I would like to take this chance to thank all the organizers who helped to host this amazing event. Thank you very much the MoT Team! 
Looking back at Day 1, I only knew very limited vocab about AI in testing - AI, ChatGPT, Google Gemini, Prompt - that’s all. But going through the whole month, although I could not have enough capacity to participate in all challenges, it is really a great journey for myself following the 30 days chain of thoughts of AI in testing.
-
The heat on the first few days was super strong and encouraging. Everyone’s contribution really helped build the learning environment and push me to dedicate daily time to study on the topic.
-
@poojitha-chandra @adrianjr Thank you for the tag! I also appreciate you and a lot of the other enthusiastic fellows giving so many likes and replies to each other’s posts. The positive energy is so nice
-
Having the chance to collaborate with @parwalrahul is a very new experience for me, as I am a super introvert who never try to meet new people
It feels like having a buddy oversea to learn things about testing together. The mind maps, YouTube videos and daily blogs are also very good sharing. I am so glad to meet you through this event 
There were a lot of insightful comments which I could not tag all of them here. But I would like to highlight some of them below:
- @nao sharing some practical examples of LLM usage 🤖 Day 9: Evaluate prompt quality and try to improve it - #5 by nao 🤖 Day 14: Generate AI test code and share your experience - #21 by nao
- @billmatthews sharing how to use LlamaFile to setup your local LLM 🤖 Day 11: Generate test data using AI and evaluate its efficacy - #46 by billmatthews
- @p.schrijver sharing his view comparing different tools related to self-healing test 🤖 Day 20: Learn about AI self-healing tests and evaluate how effective they are - #16 by p.schrijver
- @lisboalien sharing her experience on some bug reporting tools 🤖 Day 17: Automate bug reporting with AI and share your process and evaluation - #6 by lisboalien
- @mirekdlugosz thoughts on carbon footprint in some different dimensions 🤖 Day 26: Investigate strategies to minimise the carbon footprint of AI in testing - #7 by mirekdlugosz
- @manojk the hardworking researcher - I specifically like his AI in Testing Champion Proposal with the suggested % of resources allocation 🤖 Day 13: Develop a testing approach and become an AI in testing champion! - #6 by manojk
and more…
One interesting thing is that I see many of us also use LLM tools to assist in drafting the task replies or doing the research. This practice itself means a lot, of how we as a group of collaborative testers really adopting AI in the field.