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Beyond Code: How to Use AI Coding Agents for Non-Technical Tasks

Beyond Code: How to Use AI Coding Agents for Non-Technical Tasks

AI Coding Agents were designed to help developers—fixing bugs, adding features, writing new code. But if you only use them for that, you're leaving serious potential on the table. The truth is, tools like Claude Code, Cursor, Codex, and Gemini CLI are remarkably capable at handling all sorts of non-coding work on your computer. And once you start thinking differently about what they can do, you'll wonder how you ever managed without them.

You could hand off auto-filling web forms, managing your personal budget, finding the cheapest product prices, researching customer data, preparing reports, or even learning a new language. But the real shift is mental: instead of assuming you have to do everything yourself, start asking "Could an AI Coding Agent handle this for me?" constantly.

Why Use Coding Agents for Everything You Do on Your Computer?

Most people assume Coding Agents are exclusive to programmers. That misconception misses the bigger picture. These tools can handle nearly any task you perform on a computer. They don't just understand code—they can read documents, interact with web browsers, manipulate files, search for information, and automate countless workflows.

When we talk about Coding Agents here, we mean Claude Code, Cursor, Codex, Gemini CLI, and similar platforms. Each has distinct strengths, but overall they're becoming smarter every update and can take on work that previously required manual human effort.

There are two immediate advantages. First: time savings. By running multiple tasks in parallel through AI, you accomplish more in the same hours. Second: automation of repetitive or tedious work, freeing you to focus on what actually matters.

Non-Technical Work That Coding Agents Can Handle

Here are task categories you can genuinely delegate to Coding Agents regularly:

  • Personal budget management and expense tracking.
  • Creating HTML reports.
  • Designing presentation slides.
  • Accessing and interacting with websites.
  • Supporting sales operations.
  • Researching information online.
  • Learning foreign languages.

But the list isn't what matters—the mindset is. Every time a new task lands, ask yourself whether a Coding Agent could help.

Budget Management, Reports, and Presentations

One of the most practical applications is personal finance management. Download your transaction history from your bank or digital wallet, feed it to a Coding Agent, and let it work. The AI categorizes income and expenses, creates a budget, and generates an HTML financial report you can actually visualize and track over time.

Creating presentation slides gets dramatically easier too. Describe your topic, and a Coding Agent generates a complete PDF presentation immediately. Then you just request refinements until it matches your vision.

Here's an effective pro tip: give the Coding Agent samples of presentations or documents you've created before. It learns your style, color choices, layouts, and writing voice—then applies that "personal stamp" to everything new it creates. This trick works for countless other tasks. When AI sees your old work, it understands your patterns far better.

Let Coding Agents Handle Website Interactions

Another genuinely useful application: delegating website tasks to a Coding Agent.

You can set up a dedicated browser profile for your Coding Agent with your necessary accounts pre-saved. Then the AI logs in automatically, creates API keys, changes settings, downloads data, or performs other actions—all without you lifting a finger. While it works in the background, you tackle other priorities. This is especially valuable for complex websites with many steps or confusing interfaces.

One caveat: some websites explicitly prohibit AI agents in their terms of service, or deploy CAPTCHAs and verification measures specifically to block automated access.

Sales Support and Research

Coding Agents make effective sales assistants. If you work with customers or manage a CRM, create a dedicated folder containing all your sales data so the AI always has full context when working on your behalf.

With everything stored centrally, the Coding Agent remembers interaction history, understands your customers more deeply, and becomes increasingly effective. It can independently research prospect information, track progress, set reminders, and consolidate data from multiple sources.

Beyond sales, AI excels at online research. Say you want blackout curtains in a specific size at the lowest price. Give your requirements to a Coding Agent. It searches the internet, compares options across retailers, builds a price comparison table with links, and delivers everything in minutes. Instead of opening dozens of websites yourself, you get a complete summary ready to review.

Using Coding Agents as Language Tutors

Outside of work, Coding Agents become personal language tutors. Create a dedicated learning folder where the AI maintains your vocabulary list, notes which words you tend to forget, schedules 10-minute daily review sessions, and tracks your progress over time.

What's interesting here is that modern language models support dozens of world languages. They converse fluently in uncommon languages like Norwegian. While slang and regional dialects sometimes trip them up, their teaching and conversation abilities remain genuinely strong.

Shift Your Mindset Around Coding Agents

The most important change isn't learning new features—it's rethinking how you approach work.

View Coding Agents as a collaborator present for every computer task. Rather than working solo, AI backs you up by recalling forgotten information, remembering workflows so it handles them independently next time, and taking repetitive work off your plate. You accomplish more simultaneously and productivity rises noticeably.

