Everything we publish — product, engineering, and GTM.
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March 8, 2026 · 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
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
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
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
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
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
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
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
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
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
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
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
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
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 23, 2026
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
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.
February 22, 2026
Katherine Holland · 3 min read
Choosing between a single giant dense model and alternatives like Mixtures of Experts (MoE) or model merging is less about ideology and more about trade-offs: how much quality you can buy, how predictably you can run it, and how painful it will be to ship and maintain. Each approach can win depending on whether you care most about peak accuracy, serving cost, iteration speed, or operational simplicity.
February 21, 2026
Lillian Kim · 3 min read
GPUs keep winning in AI not because they’re “perfect,” but because they hit a rare combination: high throughput, strong software support, flexible programmability, and a supply chain that can actually deliver millions of chips into real systems. Custom accelerators and NPUs can outperform GPUs on specific workloads, yet they often struggle to match the broad usefulness and frictionless adoption that make GPUs the default choice for training and increasingly for inference.
February 20, 2026
Ben Larson · 3 min read
In the current AI boom, AI Agent and Agentic AI are often treated as synonyms, but the gap between them is the difference between a tool that waits for orders and a teammate that pursues goals. Understanding this shift—from the diligent clerk to the autonomous strategist—is key to seeing where automation is heading.
February 19, 2026
Ben Larson · 3 min read
If you’ve ever tried to “just extract the text” from a Word document and keep all the formatting, you’ve probably discovered it’s anything but “just.” Under the hood, Word is closer to a layout engine plus a tiny CMS than a simple text editor, and that makes faithful extraction surprisingly hard.
February 18, 2026
Annie Hayes · 3 min read
Agentic AI is a new generation of artificial intelligence that does not just answer questions, but can set sub‑goals, make decisions, and take actions on its own to achieve an outcome with minimal supervision. It is popular because it promises a step change from “smart assistants” to semi‑autonomous digital workers that can operate across many systems and workflows.
February 17, 2026
Annie Hayes · 3 min read
RAG, short for Retrieval-Augmented Generation, is one of the most practical ways to make AI chatbots and assistants more useful for real work. It combines two things: searching for relevant information and generating a natural-language answer. The result is an AI system that can respond with content grounded in specific documents rather than relying only on what it learned during training.
February 16, 2026
Dustin Collins · 3 min read
Chunking is one of those quiet decisions that can make AI search feel crisp and helpful—or scattered and frustrating. Good chunking turns long documents into search-friendly pieces that match user questions, preserve meaning, and keep retrieval costs under control. Below are practical strategies you can apply to get better results from keyword search, vector search, or hybrid retrieval.
February 16, 2026
Jessy Chan · 3 min read
Enterprise reporting often suffers from the same recurring issues: inconsistent definitions, manual data prep, slow cycles, and dashboards that look polished but don’t answer the questions leaders actually have. AI can help—but only if it’s applied to the right parts of the reporting workflow and supported by solid data practices.
