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Computer Science and Programming

Computer Science and Programming

الذهاب إلى القناة على Telegram

Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science

إظهار المزيد

📈 نظرة تحليلية على قناة تيليجرام Computer Science and Programming

تُعد قناة Computer Science and Programming (@computer_science_and_programming) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 140 565 مشتركاً، محتلاً المرتبة 804 في فئة التكنولوجيات والتطبيقات والمرتبة 88 في منطقة إيطاليا.

📊 مؤشرات الجمهور والحراك

منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 140 565 مشتركاً.

بحسب آخر البيانات بتاريخ 29 أغسطس, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار -741، وفي آخر 24 ساعة بمقدار -33، مع بقاء الوصول العام مرتفعاً.

  • حالة التحقق: غير موثّقة
  • معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 7.84‎%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.89‎% من ردود الفعل نسبةً إلى إجمالي المشتركين.
  • وصول المنشورات: يحصل كل منشور على متوسط 11 021 مشاهدة. وخلال اليوم الأول يجمع عادةً 2 656 مشاهدة.
  • التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 14.
  • الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل sellerflash, github, developer, pricing, waybienad.

📝 الوصف وسياسة المحتوى

يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_sc...

بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 30 أغسطس, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التكنولوجيات والتطبيقات.

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The fastest was to learn is from writing not reading I truly recommend using this steps. If you’re learning something new-coding, design, strategy, systems, writing about it will double the speed and depth of your learning. It doesn’t have to be public. It doesn’t have to be pretty. But it has to be real. Because writing forces you to slow down, organize your thoughts, spot your blind spots, and sharpen your understanding in ways passive consumption never will. Passive learning ≠ real learning You can binge a 3-hour YouTube Scroll 20 well-crafted Twitter threads on business models. Maybe even highlight a few Medium articles. And you’ll feel smart afterward. But until you try to explain the concept in your own words, you won’t know what you actually understand - and what you’ve just memorized. “If you can’t write it clearly, you don’t understand it deeply.” Writing is the cheapest, fastest test of comprehension. No grades. No teacher. Just a blinking cursor asking, “Do you actually get this?” Ever try explaining something and halfway through…your brain just blanks? That’s not failure - it’s discovery. It’s your mind running into a gap you didn’t know existed. And once you see it, you can fix it. Writing makes those invisible gaps visible. It turns “I think I get it” into “I know where I’m lost.” This is where compounding kicks in After 10 write-ups on how you built or solved something, you start seeing patterns: → The same problems keep showing up → Certain assumptions fail every time → The same tools quietly carry the weight Those patterns become systems. And once you have systems, learning stops being a linear grind - it starts compounding. You don’t have to be a “writer” Pick a simple rhythm and stick to it: Mon → “What did I struggle with today?” Wed → “What finally clicked?” Fri → “What would I tell someone learning this?” Keep each entry under 200 words. Don’t over-edit. The goal isn’t to look clever - it’s to think clearly. The people who learn fastest aren’t the ones with the most raw talent. They’re the ones who write. Not for likes. Not for followers. But because writing is how they process, refine, and absorb faster than everyone else around them. Thinking is messy. Writing makes it real. Do you write while you’re learning - or only once you feel ready to “share”?

Openness (Open Data Architecture) Openness in data architecture refers to building sustainable and trustworthy systems throug
Openness (Open Data Architecture)
Openness in data architecture refers to building sustainable and trustworthy systems through open-source and standardized formats. It promotes collaboration, avoids vendor lock-in, and maximizes the utility of open-source tools. Key components include data lakes, data warehouses, and orchestration, which support data management ecosystems like the data lakehouse and modern data stacks

anthropics/prompt-eng-interactive-tutorial: Anthropic's Interactive Prompt Engineering Tutorial This tutorial provides a stru
anthropics/prompt-eng-interactive-tutorial: Anthropic's Interactive Prompt Engineering Tutorial
This tutorial provides a structured guide to mastering prompt engineering with Claude, including nine chapters with exercises and an appendix of advanced methods. Users will learn to create optimal prompts, recognize common errors, and utilize areas like an Example Playground to experiment. The tutorial uses Claude 3 Haiku and recommends using the Google Sheets extension for convenience.

Open Data Standards: Postgres, OTel, and Iceberg The post discusses emerging open data standards in the data world including
Open Data Standards: Postgres, OTel, and Iceberg
The post discusses emerging open data standards in the data world including Postgres, Open Telemetry (OTel), and Iceberg. These standards are underpinned by important open source tenets: OSI-approved licensing, the feasibility of self-hosting, and vendor neutrality. Postgres has become a standard due to its compatibility across platforms and non-ownership by any single entity. OTel is gaining traction among major cloud providers for its telemetry capabilities, while Iceberg is leading in OLAP standards. The emphasis is on achieving portability and interoperability, particularly with AWS's S3, enhancing data management and reducing vendor lock-in.

