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Dorsey's Block cutting up to 10% of staff

https://www.reuters.com/business/dorseys-block-cutting-up-10-staff-bloomberg-news-reports-2026-02...
1•dev_tty01•2m ago•0 comments

Show HN: Freenet Lives – Real-Time Decentralized Apps at Scale [video]

https://www.youtube.com/watch?v=3SxNBz1VTE0
1•sanity•4m ago•1 comments

In the AI age, 'slow and steady' doesn't win

https://www.semafor.com/article/01/30/2026/in-the-ai-age-slow-and-steady-is-on-the-outs
1•mooreds•11m ago•1 comments

Administration won't let student deported to Honduras return

https://www.reuters.com/world/us/trump-administration-wont-let-student-deported-honduras-return-2...
1•petethomas•11m ago•0 comments

How were the NIST ECDSA curve parameters generated? (2023)

https://saweis.net/posts/nist-curve-seed-origins.html
1•mooreds•12m ago•0 comments

AI, networks and Mechanical Turks (2025)

https://www.ben-evans.com/benedictevans/2025/11/23/ai-networks-and-mechanical-turks
1•mooreds•12m ago•0 comments

Goto Considered Awesome [video]

https://www.youtube.com/watch?v=1UKVEUGEk6Y
1•linkdd•14m ago•0 comments

Show HN: I Built a Free AI LinkedIn Carousel Generator

https://carousel-ai.intellisell.ai/
1•troyethaniel•16m ago•0 comments

Implementing Auto Tiling with Just 5 Tiles

https://www.kyledunbar.dev/2026/02/05/Implementing-auto-tiling-with-just-5-tiles.html
1•todsacerdoti•17m ago•0 comments

Open Challange (Get all Universities involved

https://x.com/i/grok/share/3513b9001b8445e49e4795c93bcb1855
1•rwilliamspbgops•18m ago•0 comments

Apple Tried to Tamper Proof AirTag 2 Speakers – I Broke It [video]

https://www.youtube.com/watch?v=QLK6ixQpQsQ
2•gnabgib•20m ago•0 comments

Show HN: Isolating AI-generated code from human code | Vibe as a Code

https://www.npmjs.com/package/@gace/vaac
1•bstrama•21m ago•0 comments

Show HN: More beautiful and usable Hacker News

https://twitter.com/shivamhwp/status/2020125417995436090
3•shivamhwp•21m ago•0 comments

Toledo Derailment Rescue [video]

https://www.youtube.com/watch?v=wPHh5yHxkfU
1•samsolomon•24m ago•0 comments

War Department Cuts Ties with Harvard University

https://www.war.gov/News/News-Stories/Article/Article/4399812/war-department-cuts-ties-with-harva...
6•geox•27m ago•0 comments

Show HN: LocalGPT – A local-first AI assistant in Rust with persistent memory

https://github.com/localgpt-app/localgpt
1•yi_wang•28m ago•0 comments

A Bid-Based NFT Advertising Grid

https://bidsabillion.com/
1•chainbuilder•32m ago•1 comments

AI readability score for your documentation

https://docsalot.dev/tools/docsagent-score
1•fazkan•39m ago•0 comments

NASA Study: Non-Biologic Processes Don't Explain Mars Organics

https://science.nasa.gov/blogs/science-news/2026/02/06/nasa-study-non-biologic-processes-dont-ful...
2•bediger4000•42m ago•2 comments

I inhaled traffic fumes to find out where air pollution goes in my body

https://www.bbc.com/news/articles/c74w48d8epgo
2•dabinat•43m ago•0 comments

X said it would give $1M to a user who had previously shared racist posts

https://www.nbcnews.com/tech/internet/x-pays-1-million-prize-creator-history-racist-posts-rcna257768
4•doener•45m ago•1 comments

155M US land parcel boundaries

https://www.kaggle.com/datasets/landrecordsus/us-parcel-layer
2•tjwebbnorfolk•50m ago•0 comments

Private Inference

https://confer.to/blog/2026/01/private-inference/
2•jbegley•53m ago•1 comments

Font Rendering from First Principles

https://mccloskeybr.com/articles/font_rendering.html
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Show HN: Seedance 2.0 AI video generator for creators and ecommerce

https://seedance-2.net
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Wally: A fun, reliable voice assistant in the shape of a penguin

https://github.com/JLW-7/Wally
2•PaulHoule•1h ago•0 comments

Rewriting Pycparser with the Help of an LLM

https://eli.thegreenplace.net/2026/rewriting-pycparser-with-the-help-of-an-llm/
2•y1n0•1h ago•0 comments

Lobsters Vibecoding Challenge

https://gist.github.com/MostAwesomeDude/bb8cbfd005a33f5dd262d1f20a63a693
2•tolerance•1h ago•0 comments

E-Commerce vs. Social Commerce

https://moondala.one/
1•HamoodBahzar•1h ago•1 comments

Avoiding Modern C++ – Anton Mikhailov [video]

https://www.youtube.com/watch?v=ShSGHb65f3M
2•linkdd•1h 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.