AI Tools & Reviews

OpenAI has launched a new ChatGPT Study Mode, offering learners a new learning experience that helps them work through problems step-by-step instead of just getting an answer. In the most recent tweet, OpenAI stated that ChatGPT has become the go-to tool for students; they want to ensure that it encourages deeper understanding and learning.

We know that AI’s classroom presence has grown in recent times, so do the questions: Are these AI tools genuinely helping us understand the material, or are they just giving quick answers that disappear once the browser closes?

What is the new ChatGPT Study Mode?

That’s where this new ChatGPT Study Mode steps in as a new feature that can turn passive answer-seeking into active, step-by-step learning. This is where ChatGPT truly becomes a hands-on tutor who won’t just give you the answer and move on. Instead, it guides, quizzes, and nudges you to think, making sure you actually get what’s going on before you click away.

How Study Mode Works

Traditional ChatGPT chats often turn into answer vending machines. Study Mode inserts “productive friction.” Ask a trigonometry question and, before showing any sine or cosine, ChatGPT probes your current understanding, offers a scaffolded outline, and checks whether the explanation sticks. The system runs on carefully engineered instructions drafted with learning‑science researchers from roughly 40 institutions; OpenAI can tweak those instructions quickly while it gathers student feedback.

Main Features and Highlights of New ChatGPT Study Mode:

• Interactive Prompts: Instead of spilling out answers, Study Mode uses guiding questions, hints, and self-reflection nudges. The aim? Help you connect the dots and genuinely understand a topic, much like a real-life tutor might.
• Step-by-Step Guidance: Rather than providing immediate answers, the feature works with students incrementally, helping them get to the correct answer.
• Scaffolded, Easy-to-Follow Explanations: To cut down on confusion, information is packaged in organized, digestible steps—highlighting key ideas and keeping overwhelm in check, even when unraveling complex concepts.
• Personalized Learning: Study Mode adapts its approach based on your skill level and learning history (drawing from previous chats). The result is support that feels custom-fit, whether you’re skimming the basics or going deeper.
• Knowledge Checks: Regular quizzes and open-ended questions are sprinkled throughout. Get instant, personalized feedback—so you know what’s sticking, and what needs another look.
• Flexible On/Off: You can toggle Study Mode however you like, turning it into an always-on tutor, or dipping in when you come across a tricky question.

The Science Behind the Scenes

Study Mode’s “teaching style” comes from ongoing collaborations between OpenAI and a blend of teachers, cognitive scientists, and learning experts. The engine relies on research-backed methods: keeping you engaged, preventing info overload, and encouraging reflection. There’s even a dash of Socratic questioning—think more “Why do you think that happened?” than “Here’s the answer.”

What Real Students Say

Early feedback from college students reads like reviews of a favorite professor: “An all-knowing, 24/7 office hour.” Others praise how it breaks down tough topics into easily digestible bits, and that it’s patient—never tapping its foot if you ask the same thing twice.

How to Use It

Find the “Study and learn” tool within ChatGPT (it’s available to all logged-in users: Free, Plus, Pro, and Team, with Edu coming soon). Set your learning goals, choose your topic, and let Study Mode walk you through custom steps.

What’s Next

This is just the starting line. OpenAI plans to keep tuning the experience—adding clearer visuals, enhanced goal tracking, and even deeper personalization—based on more research and student feedback. Collaborations with education experts at Stanford and partnerships like NextGenAI are fueling this continuous learning engine, trying to reimagine how AI can truly boost understanding, not just get you to the right answer.

Conclusion

The new ChatGPT Study Mode isn’t trying to replace teachers or traditional learning. Instead, it’s an attempt to make AI a real educational partner—encouraging curiosity, critical thinking, and mastery rather than shortcuts. As more students test it out and experts weigh in on what works, one thing feels clear: the future of learning with AI is about guidance, not just graduation from one answer to the next.

AI Tools Club

Artificial intelligence (AI) is directly or indirectly part of over 90% of booming businesses in 2025. You may have seen a new AI tool or agent going viral every week, if not every day. While most are just hype, there are several viral AI tools and agents that are worth adding to your workflow. AI tools and agents go viral for many reasons, but those that are worth it can boost your productivity, unlock creativity, and provide a competitive edge in an increasingly automated world.

In this article, we will introduce you to the top 12 viral AI tools and agents for July 2025, from AI tools streamlining our inboxes to an autonomous AI agent that can help you build intelligent full-stack apps. The following 12 AI tools and agents stand out for going viral, not out of hype, but because they genuinely deliver simpler, smarter, and sometimes a little more playful ways of getting things done.

