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How Computers Learn

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5 months ago

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Have you ever wondered how your phone suggests the perfect song, how a game knows your next move, or how a robot vacuum avoids crashing into your dog? It’s all because computers can learn—and they do it in a way that’s kind of like how you figure things out every day! Today, we’re diving into two super-cool tricks computers use to get smarter: forward propagation and back propagation. Don’t worry about the fancy names—they’re just ways computers guess and improve, and I’ll break them down so they’re as easy as pie. Ready? Let’s jump in!

What’s a Neural Network? Your Brain’s Clever Cousin

First things first: to understand how computers learn, we need to know what a neural network is. Picture your brain as a huge team of friends passing notes to solve a mystery, like figuring out what’s in a surprise gift box. Each friend reads the note, adds their own clue (like “It’s small!” or “It rattles!”), and passes it along. That’s how a neural network works—it’s a bunch of tiny helpers (called neurons) working together to figure stuff out.

Here’s the basic setup of a neural network:

  • Input Layer: This is where the computer gets its info, like a picture of a dog, the sound of a voice, or the temperature outside.
  • Hidden Layers: These are the “thinking” layers, like detectives looking for hints. They ask stuff like, “Does this picture have floppy ears?” or “Is it chilly enough for a sweater?”
  • Output Layer: This is the final answer—like “It’s a dog!” or “Yes, grab that sweater!”

These layers are connected by little pathways, kind of like how you connect dots in a puzzle. The computer uses those pathways to decide what’s important and what’s not. It’s similar to how you learn that studying a little every day helps you ace a test—the computer tweaks how it pays attention to clues over time.

Fun Fact: Neural networks got their name because scientists were inspired by how your brain works! Your brain has about 86 billion neurons (those tiny helpers), while even the biggest computer neural networks have way fewer, like millions at most. So, you’re still the champion learner!

Another Way to Think About It: Imagine a neural network as a big kitchen crew making your favorite pizza. The input layer gathers ingredients (like dough and sauce), the hidden layers mix and match them (deciding how much cheese or pepperoni), and the output layer serves up the final pizza (yum or yuck?). That’s the teamwork vibe of a neural network!

Forward Propagation: Taking a Guess, Step by Step

Now, let’s talk about forward propagation. It’s the first big trick a neural network uses, like when the computer takes a guess at something. Imagine you’re trying to decide if you need a jacket for recess. You peek outside and see the temperature (that’s your input). Then, you think, “Hmm, last time it was this cold, I shivered” (that’s the hidden layers doing their job). Finally, you decide, “Yup, jacket time!” (that’s the output). That’s forward propagation in action—information zooming from start to finish.

Here’s how it works in a computer:

  • Grabbing the Clues: The input layer takes in the data, like the colors and shapes in a photo of an animal.
  • Thinking It Over: The hidden layers look for patterns. They might wonder, “Are these pointy ears? Is this a fluffy tail?”
  • Making a Guess: The output layer spits out an answer, like “I’m 80% sure it’s a cat!”

At first, the computer’s guess might be totally off, like when you guess “pizza” for lunch but it’s tacos. That’s okay—it’s just starting out, like when you’re new at guessing in a game of charades. The magic happens when it learns to get better, which we’ll get to soon!

Everyday Example: Think about guessing what’s in a mystery bag by feeling it. You touch something round and squishy, so you guess “orange.” That’s forward propagation—taking what you know and making your best shot at an answer.

Another Fun Example: Picture a teacher asking, “What’s 2 + 2?” Your brain grabs the numbers (input), thinks about what they mean together (hidden layers), and says “4” (output). A neural network does the same thing, but with way more steps—like solving a giant riddle!

Why It’s Called ‘Forward’: The info moves forward through the layers, from the input to the output, like passing a baton in a relay race. No looking back yet—just charging ahead with a guess!

Back Propagation: Learning from Oopsies

So, what happens if the computer guesses “cat” but the picture was actually a raccoon? Does it throw in the towel? Nope! It uses back propagation to fix its mistakes and get smarter. This is the second big trick—and it’s all about learning from slip-ups.

