I’ve found most “intelligence” products fall into one of three buckets:
* expensive but opaque (e.g. Bloomberg Terminal)
* fast but noisy (Twitter/X)
* thoughtful but opinion-heavy (Substack)
What I actually wanted was cause-first analysis — not just what happened, but what mechanism produced it, and what that implies downstream.
So I started building something for myself: a daily pipeline that ingests ~15 public sources (PubMed, FRED, financial APIs, news feeds, etc.) via scheduled Python scrapers, then synthesizes each domain into a structured briefing using an OMIT framework:
* Origin (what triggered this?)
* Mechanism (how does it propagate?)
* Impact (what changes?)
* Trajectory (what happens next?)
Forcing the model to fill those fields seems to shift it away from headline summarization toward causal reasoning.
Each morning the system generates briefings across geopolitics, markets, AI/tech, supply chains, and biotech — writes them to MongoDB and a GCS-hosted JSON CDN simultaneously. The frontend loads from CDN on first paint to avoid cold-start latency; the API refreshes silently in the background.
The OMIT tags effectively create a structured metadata layer on top of the content — next step is exposing those as filters (e.g. query by mechanism type or trajectory horizon).
It’s a solo project and has been running unattended for the past few week. I genuinely don’t know if this is useful beyond my own workflow — curious if others here would want something like this, or if I’m just reinventing RSS with extra steps.
I’ve found most “intelligence” products fall into one of three buckets:
* expensive but opaque (e.g. Bloomberg Terminal) * fast but noisy (Twitter/X) * thoughtful but opinion-heavy (Substack)
What I actually wanted was cause-first analysis — not just what happened, but what mechanism produced it, and what that implies downstream.
So I started building something for myself: a daily pipeline that ingests ~15 public sources (PubMed, FRED, financial APIs, news feeds, etc.) via scheduled Python scrapers, then synthesizes each domain into a structured briefing using an OMIT framework:
* Origin (what triggered this?) * Mechanism (how does it propagate?) * Impact (what changes?) * Trajectory (what happens next?)
Forcing the model to fill those fields seems to shift it away from headline summarization toward causal reasoning.
Each morning the system generates briefings across geopolitics, markets, AI/tech, supply chains, and biotech — writes them to MongoDB and a GCS-hosted JSON CDN simultaneously. The frontend loads from CDN on first paint to avoid cold-start latency; the API refreshes silently in the background.
The UI uses the same data stream to render:
* compact “signal” rows (machine-generated) * editorial rows (headlines, excerpts, bylines)
The OMIT tags effectively create a structured metadata layer on top of the content — next step is exposing those as filters (e.g. query by mechanism type or trajectory horizon).
It’s a solo project and has been running unattended for the past few week. I genuinely don’t know if this is useful beyond my own workflow — curious if others here would want something like this, or if I’m just reinventing RSS with extra steps.