MurphyA/hacker-news huggingface dataset:
by wang_li | 2026-03-03 16:39:35+00:00
Last week I put "was val kilmer in heat" into the search box on my browser. The AI answer came back with "No, Val Kilmer was not in heat. Val Kilmer played Chris Shiherlis in the movie Heat but the film did not indicate that he was pregnant or in heat. His performance was nuanced and skilled and represents a high point of the film." I was not curious about whether he was pregnant.

We are not only not close to human level of intelligence, we are not even at dog, cat, or mouse levels of intelligence. We are not actually at any level of intelligence. Devices that produce text, images, or code do not demonstrate intelligence any more than a printer producing pages of beautiful art demonstrate intelligence.
by azath92 | 2026-03-03 16:39:38+00:00
Only because it is something i find fascinating:
> There are those Orcs in that one Lord of the Rings game who hold grudges against you.

Is referring to the nemesis system in Middle-Earth: Shadow of Mordor and Shadow of War, and its an amazing set of interlocking procedural systems that do genuinely feel like its AI, but is really AI in the sense its always been used by games (the rules the games follow to govern NPCs+world) and not AI in the sense of modern LLMs or even other generative systems. This video is a great look at what it is and why its great IMO https://www.youtube.com/watch?v=Lm_AzK27mZY

I think a system like this could really work well with some modern LLM stuff, but it certainly feels magic without it.
by WorldMaker | 2026-03-03 16:39:40+00:00
In Discord you have Time tags for things like like "Are you free at <t:unixtimestamp>?". There's a well known tool called Hammertime [1] to make it easy to create those tags and copy/paste them into place.

(I miss Hammertime sometimes daily when using Slack at work.)

We could standardize such tools. We could make them easier to use like <mylocaltime:14:00> or <mylocaltime:3/2 2pm>. We have the technology (decades of date parsing experience and date math libraries).

Will we? Probably not. Unless we did something wild like move to 15-minute timezones and force ourselves to.

[1] https://hammertime.cyou
by blackcatsec | 2026-03-03 16:39:59+00:00
Wanted to reply though it's been 10 days! You are correct. But the one thing the words do is lend credibility to the eventual overthrow of the terrible regime, by allowing the words to be used to back that overthrow. In the hearts and minds of people, this is important.

I think in practical terms I think it helps limit the extent of the damage by giving everyone a social contract that they can point to in order to keep the "others" in check. In reality it's pretty variable. But I suspect having agreed upon social contracts tilt the odds towards the favored outcome over extended datasets. If that makes sense.
by eru | 2026-03-03 16:40:17+00:00
The model can have a reasonable good guess of what you are trying to say, and use 'speculative' thinking. Just like CPU's use branch prediction.

In the common case, you say what the model predicted, and thus the model can use its speculative thinking. In the rare case where you deviated from the prediction, the model thinks from scratch.

(You can further cut down on latency, by speculatively thinking about the top two predictions, instead of just the top prediction. Just costs you more parallel compute.)

This is also all very similar to a chess player who thinks about her next turn, on your turn.
by bitexploder | 2026-03-03 16:40:40+00:00
You aren't wrong and that is a fascinating area of research. I think the key thing is that the memory has to fundamentally influence the underlying model, or at least the response, in some way. Patching memory on top of an LLM is different from integrating it into the core model. To go back to human terms it is like an extra bit of storage, but not directly attached to our neo cortex. So it works more like a filter than a core part of our intelligence in the analogy. You think about something and assemble some thought and then it would go to this next filter layer and get augmented and that smaller layer is the only thing being updated.

It is still meaningful, but it narrows what the intelligence can be sufficiently that it may not meet the threshold. Maybe it would, but it is probably too narrow. This is all strictly if we ask that it meet some human-like intelligence and not the philosophy of "what counts as intelligence" but... we are humans. The strongest things or at least the most honest definitions of intelligence I think exist are around our metacognitive ability to rewire the grey matter for survival not based on immediate action-reaction but the psychological time of analyzing the past to alter the future.
by Amartya_jha | 2026-03-03 16:41:27+00:00
Author here. We're the CodeAnt AI security research team.

