MurphyA/hacker-news huggingface dataset:
by chneu | 2026-03-10 17:26:14+00:00
Having children in 2026 is a very selfish and completely asshole thing to do.

We're destroying the world as fast as we can. Our emissions are going up FAST. We're melting every bit of ice on the planet. We're deforesting for more beef at an insane rate. We're using more plastic than ever.

If you have a child today, you're a selfish asshole. Their world is going to be so, so bad. Famine, crazy weather patterns, mass human migration, wars, resource scarcity. This is what you're gifting your children.

Inb4 everyone has their excuse. Nobody is willing to sacrifice, at all, for the greater good.

Everyone wants to blame boomers but nobody since has done SHIT and when young folks stand up, suing for their right to a livable environment, most people tell them they're being "doomers" or "cynical". We're just as bad as our grandparents, we just have more excuses than they do.
by PowerfulWizard | 2026-03-10 17:26:23+00:00
On judging vs. making, also someone has to take time away from development to do code review. If the code being reviewed is written by someone who is involved and interested then at least there's a benefit to training and consensus building in discussing the code and the project in the review phase. The time and energy of developers who are qualified to review is quite possibly the bottleneck on development speed too so wasting review time will slow down development.

For AI generated code if previous PRs aren't loaded into context then there's no lasting benefit from the time taken to review and it's blank slate each time. I think ultimately it can be solved with workflow changes (i.e. AI written code should be attributed to the AI in VCS, the full trace and manual edits should be visible for review, all human input prompts to the AI should be browsable during review without having scroll 10k lines of AI reasoning.)
Show HN: Emotive Engine – I wrote 8 elemental shaders to prove one pattern works
by emotiveengine | 2026-03-10 17:26:25+00:00
Live demo (click any gesture, no install): https://joshtol.github.io/emotive-engine/examples/3d/element...

Open-source character animation engine, MIT licensed. About 6 months of work. Renders animated mascots in Canvas 2D or WebGL 3D.

For v3.4.1 I wrote 8 elemental shader systems from scratch: fire, water, ice, electricity, earth, nature, light, void. Each one has its own GLSL fragment shader, GPU-instanced models, an overlay that paints onto the mascot mesh, bloom control, and atmospherics. Ice does Snell's law refraction with chromatic dispersion. Electricity is 3-scale 3D Voronoi edge-distance. Fire decouples color from alpha so additive stacking doesn't wash out.

I didn't need 8. I built them to see if the architecture actually repeats. Every element follows the same template: factory, instanced material, overlay shader, gesture configs, registration. 161 gestures across all 8, composed from about 12 archetypes (crown, helix, vortex, barrage, pillar, etc).

The idea is that you can clone one element, swap colors and models, and have something working in days. If you write shaders, go deep on the material. If you don't, the defaults are fine.

Hit shuffle in the demo. Press G for the GPU monitor.

`npm install @joshtol/emotive-engine`
by harperlabs | 2026-03-10 17:26:59+00:00
Cross-session memory drift is a great addition -- we've seen exactly this. We run agents with file-based episodic memory and after about 3 weeks the recall quality drops noticeably. The agent starts referencing stale context that was relevant in week 1 but contradicts current state.

Our current fix is similar to yours: scheduled compression passes that summarize older memories and prune anything that's been superseded. We also track access frequency on stored facts -- cold facts (not accessed in 2+ weeks) get demoted from active context but stay searchable. That alone cut our context pollution by roughly 40%.

The checkpoint gates for cascade failures are smart. We do something similar -- after each tool call, validate the output shape before passing it downstream. Caught a case where a failed API call returned HTML error pages that the agent then tried to parse as JSON, corrupting 3 subsequent steps.

Will check out the playbook. Thanks for sharing.
by rectang | 2026-03-10 17:27:23+00:00
I agree that under default settings, LLMs introduce way too many changes and are way too willing to refactor everything. I was only able to get the situation under control by adding this standing instruction:

---
applyTo: '**'
---
By default:
Make the smallest possible change.
Do not refactor existing code unless I explicitly ask.

