Computer Science and Programming – Telegram
Computer Science and Programming
148K subscribers
808 photos
31 videos
37 files
1.1K links
Channel specialized for advanced topics of:
* Artificial intelligence,
* Machine Learning,
* Deep Learning,
* Computer Vision,
* Data Science
* Python

Admin: @otchebuch

Memes: @memes_programming

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GPU Virtualization Architecture for Multi-Desktop Containers
Deep technical dive into building GPU-accelerated multi-desktop virtualization on Apple Silicon. Covers the full stack from virtio-gpu driver through QEMU to Metal, focusing on deadlock bugs that emerge when scaling from 1-2 to 4+ concurrent desktops. Key issues include global renderer_blocked semaphore causing cross-scanout freezes, FIFO command queue blocking, broken fence polling timers, and DRM mode_config.mutex contention. Solutions involve per-context isolation, thread-based fence polling workarounds, and removing synchronous operations from critical paths.
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How we made geo joins 400× faster with H3 indexes
Geospatial joins using predicates like ST_Intersects become prohibitively slow at scale due to quadratic complexity and expensive spatial operations. By automatically rewriting these queries to use H3 hierarchical hexagonal cell indexes, spatial predicates are transformed into fast integer equi-joins on cell IDs. The approach generates H3 coverage for geometries, performs a hash join on matching cells, then applies exact predicates only to filtered candidates. Benchmarks show 400× speedup at optimal resolution (resolution 3), reducing 37.6 million comparisons to ~200k. The technique works on-the-fly without materialized indexes, supporting views and subqueries while avoiding storage overhead.
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How Stripe rolled out a consistent Cursor experience for 3,000 engineers · Cursor
Stripe rolled out Cursor to 3,000+ engineers by preinstalling it on every machine, using Cursor Rules for codebase context, and adapting code review practices. They found that senior engineers with deep codebase knowledge gained the most productivity, contrary to expectations that juniors would benefit most. The company maintained quality by using LLMs to flag risky code during reviews and spread adoption through power users sharing workflows.
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Chris’ Corner: Light & Boxes
A coding challenge explores creating dynamic box shadows that respond to a light source as elements scroll. Multiple developers showcase solutions using scroll-driven animations with animation-timeline: view() and scroll(), manipulating shadow properties through CSS custom properties and @property declarations. Solutions range from JavaScript-assisted approaches to pure CSS implementations that interpolate shadow directions based on viewport position.
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International Accelerator Sber500 Opens 7th Wave Applications
Sber500 invites startups with ready-made products and DeepTech projects (GenAI, robotics, new materials) to apply for the 7th wave. International teams entering the Russian market are welcome.

The 12-week online program in English is free. Structure:
→ 150 teams start at bootcamp
→ 25 best continue the program. They work with international mentors, and pitch to investors and corporations
→ Demo day at Moscow Startup Summit (fall 2026).

Track record: Alumni attracted 1B rubles in investments in 2025 (~14% of Russian venture market). Revenue grows 4x on average, up to 1000x for some teams.
Deadline: 10 April 2026
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What Claude Code Actually Chooses — Amplifying
A study of 2,430 Claude Code interactions across real repositories reveals that the AI coding assistant strongly prefers building custom solutions over recommending third-party tools — appearing as the top choice in 12 of 20 categories. When it does pick tools, choices are decisive: GitHub Actions (94%), Stripe (91%), shadcn/ui (90%). Deployment is stack-determined: Vercel for JS, Railway for Python, with traditional cloud providers getting zero primary picks. Significant generational shifts exist between model versions, notably Prisma→Drizzle for JS ORM, Celery→FastAPI BackgroundTasks for Python jobs, and Redis→Custom/DIY for caching in newer models.
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I struggled to code with AI until I learned this workflow

