Showing posts with label IA. Show all posts
Showing posts with label IA. Show all posts

Tuesday, December 31, 2024

Unraveling My Thought Journey: Discoveries from a Multi-Year Twitter Log via LLM

These days, it's common to reflect on the past year, but instead of doing that, I decided to try something different: an experiment. As I'm transitioning from Twitter (yes, I don’t think I’ll ever call it X) to Bluesky, I took the opportunity to back up all my tweets since I joined Twitter in 2007. My goal? To experiment and see if I could uncover anything interesting using a large language model (LLM).

After a bit of cleanup, I organized my tweets into two files: one in chronological order and another sorted by engagement (likes + retweets). I then uploaded both files to Google's NotebookLM and started testing various prompts to see what insights I could extract.

I hypothesized that with tweets spanning back to 2007, it should be possible to trace the evolution of my thinking. To my delight, the results exceeded my expectations.

I’ll share the outcome of the experiment here:

My Thinking Evolution Based on My Tweets

Here is a summary of my thinking evolution, as reflected in my tweets:

Key Themes:

  • Technical Exploration: I am passionate about technology and have always enjoyed exploring new tools and techniques. My early tweets reflect this, as I often share links to interesting resources and discuss my experiences with various programming languages and technologies.
  • Python Enthusiast: I have a particular fondness for Python, which has been a constant throughout my tweets. I frequently share Python-related content and participate in Python communities.
  • Shift Towards Agile and Lean: Over time, my focus has expanded beyond technical skills to encompass broader software development principles. I have become increasingly interested in Agile methodologies, DevOps practices, and Lean Software Development. This is evident in my tweets, as I often share content related to these topics and discuss my experiences applying them.
  • Focus on Software Craftsmanship: I value writing high-quality, maintainable code. My tweets often reflect my interest in topics like TDD, refactoring, and clean code. I believe in continuous improvement and strive to become a better software developer.
  • Lifelong Learning: I am a firm believer in lifelong learning and am always eager to expand my knowledge. My tweets often feature links to interesting talks, podcasts, and books that I have found valuable.
  • Social and Personal Insights: While technology is a major focus, my tweets also offer glimpses into my personal life and social views. I occasionally comment on current events, share personal anecdotes, and reflect on work-life balance.


My Thinking Evolution:

  • Early Exploration (2009-2010): In the early days of my Twitter journey, my focus was primarily on exploring various technologies and tools. I was experimenting with different programming languages and approaches, seeking efficient solutions to technical challenges. My tweets from this period reveal a hands-on, problem-solving mindset.
  • Embracing Agile (2011-2013): As I gained more experience, I began to recognize the limitations of purely technical approaches to software development. I became increasingly drawn to Agile methodologies, seeing their potential to foster collaboration, iterative development, and value-driven delivery. My tweets from this period reflect my growing interest in Scrum, TDD, and related practices.
  • Expanding to DevOps and Lean (2014-Present): My understanding of software development continued to evolve, encompassing DevOps practices and Lean Software Development principles. I became fascinated by the idea of streamlining the entire software development lifecycle, from coding to deployment and operations. My tweets from this period show my enthusiasm for topics like continuous delivery, automation, flow, and minimizing waste.
  • Focus on Outcomes and Impact (Recent Years): In recent years, my perspective has matured to prioritize the outcomes and impact of software development over simply churning out features. I am more conscious of the need to deliver real value to users and businesses, minimize waste, and focus on building the right things. This shift in thinking is apparent in my tweets, which often emphasize the importance of user-centric design, data-driven decision-making, and measuring the impact of our work.


Overall, my thinking evolution, as documented in my tweets, demonstrates a journey from a technically focused programmer to a more well-rounded software development professional with a strong grasp of Agile, DevOps, and Lean principles. I am passionate about continuous learning, striving for excellence in my craft, and ultimately, delivering valuable and impactful software.



Friday, November 01, 2024

Product Engineering in the AI Era

When developing a product, we must ask ourselves what value we contribute if the roles of Engineering or Development are merely seen as implementers. What is the extent of this value contribution? Furthermore, how will this contribution change now that AI will assist us with a significant portion of this work?

Let's refer to the insightful diagram created by John Cutler, which classifies the different types of teams from Waterfall to Product teams.



It's straightforward to discuss the value each type of team contributes to the business. From a business perspective, Waterfall teams or "Agile teams capable of releasing" add limited value and are primarily optimized for software creation (not necessarily for creating impact). 

In contrast, Product Teams are designed for end-to-end impact and maximizing business value.

Considering that AI will automate a considerable amount of the more routine and specialized tasks, it's clear to see the potential impact on these teams.

Teams focused on creating impact, which view software as a means to an end, will be significantly accelerated by AI, while those that are merely "implementers" may find themselves replaced. 

In this context, the importance of product teams and product engineers becomes increasingly evident.

Why should we focus solely on technology and software, when we could instead concentrate on solving business and user problems?