Giorgos Siamantas | Activities
earned:
A testerās guide to AI guardrails
thanked contributors on:
You cannot claim a system is or uses "Responsible AI" just because there is a human button-pusher. If the system is designed to move faster than that human can think, how do you prevent your busine...
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Human in the Loop (HITL) wanted...
awarded Aj Wilson for:
Human in the Loop (HITL) wanted...
awarded Damian Pereira for:
Fascinating to see how AI evals can be used for an API test automation agent.Ā I got some new insights from the Masterclass š
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Evals in practice for an AI coding agent
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Integrate monitoring, observability, and alerting into the core quality engineering process to ensure systems are as diagnosable as they are functional
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The forgotten part of quality: paying attention to production
awarded Stuart Thomas for:
The forgotten part of quality: paying attention to production
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How to think like a strategic genius (5d thinking)
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This badge is awarded to members who create their first collection, organising learning content for themselves or others.
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A testerās guide to AI guardrails
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Identify, test and improve AI guardrails through a structured, scenario-based framework that addresses common implementation failures and attack patterns.
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Claude Code Best Practices - a collection to bookmark
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Harness engineering: leveraging Codex in an agent-first world
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An AI state of the union: Weāve passed the inflection point & dark factories are coming
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Joined AI Chapter chapter
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Question by Rosie Sherry: Ā How can we practice testability within context engineering? Response:Fantastic question. Not many people talk about it (unfortunately).Good news: We at MoT are now ...
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AMA Answer: How can we practice testability within context engineering?
awarded Rahul Parwal for:
AMA Answer: How can we practice testability within context engineering?
awarded Rosie Sherry for:
AMA Answer: How can we practice testability within context engineering?
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This badge is awarded to members who read their first Insights article on the MoTaverse.
awarded James Pearce for:
The problem with non-deterministic inputs and outputs
awarded Simon Tomes for:
The problem with non-deterministic inputs and outputs
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The problem with non-deterministic inputs and outputs
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How do AI guardrails deal with millions of possibilities?
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The problem with non-deterministic inputs and outputs
awarded James Pearce for:
The problem with non-deterministic inputs and outputs
awarded Simon Tomes for:
The problem with non-deterministic inputs and outputs
awarded Dragan Spiridonov for:
Great, actionable information about agentic QE
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Agentic QE: From AI Assistant to AI Workforce
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Shift from treating AI as a "single source of truth" to using it as a "clarity booster" for human-led ideas.
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Stop delegating your thinking: Using 5 whys to check AI documentation
awarded Cristina Lanca Carrageis for:
Stop delegating your thinking: Using 5 whys to check AI documentation
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We have been collecting data from Moltbook
awarded Beren Van Daele for:
For the RiskStorming: AI - very good to see how this can work with the AI element in
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RiskStorming: Artificial Intelligence
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Evals in practice for an AI coding agent
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Master this practical framework for building a layered evaluation strategy to systematically verify, secure, and improve the output quality of AI coding agents using both deterministic and model-grade
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Nano Banana 2: Combining Pro capabilities with lightning-fast speed
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We can test in production: An introduction to shifting testing right.
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Study real-world failures to understand how quality is created, lost and strengthened in complex systems.
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How quality is created, maintained and lost in complex software systems
awarded Jitesh Gosai for:
How quality is created, maintained and lost in complex software systems
awarded Jitesh Gosai for:
How quality is created, maintained and lost in complex software systems
earned:
How quality is created, maintained and lost in complex software systems
is Open to Review Conference Proposals
is Open to Write
Giorgos Siamantas
Senior Quality Engineer
He/Him
10+ years in quality engineering for complex systems. Test strategy, failure modes, testability.
Exploratory testing for surprises, automation for predictable risks.
Systems thinking in uncertainty.
Open To
Work
Mentor
Write
Review Conference Proposals
Meet at MoTaCon 2026
