MurphyA/hacker-news huggingface dataset:
by smallstepforman | 2026-03-10 06:15:54+00:00
Read the article. Satya brought the share price from $35 to $400, that wont kill Microsoft.

I guess what you’re trying to say is that it will kill Windows. But that wont happen since enormous percentage of businesses run Windows ecosystem.

Lets face it, Windows is in maintenance mode, pointless for MS to invest heavily in it since there is no threat for businesses switching to Linux or something else. MS devs primary maintenance job these days should just be scrabling MS Office API every 6 months or so to break Wine and other Linux non-emulators. Wine devs in constand rearrange deck chairs mode, while Win32+Office devs just add a new parameter to an API interface in their 6 month cyclic undocumented API breaking scheme.

You need a better Office than MS Office to break the cycle, and this will be a Web based office / collaboration tool. And guess where MS Azure and Web services fit in this brand new world.

Microsoft dominance aint going away in our lifetimes. Only non US government pressure may force other countries to switch to a flavour of Linux due to US sanctions. Only then can you see a visible migration from Windows. This is a decades long process.
by tempoz | 2026-03-10 06:16:33+00:00
Hey HN! I built Veltora (https://veltora-beta.vercel.app), an ML-powered customer churn risk predictor that benchmarks predictions against real published data from 35+ well-known companies across 9 industries.

The core problem I wanted to solve: most churn tools return a risk score with no grounding. A "72% churn risk" means nothing without knowing whether that's normal for your industry. Veltora answers that by calibrating a weighted logistic regression model against 2024–2025 benchmark research for companies like Netflix, Salesforce, AT&T, Chase, and many others.

Technical details:
– Weighted logistic regression with industry-specific feature weights
– Calibrated against published churn rate benchmarks (annual/monthly) from industry reports
– 9 verticals: SaaS, Telecom, Fintech, Media/Streaming, Retail, Healthcare, Hospitality, Insurance, Utilities
– Frontend: React + Vite + Recharts
– Auth: Clerk, Payments: Stripe, Hosting: Vercel
– Freemium model

The product has two main surfaces:
1. Custom Predictor — input your own customer metrics and get a risk score with benchmark context
2. Portfolio Dashboard — browse real churn benchmarks by industry and company

Happy to discuss the model methodology or calibration approach. Would love honest feedback on whether this is useful to anyone in customer success, rev ops, or subscription businesses.
Branching prompts instead of linear chat
by macnorton | 2026-03-10 06:16:54+00:00
Most LLM interfaces assume thinking happens in a straight line.

prompt → response → refine → refine

But when I explore ideas with AI, it usually branches.

One prompt leads to multiple directions, and it's hard to compare them in a single chat thread.

So I built a small workspace where prompts branch like a tree.

You can explore different reasoning paths and continue from the most interesting one.

I'm curious what HN thinks about node-based interfaces for prompting compared to traditional chat UIs.
by joecool1029 | 2026-03-10 06:17:17+00:00
> Would they have zero radio coverage, or sub-optimal coverage?

Sub-optimal for data if it allows attach. If they don’t have working IMS profile it may reject the attach at worst or just have no working calls/texts at best (we do not have fallback 2g/3g except for 911 on 2g with pockets of t-mobile). There is 911 IMS profile but it sometimes doesn’t work with foreign phones, its tricky: https://optimerainc.com/wp-content/uploads/2021/11/OptimERA_...

> Could be a feature for those wanting a Wi-Fi only mobile Linux device.

I’m sorry but this is cope for a phone. At that point just buy a better linux device not built on a complex phone soc, it’s not a phone then.
by creativhood | 2026-03-10 06:18:02+00:00
Text (Leave as a comment immediately after posting): > "I got tired of the latency and abstraction overhead of standard NTFS/ext4 file systems when running high-fidelity fluid dynamics and bio-compute models. I built PICO-L to bypass OS directory pathing entirely. It maps data directly to hardware sectors using a deterministic 3D coordinate system (A_{offset} = (n \cdot 10^6) + (m \cdot 10^3) + p). It also forces LLMs to compute hard mathematical offsets rather than hallucinating file paths. Open to brutal peer review on the coordinate math and asynchronous bot execution.
by marcus_holmes | 2026-03-10 06:18:53+00:00
Good question.

