G’day, welcome to my Digital Garden, where I keep my notes on thoughts, experiments, and how-to’s.
I’m a software & dev-ops engineer, and a pricing & revenue management analyst.
Got an idea for a subject I should investigate? Reach out on LinkedIn or Email Me
Looking for content? Take a look in the explorer (side or bottom), search a keyword in the search box, or check out my list of current interests below.
Topics of Recent Interest:
Things I’ve either been reading up on, or intend to write an article about in future
Tech
- Nix OS
- DuckDB
- SIMD Intrinsics
- Polars
- Using Ollama + continue.dev open source tools for free AI code-completion
- Synthetic Sales Data
- Basic Python Modelling Pipelines
- DataFrame Cheat Sheets
- Positron IDE
- Database, Data Warehouse, Data Lake & Data Lakehoues
- Time series forecasting with LLM’s vs traditional methods
AI/ML Modelling Examples
I want to make an essentials cheat sheet, containing basic implementation examples of most common AI/ML models, applied as simply as possible to use cases as easily comprehensible as possible
- GAM & GLM
- XGBoost Regression & Classification Simple Examples
- Neural Networks
- ANN
- RNN
- CNN
Finance
- Valuing Stock Options as a SaaS employee
- Basics of Valuing a Business
Pricing & Revenue Management
- Basic Principles
- Price Elasticity of Demand
- Price Realisation
- Product Mix
- AI Modelling
- Feature Importance (how well can different attributes of data can predict others)
- Customer Segmentation
- Elasticity & Demand modelling
- Win Rates in Bid/Quote Competitive Environment
- Modelling Return on Promotional Spend & Impact Simulation
- RNN for Time Series Modelling
- Cross Portfolio Cannibalisation
- Market Pressure Elasticity Impact
Self Education Resources
Note, I haven’t checked the quality of these yet, they’re bookmarks to check out for review. I’m not yet an advocate for the contents therein
- Readlist
- Watchlist