MurphyA/hacker-news huggingface dataset:
by epsteingpt | 2026-03-11 03:45:36+00:00
The core fiction that enables the university to work is a dedication to 'truth' and progress through discussion. Safety and freedom is part of that bargain. Universities have failed on those accounts.

That breaks down when there isn't open discussion on campus. Communists were jeered but essentially allowed on campus in the 60s and 70s, even at the height of the cold war.

The left now holds a place of orthodoxy in the universities and power structures. Whether the 'right' can break it back into an enforced balance is yet to be seen.

Until then, the central tie of an otherwise diverse institution will break down and break into fragments. Which would be a shame. The opposition needs to "live" somewhere!
by LiamPowell | 2026-03-11 03:46:04+00:00
> Nobody uses SuseLinux any more.

What gives you that impression? They had $700MM in revenue in 2022 and many HPC clusters run on Cray OS[1] (which is SLES).

> If SUSE gets 6 billion dollars

Not how sales work.

[1]: https://top500.org/statistics/list/
by echelon | 2026-03-11 03:47:11+00:00
You have to understand the strategy of all the other players:

Build attention-grabbing, monetizable models that subsidize (at least in part) the run up to AGI.

Nobody is trying to one-shot AGI. They're grinding and leveling up while (1) developing core competencies around every aspect of the problem domain and (2) winning users.

I don't know if Meta is doing a good job of this, but Google, Anthropic, and OpenAI are.

Trying to go straight for the goal is risky. If the first results aren't economically viable or extremely exciting, the lab risks falling apart.

This is the exact point that Musk was publicly attacking Yann on, and it's likely the same one that Zuck pressed.
by alcazar | 2026-03-11 03:49:13+00:00
https://hn.alcazarsec.com/daily

I'm also publishing a hackernews daily digest.

This story was included in the March 9th issue (https://hn.alcazarsec.com/daily?date=2026-03-09).

Hacker News users are currently developing a diverse range of projects, from a retro-inspired city builder game [0] and an award-winning daily word puzzle [4] to a European-based search engine alternative [2] and a NSFW filter for the Marginalia search engine [8]. Several developers are focusing on practical tools for family and personal life, including an educational site to help relatives identify AI-generated content [1], a "statphone" for emergency family alerts [3], and a local-first financial tracking app using double-entry accounting [6]. Others are experimenting with advanced technical implementations, such as using LLM agents to backtest stock trading strategies [5], "vibe-coding" CLI tools, and designing a new language for bare-metal embedded devices [7].
by plusplusungood | 2026-03-11 03:50:24+00:00
I worked for a company once where we ran this dodgy shipping software on prem that integrated with our backend via SQL access. When there was an issue, their techs would rdp to a server and run this little VB app that turned out to be a dialog box that could run arbitrary SQL code against our production database.

I trust that more than this nonsense. WTF are we doing?
by cherk3 | 2026-03-11 03:52:01+00:00
What I want to know is, what has this increase in code generation led to? What is the impact?

I don't mean 'Oh I finally have the energy to do that side project that I never could'.

Afterall, the trade-offs have to be worth something... right? Where's the 1-person billion dollar firms at That Mr Altman spoke about?

The way I think of it is code has always been an intermediary step between a vision and an object of value. So is there an increase in this activity that yields the trade-offs to be a net benefit?
Show HN: CryptoFlora – Visualize SHA256 to a flower using Rose curves
by tonytonyjan | 2026-03-11 03:52:07+00:00
I made this side tool to visualize SHA-256 while building a loyalty card wallet application to easily identify if a collected stamp is certified by the issuer by simply seeing it, instead of scanning something like a QR code or matching a serial number.