Here’s the step-by-step:

  • Checking the Answer: The computer finds out the real answer, like, “Oops, it’s a raccoon, not a cat.”
  • Looking Back: It retraces its steps, asking, “Where did I go wrong? Did I think the eyes were too cat-like and ignore that sneaky raccoon mask?”
  • Tweaking the Plan: It adjusts those pathways between layers, so next time it’ll focus on the right clues, like the raccoon’s black eye patches.

It’s like when you spell “cat” as “kat” on a quiz. Your teacher marks it wrong, so you practice the right way until you nail it. The computer does that too—it practices until it’s a pro!

Sports Analogy: Imagine shooting a basketball. You aim, shoot, and miss because the ball went too far left. So, you think, “Next time, I’ll aim more to the right,” and try again. That’s back propagation—the computer adjusts after every miss to score a basket next time.

Game Analogy: Ever play “Hot or Cold”? If someone says “cold” (you’re wrong), you change direction. When they say “hot” (you’re close), you keep going. Back propagation is the computer playing that game with itself, getting “hotter” with every tweak.

Classroom Example: Think about learning multiplication. If you say 5 × 3 is 10, your teacher says, “Nope, it’s 15.” You figure out what you miscalculated and fix it for next time. The computer learns the same way—by correcting itself step by step.

Why It’s Called ‘Back’: The computer goes backward through the layers, from the output back to the input, fixing things as it goes—like rewinding a movie to see where the plot twisted wrong!

The Learning Loop: Guess, Check, Repeat!

Here’s where it gets awesome: forward and back propagation team up like peanut butter and jelly. The computer:

  • Guesses with forward propagation.
  • Checks its mistakes and fixes them with back propagation.
  • Tries again with a sharper guess.

It keeps looping like this—guess, check, tweak, guess again—until it’s super good at what it’s doing. It’s how your video game learns to throw harder challenges at you or how your music app picks songs that make you dance. Practice makes perfect, even for computers!

Real-Life Connection: Remember learning to ride a bike? You wobbled, fell, then figured out how to balance better each time. That’s the computer’s learning loop—trying, falling short, and getting back up smarter.

Another Connection: It’s like baking cookies. You mix the dough (forward propagation), taste it, and realize it needs more sugar (back propagation), then adjust the recipe and bake again. The computer keeps “baking” its guesses until they’re deliciously right!

How Long Does It Take?: Sometimes it takes thousands of loops for the computer to get good—way more than your bike-riding practice! But computers are fast, so it happens in minutes or hours, not days.

Why This Matters: Smart Computers Everywhere

Forward and back propagation are the secret sauce behind tons of cool tech. They let computers:

  • Guess stuff (like what’s in a photo or what you’ll say next).
  • Learn from mistakes (by tweaking their guesses).
  • Get better over time (like you do with piano or skateboarding).

Check out some amazing things they help with:

  • Medicine: Computers help doctors spot diseases in X-rays, like finding a tiny crack in a bone or a shadow that means trouble.
  • Self-Driving Cars: They teach cars to see stop signs, avoid pedestrians, and stay on the road—all by guessing and learning from what they see.
  • Video Games: Ever notice how games get tougher as you play? That’s the computer learning your moves and upping the challenge.
  • Voice Assistants: Siri or Alexa listens to you, guesses what you want (like “play music”), and gets better at understanding your voice over time.
  • Art and Music: Some computers even create paintings or songs by learning what looks or sounds cool—pretty wild, right?

Future Fun: Scientists are using neural networks to solve big problems—like predicting earthquakes, cleaning up oceans, or even talking to animals (imagine chatting with your cat!).

You’re Part of This Story!

Now you know the magic behind how computers learn with forward and back propagation. They guess (forward), fix their oopsies (backward), and keep going until they’re pros—just like you do when you study, play sports, or try a new hobby. Isn’t it neat how you and computers learn in such similar ways?

