MurphyA/hacker-news huggingface dataset:
by _DeadFred_ | 2026-02-26 05:33:33+00:00
If you are able to leverage the current value of stocks to gain personal benefit, you should be taxed on them as if you recognized the value. If you just let them sit, don't use them as collateral, don't take out loans against them, then they shouldn't be taxed.

But if you recognize some benefit based on their value, you absolutely should pay taxes on that value.
by josephg | 2026-02-26 05:33:57+00:00
> The only way they will is if the hyperscalers and AI companies start to implode -- which will kill a huge portion of the US economy and lead to global recession, so, cheap RAM but nobody can afford it

I disagree.

Modern RAM is made in fabs, which are ridiculously expensive to manufacture. Modern EUV lithography machines cost around 500M each. They're manufactured by hand. Only one company in the world knows how to manufacture them right now. So we can't exactly increase global manufacturing capacity overnight.

The way I see, there's 2 ways this goes:

1. AI is a fad. RAM and storage demand falls. Prices drop back to normal.

2. AI is not a fad. Over time, more and more fabs come online to meet the supply needs of the AI industry. The price comes down as manufacturing supply increases.

Or some combination of the two.

The high prices right now are because there's a demand shock. There's way more demand for RAM than anyone expected, so the RAM that is produced sells at a premium. High prices aren't because RAM costs more to manufacture than it did a couple years ago. There's just not enough to go around. In 5-10 years, manufacturing capacity will match demand and prices will drop. Just give it time.
by Scramblejams | 2026-02-26 05:34:38+00:00
You're right, packaging would definitely be a challenge. The cylinder head would need to be low profile, conformal exhaust headers, dry sumped, and put all the plumbing you can (including the turbo and wastegate) behind the block. Would be an interesting puzzle!

The peeps I've talked to who've done LS swaps seemed more interested in the economic and technological leaps made over the certified air-cooled default choices rather than the power, but YMMV...
by fragmede | 2026-02-26 05:35:51+00:00
Yes. It's a fundamental attitude problem of "that's not my job, it's your problem". It's like after your car gets into a crash. You just want your car back the way it was before the crash, assuming you don't take the insurance pay out. You want to be taken care of. But no, you gotta take it to a mechanic, then a body shop, and then a glass place. Or for software "well, it works on my machine". Even if we take the attitude out of the picture, if you're operating a business, you don't have time to spare. Having to find vendors for each step of the process takes time that could be better spent working on the product.
by decimalenough | 2026-02-26 05:36:37+00:00
The Gemini API is not enabled by default, it has to be explicitly enabled for each project.

The problem here is that people create an API key for use X, then enable Gemini on the same project to do something else, not realizing that the old key now allows access to Gemini as well.

Takeaway: GCP projects are free and provide strong security boundaries, so use them liberally and never reuse them for anything public-facing.
Show HN: Free online audio translator that translates voice instantly
by Katherine603 | 2026-02-26 05:36:49+00:00
I've developed a simple AI-powered audio translator that converts your audio and video files into text or multiple languages within minutes. Fast, accurate, and secure.
Simply drag and drop your audio file or import it from your device, select the target translation language, and let our AI convert your audio into precise text. Once converted, you can download the translated transcript in multiple formats. Everything runs entirely within your browser.
I'm primarily seeking feedback on output quality, user experience, and any technical improvements worth exploring.
by SilverElfin | 2026-02-26 05:37:38+00:00
The Samsung masking tech can selectively mask parts of the screen. And it has configurability so it automatically is turned on only in certain apps and things like that. Because it dims the screen slightly (when you look at it straight on), just like a screen protector, it’s really useful that it can be customized in these ways. You get to keep the quality of the screen high to a much greater amount while still keeping your privacy.
by WalterGR | 2026-02-26 05:37:43+00:00
It has a name. The Rule of Threes. https://en.wikipedia.org/wiki/Rule_of_three_(writing)

“The rule of three is a writing principle which suggests that a trio of entities such as events or characters is more satisfying, effective, or humorous than other numbers, hence also more memorable, because it combines both brevity and rhythm with the smallest amount of information needed to create a pattern.”

