TL;DR: Infrastructure should be application-aware, statically typed, and predictive.
Generic orchestration fails because it ignores workload-specific dynamics.
I'm designing type-theory-based cloud primitives that let applications deploy themselves.
Long-Term Vision: Autonomous Infrastructure
I'm building toward distributed systems that manage themselves: self-aware,
self-regulating, self-scaling, self-healing, self-governing.
Norbert Wiener described cybernetic feedback systems in 1948.
IBM's Autonomic Computing manifesto (2001) outlined the same goal. Kubernetes got
partway there with reconciliation loops. DePIN adds economic self-governance.
The vision keeps recurring because it's the right direction. Execution is hard.
I'm executing it for a specific domain where the value is concrete and measurable.
Why Kubernetes Failed (And My Approach)
Problem 1: Centralized Control Plane
Kubernetes complexity is a direct consequence of client-server architecture. One control plane
(API server, etcd, scheduler, controller-manager) must coordinate all nodes. This creates:
single point of failure, HA complexity (etcd quorum, leader election), scaling bottlenecks
(all decisions through one brain), and operational burden (managing the control plane is a job).
My approach: P2P choreography. Each node runs its own orchestrator and
coordinates with peers through discovery. No central brain to fail, scale, or operate.
Like a flock of birds—no conductor, yet perfectly coordinated. The complexity of K8s
evaporates when you remove the requirement for a single point of coordination.
Problem 2: The Reconciliation Tax (State Drift)
Kubernetes maintains a model of cluster state in etcd—a centralized representation
of what the control plane believes is true. But the map is not the territory. Reality lives
at the nodes, and the model is always an approximation, always slightly stale, always drifting.
This creates the reconciliation tax: continuous CPU cycles diffing desired vs actual
state, network bandwidth syncing state to the center, exponential complexity handling edge cases
(what if reconciliation itself fails?). The control plane is perpetually playing catch-up with
a reality it can never fully observe. Network partitions turn drift into divergence. The model
becomes fiction.
My approach: truth at the edge. In P2P architecture, each node IS the authoritative
source of its own state. There is no central model to drift from reality—the state is distributed
where the work happens. When you need to know a node's state, you ask the node. No reconciliation
overhead. No stale cache. No fiction.
The deeper insight: if you encode all valid state transitions using type theory and mathematical
analysis, invalid states become unrepresentable at compile time. The "model" becomes the
code itself, which IS reality. No runtime reconciliation needed—the types prevent invalid states
from existing. K8s fixes drift at runtime; we prevent it at compile time.
Problem 3: Workload Blindness
Kubernetes treats all pods as identical boxes. It knows CPU% and memory%, but not
KV cache growth rates, request batch patterns, or token generation curves.
Generic orchestration is mediocre at everything.
In practice, this shows up as OOM kills during peak token generation or late-night paging
due to slow KV cache growth that K8s never saw coming.
My approach: application-aware autonomy. Every application type has
characteristic stochastic patterns, extractable via DSP/FFT signal processing.
Once you know the frequencies, you can predict ("memory pressure peaks in 47 minutes"),
preempt ("scale up before daily traffic spike"), and diagnose ("this oscillation is anomalous").
Capacity Expansion, Not Cost Reduction
The industry fixates on cost savings; I focus on capacity expansion.
By orchestrating spot instances and treating compute as a fluid resource, we amplify the total
computational throughput available for inference and heavy workloads while simultaneously reducing expenditure.
A 70% cost reduction is actually a 3x capacity multiplier. The same budget buys three times more compute.
This reframe matters: we're not making infrastructure cheaper—we're making previously impossible workloads possible.
The constraint isn't money, it's capacity. Spot orchestration unlocks stranded compute that would otherwise sit idle.
Type-Theory-Based Cloud Primitives
Kubernetes uses untyped YAML. Errors surface at runtime, often at 3am.
My approach: design type-theory-based cloud primitives.
Each deployment pattern is a type. Workload requirements are types. Infrastructure capabilities are types.
