AI Archives - Page 2 of 7

Stop Overpaying for GPT-4o: Smart Query Routing with RouteLLM

July 18, 2026

Is GPT-4o draining your budget? Learn how to use RouteLLM to automatically route simple queries to cheaper models, cutting your API costs by up to 50% without sacrificing response quality.

Running Mixtral and Qwen-MoE Locally: High-Performance AI on Consumer Gear

July 17, 2026

Run GPT-4 class models on your own desk. This guide shows you how to use Ollama to deploy Mixtral and Qwen-MoE models locally, with specific tips for VRAM optimization.

LLM Merging with Mergekit: Build Custom Models Without the GPU Tax

June 24, 2026

Ditch the expensive GPU rentals. Learn how to use Mergekit on Linux to fuse LLMs using SLERP, TIES, and Passthrough techniques for custom AI performance.

Ditch the API Fees: High-Quality AI Voice Cloning with Kokoro-TTS

June 20, 2026

Ditch expensive cloud APIs. Learn how to build a private, high-quality Text-to-Speech system using Kokoro-TTS and Python. Perfect for developers needing low-latency, cost-effective AI voices.

Speeding Up Vietnamese Speech-to-Text: From Vanilla Whisper to Faster-Whisper in Production

June 14, 2026

Don't settle for slow transcription. Discover how to build a high-performance Vietnamese STT system using Faster-Whisper, LoRA fine-tuning, and CTranslate2 for 4x faster results.

Advanced RAG: Solving the ‘Broken Table’ Problem with Multi-vector Retrieval

June 7, 2026

Standard RAG fails when tables are split across chunks. This guide shows how to use Multi-vector Retrieval to link searchable summaries to full-context tables and images, boosting accuracy for complex documents.

Stop Feeding Your RAG Garbage: Better Extraction with LlamaParse and LlamaIndex

June 6, 2026

Learn how to build a high-accuracy RAG pipeline using LlamaParse and LlamaIndex. Avoid common PDF parsing traps and extract clean, structured data from complex tables and layouts.

Automating Prompt Optimization with DSPy: Declarative LLM Programming That Replaces Manual Prompt Engineering

June 3, 2026

DSPy lets you define LLM pipelines declaratively — specifying inputs and outputs instead of writing prompts by hand — then automatically optimizes them using labeled examples. This guide covers the trade-offs versus manual prompt engineering and walks through a full working implementation with code examples.

Automating Technical Documentation with LLMs and GitHub Actions

June 1, 2026

Tired of outdated READMEs and missing changelogs? Learn how to build a GitHub Actions pipeline that uses LLMs to automatically update your technical documentation whenever code changes.

Extract Structured Data from LLMs with Instructor and Pydantic: No More JSON Parsing Nightmares

June 1, 2026

Stop fighting with malformed JSON from LLMs. Learn how to use Python's Instructor library and Pydantic to extract reliable, type-safe structured data for production AI applications.