Here are the top 12 viral AI tools and agents in July 2025:

1. SaneBox: AI Tool for Indox^*

SaneBox is an AI-powered email management tool that promises to reclaim hours of your week from the clutches of your inbox, which is often filled with time-consuming junk. It intelligently identifies and filters unimportant emails, so you can focus on what truly matters.

• AI-Powered Filtering: SaneBox’s AI learns your email habits and automatically filters incoming messages into different folders based on their importance.
• One-Click Unsubscribe: The “BlackHole” feature allows you to drag an email from an unwanted sender and never hear from them again.
• Follow-Up Reminders: The tool can notify you if an email hasn’t received a response by a certain time, ensuring nothing falls through the cracks.

2. Adcreative.ai: AI Tool for Advertisements^*

Adcreative.ai is a complete AI platform for generating high-conversion ad creatives, from banners and text to product photoshoots and videos. It’s designed to help businesses of all sizes create compelling ad campaigns without the need for a design team.

• AI-Powered Ad Generation: The platform can generate a wide range of ad assets, including banners, text, and videos, tailored to your brand.
• Higher ROI: Claims up to get you up to 14× higher conversion rates and click-through rates using its proprietary AI models.
• Competitor Insights: The AI tool allows you to analyze your competitors’ best-performing ads to gain a competitive edge.

3. Google Opal: AI Agent to Build Mini-AI Apps

Opal is an experimental app from Google that allows you to build, edit, and share mini-AI applications using natural language. It’s a user-friendly platform for creating customized AI tools without writing a single line of code.

• Natural Language App Building: Describe the app you want to create, and Opal will build it for you.
• Visual Workflow: The app provides a visual representation of your mini-AI app’s workflow, making it easy to understand and edit.
• Shareable Creations: Once you’ve built your mini-AI app, you can easily share it with others.

4. Lovable: AI Agent to Chat Your Way to a New App^*

Lovable is a platform that lets you create websites and applications by simply chatting with an AI. It uses the vibe coding approach for app development that allows anyone to bring their ideas to life.

• Conversational Development: Describe your desired app or website in plain English, and Lovable’s AI will build it for you.
• Community-Driven Templates: The platform features a community-driven library of templates that you can customize and remix.
• From Prototype to Production: Lovable can be used to create everything from simple prototypes to fully functional consumer apps.

5. SlideSpeak: AI Tool for Presentations^*

SlideSpeak is an AI-powered tool that helps you create, summarize, and improve presentations. It’s like having a personal presentation AI assistant that can manage the heavy lifting, so you can focus on delivering your message.

• AI Presentation Generation: Create presentations from text, PDFs, Word documents, or even websites.
• Document Summarization: Upload a PowerPoint, Word, or PDF file and get a concise summary in seconds.
• ChatGPT for Presentations: Ask questions about your documents and get instant answers from the AI.

6. String by Pipedream: Build and Run AI Agents

String is an AI agent builder from Pipedream that allows you to automate different tasks like sending emails, Slack messages, generating tweetstorms, summarizing earnings calls, and more.

• Prompt-Based Automation: Simply describe the task you want to automate, and String will build an AI agent to handle it.
• Pre-Built Templates: The AI agent offers different pre-built templates for common automation tasks.
• Seamless Integration: String can be integrated with a wide range of services, including Stripe, HubSpot, Gmail, and Slack.

7. Context AI: AI Agent as Office Suite^*

Context AI is a comprehensive AI-powered office suite that helps you work smarter and faster. It integrates with your existing tools to simplify your workflow and turn scattered inputs into presentation-ready deliverables.

• AI-Powered Document Creation: Create polished presentations, documents, and spreadsheets with the help of AI.
• Deep Research Capabilities: Context AI can research internal and external data sources to provide you with data-driven insights.
• Seamless Integration: The platform integrates with over 300 tools, including Gmail, Salesforce, Slack, and Excel.

8. Memories.ai: AI Agents for Videos

Memories.ai is a video analysis platform that uses AI to unlock insights from your video content. It’s a powerful tool for everything from security and safety to video marketing and content creation.

• Large Visual Memory Model: The platform is powered by the world’s first large visual memory model, which allows it to understand and remember video content like a human.
• AI-Powered Video Search: Search your video archives using natural language to quickly find relevant clips.
• Automated Video Creation: The platform’s AI agents can automatically create video content, from storyboards to fully edited videos.

9. HeyGen: AI Tool to Generate Video^*

HeyGen is an AI video generator that allows you to create studio-quality videos from text and images. It’s a powerful tool for creating everything from video ads and social media content to product explainers and learning courses.

• AI-Powered Video Creation: Convert your text prompts or images into high-quality videos with a script, voice, and editing handled by the AI.
• Lifelike AI Avatars: Choose from different lifelike AI avatars or create your own to bring your videos to life.
• Video Translation: Translate your videos into over 175 languages with synchronized lip movements and voice output.

