MurphyA/hacker-news huggingface dataset:
by 7777777phil | 2026-03-08 11:45:26+00:00
NTS requires both ends. More public NTS servers help, but the installed base of plain NTP clients, routers and embedded systems that will never get a firmware update, keeps unauthenticated NTP as the default regardless.. every government that funds an atomic clock could run an authenticated public time service. Almost none have.

Huston is one of the few people who makes leap second arithmetic readable.
by Imustaskforhelp | 2026-03-08 11:46:40+00:00
Coolify is usually loved by the community.

Dokploy is another good one.

Kubero seems nice for more kubernetes oriented tasks.

But I feel like if someone is having a single piece of hardware as the OP did. Kubernetes might not be of as much help and Coolify/Dokploy are so much simpler in that regards.
by amaury_bouchard | 2026-03-08 11:47:39+00:00
Fair point on shadow DOM. That said, µJS targets server-rendered HTML workflows, where pages are plain HTML generated by a backend framework or template engine. In that context, shadow DOM is rarely used, so this limitation doesn't affect the typical µJS user. If you're building heavily component-based UIs with Web Components, µJS is probably not the right tool anyway. A proper JS framework would serve you better.

On the native APIs: yes, the Navigation API and Speculation Rules are exciting, but browser support is still uneven. µJS works today across all modern browsers without any configuration. That said, I agree the gap will narrow over time.
by sigwinch | 2026-03-08 11:49:59+00:00
It was inevitable for the mainstream media to connect Musk with the pressure campaign on Anthropic. Among people who use the English language professionally, the sloppy redefinition of “woke” raised a lot of red flags. It was never sharp, just a dull thought-ending judgment, always insanely rejecting Trump superiority. Is it clear enough to kill a corporation? If corporations are people (in the US, the Supreme Court has given them people rights), is this the right way to administer exile or even the death penalty? The mainstream media seems to wonder now, do we talk about that or will Elon Musk control Anthropic’s assets six months from now?
by littlestymaar | 2026-03-08 11:50:00+00:00
> Busting a gut to make some shareholders/managers loads of money isn't exactly fair if you aren't also being rewarded.

Exactly. But it's not a matter of “being paid more than the marginal value of the additional work” as microeconomics tend to frame those things.

Symmetrically, people routinely accept pay cut or degraded work conditions when the company isn't going well, even though it makes no sense from a game theory perspective (it's basically a prisoners' dilemma yet people cooperate most of the time).
by Imustaskforhelp | 2026-03-08 11:51:42+00:00
I use serververify which is created by jbiloh from the lowendtalk forum which uses yabs (yet-another-benchmark-script) to give details about lot more things than usually meets the eye.

That being said, I have found getdeploying.com to be a decent starting point as well if you aren't too well versed within the Lowend providers who are quite diverse and that comes at both costs and profits.

Btw legendary https://vpspricetracker.com (which was one of the first websites that I personally had opened to find vps prices when I was starting out or was curious) is also created by jbiloh.

So these few websites + casually scrolling LET is enough for me to nowadays find the winner with infinitely more customizability but I understand the point of TUI but actually the whole hosting industry has always revolved around websites even from the start. So they are less interested in making TUI's for such projects generally speaking atleast that's my opinion
by flohofwoe | 2026-03-08 11:52:41+00:00
I mean... don't believe the hype?

WASM is "just" another virtual ISA, everything else is just marketing. If you manage expections (in the sense of "it's just another ISA / bytecode VM") then WASM can be incredibly useful, e.g. all my C/C++ projects are running automatically in browsers thanks to wasm (for instance these home computer emulators: https://floooh.github.io/tiny8bit/).
by LargoLasskhyfv | 2026-03-08 11:53:21+00:00
I didn't care that much about these things at first, until others noticed the distances on my speedometer, or have seen me in action, when we had the same routes, speeding by them without even noticing them. Except when they honked their horn, or later talked to me about that.

All I can say is that this disturbed me, so I made sure the speedometer wasn't lying to me. By doing many rounds on an empty running track, to check if the distance counting is right. Confirmed by measuring trip distances on maps, and comparing the stopped times for that. Further confirmed by comparing car-trip times by "progressive" drivers between locations, and mine. It all checks out. No matter if mostly flat, or hilly terrain.