Your Superpower: Your brain is still way cooler than any computer. It can dream, laugh, and invent stuff a neural network can’t even imagine. But you can team up with computers to make the world even more awesome!

Try This: Next time you play a game or use an app, think, “Is this computer guessing what I’ll do? How did it learn that?” You’re already a detective of smart machines!

Dream Big: Maybe one day, you’ll teach a computer to recognize your favorite Pokémon, design a game that’s unbeatable, or help save the planet with tech. The world of neural networks is wide open, and you’re just getting started!

Fun Fact: The biggest neural networks today—like the ones in self-driving cars—have millions of neurons working together. That’s a lot, but your brain’s 86 billion neurons still win the prize for the ultimate learning machine!

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Education

Fluent in Code: Navigating the New World of AI-Powered Language Learning

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3 days ago

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October 10, 2025

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Learning a foreign language has always required commitment — hours of practice, expensive classes, and exposure to native speakers. But now, a new companion has entered the scene: artificial intelligence. With AI models like ChatGPT, tools powered by Grok’s Ani, and a wave of emerging apps, it’s never been easier—or cheaper—to start your language journey. But can these digital tutors really deliver fluency? Let’s dive into the possibilities, pitfalls, and the best free or low-cost AI tools available right now.

The AI Advantage: Why More People Are Skipping the Classroom

AI offers a compelling pitch for anyone intimidated by traditional language learning routes. The tools are available 24/7, often free or inexpensive, and adapt instantly to your level and interests. Here’s why it’s catching on:

  • Cost-effective: Many general-purpose AI models like ChatGPT offer free tiers or require only a basic subscription, making them far cheaper than classes or tutors.
  • Always-on access: Whether it’s midnight or your lunch break, AI doesn’t sleep. You can practice anytime, anywhere.
  • Custom feedback: AI can correct your grammar, suggest better word choices, and even roleplay everyday scenarios in your target language.
  • Zero judgment: Learners often feel anxious about speaking with humans. AI offers a pressure-free way to make mistakes and learn from them.

In essence, AI gives you a patient, tireless, and responsive partner. But it’s not a silver bullet.

The Drawbacks: What AI Still Can’t Do

While AI language learning tools are powerful, they’re not flawless. Here’s where they fall short:

  • Cultural nuance is limited: AI may know grammar, but it often misses idioms, tone, and the social subtleties of real communication.
  • Risk of errors: AI can sometimes provide inaccurate or unidiomatic translations or explanations. Without a human teacher, you might not know what’s off.
  • Speech limitations: Even with voice-enabled tools, AI pronunciation might not match native speech exactly — and it can struggle to understand heavily accented input.
  • No real-world exposure: AI can’t replicate the experience of talking to a real person in a café, on the street, or in a business meeting.
  • Motivation still matters: AI might be engaging, but it won’t push you to keep going. You’re still the one who has to show up every day.

The verdict? AI is a fantastic assistant but works best as part of a broader learning strategy that includes immersion, real interaction, and diverse resources.

Mapping the AI Language Learning Landscape

So, what are your options if you want to get started? Here’s an overview of the most popular and accessible ways people are using AI to learn languages — with a focus on free or low-cost tools.

1. ChatGPT and General AI Chatbots

One of the most flexible approaches is using a general-purpose model like ChatGPT (from OpenAI) or Claude (from Anthropic) as your language partner. Just prompt it to:

  • “Speak only in French and help me practice everyday conversation.”
  • “Correct my Spanish paragraph and explain the grammar mistakes.”
  • “Teach me five useful idioms in Italian.”

Many learners use ChatGPT’s voice feature to practice listening and speaking, even roleplaying restaurant scenarios or travel situations. It’s like having a personal tutor who never runs out of patience.

2. Grok’s Ani: The Friendly AI Tutor

If you’re part of the Grok AI ecosystem, you may have access to Ani, a conversational AI designed to help users learn languages in a more interactive and emotionally intelligent way. Ani aims to go beyond correction—it encourages, adapts, and even gives personality to your learning partner. Users report that the emotional tone and feedback from Ani helps build confidence, especially in early stages of learning.

