The power rules for derivatives and integrals, quotient rule, chain rule, product rule, reciprocal rule, and integration by parts.
These are the core tools for finding rates of change and accumulated quantities.
They drive applications in physics (motion and forces), engineering (system optimization), economics (marginal costs and trends), biology (population models), and data science.
Who are your top 5 programmers of all time and why?
Mine:
1. Fabrice Bellard (ffmpeg, tinycc, quickjs)
2. John Carmack (doom, quake)
3. John McCarthy (lisp, father of AI, invented GC, timesharing)
4. Linus (linux man)
5. Dennis Ritchie (C and unix, K&R book)
A teenager in the United States started publishing software at 14 in 1998, built the entire online infrastructure for the Occupy Wall Street movement in 2011, joined Google as a software engineer, quit in 2018, and then spent five years writing a C library that does something the entire industry said was impossible.
Then she combined it with llama.cpp and shipped the easiest way on the planet to run a large language model on any computer.
Her name is Justine Tunney.
Here is the story, because almost nobody outside the low level systems world knows what one engineer has built.
Justine was born in 1984. She started writing and publishing software at 14, back when distribution meant uploading binaries to BBS systems and chat networks. She picked up the handle jart, which she still uses on GitHub today. She did the work most teenagers her age were not doing. She read the systems programming literature. She studied compilers. She fell in love with C.
In July 2011 she registered the @occupywallst Twitter handle and the occupywallst dot org domain. Within weeks the protest movement that began in Zuccotti Park in New York had become a global phenomenon, and her infrastructure was the digital backbone of the entire thing. She handled the social media, the website, the donations, the coordination. She built the platform that pushed the movement to reach millions.
After Occupy she joined Google as a software engineer. She worked on TensorBoard, the visualization tool for TensorFlow, and on site reliability for Google infrastructure. She stayed for years. Then in 2018 she left Google Brain to work on a personal project.
The project was called Cosmopolitan Libc.
Cosmopolitan does something most C programmers would tell you is mathematically impossible. It lets you compile a C program once and have the resulting binary run natively on Linux, Windows, macOS, FreeBSD, OpenBSD, and NetBSD with no modification. One file. Six operating systems. No virtual machines. No interpreters. No recompilation. The technique she invented is called Actually Portable Executable.
The implications are wild. Cosmopolitan binaries violate every assumption about how operating systems load programs. They are at once a Windows PE file, a Linux ELF binary, a macOS Mach-O binary, and a shell script. The same bytes run on every platform.
For five years she worked on it mostly alone. She funded the development partly through Mozilla's MIECO program, which sponsored her work on Cosmopolitan 3.0, released on October 31, 2023.
A month later she shipped llamafile.
llamafile is what happens when you combine Cosmopolitan with llama.cpp. You take any LLM weights file in the standard GGUF format, you wrap it in Justine's binary, and you get a single file that runs on six operating systems without installation. No Python. No CUDA setup. No dependency hell. Just one file that you double click and it works.
Mozilla launched it as an official project of their innovation group on November 29, 2023. It went viral immediately. The repository, hosted at github .com/mozilla-ai/llamafile, now has 24,600 stars. The license is Apache 2.0.
Justine kept shipping. She added GPU support to Cosmopolitan, a task systems engineers thought would require rewriting the whole thing. She added dlopen support, another thing nobody else had figured out. She wrote whisperfile, a single file version of OpenAI's Whisper speech-to-text model based on the same architecture.
Her GitHub profile lists projects most engineers would consider impossible. sectorlisp, a Lisp interpreter that fits in a boot sector. blink, the tiniest x86-64-linux emulator on Earth. bestline, a teletypewriter command session library. redbean, a complete web server inside a single zip file.
A teenager who shipped software in 1998 grew up to write the C library that the entire local AI movement now runs on top of.
She did most of it alone, and most people scrolling AI Twitter cannot name her.
I just spent months handwriting a 200 page guide on the entirety of ML foundations and math from scratch.
The guide features:
- Neural Nets (Backprop, Adam, SGD, Batch Norm)
- ML Algorithms (SVM, Grad Boosting, K-means, PCA)
- Hardware (Tensor Cores, Systolic Arrays, CUDA)
- Transformers (Multi-Head Attn, KV Cache, LoRA)
- Vision (ViT, Convolutions, MAE, IoU, NMS, VLM)
- Agents (OpenClaw, ReAct, Memory, Orchestration)
Everything I wish I had years ago, for free.
Unlock the core of vector calculus with this visual guide.
The Del operator (∇) drives three key operations on fields:
- Gradient (∇Φ) points to the steepest increase of a scalar;
- Divergence (∇ · F) quantifies net outflow or inflow;
- Curl (∇ × F) measures local rotation.
> Gauss’s Divergence Theorem states
∯_S F · dA = ∭_V (∇ · F) dV, relating surface flux to volume divergence.
> Stokes’ Theorem, ∮_C F · dl = ∬_S (∇ × F) · dA, relates circulation around a curve to curl over the surface.
These are foundational in electromagnetism, fluid dynamics, and engineering.
What if you could take three completely different model families… and distill them into one tiny model? 🤯
📜 Paper: https://t.co/K2iKD4xFvp
MOPD (Multi-Teacher On-Policy Distillation) has become a standard procedure in post-training. We already distill multiple specialized variants of the same model into a single set of weights.
But what if we could go further - and distill models from entirely different families? Turns out, it is possible.
Today we’re releasing a paper on cross-tokenizer distillation - our first steps in this exciting direction. 📄
We distilled Qwen3-4B, Phi-4-Mini, and Llama-3B into Llama-3.2-1B.
MMLU jumped from 32.05 → 46.32 when using multiple teachers. 📈
The team is now working on Nemo-RL integration so the community can try this method in their own settings. Plus, we are scaling experiments up. 🚀