It’s how I was taught to write, but I understand that my personal experience can’t be generalized to make sweeping statements.

Do you have data that suggests it’s uncommon in human-authored blog posts and more common in LLM-generated text?
by lelanthran | 2026-02-26 05:38:04+00:00
> But the end product is notably worse than native interface.

That was my point - a lot of superior products were eaten by poor bundled replacements.

Last I checked, copilot has more users than ChatGPT simply because users are using it from within Excel, Word, Outlook and Teams, without even knowing that they are using copilot. It's bundled into Windows.

Right now, copilot is more useful to users than ChatGPT because it is embedded into their workflows.
by lunaprompts_hn | 2026-02-26 05:38:21+00:00
One thing that doesn't get discussed enough: the gap between people who can use AI tools effectively and those who can't is widening fast.

The engineers who thrive are the ones who understand how the underlying models actually work. They know when to trust the output, when to add constraints, and how to structure prompts for reliable results across different contexts.

The ones struggling tend to treat LLMs as magic boxes. They get inconsistent results and don't know why.

This is creating a real challenge for hiring managers. Standard coding interviews do not surface which category a candidate falls into at all.
by lunaprompts_hn | 2026-02-26 05:38:30+00:00
The framing of "bad vs none" is interesting but I think the more useful question is: what makes an Agent.md actually good?

From what I have seen, the ones that work well are specific about failure modes rather than capabilities. Instead of "you can search the web", something like "when you search, prefer recent sources and flag anything older than 6 months as potentially outdated."

Constraints and edge cases matter more than listing capabilities. Any agent will try to be helpful. The ones that go wrong usually do so at boundaries the original author didn't think about.
by chipsa | 2026-02-26 05:38:36+00:00
Very much depends on the rating. A residential panel is something like 65”x40”. A commercial sized panel is something like 80”x40”. The cell size is relatively constant, but the bigger panels are 6x12 cells instead of 6x10. Newer panels have more efficient cells, and so higher power.

Panel manufacturers can also do odder sizes as required. Example: q-cell does a 94x51” panel. This is 6x22 cells, but different sized cells as well.

Most panels are 6x, because that results in an open circuit voltage of just shy of 50V, which is convenient for code compliance.
by lunaprompts_hn | 2026-02-26 05:38:40+00:00
The real-world benchmark approach is the right direction. Most agent evals I've seen test for task completion on clean inputs. That's not how production use looks.

What tends to break agents in the wild: ambiguous instructions that have multiple valid interpretations, state that changes mid-task, and error recovery when a sub-step fails silently rather than loudly.

The hardest thing to benchmark is graceful degradation. A good agent should know when to stop and ask for clarification rather than confidently completing the wrong task.
by amyjess | 2026-02-26 05:38:53+00:00
Are there any text editors for Windows that are feature-for-feature and bug-for-bug compatible with the Windows 95 build of Notepad?

I'm looking for a plain and simple text editor with no programmers' features—no line numbers, syntax highlighting, etc.—no tabs or MDI whatsoever. No, I'm not looking for something with "you can turn these off", just a complete reimplementation of Win95 Notepad made via black-box testing.
Show HN: CVJ-1 VJ Deck / VISUALZ audio-reactive VJ software
by madchops1 | 2026-02-26 05:38:56+00:00
Hey HN —

I’ve been building real-time audio-reactive graphics software for the past 7 years. It started as a side project while working full-time as a software engineer, and slowly evolved into VISUALZ — a GPU-accelerated VJ engine focused on low-latency audio analysis, and reactive visuals.

Two years ago I decided to take it further and prototype dedicated hardware: the CVJ-1. It’s essentially a compact, touch-screen VJ deck designed mainly for live performance.

We're hoping to get some pre-orders. To get the next phase of manufacturing moving forward.
by renjimen | 2026-02-26 05:39:16+00:00
Having less garbage is a whole other issue. Small utility vehicles makes a lot of sense but doesn't seem to be the way it's done in NA. Maybe it’s a labour cost thing. But even the long haul trucks are huge here compared to the rest of the world. They haul the same amount but the cab and engine are enormous. Maybe because the roads are wider they can just make everything bigger.
by daxfohl | 2026-02-26 05:39:42+00:00
I just wonder how long it'll take local models to be good enough for 99% of use cases. It seems like it has to happen sooner or later.

