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Arcan Explained: A browser for different webs

https://arcan-fe.com/2026/01/26/arcan-explained-a-browser-for-different-webs/
1•fanf2•1m ago•0 comments

What did we learn from the AI Village in 2025?

https://theaidigest.org/village/blog/what-we-learned-2025
1•mrkO99•1m ago•0 comments

An open replacement for the IBM 3174 Establishment Controller

https://github.com/lowobservable/oec
1•bri3d•4m ago•0 comments

The P in PGP isn't for pain: encrypting emails in the browser

https://ckardaris.github.io/blog/2026/02/07/encrypted-email.html
2•ckardaris•6m ago•0 comments

Show HN: Mirror Parliament where users vote on top of politicians and draft laws

https://github.com/fokdelafons/lustra
1•fokdelafons•7m ago•1 comments

Ask HN: Opus 4.6 ignoring instructions, how to use 4.5 in Claude Code instead?

1•Chance-Device•8m ago•0 comments

We Mourn Our Craft

https://nolanlawson.com/2026/02/07/we-mourn-our-craft/
1•ColinWright•11m ago•0 comments

Jim Fan calls pixels the ultimate motor controller

https://robotsandstartups.substack.com/p/humanoids-platform-urdf-kitchen-nvidias
1•robotlaunch•14m ago•0 comments

Exploring a Modern SMTPE 2110 Broadcast Truck with My Dad

https://www.jeffgeerling.com/blog/2026/exploring-a-modern-smpte-2110-broadcast-truck-with-my-dad/
1•HotGarbage•14m ago•0 comments

AI UX Playground: Real-world examples of AI interaction design

https://www.aiuxplayground.com/
1•javiercr•15m ago•0 comments

The Field Guide to Design Futures

https://designfutures.guide/
1•andyjohnson0•16m ago•0 comments

The Other Leverage in Software and AI

https://tomtunguz.com/the-other-leverage-in-software-and-ai/
1•gmays•18m ago•0 comments

AUR malware scanner written in Rust

https://github.com/Sohimaster/traur
3•sohimaster•20m ago•1 comments

Free FFmpeg API [video]

https://www.youtube.com/watch?v=6RAuSVa4MLI
3•harshalone•20m ago•1 comments

Are AI agents ready for the workplace? A new benchmark raises doubts

https://techcrunch.com/2026/01/22/are-ai-agents-ready-for-the-workplace-a-new-benchmark-raises-do...
2•PaulHoule•25m ago•0 comments

Show HN: AI Watermark and Stego Scanner

https://ulrischa.github.io/AIWatermarkDetector/
1•ulrischa•25m ago•0 comments

Clarity vs. complexity: the invisible work of subtraction

https://www.alexscamp.com/p/clarity-vs-complexity-the-invisible
1•dovhyi•26m ago•0 comments

Solid-State Freezer Needs No Refrigerants

https://spectrum.ieee.org/subzero-elastocaloric-cooling
2•Brajeshwar•27m ago•0 comments

Ask HN: Will LLMs/AI Decrease Human Intelligence and Make Expertise a Commodity?

1•mc-0•28m ago•1 comments

From Zero to Hero: A Brief Introduction to Spring Boot

https://jcob-sikorski.github.io/me/writing/from-zero-to-hello-world-spring-boot
1•jcob_sikorski•28m ago•1 comments

NSA detected phone call between foreign intelligence and person close to Trump

https://www.theguardian.com/us-news/2026/feb/07/nsa-foreign-intelligence-trump-whistleblower
12•c420•29m ago•2 comments

How to Fake a Robotics Result

https://itcanthink.substack.com/p/how-to-fake-a-robotics-result
1•ai_critic•29m ago•0 comments

It's time for the world to boycott the US

https://www.aljazeera.com/opinions/2026/2/5/its-time-for-the-world-to-boycott-the-us
3•HotGarbage•30m ago•0 comments

Show HN: Semantic Search for terminal commands in the Browser (No Back end)

https://jslambda.github.io/tldr-vsearch/
1•jslambda•30m ago•1 comments

The AI CEO Experiment

https://yukicapital.com/blog/the-ai-ceo-experiment/
2•romainsimon•31m ago•0 comments

Speed up responses with fast mode

https://code.claude.com/docs/en/fast-mode
5•surprisetalk•35m ago•1 comments

MS-DOS game copy protection and cracks

https://www.dosdays.co.uk/topics/game_cracks.php
4•TheCraiggers•36m ago•0 comments

