LLM

When I’m in AI Studio mode (rapid prototyping, lots of experiments), I want to spend my time on prompts, evals and breaking the code — not on re-learning yet another SDK. That’s why I use aisuite for most of my AI projects: One client API across providers Switching models is usually just changing a string like openai:gpt-5-mini → minimax:MiniMax-M2.1-lightning or even local ollama It stays close to the OpenAI-style shape, so it’s easy to adopt Lately I use quite a bit Minimax for coding tasks because it hits a great ratio of price vs quality....

Running local LLMs is kinda addictive. New model drops? Gotta try it. But here’s the thing—you need a quick way to check if a model’s actually thinking or just spitting out vibes. The Prompt Here’s my go-to sanity check: What is the number that rhymes with the word we use to describe a tall plant? That’s it. Dead simple. Why This Works It’s not about being hard. It’s about being consistent....

Large Language Models are superuseful right, but they have a well-known weakness: hallucinations. These are confident-sounding responses that are factually incorrect or completely fabricated. While no technique eliminates hallucinations entirely, these three strategies significantly reduce their occurrence in production systems. 1. Provide an Escape Hatch One of the most effective ways to reduce hallucinations is giving the model permission to admit uncertainty. LLMs are trained to be helpful, which sometimes leads them to generate plausible-sounding answers even when they lack sufficient information....