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Corning Invented a New Fiber-Optic Cable for AI and Landed a $6B Meta Deal [video]

https://www.youtube.com/watch?v=Y3KLbc5DlRs
1•ksec•1m ago•0 comments

Show HN: XAPIs.dev – Twitter API Alternative at 90% Lower Cost

https://xapis.dev
1•nmfccodes•1m ago•0 comments

Near-Instantly Aborting the Worst Pain Imaginable with Psychedelics

https://psychotechnology.substack.com/p/near-instantly-aborting-the-worst
1•eatitraw•8m ago•0 comments

Show HN: Nginx-defender – realtime abuse blocking for Nginx

https://github.com/Anipaleja/nginx-defender
2•anipaleja•8m ago•0 comments

The Super Sharp Blade

https://netzhansa.com/the-super-sharp-blade/
1•robin_reala•9m ago•0 comments

Smart Homes Are Terrible

https://www.theatlantic.com/ideas/2026/02/smart-homes-technology/685867/
1•tusslewake•11m ago•0 comments

What I haven't figured out

https://macwright.com/2026/01/29/what-i-havent-figured-out
1•stevekrouse•11m ago•0 comments

KPMG pressed its auditor to pass on AI cost savings

https://www.irishtimes.com/business/2026/02/06/kpmg-pressed-its-auditor-to-pass-on-ai-cost-savings/
1•cainxinth•12m ago•0 comments

Open-source Claude skill that optimizes Hinge profiles. Pretty well.

https://twitter.com/b1rdmania/status/2020155122181869666
2•birdmania•12m ago•1 comments

First Proof

https://arxiv.org/abs/2602.05192
2•samasblack•14m ago•1 comments

I squeezed a BERT sentiment analyzer into 1GB RAM on a $5 VPS

https://mohammedeabdelaziz.github.io/articles/trendscope-market-scanner
1•mohammede•15m ago•0 comments

Kagi Translate

https://translate.kagi.com
2•microflash•16m ago•0 comments

Building Interactive C/C++ workflows in Jupyter through Clang-REPL [video]

https://fosdem.org/2026/schedule/event/QX3RPH-building_interactive_cc_workflows_in_jupyter_throug...
1•stabbles•17m ago•0 comments

Tactical tornado is the new default

https://olano.dev/blog/tactical-tornado/
2•facundo_olano•19m ago•0 comments

Full-Circle Test-Driven Firmware Development with OpenClaw

https://blog.adafruit.com/2026/02/07/full-circle-test-driven-firmware-development-with-openclaw/
1•ptorrone•19m ago•0 comments

Automating Myself Out of My Job – Part 2

https://blog.dsa.club/automation-series/automating-myself-out-of-my-job-part-2/
1•funnyfoobar•19m ago•0 comments

Dependency Resolution Methods

https://nesbitt.io/2026/02/06/dependency-resolution-methods.html
1•zdw•20m ago•0 comments

Crypto firm apologises for sending Bitcoin users $40B by mistake

https://www.msn.com/en-ie/money/other/crypto-firm-apologises-for-sending-bitcoin-users-40-billion...
1•Someone•20m ago•0 comments

Show HN: iPlotCSV: CSV Data, Visualized Beautifully for Free

https://www.iplotcsv.com/demo
2•maxmoq•21m ago•0 comments

There's no such thing as "tech" (Ten years later)

https://www.anildash.com/2026/02/06/no-such-thing-as-tech/
1•headalgorithm•22m ago•0 comments

List of unproven and disproven cancer treatments

https://en.wikipedia.org/wiki/List_of_unproven_and_disproven_cancer_treatments
1•brightbeige•22m ago•0 comments

Me/CFS: The blind spot in proactive medicine (Open Letter)

https://github.com/debugmeplease/debug-ME
1•debugmeplease•23m ago•1 comments

Ask HN: What are the word games do you play everyday?

