Top all-time · #13587
Ji-Ha Kim
Personal blog · jiha-kim.github.ioaimathprogramming
Ji-Ha Kim writes Ji-Ha's Blog, a personal blog at jiha-kim.github.io about ai, math and programming. Since 2025, 5 different Hacker News readers have linked it 23 times, most often “Introduction to Stochastic Calculus”. It is still writing: the last post was on 2026-05-06, and you can follow it by feed.
Too few sampled comments (2) to say how often HN recommends it.?
Why HN links it
HN readers link this blog's introduction to stochastic calculus as an intuitive starting point. One commenter summarises it as a way to define integration of random walks.
Summarised by Claude from a sample of 2 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
Most-linked posts?
- Introduction to Stochastic Calculus2 people · 2 mentions · first linked 2025-02-24
- The Mean-Ing of Loss Functions2 people · 2 mentions · first linked 2025-03-28
What Hacker News says?
the article goes into the details in https://jiha-kim.github.io/posts/introduction-to-stochastic-... but the TLDR is it's a way to define integration of random walks.
Comment linking /posts/introduction-to-stochastic-calculus/
on “Introduction to Stochastic Calculus”Submitted Introduction to Stochastic Calculus as a story · 444 points
HN discussionIf you just want some intuition, I found this previous HN submission https://jiha-kim.github.io/posts/introduction-to-stochastic-... pretty approachable at giving you some key ideas without being too rigorous. It's not useful for doing calculating anything practical of course but it can either be a starting point or just a way to satisfy that curiosity.
Comment linking /posts/introduction-to-stochastic-calculus/
on “An Introduction to Stochastic Calculus (2022)”Submitted Rethinking Probability – Mass, Averages, and Granularity as a story · 3 points
HN discussionSubmitted The Mean-Ing of Loss Functions as a story · 3 points
HN discussionSubmitted Autoregression vs. Diffusion – Understanding Sampling via Optimal Transport as a story · 2 points
HN discussionSubmitted Rational Matrix Iterations for Polar Decomposition as a story · 2 points
HN discussionSubmitted Dynamics of Higher-Order Residual Networks as a story · 2 points
HN discussionSubmitted When Equivalent Weights Train Differently as a story · 1 points
HN discussionSubmitted A Simpler Parametrization for Modern Optimizers as a story · 1 points
HN discussionSubmitted Fast Tight Spectral-Norm Bounds as a story · 1 points
HN discussionSubmitted Lion-K CCWD: Corrected Cautious Weight Decay and Hyperparameter Transfer as a story · 1 points
HN discussionSubmitted Transformers as Constrained Optimization as a story · 1 points
HN discussionSubmitted Optimizers and Odes as a story · 1 points
HN discussionSubmitted Frequency-Domain Muon for Conv Filters – Orthogonalizing the Operator as a story · 1 points
HN discussionSubmitted Polar Factor Beyond Newton-Schulz – Fast Matrix Inverse Square Root as a story · 1 points
HN discussionSubmitted Matrix Inverse Roots with Fixed-Budget GEMM Kernels as a story · 1 points
HN discussionSubmitted Matrix Inverse Square Root (SPD) with Fixed-Budget GEMM Kernels as a story · 1 points
HN discussionSubmitted Deriving Reverse-Time Stochastic Differential Equations (SDEs) as a story · 1 points
HN discussionSubmitted Discrete Calculus as a story · 1 points
HN discussionSubmitted Life Update – On Health as a story · 1 points
HN discussionSubmitted The Mean-Ing of Loss Functions as a story · 1 points
HN discussionSubmitted Automatic Stochastic Differentiation as a story · 1 points
HN discussion
