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Managers Have Been Vibe Coding All Along

https://yusufaytas.com/managers-have-been-vibe-coding-all-along
2•wyajmd•28s ago•0 comments

Anthropic on track for first profitable quarter

https://www.ft.com/content/a67248e7-f819-4dba-b0f7-3847df0a75f3
1•throwaway2037•3m ago•0 comments

Show HN: Real-time virtual try-on using hand gestures and live video diffusion

https://github.com/manas15/try-on
2•manas95•4m ago•0 comments

Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multiview Captures

https://apple.github.io/ml-headsup/
1•epaga•4m ago•0 comments

AI Engineering from Scratch

https://aiengineeringfromscratch.com
1•rippeltippel•5m ago•0 comments

The US space enterprise is desperately waiting for Starship–will it deliver?

https://arstechnica.com/space/2026/05/the-us-space-enterprise-is-desperately-waiting-for-starship...
1•rbanffy•6m ago•0 comments

Anthropic is paying SpaceX $1.25B/month and other things hidden in the S-1

https://italianelite.eu/articles/spacex-s1-deep-dive.html
1•johntiror•6m ago•0 comments

I built a tool that critiques SaaS landing pages

https://pagegains.com
1•solopreneur_dad•7m ago•0 comments

Earth's supervolcanoes are waking up. Here's what that means for the planet

https://www.sciencefocus.com/planet-earth/earths-supervolcanoes-are-waking-up-heres-what-that-mea...
1•bryanrasmussen•7m ago•0 comments

Fuck YAML

https://github.com/IronScheme/IronScheme/commit/2f847793946935bd9143cdfb064f9006f763df68
1•theanonymousone•11m ago•0 comments

The lesson from John Travolta's dramatic new look: dress for the job you want

https://www.theguardian.com/commentisfree/2026/may/20/john-travolta-cannes-festival-beret-glasses...
1•Michelangelo11•11m ago•1 comments

Why Optimistic Merging Works Better

http://hintjens.com/blog:106
1•jimsojim•16m ago•0 comments

Simplifying our homepage helped increase trial signups

https://plausible.io/blog/homepage-edits-conversion-lift
1•markosaric•16m ago•0 comments

The Mislabeled Brick of Utopia

https://orib.dev/nexus.html
1•enemyz0r•18m ago•0 comments

PinePhone by PINE64

https://pine64.org/devices/pinephone/
1•mentalgear•19m ago•0 comments

Why Ruby Still Feels Like Home After All These Years

https://caio.ca/blog/why-ruby-still-feels-like-home
1•vinhnx•23m ago•2 comments

Fast local spreadsheet viewer based on GPUI

https://github.com/samuelcolvin/spread
2•scolvin•23m ago•0 comments

Blink-AI Assistant. A knowledge destination

https://blink-oi.vercel.app
1•Pascal1997•24m ago•0 comments

The famous O3 "GeoGuessr" prompt did not work

https://www.seangoedecke.com/the-o3-geoguessr-prompt-did-not-work/
3•ingve•27m ago•0 comments

Agent Bazaar: Enabling Economic Alignment in Multi-Agent Marketplaces

https://www.chatpaper.ai/dashboard/paper/80941351-e4d5-4566-a458-aa93c4f7dbff
3•doener•30m ago•0 comments

How to Speed Up Phrase Search with Bigram_index

https://manticoresearch.com/blog/how-to-speed-up-phrase-search-with-bigram-index/
2•snikolaev•30m ago•0 comments

The Economics of Human Extinction

https://www.andrewleigh.com/speech_the_economics_of_human_extinction_21_may_2026
2•ajdlinux•32m ago•0 comments

Show HN: The-knowledge-guy – ask, walk, and skim every book on your shelf

https://github.com/vitalysim/the-knowledge-guy
1•vitalysim•32m ago•0 comments

We Backtest High-Frequency Options Spread Strategies with Volatility Timing

https://medium.com/@DolphinDB_Inc/backtesting-application-medium-and-high-frequency-options-sprea...
2•CrazyTomato•34m ago•0 comments

What it was like working on LLMs and security at Meta (2022-2026)

https://joshuasaxe181906.substack.com/p/what-it-was-like-working-on-llms
2•takira•37m ago•0 comments

AltTab Pro

https://alt-tab.app
1•frizlab•37m ago•2 comments

CAISI states open models lag behind the American frontier, with the gap widening

https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro
1•nilen•39m ago•1 comments

Ask HN: Is anyone using vibe coding harnesses like OmO or Ralph loops at work?