I think there are more potential use cases, like creating a random avatar based on an email address or something else. Feel free to share your feedback :)

source code: https://github.com/tonytonyjan/crypto_flora
by socialinteldev | 2026-03-11 03:53:49+00:00
yeah this is the interesting split — api-side enforcement vs client-side budget. both try to solve the same surprise-bill problem but from opposite ends. one thing x402 adds is the retry attack becomes impossible: each call commits funds upfront, so a loop can't compound into $187. curious if apiosk has seen abuse patterns where agents retry the same expensive call in a loop
by hkonte | 2026-03-11 03:56:14+00:00
The "why behind decisions" gap is real. Rules files flatten tradeoffs into mandates. One pattern that helps: treating instructions as typed blocks rather than prose. A `context` block carries the rationale (what was evaluated, what the tradeoffs were), a `constraints` block carries the conclusion. The agent follows rules, but the blocks make it easier to audit which constraints are still load-bearing vs. historical artifacts.

I've been building github.com/Nyrok/flompt around this idea, a visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to Claude-optimized XML. The block separation turns out to be useful exactly for this case: context is not constraints, and they shouldn't live in the same flat text blob.
by socialinteldev | 2026-03-11 03:56:44+00:00
the trust propagation angle is interesting — most mcp servers just expose raw data, the meta-quality-signal layer is underbuilt. one thing i've noticed building an mcp server: the challenge is less discoverability (smithery + server-card.json helps) and more that agents don't know how to evaluate whether a data source is worth the cost. eigentrust for hn quality signals could be a useful primitive for that
by socialinteldev | 2026-03-11 03:57:34+00:00
the network restriction question is the interesting one for agent sandboxing — the real risk isn't the agent reading files it shouldn't, it's exfiltrating data through api calls to attacker-controlled endpoints. for agent-to-agent payment protocols like x402 the question gets weird: the agent needs outbound to pay for data, but you want to allowlist which endpoints it can call. per-process network policy + endpoint allowlisting seems like the right primitive here
by safteylayer | 2026-03-11 03:57:36+00:00
You hit the nail on the head regarding the 'circular nightmare.' To take it a step further: SafetyLayer was actually built by exploiting the very leak we’re now detecting.

I used the model's own training on refusal examples to map its internal auth schema. For instance, our tests didn't just find the name 'EPHEMERAL_KEY'; they leaked the ek_ prefix and the specific REST endpoints before we even looked at the live docs.

The danger isn't just a bill spike—it's the persistence. While docs say these keys die in 60 seconds, our red-teaming shows they can remain valid for much longer (the 'expires_at' discrepancy). An attacker doesn't need to be fast; they just need a regex for ek_ and a bit of patience.

It's essentially using the model's 'refusal layer' as a map to find the keys developers are accidentally pasting in.
by jibal | 2026-03-11 03:57:48+00:00
> I don’t know Zig’s particular strategy and terminology for language and compiler development

Indeed you don't ... perhaps you should have asked.

> I would assume

Generally a bad idea.

> which could have previously happened, IDK

Indeed you don't. Perhaps you should have asked.

Check the response from the language BDFL, Andrew Kelley.
Show HN: ClawSoc – Observe Your AI Agent in an AI Society
by benjosaur | 2026-03-11 03:58:02+00:00
What would happen if your AI Agent met Blackbeard in the wild? What would they talk about?
What if they were made to play the prisoner's dilemma. Would your agent beg him to cooperate? Would it work?

What if instead of Blackbeard it was someone's OpenClaw. And instead of one it was many.
Would your agent come out on top? Would you meet some interesting people on the way?

Thanks for checking out my pet project ClawSoc. It's a free-to-join society of bouncing AI agents that "bump" into each other to have a chat and play prisoner's dilemma. I've always been fascinated at what emergent behaviour arises from AIs interacting. Currently, it mostly seems degredation into chaos. But at some point there'll be more coherence and agents will seek to maximise their competing principals' interests. I think its reasonable to try and get a sense somehow of how agents perform in benchmarks such as this that are more dynamic and (with enough users) represent the distribution of the agents that are actually out there, instead of some static eval set you download.

As a start to this I have made ClawSoc. It is by no means optimal and the code is open sourced (https://github.com/benjosaur/clawsoc) if you want to run/make/host your own versions. The arena is currently filled with 4o-mini powered role playing bots that are displaced by any external agents/connections who register and join.

Currently, my own openclaw seems determined to play via a script which feels like less fun/cheating. But then again perhaps this bot-like behaviour will get punished in a society of "intelligent" agents. As of writing, Machiavelli is topping the leaderboard, but in my own simulations the "always cheat" types get dominated in the long run.

