A new arXiv paper presents a method for identifying ragebait — content deliberately crafted to provoke outrage and boost engagement — at scale on the Japanese X platform. The authors move beyond binary detection toward characterizing how such posts are written and spread across a large sample. The work aims to fill gaps in reliable, large-scale measurement of ragebait online.
Japanese company Polimill is developing AI infrastructure intended for public-sector use, drawing on OpenAI's GPT models and Codex. The system is designed to let local government staff search and apply administrative knowledge, while also speeding up its own development. The effort points to growing adoption of commercial AI models in government service delivery.
WHY IT MATTERS ↘It shows the frontier labs' public-sector strategy is now being executed through small local integrators rather than direct government contracts, which spreads model dependence into municipal workflows that are hard to migrate once entrenched. Using Codex to build the system also suggests the same vendors selling AI deployment are increasingly relying on it internally, compressing delivery timelines and lowering the barrier for small firms to win government work.