HTTP/1.1 must die: the desync endgame
23 by sprawl_ | 2 comments on Hacker News.
Friday, August 15, 2025
Thursday, August 14, 2025
Wednesday, August 13, 2025
Tuesday, August 12, 2025
Monday, August 11, 2025
Sunday, August 10, 2025
New top story on Hacker News: Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching
Show HN: Llmswap – Python package to reduce LLM API costs by 50-90% with caching
8 by sreenathmenon | 1 comments on Hacker News.
I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development. It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%. Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables from llmswap import LLMClient client = LLMClient(cache_enabled=True) response = client.query("Explain quantum computing") # Second identical query returns from cache instantly (free) The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications. Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development. GitHub: https://ift.tt/AGBkUiI PyPI: https://ift.tt/IhBwHSL
8 by sreenathmenon | 1 comments on Hacker News.
I built llmswap to solve a problem I kept hitting in hackathons - burning through API credits while testing the same prompts repeatedly during development. It's a simple Python package that provides a unified interface for OpenAI, Anthropic, Google Gemini, and local models (Ollama), with built-in response caching that can cut API costs by 50-90%. Key features: - Intelligent caching with TTL and memory limits - Context-aware caching for multi-user apps - Auto-fallback between providers when one fails - Zero configuration - works with environment variables from llmswap import LLMClient client = LLMClient(cache_enabled=True) response = client.query("Explain quantum computing") # Second identical query returns from cache instantly (free) The caching is disabled by default for security. When enabled, it's thread-safe and includes context isolation for multi-user applications. Built this from components of a hackathon project. Already at 2.2k downloads on PyPI. Hope it helps others save on API costs during development. GitHub: https://ift.tt/AGBkUiI PyPI: https://ift.tt/IhBwHSL