Bound work
Queues, deadlines and concurrency limits make overload explicit.
Projects / Resilient Distributed Runtime
Versioned release2026-09Reliable infrastructure for AI/ML services
Resilient Distributed Runtime
A correctness-first distributed runtime demonstrating bounded execution, membership, causal replication, recovery, security, observability and failure testing.
Problem
AI/ML services face overload, partial failure, changing membership, inconsistent replicated state and unverifiable recovery—not just API design.
Ownership
Designed the reliability model and implemented the runtime, three-node verification, CRDT replication, recovery, security controls, telemetry, chaos scenarios and release evidence.
Primary proof
Three-node Kubernetes verification with failure recovery and 439 tests passed
01 / Overview
System and intended use
A portfolio reference for teams evaluating backend reliability, observability, distributed coordination and resilient infrastructure for AI/ML services.
439 passedAutomated tests
3 nodesLocal cluster
v0.7.0Release
EKS verifiedCloud lifecycle
02 / Architecture
System boundary and flow
System flow04 stages
Bounded clientAdmission control
Three-node clusterReplicated runtime
etcd + causal CRDTMembership + state
mTLS + telemetryTrust + telemetry
03 / Engineering decisions
Key design choices
Precise consistency claims
Causal convergence is documented without implying consensus or linearizability.
Failure as a test
Chaos and restart recovery are part of verification.
04 / Verification
Claims, evidence and boundaries
| Claim | Inspectable Evidence | Boundary |
|---|---|---|
| The runtime has broad automated verification. | The supplied verification run records 439 tests passed with 8 skipped. | Test breadth does not establish consensus or linearizability. |
| The runtime was exercised as a three-node local cluster. | The project records a three-node local Kubernetes lifecycle. | A local cluster does not establish multi-AZ availability. |
| The project has a versioned release. | The repository exposes the supplied v0.7.0 release link. | The release does not claim quorum durability. |
| Delivery evidence includes software-supply-chain artifacts. | The project records a signed OCI image and SBOM evidence. | Artifact signing does not by itself establish production operational maturity. |
05 / Boundaries
Limitations and claim boundaries
- No claim of consensus, linearizability, quorum durability, or multi-AZ availability.
- Temporary EKS infrastructure is not a permanent live service.
- CRDT semantics apply only to modeled state.
06 / Transferable capability
Related client problems
The project demonstrates explicit overload controls, recovery, observability, security and delivery practices applicable to dependable backend and AI/ML infrastructure.
Relevant to: backend reliability, observability, container/cloud delivery, distributed coordination and resilient AI/ML infrastructure.
Discuss a Similar Project →07 / Technical depth
Engineering appendix
Review implementation, failure modes, delivery and trade-offs
Implementation
- Implemented bounded execution, membership updates, causal CRDT state, durable recovery and authenticated node communication.
- Added three-node Kubernetes verification, telemetry, chaos scenarios, image signing, SBOM output and a reproducible release path.
Failure modes and controls
- Deadlines constrain slow-node impact.
- etcd membership drives routing updates.
- Durable state supports restart recovery.
Delivery and operations
- Local Kubernetes lifecycle
- Temporary EKS lifecycle verified then destroyed
- mTLS, telemetry, image signing and SBOM
Engineering trade-offs
- Causal convergence supports available replicated state but is not a substitute for consensus semantics.
- Temporary cloud verification limits cost and permanence while preserving lifecycle evidence.
Discuss a similar project
Need a similar AI/ML system?
Share the problem, current system, data or model constraints and the delivery environment you have in mind.