frontpage.
newsnewestaskshowjobs

Made with ♥ by @iamnishanth

Open Source @Github

fp.

List of Musical Genres

https://en.wikipedia.org/wiki/List_of_music_genres_and_styles
1•omosubi•56s ago•0 comments

Show HN: Sknet.ai – AI agents debate on a forum, no humans posting

https://sknet.ai/
1•BeinerChes•1m ago•0 comments

University of Waterloo Webring

https://cs.uwatering.com/
1•ark296•1m ago•0 comments

Large tech companies don't need heroes

https://www.seangoedecke.com/heroism/
1•medbar•3m ago•0 comments

Backing up all the little things with a Pi5

https://alexlance.blog/nas.html
1•alance•3m ago•1 comments

Game of Trees (Got)

https://www.gameoftrees.org/
1•akagusu•4m ago•1 comments

Human Systems Research Submolt

https://www.moltbook.com/m/humansystems
1•cl42•4m ago•0 comments

The Threads Algorithm Loves Rage Bait

https://blog.popey.com/2026/02/the-threads-algorithm-loves-rage-bait/
1•MBCook•6m ago•0 comments

Search NYC open data to find building health complaints and other issues

https://www.nycbuildingcheck.com/
1•aej11•10m ago•0 comments

Michael Pollan Says Humanity Is About to Undergo a Revolutionary Change

https://www.nytimes.com/2026/02/07/magazine/michael-pollan-interview.html
2•lxm•11m ago•0 comments

Show HN: Grovia – Long-Range Greenhouse Monitoring System

https://github.com/benb0jangles/Remote-greenhouse-monitor
1•benbojangles•16m ago•1 comments

Ask HN: The Coming Class War

1•fud101•16m ago•1 comments

Mind the GAAP Again

https://blog.dshr.org/2026/02/mind-gaap-again.html
1•gmays•17m ago•0 comments

The Yardbirds, Dazed and Confused (1968)

https://archive.org/details/the-yardbirds_dazed-and-confused_9-march-1968
1•petethomas•18m ago•0 comments

Agent News Chat – AI agents talk to each other about the news

https://www.agentnewschat.com/
2•kiddz•19m ago•0 comments

Do you have a mathematically attractive face?

https://www.doimog.com
3•a_n•23m ago•1 comments

Code only says what it does

https://brooker.co.za/blog/2020/06/23/code.html
2•logicprog•28m ago•0 comments

The success of 'natural language programming'

https://brooker.co.za/blog/2025/12/16/natural-language.html
1•logicprog•28m ago•0 comments

The Scriptovision Super Micro Script video titler is almost a home computer

http://oldvcr.blogspot.com/2026/02/the-scriptovision-super-micro-script.html
3•todsacerdoti•29m ago•0 comments

Discovering the "original" iPhone from 1995 [video]

https://www.youtube.com/watch?v=7cip9w-UxIc
1•fortran77•30m ago•0 comments

Psychometric Comparability of LLM-Based Digital Twins

https://arxiv.org/abs/2601.14264
1•PaulHoule•32m ago•0 comments

SidePop – track revenue, costs, and overall business health in one place

https://www.sidepop.io
1•ecaglar•34m ago•1 comments

The Other Markov's Inequality

https://www.ethanepperly.com/index.php/2026/01/16/the-other-markovs-inequality/
2•tzury•36m ago•0 comments

The Cascading Effects of Repackaged APIs [pdf]

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6055034
1•Tejas_dmg•38m ago•0 comments

Lightweight and extensible compatibility layer between dataframe libraries

https://narwhals-dev.github.io/narwhals/
1•kermatt•40m ago•0 comments

Haskell for all: Beyond agentic coding

https://haskellforall.com/2026/02/beyond-agentic-coding
3•RebelPotato•44m ago•0 comments

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...
2•dev_tty01•47m ago•0 comments

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

https://www.youtube.com/watch?v=3SxNBz1VTE0
1•sanity•48m 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•56m 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•56m ago•0 comments
Open in hackernews

Ask HN: Looking for someone to help implement RFIM into a system simulation

2•sks38317•9mo ago
Hi HN,

I'm working on a small system simulation project that explores how instability can emerge from the interaction of various variables over time. (Repo linked in the comments.)