Mismatches are caught statically, before deployment.
The orchestrator ships with the application as a library, not as a separate
system the application is deployed onto. Static analysis deduces workload
requirements from the code itself. Applied category theory provides the mathematical
foundation for mapping between workload types and infrastructure types.
The meta-shift: Traditional infrastructure deploys applications (passive).
In this model, applications become autonomous agents that navigate decentralized infrastructure,
find suitable nodes, self-deploy, and self-serve. The application does not wait to be
orchestrated. It orchestrates itself.
The Endgame: Decentralized Autonomy
Self-governing systems need trustless coordination. If no single entity should control
scheduling decisions, then the settlement layer must be permissionless.
On-chain settlement (Solana) enables this: off-chain execution for fast operational
decisions, on-chain coordination for disputes, payments, and reputation. The DePIN model
applied to GPU orchestration.
Read the full decentralization thesis →
TL;DR: Building toward decentralized autonomous infrastructure.
Phase 1 validates the algorithms. Phase 2 proves production viability.
Phase 3 removes the central operator via on-chain settlement.
The Vision
Infrastructure that manages itself: self-aware, self-healing, self-governing.
Not another Kubernetes—application-aware orchestration that understands workload-specific patterns.
The logical endpoint: permissionless, decentralized operation. No single entity controls
scheduling decisions. Trustless coordination via on-chain settlement.
Three-Phase Roadmap
- Kuhn-Munkres optimal migration (7-46% improvement over naive)
- Stateless failover with graceful draining
- Discrete-event simulation engine
- 2,191 LOC Rust, 32 tests, 243-scenario validation
- AWS multi-region integration
- Prognostics engine (ARIMA + FFT/DSP prediction)
- Pilot validation with early adopters
- European provider adapters (Hetzner, OVHcloud)
- Solana on-chain settlement layer
- Trustless compute verification
- Permissionless node participation
- Economic self-governance (DePIN model)
Why Solana
The Problem with Centralized Orchestration
Current orchestration systems require trust in a central operator.
Scheduling decisions, compute verification, and dispute resolution all flow through
a single point of control. This creates vendor lock-in at the orchestration layer—the
very infrastructure meant to provide flexibility.
P2P Architecture: No Central Controller
Synkti's architecture is peer-to-peer from day one. Each node runs its own orchestrator,
discovers peers dynamically, and makes autonomous decisions. Self-aware, self-monitoring,
self-healing. No central control plane to fail, scale, or pay for. This P2P foundation
makes the transition to decentralized networks natural—only the discovery layer changes
(EC2 tags → libp2p).
The Solution: On-Chain Settlement
Separate execution from settlement. Off-chain execution handles fast
operational decisions—migrations, failovers, scaling. On-chain settlement
handles trustless coordination—disputes, payments, reputation. No single entity controls
the feedback loop.
Why Solana Specifically
- 400ms finality — Spot preemptions need sub-second response
- Sealevel runtime — Parallel state access enables non-blocking settlements across thousands of concurrent nodes
- DePIN ecosystem — Helium, Render Network precedent for physical infrastructure
- Rust-native — Same language as Synkti core, minimal context switching
The DePIN Thesis
Decentralized Physical Infrastructure Networks coordinate real-world resources without
central operators. GPU compute is physical infrastructure with volatile supply.
Synkti brings orchestration intelligence—optimal migration, stateless failover, workload prediction.
Solana brings trustless coordination—permissionless participation, cryptographic verification,
economic alignment. Together: a permissionless GPU marketplace with production-grade reliability.
Technical Foundation
- P2P peer discovery — EC2 tags (Phase 2) → libp2p DHT (Phase 3)
- Provably optimal algorithms — Kuhn-Munkres vs. greedy heuristics
- Domain-agnostic architecture — Same core for inference, training, batch
- DSP/FFT signal processing — Extract workload patterns for prediction
- Type-theoretic primitives — Compile-time guarantees for cloud operations