MurphyA/hacker-news huggingface dataset:
by tetris11 | 2026-02-15 13:02:53+00:00
Attak by JohnnyTwoShoes[0,1], is one of the standout games from that era: super-saiyan type battles using an underlying physics/ragdoll engine with fully destructible buildings and terrain.

Half the strategy was digging out an underground bunker to shield yourself from your enemy's 500m vertical drop attacks. It was an incredible feat of programming for the time.

The game unfortunately is in that final tiny percentage of AS3 games with missing features and thus does not work with ruffle yet, I suspect because the game was so cutting edge.

0: https://flashpointproject.github.io/flashpoint-database/sear...

1: https://www.youtube.com/watch?v=CTiO6o-NERw
by littlestymaar | 2026-02-15 13:04:12+00:00
How do you think that works?!

With the exception of diffusion language models that don't work this way, but are very niche, language models are autoregressive, which means you indeed need to process token in order.

And that's why model speed is such a big deal, you can't just throw more hardware at the problem because the problem is latency, not compute.
by datsci_est_2015 | 2026-02-15 13:04:24+00:00
I mean why set the bar any lower for Altman? Shouldn’t AI be able to produce an executable that minimizes RAM and storage? If not, why not?

If it can’t produce an executable that minimizes RAM and storage for something like Slack, are we supposed to believe it can for something more traditionally power-hungry (browsers), or storage hungry (Call of Duty, iPhone apps)?

Shouldn’t AI be on the forefront for reducing resource usage in general (not just digital resources), or is just a giant exercise in induced demand / Braess’ paradox?
by everdrive | 2026-02-15 13:04:45+00:00
HN has also been taking a turn lately. Part of it is a large influx of new users, part of it (I suspect) is just a growing disenfranchisement with the technology scene. I'm partly to blame for this as well. I've tried to stop commenting most of the time since my first and strongest response has just been to express my anger and frustration at the direction most technology is taking.
by bell-cot | 2026-02-15 13:05:27+00:00
> Lets say someone has lower urinary tract symptoms. And is 60 years old. An MRI could visualize as well as ...

Not a doctor - but maybe start with some quick & cheap tests of their blood & urine, polite questions about their sexual partners, and possibly an ultrasound peek at things?

At least in America, high-tech scans are treated as a cash cow. And cheap & reasonable tests, if done, are merely an afterthought - after the patient has been milked for all the scan-bucks that their insurance will pay out.

Source: Bitter personal experience.
by lm28469 | 2026-02-15 13:05:36+00:00
I ended up on a yt rabbit hole of "developer influencers" shilling these tools to their audience.

It looks like a sub category of wantrepreneurs who pray on wannabe developers. They never worked on anything of importance but cosplay as domain experts and sell their expertise to people who don't know better. "how I launched 23 successful SaaS in q4 2025 with clawdbot" type of videos
by hallh | 2026-02-15 13:05:58+00:00
We've tackled this problem slightly differently where I work. We have AI agents contribute in a large legacy codebase, and without proper guidance, the agents quickly get lost or reimplement existing functionality.

To help the agents understand the codebase, we indexed our code into a graph database using an AST, allowing the agent to easily find linked pages, features, databases, tests, etc from any one point in the code, which helped it produce much more accurate plans with less human intervention and guidance. This is combined with semantic search, where we've indexed the code based on our application's terminology, so when an agent is asked to investigate a task or bug for a specific feature, it'll find the place in the code that implements that feature, and can navigate the graph of dependencies from there to get the big picture.

We provide these tools to the coding agents via MCP and it has worked really well for us. Devs and QAs can find the blast radius of bugs and critical changes very quickly, and the first draft quality of AI generated plans requires much less feedback and corrections for larger changes.

In our case, I doubt that a general purpose AST would work as well. It might be better than a simple grep, especially for indirect dependencies or relationships. But IMO, it'd be far more interesting to start looking at application frameworks or even programming languages that provide this direct traversability out of the box. I remember when reading about Wasp[0] that I thought it would be interesting to see it go this way, and provide tooling specifically for AI agents.