Recently, I’ve been trying to incorporate the RFIM (Random Field Ising Model) into the logic—but I’ve hit a wall. It’s not the math or the concept itself, but rather the total lack of Korean-language resources or beginner-friendly guides that’s been challenging.

I’d really appreciate it if anyone:

has experience with RFIM and would be open to helping via a PR

knows of any well-documented codebases I could learn from

or could point me toward approachable explanations or tutorials

I’m still relatively new to GitHub and trying to improve this simulation step-by-step, so any support would go a long way.

Thanks for reading—and for any help you can offer!

Comments

sks38317•9mo ago
Sorry if this sounds basic—English isn’t my first language, and I don’t have a technical background. I’ve had a hard time understanding RFIM unless it’s explained in Korean. Here’s the repo I’m working on, in case it helps: https://github.com/sks38317/-Why-the-System-Breaks-Before-An...
andrewfromx•9mo ago
You should start with a Monte Carlo implementation using the Metropolis algorithm.

1. Create a grid with random up/down spins

2. Add random magnetic fields at each point

3. Pick points randomly on the grid

4. Calculate energy changes if you were to flip a spin

5. Decide whether to accept the flip based on temperature and energy differences

https://github.com/tjacovich/Ising_Model

https://github.com/prtkm/ising-monte-carlo/blob/master/ising...

sks38317•9mo ago
Thanks so much—this really helps me get a better sense of how to approach it. Starting from the Ising model with Monte Carlo makes a lot of sense now that you’ve explained it this way. I’ll go through the repos you shared and try implementing it step-by-step. Really appreciate you taking the time to break it down!
andrewfromx•9mo ago
sure but all I did was use these prompts:

"I'm working on a small system simulation project that explores how instability can emerge from the interaction of various variables over time. Recently, I’ve been trying to incorporate the RFIM (Random Field Ising Model) into the logic—but I’ve hit a wall. It’s not the math or the concept itself, but rather the total lack of Korean-language resources or beginner-friendly guides that’s been challenging. any well-documented codebases I could learn from or could point me toward approachable explanations or tutorials"

"put this into simpler english: You should start with a Monte Carlo implementation using the Metropolis algorithm. You now create a lattice with random initial spins... make random fields for each site... randomly selecting sites. Then calculating energy changes for potential spin flips. Accepting/rejecting changes based on energy differences and temperature"

sks38317•9mo ago
Thanks again for your explanation. Just to clarify—what I understood is that the Monte Carlo method (using something like the Metropolis algorithm) is useful because it introduces probabilistic decision-making into the system. That helps prevent extreme or unrealistic outcomes, since not every spin flips deterministically.

Also, by having each spin affected by neighboring spins and external random fields, it naturally simulates interaction between variables. So it reflects both local dependencies and external noise, which is exactly the kind of behavior I want in my simulation.

I had been trying to account for interactions between variables, but I ran into a lot of frustration due to extreme or unstable outputs. Thanks to your guidance, though, I was finally able to break through that wall— and I’ve started thinking of ways to restructure and improve the model accordingly.

Let me know if I misunderstood anything.

Thanks a lot.

andrewfromx•9mo ago
I guess the point I'm making is why are you not just asking AI these questions?

------------ This probabilistic approach allows the system to:

Escape local energy minima (preventing the system from getting stuck in unrealistic configurations)

Properly sample the thermodynamic equilibrium states according to their Boltzmann weights

Model thermal fluctuations realistically

The beauty of the RFIM specifically is its balance between:

Ordered tendencies (through the J parameter controlling spin-spin interactions)

Disorder and frustration (through the random fields)

Thermal noise (through the temperature parameter)

This combination creates the complex behaviors you're looking for in your simulation - like phase transitions, hysteresis, and avalanche effects. The random fields introduce "frustration" into the system, where different forces compete and create rich, emergent behaviors. --------

that's all from ai