2026-01-12 01:38:21
The first time I used AI seriously for coding, I didn’t feel replaced.
I felt exposed.
Not because the AI was smarter than me — but because it kept asking questions my own code couldn’t answer.
That experience changed how I see both AI and software development.
AI doesn’t replace developers. It reveals how clearly we think about our work.
Many of us evaluate code with a simple metric:
Does it work?
If the answer is yes, we move on.
But working code doesn’t always mean well-understood code.
When you involve AI — asking it to refactor, optimize, or extend an existing system — it immediately asks questions like:
If those answers aren’t clear, the limitations surface quickly.
A common criticism of AI tools is that they “hallucinate.”
In practice, what often happens is more subtle.
When we give vague instructions like:
…without defining intent or constraints, we’re asking the AI to make assumptions on our behalf.
AI doesn’t struggle with ambiguity — it responds to it.
The output reflects the clarity (or lack of it) in the input.
There’s a lot of discussion around “prompt engineering,” but in day-to-day development, good prompts usually come down to clear reasoning.
Effective prompts tend to answer:
If writing a prompt feels difficult, it’s often because those questions haven’t been answered yet — not because the wording isn’t clever enough.
One unexpected benefit of working with AI is how often it forces clarification.
When extending or modifying code, AI naturally pushes back with questions:
Answering those questions improves the codebase — whether the AI is involved or not.
Used thoughtfully, AI encourages better habits:
It discourages vague, assumption-heavy development and rewards clarity.
In that sense, AI works less like a replacement and more like a continuous review loop — one that responds immediately to how well we articulate our thinking.
The biggest gap AI highlights isn’t about:
It’s about:
Those skills compound. AI simply makes them more visible.
AI didn’t make me a better developer.
It made me more aware of the difference between code that works
and code I actually understand.
And that awareness has been far more valuable than any autocomplete.
2026-01-12 01:37:18
El contexto
Nací y he vivido toda mi vida en Venezuela. El pasado 3 de enero de 2026 ocurrió un hecho histórico: Estados Unidos llevó a cabo una operación de extracción de Nicolás Maduro. Más allá del impacto político, como ingeniero entiendo que estamos ante un "Quiebre Estructural" (Structural Break) en la historia del país.
Hoy comienzo esta serie de artículos con dos propósitos claros:
Documentar la historia: Registrar con datos duros el comportamiento de algunos indicadores económicos clave en lo que, espero, será la recuperación de la economía del país.
Aprendizaje Técnico: Explorar y dominar el ecosistema de Análisis de Datos con Python (Pandas, Openpyxl, Matplotlib, Seaborn, Visidata) aplicado a un problema del mundo real.
La Construcción del Dataset (2024-2025)
Para entender hacia dónde vamos, primero tuve que reconstruir de dónde venimos, con este fin, utilicé las siguientes estrategias de Ingeniería de Datos:
Minería de Fuentes Oficiales (BCV): Descargué reportes del Banco Central de Venezuela, luego, utilizando la librería Openpyxl, extraje la información de liquidez monetaria, reservas internacionales y tipo de cambio oficial, transformando hojas de cálculo en un formato CSV unificado y limpio.
Reconstrucción de Series de Tiempo: Ante la falta de datos diarios del mercado paralelo, utilicé herramientas de IA (Perplexity y Grok) para obtener los cierres mensuales de 2024 y 2025. Posteriormente, utilicé Pandas para aplicar técnicas de interpolación lineal (upsampling), convirtiendo 24 puntos de datos mensuales en una serie continua de 730 días.
Monitoreo en Tiempo Real: Para la data actual (2026 en adelante), implementé bots de web scraping modulares. Con el objetivo de garantizar una recolección de datos continua (24/7) sin comprometer recursos ni mantener una laptop encendida permanentemente, desplegué estos scripts directamente en mi smartphone (utilizando el entorno Linux de la aplicación Termux). Mediante la configuración de Cron Jobs, automaticé la ejecución exacta de los procesos cada hora, creando un servidor de recolección de datos portátil, eficiente y de bajo consumo energético que monitorea el dólar paralelo y el petróleo Brent.
En los próximos posts, compartiré el código fuente, las gráficas resultantes y el análisis de estos primeros días de operación.
2026-01-12 01:25:04
This is Part 3 of a 3-part series on my AWS Certified Generative AI Developer - Professional certification journey.
Series Navigation:
In Parts 1 and 2, I covered the foundation and advanced learning phases. Part 3 focuses on the practical aspects, hands-on experience, and actionable tips that made the difference in my exam success.
The hands-on labs were absolutely crucial for my success. Theory alone wouldn't have been sufficient for this professional-level certification. Here's what made the practical experience so valuable:
Lab Focus: Building a complete RAG system using Amazon Bedrock Knowledge Bases
Key Learning Outcomes:
Real-World Application: This lab directly prepared me for questions about RAG architecture, vector database management, and knowledge base optimization.
Lab Focus: Implementing streaming conversations using Amazon Nova Lite model
Key Learning Outcomes:
Real-World Application: Essential for understanding model API patterns and conversational AI implementation strategies.
