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135,000 OpenClaw Users Just Got a 50x Price Hike. Anthropic Says It's 'Unsustainable.'
Originally published at news.skila.ai A single OpenClaw session can burn through $1,000 to $5,000 in compute. Anthropic was eating that cost on a $200/month Max plan. As of April 4, 2026 at 12pm PT, that arrangement is dead. More than 135,000 OpenClaw instances were running when Anthropic flipped the switch. Claude Pro ($20/month) and Max ($200/month) subscribers can no longer route their flat-rate plans through OpenClaw or any third-party agentic tool. The affected users now face cost increases of up to 50 times what they were paying. This is the biggest pricing disruption in the AI developer tool space since OpenAI killed free API access in 2023. And the ripple effects reach far beyond Anthropic's customer base. What Actually Happened (and Why) Boris Cherny, Head of Claude Code at Anthro
Gemma 4 Complete Guide: Architecture, Models, and Deployment in 2026
Google DeepMind released Gemma 4 on April 3, 2026 under Apache 2.0 — a significant licensing shift from previous Gemma releases that makes it genuinely usable for commercial products without legal ambiguity. This guide covers the full model family, architecture decisions worth understanding, and practical deployment paths across cloud, local, and mobile. The Four Models and When to Use Each Gemma 4 ships in four sizes with meaningfully different architectures: Model Params Active Architecture VRAM (4-bit) Target E2B ~2.3B all Dense + PLE ~2GB Mobile / edge E4B ~4.5B all Dense + PLE ~3.6GB Laptop / tablet 26B A4B 25.2B 3.8B MoE ~16GB Consumer GPU 31B 30.7B all Dense ~18GB Workstation The E2B result is the most surprising: multiple community benchmarks confirm it outperforms Gemma 3 27B on s
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Один промпт заменил мне 3 часа дебага в день
Вечерами, когда большинство уже отдыхает, я зависаю в своём офисе и ковыряюсь с кодом. Тот 14 августа, в 21:45, не был исключением. Я опять сидел над этой задачей, которая съедала по три часа каждый день. Почему это была боль Всё началось с простого: проект на Python, который выглядел как очередное рутинное задание. Однако вычисления упорно выдавали ошибочные результаты. Три дня подряд я безуспешно искал причину. Как обычно, приходилось проверять каждую строчку, каждую переменную. Это было настоящим адом. Для фрилансера с жесткими сроками это катастрофа - теряешь время, не зарабатываешь, а заказчик ждёт. Я собрал промпты по этой теме в PDF. Забери бесплатно: https://t.me/airozov_bot Как я нашёл решение Тогда я решил попробовать ChatGPT, хотя и не особо верил в его чудеса. Вбил проблему в п
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