Profile 10 min read

Lessons from Thomas Rowe Price Jr.

Thomas Rowe Price Jr., a growth-investing pioneer, shifted attention from depressed assets toward companies with expanding long-term earnings. His framework joins future earning power, management quality, economic cycles, corporate life stages, and fees aligned with clients.
Profile 9 min read

Lessons from Philip Fisher

Philip Fisher, author of Common Stocks and Uncommon Profits, helped move investing beyond statistical bargains toward growth analysis. His “scuttlebutt” method supports concentrated ownership of fast-growing companies and the patience to hold them for decades.
Profile 8 min read

Lessons from John Templeton

John Templeton, manager of the Templeton Growth Fund, invested globally at moments of extreme panic. His principle of “maximum pessimism” frames buying discounted assets amid fear within a broader study of market cycles, contrarian psychology, science, and religion.
Digest 6 min read

Daily Digest - 2026-06-04

Trust is becoming the decisive layer of AI deployment: agents need governance, codified judgment, and realistic evaluation before they can operate across fragmented systems or deliver useful work.
Digest 7 min read

Daily Digest - 2026-06-03

Rising token bills are symptoms of architecture, not prompting: efficient agents depend on clean context, compiled workflows, thoughtful routing, and an integration layer that governs tools, permissions, and costs.
Digest 7 min read

Daily Digest - 2026-06-02

Scaling agents requires treating memory, workflow, and hardware as purposeful infrastructure: retained outcomes guide future action, executable plans lower coordination costs, and better cooling expands practical compute capacity.
Digest 7 min read

Daily Digest - 2026-06-01

As model capability becomes cheaper and more accessible, competitive advantage shifts to software factories, governed context, and deployment infrastructure that lets agents operate safely inside real organizations at scale.
Research Explainers 6 min read

The Agent That Updates the Harness Is Not the Bottleneck

A paper separates the model that improves an agent's harness from the model that must use it. Cheap models may propose useful scaffold changes, but task performance still depends on an executor capable enough to load and follow them.

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