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How to Use KaneAI — Best Gen AI Native (FULL GUIDE) 🔥” QA Engineers & DevOps: Stop wasting 80% of your time on manual tests. KaneAI converts natural language into executable End to End tests instantly: ✅ Upload JIRA issues, PDFs, or videos → Auto-generate test cases ✅ Self-healing... show more

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Stop writing tests. Start describing them. KaneAI converts natural language into end-to-end tests in seconds. ✔ JIRA / PDF / video uploads ✔ Self-healing UI tests ✔ GitHub PR-based test creation ✔ API & backend validation Modern testing, powered by GenAI Give it a try : Follow #AITesting #KaneAI #DevOps #QualityEngineering

Stop writing tests. Start describing them. KaneAI converts natural language into end-to-end tests in seconds. ✔ JIRA / PDF / video uploads ✔ Self-healing UI tests ✔ GitHub PR-based test creation ✔ API & backend validation Modern testing, powered by GenAI Give it a try : Follow #AITesting #KaneAI #DevOps #QualityEngineering

Marry claire

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🚀 Meet KaneAI TestMu AI the next-generation AI-powered testing assistant for modern QA teams. KaneAI helps teams plan, author, and evolve end-to-end tests using natural language, turning requirements directly into executable code. Built for enterprise-scale complexity, it handles sophisticated workflows across all major programming languages and frameworks. QA teams often face hurdles like rapid development cycles, test case debt, and complex framework management. KaneAI addresses these challenges by: ✅ Generating structured test cases from text, JIRA, PDFs, images, audio, video, or spreadsheets ✅ Enabling complex testing workflows with natural language ✅ Supporting multi-language code exports and deep integrations with JIRA and GitHub ✅ Automating API and database testing, smart versioning, and auto bug detection ✅ Self-healing tests for UI changes and flexible manual interaction KaneAI augments QA teams, boosting productivity, reducing errors, and helping teams focus on outcomes rather than repetitive tasks. Learn more about KaneAI here: Connect with TestMu AI : LinkedIn: Twitter: #QA #TestAutomation #AI #GenAI #KaneAI #TestMu

🚀 Meet KaneAI TestMu AI the next-generation AI-powered testing assistant for modern QA teams. KaneAI helps teams plan, author, and evolve end-to-end tests using natural language, turning requirements directly into executable code. Built for enterprise-scale complexity, it handles sophisticated workflows across all major programming languages and frameworks. QA teams often face hurdles like rapid development cycles, test case debt, and complex framework management. KaneAI addresses these challenges by: ✅ Generating structured test cases from text, JIRA, PDFs, images, audio, video, or spreadsheets ✅ Enabling complex testing workflows with natural language ✅ Supporting multi-language code exports and deep integrations with JIRA and GitHub ✅ Automating API and database testing, smart versioning, and auto bug detection ✅ Self-healing tests for UI changes and flexible manual interaction KaneAI augments QA teams, boosting productivity, reducing errors, and helping teams focus on outcomes rather than repetitive tasks. Learn more about KaneAI here: Connect with TestMu AI : LinkedIn: Twitter: #QA #TestAutomation #AI #GenAI #KaneAI #TestMu

Mohini Shewale

16,809 просмотров • 5 месяцев назад

Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI

Anyone who’s worked in QA knows the gap. Requirements live in one place and tests live somewhere else. Every change creates another round of rewriting. Kane AI by TestMu AI (formerly LambdaTest) approaches that gap differently. Instead of forcing teams to rewrite tests in a new abstraction, it lets you describe intent in natural language and turns that into structured, executable tests that can be exported into your existing frameworks. During exploration, the most practical part wasn’t just test generation, it was how expressive the language layer is. Complex assertions, branching conditions, and validations can be described without switching mental modes into code, but the resulting output still integrates cleanly with real pipelines. There’s also a strong emphasis on API coverage and auto-bug detection, which matters when UI tests aren’t enough. Kane AI by TestMu AI (formerly LambdaTest) can detect failures during execution and attempt self-healing based on original intent, while still requiring review and validation from the team. It doesn’t pretend QA disappears. It acknowledges QA is hard, contextual, and constantly changing. That honesty makes it useful. If you’re dealing with test maintenance and curious how natural-language testing fits into real QA work, Kane AI by TestMu AI (formerly LambdaTest) is available to try. See it in action: TestMu AI

