Show HN: Run agents locally, share them globally, collect feedback instantly (syngularai.com)

A new tool showcased on Hacker News advertises a simple workflow for building chat agents that run on your machine, can be exposed globally, and immediately collect user feedback. The short example uses a Syngular AI SDK: you annotate a Python generator with @entrypoint('@welcome'), yield MarkdownMessage objects (the agent’s outputs) alongside ThumbsFeedback options, and call dev_listen('<api_key>') to open a development endpoint. The snippet demonstrates a minimal welcome agent that returns two messages and wires a thumbs-up/downs feedback control into each response. This is significant because it lowers friction for rapid agent development, iteration, and data collection. Technically, the pattern—generator-based handlers that emit content+feedback—lets developers instrument UIs and backend pipelines for immediate feedback aggregation, which can feed analytics, A/B testing, or supervised/RL fine-tuning workflows. Running agents locally preserves compute and data control while dev_listen enables temporary public exposure for testing or demos. Implications include faster prototyping and community sharing of agents, but also raises considerations around privacy, misuse, and how collected feedback will be stored or used for model improvement.