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AI & Coding Resources 👨‍💻📑🚀

AI & Coding Resources 👨‍💻📑🚀

前往频道在 Telegram

👉 Sharing Free Technical and Coding realted Resources and handwritten Notes 🤩📑👨‍💻. 👉 Follow on LinkedIn for more content :- https://www.linkedin.com/in/manish-kumar-shah 👉 Follow on Instagram for Short Notes :- https://instagram.com/codes.manish

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📈 Telegram 频道 AI & Coding Resources 👨‍💻📑🚀 的分析概览

频道 AI & Coding Resources 👨‍💻📑🚀 (@codetreasure) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 37 344 名订阅者,在 技术与应用 类别中位列第 3 462,并在 印度 地区排名第 10 562

📊 受众指标与增长动态

невідомо 创建以来,项目保持高速增长,吸引了 37 344 名订阅者。

根据 26 八月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 -534,过去 24 小时变化为 -17,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 15.80%。内容发布后 24 小时内通常能获得 16.88% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 5 900 次浏览,首日通常累积 6 303 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 11
  • 主题关注点: 内容集中在 humva, hunt, techinnovation, integration, insight 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
👉 Sharing Free Technical and Coding realted Resources and handwritten Notes 🤩📑👨‍💻. 👉 Follow on LinkedIn for more content :- https://www.linkedin.com/in/manish-kumar-shah 👉 Follow on Instagram for Short Notes :- https://instagram.com/codes.m...

凭借高频更新(最新数据采集于 27 八月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 技术与应用 类别中的关键影响点。

37 344
订阅者
-1724 小时
-1197
-53430
帖子存档
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I use AI almost every day for my work. But recently, I realized something: using AI every day doesn’t necessarily mean you’re
I use AI almost every day for my work. But recently, I realized something: using AI every day doesn’t necessarily mean you’re using it well. I wanted to get better at researching, brainstorming, organizing information, improving my writing, and using AI more effectively for my daily tasks. That’s what caught my attention about the Google AI Professional Certificate on Coursera. It focuses on practical AI skills, including how to: → Work with AI tools like Gemini, NotebookLM, and AI Studio → Research, brainstorm, and organize information → Improve writing, communication, and productivity → Build AI-powered solutions → Apply AI to real workplace tasks → Complete hands-on projects and a capstone And for a limited time, enrolling in the certificate also includes 3 months of Google AI Pro at no extra cost. If you’re looking to build practical AI skills that you can actually use in your work, this is worth checking out. 🔗 Enroll here: https://imp.i384100.net/c/4788814/3979813/14726?sharedid=gwgmanishai

Over the past year, I've realized that AI has become an important skill, no matter what field you work in. Since I create AI content every day, I'm always looking for better ways to use AI in my work, not just write better prompts. That's why I decided to explore the Google AI Professional Certificate. Check out here: https://imp.i384100.net/c/4788814/3979813/14726?sharedid=gwgmanishai What I like about the program is that it's focused on practical learning. You get to use tools like Gemini, NotebookLM, and AI Studio to: • Organize research and study materials • Brainstorm ideas • Save time on everyday tasks • Build AI powered projects The program also includes hands on exercises and a capstone project, so you can apply what you learn, build a portfolio, and earn an industry recognized Professional Certificate from Google. For a limited time, when you enroll in the Google AI Professional Certificate, you'll also get 3 months of Google AI Pro at no extra cost. Enroll now: https://imp.i384100.net/c/4788814/3979813/14726?sharedid=gwgmanishai It can help you: • Understand complex topics more easily • Organize your study materials • Generate ideas for projects • Build projects more efficiently • Prepare your resume and practice interview questions Whether you're a student, creator, developer, or working professional, it's a practical way to build job ready AI skills that you can use in your everyday work. If you've been thinking about learning AI in a more practical way, this is worth checking out. 🔗 Enroll here: https://imp.i384100.net/c/4788814/3979813/14726?sharedid=gwgmanishai