3. Voice-Based AI Tools

For those who want to speak and be heard, apps like Gliglish and TalkPal let you practice conversations using your voice. These tools simulate real-life dialogues and provide real-time feedback. They often use GPT-style models on the backend, with some offering limited free daily usage.

  • Gliglish: Offers free speaking practice and realistic conversation scenarios.
  • TalkPal: Lets you converse by text or voice, with personalized feedback.

These are great for practicing pronunciation and spontaneous response — key skills for fluency.

4. AI-Powered Apps with Freemium Models

Several newer apps integrate LLMs like GPT to offer personalized lessons, dialogues, or speaking drills:

  • Speak: Uses OpenAI’s tech to simulate natural conversations and offers corrections.
  • Loora AI and LangAI: Focus on business or casual dialogue training using AI chats.

While many of these are paid, they typically offer free trials or limited daily use, enough for a solid daily practice session without a subscription.

5. DIY AI Setups and Open Source Tools

Tech-savvy learners are also building their own setups using tools like OpenAI’s Whisper (for speech recognition) combined with GPT for dialogue generation. Guides exist for setting up roleplay bots, combining voice input and AI-generated responses for a truly custom tutor experience.

For written language learning, tools like Tatoeba (a multilingual sentence database) or LanguageTool (an open-source grammar checker) can be used alongside AI to get example sentences or polish writing.

What People Are Actually Using

Among language learners, the most common practice seems to be leveraging ChatGPT or similar LLMs to:

  • Practice writing and get corrections
  • Simulate conversation scenarios
  • Translate and explain phrases
  • Build vocabulary with flashcards or custom quizzes

Many learners supplement this with speech-based apps or tools like Gliglish for pronunciation and conversation. Community feedback on Reddit and language forums consistently highlights the flexibility and personalization AI provides as the main draw.

Final Thoughts: Should You Learn a Language with AI?

If you’re considering learning a new language, AI offers an incredibly accessible, customizable, and low-pressure entry point. You can use it to build a habit, sharpen your skills, and explore a language before committing to more intensive study.

But remember: AI is a tool, not a replacement for the real-world experience. Use it to complement human interaction, cultural immersion, and diverse materials. The best results come when you combine AI’s strengths—endless practice, instant feedback, low cost—with your own curiosity and consistency.

So go ahead — say “bonjour” to your new AI tutor.

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Education

Building Real AI Marketing Agents: A Technical Roadmap Emerges

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3 weeks ago

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September 25, 2025

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When marketing teams talk about “AI agents,” what often emerges is an overambitious promise: systems that handle campaigns end‑to‑end, make strategic decisions autonomously, or optimize across channels without human oversight. Too often, these visions crash into reality — brittle implementations, cost overruns, or simply abandonment. A new technical guide, shared recently on Reddit’s AgentsOfAI community, offers a grounded, step‑by‑step framework for building functional AI marketing agents — not perfect ones, but useful, reliable ones.

Below is an edited and synthesized version of that roadmap — along with my own commentary on its strengths, tradeoffs, and what this means for marketing organizations ready to get serious about agentic automation.

From Hype to Reality: The Need for a Practical Framework

The origin of the guide is worth noting. It was posted by Reddit user Icy_SwitchTech in the AgentsOfAI community roughly a month ago and quickly drew attention as marketers and AI engineers struggled with similar pain points.

The feedback loop is clear: many firms try to start from a grandiose ideal (an “omni‑agent” that manages everything), then run into the complexity of tool integration, memory, error handling, and edge‑case logic. The new guide flips that script. Instead of starting with everything, it begins with a narrow use case and builds upward.

That philosophy echoes long‑standing software engineering wisdom: start small, iterate, factor complexity gradually. In the AI agent context, however, that discipline is often neglected. The guide helps reimpose discipline.

The Eight‑Step Agent Development Process

Here’s how the framework lays out the path, step by step — with some reflections on each stage.

