<feed xmlns="http://www.w3.org/2005/Atom"> <id>https://sgryt.com/</id><title>Sergii Grytsaienko</title><subtitle>Sergii Grytsaienko's blog about Leadership in the Digital Age, Tech Insights, Ideas, and Innovations</subtitle> <updated>2025-08-05T12:18:35-05:00</updated> <author> <name>Sergii Grytsaienko</name> <uri>https://sgryt.com/</uri> </author><link rel="self" type="application/atom+xml" href="https://sgryt.com/feed.xml"/><link rel="alternate" type="text/html" hreflang="en" href="https://sgryt.com/"/> <generator uri="https://jekyllrb.com/" version="4.3.2">Jekyll</generator> <rights> © 2025 Sergii Grytsaienko </rights> <icon>/assets/img/favicons/favicon.ico</icon> <logo>/assets/img/favicons/favicon-96x96.png</logo> <entry><title>Self-Hosted vs API-Driven RAG: A Strategic Architecture Guide</title><link href="https://sgryt.com/posts/self-hosted-vs-api-driven-rag/" rel="alternate" type="text/html" title="Self-Hosted vs API-Driven RAG: A Strategic Architecture Guide" /><published>2025-08-05T12:00:00-05:00</published> <updated>2025-08-05T12:00:00-05:00</updated> <id>https://sgryt.com/posts/self-hosted-vs-api-driven-rag/</id> <content src="https://sgryt.com/posts/self-hosted-vs-api-driven-rag/" /> <author> <name>s-gryt</name> </author> <category term="AI Strategy" /> <category term="RAG Architecture" /> <category term="Enterprise AI" /> <category term="LLM Operations" /> <category term="Cybersecurity" /> <summary> The Strategic Fork in the Road: Self-Hosted vs. API-Driven RAG In the landscape of enterprise AI, the deployment of Retrieval-Augmented Generation (RAG) systems represents a critical architectural decision with far-reaching strategic implications. The fundamental choice between self-hosted and API-driven approaches—exemplified by frameworks like Ollama versus cloud services like OpenAI—extends... </summary> </entry> <entry><title>Parallel AI Development with Git Worktrees: A Strategic Implementation Guide</title><link href="https://sgryt.com/posts/git-worktree-parallel-ai-development/" rel="alternate" type="text/html" title="Parallel AI Development with Git Worktrees: A Strategic Implementation Guide" /><published>2025-07-30T18:00:00-05:00</published> <updated>2025-07-30T18:00:00-05:00</updated> <id>https://sgryt.com/posts/git-worktree-parallel-ai-development/</id> <content src="https://sgryt.com/posts/git-worktree-parallel-ai-development/" /> <author> <name>s-gryt</name> </author> <category term="AI Development" /> <category term="Developer Productivity" /> <category term="Git Workflows" /> <category term="Software Development" /> <category term="Developer Tools" /> <category term="Automation" /> <category term="Version Control" /> <category term="AI Agents" /> <summary> The Context Switching Nightmare Every Developer Knows Consider a typical development scenario: You’re implementing a complex feature with an AI coding assistant that has accumulated substantial context about your codebase architecture. A critical production issue surfaces, requiring immediate attention. The conventional response follows a predictable pattern: git stash push -m "WIP: complex f... </summary> </entry> <entry><title>Can AI Find Complex Bugs in Entire Project Codebases?</title><link href="https://sgryt.com/ai-complex-bug-detection-projects/" rel="alternate" type="text/html" title="Can AI Find Complex Bugs in Entire Project Codebases?" /><published>2025-07-16T02:00:00-05:00</published> <updated>2025-07-16T02:00:00-05:00</updated> <id>https://sgryt.com/ai-complex-bug-detection-projects/</id> <content src="https://sgryt.com/ai-complex-bug-detection-projects/" /> <author> <name>s-gryt</name> </author> <category term="AI" /> <category term="Code Review" /> <category term="Software Quality" /> <category term=".NET" /> <summary> Can AI Find Complex Bugs in Entire Project Codebases? TL;DR: Modern AI with reasoning capabilities can detect sophisticated bugs in large codebases. Here’s how AI found a subtle GetHashCode bug that was causing duplicate artifacts in production - a bug that stumped experienced developers for days. The Skepticism: AI vs Real-World Bugs Many developers believe AI tools are only good for ca... </summary> </entry> <entry><title>How to Stay Focused When Working with AI: My Journey to Automation</title><link href="https://sgryt.com/ai-focus-hacks-notifications/" rel="alternate" type="text/html" title="How to Stay Focused When Working with AI: My Journey to Automation" /><published>2025-06-21T07:00:00-05:00</published> <updated>2025-06-21T07:00:00-05:00</updated> <id>https://sgryt.com/ai-focus-hacks-notifications/</id> <content src="https://sgryt.com/ai-focus-hacks-notifications/" /> <author> <name>s-gryt</name> </author> <category term="AI" /> <category term="Productivity" /> <category term="Automation" /> <category term="Python" /> <summary> How to Stay Focused When Working with AI TL;DR: If you want to optimize your workflow and maintain focus while working with AI or LLM tasks, use notifications and automation to bring your attention back at the right moment. Here’s how I approach this, with practical examples for Jupyter, macOS, and more. Ever Launched an AI Task and Wondered, “Now What?” You kick off a long-running AI jo... </summary> </entry> <entry><title>You Got Breached: Data Leaks, Prompt Injection, and AI Security - How to Protect Your LLMs from Becoming Security Vulnerabilities</title><link href="https://sgryt.com/posts/llm-ai-security-prompt-injection-data-leak/" rel="alternate" type="text/html" title="You Got Breached: Data Leaks, Prompt Injection, and AI Security - How to Protect Your LLMs from Becoming Security Vulnerabilities" /><published>2025-06-04T01:00:00-05:00</published> <updated>2025-06-04T01:00:00-05:00</updated> <id>https://sgryt.com/posts/llm-ai-security-prompt-injection-data-leak/</id> <content src="https://sgryt.com/posts/llm-ai-security-prompt-injection-data-leak/" /> <author> <name>s-gryt</name> </author> <category term="AI Security" /> <category term="LLM Protection" /> <category term="Cybersecurity" /> <category term="Data Privacy" /> <category term="Prompt Engineering" /> <category term="AI Governance" /> <category term="Compliance" /> <category term="Risk Management" /> <summary> Your company just deployed an AI chatbot to streamline customer service. Within hours, a malicious user tricks it into revealing confidential customer data from another account. Sound like a nightmare? It’s happening every day to organizations that underestimate AI security risks. See real-world incidents in the OWASP Top 10 for LLM Applications or Gartner’s AI Security Hype Cycle. The rapid a... </summary> </entry> </feed>
