Posts

Step-by-step setup for vLLM’s disaggregated serving on NVIDIA GB200: separate prefill and decode workers, expert parallelism, FP4 quantization, and monitoring.

Complete API guide for Seedance 2.0: text-to-video, image-to-video, multimodal inputs, and phoneme-accurate lip sync in 8+ languages with native audio.

Combine BM25 keyword search with Qwen3-Embedding-8B dense vectors in Qdrant to build a retrieval pipeline that beats either approach alone—with working Python code.

Set up InternVL3 on your own hardware for document understanding tasks—OCR, tables, charts—with practical code and quantization options for consumer GPUs.

Wire up an orchestrator agent and specialized subagents through MCP tool servers — with working Python, task splitting, and result aggregation.

Learn how to send entire codebases, legal archives, or research corpora to Claude Sonnet 4.6 in one shot and get accurate answers back.

Learn to build a multi-model router that sends simple queries to cheap LLMs and complex ones to GPT-4o or Claude, with fallbacks and cost monitoring.

Stand up a working MCP server from scratch with Python’s FastMCP SDK, define custom tools with type hints, and connect it to Claude Desktop in under 30 minutes.

Build synthetic training datasets using distilabel pipelines, then validate diversity and deduplicate to keep your model from collapsing on its own outputs.

Add a two-stage constitutional classifier layer to your FastAPI endpoint — blocks 95%+ of jailbreaks at ~1% extra compute cost per Anthropic’s research.

Turn still photos into animated talking videos by driving facial expressions and lip sync with audio input

Protect sensitive data while training ML models with proven anonymization techniques and ready-to-use Python code for real datasets.

Build hands-off retraining pipelines that fetch data, train, evaluate, and promote models only when metrics improve

Scale LLM inference pods up and down automatically based on request queue depth and GPU utilization

Build a GPU benchmarking toolkit that measures memory bandwidth, compute throughput, and training speed across precisions.

Detect, decode, and annotate barcodes and QR codes from images and webcams with Python and OpenCV

Step-by-step guide to creating a browser automation agent that observes pages, decides actions, and extracts data with LLMs.

Create a code-writing agent that generates Python, runs it safely in a sandbox, and self-corrects on errors

Automate code reviews with an LLM-powered agent that reads Git diffs and suggests improvements

Build a color extraction pipeline that identifies dominant colors, matches named colors, and creates palette swatches from any image.

Create a contract Q&A agent that pulls payment terms, termination clauses, and liabilities from any PDF automatically.

Resolve pronouns and noun phrases to their referents using spaCy coreferee and transformer-based models for cleaner NLP output.

Create an intelligent cron job manager that interprets natural language schedules and handles task execution with LLMs

Create a support agent that answers from a knowledge base, checks orders, and escalates when unsure

Create agents that write and run Python to answer data questions, generate charts, and query databases from plain English prompts.

Build a complete annotation workflow with Argilla to label data, collect feedback, and improve your models

Augment tabular datasets for ML training using oversampling, synthetic data generation, and noise injection techniques.

Find and flag contaminated samples in your training data before they leak benchmark answers into your model

Detect data drift early using whylogs profiles, statistical tests, and automated constraint validation in Python

Detect stale training data and broken pipelines early with automated freshness checks and Slack alerts in Python

Build production labeling pipelines that combine human annotators with ML-assisted pre-labeling using Label Studio

Ingest raw data into a MinIO data lake as partitioned Parquet files using PyArrow and Python

Build a lightweight data lineage system that records every transformation from raw data to training set

Detect and remove outliers from your training data using multiple PyOD detectors, model combination, and automated scoring

Create a conversational agent that takes plain English data requests and autonomously generates, runs, and debugs pandas transformations.

Profile messy datasets and auto-fix missing values, outliers, type errors, and duplicates with a reusable Python pipeline

Find label errors, outliers, and duplicates in your training data using Cleanlab’s Python library with any classifier

Compare ML training set versions automatically and catch drift, schema changes, and distribution shifts before they break your models.

Sample large datasets intelligently for ML training using stratified splits, importance weighting, and Polars

Manage column additions, renames, and type changes in ML datasets with automated schema migrations

Find weak spots in your model by slicing data into segments and evaluating per-slice performance.

Catch bad data before it ruins your model by combining Pydantic and Pandera into a single validation pipeline.

Version your ML datasets with Delta Lake and get time travel, schema enforcement, and audit history

Build automated checks that catch dataset biases before they poison your model training

Generate standardized dataset documentation with statistics, distribution plots, and bias checks automatically

Detect added, removed, and modified rows between dataset versions with a hash-based diff pipeline that produces clear changelogs.

Export ML datasets to any format your pipeline needs with a unified Python conversion toolkit

Merge multiple annotation sources into a single clean dataset with automated conflict resolution strategies

Build a pipeline that catches bad data before training starts using GX expectation suites and Airflow’s TaskFlow API

Set up lakeFS locally and use its Python SDK to version, branch, diff, and merge datasets in your ML pipeline