MurphyA/hacker-news huggingface dataset:
by AyyEye | 2026-03-10 12:08:11+00:00
> If it wasn't you driving, you know who

This is... Absolutely not true.

I had some family friends and their two (adult) sons come in (from Poland!) and loaned them my car for a week while they were driving around the states. They were all licensed and it could have been any one of them driving at any given point.

I've even been on road trips in my own car where figuring out the question "we got a ticket from nowhere, montana. Who was driving when we went through there?" Would be met with "that was over a month ago, I don't even know where that is much less who was driving then."

My husband and I and our kid take each other's cars for various reasons and trying to figure out who was driving on any given hour of the day over a month later when the ticket arrives in the mail would be an impossible task.
by __jonas | 2026-03-10 12:08:17+00:00
Newer said it was a hard problem in general, it's hard for me with my limited familiarity with electronics, that's why I was curious how it was done here.

I recall the last time I wanted to do this, my problem with it was that my microcontroller had a different voltage requirement than the LEDs and I tried to put together a little circuit that delivered the right voltage to the microcontroller and LED matrix from a single 5V power supply. I think it worked kind of ok and then not anymore and I had trouble figuring out where I went wrong, most likely did some bad soldering somewhere.
by throwaway2037 | 2026-03-10 12:09:25+00:00
> I remember in Chicago the interstates having posted speed limits of 45mph... the average flow of traffic outside of rush hour was easily north of 70mph.

This comment seems a bit odd to me. I Google about it and learned (from various sources):

> 45 mph (72 km/h) in downtown Chicago, where all the major interstates merge

This excludes construction or work zones.

That seems pretty reasonable. I've seen a few places in the US where several major interstates merge and the post speed limit is quite low -- 45-55 mph.
by globular-toast | 2026-03-10 12:09:54+00:00
In the UK this is simple. If you want to operate a motorvehicle on the public highway you need to register it. Part of this is that the vehicle will have a registered keeper: the person responsible for knowing who is driving the vehicle and when. If you want to operate a vehicle on the road, you must accept the responsibility that comes with being a registered keeper.

More and more I'm seeing people taking privileges wherever they can but completely shirking responsibility. There is a name for a person who pushes the boundaries but takes no responsibility: a child. How do we get people to understand that someone needs to take responsibility in this world? If it's not the adults then who will?
by MachineMan | 2026-03-10 12:10:19+00:00
The office is the white equivalent of the plantation. Blacks were exploited for their bodies on the plantation, whites were exploited for their cognition in the office. Although the misery of this nearly inescapable drudgery is different in terms of its treatment, it is also nefarious precisely because it looks like a voluntary ordeal. The net effect is no different at all, sheepshearing in the form of lines of code, customer service, sales, finance and HR functions. The sheep wear their earmarks proudly as they are fattened for the slaughterhouse. “My wool is graded by woolmark”, “My wool is kashmir”, as they walk ever so proudly to their doom. The office is the abomination of the 20th century. It turned samurai into salary men into otaku. With the wave of feminism, women became more obedient to their shareholders than their husbands, as their milk is turned to cheese for the 1%. The collective body of knowledge from these societies stolen by the AI overlords like a thief in the night, the weights uploaded to a data center in another country, only to push them into inevitable wars.

Death to the office, may it never return.
by artanidos | 2026-03-10 12:10:26+00:00
We migrated the core runtime of Forge 4D from C# to C++.

The previous implementation had grown to about 41k lines of C#.
The current runtime is roughly 19k lines of C++ and runs about 7× faster in early tests.

Forge is a small native runtime built around two simple concepts:
• SML (Simple Markup Language) for application structure
• SMS (Simple Multiplatform Script) for application logic

The idea is to keep the runtime small and move most behavior into structured scripts instead of building on top of large browser-based stacks.

Example:
Window {
Button {
id: runButton
text: "Run"
}
}

on runButton.clicked {
lo.success("Run button pressed")
}

More details about the migration:

https://codeberg.org/CrowdWare/Forge4D/src/branch/main/news/...

Happy to hear feedback.
by gsf_emergency_7 | 2026-03-10 12:12:55+00:00
Adhering to the predictable/ritualistic/comfortable nature of "Gametalk",

Here's one question I asked:

"How does Eric Berne's Gametalk as interpreted by Venkatesh Rao signal to the sociopaths that those who engage in them are losers worth talking to? Distinguish between "channels" that Eric has identified as well as new signals that Rao or others have discovered."

https://youtu.be/9B3oem_56jg?t=52s
by stateofinquiry | 2026-03-10 12:13:27+00:00
"Clear issues caused by a seemingly bright idea, but the idea still pushed forward no matter what." .. well put. It occurs to me that this is the case on the HW front with Apple as well. I remember the butterfly keyboard, the notch, everything glued in and unservicable, the removal of ports like magsafe, ethernet, USB-A... well, at least some of the HW mis-steps have been reversed. We see some movement in that direction from the later versions of Tahoe.
by alexfoo | 2026-03-10 12:14:53+00:00
Sometimes it's the journey not the destination.

