wire bonding quality but yes you're not supposed to sonic wash anything with an oscillator. This includes ultra and mega sonic. I had always thought it was because you could damage the crystal or mems structures, so color me surprised to see this failure mode, though there still could be a shift in frequency that the scoping wasn't able to see.

I tried looking at an exemplar ECS tcxo datasheet and didn't see anything in there about washing which is surprising but it also doesn't say not to crush it with a hammer so maybe it was assumed. That's bad on them.

As for SMA to 0.1" headers: yes these are very cursed. But RF designers love putting SMAs for every connector on an eval board (power, enable, whatever) and those come in handy.
by hparadiz | 2026-03-10 11:36:22+00:00
It's (c) copyright of the operator.

For one simple reason. Intention.

Here's some code for example: https://i.imgur.com/dp0QHBp.png

Both sides written by an LLM. Both sides written based on my explicit prompts explaining exactly how I want it to behave, then testing, retesting, and generally doing all the normal software eng due diligence necessary for basic QA. Sometimes the prompts are explicitly "change this variable name" and it ends up changing 2 lines of code no different from a find/replace.

Also I'm watching it reason in real time by running terminal commands to probe runtime data and extrapolate the right code. I've already seen it fix basic bugs because an RFC wasn't adhered to perfectly. Even leaving a nice comment explaining why we're ignoring the RFC in that one spot.

Eventually these arguments are kinda exhausting. People will use it to build stuff and the stuff they build ends up retraining it so we're already hundreds of generations deep on the retraining already and talking about licenses at this point feels absurd to me.
by Random403 | 2026-03-10 11:36:28+00:00
I've been building ANIMA, a locally-hosted persistent AI agent that maintains beliefs and memory across sessions. Qwen3-32B Q3_K_M instruct via llama.cpp, Vulkan on consumer AMD hardware (RX 7900 XT). No cloud, no API.
The project runs continuously and explores its own belief graph between interactions. Every run ("soak") logs telemetry: belief growth, graph structure, dream synthesis, and exploration behavior.
I'm documenting the system as it develops — including the failures.
Some examples from the last week:

belief pollution from unfiltered memory ingestion
a base-model swap experiment that collapsed in hours
a governance gap that allowed self-reinforcing dream loops

The newest article looks at three slow failure modes persistent agents run into: echo chambers, belief crystallization, and curiosity collapse. All three can appear as normal system activity until the structure is examined.
The series is a running log rather than a retrospective. Real failures, real telemetry, no hype.
by Signez | 2026-03-10 11:38:30+00:00
Hm, Singapour looks more like "one of their base"; they will have offices in Paris, Montréal, Singapour and New York (according to both this article and the interview Yann Le Cun did this morning on France Inter, the most listened radio in France).

Of course, each relevant newspaper on those areas highlight that it's coming to their place, but it really seems to be distributed.
by A7OM | 2026-03-10 11:39:05+00:00
Building ATOM — a global price benchmark for AI inference.
The problem: inference pricing is fragmented across 40+ vendors, 6 modalities, and constantly changing. Developers and FinOps teams have no reliable way to compare prices or know if they’re overpaying.
What we built: a live index tracking 1,625 SKUs across 40+ vendors — text, image, audio, video, voice, and multimodal. We also publish 14 weekly benchmark indexes and an MCP server that lets agents query live pricing directly inside Claude, Cursor, or Windsurf.
The data is striking — the same model can cost 5-10x more depending on which vendor you use. Open-source models price ~82% below proprietary. Neocloud providers run ~75% cheaper than cloud marketplaces.
Free to explore at a7om.com. MCP is free to connect, PRO unlocks all 8 tools.
by bronlund | 2026-03-10 11:39:40+00:00
Yeah, I could have formulated that in another way - 'missing out' would be a better term. I do not mean to be condescending in the meaning that I look down on people that have other preferences than me. Take books as an example. I do not believe that people that can't read is of any lesser value than me, and whether you can read or not, is a poor indication of how intelligent you are - or how happy you are for that matter. Sure, information is important, but there are a lot of different types of information and a lot of different ways of acquire it - so I do not believe that my way is any better than someone else's. That being said, me being able to read and appreciate books, do believe that people who can't, are missing out.

We know books has been used for both good and evil, but I still think books are a wonderful invention. Same with television or the internet, the quality of the content on there doesn't really take anything away from the fact that the technology in itself is absolutely amazing. In hindsight - it has been a minute since Gutenberg - how the society has adopted the written word do have real implications for the people living in it though. If you can't read today, you will struggle. Not because there are anything wrong with you, but because the system more or less take it for granted that you can.

And it is going to be the same with AI, but even more so. The ones who learns to master it will dominate those who don't. It will create a new form of class divide, where access to tokens and knowing how to use them, is going to be the main drivers. AI is still in it's early stages and we see that not everything is alright with it, take for instance the economics around it or the environmental impact it has. But still, I do believe that it is an amazing invention, and that if you do not embrace it, you are missing out.
by jungard | 2026-03-10 11:40:40+00:00
180+ production-ready skills and plugins for Claude Code, OpenAI Codex, Gemini CLI, and OpenClaw — reusable expertise bundles that transform AI coding agents into specialized professionals across engineering, product, marketing, compliance, and more.

Skills are modular instruction packages (plugins) that give AI coding agents domain expertise they don't have out of the box. Each skill includes a SKILL.md (instructions + workflows), Python CLI tools, and reference documentation — everything the agent needs to perform like a specialist.