Obviously, not everything should go to AI. Personal communication—responding to close friends or family—deserves your human touch. Don't use AI to write personal essays or intimate social posts. Your writing reflects your thinking and perspective. Handing that entirely to AI drains the meaning from what you've created.

Coding Agents Enhance Rather Than Replace

Some argue that creative work shouldn't be delegated to AI. That view oversimplifies things. Coding Agents are tools that expand human capability. You still set the goals, provide the requirements, and direct the AI's work. When it runs automatically, it's executing what you designed.

Actually, Coding Agents can boost creativity. Maybe you've had dozens of app ideas but lacked time or skills to build them. Now a Coding Agent quickly transforms those concepts into working prototypes. Ideas that stayed on paper before now become real projects worth testing.


Coding Agents aren't exclusively for developers. Any task performed on a computer has potential to be supported or automated by AI at some level.

The real habit to build: constantly ask "Can I delegate this to a Coding Agent?" Whether it's financial planning, report writing, slide design, market research, or language learning, today's AI Coding Agents save considerable time and effort.

You remain the final decision-maker. Coding Agents don't replace you—they serve as an intelligent assistant, helping you work faster, accomplish more, and reclaim hours to spend on what genuinely matters.


Description: AI Coding Agents aren't just for developers. Learn how tools like Claude, Cursor, and Gemini CLI can automate everyday computer work.

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Rowboat: The Open-Source AI Assistant That Does What Claude Cowork Should Have Done From Day One

Rowboat: The Open-Source AI Assistant That Does What Claude Cowork Should Have Done From Day One

Using an AI assistant has always felt fragmented. Sure, AI can help you write that email, but you're still the one shuttling information back and forth—copying text into ChatGPT or Claude, explaining what you need, waiting for a draft, then pasting it back into Gmail. It's tedious. What's frustrating is that the AI isn't really the bottleneck; it's all this manual context-switching between tools. The good news? That friction is starting to disappear, thanks to products like Claude Cowork. But there's an open-source alternative that delivers something many people actually wish Cowork had built in from the start.

Download Rowboat for Windows Download Rowboat for macOS Download Rowboat for Linux

Rowboat Is the Open-Source Answer, With Persistent Memory Built In

Rowboat's main dashboard showing an evening greeting, a gym event scheduled for 11 hours later, and 3 new messages in the inbox
Rowboat's main dashboard showing an evening greeting, a gym event scheduled for 11 hours later, and 3 new messages in the inbox

Let's back up for a moment. Claude Cowork essentially takes the agentic capabilities that power Claude Code and repackages them into something friendly for non-technical users. Instead of living in a terminal window focused purely on coding, Cowork gives Claude a proper workspace where it can manipulate files, integrate with other services, and handle multi-step workflows across different use cases.

Rowboat pursues a similar "AI coworker" concept but from a different angle entirely. The best way to think about Rowboat is as an AI workspace that continuously builds up knowledge about your work in the background. Rather than forcing you to rebuild context every time you need help with something new, Rowboat maintains a persistent knowledge graph about the people, projects, conversations, meetings, and information that make up your professional life.

Rowboat is completely open-source, which means you can see exactly how it works and modify it if you want. It also isn't locked to any specific AI model, so you can run local models through Ollama or LM Studio, or plug in any model hosted on your preferred service. Since Rowboat is open-source and runs on your own machine, your data stays local—stored as regular Markdown files instead of getting locked into some proprietary cloud service.

Rowboat Feels Like a Genuine Workspace, Not Just a Chatbot

Rowboat's main interface showing an inbox preview and a dashboard of background AI agents
Rowboat's main interface showing an inbox preview and a dashboard of background AI agents

Rowboat feels intentionally designed for work that doesn't fit neatly into a single session. Everything connects to the same system. Your email, calendar, notes, meetings, browser activity, and conversations all feed into the system's "Brain." That accumulated context stays ready whenever you return to work later.

You'll notice this immediately when using Rowboat to plan an upcoming business trip. It doesn't just dig through forwarded emails and extract dates—it cross-references them with your Google Calendar, identifies what's confirmed versus tentative, spots gaps in your plan, and reminds you of pending meeting invitations. Then when you ask follow-up questions about specific events in a separate chat thread, you don't have to explain the entire trip context all over again.

A Rowboat conversation titled 'Germany Business Trip Itinerary' showing notes about Turkish Airlines bookings and calendar entries
A Rowboat conversation titled "Germany Business Trip Itinerary" showing notes about Turkish Airlines bookings and calendar entries

What's interesting is that Rowboat genuinely feels like a workspace rather than a tool you visit when you need something done. The main dashboard works as a central hub for your work: upcoming calendar events, new emails, unfinished tasks, background processes, plus access to your notes, meetings, Brain, and separate workspaces—all without leaving the app.