My hunch is that in five years we'll look back and see current OpenAI as something like a 1970's VAX system. Once PCs could do most of what they could, nobody wanted a VAX anymore. I have a hard time imagining that all the big players today will survive that shift. (And if that particular shift doesn't materialize, it's so early in the game; some other equally disruptive thing will.)
by decimalenough | 2026-02-26 05:40:49+00:00
Gemini API is not enabled by default, a project owner has to go explicitly enable it.

The problem described here is that developer X creates an API key intended for Maps or something, developer Y turns on Gemini, and now X's key can access Gemini without either X or Y realizing that this is the case.

The solution is to not reuse GCP projects for multiple purposes, especially in prod.
by 9rx | 2026-02-26 05:42:55+00:00
I didn't take that the author was trying to make a point, rather pondering about when a word becomes worthy of inclusion in the dictionary. "Boiling water" is a word that hasn't (yet) been deemed worthy. On the other hand, "hot water" is a word that is included in the dictionary, just as "still water", "warm water", etc. are. Somewhere between those included and "boiling water" is line where a word becomes worthy of inclusion. But where, exactly, is that line?
by mvdtnz | 2026-02-26 05:42:56+00:00
It's very strange to look at how the world's richest country just can't do basics. I live in a country with way, way, way, way, way less money (any way you want to measure it from natural resources to gdp percent capital to average income etc) and things are far from perfect here but we get the basics right.

You guys have so. Much. Money. How can you not pave a road properly?
by spiralcoaster | 2026-02-26 05:43:10+00:00
That's great, but it doesn't make you or any of us engineers.

Just because I drive my car with immense focus, make precision shifts, and hit the apex of all of my turns when getting onto and off of the freeway doesn't make me a race car driver.

Engineers don't just feel good vibes about science and mix it into their work. It is the core of their work.

Simply having a methodology absolutely is not sufficient for being an engineer.

And great, you have an arbitrary system of ethics, like everyone does I imagine. But no one holds you to these ethics.
by qsera | 2026-02-26 05:43:37+00:00
>I’m just curious, what would need to happen for you to change your opinion about this?

Imagine a machine that can calculate using logic circuits and one that uses a lookup table.

LLMs right now is the latter (please don't take literally, It is just an example). You can argue that the look up table is so huge that it works most of the time.

But I (and probably the parent commenter) need it to be the former. And that answers your question.

So it does not matter how huge the lookup table will grow in the future so that it will work more often, it is still a lookup table.

So people are divided into two groups right now. One group that goes by appearance, and one that goes by what the thing actually is fundamentally, despite the appearances.
by matheus-rr | 2026-02-26 05:45:02+00:00
The context window cost is the real story here. Every MCP tool description gets sent on every request regardless of whether the model needs it. If you have 20 tools loaded, that's potentially thousands of tokens of tool descriptions burned before the model even starts thinking about your actual task.

CLI tools sidestep this completely because the agent only needs to know the tool exists and what flags it takes. The actual output is piped and processed, not dumped wholesale into context. And you get composability for free - pipe to jq, grep, head, whatever.

The auth story is where MCP still wins though. If you need a user to connect their Slack or GitHub through a web UI, you need that OAuth dance somewhere. CLI tools assume you already have credentials configured locally, which is fine for developer tooling but doesn't work for consumer-facing AI products.

For developer workflows specifically, I think the sweet spot is what some people are calling SKILL files - a markdown doc that tells the agent what CLI tools are available and when to use them. Tiny context footprint, full composability, and the agent can read the skill doc once and cache it.
by jacquesm | 2026-02-26 05:46:59+00:00
> And then memory expanded so much that all kinds of “optimal” patterns for programming just become nearly irrelevant.

I don't think that ever happened. Using relatively sparse amount of memory turns into better cache management which in turn usually improves performance drastically.

And in embedded stuff being good with memory management can make the difference between 'works' and 'fail'.
by hiAndrewQuinn | 2026-02-26 05:47:11+00:00
>I post under my real name here, pretty much the only place I post. It keeps me honest and straight in what I say when I choose to say it.