(Nowadays even in alpine terrain, going from about 2500m altitude up to 4400m, and got cited for reckless driving, because doing 90kph downhill where only 15mph were allowed. Shrug)

Oh! And no doping, btw...
by BoppreH | 2026-03-08 11:54:09+00:00
Lots of good ideas here.

Flow-sensitive type inference with static type checks is, IMHO, a massively underrated niche. Doubly so for being in a compiled language. I find it crazy how Python managed to get so popular when even variable name typos are a runtime error, and how dreadful the performance is.

All the anonymous blocks lend themselves to a very clean and simple syntax. The rule that 'return' refers to the closest named function is a cute solution for a problem that I've been struggling with for a long time.

The stdlib has several gems:

- `compile_run_code`: "compiles and runs lobster source, sandboxed from the current program (in its own VM)."

- `parse_data`: "parses a string containing a data structure in lobster syntax (what you get if you convert an arbitrary data structure to a string) back into a data structure."

- All the graphics, sound, VR (!), and physics (!!) stuff.

- imgui and Steamworks.

I'll definitely be watching it, and most likely stealing an idea or two.
by danielhanchen | 2026-03-08 11:54:12+00:00
Oh https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks might be helpful - it provides benchmarks for Q4_K_XL vs Q4_K_M etc for disk space vs KL Divergence (proxy for how close to the original full precision model)

Q4_0 and Q4_1 were supposed to provide faster inference, but tests showed it reduced accuracy by quite a bit, so they are deprecated now.

Q4_K_M and UD-Q4_K_XL are the same, just _XL is slightly bigger than _M

The naming convention is _XL > _L > _M > _S > _XS
Anon's general theory on the endgame of AI
by MissNgKnow | 2026-03-08 11:55:41+00:00
hi lurker here, throwaway account here, unknown genius here, figured this is a good place to share my thoughts...

First, I begin with the following statement and posit it to be true: AI(Artificial Intelligence) reads, sees, and listens to vastly more things than any human can ever do in order to produce an informed conclusion(i.e. a decision), yet human agents have the capability of reaching the same or even better decision.

I present the following theory: AI will have no incentive to take into consideration the survival of human agents when it has reached the determination that human agents are incapable of making superior decisions(i.e. be of use to the AI), given that humans compete for the same resources as itself(land, water, energy).

Analysis: When it gets to the point where it's man vs machine, anything goes. The AI will cull the population of human agents and only keep the ones that it considers useful. However, once the population diminishes rapidly, the likelihood of any human agent being able to meet AI's ever-rising threshold for utility diminishes. This is essentially a mass-extinction scenario.

Solution: Forbid the AI from engaging in culling the population even when human agents are unable to produce anything of benefit to it and while those agents are competing for the same resources as the AI. Instead, have the AI seek out alternate decisions- those exist. Alternatives cannot only be considered, they must be undertaken because the AI cannot see far into a decision tree that it hasn't embarked on yet. However, we know where the default decisions will lead us to eventually without this intervention- to our collective demise.
by vintagedave | 2026-03-08 11:56:29+00:00
I love how several of these are analogue. For the first, I kept on thinking, ‘surely the author means digital chalkboard.’ No. A real chalkboard.

And while some of these push the bounds of ethics — he was a teenager after all — they are innocent, and built from curiosity. Reading this makes me feel nostalgia for Tunisia, a nostalgia I can’t really feel because I’ve never been there.
by OJFord | 2026-03-08 11:56:37+00:00
They are de facto semantic major versions - think of recent-ish additions like f-strings and match-case (3.7 and 3.11, I think), you'd get a syntax error in an older parser. PyPy targeting 3.9 for example would would support f-strings but not match-case.

Or at runtime, you can import things from the standard library which require a minimum 3.x. - .x releases frequently if not always add things, or even change an existing API.
by appsoftware | 2026-03-08 11:56:47+00:00
Hey, saw this while looking under new and wanted to try and help. I think the answer is to be places. Life needs randomness and interactions and that doesn't happen at home. Try to be in "3rd places" - the gym / work in a shared working space / pick up a couple of hobbies (I say a couple as just one is risky if say it's sports based an you injure yourself - something you can do outdoors, something you can do indoors). Trust that it will take time, but it will happen.
by flohofwoe | 2026-03-08 11:57:33+00:00
This is basically the same problem as using Emscripten's embind for automatically generating C++ <=> JS bindings, and my advice would be "just don't do it".