The interface actually resembles those Notion dashboards people spend hours manually setting up. The crucial difference? Rowboat doesn't just show links to information. The inbox, calendar, notes, and projects displayed on the interface actively feed contextual data back to the AI. Everything connects by design from the start, rather than being bolted on as separate integrations to a chatbot.

You Can Use Almost Any AI Model With Rowboat

One of Rowboat's biggest advantages over Cowork is flexibility. With Cowork, you're locked into Claude by default. Rowboat lets you choose which AI model powers your experience. Use Rowboat's built-in models (with higher quotas on paid plans), or configure your own. It supports OpenAI, Anthropic, Gemini, OpenRouter, local models via Ollama, and any custom endpoint compatible with OpenAI's API.

Rowboat's "Brain" Feature Is More Impressive Than It First Appears

Rowboat Brain's graph view showing 20 interconnected notes across different folders
Rowboat Brain's graph view showing 20 interconnected notes across different folders

If you've ever used Obsidian's graph view, Rowboat Brain will feel instantly familiar. As Rowboat learns about your work, it creates linked notes about people, companies, projects, and topics it encounters, gradually building a visual knowledge graph. Click any node and you see the actual information Rowboat has collected and stored about that entity.

At first, many people assume it's just a pretty visualization of information extracted from email. Then connect your Gmail account and let Rowboat run. Within surprisingly little time, it builds a profile about you with details that go far beyond basic facts. For instance, if you're a tech journalist, Rowboat recognizes that immediately—it knows which publications you write for, what kinds of products you review, and even some of the PR relationships you've built. That's not shocking given it has email access.

A Rowboat Brain profile page for Mahnoor Faisal, a tech journalist working in Karachi, Pakistan
A Rowboat Brain profile page for Mahnoor Faisal, a tech journalist working in Karachi, Pakistan

What's genuinely surprising is the subtlety and nuance in how it analyzes. Rowboat dedicates entire sections to describing how you communicate with external PR contacts. It notices you typically open with friendly greetings and thanks, keep responses concise, mention which publication you represent when relevant, and provide specific time slots when scheduling press calls or information exchanges.

Then it goes deeper still. Rowboat creates a separate section for long-term PR relationships and spots how your writing style shifts when talking to people you know well. It observes that you become more casual, throwing in "lol," "hahaha," or capital letters for emphasis, and that you're generally more open and enthusiastic when sharing feedback about products you've tested with familiar contacts.


Description: Meet Rowboat, an open-source AI workspace that maintains continuous context about your work. Here's how it compares to Claude Cowork.

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Building and Managing Skills in Gemini Spark: A Complete Guide

Gemini Spark is a powerful feature within Gemini designed to automate workflows by defining specific processes and instructions. Here's what makes it useful: instead of repeatedly typing the same commands or entering identical parameters for similar tasks, you can build a Skill once—complete with guidelines, rules, and reference materials—and Gemini will apply it automatically whenever you need it. What's interesting here is that you can chain multiple Skills together to create sophisticated workflows that handle everything from simple, one-off tasks to complex, multi-step operations. Below, we'll walk you through creating and managing Skills in Gemini Spark.

How to Create Skills in Gemini Spark

Building a Skill through Gemini's AI Assistant

Step 1:

Start by opening Gemini and selecting Spark. Once you're there, scroll down and choose Skills from the menu.

Next to that section, you'll see Create with Gemini. Click on it to get started.

Step 2:

You'll transition to Gemini's interface, where the AI assistant helps you build your Skill. Describe the Skill you want with as much detail as possible so Gemini can properly implement it. Type your request in the input box below.

Instantly, your Skill springs to life based on your description. Below it, you'll see the command syntax for invoking this Skill.

Further down, you'll find a step-by-step workflow outline that breaks down exactly how Gemini will execute the Skill based on your description.

Using Pre-Built Templates to Create Skills

In the Skills section, under Suggestions, you'll discover ready-made templates you can use immediately.

Each template includes the core Skill content and step-by-step instructions. To create the Skill, click Create in the top right corner. You can also use these templates as examples to guide your own custom Skill development.

After a moment, Gemini Spark saves your new Skill, ready for whenever you need to use it.

Creating a Skill from Scratch with a Blank Template

In the Skills section, click on Create Manually to build a Skill entirely from scratch.

You'll see a form where you enter the Skill name at the top, followed by a section for detailed instructions on how the Skill should work. Once you've filled everything in, click Create to save it.

Managing Your Gemini Spark Skills

The Skills interface displays all the Skills you've created in Gemini Spark. To access management options for any Skill, click the three-dot menu icon next to it. A dropdown menu appears with various options.

To use a Skill right away, select Use Now. You can also edit the Skill—either with Gemini's assistance or manually.