I do the same thing, and I think I'm a much better person for it. The Internet is not, in my final analysis, some indiscriminate dumping ground for my personal issues and moods. It's a place where I can relax and practice putting forward a more prosocial form of myself, even when what I actually have to say is uncomfortable.

While we can't predict how the adversary will read and respond to our moves, I suspect the easier marks are the people who choose to publicly drench everything they touch in negativity and cynicism. It's a sign of an already compromised social immune system.
by inkyoto | 2026-02-26 05:47:31+00:00
Before the wide adoption of Unicode in mainstream operating systems, quite a few people used -- (two ASCII minus signs) to differentiate between a hyphen and a dash (of either pedigree), and some people used -- in emails and online where a dash was required.

Most think that it came from TeX, which had -- (for an en dash) and --- (for an em dash, although I don't think I have ever observed it out in the wild outside TeX), but in fact, the habit well predates TeX and goes all the way back to typewriters where typists habitually hit two hyphens in a row to approximate an em dash. The approximated em dash was described in hard-copy manuscript preparation rules such as The Chicago Manual of Style.

So, if you have ever used a typewriter or TeX, you can claim an even richer than 20 years’ heritage of using the em dash.
by oska | 2026-02-26 05:48:31+00:00
> because in 2024 their boss told them

I am not commenting on your specific example of DEI but I want to make the general point that you are always responsible for what you do, irregardless of whether you were told to do it by your boss, or commanding officer, or whatever.

So again, I don't care about the specific example you used but if something is 'in fashion' and you go along with it, including at work, then you are ultimately responsible for that choice. Because it is always a choice, including being a hard choice that results in you losing your job.
by somenameforme | 2026-02-26 05:48:36+00:00
Imagine the result if everybody took to this mindset. Look at everything that's on your desk right now, and what percent of it was made in e.g. China. Imagine if they decided to just start jamming political slogans onto everything. Or for something closer to home, surgeons and anesthesiologists are largely conservative. [1] Imagine if they started signaling their politics. Many people, ironically those most predisposed to try to make their own political views highly visible, do a poor job of tolerating the views of others. This sort of behavior would just cause complete chaos and disorder and make everybody even more pissed off at each other than they already are.

And political signaling can also make you look bad even to the audience that might ideologically agree with you. For instance notepad++ takes a position on essentially every big controversial US geopolitical issue, but they are conspicuously silent on the Gaza issue. If they hadn't taken on any political positions, this isn't an issue. But when they take a position on every divisive issue, suddenly their not taking a position on one like this effectively is taking a position, but it's one that (for once) they don't want to say.

[1] - https://www.medscape.com/viewarticle/870192?form=fpf
by pushedx | 2026-02-26 05:48:43+00:00
Yes, most people (including myself) do not understand how modern LLMs work (especially if we consider the most recent architectural and training improvements).

There's the 3b1b video series which does a pretty good job, but now we are interfacing with models that probably have parameter counts in each layer larger than the first models that we interacted with.

The novel insights that these models can produce is truly shocking, I would guess even for someone who does understand the latest techniques.
by rTX5CMRXIfFG | 2026-02-26 05:49:32+00:00
I never really bought in to the anti-Leetcode crowd’s sentiment that it’s irrelevant. It has always mattered as a competitive edge, against other job candidates if you’re an employee or the competition of you’re a company. It only looked irrelevant because opportunities were everywhere during ZIRP, but good times never last.
by hiAndrewQuinn | 2026-02-26 05:50:00+00:00
One could just as easily make the opposite argument. Given that your values and priorities may change significantly over the decades, a smart investment now into a solid, stable, and prosocial public identity may reap considerable and wide-ranging benefits in ways you couldn't even predict. This is especially true if you take seriously the idea that it's not what you say but how you say it that matters in the end.
by energy123 | 2026-02-26 05:50:40+00:00
I pay OpenAI but I would also be a happy Anthropic customer.

My view is that OpenAI, Anthropic and Google have a good moat. It's now an oligopolistic market with extreme barriers to entry due to needed scale. The moat will keep growing as the payoffs from scale keep growing. They have internal scale and scope economies as the breadth of synthetic data expands. The small differences between the labs now are the initial conditions that will magnify the differences later.

