Text so far
The best pet is a
Local AI / Systematic Research / Software / Public Notebook
Harris Oldroyd
Local AI, systematic research, and the systems behind both.
A public research notebook built from the hardware up: running models locally, testing trading ideas against data, and building the software the work needs.
What I work on
Three areas, one notebook.
Local AI is the deepest area right now; the research and software around it stay public alongside it.
Local AI
Run and understand models locally.
Hardware, quantization, runtimes, benchmarks, and interactive explanations for local inference.
Explore local ai →ResearchSystematic trading evidence.
Reproducible experiments on market regimes, filters, and execution — including the ideas that fail against data.
Explore research →SoftwareTools that make research repeatable.
Local-first projects for data, measurement, and research workflows.
Explore software →Local AI
The deepest area of the notebook.
Hardware, models, runtimes, and benchmarks for running AI locally — with a learning hub for going deeper.
Learning hub
Learn how local AI actually works.
Learning paths for fundamentals, hardware, models, and software, with interactive explainers for tokenization, attention, and the KV cache.
The featured guides below cover the decisions that matter most: choosing hardware, understanding quantization, and what a 3090-class build can actually run.
Interactive explainer
From prompt to next token
Step through the small loop a language model repeats while generating text.
Tokens and IDs
TheID 791bestID 1888petID 5207isID 374aID 264
The tokenizer splits text and gives every token a numeric ID.
Meaning and context
pet · 5207
[0.2, −0.7, …]Embeddings represent meaning. Attention mixes in useful context from the other tokens.
Possible next tokens
dog
cat
fish
One token is added
The best pet is a dog
Then the same loop starts again.
Step 1 of 5
Featured Local AI guides
Guides worth starting with.
Selected hardware, fundamentals, benchmark, and model-analysis work rather than a chronological feed.
Hardware
I Built a 48 GB VRAM Local AI Workstation with Two RTX 3090s
A dual RTX 3090 local AI workstation with 48 GB of distributed VRAM, Threadripper Pro, WRX80, 128 GB ECC RAM and enough expansion room for larger multi-GPU experiments.
Read the guide →Buying guideBest GPUs and Devices for Local AI Inference
A comparison of GPUs, workstation cards, unified-memory devices, and CPU inference setups for local AI, focused on price, speed, memory capacity, software support, and practical tradeoffs.
Read the guide →FundamentalsLLM Quantization Without the Alphabet Soup
A beginner's guide to local LLM quantization: what bits per weight means, how popular GGUF formats compare, when to choose Q2 through Q8, and why Q4 is a strong starting point.
Read the guide →BenchmarkRTX 3090 for Local AI Inference
A practical look at the RTX 3090 for local AI workloads, covering specs, VRAM, bandwidth, TFLOPS, used pricing, power limiting, LLM performance, image generation, and why it remains one of the best-value GPUs for running open models at home.
Read the guide →Recent work
Newest across the notebook.
The latest from every area — Local AI, research, and software.
Hardware10 min read
I Built a 48 GB VRAM Local AI Workstation with Two RTX 3090s
A dual RTX 3090 local AI workstation with 48 GB of distributed VRAM, Threadripper Pro, WRX80, 128 GB ECC RAM and enough expansion room for larger multi-GPU experiments.
research7 min read
I Abliterated Qwen3.5-9B and Traced the Refusal Through the Weights
I weakened refusal in Qwen3.5-9B and found that most of the change came from one simple update in layer 15.
Software8 min read
How to Access Your Local AI From Anywhere
A step-by-step guide to using Tailscale and SSH for private remote access to a home local AI server, with Ollama and Open WebUI examples.
Fundamentals8 min read
GPU Offloading for Local LLMs
A beginner's guide to splitting a local LLM between GPU VRAM and system RAM, with practical llama.cpp settings and advice for dense and mixture-of-experts models.
Elsewhere in the notebook
The wider research practice.
Quant research, software projects, and technical writing each have their own section.
Quant research
Research notes and experiments.
Methodology, testing, failures, and iteration on systematic strategies.
Browse quant research →Software projectsSystems, tools, and experiments.
Projects spanning quantitative research, automation, data engineering, and personal interests.
Browse software projects →Essays and notesFollowing interesting questions.
Long-form writing wherever curiosity leads: technology, markets, AI, and decision-making.
Browse essays and notes →