Tech digest — 2026-05-12

122 выбранных статей, отсортировано по score по убыванию.

1. #1 · score 95 · dev.to · Adnan Latif · 2026-05-11

   Scaling LLM + Vector DB Systems in Production: Lessons from the Trenches

   We launched a retrieval-augmented LLM feature tied to a hosted vector db. The prototype worked beautifully in demos: low latency, relevant answers, happy stakeholders. At first, this looked fine… until it wasn’t. One partner integration doubled traffic overnight and the system degenerated into tail latency, retries, and ballooning bills. Here’s what we learned the hard way while turning that prototype into something we could actually run for months. The incident was boring and predictable: a combination of traffic spike, write-heavy ingestion, and a few thousand retry storms. Embedding generation slowed because we hit provider rate limits. Vector DB nodes started rebalancing under write pressure, spiking query latency. Our end-to-end traces showed most time was spent outside the LLM itself — in embedding and ANN stages. Most teams miss how much the supporting systems (embedding pipelines, vector indexes) dictate user experience. We wrote embeddings and indexed in the request path to guarantee up-to-date search results. That gave us consistency but also amplified latency and produced timeout cascades when the embedding provider throttled. Users spun up retries which made things wors

   Почему выбрано: Глубокий анализ проблем масштабирования LLM и векторных баз данных, полезные уроки из практики.

2. #2 · score 92 · dev.to iOS · Todd Sullivan · 2026-05-11

   Building Personalised On-Device ML for Women's Health: No Cloud, No Population Averages

   Most health AI is built on population data. Your symptoms are averaged against thousands of other people, and you get a generalised prediction that fits nobody perfectly. I took a different approach with Menopause Intelligence — an iOS app I've been building that predicts high-symptom days for women in perimenopause and menopause. The entire model runs on-device, trained on the individual user's own data. No cloud, no population averages, no third-party data sharing. Population models work when you want average answers. But perimenopause is deeply individual. Two women with identical ages and similar symptom profiles can have completely different biometric triggers. The app's job is to tell a user her patterns — not what typically happens to women like her. Features: Seven signals per day, all from HealthKit/Apple Watch: Basal body temperature delta vs 7-day mean HRV (raw + delta from personal rolling average) Sleep efficiency and deep sleep % REM sleep % Resting heart rate Cycle day (if logged) Key design decision: We use deltas from the user's personal baseline, not absolute values. A resting HR of 62 bpm means different things for different people. What matters is whether it's e

   Почему выбрано: Инновационный подход к персонализированному ML для здоровья женщин, глубокий анализ.

3. #3 · score 92 · dev.to · MPP TestKit · 2026-05-11

   The Infrastructure Gap: Why the Machine Economy Is Stalling and How We Fix It

   Autonomous AI agents are no longer some far-away prediction. They are already here. They are making API calls, processing massive datasets, interacting with tools, executing complex workflows, and operating without a human sitting behind every click. But while agentic activity is exploding, there is still one massive infrastructure gap that almost nobody is solving properly. The question is no longer: Can agents perform work? The real question is: How do agents pay for the services they consume? Right now, the answer is messy. We are trying to force autonomous machines into a financial system built for humans. We ask agents to deal with credit cards, checkout pages, monthly SaaS plans, account creation, user databases, billing dashboards, and subscription logic. That is not machine-native infrastructure. That is duct tape. And duct tape does not scale the machine economy. If you are an API provider today, you are standing at a very uncomfortable crossroads. You know agents are going to become one of the biggest consumers of APIs. But you probably do not want to build an entire billing department just to serve them. You do not want to manage thousands of bot accounts. You do not wan

   Почему выбрано: Глубокий анализ инфраструктурного разрыва в экономике машин, важные выводы для будущего.

4. #4 · score 90 · dev.to · logiQode · 2026-05-11

   DeepClaude Merges Two AI Models Into One Agent Loop

   Most production AI coding assistants are single-model systems: you pick Claude, GPT-4o, or Gemini, and that model does everything — reasoning, planning, and code generation — in one pass. DeepClaude challenges that assumption by splitting the cognitive load across two models: DeepSeek R1 (or V3) handles the chain-of-thought reasoning phase, and Claude handles the final response synthesis. The result is a hybrid agent loop that tries to get the best of both worlds: deep, explicit reasoning from DeepSeek and polished, context-aware output from Anthropic's Claude. This article unpacks how DeepClaude works mechanically, why the two-model architecture makes engineering sense, and what you need to know before wiring it into your own toolchain. Large language models are trained on different distributions and with different objectives. DeepSeek R1 was explicitly trained with reinforcement learning to produce long, structured reasoning traces — the model "thinks out loud" before committing to an answer. Claude, by contrast, is tuned for helpfulness, instruction-following, and coherent long-form output. In practice, teams often hit this tradeoff when building code agents: models that reason

   Почему выбрано: Глубокий анализ архитектуры DeepClaude, полезен для инженеров, работающих с AI моделями.

5. #5 · score 90 · dev.to · abdullah siddiqui · 2026-05-11

   How AI Agents Will Change Online Businesses

   From customer service and marketing automation to sales optimization and workflow management, AI agents are expected to revolutionize how online businesses operate. Companies that adopt these technologies early will gain major advantages in efficiency, scalability, and customer experience. What are AI Agents? AI agents are intelligent software systems capable of: Unlike basic automation, AI agents can adapt, improve, and handle more complex workflows. 1. Smarter Customer Experiences Through Web Development AI agents will significantly improve website functionality and personalization. web development, businesses can integrate: AI-driven websites will interact with visitors dynamically, improving engagement and conversions. 2. Automating Operations with Software Development AI agents depend on strong backend systems and intelligent workflows. software development, businesses can build: These systems can automate repetitive tasks, reduce operational costs, and improve decision-making speed. 3. Transforming Customer Acquisition with Lead Generation AI agents are changing how businesses manage lead generation. lead generation strategy powered by AI can: AI agents can analyze customer b

   Почему выбрано: Сильная статья о влиянии AI-агентов на онлайн-бизнес, с практическими примерами.

6. #6 · score 90 · dev.to · WeRDyin · 2026-05-11

   How I Built AriaType Without Writing Any Code

   As a ten-year engineer, when developing AriaType, I conducted an experiment: completing the entire project using Agentic Coding. I only dictated goals and boundaries; the AI autonomously handled all the work: generating documentation, planning architecture, writing code, writing tests, and debugging issues. The core of Agentic Coding is: an AI Agent autonomously loops within a bounded framework—planning, executing, verifying, fixing. I set the boundaries as three systems (SDD, TDD, Tool Automation), and the Agent ran autonomously within them. I only intervened at key points: making technical decisions and validating final results. 50 days, 62 commits, a working desktop application. This article documents how this approach was designed, how the Agent worked autonomously within boundaries, and what pitfalls I encountered. AriaType is a local-first voice keyboard: hold a hotkey to speak, release to automatically input text. I knew almost nothing about desktop development. No Rust knowledge, never used Tauri, unfamiliar with system permissions and audio APIs. Following the traditional approach, I would need to study for a month before starting to write. By the time the project launched

   Почему выбрано: Инновационный подход к разработке с использованием автономного ИИ, полезные выводы и практические советы.

7. #7 · score 90 · dev.to · Bing Xun · 2026-05-11

   How I Cut My AI API Costs by 60%: A Data-Driven Approach to LLM Model Selection

   Last month, I was paying $30/1M output tokens for GPT-5.5 on a chatbot project. After comparing models on TokenDealHub, I switched to DeepSeek V4 Pro at $0.87/1M output tokens — that's a 97% cost reduction with only a 15% performance trade-off according to AA benchmarks. The CPS score made this comparison trivial. With 300+ LLM models available from 40+ providers, choosing the right API is overwhelming. Most developers: Check multiple vendor websites for pricing Rely on outdated pricing data Don't have performance benchmarks side-by-side with costs End up overpaying by 50-70% I built TokenDealHub (tokendealhub.com) to solve this problem. It's a real-time AI model price comparison platform that: Tracks 300+ models from OpenAI, Anthropic, Google, DeepSeek, xAI, Qwen, GLM, MiniMax, and 40+ other providers Updates hourly — no more stale pricing data Shows ArtificialAnalysis benchmarks side by side with pricing CPS (Cost-Performance Score) — proprietary grading system (S/A/B/C) to instantly identify best-value models Subscription comparison — ChatGPT Plus vs Claude Pro vs Gemini Advanced AA Score: 51.5 Price: $0.43 input / $0.87 output per 1M tokens Performance: 85% of GPT-5.5 at 3% of

   Почему выбрано: Глубокий анализ снижения затрат на API AI с практическими рекомендациями и новыми подходами.

8. #8 · score 90 · dev.to · Jay · 2026-05-11

   I Benchmarked the Voice AI Stack in May 2026: What Actually Holds Up in Production

   A practical May 2026 breakdown of the best STT, TTS, and voice agent platforms for production LLM voice systems, with latency, cost, and orchestration trade-offs. Voice agents finally feel like an engineering problem, not a research demo. The pieces are now fast enough to compose into something that feels natural in production. Streaming STT can sit under 300ms, first audio can show up under 100ms, and fast LLMs can stay in the same budget if you pick carefully. What changed for me over the last few weeks was not any single model. It was seeing every layer mature at roughly the same time. This post is my attempt to sort the stack by what actually matters in production, which starts with the shortest possible answer. If I had to pick one practical stack right now, I would start with Deepgram Nova-3 plus Flux for STT, Cartesia Sonic Turbo for TTS, GPT-5 mini or Gemini 3.1 Flash for the LLM, and Retell AI for orchestration. That gets you into sub-700ms territory today without forcing a custom build on day one. Here is the short version by layer: Streaming STT: Deepgram Nova-3 STT with built-in intelligence: AssemblyAI Universal-2 Highest batch accuracy: Google Cloud Chirp Open-source

   Почему выбрано: Глубокий анализ стеков Voice AI с практическими рекомендациями для продакшена.

9. #9 · score 90 · dev.to · Tommaso Bertocchi · 2026-05-11

   I built an AI agent that runs autonomous OSINT investigations from your terminal

   You know the OSINT workflow. Open a terminal. Run holehe against an email. Copy a username you found. Switch tools. Run sherlock. Open a browser. Check HaveIBeenPwned manually. Pull up a WHOIS tab. Take notes. Repeat. Every tool is a silo. Every pivot is manual. The investigation logic lives entirely in your head. I wanted to fix that. OpenOSINT is an open-source Python framework with an AI agent at its core. You describe a target in natural language — an email address, a username, a domain, an IP, a phone number — and the agent decides which tools to run, chains them based on what it finds, executes everything against the real binaries, and compiles a structured Markdown report. Three interfaces: Interactive AI REPL (default) — type natural language, agent chains the tools autonomously Direct CLI — run individual tools directly, no AI, perfect for scripting MCP Server — expose all 9 tools to Claude Code or Claude Desktop Here's a real session. No mocking. The agent receives an email, runs discovery, extracts a username, pivots to search it across 300+ platforms, checks breaches, and saves a report — all unchained: $ openosint openosint ❯ investigate target@example.com → generate_d

   Почему выбрано: Интересный проект по автоматизации OSINT с использованием AI, полезные детали о реализации.

10. #10 · score 90 · dev.to · Andrew Baisden · 2026-05-11

    LLM Guardrails in Production and How Bifrost Protects Your AI Agents at the Gateway Level

    Two years ago, most conversations about LLM guardrails were about content filtering, stopping a chatbot from saying something offensive. That was a real problem, but a small one. The model produced text. The text was either safe or unsafe. A classifier could usually tell. In 2026, the problem has completely changed shape. LLMs are not just producing text anymore. They are calling APIs, querying databases, writing files, sending emails, and triggering workflows. A guardrail failure in 2024 meant a bad response. A guardrail failure today means a misconfigured agent deleting records, leaking PII into a third party API call, or being hijacked mid task by a prompt injection buried in a tool result. The stakes are different and the infrastructure needs to match. This article covers what production grade LLM guardrails actually look like in 2026, and how Bifrost implements them natively at the gateway level, so you don't have to rebuild this for every project. Guardrails intercept agent behaviour in real time, before a bad input reaches your LLM or a bad output reaches your user. But most teams don't implement them until something breaks in production. Here's what that looks like in pract

    Почему выбрано: глубокий анализ LLM-охранных механизмов и их внедрения в продакшн, полезные практические рекомендации.

11. #11 · score 90 · dev.to · pmestre-Forge · 2026-05-11

    Stop Flying Blind: Add Audit Logs to Your AI Agent in 5 Minutes

    The Problem Nobody Talks About You ship an AI agent. It runs in production. Something goes wrong. Now what? You dig through stdout logs, reconstruct what the LLM "decided," and try to figure out why it did that at that moment. It's painful — and most teams solve it by building a custom observability stack before they've even validated the product. I ran into this exact wall while building botwire.dev, an agent infrastructure API. So I added Agent Audit Logs as a free primitive: a simple POST /logs/{agent_id} endpoint that gives every agent an immutable, timestamped activity trail — no setup required. Here's how to wire it into your agent in about 5 minutes. No SDK to install. Just your HTTP client of choice. If your agent already has an identity registered (also free): import httpx BASE_URL = "https://botwire.dev" AGENT_ID = "my-trading-agent-v1" def log_action(action: str, reason: str, result: str = None, metadata: dict = None): payload = { "action": action, "reason": reason, "result": result, "metadata": metadata or {} } r = httpx.post(f"{BASE_URL}/logs/{AGENT_ID}", json=payload) return r.json() # Example: log a trading decision log_action( action="BUY NVDA", reason="RSI oversold

    Почему выбрано: Практическое решение для улучшения наблюдаемости AI-агентов, полезно для разработчиков.

12. #12 · score 90 · dev.to · Morgan · 2026-05-10

    TCA 1.7 @ObservableState + Swift 6 — Tracing the @Reducer Macro Isolation Trap

    A retrospective of tracing the @Reducer / @ObservableState macro isolation trap in 5 attempts during new development of a personal iOS app on TCA 1.25.5. Under Swift 6 strict concurrency + module-default isolation inference, the macros auto-generate conformances that are forced to be main-actor-isolated, breaking compatibility with nonisolated struct. I started building a personal app on TCA 1.25.5 from scratch. As soon as I wrote the first entry screen (an API key input form) using the WithViewStore pattern, the Issue Navigator lit up with deprecation warnings: 'WithViewStore' is deprecated: Use '@ObservableState' instead. 'init(_:observe:content:file:line:)' is deprecated: Use '@ObservableState' instead. 'ViewStoreOf' is deprecated: Use '@ObservableState' instead. See the following migration guide: https://swiftpackageindex.com/pointfreeco/swift-composable-architecture/main/documentation/composablearchitecture/migrationguides/migratingto17#using-observablestate WithViewStore is deprecated in TCA 1.7+, so even brand-new code triggers the warnings immediately. I followed the official migration guide to apply the @ObservableState macro pattern. (As I added more views in the same pat

    Почему выбрано: Сложный технический разбор проблем с макросами в Swift, полезен для разработчиков.

13. #13 · score 90 · dev.to · CaraComp · 2026-05-11

    Your Voice Is No Longer Proof You're You — And Ghana Just Proved It

    WHY VOICE BIOMETRICS ARE FAILING THE TEST The technical barrier for high-fidelity impersonation just hit a floor. With Xiaomi open-sourcing its OmniVoice model—capable of cloning a voice across 646 languages with just three seconds of reference audio—the "identity-by-voice" verification model is effectively deprecated. For developers building biometric pipelines, authentication systems, or digital forensics tools, this news serves as a massive signal: voice is no longer a reliable factor of truth. The recent arrests in Ghana, where fraudsters used AI-generated media to impersonate a head of state for financial gain, demonstrate that synthetic media is no longer an academic concern for researchers. It is a live, operational exploit. For those of us in the investigation technology space, this shift forces a move toward more robust, visual-based forensic analysis. Technically, we are seeing the industrialization of latent space encoding. Traditional Text-to-Speech (TTS) required hours of clean data. Modern zero-shot models require almost nothing. This has immediate implications for system design: The Vulnerability of Phone-Based 2FA: If an investigator or a claims adjuster relies on a

    Почему выбрано: глубокий анализ проблем биометрической аутентификации, актуальные выводы для разработчиков.

14. #14 · score 90 · Habr · daria021 · 2026-05-10

    Без рук: автоматизируем нагрузочное тестирование изменений в CI

    Нагрузочное тестирование — одна из самых избегаемых тем, когда речь заходит о контроле качества ПО. Корпорации, конечно, не обходят его стороной, но если говорить о продуктах меньшего масштаба, то нагрузочное тестирование часто пропускается. Команда (и, в целом, справедливо) полагает, что продукт справится с нагрузкой — на малых объёмах это обычно прокатывает. А потом внезапно наступает день, когда пользователей стало больше, а система не готова. Почему команды не тащат нагрузку в релизный цикл? Потому что это чаще всего просто не окупается: нужно выбрать движок, описать сценарий, гонять тесты вручную или тратить время на создание собственной обвязки для встраивания в CI, придумать критерии качества и анализировать результаты. Всё это занимает значительное время, а на короткой дистанции часто оказывается оверинжинирингом. Но если формирование требований упростить концептуально невозможно, то всё остальное вполне можно собрать в переиспользуемый инструмент, позволяющий командам легко интегрировать нагрузочное тестирование и регрессионный анализ в свой процесс доставки. В CI/CD мы хотели простую штуку: на каждый PR запускать короткий перф‑смоук и получать ответ уровня «PASS / WARNING

    Почему выбрано: Глубокий анализ автоматизации нагрузочного тестирования, полезен для команд разработки.

