Generative BI is not just an evolution of Business Intelligence.
It’s a structural shift in how organizations think, interact, and decide with data.
For years, BI promised democratization. In reality, many companies are still stuck between: IT bottlenecks Low data literacy
GENERATIVE AI
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Generative BI: Beyond Evolution, a Structural Shift in Data
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AI Model Selection and Prompting for Rare Disease Applications
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Pas les bons prompts, mauvaise sélection du bon modèle, et peut être pas une version payante. Normal. https://
bit.ly/grandir-avec-m
aladies-rares
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Anthropic publishes research on AI product risks and disempowerment
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Anthropic publishing research on risks of their own product is something more AI companies should do. The disempowerment patterns finding deserves way more attention than it got.
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Adding Diverse News Without Re-ranking: Simple AI Intervention
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The finding that just adding diverse news worked without re-ranking is really interesting. Feels like the simplest possible intervention.
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Dialogue Format Depolarizes AI Models Across Political Alignments
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Really interesting that the depolarizing effect holds across models regardless of political leaning. Suggests it's something about the dialogue format itself, not the model's alignment.
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RAG Pipeline Over Raw Token Storage Strategy
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The ideal is not “store everything,” it’s “keep only what is worth keeping” — more like a really good RAG pipeline than a giant token dump.
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Context Size vs. Quality: When Less Compression Hurts Performance
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Bigger context is useful, but not if it keeps carrying forward noise that should have been compressed away. In some cases that extra clutter can even hurt performance relative to a stronger compaction approach that preserves only the minimum viable context needed for the task.
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Opus to Opus 1M Switch: Daily Limits and Token Efficiency Issues
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This is also why the switch between Opus and Opus 1M is pretty annoying to me: I hit daily limits much earlier, and part of it feels like compaction happens less aggressively, so way more tokens get sent every time (and probably because they keep reducing that daily limit).
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Better Compaction System for Token-Efficient AI Processing
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A much better compaction system would keep the signal and discard the rest, making the whole stack far more token-efficient. That means lower latency, less compute, and lower cost for users.
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Long contexts contain noise and low-value information
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A lot of what gets sent in long contexts is just noise: stale history, repeated reasoning, intermediary thinking tokens, unrelated discussions and other low-value baggage.