Machine Learning

The Popular Comparison A teenager learns to drive in 10 hours, but an AI system needs millions of simulations and millions of hours of simulated data. This comparison appears frequently in AI discussions, but once you look closely, it’s not fair. Here’s why. Where This Example Comes From I recently saw this example in an interview with Ilya Sutskever, co-founder of OpenAI who later started his own AI startup with significant investment backing. ...

I want to talk about an exciting research paper that has been on my list since its release last month. I finally had the opportunity to dive deep into it, and I believe this represents a fundamental shift in how AI understands code. What Are Code World Models? Let’s start with the basics and some impressive numbers. Meta’s Code World Model is an open weights large language model with 32 billion parameters. It features a dense, decoder-only architecture with a 131K token context size. While these specifications are noteworthy, the truly exciting aspect lies in what this model actually does. ...

When StarRocks introduced vector search, I had to test and see myself. Is it just a toy feature just bolted on an OLAP DB or not? Turns out this thing is actually good enough that you can fold your approximate nearest-neighbor (ANN) workloads into the same cluster that’s already powering your analytics dashboards. In this article, I decided to skip skip the benchmarking details and focus on what you actually need to get vector search running. ...

At the heart of both philosophy and computer science lies a single, crucial question: How do we build a model of the world? Plato and Aristotle approached this question from completely opposite directions, and their answers map almost perfectly onto modern programming and machine learning approaches. Plato’s Perfect Forms in Your Code Plato believed everything we see is an imperfect copy of a perfect, unchanging form. Take a circle you draw on paper, according to Plato, it’s just an imperfect representation of a perfect circle that exists as an abstract form, with exact mathematical properties no physical circle can fully achieve. ...

OpenAI recently released the GPT-5 family, introducing three distinct options for Pro users: GPT-5 Fast, GPT-5 Thinking, and GPT-5 Pro. While it’s common knowledge that GPT-5 Fast handles simple tasks and GPT-5 Pro tackles complex ones, the underlying mechanisms remain unclear to many users. This post provides a concise explanation of how each variant operates and when to use them effectively. GPT-5 Fast For the context of this article, we will use GPT-5 Fast as our base model, think of it as a black box optimized for speed, good old LLM. When you submit a query, it processes the request and delivers an answer quickly without extensive deliberation. ...

We keep hearing this: “AGI is coming.” But what exactly is AGI? Let’s explore this critical question. Three Different Lenses Let’s look at AI through three different lenses. The Economics Lens OpenAI defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Their focus is entirely economic—it’s about jobs and economic productivity. When framed this way, the definition essentially states: if AI surpasses humans at most work that people are paid to perform, then we have achieved AGI. The moment AI can produce the majority of economically valuable work, we’ve crossed the AGI threshold. ...

Table of Contents Introduction Prerequisites Project Setup Architecture Overview Exporting Models to ONNX Loading an ONNX Model Text Generation Pipeline Building the CLI Chat Interface Going Further Conversation Memory Temperature & Top-p Sampling Streaming Tokens Performance Optimizations Testing Deployment Considerations Conclusion TLDR ...