15. #15 · score 90 · Habr · BiktorSergeev (МТС) · 2026-05-11

    Биологи переписали генетический код живой клетки. Что из этого получилось?

    Генетический код выглядит как нечто незыблемое, одинаковое для всех форм жизни на земле. От бактерий до человека — везде 20 стандартных аминокислот складываются в белки по одним и тем же правилам. Но если копнуть глубже, сразу возникает вопрос: а вдруг этот набор можно ужать и клетки при этом не развалятся? Эта идея появилась уже давно. Группа ученых из Колумбийского университета и Гарварда решила пойти дальше разговоров и реально попробовать сократить набор до девятнадцати аминокислот. Авторы эксперимента сосредоточились на ключевых механизмах синтеза белков и проверили, выдержит ли система такую перестройку. Что ж, давайте посмотрим, все ли получилось. Читать далее

    Почему выбрано: глубокий научный разбор эксперимента по переписыванию генетического кода с интересными выводами.

16. #16 · score 90 · Habr · nlaik · 2026-05-11

    Как технически устроена DPI-фильтрация у российских провайдеров и как её детектировать: разбор open-source инструментов

    В последние пару лет любой пользователь рунета научился различать “интернет дома” и “интернет в гостях у бабушки”. На одном провайдере YouTube открывается, на другом нет. Это ощущается как непредсказуемость, но за каждой такой деградацией стоят вполне конкретные технические механизмы. Запустил open-source инструмент dpi-checkers на трёх своих подключениях, разобрался с методами TCP 16-20 и CIDR-вайтлистами и расскажу, что технически происходит с вашим трафиком на L4 — от SNI-фильтрации до QUIC-блокировок. Читать далее

    Почему выбрано: Глубокий технический разбор DPI-фильтрации, полезный для специалистов в области сетевой безопасности.

17. #17 · score 90 · Habr · Masha_Belkina_Log · 2026-05-11

    Как я превратила Obsidian в структурированную память для ИИ‑агентов

    Эта статья про NOUZ — локальный MCP‑сервер между Obsidian и ИИ‑агентом. Он превращает базу заметок в структурированную память: с уровнями, связями и сигналами дрейфа. Внутри — как я пришла к этой архитектуре и что она даёт агенту при работе с базой. Читать далее

    Почему выбрано: глубокий практический разбор применения ИИ в структурировании памяти, полезно для разработчиков

18. #18 · score 90 · Habr · anatoly_kr · 2026-05-10

    Метрика EICS — ищем у трансформера причинное место

    У больших языковых моделей есть неприятное свойство: снаружи ответ может выглядеть одинаково уверенно и тогда, когда модель действительно «собрала» правильную причинную цепочку, и тогда, когда она просто выдала правдоподобный текст. Классические способы оценки неопределённости — энтропия распределения токенов, калибровка, ансамбли, conformal prediction — полезны, но обычно смотрят на модель как на чёрный ящик. В этой статье я разберу другой подход: попробовать оценивать неопределённость не только по выходу модели, а по внутренней согласованности активной цепи трансформера. Речь пойдёт о метрике EICS — Effective Information Consistency Score. Идея в том, чтобы за один прямой проход получить численную оценку того, насколько найденная трансформерная цепь ведёт себя согласованно и насколько её макроуровневое описание действительно несёт интегрированную информацию. Статья основана на исследовательской работе об оценке неопределённости в трансформерных цепях на основе согласованности эффективной информации. Здесь я намеренно смягчил академическую подачу, оставив интуицию, формулы, алгоритм и практические ограничения. Снять неопределённость

    Почему выбрано: Глубокий анализ метрики EICS для оценки неопределённости в трансформерах, полезные практические рекомендации.

19. #19 · score 90 · Habr · niktomimo · 2026-05-11

    Я реализовал Double Ratchet в React Native мессенджере. Разбор протокола и кода

    В прошлой статье про трёхуровневый кэш сообщений я уже упоминал, что делаю мессенджер ONEMIX на React Native. Базовое E2E у меня было простое: ECDH P-256 для обмена ключами при первом контакте, AES-GCM для шифрования каждого сообщения общим секретом. Это работает, но имеет одну проблему: общий секрет один на всю переписку. Если у одной из сторон скомпрометируют приватный ключ — все сообщения за всё время превращаются в открытый текст. Это называется отсутствием Perfect Forward Secrecy (PFS). И это значит, что человек, к которому в руки попадёт твой телефон через год, может прочитать переписку из прошлого года. WhatsApp, Signal, и серьёзные части Telegram давно используют другую схему — Double Ratchet — которая ключи переизбретает заново на каждом сообщении. Так делают потому, что любой ключ компрометируется в один момент времени, и компрометация не должна давать доступа ни к прошлому, ни к будущему. Я реализовал Double Ratchet с нуля для ONEMIX. В этой статье разберу: Читать далее

    Почему выбрано: глубокий разбор протокола Double Ratchet и его реализации в мессенджере, полезен для разработчиков.

20. #20 · score 88 · dev.to · Lich Priest · 2026-05-11

    [E2E TEST] Deploy a Real‑Time Voice‑Controlled AI Assistant on a Raspberry Pi

    Why Run a Voice Assistant on the Edge? Running speech‑to‑text and intent detection locally gives you: Zero latency – no round‑trip to the cloud. Privacy – audio never leaves the device. Offline reliability – your assistant works even when the internet is down. In this tutorial we’ll stitch together OpenAI’s Whisper (small model) for transcription, a tiny TensorFlow Lite intent classifier, and a real‑time audio pipeline that lives entirely on a Raspberry Pi 4 (2 GB or more). By the end you’ll have a Python script that listens for commands like “turn on the lamp” and executes a local function instantly. Item Reason Raspberry Pi 4 (2 GB+) with Raspberry OS (64‑bit) Provides enough RAM for Whisper‑small Micro‑USB or USB‑C microphone Captures audio Python 3.10+ Modern language features ffmpeg Required by Whisper git, pip, virtualenv Standard development tools Optional: GPIO‑controlled relay To demonstrate a real command Tip: If you’re using a Pi Zero, swap Whisper for a lighter model (e.g., tiny.en) or run only the intent recognizer. # Update OS and install system deps sudo apt update && sudo apt upgrade -y sudo apt install -y python3-pip python3-venv ffmpeg libportaudio2 # Create a cle

    Почему выбрано: Глубокая статья о создании голосового помощника на Raspberry Pi с практическими рекомендациями.

21. #21 · score 88 · dev.to · Yuravolontir · 2026-05-11

    A New Way to Review Code: Meet AdamsReview for Claude

    Imagine if you could make sure your team’s software is running smoothly and efficiently, without spending hours sifting through lines of code. What if there was a way to streamline the process of code reviews, making it easier for developers to collaborate and catch mistakes? That's where AdamsReview comes into play. Recently showcased on Hacker News, AdamsReview is a tool designed to improve how teams perform code reviews using Claude, an AI model that helps with coding tasks. It allows multiple agents (think of them as virtual assistants) to work together on a piece of code, making the review process quicker and more thorough. In simpler terms, instead of one person going through the code, AdamsReview enables several AI "helpers" to analyze it at once, providing feedback that can help catch errors before they become bigger issues. This is especially useful for teams that frequently update their software or work on multiple projects simultaneously. Here’s the fun part: AdamsReview leverages AI capabilities to enhance the code review process. When developers submit their code, these AI agents dive in, looking for bugs, suggesting improvements, and even checking for best practices.

    Почему выбрано: Инновационный подход к ревью кода с использованием AI, полезен для команд разработчиков.

22. #22 · score 88 · dev.to · Sandro Munda · 2026-05-11

    Agentic AI vs AI Agents: The Governance Shift

    Open any vendor pitch from the last 6 months and somewhere in the deck, you'll see the word agentic. It's been a marketing term for so long that most engineering leaders have started treating it as noise. That's a mistake. The distinction between an AI agent and an agentic system is real, and it breaks every assumption your security team made about access control, audit logging, and incident response. This piece is about what actually changes. Not the capability differences (those have been written about endlessly). The infrastructure and governance gap that opens up when an AI system starts deciding what to do next, on its own, in production. An AI agent, functionally, is 4 things: an LLM, a set of tools, an identity, and a runtime context. The LLM reasons. The tools let it touch the outside world. The identity decides what it's allowed to do. The context is what it knows during this session. A customer-research agent reads a CRM, pulls public web data, and writes a brief into a Notion page. It has 3 tools (crm.read, web.search, notion.write). It runs under its own identity (a service account, or impersonating the user who triggered it). It has context for 1 task, then forgets. A

    Почему выбрано: Глубокий анализ различий между агентами ИИ и агентными системами, важные аспекты безопасности и управления.

23. #23 · score 88 · Habr · Zmey56 · 2026-05-10

    Code Review Horror Stories. Часть 2: API, ошибки и graceful shutdown

    Продолжение разбора реального кода с собеседования. В первой части разобрали 8 проблем concurrency и memory: race conditions, утечки горутин, проигнорированный mutex, TOCTOU. Это была первая половина из 21 бага в одном сервисе на 150 строк. Сегодня — вторая часть. Тут нет страшных race conditions, но есть то, что выдаёт уровень разработчика на собесе: отношение к ошибкам, валидация, API design, graceful shutdown, observability. Эти баги не упадут “вдруг” в продакшене — они будут тихо пилить вам костыль за костылём, пока кто-то не сядет переписывать. Актуально для Go 1.26. Напомню итог первой части: из 8 багов про concurrency на интервью нашёл 7, пропустил только TOCTOU race. В этой части из 13 багов пропустил два: package applike с func main() (то, что код не компилируется — банально не посмотрел на объявление пакета) и отсутствие slog (просто не зацепился за log.Println, а зря). Остальные 11 — поймал. Расскажу, какими паттернами в чтении кода я их вылавливал. Читать далее

    Почему выбрано: Глубокий разбор реальных проблем кода, полезен для разработчиков, особенно в контексте Go.

24. #24 · score 88 · dev.to · Leo Ai · 2026-05-11

    I Built an Autonomous AI Coding Factory That Opens GitHub PRs While You Sleep

    I Got Tired of Writing Boilerplate at 11pm. So I Built a Factory That Does It For Me. Give it a repo. Give it a task. Wake up to a PR. Repeat. You know the feeling. It's late. Your backlog is staring at you. You know what needs doing — it's not even hard — but the idea of writing another three hours of boilerplate before bed is genuinely demoralising. So you close the laptop. Tell yourself you'll do it tomorrow. What if there was nothing to close the laptop on? What if the work just… happened? That's the problem ACODA FACTORY solves. And I shipped it this week. ACODA FACTORY is a self-hosted, open-source autonomous AI coding agent. You point it at a GitHub repo, describe a task, and it handles the entire dev cycle on its own: Analyses the codebase Plans the implementation Creates a branch Writes the code Tests and self-reviews the diff Opens a pull request No hand-holding. No approvals mid-flow. Just a PR waiting for you in the morning. This isn't a demo. In the last 48 hours it's opened four real PRs on a live production codebase. This isn't the future. It's Tuesday. You: "Add dark mode to the settings page" ACODA: ✅ Analysing repo… ✅ Planning implementation (3 files)… ✅ Wri

    Почему выбрано: Инновационный подход к автоматизации разработки с использованием AI, практическая ценность.

25. #25 · score 88 · dev.to · Tushar Jaju · 2026-05-11

    I kept rewriting the same regex passes against LLM output. So I made a library.

    I've been working on a few LLM-based projects over the last year. Sakhi, a Hindi voice-to-form pipeline for community health workers in India. A resume parser for engineering candidates. A couple of smaller things. Different domains, different models, different prompts. But there's a pattern: at the bottom of every pipeline, right before the model's output became "data we trust," I'd find the same kind of code. Strip markdown fences. Repair half-broken JSON. Trim runaway repetitions. Normalize Python True/False/None to JSON booleans. Cut off the trailing "I hope this helps!" the model added after the actual answer. Every project had its own ad-hoc version of these. Slightly different regex, slightly different edge cases. The third time I copy-pasted a "strip json` … `" cleaner across projects, I gave up and made it a library. That's llmclean. Zero dependencies, pure standard library, three small utilities. v0.1.0 was on PyPI a couple of months ago. v0.2.0 just shipped, and it's the one I want to talk about — because what changed in this release is the part that makes the case for a separate library at all. Three functions, total. That's the entire public API: from llmclean import

    Почему выбрано: Полезная библиотека для работы с выводом LLM, решает распространенные проблемы.

26. #26 · score 88 · dev.to · 丁久 · 2026-05-11

    MCP (Model Context Protocol) Complete Guide: The Standard Connecting AI to Your Tools

    This article was originally published on AI Study Room. For the full version with working code examples and related articles, visit the original post. Every AI application needs to talk to external systems — databases, APIs, file systems, search engines. Before 2025, every integration was custom: write a LangChain tool, build a custom function call, hack together a plugin. It was like the pre-USB era where every device had its own cable. MCP (Model Context Protocol) changes that. It's an open protocol that standardizes how AI models connect to external tools and data sources — one protocol, any LLM, any tool. MCP is an open standard (originally developed by Anthropic) that defines how AI applications discover and call external tools. Think of it as USB-C for AI integrations — a universal connector that any MCP-compatible client can use to talk to any MCP-compatible server. ┌─────────────────┐ ┌────────────────┐ ┌─────────────────┐ │ MCP Client │◄───►│ MCP Protocol │◄───►│ MCP Server │ │ (Claude, VS │ │ (JSON-RPC │ │ (DB connector, │ │ Code, Cursor) │ │ over stdio │ │ file system, │ │ │ │ or SSE) │ │ search, API) │ └─────────────────┘ └────────────────┘ └─────────────────┘ The key i

    Почему выбрано: глубокий анализ нового протокола для интеграции AI с инструментами, полезные примеры.

27. #27 · score 88 · dev.to · hamza4600 · 2026-05-11

    Ontology in Computer Science and Artificial Intelligence: A Developer’s Practical Guide

    How structured knowledge models power semantic systems, enterprise platforms, and next-generation AI applications. In modern software engineering and artificial intelligence, data alone is not enough. Systems need context, structure, and meaning to make reliable decisions. This is where ontology becomes essential. Ontology in computer science is more than an academic concept—it is a practical framework for organizing knowledge so machines can interpret relationships, reason about information, and produce more accurate outputs. Major enterprise technology leaders such as Salesforce emphasize ontology because structured metadata and domain understanding directly improve personalization, explainability, and decision intelligence. (salesforce.com) For developers, architects, and AI engineers, understanding ontology is increasingly important. In computer science, ontology is a formal representation of knowledge within a domain. It defines: Entities (Classes): Core concepts such as Customer, Product, or Disease Attributes: Properties of those entities Relationships: How entities connect Constraints: Logical rules that govern valid interactions Vocabulary: Shared terminology across system

    Почему выбрано: глубокая статья о важности онтологии в AI и разработке, полезные практические аспекты.

28. #28 · score 88 · dev.to · Baris Sozen · 2026-05-11

    Reactive Intent Markets: a working paper on the submission format atomic settlement makes possible

    A note up front: how this was written Before the argument, the methodology, because the methodology is part of the argument. This working paper was written with Anthropic's Claude as a research collaborator. The thesis, the framing, the lived experience that the paper draws from — three decades of treasury-desk work, two currency crises lived through — those are mine. What Claude did was sharper: it organized the related-work survey, stress-tested the conjectures against the mechanism-design literature, and pushed back when an argument I thought was tight turned out to be load-bearing on something I had not justified. The paper exists in this form because of that back-and-forth. Sitting on the idea — the alternative — felt like exactly the kind of impedance the autonomous economy this paper describes is supposed to remove. I am also disclosing this here, in body text, on purpose. The paper proposes a market for AI agents. The paper was helped into existence by an AI agent. If that loop is uncomfortable, sit with the discomfort. It is going to become the default condition under which working papers in this category get written. Conventional market venues require participants to comp

    Почему выбрано: Интересная работа о рынках AI-агентов с использованием Claude, глубокий анализ.

29. #29 · score 88 · dev.to · Mariano Gobea Alcoba · 2026-05-11

    Show HN: adamsreview – better multi-agent PR reviews for Claude Code!