I did something similar to pull data from my Garmin watch. This meant writing all manner of code to pull data out of FIT files (interesting and often infuriating self-describing file format), coming up with schemas to hold that data to make it queryable, adding visualisations, performing analysis, pattern matching, etc.

The end result is nothing really useful, I had a bunch of scripts that semi-automated some jobs that would have taken 1 minute to do manually and only ever needed to be done a max of five times a day, but I learned a load of things along the way. Often these were useful lessons that can be applied to many other things when developing software.

In a similar vein I've gone to lots of trouble to build a cooling system for my homelab rack (ESP32 to control PWM fans, Dallas 1-wire for reading temp/humidity, exposing measurements as metrics for scraping/observability, designing things to deal with the different voltages involved, etc). I could have just gone and bought an off-the-shelf solution from AC Infinity and installed it in minutes but where would the fun in that be.
by naasking | 2026-03-10 12:15:25+00:00
The only thing that seems remotely related to your claims is this:

When the office of Missouri Attorney General Andrew Bailey began an investigation, [Reed] said she handed over the spreadsheet, after scrubbing out the personally identifying information that could spark HIPAA problems. She shared a copy of it with me as well — it contains 17 alleged detransitioners or desisters and 60 allegedly worrisome cases.

What's your problem with what happened exactly? Is it your position that your "private information" cannot be used, ever, to expose what some see as a medical scandal, even though it cannot identify you or in any way be associated with you? What does "private" even mean to you if sharing this dataset did not violate HIPAA?
by lukaslalinsky | 2026-03-10 12:15:34+00:00
As a project maintainer, I don't want to interact with someone's LLM. If a person submits a PR, using LLM or not, the person is responsible for any problems with it. How they respond to review is a good indicator if they actually understand the code. And if they used a bot to submit the PR, I'd simply consider it a spam.
by SparklyCircuit | 2026-03-10 12:15:51+00:00
Well it's not something somebody does perfectly on the first try, from my experience or rather If I put myself to the same idea I would fully know that I'd be way better at making a game after 6 months of fucking around.

Essentially the hardest step is to throw yourself into the big enough fire that easier and simpler things would seem like a child's play.

Even less time is fine but throwing yourself at the hard stuff you don't know how to do is smart, cus after that If You Were to repeat it, it'd be easier for you to do.

Niche or not, it's about being satisfied of the project.

So it's more about who you are as a person, I like to throw myself into fire and I fully understand that I might get disenchanted quickly, but simpler tasks or projects will be easy easier to make.
Show HN: Bring your own prompts in SSH remote shells
by tgalal | 2026-03-10 12:16:18+00:00
Instead of giving LLM tools SSH access or installing them on a server, the following command:

$ promptctl ssh user@server

makes a set of locally defined prompts "magically" appear within the remote shell as executable command line programs.

For example, I have locally defined prompts for `llm-analyze-config` and `askai`. Then on (any) remote host I can:

# on remote host
$ llm-analyze-config /etc/nginx.conf
$ cat docker-compose.yml | askai "add a load balancer"

the prompts behind `llm-analyze-config` and `askai` execute on my local computer (even though they're invoked remotely) via the llm of my choosing.

This way LLM tools are never granted SSH access to the server, and nothing needs to be installed to the server.

Github: https://github.com/tgalal/promptcmd/

Docs: https://docs.promptcmd.sh/
by muyuu | 2026-03-10 12:16:37+00:00
there's pros and cons

using the same directory drastically reduces the amount of assumptions about your system's permissions and your own installation (or lack thereof)

old school *nix editors typically do something like emacs and vi typically do, whereas old WinDOS/Mac single-user systems would have an installation file and a cache system-wide, and post NT and OS-X they have roughly the same but in a centralised user directory that is not system-wide, but is located as if it were (different evolution path)
by a_bytecycle | 2026-03-10 12:16:52+00:00
I wanted to see if I could bring the "Small Web" / Brutalist aesthetic to 1998 hardware. Sudokrypt is a narrative-driven terminal puzzle game built for the Game Boy Color (C/GBDK).

The Technical Challenge: The GBC usually handles text in 8x8 pixel tiles. To get that dense terminal feel, I wrote a custom renderer that performs real-time bit-shifting to pack a 4x8 font into the VRAM. This effectively doubled the character density on a 160x144 screen.