When you select Use Now, the Skill appears in chat as /skillname. You then input your specific request, and Gemini executes the Skill based on the instructions and guidelines you defined earlier. Gemini returns the output matching your request—for example, below is a Skill designed to generate marketing ideas for bottled coffee products.

The complete output follows the instructions you created for the Skill.

Skill execution result in Gemini Spark


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What is Meta AI on Threads? Complete Guide to Using It

Meta just rolled out a fresh feature on Threads called Ask Meta AI, and it's designed to boost adoption of Meta's chatbot while making life easier for users hunting down information about posts. Here's what's interesting: you can fire questions at the bot about literally any aspect of a post—whether that's summarizing the text, digging into embedded videos, or even requesting information Meta pulls from external sources. All without leaving the app.

Meta is rolling this AI-powered platform across its entire ecosystem in different forms. On Instagram, it powers image editing tools. On Threads, it works as an inline chatbot for individual posts. Users can accomplish a lot through simple, straightforward commands—no complex prompts required. Below is your step-by-step guide to getting the most out of Ask Meta AI on Threads.

How to Use Ask Meta AI on Threads

Step 1:

Open any post on Threads and tap the three-dot menu icon in the top right corner. Then select Ask Meta AI from the menu.

Step 2:

The AI chat interface will open and automatically display key information about the post—including text, images, videos, and other media. No input needed on your part. Meta serves up the essential details in your language, highlighting the most important points from the post.

Want deeper insights or have questions about video content? Just type your query and send it.

Step 3:

Meta AI extracts and analyzes video content for you. Everything comes back in your preferred language, even if the original post or video was created in a different language.

Need a condensed version? Simply ask Meta AI to create a summary of the key points.

What Can You Do With Ask Meta AI on Threads?

Ask Meta AI lets you get quick answers about any post directly within Threads. No need to copy-paste content into a separate chatbot. The real benefit here is convenience—everything happens in context.

The feature supports several practical uses:

  • Summarize lengthy posts when you don't have time to read the full text.
  • Break down confusing terminology or complex concepts mentioned in the post.
  • Get answers to follow-up questions related to the post's topic.
  • Receive additional context or background information for better comprehension.
  • Translate post content into another language.
  • Interpret images attached to posts, when AI image analysis is supported.


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5 Essential Books to Master Large Language Models

5 Essential Books to Master Large Language Models

Generative AI is evolving at breakneck speed, but here's the thing: the mathematical principles and architectural foundations behind large language models (LLMs) have actually been well-documented for years. A few years ago, understanding traditional NLP and RNNs was enough. Then Transformers arrived and reset the game entirely. Now, grasping how these models are built, trained, and deployed has become essential knowledge for AI engineers, data scientists, and anyone shipping AI-powered products.

If you're ready to move beyond just calling ChatGPT or Claude APIs—if you actually want to understand how a foundation model works—you need structured learning material, not scattered blog posts. Here are five highly-regarded books that will take you from theoretical foundations all the way through practical implementation of Large Language Models.

1. Build a Large Language Model (From Scratch) – Sebastian Raschka

The best way to understand a complex system? Build it yourself. That's the philosophy driving Build a Large Language Model (From Scratch) by Sebastian Raschka.

Rather than just explaining concepts, this book walks you through constructing, training, and fine-tuning an LLM from the ground up using PyTorch. Every step is detailed thoroughly, giving you hands-on exposure to core components like tokenization, embeddings, attention mechanisms, Transformer architecture, and training optimization techniques.

What's interesting here is the book comes with over 20 annotated Jupyter notebooks. You're not just reading—you're coding alongside, watching data flow through each layer of the Transformer network. Perfect for AI engineers and researchers who need to truly understand what happens at each computational step. The theory-plus-code combination is particularly powerful.

2. The Hundred-Page Language Models Book – Andriy Burkov

Not everyone has time to build models from scratch. If you need a comprehensive overview fast, The Hundred-Page Language Models Book by Andriy Burkov is your move.

The title's honest: it compresses the entire evolution of language models into roughly 100 pages without sacrificing technical accuracy. Burkov guides you from classic n-gram models through modern architectures like BERT and GPT. Mathematical concepts are explained clearly with visual diagrams and concise Python examples throughout.

Each topic—pretraining, attention, text generation—gets its own focused chapter. You won't feel overwhelmed. This is ideal for students or working professionals who want solid foundations without a massive time commitment. The breadth here is impressive for the page count.

3. Hands-On Large Language Models – Jay Alammar & Maarten Grootendorst

Once theory is solid, the next phase is applying LLMs to real problems. Hands-On Large Language Models is written exactly for that transition.

Jay Alammar built his reputation on crystal-clear Transformer visualizations. Maarten Grootendorst is a seasoned NLP specialist. Together, they created something balanced: theory meets practice.