10. Lumo by Proton: Privacy-First AI Tool

Lumo is a privacy-first AI assistant from the creators of Proton Mail and Proton VPN. It’s designed to provide you with all the benefits of an AI chatbot without compromising your privacy and data security.

• Confidential Conversations: Lumo keeps no logs of your chats, and your conversations can’t be seen, shared, or used to profile you.
• Zero-Access Encryption: Your saved conversations are encrypted and can only be decoded on your device.
• Open-Source Code: Lumo’s code is fully open source, so anyone can independently verify its privacy and security.

11. PodClips: AI Tool to Turn Your Podcast into Viral Video Content

PodClips is an AI-powered tool that helps you turn your podcast episodes into viral video content for social media. It’s a must-have tool for any podcaster looking to grow their audience.

• Automated Clip Creation: The platform automatically generates engaging short-form and mid-form video clips from your podcast episodes.
• Top Podcaster Captions: Get captions styled like the biggest podcasters in the game.
• B-Rolls and Thumbnails: The tool also provides B-rolls and thumbnails to make your videos even more engaging.

12. GitHub Spark: AI Agent to Turn Idea to App in a Click

GitHub Spark is an AI-powered platform that helps you build and deploy intelligent apps with a single click. It’s a powerful tool for developers and non-developers, allowing you to turn your ideas into full-stack applications without any setup.

• Natural Language App Building: Describe the app you want to build in plain language, and Spark will generate a working application for you.
• One-Click Deployment: Deploy your app with a single click, with secure hosting and authentication handled by GitHub.
• Seamless Integration with GitHub: Spark is fully integrated with the GitHub ecosystem so that you can use all the powerful tools for version control, security, and collaboration.

In Conclusion:

2025 has proved that “AI everywhere” isn’t a slogan—it’s the reality. Not all viral AI tools and agents are worth it, but those that are should be part of your workflow and not ignored. A good AI tool and agent help you automate, simplify, and boost your productivity while leaving you room to do your best. There were surely other viral AI tools and agents we missed, but those in this list are definitely worth a try. We can expect to see even more innovative and powerful applications that will further transform the way we work, create, and live.

^{*Affiliate: We do make a small profit from the sales of this AI product through affiliate marketing.}

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If you talk to a modern language model, what matters even more than the model itself is what you feed it. Garbage in, garbage out. You’ve probably heard of “prompt engineering,” the art of writing the perfect question to get the best answer from generative AI tools like ChatGPT, Gemini, Claude, and others. For a while, prompt engineering has been the most popular approach to achieving the best output and unlocking the full potential of AI.

But what if I told you there’s a new, more powerful skill emerging, one that’s less about asking the right questions and more about creating the right environment for the AI to think? This new frontier is called “context engineering,” and it’s the secret ingredient that’s transforming AI from a clever chatbot into a truly intelligent partner.

From Prompts to Context: A New Era of AI

By default, large language models (LLMs) don’t “know” anything. They predict text based on the context window you hand them, and if that window is stuffed with stale chat history, random API responses, or half‑formatted code, even the world’s best model will struggle. However, on the other hand, a lean bundle of instructions, examples, and up-to-date facts can make a smaller, cheaper model stand out; that is, context engineering.

Why is this such a game-changer?

• LLMs can’t read minds. They respond only to what they “see.”
• The right context means better, safer, more relevant results, every time.
• As AI tools become more integrated in our daily work, “context” is now table stakes for reliability and trust.

What is Context Engineering?

So, what exactly is context engineering? To put it in the right words, context engineering is the practice of strategically organizing everything an AI needs (information, tools, background details, and history) to allow the AI model to perform a task just like a human would.

Think of an AI’s “context window” as its short-term memory. Prompt engineering is about what you write in that window. Context engineering, on the other hand, is about everything else you can put in there to help the AI succeed. It’s the difference between asking a stranger for guidance (prompt engineering) and giving them a map, a compass, and the address of your destination (context engineering).

This broader approach is becoming increasingly important as AI systems, often called “autonomous AI agents,” are asked to perform more complex, multi-step tasks. These agents need more than just a single instruction; they need a rich, dynamic environment of information to draw from. Most of the time, when an AI agent fails, it’s not because the AI model itself isn’t smart enough, but because it was missing a crucial piece of information, a “context failure.”

How is it different from prompt engineering?

Think of prompt engineering as writing the last mile of instructions (tone, persona, format). Context engineering is the supply-chain management that ensures those instructions sit on top of accurate and relevant data. In many applications, the context itself dwarfs the literal prompt. That’s why LangChain’s engineers argue prompt writing is now “just one tool in the context engineer’s kit.”