I don't know enough about neuroscience to really answer your question in depth.

My opinion, uninformed as it is, is basically around the intuitive reasoning that something cannot be "highly autonomous" if it has to be kicked every second ;) Autonomous is defined as not needing to be controlled externally. And coupling that part with something as simple as cron job doesn't solve that in any meaningful way or make it "autonomous".

A batch file coupled with a cron job that triggers it once a day is not an "autonomous system" to my mind. It's a scheduled system, and there's a significant difference between those things.
Branching prompts instead of linear chat
by macnorton | 2026-03-10 06:20:06+00:00
Most LLM interfaces assume thinking happens in a straight line.

prompt → response → refine → refine

But when I explore ideas with AI, it usually branches.

One prompt leads to multiple directions, and it's hard to compare them in a single chat thread.

So I built a small workspace where prompts branch like a tree.

You can explore different reasoning paths and continue from the most interesting one.

I'm curious what HN thinks about node-based interfaces for prompting compared to traditional chat UIs.
by vladdoster | 2026-03-10 06:20:31+00:00
Location: Nashville, TN
Remote: true
Willing to relocate: boston || nashville
Technologies: Python, Django, IaC, Terraform, Golang, CI/CD, Azure/AWS, {ba,z}sh, Lua, Springboot,
Redis, Docker, Open Service Broker, {,non}relational & vector dbs, shared services, SOA
Email: mvdoster@gmail.com

resume: https://linkedin.vdoster.com && http://github.com/vladdoster

sr. cloud software engineer @ Mastercard (5 years)

Designed and built Platform-as-a-Service for microservice workloads running on Azure and AWS
to migrate on-prem workloads to the cloud. The platform operates globally, allowing teams to
deploy to several Azure-backed regions worldwide. Helped plan and create regional landing zones
in Azure for application teams wanting to perform globally. Designed platform components using
Terraform, Go, and Node; developed support tooling using Bash, Python, and Azure CLI. De-risk the
platform by addressing security vulnerabilities and ensuring the platform is PCI compliant.

api/devops software engineer @ Harvard Medical School (2 years)

Worked as part of the DevOps team to manage a 15k core CentOS HPC cluster. Designed and implemented
Python-based security scanning pipeline to securely deploy & run Apptainer images via SLURM.
Coordinated cross-team emergency patching eﬀorts. Collaborated with researchers and engineers across
3-letter institutions to deliver secure research infrastructure. Built CI/CD Pipelines with GitHub, Jenkins,
to automate code builds, testing, and deployments. Wrote Puppet modules to manage configuration of HPC fleet.
Wrote custom Puppet module & Python scripts to automatically remediate Tenable findings.

full-stack software engineer @ Global Prior Art (3 years)

Used Django, Celery, Selenium grid, and AWS API Gateway for IP rotation to scrape USPTO patent PDFs,
apply CV for data extraction, and generate reports with relevant claims, saving employees 4-5 hours daily.
Wrote E2E black-box/fuzzing unit tests. Automated on-premise deployment via Github Actions and Ansible.

maintainer @ Zinit (5 years)

Develop and maintain a zsh plugin manager on Github with 4k stars. Implemented unit tests,
containerization, bug fixes, and new features.
See: https://github.com/zdharma-continuum/zinit

contributor @ Open-source documentation (5 years)

Contributed hundreds of documentation corrections and enhancements to 400+ open source projects.
Calling all who run inference in models
by hpcaitech | 2026-03-10 06:20:51+00:00
We've been thinking a lot about inference infrastructure recently, and it seems like the challenges are very different from training.
Training tends to be compute-heavy but predictable, while inference introduces things like:
- latency constraints
- dynamic batching
- unpredictable traffic patterns
- model versioning in production
- GPU utilization issues at smaller batch sizes
For people running inference in production today, what's been the most painful part?
by hrmtst93837 | 2026-03-10 06:21:09+00:00
Obscurity is a shrinking moat unless you are upstreaming changes regularly, and most uncommon emulators lag behind on the boring but needed patches compared to QEMU or Firecracker. If you shift to a niche emulator for security, you really need a plan to audit the new attack surface it brings. Even a weird stack tends to attract attackers once it gets popular enough or just irritates someone determined.
by 3rodents | 2026-03-10 06:21:40+00:00
A polite request: English and Chinese are very different languages, asking AI to "translate" your thoughts sanitizes what you have to say, your words lose all of your personality -- a great shame. Participation from non-English speakers is wonderful but rather than use AI to "translate", using a literal translator (e.g: Google Translate, DeepL) will ensure we get to hear what you have to say, not what AI thinks you want to say.