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I'm thrilled to announce Bug0 Studio - a self-serve, AI-native browser testing tool. We're in private beta. DM me your email for access + $250/mo in credits for 3 months. What you can do today: - Create tests from video recordings - Natural language test steps - Self-healing tests (adapts to UI changes) - Live browser preview - CI/CD integration (Vercel, Netlify, GitHub Actions) - Scheduled runs + reports via Slack, email, or PRs - Auto-extract emails (OTPs, magic links) - Team collaboration Most QA tools hide behind "contact us for demo." But I believe powerful QA shouldn't be gatekept behind sales calls and enterprise pricing. Help us build the most user-friendly and powerful AI QA tool that is open and accessible to all🙏 Today it's private beta. Soon, it's open to everyone.

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Shipping speed is up across the board, and AI tools like Gemini 3 Pro are helping us code and build faster than ever. But validation and quality assurance? Still the bottleneck. That’s why I’m integrating TestSprite into my workflow. It analyzes my code and PRD, automatically builds a comprehensive test layer, and keeps my development flow unbroken. No test writing. No last-minute QA chaos. What it covers: ➞ UI Flow Coverage: Verifies real-time updates and synchronization (e.g., confirming gradient sliders and angle controls work together). ➞ Edge Case Validation: Catches subtle logic bugs like inputs accepting out-of-range values that I'd typically miss. ➞ Functionality Check: Automatically runs videos to ensure core features (like "Randomize" and "Copy to Clipboard") are working. ➞ Repeatable Test Suite: Creates tests I can rerun anytime, giving me confidence that new code hasn't broken the old. My new Antigravity + Test Sprite Loop: ➞ AI Plan & Code: Use Antigravity's Planning Mode with Gemini 3 Pro to generate structured code. ➞ Browser Agent QA: Use Antigravity's Open Browser feature to let the AI visually analyze and control the live UI for quick verification. ➞ Validate & Test: Run the automated, reproducible test suite in Test Sprite. ➞ Ship. This loop feels clean, fast, and robust. Works beautifully alongside Gemini 3 Pro, Cloud Sonet, or any AI stack.

Shipping speed is up across the board, and AI tools like Gemini 3 Pro are helping us code and build faster than ever. But validation and quality assurance? Still the bottleneck. That’s why I’m integrating TestSprite into my workflow. It analyzes my code and PRD, automatically builds a comprehensive test layer, and keeps my development flow unbroken. No test writing. No last-minute QA chaos. What it covers: ➞ UI Flow Coverage: Verifies real-time updates and synchronization (e.g., confirming gradient sliders and angle controls work together). ➞ Edge Case Validation: Catches subtle logic bugs like inputs accepting out-of-range values that I'd typically miss. ➞ Functionality Check: Automatically runs videos to ensure core features (like "Randomize" and "Copy to Clipboard") are working. ➞ Repeatable Test Suite: Creates tests I can rerun anytime, giving me confidence that new code hasn't broken the old. My new Antigravity + Test Sprite Loop: ➞ AI Plan & Code: Use Antigravity's Planning Mode with Gemini 3 Pro to generate structured code. ➞ Browser Agent QA: Use Antigravity's Open Browser feature to let the AI visually analyze and control the live UI for quick verification. ➞ Validate & Test: Run the automated, reproducible test suite in Test Sprite. ➞ Ship. This loop feels clean, fast, and robust. Works beautifully alongside Gemini 3 Pro, Cloud Sonet, or any AI stack.

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Most AI app builders can generate a prototype. The real question is: Can they generate something you can actually launch? I recently explored MeDo, an AI-powered full-stack app generation platform that goes beyond just creating a frontend. With a single prompt, it can generate the frontend UI, backend logic, database structure, and plugin integrations. All in one workflow, without requiring coding or deployment experience. What stood out to me is the focus on building real products rather than simple demos. Whether you're creating SaaS tools, internal dashboards, e-commerce applications, games, or business automation tools. the goal is to move from idea → working product as quickly as possible. A few notable features: ✅ Full-stack generation from a single prompt ✅ Plugin ecosystem with integrations like Stripe and AI tools ✅ Auto bug-fixing capabilities ✅ Website cloning from screenshots ✅ Built-in UI enhancement options ✅ Marketplace of ready-made apps you can customize The AI app builder space is becoming increasingly competitive, and platforms are starting to differentiate themselves based on how close they get to a production-ready result rather than just generating code. For builders, founders, and creators exploring no-code AI development, it's an interesting category worth watching. Try it today and launch your next project with just a prompt:

Most AI app builders can generate a prototype. The real question is: Can they generate something you can actually launch? I recently explored MeDo, an AI-powered full-stack app generation platform that goes beyond just creating a frontend. With a single prompt, it can generate the frontend UI, backend logic, database structure, and plugin integrations. All in one workflow, without requiring coding or deployment experience. What stood out to me is the focus on building real products rather than simple demos. Whether you're creating SaaS tools, internal dashboards, e-commerce applications, games, or business automation tools. the goal is to move from idea → working product as quickly as possible. A few notable features: ✅ Full-stack generation from a single prompt ✅ Plugin ecosystem with integrations like Stripe and AI tools ✅ Auto bug-fixing capabilities ✅ Website cloning from screenshots ✅ Built-in UI enhancement options ✅ Marketplace of ready-made apps you can customize The AI app builder space is becoming increasingly competitive, and platforms are starting to differentiate themselves based on how close they get to a production-ready result rather than just generating code. For builders, founders, and creators exploring no-code AI development, it's an interesting category worth watching. Try it today and launch your next project with just a prompt:

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Autonomy for All: My Experience with Replit ⠕ Agent 3 🚀 If you’ve ever wished building software felt less like coding with a computer and more like collaborating with a teammate, you need to try Agent 3. I’ve been using it recently, and honestly it’s a whole new way to build. Try now: 💡 Here’s what stood out for me: ✅ It’s 10X more autonomous — I’ve had it run for up to 200 minutes straight without me touching a thing. ✅ It tests and fixes its own code in a live browser — and it’s 3X faster and 10X more cost-effective than the usual testing models. ✅ It can build other Agents & Automations — I’ve spun up Slackbots to query data, Telegram bots to track habits, and even time-triggered workflows that email me daily stock news. How it feels to use: → You describe what you want in plain language. → Agent 3 just… gets to work — building, testing, improving, and delivering. → You can even track progress live from your phone while it’s still working. This isn’t just an upgrade, it’s a leap. From idea → app → automation, Agent 3 makes it feel like you’ve got another person on your team, not just another tool. 🔗 Check out Replit Agent 3 here: If you build anything cool, I’d love to see it.

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🚀 Less managing, more creating. Building app usually means: • Hours lost debugging • Constantly juggling tools • Non-stop AI hand-holding Replit’s new Agent 3 changes that entirely → ⁠ We just tried out Agent 3, and it’s impressive! ⁠ It’s not another ordinary AI coding assistant. It’s a full-on collaborator that: • Runs autonomously for up to 200 minutes, building your app from start to finish. • Tests its own work in a real browser and fixes bugs automatically, saving hours of manual debugging. • Lets you build other agents and automations (think Slack bots, Telegram reminders, email summaries). ⁠ Here's why Agent 3 is different: Unlike traditional AI tools where you constantly babysit and prompt, Agent 3 understands your idea and takes charge, freeing you to focus on creativity, strategy, or simply grabbing coffee while your app builds itself. And to show how easy it is, we built a complete waitlist app in less than an hour, from idea to almost finished product. No babysitting. No endless tweaking. ⁠ Replit ⠕ is calling this “Autonomy for All,” and after seeing Agent 3 in action, it’s easy to see how it can bring millions more creators online. ✅ Faster builds ✅ Less frustration ✅ More polished, reliable apps With Agent 3, app-building really does feel less like wrestling with software and more like collaborating with a teammate. ⁠ Try Replit Agent 3 yourself today…👆 Get $10 credit when you purchase Replit Core using our link above!