It wouldn't be surprising to also see consolidation of the industry in the next 2 years which makes it even more difficult to compete, as 2 or 3 winners gobble up everyone and solidify their leads.
by nuuuwan | 2026-02-26 05:51:13+00:00
Does Sri Lanka Have a "Tourist Season"?

Yes, but it's complicated

---

Ask anyone who has travelled to Sri Lanka (or tried to sell a holiday there) and they'll tell you the same thing: "December and March is the season". In other words, our tourist season, by popular consensus, is the northern-hemisphere winter.

This feels intuitive.

But is one single "season" really the whole story?

Does every tourist-source market behave the same way?

READ ON: https://github.com/nuuuwan/lk_tourism_2025/blob/main/README....
by fulafel | 2026-02-26 05:51:49+00:00
You're right in terms of fitting your program to memory, so that it can run in the first place.

But in performance work, the relative speed of RAM relative to computation has dropped such that it's a common wisdom to treat today's cache as RAM of old (and today's RAM as disk of old, etc).

In software performance work it's been all about hitting the cache for a long time. LLMs aren't too amenable to caching though.
by raullen | 2026-02-26 05:52:47+00:00
I've been working on a fork of vllm-mlx (OpenAI-compatible LLM server for Apple Silicon) to make it actually usable for coding agents. The upstream project is great but was
missing production-grade tool calling, reasoning separation, and multi-turn performance.

What I added (37 commits):

- Tool calling that works — streaming + non-streaming, supports MiniMax and Hermes/Qwen3 formats. 4/4 accuracy on structured function calling benchmarks.
- Reasoning separation — MiniMax-M2.5 mixes reasoning into its output with no tags. Built a heuristic parser that cleanly separates reasoning from content (0% leak rate, was 60%
with the generic parser).
- Prompt cache for SimpleEngine — persistent KV cache across requests. On 33K-token coding agent contexts: TTFT goes from 28s to 0.3s on cache hit. This is the single biggest
improvement for multi-turn use.
- 1500+ tests — parsers, engine, server, tool calling. The upstream had minimal test coverage.

Benchmarks (Mac Studio M3 Ultra, 256GB):

Qwen3-Coder-Next-6bit (80B MoE, 3B active):
- Decode: 65 tok/s
- Prefill: 1090-1440 tok/s
- TTFT (cache hit, 33K context): 0.3s

MiniMax-M2.5-4bit (229B MoE):
- Decode: 33-38 tok/s
- Deep reasoning with tool calling

I built this to run OpenClaw locally on my Mac instead of paying for cloud APIs. Qwen3-Coder-Next at 65 tok/s with tool calling is genuinely usable — not a toy demo.

Quick start:

pip install git+https://github.com/raullenchai/vllm-mlx.git
python -m vllm_mlx.server \
--model lmstudio-community/Qwen3-Coder-Next-MLX-6bit \
--tool-call-parser hermes --port 8000

GitHub: https://github.com/raullenchai/vllm-mlx
by bryanrasmussen | 2026-02-26 05:53:05+00:00
All of these articles as they come up discuss one aspect of the de-skillification process, that people no longer practice a skill, but I recently (some minutes ago) realized there is also another thing driving de-skillification

https://news.ycombinator.com/item?id=47162170

and that is as LLMs become more skilled and able to impersonate humans, humans start to think demonstrations of skill are the symptoms of being an LLM, thus in order to avoid being called an LLM people must decrease their skills on their own.

This has been observed by others, where people will throw in deliberate misspellings that LLMs would not make to demonstrate humanity.

It is not certain how people think writing less competently than the LLMs will lead to an eventual human triumph.
by palmotea | 2026-02-26 05:53:08+00:00
> It has a name. The Rule of Threes. https://en.wikipedia.org/wiki/Rule_of_three_(writing)

I don't think that's exactly it.

Speaking of LLM-writing in general, it seems to greatly overuse certain types of constructions or use them in uncommon contexts. So that probably isn't so much using the rule of threes, but overusing the rule of threes in certain specific ways in certain specific contexts.