Profil von QUD7 | Binance Square

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HOW IS IT BUILT? On August 6th, 2025, mefai.io/news went live, and we introduced it as an AI powered news page. At this page, All news from the past 6 months can be viewed by going back through the archives. As we now bid farewell to that page, Mefai Browser in Browser introduces a new news page inside the platform. So what was this feature? From day one, we could have just outputted random data. Instead, we made sure it delivers the right news the right way. You can now see every single relevant news item from the past six months on this page. For AI, the first requirement is data. And if you want a truly independent AI, almost the only thing you need is this. All six months of data are now fully accessible. Over six months, we tracked which platform first published each news item. For example, if a news piece is about a certain token, we elevated the score of the originating platforms. We started with Deep Enrichment for training. What did we learn, and why do we now feel entitled to share it with people? The system runs as a multi layer AI, Layer 0 (ML v2): TF IDF + Ensemble model trained from scratch (NB + RF + LR) — 88.95% accuracy, trained on 4,977 news articles. Layer 1 (v1): Keyword scoring always active. First, we classified the news as bearish, bullish, or neutral and calculated a confidence score. We focused on the most impactful part of the news and analyzed it. Later, we even generated a buy, sell, or hodl recommendation. We tracked the price at the moment the news arrived and the price after the news. Weight adjustments were applied based on the accuracy of the outcomes. For example, Sentiment, bearish, Score: 7/10 AI Summary: "Circle stock crashes 22% to below $100 as Senate bill revision reportedly bans stablecoin rewards..." Action: sell Techniques used,Keyword frequency analysis Weighted scoring with learned adjustments Source credibility weighting Token name extraction (NER like pattern matching) Round robin API key rotation (5 keys) In memory caching (1 hour TTL) Stale while revalidate pattern TF IDF Word embeddings Neural network

HOW IS IT BUILT?

On August 6th, 2025, mefai.io/news went live, and we introduced it as an AI powered news page. At this page, All news from the past 6 months can be viewed by going back through the archives.

As we now bid farewell to that page, Mefai Browser in Browser introduces a new news page inside the platform.

So what was this feature? From day one, we could have just outputted random data. Instead, we made sure it delivers the right news the right way. You can now see every single relevant news item from the past six months on this page.
For AI, the first requirement is data. And if you want a truly independent AI, almost the only thing you need is this. All six months of data are now fully accessible.
Over six months, we tracked which platform first published each news item. For example, if a news piece is about a certain token, we elevated the score of the originating platforms.
We started with Deep Enrichment for training. What did we learn, and why do we now feel entitled to share it with people?
The system runs as a multi layer AI,
Layer 0 (ML v2): TF IDF + Ensemble model trained from scratch (NB + RF + LR) — 88.95% accuracy, trained on 4,977 news articles.
Layer 1 (v1): Keyword scoring always active.

First, we classified the news as bearish, bullish, or neutral and calculated a confidence score.

We focused on the most impactful part of the news and analyzed it.

Later, we even generated a buy, sell, or hodl recommendation.
We tracked the price at the moment the news arrived and the price after the news. Weight adjustments were applied based on the accuracy of the outcomes. For example,

Sentiment, bearish, Score: 7/10
AI Summary: "Circle stock crashes 22% to below $100 as Senate bill revision reportedly bans stablecoin rewards..."
Action: sell
Techniques used,Keyword frequency analysis
Weighted scoring with learned adjustments
Source credibility weighting
Token name extraction (NER like pattern matching)
Round robin API key rotation (5 keys)
In memory caching (1 hour TTL)
Stale while revalidate pattern
TF IDF
Word embeddings
Neural network

QUD7

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