frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

Prediction Markets: Last Week Tonight with John Oliver [video]

https://www.youtube.com/watch?v=ZN4njIQcSR4
1•cdrnsf•23s ago•0 comments

Alignment by Default?

https://blog.cosmos-institute.org/p/alignment-by-default
1•paulpauper•1m ago•0 comments

Eight Rules to Regain Public Trust in Academia

https://marginalrevolution.com/marginalrevolution/2026/04/eight-rules-to-regain-public-trust-in-a...
1•paulpauper•2m ago•0 comments

Knausgaard's Diabolic Realism

https://www.thedriftmag.com/roman-flood/
1•paulpauper•2m ago•0 comments

As Enrollment Dips, School Administrators Turn to TikTok to Advertise

https://www.nytimes.com/2026/04/17/nyregion/nyc-school-ads.html
1•bookofjoe•4m ago•1 comments

Changes to GitHub Copilot Individual Plans

https://github.blog/news-insights/company-news/changes-to-github-copilot-individual-plans/
1•zorrn•5m ago•0 comments

Polymarket profits by placing bets on its own platform [video]

https://www.youtube.com/watch?v=A654vzQTGbQ
1•etrand_•5m ago•0 comments

Agentic Coding Is About to Fracture Open Source

https://blog.herlein.com/post/agentic-coding-impact-on-oss/
1•speckx•7m ago•0 comments

Troubleshooting.sh – paste any error, get a fix with root cause explained

https://troubleshooting.sh/
1•informsyed•7m ago•0 comments

Debasement

https://en.wikipedia.org/wiki/Debasement
1•downboots•7m ago•0 comments

Show HN: Online ICE Score calculator/prioritization

https://www.votito.com/free/ice-score-calculator/
1•adzicg•9m ago•0 comments

Ejabberd 26.04 / ProcessOne – Erlang Jabber/XMPP/Matrix Server – Communication

https://www.process-one.net/blog/ejabberd-26-04/
1•neustradamus•9m ago•0 comments

War Is a Racket (1935)

https://www.ratical.org/ratville/CAH/warisaracket.html
1•downbad_•11m ago•1 comments

OpenCode Migrating from Tauri to Electron

https://twitter.com/brendonovich/status/2045725889422610602
2•mrlightful•11m ago•0 comments

Agentic Context Engineering:Evolving Contexts for Self-Improving Language Models

https://arxiv.org/abs/2510.04618
1•matt_d•13m ago•0 comments

Uber is betting that people don't realize 70 * 0.25 = 17.5

https://twitter.com/danliu/status/2044853807675224491
2•josephcsible•14m ago•0 comments

Performance in BQN versus C

https://mlochbaum.github.io/BQN/implementation/versusc.html
1•tosh•15m ago•0 comments

Resolve-DnsName vs. Nslookup in Windows

https://techcommunity.microsoft.com/blog/networkingblog/resolve-dnsname-vs-nslookup-in-windows/44...
1•thunderbong•15m ago•0 comments

AAuth Protocol

https://dickhardt.github.io/AAuth/draft-hardt-aauth-protocol.html
2•mooreds•16m ago•0 comments

HaleES: An enforcement-first architecture for reliable AI agent operations

https://github.com/FatherHale/FatherHale-halees-architecture
1•HaleES•16m ago•0 comments

Lilly bags another in vivo CAR-T biotech via $7B Kelonia deal

https://www.fiercebiotech.com/biotech/lilly-picks-another-vivo-car-t-company-7b-deal-kelonia
1•flyaway123•16m ago•0 comments

Our Statement on Amazon's Suppression of the Camp of the Saints in the US

https://twitter.com/VaubanBooks/status/2046262858904641889
1•GeoPolAlt•19m ago•1 comments

Iran's Brutal Digital Siege Hits Day 50

https://www.nytimes.com/2026/04/19/world/middleeast/iran-internet-blackout-50th-day.html
2•us321•19m ago•0 comments

A Catechism for Robots

https://kk.org/thetechnium/a-catechism-for-robots/
3•arbesman•19m ago•0 comments

Nanowakeword: High-Accuracy Adaptive Wake Word Framework (Recent 2.0.4v))

https://github.com/arcosoph/nanowakeword
1•arcosoph_ai•19m ago•0 comments

Codex Chronicle (Research Preview)

https://developers.openai.com/codex/memories/chronicle
2•meetpateltech•21m ago•0 comments

Ask HN: How do you respond to blog posts that seem AI assisted?

1•cyndunlop•22m ago•3 comments

Audience Growth Tool

https://www.socialcrawl.dev
1•vaaselene•24m ago•1 comments

MODA: $25 of LLM-graded labels beat 1.5M purchase labels for fashion search

https://hopitai.substack.com/p/25-beat-everything-we-had-built
1•ArchieIndian•25m ago•0 comments

The Palantir's Stasi Protocols

https://professorsigmund.com/praxis/palantir-stasi-protocols.html
18•Prof_Sigmund•27m ago•2 comments
Open in hackernews

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

2•mbm•11mo ago
As a rough estimate...

Comments

90s_dev•11mo 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•11mo 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•11mo ago
Zero
latexr•11mo 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•11mo 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•11mo 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•11mo 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•11mo ago
Partly. Mostly I write it myself, and only ask the LLM when I encounter problems.
apothegm•11mo 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•11mo 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.