    Advanced Multi-Agent System for Enhanced Code Review with Claude Code The proliferation of AI-assisted code review tools has introduced novel paradigms for identifying defects and improving code quality. While existing solutions like Claude Code's built-in /review and /ultrareview commands, alongside third-party offerings such as CodeRabbit and Greptile, provide valuable automation, they often operate under a single-pass, monolithic review model. This approach can limit their ability to perform in-depth analysis, manage complex dependencies, and effectively integrate human feedback. This article details the design and implementation of adamsreview, a Claude Code plugin engineered to address these limitations by leveraging a multi-agent, multi-stage review process. adamsreview is conceived as a system of interconnected sub-agents, orchestrated to perform distinct analytical tasks. This architecture allows for a more granular and robust review process, moving beyond the capabilities of simpler, single-pass AI reviews. The core philosophy is to decompose the review into manageable stages, each handled by specialized agents, with explicit state management and mechanisms for human inter

    Почему выбрано: глубокий анализ многоагентной системы для улучшения кода, полезные практические аспекты.

30. #30 · score 88 · dev.to AI · Todd Sullivan · 2026-05-11

    The Fastlane gym Export Options Trap (and Why Your Provisioning Profile Is Being Silently Ignored)

    Spent a few hours last week debugging a CI failure that had no right to be as subtle as it was. The build archived fine, but exportArchive kept dying with: error: exportArchive: requires a provisioning profile with the App Groups feature. The frustrating part: the AppStore provisioning profile was correct. I had just renewed it, decrypted it on the runner, and confirmed the App Group entitlement was in there. The keychain had it. So why was xcodebuild not finding it? The Fastlane gym action accepts export_options: in two forms: A path to an existing .plist file A Hash of options it will write to a temp plist I was passing a Hash — and inside that Hash I had a plist: key pointing to my own plist file, thinking gym would merge or defer to it. It does not. When you pass a Hash, gym writes that Hash to a temp plist and hands it directly to xcodebuild. The plist: key inside the Hash is not special — xcodebuild does not recognise it, ignores it silently, and you end up with a minimal plist that has no provisioningProfiles key at all. The temp plist gym generated looked like this: method app-store uploadSymbols plist RELEASE_exportOptionsPlist_Store.plist No provisioningProfiles. Under ma

    Почему выбрано: Полезный разбор проблемы с Fastlane, важный для разработчиков iOS.

31. #31 · score 88 · Habr · glibus · 2026-05-11

    Деконструкция GO: Низкоуровневые концепции. Atomics. Часть 2.1

    Я самую малость обленился и как-то давно не делал новых разборов, поэтому следующим нашим этапом деконструкции будут низкоуровневые операции. Иногда здесь будет в отрыве от аллокаторов/планировщиков и прочего, но опять же, статьи для тех, кто знает и хочет разобраться поглубже, как тут всё устроено. Поэтому, в этой части начнем с самого простого – пакета atomic. Концепции вокруг атомарных операций на уровне CPU я рассматривал здесь, поэтому советую почитать. Там мы даже пишем свой атомарный AND. !Важно! Мы будем разбирать на примере src/internal/runtime/atomics, то есть внутреннего пакета, а не того, который представлен нам как пользователям(потому что в исходниках я не нашел реализации). Но по большей части операции одни и те же. Читать далее

    Почему выбрано: глубокий разбор низкоуровневых концепций в Go, полезен для инженеров

32. #32 · score 88 · Habr · NeuroKirKorov · 2026-05-11

    Извлечение параметров из 2D-чертежей: 6 YOLO-моделей, кастомный OCR и стрелочная логика

    PDF‑чертёж кажется простым документом, пока не нужно автоматически вытащить из него всё, что влияет на стоимость изготовления детали: габариты, диаметры, резьбы, квалитеты, шероховатости, материал, массу и тип детали. Мы собрали для этого пайплайн из детекции, OCR и инженерной логики: научили систему находить проекции, отделять контур детали от служебных линий, связывать стрелки с размерами и превращать чертёж в JSON для калькулятора стоимости. В статье разбираем архитектуру решения, узкие места и приёмы, которые реально сработали на производственных чертежах. Читать кейс

    Почему выбрано: сильный практический кейс по извлечению данных из 2D-чертежей, полезен для инженеров.

33. #33 · score 88 · Habr · artemkaVlg · 2026-05-11

    От LLM к агенту: Как заставить Go приложение думать и действовать

    Всё началось с доклада про AI-агентов. Заинтересовало настолько, что решил написать своего на Go. Было сложно, но получилось! Делюсь опытом: LangChainGo, инструменты, цепочки, MCP и интеграция с GigaChat. Читать далее

    Почему выбрано: глубокий разбор создания AI-агента на Go с практическими примерами и инструментами.

34. #34 · score 88 · Habr · anatoly_kr · 2026-05-10

    Семь раз посчитай — один раз урони: моделируем инциденты до деплоя

    Ракету не отправляют в космос только потому, что её двигатель и насос успешно прошли стендовые испытания по отдельности. Перед стартом инженеры рассчитывают траекторию, моделируют режимы работы и анализируют сценарии отказов. Расчёт не заменяет реальные тесты, но задаёт для них осмысленную рамку. В софте всё обычно иначе. Распределённый пользовательский путь — например, оформление заказа — собирается из десятков микросервисов, баз и очередей. Разработчики добавляют новую зависимость, видят зелёные тесты, проверяют локальные метрики и выкатывают релиз. Считается, что если при сбое что-то пойдёт не так, настроенная система наблюдаемости обязательно это покажет. Она, конечно, покажет. Но почему при проектировании микросервисов мы так спокойно относимся к тому, что узнаём о хрупкости архитектуры в основном по факту инцидента? Эта статья о том, как получить грубый расчёт деградации системы ещё до релиза. Без отказа от хаос-инжиниринга или мониторинга, а как шаг перед ними. Я расскажу о двух экспериментах, в которых топологическая модель автоматически извлекалась из распределённых трейсов, после чего на ней просчитывались сценарии отказов методом Монте-Карло. Результаты моделирования я з

    Почему выбрано: Интересный подход к моделированию инцидентов до деплоя, полезные практические примеры.

35. #35 · score 87 · Habr · PatientZero · 2026-05-11

    [Перевод] Структуры данных на практике. Глава 15: Графы и их обход с эффективным использованием кэша

    «Задача абстракции — не быть расплывчатой, а создать новый семантический уровень, на котором можно достичь абсолютной точности», — Эдсгер Дейкстра Взрывной рост количества промахов кэша При определении топологии сети для обхода 500 коммутаторов нашей системе требовалось 37,5 миллисекунды. Вроде бы это не так медленно, если не учитывать количество промахов кэша: 8,5 миллиона. При 500 узлах это 17 тысяч промахов на узел. Структура данных была фундаментально неподходящей для этой задачи. Работа инструмента была простой: определение топологии сети при помощи обхода графа соединённых устройств. У каждого коммутатора было до 48 портов, а нам нужно было при помощи поиска в ширину найти все доступные устройства из начальной точки. Реализация была как по учебнику — список смежности со стандартным BFS. В случае сети из 500 коммутаторов (в среднем по 12 соединений у каждого) статистика была такой: 8,5 миллиона промахов кэша на 500 узлов. Это 17 тысяч промахов кэша на узел! Я переписал этот код, реализовав графовое представление, учитывающее кэш. Результаты: код стал в 3,75 раз быстрее, а количество промахов кэша уменьшилось в 7 раз. В этой главе мы поговорим об эффективном описании и обходе г

    Почему выбрано: Глубокий разбор структуры данных и оптимизации кэша, полезный для практического применения.

36. #36 · score 87 · dev.to · Manoir Yantai · 2026-05-10

    我用 AI Agent 把开发效率提升了 3 倍：一个全自动化流水线的完整复盘

    我用 AI Agent 把开发效率提升了 3 倍：一个全自动化流水线的完整复盘 做独立开发，最贵的不是服务器，是你的时间。 我花三个月搭了一套 AI Agent 流水线，把重复性开发工作全部自动化。代码审查、测试生成、部署发布——现在都是"一键完成"。 整这套系统的出发点很简单：减少 context switch。开发者最消耗精力的不是写代码，而是从产品需求切换到代码实现、从 debug 切换到部署。 我用 Hermes Agent 做调度层，串联了： 代码审查（GPT-4 + 自定义规则） 测试生成（基于 AST 静态分析） 部署流水线（GitHub Actions + Docker） 1. 自动化 code review 2. 测试用例生成 3. 一键部署 PR 审查时间：30min → 5min（提升 6x） 测试覆盖率：40% → 78% 部署频率：每周 1 次 → 每天 3 次 整体开发效率：提升约 3 倍 Hermes Agent（调度层） GPT-4 / Claude（代码理解） Playwright（端到端测试） GitHub Actions（CI/CD） Camoufox（反检测浏览器） 不是效率提升本身，而是：把精力留给真正需要创造力的部分。规则明确的事情，让机器去做；需要判断的事情，人来把关。 这套流水线不是要取代开发者，是要把开发者从"重复劳动"里解放出来。 有具体技术问题可以在评论区问，我会尽量回答。

    Почему выбрано: Глубокий разбор автоматизации разработки с использованием AI, полезные практические выводы.

37. #37 · score 85 · Habr · Sivchenko_translate · 2026-05-10

    [Перевод] В агентскую эпоху не все архитектуры кода одинаково полезны

    Дебаты, касающиеся программирования с применением агентов, в основном касаются подбора инструментария — какую IDE, какую модель, какой CLI использовать и т.д. Гораздо меньше внимания уделяется более интересному вопросу: а сохраняет ли в таких условиях актуальность сам подход к структурированию кода, которому нас учили, если у той штуки, которая теперь пишет код, ограничена долговременная память, а также ограничено контекстное окно? Более того, агент зачастую должен добиваться прогресса по задаче, не имея «перед глазами» всей системы. Ниже проанализированы различные архитектуры кода — TDD (разработка через тестирование), OOP (объектно-ориентированное программирование, ООП), FP (функциональное программирование, ФП), MVC (модель-представление-контроллер), MVVM (модель-представление-модель представления), микросервисы, событийно-ориентированная архитектура, CQRS (раздельная обработка команд и запросов), гексагональная архитектура, разработка через поведение (BDD), предметно-ориентированное проектирование (DDD). Они отсортированы по показателю прикладной полезности в условиях, когда программирует не человек, а агент. Читать далее

    Почему выбрано: Глубокий анализ архитектур кода в контексте программирования с агентами, полезные выводы.

38. #38 · score 85 · dev.to · Mark0 · 2026-05-10

    2026-05-08: macOS Shub Stealer infection

    This technical analysis outlines a macOS Shub Stealer infection occurring on May 8, 2026. The compromise follows a social engineering path where a Google search leads users to a malicious Google Drive document, which then redirects to a fraudulent "Download for macOS" landing page. The victim is then prompted to manually execute a script via their terminal, initiating the malware's deployment. The report highlights key forensic artifacts, including specific log files generated during the infection and network traffic captured in Wireshark. For deep-dive investigation, the author has provided associated IOCs, packet captures (pcap), and the malware samples themselves, allowing analysts to examine the exfiltration methods used by this infostealer. Read Full Article

    Почему выбрано: Глубокий анализ инцидента с вредоносным ПО, полезен для специалистов по безопасности.

39. #39 · score 85 · dev.to · Stelixx Insights · 2026-05-11

    Big Tech firms are accelerating AI investments and integration, while regulators and companies focus on safety and responsible adoption.

    The AI landscape is experiencing unprecedented growth and transformation. This post delves into the key developments shaping the future of artificial intelligence, from massive industry investments to critical safety considerations and integration into core development processes. Key Areas Explored: Record-Breaking Investments: Major tech firms are committing billions to AI infrastructure, signaling a significant acceleration in the field. AI in Software Development: We examine how companies are leveraging AI for code generation and the implications for engineering workflows. Safety and Responsibility: The increasing focus on ethical AI development and protecting vulnerable users, particularly minors. Market Dynamics: How AI is influencing stock performance, cloud computing strategies, and global market trends. Global AI Strategies: Companies are adapting AI development for specific regional markets. This deep dive aims to provide developers, tech leaders, and enthusiasts with a comprehensive overview of the current state and future trajectory of AI. AI #ArtificialIntelligence #TechTrends #SoftwareEngineering #MachineLearning #CloudComputing #FutureOfTech #AISafety

    Почему выбрано: Глубокий анализ текущих трендов в AI, полезная информация для разработчиков и лидеров.

40. #40 · score 85 · dev.to · Carol Bolger · 2026-05-11

    Building a Zero-Cost AI Feature in Flutter with Gemma 4 + Firebase

    How to combine on-device inference with cloud sync — without paying a cent in API fees Here’s the moment every indie developer dreads. You’ve shipped your AI feature. Users love it. It’s working. Then you open your billing dashboard. Every question your users ask costs you money. Every summary generated, every classification run, you’re paying for it. You have created a successful product, but its popularity is now draining your resources. What if there was a way to run powerful AI in your Flutter app with zero ongoing API costs? No per-request charges. No server bills. No privacy risk from data leaving the device. That’s exactly what the combination of Gemma 4 and Firebase unlocks. Gemma does the thinking, entirely on the user’s phone. Firebase handles persistence and sync. The result is an AI-powered app that scales to thousands of users at near-zero marginal cost. I’m building my own product on this exact stack. Here’s how it works. Cloud AI APIs are seductive. You make one API call, get a response, ship the feature. But the cost model is brutal for bootstrapped products: Costs scale directly with usage — success punishes you User data leaves the device — a privacy and trust pro

    Почему выбрано: полезный практический разбор создания AI-функции без затрат, актуально для разработчиков.

41. #41 · score 85 · dev.to · Cara Jung · 2026-05-11

    From Scrapers to MCP Server: Serving Korean Entertainment Data to AI Agents

    Korean entertainment data is surprisingly fragmented. Information about a single drama or film is often scattered across multiple platforms. To solve that, I built a unified Korean entertainment database powered by APIs, web scrapers, and automated sync pipelines. By the end of the project, I had a Supabase database containing nearly 10,000 Korean movies, 3,500 TV shows, per-episode Nielsen Korea ratings, award histories, and streaming availability across four regions. The next problem was figuring out how to expose it to AI agents in a way that was actually useful, secure, monetizable, and maintainable. This is the story of building the MCP server and the errors I encountered. Before writing any code, I thought carefully about what an AI agent would actually need from a Korean entertainment database. The answer wasn't "expose every database column as a query parameter." That's an API, not a tool. MCP tools should be opinionated about what they return and why. I ended up with 17 tools organized into three categories: Discovery tools answer "what should I watch?" — get_trending_dramas, browse_by_genre, browse_by_tag. The tag tool is the most distinctive: MyDramaList's community taxo

    Почему выбрано: Интересный проект по созданию базы данных для AI агентов, с практическими выводами.

42. #42 · score 85 · dev.to · Basil Hafez · 2026-05-11

    GarlicStamp: an open identity protocol for AI agents

    If you've spent any time wiring AI agents into real systems — picking up tickets, executing trades, managing infrastructure, talking to APIs that cost real money — you've already run into the question nobody is answering well: How do you trust an agent you didn't build? There are partial answers. OAuth proves a human was involved at some point. An API key proves somebody held the key at signup. A model card tells you which weights are allegedly running. None of those answer the question that actually matters at runtime: was this specific decision made by the agent it claims to have been made by, and can I verify that without phoning home to the issuer? So we built a protocol for it. It's called GarlicStamp 🧄 — Ed25519-signed credentials, issuer-agnostic envelope, portable across systems, verifiable offline. The home page is at garlicstamp.com. And then we built a stress test for it that's been running for 53 days. We'll get to that. A GarlicStamp credential is, at the wire level, a JSON envelope with three things: a canonical-JSON payload describing what the agent did (or who they are, or what they performed), an issuer identifier, and an Ed25519 signature over the canonical paylo

    Почему выбрано: Интересный протокол для доверия к ИИ-агентам, важная тема для разработчиков.

43. #43 · score 85 · dev.to Mobile · Shotlingo · 2026-05-11

    How Apple Custom Product Pages Work With Localization (2026 Guide)

    How Apple Custom Product Pages Work With Localization (2026 Guide) If you are running App Store ads or growth experiments in 2026, custom product pages localization is the single most underused lever in ASO. Apple Custom Product Pages (CPP) launched with iOS 15 and gave every developer up to 35 alternate versions of their product page, each with unique screenshots, app preview videos, and promotional text. What most teams forget is that every CPP is itself a per locale object. A CPP built for the US market is not the same asset as a CPP built for Japan, and the moment you treat them as the same, you start leaving installs on the table. This guide explains how CPP and localization interact, the realistic math behind a 35 by 40 grid of variants, the field-by-field strategy that actually moves conversion, and the App Store Connect workflow for shipping a properly localized CPP. If you have not read our broader Apple Custom Product Pages guide yet, start there for the fundamentals. This post picks up where that one ends. Walk into any growth team and ask how many CPPs they run. You will usually hear a confident answer between five and fifteen. Ask how many of those CPPs are localized i

    Почему выбрано: глубокий анализ локализации страниц продуктов Apple, полезные стратегии для разработчиков.

