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March 14, 202615 min read

Why LLM-Generated Code Accumulated 172 TypeScript Errors: A Technical Deep Dive

During a large-scale refactoring, we discovered 172 hidden TypeScript errors from LLM-generated code. This wasn't random—it revealed systematic patterns in how AI handles legacy removal, type inference, and build configurations. Learn the root causes and how to prevent them.

#vibe coding#TypeScript#LLM code generation

#architecture#design-patterns#messaging

Pub/Sub Architecture: A Complete Guide to Publish-Subscribe Pattern

Master the pub/sub messaging pattern with real-world examples, implementation guides, and comprehensive tutorials for distributed systems.

March 1, 202626 min read

January 6, 202610 min read

The Generative Core: Understanding Hallucination in Large Language Models

Hallucination isn't a bug in LLMs—it's the generative process itself. Understanding this changes how we should think about working with these tools.

#LLM hallucination#AI generation#confabulation

March 12, 202624 min read

The LLM Collaboration Guide: How to Avoid 20 Critical Bugs in Production

Learn how to turn LLMs from a liability into your most powerful engineering tool. Discover the three-phase workflow, constraint matrix framework, and production readiness checklist that prevents critical bugs in production.

#LLM#AI-Assisted Code#Production

January 14, 202612 min read

The Hidden Cost of "Ship It Fast": When Velocity Becomes Technical Debt

A post-mortem on vibe coding—the practice of prioritizing momentum over maintainability. Learn why shipping fast without foundations is the most expensive shortcut you'll ever take, and how to recover.

#vibe-coding#technical-debt#software-engineering

January 16, 202611 min read

Testing LLM Reasoning on Novel Problems: What 400 Experiments Reveal

We ran 400 experiments on competitive programming problems LLMs have never seen before. The results reveal that when memorization is eliminated, prompt formatting matters far less than model selection.

#vibe-coding#LLM-benchmarks#novel-problems

January 6, 20269 min read

Why LLMs Can't Revise Tokens: The Feed-Forward Constraint Every Developer Must Understand

Learn why LLMs hallucinate and can't self-correct. Understand feed-forward token generation and master vibe coding strategies for better AI-assisted development.

#LLM#token generation#feed-forward

#Node.js#JavaScript#Async

Node.js Event Loop: Master Non-Blocking I/O (Complete Guide)

Understand how Node.js event loop works, master async patterns, and build scalable servers handling 10,000+ concurrent requests.

March 7, 202610 min read

January 6, 202611 min read

Why LLMs Fail at Complex Tasks: The Math Behind Compounding Error in AI Code Generation

Learn why LLMs fail on long reasoning chains due to compounding error. Includes mathematical proofs, real-world AI coding failures, and actionable engineering strategies to mitigate AI hallucinations in software development.

#LLM limitations#compounding error#AI hallucinations

January 25, 202610 min read

Temperature Sensitivity in LLM Code Generation: A 752-Run Experiment

We tested how temperature (0, 0.3, 0.7, 1.0) affects code generation accuracy on 100 competitive programming problems. The surprising result: temperature barely matters for problem-solving, but model selection does.

#vibe coding#LLM temperature#Gemini

January 15, 20269 min read

XML vs Natural Language Prompts: An Experimental Comparison

We tested XML-structured versus natural language prompts across 144 experiments spanning code generation, mathematics, reasoning, and complex multi-step tasks. The results reveal significant efficiency gains—but with important caveats.

#vibe-coding#prompt-engineering#XML-prompts

#TypeScript#JavaScript#Type Safety

TypeScript Advanced Types: Generics, Discriminated Unions & Mapped Types

Master TypeScript advanced types, generics, conditional types, and build bulletproof type-safe applications.

March 7, 20269 min read

#LLM#AI-Assisted Code#Debugging

The Silent Failure Cascade: Why LLM-Powered Code Breaks in Production

LLM-generated code often fails silently in production due to implicit assumptions. Learn why this happens, how to detect it, and proven strategies to write defensive code that survives the real world.

March 7, 202612 min read

February 5, 202612 min read

The Full Papercut Audit: Where AI Coding Tools Break Down

A comprehensive friction analysis across five dimensions: bracket completion, comment verbosity, over-engineering, multi-file projects, and iterative modification. Gemini CLI achieves perfect zero friction.

#ai-coding#claude-code#codex-cli

January 6, 202612 min read

Attention Mechanisms in LLMs: The Complete Guide to How Transformers Really Work

Learn how attention mechanisms power large language models (LLMs) like GPT-4 and Claude. This in-depth guide explains Query-Key-Value math, multi-head attention, and long-context processing with real code examples.

#attention mechanisms#transformers#LLM

AWS Lambda & Serverless: Scale Without Servers (Complete Guide)

Master AWS Lambda, build serverless applications, scale to 1M requests/month for $2/month instead of $500/month.

March 7, 20267 min read

#AWS#Serverless#Lambda

#JavaScript#Async#Promises

JavaScript Promises vs Async/Await: Master Async Code (Complete Guide)

Master JavaScript promises and async/await, parallelize operations, speed up code 3x, handle errors correctly.

March 7, 20267 min read

January 21, 202615 min read

Few-Shot Prompting for STEM: Claude vs Gemini Across 128 Experiments

We ran 128 experiments comparing Claude Sonnet 4 and Gemini 2.0 Flash on STEM problems with zero-shot through 5-shot prompting. The results challenge common assumptions: accuracy stayed flat while format consistency and token efficiency improved dramatically.

#few-shot prompting#STEM education#prompt engineering