Under this, Claude Opus at least produces pretty reliable code with my methodology even under surprisingly challenging circumstances, and recent ChatGPTs weren't bad either (though I'm no longer using them). Less powerful LLMs struggle, though.
by mrguyorama | 2026-03-10 17:27:32+00:00
>We don’t think the tasks that a cat can accomplish are smart, but in fact, they are.

https://en.wikipedia.org/wiki/Moravec%27s_paradox

All the things we look at as "Smart" seem to be the things we struggle with, not what is objectively difficult, if that can even be defined.
by abe312 | 2026-03-10 17:27:33+00:00
Location: Silicon Valley / San Francisco Bay Area (CA) USA, NYC (PST, ET, CET)
Remote: Yes
Willing to relocate: Maybe
Technologies: next.js, react.js, amp, seo, incremental migration, web performance, web accessibility
Résumé/CV: contact
Email: contact+hn@abhineet.me
Résumé/CV: https://abhineet.me

Need help with migrating to nextjs?
Looking for a React developer who would be productive from day 1?

Hi. I'm looking for full-time remote roles in a well-funded stable company (I will work in the company's core office hours. 0 issues with timezones. Been working remotely before covid)

Building websites since 2016, working with React since 2018 and have 2 years of production next.js experience

Super comfortable with Next.js, React, Redux (with sagas), tailwind, material ui, styled-components, SEO schemas, AMP, MERN, scalable deployment on GCP/AWS/Vercel, incremental migration and parallel deployment (WordPress blogs, new next.js code and old code working on the same domain)

I value web performance (web vitals) and accessibility and could create high conversion dynamic pages
by ShawnC21 | 2026-03-10 17:27:38+00:00
The prompt injection risk CNCERT is describing is structural.. it's not specific to OpenClaw. Any agent runtime where model output can reach privileged tools without an enforcement boundary has this exposure.

The agencies' advice to "check permission configurations" is the right instinct but it's a manual process that doesn't scale. The architectural fix is deterministic enforcement at the execution sink level: model output with untrusted provenance cannot invoke shell, credentials, filesystem, or network calls regardless of what the prompt says.

That's what MVAR enforces > UNTRUSTED input + CRITICAL sink → BLOCK. No classification, no configuration checklist — the boundary holds automatically.

More on this here : github.com/mvar-security/mvar
by bonesss | 2026-03-10 17:27:51+00:00
That’s the reverse-centaur issue I see: humans are not great at repetitive nuanced similar seeming tasks, putting the onus on humans to retroactively approve high volumes of critical code has them managing a critical failure mode at their weakest and worst. Automated reviews should be enhancing known good-faith code, manual reviews of high volume superficially sound but subversive code is begging for issues over time.

Which results the software engineering issue I’m not seeing addressed by the hype: bugs cost tens to hundreds of times their coding cost to resolve if they require internal or external communication to address. Even if everyone has been 10x’ed, the math still strongly favours not making mistakes in the first place.

An LLM workflow that yields 10x an engineer but psychopathically lies and sabotages client facing processes/resources once a quarter is likely a NNPP (net negative producing programmer), once opportunity and volatility costs are factored in.
by troupo | 2026-03-10 17:28:11+00:00
> Gas turbines do 16% of nameplate capacity per minute without catching a sweat. 5% per minute isn't particularly extreme.

If you keep jumping around with your arguments, nothing is extreme.

Your original claim started with claiming cold starts (which most power plants including gas turbines don't do, ever) and that coal and nuclear aren't fast.

Nuclear is plenty fast.

I never claimed gas power stations were slow, or that they were slower than nuclear.

> If you look at table 2.4 on the same page it states that it (the Russian VVER-1200) can do the 5% per second/20% per minute emergency change

Let me slowly walk you through that statement:

--- start quote ---

can do the 5% per second/20% per minute emergency change

emergency change

emergency

--- end quote ---

> And that was the point of my footnote: that nuclear plants technically can go faster than 5% but not up and down on a minute-by-minute basis.