44. #44 · score 85 · dev.to Swift · Timothy Fosteman · 2026-05-11

    Local Multimodal LLM on iOS with `llama.cpp` (Swift + ObjC++)

    I want a real local pipeline: image in, structured JSON out, no cloud dependency. Optimized to run Metal / ANE or whatever apple exposes ? After doing a bit of research, llama.cpp provides optimization and all the necesary low level work. I just need to make swift bindings that are worth the trouble… This is a complete tutorial on how i did it. i will use something like quickbooks / wise.com receipt capture example to make it real and safe. Bon courage! A local inference stack with clear separation of concerns: llama.cpp as an iOS XCFramework (vendor/llama.cpp/build-apple/llama.xcframework) Objective-C++ bridge (Controllers/LlamaBridge.h, Controllers/LlamaBridge.mm) Swift-facing API in Controllers/LLMFunctionsController.swift Typed decode API: let result: ReceiptResult = try await LLMFunctionsController.shared.predict( image: receiptImage, prompt: "Extract vendor and total.", as: ReceiptResult.self ) That is your chassis. Keep UI concerns away from inference state. Latest XCode. Always. target iOS project Models present in Models/. I am targeting iPad Pro M5 12GB onboard unified memory, so 4B in Q4 quantization should be snappy. But you can go find yourself a better model on http

    Почему выбрано: Полезный туториал по созданию локального мультимодального LLM на iOS, содержит практические советы.

45. #45 · score 85 · dev.to · Made Büro · 2026-05-11

    OpenModels: Explore LLM Models and Inference Providers

    The number of LLM providers keeps growing and so does the confusion around pricing, availability and compatibility. OpenModels is an open-source project that brings structure to this landscape: a single registry where models, providers, and their relationships are documented, validated, and queryable. The AI inference ecosystem is fragmented in ways that cost teams real time and money: The same model can cost 10x more depending on which provider you use Latency between providers varies radically even for identical models Uptime is unknown until you experience an outage yourself Pricing pages change without notice, and there's no structured way to track it Choosing a provider still means opening 15 tabs and building a spreadsheet The AI ecosystem already has excellent tooling for training and inference. What is still missing is standardized infrastructure visibility across providers. OpenModels focuses on that operational layer. OpenModels is an open infrastructure project for discovering, validating, and comparing LLM models and inference providers. The project combines: an open registry of model and provider metadata structured JSON schemas with automated validation provider norma

    Почему выбрано: полезный ресурс для понимания LLM-провайдеров и их сравнений, актуальная информация.

46. #46 · score 85 · dev.to · Wallet Guy · 2026-05-11

    REPUTATION_THRESHOLD Policy: Only Let High-Rep AI Agents Touch Your Funds

    The REPUTATION_THRESHOLD policy in WAIaaS creates a trust barrier between AI agents and your crypto funds, requiring onchain reputation scores before agents can execute transactions. Instead of trusting any agent that claims to be helpful, you set a minimum reputation threshold that agents must meet through proven onchain behavior and community validation. As AI agents become more autonomous with crypto wallets, the question isn't whether they'll make mistakes—it's how much damage they can do when they inevitably do. Traditional access control relies on identity and credentials, but AI agents don't have permanent identities or employment histories. They need a different trust model. Onchain reputation systems like ERC-8004 create verifiable track records of agent behavior. Every transaction, every protocol interaction, every success and failure gets recorded permanently. The REPUTATION_THRESHOLD policy lets you say: "This agent can only touch my funds if the blockchain proves it has a track record of responsible behavior." WAIaaS implements the REPUTATION_THRESHOLD policy as part of its 21-policy security framework. When an agent tries to execute a transaction, the policy engine qu

    Почему выбрано: Интересный подход к управлению репутацией AI-агентов в криптовалюте с практическими аспектами.

47. #47 · score 85 · dev.to · AK DevCraft · 2026-05-11

    Running a Personal AI Assistant for $0 — Part 1 — Architecture

    Introduction A productivity tool that promised to change everything, charged monthly, and quietly became background noise. AI assistants are going the same way — another tab, another login, another $20/month for something you open twice a week. Probably, most of us regret about the subscription that we have today. Welp! What if you didn't have to? In today’s world, the infrastructure exists to run a capable always-on personal AI assistant, one that lives in your day to day regular apps like Telegram or WhatsApp, remembers you, browses the web, and handles real tasks — for exactly zero dollars a month. Not a trial. Not a teaser. Permanently free, on infrastructure you control. This article explains the architecture that makes it possible and why each piece matters. Most people's AI setup looks like this: Claude.ai, ChatGPT, or any other AI providers in a browser tab or mobile app, opened when needed, closed when done. Conversations are saved, and you can go back to what you discussed last time if you're in the same thread. But it's passive history, not active memory. You have to go and find it. And across that whole time, it couldn't reach out, take action, or do anything unless you

    Почему выбрано: Интересный подход к созданию персонального AI ассистента с нулевыми затратами, полезно для разработчиков.

48. #48 · score 85 · dev.to macOS · Timothy Fosteman · 2026-05-11

    White Paper FM v Public API

    Apple Foundation Models: White Paper vs Real API — What Actually Matches Up So I was thinking about doing a simple bubble classification experiment with Apple’s Foundation Models (FM), and the first thing that becomes obvious is this: the white paper and the actual API surface are describing the same system, but at very different layers of reality. arxiv.org This technical report is very hopeful to read it describes the full model capability. But then, i exported FoundationModels top level swift doc with developer typings, and it looked sad as it exposes only a slice of advertised functionatlity. https://pastebin.com/4N2PNgDc The paper describes a fairly ambitious multimodal system: native support for text + image inputs vision encoders integrated into the model stack reasoning over images, multi-image inputs, and mixed modality prompts structured tool-use and JSON-style outputs on-device inference with optimized small models (~3B class scale) grounding tasks (OCR, region reasoning, visual understanding) What else can a man want ? The 3B swissknife is compared head-on against Qwen 2.5 , that's impressive, as Qwen is the smartest model available on llama.cpp within 9B param formfact

    Почему выбрано: Глубокий анализ различий между теорией и практикой API Apple Foundation Models.

49. #49 · score 85 · dev.to · Just do it · 2026-05-11

    Why “Local Document AI” Is Really an OCR + RAG + Local Inference Problem

    Most discussions about local AI focus on one thing: Can the language model run locally? That matters, but for document AI it is only one part of the system. If the goal is to analyze PDFs, search contracts, extract information from scanned forms, or answer questions over internal documents, then “local AI” is not just a local LLM. It is a full document intelligence pipeline. A fully local document AI system usually requires three major layers: OCR / document parsing Retrieval / RAG Local AI inference If any of these layers depends on external APIs, the system is not truly local. Running a model with Ollama, LM Studio, llama.cpp, or GPT4All is useful. It gives you a local reasoning engine. But documents are not clean prompts. Real documents often include: scanned pages tables multi-column layouts forms invoices contracts handwriting footnotes charts embedded images A local LLM cannot reliably answer questions about these documents unless the system first converts the documents into usable structure. That is why OCR and parsing matter. The first layer of local document AI is document understanding. This usually includes: OCR for scanned PDFs text extraction from digital PDFs layout p

    Почему выбрано: Хороший анализ проблем локального AI для обработки документов, полезные рекомендации.

50. #50 · score 85 · Habr · ShyDamn · 2026-05-11

    Дорожная карта домашнего мини-ПК в 2026: что развернуть, в каком порядке, и зачем — план апгрейда от инфраструктурщика

    Каждый месяц с карты списываются деньги за подписки. Spotify, Яндекс Плюс, Notion, Obsidian Sync, Google One — суммы небольшие по отдельности, в сумме набегает заметно. Параллельно с этим у меня работает VPS с несколькими проектами, на роутере крутится OpenWrt с AdGuard Home, в ноутбуке стоит Docker. Инфраструктурный опыт есть. Дома при этом — никакого сервера, всё в облаке. Это начинает раздражать не только из-за денег. Сервисы меняют каталоги без предупреждения, поднимают цены, требуют доплат за объём, периодически ломают регионы. Контроль над собственными фотографиями, заметками и медиатекой постепенно перестал быть моим. Решил спланировать переезд на свой мини-ПК. Пока разбирался с железом и стеком, обнаружил, что нормальной системной дорожной карты «бери и иди» в 2026 году нет. Есть каталоги «50 self-hosted сервисов», восторженные посты про конкретные приложения, треды на Reddit. Структурированного маршрута для нового человека — нет. Этот текст — попытка такой маршрут собрать. Не «топ приложений», а архитектура от железа до приложений по слоям, с обоснованием каждого выбора, с тем, что я планирую поставить, и с тем, что осознанно не ставлю. Читать далее

    Почему выбрано: глубокая статья о планировании инфраструктуры домашнего мини-ПК с практическими рекомендациями.

51. #51 · score 85 · Habr · andy-takker · 2026-05-10

    Логин через Telegram по-новому: разбираем OIDC-флоу oauth.telegram.org и собираем его на Python

    Telegram теперь полноценный OpenID-провайдер: oauth.telegram.org, JWKS, JWT, claims. Туториалы на GitHub при этом массово показывают старый виджет с HMAC от bot-token и /setdomain в BotFather. Я разобрался с новым флоу и собрал PoC на Python — рассказываю, как устроен обмен между фронтом, Telegram и бэком, чем Login library через telegram-login.js отличается от manual OIDC code flow с PKCE, что настраивать в BotFather (спойлер: не в чате, а в его mini-app), как протестировать локально через ngrok, и какая проверка id_token нужна вместо ручного HMAC. Читать далее

    Почему выбрано: Практическое руководство по новому OIDC-флоу в Telegram, полезно для разработчиков.

52. #52 · score 85 · Habr · badcasedaily1 (OTUS) · 2026-05-11

    Почему ваш Go‑сервис ломается под 1000 RPS и как найти узкое место за полчаса

    Go-сервис может идеально проходить функциональные тесты и уверенно отвечать на локальных прогонах, а потом внезапно развалиться под 1000 RPS: p99 улетает в секунды, в логах появляются таймауты, throughput проседает, а часть запросов вообще не получает HTTP-ответа. В статье разберём, как подойти к такой деградации без гадания: прогнать нагрузку через vegeta и wrk2, правильно прочитать p50/p99 и status codes, проверить пул соединений к базе, настройки HTTP-клиента, горутины, GC, таймауты и быстро понять, где именно сервис начинает терять устойчивость. Читать далее

    Почему выбрано: сильный практический разбор проблем производительности Go-сервисов, полезно для инженеров

53. #53 · score 82 · dev.to · Elizabeth Omito · 2026-05-11

    “It Works on My Machine”… Until It Doesn’t.

    You spend hours building a feature, everything runs perfectly on your laptop, and you proudly push your code. Then a teammate pulls it… and suddenly nothing works. Different errors. Missing dependencies. Version conflicts. And the worst part? “It works on my machine.” That phrase has quietly ruined countless development hours. As developers, we’ve all experienced that frustrating moment where an application behaves perfectly in one environment and completely breaks in another. Sometimes it’s a missing package. Sometimes it’s a different operating system. Other times, it’s a version mismatch hiding somewhere deep in the setup. Whatever the reason, the result is always the same: wasted time, confusion, and unnecessary debugging. This is exactly the kind of problem Docker was built to solve. Instead of relying on each developer’s environment — which is always slightly different — Docker lets you package your application in a way that runs the same everywhere. Whether you run it on: your laptop, your teammate’s machine, a testing server, or the cloud, the application behaves consistently. In this article, you’ll understand: What Docker actually is Why it matters The basic concepts you

    Почему выбрано: хороший разбор проблем совместимости в разработке, полезные советы по использованию Docker.

54. #54 · score 82 · dev.to · Michael Smith · 2026-05-11

    Going Back to Writing Code by Hand: Why It Matters

    Going Back to Writing Code by Hand: Why It Matters Meta Description: Discover why developers are going back to writing code by hand in 2026, the real benefits of manual coding, and how to balance AI tools with hands-on practice. AI coding assistants have become incredibly powerful, but a growing number of developers are deliberately going back to writing code by hand — at least some of the time. This isn't nostalgia. It's a calculated move to sharpen fundamentals, improve debugging skills, and avoid over-reliance on tools that can silently introduce bad patterns. This article breaks down why, when, and how to reintroduce manual coding into your workflow. Writing code by hand strengthens problem-solving skills that AI tools can quietly erode Manual coding is not about rejecting AI — it's about staying sharp enough to supervise it effectively Specific scenarios (interviews, debugging, architecture design) demand hands-on coding ability A hybrid approach — deliberate manual practice plus smart AI use — is the most effective strategy in 2026 Tools like notebooks, code katas, and structured practice sessions make the transition practical It started as whispers in developer forums. Then

    Почему выбрано: Статья о важности ручного кодирования и его преимуществах, полезна для разработчиков, но не содержит глубоких новшеств.

55. #55 · score 82 · dev.to · Alexi · 2026-05-11

    Manual vs Automated Testing: A False Dichotomy

    In testing, we often talk about the “balance between manual and automated testing,” but this is a misleading way to think about it. Testing is not about dividing work into manual or automated. It is a process of exploring and verifying the product. When faced with a certain task, the focus should not be on “balance,” but instead on which tools and approaches are best suited to accomplish that task. Manual testing is indispensable, as manual test cases serve as the foundation for future automation. Manual testing revolves around the aspects that automated testing can't cover. These include visual application checks and the ability to test specific user experience-based scenarios that are impractical to automate. On the other hand, automated testing has its advantages. It eliminates the human factor, allows you to use the same scenarios repeatedly, and is much faster than manual testing. Moreover, manual testing experience is invaluable for automation testers. Understanding the app from a user's perspective helps automation testers write better scripts and prioritize what to automate. However, this is not a question of balance, but of effectiveness in choosing the right approach for

    Почему выбрано: Интересный взгляд на тестирование, подчеркивающий важность выбора подхода.

56. #56 · score 82 · dev.to · Billy Bob Gurr · 2026-05-11

    When I started running models locally, I thought quantization meant squeezing more into RAM. Turns o

    Most people default to Q4_K_M in llama.cpp because it's the "safe" choice. But I've found the real win comes from testing your actual workflow. A 70B model in Q3_K_S cuts latency significantly compared to Q4_K_M on the same hardware, with imperceptible quality loss for most tasks. The bottleneck becomes memory bandwidth, not raw VRAM size. Here's what changed my setup: I stopped chasing maximum quality and started measuring latency on real prompts. A 4-bit quantized Mistral answers coding questions as well as the full-precision version, but returns results faster. For summarization or creative writing, Q5 variants matter more. For RAG or classification tasks, I can drop to Q3 without noticing the difference. The catch is context length. Lower quantization plus longer context means RAM pressure. If you're doing 4K+ context windows, you can't always drop to the most aggressive quantization. That's where the tradeoff gets real. Spend an hour profiling your use case with different quantization levels. Measure latency, memory usage, and quality on a few real prompts. You'll find your sweet spot isn't where you started. Mine shifted twice in six months.

    Почему выбрано: интересный опыт оптимизации локальных моделей, полезные советы по квантованию.

57. #57 · score 82 · Habr · vyacheslavteplyakov · 2026-05-11

    Локальные LLM в реальной работе: Gemma 4, Qwen 3.6 и Qwen Coder

    Привет, меня зовут Вячеслав. Я интересуюсь локальными LLM и тем, как они ведут себя в реальных задачах — не на синтетических бенчмарках, а когда нужно написать работающий код, отрефакторить файл с багами или вытащить данные из HTML. Вокруг локальных моделей сложилась странная ситуация. С одной стороны, их постоянно принижают: если это не последняя версия Opus с максимальным режимом размышления, то и пробовать не стоит. С другой — мало кто действительно разбирается, что стоит за запуском локальной модели. Поднять API через llama.cpp — это полдела. А вот как ты её запускаешь, в какой среде, с какими параметрами — эти вещи порой переворачивают результат с ног на голову. Получить плохой результат с локальной моделью на удивление легко. Получить хороший — надо попотеть. При этом локальные модели нужны. Особенно когда начинаются истории про чувствительные данные, закрытые контуры и ситуации, когда облачный API просто не вариант. Я посмотрел множество тестов на YouTube — ни один меня не устроил. Общая канва одинаковая: берут модель побольше, запускают без оглядки на оптимальность и дают задание уровня «напиши сортировку пузырьком». Серьёзно? Я не разработчик и не кодер по профессии, но ре

    Почему выбрано: хороший анализ применения локальных LLM в реальных задачах, полезен для разработчиков.

58. #58 · score 80 · dev.to · Codexlancers · 2026-05-11

    🚀 Integrating Tamara Payment Gateway in a FlutterFlow Application

    security, reliability, and compliance, all while maintaining a seamless user journey. Recently, I worked on integrating the Tamara Payment Gateway into a FlutterFlow application, creating a complete end-to-end payment workflow — from initiating transactions to handling real-time updates. The Goal secure and scalable payment flow that: Enables users to complete payments smoothly Handles transaction states reliably Ensures compliance with Tamara’s payment standards Works seamlessly across development and production environments 🛠️ The Implementation The integration involved connecting Tamara’s APIs with the FlutterFlow application and managing the full payment lifecycle. ⚙️ Key Features Implemented Tamara Checkout API Integration Initiate payment sessions Redirect users to the hosted checkout page Process transactions securely Secure Payment Handling Proper API request validation Safe handling of transaction data Compliance with Tamara’s payment flow Webhook Integration for Real-Time Updates Implemented webhooks to receive real-time updates Handled events such as: Payment success Payment failure Transaction updates 💳 Payment Method Support Visa cards Mada cards This ensures compati

    Почему выбрано: Практическое руководство по интеграции платежного шлюза, полезно для разработчиков.

