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Simon Tomes

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Simon Tomes
Community Lead at MoTaverse
he/him

Hello, I'm Simon. Since 2003 I've had various roles in testing, tech leadership and coaching. I believe in the power of collaboration, creativity and community. 🎓 MoT-STEC qualified. I get real joy working with and supporting our wonderful MoTaverse community.

“Thanks for making me feel so confident about myself.” | “I'm so grateful for all you do in the MoTaverse community, I learn so much as a result!” | “A simple chat with Simon in person absolutely changed everything.” | “Simon is one of the best people that I've ever met to have conversations with. He's so grounded in his way of thinking and articulates his thoughts so well and his feelings and his knowledge about community and everything.”

🎂 MoTaBirthday | October 24, 2016
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MoTaverse Team
Attending MoTaCon 🤝
Chapter Lead
Bug is flying! image
After a very successful day at MoTaCon, Bug could not contain they're happiness!Did Bug gain wings? Did the riding star gained power, or did Bug magically developed human legs? 🤔In my opinion, Bug ...
9 Oct
Good people reflect on good times at MoTaCon festival week image
It's important to gather after something special like MoTaCon festival week. It's good to reflect and just to have a chat about things.I have no other key learnings to share. That is all.Good peopl...
9 Oct
Leading with attention to event running details image
30/09/26, 7:35am. It's early morning and a few of us are prepping the room for the Leading With Quality day. We're working out how to use the amplifier in case we need to use the lectern microphone...
8 Oct
A prompt paints a thousand words image
I decided to take on Ady's AI challenge to ask about myself and the MoTaverse.https://www.ministryoftesting.com/moments/ask-ai-about-you-and-mot-challengeI do have to say I am still trying to work ...
7 Oct
Leading With Quality - Slides & Resources image
Grab the slides and resources from all the sessions at Leading With Quality 2026
6 Oct
A GROW model for AI adoption image
Four mindsets to guide your AI strategy and implementation
24 Sep
Community stars shine at MoTaCon festival, with moments to remember image
A post-MoTaCon reflection episode celebrating the festival week’s workshops, talks, volunteering, community connections, Moments, stars, swag... and many unexpected memories made in Brighton.
9 Oct
But I'm not a developer: the quality route into head of engineering image
Follow one quality leader's move from QE into head of engineering, and find the mission and principles that can guide you too.
8 Oct
Edge case Definition: An edge case is a scenario that sits at or just beyond the limits of how a system is expected to be used, where behaviour can tip from expected to unexpected. It often involves extreme inputs, an unusual state a user reaches by stepping outside the designed path, or a condition rare enough to be easy to overlook. Where the limit is clearly specified, the edge is easy to pin down. Where it is fuzzy, such as how much load a system can take before it struggles, part of the work is finding where the edge actually lies. So what? The label is used loosely, so it helps to be clear which meaning is in play: one person's "edge case" is another's boundary, corner case, or plain bug. Naming something an edge case is usually the start of a risk conversation, covering how likely it is, what happens if it occurs, and whether it is worth handling now or accepting. Spotting the ones that matter tends to draw on domain knowledge and creative thinking rather than a fixed checklist. Examples: A user reaching an application state the designed workflow never intended, by combining actions in an order no one anticipated. A field accepting an age from 18 to 65 behaving differently at 17, 18, 65 and 66. A system specified for 200 connected devices being asked to handle 600 after a customer grows faster than planned. Contextual Practice Contextual practice is an antonym of best practice. Best practice would suggest there's only one best way to achieve an outcome, goal or something. On the other hand, contextual practice welcomes approaches from several different sources and chooses what to do with that information for its own particular context (situation). Contextual practice attempts to be aware of its biases and seeks to only be inspired by other so called best practices yet isn't ruled by them.For example, rather than adopting the test automation pyramid by default, a team building a content-heavy website draws on several testing models and its own incident history, then weights its tests towards integration and visual checks because that's where its real risks lie. Read-Eval-Print Loop (REPL) Definition: An interactive programming environment that reads a single input, evaluates it, prints the result, then loops back to wait for the next input. It lets you run code a piece at a time rather than compiling and running a whole program.So what? A REPL gives immediate feedback, which makes it handy for exploratory coding, quick debugging, and confirming how a snippet behaves before committing to it.Examples: Typing node in a terminal to open the Node.js REPL and running expressions line by line; using Python's interactive shell to try a function before adding it to a test. Spec-Kit Definition: An open-source toolkit from GitHub for spec-driven development with AI coding agents. It provides a CLI, templates and prompts that move work through defined phases (specify, plan, tasks, then implement), treating the specification as the source of truth that the AI builds against. It is tool-agnostic and works with agents such as GitHub Copilot, Claude Code and Gemini CLI.So what? It aims to move teams beyond ad-hoc "vibe coding" towards a repeatable, verifiable workflow, so an AI agent builds what was intended rather than a generic solution based on common patterns.Example: Using slash commands such as /specify, /plan and /tasks to turn a written specification into a technical plan and a set of small, testable tasks for an AI agent to implement. BMAD (Breakthrough Method for Agile AI-Driven Development) Definition: An open-source framework for building software with AI, using specialised agent personas (such as Analyst, Product Manager, Architect, Developer and QA) that work through structured, phase-by-phase workflows mirroring a real engineering team. It defines what each agent does and what artefacts they produce, and can sit alongside existing agile approaches like Scrum or Kanban rather than replacing them.So what? It brings agile structure and documentation to AI-assisted development so outputs are more repeatable and context carries forward between phases, addressing a common failure point where intent is lost across a long conversation.Example: A planning phase produces specifications that are then split into self-contained story files, each carrying the requirements, constraints and acceptance criteria a Developer or QA agent needs to complete that unit of work.
08 Oct
asos
social-engineering
Claude’s Constitution (www.anthropic.com)
03 Oct
claude
ai-ethics
03 Oct
microsoft
humanist-ai
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