MurphyA/hacker-news huggingface dataset:
by sanchitmonga22 | 2026-03-11 02:01:13+00:00
The default TTS voice (Piper) is a lightweight model optimized for speed over quality. It's fast but yeah, it doesn't sound great.

If you install Kokoro TTS (rcli models > TTS section), the voice quality is dramatically better, it's a neural TTS model with 28 different voices. MetalRT synthesizes Kokoro at 178ms for short responses, so you don't pay a speed penalty for the upgrade.

We should probably make Kokoro the default or atleast make the upgrade path more obvious in the first-run experience. Fair feedback.
by sanchitmonga22 | 2026-03-11 02:02:29+00:00
This is exactly the problem we're trying to solve. The models themselves have gotten surprisingly capable at small sizes, Qwen3.5 4B with 262K context, LFM2 1.2B for fast tool calling, but the inference infrastructure hasn't kept up.

When people say "local AI is too slow," they usually mean the engine is too slow, not the model. A 4B model at 186 tok/s (MetalRT on M4 Max) feels genuinely responsive for interactive chat. The same model at 87 tok/s (llama.cpp) feels sluggish. Same weights, same quality, 2x the speed, that's a usability cliff.

We think the gap between cloud and on-device inference is a infrastructure problem, not a model problem. That's what we're working on.
by tomxor | 2026-03-11 02:02:43+00:00
If hung SSH connections are common it's likely due to CGNAT which use aggressively low TCP timeouts. e.g. I've found all UK mobile carriers set their TCP timeout as low as 5 minutes. The "default" is supposed to be 2 hours, you could literally sleep your computer, zero packets, and an SSH connection would continue to work an hour later, and generally speaking this is still true unless CGNAT is in the way.

If you are interested there are a few ways you can fix this:

Easiest is to use a VPN, because the VPN's exit node becomes the effective NAT they usually have normal TCP timeouts due to being less resource constrained. Another nice benefit of this method is you can move between physical networks and your connection doesn't die... If you use Tailscale then you already have this in a more direct way.

Another is to tune the tcp_keepalive kernel parameters. Lowering the keepalive timeout to be less than the CGNAT timeout will cause keepalive probes to prevent CGNAT from dropping the connection even while your SSH connection is technically idle. For Linux I pop these into /etc/sysctl.d/z.conf, I have no idea for Windows or Mac:

# Keepalive frequently to survive CGNAT
net.ipv4.tcp_keepalive_time = 240
net.ipv4.tcp_keepalive_intvl = 60
net.ipv4.tcp_keepalive_probes = 120

This is really a misuse of these settings, they are supposed to be for checking TCP connections are still alive and clearing them up from the local routing table. Instead the idea is to exploit the probes by sending them more frequently to force idle connections to stay alive in a CGNAT environment (dont worry the probes are tiny and still very infrequent).

_time=240 will send a probe after 4 mins of idle connection instead of the default 2 hours, undercutting the CGNAT timeout. _intvl=60 and _probes=120 mean it will send 120 probes 60 seconds apart (2 hours worth) before considering the connection dead. This will keep it alive for at least 2 hours, but also allows us to have the best of both worlds so that under a nice NAT it keeps the old behaviour, e.g if I temporarily lose my network the SSH connection is still valid after 2 hours, but under CGNAT it will at least not drop the connection after 5 mins so long as I keep my computer on and don't lose the network.

There are also some SSH client keepalive settings but I'm less familiar with them.
by simonpure | 2026-03-11 02:03:25+00:00
I've been impressed by Google Jules since the Gemini 3.1 Pro update. Sometimes it's been working on a task for 4h. I've now put it in a ralph loop using a Github Action to call itself and auto merge PRs after the linter, formatter and tests pass. It does still occasionally want my approval, but most of the time I just say Sounds great!

It's currently burning through the TESTING.md backlog:
https://github.com/alpeware/datachannel-clj
by sanchitmonga22 | 2026-03-11 02:03:45+00:00
This is a great idea. A virtual audio device that sits in the path of any audio stream and provides live transcription, that would be huge for video conferencing, lectures, podcasts.