One repo, four platforms: Works natively as Claude Code plugins, OpenAI Codex CLI and agents, Gemini CLI skills, and OpenClaw skills. All 237 Python tools are stdlib-only and verified working.
by balintkorosi | 2026-03-10 11:40:53+00:00
I spent the first part of my career gaming search results. Exact-match domains, spun articles, AdSense. It worked until Google banned the
account permanently with no appeal.

The second part was building conversion infrastructure for online
education companies. Better work, but I'm not sure it was a different game.

The article is an attempt to trace the pattern honestly. I don't think it resolves cleanly, so I didn't try to make it.
by orthoxerox | 2026-03-10 11:41:30+00:00
Scott took it too literally. See also how the broader rationalist community took issue with Sam Kriss for inventing a not-obviously-fake historical figure.

The biggest takeaway for me is that you shouldn't expect to succeed as a manager by meeting (or exceeding) KPIs. It's about as effective as being a "nice guy" and expecting intimacy in return.

The KPIs are there for assigning blame, not for identifying key personnel. You can game them to increase your compensation if you are already doing something that an even bigger manager finds useful and important. Conversely, you can get away with half-assing every official performance indicator as long as you keep delivering the real thing.
by shintomura | 2026-03-10 11:41:34+00:00
Building a terminal UI in pure C is a fantastic challenge!

I wrote a simulation game in C about a decade ago, but I relied heavily on a wrapper library to handle all the complex UI and system loops because it was too daunting.

Seeing you tackle ncurses and GStreamer directly for a learning project is really impressive. It’s a great, hardcore way to truly understand what's happening under the hood. The feature set, especially the radio stream playback, looks surprisingly complete for a terminal app.

Great work, and thanks for sharing your learning journey!
Ask HN: P2P Trust Network for reviews in the 2020s
by JCharante | 2026-03-10 11:41:44+00:00
I'm wondering why nobody has created a review network where it only shows you reviews from someone you're connected to. Reviews would be sorted by the number of degrees of separation. Bot accounts wouldn't work because you'd need someone to endorse the bot account, and if you endorse a bot account it'd be easy to end up on a user blacklist. If you endorse someone on a blacklist then people who endorsed you would be warned and asked if they want to disconnect from you. Of course there's tons of different ways to implement the propagation of positive & negative feedback re: trust within the network.

Is it just the network effect? Would piggybacking off established social media networks not work?
by Unearned5161 | 2026-03-10 11:41:52+00:00
I have a pet peeve with the concept of "a genuinely novel discovery or invention", what do you imagine this to be? Can you point me towards a discovery or invention that was "genuinely novel", ever?

I don't think it makes sense conceptually unless you're literally referring to discovering new physical things like elements or something.

Humans are remixers of ideas. That's all we do all the time. Our thoughts and actions are dictated by our environment and memories; everything must necessarily be built up from pre-existing parts.
by trymas | 2026-03-10 11:42:03+00:00
my other comment on this post: https://news.ycombinator.com/item?id=47321852

Traffic light buttons were already equidistant to the edge of the window. Now they are trying to center circles in squircles[1], breaking window edges and draggability, etc.

> It creates a larger inconsistency than the "consistency" it supposedly brings.

That's why I am baffled (as many commenters here) - how did this went out all the way to release, instead of ending as an experiment at design floor.

[1] parent comment: https://news.ycombinator.com/item?id=47321065
by Oras | 2026-03-10 11:42:04+00:00
> But this is not an applied AI company.

There is absolutely no doubt about Yann's impact on AI/ML, but he had access to many more resources in Meta, and we didn't see anything.

It could be a management issue, though, and I sincerely wish we will see more competition, but from what I quoted above, it does not seem like it.

Understanding world through videos (mentioned in the article), is just what video models have already done, and they are getting pretty good (see Seedance, Kling, Sora .. etc). So I'm not quite sure how what he proposed would work.
by myself248 | 2026-03-10 11:42:28+00:00
I've always been cautioned against ultrasonic cleaning of boards that have crystal oscillators, and indeed it's in most XO datasheets.

I've also heard that one shouldn't trim the leads of a through-hole XO before soldering it into the board, since the mechanical shock of the lead breaking can ring the whole package and similarly shake it apart. I'm curious if anyone here has seen that in practice!
by BoredPositron | 2026-03-10 11:42:36+00:00
I definitely see the appeal, but I can’t help feeling it’s the same trap we fall into with product data and telemetry. So much of what we collect ends up being noise; my worry is that if we dig through enough of it, we’ll eventually talk ourselves into seeing a pattern that isn't actually there.

I’ve started applying this to my personal life by using Memos (https://usememos.com/ - OSS and selfhosted) for tweet style journaling and only tracking outlier data for sleep, fitness, and health. What over tracking and over planning taught me is that anything normal is effectively just noise. If the data isn't an anomaly, it isn't actionable.
by nachocoll | 2026-03-10 11:42:45+00:00
The slot machine analogy is apt — the variable reward schedule of "sometimes it works perfectly, sometimes it generates nonsense" creates exactly the compulsive checking behavior described. But I think the addiction framing points to something structurally important beyond the psychological hook.

The deeper problem with slot-machine vibe coding is what happens to the codebase over time. Each "spin" produces output without accountability for whether it fits the broader system design. You get dopamine hits from features that appear to work, but accumulated inconsistency, undocumented assumptions, and untraceable decisions pile up below the surface.

This is why the Agile Vibe Coding Manifesto emphasizes "traceable intent over opaque implementation" as a core value — not just for code quality, but because without it you can't distinguish a genuine win from a lucky spin. When every change is opaque, you're always gambling even when you feel like you're in control.

Relevant for anyone trying to build sustainable workflows with AI-assisted development: https://agilevibecoding.org