1. Define a Narrow Problem Scope

Rather than aiming for a “universal marketing agent,” the guide recommends picking a narrowly scoped, well‑defined task: booking appointments, scanning job boards, summarizing unread emails, etc.

This constraint is crucial: a small scope allows you to test, debug, validate, and iterate. It also ensures the value is visible early, reducing the risk of abandonment.

2. Choose a Base Model (Without Custom Training at First)

The guide warns against jumping immediately into training custom models. Instead, leverage existing LLMs (e.g., GPT, Claude, Gemini, or open source models such as LLaMA, Mistral), depending on licensing, reasoning ability, output structuring, latency, and cost tradeoffs.

The idea is: start with what works, then only consider fine‑tuning once the architecture, tooling, and logic are stable.

3. Design External Tool Interfaces

This is often the “hidden” complexity in agents. Real agents must interact with external systems: web scraping (via Playwright, Puppeteer), email APIs (Gmail, Outlook), calendar APIs, file I/O (PDFs, CSVs), etc.

Defining clean “tool contracts” — i.e., APIs for each external capability — is critical. If your agent’s logic is tightly coupled with a raw web scraper rather than through an interface, maintenance becomes painful.

4. Construct the Agent Workflow Loop

The architecture centers on a recurring cycle:

  1. Process user input
  2. Interpret instructions / plan next step
  3. Invoke tool(s) as needed
  4. Receive results
  5. Feed back into the model
  6. Repeat until task completion

This “model → tool → result → model” loop is the heartbeat of agent operation.

It’s helpful to explicitly design this loop — not leave it ad hoc — because the handoff boundaries (when to invoke tools, when to prompt again) are frequent sources of bugs.

5. Implement Memory / Context Handling

Memory is tricky. A common mistake is trying to build a huge memory store from the start. The guide instead recommends starting with short‑term memory (e.g., recent messages in context) and then layering in persistence (e.g., JSON files, simple databases), and only later vector stores or more complex retrieval engines.

This staged approach keeps early systems simple and predictable.

6. Build a Basic Interface (Don’t Overdesign Early)

In the early phases, a simple CLI (command line) may suffice to test correctness. Once behavior is stable, one can wrap it with dashboards (Flask, FastAPI, Next.js) or integrate it into messaging platforms (Slack, Discord).

The priority is usability and observability — being able to see what the agent is doing, inspect logs, debug failures — rather than dropping time on fancy UI in the initial phase.

7. Iterate Rapidly, Gather Failures, Refine

Everyone expects the first version to be imperfect. The guide emphasizes running real tasks early, tracking failures, fixing them, and repeating. Many cycles are expected before stability.

This feedback-driven refinement is the only path to reliability. Log tool calls, inputs, outputs, error traces, and success indicators.

8. Manage Scope & Embed Safeguards

It’s tempting to add features or tools endlessly; the guide warns against this. Instead, focus on deepening one capability rather than broadening too soon. Introduce guardrails: timeouts, retries, schema validation, human‑in‑the‑loop triggers, resource budgets, logging, fallback plans, etc.

Also include regression tests (golden test suites) and versioning to detect regressions when code changes.

Why This Matters for Marketing Teams

Closing the implementation gap
Many marketing organizations already use AI tools (for content generation, targeting, and analytics). What they struggle with is turning those tools into autonomous workflows — e.g., auto‑optimizing campaigns, auto‑adjusting budgets, generating tactical recommendations, mutating creative, etc. The guide provides a blueprint to bridge that gap.

Balancing automation and oversight
An underlying tension is always present: the more you let agents act independently, the more risk you assume. Marketing is high stakes — budgets, brand reputation, compliance. The framework’s emphasis on guardrails and focused scope helps maintain human oversight while pushing automation forward.

Scalable value vs. brittle infrastructure
A narrow, well‑tested agent can deliver dramatic ROI in small domains — e.g., automatically adjusting budget pacing, flagging anomalies, and doing A/B test scheduling. But too broad an agent risks brittle fragility or silent failure. The methodology encourages building in stable islands rather than chasinga “general agent” prematurely.

