1. The Kernel Concept
Think of a PRISM kernel as a compressed instruction set – like a mini-operating system for the AI. Instead of writing verbose prompts, you encode: The context (what mode to enter)
The constraints (what rules to follow)
The output format (how to structure
@godofprompt
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PRISM Kernel: Compressed AI Instruction Set
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GPT-4 Agents Outperformed Without Weight Adjustments
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The fact that it beats GPT-4 agents without touching weights… That’s a turning point.
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Google’s Self-Organizing AI System: TUMIX
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Google just built an AI that organizes itself. It’s called TUMIX, and it might be the most interesting paper Google has published this year. Instead of training a bigger model, the team built a system where multiple AIs work together at test time. Each agent uses different
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Guide to Customizing ChatGPT
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ChatGPT forgets threads, but 'saved memory' doesn’t. Write your user manual once, and every conversation follows it. Examples worth borrowing:
• "Provide one solution only, no options unless requested."
• "If the answer would be shallow or misleading, say so."
• "Debug like a senior engineer: include unit tests." -
AI and analog computing revolutionize transformers
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Sources:
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https://research.ibm.com/blog/how-can-analog-in-memory-computing-power-transformer-models?utm_source=chatgpt.com
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https://nature.com/articles/s43588-025-00854-1?utm_source=chatgpt.com
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Smarter physics, not bigger chips, for AI
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it’s no longer “bigger chips = better AI” it’s “smarter physics = better AI.” With analog IMC, a model doesn’t waste energy copying weights back and forth. That overhead vanishes. IBM and others already show that MoE models map well to analog designs, boosting energy and
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AI Models Moving to Devices: Key Considerations
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ai models might soon live in your device, not just in giant datacenters. if you’re a founder / engineer / prompt architect, start thinking:
→ designing models aware of analog constraints
→ hybrid architectures: analog + digital fallbacks
→ compression, pruning, or -
Offline AI possible with analog in-memory computing
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HOLY SH*T. OFFLINE AI IS POSSIBLE? Researchers published a new analog in-memory computing (IMC) architecture in Nature Computational Science, targeting the transformer attention mechanism. Nature Instead of shuffling data between memory and compute (the classic GPU bottleneck),