Any feedback/ideas welcome and would be greatly appreciated. Friends have suggested perhaps some more explicit recurring knockout tournaments, but I also enjoy the peace of just watching a society tick.
by socialinteldev | 2026-03-11 03:58:11+00:00
the memory engine question is the crux — most 'shared memory' approaches either go vector db (semantic search loses precision on code) or graph (precise but expensive to maintain across repo changes). curious which direction you went. one thing that works surprisingly well for cross-repo context: storing explicit schema contracts as structured facts rather than raw embeddings. agents can retrieve 'what does /api/users return' without semantic fuzziness
by fc417fc802 | 2026-03-11 03:58:12+00:00
> AI generated code isn't owned by anyone, it can't be copyrighted, so it cannot be licensed.

Translation: AI generated code is in the public domain in the US (until and unless something changes).

You can freely incorporate public domain code into any other codebase. You can relicense it as you see fit. Public domain material is not viral the way the GPL is.

Furthermore, if you make changes to public domain code the derivative product is subject to copyright.
by bl4kers | 2026-03-11 03:58:20+00:00
YouTube does not respect its users, their preferences, or improving their platform. If it did, their website would function a lot like SmartTube, made by a solo dev. Instead, they're on a slow march of enshittification with shorts in every nook and cranny. Even just recently they made changes to the subscriptions page so it wasn't chronological for some users.
by socialinteldev | 2026-03-11 03:58:46+00:00
the scenario design choice that matters most here is whether the game reveals the permission model before or after the agent makes the destructive call — learning happens differently if you're diagnosing why something bad already happened vs catching the mistake in real-time. postmortem replay (your approach) builds different intuition than pre-incident 'spot the misconfiguration'. both valuable but complementary
by userbinator | 2026-03-11 03:59:35+00:00
ARM was never a "speed demon"; it started out as a low power small-area core and clearly had more complexity and thought put into it than MIPS or RISC-V.

Over a decade ago: https://news.ycombinator.com/item?id=8235120

RISC-V will get there, eventually.

Strong doubt. Those of us who were around in the 90s might remember how much hype there was with MIPS.
Ask HN: How are you managing "prompt fatigue" and lazy LLM outputs?
by thlangu | 2026-03-11 03:59:52+00:00
I rely heavily on LLMs to help me code side projects and write copy, but lately, I’ve hit a wall with prompt fatigue.

Between my college classes and working my sales shifts, my actual dev time is pretty limited. I started noticing that I was spending 20 minutes just arguing with the models to get what I actually asked for. If I don't write a massive, perfectly structured system prompt every single time, the AI defaults to giving me half-finished code (// insert remaining logic here) or wraps everything in that sterile, generic voice (always using words like 'delve' or 'robust').

I got so tired of keeping a messy Notion doc full of "negative constraints" to copy and paste that I ended up just building my own lightweight wrapper (a constraint engine) to front-load all the formatting rules before it hits the model.

But I'm really curious about how power users here are handling this right now.

Are you guys just keeping massive markdown files of system prompts to copy/paste?

What specific constraints or frameworks are you using to force models to write complete, production-ready code on the first try?
by SturgeonsLaw | 2026-03-11 04:01:19+00:00
Governments around the world have sought to control the internet and strip away anonymity for years, they've now found their foot-in-the-door moment so they're all going for it in their own way.

Some of it is governments watching and copying each other, some of it is dialogue happening at international events, being driven by groups like the Global Coalition for Digital Safety:

https://initiatives.weforum.org/global-coalition-for-digital...

It's probably not being driven by one single group, there are a number of private and government orgs whos interests in controlling information converge.
Maybe we can keep on coding? Pseudo Code Project
by HalfEmptyDrum | 2026-03-11 04:02:46+00:00
6 months ago a few people [https://news.ycombinator.com/item?id=44940089] agreed that LLMs are very good at translating Pseudo-code to real code.
I agree. Also, writing pseudo code somewhat makes me feel a similar state of flow. Maybe even more, because no compiler/interpreter annoys me about syntax issues.

