AskHandle Blog

Featured insights from the AskHandle team.

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How Can You Build an AI Agent to Log In and Export Data?

March 24, 2026 · Melissa Olson · 3 min read

If your team is still manually logging into dashboards just to export CSV files, you’re spending valuable time on something that can be automated in a few dozen lines of code—this guide shows how to build a simple AI-powered agent that logs in, navigates your internal dashboard, and downloads data reliably, along with the practical considerations that separate a quick script from a production-ready system.

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March 23, 2026
Build a Chess AI That Thinks Like a Human — A Beginner-Friendly Guide with ChatGPT
Emily Henderson · 3 min read
What if your chess app didn’t try to calculate every possible move like old-school engines, but instead played more like a human—using intuition, simple plans, and a bit of “thinking”? The good news is: with today’s tools like ChatGPT and open-source libraries, even a high school student can build a chess AI that feels intelligent without needing massive computing power or years of research.
March 21, 2026
Which regex turns plain-text URLs into clickable HTML links?
Nina Kimes · 3 min read
Turning plain-text links into clickable HTML anchors is one of those tasks that looks simple until you run into punctuation, parentheses, query strings, and edge cases like already-linked URLs. Regex can help a lot, but the “best” pattern depends on what you consider a valid URL, what inputs you expect, and how safe you need the output to be.
March 20, 2026
Why Are GPUs Still the Top Choice for AI Training Over NPUs?
Dustin Collins · 3 min read
AI training is mostly a story of moving and multiplying huge matrices, again and again, across weeks of iteration and constant model changes. In that world, GPUs continue to win mindshare and budgets even as NPUs (neural processing units) improve quickly. The reason isn’t that NPUs are “bad” or that GPUs are “perfect”; it’s that training demands a mix of flexibility, mature software, memory scale, and predictable performance across many model types—areas where GPUs have built a long lead.
March 19, 2026
Can You Train an AI Stock Trader on Historical Market Data?
Lillian Kim · 3 min read
Training an AI stock trader on historical market data is possible, and people do it every day in research labs, hedge funds, and personal projects—but “possible” is not the same as “profitable” or “reliable.” Historical prices, volumes, order book snapshots, and related indicators can teach models to detect patterns and make predictions or trading decisions. The real question is whether those learned patterns will hold up when the market changes, costs are applied, and risk is measured in real money.
March 18, 2026
Why Is It So Hard for AI to Search and Find the Right Number in Excel?
Katherine Holland · 3 min read
If you've ever asked an AI assistant to look up a value in your spreadsheet and gotten back a confidently wrong answer, you're not imagining things — and it's not a random glitch. AI feels dumb with Excel because, under the hood, it is not doing cell-by-cell lookup the way Excel does. Instead, it is doing fuzzy pattern matching over text, which is great for language but terrible for finding exact numbers in specific rows and columns. That fundamental mismatch is exactly why AI keeps mixing and matching data that you, as a human, can see is obviously separate.
March 17, 2026
How AI Coding Solves Real Problems
Lillian Kim · 5 min read
Large language models are often treated like writing assistants, but one of their most useful abilities is coding. When you ask AI to write code that produces a result, you move beyond getting a one-time answer and start building a tool that can solve the same problem again and again. That is what makes AI coding so practical. Many everyday tasks are not just writing tasks. They are formatting tasks, data tasks, automation tasks, and workflow tasks. Instead of asking AI to only “help,” you can ask it to build the thing that does the work.
March 16, 2026
Why Box-Lifting Humanoids Are Hard
Lillian Kim · 3 min read
At first glance, this problem seems like it should already be solved. Vision models can detect boxes. Speech systems can understand simple commands. Robots can run, jump, dance, and even play the piano in carefully staged demos. So why is pick up that box still such a hard problem for humanoids in factories? The answer is that the hard part is not object detection. The hard part is integrating perception, language, manipulation, balance, safety, and error recovery into one system that works reliably in the real world. In robotics terms, the challenge is not a single model. It is the full stack.
March 15, 2026
Which AI chips lead now?
Ben Larson · 3 min read
The answer to “what is the most powerful AI chip?” depends on what kind of power you care about. One class leads in giant GPU servers for training and reasoning, another wins on memory-heavy accelerator design, another is the biggest single piece of AI silicon ever sold, and a newer workstation card brings serious AI work much closer to a normal desk. That means the best chip is not one universal winner. It is the one that fits the way you plan to run models, store weights, cool the hardware, and pay the power bill. ([nvidia.com](https://www.nvidia.com/en-us/data-center/dgx-b300/?utm_source=openai))
March 14, 2026
How Should You Chunk Documents for AI?
Lillian Kim · 3 min read
Document chunking sounds simple until you try to build it for a real AI system. Split text too aggressively and the model loses context. Make chunks too large and retrieval gets noisy, slow, and expensive. Good chunking sits in the middle: small enough to keep results precise, large enough to preserve meaning. If you want better search, cleaner summaries, and stronger question answering, chunking deserves careful thought from the start.
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
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
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.