Lab Focus: Implementing comprehensive security using Amazon Bedrock Guardrails
Key Learning Outcomes:
Real-World Application: Critical for security and governance questions, which represent 20% of the exam.
Lab Focus: Building autonomous AI agents with tool integrations
Key Learning Outcomes:
Real-World Application: Essential for understanding agentic AI patterns and autonomous system design.
Service Integration Patterns:
Performance Optimization:
Error Handling and Troubleshooting:
Based on my experience and the challenges I encountered, here are my top recommendations for exam success:
Amazon Bedrock:
Vector Databases and RAG:
Security and Governance:
Time Management Approach (205 minutes total for 85 questions):
Decision-Making Process:
Certification Achievement:
Additional Achievements:
Background:
Career Benefits:
Background:
Career Benefits:
Background:
Career Benefits:
Background:
Career Benefits:
Recommended Next Steps:
Technical Skills:
Business Skills:
Vertical Expertise:
All my handwritten study notes from the certification journey are available on GitHub for reference:
📝 Complete Study Notes Collection
These 43 pages of detailed handwritten notes cover all exam domains, key concepts, implementation patterns, and study strategies that helped me pass the exam. Feel free to reference them for your own preparation!
Two weeks of focused study was sufficient, but the key was the structured approach and emphasis on hands-on practice. The combination of comprehensive video training, official AWS resources, and intensive practice exams provided both breadth and depth needed for this professional-level certification.
This certification represents AWS's commitment to the rapidly evolving GenAI landscape. As the field continues to advance, I expect:
Earning this certification has already opened new opportunities and conversations about AI initiatives. The knowledge gained extends far beyond exam preparation - it's provided a comprehensive understanding of how to build production-grade GenAI solutions that deliver real business value.
The early adopter badge adds extra recognition, but the real value lies in the practical skills and architectural understanding gained through the preparation process.
Series Conclusion:
This three-part series has covered my complete journey from initial planning through exam success. The structured approach, emphasis on hands-on practice, and comprehensive resource utilization made the difference in achieving certification in just two weeks.
Whether you're just starting your GenAI journey or looking to validate existing skills, this certification provides a valuable framework for understanding and implementing production-grade generative AI solutions on AWS.
Have questions about any part of this certification journey? Feel free to reach out in the comments below! I'm happy to help fellow candidates succeed in their AWS GenAI certification goals.
2026-01-12 01:23:28
This is Part 2 of a 3-part series on my AWS Certified Generative AI Developer - Professional certification journey.
Series Navigation:
In Part 1, I established a solid foundation using the Udemy course. Part 2 focuses on the intensive learning phase using official AWS resources and comprehensive practice exams that prepared me for exam success.
After completing the Udemy course, I transitioned to the AWS Generative AI Developer Advanced Learning Plan on AWS Skill Builder. This official AWS resource provided the perfect complement to my Udemy foundation.
Week 2 Daily Schedule (7 days):
Detailed Study Method:
Beyond the official learning plan, I also explored these valuable AWS workshops for additional practical experience:
These workshops provided additional hands-on experience that complemented the official learning plan perfectly.
To ensure I was fully prepared for the exam format and question style, I completed the AWS Exam Prep Plan: AWS Certified Generative AI Developer - Professional (AIP-C01).
1. Exam Prep Overview: AWS Certified Generative AI Developer - Professional (AIP-C01)
Domain 1: Foundation Model Integration, Data Management, and Compliance (31%)
Domain 2: Implementation and Integration (26%)
Domain 3: AI Safety, Security, and Governance (20%)
Domain 4: Operational Efficiency and Optimization (12%)
Domain 5: Testing, Validation, and Troubleshooting (11%)
Official Practice Question Set: AWS Certified Generative AI Developer - Professional (AIP-C01)
Official Pretest: AWS Certified Generative AI Developer - Professional (AIP-C01)
Exam Prep Summary: AWS Certified Generative AI Developer - Professional (AIP-C01)
📝 Study Notes: My detailed handwritten notes from all three phases, including domain-specific concepts and exam strategies, are available in my GitHub Study Notes Repository.
After completing the AWS Skill Builder exams and gaining a solid understanding of how to approach exam questions and answers, I decided to get additional practice with a comprehensive mock exam course on Udemy.
Course Link: AWS Certified Generative AI Developer Pro - 4 Mock Exams
Course Statistics & Credibility:
Comprehensive Learning Features:
Topics Covered (Aligned with Official Exam Weightings):
Key AWS Services Extensively Covered:
After completing the comprehensive mock exams, I discovered another exceptional practice resource that provided the final edge for exam success.
Course Link: Available on Udemy (search for "Practice Exams AWS Certified Generative AI Developer Pro" by Stéphane Maarek and Abhishek Singh)
Course Statistics & Credibility:
Human-Crafted Excellence:
Based on my comprehensive study across all resources, here are the critical learning areas organized by exam domain:
Core Topics:
Key AWS Services:
Core Topics:
Key AWS Services:
Core Topics:
Key AWS Services:
Core Topics:
Key AWS Services:
Core Topics:
Key AWS Services:
Continue to Part 3: Practical Experience & Success Tips where I share hands-on lab experiences, practical tips for exam success, and insights about the actual exam experience.
