Hai there! :-) I'm Libor Burian or Burny! I'm looking to collaborate! :-) ## Focus - [[Artificial Intelligence|ML and AI research and engineering ]] - Technical writing - Community building ![[Images/dd8ede9a226ac6782d4f102f2e25224a_MD5.jpeg|200]]![[Images/35727f7355f3216747dce9cc72ebe34b_MD5.jpg|200]] ## I am constantly following, reading, writing about, playing with, expanding, etc. on various new research papers and industry news, with others, in: - [[Artificial Intelligence|AI progress, such as new LLMs, novel AI architectures, agents, reinforcement learning, etc.]] - [[Mathematical theory of artificial intelligence|The science of AI/ML/deep learning]], [[Mechanistic interpretability|AI interpretability]] that tries to reverse engineer how AI systems work, etc. - [[AI engineering]], applying AI in industry, to [[Artificial intelligence x Programming Coding Software Engineering|software engineering]], to [[Artificial Intelligence x Physics|physics]], to other [[Artificial intelligence x Engineering|engineering]], to other [[Artificial intelligence x Science|sciences]], etc. - [[Artificial Intelligence|And other types of AI research papers and industry news]] - I'm highly curious and also like to sometimes explore some [[Mathematics|math]], [[physics]], science of [[intelligence]], [[cognitive science]], [[neuroscience]], [[philosophy]], [[computer science]], [[Future of humanity, AI, sentience, futurology, politics|futurology]], scifi, etc. ## Experience - For 3+ years following various new AI research papers and industry news - For 2+ years working in AI industry on LLMs, agents, etc. for various clients - For 10+ years all sorts of community building activities - For 10+ years general education and experimenting and curiosity about [[STEM]] related things, mostly computer science ## Education - Since 2020: [CTU FIT](https://en.wikipedia.org/wiki/Faculty_of_Information_Technology,_Czech_Technical_University_in_Prague) computer science university, took some courses also from [MFF CUNI](https://www.mff.cuni.cz/en) and [CTU FEE](https://fel.cvut.cz/en) - [[Artificial Intelligence|Going through more online courses from MIT, Stanford, and others. And books.]] ## Community building I do - Almost 20 000 followers on X (Twitter): [Burny - Effective Curiosity](https://x.com/burny_tech), used to operate Youtube channel with 35000 subscribers. - Moderating bunch of active discord servers ([Mechanistic Interpretability](https://mechinterp.com/) with [weekly research paper reading group](https://www.youtube.com/@MIDiscordServerRecordings), [Machine Learning Street Talk](https://www.youtube.com/c/MachineLearningStreetTalk) with weekly debates), created bunch of them that are/were active (such as [Qualia Research Institute](https://qri.org/), Burny's server), and moderated more in the past - Active in other communities on Discord and other social media: AI research communities (such as [EleutherAI](https://en.wikipedia.org/wiki/EleutherAI), [Yannic Kilcher](https://www.youtube.com/yannickilcher), [Latent Space](https://www.youtube.com/@LatentSpacePod)) and other comunities ([LessWrong](https://www.lesswrong.com/), [Effective Altruism](https://www.effectivealtruism.org/), [AstralCodexTen](https://www.astralcodexten.com/), tpot), Czech universities I just mentioned ## Wikis I develop - [AI interpretability wiki](https://aiinterpretability.miraheze.org/wiki/Main_Page): Wiki of the AI interpretability scientific field. AI interpretability tries to understand how AI systems work using science. These findings help causally predict and explain the behavior and evolution of AI systems, making them more interpretable, transparent, steerable, safe, and so on. It does so by empirically finding representations and circuits, as in biology or reverse engineering of software (the mechanistic interpretability subfield); by empirically finding mathematical laws, as in physics, or by analytically deriving mathematical properties, as in theoretical physics (the deep learning theory subfield). - [[Exocortex]]: My personal wiki of everything I am curious about that I explore. Full of interconnected topics, taxonomies, links to resources, written down thoughts, ideas, articles, projects, and so on. ## Some of the projects I work on ### Code and math #### Research primarily for science ##### Artificial intelligence / machine learning ###### Mechanistic Interpretability - [Attention Head Zoo: 2-Layer Attention-Only Transformer](https://github.com/BurnyCoder/attention-head-zoo-2-layer-attention-only-transformer): Manually cataloguing and classifying the functional roles of all 24 attention heads in a 2-layer attention-only transformer, using TransformerLens and circuitsvis for mechanistic