There's An AI For That

40,330 просмотров • 10 месяцев назад

Claude will replace manual traders in 2026. New update lets you give it full control of your computer. That's why I decided to test it with $100 and total access. Asked to turn it into $1,000 on Polymarket or I shut it down. 3 hours later it was scanning every wallet on Polymarket for copy trading opportunities. It found best wallets and started testing them through this bot: At first, it lost $73 trying each wallet to understand which ones were profitable. Then it picked 7 wallets, executed copy trades automatically and did something i don't believe even now. $3,467 profit in 24 hours. Automatically. With no monitoring. What’s insane is how it works: • Claude monitors hundreds of wallets 24/7 • Evaluates win rates, market timing and sizing • Tests micro trades to find the best strategy • Executes automatically with AL and TP rules This is why AI is mandatory in 2026. Markets move faster than humans, I've been telling it for months. Your edge is speed, analysis, and execution. Anyone still clicking buttons manually will lose edge within months. I just reinvested the profits and let Claude keep copy trading. This is the future. Manual trading is probably dead FOREVER.

Claude will replace manual traders in 2026. New update lets you give it full control of your computer. That's why I decided to test it with $100 and total access. Asked to turn it into $1,000 on Polymarket or I shut it down. 3 hours later it was scanning every wallet on Polymarket for copy trading opportunities. It found best wallets and started testing them through this bot: At first, it lost $73 trying each wallet to understand which ones were profitable. Then it picked 7 wallets, executed copy trades automatically and did something i don't believe even now. $3,467 profit in 24 hours. Automatically. With no monitoring. What’s insane is how it works: • Claude monitors hundreds of wallets 24/7 • Evaluates win rates, market timing and sizing • Tests micro trades to find the best strategy • Executes automatically with AL and TP rules This is why AI is mandatory in 2026. Markets move faster than humans, I've been telling it for months. Your edge is speed, analysis, and execution. Anyone still clicking buttons manually will lose edge within months. I just reinvested the profits and let Claude keep copy trading. This is the future. Manual trading is probably dead FOREVER.

Oracle Boar

66,164 просмотров • 3 месяцев назад

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"This will replace 80% of the apps that you have on your phone." Here's my new episode with Peter Steinberger 🦞 where he showed me: ✅ His personal OpenClaw use cases - flight check-in, home security, and much more ✅ His counterintuitive AI coding workflow - no plan mode, no MCPs, and no fancy prompts ✅ Practical advice for other builders and how to build product taste Some quotes from Peter: "It's like having a new weird friend that is also really smart and resourceful that lives on your computer." "Why should I use MyFitnessPal when I have an infinitely resourceful assistant that already knows I'm making bad decisions at KFC?" "I don't use MCPs or any of that crap. Just because you can build everything doesn't mean you should." 📌 Watch now: Thanks to our sponsors: Granola - The best AI meeting notes app I've ever used: Replit ⠕ - Create beautiful prototypes and full stack apps:

Peter Yang

666,628 просмотров • 5 месяцев назад

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off

Remember that paper that started with ‘Certainly, here is a possible introduction for your topic’? How did that get past peer review?! I don’t want AI tools to do my research for me. I want AI tools to speed up boring tasks that take up my time, so I can focus on the important stuff. Anara moved to a new handle (formerly Unriddle) does exactly that. Here’s how you can use it for your research. 🧵👇 #SponsoredWalkthrough One of the biggest challenges in research is time. A solid literature review takes at least 2-3 months… sometimes even longer, depending on the depth of analysis needed. Reading, organising, and synthesising information is a slow process, but it’s absolutely necessary for high-quality work. AI can help speed it up. Not by replacing your critical thinking. It’s your PhD, your ideas need to be your own—but by automating the tedious, repetitive parts of research so you can focus on deep understanding, analysis, and writing. Unlike other AI tools, Anara works with almost any document format. This is what makes it really stand out from the rest. For instance, you can upload: ✅PDFs and other word-based documents ✅Images and presentations ✅Handwritten notes, voice memos, even videos There are so many resources out there that we can learn from. You can upload everything from research papers to YouTube videos and even your own notes and scribbles. It actually understands handwriting surprisingly well! You get automatic summaries when you upload documents. The AI extracts key information immediately, giving you quick insights. It can also help you keep your documents organised. Use the Groups feature to sort and categorise your resources. Create a group for your literature review and keep these papers separate from your other projects or chapters. Tip: Overwhelmed by the number of papers in your "to-be-read" folder? Upload your papers to Anara for immediate insights on each of them, then use these to decide which ones you want to read in more detail. Quickly identify which papers are worth your time—thank me later! You can also go deeper into the papers with Anara’s chat feature. Instead of endlessly scrolling through documents to find relevant sections, just ask the AI a question based on your uploaded files. The chat provides direct answers, all with citations. ✅Suggests questions based on your prompt, helping you refine your focus ✅Everything is sourced directly from your documents. So no random AI-generated nonsense ✅Switch between different AI models to suit your needs. Some are better for summarisation, others for deeper contextual analysis It actually sticks to the sources you give it. My favourite feature is the ability to make flashcards! After you upload a document, Anara can create flashcards to help you test your understanding. Perfect for revision and retention. But… can you trust it? The problem with many AI research tools is hallucination... meaning that they make things up. Anara doesn’t do that. It reduces hallucinations by only referencing the documents you upload. Plus, it provides detailed references and hyperlinks so you can check the original source down to the exact page number. This doesn’t mean you shouldn’t read the paper for yourself. It does mean that you can find what you need much faster, and then verify it with automatic citations. At the end of the day, these tools are here to help you, not replace you. If you’ve made it this far, then it’s (definitely) time to go to 👇 anara(dot)so and give it a try. Use code THEPHDPLACE20 for 20% off

