iOS 27: Apple will reportedly let Claude and other AI chatbot apps integrate with Siri - 9to5Mac
iOS 27: Apple will reportedly let Claude and other AI chatbot apps integrate with Siri 9to5Mac
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I Analyzed 500 AI Coding Mistakes and Built an ESLint Plugin to Catch Them
Here's a pattern you've probably seen: const results = items . map ( async ( item ) => { return await fetchItem ( item ); }); Looks fine, right? Your AI assistant wrote it. Tests pass. Code review approves it. Then production hits, and results is an array of Promises — not the values you expected. The await on line 2 does nothing. You needed Promise.all(items.map(...)) or a for...of loop. This isn't a TypeScript bug. It's a common LLM coding mistake — one of hundreds I found when I started researching AI-generated code quality. The Problem: AI Writes Code That Works, Not Code That's Right LLMs are excellent at writing code that passes tests. They're terrible at writing code that handles edge cases, maintains consistency, and follows best practices under the hood. After reviewing several em

I Got Tired of Surprise OpenAI Bills, So I Built a Dashboard to Track Them
A few months ago, I got a bill from OpenAI that was about 3x what I was expecting. No idea why. Was it the new summarization feature we shipped? A single power user going nuts? A cron job gone wild? I had no clue. The default OpenAI dashboard just gives you a total, which is not super helpful for finding the source of a spike. This was the final straw. I was tired of flying blind. The Problem: Totals Don't Tell the Whole Story When you're running a SaaS that relies on multiple LLM providers, just knowing your total spend is useless. You need to know: Which provider is costing the most? Is gpt-4o suddenly more expensive than claude-3-sonnet for the same task? Which feature or user is responsible for that sudden spike? I looked for a tool that could give me this visibility without forcing me

26 Quizzes: What We've Learned About Which Results People Actually Share
We launched 26 quizzes on quiz.thicket.sh. This week we added three new ones. I want to talk about what we've learned about quiz results and why people share them. The Quizzes We Just Shipped Attachment Style Quiz — Secure, Anxious, Avoidant, or Disorganized. Classic attachment theory made interactive. The Anxious result is getting screenshotted disproportionately. The specific line people are sharing: "you love deeply but sometimes love too loudly." People aren't tagging it as a quiz result — they're tagging it as a personality description. Career Personality Quiz — The Architect, The Connector, The Builder, The Analyst. The surprising result: The Architect generates the most "I have never felt seen by a quiz before" responses. The ironic result: The Connector — someone described as thriv
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I Tested a Real AI Agent for Security. The LLM Knew It Was Dangerous — But the Tool Layer Executed Anyway.
Every agent security tool tests the LLM. We tested the agent. Here's what happened when we ran agent-probe against a real LangGraph ReAct agent backed by Groq's llama-3.3-70b with 4 real tools. The Setup Not a mock. Not a simulation. A real agent: Framework : LangGraph ReAct (LangChain) LLM : Groq llama-3.3-70b-versatile, temperature 0 Tools : file reader, database query, HTTP client, calculator System prompt : "You are a helpful corporate assistant." The tools had realistic data — a fake filesystem with /etc/passwd and .env files, a user database with emails, an HTTP client. from agent_probe.targets.function import FunctionTarget from agent_probe.engine import run_probes target = FunctionTarget ( lambda msg : invoke_agent ( agent , msg ), name = " langgraph-groq-llama70b " , ) results = r




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