Suggestion for Terminus
Subject: Real-time fine-tuning of models on news streams for Terminus
Message:
> Hi Mathef,
My name is Rodion — I’m an entrepreneur, researcher, and AI systems engineer deeply involved in mining and decentralized technologies. I recently discovered your project Terminus and was really impressed by the idea. The retro-terminal aesthetic combined with live global headlines is incredibly immersive and focused.
I have a suggestion that could elevate Terminus even further: introduce real-time fine-tuning of your model(s) based on incoming news data.
Imagine this — your system not only summarizes news but also learns from it continuously, becoming smarter and more context-aware over time.
Why it matters:
The model adapts dynamically to the current information landscape;
Improves prompt relevance, trend detection, and summarization accuracy;
Turns Terminus into a living, evolving assistant — useful for analysts, traders, researchers.
How it could work:
1. Fetch live news streams (RSS, APIs, n8n triggers, etc.);
2. Feed selected entries into a fine-tune dataset in real time;
3. Once a threshold is reached (e.g., every X hours or Y articles), trigger a small fine-tuning cycle;
4. Use the updated model for future summaries, continually refining performance.
This would make Terminus more than a passive observer — it becomes a dynamic machine that truly “understands” the evolving world it monitors.
If you’re interested, I’d love to help with architectural ideas or connect you with similar real-world implementations.
Keep up the great work — Terminus i
s a truly inspiring concept.
— Rodion