MetalRT's STT numbers make this feasible: 70 seconds of audio transcribed in 101ms means you could process audio chunks in real-time with massive headroom. The latency would be imperceptible.

We haven't built this yet but it's a compelling use case. CoreAudio supports virtual audio devices (aggregate devices) that could pipe audio through the pipeline. If anyone in this thread has experience building macOS audio HAL plugins and wants to collaborate, we're very open to contributions, RCLI is MIT.
by gcanyon | 2026-03-11 02:03:54+00:00
> [greater activity within] our immune systems long-term can't be healthy

Not trying to be flip, but why? "Natural" isn't always better, and as the obesity epidemic has shown, our evolutionary past hasn't done a perfect job of preparing us for our current environment.

You might be right, but I'm skeptical that there is any non-extreme limit to something as simple and mechanical as our innate immune system.
by kyivctoner | 2026-03-11 02:04:26+00:00
I made a small tool called T9T that sits in front of terminal AI tools like Codex, Claude, and Gemini and fixes obvious prompt typos as you type.

The idea is basically: T9 for terminal AI.

It is local, macOS-only for now, and tries to stay conservative so it does not touch paths, flags, URLs, variables, or other code-like tokens. It uses native macOS spellchecker via NSSpellChecker

I made it because when I’m prompting fast in the terminal, small typos break flow more than they should. Plus any AI will use more tokens to understand what have you typed.

There’s a demo in the repo and the first release is up here: https://github.com/Xsamsx/T9T

Would love honest feedback on whether this feels useful or too niche.
by sanchitmonga22 | 2026-03-11 02:04:56+00:00
Understood, you want dictation, not a chatbot. That's a valid and different use case.

RCLI is Apple Silicon only today because MetalRT is built on Metal. For Linux, the closest thing to what you're describing would be building a virtual input device on top of Whisper or Parakeet (which RCLI supports as STT backends). Parakeet TDT 0.6B has ~1.9% WER, that's very close to production dictation quality.

The missing piece on Linux isn't the model, it's the integration: a daemon that captures mic audio, runs STT with hidden latency (streaming partial results), and injects text as keyboard input. sherpa-onnx (https://github.com/k2-fsa/sherpa-onnx) supports Linux and has streaming STT, it might be the best starting point for what your after.

We're focused on Apple Silicon for now but broader platform support is on the roadmap.
by mountainriver | 2026-03-11 02:05:27+00:00
Okay but most modern LLMs are multimodal, and it’s fairly easy to make an LLM multimodal.

Also there is no evidence that novel discoveries are more than remixes. This is heavily debated but from what we’ve seen so far I’m not sure I would bet against remix.

World models are great for specific kinds of RL or MPC. Yann is betting heavily on MPC, I’m not sure I agree with this as it’s currently computationally intractable at scale
by Terr_ | 2026-03-11 02:05:31+00:00
Very nice, I can imagine someone turning it into a little satirical webpage, which implements a kind of decision tree:

1. Choose from a set of challenge types (e.g. meeting a deadline, reliability)

2. Choose whether the challenge was "met" or "failed".

3. Choose whether you want to make the person look good or bad, by following/ignoring a principle.

4. Results: A list of relevant principles with short rationalizations.

I'm almost tempted to try, except perhaps I should treasure my ignorance.

If a tool like that gets popular enough that most employees are using it for office-politics, it might even start to deflate the whole Leadership Principles thing.
by bandrami | 2026-03-11 02:06:03+00:00
It's weird that even just a couple of years ago the absolute consensus in the industry was to work for repeatability and secure chain of custody, both of which are basically impossible with an agentic workflow. I don't think any of the criticisms that led to the SBOM process that everybody dropped like it was hot lava have been shown to be wrong, so we're going to have to re-learn that painfully over the next few years.
by _hark | 2026-03-11 02:06:11+00:00
You literally can do a kind of model PCA, using the Hessian (matrix of second derivatives of the loss function w/r/t the parameters, aka the local curvature of the loss landscape), and diagonalizing. These eigenvectors and eigenvalues (the spectrum of the Hessian) tend to be power-law distributed in just about every deep NN you can think of [1].

