Posts

Automate your inbox with a Python agent that reads, classifies, drafts replies, and files emails using IMAP and LLMs.

Classify text into 27 emotion categories with fine-tuned Transformers and serve predictions via FastAPI

Extract precise answers from documents with transformer-based QA models that point to the exact text span

Generate accurate image descriptions with BLIP models using a production-ready captioning pipeline in Python

Step-by-step guide to creating an MCP server that gives AI agents access to custom tools and data sources.

Extract text from images and scanned documents using PaddleOCR and Tesseract with confidence scoring and batch processing

Add age-appropriate content filters to your AI app with keyword screening, ML classification, and LLM-based review

Catch demographic biases in your LLM app before users do with automated prompt-based auditing

Automate GDPR-compliant data retention with time-based deletion, audit trails, and dry-run support

Test LLM outputs for demographic bias with automated fairness checks using Python and statistical analysis

Build a multi-layer hate speech detection system with classifier models and content filtering rules.

Detect prompt injection and jailbreak attempts before they reach your LLM with a multi-layer detection pipeline

Catch unsafe LLM outputs before they reach users with classifier pipelines that flag toxicity, bias, and policy violations.

Catch PII leaks in LLM outputs automatically with a testing framework built on Presidio and pytest

Build automated tests that catch prompt leakage before it reaches production with Python and regex guards

Catch stereotypical patterns in your LLM app with automated test suites based on StereoSet-style probing

Detect toxic comments in production with a multi-model ensemble that catches what single classifiers miss

Stop hand-tuning prompts — let DSPy compile and optimize them for your specific task and metrics

Run two model versions side by side, validate the new one with health checks, and swap traffic instantly with rollback

Implement user consent tracking, data deletion requests, and opt-out workflows for AI training pipelines.

Build a prompt router that automatically picks the best model for each query using vector similarity

Detect and flag potentially copyrighted text in training datasets with n-gram fingerprinting and fuzzy matching

Save thousands on GPU training by using spot instances with automatic checkpointing and multi-cloud fallback

Verify your training data meets privacy guarantees by building DP tests with Opacus and membership inference

Turn photos of documents into clean, flat scans using OpenCV perspective warping in Python

Save costs and boost reliability by routing each prompt to the best model with automatic cascading.

Use Fairlearn to measure demographic parity, equalized odds, and fix biased classifiers in Python

Safely deploy ML models with percentage-based traffic splitting, shadow mode, and instant rollback using feature flag systems

Create few-shot prompts that automatically pick the best examples for each query using vector similarity.

Catch LLM hallucinations automatically by scoring generated text against source documents using NLI-based verification

Detect and classify hand gestures in real time with MediaPipe landmarks and a simple rule-based classifier

Protect multi-agent systems from prompt injection and harmful outputs with boundary guardrails at each agent step

Wrap any Python ML model in a web UI with Gradio and deploy it to Hugging Face Spaces in minutes

Create an automatic license plate reader with YOLO detection, OCR text extraction, and real-time video support in Python

Stop bad LLM outputs before they reach users with validators, PII detection, and automatic retries

Get structured, validated data from LLMs every time using Instructor’s patched client with Pydantic schema enforcement.

Train a DenseNet-121 model to detect 14 chest X-ray pathologies and visualize predictions with Grad-CAM attention maps.

Audit whether your ML model leaks training data by building a membership inference detector from scratch with Python

Speed up your ML API with prediction caching and smart batching. Cut response times by 90% and double your GPU throughput with working code.

Write clear model documentation that helps users understand what your AI can and can’t do safely

Send prompts in any language and get responses back, with automatic translation and language detection

Track multiple objects across video frames by pairing YOLOv8 detections with DeepSORT identity matching

Chain multiple LLM calls with validated JSON schemas to build reliable AI data pipelines that never break

Give your LLM chatbots real conversation memory that persists across turns without blowing up your context window

Track pixel-level motion between frames using RAFT optical flow from torchvision in a few lines of code

Stop hallucinations before they reach users with claim-level grounding checks and NLI-based verification for RAG apps.

Speed up your LLM apps by running multiple tool calls at once instead of waiting for each one sequentially.

Fine-tune large language models with prefix tuning using PEFT, cutting GPU memory by 90% while matching full fine-tuning quality

Skip manual labeling. Use Snorkel’s weak supervision to programmatically label thousands of examples in minutes with Python code

Reduce LLM API costs by 40-60% with prompt caching strategies that eliminate redundant token processing across conversation turns