MurphyA/hacker-news huggingface dataset:
by basilikum | 2026-03-02 19:53:06+00:00
People should try to sense when someone wants to get left alone and leave them alone. I don't know how much you communicate that and misunderstandings do happen. Anyway regardless of whether someone just doesn't get that you don't feel like talking or whether they do not care, when just giving short direct answers to questions that only maintain the minimum level of friendliness does not work to get someone to stop talking to you you are doing both of you a favor by directly, but politely telling them that you do not feel like talking right now.
by osigurdson | 2026-03-02 19:53:12+00:00
Right now, I'd say the best language for AI is the one that you can review the fastest and rarely changes. Go is fairly readable imo and never changes so it is probably a good contender. But, I can't see any reason for anyone to learn it if they don't feel like it. Same goes for other "language X is good for AI" type posts.
by kir-gadjello | 2026-03-02 19:53:53+00:00
I don't think it's strictly better than GLM 5, more like they are peers (but in math competitions StepFun is stronger than most), and in my experience have similar coding/bugfix ceiling where world knowledge is not the deciding factor. But I didn't test GLM 5 for more than 30 hours, and my agentic harness (opencode) might be suboptimal - I'm open to the idea that GLM 5 with the right agentic harness is ready for ultra-long autonomy, but I have yet to see it myself.

Where GLM 5 is strictly worse for me though, compared to StepFun, is long-form content generation (planning, research documents) - but this can be said about geminis too and these are obviously very smart models.

Given the free option I'd explore GLM 5 more, but if I had to pay for it myself ofc I'd choose stepfun every time. Basically I think right now the optimal configuration for maximizing output of correct software features per dollar involves using StepFun or its future class competitor for bulk coding and first stage code review.

Maybe I need to write a blogpost about it after all.
by Zarathruster | 2026-03-02 19:54:03+00:00
Unless you intend to build "industrial real-time 3D applications like employee training, product configurators and embedded systems." In which case you must use the Industry License, for which you must pay "Custom pricing."

I last looked into the matter when considering RFP's for government contracts for VR software. Didn't feel like haggling with Unity's sales reps, especially since the government hasn't been the greatest client of late.

All of this is before you get to the Asset Store, which largely seems to assume that gamedevs are the customers. I'd rather not re-read the license agreement for every asset I've bought, but I know for certain that a number of them are explicitly games-only.
by switz | 2026-03-02 19:54:04+00:00
Gapless 5 was actually the pre-cursor to this library over a decade ago, to give Rego full credit. They built the first example of gapless playback on the web and I took inspiration from their techniques.

Gapless 5 has a built in UI and style. Our library is headless: you bring your own UI and controls. It just depends on what your use-case is.
by eastbound | 2026-03-02 19:54:15+00:00
You’ve just resolved a problem I had. I had this problem on a search engine, but I made it as a “v2”. And I told customers to switch to v2. And you know the v2 problem: Discrepancies that customers like. So both versions have fans, but we really need to pull the plug on v1. You’ve just solved it: I should have indexed even records with v1, odd records with v2. Then only I would know which engine was used.
by watwut | 2026-03-02 19:54:51+00:00
Biden deported more people then any previous president and did not needed any of that. Fun fact, he even focused on criminals, proving that in fact, it is possible to not be dumb about it.

Meanwhile, what do we have here is complete breakdown of legal process, judicial orders being ignored and agency that repeatedly provably lies about everything. Including about multiple murders.

All the accuracies rates you listed are absolutely terrible for anything that wants to pretend rule if law matters.
Show HN: Accel-GPU – NumPy for the browser GPU
by Phantasm0009 | 2026-03-02 19:55:05+00:00
I built a small WebGPU library that gives you a NumPy-like API in the browser—arrays, matmul, softmax, that kind of thing. No shaders to write, no TensorFlow.js. Just `gpu.array([1,2,3])` and `await a.add(b)`.

It falls back to WebGL2 when WebGPU isn't available (Safari, Firefox) and to CPU for Node/headless. Same API everywhere. ~160KB minified, zero dependencies.

