Posts

Split traffic between prompt variants, collect quality metrics, and pick winners with statistical confidence

Protect your LLM application from jailbreaks, off-topic use, and harmful outputs in under 50 lines

Build agents that pause for human approval before taking risky actions, with working LangGraph interrupt and resume code.

Give your AI agents memory that survives restarts using LangGraph’s checkpointer system with real database backends.

Build augmentation pipelines for CV and NLP datasets with practical Python examples

Detect, embed, and match faces in Python with InsightFace’s buffalo_l model and a few lines of code

Build a production-ready feature store using Feast with entity definitions, feature views, and materialization

Encode text in 50+ languages into a shared vector space for search, classification, and similarity scoring.

Extract named entities from any text using spaCy pretrained models, transformer-based NER, and zero-shot GLiNER.

Ground your LLM answers in real documents with a working RAG pipeline you can run locally

Build your own reasoning-and-acting agent from scratch without frameworks, and understand every line

Detect body landmarks, draw skeletons, and calculate joint angles with MediaPipe and a webcam

Create a search engine that understands meaning, not just keywords, using OpenAI embeddings

Ship a sentiment analysis endpoint in under 100 lines of Python using a fine-tuned RoBERTa model and FastAPI.

Build a pipeline that turns plain English questions into validated SQL queries you can run against any database.

Create a Python agent that can search the web, run calculations, and chain tool calls autonomously

Set up a production-ready Pinecone pipeline with serverless indexes, batch upserts, metadata filtering, and cost optimization.

Create collaborating AI agents with AutoGen’s group chat, role-based agents, and sandboxed code execution for real tasks.

Step-by-step guide to creating reliable AI agents with LangGraph’s graph-based architecture and built-in persistence.

Ship production agents with built-in tools, custom MCP servers, hooks, and subagents in Python

Use Cohere’s chat, embed, rerank, and classify endpoints to build assistants with built-in citation grounding and tool use.

Chain prompts, models, and parsers into clean AI workflows using LCEL’s pipe syntax and built-in streaming

Search images by text or visual similarity using CLIP embeddings and a FAISS vector index

Build production apps with Gemini using the Python SDK for text generation, multimodal input, tool use, and streaming

Create agents that call functions, execute code, and manage conversations with persistent threads using the Assistants API.

Practical CoT prompting patterns that measurably improve LLM reasoning on math, code, and logic tasks.

Set up Airflow with Docker and create production-ready DAGs that extract, clean, validate, and load ML training data on a schedule

Build transparent ML models by adding SHAP and LIME explanations to credit scoring, tree models, and neural networks.

Step-by-step guide to creating reproducible ML workflows with KFP v2 on Kubernetes, from local setup to production pipelines.

Create teams of AI agents that collaborate on tasks using CrewAI’s role-based framework and process orchestration.

Stop LLM hallucinations by wiring up retrieval-augmented generation with LangChain and ChromaDB

Load a Vision Transformer, preprocess images, run inference, and fine-tune on your own dataset

Build a text classification pipeline with LLMs that handles any label set without training data or fine-tuning.

Clean messy training data and find near-duplicate records with pandas, datasketch, and text-dedup in practical Python workflows

Build custom voice pipelines using OpenAI’s steerable TTS and ElevenLabs’ voice cloning API with working Python examples

Get precise control over AI image generation using ControlNet spatial conditioning and IP-Adapter style transfer

Use Claude or GPT-4 to create labeled training data when real data is scarce or expensive

Ship a containerized LLM inference server with streaming, concurrency handling, and production hardening

Ship your models to edge hardware fast with a proven ONNX-to-TensorRT pipeline that actually works

Apply statistical watermarks during text generation and verify them with open-source detectors in Python

Find and fix unfair predictions in your ML pipeline with MetricFrame, ThresholdOptimizer, and ExponentiatedGradient

Strip sensitive data from text in a few lines of Python using Presidio’s analyzer and anonymizer engines.

Stop your LLM app from making things up with practical detection methods, validators, and grounding strategies you can ship today.

Catch silent model degradation early using drift detection, statistical tests, and automated monitoring pipelines

Set up YOLOv8 for image and video object detection with just a few lines of Python

Replace or remove objects in images with prompt-guided AI inpainting running locally on your GPU

Turn any photo into a depth map with Depth Anything V2 using three lines of Python or full manual control

Build automated LLM evaluation suites using DeepEval’s built-in and custom metrics, integrated directly into your pytest workflow.

Build a pipeline that parses invoices and receipts from PDF to validated, typed JSON in under 50 lines of Python.

Replace Tesseract with vision LLMs that read messy documents, handwriting, and tables accurately