Publications tagged "Llm"

AI coding assistants are like an extra gear on a bicycle: they help you go faster but don’t replace the rider. This post explores the hidden patterns, from backwards compatibility obsession to test regression and security gaps, and how to stay in control.

Hallucinations in large language models (LLMs) are no longer a niche concern. With real-world incidents causing significant damage, the question remains: can you trust an LLM?

Deploy state-of-the-art LLMs locally with Ollama! This guide walks through how to set it up, then dives into practical workflows: offline embedding search, secure code summarization, local chat agents, and even smart-home integration with Home Assistant. Whether you’re tinkering on a laptop or running a 70B model on dual GPUs, local AI is finally usable and useful.

LLMs blur the line between code and data – collapsing the security boundary traditional systems rely on. This post diagrams the architectural flaw behind prompt injection, explains why structured output and JSON schemas can’t restore trust, and explores layered defenses from runtime controls to agent isolation.

A critical vulnerability for Large Language Models (LLMs), prompt injection is easy to trigger, hard to contain, and widely misunderstood. This post revisits early ChatGPT attacks with real screenshots, showing how I bypassed safety layers and what it revealed about the fragility of prompt-based controls.