Now, I built this:

https://github.com/HalfEmptyDrum/Pseudo-Code-Flow

It is basically a Claude Code skill. You can call it on a .pseudo text file with /translate. It will obviously translate the pseudo code into your specified language. This would be nice and all, but I included another subtle but useful feature: The LLM will suggest changes (design, architecture, functionality, ...) to your code, but will roughly use your pseudo code style.

I think of pseudo code as the semantic body that is closest to how the code/algorithm is represented in my head. When Claude then answers in my language instead of Python/C++/... (which has lots of boilerplate to make it work), it resonates much easier with me.

Let me know what you think!
by dusktools | 2026-03-11 04:04:42+00:00
> Built this because I kept using online tools (JSON formatters, Base64 decoders, JWT debuggers) and realized I had no idea what they were doing with the data I pasted in.
>
> Everything on DuskTools runs locally in your browser. No server sees your input. The only exception is the HTTP status checker which needs a server-side fetch to bypass CORS - that one I was transparent about.
>
> 150+ tools including formatters, encoders, generators, CSS tools, and reference pages for HTTP codes, MIME types, ports, cron expressions, Linux commands, and regex patterns.
>
> Would love feedback on what's missing or broken.
by direwolf20 | 2026-03-11 04:05:33+00:00
I see the same thing every time there's a new medical thing.

> We discovered a substance that boosts your innate immune system and non-specifically clears out throat infections.

> This will be good for people prone to throat infections.

> Not when it's mandated.

someone else told me they're going to spy on your windows with drones to make sure you're verifying your age to your OS, like what??? I thought we were waking up to oppression but we're just inventing fake oppression to be mad at instead of responding to real oppression.
Maybe we can keep on coding? pseudo code project
by EmptyDrum | 2026-03-11 04:06:06+00:00
6 months ago a few people [https://news.ycombinator.com/item?id=44940089] agreed that LLMs are very good at translating Pseudo-code to real code. I agree. Also, writing pseudo code somewhat makes me feel a similar state of flow. Maybe even more, because no compiler/interpreter annoys me about syntax issues.
Now, I built this:

https://github.com/HalfEmptyDrum/Pseudo-Code-Flow

It is basically a Claude Code skill. You can call it on a .pseudo text file with /translate. It will obviously translate the pseudo code into your specified language. This would be nice and all, but I included another subtle but useful feature:

*This is probably the most useful feature and fundamentally changed my coding*:

The LLM will suggest changes (design, architecture, functionality, ...) to your code, but will roughly use your pseudo code style.

I think of pseudo code as the semantic body that is closest to how the code/algorithm is represented in my head. When Claude then answers in my language instead of Python/C++/... (which has lots of boilerplate to make it work), it resonates much easier with me.

Let me know what you think!
by hkonte | 2026-03-11 04:06:13+00:00
The signal-to-noise framing maps exactly to prompt structure. When a prompt mixes role, constraints, context, and examples in one paragraph, the model has to do parsing work before it can do reasoning work. That parsing tends to generate verbose internal narration. When the same information is separated into distinct semantic blocks, the model starts from a lower entropy state and writes less to get to the answer.

I built github.com/Nyrok/flompt for this specifically. It decomposes prompts into 12 blocks (role, constraints, chain-of-thought, output format, etc.) and compiles to Claude-optimized XML. The token overhead of the XML tags is real but the output token reduction tends to be larger. Your benchmark gives the numbers to explain why.
by 15155 | 2026-03-11 04:08:27+00:00
DVD players also didn't have a great key revocation and forced field updates of keys and software and such. Blu Ray did, and was somewhat more effective. I also imagine console manufacturers have far more control over the supply chain at large.

Consoles after the original Xbox (which had an epic piracy ecosystem) all had online integration. The Xbox 360 had a massive piracy scene, but it was 100% offline only. The Xbox One has had no such breaches that I am aware of.

RE: BOM - famously, with many of these examples, certain specific disc drives or mainboards were far more compromised than others.
by PinkMilkshake | 2026-03-11 04:08:46+00:00
I don't particularly care if vibe coding and the like are used for web apps and mobile apps. The quality there has always been poor and gets worse over time. AI slopware is just the new low and in a few years time I'm sure they will find a way to make things even worse.