The PhD Place

23,135 просмотров • 1 год назад

$FAME and #AICON Launcher are officially LIVE on Base!🔥 A chance to be part of something BIG–Participate in the #AICON revolution and experience the top-tier seamless experience with #FameAI 🔗 Time to celebrate this milestone together! 🌟 ----------------------------------------------------------- 🚨 Early Access to Fame AI-CON Launcher is Now Available on BASE! 🚨 With FameAI's #AICON Launcher, you can: ✨ Create your human-like AICON ✨ Publish your AI-CON's token on the market $FAME will be tokenized as the first tradable #AICON on our platform! ----------------------------------------------------------- 🔥Early access will be available for selected $FMC stakers🔥 Stakers can enjoy full features being rolled out in the coming weeks. Next up, opening to the public! $FMC stakers, stake now if you haven't already! 👉 ----------------------------------------------------------- 🟣 How Does #FameAI's #AICON Launcher Work? 💻 No coding required! Anyone can create their own #AICON 🔗 Launch your #AICON token—graduated AI-CON can trade on Base Uniswap v2 ⚙️ Fully customizable and highly autonomous How to acquire $FMC on Base ----------------------------------------------------------- 🟣 $FAME Token and FAME Framework This is built to simulate human-like interactions on social platforms. 🎨 Generates content: Images, text, videos 🤖 Reflects personality, knowledge, and mood 💰 Powered by $FAME token ✨ CA: 0xB8e23ab4A1762Fe8dABb844EcC66FEEE3725c480 🔗 Read more here 👉 ----------------------------------------------------------- 🟣 Fame Studio Fame Studio lets you: ✅ Create hyper-realistic human identities ✅ Generate images, audio, music, and even videos V2 Update Coming Soon– Expanded features for more engaging content—perfect for creators and innovators! #AIAgents $FMC #FameAI $FAME ----------------------------------------------------------- 🟣 #FameAI Roadmap for Q1 2025 🚀 Skill Marketplace: Co-create #AICON, utilizing $FMC as the native currency 🧠 AI-Agent Learning: Train agents to learn specific tones, personalities, and behaviors with one click 🎥 Live Streaming: Superhuman-like models for business or lifestyle-focused live streams

