GerardableA space for active experimentation, where strategic theory meets production-ready code.
// Technical, data-driven, and transparent.
// Think "Developer Blog" meets "Architect’s Blueprint."
Explra.com: Architecting an AI-Native Travel Planner.
The Problem
Designing a seamless, generative experience for complex travel itineraries that feels personal and accurate, moving beyond generic listicles.
The Execution
- Full-Stack: Personally architected, coded, and deployed the entire platform.
- Orchestration: Leveraged OpenAI and Langchain for core generative features, managing context windows and structured outputs.
Key Takeaway: Demonstrates the ability to solve complex problems by applying cutting-edge AI technologies directly to a product, rather than just theorizing.
from langchain.agents import Tool, AgentExecutor
from langchain.prompts import PromptTemplate
class TravelPlannerAgent:
"""Orchestrates the itinerary generation process."""
def __init__(self, model="gpt-4-turbo"):
self.llm = ChatOpenAI(model=model, temperature=0.7)
self.memory = ConversationBufferMemory()
async def generate_itinerary(self, destination, days, interests):
# Define the structured output parser
parser = PydanticOutputParser(pydantic_object=Itinerary)
prompt = PromptTemplate(
template="Create a {days}-day trip to {destination}...",
input_variables=["destination", "days"],
partial_variables={"format_instructions": parser.get_format_instructions()}
)
return await self.chain.arun(prompt)Gift Mate: Agentic Secret Santa & Gift Recommendation.
The Problem
Secret Santa exchanges are often plagued by logistical headaches and generic gifts. Groups struggle to coordinate, and finding meaningful gifts for acquaintances can be stressful.
The Execution
- Intelligent Agents: Developed an AI agent that interviews users about their interests and generates highly personalized gift recommendations.
- Seamless UX: A modern React frontend that simplifies group creation, invitations, and the anonymous exchange process.
Key Takeaway: Integrating LLMs into social workflows can transform a chore into a delightful, personalized experience.
import { generateObject } from 'ai';
import { openai } from '@ai-sdk/openai';
export async function recommendGifts(userProfile) {
const { object } = await generateObject({
model: openai('gpt-4-turbo'),
schema: z.object({
recommendations: z.array(
z.object({
giftName: z.string(),
reasoning: z.string(),
priceRange: z.string(),
searchQuery: z.string(),
})
),
}),
prompt: `Based on the following user profile,
suggest 3 unique gift ideas:
Interests: ${userProfile.interests}
Age: ${userProfile.age}
Budget: ${userProfile.budget}`,
});
return object.recommendations;
}The "Meta" Proof
High-Velocity Development via AI-Augmented Workflows.
The Toolkit
- Cursor For context-aware code generation and refactoring.
- Aider For CLI-based pair programming and git-aware edits.
- Claude Code For complex reasoning and architectural planning.
Expert Perspective
Applying AI isn't just about faster typing—it's about handling broader context windows for advanced debugging and maintaining flow state. I proactively experiment with new models to assess their potential for innovative solutions.
Technical Stack Reference
Validated across various experiments and production environments.
| Category | Practical Application |
|---|---|
| AI/ML | Langchain , Hugging Face , Data Lakes (Databricks) , Machine Learning Workflows |
| Cloud & Infra | AWS (production-grade, fault-tolerant systems) , Docker , CI/CD pipelines (BuildKite, Jenkins) |
| Programming | Python (FastAPI, Django, Flask) , PHP (Laravel, Symfony) , JavaScript (React, Angular) |
| Architecture | Microservices , Event-Driven Architecture , PostGIS for geospatial data |
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