Now Archive

Past experiments are kept visible for continuity.

6/7/2026 · ventures

Developing an enterprise Django hub designed to help salespeople around the world use internal AI tools more often and more effectively.

The experience brings enablement into one place: sellers can complete practical tasks, earn badges, train on AI-assisted workflows, and share field-tested examples with their peers.

The application is still in active development and depends on close collaboration across engineering, sales, and leadership.

6/7/2026 · tools

Using Cursor as my primary AI development assistant while building the Django application, with Claude Code helping maintain and evolve the supporting dbt models.

The goal is to combine fast AI-assisted iteration with reliable data foundations as the product and its measurement needs take shape.

6/7/2026 · ideas

Exploring how activation, learning, and community can work together to make enterprise AI adoption practical rather than abstract.

Sellers will learn where agents can take on repetitive work and how an AI sales assistant can accelerate research, deck building, and analysis, while shared field experiences help the most useful workflows spread.

6/7/2026 · models

At work, Claude Opus 4.8 is my default model in Cursor. In my experience, its reasoning is unmatched for building applications, debugging, refining interfaces, and turning loosely formed ideas into reliable implementations with fewer corrections.

Cursor's Auto mode is convenient, but I still prefer choosing Opus directly; handoffs to models such as Composer 2.5 have been less consistent for the complex work I give it.

Outside work, I gravitate toward ChatGPT for everyday questions, research, and browsing. I have also been impressed by GPT-5.5 in the Codex app, which gives me a powerful personal development workflow through a standard subscription without requiring the higher-cost plan I previously needed to get sustained value from Claude Code.

2/20/2026 · models

MODELS

Designing scalable data models that turn raw inputs into decision-ready infrastructure. Using Snowflake and dbt, I'm building governed, tested datasets that support analytics, experimentation, and AI systems.

Clean structures. Clear metrics. Reliable foundations.

Keep hardening model and metric definitions. If the model is wrong, everything built on top drifts.

2/20/2026 · tools

TOOLS

Using SQL, Python, Snowflake, dbt, and Streamlit to turn models into working systems. Leaning heavily into AI-assisted engineering with Cursor and advanced models to refactor code, accelerate transformations, and automate repetitive analytics work.

Continue reducing friction between question and answer by systematizing repeatable analytics workflows.

2/20/2026 · ideas

IDEAS

Writing and thinking about AI as a compounding system that is starting to automate its own improvement.

A year ago AI helped polish emails. Today it helps refactor systems, build one-shot apps, generate production-ready data models, and orchestrate multi-step workflows.

Keep exploring what this structural acceleration means for builders, teams, and the people who adapt.

2/20/2026 · ventures

VENTURES

Building and supporting products and businesses across finance AI, coaching, and fitness studio operations.

Pantheon is an autonomous hedge fund concept with 12 specialized AI agents running staggered 15-minute cycles from ingestion to execution and reporting.

InterCoach is an AI interview prep platform using conversational avatars and real-time feedback with WebRTC.

Sam's Studio is a yoga and barre business platform built with Next.js, Supabase, Stripe, scheduling, waitlists, and a mobile app via Capacitor.

2/4/2026 · models

Multi-agent reliability sweep

Tried: Benchmarked retry and reconciliation strategies for parallel coding sessions.

Outcome: Queue-based retries improved successful merges and reduced dead-end sessions.

Next step: Add conflict scoring and weighted merge heuristics.

11/15/2025 · ideas

Context-window operations notes

Tried: Documented prompt packing patterns for analytics copilots.

Outcome: Prompt budgets were easier to tune with structured retrieval chunks.

Next step: Package patterns into reusable toolkit modules.