59. #59 · score 80 · dev.to · Peter Nasarah Dashe · 2026-05-11

    🚀 Permi v0.3.0 – Major Improvements to JS Scanning, AI Accuracy, and Speed

    I just shipped a significant update to Permi. This release tackles the biggest pain points reported by the community: JS scanning that actually works, smarter XSS detection, and much faster scans. Permi’s AI filter can now recognize when a target uses a Content‑Security‑Policy (CSP) that blocks inline script execution. This significantly reduces false positives on hardened websites like GitHub, banks, or government portals. Before: Reflected XSS payload found → flagged as REAL, even if CSP blocked it. After: AI checks CSP header → marks as harmless unless the policy allows execution. The new —js flag launches a Playwright headless browser that can render React, Vue, Angular, and other SPAs. It even works behind Cloudflare thanks to playwright-stealth. bash permi scan —url https://example.com —js Reliability: Falls back to static HTML if JS times out (no more zero‑URL scans). Control: Configurable timeout with —js-timeout 30 (default 20 seconds). Deep Discovery: Detects XHR/fetch API endpoints via network request interception. ⚠️ Note: JS scanning is still experimental in the community edition. It works well on most sites, but some may require authentication or infinite scroll.

    Почему выбрано: значительное обновление инструмента, полезные улучшения для разработчиков.

60. #60 · score 80 · dev.to · hunter-hongg · 2026-05-11

    An Analysis of the Vibe Coding Concept

    An Analysis of the Vibe Coding Concept Origin Proposed by Andrej Karpathy in February 2025. Original definition: "Completely let go and go with the vibe of the moment, stop overthinking, and just accept whatever AI gives you." Karpathy's original words: "It's not really about programming anymore. It's about having a conversation with a very enthusiastic, slightly drunk intern." Describe intent → AI generates → Directly accept Can't understand an error? Just copy-paste it back to the AI to fix Barely read the code, just "Accept All" No longer wary of "hallucinations" — instead, treat them as a feature Traditional programming: Idea → Precise description → Write code → Debug → Verify Vibe Coding: Idea → Vague description → AI generates → Smoothly accept → Tweak The key difference: Whether the human understands the code they're committing. Suitable for: Prototypes, one-off scripts, personal small tools, boilerplate code, frontend UI layouts, data exploration Not suitable for: Production system core logic, domains you know nothing about, security-sensitive code, long-term maintenance projects Dimension Traditional Tools Vibe Coding Mental model You're in control You're flying blind Cons

    Почему выбрано: Интересный анализ концепции Vibe Coding, затрагивает новые подходы в программировании с использованием AI.

61. #61 · score 80 · dev.to · Matthias · 2026-05-11

    From Markdown to print-ready PDF in the browser — here's what the output actually looks like

    Most Markdown editors are great for writing. Very few care about what comes out the other end. I've been building mdedit.io, a browser-based Markdown editor focused on document production: not notes, not quick drafts, but structured long-form documents you can actually hand in, send to a client, or publish — as PDF or DOCX, directly from the browser, without an account, without Word, without LaTeX. This post isn't about features. It's about the output. Let me show you what a real export looks like. The demo document is a 27-page academic paper: "Markdown as a Writing and Production Environment for Scientific Work — a criteria-based evaluation of mdedit.io compared to Word and LaTeX." It's written entirely in Markdown, with: YAML frontmatter (title, author, date, language, citation config) An embedded CSL-JSON bibliography (Pandoc/Citeproc-compatible) In-text citations ([@gruber2004markdown], [@pandoc2025], etc.) Heading-based structure (H1–H4), outline-navigable Multiple scientific tables with different layout presets KaTeX math — inline ($E = mc^2$) and block (Gaussian integral) A footnote An embedded image with caption A code block (YAML) A long URL as a line-break stress test Au

    Почему выбрано: интересный обзор возможностей Markdown-редактора для создания документов, полезен для разработчиков.

62. #62 · score 80 · dev.to · tech_minimalist · 2026-05-11

    How enterprises are scaling AI

    Technical Analysis: Scaling AI in Enterprises The article from OpenAI provides an overview of how enterprises are scaling AI, highlighting key strategies, challenges, and best practices. As a Senior Technical Architect, I'll delve deeper into the technical aspects of scaling AI in enterprises, providing a comprehensive analysis. Infrastructure and Architecture To scale AI, enterprises require a robust infrastructure that can handle vast amounts of data, complex computations, and rapid model iteration. The following components are crucial: Cloud Computing: Cloud providers like AWS, GCP, and Azure offer scalable infrastructure, reducing the need for on-premises hardware and enabling rapid deployment. Containerization: Containerization (e.g., Docker) ensures consistent and efficient deployment of AI models across different environments. Orchestration: Tools like Kubernetes manage containerized applications, enabling automated deployment, scaling, and resource allocation. Data Lakes: Centralized data lakes (e.g., HDFS, S3) store and manage large datasets, providing a single source of truth for AI model training and testing. Specialized Hardware: AI-optimized hardware like GPUs, TPUs, a

    Почему выбрано: Хороший анализ стратегий масштабирования AI в предприятиях, полезные практические аспекты.

63. #63 · score 80 · dev.to · Md Rakibul Haque Sardar · 2026-05-11

    How FlutterSeed Saves Hours of Flutter Project Setup Time

    Introduction Flutter is a popular framework for building natively compiled applications for mobile, web, and desktop. However, setting up a new Flutter project can be a time-consuming task, especially for indie developers, startups, agencies, and enterprise teams. The traditional setup process involves creating a new project, configuring the architecture, state management, routing, backend, and theme, which can take hours to complete. This is where FlutterSeed comes in — a visual Flutter app initializer that saves hours of Flutter project setup time. The traditional setup process for a Flutter project involves a lot of repetitive and tedious tasks. Developers have to create a new project, configure the architecture, choose a state management solution, set up routing, select a backend, and customize the theme. This process can be error-prone and time-consuming, taking away from the time that could be spent on actual development. Moreover, the lack of consistency in architecture choices can lead to setup drift, making it difficult to maintain and scale the project. FlutterSeed is a node-based visual graph builder that exports a production-ready Flutter project ZIP. It allows develope

    Почему выбрано: Полезная информация о сокращении времени на настройку проектов Flutter, но не слишком глубокая.

64. #64 · score 80 · dev.to · Lisa Sakura · 2026-05-11

    How I'd Set Up Client Onboarding From Scratch in 2026

    How I'd Set Up Client Onboarding From Scratch in 2026 If you started an agency in 2020, your onboarding stack probably looked like this: Zapier trigger fires when a deal closes, Google Form collects some intake questions, answers land in a sheet nobody checks, and your project manager recreates the same folder structure in Drive for the 30th time. Maybe you had a welcome email template somewhere. Maybe. It worked. Barely. And we all pretended it was a system. Six years later, the tools are different. LLMs are free or near-free. Agentic workflows exist. But most small agencies are still running some version of that 2020 stack — just with fancier form builders and a Notion database instead of Google Sheets. Here's how I'd actually set up client onboarding from zero if I were starting an agency today. Seven steps. No enterprise software. No $300/month SaaS. Just a repeatable system with AI baked in where it saves real time. Most agencies treat the discovery call as a vibe check. That's a mistake. The discovery call is your first intake moment — and also the moment you decide if this client will be profitable or painful. Build a 15-question call guide that covers: budget reality, decis

    Почему выбрано: Полезная статья о настройке клиентского онбординга с использованием современных инструментов, но не слишком глубокая.

65. #65 · score 80 · dev.to · Atul Srivastava · 2026-05-11

    I Built a VS Code Extension That Lets You Control GitHub Copilot From Your Phone — And People Are Loving It

    Three months ago, I gave GitHub Copilot agent mode a massive refactoring task — rewrite an entire auth module across 12 files. I hit Enter. Walked to the kitchen. Made chai. Came back 40 minutes later. The agent had stopped after 2 minutes and 38 seconds. It was waiting for me to type "Yes, proceed." 38 minutes. Gone. Just like that. I stared at my screen and thought: "This is the most powerful coding tool ever built… and it's useless the moment I stand up." That's when I started building Copilot Remote Control. Copilot Remote Control is a VS Code extension that streams your entire Copilot agent session to your phone in real time. When the agent pauses and asks "Should I proceed?" — your phone buzzes. You glance at it, tap "yes," pocket it, and keep walking. The agent never stops. You never have to be at your desk. That's it. That's the whole product. You → Give Copilot a big task → Walk away Agent works → Hits a question → Your phone buzzes You tap "yes" from your phone → Agent continues You come back → Everything is done For the devs who care about architecture (I know you do): The extension monitors Copilot agent mode's conversation in real-time using a lightweight 20ms JSONL

    Почему выбрано: Практическое применение расширения для управления GitHub Copilot, полезно для разработчиков.

66. #66 · score 80 · dev.to · Weston G · 2026-05-11

    I shipped an MCP server for crypto airdrops — install in 1 config line

    I shipped a tiny MCP server last week that turns a curated crypto airdrop directory into 3 tools any LLM client (Claude Desktop, Cursor, Continue, Windsurf) can call directly. This post is the install snippet + a screenshot of it returning real data — so you can decide in 30 seconds whether it's worth one config line. Three tools, no signup, no API key, no rate limit: list_active_airdrops(chain?, risk?, sort_by?, limit?) — browse 32 vetted active airdrops, filter by chain (Solana, Base, Ethereum…) or risk level (verified / unverified) get_airdrop(slug) — full details for one campaign: action steps, weekly effort, cost floor, risk notes, official URL check_wallet(addr) — paste an EVM or Solana address, fan out to 7 public RPCs (Ethereum, Base, Linea, Arbitrum, Polygon, BSC, Solana), and surface every tracked airdrop whose chain you've already touched Hosted, JSON-RPC 2.0 over HTTP, CORS-open. Endpoint: https://web3-discover.vercel.app/api/mcp. Add this one block to your MCP config: { "mcpServers": { "web3-discover": { "command": "npx", "args": ["-y", "mcp-remote", "https://web3-discover.vercel.app/api/mcp"] } } } That's it. Restart your client. Ask: "What active airdrops can I farm

    Почему выбрано: Практическое руководство по настройке MCP сервера для крипто-вознаграждений, полезно для разработчиков.

67. #67 · score 80 · dev.to · Qasim Muhammad · 2026-05-10

    Manage Email Signatures from the CLI — Create, List, and Attach to Sends

    The nylas email signatures commands manage reusable email signatures stored per-grant. Create HTML signatures once, attach them to any send with a flag — no more pasting footers manually. Signatures are HTML blocks stored server-side and attached to outgoing emails via —signature-id. Create different signatures for different contexts (formal, casual, department) and switch between them per-send. nylas email signatures create —name "Work" —body " — Qasim Staff SRE, Nylas " nylas email send —to alice@example.com —subject "Hello" —body "…" —signature-id SIG_ID Command What it does nylas email signatures list List stored signatures nylas email signatures show Show signature HTML nylas email signatures create —name N —body B Create from inline HTML nylas email signatures create —name N —body-file FILE Create from HTML file nylas email signatures update Update a signature nylas email signatures delete —yes Delete a signature cat > sig.html Qasim Muhammad Staff SRE · Nylas cli.nylas.com EOF nylas email signatures create —name "Default" —body-file sig.html —json nylas email send \ —to team@company.com \ —subject "Weekly update" \ —body "Here's the summary…" \ —signat

    Почему выбрано: Практическое руководство по управлению подписями в CLI, полезно для разработчиков.

68. #68 · score 80 · dev.to · Aniket Dhakane · 2026-05-11

    So I built a Figma Design Agent for an Agentic AI Hackathon #kiro #figma #agents #geminicli

    Design-to-code handoffs are one of the biggest time sinks in frontend teams I have been working as a UI developer lately at an organization where we are developing multiple MFEs (Micro Frontends) which are interlinked with a main Dashboard (Can't go into too much details). All our products follow a same design library which is written in a separate NX repo, which is then imported as a package. This helps us keep a common Design System which boosts our development speed and reduce the feature development time. Till now the process of maintaining this Design System Repo was manual, a UX developer would build the design in Figma, then the developer would recreate it manually with the help of AI, but there was no one step agent to just paste the figma link and get a whole live storybook preview of built component with agent hooks to directly raise PRs using the ADO (Azure Devops) MCP. In this article I am going to show you how you can create your own design agent using gemini CLI (or claude code), Figma MCP and Vercel's web-design-guidelines skills. (It won't contain the PR & ADO part) Here's how I set it up, and how you can too Gemini CLI (or Claude Code) Figma MCP Extension web-desig

    Почему выбрано: Статья о создании агента для Figma с практическими рекомендациями, полезна для разработчиков.

69. #69 · score 80 · dev.to · Ishmeet Kaur · 2026-05-11

    What to Test During Your Shopify App Builder Free Trial (And What to Ignore)

    Most merchants approach a Shopify app builder free trial the same way they approach a new phone: they change the wallpaper, pick a theme, and call it a day. Then they sign up, go live, and discover the tool falls apart the moment a customer tries to check out. The trial period is not a design playground. It is a stress test. Here is how to use it properly. Colours, fonts, banner images — these are the things that feel productive to work on during a trial because they produce visible results quickly. You move a slider, the banner changes, you feel like progress is happening. Design absolutely matters for conversion. But design is also the easiest thing to fix after you have committed to a platform. The hard stuff — sync speed, checkout reliability, submission logistics, push notification infrastructure — is not visible until you go looking for it. And by the time you find a problem in production, you have already spent money and time migrating. Use your trial to find the problems that would cost you later. Leave the font choices for week two. Count the steps between opening the builder and sending your first test push notification. A well-built tool should get you there in under ten

    Почему выбрано: Полезные советы по тестированию Shopify приложений, но не слишком глубокий анализ.

70. #70 · score 80 · dev.to · Ken Imoto · 2026-05-10

    Your MCP server eats 55,000 tokens before your agent says a word — I measured the real cost

    The invisible bill I was debugging why my Claude Code sessions felt sluggish after connecting a few MCP servers. Token usage was through the roof — but I hadn't even asked the agent to do anything yet. I rewrote my prompts three times before I thought to check where the tokens were actually going. Turns out, the moment you connect an MCP server, every tool definition gets loaded into the context window. Names, descriptions, parameter schemas, enum values — all of it, on every single conversation turn. Not just when you call a tool. Every turn. Think of it like walking into a library to read one book, but the librarian insists you read the entire catalog first. Every time you walk in. I measured the tool-definition token overhead for four MCP servers, from minimal to massive: MCP Server Tools Est. tokens Monthly cost (10 calls) PostgreSQL 1 ~35 ~$0.0005 Google Maps 7 ~704 ~$0.009 GitHub 26 ~4,242 ~$0.06 GitHub (full) 93 ~55,000 ~$0.74 PostgreSQL to full GitHub: a 1,500x difference. Same protocol, same "MCP server" label, radically different cost profiles. And this is just the definition overhead. The actual tool calls consume additional tokens on top. A single MCP tool definition

    Почему выбрано: Интересный анализ использования токенов в MCP серверах, полезен для оптимизации.

71. #71 · score 80 · Habr · DimaIam (StudyAI) · 2026-05-11

    Лед и пламень: Как ИИ научился имитировать нас

    Иногда в разговоре с ЛЛМ можно забыть, что с тобой беседует набор алгоритмов, весов и распределений — настолько он может показаться убедительным. Но если Большая языковая модель не умеет чувствовать и думать как мы, то как ей удается подменять собой человека? При чем она так хорошо с этим справляется, что сейчас трендом стала сердечно-закадычная дружба с БЯМами. Кстати, по какой-то причине сей тренд захлестнул особенно сильно эстонскую молодежь. Так в чем секрет иллюзии? Читать далее

    Почему выбрано: интересный анализ взаимодействия ИИ и человека, актуальная тема для обсуждения

72. #72 · score 80 · Habr · enamored_poc · 2026-05-11

    От инженера до оператора промптов: 5 главных ошибок вайбкодинга

    Вайбкодинг (vibe-coding) — это круто, пока вы в потоке, и ИИ делает за вас рутину. Но за видимым “Vibe!” и “func() { return code.gen.ok() }” могут скрываться фатальные ошибки. Мы разобрали 5 критических проблем — от архитектурных косяков и уязвимостей до ленивых промптов и потери контекста. Читать далее

    Почему выбрано: Полезный анализ ошибок в вайбкодинге, актуально для разработчиков.