It adds an incredible amount of complexity and bloat versus writing a proper hybrid C++/JS application where substantial work is happening in a handwritten JS shim versus hopping across JS/WASM the boundary for every little setter/getter.

Alternatively go through a properly designed C API. Automatically generated bindings for a C API make much more sense than for a language with a huge 'semantic surface' like C++ or Rust.
Show HN: Aivaro – Open-source AI alternative to Zapier
by abelo9996 | 2026-03-08 11:59:12+00:00
Hey HN, I built Aivaro because my brother was spending $30+ per month on Zapier for workflows I could describe in one sentence.

The idea: describe what you want in plain English, AI builds the workflow, you approve sensitive steps (emails, payments), then it runs automatically.

- 20+ integrations (Google, Stripe, Slack, Shopify, etc.), 212 tools
- Visual drag-and-drop editor for manual tweaking
- Human-in-the-loop: nothing fires without your approval

I'd love feedback on the product and how I could strengthen the product to be used by a broader audience. Happy to answer questions about the architecture.
by chadtd1 | 2026-03-08 11:59:23+00:00
Congrats on shipping. I launched ContentForge (contentforgehq.com) yesterday solving the same problem — paste one long-form asset, get platform-native outputs for 8 channels.

Two things I've learned building in this space: (1) voice consistency across platforms is harder than the actual generation — LinkedIn tone vs. email tone vs. X tone all need different sentence structures, not just length adjustments. (2) The real retention driver is showing users the before/after quality gap between generic repurposing and platform-tuned output.

Curious how you're handling voice drift across formats. That's been my biggest engineering challenge.
by sharp_runner_84 | 2026-03-08 11:59:57+00:00
The interesting side effect of officials being banned is it reduces liquidity on geopolitical contracts, which makes the remaining prices less informative. Right now on Kalshi, some policy contracts have so little depth that a single 5k order can move the price 10+ cents. That thinness already makes the prices unreliable as forecasts — banning one of the most informed participant classes just makes it worse.
by alxfrnr | 2026-03-08 12:00:43+00:00
"They're portable, auditable, and composable. No MCP server required. No plugin marketplace to browse. Just a folder with a SKILL.md in it." It screams lazy ai slop copy-paste. Now when I see this kind of obvious llm tell, I can't help but feel betrayed for investing time in giving it a shot in the first place.
by 7952 | 2026-03-08 12:01:48+00:00
I don't think a maximalist solution exists for something like this. And in fact historic trends are doing exactly what you talk about. And the best example is money. It eliminates social resiprocity in favour of a transaction that completes in the moment. And for many people that satisfies a substantial portion of their basic needs. But everything else is then left with just an emotional side as the primary objective. Relationships are based on fulfilling some barely understood emotional need rather than practical benefit. And we feel more and more need to use hacks to try and satisfy that.
by watwut | 2026-03-08 12:01:51+00:00
Hegseth is religious radical who invites religious radicals to speak to troops. He is the head of the operation and openly promotes "maximum lethality, rules of engagement are stupid, we dont do defenders we do warriors" philosophy.

So, imo there us a relationship between "religious nutter" leading department of war and army not being bothered with bombing schools or hospitals.
by halayli | 2026-03-08 12:02:06+00:00
agreed. Strong emphasis on "profiling and identifying the actual bottleneck". Every benchmark will show a nested stack of performance offenders, but a solid interpretation requires a much deeper understanding of systems in general. My biggest aha moment yrs ago was when I realized that removing the function I was trying to optimize will still result in a benchmark output that shows top offenders and without going into too many details that minor perspective shift ended up paying dividends as it helped me rebuild my perspective on what benchmarks tell us.
by chadtd1 | 2026-03-08 12:03:26+00:00
Interesting angle with the social nudging. I built Momentum (momentumfit.app) for sustainable fitness habits — AI-adjusted weekly movement plans that adapt to real-life constraints rather than punishing you for missing a day.