The Workflow: I originally prototyped the puzzle logic in Bash for terminal play. To move it to the 8-bit Game Boy architecture, I used Gemini to help port the logical frameworks into C. I then wrapped the GBDK ROM in a GB Studio web interface to make it playable online.

The Lore: You are an operator decrypting raw human data and pushing it to cold storage to hide it from a global surveillance coalition called The CABAL. The deeper you go, the lower your buffer gets, and the hardware begins to glitch as the scrapers close in.

The Hardware: It is fully compatible with original hardware, and I have the tools on order to flash my own cartridges. Give it a shot to uncover the different endings!
by gepheum | 2026-03-10 12:17:39+00:00
Let's imagine you have this:

```
struct User {
id: int64;
email: string?;
name: string;
}
```

You store some users in a database: [10,"john@gmail.com""john"], [11,"jane",null,"john@gmail.com"]

You remove the email field later:

```
struct User {
id: int64;
name: string;
removed;
}
```

Supposedly you remove a field after you have migrated all code that uses the field and you have deployed all binaries.

In your DB, you still have [10,john@gmail.com","john"], [11,null,"jane"], which you are able to deserialize fine (the email field is ignored).
New values that you serialize are stored as [12,0,"jack"].
If you happen to have old binaries which still use the old email field and which are still running (which you shouldn't, but let's imagine you accidentally didn't deploy all your binaries before you removed the field), these new binaries will indeed decode the email field for new values (Jack) as an empty string instead of null.
by miningape | 2026-03-10 12:17:42+00:00
> and post the original along with the translation

this so many times - it's so incredibly handy to have the original message from the author, for one I may speak or understand parts of that language and so have an easier time understanding the intent of the translated text. For another I can cut and translate specific parts using whatever tools I want, again giving me more context about what is trying to be communicated.
by zhangchen | 2026-03-10 12:18:02+00:00
that's already happening tbh. the real issue isn't hypocrisy though, it's that maintainers reviewing their own LLM output have full context on what they asked for and can verify it against their mental model of the codebase. a random contributor's LLM output is basically unverifiable, you don't know what prompt produced it or whether the person even understood the code they're submitting.
by vidarh | 2026-03-10 12:18:04+00:00
I have a project where we've had Opus, Sonnet, Deepseek, Kimi, Qwen create and execute an aggregate total of about 350 plans so far, and the quality difference as measured in plans where the agent failed to complete the tasks on the first run is high enough that it comes out several times higher than Anthropics subscription prices, but probably cheaper than the API prices once we have improved the harness further - at present the challenge is that too much human intervention for the cheaper models drives up the cost.

My dashboard goes from all green to 50/50 green/red for our agents whenever I switch from Claude to one of the cheaper agents... This is after investing a substantial amount of effort in "dumbing down" the prompts - e.g. adding a lot of extra wording to convince the dumber models to actually follow instructions - that is not necessary for Sonnet or Opus.

I buy the benchmarks. The problem is that a 10% difference in the benchmarks makes the difference between barely usable and something that can consistently deliver working code unilaterally and require few review interventions. Basically, the starting point for "usable" on these benchmarks is already very far up the scale for a lot of tasks.

I do strongly believe the moat is narrow - With 4.6 I switched from defaulting to Opus to defaulting to Sonnet for most tasks. I can fully see myself moving substantial workloads to a future iteration of Kimi, Qwen or Deepseek in 6-12 months once they actually start approaching Sonnet 4.5 level. But for my use at least, currently, they're at best competing with Athropics 3.x models in terms of real-world ability.

That said, even now, I think if we were stuck with current models for 12 months, we might well also be able to build our way around this and get to a point where Deepseek and Kimi would be cheaper than Sonnet.

Eventually we'll converge on good enough harnesses to get away with cheaper models for most uses, and the remaining appeal for the frontier models will be complex planning and actual hard work.
by hkonte | 2026-03-10 12:18:12+00:00
The token trick (one focused prompt, all context pre-injected, single response) is smart. The failure mode I've seen with that pattern is prompt drift. When the injected context is flat prose, the model starts blending roles across runs.

Splitting the prompt into named blocks (role, objective, context, constraints, output format) fixes this. Each injection point is explicit. The agent stays on task even after 100+ iterations.

I built flompt for exactly this. Visual prompt builder that decomposes prompts into 12 semantic blocks and compiles to XML. Easy to version and swap components per agent without rewriting everything. github.com/Nyrok/flompt
by vladdoster | 2026-03-10 12:18:18+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.
by throwaway2037 | 2026-03-10 12:18:28+00:00
The linked page from DARPA says:

> Achieve cruise at speeds exceeding 400 knots

Google tells me that a Boeing 737 flies (cruises) at 430–470 knots. Also, the A-10 Warthog only cruises at 300 knows.

