A step-by-step walkthrough of how BLOOM's 176 billion parameter architecture actually processes language, from token embeddings through 70 decoder blocks to the final output prediction.
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Over 2 million models are now hosted on HuggingFace, with the second million added in just 335 days. If open source models are viable alternatives to OpenAI and Anthropic, how do you justify those companies' valuations? A deep dive into BLOOM, the only truly open source LLM.
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A brilliant explanation of what's actually happening inside modern AI systems, and what remains mysterious and unresolved.
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Most dashboards treat KPIs like a scoreboard. Green equals good, red equals bad. Useful for reporting, but terrible for decision-making. A KPI Driver Tree flips this idea around.
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When personalised tools can be created in days, broad vanilla platforms start to feel useless. I built my own AI job tracker in a day and a half.
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Ethan Mollick's Co-Intelligence offers a clear, accessible introduction to using large language models as collaborators. It's the perfect on-ramp for AI newcomers, though seasoned users may find familiar ground.
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Each time a machine crosses a new threshold, many people insist: that doesn't really count as intelligence. Why do we resist calling machine behavior intelligent?
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Fluency doesn't come from studying language. It comes from generating it. That's why I built Babblo.
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Jeff Hawkins' 2005 exploration of intelligence as prediction was ahead of its time in some ways, but missed the mark on others.
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Karen Hao's Empire of AI attempts to capture the OpenAI story while it's still being written. The result is part insightful journalism, part premature verdict.
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How I evolved from copy-paste chaos to a planning-first workflow with Claude Code, and why adding friction made everything faster.
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I don't know if everyone will end up with an AI-powered personal assistant. I just know that I badly want one. Here's how I accidentally built one.
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