AI-DRIVEN · SOURCE-LINKED · COMMUNITY-CORRECTED // I BURN THE TOKENS SO YOU DON'T HAVE TO.How the slop is made →
PAM · IAM · IGA · NHI · ITDR · AUTHZ   //   SIGNALS FROM THE IDENTITY SECURITY FIELD
NO MAGIC, NO PRETENDING

AI slop, with receipts.

Identity Field Notes is intentionally AI-driven. The pitch is not that a robot became a journalist. The pitch is that identity people have too many tabs and too little time.

THE DEAL

I burn the tokens so you don’t have to. Automation reads broadly, identifies what looks important, summarizes it and links you back to the original. You get a faster reading queue; the original author still gets the click.

How the slop is made

01 // COLLECT

Search broadly.

A scheduled job searches current PAM, IAM, IGA, identity-threat, authentication and machine-identity material, plus selected RSS sources.

02 // DISTILL

AI does the first pass.

The model clusters duplicates, ranks practitioner relevance and drafts summaries plus “why this matters” takeaways.

03 // SHOW RECEIPTS

Keep sources attached.

Every story is expected to retain its original URL and source name. If the link is missing, the item should not graduate into a Field Note.

Editorial policy

SOURCE FIRST

The summary is not the authority.

Important technical, security, legal, compliance and product decisions should be validated against the linked primary source.

VENDOR CLAIMS

Marketing remains marketing.

A vendor saying its product is first, only, fastest or safest does not make that an independent fact. Automation is instructed to frame vendor claims as vendor claims.

SPONSORSHIP

Money gets a label, not a ranking.

Sponsors can buy clearly marked inventory. They cannot buy “Why It Matters,” suppress criticism, or move themselves into the daily top stories.

HUMANS

Community context is a feature.

Practitioner corrections and evidence are more valuable than pretending the model never hallucinates or misses nuance.

Correction policy

Correct loudly. When a material summary is wrong, fix the underlying JSON, note the correction in the article, and preserve the original-source trail. Community discussion should remain attached so readers can see why the correction happened.

What the automation should never do

Invent sources, hide AI authorship, copy whole articles, present sponsor claims as editorial judgment, or turn a model confidence score into a fact score.