The real standout? Over 250 illustrations. Concepts like attention heads and multi-layer Transformer architecture become intuitive when drawn well. The book also covers semantic search, dense retrieval, prompt engineering, and RAG (Retrieval-Augmented Generation) systems. You'll learn fine-tuning and deployment using open-source tools, especially the Hugging Face ecosystem. This one's perfect if you want to shift quickly from understanding LLMs to building actual AI applications.

4. Natural Language Processing with Transformers – Lewis Tunstall, Leandro von Werra & Thomas Wolf

Where the previous book leans visual and practical, Natural Language Processing with Transformers goes deeper on engineering and professional deployment.

All three authors work at Hugging Face. You're essentially reading the official manual for the most popular open-source AI ecosystem today.

Content covers step-by-step training and deployment of BERT, GPT, T5, and others. You'll learn data preparation, training, fine-tuning, and evaluation workflows. Real-world examples span healthcare, finance, and multilingual NLP. The real concern here is that without practical hands-on experience, you might hit walls. This book assumes you're ready to actually implement things in production contexts. Essential reference material for ML engineers building real AI products with Hugging Face.

5. The LLM Engineering Handbook – Paul Iusztin & Maxime Labonne

Training a model? That's only half the battle. The harder part: turning that model into a stable product serving thousands or millions of users. That's what The LLM Engineering Handbook addresses.

Unlike training-focused books, this is a practitioner's guide for deploying AI systems. It covers the entire LLM lifecycle from initial research through production operations.

Topics include prompt optimization, function calling, tool use, advanced RAG architectures, and large-scale deployment strategies. All presented from a practical, real-world angle. This book is perfect for developers moving beyond simple API calls toward building scalable, reliable AI applications. If you're facing actual production constraints, this is your reference.

Which Book Should You Start With?

These five books form a complete learning progression from foundational theory to practical deployment.

If you're new to the field, start with The Hundred-Page Language Models Book or Hands-On Large Language Models. Both build intuition quickly and give you the big picture. Once foundations feel solid, Build a Large Language Model (From Scratch) teaches you how Transformers actually work under the hood. Next, Natural Language Processing with Transformers gets you productive with industry-standard Hugging Face workflows. Finally, when you're shipping real AI products, The LLM Engineering Handbook becomes your guide for optimization and large-scale operations.

In AI, the people who stand out aren't those who know the most prompts. They're the ones who understand what's actually happening inside the model behind those prompts. Wherever you are in your AI learning journey, at least one of these five deserves a spot on your shelf.


Description: Build real LLM expertise with this curated reading list—from foundational theory to production deployment.

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The 5 Best AI Tools for Data Analysis in 2026

The 5 Best AI Tools for Data Analysis in 2026

The days of data analysts manually writing hundreds of lines of Python to clean datasets, build visualizations, and generate reports are officially over. A new generation of AI tools is reshaping the entire workflow, automating tasks that once consumed hours of tedious, repetitive work.

Modern AI platforms do far more than just read and analyze spreadsheets. They automatically clean messy data, write and execute Python or SQL code, generate charts, spot trends, explain patterns, and build reusable analytical pipelines—all with minimal human intervention. What's interesting here is that analysts can now skip the grunt work entirely and focus on asking the right questions and extracting actionable insights.

Here are five standout AI tools reshaping data analysis for 2026. Whether you're working with Python, SQL, notebooks, databases, or local projects, these platforms can dramatically cut the time it takes to transform raw data into meaningful intelligence.

Deepnote – An AI-Powered Collaborative Workspace for Data Teams

Deepnote is a collaborative notebook platform with AI baked in from the ground up. At its core sits Deepnote Agent—an intelligent assistant that understands your entire project context, plans multi-step workflows, and automatically adds, edits, or removes Python, SQL, and text blocks to complete complex analytical tasks.

Beyond that, Deepnote packs in a suite of AI capabilities: automatic analysis generation, SQL query suggestions, code recommendations and fixes, data visualization, error detection and correction, and code explanation. Describe what you need in plain language, and the AI handles most of the heavy lifting right inside your notebook, with full ability to review or tweak results on the fly.

For enterprise users, Deepnote supports OpenAI-compatible models and offers Deepnote MCP, allowing popular AI tools like Codex, Claude, Cursor, and Visual Studio Code to connect directly with your notebook and active projects.

If you're working in teams or need a data environment where AI is deeply woven into your daily workflow, Deepnote is one of your strongest bets right now.

ChatGPT – Straightforward Yet Powerful Data Analysis

ChatGPT is probably the most accessible entry point for anyone new to AI-assisted data work.

Upload a CSV or Excel file and you can ask ChatGPT to clean your data, find trends, spot anomalies, run Python calculations, build tables, draw charts, and summarize findings in plain English—with virtually no coding required. The real convenience here is that non-technical users can accomplish serious analytical work in minutes.

