Blog
Notes on AI Research, OSINT, and Building Workflows That Hold Up
-
Understanding AI Context Windows
If AI sometimes feels brilliant in one prompt and forgetful in the next, context windows are usually the hidden constraint, and understanding them changes how you work.
-
NotebookLM for High-Stakes Case Research: Why Source-Grounded AI Matters
When accuracy and defensibility matter, NotebookLM offers a more grounded way to work with source material, and the difference becomes obvious once the stakes are real.
-
Context Engineering for Reliable AI Workflows
Better prompts help, but reliable results usually come from better context design, which is where serious AI workflows start to separate themselves.
-
Using AI to Find and Verify News Sources
AI is excellent at surfacing leads and patterns in news research, but the real advantage comes from knowing how to turn those leads into verified source trails.
-
AI Misuse in the Real World: Why Bad Workflows Fail Faster
AI does not just speed up good workflows; it accelerates bad ones too, and the lesson from real failures is that a few disciplined checks can prevent expensive mistakes.
-
Building an AI Knowledge Base with Obsidian Notes
AI gets far more useful when it is working against notes that are local, linked, and structured, which is why Obsidian can become more than just a note-taking app.
-
Advanced AI Scripting for Research Workflows
AI can now help non-programmers build serious automation, but the real skill is knowing where speed helps, where risk starts, and how to stay on the right side of both.
-
Advanced AI Workflows in Cursor and VS Code
Cursor and VS Code are no longer just for engineers; used properly, they can become structured workbenches for teams producing timelines, notes, evidence logs, and briefings.
-
Prompt Injection: The Attack That Rewrites Your AI's Instructions
Prompt injection can cause outside content to steer your AI in ways you never intended, which is why high-trust workflows need clear boundaries between source material and system instructions.
-
Private AI Infrastructure for Sensitive Casework
Private AI infrastructure can give sensitive teams tighter control over data and model behavior, but the real question is when that control is worth the added operational burden.