My own AI stack
Most people still use AI one prompt at a time.
Open ChatGPT. Explain the problem. Paste some context. Get an answer. Copy it somewhere. Close the tab.
Next day, do it again.
That was me for a long time. The answers were useful, but the process was still annoying. Every session started from zero, and I was the one carrying the context.
This works for small tasks.
It breaks down for real data science work, because real DS work has memory.
You need to remember:
→ which table has the correct metric
→ which stakeholder asked for which cut
→ which analysis version was trusted
→ which caveat matters
→ which meeting changed the direction
→ which experiment had logging issues
→ which dashboard is outdated but still used by leadership.
A lot of data science is not writing code. It is carrying context.
So my goal became simple:
Stop re-explaining my work to AI.
Below is the setup I use across personal and work projects.
1. Second Brain — the foundation
The most important part of my setup is a structured workspace.
I have seen many people and companies already call this kind of system a Second Brain, so why not keep the same name?
It is a set of files that describe my active projects, stakeholders, metric definitions, useful links, decisions, notes, and recurring workflows.
I organize it roughly using PARA:
Projects
Areas
Resources
Archives
It lives in Google Drive, so it survives across machines and sessions. The key part: my AI coding assistant can read it when I start working.
So instead of saying:
Here is the background, here are the tables, here is the metric definition, here is what happened last week
I can say:
Continue the campaign performance analysis. Check if the WoW change still holds after excluding market B.
That is a very different interaction. The AI is no longer starting from a blank page.
What I keep inside
For each active project, I usually keep:
project goal
current status
main stakeholders
important links
table names
metric definitions
known caveats
decisions already made
open questions
next steps
This is boring.
Boring is good.
Most productivity systems fail because they try to be too clever. This one works because it is just structured context.
The compounding effect is real. Every time I save a note, capture a decision, or update a project file, the next session gets better. After a few weeks of feeding it, you stop noticing the setup cost — you just describe what you need and it happens.
One thing I got wrong early: I kept stuffing everything into one big context file. It grew to 700+ lines and actually made the agent slower and less focused. What works better is a layered approach — a slim top-level file that points to deeper context, loaded only when relevant.
2. Claude Code — the hands
Second Brain is the memory. Claude Code is the hands.
For me as a data scientist, this means: writing and iterating on SQL, building analysis scripts, generating reports, editing documents, creating presentations. It’s not a chatbot — it’s a coding partner that lives in my terminal and can reach into my file system.
The way I use it most: I’ll describe an analysis I want to run — say, “I need to understand the week-over-week trend in campaign performance across these three markets, broken down by format” — and Claude Code will write the query, run it, iterate if something looks off, and produce a structured output. I steer, it executes.
Where this really shines is when the analysis isn’t a one-liner. Anything that involves multiple tables, joins, validation steps, or formatting into a deliverable — that’s where the autonomous loop saves me hours. I used to spend a full afternoon on what’s now a 20-minute conversation.
I also use it for things that aren’t strictly “data science” — writing documentation, drafting comms, structuring my thoughts into something coherent. It’s become the default interface for anything that requires sustained thinking plus output.
3. Wispr Flow — capturing thoughts before they disappear
This one sounds trivial but genuinely changed my workflow.
Wispr Flow is voice-to-text.
I speak, it writes.
Why does a data scientist need voice-to-text? Because my best thinking doesn’t happen at the keyboard.
I use Wispr to:
Save thoughts in transit. Walking between meetings, on the tube, making coffee — ideas come and I used to lose them. Now I speak them into a note and they’re captured. I have a running “thoughts” doc that I periodically feed back into my Second Brain.
Write richer prompts. When I’m describing a complex analysis to Claude, speaking naturally produces way more context than typing. I’ll ramble for 30 seconds about what I actually want, and the prompt comes out better than anything I’d type.
Draft first versions of documents. Instead of staring at a blank page, I talk through what I want to say. The structure is messy, but the ideas are there — then Claude cleans it up.
Process meetings in real-time. Right after a meeting, while context is fresh, I’ll dictate my takeaways and action items. Thirty seconds of talking captures what would otherwise evaporate by the time I sit back down.
The speed difference matters more than you’d think. Speaking is 3-4x faster than typing, and for some reason it’s also more honest — I say what I actually think instead of self-editing as I type.
4. MyClaw — the always-on watcher
MyClaw is my always-on agent. It monitors things I care about and surfaces them without me having to go looking.
The setup: I’ve configured it to watch specific chat channels, track mentions of topics I care about, and generate periodic summaries. Think of it as a personalized news feed for my work context — but instead of me scrolling through hundreds of messages, it distills what matters.
I get:
A daily digest of what happened in key channels while I was offline or in meetings
Alerts when specific topics come up (project names, metric names, stakeholder names)
Weekly summaries that help me draft my own updates
It’s not glamorous. It’s not even particularly “smart.” But it solves a real problem: I was spending 30-40 minutes a day just reading to stay in the loop. Now the staying-in-the-loop happens in the background, and I get a 2-minute summary instead.
5. NotebookLM — the research partner for messy, multi-document problems
NotebookLM is Google’s tool for reasoning over a collection of documents. You upload sources — PDFs, docs, web pages — and it lets you ask questions across them, synthesize, compare, find contradictions.
I use it for two very different purposes:
Work: synthesizing domain research. When I’m scoping a new analysis area or trying to understand a complex business question, I’ll throw 10-20 relevant documents into NotebookLM — past analyses, strategy docs, meeting notes, industry reports — and have it build me a structured understanding. It finds connections I’d miss reading sequentially, and it cites everything so I can verify.
Personal: my Global Talent visa application. This is the use case that sold me on the tool completely. The UK Global Talent visa requires you to demonstrate “exceptional talent or exceptional promise” in your field. The application involves pulling together evidence from multiple sources — publications, reference letters, proof of impact, immigration guidelines, successful application examples.
I uploaded everything:
→ the official government guidance
→ successful application examples I found
→ my own CV
→ reference letter drafts
→ proof of work documents
→ relevant immigration forum threads.
Then I used NotebookLM as my research partner — asking it things like “what evidence do I have that maps to criterion X?”, “what gaps exist in my application compared to successful examples?”, “how should I frame this achievement in the context of the guidance?”
It’s essentially a research assistant that holds a hell of a context at once and lets you have a conversation with it. For any multi-document problem where the answer lives between documents rather than in any single one, it’s an awesome tool.
6. Zapier / n8n — the glue and the automations
The last piece is automation. Zapier (and more recently n8n for things I want more control over) connects everything together and handles the recurring stuff.