No idea what your footnote was about, and how it is relevant.
by _libertine_ | 2026-03-10 17:28:19+00:00
Location: Seattle, WA

Remote: Optional; remote or in-office ok

Willing to relocate: Only to CA; WA or remote preferred

Technologies: AWS, C++, Python, Java, TypeScript + JavaScript, Docker, Kubernetes, Claude, all manner of CI/CD, automation and monitoring, DevOps. Automation, generally.

Résumé/CV: https://drive.google.com/file/d/1Zlcxp2hkeAxJv00FgD4PkcNUQna...

Email: morganjweaver@gmail.com

About: ~10-yr Backend Engineer with experience across AI/ML model and platform development, blockchain security, FAANG container systems, research and tech journalism, security standards writing, and large, multi-team, high-risk projects spanning months and effecting billions of people. Seeking non-coding role with opportunity to lead teams and optimize troublesome systems.
by codygman | 2026-03-10 17:28:33+00:00
No, you need to use -nw with emacs to make it apples to apples. Then it's emacs 0m0.095s vs nvim 0m0.057s:

$ time nvim -es --cmd 'vim.cmd("q")'

real 0m0.057s
user 0m0.016s
sys 0m0.017s

$ time emacs -Q -e kill-emacs

real 0m0.230s
user 0m0.165s
sys 0m0.064s

$ time emacs -nw -Q -e kill-emacs

real 0m0.095s
user 0m0.057s
sys 0m0.017s
by Joel_Mckay | 2026-03-10 17:29:14+00:00
The premise LLM are "AI" is false, but are good at problems like context search, and isomorphic plagiarism.

Given the liabilities of relying on public and chat users markdown data to sell to other users without compensation raises a number of issues:

1. Copyright: LLM generated content can't be assigned copyright (USA), and thus may contaminate licensing agreements. It is likely public-domain, but also may conflict with GPL/LGPL when stolen IP bleeds through weak obfuscation. The risk has zero precedent cases so far (the Disney case slightly differs), but is likely a legal liability waiting to surface eventually.

2. Workmanship: All software is terrible, but some of it is useful. People that don't care about black-box obfuscated generated content, are also a maintenance and security liability. Seriously, folks should just retire if they can't be arsed to improve readable source tree structure.

3. Repeatability: As the models started consuming other LLM content, the behavioral vectors often also change the content output. Humans know when they don't know something, but an LLM will inject utter random nonsense every time. More importantly, the energy cost to get that error rate lower balloons exponentially.

4. Psychology: People do not think critically when something seems right 80% of the time. The LLM accuracy depends mostly on stealing content, but it stops working when there is nothing left to commit theft of service on. The web is now >53% slop and growing. Only the human user chat data is worth stealing now.

5. Manipulation: The frequency of bad bots AstroTurf forums with poisoned discourse is biasing the delusional. Some react emotionally instead of engaging the community in good faith, or shill hard for their cult of choice.

6. Sustainability: FOSS like all ecosystems is vulnerable to peer review exhaustion like the recent xz CVE fiasco. The LLM hidden hostile agent problem is currently impossible to solve, and thus cannot be trusted in hostile environments.

7. Ethics: Every LLM ruined town economic simulations, nuked humanity 94% of the time in every war game, and encouraged the delusional to kill IRL

While I am all for assistive technologies like better voice recognition, TTS, and individuals computer-user interfaces. Most will draw a line at slop code, and branch to a less chaotic source tree to work on.

I think it is hilarious some LLM proponents immediately assume everyone also has no clue how these models are implemented. =3

"A Day in the Life of an Ensh*ttificator "

https://www.youtube.com/watch?v=T4Upf_B9RLQ
by jaggederest | 2026-03-10 17:29:28+00:00
I think we're going to see a lot more of this. I've done a similar thing, hosting a toy language on haskell, and it was remarkably easy to get something useful and usable, in basically a weekend. If you keep the surface area small enough you can now make a fully fledged, compiled language for basically every single purpose you'd like, and coevolve the language, the code, and the compiler
by rsync | 2026-03-10 17:29:33+00:00
"14.4 is a maintenance release. If you're installing FreeBSD today, use 15.0"

Translation: whatever investments in time, tooling, training and documentation you made for 14.x, they were useful for about 18 months.

x.0 shouldn't be deployed in production because it is brand new ...