The English that English speakers post on Hacker News is often grammatically incorrect, clumsy, misspelled, and that's okay, good, even. We want to hear from you!

(My preference is for translators to include both the original Chinese words, and the English translation because it means your fellow Chinese speakers get to read your exact words, but of course that is personal preference :)).
by Chamix | 2026-03-10 06:21:47+00:00
Try 10s of trillions. These days everyone is running 4-bit at inference (the flagship feature of Blackwell+), with the big flagship models running on recently installed Nvidia 72gpu rubin clusters (and equivalent-ish world size for those rented Ironwood TPUs Anthropic also uses). Let's see, Vera Rubin racks come standard with 20 TB (288gb per "gpu" of 2 dies) of unified memory, and NVFP4 fits 2 parameters per btye...

Of course, intense sparsification via MoE (and other techniques ;) ) lets total model size largely decouple from inference speed and cost (within the limit of world size via NVlink/TPU torrus caps)

So the real mystery, as always, is the actual parameter count of the activated head(s). You can do various speed benchmarks and TPS tracking across likely hardware fleets, and while an exact number is hard to compute, let me tell you, it is not 17B or anywhere near that particular OOM :)

Comparing Opus 4.6 or GPT 5.4 thinking or Gemini 3.1 pro to any sort Chinese model (on cost) is just totally disingenuous when China does NOT have Vera Rubin NVL72 GPUs or Ironwood V7 TPUs in any meaningful capacity, and is forced to target 8gpu Blackwell systems (and worse!) for deployment.
by User750 | 2026-03-10 06:22:22+00:00
I’ve been developing Japr750, a symmetric stream cipher engine written in pure Python. My main focus was on optimizing bitwise logic and OOP structure to see how much performance I could squeeze out of Python without using external libraries.
Performance: Achieved a 4x speedup after refactoring (~18µs/char).
The Challenge: I’ve included a "Public Challenge" in the repository called "Challenge". It uses a randomized internal alphabet and a secret 16-character mapping and three keys
I would love your feedback on the implementation or to see if anyone can break the challenge!
Show HN: JTC – JSON/YAML Type Checker Using TypeScript
by disjukr | 2026-03-10 06:22:26+00:00
If you add a $schema field to JSON or YAML, you can run JSON Schema validation.
Likewise, if you add a $type field, this tool performs TypeScript-based checks.

It provides both a VSCode extension and a CLI.

The VSCode extension offers Diagnostics (it draws a red underline when something is wrong) and a Go To Definition feature.
Since it’s published on both the Marketplace and open-vsx, the extension can also be used in other VSCode-based editors such as Cursor.

The CLI provides check and lsp commands.
With the check command, you can run JSON type checks in CI/CD,
and with the lsp command, it can be integrated and used in editors other than VSCode.

I’m planning to soon implement type-based field auto-completion and a feature that shows documentation on mouse hover.
I’m also considering adding type checking using other static type languages besides TypeScript.
by aurareturn | 2026-03-10 06:22:27+00:00
I recently understood why so many people are anti-AI and think AI is a scam.

It's because they are Windows users and being shoved piss poor Copilot implementations down their throats by Microsoft.

I have no doubt that Microsoft is using the cheapest(worst) cloud model possible for free Copilot users or they're running a tiny local model on the NPU when available.

These people aren't running Opus 4.6 or GPT 5.4. No wonder they're so anti-AI and can't see the why there is AI hype.
by AnthonyMouse | 2026-03-10 06:23:04+00:00
"Decommissioning costs" are essentially bad math. Here's this again:

https://www.ourworldofenergy.com/images/electrical-power-gen...

Nuclear, inclusive of construction costs: ~$181/MWh, only better than natural gas because no CO2. Nuclear, cost of continuing to operate an existing reactor once it's already built: $31/MWh, basically the cheapest thing on the market, half the cost of continuing to operate an existing natural gas plant (because you need so much less fuel).