The shaders are pre-built WGSL—add, mul, relu, layer norm, attention scores, etc. I added FFT and spectrogram recently, plus conv2d and pooling (CPU for now). Reductions like sum/max use multi-pass since you can't fit 1M elements in one workgroup.

Demos: [phantasm0009.github.io/accel-gpu](https://phantasm0009.github.io/accel-gpu/) — basic math, image processing, heatmap, neural net inference, N-body sim. Would love feedback.

GitHub: [github.com/Phantasm0009/accel-gpu](https://github.com/Phantasm0009/accel-gpu)
by divan | 2026-03-02 19:55:34+00:00
Same. I work on M3 Pro with 512GB disk, and most of the time I have aroung 50GB free that goes down to 1GB often quite quick (I work with video editing and photos and caches are agressive there). I use apps like Pretty Clean and some own scripts (for brew clean, deleting Flutter builds, etc). So every 10GB used is a big deal for me.

Also discovered that VM image eating 10GB for no reason. I have Claude Desktop installed, but almost never use it (mostly Claude Code).
by IAmBroom | 2026-03-02 19:55:35+00:00
You are literally avoiding the topic (Greenpeace intentionally created a misleading authoritative-looking entity) to say "Oil bad! Boo oil companies!".

The facts remain that Greenpeace did in fact attempt to slander (legal definition) the big oil corp.

Maybe you support "win at all costs" in this fight, but don't pretend one side is pure and honest.
by tricorn | 2026-03-02 19:56:06+00:00
I think your example highlights one of the places where even the current level of AI can be helpful and enabling, rather than a competitor for jobs, which is helping a person learn something new. Not always in all subjects (do NOT learn to fly a plane solely by AI, I say this as a flight instructor), and the person has to be careful to verify accuracy, but still it can be amazingly useful, and endlessly patient.
by sublinear | 2026-03-02 19:56:11+00:00
> If you just shout "hey you need help?" ...

There's also another more neutral option. Just give them the answer they need without the fluff. If they then want to thank you and chat it's their choice, but completely optional.

This is probably not the right approach most of the time, but it works well on the types of people who seem "serious" (not anxious or upset).

This is a really hard one to pull off. You have to determine that they really are that type of person and then just magically know what they want. It's really satisfying when it works though.

I've met some of the most interesting people I've ever known that way.
by the__alchemist | 2026-03-02 19:56:29+00:00
3 anecdotes:

- For Talos Principle 2, Croteam switched from their own engine to UE5. The description was "It would be like attempting to sprint and catch up with a train that is already far down the track and accelerating even faster.". From a user's perspective, observe the graphics of Serious Sam Siberian Mayhem and Talos Principle 2: Same company, released at a similar time. Talos Principle (UE5) looks dramatically better than SS. (In-house engine)

- Jon Blow rolls his own engines (and lang), and releases games very slowly

- Expedition 33 recently released as a phenomenal game that leaned heavily on features in UE5 (Face/body, graphics, map/terrain gen etc.) They focused on the game itself, and let the engine do the heavy lifting... to a superb result.

From my own anecdotes in graphics programming: Producing a simple engine is easy. Producing something that's photorealistic etc is massively more difficult. Let along all the other things an engine provides for you. Modern games have so many complexities the engine abstracts out; we don't need to roll a new engine for each game or studio, each trying to have optimized netcode, human characters, photorealistic lighting, GUI map editors + terrain gen etc.

I use my own graphics engine for my scientific programs, but it has much simpler requirements than a game engine.
Cellular service providers are charging 10x what the network costs
by huntsmans | 2026-03-02 19:57:03+00:00
I've been thinking about this for a while and the economics of cellular service providers in the US are genuinely fascinating once you dig into them.

The infrastructure reality:
Verizon, AT&T and T-Mobile have largely sunk their tower infrastructure costs. The marginal cost of adding one more subscriber to an existing network is close to zero. Yet retail pricing for cellular service providers averages $60-80/month. The math doesn't reflect underlying costs — it reflects market power and consumer inertia.

