I build and operate full-stack/platform systems where reliability is a feature, not a nice-to-have.
Current obsession: AI-assisted and agentic engineering workflows that improve implementation quality and make products easier to interact with.

• Build full-stack/platform systems that stay calm when production gets noisy: load spikes, partial failures, and weird edge cases.
• Focus on resilience, performance, and operability under real pressure, not only happy-path benchmarks.
• Treat debugging as engineering, not heroics: hypotheses, instrumentation, fast feedback loops, and root-cause discipline.
• Build personal products/services from curiosity and craft: things I actually use, iterate on, and refine to production quality.
• Bridge product and infrastructure so the system behaves like a well-automated factory line (yes, strong Factorio energy): clear flows, visible bottlenecks, and smooth handoffs.
• Work end-to-end to turn ideas into robust systems that are usable, observable, and maintainable.

• Building AI-assisted workflows that make implementation more useful and make products easier to interact with.
• Creating agentic automations for everyday engineering tasks (debugging, triage, repetitive ops) so teams can focus on higher-value work.
• Integrating LLM tools into real product and engineering loops with emphasis on clarity, reliability, and practical outcomes.
• Exploring how product systems and AI systems can reinforce each other: better context, better decisions, better user experience.
• Pushing this further through hands-on experiments in personal tools and services to find what is genuinely useful beyond demos.

• CI/CD migration: Bamboo -> GitHub Actions across 30+ repositories.
• Home AI voice assistant: Home Assistant + Node-RED + LLM orchestration.
• Linux debugging deep dive: suspend/resume reliability on modern AMD laptops.
• AI-assisted operations: workflows for log analysis and issue triage.
• Self-hosted observability lessons: practical trade-offs from running monitoring stacks in real environments.

Full-Stack & Architecture

• Service/API design, application architecture, event-driven patterns, system decomposition.
• High-load system design based on real platform constraints and capabilities (runtime, database, queue, infrastructure limits).
• Capacity-aware technical decisions: choosing patterns that fit throughput, latency, and operational realities.

Platform & Operations

• CI/CD architecture, infrastructure automation, observability, production diagnostics.

AI-assisted Engineering

• Agentic workflows, LLM-assisted debugging, automation-first developer tooling.

• GitHub: github.com/Palgogo
• LinkedIn: linkedin.com/in/palgogo
• Email: dev@palgogo.com