π Seattle | β Sr. Data Science Manager @ Starbucks | π€ Local AI Nerd | βοΈ Writing at Asymptotic
I have a computer science & data science background. Currently leading large-scale recommender systems & experimentation projects for a global consumer brand. In my spare time, I like exploring how to assess reliability & trust in LLMs, along with ultra-low cost implementations of LLMs in Data Science & ML use cases. I believe in human-centric approaches to using new technology, not replacing them.
LLM + Data Science & ML Use Cases
- How I Prompt β analyze your prompting activity and language patterns for Claude, Codex, Pi, OpenCode etc.
- A/B Testing Memory Game β live A/B test using a memory game puzzle concept, with always-on stats & python analysis
- Weather App β weather explorer for US ZIP codes with current, historical, and forecast data
Local AI Execution, LLM Benchmarks & Evals
- Local LLM Visual Bench β benchmarking local AI models such as Qwen 3.6 and Gemma 4 with visual / animation prompts
Some useful tools
- age-vault - cli for encrypting / decrytping files using age (very useful for protecting files & folders from LLMs)
- Quizzard - if you don't want to pay for Kahoot
- Sidequests - track the health & get AI summaries of your slopware sidequests (maybe it helps you clean things up)
Reach Me | LinkedIn | Twitter / X | eeshans.com


