Overview & Result
Processing millions of live social posts or event streams with frontier LLMs is financially impossible. Firehose Judge processes incoming messages through Jev Choice at micro-cent costs, discarding noise and routing high-value signals to deep-research agents.
How Jev fits in the loop
- Ingest live events from webhooks or streaming protocols.
- Submit post text to Jev Choice with structured relevance criteria.
- Filter out 98%+ of background chatter with zero generative latency.
- Dispatch verified high-intent posts to downstream agent queues for deep summarization.
How to reproduce
- Connect to the Bluesky firehose websocket stream using the project setup.
- Configure classification criteria (e.g. tech lead signals, security alerts, noise).
- Process streaming posts through Jev Choice and route high-scoring items to a triage database or notification bot.
Why this build matters
Shows how System 1 decision engines make real-time event-driven agent architectures economically viable.
Reported performance
Reported by authorCost: ~$0.00003 per post
Measured during continuous live streaming ingestion of the public Bluesky firehose.
Limitations
- High-volume streams require local message queue buffering to handle burst spikes.
- Short text posts lacking context may produce false negatives without account metadata.
- Generative synthesis must still be performed by downstream specialized models.