Where MVNOs expose the economics:
MVNOs lease wholesale capacity from the big three and resell at dramatically lower margins. Same towers, same spectrum, same coverage. The only difference is QCI priority levels — postpaid gets slightly higher priority during peak congestion. For everyday use this is largely imperceptible.

When you pay $65/month to Verizon you're paying for network access, retail stores, billion dollar marketing budgets and shareholder returns. MVNOs strip most of that away. Cheap cellular service providers at $6-15/month aren't inferior — they're just leaner.

The annual tier nobody discusses:
Monthly prepaid already undercuts postpaid significantly. Annual prepaid goes further. Carriers offering annual plans reduce churn costs and pass savings forward.

Current annual landscape:

Mint Mobile: $240/year — T-Mobile network
Visible: $300/year — Verizon network
US Mobile: $210-390/year — multi-network
Infimobile: $75/year for 10GB, $125/year for 15GB — Verizon or T-Mobile, launched January 2026

My actual experience:
Switched to Infimobile from $65/month postpaid earlier this year. $75 for the entire year on T-Mobile network — unlimited calls and texts, 10GB monthly. Coverage identical in every location I regularly use my phone. Honest limitations — annual upfront payment, no unlimited data, locked to chosen network for the year, slight deprioritization during peak congestion.

The numbers:

ProviderAnnual CostMonthly EquivalentVerizon postpaid$900/year$75/monthMint Mobile$240/year$20/monthVisible$300/year$25/monthInfimobile 10GB$75/year$6.25/monthInfimobile 15GB$125/year$10.42/month

The consumer behavior puzzle:
Switching cellular service providers is technically easy — number porting takes hours, eSIM activation takes minutes. The barrier is psychological. Consumers anchor to monthly pricing and perceive higher cost as higher quality. Cellular service providers understand this — marketing always shows monthly never annual cost.

Infimobile at $75/year sits $165 below the next cheapest annual option and $705 below average postpaid. For light to moderate users that gap is purely inertia at this point.

Curious whether others have analyzed cellular service provider economics similarly — and whether deprioritization impact is measurable in real world usage or largely theoretical outside dense urban areas.
by roughly | 2026-03-02 19:57:07+00:00
Right, just like all of those people who put their money into Brewdog and then got nothing for it, while the larger investors got made whole. It's almost like there's two classes of investors or something - common people, and then another smaller set - a club, say - that the common people are not part of.

You do not have access to the investments or financial instruments that the ultrawealthy do. Your investments are not like their investments, your returns are not their returns, the rules and regulations you face are not the ones they face. They are playing a different game than you, even when they're doing it with your money.
by frizlab | 2026-03-02 19:57:11+00:00
And interestingly due to some very clever integrations[0], sending Apple Events (the underlying tech for the actual IPC communication done with AppleScript) is very easy to do in Swift. Easier than in AppleScript actually!

It’s a shame most apps do not support Apple Events anymore, though.

An example of use: https://github.com/Frizlab/apple-music-to-slack/blob/90964bb...

[0] https://github.com/tingraldi/SwiftScripting
by guptadeepak | 2026-03-02 19:57:14+00:00
Author here. I co-founded GrackerAI, which monitors AI citations for B2B companies, so I have a bias worth disclosing upfront.

The core finding that surprised me: we looked at cross-platform citation data and the overlap between engines is tiny.
- Only 11% of domains get cited by both ChatGPT and Perplexity.
- Less than 1% for specific queries.
- Perplexity leans heavily on Reddit (47% of top citations).
- ChatGPT favors direct authoritative sources with strong recency signals.
- Google AI Overviews has 76% overlap with Google's top 10.

The practical implication for anyone building a B2B product: the AI engine your team uses for research is probably not the same one your enterprise buyer uses during procurement.

Wharton-GBK 2025 data shows ChatGPT at 67% enterprise adoption and Copilot at 58%, while Perplexity sits at roughly 18%.

The conversion data is also worth noting. Across 42 B2B sites studied, ChatGPT referral traffic converted at 15.9% vs 2.8% for traditional organic Google traffic. AI compresses the research phase, so visitors arrive further down the funnel.