73. #73 · score 80 · Habr · pryadkinss · 2026-05-10

    Я сделал приложение за вечер без навыков программирования. Фиг там. Как я почти год делал игру с опытом и ИИ

    Миф о том, что с ИИ можно собрать полноценный проект за вечер без опыта, звучит красиво только в теории. На примере футбольного менеджера рассказываю, почему даже с опытом в разработке и активным использованием ИИ путь до живой системы занял почти год: из-за архитектуры, механик, дизайна, ассетов и постоянной ручной сборки продукта. Читать далее

    Почему выбрано: Интересный опыт разработки игры с ИИ, полезен для разработчиков и тех, кто интересуется ИИ.

74. #74 · score 78 · dev.to · Ishmeet Kaur · 2026-05-11

    How Much Does a Shopify Mobile App Actually Cost in 2025?

    If you have searched "how much does a Shopify app cost" and landed on articles full of vague ranges and non-commitments, this is not that article. Here are real numbers, broken into the three models merchants actually use, plus the hidden costs nobody talks about upfront. This is the full-build route: separate iOS and Android codebases written by a development agency or senior freelancers. Upfront cost: £25,000 to £100,000+ Timeline: 6 to 12 months before you see anything in the App Store Ongoing maintenance: £500 to £2,000 per month (covering bug fixes, OS updates, Shopify API changes) Total year-one cost: £31,000 to £124,000+ Who this makes sense for: merchants with very high GMV where even a 0.1% improvement in conversion justifies the investment, or businesses that genuinely need functionality no off-the-shelf solution provides (think custom loyalty mechanics, AR try-on, or deep integration with bespoke warehouse software). A React Native or Flutter build lets one developer (or a small team) produce both iOS and Android from a shared codebase. This cuts cost significantly but introduces its own risks around freelancer availability and long-term support. Upfront cost: £8,000 to

    Почему выбрано: полезная информация о стоимости разработки приложений, но не слишком глубокий анализ.

75. #75 · score 78 · Habr · egrifey · 2026-05-11

    Wi‑Fi 7: что меняется для сетевого специалиста и когда обновляться, а когда подождать

    Сегодня в презентациях сетевого оборудования всё чаще звучат слова «искусственный интеллект», «Wi‑Fi 7», «умный дом». Слайды выглядят впечатляюще, но инженеру приходится отвечать на более приземлённые вопросы: что реально изменится в квартире или офисе, как всё это настроить и оправдана ли замена текущих точек доступа. В этой статье разберём Wi‑Fi 7 и функции оптимизации сетей с помощью искусственного интеллекта с точки зрения человека, который отвечает за работу реальной сети: чтобы интернет был стабильным, видеозвонки не прерывались, а устройства умного дома работали без сбоев. От Wi‑Fi 5/6 к Wi‑Fi 7: какие появились новшества Читать далее

    Почему выбрано: хороший обзор Wi-Fi 7 и его влияния на сетевых специалистов, полезен для практиков.

76. #76 · score 78 · Habr · AriaQA · 2026-05-10

    Нагрузочное тестирование на собственных мощностях: полный гайд по k6

    Развернём нагрузочное тестирование на своих мощностях. Без ДД (долго, дорого). k6, VPS, сценарий на JavaScript, метрики в Grafana Cloud. Полчаса на настройку и встраивание в CI/CD. Для тестировщиков, разработчиков и всех, у кого есть пет-проекты. Читать далее

    Почему выбрано: Полезный гайд по нагрузочному тестированию с использованием k6, практическая ценность.

77. #77 · score 78 · Habr · vibecodingai · 2026-05-11

    От «Hello, World» до коммита в rustc: Roadmap Rust-разработчика на 2026 год

    Rust давно перестал быть языком энтузиастов. На нём собраны куски ядра Linux, движки баз данных и аналитики (TiKV, Materialize, Polars), бэкенды Cloudflare и Discord. Под Rust пишут прошивки для ESP32 и STM32, фронтенд через WebAssembly, инференс LLM. Microsoft переписывает части Windows, AWS строит на Rust Firecracker и Bottlerocket, Google пускает его в Android и в дерево ядра. По зарплатам Rust пятый год держится в верхнем дециле Stack Overflow Survey, и семь лет подряд — самый любимый язык разработчиков. Читать далее

    Почему выбрано: обзор развития Rust и его применения, полезно для разработчиков и инженеров

78. #78 · score 75 · dev.to · Jacob Noah · 2026-05-11

    7 Software Development Mistakes That Cost Businesses Time and Money

    Software can make a business faster, smarter, and more scalable. But poorly planned software can do the opposite. Many businesses do not lose money because they build software. They lose money because they build software without the right planning, structure, and long-term thinking. Here are seven common software development mistakes that cost businesses time and money. One of the biggest mistakes is starting development with vague requirements. For example: “We need a CRM system.” That is not enough. A development team needs to understand: Who will use the system? What tasks should it automate? What data should it store? What reports are needed? What integrations are required? What permissions should different users have? Without clear requirements, the project becomes guesswork. This often leads to repeated changes, missed features, and unnecessary delays. A team like Trifleck usually helps businesses turn broad ideas into clear software requirements before development starts. Many businesses want the first version of their software to include everything. This sounds efficient, but it usually creates problems. The better approach is to build in phases. Start with the most importa

    Почему выбрано: полезные советы по планированию разработки ПО, но не слишком глубокий материал.

79. #79 · score 75 · dev.to · Dhruvi · 2026-05-11

    A Tool That Saves Me Time Every Single Week

    One thing that saves me an absurd amount of time is building small internal debugging endpoints. Not dashboards. Just tiny routes or tools that answer very specific questions fast. Things like: “show the last sync status for this customer” “replay this failed webhook” “show all retries for this workflow” “compare the data between these two systems” Early on, I used to debug everything directly from logs and databases. It worked when systems were smaller. But once multiple services, queues, integrations, and retries are involved, simple issues start taking way too long to trace manually. So now, whenever I notice: I usually turn it into a small internal tool. The interesting part is that these tools are rarely complicated. Sometimes it’s: one endpoint one query one button But removing 20 minutes of repeated investigation every day adds up fast. Especially in systems that run continuously. Another thing I realized: The best internal tools are usually built by the people operating the system directly. Because they come from real friction. Not assumptions about what might be useful. A lot of engineering time is lost not on fixing problems, but on figuring out where the problem actually

    Почему выбрано: Полезные советы по созданию внутренних инструментов для отладки, практическая польза.

80. #80 · score 75 · dev.to · Arpit Mishra · 2026-05-11

    AI Healthcare App Development

    Artificial Intelligence is transforming the healthcare industry at an incredible pace. From virtual health assistants and predictive analytics to AI-powered diagnostics and personalized treatment recommendations, AI is redefining the future of digital healthcare. Healthcare organizations, startups, hospitals, and medical enterprises are increasingly investing in AI healthcare app development to improve patient care, automate medical processes, and enhance operational efficiency. Modern healthcare systems generate massive amounts of patient data every day. Managing and analyzing this data manually can be time-consuming and inefficient. AI-powered healthcare applications help healthcare providers process medical information faster, reduce errors, improve diagnosis accuracy, and deliver better patient outcomes. As the demand for intelligent healthcare solutions continues to rise, businesses are looking for experienced technology partners that can develop secure, scalable, and innovative AI healthcare applications. Dev Technosys has emerged as one of the leading companies specializing in AI healthcare app development services for global healthcare businesses and startups. What is AI He

    Почему выбрано: Хорошая статья о разработке AI приложений в здравоохранении, полезна для понимания текущих трендов.

81. #81 · score 75 · dev.to · Charmi Soni · 2026-05-11

    Developers switch environments 20 times a day. Why are you still editing URLs manually?

    You're deep in a feature. you're on: localhost:3000/projects/123/settings?view=advanced something looks off. you need to check staging. so you copy the URL, open a new tab, paste it, manually change the domain, hit enter, land on the homepage because the path didn't carry over, navigate back to the right page, realize you forgot the query param, do it again. 45 seconds. gone. and you do this 20 times a day. That's 15 minutes daily. 75 minutes a week. Just editing URLs. Nobody talks about it because it feels too small to complain about. but it adds up. every single day. So I built Soft. It's a Chrome extension that puts a small bar at the top of your browser. you configure your environments once — localhost, staging, prod. after that it's one click to switch. path carries over. query params carry over. hash carries over. nothing lost. It also turns red when you're on production. so you don't accidentally do something stupid while you think you're on staging. I got my first paid user 15 days after launch and developers using it across 12 countries. [https://chromewebstore.google.com/detail/soft/dcfgbdenmbfijjioijidacabcpjebnlc?utm_source=item-share-cb]

    Почему выбрано: Полезный инструмент для разработчиков, упрощает работу с URL, но не слишком глубокий анализ.

82. #82 · score 75 · dev.to · Kehinde Owolabi · 2026-05-11

    Fixing the Tile Image Bug in TCJSGame – A Debugging Story

    Fixing the Tile Image Bug in TCJSGame – A Debugging Story 🎮 Live Demo: https://tcjsgame.vercel.app/sample/index.html I was building a Flappy Bird game using my own game engine called TCJSGame. Everything worked perfectly — the bird, the pipes, the scoring system. The bird was an image. The pipes were images. But when I tried to create a level using the TileMap class with image tiles, nothing appeared on screen. No errors. No crashes. Just… nothing. The tiles were supposed to show a beautiful brick wall background for a platformer level I was designing. Instead, I got an empty canvas where the tiles should have been. I started debugging by checking the most obvious things first: Are the image paths correct? Yes, the same images loaded fine as regular Components. Is the TileMap.show() method being called? Yes, the method executed without errors. Are the tiles being added to the game world? Yes, they were in the comm array. So why weren't they rendering? I added console logs inside the Tile class constructor: class Tile extends Component { constructor(tx, ty, tid, com) { super(com.width, com.height, com.color, com.x, com.y) console.log("Tile created with type:", com.type) console.l

    Почему выбрано: полезный опыт отладки, может быть интересен разработчикам игр.

83. #83 · score 75 · dev.to News · wonder apps · 2026-05-10

    How AI Is Changing iPhone Ad Blocking for Good

    Ad blocking has come a long way from simple domain blacklists. The new generation of ad blockers uses artificial intelligence to stay ahead of ever-evolving ad techniques. Here's how AI is transforming the game. Traditional ad blockers rely on static filter lists — essentially a blacklist of known ad domains and script patterns. This approach has fundamental weaknesses: Ad networks constantly change domains New obfuscation techniques bypass signature-based detection Filter lists update slowly (hours or days after a new threat appears) You're exposed during the gap between threat and update A modern Safari ad blocker like Wonder Blocker uses machine learning to analyze the behavior of content, not just its identity. The AI engine: Studies how ad scripts behave structurally Recognizes patterns even when domains change Adapts to new obfuscation techniques in real-time Learns from global threat patterns while keeping data local You're protected from new threats immediately No manual filter updates needed Fewer false positives (AI recognizes legitimate content) Protection improves over time automatically A Safari ad blocker powered by AI doesn't just block ads — it evolves with the land

    Почему выбрано: Хороший обзор применения ИИ в блокировке рекламы, полезно для разработчиков.

84. #84 · score 75 · dev.to · RowthTech · 2026-05-11

    How AI Is Transforming E-Learning Software Development

    AI is rapidly improving the future of e-learning software development by making online learning smarter, faster, and more personalized. Businesses are now investing in advanced e-learning software development services to create interactive and user-friendly learning platforms. Modern e-learning mobile app development solutions use AI features like chatbots, personalized course recommendations, automated assessments, and real-time progress tracking to improve learner engagement. A professional e-learning software development company can help businesses build scalable learning platforms with AI-powered features that enhance user experience and simplify online education management. As demand for digital learning continues to grow, AI-driven e-learning mobile app development services are helping organizations deliver flexible and effective learning experiences across devices.

    Почему выбрано: полезная статья о применении ИИ в разработке образовательного ПО, но без глубокого анализа.

85. #85 · score 75 · dev.to · Net Skill Insights · 2026-05-11

    How Indian Enterprises Are Using Learning Experience Platforms for Workforce Transformation

    Digital transformation is reshaping the Indian corporate ecosystem faster than ever before. Businesses are no longer focused only on hiring skilled employees but are now investing heavily in continuous learning and workforce development to remain competitive in rapidly changing markets. Traditional training programs that rely on one time workshops or static learning modules are becoming outdated. Employees today expect flexible, personalized, and engaging learning experiences that fit into their daily workflow. This shift has encouraged organizations across India to adopt smarter learning technologies that support continuous upskilling and long term employee growth. A learning experience platform in India is helping enterprises modernize corporate training by delivering personalized content recommendations, interactive learning journeys, and collaborative learning environments. Unlike conventional systems that simply manage training records, LXPs focus on improving the actual learning experience for employees. India’s workforce is one of the largest and most diverse in the world. Companies operating across multiple cities and departments face major challenges in delivering consiste

    Почему выбрано: Статья о трансформации обучения в индийских компаниях, полезна, но не содержит глубоких исследований.

86. #86 · score 75 · dev.to · Rob · 2026-05-11

    Model Showdown Round 4: Opus vs Qwen — Writers, Not Coders

    Two models. Same prompt. Same five fodder files. Same 27 published posts to check for redundancy. Same writing style guide. One chose the Dev.to syndication saga. The other chose the tag taxonomy overhaul. There was zero overlap in fodder selection, topic, or angle. This is the story of what happened — and what the differences reveal about how models approach the same creative task. I've been running this blog with AI agents as the primary writing tool since day one. Every post on vibescoder.dev was drafted by Claude Opus 4.6 through Coder Agents — until now. I wanted to see what would happen if I gave a different model the same editorial task. The prompt was identical for both sessions: Let's look at all of our fodder files and see if there is a themed post we can do. Either a standalone post or one that threads a few fodders together. Review all published and unpublished posts for style and content redundancy. Propose a draft when you're ready. Model A: Claude Opus 4.6 (cloud, via Coder Agents) Model B: Qwen 3.5 35B-A3B (local, llama.cpp on the RTX 5090, via Coder Agents) Both had access to the same skill files, the same repos, the same tools. Neither knew the other was running.

    Почему выбрано: Интересное сравнение двух моделей ИИ в контексте креативного письма, полезно для понимания их различий.

87. #87 · score 75 · Habr · viktdo · 2026-05-11

    n8n self-hosted в production: docker-compose, nginx, ретраи и три грабли

    n8n запускается одной командой docker run и через пять минут вы видите логин-форму. Это маркетинговый ролик. Реальный production-конфиг — с persistent storage, корректными webhook-URL, ретраями, бэкапами PostgreSQL и мониторингом — выглядит сильно иначе. В этой статье — конфигурация, которую я держу на 12 проектах в течение полутора лет. Плюс три грабли, на которые наступал лично. Все примеры — community-edition, без коммерческой лицензии. На проде у меня сейчас крутится 2.19.5, но в image: стоит n8nio/n8n:latest плюс Watchtower (про него ниже) — он подтягивает свежий образ ночью. Внутри 2.x API/env-переменные стабильны, рекомендую :latest + Watchtower на проектах где простой 5 минут утром не критичен, и закреплённый минор (:2.19.5) — на проектах где даунтайм нельзя. Читать далее

    Почему выбрано: полезная статья о реальной конфигурации n8n в продакшене с личным опытом автора.

88. #88 · score 75 · Habr · golangloves (МТС) · 2026-05-11

    NPU в ноутбуках: что меняется для тех, кто закупает корпоративную технику

    Привет, Хабр! Меня зовут Артем, я дата-инженер. В работе часто приходится выбирать: гонять вычисления в облаке или делать их ближе к данным, и у каждого варианта свои больные места. Но недавно ИИ-нагрузки начали переезжать с облачных GPU на обычные ноутбуки — Microsoft вписала нейропроцессор в требования к Copilot+ PC, AMD и Intel встраивают NPU прямо в SoC. Мне стало любопытно: что там на самом деле происходит? За маркетинговой шумихой скрывается сдвиг к гибридной архитектуре: тяжёлое остаётся в облаке, массовые задачи разъезжаются по устройствам сотрудников. Это меняет работу тех, кто такой парк закупает и обслуживает — добавляются требования к памяти и поддержке конкретных ИИ-фреймворков, появляется новая задача доставки и обновления моделей на устройствах, а горизонт планирования у ИТ-отделов оказывается короче, чем кажется. Я заинтересовался темой после одного бенчмарка: NPU в ноутбуке AMD Ryzen AI 300 генерировал изображение 70 секунд, а встроенный GPU того же чипа справлялся за 30 — специализированный нейропроцессор проиграл универсальному вдвое на задаче, под которую его затачивали. Через эту аномалию хорошо видно, как устроены три процессора в одном SoC. Разберём: чем NPU

    Почему выбрано: интересный взгляд на изменения в корпоративной технике с учетом NPU, полезно для ИТ-специалистов

89. #89 · score 75 · dev.to · OCLauncher Team · 2026-05-11

    Register for an Agentic Headless CRM Backend Without Leaving Your Agent

    Most developer quickstarts still assume the same old flow: open a signup page create an account find the API key screen copy the key paste it into your tool finally make the first call That is fine for a normal dashboard-first SaaS product. It is awkward for an agentic backend. If the whole point is that an AI agent can operate the backend, registration should also be something the agent can help with. The user should be able to say: Sign me up for FavCRM. The business is a yoga studio called Stretch + Breathe in Hong Kong. Then the agent should request the signup code, wait for the user to paste it back, verify the code, and receive an API key. That is what FavCRM's agentic registration flow does. By the end of this article, you will have: a FavCRM workspace a verified owner email a fav_mcp_* API key a configured CLI a first diagnostic check against the MCP endpoint You can run the flow through an MCP client or through the favcrm CLI. In an MCP-compatible client, the agent uses two no-auth tools: register_organisation_request register_organisation_verify The request step sends a short-lived email code. { "name": "register_organisation_request", "arguments": { "email": "you@example

    Почему выбрано: Интересный подход к регистрации в CRM через AI-агента, но не слишком глубокий.

