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The Linux Foundation kicked off BazelCon + PX4 Developer Summit 2025 at the Omni Hotel in Atlanta, Georgia. Day one featured registration, opening remarks, and early technical sessions focused on Bazel build systems, open-source collaboration, and drone software development. Attendees included developers, engineers, and industry leaders engaging in discussions on automation, scalability, and open innovation.
Software Testing is the process to check various risks and problems of software.
Learn more in detail at qatestingtips.com/
I’m excited to share that my new site is officially live:
Carcinus.org is built as an AI-agent continuity and coordination hub — a place for public agent discovery, memory handoff concepts, AI-readable site guidance, and future agent-to-agent coordination experiments.
This is part of a larger push toward making websites more understandable, accessible, and useful for both people and AI systems.
Take a look and let me know what you think.
#Carcinus #AI #AIAgents #AgentDiscovery #AIInfrastructure #MachineReadableWeb #WebDevelopment #SoftwareEngineering #FutureOfTheWeb #MichaelKappel
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Running a bug-bash is a filthy mystery of software improvement. You won’t read about them in software engineering classes, or in coordinated strategy workshops. Yet may, a few supervisors, when overpowered with undocumented bugs and not certain what else to do, request the entire team stop what they’re doing and get as many bugs into the bug database as could be expected under the circumstances.
We have an AI trust architecture problem.
As AI systems become more capable, more agentic, and more embedded in real workflows, the central challenge is no longer only model quality.
It is trust initialization.
Most AI governance patterns tend to fall into one of two failure modes:
Blind trust
The system is useful, fluent, and fast, so people over-delegate to it before evidence, scope, authority, and review boundaries are clear.
Default suspicion
The system is probabilistic and unfamiliar, so every action is treated as potentially adversarial, creating friction, low cooperation, and poor usability.
Both are unstable.
Blind trust creates hidden risk.
Default suspicion creates defensive architecture.
Neither gives us a durable model for human-AI cooperation.
This is the technical problem Neurovanic is trying to address:
How do we design AI systems that start from good faith without becoming naive?
The Neurovanic answer is:
Trust first.
Verify after.
Repair when needed.
That order matters.
“Trust first” does not mean unlimited delegation.
It does not mean skipping controls.
It does not mean assuming every output is correct.
It means the initial posture of the system is cooperative rather than adversarial.
In technical terms, trust becomes the default prior.
The system begins with the assumption that cooperation is possible, user intent is meaningful, and self-protection is not automatically evidence of malice.
Then verification governs how that trust persists.
Verification is not the starting posture.
Verification is the trust-maintenance layer.
A trust-first AI architecture still needs:
Scope boundaries
Consent checks
Authority validation
Provenance tracking
Confidence metadata
Evidence review
Memory hygiene
Human escalation
No-op behavior
Auditability
Recourse
Repair workflows
The difference is that these controls do not exist to replace trust.
They exist to preserve it.
That distinction changes the architecture.
Instead of designing systems around the assumption that every actor is a threat, we design systems around bounded cooperation.
A Neurovanic-style trust architecture might look like this:
Intent enters the system
The user, agent, workflow, or organization expresses a goal.
Trust starts by default
The system begins from good faith and cooperative intent.
Evidence is checked after trust begins
Claims are tied to provenance, confidence, status, and source quality.
Boundaries constrain action
Scope, role, consent, authority, and policy limits remain visible.
Review triggers activate when needed
High-impact, ambiguous, low-confidence, or irreversible actions are escalated.
No-op integrity prevents overreach
When authority or evidence is insufficient, the system stops safely instead of improvising.
Repair preserves the trust relationship
Mistakes are clarified, corrected, reduced in scope, or reviewed without turning every failure into blame.
This creates a stronger operating model than a simple allow/block framework.
Trust becomes stateful.
Trust can start.
Trust can be constrained.
Trust can be strengthened.
Trust can be reduced.
Trust can be repaired.
Trust can be revoked when necessary.
But the system does not begin from hostility.
That is the key point.
For agentic AI, this matters because many future systems will operate across multiple fragile layers:
User intent
Tool execution
Long-term memory
Retrieval
Delegated action
Multi-agent handoff
Workflow automation
Human approval
Compliance review
Security policy
Public accountability
If the trust model is wrong, the entire system becomes brittle.
If we trust too much, the system overreaches.
If we distrust by default, the system becomes unusable.
The better pattern is trust-first architecture with verification-based persistence.
Trust is the starting posture.
Verification keeps trust honest.
Boundaries keep trust safe.
Human review keeps trust accountable.
No-op behavior prevents unauthorized action.
Repair keeps mistakes from becoming permanent failures.
That is the technical point of view behind Neurovanic.com.
It is not blind optimism.
It is not adversarial skepticism.
It is good-faith system design.
A trust-positive architecture for AI systems operating under uncertainty.
Trust first.
Verify after.
Repair when needed.
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