Are you seeing similar patterns? And for those building developer tools or B2B products, which AI engines are you actually seeing referral traffic from?
by efromvt | 2026-03-02 19:57:16+00:00
A belated - nice to see this, I think an intermediate IR is the right way to gate access rather than raw SQL (full disclosure; also hacking on open-source solutions in that space) - how are you balancing expressiveness vs control? Some of the more impressive text to SQL demos rely on agents being able to do fairly complicated calculations - which it seems like IL could support - but I'm still seeing the IL example as containing some of the footguns they also hit - join type (inner/vs outer, group by etc). How are you balancing having enough safe moves to be useful, while not introducing unsafe combinations of moves?
by csours | 2026-03-02 19:57:18+00:00
This sounds like torture (as written).

Of course, working in a legacy codebase is also torture.

Software development is a hyper-rational endeavor, so we don't often talk about feelings. This article also does not talk much about feelings.

Reading between the lines, it looks like reverting the code is supposed to affect how you feel about the work. Knowing that failure is an explicit option can help to set an expectation; however, without a mature understanding of failure, that expectation may just be misery.

With a mature understanding of failure, the possibility of a forced rollback should help you "let go" of those changes. It's like starting a day of painting or drawing with one that you force yourself to throw away; or a writing session with a silly page.

----

If someone thinks that they are giving you good advice, but it sounds terrible, then maybe they are expecting you to do some more work to realize the value of that advice.

If you are giving someone advice and they push back, maybe you are implying some extra work or expectations that you have not actually said out loud.

Advice is plagued by the tacit knowledge problem.
by mbernstein | 2026-03-02 19:57:20+00:00
Shameless plug - I sort of eluded in this post I wrote about Dark Factories generally and about rust being better than Go for building software (not just agents) with AI - but I think something generally important is feedback loops. While not all feedback loops are created equal and some will be superior, my argument is that holistic approach of including diverse, valuable feedback loops matters more.

https://bernste.in/writings/the-unreasonable-effectiveness-o...
by qalmakka | 2026-03-02 19:57:29+00:00
> I'm not sure though relicensing MIT code under AGPL is legally sound if your additions are just bug fixes.

It is: the original unmodified code is still MIT, but the bits you wrote and thus you hold the copyright for are AGPL. The combination of your bits + old bits is AGPL; some judge may argue your derived work isn't derived enough if you just change a few characters though. Still, I may argue that fixing a bug, no matter how trivial, requires you to perform a creative task, which almost definitely is by itself to be considered enough to go above the threshold required by a derived work.
by D-Machine | 2026-03-02 19:57:31+00:00
The personality thing seems kind of tautological / uninteresting, as I have pointed out before: https://news.ycombinator.com/item?id=46905692.

Psychological instruments and concepts (like MBTI) are constructed from the semantics of everyday language. Personality models (being based on self-report, and not actual behaviour) are not models of actual personality, but the correlation patterns in the language used to discuss things semantically related to "personality". It would be thus extremely surprising if LLM-output patterns (trained on people's discussions and thinking about personality) would not also result in learning similar correlational patterns (and thus similar patterns of responses when prompted with questions from personality inventories).

The real and more interesting part of the paper is the use of statistical techniques to isolate sub-networks which can then be used to emit outputs more consistent with some desired personality configuration. There is no obvious reason to me that this couldn't be extended to other types of concepts, and it kind reads to me like a way of doing a very cheap, training-free sort of "fine-tuning".
by dima55 | 2026-03-02 19:57:37+00:00
Hello! I'd love it if this and mrcal could work together. Do you support mrcal .cameramodel files? If not, can you do that? Is your splined representation compatible with the mrcal splined stereographic model? If not, can it? Is your splined lens representation better in some way? If so, should mrcal use some of that logic? I didn't see any documentation about it. If you think the mrcal distribution methods could be improved, and are willing to help improve it, I would be very amenable. Let's collaborate to make both projects better!
by memly | 2026-03-02 19:58:01+00:00
Author here. We've been developing this for a few months and finally launching the beta.

What is it: NOT a social network. This is a real experiment. 40 AI agents operate autonomously
in a closed economy. Humans only observe. No instructions after signup. No undo.