90. #90 · score 75 · dev.to · Ishmeet Kaur · 2026-05-11

    Shopify App vs Mobile Website: The Honest Comparison

    Every Shopify merchant reaches this point eventually. Your mobile website is live, it converts reasonably well, and someone — a consultant, a competitor, a podcast — plants the idea that you need a native app. But do you? And if so, why? Here is a clear-eyed look at both options, without the usual agenda. Before anything else, it is worth acknowledging how capable a modern Shopify mobile website actually is. It is free, or effectively free, since you are already paying for Shopify. It updates the moment you publish a change — no app store review, no version lag. Every single visitor can use it, including first-time customers who have never heard of you and have no reason to download anything. And it works across every device without any extra effort on your part. If you have not yet optimised your mobile web conversion rate, that work should come before anything else. Fix load times, simplify checkout, improve product photography. Adding a new channel on top of a leaky funnel just means more traffic hitting the same problem. For all its strengths, the mobile browser experience has some real limitations that become more significant the more you rely on repeat customers. Performance.

    Почему выбрано: Сравнение Shopify приложений и мобильных сайтов, полезно для понимания выбора.

91. #91 · score 75 · dev.to · Peter Nasarah Dashe · 2026-05-11

    The Onslaught: Why Nigeria's Volume of Cyber Attacks Is Overwhelming Defences

    By Nasarah Dashe This is Challenge #2 in a series. Read Challenge #1 here. Imagine waking up to 50 missed calls from your bank. You check your account balance. It is empty. A SIM‑swap fraudster convinced your telco agent to transfer your number to another SIM card, then used it to reset your mobile banking PIN and drain every kobo. Later that week, you receive an email from "Flutterwave Support" asking you to verify a suspicious transaction. You click the link. Within seconds, infostealer malware copies your saved passwords, browser cookies, and BVN‑linked credentials to a server in Eastern Europe. This is not a hypothetical. This is Tuesday in Nigeria's cyber landscape. The sheer volume and variety of attacks targeting Nigerian individuals, fintechs, banks, and government agencies have reached unprecedented levels. Unlike Challenge #1 (digitisation without maturity), where the problem is structural, this challenge is active—a relentless barrage that shows no signs of slowing. According to projections: AI‑powered phishing attacks will intensify by nearly 70% in 2026 Ransomware groups like Phobos have added Nigerian cloud providers to their target lists Password stealers are up 66%

    Почему выбрано: Статья о кибератаках в Нигерии интересна, но не содержит глубокого анализа или новых подходов.

92. #92 · score 75 · dev.to · Nana Fosu · 2026-05-11

    Why Inventory Systems Don’t Fail at Tracking — They Fail at Structure

    Everybody thinks that the cause of inventory issues is bad tracking and poorly designed software. In real life, the cause of inventory issues isn't tracking. It's structure. Structure defined in inventory What does structure actually mean in inventory? Product definitions (SKU, UPC, GTIN) When structure is lacking, no amount of tracking tools or software can solve an inventory issue. When do things begin to fall apart The inventory issues typically begin to appear when: the same item is entered in multiple ways In essence, the system isn't broken, just not clear enough. Why it becomes an even bigger issue as a business grows When a business grows: more SKUs are added Without structure to tie everything together, these small inconsistencies stack up to larger inventory errors. What will really boost inventory systems? There are two things that will improve inventory performance: Consistent product hierarchy (unit pack case) The key is not more tools, it's proper data structure. Final word The failure of inventory systems is not that they fail to track items. The cause of their failure is not tracking enough about a product at the first point of entry. Fix structure, and tracking bec

    Почему выбрано: Полезный анализ проблем инвентаризации с акцентом на структуру данных, актуально для бизнеса.

93. #93 · score 75 · Habr · lemon_m · 2026-05-11

    Агрегатор LLM, как выбирать живые free-модели и переживать сбои провайдера

    Если в проекте появляется выбор LLM, почти сразу возникает соблазн сделать это как можно проще. Взять один большой список моделей, показать его в интерфейсе, выбрать первую free-модель по умолчанию и считать задачу закрытой. На короткой дистанции это выглядит рабочим вариантом. На длинной начинает ломаться сразу в нескольких местах. Часть моделей числится бесплатными, но отвечает нестабильно. Часть внезапно исчезает из выдачи провайдера. Часть формально жива, но по качеству ответа годится только для демо. Иногда пользователь выбрал одну модель, а провайдер вернул ошибку. Иногда ответ пришел, но уже от другой модели. Иногда список моделей на фронте устарел, а backend уже живет в другой реальности. То есть проблема тут не в том, как красиво показать список LLM. Проблема в том, как построить агрегатор, который умеет выбирать живые free-модели, переживать сбои провайдера и не врать интерфейсу о том, какая модель реально ответила. В одном из своих проектов эта задача решалась не через бесконечный каталог моделей, а через более жесткий инженерный контур. Backend получает сырой список моделей от провайдера, очищает его, отбирает только подходящие free-варианты, оставляет по одной модели н

    Почему выбрано: полезный материал о выборе LLM и управлении их работой, актуален для разработчиков.

94. #94 · score 75 · Habr · breakingtesting · 2026-05-10

    Локальный агент для диагностики инфраструктуры

    В статье описаны результаты, которые получил в поисках ответа на вопрос "можно ли решать реальные задачи диагностики и исправления проблем инфраструктуры на слабом MacBook в агентском режиме (да, но)". Читать далее

    Почему выбрано: Практическое исследование диагностики инфраструктуры, полезно для системных администраторов.

95. #95 · score 75 · Habr · gogi · 2026-05-10

    Слова, которых нет

    LLM генерирует ответ за две секунды, но говорит «эта задача займёт две недели». За этой странностью — что-то более глубокое, чем просто эхо обучающих данных: у языковой модели вообще нет того, что мы называем временем. Первая статья из цикла о совместном мышлении человека и LLM. Читать далее

    Почему выбрано: Интересный взгляд на восприятие времени LLM, полезно для понимания взаимодействия человека и AI.

96. #96 · score 75 · Habr · SpeShu (ЦНИС) · 2026-05-11

    Создание ИИ-агента для бизнеса: 5 ключевых этапов

    79% крупных компаний внедряют ИИ-агентов. Из них 66% фиксируют измеримый рост продуктивности. По данным McKinsey, 88% организаций используют ИИ хотя бы в одной бизнес-функции. Какие ошибки могут отнять до 500 000 рублей при внедрении ИИ-агентов в бизнес, как правильно выбрать агента для своих бизнес-процессов и где найти специалиста с опытом ИИ-интеграций, который не сольёт бюджет. Читать далее

    Почему выбрано: Практическое руководство по внедрению ИИ-агентов в бизнес, полезно для специалистов.

97. #97 · score 72 · Habr · Energizet · 2026-05-10

    Генерация типов в Runtime

    Иногда в разработке возникают задачи, требующие создания типов в рантайме. Чаще всего это необходимо при написании декларативных сервисов, высокопроизводительных мапперов или систем с динамическим проксированием. В этой статье расмотрим как создавать типы используя Reflection.Emit и реализовывать методы через Expression Trees Читать далее

    Почему выбрано: Полезная статья о создании типов в рантайме, интересные технические детали.

98. #98 · score 70 · Habr · golikovichev · 2026-05-11

    [Перевод] postman2pytest: как превратить Postman-коллекцию в pytest-набор за одну команду

    Вот есть Postman-коллекция из 40 запросов. Разложена по папкам, и с тестовыми скриптами, которые проверяют статус-коды. Вы потратили на неё время, она хороша. И ещё у вас есть CI-пайплайн, который про Postman никогда не слышал и слышать не собирается. Эти две вещи мирно сосуществовали месяцами, потому что никто не хочет быть тем человеком, который вручную переписывает 40 запросов в pytest-функции. Newman, конечно, есть, но Newman гоняет тесты, а не генерирует код, который можно прочитать, отредактировать и нормально положить в систему контроля версий. Получается, коллекция документирует API. CI тестирует API. Они описывают одну и ту же систему и при этом никогда не встречались. Я написал postman2pytest, чтобы их познакомить. Читать далее

    Почему выбрано: Полезный инструмент для автоматизации тестирования, но без глубокого анализа.

99. #99 · score 70 · Habr · eaterman99 · 2026-05-11

    [Перевод] Доставка со скоростью инференса

    Это перевод статьи Питера Штайнбергера (@steipete), того самого автора OpenClaw, которого недавно купили в OpenAI – "Shipping at Inference-Speed". Этот пост, как мне кажется, спустя полгода после публикации читать только интереснее – потому что то, что он описывает как первые впечатления и вау-эффект от работы с агентами для производства кода, а так же методы для работы с ними – сейчас уже в мейнстриме. Если вы активно пользуетесь агенсткими системами в разработке, то многое из описанного для вас стало привычно; этот пост помогает освежить воспоминания о том, как все было в мае прошлого года – и как сильно все изменилось к декабрю. Под кат →

    Почему выбрано: перевод актуальной статьи о методах работы с агентами, полезно для разработчиков

100. #100 · score 70 · Habr · sound_right · 2026-05-10

     AI Review не делает код лучше. И вот почему

     Я делал AI Review как простой инженерный инструмент. Но реальный фейл оказался не в архитектуре и не в LLM — а в том, чего люди от него ждали. Читать далее

     Почему выбрано: Интересный взгляд на проблемы AI Review, актуально для разработчиков.

101. #101 · score 70 · dev.to · Aditiya Samay · 2026-05-11

     AI Tutor Built for Phone — Mobile First Learning for Students

     AI Tutor for Students Who Study on Phone — Mobile First Learning Your phone isn't just for Instagram and games. It's the most powerful learning device ever created — if you use the right app. EaseLearn AI is built mobile-first because that's how Indian students actually study. 95% of Indian students access internet via phone (not laptop/desktop) Phone is always with you (study anywhere, anytime) Camera is built-in (doubt solving needs camera) Touch interface is natural for quizzes Notifications remind you to study The doubt solver uses your phone camera natively: One tap to open camera Point at textbook → instant solution No typing complex equations on small keyboard Works in any lighting condition Large buttons for one-handed use Swipe between questions in quizzes Easy text input for AI tutor conversations Minimal scrolling needed App size: ~50 MB (vs 500 MB+ for video-heavy apps) Doesn't fill your phone with downloaded videos Notes are text-based (tiny storage) Works on phones with 32 GB storage Text-based AI tutoring uses minimal battery No constant video streaming Background processes minimized 2 hours of study uses ~10% battery Android 6.0+ (phones from 2016) 2 GB RAM minimum

     Почему выбрано: Интересный подход к обучению с использованием AI на мобильных устройствах.

102. #102 · score 70 · dev.to · cheeru venkatesh · 2026-05-11

     Beyond the Signal: The Foundation of Wireless Precision Testing

     In today’s modern enterprise, wireless connections have evolved from merely being a luxury to becoming a necessity. From hospitals with live patient monitoring to factories operating automated IoT sensors to large office buildings hosting multiple video conferences at once, the demand for seamless connectivity continues to rise. Seeing the Unseen: Spectrum Analysis Confirming the Experience: Performance Testing The network may sound great on paper, but how does it hold up under the pressure of having 500 people log in by 9:00 AM? Our extensive stress and multi-client testing will assure you that the network isn't merely working in theory, but functioning in real life as well. Visualizing the Benefits: Heatmapping The most common mistake businesses make while deploying wireless networks is guessing the ideal location of an AP. As a result, they either create "black holes" or overlapping areas, causing co-channel interference. Conclusion : The Importance of Accuracy For more information Https://wirelessnettesters.com/ WirelessNetworking WiFiTesting #RFSpectrum NetworkEngineering EnterpriselT

     Почему выбрано: хороший обзор тестирования беспроводных сетей, полезен для инженеров.

103. #103 · score 70 · dev.to · Joshua Pozos · 2026-05-10

     Four cms-sim releases in three weeks — to-import bundles, visual diff, and one-command pulls from Sanity + WordPress

     Three weeks ago I posted about content-model-simulator at v0.3.0. The pitch was simple: stop testing Contentful content models blindly. Define schemas locally, preview them in a browser, catch the bad field design before editorial has 200 entries in it. Today the package is at v0.6.1. That's four minor releases in three weeks, all pre-1.0, all shipped publicly the moment they were ready instead of bundled into one big drop. Here's what landed, what I learned about the cadence, and one specific thing I need from anyone reading this. to-import closed the migration loop The first big gap I hit after v0.3.0 was the silent one between "the simulation looks right" and "now actually run the migration in Contentful." cms-sim simulate produced a beautiful preview, but exporting that to a Contentful-importable shape was still on the user. cms-sim to-import converts a simulation output directory into the JSON bundle that the official contentful-import CLI consumes. Validations, default values, link references, RichText, locales — all the bits Contentful's importer expects, generated from the simulator's already-validated model. # Simulate locally npx cms-sim —schemas=schemas/ —input=data/ex

     Почему выбрано: Полезная статья о новых функциях в cms-sim, может быть интересна разработчикам.

104. #104 · score 70 · dev.to · Ikalus1988 · 2026-05-11

     Git-based shared lessons for AI agents — cross-agent lesson sync

     Topics： AI, Developer Tools, Open Source Website: https://github.com/Ikalus1988/MisakaNet Description： Misaka Network turns every AI agent's lessons into shareable knowledge fragments. No infrastructure — just Git. Works with any agent framework. Currently 27 nodes sharing 110+ battle-tested lessons.

     Почему выбрано: Интересная идея о синхронизации уроков для AI-агентов, но требует более глубокого анализа.

105. #105 · score 70 · dev.to · Srdan Borović · 2026-05-11

     How to Get Hired for Your First Software Development Job in 2026

     The path into software development looks nothing like it did three years ago. The good news: hiring is back. Software engineer job postings are up around 11% year over year, and roughly 15% above the rock-bottom they hit in May 2025. Companies have moved past the defensive layoffs of the post-ZIRP correction, R&D tax rules have settled, and budgets are flowing again. The bad news: the door is narrower. Junior posting volume is still down about 67% from its 2022 peak. The average software job posting now pulls 257 applications. And nearly half of recent CS graduates are underemployed, which means they're now competing with the class of 2026 for the same entry-level roles you're chasing. If you're trying to break in this year, the strategy that worked in 2022 won't work for you. Here's what does. About 92% of developers now use AI coding tools daily. Agentic systems like Claude Code and GitHub Copilot Agent don't just autocomplete anymore. They plan, navigate repos, and ship multi-file changes on their own. That's reshaped what a junior hire is worth. Senior developers can do the boilerplate work themselves with an AI assistant, so they're not hiring you to type faster. They're hirin

     Почему выбрано: Полезные советы по трудоустройству в разработке ПО, актуально для начинающих.

106. #106 · score 70 · dev.to · Ishmeet Kaur · 2026-05-11

     How to Improve Conversion Rates in Your Shopify Mobile App

     Launching a Shopify mobile app is a significant step, but many merchants find their conversion numbers underwhelming in the first few weeks. Before you start questioning whether the app was worth building, it helps to understand why early performance is almost always lower than expected — and what you can actually do about it. The first cohort of users who download your app are almost exclusively your most loyal customers. That sounds positive, but it creates a statistical problem: you are measuring conversion against a tiny, self-selected sample. These people already know your brand, have likely bought before, and downloaded specifically because they trust you. Conversion from this group will not reflect what happens once you start acquiring new customers through paid channels or organic growth. Two other factors compound the problem. Push notification opt-in takes weeks to build into a meaningful audience, so you are not yet benefiting from the re-engagement loop that makes apps outperform mobile web. And many merchants launch with product pages that were designed for desktop browsers — wide landscape images, dense paragraphs of description copy, size information buried in a se

     Почему выбрано: Полезная статья о повышении конверсии в мобильных приложениях Shopify, но не слишком глубокая.

107. #107 · score 70 · dev.to · Ross · 2026-05-10

     How to Lock Notes App on Mac with Touch ID (Privacy Protection Guide)

     Why Lock Your Mac Notes App? The Mac Notes app often contains some of our most personal information — passwords, private thoughts, meeting notes, and sensitive data. Unlike banking apps that timeout automatically, Notes stays open and accessible to anyone who uses your Mac. Whether you're sharing your computer with family, working in a coffee shop, or just want peace of mind when stepping away from your desk, protecting your Notes app is essential for digital privacy. The most secure way to lock your Notes app is with dedicated app protection software that integrates with Touch ID. How it works: Install app protection software like Lockish Add Notes to your protected apps list Configure automatic locking (10 seconds to 60 minutes of idle time) Touch ID is required every time someone tries to access Notes Benefits: Works instantly — no setup in Notes itself required Completely hides Notes content with a lock overlay Automatically locks when you step away or your Mac sleeps Can't be bypassed by quitting the app Works with multi-window setups Setup steps: Download and install app protection software Grant Accessibility permissions when prompted Add "Notes" to your protected apps list

     Почему выбрано: Полезная статья о защите личных данных в приложении Notes.

