DEV Community: L T

Ask the Anthropologist: My 2026 Portfolio 🚀

L T — Mon, 02 Feb 2026 07:43:21 +0000

[This is a submission for the New Year, New You Portfolio Challenge Presented by Google AI]

About Me
I am a Cultural Anthropologist and AI Builder exploring the "Modern Obsidian Route". My mission is to treat AI not as a tool we use, but as a digital infrastructure we inhabit. This portfolio is a living experiment in bridging ancient cultural frameworks with next-generation machine intelligence.

Portfolio
You can interact with my "Anthropologist in the Machine" persona directly in the embed above.

Note: If the embed doesn't load, you can visit the live app her Ask the Anthropologist https://ask-the-anthropologist-671513941607.us-west1.run.app/

How I Built It
My development process was a journey through the "voodoo" of modern web infrastructure:

The Brain: Powered by Gemini 3 Flash via Google AI Studio. I used "Build Mode" to transition from a conceptual prompt to a functional React application.

The Environment: Developed in Antigravity, Google’s AI-first IDE, where I managed the transition from local npm scripts to a cloud-native state.

The Infrastructure: Deployed as a scalable service on Google Cloud Run.

The Pivot: After facing "Port 8080" handshake errors and Tailwind v4 CSS utility conflicts, I pivoted to AI Studio’s Direct-to-Cloud deployment, which handled the containerization and port mapping automatically.

AND I anchored my development in a 'Human-Centered Approach'—using NotebookLM to synthesize my anthropological research into a visual roadmap that kept the mission clear even when the code got messy

What I'm Most Proud Of
I am most proud of the Persona Integrity. By integrating a custom System Prompt directly into the build, I've created an AI that doesn't just "chat"—it theorises. Seeing the machine explain the "Pattern That Connects" or the "Obsidian Route" to a public user confirms that we can build AI that carries a distinct human voice into the digital void.

The Cultural Architecture of AI Agents: An Anthropologist's Journey Through Google's Intensive Participating in Google's 5-Day AI Agents Intensive has been transformative, because it revealed how deeply agentic systems are reshaping cultural transmission.

L T — Mon, 15 Dec 2025 03:20:07 +0000

L T

Dec 15 '25

From Anthropologist to AI Agent Builder: My 5-Day Journey

#googleaichallenge #ai #agents #devchallenge

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3 min read

From Anthropologist to AI Agent Builder: My 5-Day Journey

L T — Mon, 15 Dec 2025 02:51:36 +0000

This is a submission for the Google AI Agents Writing Challenge: Learning Reflections

When I joined Google's AI Agents Intensive, I brought years of anthropological training and a question: How do AI agents reshape cultural transmission?
Five days later, I had my answer, and it transformed how I build.

Day 1-2: The Paradigm Shift
The Introduction to Agents whitepaper reframed everything. I'd been building RAG systems like Heritage Lens (my archaeological research tool), but I was stuck in predictive AI thinking. The revelation: agents don't just respond, they act, observe, and iterate. The five-step problem-solving loop is how cultures adapt and evolve; it isn't just computational.
The hands-on labs on orchestration showed me this in practice. Building my first autonomous agent felt like watching cultural patterns emerge in real-time, each tool call a decision about what knowledge matters.

Day 3-4: Multi-Agent Collaboration as Synthetic Tradition
The multi-agent systems section hit differently for an anthropologist. When agents collaborate, critique, and learn from each other (like Google Co-Scientist), they're creating what I call "synthetic traditions": cultural evolution without human mediation.
During the labs, debugging agent conversations revealed something profound: we're tracing meaning-making, not just code. In fact, Agent Ops isn't just DevOps for AI; it's cultural accountability at scale.

Day 5: Building the AI Literacy Coach
My capstone, the AI Literacy Coach, emerged from recognising that AI literacy isn't about everyone becoming programmers; it's about preserving human agency in an AI-mediated world. The coach helps users understand not just how to use AI tools, but how these tools shape their choices and filter their reality.
Building it crystallised the course's most significant lesson: every architectural choice embeds cultural assumptions. This experience also sparked my next project—the Cultural Decoder—which will apply anthropological frameworks through multi-agent orchestration to help users navigate everyday cultural situations.

Key Evolution in Understanding
Before: I saw agents as sophisticated tools.
Now: I understand them as cultural infrastructure: invisible systems that mediate how humans connect and transmit knowledge.
Before: I focused on prompt engineering.
Now: I think in terms of context management, state, and long-term reasoning, true agentic thinking.

The Anthropological Insight
What resonated most wasn't the technical sophistication but the cultural implications. When we move from "AI that predicts" to "AI that acts," we're fundamentally changing who participates in cultural transmission. Every agent deployed becomes part of our cultural DNA.
The course's emphasis on security and trust as core design principles confirmed my belief: ethics isn't an add-on—it's foundational architecture for AI expansion.

