OpenCompress
OpenCompress is an open source AI optimization layer designed to reduce the cost, latency, and token usage of large language model interactions by compressing both input prompts and generated outputs without significantly affecting quality. It works as a drop-in middleware that sits in front of any LLM provider, allowing developers to use models like GPT, Claude, Gemini, and others while automatically optimizing every request behind the scenes. It focuses on reducing token waste through a multi-stage pipeline that includes techniques such as code minification, dictionary aliasing, and structured compression of repeated content, enabling more efficient use of context windows and lowering computational overhead. It is model-agnostic and integrates seamlessly with any provider that supports an OpenAI-compatible API, meaning developers can adopt it without changing their existing workflows or infrastructure.
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ArchiverFS
ArchiverFS is a lightweight file archiving solution for servers and network storage that lets you use any NAS, SAN, or cloud platform as second-tier storage. With no databases or proprietary formats, it runs on pure NTFS from start to finish. Old, unused, or unstructured files can be moved in bulk from expensive primary storage to cheaper secondary devices while preserving directory structures, attributes, and permissions. If it can be formatted with NTFS and shared via a UNC path, ArchiverFS can use it. Features include support for cloud, DFS, replication, de-duplication, and compression. Optional link stubs (including seamless symbolic links) can be left in place of moved files, so users see them exactly as before. By reclaiming valuable space on primary storage without adding complexity, ArchiverFS helps organizations reduce costs, improve performance, and manage file growth with complete transparency.
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PromptUnit
PromptUnit is an AI inference proxy that reduces AI costs automatically by sitting between an app and its AI providers with no code changes required. Teams swap the base URL, keep the same SDK, endpoints, response parsing, and error handling, then PromptUnit handles routing, failover, cost tracking, and quality validation. It logs every API call by model, feature, user segment, token count, latency, and cost, giving real-time visibility into where AI spend is going before any routing changes go live. In observation mode, PromptUnit watches traffic, shadow-classifies requests, forecasts savings, and explains routing decisions so teams can see exact savings before enabling live routing. Once enabled, Smart Routing uses task classification to route each request to the cheapest model that clears the configured quality bar. PromptUnit also includes prompt compression, token inflation defense, prompt efficiency scoring, semantic request caching, and multi-model consensus.
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condense.chat
condense.chat is an LLM input compression API and drop-in proxy that shrinks prompts, retrieved documents, tool outputs, and repeated agent context before they hit upstream models. Less context, same Claude Code; its harness intercepts an agent’s growing session history and passes it through compression models before it reaches the main model, helping long-running coding agents start each next turn with fewer tokens. Condense sits between an app and the upstream LLM provider, tracks the conversation as a content-addressed chain, and transparently compresses repeated context on the way upstream. Developers can point their SDK at the Condense provider route, add a Condense key, keep their existing provider key, and change nothing else. It supports Anthropic and OpenAI-compatible routes, plus pass-through behavior for other provider paths such as model lists and embeddings.
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