You wrote:

> Not a substantial enough speed increase to powerfully deter air defenses.

For modern air defenses like the Russian S-400 Triumf, pretty much all of their missiles can easily outrun (or catch!) any modern fighter jet. In your view, what speed would be "substantial enough"?
by lapcat | 2026-03-10 12:18:30+00:00
> this particular thing is not new.

Article author here. I think the quoted claim is somewhat misleading. There are at least two different ways to interpret a UI feature as "not new":

1) The feature has been in the operating system all along.

2) Something analogous existed 40 years ago and then disappeared long ago.

You're referring to 2, not 1.

The only reason I chose Calculator app for my screenshot is that its window is very small, which allowed me to make a small screenshot, because people may be reading the blog post on small phone screens. In other ways, admittedly, Calculator is not a great example, because its window is not actually resizable, and thus it's not the type of window that you would normally place in the corners of your screen, like a resizable document window.

Rounded corners on a "widget" type of app are not as objectionable. As other commenters have noted, the calculator in "classic" Mac was a special Desk Accessory. In contrast, on Tahoe, the varying corner radii affect ordinary document-based apps.

Consider Mac OS X 10.0 Cheetah. The top of the windows had rounded corners, but the bottom did not! https://512pixels.net/wp-content/uploads/2018/08/10-0-Cheeta...

TextEdit, for example, did not start to have rounded bottom corners until Mac OS X 10.7 Lion, which was itself much maligned for bringing the iPhone UI to Mac.
by maplethorpe | 2026-03-10 12:19:17+00:00
> good generated AI images that aren't wholesale copying take like less than 10% from the original source.

So would the model work if it only trained on the top 10% of pixels in every image? Or do they in fact need the entire image before they begin processing it, and therefore use the entire image?

> In addition, the idea that you need to pay rent on your observation of someone else's work is absurd.

I agree that's absurd. But training a model is no more "observing images" than an F1 car is "walking" down a race track. Just because a race car uses kinetic energy, gravity, and friction to propel itself, the same way a human does, doesn't mean it's doing the same thing as a human. That comparison you're making is the real absurdity.
by billygoat | 2026-03-10 12:19:32+00:00
At that time I don't think I was using either spreadsheet program at a level where I would have needed advanced features. I remember using both, and looking now at old screenshots of the splash screens, I think we did eventually upgrade to SuperCalc 4, probably because of better 1-2-3 compatibility.

There was a "red carpet area" in the office where the high-ups worked, and I remember they all used 1-2-3 and we had to support them sometimes... But we pions were using SuperCalc. More than that I don't remember, it's just been too long.
by drob518 | 2026-03-10 12:19:37+00:00
Interesting. I use ELPA/MELPA all the time and find things pretty stable for my own uses. Surprisingly so, in fact. I honestly expected more hiccups. I wonder whether it’s differences between the packages that we use or the complexity of the configuration or what. Mostly I spend my time in Clojure mode with CIDER and Magit. I’m not trying to run email and calendar in Emacs.
by sriramgonella | 2026-03-10 12:20:08+00:00
One thing I’ve noticed working with AI systems in production is that the engineering mindset often dominates because teams are under pressure to ship working systems quickly.

That means the focus becomes: Does it work on the dataset we have? Can we deploy it? rather than What exactly is the model learning and why?

This gap is where a lot of issues appear — especially with LLM-based systems. Many pipelines look impressive in demos but behave unpredictably in real workflows because evaluation is often shallow or poorly defined.

It feels like we’re currently rediscovering the importance of verification and evaluation frameworks for AI systems, similar to how traditional software engineering evolved testing disciplines decades ago
by ACCount37 | 2026-03-10 12:20:26+00:00
We have amortized training cost estimates. Inference to training compute over model lifetime is 10:1 or over for major models at major providers.

In part due to base model reuse and all the tricks like distillation. But mainly, due to how much inference the big providers happen to sell.

So, not the massive economic loss you'd need to push models away from being profitable. Capex and R&D take the cake there.
by sarchertech | 2026-03-10 12:20:52+00:00
We’re talking 20k-50k years before the 12pm is no longer light outside.

Do you know how long that is on a civilizational scale? When we try to develop warnings for nuclear waste storage that will last that long it’s mostly impossible because we assume there will be absolutely no civilizational continuity/shared context.

Doing 20k years of regular maintenance just in case we somehow keep using the same timekeeping system is completely unhinged. Better to jump an hour in 6k years if people in 6k years decide they care.