Here are some of the key elements that make up an AI’s context:

• Instructions and System Prompts: These are the foundational rules and guidelines that tell the AI how to behave. This is where you can set the AI’s persona, its goals, and any constraints it needs to follow.
• User Input: This is the immediate question or task you give the AI.
• Short-Term Memory: This includes the history of your current conversation, allowing the AI to remember what you’ve already discussed and build upon it.
• Long-Term Memory: This is a persistent knowledge base that the AI can access across multiple conversations. It can include your preferences, past projects, or any other information you’ve told it to remember.
• Retrieved Information (RAG): This is where things get really powerful. Retrieval-Augmented Generation (RAG) allows AI to pull in external, up-to-date information from documents, databases, or the internet to answer your questions.
• Tools: You can give an AI access to “tools,” which are essentially functions it can call to perform specific actions, like checking your calendar, sending an email, or searching the web.
• Structured Output: You can also provide the AI with a specific format for its response, like a JSON object, which is incredibly useful for making AI-powered applications more reliable.

From “just write a clever prompt” to a repeatable process

Early adopters treated prompt writing like haiku—short, mysterious, and occasionally brilliant. As apps improved and became multi-step agents, that broke down. Andrej Karpathy nailed the change in a recent tweet: “Prompt engineering is what you do inside the window. Context engineering is how you decide what fills the window.”

Today, best‑practice teams run context engineering as a pipeline:

1. Collect user input, tool outputs, database records, and relevant documents.
2. Select the smallest subset that actually helps.
3. Transform it into a format the model understands, often JSON or structured markdown.
4. Evaluate the result using both automated tests and human review, then refine the loop.

Why Context Engineering Is Replacing Prompt Engineering

Business and technical leaders are moving away from one-off quick fixes to scalable, systematic solutions. Here’s how context engineering stands apart:

• Prompt Engineering:

Prompt engineering focuses on writing better questions or commands, relying on trial and error, and is most effective for simple, single-shot requests.

• Context Engineering:

Context engineering focuses on providing the right knowledge, memory, and tools. It allows continuous, personalized, and multi-step interactions. Context engineering supports more complex use cases: customer support, digital assistants, automated research, enterprise chatbots, and more.

How Context Engineering Works in Practice

• Write: Save important details outside the main prompt, ready to be used as needed.
• Select: Pull the most relevant information into the conversation at the right moment.
• Compress: Process large data sets into summaries that fit within the AI’s “working memory.”
• Isolate: Organize bits of information so they can be referenced without confusion.

Real‑world scenarios you already know

From Clunky to “Magical”: Why Context Engineering Matters

The difference between an AI with good context and one without is the difference between a clunky, unhelpful tool and a “magical” assistant that seems to anticipate your needs. Imagine you ask an AI to schedule a meeting for “tomorrow.” A basic AI, with no context, might respond with a generic, “What time works for you?”

But an AI powered by rich context would be a different story. Before responding, it would have access to your calendar, see that you’re fully booked, and know from your past emails with this person that you have an informal relationship. It might even have a tool to send a calendar invite. With all this information, it could generate a much more helpful response, like: “Hey Jim! Tomorrow’s packed on my end, back-to-back all day. Thursday AM free if that works for you? Sent an invite, lmk if it works.”

This is the power of context engineering. It’s not about a smarter AI model; it’s about providing the right information, in the right format, at the right time.

In Conclusion:

If prompt engineering taught us to speak to machines, context engineering teaches us to set the conversation’s stage. It’s less glamorous than inventing new neural nets, but it’s where real‑world AI succeeds. Context engineering flips the script on “prompt magic.” It’s not about tricking an AI with clever phrasing; it’s about building a smarter environment so the AI helps you solve real problems reliably.

Context engineering recognizes that the key to opening an AI’s true potential lies not just in the questions we ask, but in the rich, dynamic environment of information we provide. We can turn an AI from a mere “chatbot” into a truly intelligent and autonomous agent by thoughtfully curating an AI’s context, i.e., core instructions and memory, the tools and data it can access. Because in the era of smart automation, it’s not only what AI can do—it’s what you give it to work with, and how you set the stage for success.

Sources:
https://www.promptingguide.ai/guides/context-engineering-guide
https://rlancemartin.github.io/2025/06/23/context_engineering/
https://x.com/karpathy/status/1937902205765607626
https://www.philschmid.de/context-engineering
https://simple.ai/p/the-skill-thats-replacing-prompt-engineering
https://github.com/humanlayer/12-factor-agents
https://blog.langchain.com/the-rise-of-context-engineering/

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