Two MoTacon attendees are on the left. The MoTaacon logo is in the center, and to the right a prompt to Get Your Ticket.

Simon Tomes

Simon Tomes profile image
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
Thoughts before MoTaCon: Into hell and back again image
Continuing with an experiment. Let's look at the talk and workshop descriptions of MoTaCon 2026 and share my thoughts and reflections before the talk/workshop has happened. And then, hopefully, sh...
11 Sep
Have you played the call out the elephant game? image
"We often end up discovering that the elephant in the room is just a mouse." – Susanne Abdelrahman I heard an excellent story from Susanne that reminded me that we have to be brave when asking ques...
10 Sep
Small things are worth sharing image
During a Call for Insights with Simon Tomes, continuous learning came up as a broad topic. One thread stood out: sharing. So here's a share about sharing. If something you read, learned, or stumble...
10 Sep
Community as a water park image
I stood patiently in a queue at the highest point of the water park. Music blared out as several people spoke loudly in multiple languages as anticipation grew. I reflected on how many people had p...
7 Sep
Anything is a lesson: why continuous curiosity matters image
The key to continuous learning is continuous curiosity
11 Sep
How we overhauled the MoTaverse profile page image
Using the GPS Model to deliver software with quality and care
25 Aug
Community as a service: worth saying, worth repeating image
Discover why community as a service deserves saying out loud, covering paid membership, monetisation, and shifting community trends.
10 Sep
Curious but sceptical: exploring AI as an engineering manager image
Explore how AI can support your decisions, research, and communication as an engineering manager, without replacing human connection
21 Aug
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. GSD (Get Stuff Done) Definition: An open-source, spec-driven development system for AI coding agents, focused on context engineering to keep an agent on track across long or multi-session builds. It sits on top of an existing AI coding tool (such as Claude Code) and adds a repeatable phase loop, specialist subagents, and persistent planning artefacts, rather than replacing the editor or assistant.So what? Long agentic coding sessions tend to suffer from "context rot", where output quality drops as the context window fills with accumulated discussion; GSD is aimed at managing that so work stays reliable over time.Example: Running planning and execution steps through fresh-context subagents, with state files that persist across a context reset so the agent does not lose track of requirements partway through a build. The GPS Model The GPS ModelOne approach to quality is not enough, the combination of a multitude of approaches that revealed layers of quality and care: Group work Pair work Solo work
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