interpretability. - [LLM steering vectors for physics](https://github.com/BurnyCoder/llm-steering-vectors-for-physics): Finding and applying steering vectors to LLMs to increase performance on physics problems. LLM steering vectors are directions in a model's activation space that, when added to its hidden states at inference time, push the model's behavior toward or away from a specific concept or trait (like honesty, refusal, or sycophancy) without retraining the weights. - Mechanistic interpretability of text to speech models using sparse autoendoers: Sparse autoencoders learn sets of sparsely activating features that are more interpretable and monosemantic than directions identified by alternative mechanistic interpretability approaches. ###### Other - [Autoresearch Bootstrap](https://github.com/BurnyCoder/autoresearch-bootstrap): An autonomous LLM agent for running AI research end to end with minimal human intervention. It works by an agent such as Codex or Claude Code looping over specs specifying AI researcher workflow. Expansion of Andrej Karpathy's autoresearch setup. - [Auto Picbreeder](https://github.com/BurnyCoder/auto-picbreeder): Picbreeder but LLMs can play the role of humans. Picbreeder evolves images without any training data using Compositional Pattern Producing Networks (CPPNs) evolved by NEAT (NeuroEvolution of Augmenting Topologies). - [Diverse Group Relative Policy Optimization (DGRPO)](https://github.com/BurnyCoder/diverse-group-relative-policy-optimization)): Reinforcement learning algorithm to make LLMs reason more creatively by incorporating solution diversity into the LLM RL GRPO advantage calculation through upweighting less likely but correct solutions to incentivize rare solutions - [LLMs take autism test](https://github.com/BurnyCoder/llms-take-autism-test): Benchmark in which various current LLMs answer sampled items from the Autism Spectrum Quotient through OpenRouter, then aggregates the results into reproducible artifacts and a LaTeX paper. #### Engineering primarily for science #### Research primarily for industry ##### Closed source for clients - AI Agent company: A hierarchical team of AI agents that form a company with CEO, CTO, engineers, and so on. CEO recruits new agents as the company runs. Orchestrator adds requested MCP tools. Powered by Claude Code agents, Codex agents, OpenClaw agents, Hermes agents, or OpenCode agents. #### Engineering primarily for industry ##### Closed source for clients - AI Agent GUI specialized for company's usecase: Research company's products, social media content planning, competitor analysis, customer-response drafting, and internal knowledge base building. - Customer support phone AI agent - AI Agent explorer: Platform to explore and interact with multiple AI agents including a MultiAgent AI Coding system (for collaborative code generation and review), a Deep Research agent (for thorough research with clarifying questions), and a Synthesis agent (for combining responses from multiple LLMs) with a modern React frontend and Python backend. #### Engineering primarily for myself ##### Artificial intelligence / machine learning - [Wikipedia AI agent research assistant](https://github.com/BurnyCoder/wikipedia-ai-agent) Wikipedia AI agent research assistant. LangChain's LangGraph's ReAct agent architecture, LLMs (OpenAI, Anthropic, Google), Wikipedia API, RAG with FAISS vector db, semantic chunking, GraphRAG, Streamlit frontend, terminal and web interfaces - [Speech to Text (OpenAI gpt-4o-transcribe)](https://github.com/BurnyCoder/speech-to-text-openai): Transcribe audio files using OpenAI's GPT-4o-transcribe model using OpenAI API. - [FL Studio Piano Roll MCP](https://github.com/BurnyCoder/fl-studio-mcp): Fork of calvinw/fl-studio-mcp with improved Windows support (FL64.exe detection, window class name fix). An MCP (Model Context Protocol) server that enables AI assistants like Claude to interact with FL Studio's piano roll. Create melodies, chord progressions, and musical patterns through natural language conversation with automatic, real-time updates. - [AI Youtube Assistant](https://github.com/BurnyCoder/ai-youtube-assistant): AI assitant that let's you talk about the contents of Youtube videos using Langchain, Streamlit, FAISS, GPT3.5 ##### Other - [Multi-Interval Habit Tracker](https://github.com/BurnyCoder/multi-interval-habit-tracker): Additional functionality to complement Habitica, a browser-based app for tracking habits that are completed on a recurring schedule (e.g. every N days), persisting data in your project folder via a local backend service. #### Learning ##### Artificial intelligence / machine learning - [AI From Scratch](https://github.com/BurnyCoder/ai-from-scratch): This