Local-first No cloud dependency No proof, no execution Ghost Protocol Offline-First PWA

We are done trusting AI outputs.

VibeKLR is not a chatbot, a copilot, or a creativity tool. It is a verification-first execution system for AI-assisted work.

If an action cannot be proven safe, correct, and authorized, it does not execute. There are no exceptions.

The problem

Modern AI systems are probabilistic. Enterprises deploy them as if they were deterministic.

This mismatch has created a growing liability gap across software, finance, healthcare, infrastructure, and government.

Hallucinated code, fabricated citations, unsafe automation, and unverifiable decision paths are not edge cases. They are structural failures.

What VibeKLR does

• Intercepts AI-proposed actions before execution
• Classifies each action by side-effect risk
• Blocks irreversible actions without human authorization
• Enforces logic, schema, and invariant verification
• Produces immutable audit artifacts for every decision

Unknown or ambiguous actions default to stop.

Execution model

• Epistemic — read-only, auto-approved
• Mutable — reversible, logged
• Effective — irreversible, human-gated

This is enforced in code, not policy documents.

Execution roadmap

This roadmap reflects committed execution phases. Each phase advances only if its verification gates are met.

Privacy by construction, not by promise

VibeKLR does not require surveillance to increase safety.
The system becomes less invasive as safety increases.

This is possible because we verify actions, not people.

Technical mechanism: How this actually works

1. Action-Centric Security (Not Identity-Centric)

Most systems ask: "Who is doing this?"

We ask: "What would happen if this executes?"

Every proposed action is classified into exactly one category:

• Epistemic – read-only, no state change
• Mutable – reversible state change
• Effective – irreversible state change

If an action is Effective, it is blocked by default. This is enforced in code.

We don't need to monitor users continuously if the system cannot execute irreversible actions silently.

2. Zero-Knowledge Authorization

When human approval is required, the system does not store:

• the human's identity
• their history
• their behavior profile

Instead, it stores a cryptographic proof that:

• a valid authorization existed
• at a specific time
• for a specific action hash

"Prove that authorization occurred, without revealing who authorized or why."

3. Audit Without Surveillance

The audit log records: action hash, classification result, gate decision, proof reference.

It does not record: keystrokes, content of private communications, user intent beyond the formal action contract.

Auditors can verify correctness of enforcement without inspecting human behavior.

4. Local-First Execution

The system is designed to function offline, air-gapped, without cloud dependency.

If the system can't phone home, it can't spy.

Cited technical foundations

Zero-Knowledge Proofs

Goldwasser, S., Micali, S., & Rackoff, C. (1985). "The Knowledge Complexity of Interactive Proof Systems." SIAM Journal on Computing, 18(1), 186-208.

Used in production: Zcash, zk-SNARK compliance systems, privacy-preserving authentication.

Proof-Carrying Code

Necula, G. C. (1997). "Proof-Carrying Code." Proceedings of the 24th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages.

Standard in avionics, medical devices, and safety-critical systems.

Capability-Based Security

Dennis, J. B., & Van Horn, E. C. (1966). "Programming Semantics for Multiprogrammed Computations." Communications of the ACM, 9(3), 143-155.

Foundation for object-capability systems, used in Chromium sandboxing, seL4, and modern isolation architectures.

Local-First Computing

Kleppmann, M., et al. (2019). "Local-First Software: You Own Your Data, in Spite of the Cloud." Proceedings of the 2019 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software.

Join the mission

We are building infrastructure for the next 20 years of safe AI execution. This is not a startup hobby. This is a long-term commitment to accountability.

Ways to help right now

• ⭐
  Star the repo
  Signal matters. Stars help discoverability.
• 📣
  Share with your network
  Engineers, compliance officers, anyone tired of AI theater.
• 🔍
  Try to break it
  Report edge cases, logic holes, or bypasses. We pay bounties.
• 📝
  Write about it
  Blog posts, threads, critiques. Honest coverage helps.
• 🏛️
  Connect us with regulated industries
  Healthcare, finance, government, defense, justice system. They need this.
• 🛠️
  Contribute expertise
  Code review, formal verification, documentation, security audits, domain knowledge.

Execution protocols

VibeKLR supports multiple execution protocols, each optimized for different requirements. Select based on your privacy, speed, and verification needs.

Workforce development & re-entry programs

We believe verification is a learnable skill, and meaningful work reduces recidivism more reliably than punishment.

We are building training pipelines for:

• Intent modeling
• Data curation and labeling
• Verification review
• Audit analysis

This is not charity. It is capacity building.

Current partners include workforce development organizations and re-entry programs in Oregon and beyond. If you work in this space, reach out.

What we will not do

• Sell user data
• Run opaque cloud-only systems
• Deploy unverifiable automation into critical workflows
• Trade safety for speed
• Pretend probabilistic output is "good enough"
• Expand surveillance under the guise of safety

If that is what you are looking for, this is not the right project.