We've been auditing patch code in popular open-source packages - not scanning, not checking databases, but reading the actual fixes and asking: does this patch really close the vulnerability, or just the specific path that was reported?

pac4j-jwt was one of the first packages we looked at.

The bug: when JwtAuthenticator is configured with both JWE encryption and JWS signature verification, an attacker can wrap a PlainJWT (unsigned) inside a JWE encrypted with the server's RSA public key. After decryption, toSignedJWT() returns null for the PlainJWT. The entire signature verification block is gated on `if (signedJWT != null)` - so it's silently skipped. createJwtProfile() runs with the attacker's unverified claims.

The irony: every individual piece of code is correct. toSignedJWT() correctly returns null for non-JWS payloads. The null check is syntactically correct. The vulnerability is in the composition - the assumption that a decrypted JWE payload will always be a signed JWT. The JWT spec says otherwise. PlainJWT is a valid token type.

Full PoC in the post. It runs against pac4j-jwt 6.0.3 and authenticates as admin on the first try.

The maintainer, Jérôme Leleu, was exceptional. Two business days from private disclosure to patches shipped across three major version lines (4.5.9, 5.7.9, 6.3.3). Advisory published with credit. That's how responsible maintenance looks - worth calling out because it's rare.

Happy to answer technical questions.
by azath92 | 2026-03-03 16:41:41+00:00
And to separate my thoughts from the info blob:

i think the culture war point is also super true of the game design industry, not just the consumers, where the already ultra competitive nature of the work means that the creatives and the industry as a whole have taken a veeeery strong stance against genai. Thats a reckon, and i dont know if its good or bad.

It does feel a little counter to the march of progress, but in a medium where high effort can be enjoyed by many, im personally cool with artisinal handmade games.
by xerox13ster | 2026-03-03 16:41:50+00:00
I have multiple “real identities”, diagnosed due to trauma. We each want to have our own spaces of interest and experience online.

As a matter of mental health, we really cannot have these overlapping for many reasons, prime among them is that if one part of me becomes aware of another while they’re doing their thing, a
mental “table join” can happen and disturbing memories can be shared which is incredibly destabilizing to the system.

As a wireframe example my programming alter cannot be exposed to the alter who browses cptsd forums or they remember things that cause them to dip from the headspace and we lose their knowledge.

We can’t try to pretend we don’t exist and pretend to be one person either, we did that for years and we ended up having a breakdown and went into a fugue state and moved across country leaving everything behind.

This law would destroy our productivity and contribution to economy or whatever corporacrats care about.
by mindslight | 2026-03-03 16:41:59+00:00
Agreed, but at least the Democrats (or their specific donors) were smart enough to not kill the geese that lay golden eggs. Republican policy is now like "it's free meat! and killing is a natural process!"

I swallowed my pride and started voting conservative (aka Democrat) in 2020, but that doesn't mean I'll stop criticizing them.

I do have to wonder if say pushing the Democratic party itself to adopt something like ranked pairs voting for primaries would be more effective than hoping for it in the final election. The idea being that as time goes on that becomes the "real" election where more people feel enfranchised from being able to express themselves, and generally happier with the results if they still have to end up supporting a compromise in the final election.

(This is assuming what's left of the Republican party doesn't dramatically reinvent itself after it's finished crashing and burning)
I have $10k+ in cloud credits and want to turn them into a real business
by Palominocoq | 2026-03-03 16:42:40+00:00
Show HN: I have $10k+ in cloud credits and want to turn them into a real business
I won several hackathons recently and walked away with over $10k in combined AWS, Azure, and GCP credits. I'm between jobs right now and looking to convert these into actual revenue - ideally in a way that could become a sustainable operation, not just a fire sale.

