#llm

1 week ago 913

AI Programming

How to Prompt Claude Fable 5 Efficiently: A Practical Guide

A hands-on guide to prompting Claude Fable 5 efficiently: front-loaded task briefs, effort sweeps with runnable Python, prompt caching that bills repeat prefixes at a tenth of the price.

2 weeks ago 170

AI Architecture Programming

The AI App Stack in 2026: Models, Tools, Queues, Vector Stores, and Observability

An architecture map of a production AI application in 2026 model gateway, orchestration, queues and workers, the vector/cache/database data layer with the decisions that matter at scale.

3 weeks ago 161

AI Programming

RAG Is Not Just Search: Chunking, Ranking, Reranking, and Hallucination Control

Go beyond naive vector search chunking strategies, hybrid keyword+semantic ranking with RRF, cross-encoder reranking, context assembly, and layered hallucination control, with each change helps.

4 weeks ago 176

AI Databases Programming

Building a Private RAG System: Architecture, Embeddings, Vector DBs, and Evaluation

A full architecture walkthrough for a private, self-hosted RAG system ingestion, chunking, embeddings, vector databases and the evaluation loop that makes it trustworthy.

1 month ago 101

Python AI

A Practical Introduction to LLMs and RAG in Python, and Where to Go Next

The finale: LLMs explained with the concepts you already own, tokens and prompts, your first Claude API call from Python, a toy RAG engine in the browser, and the full map into the production LLM and MCP series.

1 month ago 312

AI Linux Programming

Local AI in 2026: Ollama, vLLM, Docker Model Runner, and When to Use Each

An honest comparison of local AI tooling in 2026 — Ollama for laptops, vLLM for high-throughput GPU serving, and Docker Model Runner for container-native models, with a decision framework and VRAM sizing advice.

1 month ago 72

AI & LLM

Calling LLMs from Python: The Request Loop, Tokens, Cost, and Retries

Call language models from Python with the Claude SDK: the messages loop, tokens and cost, and a client with timeouts and retries you can inject and test.

1 month ago 72

AI & LLM

Structured Outputs and Function Calling: Getting Reliable JSON from LLMs

Get dependable JSON from language models with structured outputs and function calling, then validate with Pydantic so your code works with typed objects.

1 month ago 82

AI & LLM

Streaming LLM Responses to the Browser with FastAPI and SSE

Stream a real model response to the browser: consume the model stream in Python, forward it through a FastAPI SSE endpoint, and render it live.

1 month ago 67

AI & LLM

Building an AI Agent API: Tool Calls, Memory, and Guardrails

Build a small AI agent API: the tool calling loop, conversation memory, and the guardrails that keep an action taking agent safe and bounded.