A documented year of deep exploration: building prototypes, studying the AI stack, and thinking seriously about how this technology transforms how we work and organize.
As I learn more, the nodes in the digital brain grows.
As a sociologist, economist, and on-leave manager in the public service, I am drawn to the enormous transformational potential of AI — as a normal technology. Throughout my career, I have been involved in various projects studying the trends and impacts of technology on society and the economy from a statistical and research perspective. For the next year, I will be shifting my focus towards deep exploration of the capabilities, possibilities, and risks of this quickly evolving technology. I want to be able to not only understand the ecosystem that enables this technology, but also to be able to fluently use and architect realistic business solutions with it.
Over the next year (and probably longer!), I will be building functional prototypes, experimenting with new workflows, and thinking deeply about how AI can transform organizational strategy and operations. My work will likely fall along several buckets:
As a non-IT professional, there is a lot for me to learn about the technical ecosystem in which AI technologies are embedded. To do this, I am diving deep into the end-to-end AI stack, including but not limited to:
I want to find that middle-ground where I can contribute ambitious, innovative ideas and solutions that are well-thought-out and grounded in technical understanding.
This diagram maps how I currently experiment and develop prototypes — the autonomous vs. manual aspects of the decision-making, execution, and delivery parts of my workflow.
%%{init: {"securityLevel": "loose", "flowchart": {"padding": 20, "nodeSpacing": 50, "rankSpacing": 70}, "theme": "dark"}}%%
flowchart LR
IDEA["💡 Ideas & Research"]
DECIDE["🧠 Decide"]
EXECUTE["🛠️ Build with AI"]
DELIVER["🚀 Ship & Test"]
IDEA --> DECIDE --> EXECUTE --> DELIVER
DELIVER -.->|feedback loops back| IDEA
linkStyle default stroke:#D9B08C,stroke-width:2px;
class IDEA,DECIDE,EXECUTE,DELIVER brand
%%{init: {"securityLevel": "loose", "flowchart": {"padding": 30, "rankSpacing": 80, "nodeSpacing": 60}, "theme": "dark"}}%%
flowchart TD
subgraph DECIDE["1. Decision making"]
subgraph STREAM1[Stream A - Direct Human Prompting]
H[Human with\nDomain Knowledge]
end
subgraph STREAM2[Stream B - Autonomous Idea Pipeline]
M[(Obsidian Vault)]
N[Daily Review Agent]
M -->|Daily scheduled review| N
end
R[Research and Analysis]
R -->|Informs and shapes| H
R -->|Captures insights| M
H -->|Direct prompt| O
H ~~~ N
N -.->|"Suggested prompt"| H
O[Antigravity AI]
end
subgraph EXECUTE["2. Execution"]
UI[System and UI Design]
DM[Data Management]
MCP[Model Context Protocol]
subgraph EXISTING[Handled by Existing Systems]
LLM[LLM APIs and Orchestration]
SEC[Security and Optimization]
end
end
UI --> LLM
UI --> DM
UI --> MCP
DM --> LLM
MCP --> LLM
LLM --> SEC
subgraph DELIVER["3. Delivery"]
G[Local Testing and Debugging]
DP{Deployment}
I[Vercel and Netlify]
J[Hugging Face Spaces]
K[GitHub Codebase / CI]
P[Portfolio Site Showcase]
W[Web3Forms Contact]
L[Iterative Feedback]
G --> DP
DP -->|Web Apps & UIs| I
DP -->|AI Models & Python Backends| J
DP -->|All Source Code| K
DP -->|Curated Highlights| P
P --> W
I --> L
J --> L
K --> L
P --> L
W --> L
end
DECIDE ~~~ EXECUTE
EXECUTE ~~~ DELIVER
O -->|Scaffold and generate code| UI
SEC --> G
R <-->|"Refine direction"| L
M <-->|"Capture new ideas"| L
H <-->|"Direct follow-up"| L
H <-->|"Test results"| G
%% Interactive Tooltips and Clicks
click MCP "javascript:void(0)" "Example Project: statcan_mcp"
%% Classes are now styled via external CSS in style.css for Light/Dark mode support
class R,H human
class M obsidian
class N agent
class O ai
class UI,DM,MCP execute
class LLM,SEC existing
class G,DP,I,J,K,P,W deliver
class L feedback
A living log of milestones, prototypes, self-study, and reflections. I'll keep adding entries as the journey unfolds.