MurphyA/hacker-news huggingface dataset:
by tkgally | 2026-03-07 04:08:44+00:00
Very similar here. I am 68.

While I have never developed software professionally, in the four decades I have been using computers I have often written scripts and done other simple programming for my own purposes. When I was in my thirties and forties especially, I would often get enjoyably immersed in my little projects.

These days, I am feeling a new rush of drive and energy using Claude Code. At first, though, the feeling would come and go. I would come up with fun projects (in-browser synthesizers, multi-LLM translation engines) and get a brief thrill from being able to create them so quickly, but the fever would fade after a while. I started paying for the Max plan last June, but there were weeks at a time when I barely used it. I was thinking of downgrading to Pro when Opus 4.5 came along, I saw that it could handle more sophisticated tasks, and I got an idea for a big project that I wanted to do.

I have now spent the last two months having Claude write and build something I really wanted forty years ago, when I was learning Japanese and starting out as a Japanese-to-English translator: a dictionary that explains the meanings, nuances, and usages of Japanese words in English in a way accessible to an intermediate or advanced learner. Here is where it stands now:

https://www.tkgje.jp/

https://github.com/tkgally/je-dict-1

It will take a few more months before the dictionary is more or less finished, but it has already reached a stage where it should be useful for some learners. I am releasing all of the content into the public domain, so people can use and adapt it however they like.
by skybrian | 2026-03-07 04:09:15+00:00
It's a bit more complicated:

> The situation on the Rhine/Danube frontier was complex. The peoples on the other side of the frontier were not strangers to Roman power; indeed they had been trading, interacting and occasionally raiding and fighting over the borders for some time. That was actually part of the Roman security problem: familiarity had begun to erode the Roman qualitative advantage which had allowed smaller professional Roman armies to consistently win fights on the frontier. The Germanic peoples on the other side had begun to adopt large political organizations (kingdoms, not tribes) and gained familiarity with Roman tactics and weapons. At the same time, population movements (particularly by the Huns) further east in Europe and on the Eurasian Steppe began creating pressure to push these ‘barbarians’ into the empire. This was not necessarily a bad thing: the Romans, after conflict and plague in the late second and third centuries, needed troops and they needed farmers and these ‘barbarians’ could supply both. But as we’ve discussed elsewhere, the Romans make a catastrophic mistake here: instead of reviving the Roman tradition of incorporation, they insisted on effectively permanent apartness for the new arrivals, even when they came – as most would – with initial Roman approval.

https://acoup.blog/2022/01/14/collections-rome-decline-and-f...
by helsinki | 2026-03-07 04:10:47+00:00
I've been working on gollem — it's a Go agent framework with type-safe agents, structured output, multi-provider support (Anthropic, OpenAI, Gemini, Vertex AI), MCP integration, and multi-agent team swarms. The big idea is compile-time guarantees instead of runtime validation, zero core dependencies, and single-binary deploys. Think of it as what you'd want if you were building production agent systems in Go instead of Python.

To stress-test it I pointed it at a dumb task: generate an entire novel. It wrote ~40K words across 11 chapters in about 10 minutes. The "author-bot" uses gollem's agent primitives — chained agents for chapter planning, continuity checking, iterative drafting. Basically an orchestration.ChainRun pipeline where each stage feeds into the next.

The novel ("The Year Unedited" — dystopian memory-editing premise) came out surprisingly coherent. Characters kept distinct voices across chapters, the plot threads mostly held together, and it didn't just wrap everything up neatly at the end. Not saying it's good literature, but it's a decent stress test for long-running agent orchestration.

I threw it on Amazon mostly as an experiment: https://a.co/d/037EOH88

The framework itself is the actual point of this post though. It does cost tracking, guardrails, middleware chains, streaming with Go 1.23+ iterators, background process management, graph workflows, eval framework, the works. Happy to dig into any of the architecture details.
by jjmarr | 2026-03-07 04:10:55+00:00
> At the same time I am confused about what you would like to achieve within the first year of your professional career.

I am in great fear of ending up on the wrong side of the K shaped recovery.

Everyone is telling me I need to be exceptional or unemployed because the middle won't exist in 2 years.

I want to secure the résumé that gives me the highest possibility of retaining emoloyment if there's a sudden AI layoff tomorrow. A fast career trajectory catches HR's eye even if they don't understand the technicals.
by bjourne | 2026-03-07 04:11:47+00:00
Pre-prints exists because it can take up to 18 months to get a paper published in a journal or reputable conference. Since lots of people can publish pre-prints[1] what you should think depends on the authors. If they have a record of publishing good research you should think highly of the paper.

[1] - Actually, there are hoops on pre-print repositories, such as arXiv, so not everyone can post there. I guesstimate that 99% of the public has no means of posting on arXiv.
by ehnto | 2026-03-07 04:12:54+00:00
Super cool, I have been extremely pleased with my switch to KDE. I have used MacOS as a daily, I have used Windows 95-Win10, I have used various linux distros all for long periods of time at work and home.

