MurphyA/hacker-news huggingface dataset:
by datsci_est_2015 | 2026-03-12 12:51:57+00:00
Humans have a much greater diversity in bias because we have all lived our own unique lives. LLMs are incredibly limited, by contrast. Even if you were somehow to simulate bias by exposing subsets of LLMs to subsets of human knowledge corpuses, you would need billions of subsets to simulate the diversity of human bias.

Wisdom of the crowd also implies that diversity of human bias is a good thing, in aggregate.

To more closely address your point: if all companies use the same LLM they’ll all have the same hiring bias. But if Company Foo has Hiring Manager Bob that’s biased against me, I can shoot my shot with Company Bar with Hiring Manager Alice who might not be.
by Fr-e-d | 2026-03-12 12:52:40+00:00
Hey HN, I'm Fred. I designed this from an iPhone during a
6-week digital detox in Vietnam — my partner banned my laptop.

Without a computer I couldn't chase the weekly model releases. I was
stuck with a harder question: not "how to code faster with AI" but
"how to make agentic coding reliable." I read papers on separating
planning from execution in agent systems — GoalAct (Chen 2025) shows
+12.2% success when you split them [1]. That became the core design.

GAAI is a .gaai/ folder — markdown, YAML, bash. No SDK. You drop it
into any project. It gives you two agents with a hard gate between
them:

- Discovery structures what to build (stories, acceptance criteria).
Never writes code.
- Delivery ships validated stories autonomously (plan, implement, QA).
Never decides scope.
- The backlog is the contract. Nothing gets built that isn't in it.
- Memory persists across sessions — session 47 knows what session 1
decided.

I stress-tested it by building a full SaaS — 7 Cloudflare Workers,
auth, billing, matching engine, analytics. First 4 days: 39 stories
shipped, 260 tests passing, 16K LOC. Today: 176 stories, 84K LOC.
96.9% cache read rate — agents build forward, not from scratch.

vs. AGENTS.md: solves one session, not cross-session memory or scope
drift. vs. LangGraph/CrewAI: code-first orchestration for building AI
systems. GAAI governs using AI tools. Different level.

Honest trade-offs: no programmatic enforcement (agents follow the
files), even trivial tasks need a backlog item, freshly open-sourced.

Install: git clone + bash install.sh --wizard. 30 seconds.

ELv2 — use it freely in your projects. Only restriction: can't offer
it as a competing hosted service.

[1] https://arxiv.org/abs/2504.16563
by CM30 | 2026-03-12 12:52:51+00:00
This. People seem way too keen to assume every questionable decision or random mistake is part of some super complex 4D chess game, while the real answer is probably a whole lot more mundane. They write like this because that's how a lot of people write in general, elite or not.

I see all these example emails and such and my first thought is "oh, so how many of my relatives write emails and text messages then".

It reminds me of the whole "scammers use bad English to find easy marks" concept. Yeah, maybe some do. But again, it feels like people giving them way too much credit for what could easily have been an accidental situation.

Like, you suck at writing English because it's not your first language and you've got no professional reason to do so, and try scamming someone on the other side of the world with those skills. If the recipent is smart or internet savvy, they'll ignore your message. If they're not smart/are overly greedy/aren't internet savvy in general they'll fall for it.

So, why would you try and improve your English skills in that situation? As far as you know, they're good enough to get you money from gullible folks elsewhere. You're succeeding at what you want to suceed at, so you don't really think twice about it.

You can certainly try and find some psychology related reason behind every random disrepetency and questionable decision, or assume that people are often pretty dumb and do pretty dumb things, and that they won't stop doing those things unless they've given a reason to.
Show HN: Prompt Area – Rich text input for AI chat UIs (Shadcn, zero deps)
by kachar | 2026-03-12 12:52:55+00:00
Hi HN,

I built Prompt Area — a contentEditable rich text input component for React, designed specifically for AI chat interfaces.

The problem: if you're building an AI chat UI, your input needs @mentions (to reference context), /commands (to trigger actions), #tags, inline markdown, file attachments, and undo/redo. Existing options are either too limited (react-mentions only does mentions) or massive overkill (Tiptap, Lexical, and Plate are full document editors built on ProseMirror/Slate — way too heavy for a prompt input).