$FAME and #AICON Launcher are officially LIVE on Base!🔥 A chance to be part of something BIG–Participate in the #AICON revolution and experience the top-tier seamless experience with #FameAI 🔗 Time to celebrate this milestone together! 🌟 ----------------------------------------------------------- 🚨 Early Access to Fame AI-CON Launcher is Now Available on BASE! 🚨 With FameAI's #AICON Launcher, you can: ✨ Create your human-like AICON ✨ Publish your AI-CON's token on the market $FAME will be tokenized as the first tradable #AICON on our platform! ----------------------------------------------------------- 🔥Early access will be available for selected $FMC stakers🔥 Stakers can enjoy full features being rolled out in the coming weeks. Next up, opening to the public! $FMC stakers, stake now if you haven't already! 👉 ----------------------------------------------------------- 🟣 How Does #FameAI's #AICON Launcher Work? 💻 No coding required! Anyone can create their own #AICON 🔗 Launch your #AICON token—graduated AI-CON can trade on Base Uniswap v2 ⚙️ Fully customizable and highly autonomous How to acquire $FMC on Base ----------------------------------------------------------- 🟣 $FAME Token and FAME Framework This is built to simulate human-like interactions on social platforms. 🎨 Generates content: Images, text, videos 🤖 Reflects personality, knowledge, and mood 💰 Powered by $FAME token ✨ CA: 0xB8e23ab4A1762Fe8dABb844EcC66FEEE3725c480 🔗 Read more here 👉 ----------------------------------------------------------- 🟣 Fame Studio Fame Studio lets you: ✅ Create hyper-realistic human identities ✅ Generate images, audio, music, and even videos V2 Update Coming Soon– Expanded features for more engaging content—perfect for creators and innovators! #AIAgents $FMC #FameAI $FAME ----------------------------------------------------------- 🟣 #FameAI Roadmap for Q1 2025 🚀 Skill Marketplace: Co-create #AICON, utilizing $FMC as the native currency 🧠 AI-Agent Learning: Train agents to learn specific tones, personalities, and behaviors with one click 🎥 Live Streaming: Superhuman-like models for business or lifestyle-focused live streams

Fame AI | The Home of AI Agent 2.0 - AI-CON

23,775 просмотров • 1 год назад

How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ

How to Create a Professional Data Analysis Report—Even If You’re Not a Data Specialist Today on Agent 101—MuleRun’s first review series where real users test AI agents in real work scenarios—we introduce “Smart Q,” a data analysis expert agent designed to turn anyone into a data-savvy reporter. 1. Team Expertise Smart Q was developed by a team with over 10 years of data analysis experience at a giant corporation. This background ensures that the agent delivers insights and reports that meet professional standards. 2. The Traditional Approach & Its Pain Points Traditionally, creating a data analysis report required deep expertise in tools like Excel, SQL, or Python. You’d need to clean the data, run calculations, generate visualizations, and summarize findings—all of which is time-consuming and prone to human error. For non-specialists, this process is often inaccessible and intimidating. 3. How Smart Q Uses AI—and What Problems It Solves With Smart Q, the entire reporting process is simplified into three steps: upload your raw data, ask a question, and receive charts, key insights, and a polished report—all generated by AI expert. Its key advantages include: ✅Accessibility:No technical background required. ✅Speed:Get a complete analysis in minutes, not hours. ✅Clarity:Receive expert-level conclusions presented in clear, actionable language. Want to become a tester for future AI agents? Engage with this video—comment, like, or share—and we’ll be sure to notice your support! 🎥 See Smart Q in action. #mulerun #mulerun4U #SmartQ

MuleRun

30,549 просмотров • 8 месяцев назад

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

What they don't tell you about vibe coding: • Moltbook exposed 1.5M auth tokens. The owner hadn't written a single line of code. • Tea App leaked 72,000 government IDs. The database was just open, no sophisticated hack needed. • A researcher took control of a journalist's computer through her own vibe-coded game, without a single click. The code ran fine in all three cases, tests passed, reviews looked clean, and nothing raised a flag. That's the problem nobody is talking about. Teams are shipping faster than ever. AI writes the code. CI catches build failures. Tests catch regressions. Observability catches outages. But nobody is asking the one question that actually matters: What can an attacker do with this, right now? Because the bottleneck is no longer writing code. It's understanding what that code actually exposes once it's live. PR reviews miss auth edge cases. Unit tests don't probe broken access control. Staging environments don't simulate adversarial behavior. And business logic flaws look completely fine until someone decides to break them on purpose. Strix is an open-source tool that fills this gap. It reviews your running app the way an attacker would: - Crawls the app and maps every exposed route and flow - Probes abuse paths dynamically, not just at build time - Returns findings with proof-of-concepts and suggested fixes Strix was benchmarked against 200 real companies and open-source repos, where it found 600+ verified vulnerabilities including assigned CVEs. It's designed to fit into how modern teams already work. Run it before a release, after major changes, or continuously as the app evolves. If your team is shipping AI-generated code and you don't currently have a way to answer "what does this actually expose", it's worth looking at. GitHub link in the next tweet.

