🧠 AI & Machine Learning
6 min read
Before You Build a Custom ML Model, Prove You Need One
A practical framework for deciding when off-the-shelf models have reached their limit—and when your data, economics, and constraints justify building your own.
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#ai
AI & Machine Learning
LLMs, GPU infrastructure, inference latency, agent patterns, and production ML systems.
7 essays▲ 5 reactions
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Showing 7 of 7 essays
🧠 AI & Machine Learning
7 min read
From Traces to Tests: Closing the Continuous Improvement Loop for AI Agents
A practical loop for keeping AI agent evals fresh: capture production traces with outcome labels, curate the failures worth testing, convert them into property-based eval cases, and verify fixes against both evals and production failure rates.
🧠 AI & Machine Learning
7 min read
AI Search Is Moving Work Out of the Request Path
Retrieve-for-Train suggests a practical design shift: learn repeatable search behavior offline, but keep live facts and permissions online.
🧠 AI & Machine Learning
7 min read
Your AI Agent Needs a Memory Policy, Not Just a Vector Database
Persistent memory is becoming an infrastructure feature. The harder problem is deciding what an agent may remember, trust, update, and forget.
🧠 AI & Machine Learning
10 min read
Jev and the Return of the Smart If Statement
TypeSafe AI's Jev does not want to write your email, explain quantum mechanics or generate an application. It wants to answer a smaller question quickly: given this state, which bounded decision should software make next?
🧠 AI & Machine Learning
22 min read
Cybersecurity Threats and Data Breaches in Healthcare Organizations
🧠 AI & Machine Learning
1 min read
From Bigger Models to Better Decisions The New AI Advantage
The competitive edge is shifting from raw model size to disciplined systems that combine reasoning, tools, data and human judgment.