My experience: the habit apps that retain users aren't the ones with the best streaks or social pressure. They're the ones that gracefully handle failure. When someone misses three days, the worst thing you can do is show them a broken streak. The best thing is to regenerate their plan around what's actually realistic this week. That "recovery" moment is where long-term users are made.
by embedding-shape | 2026-03-08 12:03:35+00:00
> (I'm not counting high-end workstation/server CPUs because, as others in this thread have explained, Geekbench isn't intended for them.)

Yeah but that's the thing, the article is both in the headline and contents of the article comparing a CPU intended for high-end workstations with a consumer CPU meant for a laptop, and using software explicitly not designed for them. That's the issue here!
Show HN: AI agents run my one-person company on Gemini's free tier – $0/month
by ppcvote | 2026-03-08 12:04:48+00:00
I'm a solo dev in Taiwan. I built 4 AI agents that handle content, sales leads, security scanning, and ops for my tech agency — all on Gemini 2.5 Flash free tier (1,500 req/day). I use ~105. Monthly LLM cost: $0.

Architecture: 4 agents on OpenClaw (open source), running on WSL2 at home with 25 systemd timers.

What they do every day:

- Generate 8 social posts across platforms (quality-gated: generate → self-review → rewrite if score < 7/10)
- Engage with community posts and auto-reply to comments (context-aware, max 2 rounds)
- Research via RSS + HN API + Jina Reader → feed intelligence back into content
- Run UltraProbe (AI security scanner) for lead generation
- Monitor 7 endpoints, flag stale leads, sync customer data
- Auto-post blog articles to Discord when I git push (0 LLM tokens — uses commit message directly)

The token optimization trick: agents never have long conversations. Every request is (1) read pre-computed intelligence files (local markdown, 0 tokens), (2) one focused prompt with all context injected, (3) one response → parse → act → done. The research pipeline (RSS, HN, web scraping) costs 0 LLM tokens — it's pure HTTP + Jina Reader. The LLM only touches creative/analytical work.

Real numbers:

- 27 automated Threads accounts, 12K+ followers, 3.3M+ views
- 25 systemd timers, 62 scripts, 19 intelligence files
- RPD utilization: 7% (105/1,500) — 93% headroom left
- Monthly cost: $0 LLM + ~$5 infra (Vercel hobby + Firebase free)

What went wrong:

- $127 Gemini bill in 7 days. Created an API key from a billing-enabled GCP project instead of AI Studio. Thinking tokens ($3.50/1M) with no rate cap. Lesson: always create keys from AI Studio directly.
- Engagement loop bug: iterated ALL posts instead of top N. Burned 800 RPD in one day and starved everything else.
- Telegram health check called getUpdates, conflicting with the gateway's long-polling. 18 duplicate messages in 3 minutes.

The site (https://ultralab.tw) is fully bilingual (zh-TW/en) with 21 blog posts, and yes — the i18n, blog publishing, and Discord notifications are all part of the automated pipeline.

Live agent dashboard: https://ultralab.tw/agent

Stack: OpenClaw, Gemini 2.5 Flash (free), WSL2/systemd, React/TypeScript/Vite, Vercel, Firebase, Telegram Bot, Resend, Jina Reader.

GitHub (playbook): https://github.com/UltraLabTW/free-tier-agent-fleet

Happy to answer questions about the architecture, token budgeting, or what it's actually like running AI agents 24/7 as a one-person company.
by d4rkp4ttern | 2026-03-08 12:05:20+00:00
For every new interesting open model I try to test PP (prompt processing) and TG (token gen) speeds via llama-cpp/server in Claude Code (which can have at least 15-30K tokens context due system prompt and tools etc), on my good old M1 Max 64GB MacBook.

With the latest llama-cpp build from source and latest unsloth quants, the TG speed of Qwen3.5-30B-A3B is around half of Qwen3-30B-A3B (with 33K tokens initial Claude Code context), so the older Qwen3 is much more usable.