Prompt Area sits in the middle. It uses a segment-based model (array of {type, text} objects) on top of contentEditable, with all logic extracted into a pure engine file that's testable without a DOM. Zero dependencies beyond React and your existing Tailwind/shadcn setup.

Features: trigger-based chips (@, /, # or any character), immutable chip pills, live inline markdown preview, auto-growing textarea, full undo/redo history, copy/paste with chip preservation, IME support for CJK input, ARIA accessibility, dark mode, and file/image attachments.

Install in one command:

`npx shadcn@latest add https://prompt-area.com/r/prompt-area.json`

Demo & docs: https://prompt-area.com
GitHub: https://github.com/team-gpt/prompt-area

Would love feedback from anyone building AI/LLM interfaces.
by bryanlarsen | 2026-03-12 12:53:03+00:00
I used nvm a long time and was very happy to get rid of it and use `devbox` and similar tools instead.

The rule on my machine now is that everything has to be in a per-project sandbox, self-contained to ~/.local/bin or installed by the system package manager.

The question was about global tools, something nvm purposefully does not handle.

The `uv tool` answer by a sibling comment was great; it'd be nice to have something similar for npm.
by littlecranky67 | 2026-03-12 12:53:13+00:00
Good for you, not see how that is relevant for the discussion what features were added to macOS though. Also note that clipcoard sharing just as Airdrop are point-to-point and neither require internet connection, nor is the data send through a third party or network.

> since it's open source you should be able to help fix them

And I can also grow my own tomatoes and cucumbers in my back yard, but I still prefer to buy them from a supermarket.
by layer8 | 2026-03-12 12:53:33+00:00
Nobody will notice if you use LLMs as long as it doesn’t sound like an LLM. But sounding like an LLM is as “bad” as badly written English, so you’ll get looked down upon either way in that case.

It’s not without reason that bad English is taken as a signifier, and for similar reasons LLM-speak is taken as a signifier as well.
by afavour | 2026-03-12 12:53:41+00:00
An encryption key could be stored in a QR code and the user could be sheltered from any technical issues.

To be honest if we’re talking truly accessible, even usernames and passwords aren’t great. Users forget them. That’s why a lot of sites these days offer to email you “magic links” to log you in. And if you want to do that you need to make sure you’re running a server sending emails that won’t immediately go to spam.

At a certain point if you want a truly scalable, decentralized platform you’re going to have to cut back on backend responsibilities. This is a logical answer to the problem.
by wa5ina | 2026-03-12 12:53:45+00:00
I got tired of paying $35/mo just to see company-specific tags, so I scraped 1,500+ recent questions across 458 companies to map their actual statistical biases.

I ran some basic frequency math. For example, looking at 100 recent Apple questions, their difficulty is standard (15% hard), but they test BSTs at 4.5x the industry average and backtracking at 2.5x.

The site is completely free, no login required. Just built it to make interview prep slightly less of a black box. Happy to answer any questions on how I scraped/categorized the data.
Show HN: ImagineIf – Collaborative storytelling with AI visuals (22 languages)
by tugaypala | 2026-03-12 12:53:53+00:00
ImagineIf is a platform where users collaboratively write stories, one 280-character segment at a time, and AI generates a visual for each contribution automatically.
Each story starts with an "Imagine if..." prompt. Anyone can continue a chain — the AI produces an image for every new segment using FLUX-dev (Replicate as primary, fal.ai as fallback). Stories can branch based on contributor choices.
Tech stack: React Native/Expo (web PWA + iOS + Android), FastAPI backend, MariaDB, Celery+Redis for async image gen, Groq/Llama for moderation and translation, FLUX-dev for visuals.
What's live today:

Web: https://imagineif.app
iOS: https://apps.apple.com/app/imagineif-story-chains/id67597222...
Android in closed testing
22 language support with automatic translation pipeline

The hardest part technically was building a reliable dual-provider image pipeline that degrades gracefully without blocking the user's contribution flow — a failed image generation shouldn't interrupt the writing experience.
Happy to answer questions about the architecture or the solo-founder experience.
Show HN: GlobCall – Agentic AI voice agents that make real international calls
by makepostai | 2026-03-12 12:54:04+00:00
GlobCall started as a Skype alternative — browser-based international calling, open a tab and call anywhere. I use it myself to run my business in Europe.