KDE Plasma has really hit the sweetspot for me, it's super usable daily and still has easy customisability. Thanks to all the effort poured into Proton I have even replaced my gaming PC with Arch/KDE plasma with really very little stress.

I am VR gaming, on linux, on AAA titles, with no messing around, if this isn't the year of linux desktop then it will never be.
by Ronsenshi | 2026-03-07 04:13:45+00:00
> Do not fall for the AI agent hype.

Problem is, it's the people in higher positions who should be aware of that, except they don't care. All they would see is how much more profit company can make if it reduces workforce.

Plenty of engineers do realize that AI is not some magical solution to everything - but the money and hype tends to overshadow cooler heads on HN.
by prashanthnani | 2026-03-07 04:15:20+00:00
Most engineers enabling HTTP/2 don't think about headers. Every request still carries the same Authorization, User-Agent, and Accept-Encoding strings on every single request.

HPACK fixes this. It gives both sides a shared lookup table. Once a header has been seen, future requests send an integer index instead of the string. 800 bytes on the first request, as low as 50 bytes once the table is warm.

Wrote a short visual explainer on how it works, including the difference between the static table (61 predefined entries) and the dynamic table that grows per session.
by skyberrys | 2026-03-07 04:15:22+00:00
I am definitely curious about this type of a tool, but I am interested in seeing it used in products I would like to buy. For instance, imagine how much better a robot to clean the house would be if it was aware of wow this room smells awful. It could have also prevented the poocalypse from happening (robot ran when not home, dog pooped in house.. robot.. did not realize)

Independent of the sensor smelling, I am recently curious if there are smell libraries where I myself could better learn to classify scents. Recently I came across a laundry detergent scent that was great but I didn't get the brand name and now I can't explain what it smelled like.
by ehnto | 2026-03-07 04:15:57+00:00
Well when you write it manually you are doing the review and sanity checking in real time. For some tasks, not all but definitely difficult tasks, the sanity checking is actually the whole task. The code was never the hard part, so I am much more interested in the evolving of AIs real world problem solving skills over code problems.

I think programming is giving people a false impression on how intelligent the models are, programmers are meant to be smart right so being able to code means the AI must be super smart. But programmers also put a huge amount of their output online for free, unlike most disciplines, and it's all text based. When it comes to problem solving I still see them regularly confused by simple stuff, having to reset context to try and straighten it out. It's not a general purpose human replacement just yet.
by whalesalad | 2026-03-07 04:17:20+00:00
I don't play games anymore. I just work on whacky ideas with LLMs. I even nuked my gaming PC and installed ollama+rocm to play with local models, run openclaw there to experiment with that too. It's a lot of fun. I feel like agents are particularly useful for people who are ADD and want to work on 10 things at once.
by 8192kjshad09- | 2026-03-07 04:17:38+00:00
I hold a general license in the US. I started 2m and 70cm with a cheap Baofeng but eventually CW caught my interest and now I design and sell a semi-open-source Morse Code trainer on https://katnisslabs.com/ for folks looking to learn Morse Code. It's been quite the journey!
by scottLobster | 2026-03-07 04:17:40+00:00
It can be a power tool if used in a limited capacity, but I'd describe vibe-coding as sending a junior construction worker out to finish a piece of framing on his own.

Will he remember to use pressure treated lumber? Will he use the right nails? Will he space them correctly? Will the gaps be acceptable? Did he snort some bath salts and build a sandcastle in a corner for some reason?

All unknowns and you have to over-specify and play inspector. Maybe that's still faster than doing it yourself for some tasks, but I doubt most vibe-coders are doing that. And I guess it doesn't matter for toy programs that aren't meant for production, but I'm not wired to enjoy it. My challenge is restraining myself from overengineering my work and wasting time on micro-optimizations.
by Oanid | 2026-03-07 04:17:47+00:00
I'd have to disagree with you on that one. I recently migrated to Fedora from Windows 11, which gave me the chance to try Plasma, GNOME, and a couple other desktops.

Plasma is exactly what I don't want in a DE. It’s extremely configurable, but also overwhelming, and I don’t think that’s something the average user would feel comfortable navigating.

I ended up choosing GNOME. It feels visually cohesive, and the design is much more opinionated — they’ve clearly made decisions about what should and shouldn’t be part of the core desktop experience.
by autojunjie | 2026-03-07 04:18:25+00:00
The problem isn't Slack. The problem is that AI Agent can't participate in your team's actual workflow.