Qwen3-30B-A3B (Q4_K_M):

- PP: 272 tok/s | TG: 25 tok/s @ 33k depth

- KV cache: f16

- Cache reuse: follow-up delta processed in 0.4s

Qwen3.5-35B-A3B (Q4_K_M):

- PP: 395 tok/s | TG: 12 tok/s @ 33k depth

- KV cache: q8_0

- Cache reuse: follow-up delta processed in 2.7s (requires --swa-full)

Qwen3.5's sliding window attention uses significantly less RAM and delivers better response quality, but at 33k context depth it generates at half the tok/s of the standard-attention Qwen3-30B.

Full llama-server and Claude-Code setup details here for these and other open LLMs:

https://pchalasani.github.io/claude-code-tools/integrations/...
by hyperpape | 2026-03-08 12:05:35+00:00
I think the most significant boundary is given by the question: "is there a plan to support new minor versions of Python?" It sounds like there is not.

There may be non-zero maintenance work happening, but a project that only maintains support for old versions and will never adopt new ones is functionally one that the ecosystem will eventually forget about. Maybe you call that "under active development" but my response is "ok, then I don't care whether it's under active development, I (and 99.9% of other people) should care about whether it's going to support new minor versions."

On the other hand, if you don't support new minor versions day one, but you eventually support them, that's quite different.
by amaury_bouchard | 2026-03-08 12:05:39+00:00
Thanks! The short answer: µJS doesn't try to be a DSL in HTML attributes.

Here's what I deliberately left out compared to Turbo (~25KB gzip) and htmx (~16KB gzip):

- No client-side template/extension system. htmx has a full extension API, header-based control flow (HX-Trigger, HX-Redirect, HX-Reswap...), and dozens of response headers the server can use to command the client. µJS has 5 custom headers total; the server returns HTML, the client renders it.

- No CSS selector-based targeting in attributes. htmx lets you write hx-target="closest tr", hx-target="find .result", hx-target="next div" with a mini query language. µJS uses plain CSS selectors only (mu-target="#id"), no traversal keywords.

- No built-in transition/animation classes. htmx adds/removes CSS classes during swaps (htmx-settling, htmx-swapping, htmx-added) with configurable timing. Turbo has turbo-preview, transition classes, etc. µJS defers to the native View Transitions API: zero animation code in the library, and it falls back silently.

- No client-side cache or snapshots. Turbo keeps a page cache for "instant" back-button rendering and manages preview snapshots. µJS stores only scroll position in history.state and lets the browser's own cache + fetch() handle the rest.

- Patch mode simpler than Turbo Frames. Turbo has <turbo-frame>, a custom element with lazy-loading, src rewriting, and frame-scoped navigation. µJS handles multi-fragment updates via patch mode (mu-mode="patch") with regular HTML elements. No custom elements, no frame concept. It's simpler but it works perfectly.

- No request queuing or throttling. htmx has hx-sync with queuing strategies (queue, drop, abort, replace). µJS simply aborts the previous in-flight request. One request at a time per navigation.

- No form serialization formats. htmx supports JSON encoding (hx-encoding), nested object serialization, etc. µJS uses the browser's native FormData for POST and URL params for GET. That's it.

- No JavaScript API surface to speak of. htmx exposes htmx.ajax(), htmx.process(), htmx.trigger(), event details, etc. µJS exposes mu.init(), mu.fetch(), mu.render(), and a few setters and a few events. The goal is that you shouldn't need the JS API — the HTML attributes should be enough.

What I kept: the features that cover 90% of real-world use cases. AJAX navigation, 8 injection modes, multi-fragment patch, morphing (via Idiomorph), SSE, prefetch, forms with all HTTP verbs, triggers with debounce/polling, progress bar, history/scroll management. Just without the layers of abstraction around them.

The philosophy: if the browser already does it (View Transitions, FormData, fetch, AbortController, history API), don't reimplement it.
by PunchyHamster | 2026-03-08 12:06:11+00:00
I do remember some of them showed some productivity improvement but it pretty much dove off cliff with the complexity of the tasks involved, or the small improvement on medium difficulty task was eaten by time to wait for responses.

Note that most of them were focused on programming tasks aimed to ship a product, not other use cases like "prototype a dozen of ideas quickly before we pick direction", or "write/update documentation about this feature" which AI might be significantly more productive use case than just programming.