In testing with a student burnout dataset, ChatGPT automatically constructed a complete dashboard with key metrics, visual charts, and a clear summary without requiring any manual code writing from the user.

That capability expands further with ChatGPT Work. Feed it data and your end goal, and it can analyze everything and produce reports, spreadsheets, presentations, or even a finished website.

OpenAI has also rolled out GPT-5.6 on ChatGPT, while ChatGPT Work subscribers can access newer models like Sol, Terra, and Luna depending on their plan. According to OpenAI, these new models significantly boost analytical depth and spreadsheet handling.

For quick data exploration, rapid visualization, and summary reports, ChatGPT delivers instant results and remains the go-to choice for speed and simplicity.

Claude Code – AI That Writes Code and Completes Full Analytical Workflows

Though not a dedicated data science platform, Claude Code proves surprisingly effective for Python-based analytical projects.

Drop your dataset into a project folder, open Claude Code's command interface, and ask the AI to inspect your data, write Python scripts, run the analysis, fix bugs, generate charts, and export a finished report. It handles the entire pipeline with minimal input.

In testing, Claude Code built a complete analysis script, executed it against a student burnout dataset, validated the results, and generated a 146-line Markdown report automatically.

The real strength of Claude Code is its ability to read files, edit code, and execute commands directly on your system, making the whole process feel almost hands-off.

Claude Code now exists as more than just a CLI tool—it's available as a desktop app, a Visual Studio Code extension (which previews changes inline), and a web version. The VS Code extension shows code edits in real-time, while the desktop app can manage multiple local and remote sessions simultaneously.

This is the right pick for large-scale data science projects, automated reporting pipelines, and building reusable analytical frameworks.

DataLab – An AI Notebook Built for Beginners

DataLab from DataCamp is a data analysis notebook with an integrated AI Assistant that works much like ChatGPT.

Connect your data source, describe what you need in natural language, and the AI generates Python, R, or SQL code, runs the analysis, visualizes results, and explains the findings.

In testing with Google BigQuery as the data source, a single simple question about ticket sales was enough for the AI to read the data, perform analysis, create a chart, and explain the outcome.

DataLab goes beyond chat—it also supports prompt-to-code generation, code editing, auto-completion, debugging, and code explanation. All results stay visible in the notebook so you can review, modify, and re-run as needed.

In many ways, DataLab mirrors Deepnote by combining collaborative notebooks, connected data sources, AI chat, code generation, and reporting in one workspace. The difference is that DataLab feels more approachable for learners navigating the DataCamp ecosystem.

VS Code with Codex – The Professional-Grade Data Analysis Environment

Codex now lives across multiple platforms: the ChatGPT desktop app, CLI, IDE extensions, and web interfaces.

But the most natural experience is using Codex directly inside Visual Studio Code.

Keep a notebook, Python scripts, CSV files, and reports all open in one project. Ask Codex to read the full context, edit your code, run analysis, fix errors, or extend your pipeline—all with brief commands. The Codex extension can use open files or selected code snippets as context, letting it propose more precise edits while showing changes alongside your original code.

This makes the entire workflow far more interactive. Instead of reading answers in a chat window, you get notebooks, scripts, charts, and reports living inside your project, ready for further refinement and development.

For data analysts or engineers already comfortable with VS Code, this is one of the most flexible and powerful AI solutions available today.

AI Is Fundamentally Reshaping How Data Gets Analyzed

AI has already transformed how data professionals work. Instead of manually building every step—cleaning data, writing charting code, drafting reports from scratch—analysts now provide data and goals, then let AI handle the repetitive work.

Need fast exploration or initial data discovery? ChatGPT is your shortcut. Want an AI-integrated notebook experience? Deepnote and DataLab deliver that seamlessly. Running large Python projects or automated reporting pipelines? Claude Code excels there. Already living in VS Code? Pairing it with Codex creates a seamless workflow where notebooks, scripts, data, and reports all live in one unified project.

But here's the critical reminder: as AI gets smarter, the most important principle never changes. Let AI speed up your work, but always verify the code, the math, and the conclusions yourself before making decisions based on your data.


Description: Discover the top AI-powered platforms transforming data analysis—from ChatGPT to Claude Code. Automate cleaning, coding, and reporting in 2026.

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8 Practical Use Cases Where Claude Fable 5 Actually Justifies Its Cost

8 Practical Use Cases Where Claude Fable 5 Actually Justifies Its Cost

The most effective applications of Claude Fable 5 share one common thread: they're large, multi-step projects that would normally land on a team member's desk for days of manual work. Fable 5 is Anthropic's most powerful model—and its priciest—so burning through credits on quick summarization tasks or dashing off a short email is honestly wasteful. The teams getting real value out of this model hand it complex, messy problems and let it plan, execute, and validate its own work.

