Kubernetes migration almost killed our startup. Where we were: - 8 EC2 instances - Ansible for deploys - Boring but working - $1200/month AWS bill Why we migrated: - New investor wanted 'cloud-native' - Engineers wanted K8s experience - Competitors were using it - Seemed like the future 6 months later: - 3 engineers spending full-time on K8s - AWS bill at $4500/month - Deploys took longer than before - More outages, not fewer - Product development stalled We rolled back: - Moved to ECS Fargate - 2 week migration - Back to $1800/month - Engineers back on features K8s is amazing for scale. We weren't at scale. Technology should solve problems you actually have.

🇨🇳 Alibaba-backed Moonshot releases its second AI update in four months as China’s AI race heats up. Head to head with GPT-5, Sonnet 4.5, Gemini 2.5 Pro, and Grok 4, while being 6x cheaper. On some benchmarks, the model now outperforms OpenAI’s GPT-5, Anthropic’s Claude Sonnet 4.5 (Thinking mode), and xAI's Grok-4 on several standard evaluations — an inflection point for the competitiveness of open AI systems. Kimi K2 Thinking is an open reasoning model that brings frontier level agent behavior to everyone, with 44.9% HLE (Humanity’s Last Exam), 60.2% BrowseComp, 256K context, and 200-300 sequential tool calls that enable strong reasoning, search, and coding. K2 Thinking uses a Reasoning MoE design with 1T total parameters and 32B active per token, so it scales capacity while keeping each step’s compute manageable. The system is built for test time scaling where it spends more thinking tokens and more tool call turns on hard problems, which lets it plan, verify, and revise over long chains without help. Interleaved thinking means it inserts private reasoning between actions and tools, so it can read, think, call a tool, think again, and keep context across hundreds of steps. Tool calls here are structured functions for search, code execution, or other services, and the model chains them to gather facts, run code, and use results in the next decision. The 256K context window lets it load long documents, extended chats, or many tool outputs at once, then focus attention on the right spans as the plan evolves. Serving is optimized with INT4 QAT (Quantization aware training) on the MoE parts, which yields roughly 2x faster generation while preserving accuracy, and the reported scores are under native INT4 inference. With QAT the model learns during post training to live with 4bit weights, which reduces the usual accuracy loss seen with after the fact quantization.

Huge congrats to @Kimi_Moonshot for releasing Kimi-K2-Thinking, and we've just integrated it into @YouWareAI. The results are impressive. Love seeing open-source models hitting SOTA levels.

🚀 Hello, Kimi K2 Thinking! The Open-Source Thinking Agent Model is here. 🔹 SOTA on HLE (44.9%) and BrowseComp (60.2%) 🔹 Executes up to 200 – 300 sequential tool calls without human interference 🔹 Excels in reasoning, agentic search, and coding 🔹 256K context window Built as a thinking agent, K2 Thinking marks our latest efforts in test-time scaling — scaling both thinking tokens and tool-calling turns. K2 Thinking is now live on kimi.com in chat mode, with full agentic mode coming soon. It is also accessible via API. 🔌 API is live: platform.moonshot.ai 🔗 Tech blog: moonshotai.github.io/Kimi-K2… 🔗 Weights & code: huggingface.co/moonshotai

Kubernetes migration almost killed our startup. Where we were: - 8 EC2 instances - Ansible for deploys - Boring but working - $1200/month AWS bill Why we migrated: - New investor wanted 'cloud-native' - Engineers wanted K8s experience - Competitors were using it - Seemed like the future 6 months later: - 3 engineers spending full-time on K8s - AWS bill at $4500/month - Deploys took longer than before - More outages, not fewer - Product development stalled We rolled back: - Moved to ECS Fargate - 2 week migration - Back to $1800/month - Engineers back on features K8s is amazing for scale. We weren't at scale. Technology should solve problems you actually have.

You can now provision dedicated PgBouncers for your Postgres replicas. This gives you a connection pool that evenly distributes connections across your available replicas, making it simple to scale out read-only traffic.

how can i short OpenAI?

Lately I’ve been testing both CLI and MCP in Claude Code... and honestly, the CLI wins I list all my installed tools (vercel, supabase, gh, etc.) in CLAUDE .md and let Claude use them to check logs, run queries, or tweak configs. Setup is simpler, auth is native, and observability is crystal clear. There’s still debate on CLI vs MCP, but in my workflow, CLI is miles ahead