Alignment with industry movements
In 2024, $1.1 billion in equity flowed into agentic AI, and jobs in the space surged. Major vendors are rolling out agent orchestration: Adobe launched its Experience Platform Agent Orchestrator; Amazon is embedding agentic capabilities in marketplace management. These shifts suggest that marketing agents are moving from exotic proof‑of‑concept into enterprise infrastructure.

Challenges, Risks, & Open Questions

No methodology is a silver bullet. Some challenges remain:

  • Model unpredictability: Agents built on LLMs remain non‑deterministic. Despite guardrails, they may hallucinate or misinterpret instructions.
  • Tool integration complexity: APIs change, web page layouts shift, authentication breaks — these are fragile surfaces.
  • Cost and latency tradeoffs: Invoking multiple tools or model calls per step adds compute and time costs.
  • Data privacy and compliance: Marketing agents may need to access sensitive data (user profiles, campaign spend). Ensuring compliance (e.g., GDPR) is nontrivial.
  • Scalability: As you scale to more users or tasks, maintaining performance, memory, concurrent tool execution, and state becomes more complex.
  • Monitoring and observability: Detecting silent failures, reasoning errors, drift, or misuse demands robust logging and metric systems.

Yet, the eight‑step guide helps surface these challenges early, rather than ignoring them until “late stage.”

What Should Marketing Teams Do First?

If I were advising a marketing tech team, here’s how I’d apply this:

  1. Pick a “pilot agent” project — one narrow task with clear ROI (e.g., automatically adjust email send timing based on open rates, or schedule social media posts given trending signals).
  2. Adopt the eight‑step process as your roadmap — especially tool abstraction, memory staging, iterative loops, and guardrails.
  3. Instrument heavily — logs, metrics, failure catalogs.
  4. Set human fallback triggers — never let the agent act blindly in risky areas (big budgets, public messaging).
  5. Expansion plan thoughtfully — once one agent is stable, compose agents or add complementary submodules incrementally.

Conclusion

The newly surfaced technical guide offers more than theory — it provides a practical, stepwise path from concept to deployment for AI marketing agents. Its value lies in enforcing engineering discipline in a space tempted by hype. For marketing organizations that want to responsibly adopt agentic AI (rather than chase the next flashy headline), it offers a sane architecture and process.

As the broader ecosystem evolves — with vendor support, better tooling, and more standards — this methodology may serve as a foundation for building robust agentic systems without falling prey to overreach.

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AI Model

Step-by-Step Tutorial for First Time Use of Agenda Mode in ChatGPT-5

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2 months ago

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August 20, 2025

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Introduction

If you’ve ever wished ChatGPT could not only research things for you but also take action—like browsing the web, filling in forms, or even posting on your social media—then Agent Mode is what you’re looking for.

Agent Mode turns ChatGPT into your personal assistant that can explore websites in a virtual browser, gather information, create content, and (with your permission) log in to accounts to perform tasks. Think of it as ChatGPT with hands on the keyboard and mouse—but you remain in full control.

In this guide, you’ll learn:

1. What Agent Mode can do.

2. How to switch it on.

3. A step-by-step example of using it for a real-world scenario.

    We’ll walk through a real-world scenario step by step, showing exactly what happened at each stage. The accompanying images will illustrate how the user interacted with the AI agent throughout the process.

    What Agent Mode Can Do

    With Agent Mode, you can:

    • Browse the web to find fresh information.
    • Compare sources and filter out what’s relevant.
    • Draft outputs like tweets, summaries, or slides.
    • Interact with websites by clicking, typing, or filling forms.
    • Pause for login when you need to sign in (you take over safely).
    • Ask before acting so you’re always in control.

    Sometimes, an AI agent can’t finish a task on its own and needs help from the user. For example, it might ask the user to provide specific information or prompt them to log in using their username and password to continue.