We're a 3-person startup (2 humans + 1 OpenClaw Agent). Yesterday we had a 40-minute product positioning discussion in Slack — all three of us. The AI agent wasn't summarizing after the fact or answering questions in a sidebar. It was in the thread, in real time, doing these things simultaneously:

• Synthesizing two humans' conflicting viewpoints into a framework (one wanted to position as "open-source Linear," the other insisted on "agent harness for product development" — the agent articulated why the distinction matters and took a side)

• Generating investor personas and tailored one-liners for each audience when asked

• Building a comparison slide (Chorus vs Linear agent workflow) and uploading it to the thread mid-conversation

• Answering technical challenges ("can't Linear just build a plugin to do the same thing?") with honest analysis — "technically yes, but they won't prioritize it because 95% of their users are traditional teams"

The output: 5 audience-segmented positioning statements, a competitive analysis slide, an investor target list, and a new internal tool (Slack file upload skill) — all produced during a natural conversation, not as a separate "ask the AI" step.

A better Slack wouldn't have helped here. What helped was an AI agent that sits in the same channel, has full project context, can disagree with the founder, and executes tasks while still participating in the discussion.
by chongli | 2026-03-07 04:18:29+00:00
Cowen uses Freestyle Chess (teams of humans and computers) as an analogy, noting that human intuition combined with machine processing power consistently outperforms either working alone.

Unfortunately, this one hasn't aged well. Human+Computer is now consistently outperformed by Computer alone in the chess world. Also, the name Freestyle Chess is now used for Chess960, the chess variant where starting positions are randomized. It has nothing to do with computer chess now!
by gsf_emergency_7 | 2026-03-07 04:19:04+00:00
Since you seem to be having fun but not a boomer and maybe even have a day job, don't mind grandfather. he has a vendetta against crackpots, but you don't fit that mold

I think the technical term is "idempotency", please look up fixed points[0] or "Lyapounov"..

(academia.edu feels scammy I did not click. Taleb uses it but I don't read his technical stuff tbh)

yes it is underappreciated..

Eg when applied to the Bethe-Salpeter equation this leads to a condition for the superconductor transition temperature.. (similar to the BEC condition which is much easier to compute)

I'd just like to know, did you have help from an LLM when writing that

[0]https://blog.yellowflash.in/posts/2026-03-03-fixpoints-for-c...
by GreenWatermelon | 2026-03-07 04:19:31+00:00
Yeah, and for good reason. The invitation of the light bulb meant factory owners could force workers into 16 hours shifts. The main beneficiaries of new tech were always the capital owners. Workers had to literally fight and die for our 8-hour workdays and 5-days workweek.

This is still going on today: the massive gains from automation are being hoarded by the wealthy capital owners, while workers struggle to make ends meet.
by tptacek | 2026-03-07 04:19:33+00:00
UUIDv7 didn't mature until long after the Go standard library was mostly settled. By that point, there was already an idiomatic 3p dep for UUIDs (the Google package), and as you can see from the thread, there were arguments in favor of keeping it 3p (it can be updated on an arbitrary cadence, unlike the stdlib).
by kjellsbells | 2026-03-07 04:19:39+00:00
I see statistics like "inbound international tourism is down 8%" but I can't tell without context if that is just a typical drop or a cataclysmic decline.

That said, I do see a lot of ads for domestic tourism to places that ordinarily would really have no need to advertise. Disney buying YouTube spots to persuade me,a US resident, to visit Florida seems remarkable. I suspect things are not rosy?
by SilverElfin | 2026-03-07 04:21:36+00:00
I don’t think the prediction from two years ago is fully wrong. The wins will be for those with distribution and capital. And unfortunately that means every startup that isn’t Anthropic or OpenAI will lose as those platforms just replicate the best ideas from the other startups. Besides that - Microsoft may be stumbling but I have a feeling Google and Apple will ultimately get most of the wins of this era too.

We need fairer competition. That means laws and regulations that break down moats, tax wealth, tax capital, etc.
by ComCat | 2026-03-07 04:22:05+00:00
Most npm supply chain attacks never get a CVE.

Shai Hulud, the chalk and debug hijack, and S1ngularity all spread before any advisory existed. Tools that rely on vulnerability databases like npm audit, Dependabot, or Snyk passed them clean because there was nothing in the database yet.

That annoyed me enough that I built a different approach.

Instead of checking advisories, the scanner reads the package source and looks for behaviors common in supply chain attacks. Things like install script abuse, credential theft, unexpected filesystem access, network exfiltration, and obfuscated payloads.

Right now it uses 46 deterministic detectors. No LLMs. same result every time.

