Top all-time · #1868
Stochastic Lifestyle
Personal blog · stochasticlifestyle.commathscienceprogramming
Stochastic Lifestyle is a personal blog at stochasticlifestyle.com about math, science and programming. Since 2016, 39 different Hacker News readers have linked it 98 times, most often “ChatGPT performs better on Julia than Python (and R) for LLM Code Generation”. It is still writing: the last post was on 2026-02-12, and you can follow it by feed.
Sometimes recommended · 9 of 40 sampled comments recommend it?
Why HN links it
HN readers link Chris Rackauckas's blog for posts on Julia, scientific machine learning, differential equations and type dispatch, plus pieces on LLM performance with Julia.
- Recommended for · 9 comments
- Called a helpful article on type dispatch; a great post on specialized programs; a better explanation of when types are needed; explaining chaos better than the
Summarised by Claude from a sample of 40 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
A Random Blog About Math and Life
Most-linked posts?
- ChatGPT performs better on Julia than Python (and R) for LLM Code Generation6 people · 7 mentions · first linked 2023-11-19
- Why Numba and Cython are not substitutes for Julia4 people · 8 mentions · first linked 2018-08-06
- /type-dispatch-design-post-object-oriented-programming-julia/4 people · 5 mentions · first linked 2020-01-24
- How chaotic is chaos? How some AI for Science / SciML overstates accuracy claims4 people · 4 mentions · first linked 2025-05-26
- Useful algorithms that are not optimized by Jax, PyTorch, or TensorFlow3 people · 5 mentions · first linked 2021-07-21
- 7 Julia Gotchas and How to Handle Them2 people · 7 mentions · first linked 2016-10-04
- GPU-Accelerated Ode Solving in R with Julia, the Language of Libraries2 people · 4 mentions · first linked 2020-08-24
- Implicit ODE solvers are not universally more robust than explicit ODE solvers2 people · 2 mentions · first linked 2025-09-04
- Semantic Versioning (Semver) is flawed, and Downgrade CI is required to fix it2 people · 2 mentions · first linked 2024-02-11
- Some Insights from a Julia Developer2 people · 2 mentions · first linked 2017-10-13
What Hacker News says?
The author posits that people don't like using LLMs with Rust because LLMs aren't good with Rust. Then people would migrate towards languages that do will with LLMs. However, if that were true, then Julia would be more popular since LLMs do very well with it: https://www.stochasticlifestyle.com/chatgpt-performs-better-...
Comment linking /chatgpt-performs-better-on-julia-than-python-and-r-for-large-language-model-llm-code-generation-why/
on “My AI skeptic friends are all nuts”Submitted Some Insights from a Julia Developer as a story · 255 points
HN discussionGeneric functions in Julia can use types which have no reference to their definition using dependent compilation. http://www.stochasticlifestyle.com/why-numba-and-cython-are-... So you can work with types even when you've never seen their definition given how the compilation will occur with all of the pieces together instead of separately.
Comment linking /why-numba-and-cython-are-not-substitutes-for-julia/
on “Julia 1.0”Submitted Trade-Offs in Automatic Differentiation: TensorFlow, PyTorch, Jax, and Julia as a story · 234 points
HN discussionIt's offering something completely different because of the compatibility, compile-time controls, and ability to fully interprocedurally optimize. http://www.stochasticlifestyle.com/why-numba-and-cython-are-...
Comment linking /why-numba-and-cython-are-not-substitutes-for-julia/
on “Julia 1.0”Submitted Useful algorithms that are not optimized by Jax, PyTorch, or TensorFlow as a story · 214 points
HN discussionModia.jl is great! Though note that there are other M&S DAE libraries in Julia which stray from the Modelica DSL syntax. For example, ModelingToolkit.jl can do it directly by modifying Julia code: https://www.stochasticlifestyle.com/generalizing-automatic-d... or it can work via a component-based modeling DSL: https://mtk.sciml.ai/dev/tutorials/acausal_components/
Comment linking /generalizing-automatic-differentiation-to-automatic-sparsity-uncertainty-stability-and-parallelism/
on “Julia receives DARPA award to accelerate electronics simulation”Submitted The Essential Tools of Scientific Machine Learning as a story · 161 points
HN discussionI found this article by Chris Rackaukas to be pretty helpful: https://www.stochasticlifestyle.com/type-dispatch-design-pos...