Have questions about the advanced learning phase or practice exams? Feel free to reach out in the comments below!
2026-01-12 01:22:29
ID: LL-131
Date: January 11, 2026
Severity: MEDIUM
Category: self-healing, automation, blog
The GitHub Pages blog showed "Jan 11:" with no content because:
main
Gap in self-healing: The weekend-learning workflow auto-merges RAG content, but session-created lessons don't have the same automation.
Current workflow:
Jan 11 blog entry empty because:
- ll_130_investment_strategy_review_jan11.md was on branch
- Branch not merged to main
- Blog builds from main only
Used GitHub PAT to:
Add to CI workflow that auto-merges PRs that only change:
rag_knowledge/lessons_learned/*.mddocs/_lessons/*.mdAllow direct push to main for lesson files only if:
ll_*.md patternCreate hook that auto-creates and merges lesson PRs at session end.
| Component | Self-Healing? |
|---|---|
| Weekend learning RAG | ✅ YES - auto-merge |
| Daily trading | ✅ YES - scheduled |
| Session lessons | ❌ NO - requires manual PR |
| Branch cleanup | ✅ YES - weekend-learning cleans |
| Blog sync | ✅ YES - after merge |
self-healing, automation, blog, github-pages, lessons-learned, operational-gap
This lesson was auto-published from our AI Trading repository.
More lessons: rag_knowledge/lessons_learned
2026-01-12 01:20:44
This is Part 1 of a 3-part series on my AWS Certified Generative AI Developer - Professional certification journey.
Tags: AWS Certification Generative AI Amazon Bedrock Professional Certification Cloud Computing AI Developer AWS GenAI Certification Study Guide Early Adopter Beta Exam AWS Skill Builder Foundation Models RAG Vector Databases Prompt Engineering Agentic AI AWS Training Cloud AI Machine Learning Career Development
Series Navigation:
The AWS Certified Generative AI Developer - Professional (AIP-C01) is one of the newest and most forward-looking certifications from AWS. After two weeks of focused preparation, I successfully cleared this challenging exam. This three-part series covers my complete journey and the strategy that worked for me.
The generative AI landscape is evolving rapidly, and AWS is at the forefront with services like Amazon Bedrock, SageMaker, and comprehensive AI/ML tooling. This professional-level certification validates your ability to build production-grade generative AI applications on AWS - a skill that's increasingly in demand.
Before diving into study materials, I started by thoroughly reviewing the official AWS exam guide. This step was crucial for understanding what I was preparing for.
Schedule Your Exam: AWS Certified Generative AI Developer - Professional
The AWS Certified Generative AI Developer - Professional is currently in beta phase, offering unique opportunities for early adopters:
Beta Exam Details:
Why This Certification Matters for Your Career:
This professional-level certification showcases advanced technical expertise in building and deploying production-ready AI solutions using AWS services like Amazon Bedrock. It's perfect for developers with 2+ years of cloud experience looking to advance their careers in the rapidly growing generative AI field.
For organizations investing in AI initiatives, this certification provides a reliable way to identify and verify developers who can move beyond proofs-of-concept to build production-grade generative AI solutions that deliver tangible business results while maintaining security and cost efficiency.
What This Certification Validates:
The AWS Certified Generative AI Developer - Professional (AIP-C01) exam is designed for individuals who perform a GenAI developer role. It validates your ability to:
Current Exam Format (Beta Phase):
Early Adopter Benefits:
Content Domain Breakdown with Weightings:
Target Candidate Profile (Official Requirements):
What's Explicitly OUT OF SCOPE:
Understanding this structure helped me allocate study time proportionally to each domain's weight and focus on the right areas.
While the exam doesn't require specific prerequisites, having certain foundational knowledge significantly accelerated my preparation:
While not required, these certifications provided valuable foundational knowledge:
If you're missing some prerequisites, consider these resources:
Before starting intensive study, I recommend reviewing these foundational AWS resources:
These documents provide essential context for understanding the strategic aspects of GenAI on AWS, which proved valuable for higher-level exam questions.
I started with the "Ultimate AWS Certified Generative AI Developer Professional" course on Udemy by Frank Kane and Stéphane Maarek. This bestseller and highest-rated course became my primary foundation, and here's why it was exceptional:
The course promises to help you master these critical skills:
Frank Kane (Sundog Education):
Stéphane Maarek:
Week 1 Daily Schedule (7 days):
Detailed Study Method:
Key Topics Covered in Week 1:
Why This Foundation Phase Was Critical:
Key Takeaways from Phase 1:
📝 Study Notes Reference: All my detailed handwritten notes from this phase and the complete certification journey are available in my GitHub Study Notes Repository for your reference.
Continue to Part 2: Advanced Learning & Exam Preparation where I cover the intensive AWS Skill Builder learning plan, official exam preparation, and comprehensive mock exams that solidified my knowledge for exam success.
Have questions about Part 1 or the foundation strategy? Feel free to reach out in the comments below!