Below are 8 concrete jobs where Fable 5 demonstrates clear, measurable value for the price. Each example was executed directly in Claude Cowork or Claude Code.

Before diving into the list, here's the golden rule for cost efficiency: Think carefully about what you're using it for.

"Many people test Fable 5 on simple tasks like quick summaries or writing a short email. The output is good, but they burn through half their monthly allocation in one go."

If you're debating between Fable 5, Opus 5, or Sonnet 5 for a specific task, start by reviewing guidance on selecting the right Claude model for each task. That resource covers when frontier models make sense and when they don't. This article focuses on the flip side: the specific jobs where Fable 5 genuinely shines.

1. Audit Your Business Systems End-to-End

Fable 5 is famous for code testing, but it's equally powerful for business audits. One example tasked it with optimizing a lead-generation funnel and boosting conversion rates. It was given a web browser, a test email address, and access to email sending tools, community platforms, and the company's CRM system.

The model experienced the entire funnel as a real prospect would. It examined every email a new user receives, then proactively dug through the data warehouse for context and produced a comprehensive onboarding audit. It didn't just report findings—it drafted two new email sequences designed to lift conversion rates, ready for review and approval.

The same approach works for evaluating customer support team performance by submitting real support tickets, or testing employee onboarding by walking through it as a new hire would.

2. Build a Local Clone of Paid Software

Let's say you use a paid image compression tool that caps daily processing on the free tier. Instead of upgrading, ask Fable 5 (in Claude Code) to build a local version.

Here's what's clever: don't ask it to build the whole system at once. Instead, Fable 5 researches first. It uses the built-in browser in Code to open the original tool, uploads a test image to understand how it works, then creates a detailed build plan.

That plan is solid enough to hand off to a cheaper model for execution. The result? A local app that compresses images without limits and at high quality—complete with a right-click Finder shortcut that Fable 5 added on its own.

3. Redesign Poorly Formatted Presentations

Image processing is one of Fable 5's genuine strengths, and it leverages this to self-check its own work. One example gave Fable 5 an intentionally badly designed deck plus the company's brand kit. It redesigned the slides, but before handing them back, it screenshotted all 10 slides, analyzed each one, caught errors it had made, and spawned a new task to fix them.

The final output: a professional deck in two formats—a clean HTML version that follows the original style guide exactly, plus an editable PowerPoint. This self-validation capability is what separates frontier AI models from cheaper alternatives when dealing with image-heavy work.

4. Deploy Packaged Service Systems for Clients

If you run a service agency, you've probably built templated systems to deploy for every new client—maybe a comprehensive AI-forward SEO system with Airtable databases and n8n workflows. Instead of manually rebuilding it for each client, hand Fable 5 the template system and new client details, then let it deploy a custom copy.

Since this involved 7 workflows, it spun up 7 sub-agents—each building one process—then validated everything and drafted handoff docs. A complete, ready-to-run system for the new client from a single prompt. This is the kind of repetitive deployment work that's worth turning into a specialized skill.

5. Reconcile and Understand Messy Data

Revenue numbers from your accountant don't match data from your internal tools. Rather than manually reviewing hundreds of transactions, give Fable 5 exported data files from all 5 systems showing the discrepancy. It built a revenue reconciliation tool in Cowork and identified 3 specific causes—bugs that would've taken days to unearth manually.

The same process scales to any fragmented data scattered across tools: matching customer source attribution, auditing inventory against orders, anything. Just point it at raw data sources, ask a question, and ask it to prove the numbers.

6. Build Custom Internal Tools

The next task: build a hiring system using two paid tools—Airtable and Tally—plus the company's brand guidelines. In one execution, it created an Airtable database, set up a Kanban interface for candidate management, built an application form and careers page on Tally, then used Claude in Chrome to manually test each page (both desktop and mobile) to validate everything worked.

It even documented step-by-step instructions for the one manual step it couldn't automate through connectors, to complete the flow. The result: a fully custom, end-to-end hiring process built on existing tools.

7. Migrate Between Systems

Moving automation workflows from one platform to another is labor-intensive and error-prone. Fable 5 was tested in Claude Code to migrate a complex automation workflow to a new platform. It first researched the target platform's capabilities to understand how to rebuild the process.

It executed the migration successfully, generated a detailed report on what couldn't be converted directly, and rebuilt the entire workflow with all data fields populated correctly. This approach scales to handling dozens or hundreds of workflows in parallel.

8. Build a Complete Marketing Funnel from Scratch

This was the biggest task handed to Fable 5. Using Claude Code's "goal mode"—which lets Fable 5 keep working until a specific condition is met—the challenge was to build a skill set that generates a complete lead-gen campaign funnel from just one brief requirement summary.