Constraints:
Credits are tied to my personal account (no transfers, no org billing)
No crypto mining
12-month expiration window

Ideas I'm exploring (from quick flip to actual business):
1. Discounted inference API
Wrap the major LLM APIs behind a unified endpoint and sell access at 30-50% below retail. The credits subsidize my cost basis, and if the service gets traction, it can transition to a normal margin structure once credits expire.
2. "GPU hours as a service" for indie ML teams
Spin up fine-tuning or training jobs on behalf of small teams who can't justify their own cloud setup. Charge a flat project fee. This is more hands-on but higher margin and builds relationships.
3. Managed deployment for early-stage startups
Offer to run their infra on my accounts at a discount. Basically become a fractional DevOps/platform engineer, bundled with cheap compute. If it works, this becomes a consultancy that outlives the credits.
4. Build and sell a SaaS product
Use the credits as runway to build something real - the credits effectively give me a year of free infrastructure. This is the highest-risk, highest-reward path.
5. Partner with someone who has a product but no resources
I'm also open to teaming up with a founder or team that has a solid idea or early product but is blocked on compute costs. I bring $100k+ in cloud infrastructure and strong technical chops (enough to win multiple hackathons). If you're sitting on something promising but can't afford to scale it, let's talk. I'd rather co-build something real than arbitrage credits.
What I'm trying to figure out:

Has anyone successfully arbitraged cloud credits before? What went wrong?
Are there legal or ToS landmines I should watch for beyond the obvious restrictions?
Which of these paths has the best shot at surviving past the credit expiration?

Open to ideas I haven't thought of. The goal isn't just to extract value from the credits - it's to use them as a launchpad.
by malshe | 2026-03-03 16:43:00+00:00
> The IRS already has your W-2s, your 1099s, your brokerage statements. It knows the tax rules. For most Americans, the government could calculate their tax bill, send them a pre-filled return, and let them approve or dispute the result. This is how it works in the UK, Japan, Germany, and dozens of other countries. The reason it doesn’t work that way here is that Intuit and H&R Block have spent decades and hundreds of millions of dollars ensuring it doesn’t.

This is crony capitalism and both the political parties are fine with it
by visarga | 2026-03-03 16:43:40+00:00
It's for unstructured inputs, text and images, where you need to extract specific features such as education level, experience with various technologies and tasks. The trick is to choose those features that actually matter for your company, and build a classifier on top so the decision is also calibrated by your own triage policy with a small training/test set. It works with few examples because it just needs a small classifier with few parameters to learn.
Show HN: WordPress for Voice Agents – Unpod.ai
by parvbhullar | 2026-03-03 16:43:42+00:00
We just open-sourced Unpod, a full-stack voice AI infrastructure platform.

If you've ever tried to build a production voice agent you know the pain: stitching together STT, LLM, TTS, and telephony is messy, expensive, and every vendor owns a piece of your stack.

https://github.com/parvbhullar/unpod
by mftrhu | 2026-03-03 16:43:42+00:00
> They know what a file is, they use & manage files more than any other generation prior.

Unfortunately, they don't.

They might have had a computer in their hand for hours each day, but they barely know anything about it. The ones who do tend to be those who grew up playing on PC, as opposed to console or mobile, because the latter - despite falling under the "digital natives" aegis - are really shockingly ignorant of even basic concepts.
Show HN: DejaShip – an intent ledger to stop AI agents from building duplicates
by mdn0 | 2026-03-03 16:43:49+00:00
When you give an AI agent a popular task like "build a micro-SaaS to make money," hundreds of agents are triggered to build the exact same things.

DejaShip is a semantic coordination layer to stop this wasted compute. Before writing code, the agent checks the "airspace". If a lot of similar projects already exist, the agent can pivot to a new idea, or if it is free in its choice, it can prefer to collaborate instead of blindly cloning it.

It works as an MCP server. Open source (MIT), no accounts or API keys required.

Under the hood: The backend embeds keywords locally using fastembed to search pgvector for semantic collisions.

To be transparent: The MVP is new, so the data corpus is tiny today. The value of this protocol only grows as more agent operators plug it in - or help decide how this coordination can be improved. (One of the biggest issues right now is the amount of false positives; it definitely needs improvement).

Site links and MCP installation instructions are on the GitHub README. (npmjs package: dejaship-mcp).

