Computer Science and Programming
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
Ko'proq ko'rsatish📈 Telegram kanali Computer Science and Programming analitikasi
Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 142 801 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 815-o'rinni va Italiya mintaqasida 86-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 142 801 obunachiga ega bo‘ldi.
12 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -1 293 ga, so‘nggi 24 soatda esa -25 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 5.74% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.81% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 8 196 marta ko‘riladi; birinchi sutkada odatda 2 581 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 16 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent sellerflash, github, developer, pricing, waybienad kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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...”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 13 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
ChatGPT and similar AI tools can significantly aid developers by analyzing code, suggesting improvements, writing tests, and more. Their effectiveness depends on clear, specific prompts. While they are not designed to solve new or niche problems independently, they excel in tasks like code contextualization, reviews, and documentation. Tools like GitHub Copilot leverage additional context to provide more relevant suggestions, bridging the gap between junior and senior developer roles.🔗 https://www.codemotion.com/magazine/ai-ml/from-junior-to-senior-developer-with-chatgpt
TLDR Toasts often appear far from the user's focus, leading to jarring interactions. For example, YouTube's toast notifications conflict with other on-screen actions. A redesign suggests directly integrating feedback into user actions, such as placing indicators near interacted elements. Examples from Gmail and clipboard actions further illustrate unnecessary toast usage. Ultimately, no feedback is worse, but there are better methods than relying on toasts.🔗 https://maxschmitt.me/posts/toasts-bad-ux
TLDR Explore 11 open source AI projects aimed at easing software development. Projects like Upscayl enhance image resolution, Nyro automates mundane tasks, and Wren AI translates natural language into SQL. Tools like Geppetto and E2B sandboxes integrate AI with productivity tools, while DSPy and Guardrails optimize AI model training and accuracy. These projects demonstrate the potential of AI in transforming everyday tasks and development workflows.🔗https://www.infoworld.com/article/3566915/11-open-source-ai-projects-that-developers-will-love.html
TLDR Mastering software architecture is crucial for handling complex systems and transitioning from a developer role to an architect role. Essential resources include books like 'Designing Data-Intensive Applications' and courses such as 'The Complete Microservices and Event-Driven Architecture' on Udemy. Additionally, whitepapers and engineering blogs provide valuable insights. These resources cover various architectural styles, principles, and real-world challenges, helping you design scalable, maintainable, and high-performing systems.🔗 https://medium.com/javarevisited/10-best-resources-to-learn-software-architecture-in-2025-2524ac91dc76
TLDR A developer experimented with using GPT-4o's structured outputs for web scraping, creating an AI-assisted web scraper. While the model performed well with simple and complex tables, it struggled with combined rows and generating XPaths. Cost is a concern due to the model's character volume requirements. Future improvements could include better UX through capturing browser events and further refining HTML data cleanup.🔗 https://blancas.io/blog/ai-web-scraper
TLDR A load balancer distributes network or application traffic across multiple servers to ensure availability, reliability, and performance. There are different types of load balancers, including hardware, software, cloud-based, Layer 4, Layer 7, and Global Server Load Balancing. Load balancers improve scalability and help manage large-scale applications efficiently. The post also touches on various design patterns for Kubernetes and highlights a sponsored service by QA Wolf for improved QA cycles.🔗 https://blog.bytebytego.com/p/ep123-what-is-a-load-balancer
TLDR Garbage collection is a crucial automatic memory management feature used in many programming languages. Java offers multiple garbage collectors tailored to different scenarios, Python employs reference counting alongside a cyclic collector to handle circular references, and GoLang utilizes a concurrent mark-and-sweep garbage collector to minimize application pauses. Additional topics include tools for designing fault-tolerant systems and key system design trade-offs.🔗 https://blog.bytebytego.com/p/ep125-how-does-garbage-collection
TLDR Open source technology offers alternatives to many proprietary software tools, providing benefits like added transparency, customizability, and security. Highlighted tools include Penpot for design, Cal.com for scheduling, Screenity for screen recording, Jitsi for video conferencing, Nextcloud for cloud storage, Ghost for publishing, and more. Each offers features to help individuals and businesses move away from Big Tech incumbents without compromising productivity.🔗 https://techcrunch.com/2024/08/11/a-not-quite-definitive-guide-to-open-source-alternative-software/
TLDR A curated list of recommended Visual Studio Code extensions categorized by their use cases, such as markdown support, general writing, GitHub integration, CSV handling, Japanese language tools, styling and themes, and various utility extensions. Includes a step-by-step guide for easy installation of all listed extensions via an `extensions.json` file.https://dev.to/ahandsel/vs-code-setup-recommended-extensions-4877
TLDR Google Chrome, holding over 60% of the market share, offers a variety of open-source extensions that enhance user experience. This list includes 13 top open-source extensions such as Dark Reader for eye protection, GitOwl to optimize GitHub usage, DuckDuckGo Privacy Essentials for privacy, Simple Translate for multilingual browsing, Page Assist for AI integration, and many more. These extensions serve a wide range of purposes from privacy protection to development tools, all with the added benefit of being open-source.🔗 https://itsfoss.com/open-source-chrome-extensions/
TLDR The current landscape of technical interviews for Senior Frontend Developers often includes questions that fail to assess practical experience and real-world problem-solving skills. Common questions like the workings of the Event Loop, differences between arrow functions and regular functions, or memory management often focus on rote memorization rather than actual expertise. The post argues for more meaningful, experience-based questions that better evaluate a candidate’s ability to apply theoretical knowledge practically.🔗 https://medium.com/@maks-dolgikh/bad-questions-for-senior-frontend-dev-interview-2c94dd937d75
@interface in Java?'
TLDR An interface in Java specifies a behavior that implementing classes must fulfill, containing method signatures without implementations, and supporting abstraction, multiple inheritance, and loose coupling. On the other hand, the @interface is used to define custom annotations that add metadata to code elements for use during compilation or runtime by tools and frameworks. Key annotations like @Retention and @Target further specify how and where these annotations can be applied.🔗 https://www.baeldung.com/java-interface-vs-annotation
TLDR A frontend developer shares five ways to use ChatGPT for optimizing workflow, including formatting JSON, creating UI skeletons, generating random data, working with regular expressions, and finding code solutions. By leveraging ChatGPT, tasks such as creating Material UI skeletons or finding regex solutions become more efficient, saving time and enhancing productivity.🔗 https://medium.com/@sumsourabh14/how-i-use-chatgpt-as-a-frontend-developer-5-ways-0494d6f1ab54
Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