... but x.4 is too old to be deployed because the cool kids stopped working on the 'x' branch 6 months ago.
by autoexec | 2026-03-10 17:30:05+00:00
> You were supposed to be the bastions of freedom and justice

That was a lie we told ourselves. In reality we started with slavery which is about as far from freedom and justice as you can get, and then shifted to mass incarceration (often just slavery with extra steps) locking up more of our own people than Russia or China ever did. These days our prison population is trending down as we're getting better at imprisoning people in their own homes and communities with GPS trackers and parole/probation requirements but it's still laughable to call ourselves the "land of the free"
by Avshalom | 2026-03-10 17:30:12+00:00
https://youtu.be/GcqQ1ebBqkc?t=1027 is on such but he doesn't actually say that each model has been profitable.

He says "You paid $100 million and then it made $200 million of revenue. There's some cost to inference with the model, but let's just assume in this cartoonish cartoon example that even if you add those two up, you're kind of in a good state. So, if every model was a company, the model is actually, in this example is actually profitable. What's going on is that at the same time"

importantly you'll notice that he's talking revenue, and assumes that inference is cheap enough/profitable enough that 100M + Inferance_Over_Lifetime < 200M
by julienchastang | 2026-03-10 17:30:43+00:00
> best practices and safeguards are not yet fully established

The way I am working with AI agents (codex) these days is have the AI generate a spec in a series of MD documents where the AI implementation of each document is a bite sized chunk that can be tested and evaluated by the human before moving to the next step and roughly matches a commit in version control. The version control history reflects the logical progression of the code. In this manner, I have a decent knowledge of the code, and one that I am more comfortable with than one-shotting.
by magicalist | 2026-03-10 17:30:49+00:00
>> but he had access to many more resources in Meta, and we didn't see anything

> I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did.

You were criticising his output at Facebook, though, but he was in the research group at facebook, not a product group, so it seems like we did actually see lots of things?
Ask HN: What would a developer-first alternative to Shopify look like?
by google_mfg | 2026-03-10 17:30:54+00:00
Over the past few years I’ve been working with e-commerce businesses through my small agency, Zenpexal. Most of the projects involve building or optimizing online stores using platforms like Shopify or custom stacks.

While Shopify has made it incredibly easy for people to start online stores, working with it as a developer sometimes reveals a few recurring limitations.

Some patterns I’ve noticed:

• Heavy reliance on third-party apps for even basic functionality
• Performance issues when stores scale and accumulate many apps
• Limited flexibility for developers who want deeper control over infrastructure
• Increasing long-term costs for store owners

None of these problems are unique to Shopify. They exist in many SaaS platforms where convenience and abstraction come at the cost of flexibility.

Lately I’ve been thinking about a long-term idea: building a developer-first commerce infrastructure that focuses on modular architecture and performance.

Not necessarily a direct Shopify replacement, but something different in philosophy.

Some principles I’ve been exploring:

• Modular architecture – stores composed of independent services instead of a monolithic platform
• Developer-first tooling – strong APIs, SDKs, and extensibility
• High-performance storefronts – optimized rendering and minimal bloat
• Reduced reliance on app marketplaces by integrating common features directly into the core system

Right now this is still just an early exploration. My current work is still mostly agency projects, but those projects keep revealing the same technical friction points.

So I’m curious about the perspective of people here.

For developers who have worked with e-commerce platforms:

• What are the biggest technical limitations you encounter?
• What would an ideal commerce platform look like from a developer’s perspective?
• Would a modular commerce infrastructure make sense compared to traditional SaaS platforms?

I’d love to hear thoughts from developers who have worked with Shopify, WooCommerce, headless commerce setups, or custom stacks.
by MintPaw | 2026-03-10 17:30:54+00:00
An interesting concept that stood out to me. Committing the prompts instead of the resulting code only.