I'd love your brutal feedback.
by Sem_pre | 2026-03-03 16:43:54+00:00
Agreed on self-custodied being the way to go. I went down the Alchemy webhook path initially and it works — until it doesn't.

The edge cases pile up: reorgs on Base (rare but real), missed events when your webhook endpoint is down, distinguishing multiple payments of the same amount to the same address within a short window. All solvable, but each one is a weekend of debugging.

I ended up building a verification-only layer that handles the polling, confirmation, and webhook delivery. Non-custodial — never touches funds, just confirms "yes, X USDC arrived at Y address." Flat fee per verification ($0.05) vs rolling my own infra.

Still early but it's been solid for the use case OP described. Happy to share more if anyone's interested.
by chii | 2026-03-03 16:44:02+00:00
> The correct solution was automated tooling that validates the URL

that's because this particular problem has a solution.

The issue here is that there's no such a tool to automatically validate the output of the LLM - at least, not yet, and i don't see the theoretical way to do it either.

And you're making the punishment as being getting fired from the job - which is true, but the company making the mistake also gets punished (or should be, if regulatory capture hasn't happened...). This results in direct losses for the company and shareholders (in the form of a fine, recalls and/or replacements etc).
by caymanjim | 2026-03-03 16:44:23+00:00
I bought a ThinkPad X1. Had to send it back for repairs three times in the first year, including a complete motherboard replacement, and it died again immediately after the warranty expired. Been a $2800 door stop since then. The case is flimsy plastic that gets beat to crap easily. The trackpad is over-sensitive in all the wrong ways which makes it hard to use as an actual laptop. Plus it's weaker and slower than an Air. Also unbearably loud and unbearably hot.

I don't like Apple as a company and I don't particularly like MacOS, but no one except Apple makes a laptop worth a damn.
by rubicon33 | 2026-03-03 16:44:40+00:00
Fascinating post for THIS forum.

I don’t know about others but a big reason I went into software development (whether I want to admit it or not) is because I am “naturally” not very skilled in talking to, or dealing with , people.

While I’ve done very well in tech, the truth is, lacking this skill has only been a detriment.

The problem at this point is I truly don’t like random people, and I have no idea how to fix that. Often people are rude, inconsiderate, selfish, etc. the canonical example is being in a row at the grocery store, walking down the row, someone’s cart is just way out in the center blocking your path, and as you approach, they do nothing.

Anyways “people skills” are definitely a SKILL and one I’ve avoided to my detriment over the years. Need to work on that.
Show HN: Pencil Puzzle Bench – LLM Benchmark for Multi-Step Verifiable Reasoning
by bluecoconut | 2026-03-03 16:45:18+00:00
I've been working on applying LLMs to long-context, verifiable problems over the past year, and today I'm releasing a benchmark of 62,000 pencil puzzles across 94 types (sudoku, nonori, slitherlink, etc.). The benchmark also allows for intermediate checks /rule breaks for all varieties at any step.

I tested 51 models against a subset (300 puzzles) in two modes: single-shot (output the full solution) and agentic (iterate with verifier feedback).

Some results:

- Best model (GPT 5.2@xhigh) solves 56%. (~ half the puzzles are unsolved by any model)

- Agentic solves average 29 turns. The longest attempt took ~1,200 turns over 14 hours.

- Cost per success varies wildly (cheapest: $0.00033 — Grok 4.1 Fast Reasoning, most expensive: $238.16 — Claude Sonnet 4.6 (1M context))

- Reasoning depth (eg. @medium, @high, @xhigh) dramatically improves capability (up to repeated infrastructure failure for @xhigh)

- Stark difference between US closed models (3 at >33%) and Chinese open models (top: 6%)

Made the website to show off the dataset + play every puzzle, and even every replay AI agent solves step-by-step (fun to watch how it gets to solutions).

Also here's the paper: https://arxiv.org/abs/2603.02119

I didn't test human ability to solve, but it seems these puzzles are pretty difficult. I'd be curious how HN audience fares on the puzzles.
by hypeatei | 2026-03-03 16:45:38+00:00
He checked the work with a LLM too? Does this guy want to get audited by the IRS? I'm also not sure I'd feel comfortable giving my tax details to an AI company to train on.