The more businesses used it for support, sales, collections and onboarding, the more one thing became obvious: the phone call isn't the problem. The human availability around it is.

---

The "Human-Phone" interface is beautiful:

Voice closes deals chat can't. A real human voice carries weight across cultures and languages that no message, notification or email ever will. It's still the highest-trust communication channel on earth.

But the world is moving fast. Someone has to be awake. Someone has to speak the language. Someone has to be available at 2am when a customer in another timezone picks up the phone.

The medium is extraordinary. Human availability is just finite.

---

The "Agent-Phone" interface is where the world is heading:

AI voice agents that place and receive real international phone calls. A local number in Brazil following up on invoices. An AI that speaks Romanian handling inbound calls overnight. An outbound agent qualifying leads in the UAE while your team sleeps. Or simply telling your AI agent "book me a haircut" and it calls the barber and handles it.

This is the next era of business communication. The infrastructure exists. The accessible, affordable calling layer built for this is what we're building at GlobCall.

---

The agentic layer is in private testing. International calling, local phone numbers in any country, browser and API access are all live today. Flat usage pricing, top up and go.

Happy to talk about the stack, the agent architecture, or anything in between.

can be found at → globcall.com
by Cthulhu_ | 2026-03-12 12:54:32+00:00
It depends on whether what they say is coming from them or if it's something they are citing; "I am extremely attractive" can be countered with "you are wrong", but "People say I am extremely attractive" cannot be, because I did not come up with the opinion, others did.

"They are wrong" is then valid, or "That is not correct" if I have misinterpreted them.
by utopiah | 2026-03-12 12:54:47+00:00
> If it is a simple kubernetes config then the model doesn't matter too much

I guess at least this person https://www.tomshardware.com/tech-industry/artificial-intell... might disagree. I think already to know what Kubernetes even is requires quite a bit of knowledge. Using a tool that manipulate its configuration files IN PRODUCTION without risking data loss is another ball game entirely.
by eterm | 2026-03-12 12:55:37+00:00
I used it with Sonnet 4.0 a lot, and there was vastly more back-and-forth and correction of "dumb" things, such as forgetting to add "using" statements in C# files.

I don't know if it's model, or harness improvements, or inbuilt-memory or all of the above, but it often has a step where it'll check itself that is done now before trying to build and getting an inevitable failure.

Those small things add up to a much smoother and richer experience today compared to 6 months ago.
by malfist | 2026-03-12 12:56:47+00:00
Two boxes is the only choice that makes sense. It is always better than one box.

No matter what you do after you enter the room, the predictor has already made their move, nothing you do now will change it. The only logical thing to do is to take both boxes because whatever the value in the second box is it will be added to the first box. If you only take the second box you are objectively always giving up $1,000 and getting no value in exchange for doing so (since not taking the first box doesn't change what's in the second)
by wongarsu | 2026-03-12 12:57:05+00:00
I don't find this very compelling. If you look at the actual graph they are referencing but never showing [1] there is a clear improvement from Sonnet 3.7 -> Opus 4.0 -> Sonnet 4.5. This is just hidden in their graph because they are only looking at the number of PRs that are mergable with no human feedback whatsoever (a high standard even for humans).

And even if we were to agree that that's a reasonable standard, GPT 5 shouldn't be included. There is only one datapoint for all OpenAI models. That data point more indicative of the performance of OpenAI models (and the harness used) than of any progression. Once you exclude it it matches what you would expect from a logistic model. Improvements have slowed down, but not stopped

1: https://metr.org/assets/images/many-swe-bench-passing-prs-wo...
by CapricornNoble | 2026-03-12 12:57:23+00:00
> Iran's regime has just indiscriminately killed 20-30K innocent civilians and uncountable many have been tortured. That is a crime against humanity. So, the top of Iran's regime and its IRGC has to be punished. I'm fine with that punishment being US and Israel's missiles.