It it really true the LLM's are non-deterministic? I thought if you used the exact input and seed with the temperature set to 0 you would get the same output. It would actually be interesting to probe the commit prompts to see how slight variants preformed.
by VorpalWay | 2026-03-10 17:31:11+00:00
I'm in a very similar situation: I have RSI and smarter-autocomplete style AI is a godsend. Unlike you I haven't found more complex AI (agent mode) particularly useful though for what I do (hard realtime C++ and Rust). So I avoid that. Plus it takes away the fun part of coding for me. (The journey matters more than the destination.)

The accessibility angle is really important here. What we need is a way to stop people who make contributions they don't understand and/or can not vouch they are the author for (the license question is very murky still, and no what the US supreme court said doesn't matter here in EU). This is difficult though.
by ABCLAW | 2026-03-10 17:31:34+00:00
You can cultivate good taste by intentionally taking in a lot of information about what's in the field, and what you like and what you don't like about it. This could be commenting on elements of film, fashion, photography, but it can also be having a sense of what you like to see stylistically in a contract, in a framework, or in corporate culture.

I recall reading an interview about a legendary developer, and the majority of the interview was not focused on his coding decisions or the structures he built, but it was about a notebook that he kept with voluminous notes about what was good and what wasn't. That notebook is a materialized version of 'taste', and it's certainly something almost anyone could put together with enough effort and time.
by beachy | 2026-03-10 17:31:42+00:00
This sounds innately wrong. When we think of celebrity clients traveling but skipping any identity checks because their entourage can vouch for them and don't want to hassle them - then who's to say later whether that person did or did not travel to that island or authorize that money transfer?

Instead, this should be handled not by fudging identity verification but by skipping it and maybe tagging the skip event with some verified identities of the people authorizing the skip.
by webpolis | 2026-03-10 17:32:00+00:00
The keychain-per-agent model is the right instinct, but the threat model gets complicated fast. When five agents share a keychain process, a prompt injection in agent A can exfiltrate credentials for agents B–E — one bad LLM output becomes a full company compromise.

The safer architecture is per-session credential scoping: each agent gets credentials minted for that specific task, revoked on completion. That's what we built Cyqle (https://cyqle.in) around — ephemeral sessions where the encryption key is destroyed on close, so if an agent misbehaves, the blast radius is bounded to that session.

The keychain needs the same isolation principle, but applied at the task boundary, not the agent boundary.
by ndriscoll | 2026-03-10 17:32:04+00:00
Ultimately you always have to trust people to be judicious, but that's why it doesn't make any changes itself. Only suggests mitigations (and my team knows what actions are safe, has context for recent changes, etc). It's not entirely a black box though. e.g. I've prompted it to collect and provide a concrete evidence chain (relevant commands+output, code paths) along with competing hypotheses as it works. Same as humans should be doing as they debug (e.g. don't just say "it's this"; paste your evidence as you go and be precise about what you know vs what you believe).
by visarga | 2026-03-10 17:32:12+00:00
Luck. RNNs can do it just as good, Mamba, S4, etc - for a given budget of compute and data. The larger the model the less architecture makes a difference. It will learn in any of the 10,000 variations that have been tried, and come about 10-15% close to the best. What you need is a data loop, or a data source of exceptional quality and size, data has more leverage. Architecture games reflect more on efficiency, some method can be 10x more efficient than another.
by fixxation92 | 2026-03-10 17:32:19+00:00
Ahhhh, the memories. I ran a Telegard BBS for a couple years back in those days, I remember being so proud to finally get a USRobotics 9600 baud modem that was so fast compared to everything we had before it. I remember having to take the board "down" to be able to dial into other BBSes in the area, and having to manully start the uploads and downloads with Ymodem-G and Zmodem. I can't remember exactly why, but I have memories of Xmodem not being great!
Sadly it all ended when a new user sent tricked me into running a "game" as an upload to prove he was legit, and told me that I didn't need to do the voice verification (as a good Sysop would have always done). Of course I trusted him, and ran the thing-- turned out to be some nasty trojan and all I remember was the text "file allocation table (followed by some other not so nice words)" across the screen over and over again, forgot the rest. Hard drive FUBAR'ed. Awful moment. Phone rings a few moments later and the there he was, asking how the "game" was and laughing... almost like the "did you check the children" line from When a Stranger Calls!
That was my first and last BBS... good times regardless, despite the virus incident!