Agree with this. Even the less powerful DR is severely underrated. I send it off on research tasks all day every day; love the status module on iPhone.
This nails the everyday value of Deep Research. What clicked for me is the architectural part: if you can orchestrate multi-engine pipelines with progressive learning, deduplication, and scoring, it stops being just a “longer Google” and becomes a reusable discovery system.
We built one starting in K–12, but it now pulls 2,000+ vetted resources per run across any domain — just by swapping engines, not rewriting code. Breakdown here:
Agree with this. Even the less powerful DR is severely underrated. I send it off on research tasks all day every day; love the status module on iPhone.
I love this -- like many AI-related concepts, I often ask myself "How much of this is a failure of my own imagination?"
This nails the everyday value of Deep Research. What clicked for me is the architectural part: if you can orchestrate multi-engine pipelines with progressive learning, deduplication, and scoring, it stops being just a “longer Google” and becomes a reusable discovery system.
We built one starting in K–12, but it now pulls 2,000+ vetted resources per run across any domain — just by swapping engines, not rewriting code. Breakdown here:
https://trilogyai.substack.com/p/ai-discovery
Would love to hear if you’ve tested source prioritization or confidence scoring to handle affiliate noise and low-signal results. It gets real fast.