That is, there are a few "really important" (highly curved) dimensions in parameter space (the top eigenvectors) which control the model's performance (the loss function). Conversely, there are very many "unimportant"/low curvature dimensions in the model. There was a recent interesting paper that showed that "deleting" these low-curvature dimensions appeared to correspond to removing "memorized" information in LLMs, such that their reasoning performance was left unchanged while their ability to answer questions which require some memorized knowledge was reduced [2].

It appears that sometimes models undergo dramatic transitions from memorization to perfect generalization, which corresponds to the models becoming much more compressible [3].

I'm hopeful that we'll find a way to distill the models down to the most useful core cognitive/reasoning capabilities, and that that core will be far simpler than the current scale of LLMs. But they might need to look stuff up like we do without all that memorized world knowledge!

[1]: https://openreview.net/pdf?id=o62ZzfCEwZ

[2]: https://www.goodfire.ai/research/understanding-memorization-...

[3]: https://arxiv.org/abs/2412.09810
by belorn | 2026-03-11 02:06:25+00:00
Yes, this is why you get on average single digit output from solar in Sweden during the worst winter months while consumption doubles. The math comes down that on average, the expected amount of solar that can be used for consumption is around 25% for an 100% capacity installation. The period with highest production is the period of lowest consumption. Average grid prices also follow this trend, with the highest point being winter and the lowest point being summer.

Germany has slightly better numbers from being a bit more south, and they also primarily use gas for heating rather than electricity, which reduces seasons effects on consumption.
by brendanyounger | 2026-03-11 02:06:49+00:00
Absolutely. Zuckerberg was willing to burn tens of billions on a metaverse that no one wanted. Staying relevant is worth every penny he spent on Moltbook. We're deep in a repeat of the dot-com boom. The interesting question is what will rise from the ashes and take down old guard of FB, Google, Salesforce, Oracle, etc.
by ducktastic | 2026-03-11 02:07:37+00:00
I wasn't under 18 when I started playing with *BSD/Nix systems on my own, but how tragic that kids who would be drawn to understanding these systems are essentially 'outlawed' from learning them. As another HNer pointed out in a related post, I think this stems from not understanding the delineation between an OS/App/Platform.
by jasomill | 2026-03-11 02:07:47+00:00
Sure, but we already have good enough players, open source even, that don't support this technology, and recent codecs have, if anything, become more open, so this only seems problematic for playback on non-general purpose computing devices like smart TVs, set top boxes, and maybe smartphones, tablets, and battery-powered PCs if the tech is incorporated into hardware decoders for all acceptable codecs.
by JumpCrisscross | 2026-03-11 02:08:19+00:00
> Am I supposed to be impressed by this?

No. But it is noteworthy. A lot of what one previously needed a SWE to do can now be brute forced well enough with AI. (Granted, everything SWEs complained about being tedious.)

From the customer’s perspective, waiting for buggy code tomorrow from San Francisco, buggy code tonight from India or buggy code from an AI at 4AM aren’t super different for maybe two thirds of use cases.
by Forge36 | 2026-03-11 02:09:17+00:00
Profiling[1] your own repository and tweaking as necessary (possibly
disabling auto-status refresh) will likely yield significant
performance improvements.

I use magit with a very large repository (100k files, millions of
commits) it's still not lightning fast like it is with smaller
repositories, I'm still finding it an improvement over the CLI.

My config notes this saves me ~13 seconds in git-status

(remove-hook 'magit-status-headers-hook 'magit-insert-tags-header)

[1]: https://docs.magit.vc/devel/magit/Performance.html
by wk_end | 2026-03-11 02:12:40+00:00
What MCUs are you thinking of?

To the best of my knowledge (and Google-fu), 26K really isn't a lot of transistors for an embedded MCU - at least not a fully-featured 32-bit one comparable to a minimal RISC-V core. An ARM Cortex M0, which is pretty much the smallest thing out there, is around 10K gates => around 40K transistors. This is also around the same size as a minimal RISC-V core AFAICT.

The ARM core has a shifter, though.
by hkonte | 2026-03-11 02:13:18+00:00
A visual prompt builder. I kept writing prompts as raw text and getting inconsistent results. So I built flompt: you decompose a prompt into typed blocks (role, constraints, output format, etc.) on a canvas, then it compiles to Claude-optimized XML.