AI Is Not a Black Box (Relatively Speaking) AI systems are actually more transparent than human intelligence when it comes to
AI Is Not a Black Box (Relatively Speaking)
AI systems are actually more transparent than human intelligence when it comes to understanding their internal workings. While AI is often called a "black box," researchers can inspect every neural connection in open-source models, trace concept paths through networks, and determine input importance - capabilities far beyond what's possible with human brain analysis. Even closed-source AI models can be studied through controlled interrogation and distillation techniques. The human brain remains more opaque due to physical and ethical constraints on research, making AI the more interpretable intelligence despite popular perception.

everywhere.tools A comprehensive collection of open-source tools tailored for designers and creative professionals to enhance
everywhere.tools
A comprehensive collection of open-source tools tailored for designers and creative professionals to enhance their work.

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🏎️ Are you a fan of F1? 🏁 Subscribe!@Formula1Constructors @Formula1Constructors @Formula1Constructors
🏎️ Are you a fan of F1? 🏁 Subscribe!@Formula1Constructors @Formula1Constructors @Formula1Constructors

Read That F*cking Code! AI coding tools like Claude Code enable developers to generate working code without reading it, but t
Read That F*cking Code!
AI coding tools like Claude Code enable developers to generate working code without reading it, but this practice leads to three critical issues: architectural decay, loss of domain knowledge, and security vulnerabilities. The author advocates for two responsible approaches: fast prototyping with post-session review for peripheral features, and synchronous pair-coding for core functionality. A comprehensive checklist covers architecture consistency, security scoping, meaningful tests, documentation, error handling, and performance considerations.

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Air Lab Simulator A web-based simulator that replicates Air Lab firmware functionality, allowing users to interact with virtu
Air Lab Simulator
A web-based simulator that replicates Air Lab firmware functionality, allowing users to interact with virtual environmental sensors through different simulated environments. The simulator includes device controls, USB connectivity simulation, and menu navigation features, providing a hands-on experience without requiring physical hardware.

Claude's System Prompt Changes Reveal Anthropic's Priorities Analysis of Claude 4.0's system prompt reveals how Anthropic use
Claude's System Prompt Changes Reveal Anthropic's Priorities
Analysis of Claude 4.0's system prompt reveals how Anthropic uses natural language instructions to program chatbot behavior. Key changes include removal of old hotfixes (now handled in training), encouragement of search functionality, expanded artifact use cases, context optimization for coding, and new cybersecurity guardrails. The 23,000-token system prompt consumes 11% of Claude's context window and demonstrates a user-driven development cycle where observed behaviors are first addressed through prompt modifications, then incorporated into model training.

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Sketchy Calendar The post explores the idea of combining the convenience of digital calendars with the flexibility and person
Sketchy Calendar
The post explores the idea of combining the convenience of digital calendars with the flexibility and personal touch of paper calendars. It discusses the limitations of current digital calendar apps, which often lack personalization and flexibility, and the unique advantages of paper calendars. The goal is to create a 'sketchy calendar' that offers interconnected views, personalization, and dynamic functionality while maintaining a personalized, sketch-like quality.

Liquid Glass on the Web – Frontend Masters Blog Apple's new Liquid Glass design aesthetic for version 26 operating systems cr
Liquid Glass on the Web – Frontend Masters Blog
Apple's new Liquid Glass design aesthetic for version 26 operating systems creates complex visual effects with light refraction, distortion, and frosted glass appearances. Web developers are recreating this look using CSS backdrop-filter, SVG filters like feDisplacementMap and feGaussianBlur, and React components. The technique involves multiple parameters including displacement scale, blur amount, saturation, and aberration intensity. However, implementing liquid glass effects raises significant text contrast accessibility concerns that developers must carefully address when placing text over unknown backgrounds.

Why I Switched to UTC and Never Looked Back A programmer shares his five-year experience of switching all devices to UTC inst
Why I Switched to UTC and Never Looked Back
A programmer shares his five-year experience of switching all devices to UTC instead of local time zones. The approach eliminates mental conversion overhead, provides consistency during travel, and simplifies scheduling for remote workers. While there are minor downsides like explaining the setup to others and converting 12-hour local times, the author found it significantly improved productivity and time management across global schedules

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