I’ve been experimenting with ways to reduce my social media usage while staying updated on very specific niches in the news. I put together a Python-based project that turns noisy RSS feeds into custom-formatted news briefings.

The Problem with Single Inference

Relying on a single LLM prompt to summarize news often leads to surface-level summaries or hallucinations. I wanted to see if I could treat narrative extraction as a multi-step consensus problem rather than a single task.

The Tech

• The "Council" Architecture: Multiple LLMs (via OpenRouter) take on the role of News Editors. They perform parallel judgments on a news batch, then review and rank each other’s work.
• Consensus Mechanism: The final narrative is synthesized only after the "editors" reach a consensus, which significantly mitigates individual model bias.
• Entity-Level Filtering: It goes beyond categories. I can filter by specific entities (e.g., "Show me consumer AI launches, but exclude all hardware").
• Stack: Python CLI service, aiosqlite for state, and "vibe coding" strategies for LLM ensembles.

The Value

• Social Media Reduction: This has effectively replaced my "doomscrolling" for news. I get high-signal, short-form briefings delivered exactly how I want them.
• Hallucination Guardrails: By having models critique each other’s drafts, the final output is much more reliable than a standard RAG summary.
• Deep Personalization: Once the system understands how to frame and language-translate the output for your specific needs, it becomes a powerful long-form content filter.

I’m looking for feedback on a few things:

1. Technical: For those working with LLM ensembles, have you found multi-step consensus to be worth the extra latency vs. single inference?
2. General: How are you currently consuming news? Would you trust an LLM "consensus" more than a single-model summary?

Repo: https://github.com/badgiovi/news-editor-agent