https://ninjalabz.io/weekly1.0 https://ninjalabz.io/watch/the-loop2026-09-270.8 https://ninjalabz.io/films/loop-poster.jpg?v=44b59839de The Loop: from threat chatter to a detection that didn't exist this morning One loop on live production traffic, narrated end to end. CHATTER listens to eleven threat feeds; a local sensor fingerprints hostile traffic; our own threat intel attributes it (Mirai, 0.91); NinJAFUNK responds and blocks at the edge; an AI SecOps agent closes it out with a report, a change for a human and a gated rule; Rule Forge finds the detections we are missing; Deep Read reads every item we ingested. https://ninjalabz.io/films/NinjaLabz-The-Loop-Pitch-1080p.mp4?v=1e0fbd5367 330 2026-09-27T12:00:00+00:00 yes https://ninjalabz.io/watch/chatter-deep-read2026-09-240.8 https://ninjalabz.io/films/chatter-poster.jpg?v=7bbb049f62 CHATTER: eleven threat feeds, one deep read Leak sites, dark-web mirrors, KEV, IOCs, Telegram and news, correlated to the knowledge graph and read into calibrated, falsifiable forecasts. https://ninjalabz.io/films/NinjaLabz-CHATTER-Deep-Read-1080p.mp4?v=85dfe48688 78 2026-09-24T12:00:00+00:00 yes https://ninjalabz.io/watch/adversary-dna-attribution2026-09-240.8 https://ninjalabz.io/films/attribution-poster.jpg?v=05b77fef4f Adversary DNA: we named the botnet From one suspicious request to a named Mirai botnet at 0.91 confidence, with the evidence chain and an incident Claude triages on its own. Local sensors fingerprint hostile traffic; the attribution engine combines independent evidence with noisy-OR. https://ninjalabz.io/films/NinJAFUNK-Adversary-DNA-Attribution-1080p.mp4?v=c52616a9e2 93 2026-09-24T12:00:00+00:00 yes https://ninjalabz.io/watch/secops-handoff2026-09-250.8 https://ninjalabz.io/films/secops-poster.jpg?v=0376ea6e68 SecOps hand-off: the AI writes the next detection After the edge block, an on-box SecOps agent closes the incident out: a post-incident report, change requests a human approves, and new detection rules. Claude writes each rule; the SIEM backtests it on 5,000 real events and ships it only if it catches the attacker and stays quiet on normal traffic. https://ninjalabz.io/films/NinJAFUNK-SecOps-Handoff-1080p.mp4?v=93c6364ca5 72 2026-09-25T12:00:00+00:00 yes https://ninjalabz.io/watch/rule-forge2026-09-250.8 https://ninjalabz.io/films/forge-poster.jpg?v=d0b2e5db13 Rule Forge: detections from our own threat intel, fitted to our own estate Rule Forge reads two states: what we are (what we collect, what we already detect, who is attacking us) and what the world is doing (our own threat intel: ATT&CK techniques weighted by real actor use, fresh IOC families, CISA KEV). It proposes the rules in the gap, proves each one is not already covered, that it catches real attacks and that it stays quiet, then a human adopts it with one click. https://ninjalabz.io/films/NinJAFUNK-Rule-Forge-1080p.mp4?v=4d207e3a50 63 2026-09-25T12:00:00+00:00 yes https://ninjalabz.io/watch/deep-read2026-09-250.8 https://ninjalabz.io/films/deepread-poster.jpg?v=0587848c04 Deep Read: every ingested item read by AI, hunting hidden meaning Deep Read reads every item ingested in the window, not a sample: 1,103 items from 10 feeds in 7 days. Code computes the structure first (bursts, who reports first, silences, cross-source entities, each with receipts); Claude Sonnet reads every chunk and Claude Opus synthesises the hidden meaning. Every finding carries a confidence, what would falsify it, and links to the exact items it rests on. https://ninjalabz.io/films/NinjaLabz-Deep-Read-1080p.mp4?v=7012b35e62 65 2026-09-25T12:00:00+00:00 yes https://ninjalabz.io/register0.3 https://ninjalabz.io/login0.3