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Your AI API bill can double overnight and you won't know until the invoice. Here's a 20-line fix.

I run a swarm of AI agents that call LLM APIs all day. One night a retry loop went haywire and burned through 3 days of budget in two hours. I didn't find out until the invoice email the next morning.

  • Most people discover API spend increase only after receiving the invoice.
  • Reporter experienced a bug causing AI agents to exhaust budget in two hours.
  • Python script alerts via Slack or Discord when spending exceeds configured threshold.

🌱 Touch Grass API: AI-Powered Missions to Get You Outside

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass What if AI could help you spend less time looking at a screen and more time enjoying the world outside?

  • Touch Grass API uses AI to inspire outdoor activities
  • Java-based project with Spring Boot and Ollama integration
  • Encourages users to disconnect from screens and spend time outdoors

The Runtime IS the Benchmark: Endurance Metrics for Local Agents

What I Benchmarked I tested whether the runtime around the model holds up: retrieval endurance, memory scaling, sandbox containment, context behavior.

  • Runtime comparison between local and cloud models revealed memory-based hang threshold.
  • Partitioning context into 80 stores improved performance by 3.1 times over monolithic context.
  • Runtime is the key benchmark, emphasizing minimal notebook setups for optimal performance.

Is Your Phone's LLM 37x Too Slow? Check This One Compiler Flag

Is Your Phone's LLM 37x Too Slow? Check This One Compiler Flag Part 3 of a series. Part 1: found the missing -march (2.5x faster) .

  • Debug Gradle variant uses -O0 optimization level, causing 37x slower performance
  • Generation speed drops from 18.5 tokens/second on release to 0.5 on debug
  • Changing build type to Release resolves significant performance improvement

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