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What Is Codex?

March 13, 2026 · Emily Henderson · 3 min read

When people ask what Codex is, the short answer is simple: Codex is OpenAI’s coding agent. It can read code, edit files, run commands, and help with software tasks either in local tools or in the cloud. That makes it more than a chat tool that returns snippets. It is built to take on pieces of real engineering work and hand back results you can inspect, revise, and ship. ([platform.openai.com](https://platform.openai.com/docs/codex))

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March 13, 2026
What Does a Machine Learning Algorithm Look Like?
Jessy Chan · 3 min read
If you are new to machine learning, it is normal to picture something mysterious: long code, hard math, and strange symbols on a whiteboard. The truth is much simpler. A machine learning algorithm often looks like a pattern finder. Sometimes it can be written as a short math formula. Sometimes it looks more like a list of rules or steps. In many cases, it is both: a formula plus a method for adjusting the numbers inside that formula until the predictions get better.
March 12, 2026
Why Does LLM Reasoning Cost So Many Tokens?
Billy Ewing · 3 min read
When people first hear that a language model can “reason,” they often picture a quick burst of thought followed by an answer. The reality is less dramatic and more mechanical: reasoning usually means the model generates, tests, revises, and extends many pieces of text before it reaches a response. Since tokens are the basic units used to process both the prompt and the reply, every extra step adds cost. What feels like a single answer to a person can involve a long internal path for the model, and that path is what drives token usage up.
March 11, 2026
How do you search keywords in a database?
Alicia Gopin · 3 min read
Keyword search is one of the most common tasks in software: finding rows where a word or phrase appears in a text field. It sounds simple, but the “right” method depends on your database, the size of your data, and what kind of matching you need (exact, partial, case-insensitive, multi-word, ranked results, and so on). This article walks through practical ways to implement keyword search, with tips that keep queries accurate and performant.
March 10, 2026
What does a modern pre-training data pipeline look like?
Jessy Chan · 3 min read
Pre-training looks less like “download the internet and train” and more like an industrial process: many stages that progressively turn noisy web-scale text into a shaped mixture of tokens that matches a training plan. Modern pipelines spend serious effort on deduplication, filtering, and curriculum-style mixing because model quality depends on what you feed it, not just how much you feed it.
March 9, 2026
Why don’t Chinese organizations rely on websites?
Billy Ewing · 3 min read
Many visitors to China notice a pattern: official websites look static, product pages feel incomplete, and news sections stop updating for long stretches. This can be surprising if you come from places where a website is the primary “source of truth” for a company or institution. In China, websites often play a secondary role behind apps, social channels, and direct relationship-based communication. That doesn’t mean Chinese firms are less digital; it often means they publish—and control—information differently.
March 9, 2026
FlowRL: Teaching AI to Think in More Ways Than One
Ben Larson · 3 min read
Imagine you're studying for a math test and you only ever practice one type of problem. When the real test comes and the questions look slightly different, you're stuck. That's exactly the problem that FlowRL — a new way to train AI — was built to solve.
March 8, 2026
What Is OpenClaw?
Annie Hayes · 3 min read
OpenClaw is an open-source AI assistant that runs on your own machine and works through chat apps you already use, including WhatsApp, Telegram, Discord, Slack, Teams, Signal, and iMessage. It is designed as a personal agent platform, which means it does more than answer prompts: it can remember context, connect to tools, browse the web, work with files, and carry out actions on a computer.
March 7, 2026
What are the downsides of using iframes on a website?
Emily Henderson · 3 min read
Iframes can feel like a convenient shortcut: drop a snippet into your page and instantly show a map, a video, a form, or even a whole external page. That simplicity is real, and in some cases it’s the right choice. Still, iframes come with tradeoffs that affect performance, security, accessibility, SEO, maintenance, and the overall polish of a site.
March 6, 2026
How to Make AI Search Your Website Knowledge Efficiently?
Alicia Gopin · 3 min read
Many small and medium-sized businesses run WordPress websites that already contain valuable knowledge—help center articles, FAQs, service descriptions, and documentation. When adding an AI assistant to answer customer questions, the key challenge is allowing the AI to access that knowledge quickly and reliably without constantly loading live webpages.
March 5, 2026
What Are MCP Servers and Clients?
Ben Larson · 3 min read
Modern AI applications often need to interact with real systems—databases, APIs, documents, and developer tools. But before MCP existed, integrating these systems with AI was messy and repetitive. Every AI product had to build its own custom connectors to every tool, which meant the same integrations were constantly being rebuilt in slightly different ways. As developers started building more AI assistants and agents, this fragmentation became a serious bottleneck. The Model Context Protocol (MCP) was introduced to solve this problem by providing a standardized way for AI systems to connect to external tools and data sources.
March 4, 2026
How Can You Build a Simple Neural Network on a MacBook?
Dustin Collins · 3 min read
Building a simple neural network on a MacBook is a practical way to learn the basics of machine learning without special hardware. With the right Python setup and a small project, you can train a model that recognizes patterns in data in under an hour, while also learning the core workflow: prepare data, define a model, train it, and evaluate results.
March 3, 2026
Who Owns AI-generated Artwork?
Alicia Gopin · 3 min read