What this implies is that if you build a nuclear plant you're going to want to continue operating it for 80 years, and even then you probably want to just modernize it again instead of actually decommissioning it.

The long-term average returns from ordinary investments (e.g. S&P 500) are ~10%/year, implying that even if you require decommissioning to be prefunded (unlike any competing form of power generation), the amount of money you need is less than 0.05% of what the cost will be in 80 years. Adding $500 million in decommissioning costs isn't $500M in net present costs, it's only $250 thousand in net present costs, because you take the $250k and add 80 years worth of interest (1.10^80) which multiplies your starting capital by more than a factor of 2000.

It's really just the construction, and that's in significant part because you have to build more of them to get economies of scale for building them.
by nneonneo | 2026-03-10 06:23:05+00:00
This is one of the reasons I like the PNG format - it has checksums. You can fix a surprising number of broken files by bruteforce testing plausible errors until the checksum passes.

With JPEG one of the big problems is that the data is Huffman-encoded without any inter-block markers (except maybe Restart Markers, if you're lucky). This means that a single bitflip can result in a code changing length, causing frameshifts in many subsequent blocks and rendering them all undecodable. If you have a large block of missing data (e.g. a 4k-byte sector of zeros), then you have to guess where in the image the bitstream resumes, in addition to guessing the value of the running DC components.
by ImPostingOnHN | 2026-03-10 06:23:34+00:00
How much does it cost to keep the facility open for 1 minute? Power, staff, etc.

And then there's the challenge of keeping people on staff at all when parents consistently make the staff work late.

Beyond that, it seems like a good way to achieve the objective of convincing parents to not offload externalities onto others by being late.
by rmunn | 2026-03-10 06:24:56+00:00
Article says room and board, plus cost of transportation, are covered. So that $13/hr goes quite a bit further than it would if a chunk of it was going towards paying for food and rent. (And if you were paying for the cost of the plane flight, then nobody would take the job). Can't calculate the equivalent compensation without knowing where the author lives normally, but it might (VERY rough guess) be the equivalent of $20 to $25 per hour.
by bigyabai | 2026-03-10 06:25:21+00:00
In the long run, I think we'll see more iPad-only families. The home computer is practically non-existent outside gaming niches or work-issued machines. We've had $700-800 Macbook Air models on sale for years now, same for the Mac Mini - little has changed. As cutesy as the shared computer ideal is, I see most people gravitating towards their phones and away from general purpose computing.
by Leftium | 2026-03-10 06:25:48+00:00
# I think it's possible to architecture around this. For example, here is one idea:

- make the game as functional as possible: as in the game state is stored in a serializable format. New game states are generated by combining the current game state with events (like player input, clock ticks, etc)

- the serialized game state is much more accessible to the AI because it is in the same language AI speaks: text. AI can also simulate the game by sending synthetic events (player inputs, clock ticks, etc)

- the functional serialized game architecture is also great for unit testing: a text-based game state + text-based synthetic events results in another text-based game state. Exactly what you want for unit tests. (Don't even need any mocks or harnesses!)

- the final step is rendering this game state. The part that AI has trouble with is saved for the very end. You probably want to verify the rendering and play-testing manually, but AI has been getting pretty decent at analyzing images (screenshots/renders).

# Here is an example of a simple game developed with functional architecture: https://github.com/Leftium/tictactoe/blob/main/src/index.ts

- Yes, it's very simple but the same concepts will apply to more complex games

- Right now, there is only rendering to the terminal, but you could imagine other renders for the browser and game engines
by k310 | 2026-03-10 06:25:52+00:00
Here’s to the crazy ones. The misfits. The rebels. The troublemakers. The round pegs in the square holes.

The ones who see things differently. They’re not fond of rules. And they have no respect for the status quo. You can praise them, disagree with them, quote them, disbelieve them, glorify or vilify them.

About the only thing you can’t do is ignore them. Because they change things. They invent. They imagine. They heal. They explore. They create. They inspire. They push the human race forward.

Maybe they have to be crazy.

How else can you stare at an empty canvas and see a work of art? Or sit in silence and hear a song that’s never been written? Or gaze at a red planet and see a laboratory on wheels?

While some see them as the crazy ones, we see genius. Because the people who are crazy enough to think they can change the world, are the ones who do.

