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
Show moreπ Analytical overview of Telegram channel Computer Science and Programming
Channel Computer Science and Programming (@computer_science_and_programming) in the English language segment is an active participant. Currently, the community unites 140 565 subscribers, ranking 804 in the Technologies & Applications category and 88 in the Italy region.
π Audience metrics and dynamics
Since its creation on Π½Π΅Π²ΡΠ΄ΠΎΠΌΠΎ, the project has demonstrated rapid growth, gathering an audience of 140 565 subscribers.
According to the latest data from 29 August, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by -741 over the last 30 days and by -33 over the last 24 hours, overall reach remains high.
- Verification status: Not verified
- Engagement rate (ER): The average audience engagement rate is 7.84%. Within the first 24 hours after publication, content typically collects 1.89% reactions from the total number of subscribers.
- Post reach: On average, each post receives 11 021 views. Within the first day, a publication typically gains 2 656 views.
- Reactions and interaction: The audience actively supports content: the average number of reactions per post is 14.
- Thematic interests: Content is focused on key topics such as sellerflash, github, developer, pricing, waybienad.
π Description and content policy
The author describes the resource as a platform for expressing subjective opinions:
βChannel 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...β
Thanks to the high frequency of updates (latest data received on 30 August, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Technologies & Applications category.
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A step-by-step guide to building a basic search UI using TypeSense and Laravel. Covers creating a simple Blade view with a search form, integrating TypeSense client to query a books collection, and displaying results in a styled list. The tutorial demonstrates setting up routes, handling query parameters, performing searches on the title field, and using Tailwind CSS for styling. This foundation prepares for more advanced features like instant search, result highlighting, and faceted filtering.
Tinybird's LLM SQL Generation Benchmark evaluates how 19 popular language models perform in generating SQL queries to filter and aggregate large datasets. Comparing models like OpenAI's GPT-4 Turbo and Anthropic's Claude, the benchmark measures accuracy, efficiency, and query latency, highlighting the challenges LLMs face in writing semantically correct SQL efficiently. The analysis shows humans leading in efficiency, while LLMs often struggle with contextual understanding and optimization opportunities.
Comprehensive guide covering modern React development patterns for 2025, including function components, custom hooks, Context API, TypeScript integration, and React 19 features like useOptimistic and Server Components. Explores ecosystem tools like Next.js, Remix, Vite, and Tailwind CSS, plus enterprise UI solutions like KendoReact for building scalable, maintainable applications.
Git-spice enhances development workflows by addressing challenges with large pull requests and the drawbacks of atomic PRs. It helps manage stacked pull requests effectively, boosting speed and maintaining code quality. Built by Abhinav Gupta to fit specific needs, it simplifies PR management, facilitating efficient collaboration and enabling better code reviews.
