Posts

Speed up multi-step LLM pipelines by chaining async API calls and batching independent prompts together

Wire together tool-calling steps and validated JSON parsing to build prompt chains that never lose data between steps

Score and compare LLM outputs systematically using rubric-based evaluation with Python and structured grading criteria

Create fault-tolerant prompt chains that fall back across OpenAI, Anthropic, and open-source models seamlessly

Stop getting unpredictable LLM outputs by enforcing structured schemas with Pydantic and OpenAI

Protect your LLM applications from prompt injection attacks with regex filters, fine-tuned classifiers, and production-ready middleware.

Create maintainable prompt pipelines using Jinja2 templates with variables, conditionals, and loops

Catch prompt regressions early by scoring LLM outputs with a judge model and failing CI on quality drops

Create type-safe, version-controlled prompt templates that work across OpenAI, Anthropic, and open-source models

Stop breaking your LLM app with untested prompt changes. Version prompts in YAML and run automated regression tests.

Protect your LLM API from abuse with per-user rate limits, token counting, and cost controls using Redis

Detect faces in real time from webcam feeds using MediaPipe’s blazing-fast face detection models in Python.

Set up StreamDiffusion for near-real-time text-to-image and img2img streaming with TensorRT acceleration and webcam integration.

Stream pixel-perfect segmentation masks to the browser in real time using SAM 2 and WebSocket connections

Generate speech in a cloned voice from just a few seconds of reference audio using OpenVoice V2

Set up proper random seeds to get identical results every time you run your ML training pipeline

Cut irrelevant context from your RAG pipeline and get sharper LLM answers with contextual compression

Reduce hallucinations and boost accuracy by grounding your LLM prompts with retrieved documents and citations

Add self-healing retry logic to your LLM pipelines so bad JSON, failed validations, and off-topic responses get fixed automatically.

Segment every object in images and video frames using SAM 2 automatic masks, point prompts, and box prompts

Skip the DevOps headache. Deploy production-ready AI models with automatic GPU scaling, no Kubernetes required.

Get reliable, typed data from LLMs with Pydantic parsing, validation, and retry strategies that handle real-world edge cases.

Use constrained decoding to guarantee your LLM produces valid JSON reasoning steps every time, not just most of the time.

Combine multiple prompts into one API call to cut token overhead, lower latency, and save money on LLM inference.

Classify human actions in video clips and live streams using SlowFast dual-pathway networks in PyTorch

Process live video feeds with object detection, tracking, and zone-based analytics using Python and OpenCV

Learn to create powerful image Q&A systems that understand product photos, medical scans, and documents using pretrained vision-language models.

Identify AI-generated images by detecting invisible watermarks embedded by diffusion models

Build a document classification pipeline that sorts invoices, receipts, contracts, and more

Use torch.compile to make your PyTorch models faster with one line of code and understand when it helps most

Practical techniques to compress prompts and reduce token usage without sacrificing response quality in production LLM apps.

Build agents that query databases, call APIs, and process results while preventing SQL injection and data leaks

Go from raw CSV or JSON files to a published, versioned dataset that anyone can load with one line of Python

Find defects and anomalies in images using deep learning without needing large labeled defect datasets

Build a text deduplication pipeline that scales to millions of documents using datasketch, sentence-transformers, and scikit-learn

Train a smaller, faster model that learns from GPT-4 or Claude, cutting inference costs by 10-100x

Edit photos with plain English prompts – no masks, no inpainting, just describe the change you want and let the model handle it

Pull the most important terms from any text using embedding-based keyword extraction that actually understands context

Train a custom embedding model that understands your domain’s vocabulary and retrieves better results

Set up Axolotl, prepare your dataset, configure LoRA training in YAML, and merge adapters back into the base model

Create 3D Gaussian splat scenes from text descriptions and render them from any camera angle

Generate two-host podcast episodes from any topic using LLMs for dialogue and text-to-speech for natural audio.

Build product photography pipelines that replace backgrounds, generate scenes, and keep products consistent across shots.

Master character consistency in AI-generated images using reference photos, face embeddings, and style transfer for professional results.

Create AI-generated floor plans from sketches or text prompts using ControlNet and Stable Diffusion XL

Use LCM and LCM-LoRA to turn Stable Diffusion into a near-real-time image generator with minimal quality loss

Create seamless textures and full PBR material sets from text prompts or photos with diffusers and PIL

Train accurate models on imbalanced data where rare classes matter most using resampling and loss weighting techniques

Track every AI decision your system makes with structured audit logs for regulatory compliance and debugging

Build citation pipelines that map every AI-generated claim back to its source, so users can trust and verify your app’s answers.