现已上线！2025 年 Telegram 研究 — 年度关键洞察 
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
显示更多📈 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 38 620 名订阅者，在 技术与应用 类别中位列第 3 557，并在 印度 地区排名第 10 676 位。
📊 受众指标与增长动态
自 невідомо 创建以来，项目保持高速增长，吸引了 38 620 名订阅者。
根据 09 六月, 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 -581，过去 24 小时变化为 -11，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 15.15%。内容发布后 24 小时内通常能获得 N/A% 的反应，占订阅者总量。
- 帖子覆盖： 每篇帖子平均可获得 5 852 次浏览，首日通常累积 0 次浏览。
- 互动与反馈： 受众积极参与，单帖平均反应数为 26。
- 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 10 六月, 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。
38 620
订阅者
-1124 小时
-1187 天
-58130 天
数据加载中...
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频道帖子
https://www.instagram.com/reel/DYzb5VMy0MT/?igsh=aG1lamNvNG1vOTlm
TCS Mass Hiring 2026 !
| 2 | 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? | 10 301 |
| 3 |  没有文字... | 5 352 |
| 4 | https://www.instagram.com/reel/DYXMuIxS_Jv/?igsh=MXFmMjdlamNjYmV1Zw==
Google is hiring for 2026 Summer Interns | 3 263 |
| 5 | Google Summer Internship 2026 🔥
https://www.instagram.com/reel/DYXMuIxS_Jv/?igsh=MXFmMjdlamNjYmV1Zw== | 0 |
| 6 | Google Summer Internship 2026 🔥
https://www.instagram.com/reel/DYXMuIxS_Jv/?igsh=MXFmMjdlamNjYmV1Zw== | 0 |
| 7 | 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? | 14 899 |
| 8 |  没有文字... | 2 836 |
| 9 | https://www.instagram.com/reel/DYR6ZLayelw/?igsh=d3hnazhuM3VuNHR0
HIRING REALITY 2026!😮
Do checks this out before your next Job switch. 😋 | 3 198 |
| 10 | https://www.instagram.com/p/DYKY1vEkm39/?igsh=MWZncmNnN3AwbXJhcQ==
Hello Everyone!
Just getting started on my new journey as an Instagram content creator.
Sharing what I learn, build & experience around tech, AI, career & life 💻🤖☕
First reel dropping soon👀💖
Do LIKE FOLLOW SHARE my page!
Need your immense support!💜🙏🏻💜 | 0 |
| 11 | 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? | 0 |
| 12 |  没有文字... | 0 |
| 13 | Most people trying to learn AI in 2026 are doing it wrong.
Here’s the reality of learning AI today:
↳ Tools change every week
↳ Tutorials are fragmented
↳ Fundamentals are often skipped
The problem isn’t lack of content.
It’s lack of structure
1. Start with tools first
❌ OLD: Learn ChatGPT, agents, tools first
✅ NEW: Tools change fast. Fundamentals don’t
Understanding models, data, and workflows gives long-term leverage
2. Learn from random tutorials
❌ OLD: YouTube + scattered resources are enough
✅ NEW: Random learning creates gaps
Structured paths across AI, ML, and Data Science compound better
3. Focus only on prompting
❌ OLD: Prompting = AI mastery
✅ NEW: Prompting is just the interface
Real value comes from building systems
4. Consume more, build less
❌ OLD: Keep learning before building
✅ NEW: Small projects teach faster than passive content
5. Learn AI in isolation
❌ OLD: Just learn AI
✅ NEW: AI + Data + Product thinking is the real edge
What actually works:
↳ Structured learning paths
↳ Hands-on projects
↳ Layered skill building across domains
With how fast AI is evolving right now, unstructured learning just doesn’t keep up anymore.
Recently, I shifted towards a more structured approach instead of jumping between random resources.
Having access to guided learning paths across AI and Machine Learning makes it easier to stay consistent and actually build skills.
Also noticed a Spring offer right now:
Coursera Plus is available at ₹7,999 for a year (earlier ₹13,999)
Click here to explore the Spring offer: https://imp.i384100.net/c/4788814/3812616/14726
AI isn’t just about using tools anymore.
It’s about understanding and building with them.
Are you currently building AI projects or mostly consuming content? | 0 |
| 14 |  没有文字... | 0 |
| 15 |  I tested SurfSense, and what stood out immediately is the control it gives you over your data.
Unlike most AI tools that rely on external servers, SurfSense is open source and self-hostable, which means you can run everything on your own infrastructure.
Your data stays with you. Always.
At the same time, it connects your entire workflow into one system.
You can bring in data from tools like Slack, Notion, Gmail, GitHub, and Google Drive, and turn it into a unified, searchable knowledge base.
Then just ask questions in plain English, and it pulls answers across all your sources with context.
It also goes beyond just answers.
You can generate reports, summaries, research briefs, presentations, and even videos directly from your connected data.
Everything is created from your sources, so the output stays accurate and consistent.
It feels less like a tool and more like your own private AI system.
If privacy and control matter to you, this is worth checking out.
Try it here: https://www.surfsense.com/ | 0 |
