AIsbf 0.9.8 adds caching, routing improvements, and expanded AI service support
AIsbf (AI Should Be Free) 0.9.8 is an API proxy/router that provides an OpenAI-compatible interface to various AI endpoint services, aiming to make LLM usage more cost-effective. It's multiuser and can scale from small setups to large infrastructure.
Key features in version 0.9.8
- Cache support for Redis, SQLite, MySQL, and file-based storage
- Additional context condensation methods
- Native prompt caching and request caching support
- Faster and improved semantic prompt-based routing for automatic service selection
- Full OAuth2 support for Claude.ai subscribers
- Full OAuth2 support for Amazon Kiro-cli subscribers
- Full OAuth2 support for OpenAI Codex subscribers
- Full support for Kilo.ai subscribers using tokens or OAuth2
- Multiple bug fixes and minor feature additions
This type of tool is useful for developers who work with multiple AI services and want a unified interface while optimizing costs through intelligent routing and caching.
📖 Read the full source: r/LocalLLaMA
👀 See Also
HuggingFace Agent Skills: Standardized AI Task Definitions for Coding Agents
HuggingFace Skills are self-contained folders with YAML frontmatter and guidance for AI agents to perform specific ML tasks like dataset creation, model training, and evaluation. They're interoperable with OpenAI Codex, Anthropic's Claude Code, Google Gemini CLI, and Cursor.
Self-updating translation system for OpenClaw maintains domain glossaries automatically
A Python script wraps the Kimi2.5 API to translate .srt files while preserving block indices, timestamps, and segmentation. The system uses project profiles with glossary.json, style.md, and memory.jsonl files, and includes a cron job that scans official sources every 6 hours to update terminology.
AIttache: A Read-Only MCP Server That Can't Nuke Your Prod
AIttache is an MCP server with 25+ read-only connectors (terminal, servers, weather, Steam) that physically cannot modify anything — built to give LLMs log context without autonomy.
Persistent Memory for Claude: Local Stack with MCP, 39ms Retrieval, 82% Token Reduction
A developer built a persistent memory layer for Claude using local vector search (Qdrant + Qwen3) and MCP integration, achieving 82% token reduction, 39ms hot-path retrieval, and session crystallization via L4 nodes.