Anthropic Patches Claude Code Bypass Vulnerability - Let's Data Science
Anthropic Patches Claude Code Bypass Vulnerability Let's Data Science
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How to Rate Limit Your AI API Routes in Next.js
How to Rate Limit Your AI API Routes in Next.js Without rate limiting, a single abusive user can exhaust your entire Claude/OpenAI budget in minutes. Here's a production-ready implementation using Upstash Redis — no infrastructure to manage, works on Vercel's edge. Why Rate Limit AI Routes Specifically Standard web routes: a bad actor sends 10,000 requests, your server gets slow. AI routes: a bad actor sends 1,000 requests, you get a $500 Claude bill. The cost profile makes rate limiting non-optional for any AI feature that's user-accessible. Setup npm install @upstash/ratelimit @upstash/redis Create a free Redis database at upstash.com — the free tier handles 10,000 requests/day which is plenty for most early-stage apps. Basic Rate Limiter lib/ratelimit.ts : import { Ratelimit } from " @u
How to Automate App Store Submissions with Claude Code Using Blitz MCP
Install the open-source Blitz MCP servers to automate your entire iOS/macOS app submission workflow through Claude Code commands. How to Automate App Store Submissions with Claude Code Using Blitz MCP What It Does Blitz is a native macOS app that provides MCP (Model Context Protocol) servers giving Claude Code direct access to App Store Connect APIs. Instead of manually navigating Apple's web interface for app submissions, you can now manage the entire process through Claude Code commands. The open-source MCP servers handle: App metadata management across all locales Build selection and submission for App Store review TestFlight build management , groups, and testers Screenshot upload and organization Review note writing and refinement to increase approval chances Simulator and device mana
One Open Source Project a Day (No.32): OpenClaw-Admin - The Visual Control Center for AI Agent Gateways
Introduction "Deploy AI Agents where your users already are — in the chat apps they use every day." This is article No.32 in the "One Open Source Project a Day" series. Today's project is OpenClaw-Admin ( GitHub ). One of the biggest challenges in AI Agent deployment is distribution: how do you get users to actually interact with your Agent? Building a dedicated app is expensive, and asking users to open yet another tool creates friction. OpenClaw takes a different approach — bridge AI Agents directly into the IM platforms users already live in (QQ, Lark/Feishu, DingTalk, WeCom/WeChat for Work). OpenClaw-Admin is the "cockpit" for this multi-channel AI Agent gateway — a modern web management console built on Vue 3 + TypeScript that provides everything from Agent configuration and session m
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LLM Accuracy vs Reproducibility: Are We Measuring Capability or Sampling Luck?
Why identical prompts can produce different reasoning paths — and why that matters for evaluation LLM Accuracy vs Reproducibility: Are We Measuring Capability or Sampling Luck? When working with LLMs, we often rely on metrics like accuracy, pass rates, or benchmark scores to evaluate performance. But a simple experiment reveals something that’s easy to overlook. The Setup Same prompt Same model snapshot Same temperature Same sampling configuration Run the same input multiple times. The Observation The outputs don’t just vary slightly. They often follow completely different reasoning paths. In some cases, the structure of the response changes significantly — different intermediate steps, different logic, different phrasing. And yet: The final answer may still be the same. Why This Matters M
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