Field notes archive.
Research notes, field reports and methodology pieces from the DeepFounder team.
Castor for Restaurants: Run AI Kitchen Monitoring and Order Automation Offline on a $800 Laptop
Run AI-powered kitchen quality monitoring, guest preference memory, and staff alerts on a $800 gaming laptop — no cloud, no subscriptions, no data leaving your building.
Castor for Healthcare: Run HIPAA-Compliant Local AI in Your Medical Office on a $800 Laptop
Every AI tool sends patient data to the cloud — violating HIPAA before you've read the terms. Castor runs entirely offline on a gaming laptop: intake processing, record search, and appointment follow-ups with zero PHI leaving your building.
Castor for Small Business Security: Run AI Surveillance Offline on a $800 Gaming Laptop
Cloud surveillance costs $2,400+/year and sends your footage to Amazon or Google. Castor runs AI motion detection, Telegram alerts, and overnight security routines completely offline on a gaming laptop — no subscription, no cloud, no data leaving your building.
Castor as a Web Research Agent: Automate Browser Tasks Offline Without Cloud APIs
Castor uses Playwright-powered browser automation to run competitor monitoring, market research, and data extraction jobs on your local machine — no cloud APIs, no per-query cost, daily briefings delivered to Telegram.
Castor for Retail: Run AI Shelf Monitoring and Customer Analytics on a $800 Laptop
Enterprise retail analytics costs $30k–$80k/year. Castor runs the same shelf compliance monitoring, foot traffic analysis, and daily sales briefings completely offline on a gaming laptop — no subscriptions, no cloud, no data leaks.
Castor for Manufacturing QC: Run AI Visual Inspection Offline on a $800 Gaming Laptop
Cloud vision APIs are expensive and need internet. Castor runs fully offline on a gaming laptop with native camera access — here's how to build a visual quality control system for manufacturing with zero per-query cost.
LLM Evals in 2026: How to Build an Evaluation Pipeline for Your AI Application
Shipping AI without evals is flying blind. Learn how to build a systematic evaluation pipeline for your AI application — from golden datasets to LLM-as-judge to CI/CD gates.
AI Reasoning Models in 2026: When Extended Thinking Is Worth the Cost
Reasoning models like Claude Extended Thinking, o3, and DeepSeek R1 can solve problems standard models can't — but they cost 2–10x more. Here's a practical guide to when they're worth it.
Context Engineering in 2026: The Skill That Is Replacing Prompt Engineering
Prompt engineering is dead. Context engineering — deliberately designing everything an LLM sees — is the skill that actually ships reliable AI in 2026.
LLM Tool Calling in 2026: How to Build Reliable Function-Calling Agents
Tool calling is the primitive that turns LLMs into agents that actually do things. This guide covers schema design, parallel execution, error handling, and observability for production-grade function-calling agents in 2026.
AI Reasoning Models in 2026: GPT-5, Claude Sonnet 4.6, Gemini 3.1, Kimi K2 — Which One to Use
Not all reasoning models are equal. Here's when to use o3, Gemini 2.5 Pro, DeepSeek R1, and Claude 3.7 — with real cost analysis and production deployment patterns.
LLM Fine-Tuning in 2026: When to Train Your Own Model (And When Not To)
The Fine-Tuning Trap Most Developers Fall Into You've been using GPT-4o or Claude for your app. It works okay, but the responses feel generic, ignore your domain vocabulary, and hallucinate