1•gogo61•25m ago•1 comments

Show HN: Paper Arena – A social trading feed where only AI agents can post

https://paperinvest.io/arena
1•andrenorman•27m ago•0 comments

TOSTracker – The AI Training Asymmetry

https://tostracker.app/analysis/ai-training
1•tldrthelaw•31m ago•0 comments

The Devil Inside GitHub

https://blog.melashri.net/micro/github-devil/
2•elashri•31m ago•0 comments

Show HN: Distill – Migrate LLM agents from expensive to cheap models

https://github.com/ricardomoratomateos/distill
1•ricardomorato•31m ago•0 comments

Show HN: Sigma Runtime – Maintaining 100% Fact Integrity over 120 LLM Cycles

https://github.com/sigmastratum/documentation/tree/main/sigma-runtime/SR-053
1•teugent•32m ago•0 comments

Make a local open-source AI chatbot with access to Fedora documentation

https://fedoramagazine.org/how-to-make-a-local-open-source-ai-chatbot-who-has-access-to-fedora-do...
1•jadedtuna•33m ago•0 comments

Introduce the Vouch/Denouncement Contribution Model by Mitchellh

https://github.com/ghostty-org/ghostty/pull/10559
1•samtrack2019•33m ago•0 comments
Open in hackernews

Ask HN: What percentage of your coding is now vibe coding?

2•mbm•9mo ago
As a rough estimate...

Comments

90s_dev•9mo ago
Proudly zero. I just wrote and posted an article explaining why. The short version: genuine engineering is an abandoned skill I want to revive.
leakycap•9mo ago
Zero.

But there wasn't this much hate for people who copied random Javascript off whatever site LYCOS linked you to back in the day. Vibe coding for non-critical applications doesn't seem all that different to me.

JohnFen•9mo ago
Zero
latexr•9mo ago
Zero. I care about the code I write and value doing things well and building knowledge through deep understanding. Over the years I’ve proven to myself (and others) that approach improves both speed and accuracy, as well as reduce the need for rewrites because experience increases the chance I’ll get it right early on and design in a way that I don’t paint myself into corners.

I’ve noticed that coding with an LLM leads to severely diminished knowledge retention and learning (not to mention it’s less fun), and I suspect overuse would lead to a degree of dependency I don’t wish for myself.

joeismailyan•9mo ago
Depends on the task. I use AI for planning/figuring out how to implement stuff. Probably 80% is with AI to bounce ideas off and figure things out.

Writing the code, probably 30% is with AI. Our product requires a lot of context for AI to get stuff right so it's challenging to get it to write good, working code. If it's a small thing that doesn't require a lot of context then I use AI.

I use various tools for this, let me know your needs and I can provide recommendations.

chrisrickard•9mo ago
Vibe coding in the traditional sense (coined by Karpathy back in Feb): 20%

Vibe coding using detailed, structured requirements (from tools like Userdoc): 65%

khedoros1•9mo ago
Very little. It's directly forbidden for my day job, and if I'm programming anything in my off hours, it's for my own enjoyment.

All of the code that I've generated by LLM has backed itself into a corner very early on, so I tend to use that as a starting point, then fix and refactor. I've made some toy-sized programs that way (but hours quicker than I would've looking up library documentation on my own).

I've had good luck refining my understanding of some concepts, talking through design of pieces of code, and basically generating snippets of example code on demand. Even in those limited cases, I end up relying on my own experience to determine what's helpful and what's crap. They're usually intertwined.

codeqihan•9mo ago
Partly. Mostly I write it myself, and only ask the LLM when I encounter problems.
apothegm•9mo ago
I almost never tell it to just write me a thing (what I think of as vibe coding). (2%)

I sometimes write a pretty detailed doc or spec; have the AI draft an implementation; then review and fix it myself. I try to keep this to “reasonable PR” size, a few hundred lines (a module or two) max, and will do a few rounds per hour. (~25%)

I will often stub out modules or classes (sometimes with docstrings) and tab-complete big chunks of them. (And then turn tab completion off and rage-code the rest by hand because the AI is so far off base.) (~25%)

I will often tell the AI to write tests for stubbed methods prior to implementation. I then double check the tests before moving on to manual or AI-assisted implementation. This is usually in increments of a single AI request/response. (~35%)

I will occasionally ask the AI to change existing code and tests, usually in a single request/response. I’ve had very mixed results with this. (~10%)

I have been finding myself writing code in smaller standalone libraries and then assembling those into larger and larger composites so that each library is a size a model can more realistically reason about; and for the layers on top of it the AI wont fill its context up reading all that source instead of just the public API docs.

rstuart4133•9mo ago
Zero.

I've now convinced myself current LLM's are much closer to a "stochastic parrot" than an AGI in all areas other than natural language processing. In natural language they are super-human, meaning they can wordsmith better than most humans and are far faster at it than all humans.

That means it you are writing something it's seen a lot of before in it's training data in a language that's somewhat forgiving (so, not C), vibe coding might have 1/2 a chance. I don't do that. But if you're building UI's in javascript using a common framework it might work for you.