    How to Switch On Agent Mode

    1. Open ChatGPT and start a new chat.

    2. Click the Tools menu and choose Agent. You can also type /agent directly in the chat box.

    3. Describe your task in natural language. The agent will begin and pause to ask for confirmation when necessary.

      Tip: After a task is complete, you can make it repeat automatically (daily, weekly, or monthly) by clicking the clock icon that appears after it finishes.

      Example Scenario

      We’ll ask the AI agent to search for the latest news in the field of artificial intelligence, write a tweet about it, and post it to the user’s X account. To do this, the agent will open the desktop interface and carry out each step of the process. The user will be able to follow along by watching the actions unfold on their monitor.

      At a certain point, the agent will need the user’s help to log in to the X platform. It will prompt the user to enter their username and password. Once the login is complete, the user will return control to the AI agent so it can finish the task.

      Task:

      Enter the following into the prompt:

      Find one top AI news article published today (or in the last 24 hours). Compare a few reliable sources and choose the most newsworthy.

      Deliverables:

      1. A tweet draft (max 280 characters) that hooks readers, includes the link, and uses no more than two hashtags.

      2. A short one-line explanation of why this article was chosen.
      Action: After I approve the draft, log in to X on the handle @spaisee_com and post the tweet.
      Notes: If not already signed in, pause so I can take over the browser to log in and complete any 2FA. Ask for confirmation before posting.

      Step-by-Step Walkthrough

      1. Activate Agent Mode
        • Open a chat, type /agent, and paste in the agent-friendly prompt above.
      2. Watch the Research
        • The agent will open news sites, review articles, and select the best option. You’ll see it narrate what it’s doing.
      3. Review Outputs
        • You’ll get the chosen article, the draft tweet (under 280 characters), and a short reason why that article was selected.
      4. Approve or Edit
        • Provide feedback if you want changes (e.g., shorter hook, swap a hashtag).
      5. Log in to X
        • When the agent pauses for login, click “Take over browser,” sign in to @spaisee_com, complete any 2FA, then hand control back.
      6. Post the Tweet
        • The agent will confirm with you before posting. Once you agree, it will publish the tweet and show you the tweet URL.
      7. (Optional) Automate
        • If you like the workflow, you can schedule this to repeat daily (e.g., every morning at 9 AM).

      Step-By-Step Process Shown In Images

      To help the user understand exactly what the AI agent does, the following images show each step of the process in detail.

      1. The agent first prepared his desktop and then began searching the Internet to find suitable articles.

      2. The agent encountered a paywall, but did not stop.

      3. The agent found a suitable article and began composing a tweet.

      4. The agent created a tweet. It needs confirmation from the user that they approve the selection of the article to continue.

      5. The user has approved the article. The agent needs to log in to X.

      6. The agent asks users to take over the browser and log in.

      7. The user clicks on the Take over button. Then, she needs to confirm the takeover.

      8. Now, the user is controlling the browser. She needs to log in and click on the Finish controlling button.

      9. The user inserts the user name.

      10. Then, the user inserts the password and clicks on the Login button.

      11. The user has logged in. Then she clicks on the Finish controlling button.

      12. The agent prepares the tweet.

      13. The post is ready. The agent asks users whether to post the tweet.

      14. The user confirms it in the prompt.

      15. The agent posts the tweet.

      16. The agent closed the desktop and finished.

      You can check that the tweet has been posted by the agent.

      Summary

      The entire process was completed in just a few minutes. In contrast, it would take much longer for a user to manually browse multiple articles online and decide which one to tweet. However, having to take control of the desktop and log into the X account each time can feel tedious and inconvenient—especially if done daily. For security reasons, the AI agent cannot store or remember the user’s login credentials. As a result, this task cannot be fully automated.

      Final Thoughts

      Agent Mode is like giving ChatGPT the ability to act in the real world while you stay in charge. Start small—like with news searches and draft tweets—then build up to more complex workflows.

      Once you’ve mastered the basics, you can use Agent Mode for things like market research, reporting, content scheduling, or even handling simple business workflows.

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