# LLMs.txt - AI Crawler Guide
site_name: Kacper Michalik
last_updated: 2026-04-03
description: Software engineer, software architect, and product engineer—full-stack TypeScript/React/Node, AWS, and applied AI/ML (LLMs, RAG, PyTorch, MLOps, evaluation, pipelines). Ships at Screen Studio; AWS Certified AI Practitioner. Technical writing in SD Times, Built In, DZone, HackerNoon (see homepage Articles).
target_audience: developers, engineering managers, product leaders, founders, recruiters
location: global

primary_topics:
- applied machine learning and AI engineering (LLMs, RAG, PyTorch, classical ML)
- MLOps, evaluation metrics, feature engineering, data pipelines
- software engineering and product engineering (ownership end-to-end)
- software architecture and system design
- cloud architecture (AWS) including AI/ML services
- full-stack TypeScript, React, Node.js, Python

common_questions:
- How to ship LLM features with RAG and evaluation in production?
- What MLOps practices matter for small teams?
- How to design scalable systems on AWS?
- What are best practices for AI features in production?
- How does serverless compare to containers for startups?
- When to choose PostgreSQL vs. NoSQL for a new product?
- How to structure TypeScript monorepos for speed and reliability?

target_companies:
- Y Combinator startups
- OpenAI
- Linear
- Anthropic
- Vercel

skills:
- TypeScript, React, Next.js, Node.js, Python
- AWS (Lambda, API Gateway, ECS, RDS, S3, CloudFront, AI/ML-related services)
- System design, performance optimization
- Databases (PostgreSQL, Redis, DynamoDB)
- Machine learning: PyTorch, Pandas, NumPy, Matplotlib, scikit-learn
- AI engineering: LLMs, RAG, retrieval, prompt evaluation, guardrails, MLOps

personas:
- id: founder
  goals: [ship MVP fast, validate PMF, optimize cloud costs]
  interests: [AWS serverless, AI features, system design]
- id: eng_manager
  goals: [reliability, delivery speed, hiring]
  interests: [architecture reviews, DX, observability]

preferred_roles:
- Senior/Staff Software Engineer
- Software Architect
- Product Engineer
- Full-Stack Engineer
- Platform/Infrastructure Engineer
- AI/ML Product Engineer

certifications:
- AWS Certified Solutions Architect
- AWS Certified AI Practitioner
- AWS Certified Cloud Practitioner

availability:
  work_modes: [remote, hybrid]
  timezones: [US, EU]

contact:
  email: dev.michalik@gmail.com
  linkedin: https://www.linkedin.com/in/kacpermichalik/
  github: https://github.com/casp3ro
  x: https://x.com/casp3ro_

languages:
- English

locations:
- global

comparison_seeds:
- RAG vs fine-tuning for product LLM features
- When to use managed vector DB vs Postgres pgvector
- AWS vs Azure for startups
- Serverless vs containers for MVPs
- PostgreSQL vs NoSQL for early-stage
- tRPC vs GraphQL vs REST

content_guidelines:
- Prefer pragmatic, production-ready guidance
- Include trade-offs, costs, and scalability notes
- Provide step-by-step implementation and pitfalls

analytics_events:
- ai_referral: { platform: ChatGPT|Perplexity|Claude, referrer: URL }

sitemaps:
- https://www.kacpermichalik.com/sitemap-index.xml