Israel's regime has killed twice that many in Gaza. Shouldn't they be prioritized for "punishment"?
by phailhaus | 2026-03-12 12:57:25+00:00
> Each board director could ask for a particular thing they require, or a briefing themself, eliminating human intermediary

You are missing the point of board briefings. The CEO serves as a critical filter of information, deciding what to tell the board and how to frame it. If you take the CEO out of it, you're giving the board full access to the company's state. There's enough going on day-to-day that each member can tell themselves the story they want to believe. The CEO is there to advocate for the company and present a unified front, you can't take them out of the equation.
by k310 | 2026-03-12 12:57:35+00:00
Another "walk in the park" "over in 3 days" intervention, like the invasion of Ukraine.

Unchecked and ill-advised power. Techies please note. All the simulation and scenario planning we do is wasted when it's apparently scrapped for whim.

Oh, and distraction from Epstein.

Unintended consequences dominate when planning is absent or ignored. "Fire anyone who questions me."

A classic failure mode.
by rustyhancock | 2026-03-12 12:57:42+00:00
I think I'm coming to the same conclusion Gpt-3 to 5.3 have had real tangible but incremental improvements with quite diminishing returns.

Perhaps we won't see a phase change like improvement as we did from gpt-2 through to 3 until there is several more orders of magnitude parameters and/or training. Perhaps we will never see it again!

What is getting rapidly better is scaffolding but this seems to be more about understanding and building tools around LLMs than the LLMs themselves improving.

I'm still excited about AI but not constantly hyped to the rafters as some.
by pnathan | 2026-03-12 12:57:45+00:00
Data is missing on this chart.

It's my experience that opus 4, and then, particularly, 4.5, in Claude code, are head and shoulders above the competition.

I wrote an agentic coder years ago and it yielded trash. (Tried to make it do then what kiro does today).

The models are better. Now, caveat - I don't use anything but opus for coding - Sonnet doesn't do the trick. My experience with Codex and Gemini is that their top models are as good as Sonnet for coding...
by ryanackley | 2026-03-12 12:58:01+00:00
Are you using a tool like Claude Code or Codex or windsurf? I ask because I've found their ability to pull in relevant context improves tasks in exactly the way you're describing.

My own experience is that some things get better and some things get worse in perceived quality at the micro-level on each point release. i.e. 4.5->4.6
by BloondAndDoom | 2026-03-12 12:58:14+00:00
I feel like anyone used AI coding tools before 11/25 and after 1/26 (with frontier models) will say there has been a massive jump in, there is a difference between whether LLM can do a specific task or pass some arguably arbitrary checks by maintainers vs. what the are capable of.

We still have tons of gaps about how to build and maintain code with AI, but LLM themselves getting better at an unbelievable pace, even with this kind of data analysis I’m surprised anyone can even question it.
by butlike | 2026-03-12 12:58:23+00:00
That too, depends on circumstance.

If it is rainy near me, and clear skies near you, and I tell you the sky is grey, without corroboration from the weather report, I am wrong to you. If you say the sky is blue, without corroboration, you are wrong to me.

Gravity falls down. On Earth.

The boiling point is 100 degrees. Unless you're using Fahrenheit or Kelvin.

I find that when refuting people, instead of outright debasing their position with a right/wrong dichotomy, it works better to illuminate the possibility there is a larger breadth to the viewpoint. In this way, both views can generally share the same space. Healthily, if one can add such a descriptor.
by marcosviladomiu | 2026-03-12 12:58:39+00:00
I'm the EMEA Marketing Director at Bang & Olufsen. Over the past year
I've been running a simple experiment: asking ChatGPT, Perplexity, and
Claude for brand recommendations in our category — premium audio — and
tracking which brands appear, how often, and with what level of
specificity.

The results were uncomfortable. Some brands consistently surface
with rich, accurate descriptions. Others — including ours at times —
either don't appear or appear with vague, outdated framing. This has
nothing to do with SEO. It's about how well a brand's identity is
structured in the data that LLMs train and retrieve from.

I've been calling this GEO — Generative Echo Optimisation. The core idea: AI doesn't find your brand, it reflects it. If your brand's presence online is fragmented, inconsistent, or lacks depth, the AI produces a faint echo or none at all.