[0] https://wasp.sh/
by logicprog | 2026-02-15 13:07:04+00:00
> the core skills and personality traits required to be properly good at the job are very much feminine. Precision, diligence, carefully working through a dull task and making sure that things are going to work in all cases... this is still the same set of values of care and diligence that led to people hiring women for the initial software jobs in the first place.

Mmm yay, more gender essentialism explicitly agreeing with 1950s gender stereotypes from this author.
by paperplaneflyr | 2026-02-15 13:07:59+00:00
I built HN Status Bar, a small VS Code extension that shows Hacker News stories in the status bar while coding.

What it does:

- Shows one HN story at a time
- Rotates automatically (default 20s)
- Refreshes from the HN API
- Click current story to open it
- Configurable settings (timing, item count, score visibility)

Feedback welcome.
by michaelt | 2026-02-15 13:08:06+00:00
> Hands, mechanically are fairly simple to mimic

I can assure you they are not.

Human hands have absolutely crazy performance. Human hands have 15+ degrees of freedom. Sub-millimeter precision, no backlash. Strong enough to lift 100 lbs. Gentle enough to catch a thrown egg without breaking it. Rigid enough to hammer a nail without dropping the hammer. A compact forearm for reaching into tight spaces. Water- and dust-proof. Oh, and it'll last for decades without maintenance.

Even a $100k robot hand like a Shadow Hand can't compare.
by littlestymaar | 2026-02-15 13:08:37+00:00
It doesn't need to, during inference there's little data exchange between one chip and another (just a single embedding vector per token).

It's completely different during training because of the backward pass and weight update, which put a lot of strain on the inter-chip communication, but during inference even x4 PCIe4.0 is enough to connect GPUs together and not lose speed.
by miki123211 | 2026-02-15 13:08:41+00:00
I wouldn't be surprised if LLM companies end up sponsoring certain platforms / news sites, in exchange for being able to use their content of course.

THe problem with LLMs is that a single token (or even a single book) isn't really worth that much. It's not like human writing, where we'll pay far more for "Harry Potter" and "The Art of Computer Programming" than some romance trash with three reads on Kindle.
by dredmorbius | 2026-02-15 13:10:13+00:00
Rather famously in at least the case of Google and others, with government funding:

"Google’s true origin partly lies in CIA and NSA research grants for mass surveillance" (January 28, 2025)

The intelligence community hoped that the nation’s leading computer scientists could take non-classified information and user data, combine it with what would become known as the internet, and begin to create for-profit, commercial enterprises to suit the needs of both the intelligence community and the public. They hoped to direct the supercomputing revolution from the start in order to make sense of what millions of human beings did inside this digital information network. That collaboration has made a comprehensive public-private mass surveillance state possible today.

The Massive Digital Data Systems (MDDS) ... program's stated aim was to provide more than a dozen grants of several million dollars each to advance this research concept. The grants were to be directed largely through the NSF so that the most promising, successful efforts could be captured as intellectual property and form the basis of companies attracting investments from Silicon Valley. This type of public-to-private innovation system helped launch powerful science and technology companies like Qualcomm $QCOM +1.61%, Symantec, Netscape, and others.

<https://qz.com/1145669/googles-true-origin-partly-lies-in-ci...>

The Internet itself (particularly its precursor, ARPANET), was also government funded, as was development of the World Wide Web (CERN). Oracle, the database company, grew out of the CIA's Project Oracle.

CIA Reading Room Project Oracle

<https://www.cia.gov/readingroom/document/cia-rdp80-01794r000...>

"Oracle's coziness with government goes back to its founding / Firm's growth sustained as niche established with federal, state agencies" (2002)

<https://www.sfgate.com/bayarea/article/oracle-s-coziness-wit...>

Surveillance has been baked in since their founding.
by kgwxd | 2026-02-15 13:10:13+00:00
I "Provide Feedback" every time anything annoys me, repeatedly. And I'm annoyed really easily. I have family premium, so I make sure to mention that.