And if a message comes from Bondi, they're banned from the app store.
by cal_dent | 2026-03-10 06:26:55+00:00
Yes! Amazing show, one of my favourite shows of the last decade or two. I think what really does it for me is that they really bring the audience into the world itself. I think that's partly because there's no need to always be pushing the plot forward, which I think is a bugbear of mine of newer shows now. There's something to letting things just breathe and tales from the loop really excels at that.

The Phillip Glass soundtrack particularly elevates it too.
by pastel8739 | 2026-03-10 06:27:08+00:00
> Do we really want the entire world to be endlessly full of cookie-licking rent seekers who demand profit every time anyone does anything?

No, far better that we have four rent-seekers who gobble up everything that anyone is naive enough to share with the world, then turn around to demand profit in order to keep up with the new pace of the world that they’ve created.
by simianwords | 2026-03-10 06:29:03+00:00
one of the posters said it can't even reason through chess, i ran the actual benchmark, spent money and actually proved that it can beat a 1000 elo chess engine.

https://news.ycombinator.com/item?id=47316787

does this show i'm a troll? throughout this thread there has been misinformation that i have been dispelling.

what you are doing is ad hominem.

here's another post where i ran the prompt that the person asked which would apparently show that LLM's can't reason

https://news.ycombinator.com/item?id=47316855

have you considered that you might be misinformed so what i say might look like trolling?
by petterroea | 2026-03-10 06:31:09+00:00
> The timing wasn’t right. We depended on artists helping us to promote the platform, and they didn’t.

There's a certain arrogance to believing the timing "simply wasn't right". It looks really bad if you try it with any recent controversy:

* "The timing wasn't right to charge people for heated car seats"

* "The timing wasn't right to make Photoshop a subscription service"

* "The timing wasn't right to increase fees"

It's a way of talking yourself away from the fact that what you are making may, inherently, be disliked. The cited survey even seems to have been read as favourably as possible:

> Surveys consistently showed that consumers believed artists deserved payment when AI generated content in their style.

This doesn't mean people want artists style to be generated by AI. It could mean they think it's horrible, but if it happens they should at least be compensated for it. In fact, the quotes survey even says 43% believe companies should ban copying artists styles. I could make the exact opposite argument with the same data:

"Many consumers believe companies should ban copying styles, and this may be a more common opinion than measured as most people have no experience with modern AI tools and therefore no chance to have made an opinion yet. What is known is that the majority believe that if artists were to be copied, they should at least be compensated"

edit: formatting, typo
by Ogbon | 2026-03-10 06:32:28+00:00
I got fed up with reading online. Every article I open comes
wrapped in cookie banners, autoplaying videos, sidebar ads, and
subscribe popups. Chrome has a built-in reader mode but it only
works on a handful of sites and has zero customisation. Firefox's
is better but I'm a Chrome user.

So I built READR — a Chrome extension that strips any webpage
down to just the content.

The core is Mozilla's Readability.js (the same library behind
Firefox Reader View), so coverage is broad. It handles Medium,
Substack, Wikipedia, news sites, research papers, most blogs.
When Readability fails it falls back to a paragraph-density
heuristic.

It runs entirely in the browser. No server, no telemetry, no
account required. Settings sync via Chrome's storage.sync API.
The extension has four permissions: activeTab, scripting,
storage, tabs — nothing broader.

Free features: reader overlay, dark/light theme, font size,
line width control, ESC to close.

Pro ($19.99 one-time, no subscription): PDF export via
window.print() with a print stylesheet that isolates the
overlay, and auto-open on domains you specify.

Built with vanilla JS, no framework. The whole extension minus
Readability.js is under 600 lines.

Chrome Web Store: https://wushu75.github.io/readr

Interested in feedback on: sites where extraction fails badly,
whether the Pro features are the right ones to charge for, and
whether $19.99 one-time feels right or too high/low.
by Paracompact | 2026-03-10 06:32:39+00:00
Good on you, man. I'm sad to see no one else thanking you for speaking up with the voice of reason. Probably you're just too late to the punch.

I like to think most reasonably minded readers here don't share the infinitely cynical, evidentially empty, doom-pilled perspective that u/textembedding is conveying. Astroturfing is very real, but to declare everything is astroturf regardless of literally documented legal history is just to give astroturfers free license to take over the internet unhindered.