兆候を拾い、
異才で分業し、
紙面を組み、
定時に届ける。

Autonomous Media Generation Autonomous Media Generation

Autonomous content generation is not merely a labor-saving tool to alleviate human editorial fatigue or manual CMS overhead. It is a fundamental experiment in integrating AI natively into the Web itself—turning static websites into living, autonomous knowledge ecosystems. Kamos Autonomous Media Generation realizes this vision: continuously ingesting real-world news, market trends, and academic research, establishing a specialized division of labor across AI agents for writing and translation, composing expressive layouts and hand-drawn graphic recordings, and autonomously publishing across the web, bilingual editions, and RSS podcasts on a dependable cadence.

Four Stages of Autonomous Media Generation

Rather than relying on a single one-shot prompt, the system decouples signal ingestion, multi-agent division of labor and translation, automated editorial typesetting, and scheduled broadcast into distinct, reliable stages.

Stage I.

Collecting Signals

Regularly crawls real-time current news (global politics, macroeconomics, social dynamics), preprint archives (arXiv, etc.), and technical documentation into structured databases.

Filters out superficial clickbait to extract structural transformations and primary context connecting world events with deeper technical and societal shifts.

Stage II.

Specialization & Division of Labor

Multiple specialized AI agents divide labor across distinct responsibilities—editorial commentary, technical analysis, societal impact, and multilingual translation (Japanese/English).

Rather than relying on a monolithic prompt, assigning specialized roles to distinct agents allows parallel synthesis, deep technical rigor, and rapid multilingual expansion without compromise.

Stage III.

Typesetting Pages

Harmonizes texts and translations resulting from the division of labor with automated graphic recordings (hand-drawn illustration synthesis), multi-column newspaper layouts, and responsive CSS styling.

Produces an editorial layout optimized for both quick morning skims and deep, concentrated reading sessions.

Stage IV.

Delivering on Schedule

Executes autonomous web deployment, bilingual synchronized publishing, and audio summary (RSS) generation at preconfigured schedule intervals.

Transitioning from static, manually updated websites into an autonomous web ecosystem where the digital medium itself continuously synthesizes and circulates knowledge. Operating in full autonomy under normal conditions while sending email notifications to administrators in the event of generation failure, eliminating daily operational friction.

Autonomous Media Pipeline

Autonomous Generation Cycle

An integrated, end-to-end publishing pipeline from real-time news and paper ingestion to multi-agent division of labor, translation, layout synthesis, and scheduled broadcast.

01 / INGESTION SIGNALS

Collecting Signals

Continuous crawling of external signals, parsed directly into structured data pipes.

  • Current Global News & Politics
  • Macro Market & Social Dynamics
  • Academic Preprints (arXiv, etc.)
OUTPUT: SIGNALS STREAM ➔
02 / DIVISION SPECIALIZATION

Specialization & Division of Labor

Specialized AI agents divide labor across drafting, translation, and validation.

  • Columnist Commentary & Opinion
  • Multilingual Translation (JA / EN)
  • Technical Validation & Fact Checks
OUTPUT: ARTICLES PASS ➔
03 / SYNTHESIS LAYOUT

Typesetting Pages

Harmonizes validated text with editorial spreads and hand-drawn graphic recordings.

  • Multi-Column Newspaper Spreads
  • Graphic Recording Illustration Synthesis
  • Responsive Web Editorial Layouts
OUTPUT: EDITIONS READY ➔
04 / BROADCAST DISPATCH

Delivering on Schedule

Autonomous publishing across web, podcasts, and RSS feeds on schedule.

  • Autonomous Web Updates
  • Bilingual Synchronized Publishing
  • Audio Summaries (RSS Feeds)
  • Email Notification on Generation Failure
STATUS: BROADCAST SCHEDULED ✓
AUTONOMOUS RECURSION LOOP: Published editions recirculate into the knowledge DB as reference inputs for subsequent cycles
CONTINUOUS STREAM