- Hide all shorts, everywhere on the site, not just on my PC. uBO filters are amazing, but limited.

- The root page to show my Subscriptions instead of "Your History Is Turned Off..."

- I want links to go directly to the video and fill the window. I'll use the back button to go back where I came from, and native "Full Screen" if I want it to fill my screen.

All of it could be non-default settings available with Premium only.

It also seems to make zero difference :/
by softwaredoug | 2026-02-15 13:10:59+00:00
Codex/Claude gather telemetry by default. That’s why they are subsidized. You’re giving them training data.

If you start with everything on GitHub, with maybe some manual annotated prompts for fine tuning, you get a decent base model of “if you see this code, then this other code follows” you’ll only go so far

If you can track how thousands of people actually use prompts, then the most successful tool usage patterns that result in success, then you will be able to fine tune to even more data (and train to avoid the unsuccessful ones). Now you’re training with much more data, around how people actually use the product, not theoretical scenarios.

In ML it always boils down to the training data.
by codethief | 2026-02-15 13:12:57+00:00
> in Scotland where we have (possibly as a result of the weirdly high levels of cancer)

Interesting, I had no idea and just looked this up[0]:

> Scotland had the highest overall incidence (446 for men and 379 for women per 100 000), and Wales had the second highest rate (450 and 366 per 100 000), compared with 394 and 338 per 100 000 in England and 394 and 345 per 100 000 in Northern Ireland.

This would make Scotland rank 3rd in the table on https://en.wikipedia.org/wiki/List_of_countries_by_cancer_ra... .

PS: Glad to hear your mom is doing well!

[0]: https://pmc.ncbi.nlm.nih.gov/articles/PMC1939786/
by carter2099 | 2026-02-15 13:13:02+00:00
I sent this to someone I know knowledgeable about this type of thing, here’s what he had to say, sharing because I thought it was interesting:

Pretty cool tech, silicon is very advanced. That said, this is how every wafer comes out of the fab. This process does not dice out individual chips but instead adds interonnects. I doubt they have 100% yield, but probably just don't connect that die. This type of setup is one of the reasons Apple's M series chips are so effective. Their CPU/GPU/RAM are all on one die/directly interconnected instead of going through some motherboard based connector. I think Apple doesn't have them all go through the same process so those are connected via a different process but same layed on silicon direct connection. This solves the problem data centers tend to have of tons of latency for the connections between processors. This is also similar to AMD's infinity fabric of their Zen architecture. It's cool how all of these technologies build from another.

It's also all reliant on fab from TSMC who did the heavy lifting is making the process a reality
Show HN: Respectlytics – Open-source, privacy-first mobile analytics (MIT+AGPL)
by cesncn | 2026-02-15 13:14:56+00:00
Hey HN — I built Respectlytics because I was frustrated that every mobile analytics SDK quietly collects device IDs, ad identifiers, and IP addresses, then makes you retroactively figure out compliance.

There are some solutions out there claiming that they are compliant with certain privacy regulations but when I dig into it, I observe that they actually are not that compliant as they claim to be.
I believe Respectlytics is one of the most (if not the most) privacy focused mobile analytics solutions out there but compliance is a huge topic and I leave the decision to the legal teams/advisors of users/companies.

Instead of the "trust me bro" motto, I decided to make Respectlytics totally open-source so that people do not need to trust my word, they can verify it in the code itself.

The idea of Respectlytics builds upon Return of Avoidance (ROA) which relies on data minimization in analytics data collection: What if you just... didn't collect that data in the first place?

Respectlytics stores exactly 5 fields per event: event_name, session_id, timestamp, platform, and country. That's it. IP addresses are used transiently for country lookup and immediately discarded. Session IDs rotate latest every 2 hours (or every app start) and live only in RAM — never written to disk. Multi-session tracking is architecturally disabled.

What's open source:

4 mobile SDKs (Swift, Flutter, React Native, Kotlin) — MIT licensed
Analytics server (Django + PostgreSQL) — AGPL-3.0
Self-hosting is simple: docker compose up -d. No ClickHouse, no Kafka, no Redis. Just PostgreSQL.