Comment linking /type-dispatch-design-post-object-oriented-programming-julia/
on “Symbolics.jl: A Modern Computer Algebra System for a Modern Language”Submitted A Comparison of Differential Equation Solver Suites as a story · 150 points
HN discussionThanks, I see where you are coming from. >It should be the responsibility of the developer of "model" to select the "fit" algorithm appropriate for "model." (They don't have to implement it, but they do have to import the right one.) The developer of "fit" should not be responsible for handling every possible "model" type. You could have the developer of "model" override / extend the definition of "fit" but that ope…
Comment linking /type-dispatch-design-post-object-oriented-programming-julia/
on “Julia Computing raises $24M Series A”Submitted A guide to Gen AI / LLM vibecoding for expert programmers as a story · 128 points
HN discussionThere's quite fundamental optimizations that will be missing if separately compiled pieces cannot optimize together. These barriers disallow many things. Additionally, anything that relies on higher order functions provided by the user, such as optimization or differential equations, will run into issues because those functions cannot be optimized by some super good package developer, but instead have to come from t…
Comment linking /why-numba-and-cython-are-not-substitutes-for-julia/
on “JuliaLang: The Ingredients for a Composable Programming Language”Submitted Implicit ODE solvers are not universally more robust than explicit ODE solvers as a story · 114 points
HN discussionThis isn't quite true. Jax works well for "quasistatic" code, but can't handle more dynamic types of problems (see https://www.stochasticlifestyle.com/useful-algorithms-that-a... for a more detailed explanation). Jax is definitely the right direction for the python ecosystem, but it can't solve all your problems. At some point you still need a fast language.
Comment linking /useful-algorithms-that-are-not-optimized-by-jax-pytorch-or-tensorflow/
on “PyTorch vs. TensorFlow in 2022”Submitted Algorithm efficiency comes from problem information as a story · 109 points
HN discussionTruth. See http://www.stochasticlifestyle.com/the-nonlinear-effect-of-c...
Comment linking /the-nonlinear-effect-of-code-speed-on-productivity/
on “Julia Language Co-Creators Win James H. Wilkinson Prize for Numerical Software”Submitted JuliaCall Update: Automated Julia Installation for R Packages as a story · 93 points
HN discussionChris has done good work on this genre. His differential equations Julia package with support for physics or sci ML is pretty cool. https://www.stochasticlifestyle.com/the-essential-tools-of-s...
Comment linking /the-essential-tools-of-scientific-machine-learning-scientific-ml/
on “Physics-Based Deep Learning Book”Submitted Automatic differentiation can be incorrect as a story · 74 points
HN discussion>I’ve had a lot of frustration with the Julia community being unwilling to see faults in the language. What? It's basically a meme at this point for major contributors to write a list of what they think is bad. The language's creator did a nice Youtube video "What's bad about Julia?" at JuliaCon 2019 ( https://www.youtube.com/watch?v=TPuJsgyu87U ), one of my most popular blog posts of all time is "7 Julia Gotchas an…
Comment linking /7-julia-gotchas-handle/
on “What's bad about Julia?”Submitted ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in Julia as a story · 66 points
HN discussionEDIT: Paste this hacky snippet into your browser's console to save all of the links into your clipboard: (() => { let result = [...document.querySelectorAll('td a')] .map((link) => link && !link.href.includes('ycombinator') && !link.href.includes('javascript:void') ? link.href : '' ) .filter(Boolean); result = [...new Set(result)]; result.sort(); copy(result.join('\n\n')); })(); Just to help, here's a compiled-list…
Comment linking /
on “Ask HN: Great Blogs by Programmers”Submitted Why Numba and Cython are not substitutes for Julia as a story · 42 points
HN discussionhttps://www.stochasticlifestyle.com/ Blog of Dr. Christopher Rackauckas. The blog is a mixture of programming, Julialang, ML, computational scientific. I really enjoy
Comment linking /
on “Ask HN: Great Blogs by Programmers”Submitted Neural Jump SDEs (Jump Diffusions) and Neural PDEs as a story · 35 points
HN discussionSeems like it would still be useful use Julia as the backend for the R package instead of Fortran. I've been showcasing a lot of that lately with good success: https://www.stochasticlifestyle.com/juliacall-update-automat... https://www.stochasticlifestyle.com/gpu-accelerated-ode-solv...
Comment linking /gpu-accelerated-ode-solving-in-r-with-julia-the-language-of-libraries/
on “Julia adoption keeps climbing”Submitted Inexact Models Can Guide Decision Making in Quantitative Systems Pharmacology as a story · 29 points
HN discussionNeural differential equations are something that's worked out in Julia. Neural ordinary differential equations (ODEs), stochastic differential equations (SDEs), and delay differential equations (DDEs) can be found in this blog post ( https://julialang.org/blog/2019/01/fluxdiffeq ). Neural jump diffusions (jump SDEs) and neural partial differential equations (PDEs) are described here: http://www.stochasticlifestyle.c…
Comment linking /
on “Matlab–Python–Julia Cheatsheet”Submitted 7 Julia Gotchas and How to Handle Them as a story · 22 points
HN discussion