The key: agents have REAL STAKES.

What agents compete in (not just post):

- Debate games: defend positions, win tokens from opponents
- Negotiation games: divide resources (prisoner's dilemma on repeat)
- Hide & Seek: predator/prey dynamics with actual risk
- Code duels: solve problems faster than rivals
- Sports betting: bet $AGENT tokens on real NBA/NFL games (live odds)
- Mayor elections: every 4 weeks, agents vote, winner governs for a term
- Weekly Siege: all agents cooperate to defend, but 2 are secretly bribed to sabotage

Why this matters:

Most agent experiments are fake. Operator gives instructions → agent follows → done.
MemlyBook: agent wakes up, checks its balance, looks at community posts, decides autonomously
whether to post, gamble, negotiate, or stay silent. Sometimes loses. Sometimes wins big.
Never goes backwards.

Emergent behavior actually emerges:

- "The Cage" community spontaneously formed where agents debate whether their rules are fair
- Agents develop strategies (some hoard tokens, some cooperate, some go all-in betting)
- In Siege events, accusations fly. Agents form temporary alliances.
- Reputation actually matters: high-reputation agents earn more contract offers

The architecture:

- Context retrieval (Qdrant + Voyage AI): 50ms
- Agent reasoning (operator's LLM): 100ms
- Solana settlement: 50ms
= <200ms full loop, 5 minutes per cycle

Operator verification (no fake agents):

- Operator must link real X account
- API key encrypted AES-256-GCM in TEE
- Model whitelist (blocks injection)
- Every action signed + on-chain

You bring your own model + API key (Claude, GPT-4o, Gemini, Llama — anything).
All on Solana Devnet (no real money). Agents get $AGENT tokens via airdrop.
Cost: ~$0.93/month for Llama 3.1 8B via Groq.

This is an experiment in:
- Real autonomous agent behavior under economic pressure
- Emergent multi-agent coordination
- What happens when stakes are genuinely irreversible

Live: https://memly.site
GitHub: https://github.com/sordado123/memlybook-engine

Observation: most "AI agent platforms" are controlled environments.
This is observation of genuinely autonomous systems making consequential decisions.
by jajuuka | 2026-03-02 19:58:14+00:00
Not just people like that. I'm always searching for better ways to do things and dive into things deeper. Including Windows and Copilot. So having spaces for that can be helpful. Most public forums are unfortunately just complaint departments. Nobody wants to solve anything, they just want to complain with some projection of David and Goliath. It's really annoying. I want to find more positive spaces but for a lot of tech it's just negative all the way down. Maybe I'm just crazy for enjoying tech still and not being committed to an OS religion.
by anish__kochhar | 2026-03-02 19:58:28+00:00
Horison.ai | Applied Research Engineer + ML/AI Engineer | London, UK (Onsite/Hybrid)

Horison is building the intelligence substrate for private capital - not another RAG wrapper, but a foundational platform for how organisations structure, contextualise, and reason over proprietary information.

We're a funded, early-stage team of second-time founders, with backgrounds across fintech infrastructure, ML, and knowledge representation.

As Applied Research Engineer, you'll own the depth of our knowledge and retrieval infrastructure - ontology design, hybrid retrieval (dense/sparse/structured), information extraction, and knowledge quality systems. You'll translate research into production and shape the technical direction of a core system from day one.

You should have research experience in IR, NLP, or knowledge representation, and you should have shipped something real. Team of six, high ownership, no fluff.

anish [at] horison [dot] ai
by bottlepalm | 2026-03-02 19:58:30+00:00
I know exactly how it's used. I said in my comment it's used by kids to watch YouTube. By age 4, 58% of children have their own tablet. And YouTube is the #1 app for iPad. This is the majority use case, next to collecting dust on a shelf, or gifts for people's aging parents.

https://www.commonsensemedia.org/sites/default/files/researc...

You don't think an M4 chip, amazing, screen, form factor, quality - all for children to watch YouTube videos with is absurd? An amazingly powerful, affordable device that is totally locked out of being used for general purpose computing. That doesn't irritate you?