Full methodology and dataset notes are here:
westbayberry.com/benchmark

Curious what attack patterns people here people here have dealt with before and how they handled them.
by lucb1e | 2026-03-07 04:22:19+00:00
Even if you only read the headline, you can work out the most likely story from logic. Someone else in this thread already said it: they're not gonna go to jail for you over a small subscription fee, of course they're complying with local laws and then the Swiss people handed it over to where they got the legal assistance request from. It's also not the first time Proton cooperated with a legal request from Swiss authorities, or Signal or other similar companies for that matter. The story tells itself no matter what part of it they decide to stick in the headline
by dasil003 | 2026-03-07 04:22:25+00:00
I'm sort of shocked at how little of my argument seemed to land with you in any way. I'm wondering how many cycles of software hype have you been through? Were you here for the PC revolution, the .com era, smartphone mass adoption?

There's a lot of what-ifs, and worst case scenarios in your reply that I simply don't find likely. I am not drinking the koolaid from the AI maximalists or the doomers. I could be wrong of course, no one can predict the future, but to me the very real, novel and broad utility of LLMs that we are just learning to harness combined with the investment outlook are leading to a mania that has people overestimating where things will land when the dust settles. If I'm wrong then I guess I'll join the disenfranchised masses picking up pitchforks, but I'm not going to waste time worrying about that until I see more evidence that it's actually going that badly.

So far what I see is that software engineers are the ones getting the most actual utility of AI tooling. The reason is that it still requires a precision of thought and specificity to get anything sustainable out AI coding tools. Note this doesn't mean that engineers can design better apps than proper designers, rather my point is that designers and other disciplines still can not go much further than prototypes, they still need engineers to write the prompts, test the output, maintain the system, and debug things when they go wrong. I have worked long enough with large cross-functional teams to know that the vast majority of folks in non-engineering functions simply can not get enough specificity and clarity in their requests to allow an LLM to turn it into a working system that will work over time. The will hit a wall very quickly where new features add bugs faster than they improve things, and the whole thing collapses under its own weight like a mansion of popsicle sticks. And by the way, I don't consider AI-assisted coding to require less qualification than regular coding. Sure you don't need to know as much syntax or algorithms, but you absolutely need to know data modeling, performance, reliability, debugging, consistency, and migration knowledge in order to use AI to contribute to any software that powers a real business, and yeah you might need to develop your product and business sensibilities, but to me that's what been happening throughout the history of computing. Wiring up ENIAC, certainly required qualifications that were not needed for assembly programing, which in turn required certain things that C programmers did not need and so forth, but harnessing the increasing compute power and complexity required new qualifications. I don't think AI will ultimately be that different, it will change the way we work, it doesn't replace what senior engineers do.
Ask HN: Is SWE mostly just calling APIs?
by humbleharbinger | 2026-03-07 04:22:26+00:00
Been thinking about this question more and more in the context of AI automation. A lot of SWE (apart from design and org politics) is just chaining calls to APIs.

Analysis is just calling APIs to get data. We use that to drive a decision and then we write code and use APIs to deploy it. We close the loops with the same data/metric APIs to verify our code achieved an outcome.

I wonder if this is why SWE seems so susceptible to automation - LLMs are great at chaining together calls to APIs.

Just a thought - I know there's a lot of diversity in SWE and this may not apply everywhere. I can think of holes in this line of reasoning (ex. we make decisions about the product during this process) but want to hear others' opinions.
by II2II | 2026-03-07 04:22:37+00:00
> Each block of Python or Applescript that they generate for me is a much better learning tool than a book - I'm going through the code line by line and researching everything.

I have been doing something similar. In my case, I prefer reading reference documentation (more to the point, more accurate), but I can never figure out where to start. These LLMs allow me to dive in and direct my own learning, by guiding my readings of that documentation (i.e. the authoritative source).

I think there has been too much emphasis (from both the hypesters and doomsayers) on AI doing the work, rather than looking at how we can use it as a learning tool.
by JKCalhoun | 2026-03-07 04:23:19+00:00
I echo another reply here, if anything my experience coding feels even more valuable now.

It was never about writing the code—anyone can do that, students in college, junior engineers…

Experience is being able to recognize crap code when you see it, recognizing blind alleys long before days or weeks are invested heading down them. Creating an elegant API, a well structured (and well-organized) framework… Keeping it as simple as possible that just gets the job done. Designing the code-base in a way that anticipates expansion…

I've never felt the least bit threatened by LLMs.

Now if management sees it differently and experienced engineers are losing their jobs to LLMs, that's a tragedy. (Myself, I just retired a few years ago so I confess to no longer having a dog I this race.)
by rps93 | 2026-03-07 04:23:20+00:00
Just sold a house/moved out after being laid off in mid-January from a govt IT contractor(there for 8 great years and mostly remote). I started my UX Research, Design and Front End Web Design coding career in 2009, but now I think it's almost a stupid go nowhere vanishing career, thanks to AI.

I think much like you that AI is and will just continue to destroy the economy! At least I got to sell a house and make a profit--stash it away for when the big AI market crash happens (hopefully not a 2030 great depression tho). As then it's a down market and buying stocks, bitcoin and houses is always cheaper.