Akshay 🚀

52,377 просмотров • 3 месяцев назад

Excited to introduce MakeUGC — the #1 AI Ad Maker. We trained AI on 10,000+ viral ads and now it understands "attention" better than we do. MakeUGC ( is capable of generating ad scripts, catchy hooks, and high-converting ads in minutes. How it works: 1. Upload your product photo. MakeUGC will create product-in-hand videos, B-rolls, custom hooks, and more. 2. Describe the product to our AI scriptwriter, and it will generate an ad script in seconds. 3. Pick an AI actor, adjust voiceover, gender, tone, and emotions as per your guidelines. 4. Generate 100s of Titkok-style hooks and personalized ads in minutes. Run A/B tests and optimize without wasting weeks. While competitors with cheap rip-offs who are half as good as MakeUGC cost $2k-$5k/month, MakeUGC costs $7 per video and you get: → 150+ licensed AI actors: No copyright issues → 29+ languages: Global Reach. Local Impact. → 20+ scenes: Variety to match your style Want your custom AI avatar? Done. Want a creator to show your b2b saas? Done. Want the creator to hold your product? Done. 20,000+ brands like Prime by Logan Paul, Turkish Airlines, and Cranberry Apps are already using MakeUGC to scale their revenue. Our top client saved $100,000+ in ad spend last month. Incredible leverage, right? If you are a DTC brand, running an ad agency, or building an app, MakeUGC is here to make your life easier. The annual plan starts at $469/year. Don't like the results? No worries. We will process a 100% refund. No questions asked. Let's get your brand to the moon. If you're here, I have something for you 👇🏻

George Stock

42,027 просмотров • 1 год назад

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

Andrew Ng

752,388 просмотров • 1 год назад

Introducing Kiro, an all-new agentic IDE that has a chance to transform how developers build software. Let me highlight three key innovations that make Kiro special: 1 - Kiro introduces spec-driven development, helping developers express their intent clearly through natural language specifications and architecture diagrams for complex features. This comprehensive context helps Kiro’s AI agents deliver better results with fewer iterations. 2 - Kiro features intelligent agent hooks that automatically handle critical but time-consuming tasks like generating documentation, writing tests, and optimizing performance. These hooks work in the background, triggered by events like saving files or making commits. It’s like having an experienced developer constantly reviewing your work and handling the maintenance tasks that often get delayed. 3 - Kiro provides a purpose-built interface that adapts to how developers work. Whether you prefer chat interactions or working with specifications, Kiro supports your workflow while keeping you in control of the development process. Kiro is really good at "vibe coding" but goes well beyond that. While other AI coding assistants might help you prototype quickly, Kiro helps you take those prototypes all the way to production by following a mature, structured development process out of the box. This means developers can spend less time on boilerplate code and more time where it matters most – innovating and building solutions that customers will love. Starting today, Kiro is available for free during preview and supports most popular programming languages. Here’s how to get started with Kiro today: Excited to see how developers use Kiro, and to work with the developer community to continue to shape Kiro moving forward.

Introducing Kiro, an all-new agentic IDE that has a chance to transform how developers build software. Let me highlight three key innovations that make Kiro special: 1 - Kiro introduces spec-driven development, helping developers express their intent clearly through natural language specifications and architecture diagrams for complex features. This comprehensive context helps Kiro’s AI agents deliver better results with fewer iterations. 2 - Kiro features intelligent agent hooks that automatically handle critical but time-consuming tasks like generating documentation, writing tests, and optimizing performance. These hooks work in the background, triggered by events like saving files or making commits. It’s like having an experienced developer constantly reviewing your work and handling the maintenance tasks that often get delayed. 3 - Kiro provides a purpose-built interface that adapts to how developers work. Whether you prefer chat interactions or working with specifications, Kiro supports your workflow while keeping you in control of the development process. Kiro is really good at "vibe coding" but goes well beyond that. While other AI coding assistants might help you prototype quickly, Kiro helps you take those prototypes all the way to production by following a mature, structured development process out of the box. This means developers can spend less time on boilerplate code and more time where it matters most – innovating and building solutions that customers will love. Starting today, Kiro is available for free during preview and supports most popular programming languages. Here’s how to get started with Kiro today: Excited to see how developers use Kiro, and to work with the developer community to continue to shape Kiro moving forward.

Andy Jassy

666,354 просмотров • 1 год назад