1•choam•43m ago•0 comments

AI red teaming agents change how LLMs get tested

https://www.helpnetsecurity.com/2026/05/21/ai-red-teaming-agents-research/
1•SVI•43m ago•0 comments

AVX-512 and Validating Usage on AMD EPYC

https://www.amd.com/en/blogs/2026/understanding-avx-512---validating-usage-on-amd-epyc-.html
1•tosh•44m ago•0 comments
Open in hackernews

Ask HN: Is there a general, multi-PL programming task dataset?

1•quartztz•1y ago
Hello!

Being a student interested in PL design, I have had this idea floating around for a while: the gist is finding out what programming languages LLMs might be the most proficient in, to study their design choices and syntactic features with the goal of designing the perfect language for LLMs. This is, of course, gimmicky, but I entertained the idea for a while as a fun afterschool project.

The challenge is: what would be the best way to evaluate programming performance _in specific languages_? There are two main hypotheses here:

1. There are intrinsic syntactic/structural features that the transformer architecture is uniquely able to parse/reproduce/understand best, leading to higher quality code generated. For example: Lisp dialects make parsing code structure and blocks very easy, so one could assume an LLM can "understand their code better" 2. There is so much Python/JS out there that the question isn't even worth asking, and the performance in those will beat whatever other language you throw at it. This is probably not as much of a point thanks to newer transformer architectures but the question is still up.

I suspect the answer can be made somewhat interesting by considering performance relative to language popularity, but the ground question is: is there a general dataset containing different programming challenges, of varying difficulty, in multiple languages, with standard solutions? I couldn't find anything when I looked around, but I might have missed something obvious. It wouldn't be impossible to build a simple website to crowdsource, but I'm thinking that if I missed something obvious I'd rather find out early than late. Also, if you have any input on the project itself, I'd love to hear your ideas!

Comments

Someone•1y ago
> For example: Lisp dialects make parsing code structure and blocks very easy, so one could assume an LLM can "understand their code better"

I would expect the reverse: lisp has no syntactic sugar, making it harder for a LLM to glue code fragments together in a way that produces valid lisp code. Even guaranteeing that parentheses are correctly nested already can be a challenge.

As to a set of programs: they aren’t exactly what you’re looking for, but I would consider https://projecteuler.net (does not contain solutions, but searching for project Euler solutions” finds some) or https://benchmarksgame-team.pages.debian.net/benchmarksgame.

sargstuff•1y ago
Very open ended questions. Geeks for Geeks loosely organized around computer science topics of study : https://www.geeksforgeeks.org/

nit-pick details:

Ignoring hardware differences, "performance" comparisons can be based on differences between algorithm(s) used vs. how algorithm is implimented. For a given language, "algorithm implimentation performance" can be defined as the trade-offs on how a a given algorithm is implimented in a language (compared to other programming languages, but also easy use/flexibility based on 'language generation level -> https://www.geeksforgeeks.org/generation-programming-languag... )

----------------------

1) General computation language specialty 'modules' not withstanding; "languages" are built/optimised around core algorithmic concepts / anticipated area/concentration of targeted professional environment. aka opencl (gpu), R (statistics), Lisp (engineering design), C (OS level), sql (data selection), jasper reports, cobol (business), etc. Languages tend to be 'popular' because of the ecosystem provided around/for a given language.

snarky side note -> can always write a more standard language that compiles to an esolang & provide appropriate emacs/vim/sed/spacemacs ide support.: https://esolangs.org/wiki/Main_Page

  LLM's are very useful at curating information and recognizing/summarizing "statisical" relevance. aka apl is great for engineering mind set, not so good for business use cases aka cobal.  LLM might recognize a language for a given user that combines commonly used 'apl' aspecs of user and commonly used 'cobal' aspecs of user and recommend a language(s) with suitable commonalities for given user. 


2) Search engine topic 'coding challenges' 'algorithmic coding challenges' brings up many types of answers/sites for honing one's coding skills (various languages, beginner to expert, etc). Coding 'algorithms' vs. coming up with algorithm(s) to code is sort of a side aspect. Also differences in 'competition' challenges vs. 'technical challenges' (aka 512 c64 vs. 1 raspberry pi) ; vs. "computer science coding challenges" vs. 'computational genomic challenges'

     ?? how easy / hard based on 'profession' aka artist vs. software designer 20 years experience programming in scheme; environment -- NASA vs. google vs. insurance company.

   ?? from scratch : https://synoptek.com/insights/it-blogs/10-challenges-every-software-product-developer-faces/

   ?? based on industry standards ?? ; just trying to keep skills honed ??