repository contains implementations of various AI and machine learning concepts, architectures, and exercises built from scratch. It serves as a learning resource for understanding the underlying principles of artificial intelligence and machine learning algorithms. Transformers, LLM, GPT-2, LSTM, Diffusion, U-Net, CNNs, MLP, Reinforcement learning, Bigram, Linear+Polynomial+Logistic Regression, GRPO, Selfplay, MCTS, PyTorch, Scikit-learn, Torchvision, NumPy, Matplotlib, Einops, Transformers, Plotly - [Practical AI Projects](https://github.com/BurnyCoder/practical-ai-projects): Collection of practical AI applications and implementations using various machine learning, deep learning, and natural language processing techniques. LLM (training, finetuning, reasoning reinforcement learning , RAG, multiagents), image classification&segmentation, text&image answering, text2speech, movie recommendation, dimensionality reduction, LlamaIndex, Autogen, PyTorch, TensorFlow, Transformers, TRL, Keras, fastai, NumPy, Skicit-learn, OpenAI, ElevenLabs, ResNet, LSTM, Autoencoder, SVM See more at [[projects]] and [BurnyCoder (Burny) · GitHub](https://github.com/BurnyCoder) ## Questions I love to explore - How does [[artificial intelligence]] [[Mechanistic interpretability|work]]? What's the current state of empirical research and [[Mathematical theory of artificial intelligence|mathematical theory]] in [[artificial intelligence]]? What is the state of the art in [[artificial intelligence]] [[AI engineering|engineering practice]]? - How to define and understand [[artificial general intelligence]] and [[superintelligence]]? How to [[AI safety|make it do what we want]], how to steer it, how to align it? - How to [[Artificial Intelligence#Crossovers Omnidisciplionarity|apply AI]] for reverse engineering [[Applied mathematics|mathematics behind everything]]? How to [[Artificial Intelligence#Crossovers Omnidisciplionarity|apply AI]] for good as ideally as possible as much as possible? - What is the fundamental [[mathematics]] of [[intelligence]]? What are all the different types of all the possible current and future intelligent systems? - What is the fundamental [[mathematics]] of the [[Cognitive science|brain]]? How to upgrade [[Human intelligence amplification|human intelligence]]? - How does [[Artificial Intelligence x Biological Intelligence|AI and biological intelligence compare?]] How can [[Biological intelligence|humans]] and [[Artificial Intelligence|AIs]] form even greater [[collective intelligence]]? - How does the [[Natural science|world]] work? How does [[everything]] [[Omnidisciplinarity|work]]? - What is the [[Theory of Everything|fundamental mathematics]] of the [[Physics|universe]]? What are all the equations, and [[Mathematics|mathematical]] structures more generally, governing [[Science|reality across all scales]] in physics, and in all [[Natural science|natural science]] more generally? - How to connect all [[Science|sciences]], [[Formal science|formal]] and [[Natural science|natural]]? What is the fundamental [[mathematics]] behind [[emergence]] and [[complexity]]? How does [[biology]] and other scientific fields emerge from [[physics]] and [[chemistry]]? - How to do good [[science]] using [[philosophy of science]], [[rationalism]], and other bodies of knowledge? - What is the fundamental [[mathematics]] of [[creativity]] in [[science]] and [[art]]? How to make machines creative beyond human limitations and comprehension for [[Science|scientific]] discovery, [[Artificial Intelligence x Physics|physics]], [[mathematics]], [[art]], [[philosophy]]? - What are all the concepts in [[mathematics]]? What are all the possible [[Foundations of mathematics|foundations]] and [[mathematics]] with all sorts of mathematical universes and which ones are the best in what contexts? - What is the fundamental [[mathematics]] of [[Future of humanity, AI, sentience, futurology, politics|building a great future for all where everyone flourishes]]? How to make the world better for all? How to maximize the benefits, and minimize the disadvantages, of [[Technology|technologies]] and [[Politics|political systems]]? How to think about philosophical movements such as [[effective altruism]] and [[effective accelerationism]]? What is and what will be the geopolitics of AI? What are the probabilities of different future scenarios? - What is the fundamental [[mathematics]] of [[consciousness]]? How to work with mind and find truth in scientific secular way? - What are the answers to the problems in [[philosophy]]? - See more at [[exocortex]]. I also like cute things! ![[Images/52c0b8e6fcb51e527f12db5af187083d_MD5.svg]] ## Contact me - See [[Contact and links|contact and links]]