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Finally found a meaningful scenario for running LLM in the browser, peering into the internals of LLM to see what's really happening inside

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Broooooklyn

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46,339 görüntüleme • 2 ay önce •via X (Twitter)

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Stories have shapes: a comedy rises toward joy; a tragedy falls into loss. Inside an LLM, that’s visible more literally: as an LLM reads a story, its internal activations trace a wandering path that reflects the model’s sense of what kind of story it is reading. (1/5)

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Second place: Ambien by give me a sec A daydreaming agent running inside Hermes Agent that samples a curated Obsidian corpus for resonant idea collisions and synthesizes them into published essays, leveraging the harness for iterative voice calibration that keeps the prose from reading like an LLM.
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Second place: Ambien by give me a sec A daydreaming agent running inside Hermes Agent that samples a curated Obsidian corpus for resonant idea collisions and synthesizes them into published essays, leveraging the harness for iterative voice calibration that keeps the prose from reading like an LLM.

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i'm a little sick of chatgpt giving me obviously broken code i've found a "micro agent" approach to LLM code generation can work much better the LLM first generates a *test*, and then enters a loop where it generates and iterates on the code until the tests pass source below

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Jupyter Agents - LLMs running data analysis directly in a notebook! The agent can load data, execute code, plot results and following your guidance and ideas! A very natural way to collaborate with an LLM over data and it's just scratching the surface of what's possible soon!

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Another example of the multiple TTS parallel pipelines pattern. Here's a voice AI agent that speaks both English and Arabic, using a specific model/voice for each language. These are PlayAI voices. The STT, TTS, and LLM inference is all running on Groq Inc. (The LLM is LLama 4 Maverick.)

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Met a guy making $1.6 million/year as an LLM engineer. I asked him how he learned LLMs from scratch. He sent me the exact video that got him in. A 1 hour course on how LLMs actually work. He shows how transformers inside LLMs like ChatGPT & Claude are actually built. I watched it last night. Halfway through, I realized LLM architecture is way simpler than they make it look. Bookmark this and read the article below. • 00:00 - LLM foundations • 04:21 - LLM tokenization • 05:43 - LLMs vector embeddings • 22:16 - attention mechanism of LLM • 43:42 - LLM multi head attention

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