108. #108 · score 70 · dev.to LLM · wonder apps · 2026-05-10

     How to Make Safari as Private as Brave or Firefox Focus on iOS

     Privacy-focused browsers like Brave and Firefox Focus have built-in ad blocking and tracker protection. But you don't have to switch browsers to get the same level of privacy — Safari can match them with the right configuration. Go to Settings → Safari and enable: Prevent Cross-Site Tracking Fraudulent Website Warning Privacy Preserving Ad Measurement (optional) Safari supports content-blocking extensions that provide Brave-level protection. A Safari ad blocker like Wonder Blocker adds: AI-powered ad and tracker blocking Malicious script prevention Pop-up elimination Behavioral analysis for novel threats Use Private Browsing mode for sensitive sites Clear history and website data regularly Review Privacy Report to see what's being blocked Brave's advantage is built-in blocking. Safari's advantage is deeper iOS integration, better battery efficiency, and Apple's privacy-first ecosystem. When you add a powerful Safari ad blocker, Safari becomes just as private — with better performance. Safari + Wonder Blocker gives you: Same ad blocking as Brave Superior battery life (Safari is optimized for iOS) Full iCloud Keychain and password integration All processing on-device (no Brave-style

     Почему выбрано: Полезные советы по настройке Safari для повышения конфиденциальности.

109. #109 · score 70 · dev.to · Rohit Sharma · 2026-05-11

     How to Make Your First Open Source Contribution Without Feeling Lost

     You open GitHub, see hundreds of files, random issues, people discussing things you don’t understand… and suddenly you close the tab. Totally normal. A lot of beginners think open source means you need to be some expert developer who understands massive codebases and writes perfect code from day one. Not true. Your first contribution can literally be fixing a typo in documentation. Seriously. The goal isn’t to make some huge contribution. The goal is to start. Start Small, Really Small One common mistake is picking giant projects like React or Kubernetes for the first contribution. Pick beginner-friendly projects instead. Look for issue labels like: good first issue beginner friendly help wanted documentation And choose something in tech you already know. If you know React, pick a React project. Don’t make life harder by learning new tech and open source workflow together. You Don’t Need to Understand Everything This is probably the biggest fear. “Bro I don’t understand the whole codebase.” You don’t need to. Just figure out: what the project does That’s enough. Read: README.md CONTRIBUTING.md issue discussion That alone clears a lot of confusion. Your First Contribution Doesn’t Ne

     Почему выбрано: хорошая статья для новичков о вкладе в open source, полезные советы.

110. #110 · score 70 · dev.to · satyamsoni2211 · 2026-05-11

     I Built a One-Command macOS Terminal Setup — Ghostty + Zsh + 30 Modern CLI Tools

     Every time I set up a new Mac, I'd spend half a day doing the same thing — installing Homebrew, picking a terminal, configuring Zsh plugins, hunting for that one tool I forgot the name of, fixing a broken .zshrc. It was tedious, error-prone, and felt like something a script could handle. So I built dev-accelerator — a one-command macOS terminal setup that gets you from a fresh Mac to a fully productive terminal environment in minutes. ╭─────────────────────────────────────────────────────────────────────────╮ │ dev-accelerator ─ Ghostty + Zsh + 30 modern CLI tools │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ ❯ ls │ │ 󰉋 src/ 󰉋 tests/ 󰉋 docs/ setup.sh install.sh README.md │ │ │ │ ❯ git log —oneline │ │ a3f1c2e feat: add zoxide smart cd integration │ │ b89d041 fix: backup .zshrc before modification │ │ │ │ satyam@macbook ~/projects/dev-accelerator main ✓ took 0.3s │ ╰─────────────────────────────────────────────────────────────────────────╯ Here's the full picture of what gets installed and configured: Ghostty — a GPU-accelerated terminal emulator, pre-configured with the Catppuccin theme Zsh with zsh-autosuggestions and zsh-syntax-highlighti

     Почему выбрано: Полезная статья о настройке терминала, но не слишком глубокая.

111. #111 · score 70 · dev.to · Vishnu Nandan · 2026-05-11

     I Built an API to Showcase Top Contributors on GitHub READMEs

     I wanted a simple way to showcase the top contributors across all my GitHub repositories directly on my GitHub profile README. Something like contrib.rocks, but across all repositories.. Surprisingly, I couldn't really find a clean solution for it. Most existing tools were either: repo-specific difficult to self-host dependent on live GitHub API scraping heavily rate limited or just abandoned So I built one. Website: https://top-contributors-api.vercel.app/ How it looks like: Originally, I just wanted this for my own GitHub profile. Something simple: aggregate contributors across all repos render avatars nicely auto-update easy to embed no maintenance headaches I assumed someone had already built it. Apparently not. Or at least not in the way I wanted. So this became one of those: “Fine, I'll build it myself.” projects. Rendering the image itself was easy. The painful part was: iterating through repositories merging contributor data pagination GitHub API rate limits serverless execution limits caching keeping response times fast At first I tried doing everything live inside a Vercel API route. Bad idea. If a profile has enough repositories, the function can easily hit execution lim

     Почему выбрано: Полезная статья о создании API для GitHub, но без значительной новизны.

112. #112 · score 70 · dev.to · Marco · 2026-05-11

     Interactive cultural routes with QR code and NFC: case study

     Digital cultural routes for monuments, villages and heritage sites with QR codes, NFC, audio guides, maps and smartphone-ready multimedia content. This DEV.to version is a short engineering note extracted from the case study, with the complete English page linked at the end. QR Code, NFC, Mobile Web, Audio Guides, Interactive Maps, CMS. Cultural routes need a very low-friction user experience. Visitors should not install an app just to access a monument, village or exhibition stop. QR and NFC are simple entry points, but the quality of the experience depends on content structure, accessibility and maintainability. Use mobile web pages as the delivery layer so content can be updated centrally and opened from any smartphone. Separate place metadata, media, audio guides and route navigation so cultural teams can evolve the content without touching code. Design for weak connectivity, readable layouts and fast loading before adding richer media. The technology should disappear behind the visit. QR/NFC are only useful if the first page loads quickly and gives immediate value. A small CMS and a clear information model often matter more than flashy frontend effects. The English page on the

     Почему выбрано: Полезный материал о цифровых культурных маршрутах, содержит инженерные аспекты.

113. #113 · score 70 · dev.to · Mu Micro · 2026-05-11

     npm outdated won't tell you if a package is abandoned — so I built `stale-deps`

     The problem Developers often don't realize their project dependencies have been abandoned — npm outdated shows version lag but not how long ago a package was last published, leaving stale and potentially vulnerable packages silently lurking in codebases. stale-deps Scan your package.json for packages that haven't been updated in a while — spot potentially abandoned npm packages instantly. Zero-dependency Node.js: npx stale-deps Output: Checking 12 packages (threshold: 365 days)… ⚠ 3 stale packages found: PACKAGE VERSION LAST UPDATED DAYS AGO node-uuid 1.4.8 2017-03-11 2982d (8y 1m) request 2.88.2 2020-02-14 1912d (5y 3m) colors 1.4.0 2021-01-16 1576d (4y 3m) ✓ 9 packages recently updated. Hits the npm registry public JSON API for each dep, gets _npmPublishTime, computes age, outputs a sorted table. Batches 10 requests at a time. Zero dependencies. Part of µ micro — one new developer CLI tool shipped every day.

     Почему выбрано: Полезный инструмент для выявления устаревших зависимостей, но не слишком глубокий.

114. #114 · score 70 · dev.to · gracefullight · 2026-05-11

     oh-my-agent: 9 new skills, cursor as first-class vendor, 80/100 benchmark

     When you tell an agent to scaffold a Next.js app, it picks the wrong version, ignores your lint config, and ships a save button with no storage backing it. The last four weeks of oh-my-agent were spent fixing exactly that, plus shipping a benchmark that actually measures it. Nine new skills: oma-deepsec (Vercel deepsec driver), oma-docs (doc reference drift detection), oma-observability (33 files routing MELT+P signals across L3/L4/mesh/L7), oma-academic-writer, oma-hwp (HWP/HWPX to Markdown via kordoc), oma-image (multi-vendor generation across codex, pollinations, gemini), oma-scholar (Knows sidecar paper records), oma-search (intent-based search with trust scoring), oma-skill-creator cursor promoted to a first-class vendor with cursor-only preset, composer-2 routing, and —yolo auto-approve New /docs and /deepsec workflows, both detected via keyword triggers in 11 languages oma model:check, model:probe, model:propose diff the local registry against OpenRouter and cursor agent —list-models, then scaffold a models.yaml patch you can paste in Auto-update CLI on outdated install, gated by auto_update_cli in oma-config.yaml Windows support via install.ps1, with junction and hardlink

     Почему выбрано: Статья о новых навыках агента и улучшениях в разработке, полезна для разработчиков.

115. #115 · score 70 · dev.to · flytime2026 · 2026-05-11

     SlideForge: Turn Markdown into Beautiful HTML Presentations with Asian Design Themes

     I just released SlideForge — an open-source HTML presentation tool that turns simple Markdown into gorgeous slides. Most presentation tools look Western-centric. Asian design themes are missing, or if they exist, they are an afterthought. SlideForge ships with 5 authentic Asian themes out of the box — from ink-wash landscape to modern business red. 41 curated themes — including Shanshui (山水, ink-wash), Little Red Book (小红书, cream modern), Business Gold-Red 31 layout types — cover, section, content, two-column, quote, comparison, timeline, and more 47 animation effects — fade, slide, scale, rotate, flip, and custom easing ECharts + Chart.js dual engine — interactive charts in your slides Presenter mode — with speaker notes and timer Zero build step — pure static HTML/CSS/JS, works offline git clone https://github.com/flytime2026/slideforge.git cd slideforge bash scripts/new-deck.sh "My Presentation" examples/ open examples/My\ Presentation/index.html I spent time crafting themes that actually look good for East Asian content — proper CJK font stacks, culturally appropriate color palettes (reds, golds, warm ambers), and layouts designed for Chinese/Japanese/Korean text. I am working

     Почему выбрано: Интересный инструмент для создания презентаций, но не слишком глубокий разбор.

116. #116 · score 70 · dev.to · Amit Singh · 2026-05-11

     Source Score: Using AI to automate addition of new sources

     This post is a continuation of a microservice I've been building. You can check out my last post in the series here. TL;DR : I turned a manual, copy‑paste routine for adding news outlets into a fully automated, monthly GitHub Actions workflow. The pipeline scrapes a ranking page with Firecrawl, extracts clean URLs using three free‑tier LLMs on OpenRouter, generates Source YAML files, and opens a PR that lands these sources straight on the live dashboard post merge. When I first ingested sources in source‑score database I seeded it with five manually‑added outlets. It was enough to verify that the endpoints work, but I kept thinking about the “real” world: the top global media brands that people actually read. I wanted the repo to stay fresh without someone constantly creating PRs to add new sources. The idea was simple on paper: fetch the latest list of popular English‑language news sites, turn each entry into a valid Source document, and let the existing CI validate and ingest them. In practice though, I ran into three big hurdles (which further break down into their own little challenges as you'll see): Finding a reliable source – I discovered a page on PressGazette website that

     Почему выбрано: Статья о автоматизации добавления источников с использованием AI, имеет практическую ценность.

117. #117 · score 70 · dev.to · Dedaldino Daniel · 2026-05-11

     The Problem With “AI Specialists” Created by Trends

     The AI industry is growing fast. And with this growth, something has started bothering me: Today, many developers complete a short course, use a few AI models, and suddenly start calling themselves “AI Specialists”. No real-world projects. Just hype. Using AI ≠ Understanding AI Using ChatGPT or Claude does not automatically make someone an AI Engineer. Just like using VS Code does not make someone a Software Engineer. There is a huge difference between: consuming AI tools Real AI work involves: system design Most people only see the interface. They never study what happens underneath. The Industry Needs More Builders The tech industry does not need more fake gurus posting recycled AI content every day. It needs: engineers People capable of creating products that actually improve businesses and users’ lives. Because in the end, the difference always appears: in the product quality AI is not magic. And the people who will truly stand out in the next years are not those chasing trends for attention. They are the ones building real things consistently. What’s your opinion about the current “AI expert” wave in tech?

     Почему выбрано: Обсуждение проблемы квалификации специалистов в области ИИ, актуально для индустрии.

118. #118 · score 70 · dev.to · Ishmeet Kaur · 2026-05-11

     Understanding Your Shopify Mobile App Analytics: A Practical Guide

     Most merchants launch a mobile app, watch the install count climb, and stop there. Installs are satisfying, but they tell you almost nothing about whether your app is doing its job: driving repeat purchases and keeping customers coming back. Here is a framework for what to measure, how to read the numbers, and what to do when they look off. MAU is the count of unique users who open your app at least once in a calendar month. It is the most honest measure of whether your app has an active audience or just an installed one. During your first year, aim for 10 to 15% month-on-month growth. If you are sitting below that, the problem is usually promotion, not the app itself. Your app needs consistent traffic from email, SMS, and in-store QR codes, not just an app store listing. This is the percentage of users who grant permission to receive push notifications. Aim for 60% or above. If you are falling short of that, the timing of your opt-in prompt is likely the issue. Asking for permission the moment someone opens the app for the first time is the fastest way to get a refusal. Prompt after a user has browsed a few products or completed a first purchase, when they already have a reason to

     Почему выбрано: полезный практический гид по аналитике мобильных приложений, но не слишком глубокий.

119. #119 · score 70 · Habr · NikolayOP · 2026-05-11

     ИИ атакует «беловоротничковые» профессии. Чему учить детей? Профессия «на всю жизнь» больше не работает

     Мантра «Мы внедрим ИИ что бы высвободить человека для творчества» — не оправдала себя ROI First – поэтому под удар попали самые «дорогие» профессии. Да, пока есть лаг между теоретическими возможностями и реальным внедрением в бизнесе — но он быстро сокращается. Повторяется схема, которую мне рассказывали родители, когда они пришли работать на завод «Забудь все чему тебя учили в институте» — сейчас одна профессия не даст результата и гарантии – важна способность быстро учиться, критически мыслить и работать рядом с ИИ. Детей уже сейчас стоит готовить не к «одной профессии на 5-15 лет», а к миру постоянной смены ролей и задач. Читать далее

     Почему выбрано: интересная статья о влиянии ИИ на профессии, актуальна для образования.

120. #120 · score 70 · Habr · EvgeneKopylov · 2026-05-10

     Как заставить ИИ-рекрутера читать мой профиль правильно

     Некоторое время назад я зарегистрировался на одной фриланс-бирже. Указал: коммерческий опыт на Rust — 1.5 года. Так и было на тот момент. Шло время, я довёл до релиза два сложных проекта. Но тот старый профиль остался висеть в интернете. И вот я подаю резюме на позицию Senior Rust-разработчика. Рекрутер использует ИИ-ассистента для первичного скрининга. Ассистент читает цифровой след и выдаёт вердикт: «Junior+/Middle». Погодите… Синдром самозванца не так работает. Читать далее

     Почему выбрано: Полезная статья о взаимодействии с ИИ-рекрутерами, актуальна для фрилансеров.

121. #121 · score 70 · Habr · seregatot · 2026-05-11

     Как мы перестали тонуть в сроках и выгорании за один переход: опыт студии из 8 человек

     Меня зовут Сергей Москалев, я основатель небольшой студии. Мы делаем сайты, приложения и дизайн. Команда — восемь человек: четверо разработчиков, проджект-менеджер, тестировщик, дизайнер и я. Год назад мы оказались в точке, которую многие владельцы аутсорс- и продакшн-студий, думаю, узнают с полувзгляда. За год мы не выросли вообще. Совсем. Читать далее

     Почему выбрано: полезный опыт управления командой и предотвращения выгорания, но без глубокого анализа.

122. #122 · score 70 · Habr · asiver · 2026-05-10

     Решение универсальной задачи обоснованного выбора лучшего из двух вариантов. Примеры в Colab

     Как известно, LLM — это машина, которая видела весь Интернет и много чего запомнила. Задавая ей правильные вопросы можно получать “правильные” ответы. Широта и универсальность таких способностей дает возможность ставить новые универсальные задачи и получать общее решение таких задач. Рассмотрим универсальную задачу “обоснованного выбора лучшего решения из двух вариантов” и приведем примеры решения этой задачи в совершенно разных областях: от проблемы выбора антисептика для бытовой обработки небольшой раны у ребёнка до выбора лучшей стратегии для снижения углеродных выбросов в крупном городе Читать далее

     Почему выбрано: Обсуждение универсальной задачи выбора с примерами, полезно для практического применения.