There's also a managed SaaS if people don't want to run infrastructure, but the self-hosted Community Edition has no artificial limits.

I'd love feedback on the architecture decisions — especially the choice to reject extra fields at the API level rather than just ignoring them silently.
by kromio | 2026-02-15 13:15:00+00:00
this sounds like a total game changer for anyone who wants to mess with extensions but hates dealing with manifests and background.js nonsense all day. legit been playing with a few AI tools like this recently. i actually just launched kromio.ai. Its a no-code builder where u can whip up a full chrome extension in literally one click from a prompt (like "make a tab organizer that groups by domain automatically"). But the part that really hooks me is the "Revise" feature—u can just keep chatting to improve it, like "add dark mode and make the popup resizable" or fix some bug with another prompt. No redeploying from scratch every time, its basically chat-based continuous dev for extensions... super handy for side projects and stuff. Has anyone here tried something similiar to the AI skill you're building? curious how it compares, would love to hear!
by dgxyz | 2026-02-15 13:15:03+00:00
Keeping CI entirely out of windows desktop development is the biggest efficiency and cost improvement I've seen in the last 15 years. Our CI toolchain broke so we moved back to a release manager doing it manually. It takes him 20x less time to build it and distribute it (scripted) than it does to maintain the CI pipeline and his desktop machine is several times faster than any cloud CI node we can get hold of.

Edit: Uses a shit load less actual energy than full-building a product thousands of times that never gets run.
by plaidfuji | 2026-02-15 13:15:13+00:00
Interesting idea. I do feel one of the major barriers to mass replacement of white collar workers is lack of direct integration between email/slack and LLMs. A human still needs to distill organizational needs from multiple stakeholders to write a prompt.

At the same time, I would be very surprised if companies are lining up to hand over all of their internal comms to OpenAI. Would need really strong privacy guarantees, and I’m not sure OpenAI has the goodwill to be convincing on that front.

Also, doesn’t MS still nominally have some stake in OpenAI? Would be surprised if they were chill with another competitor to Teams getting built.
by gus_massa | 2026-02-15 13:15:32+00:00
[Too much math before my first coffee in the morning.

Anyway, the harder I try to write a rebuttal, the harder it gets. Now, assuming a .1mm gap (1/32 inches) and 500nm visible light, the article translates to something like ~A 200 wavelengths single slit can be approximated as two semi-infinite screens~ that makes a lot of sense.

But I may be missing something.]

---

Back to your questions:

> - If the interference pattern was explained by diffraction by a semi-infinite plane, why don't I see it when using only one finger? I only see a blurry shadow. The second finger is needed to make the pattern appear.

I got the best results with a led lamp on the wall of my kitchen. It's a 5x5 inches square, with a white translucent plastic and a metallic frame that hides a led strip and can be bought in any electricity store near my home. I was standing like 10 foots away, in a slightly dark area (or use the other hand to cover the eyes and fingers).

I almost put my finger like 3 inches away from my eyes, I close the fingers until I see the pattern. Then I open them slightly and I see a phantom line around my finger that does not disappear when they are far away from each other. If I look very carefully, I see a second phantom line.

- All formulas that are used compute the light intensity projected on a screen. In the actual experiment, we're looking at the slit through a lens (our eye or a camera). That's not the same thing.

A standard trick is to use a lens and a screen at the focus distance instead of a screen at infinity. This is similar to the eye. The lens in the eye will have a different adjustment to make a clear image of the eye, so it will not be exactly equivalent. But it's close enough. I'd not worry too much about this part.

> - The fact that this is white light interference is handwaved away. To model it correctly, you'd need to compute what happens at each wavelength, then integrate the resulting spectrum multiplied by CIE's x, y z functions at each point, and finally do a bunch of math to bring that in the sRGB color space if you want to display the model's result on a screen.

To get a nice rainbow interference pattern, you probably need a almost puntual source of light. A diffuse source will make a blurred rainbow that is impossible to notice. But I need a diffuse source like the lamp in my kitchen to notice the dark lines because they are too weak.