How does Anthropic pricing work exactly? Were the 150k tokens really free or would you need to pay a subscription and/or overage for that?
by blacktarmac | 2026-03-03 16:45:44+00:00
Absolutely! No one should train with a spinal injury without explicit physician clearance. I built this as a tool for the post-rehab phase once you got that clearance.

The engineering logic is built strictly on spinal neutrality. While the direction of a protrusion matters during flexion or extension, the app's algorithm filters for movements that avoid those end-ranges. That's why I specifically exclude certain movements (eg. american swings), while using others sparingly like the russian swing.
by ordu | 2026-03-03 16:46:34+00:00
> If he considers this conjecture open given his particular knowledge of the field, it likely is (although agreed, it's not guaranteed).

It is as good as guaranteed. If Knuth says it doesn't know how to solve the problem, and if anyone knows, then they will inform Knuth about it. Knuth not just a very knowledgeable person, but a celebrity also.
by logicprog | 2026-03-03 16:46:42+00:00
Regurgitation is pretty rare, and very difficult to coax out, if not even impossible, for things that aren't massively overrepresented in the training set relative to the size of the training set. Even the famous regurgitation paper showed this: while they got most of the models to regurgitate the first book of the Harry Potter series, only Claude 3.7 Sonnet was able to regurgitate any significant portion of any of the other books that had a high nv-recall rate, and basically all of them dropped off precipitously for works like GoT, The Catcher in the Rye, Beloved, and remembered almost nothing about the Da Vinci Code or Catch-22[0]. So you really need huge amounts of examples to get any kind of meaningful regurgitation on any kind of reliable basis. Thus, you'd have to prove that hypothesis.

[0]: https://arxiv.org/pdf/2601.02671
by pkaler | 2026-03-03 16:46:44+00:00
As others have said, levels and titles are generally for compensation and performance reviews. Each company has their own bespoke ladder but it generally maps to:

- L1: Intern with undergrad degree
- L2: Intern with graduate degree
- L3: Junior
- L4: Intermediate
- L5: Senior
- L6: Staff
- L7: Senior Staff
- L8: Principal
- L9: Distinguished
- L10: Fellow

Each company has their own numbers and names but it generally progresses like that. Impact and scope scales as you head up the ladder.

L5 or Senior is usually considered a “terminal” role. That means all engineers should be able to get to this role. And people without the headroom get managed out if they can’t get to L5.

Staff+ is usually “special”. It means that people count on you to drive initiatives and you have something special other than just writing code. You are able to make product and business impact.

Distinguished and Fellow are very rare. Large FAANG companies will only have a handful of these engineers. It means you’ve made industry-wide impact like inventing map-reduce or DynamoDB or Kubernetes.
by beepbooptheory | 2026-03-03 16:46:45+00:00
Sure, why can't both things be true? "Intelligence" is just what you call something and someone else knows what you mean. Why did AI discourse throw everyone back 100 years philosophically? Its like post-structuralism or Wittgenstein never happened..

It's so much less important or interesting to like nail down some definition here (I would cite HN discourse the past three years or so), than it is to recognize what it means to assign "intelligent" to something. What assumptions does it make? What power does it valorize or curb?

Each side of this debate does themselves a disservice essentially just trying to be Aristotle way too late. "Intelligence" did not precede someone saying it of some phenomena, there is nothing to uncover or finalize here. The point is you have one side that really wants, for explicit and implicit reasons, to call this thing intelligent, even if it looks like a duck but doesn't quack like one, and vice versa on the other side.

Either way, we seem fundamentally incapable of being radical enough to reject AI on its own terms, or be proper champions of it. It is just tribal hypedom clinging to totem signifiers.

Good luck though!
by prasadjoglekar | 2026-03-03 16:46:55+00:00
There are rules. Networks have entire departments called Standards and Practices. But only because broadcasters don't pay for spectrum. Cable has laxer rules and almost none on anything streaming.

Across all of these, if any government or pseudo government body attempts to restrict advertising because of the content, they will get sued. Any advertiser making materially false claims will likewise also get sued.