Picking a Problem

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.

Explain Maxwell

Project: "Maxwell"
The Thermodynamic Hypervisor

The Premise
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).

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.

The Goal
Design a distributed, bare-metal hypervisor (likely orchestrating Firecracker microVMs or Wasm modules) where:
1. Compute is Currency: Processes must "pay" for every cycle using a cryptographically verifiable energy token.
2. Scheduling is an Auction: The kernel doesn't pick who runs. Processes bid for execution slots based on their self-assessed urgency.
3. Entropy is Expensive: Allocating memory is cheap. Freeing memory (erasing information) costs "tax" (Landauer's Principle).
4. Apoptosis is Default: The OS kills any process that cannot pay its energy rent.

Why this is complicated

To plan this, you must solve three interlocking paradoxes:

1. The "Proof of Useful Work" Paradox
• Problem: How does the Hypervisor know an AI agent is actually thinking and not just mining crypto or looping?
• 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?
• Difficulty: Extremely Hard. Requires bridging Cryptography and ML Compilers.

2. The "High-Frequency Auction" Paradox
• Problem: If every CPU cycle requires a bid, the auction mechanism itself consumes more compute than the workloads.
• 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?
• Difficulty: Requires inventive Data Structures (e.g., a "Probabilistic Auction Heap").

3. The "Thermal Throttling" Consensus
• Problem: In a distributed cluster, one node overheating affects the efficiency of neighbors (fan speed, power delivery).
• 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?

The Method

It isn't always easy to find an "unknown" problem to solve. I have two strategies:

1. Propose something in the future that I know I will have to eventually solve
2. Have AI ask me questions about what I am working on, then tell it to propose 5 diverse problems that we will encounter as an industry over the next few years

You can steer the complexity of the problems by using L1 - L11 as guidance.

Below is the result of the latter.

The Five Candidates

1. Ouroboros: The Infinite Context Engine

GPU Direct Storage for LLMs. Stream KV cache from NVMe into tensor cores instead of loading into VRAM. Fight the Von Neumann bottleneck. The problem: RAG is lossy, and you can't fit 50GB of legal discovery into 80GB of GPU memory economically.

2. Chimera: The Dynamic Model Compiler

Runtime linker for neural networks. Load a base skeleton and stitch together LoRA adapters dynamically based on prompt routing. Turn model training (batch, expensive) into model composition (real-time, cheap). The problem: enterprises can't maintain 100 fine-tuned 70B models.

3. Rosetta: The Semantic Lifter

Transpiler that lifts COBOL/Fortran into a semantic graph, runs formal verification with Z3, and recompiles to Rust. The problem: global banking runs on dead developers' code, and LLMs hallucinate when translating.

4. Panopticon: Kernel-Level Data Lineage

eBPF-based Information Flow Control. Taint bytes at the kernel level; drop packets if "Secret" data hits an unauthorized socket. The problem: GDPR, HIPAA, EU AI Act—enterprises can't trace where AI sends their data.

5. Maxwell: The Thermodynamic Scheduler (selected)

Treat compute as a scarce economic resource. Agents bid for CPU time; price rises with temperature. Parasitic workloads go bankrupt and die. The problem: fairness is wrong when autonomous agents compete for resources.

Why Maxwell?

The seed that caught me was Jevons Paradox: the observation that efficiency gains don't reduce consumption—they increase it. Make engines more efficient, and people drive more. Make AI inference cheaper, and people run more AI.

We're about to hit this hard. Inference is getting cheaper (quantization, speculative decoding, distillation). That means more agents, running longer, competing for the same hardware. The current answer is "buy more GPUs." But that's not a solution—it's a deferral.

What happens when you have 50 autonomous agents on one machine and they all think they're important?

The Now Problem: DePIN Verification

There's a class of networks called DePIN—Decentralized Physical Infrastructure Networks. Examples: Akash, io.net, Render, Nosana. The pitch is simple: rent GPU time from strangers instead of AWS.

The problem is trust. When you send a workload to some random node in Argentina, how do you know they actually ran it? They could return cached results. They could run a cheaper model and pocket the difference. They could just lie.

Current solutions are heavy:

• TEE (Trusted Execution Environments): Run everything in a hardware enclave. Works, but ~10% overhead and limits which GPUs you can use.
• zkML: Generate a zero-knowledge proof that the inference happened correctly. Works, but takes minutes per proof.
• Optimistic verification: Trust, but verify a random sample. Works, but doesn't catch sophisticated cheaters.

Maxwell's angle: what if you could verify by watching the power signature? Different workloads (inference vs. mining vs. looping) produce distinct thermal fingerprints. You don't need cryptographic proofs for 99% of verification—you just need continuous monitoring.

The Future Problem: Fairness is Wrong

This is the weirder, more interesting version.

Right now, your operating system treats every process equally. The Linux Completely Fair Scheduler (CFS) gives each process a fair share of CPU time. This made sense when processes were human-initiated: a browser, a text editor, a compiler. No single user should monopolize the machine.

But autonomous agents change this. Imagine your laptop running:

• An agent scanning your email for action items
• An agent monitoring your calendar and preparing briefs
• An agent running background research on a topic you mentioned
• A malware agent mining cryptocurrency
• A broken agent stuck in an infinite loop

CFS gives them all equal time. It can't distinguish between them because it doesn't understand value. It just sees "process needs cycles."

The future problem is: who decides what's important? The hyperscalers solve this with central coordination (Borg, Kubernetes). But for a personal machine, for decentralized networks, for swarms of agents you don't fully control—you need a different mechanism.

Maxwell's answer: make them pay for it. Create an internal economy. Agents that produce value earn currency; agents that consume without producing go bankrupt. The scheduler becomes an auction, and the market discovers priority.

How This Is Represented Now

This isn't entirely new territory. There's prior art:

• Economic scheduling research (1990s-2000s): Spawn, Mariposa, and other systems experimented with resource markets. Mostly academic; never hit production.
• Power-aware scheduling: Intel RAPL, thermal governors. The OS already modulates based on thermals, but it's reactive throttling, not economic prioritization.
• Kubernetes priority classes: You can mark pods as "critical" or "best-effort." But this is static assignment, not dynamic discovery.
• DePIN verification schemes: io.net, Akash, and others are all wrestling with this. Most use staking + slashing (game-theoretic) or TEE (hardware-based).

What's missing is the synthesis: using thermodynamic constraints (real physics) as the enforcement mechanism for economic prioritization. That's the gap Maxwell aims to fill.

Next Steps

Before building anything, I need to build the scaffolding:

1. Write a vision.md — what does success look like?
2. Design the architecture — what are the components and how do they interact?
3. Build a roadmap.md — what's the sequence of work?
4. Review and understand it — does this actually make sense?

Only then do I start writing code. The goal is to front-load the thinking so the building is straightforward.