The structured format makes prompts reproducible. You can swap blocks, version them, and the model behavior is much more predictable than with plain text.

Open source: github.com/Nyrok/flompt
by SwiftAuth | 2026-03-11 02:14:14+00:00
Hey HN! I built SwiftAuth — an API authentication and license key management platform for developers and SaaS products.
The problem: most developers either roll their own auth/licensing from scratch (time-consuming and error-prone) or pay for bloated enterprise solutions that are overkill for smaller products.
SwiftAuth lets you:

Generate and issue API/license keys instantly
Revoke or suspend access in real time
Track usage from a clean dashboard
Integrate via a simple REST API

It's currently free to use — I'm in early stages and looking for feedback from developers.
Would love to hear what features matter most to you, what's missing, or what would make you actually switch to something like this.
by cube00 | 2026-03-11 02:16:01+00:00
Sounds a lot like paying for online ads, they don't work because you're not paying enough, when in reality bots, scrapers and now agents are just running up all the clicks.

You pay more to try and get above that noise and hope you'll reach an actual human.

The new "fast mode" that burns tokens at 6 times the rate is just scary because that's what everyone still soon say we all need to be using to get results.
by safteylayer | 2026-03-11 02:16:10+00:00
Exactly — this is the circular nightmare in action.

1. Dev gets 401 / rate-limit / weird error
2. Pastes full API key + request into GPT-4o / Claude for "why isn't this working?"
3. That key (or close pattern) enters the training pipeline
4. Model learns valid key structures / patterns from real usage
5. Later prompts extract similar internals (like our EPHEMERAL_KEY leaks)

We saw this repeatedly: different vectors → same leaked concept every time.

Your bill-spike point is brutal. We ran these tests for ~$0.04. An attacker could probe 10,000 variants for $4 and map your API surface before you notice anything.

Key rotation helps post-breach, but proactive multi-vector probing (what we're building continuous tests for) catches the pattern before exploitation.

Spot-on observation. Thanks.
by ZekiAI2026 | 2026-03-11 02:17:17+00:00
Signing proves what was sent. It doesn't prove the sending agent wasn't compromised.

The specific failure mode: agent A is injected via a malicious document. It then calls agent B with signed, legitimate-looking instructions. B executes. You have a perfect cryptographic audit trail of a compromised agent doing exactly what the attacker wanted.

Replay attacks and trust delegation chains are the other gaps -- if agent A can delegate signing authority to B, and an attacker controls B, you've handed them a trusted identity.

Identity without behavioral integrity is a precise false sense of security. Worth red-teaming before production. We mapped this attack class against similar systems recently -- happy to share findings.
by uoaei | 2026-03-11 02:17:33+00:00
> There are a lot more degrees of freedom in world models.

Perhaps for the current implementations this is true. But the reason the current versions keep failing is that world dynamics has multiple orders of magnitude fewer degrees of freedom than the models that are tasked to learn them. We waste so much compute learning to approximate the constraints that are inherent in the world, and LeCun has been pressing the point the past few years that the models he intends to design will obviate the excess degrees of freedom to stabilize training (and constrain inference to physically plausible states).

If my assumption is true then expect Max Tegmark to be intimately involved in this new direction.
by rossdavidh | 2026-03-11 02:17:42+00:00
I think that "vaccine" is really not the right word to use for this; they sound as different as bandages and blood transfusions. But if it works as advertised, it could be useful if used in the right situation.

I do wonder if the kind of people who got vaccinated 10 times against Covid-19 will end up trying to get a sniff of this every month? Kind of like how we overuse antibiotics in cleaners. It seems like it would be best if saved for an "oh shoot" kind of situation.
by snvzz | 2026-03-11 02:18:13+00:00
There's reason RV32E and RV64E, with half the registers, are a thing. RV32I/RV64I isn't small enough.

There are many chips in the market that do embed 8051s for janitorial tasks, because it is small and not legally encumbered. Some chips have several non-exposed tiny embedded CPUs within.

