9a9e58c935
Moved from maxwell/blog to standalone repository. - Next.js research journal application - Notes 001-005 with YAML/MD content structure - Claude Code configuration for blog development Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
62 lines
3.2 KiB
YAML
62 lines
3.2 KiB
YAML
id: "001"
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slug: 001-picking-a-problem
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date: "2026-02-06"
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title: Picking a Problem
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preview: "Surveying five OS-level AI problems. Landing on Jevons Paradox and the question of fairness."
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prompts:
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- id: challenge
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label: Generate challenges
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content: |
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Here are a list of things I have been working on. I need you to give me a challenge that pushes me to prove myself above my capability. Provide 5 diverse complicated challenges that will need to be solved within the next 2-3 years.
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- id: explain
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label: Explain Maxwell
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content: |
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Explain Maxwell
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- id: maxwell-brief
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label: Maxwell brief (AI response)
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content: |
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Project: "Maxwell"
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The Thermodynamic Hypervisor
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The Premise
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Current operating systems (Linux, k8s) are blind to value. They will happily burn 1,000 Watts executing a while(true) loop if the scheduler says so. They treat compute as an infinite resource, constrained only by "time" (CPU quotas).
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As AI agents scale to millions, Energy becomes the hard constraint, not Time. We need a runtime that schedules workloads based on Information Gain per Joule.
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The Goal
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Design a distributed, bare-metal hypervisor (likely orchestrating Firecracker microVMs or Wasm modules) where:
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1. Compute is Currency: Processes must "pay" for every cycle using a cryptographically verifiable energy token.
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2. Scheduling is an Auction: The kernel doesn't pick who runs. Processes bid for execution slots based on their self-assessed urgency.
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3. Entropy is Expensive: Allocating memory is cheap. Freeing memory (erasing information) costs "tax" (Landauer's Principle).
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4. Apoptosis is Default: The OS kills any process that cannot pay its energy rent.
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Why this is complicated
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To plan this, you must solve three interlocking paradoxes:
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1. The "Proof of Useful Work" Paradox
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• Problem: How does the Hypervisor know an AI agent is actually thinking and not just mining crypto or looping?
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• Challenge: Design a "Proof of Inference" protocol. Can you use Zero-Knowledge proofs (zk-SNARKs) to prove a model layer was executed correctly without the Hypervisor re-running the computation?
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• Difficulty: Extremely Hard. Requires bridging Cryptography and ML Compilers.
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2. The "High-Frequency Auction" Paradox
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• Problem: If every CPU cycle requires a bid, the auction mechanism itself consumes more compute than the workloads.
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• Challenge: Design a Control System. How do you implement a market mechanism that runs in O(1) or O(log n) time inside the kernel scheduler?
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• Difficulty: Requires inventive Data Structures (e.g., a "Probabilistic Auction Heap").
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3. The "Thermal Throttling" Consensus
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• Problem: In a distributed cluster, one node overheating affects the efficiency of neighbors (fan speed, power delivery).
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• Challenge: Design a Gossip Protocol for Heat. How does Node A tell Node B "I am dying" in a way that causes Node B to lower its prices for compute, autonomously rebalancing the thermodynamics of the data center?
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filesCreated: []
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navigation:
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next:
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slug: 002-building-the-scaffolding
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id: "002"
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title: Understanding the Project
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