AI vs ML vs Deep Learning: A Clear Map for Beginners
Understand the landscape in one read—without hype.
AI posts
Posts in AI.
How LLMs Work (Without the Math Spiral)
Tokens, attention, training—explained like you’ll implement it.
Prompt Patterns That Produce Reliable Outputs
Reusable templates for clarity, constraints, and verification.
RAG Done Right: Make Chatbots Use Your Data Reliably
Chunking, embeddings, retrieval pitfalls, and evaluation.
Fine-tuning vs RAG: What to Choose (And When)
A practical decision guide with real scenarios.
Evaluating AI: Test Sets, Metrics, and the ‘Looks Good’ Trap
How to measure quality beyond demos.
MLOps for Small Teams: Shipping Models Without Burning Out
Versioning, pipelines, monitoring, and rollback basics.
Vector Databases Explained: Indexes, Recall, and Cost
What actually matters when picking a vector store.
Prompt Injection: What It Is and How to Defend
Threats, mitigations, and safer tool / RAG designs.
Computer Vision Starter Pack: From Pixels to Predictions
A practical CV roadmap with projects to learn fast.
Time-Series Forecasting Without Pain: Baselines First
Avoid overfitting with strong baselines and honest evaluation.
Data Labeling That Doesn’t Break Your Model
How to design labels, handle edge cases, and reduce noise.
Quantization & Pruning: Make Models Smaller Without Ruin
When it works, when it fails, and how to test impact.
On-Device AI: When Local Inference Beats the Cloud
Latency, privacy, cost—and the tradeoffs that matter.
Agents 101: Tool Use, Planning, and Guardrails
Build useful agents without turning them into chaos machines.
Bias & Fairness: What Builders Can Actually Do
Practical checks and documentation patterns.
From Prototype to Product: Shipping a GenAI Feature Safely
Logging, evaluation, safety, and user trust basics.
LLM Cost Optimization: Reduce Tokens Without Losing Quality
Prompt compression, caching, and routing strategies.