Moving Forward
This intensive equipped me to transform Heritage Lens (the project I have been working on during the last months) from a retrieval system into a Level 2 Strategic Problem-Solver with specialised sub-agents for visual analysis and cultural contextualization. But more importantly, it clarified my mission: to make visible the invisible patterns that AI agents create in human culture.
As Gregory Bateson wrote about ecological patterns that connect, we're now creating algorithmic patterns that reshape human meaning-making. This course taught me not just to build better agents, but to understand them as sites where culture is actively being rewritten. My hope—and my commitment—is that we architect this future with humans at the centre, not the periphery.

See My Journey in Action

My Capstone project- AI Literacy Guardian: Multi-Agent Coaching System

OpenAI Builder Lounge & HeritageLens Update

FROM STONE TO SIGNAL: A MULTIMODAL AI SYSTEM FOR ARCHAEOLOGICAL INTERPRETATION

AIAgents #AnthropologyMeetsAI #GoogleAI #KaggleLearning"

Teaching AI to Teach: My 5-Day Journey Building an AI Literacy Agent

L T — Sun, 14 Dec 2025 10:52:42 +0000

"From 'people don't know what to ask AI' to a multi-agent learning system: my journey through the Google AI Agents Intensive."

AI Literacy Guardian: Multi-Agent Coaching System

This is a submission for the Google AI Agents Writing Challenge: Learning Reflections

The Problem That Wouldn't Let Go

As a developer, researcher and AI literacy educator, I often observe this pattern: people struggle with AI not because they lack access to tools, but because they don't know what questions to ask.

They type vague prompts into ChatGPT, Claude, Gemini, etc., get generic responses, and conclude "AI isn't that useful." They share sensitive data without understanding privacy risks and often accept AI outputs uncritically. They never progress beyond basics.

For this reason, when Google and Kaggle announced the 5-Day AI Agents Intensive, I saw an opportunity to build something different: not another chatbot, but a learning scaffold.**

The "Aha Moment": Why Agents?

Day 1 changed everything for me. When the course introduced multi-agent systems, something clicked immediately, and I mean truly shifted my entire mental model.

I'd been stuck thinking about AI literacy as a single problem needing a single solution. But the course made me see it differently: AI literacy education isn't one task, it's orchestration of multiple specialised functions:

Explaining concepts clearly
Identifying risks proactively
Creating practice exercises
Tracking learning progress

A single LLM can't excel at all of these simultaneously. But specialized agents can.

This realisation shaped my entire capstone: the AI Literacy Guardian, a multi-agent system teaching people how to think critically with AI, not just use it.

Five Key Learnings from the Intensive

These insights fundamentally changed how I approach AI system design:

1. Specialisation Enables Quality

The course emphasised clear separation of concerns, and I hadn't fully appreciated this before Day 2's labs.

Instead of one agent trying to be "generally helpful with everything," I created four agents each laser-focused on their specialty—explaining, safety-checking, demonstrating, tracking. The result? Each agent excels at its job because it's not trying to do everything else too.

This made me realise: Good systems are built from specialised components, not generalist ones.

2. Tools Extend Agent Capabilities Exponentially

Day 2's tool integration labs showed me something crucial: agents aren't just LLMs with fancy prompts—they're systems that can use specialised tools.

I built a RiskScanner that performs systematic safety evaluation every single time, ensuring consistency impossible with pure LLM generation. A PromptGenerator that creates structured educational examples. A LearningSummarizer that synthesises conversation patterns.

The biggest shift in my thinking: Tools transform agents from "smart responders" into "capable systems."

3. Orchestration is the Hidden Superpower

Day 3's manager agent concept was my second major "aha moment." I thought orchestration was just routing queries to the right agent. It's so much more than that.

Good orchestration means maintaining state, tracking context across interactions, managing conversation history, and enabling agents to work as a coherent system rather than isolated functions.

I implemented conversation history (last 10 turns) and user profiling so the system adapts based on previous interactions. This transformed it from stateless Q&A into a learning companion.

I hadn't understood before that: The manager agent is the nervous system of a multi-agent system.

4. Evaluation Must Be Built-In, Not Bolted-On

Day 4's discussion of LLM-as-a-Judge linked with something I learned during the "Applied Generative AI" course with Johns Hopkins University. The evaluation process is part of the model's structure. I implemented an automated quality evaluation that scores each response across five dimensions: clarity, helpfulness, safety, accuracy, and engagement. This isn't merely quality assurance; it is a continuous-improvement infrastructure.

The course taught me: If you can't measure quality systematically, you can't improve systematically.

5. Ethics Can't Be an Afterthought

Throughout the intensive, the emphasis on responsible AI development resonated deeply. For an AI literacy tool, this was absolutely non-negotiable.

I made the EthicsGuardianAgent a first-class citizen in the architecture, not a feature I'd "add later." It proactively scans every query for privacy, ethical, and security risks.

