AI Development & Coding Archives

Split screen comparing successful AI chatbot demo with production customer service failures and complaints

AI Chatbots in Customer Service: The Reality Behind “It Works”

Your AI customer service chatbot works perfectly in testing. Fluent responses, fast resolution times, impressed stakeholders. Then you deploy it. Within a week: complaints spike, tickets increase, and human agents are cleaning up messes the chatbot confidently created. Here’s why “working” chatbots still fail customers and how to test them before production destroys your CSAT scores.

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Comic-style illustration: Claude (sleek blue robot) versus Ollama/local LLM (rugged orange robot) battling — visual metaphor for cloud speed vs local persistent memory.

How I Use a Local LLM to Extend Claude’s Context (Without Hitting Limits)

Claude’s context limit makes serious AI work impossible. I fixed it by running a hybrid stack, Claude for reasoning, a local LLM for recall. No paid tier, no token resets, just continuous workflow.

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Split screen showing AI-generated code passing tests on left but breaking in production with error alerts on right

Why AI-Generated Code Passes Tests But Breaks in Production

AI code passes tests but breaks in production. Why “logically correct” ≠ works for users. Real bugs, real costs, and how to use AI without shipping broken code.

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Cyclical process visualization: prompt, test, document, pull hair, repeat—representing the iterative nature of LLM development and evaluation.

Teaching LLMs the Right Way: Why Data Evaluation Isn’t Optional

Most teams skip data evaluation in LLM development. Here’s why structured testing before SFT, RAG, and function calling is the actual foundation.

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Computer screen showing spam emails overflowing from an inbox with cryptocurrency referral links and outlook.com sender addresses flooding out of a burst envelope icon

Building EAI Anti-BS Filter: From Spam Hell to Smart Learning Plugin

How I built a custom WordPress spam filter that learns from every spam comment, accidentally roasted pingbacks, and recovered everything. A deep dive into building intelligent, self-improving plugins.

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Comparison of Copilot’s generated code and a full-featured email validation app built using v0.dev, highlighting differences in output scope and depth.

From Autocomplete to Automation: How AI IDEs Are Changing What It Means to Be a Developer

AI IDEs help you code faster—but they’re also reshaping how you think. This post compares Copilot vs. v0.dev on a real task and reveals the mental tradeoffs developers need to confront. A tactical look at the tools, the results, and the real cost of speed.

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Vibe Coding with AI: How We Went from Debugging Code to Debugging Prompts

Vibe coding got you through your first JavaScript app—but is it enough? Here’s what happens when AI shortcuts replace real dev skill, and why vibe coding should be a gateway, not your whole career.

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How Developers Actually Use AI: No Hype, Just Workflow

Forget the buzzwords. This post dives into how developers are really using AI tools like ChatGPT, Copilot, and Gemini—from debugging and drafting features to avoiding overcomplicated solutions. A grounded look at what actually happens in a dev’s workflow with AI.

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Why Over-Relying on AI is Weakening New Developers’ Skills (And What to Do About It)

Over-relying on AI tools in development can be a shortcut to productivity, but it risks weakening core developer skills like problem-solving, debugging, and team collaboration. Here’s how to balance AI use for better growth and efficiency.

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