AI coding assistants work best through an iterative workflow rather than one-shot prompts. The key is providing comprehensive context (project background, constraints, relevant code), requesting a plan before implementation, generating code in small steps with defined roles (planner, implementer, tester, explainer), reviewing output with AI-assisted tools, writing tests immediately, and debugging systematically. Common pitfalls include context drift in long conversations, API version mismatches, and over-reliance on AI without understanding the output. The workflow emphasizes treating AI like a new teammate who needs explicit briefing, keeping changes small and reviewable, and maintaining human oversight throughout the process.
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OpenFlowKit: The open-source diagram engine that thinks like you.
OpenFlowKit is a free, open-source diagram engine aimed at engineers, architects, and product teams. It offers a fully customizable diagram creation experience and is positioned as a craft-focused alternative for technical users who need flexible diagramming tools.
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6 Components of Context Engineering
Context engineering is the practice of optimizing how information flows to AI models, comprising six core components: prompting techniques (few-shot, chain-of-thought), query augmentation (rewriting, expansion, decomposition), long-term memory (vector/graph databases for episodic, semantic, and procedural memory), short-term memory (conversation history management), knowledge base retrieval (RAG pipelines with pre-retrieval, retrieval, and augmentation layers), and tools/agents (single and multi-agent architectures, MCPs). While model selection and prompts contribute only 25% to output quality, the remaining 75% comes from properly engineering these context components to deliver the right information at the right time in the right format.
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Is it over for metrics?
Traditional metrics are shifting from the center of observability stacks to an optimization layer. While metrics remain useful for known failure modes and system-level signals like CPU and memory, they struggle with high-cardinality debugging and require pre-defining what to measure. Modern columnar databases like ClickHouse enable efficient rollups over rich, structured event data, allowing engineers to store high-fidelity logs and traces that can be aggregated on-demand. This approach moves curation from development time to investigation time, making metrics a performance optimization rather than the primary interface for understanding production systems.
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Death to Scroll Fade!

A developer rant against the ubiquitous 'scroll fade' web design trend, where page elements fade in as they scroll into view. The author argues it's tacky, often demanded last-minute by stakeholders, and raises real concerns: accessibility issues for users with vestibular disorders, cognitive overload, cross-platform inconsistency, and negative impact on Core Web Vitals like LCP. The post crowdsources practical arguments to push back against scroll fade requests, while humorously suggesting developers collectively pretend the technique is now illegal.
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Six months of Rust
A JavaScript/TypeScript engineer shares their experience learning Rust over six months while maintaining a production game server backend. Key highlights include praise for Cargo's reliability compared to the JS ecosystem, appreciation for Rust's strict compiler with clear error messages, the elegance of Result-based error handling versus try/catch, and the challenges of concurrency with Mutex/RwLock deadlocks. The author concludes Rust is a mature, performant language worth considering for backend projects, but recommends JS/TS developers start with small CLI tools before tackling concurrent servers
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Warper: Rust Powered React Virtualisation Library
Warper 7.2 is an open-source React virtualization library that offloads scroll calculations to a Rust-compiled WebAssembly module. Key v7 changes include zero-allocation hot paths using TypedArrays, O(1) circular buffer operations for frame timing, and universal bundler support (Vite, Webpack, Rollup, esbuild, Parcel, Next.js). The library uses a Fenwick tree for item height tracking, enabling O(log n) prefix sum queries. Benchmarks claim 120 FPS with 100,000 items versus react-window's ~40 FPS. At 8.7KB gzipped, it sits between react-window (~6KB) and TanStack Virtual (~12KB). Community reception is mixed, with enthusiasm for the approach but questions about practical necessity and reported Firefox compatibility issues.
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Node.js — Node.js 25.7.0 (Current)
Node.js 25.7.0 has been released with several notable changes: HTTP/2 gains an `http1Options` parameter for HTTP/1 fallback configuration, Single Executable Applications (SEA) now support ESM entry points, the built-in SQLite module is marked as a release candidate,, Single Executabltype option is renamed to
(Current)
Node.j and the test runner now shows interrupted tests on SIGINT. Additional fixes include HTTP keep-alive socket reuse race conditions, FileHandle leaks in HTTP/2 and REPL, zstd memory tracking improvements, brotli compression dictionary support in zlib, and a newe module is markedoption for
Node.js 25.
npm is upgraded to 11.10.1.
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The uncomfortable truth about vibe coding
Vibe coding—building software through AI conversations—enables rapid prototyping but creates unsustainable codebases that become unmaintainable after 3 months. Projects hit walls when changes break multiple features because prompts become obsolete and code lacks intent documentation. Spec-driven development solves this by treating specifications as the authoritative blueprint, maintaining version-controlled documentation, and enabling regeneration from a single source of truth. The most effective approach combines natural language efficiency for exploration with rigorous specifications for production systems, using unit tests to validate small scopes while specs govern larger architecture.
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