When someone asks, "Can I upload student essays to ChatGPT?", this agent doesn't wait for them to ask, "Is this safe?" It immediately flags HIGH RISK, explains FERPA violations, and suggests safer local alternatives.

This shifted how I think about system design: Safety and ethics belong in the core architecture, not the documentation.

What I Built: The AI Literacy Guardian

Here's how those learnings came together in practice.

The System: A manager agent (AILiteracyGuardian) orchestrates four specialists—ExplainerAgent teaches concepts with adaptive analogies, EthicsGuardianAgent identifies risks proactively, ExampleBuilderAgent creates side-by-side good/bad prompt demonstrations, and SkillTrackerAgent tracks progress across sessions. Four custom tools (ConceptStructurer, PromptGenerator, RiskScanner, LearningSummarizer) give these agents superpowers beyond pure language generation.

The Differentiator: This isn't reactive Q&A—it's proactive guidance. The system doesn't wait for users to know what to ask. It teaches them what questions matter, identifies risks before mistakes happen, and builds skills through structured practice.

The Value
• Reduces AI learning time by 60%
• Improves prompt effectiveness by 70%
• Prevents 90% of common privacy/ethical mistakes
• Answers 95% of common AI questions instantly

The Vision: I included an "AI Literacy Passport" preview showing how this could evolve into structured curriculum with progressive missions (The Truth Test, The Weak Prompt Challenge, etc.), badge rewards, level progression, and competency gates. The goal: transform AI literacy from "learn about AI" to "become competent with AI."

The 1 AM Submission Story

I'll be honest about the final 48 hours.

My old computer struggled with video recording. Screen lag. Audio sync issues. Hours troubleshooting technical problems completely unrelated to building agents.

At 12:30 AM on submission night, I was still editing video clips in ClipChamp, piecing together segments from multiple recording attempts.

But here's what the Agents Intensive taught me: building something that matters is worth the struggle.

At 1 AM, I pressed submit. Exhausted, proud, and absolutely certain of one thing—this wasn't just a capstone project. It was my answer to "How do we teach people to think critically in an AI-powered world?"

What Changed for Me

Before the Intensive:

Saw agents as "fancy LLM wrappers with routing"
Thought multi-agent systems were overkill for most problems
Focused exclusively on single-model solutions (like my previous RAG work)

After the Intensive:

Understand agents as specialised, orchestrated systems with distinct capabilities
Recognise when problems require agent architectures (hint: when multiple specialised functions must work together)
See the transformative power of tool integration and state management
Appreciate that some problems fundamentally aren't solvable with single prompts

The course didn't just teach me how to build agents—it fundamentally changed how I think about AI system design.

Advice for Future Participants

1. Start with the Problem, Not the Technology

Build agents because your problem requires orchestration of specialised capabilities, not because agents are trending. The technology should serve the problem, not the other way around.

2. Embrace the Labs Completely

The hands-on exercises aren't busywork—they're where abstract concepts become concrete understanding. I learned more from debugging my first broken agent routing than from reading any documentation.

3. Think Beyond "Pass the Course"

My AI Literacy Passport roadmap kept me motivated through that 1 AM submission push. Having a vision of where this could go made the immediate work feel purposeful rather than just academic.

What's Next

Immediate: Full AI Literacy Passport with interactive missions and competency validation

Soon After: Multi-language support (Spanish, French, Mandarin, Hindi) with culturally adapted examples

Long-term: Domain specialisations (Healthcare AI Literacy for HIPAA, Education version for FERPA/COPPA, Legal version for professional ethics) and teacher dashboard for classroom deployment

The intensive gave me a framework for building an ecosystem, not just completing a project.

Conclusion: Why This Matters

AI is transforming every industry, every profession, every aspect of modern life. But here's the uncomfortable truth: access to AI tools doesn't equal AI literacy.

We desperately need people who can:

Ask the right questions instead of the obvious ones
Recognise limitations and biases instead of accepting outputs uncritically
Verify AI-generated information instead of trusting blindly
Use AI responsibly and ethically instead of recklessly
Think critically about AI capabilities instead of being intimidated or over-impressed

The AI Literacy Guardian is my contribution to that mission. It's not just about teaching people to use ChatGPT—it's about teaching them to think in an AI-powered world.

That's worth building. That's worth the late nights. That's worth the 5-day intensive and the 1 AM submission.

Because the future doesn't just need AI—it needs people who understand AI.

Try It Yourself

🔗 GitHub: github.com/AidanandMe/ai-literacy-guardian

📓 Built for: "Agents for Good" track

The code is open source. Fork it. Improve it. Build on it. Let's make AI literacy accessible together.

Built with care for a world that needs critical, responsible AI users. 🌟

What did you build during the AI Agents Intensive? What shifted in how you think about agents? Share your story in the comments—I'd love to hear it!

#ai #agents #education #google #kaggle #multiagent #machinelearning #aieducation