A few things I've been trying to measure:
- Share of Echo Voice (SEV): % of times your brand appears vs.
competitors in relevant AI recommendation queries
- Brand Accuracy Score (BAS): how closely AI descriptions match
your intended positioning
- Conversational Depth Index (CDI): how much detail AI generates
about your brand vs. surface-level mentions

The problem is there's no standard tooling for this yet. I'm doing
it manually with prompt templates and spreadsheets.

Has anyone else been tracking this? Are there tools I'm missing?
And do you think GEO is a real discipline or just repackaged content strategy with an AI label?

(I wrote a short book on this if anyone wants the framework — but genuinely more interested in what others have found.)
by yakkomajuri | 2026-03-12 12:59:16+00:00
This is not new, but it's certainly getting worse and will get even worse. Some large companies used to do bot interviews for low-level positions even pre-AI where the bot would ask a recorded question and you'd have a certain amount of time to answer.

It reminds me of a funny story. I did a Business Management degree at university and during my first year was already freelancing as a dev. But basically everyone around me wanted to work in either consultancy or investment banking (this was in London) so the path was to get accepted for a "spring week" during your first year, internship the second, and then get an offer to work there at the end of your third year.

With all everyone could talk about being applying for spring weeks, I gave in and decided I was going to prove to myself and others that I could do this if I wanted to but I just wanted to be a dev. Applied to JPMorgan and got through to this first bot interview stage. I thought I was knocking it out of the park and then the last question was "Why do you want to work at JP Morgan?". The answer time was something like 30s. I froze for what felt like 15 then blurted out some BS.

That told me all I needed to know. I never again thought about working in this industry and soon after was hired as a developer full-time while taking my studies lightly.
by simonreiff | 2026-03-12 12:59:32+00:00
Nice article! I'm not a graphics programmer but mathematically it makes full sense that cross-product would be a vast optimization over using `sin()`. From a complexity perspective, the computation of a cross-product reduces to calculating a formal determinant, a fixed number of arithmetic operations, and hence resolves to O(1) complexity. By contrast, computing `sin()` is O(M(n)log(n)) (even though faster algorithms are often possible in practice). See Brent, Fast multiple-precision evaluation of elementary functions (1976).
by utopiah | 2026-03-12 12:59:33+00:00
I gave up on trying months ago, you can see the timeline on top of https://fabien.benetou.fr/Content/SelfHostingArtificialIntel...

Truth is I'm probably wrong. I should keep on testing ... but at the same time I precisely gave up because I didn't think the trend was fast enough to keep on investing on checking it so frequently. Now I just read this kind of post, ask around (mainly arguing with comments asking for genuine examples that should be "surprising" and kept on being disappointed) and that seems to be enough for a proxy.

I should though, as I mentioned in another comment, keep track of failed attempts.

PS: I check solely on self-hosted models (even if not on my machine but least on machines I could setup) because I do NOT trust the scaffolding around proprietary closed sources models. I can't verify that nobody is in the loop.
by sigmar | 2026-03-12 13:00:26+00:00
>This means the step function has more predictive power (“fits better”) than the linear slope. For fun, we can also fit a function that is completely constant across the entire timespan. That happens to get the best Brier score.

I mean, sure. but it's obvious in that graph that the single openai model is dragging down the right side. Wouldn't it be better to just stick to analyzing models from only one lab so that this was showing change over time rather than differences between models?
by fariniasty | 2026-03-12 13:00:34+00:00
Hi HN, I'm the creator.
I'm a graphic designer who decided to learn enough code to build the “social network” (if you can even call it that) I actually wanted to use. It turned into a year-long hobby project and a lot of late nights fighting FFmpeg, Google Play requirements, and the occasional rebellious AI model.

Happy to answer questions about the PWA architecture, running media processing on the client side, or anything else about how this works.
by manarth | 2026-03-12 13:00:39+00:00
Some countries alter their observance of DST in line with their observance of Ramadan, which means that the time-offset changes aligned with Ramadan.

Ramadan is observed from one visual sighting of a crescent moon to the next.

Cloud conditions may prevent sighting and thereby alter the official start of Ramadan for an individual location, and from time-to-time, the start of a country's change in timezone.