White leds use phosphorus to get the full spectrum, I'd try with a lamp with leds of color that are almost monochromatic. I'd try with red and blue to maximize the distance of the interference lines, but I'm not sure if the blue leds use phosphorus too. Perhaps red and green is better for this. (Perhaps a computer screen with Painbrush filled with #FF00FF is a good alternative.)
by flowerbreeze | 2026-02-15 13:17:03+00:00
I'm quite sure I've read your article before and I've thought about this one a lot. Not so much from GIT perspective, but about textual representation still being the "golden source" for what the program is when interpreted or compiled.

Of course text is so universal and allows for so many ways of editing that it's hard to give up. On the other hand, while text is great for input, it comes with overhead and core issues for (most are already in the article, but I'm writing them down anyway):

1. Substitutions such as renaming a symbol where ensuring the correctness of the operation pretty much requires having parsed the text to a graph representation first, or letting go of the guarantee of correctness in the first place and performing plain text search/replace.
2. Alternative representations requiring full and correct re-parsing such as:
- overview of flow across functions
- viewing graph based data structures, of which there tend to be many in a larger application
- imports graph and so on...
3. Querying structurally equivalent patterns when they have multiple equivalent textual representations and search in general being somewhat limited.
4. Merging changes and diffs have fewer guarantees than compared to when merging graphs or trees.
5. Correctness checks, such as cyclic imports, ensuring the validity of the program itself are all build-time unless the IDE has effectively a duplicate program graph being continuously parsed from the changes that is not equivalent to the eventual execution model.
6. Execution and build speed is also a permanent overhead as applications grow when using text as the source. Yes, parsing methods are quite fast these days and the hardware is far better, but having a correct program graph is always faster than parsing, creating & verifying a new one.

I think input as text is a must-have to start with no matter what, but what if the parsing step was performed immediately on stop symbols rather than later and merged with the program graph immediately rather than during a separate build step?

Or what if it was like "staging" step? Eg, write a separate function that gets parsed into program model immediately, then try executing it and then merge to main program graph later that can perform all necessary checks to ensure the main program graph remains valid? I think it'd be more difficult to learn, but I think having these operations and a program graph as a database, would give so much when it comes to editing, verifying and maintaining more complex programs.
by lproven | 2026-02-15 13:17:08+00:00
> If I wanted to change most MS stuff, like Office, I could grab the COM object out of a registry, and override it with mine

This goes back a very long time -- at least to the Windows 3.0 timeframe.

The IBM-only 32-bit OS/2 2.0 came out around the same time as Windows 3.1.

OS/2 2 could run Windows 3 inside what was effectively a VM (a decade before true VMs came to the x86 platform), running the Microsoft code on top of OS/2's built-in DOS emulator.

I remember an IBM person objecting to a journalist saying "so you have access to the Windows source code, and you patch it to run under OS/2?"

Reportedly, the IBM engineer looked a bit pained and said "we don't patch it -- we superclass it in memory to make it a good citizen, well-behaved inside OS/2's protected mode."

(It is over 30Y ago so forgive me if I am not verbatim.)

This was subsequently demonstrated to be true rather than a marketing claim. OS/2 2.0 and 2.1 included a "WinOS2" environment. OS/2 Warp 3 made this an option: it was sold in 2 versions, one with a blue box which contained a Windows 3.1 environment, and one with a red box which did not contain WinOS2 but could be installed on top of an existing Windows 3.1 system and then took over the entire Windows environment, complete with all your installed apps, and ran that inside OS/2.

So you kept all your installed 16-bit apps and settings but got a new 32-bit OS with full memory protection and pre-emptive multitasking as well.

Bear in mind that Windows has not activation mechanism then, so you could copy a complete Windows 3.x installation onto a new PC, change some drivers and it just worked without complaint.

So you could buy a new high-end 486 PC, copy Windows off your old 386, install OS/2 Warp over the top and have a whole new OS with all your apps and their files still running.

This was amazingly radical stuff in the first half of the 1990s.