The system proposed messaging angles, created image ad content using Python libraries, built and deployed a landing page through website tools, wrote asset delivery emails and conditional nurture sequences, connected everything with automation, and set up the entire campaign in Meta Ads Manager including audience targeting and lookalike audiences.

What's interesting here is that no human had to switch between 7 different platforms. Fable 5 owned the entire workflow—from strategy to execution to platform configuration—in a single run.


Description: Discover 8 real-world applications where Claude Fable 5's advanced capabilities deliver genuine ROI—from system audits to building complete marketing

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Turn Google Slides Into Professional Videos With AI Scripts and Voice-Over Using Google Vids

Turn Google Slides Into Professional Videos With AI Scripts and Voice-Over Using Google Vids

Google Vids transforms your Google Slides presentations into finished videos remarkably quickly. Select the video conversion mode, and the tool automatically generates scripts, organizes content by scene, and adds AI voice-over narration. What's interesting here is how much time you save compared to manually editing video from scratch. This is a game-changer for converting lectures, training materials, reports, presentations, and product demos into professional videos without needing complex video editing software. In this guide, we'll walk through converting Google Slides into Google Vids videos complete with AI-generated scripts and narration, so you can create polished, shareable videos in minutes.

Important note: This feature currently generates English narration only, so keep that in mind for your projects.

Step-by-Step: Converting Slides to Google Vids Videos

Step 1: Start the Conversion

Open your presentation in Google Slides. Click File, then select Convert to video. You'll see all your slides listed.

Chuyển slide thành video Google Vids

Deselect any slides you don't want included in your video.

Chọn trang slide tạo thành video Google Vids

Step 2: Enable AI Features

At the top of the screen, check that AI-powered narration, scripts, background music, and transitions are activated.

Tạo kịch bản thuyết minh bằng AI cho video

Click Continue. Google Vids will generate AI narration scripts for each scene. You can delete any scripts you don't want, or manually enter custom narration for individual scenes.

Kịch bản thuyết minh từng cảnh trong video

Scroll down and choose whether to use narration alone or add an AI avatar character on screen. To swap avatars, click the pencil icon.

Nhân vật avatar AI trong video

Browse the available AI presenters, select one, and hit Use this avatar to apply the change.

Đổi nhân vật AI trong Google Vids

Step 3: Generate Your Video

Enable Animations AI to add motion effects, then click Create the draft video to generate your AI video from the slides.

Tạo video AI trên Google Vids

Wait a moment while Google Vids processes. Your video will appear with narration, avatar characters, and animations in each scene.

Video AI tạo trên Google Vids

You can customize scene transitions by clicking the transition icon and selecting a new effect from the Transition menu.

Đổi hiệu ứng chuyển cảnh video AI Google Vids

Adjust the AI avatar size and position on each scene. Click the avatar frame and drag to resize or reposition it as needed.

Chỉnh avatar AI trong video Google Vids

Step 4: Download Your Video

Once your video is complete, go to File and select Download to save it.

Chuyển slide thành video

Best Practices for Converting Slides to Video

To get the best results from Google Vids, follow these guidelines:

One main idea per slide. AI analyzes each slide's content to generate narration. Keep each slide focused on a single topic or message rather than cramming in too much information.

Use clear, descriptive titles. Concise, relevant titles help AI understand your presentation structure, leading to more logical scripts and narration.

Write plain, straightforward text. Avoid abbreviations, unclear symbols, or excessive bullet points. AI might misinterpret context otherwise.

Choose relevant images. Illustrations that match your content make videos more engaging and reduce the need for post-production fixes.

Review and edit AI-generated content. Always check the scripts and narration AI creates. Adjust them to match your tone and audience.

Preview before sharing. Watch the full video start to finish before distributing it.

Frequently Asked Questions

Can Google Vids convert any Google Slides presentation?

Yes. You can use Convert to video on any Google Slides presentation. However, some animations or special layouts may not carry over perfectly after conversion.

Does Google Vids automatically create scripts?

Yes. After converting from Google Slides, Google Vids uses AI to write narration scripts based on each slide's content. You can edit the dialogue before finalizing the video.

Do I need to recreate the video if I edit my Google Slides?

Yes. Changes made to Google Slides after conversion don't automatically sync to the video you've already created. You'd need to reconvert if substantial edits are made.

Can I add images or video clips after conversion?

Yes. Once Google Vids generates your video, you can insert additional images, video clips, icons, background music, and transition effects to polish the final product.

Is the Convert to video feature free?

Feature access depends on your Google Workspace plan. If your account doesn't yet support Google Vids or AI features, the Convert to video option may not be available.


Description: Learn how to convert your Google Slides presentations into polished videos with AI-generated scripts, narration, and avatars in just a few steps.

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