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One of the biggest mistakes companies are making with AI right now? Building critical workflows around a single AI model. The AI landscape is moving too fast. Models improve, pricing changes, and new capabilities emerge constantly. If your business is locked into one provider, you're limiting your ability to adapt. That's why Rebel by Mindstone caught my attention. What started with Joshua Wohle's personal operating system has evolved into a platform designed to help organizations adopt AI at scale—without locking themselves into a single model or vendor. Instead of isolated AI tools and disconnected experiments, Rebel gives every employee their own AI agent while keeping workflows portable across different AI providers. And this isn't just a concept. Epignosis, the company behind TalentLMS (22M+ users), recently rolled Rebel out to all 250 employees across engineering, sales, finance, product, and customer success. Twelve weeks later, they reported gaining the equivalent capacity of 8 full-time employees. A few things that stood out to me: • Works across multiple AI providers, so you're never locked into one model. • Connects with email, CRM, Slack, calendars, and documents. • Can run locally, keeping sensitive data under your control. • Includes approval layers before taking actions on your behalf. • Builds shared organizational memory so knowledge compounds over time. The memory piece is especially interesting. As people create workflows, automate tasks, and solve problems, that knowledge becomes available across the organization instead of being trapped in individual chats. Mindstone has now fair-sourced Rebel, making it free for individuals and organizations with up to 100 users. If you're thinking about how to roll out AI across an entire company—not just for a handful of power users—Rebel is worth checking out. 🔗 Download Rebel: https://rebel.mindstone.com/ 🔗 Product Hunt: https://www.producthunt.com/products/mindstone-rebel #Mindstone #AI

Cybersecurity is no longer just a tech skill. It’s becoming a basic digital survival skill. Almost everything today lives online: • Banking • Remote work • Social media • Cloud storage • AI tools • Online payments & personal data Which also means cyber threats are evolving faster than ever. What surprised me recently is how many attacks still happen because of simple mistakes: • Weak passwords • Fake emails • Unsafe downloads • Poor security awareness And with AI making scams more convincing, understanding cybersecurity fundamentals is becoming valuable across almost every profession, not just IT roles. That’s one reason I’ve been exploring structured cybersecurity learning lately instead of randomly consuming content online. Platforms like Coursera have beginner friendly cybersecurity programs that help break down concepts in a practical way. Some interesting courses I came across: Cyber Security Fundamentals: https://imp.i384100.net/B5ynbx Cybersecurity Essentials: https://imp.i384100.net/GbVKVr Data Privacy: https://imp.i384100.net/1GP9Ra Ethical Hacking: https://imp.i384100.net/4amGyM Network Security: https://imp.i384100.net/yZbqjv Google Cybersecurity Certificate: https://imp.i384100.net/Or5L6G They make the learning process much easier to follow step by step. What’s one cybersecurity habit you think everyone should follow daily?

Machine Learning looks exciting when you see the final results. AI tools. Smart automations. Models doing things that felt impossible a few years ago. But when I actually started learning ML seriously, I realized how easy it is to feel completely lost. One tutorial explains algorithms. Another jumps into Python libraries. Then suddenly you’re watching a long neural network video without properly understanding the basics behind it. That’s where a lot of people get stuck. I’ve been spending more time learning Machine Learning recently, and one thing that genuinely helped me was following a more structured learning path instead of constantly switching between random resources. While exploring coursera courses, I liked how the courses are organized from foundational concepts to more advanced ML topics. You can gradually move through: • Python for ML: https://imp.i384100.net/eKJOOZ • Data preprocessing: https://imp.i384100.net/Jk26Mq • Regression + classification: https://imp.i384100.net/g1KJEA • Supervised and unsupervised learning: https://imp.i384100.net/0GP6vR • Neural networks: https://imp.i384100.net/DKrLn2 • Deep Learning projects: https://imp.i384100.net/jroLxe What personally helped me most was learning concepts in sequence instead of trying to figure everything out alone from scattered tutorials. And honestly, with AI evolving this fast, understanding the fundamentals feels more important than ever. I’ve also noticed that many people rush into using AI tools before understanding how Machine Learning actually works underneath. For anyone learning ML right now: What concept took you the longest to finally understand?

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Most people think they are learning AI. But they are actually just collecting tools. One week it is ChatGPT. Next week it is a new automation tool. Then a design or video AI platform. It feels like progress. But in reality, it is just noise. Because without a clear roadmap, every new tool resets you back to zero. The real shift happens when you stop chasing tools and start building skills step by step. A simple roadmap most people ignore: ↳ Start with basics like Python and data handling. ↳ Understand statistics and how data actually works. ↳ Learn core Machine Learning concepts. ↳ Build small real-world projects. ↳ Then explore AI tools to apply what you know. That is what creates real confidence. Right now, the people growing fastest are not the ones using the most tools. They are the ones who have strong fundamentals and a clear path. That is where structured learning makes a difference. Instead of jumping between random tutorials, you follow a guided path across AI, Data Science, Machine Learning, or even UI UX and Project Management. I recently came across a Spring offer that gives access to multiple courses under one subscription. The annual plan is currently ₹7,999 instead of ₹13,999. Explore the Spring offer here: https://imp.i384100.net/c/4788814/3812616/14726 If you are serious about upskilling this year, having everything in one place makes it easier to stay consistent and actually complete what you start. Because in the long run, tools will change. But your foundation and problem-solving ability will not. Are you building real AI skills right now, or just experimenting with tools?