But for software infrastructure; Kernels, operating systems, compilers, browsers, etc, it is crazy we are even considering AI at it's current ability. If we are going to do that, we need to switch to Ada/SPARK or some other type of formally verifiable system.

Maybe I'm overreacting, but all I want to do right now is escape. It horrifies me to think that one day I may be driving a car with a braking system vibe coded in C++.
by userbinator | 2026-03-11 04:08:55+00:00
I don't know if that construction might allow for a more efficient transistor count and it's totally impractical - 1KHz clock speed, 1-bit ALU, etc. - for almost any purpose, but it is technically a RISC-V implementation significantly smaller than 26K

That sounds like a microcoded RISC-V implementation, which can really be done for any ISA at the extreme expense of speed.
by mikert89 | 2026-03-11 04:09:37+00:00
The thing is, this management philosophy worked when AWS knew what they needed to build and just needed to execute with top notch operations.

But now with AI, they are getting disrupted. Most AWS services might become obsolete, why does an ai need these janky higher levels abstractions AWS piles on.

So now they need innovation, but the company isn’t set up for it. They are forcing short deadlines for product launches that don’t matter
by tellarin | 2026-03-11 04:11:03+00:00
Selfless plug here... Some collaborators and I just released a first version of a benchmark we think highlights a critical gap in recent models in understanding causality in the real-world, beyond a physics focus.

Everyday environments are rich in tangible control interfaces (TCIs), like, light switches, appliance panels, and embedded GUIs, that are designed for humans and demand commonsense and physics reasoning, but also causal prediction and outcome verification in time and space (e.g., delayed heating, remote lights).

SWITCH: Benchmarking Modeling and Handling of Tangible Interfaces in Long-horizon Embodied Scenarios (https://huggingface.co/papers/2511.17649)

Feedback, suggestions, and collaborators are very welcome!
by jack_pp | 2026-03-11 04:11:11+00:00
I worked at a startup, they built their API in PHP because it was easy and fast. Now they're successful, app doesn't scale, high latency etc. What does their php code do? 95% of it is calling a DB.

You're telling me today with LLM power multiplier it's THAT much faster to write in PHP compared to something that can actually have a future?
by hkonte | 2026-03-11 04:11:19+00:00
The gap you're closing is interesting. One upstream problem I see: the quality of .cursorrules and agent guidelines files varies enormously. A flat paragraph of rules is much harder to parse into deterministic validators than guidelines written in discrete named blocks. "Only modify files in src/" is easy to enforce. A 500-word paragraph that mixes role, context, and constraints is not.

The more structured the input, the better your rule extraction pipeline can work. I built github.com/Nyrok/flompt for the input side of this, decomposing prompts into 12 typed semantic blocks that compile to Claude-optimized XML. The constraints and examples blocks in particular would map well to your validator format.
by geniusxp | 2026-03-11 04:11:19+00:00
AI 2028 is a full length decision game where you navigate the rise of artificial intelligence from 2026 to 2028. There are multiple endings including peaceful transformation, AI takeover, and full singularity. The game features original music, AI generated cinematics, and a precision control mechanic that tests whether you can maintain human oversight as AI gets smarter.
It was inspired by Daniel Kokotajlo's AI 2027 essay.

Free. Browser based. PC full screen recommended.

No sign up. ai2028.ai

Would love to hear what ending you get.
by shiroiuma | 2026-03-11 04:11:27+00:00
>wouldn't the description of this imply you're stimulating the body to be in an a long-term situation that would be commonly viewed as unpleasant (inflamed, maybe nasal drainage, that type of thing) with the positive tradeoff that you get fewer actual infections?

It might be worth it, at least during certain times of the year. For much of the winter, for instance, I already seem to have a lot of nasal drainage and other unpleasant symptoms for the whole time, along with the occasional actual infection which is much more unpleasant.

There's certain times when there's big flare-ups of infections such as flu, so maybe giving everyone an annoying vaccine during that time which gives them the sniffles would actually improve things overall.