The problem is judgement,
as well as discovery.
AI launches appear across project repositories, engineering blogs, research releases and online communities. Fast collection helps, but attention alone does not tell a team whether a tool solves its problem.
The system therefore needs two distinct jobs: finding worthwhile leads and building a defensible view of them. A social spike can trigger investigation. It cannot approve a recommendation.
Four decisions shaped the build.
Check what changed before reading everything.
RSS validators, content fingerprints and persisted caches avoid repeating unchanged work. A bounded metadata decision can defer a lead before full article sampling.
Keep a reason with the result.
Evidence records retain source links, relevant bounded text, model questions, confidence and score arithmetic. Historical gaps are made explicit rather than filled with invented detail.
Separate research from publication.
The worker writes private research checkpoints. The website receives only the approved public projection. An agent cannot approve its own advice.
Make the operational limits visible.
Daily request allowances, input bounds and a single cloud writer provide a controlled starting point. Long-form editorial work and practical experiments remain separate workflows.
Unchanged content
should mean less work.
In the controlled AWS feed test, conditional requests reused the existing evidence state. The resulting checkpoint was downloaded and checked for database integrity and preservation of prior research. This verifies that test, not a detection SLA or a long-term cost saving.
A working pilot.
A deliberate next step.
The next work is to calibrate the research against useful discoveries and missed leads, run task-specific evaluations, and publish the first human-reviewed edition. The current system does not certify production experience or automatically promote products to Trial or Adopt.