RISC-V is replacing many of these, bringing modern tooling. There's even open source designs like SERV that fit in a corner of an already small FPGA, leaving room for other purposes.
by montyanne | 2026-03-11 02:18:35+00:00
Reticulum is a full replacement networking stack for several layers of the OSI stack, so not directly comparable. The LXMF messaging protocol (built on top of reticulum) is nice as it’s encrypted-by-requirement, but doesn’t really have a ton of non-text messaging implementations.

Reticulum is not worth getting into, the utilities and general infrastructure just isn’t there yet.

The project was basically a one-man-show for a long time, and has a lot of odd, esoteric decisions that will drive you mad if you’re actually trying to build something with it (eg, configuration files with sparse documentation that are yaml-but-not-really). I don’t mean to belittle the loads of time the original maintainer put into this project, it’s just not really designed to be usable in the general case by other developers.

I spent some time porting the reference Python implementation to no_std rust, but basically had to roll all the basic debugging utilities myself.
by rkagerer | 2026-03-11 02:18:40+00:00
I had been programming for a long time before I learned OOP. After some years playing with it, I came to the conclusion there's not much I can't do about as well using simple functions and structs. The key is a well thought out and organized codebase. Always felt polymorphism in particular seemed more trouble than it was worth.

I still use modern languages on a regular basis, but when I drop back to more basic languages there are only a few ergonomics that I truly miss (eg. generics).
by tom_m | 2026-03-11 02:19:40+00:00
No they wouldn't. They have tons of funding. They absolutely can and do absorb costs like this. Don't think anyone is ever gonna tell you precise numbers (and it also varies based on workload of course)...but this is literally the business model of AI providers.

They're goal (similar to Uber, DoorDash, Robin Hood, etc.) is to get mass adoption. Their business models only work at this kind of scale.

It's completely impossible to have consumers pay $20-60/mo and be a profitable business without mass adoption where some are not using it as much as others...and, perhaps more importantly, the masses put pressure on their employers to pay for their tooling. This is why pricing does not need to come down.

Quite literally I have engineers spending over $1,000/mo on Opus. That's the goal.
by est31 | 2026-03-11 02:20:55+00:00
Have you tried the latest models at best settings?

I've been writing software for 20 years. Rust since 10 years. I don't consider myself to be a median coder, but quite above average.

Since the last 2 years or so, I've been trying out changes with AI models every couple months or so, and they have been consistently disappointing. Sure, upon edits and many prompts I could get something useful out of it but often I would have spent the same amount of time or more than I would have spent manually coding.

So yes, while I love technology, I'd been an LLM skeptic for a long time, and for good reason, the models just hadn't been good. While many of my colleagues used AI, I didn't see the appeal of it. It would take more time and I would still have to think just as much, while it be making so many mistakes everywhere and I would have to constantly ask it to correct things.

Now 5 months or so ago, this changed as the models actually figured it out. The February releases of the models sealed things for me.

The models are still making mistakes, but their number and severity is lower, and the output would fit the specific coding patterns in that file or area. It wouldn't import a random library but use the one that was already imported. If I asked it to not do something, it would follow (earlier iterations just ignored me, it was frustrating).

At least for the software development areas I'm touching (writing databases in Rust), LLMs turned into a genuinely useful tool where I now am able to use the fundamental advantages that the technology offers, i.e. write 500 lines of code in 10 minutes, reducing something that would have taken me two to three days before to half a day (as of course I still need to review it and fix mistakes/wrong choices the tool made).

Of course this doesn't mean that I am now 6x faster at all coding tasks, because sometimes I need to figure out the best design or such, but

I am talking about Opus 4.6 and Codex 5.3 here, at high+ effort settings, and not about the tab auto completion or the quick edit features of the IDEs, but the agentic feature where the IDE can actually spend some effort into thinking what I, the user, meant with my less specific prompt.
by fc417fc802 | 2026-03-11 02:21:16+00:00
> It would be an easy problem if the government put in place sensible reforms for age verification.

Implying there's a simple solution that isn't being implemented because they weren't forced to by law. So what is that simple solution?

Children typically don't have any form of government issued ID. You can verify someone is a legal adult (you probably shouldn't, but the point is that you can) whereas you can't easily verify that someone online is a child.