AI-generated artwork raises a deceptively simple question: if a model was trained on millions of images made by other people, who—if anyone—owns what the model produces? The answer depends on a mix of copyright rules, contract terms, and how the artwork was created and used. It also depends on which country’s law applies, because copyright is not identical worldwide.
March 2, 2026
Will LLMs Replace Search Engines?
Elise Taylor · 3 min read
Search has long been the default way to find answers online: type a query, scan a list of results, open a few tabs, and stitch together the best information. Large language models (LLMs) offer a different promise—ask in plain language and receive a direct, coherent response. That shift feels big enough to raise a real question: are LLMs about to replace search engines, or will they simply change what search means?
March 2, 2026
How do AI models build long-term memory across sessions?
Alicia Gopin · 3 min read
Chatbots feel most helpful when they pick up where you left off: your preferences, your ongoing project, the tone you like, and the details you already shared. Traditional AI conversations reset when the session ends, but newer systems are moving toward “long-term memory” that persists across days or weeks. This shift isn’t about giving a model a human mind; it’s about engineering reliable ways to store, retrieve, and use prior context without causing privacy problems or compounding mistakes.
March 2, 2026
How can AI help evaluate job applicants?
Annie Hayes · 3 min read
Hiring teams are flooded with resumes, screening calls, interview notes, and feedback forms, yet still struggle to predict who will perform well and stay. AI can help HR move faster and make decisions more consistent—if it’s used as a structured assistant rather than an automated judge. Below are practical, “start this week” ways to use AI in applicant evaluation, with clear guardrails to keep the process fair and defensible.
February 28, 2026
Can We Shrink Billion-Parameter Models Without Losing Intelligence?
Ben Larson · 3 min read
Billion-parameter models feel like the peak of modern AI: fluent, versatile, and surprisingly capable across many tasks. Yet their size brings costs—latency, energy use, memory pressure, and deployment friction. The big question is whether “intelligence” is inseparable from sheer parameter count, or whether we can compress these models while keeping most of what makes them useful.
February 27, 2026
Will Serious LLMs Ever Run Fully On-device?
Emily Henderson · 3 min read
For years, the default way to use large language models has been to send prompts to a remote server and wait for an answer, but that pattern is starting to look less fixed than it once did. Chips are getting better, models are getting smaller and more efficient, and consumer devices now ship with dedicated AI accelerators. The real question isn’t whether on-device LLMs are possible—it’s what “serious” means for consumers, and which trade-offs people will accept.
February 26, 2026
What Is an NPU? A Simple Guide to the AI Processor in Modern Devices
Jessy Chan · 3 min read
You’ve probably started seeing laptops and phones advertised as “AI PCs” or “AI-ready devices.” The reason isn’t just software — it’s a new chip inside them called the NPU (Neural Processing Unit). Unlike a CPU that runs programs or a GPU that handles graphics, an NPU is designed specifically to run artificial intelligence directly on your device. It enables live translation, video call background blur, smart photo search, voice assistants, and even offline AI writing tools — all without sending your data to the cloud.
February 26, 2026
How does an enterprise feature release pipeline work?
Annie Hayes · 3 min read
Shipping a new feature to production in a large enterprise is rarely a single “deploy” button. It’s a coordinated pipeline that balances speed with reliability, audit needs, and shared ownership across many teams. The result is a release process that looks less like a straight line and more like a set of controlled gates, feedback loops, and automated checks.
February 25, 2026
What Is Batch Processing When Using Large Language Models (LLMs)?
Aria Singh · 3 min read
Large Language Models (LLMs) like GPT-style systems have unlocked powerful capabilities — summarization, classification, coding, search, document analysis, and conversational agents. But once you move beyond a single prompt and start building real applications, you quickly run into a practical reality: you rarely need the model once. You often need it hundreds, thousands, or millions of times. That is where batch processing comes in. Instead of sending requests one-by-one in real time, batch processing groups many LLM tasks together and runs them as a scheduled or bulk job. This changes how you design systems, manage cost, and scale AI workflows.
February 24, 2026
What Is COBOL? The Language Quietly Running the Modern World
Alicia Gopin · 3 min read
Most people assume the technology behind their banking app, paycheck, taxes, or credit card is modern — cloud servers, microservices, and shiny web APIs. In reality, a surprising portion of those transactions still depend on software originally designed when computers filled entire rooms and storage was measured in kilobytes. That software is written in COBOL (Common Business-Oriented Language), a programming language created in 1959 that never went away. It didn’t survive because companies are lazy or outdated — it survived because, for a very specific job, it worked extremely well, and replacing it turned out to be far harder than anyone expected.
February 23, 2026
Will future LLMs still hallucinate?
Nina Kimes · 3 min read
Large language models (LLMs) often feel fluent enough to be trusted, yet they can confidently state false facts, invent citations, or misread a question. That mismatch between polished language and shaky truth is what people call “hallucination,” and it’s one of the biggest barriers between today’s chatbots and dependable assistants.
February 22, 2026
What Is ngrok?
Ben Larson · 3 min read
A lot of development happens on a laptop: a local web server, a webhook endpoint, an API you’re testing, or a demo you want to show to someone. The problem is that “localhost” is private. ngrok solves that by giving your local app a temporary public URL that forwards traffic straight to your machine.