2 min read

Technical literacy series: #1 Why Technical Judgment Is a Learnable Skill

Technical judgment is accumulated pattern recognition, built by tracing decisions to their consequences rather than pretending to be an architect. Studying failures, tradeoffs, and ownership sharpens the questions operators ask and improves their participation in software-shaped decisions.
Profile 9 min read

Lessons from Mitch Joel

Mitch Joel, a digital marketer, entrepreneur, and author of Six Pixels of Separation and Ctrl Alt Delete, maps technology, human connection, and business transformation. His work asks how brands and individuals can adapt in a connected, AI-driven world.
Digest 8 min read

Monthly Learnings from the Daily Digest: April 2026

April paired digital abundance with physical scarcity. Software became easier to create, while human taste, organizational alignment, power, cooling, and critical materials emerged as the constraints shaping the next generation of AI businesses.
Digest 9 min read

Daily Digest - 2026-04-30

AI’s bottleneck is moving beyond model capability to the operating systems around it: agent-ready infrastructure, secure provisioning, workflow integration, and production iteration. Teams that redesign these layers can turn rapid prototypes into durable products and commercial growth.
Digest 8 min read

Daily Digest - 2026-04-29

Frontier models can collapse the distance from visual intent to functional software, but production value still depends on reliable inference and disciplined agent architecture. As experimentation gives way to scale, taste, decomposition, debugging, and validation become the decisive work.
Digest 9 min read

Daily Digest - 2026-04-28

AI adoption becomes real only when operating assumptions change: engineering leaders need firsthand tool fluency, sellers must frame value against labor budgets, and agent builders must design for state, safety, and recovery rather than impressive demonstrations.
Profile 7 min read

Lessons from DJ Patil

DJ Patil co-coined data scientist and served as the first U.S. Chief Data Scientist after leadership at LinkedIn and eBay. His Data Jujitsu philosophy treats technology as a multiplier while keeping ethics, storytelling, curiosity, and human judgment central.
Digest 8 min read

Daily Digest - 2026-04-27

Reliable AI work depends less on static benchmarks than on tight production loops: grade outcomes, turn failures into engineering work, and verify fixes. Automation may cover most execution, but expert judgment remains essential for quality, strategy, and final delivery.

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