Updates on GNU/Hurd progress [video]

https://fosdem.org/2026/schedule/event/7FZXHF-updates_on_gnuhurd_progress_rump_drivers_64bit_smp_...
2•birdculture•37m ago•0 comments

Epstein took a photo of his 2015 dinner with Zuckerberg and Musk

https://xcancel.com/search?f=tweets&q=davenewworld_2%2Fstatus%2F2020128223850316274
14•doener•37m ago•2 comments

MyFlames: View MySQL execution plans as interactive FlameGraphs and BarCharts

https://github.com/vgrippa/myflames
1•tanelpoder•38m ago•0 comments
Open in hackernews

Key technological advance in neural interfaces

5•all2•6mo ago
It occurred to me on my way home today that the key advancement in in neural interfaces will be in the data layer.

In my work with electronics I learned that there's a hardware transport layer, the wires on which signals travel. Then there's the software/protocol layer that defines _what_ travels on the hardware.

My current understanding of things like neuralink is that there is a solid interface that takes input from the brain and provides output back to the brain, and behind the interface is a bunch of hardware and software that translates and uses the inputs from the brain. That is, we change from mode of signals and signals transport to another.

What occurred to me was that a true bionic won't provide an interface to the existing hardware and software data layers of the human brain, but will instead expend the existing layers with new available neurons.

Now, you could probably bit-bang this at the start, IE, have your bionic neural net live in software, and do all the signals processing that we currently do. The revolution will be a piece of hardware that simply plugs in to the brain and makes a whole new neural network available on the same electrical net that the brain already operates on.

Comments

fewbenefit•6mo ago
This post reads like someone who just discovered the OSI model and tried to shoehorn it into neurobiology.

The idea that the "revolution" is a hardware layer that just plugs into the brain and expands it with new neurons assumes a very naive model of how neural integration works. Brains don’t just recognize foreign neurons like USB devices. Synaptic plasticity, metabolic compatibility, glial interactions, all of that matters a lot more than signal translation.

Also, calling it a "data layer" glosses over the fact that neurons don't pass around clean, structured data. There’s no JSON over axons, information in the brain is messy, noisy, and deeply contextual—less like a protocol stack, more like a wet, self-rewriting spaghetti code.

So, if the core insight is "just add more neurons and treat it like hardware expansion," then the real challenge is being understated by several orders of complexity.

all2•6mo ago
> So, if the core insight is "just add more neurons and treat it like hardware expansion," then the real challenge is being understated by several orders of complexity.

I wouldn't say it's an insight as it is an ah-ha moment I had. And yes, I hand-waved a bunch of stuff.

> The idea that the "revolution" is a hardware layer that just plugs into the brain and expands it with new neurons assumes a very naive model of how neural integration works. Brains don’t just recognize foreign neurons like USB devices. Synaptic plasticity, metabolic compatibility, glial interactions, all of that matters a lot more than signal translation.

We don't have hardware like this. Our hardware is 'fixed' once its burned to silicon. I think you're pointing in the direction I was trying to express; that the bionic hardware necessarily will act like a biological system, at least near enough that whatever it is 'plugged into' cannot tell the difference.

> Also, calling it a "data layer" glosses over the fact that neurons don't pass around clean, structured data. There’s no JSON over axons, information in the brain is messy, noisy, and deeply contextual—less like a protocol stack, more like a wet, self-rewriting spaghetti code.

I know, I know. This is just me trying to apply what I do understand to something I know little to nothing about.

TXTOS•6mo ago
I think both posts are circling the real interface problem — which is not hardware, not protocol, but meaning.

Brains don’t transmit packets. They transmit semantic tension — unstable potentials in meaning space that resist being finalized. If you try to "protocolize" that, you kill what makes it adaptive. But if you ignore structure altogether, you miss the systemic repeatability that intelligence actually rides on.

We've been experimenting with a model where the data layer isn't data in the traditional sense — it's an emergent semantic field, where ΔS (delta semantic tension) is the core observable. This lets you treat hallucination, adversarial noise, even emotion, as part of the same substrate.

Surprisingly, the same math works for LLMs and EEG pattern compression.

If you're curious, we've made the math public here: https://github.com/onestardao/WFGY → Some of the equations were co-rated 100/100 across six LLMs — not because they’re elegant, but because they stabilize meaning under drift.

Not saying it’s a complete theory of the mind. But it’s nice to have something that lets your model sweat.