
Mathematics Physics Computer Science
Mathematics Physics Computer Science
What it does
ScienceCreator.asia is a universal compendium bridging Mathematics, Physics, and Computer Science. It features interactive formulas with rich historical narratives, tracing discoveries from ancient India to modern AI. Covering algebra, calculus, quantum mechanics, and algorithms, each entry reveals the human stories behind the math. With a built-in scientific calculator, it serves as an engaging educational hub for students and enthusiasts to explore the origins of scientific knowledge.
Does the same job
all alternatives →- MCMaths, CS and AI CompendiumFeb 2026 · github.com · ▲88
Hey HN, I don’t know who else has the same issue, but: Textbooks often bury good ideas in dense notation, skip the intuition, assume you already know half the material, and get outdated in fast-moving fields like AI. Over the past 7 years of my AI/ML experience, I filled notebooks with intuition-first, real-world context, no hand-waving explanations of maths, computing and AI concepts. In 2024, a few friends used these notes to prep for interviews at DeepMind, OpenAI, Nvidia etc. They all got in and currently perform well in their roles. So I'm sharing. This is an open & unconventional…



Physics-CalculatorJun 2026 · github.com · ▲3a lightweight, zero-dependency computational utility engineR
- ITIntroduction to Algorithms and Machine Learning [pdf]2023 · justinmath.com · ▲6
From 2020-23 I taught at a highly accelerated math program in Pasadena, CA and was given the opportunity to develop and teach a quantitative computer science track for participating 10th-12th graders who had an interest in CS. I developed the curriculum on the fly while teaching the courses, and eventually consolidated it all into this textbook. The students had already learned a surprising amount of math for their age using a system that is now commercially available at www.mathacademy.com, where they progressed from prealgebra through AP Calculus BC in only three years (6th-8th grade), and…
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the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, May 2026
the whole month →

Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai


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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com