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Your deserves better.

  • LinkedIn doesn't speak dev

    Skills endorsed by people who've never seen your code. A profile that can't show what you actually shipped.

  • GitHub isn't a profile

    A contribution graph is not a portfolio. There's no way to tell your story, control your narrative, or showcase your work.

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About karpathy

Overview

Andrej is a world-class deep learning researcher and engineer with 16 years of GitHub history and a focus on building minimal, educational implementations of large language models (LLMs) and neural networks. He is best known for creating highly starred open-source projects like nanoGPT and llm.c, which are widely used across the AI community, signaling exceptional technical depth and the rare ability to make complex concepts accessible. With over 151,000 followers and multiple repositories exceeding 10,000 stars, he is one of the most influential AI practitioners on GitHub today.

Role Fit

AI/ML Research EngineerDeep Learning Educator / Developer AdvocateAI Infrastructure EngineerLLM Systems Architect
Strengths

Elite LLM & Deep Learning Expertise

Built nanoGPT (55k+ stars), minGPT (24k+ stars), llm.c (29k+ stars), and nanochat (50k+ stars) — all focused on training and fine-tuning large language models from first principles, demonstrating mastery of the full LLM stack.

Low-Level Systems Programming

llm.c implements LLM training in raw C and CUDA (15.8% of codebase is C, 0.6% CUDA), showing the ability to go far below Python abstractions and optimize at the hardware level — a rare skill in ML.

Exceptional Ability to Simplify Complexity

Repositories like micrograd, minGPT, and nn-zero-to-hero are explicitly designed to teach neural network concepts from scratch, with millions of learners using them globally, indicating elite-level pedagogical and communication skills.

High Community Impact & Thought Leadership

151,365 GitHub followers, multiple repos with 10k–55k stars, and a contribution to the official Python CPython repository signal that his work sets standards others follow.

Prolific & Consistent Output

63 public repositories spanning GPT architectures, tokenization (minbpe), inference engines (llama2.c), and research tooling (arxiv-sanity-preserver), demonstrating sustained, high-quality output across many years.

Standout Projects
nanoGPT

The most starred repo at 55k+ stars, it is the go-to reference implementation for training medium-sized GPT models. Its widespread adoption in academia and industry proves both technical excellence and real-world utility.

llm.c

Implementing LLM training in raw C and CUDA with 29k+ stars is an extraordinary technical feat that demonstrates systems-level mastery most ML engineers lack, and it directly influences how practitioners think about performance optimization.

nn-zero-to-hero

A Jupyter Notebook-based course with 21k+ stars that has become a canonical resource for learning neural networks from scratch, highlighting his unique ability to bridge research and education at scale.

micrograd

A tiny autograd engine with 15k+ stars that replicates PyTorch's core functionality in minimal code — widely regarded as one of the best teaching tools in deep learning, showcasing elegant, purposeful engineering.

Built differently

Your profile. Your rules.

  • Open source, all the way down

    TopSignal is fully open source. No black boxes, no vendor lock-in. Inspect it, contribute to it, or self-host.

  • We never see your code

    Analysis happens on your machine using your own API keys. You decide what gets published. Full privacy, zero compromise.

  • Make it yours

    Customize your profile. Pick your theme, highlight what matters. This is your page — not a template built for an ATS.

  • Share with recruiters, on your terms

    When you're ready, generate a clean shareable link that shows exactly what recruiters should see — skills verified by real commits, not self-reported.

  • Show off your skills

    Let your work speak for itself. Share your profile with teammates or your network and let real commit history back up your reputation.

  • See where your gaps are

    Discover skills you're close to mastering and areas worth investing in. Honest insights from your actual work history — no guesswork.

How it works

Up and running in fast.

  1. 01

    Run the CLI

    Point TopSignal at your code directories. It scans your local git repos and pulls GitHub data.

    npx topsignal analyze johndoe
  2. 02

    AI builds your profile

    Your repos are analyzed locally. No code is ever sent to our servers.

  3. 03

    Publish & share

    Push your profile with one command and get a clean, shareable link.

    npx topsignal publish
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CLI FIRST

The terminal is where agents live.

One command. Clean, structured developer profiles — piped directly into your agents, scripts, and sourcing pipelines.

topsignal — bash
$

Open source at the core

Built by developers, for developers.

Most professional networks treat your data as their product. TopSignal flips that. Your profile data is open and portable, your analysis happens on your machine using your own API keys, and you decide what gets published.

  • Your data is open

    Your developer profile data is yours — open, portable, and never locked behind a platform. No proprietary formats, no black boxes.

  • Your data, your export

    Download everything, anytime. No hostage data, no lock-in. Take your profile wherever you go.

  • We never see your code

    Analysis happens on your machine using your own API keys. You decide what gets published — code is ever sent to our servers.