
Dragonfly serves as a seamless substitute for Redis, offering enhanced performance while reducing costs. It is specifically engineered to harness the capabilities of contemporary cloud infrastructure, catering to the data requirements of today’s applications, thereby liberating developers from the constraints posed by conventional in-memory data solutions. Legacy software cannot fully exploit the advantages of modern cloud technology. With its optimization for cloud environments, Dragonfly achieves an impressive 25 times more throughput and reduces snapshotting latency by 12 times compared to older in-memory data solutions like Redis, making it easier to provide the immediate responses that users demand. The traditional single-threaded architecture of Redis leads to high expenses when scaling workloads. In contrast, Dragonfly is significantly more efficient in both computation and memory usage, potentially reducing infrastructure expenses by up to 80%. Initially, Dragonfly scales vertically, only transitioning to clustering when absolutely necessary at a very high scale, which simplifies the operational framework and enhances system reliability. Consequently, developers can focus more on innovation rather than infrastructure management.
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TinyPNG (by Tinify) is a free image optimization service built for developers and designers. It utilizes smart lossy compression to reduce the file sizes of JPEG, PNG, WebP, and AVIF files by up to 80% with no visible quality loss. That means faster load times, better SEO, and lower bandwidth.
You can compress, convert, and resize images via a clean web interface or integrate it into your workflow with the API. The platform also provides an image CDN for fast global delivery of optimized assets. SDKs are available for Python, Node.js, PHP, Java, Ruby, and .NET. WordPress plugin included, plus plenty of community-driven integrations.
No tuning, no noise, Tinify just works. Whether you're optimizing a handful of images or processing millions, it scales effortlessly. All plans include a generous free tier, and support is quick when you need it.
George the panda 🐼 approves.
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Velokey
Velokey is an AI model access platform that lets developers call text, image, and video models through one reliable API. The platform is designed for teams that want to experiment with, compare, and switch between leading AI models without rebuilding their application integrations. Velokey supports an OpenAI-compatible workflow, so existing SDK users can migrate by updating the base URL, adding a Velokey API key, and choosing a model ID. Developers can access LLMs, image generation models, and video generation models from one account and interface. Supported model families include GPT, Claude, Gemini, DeepSeek, Grok, Kimi, Qwen, MiniMax, GLM, ERNIE, Seedance, Kling, Veo, Wan, PixVerse, GPT Image, Nano Banana, Seedream, and others. Velokey helps teams compare models by capability, context, speed, billing unit, and price before adding them to production workflows. The platform includes smart model routing that can send requests to faster or more stable endpoints when available. Automatic failover helps move failed requests to a healthy fallback route when multiple providers are supported. With one console for request status, token usage, latency, errors, spend, and usage-based metering, Velokey gives developers a simpler way to build across the AI model ecosystem.
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condense.chat
Condense.chat is an innovative API designed for compressing input for language models, functioning as a drop-in proxy that effectively reduces the size of prompts, retrieved documents, tool outputs, and recurring agent contexts prior to reaching the main models. By minimizing context while maintaining the integrity of Claude Code, it intercepts an agent's expanding session history and processes it through compression models, enabling long-running coding agents to operate with fewer tokens at the start of each new turn. Acting as an intermediary between applications and upstream LLM providers, Condense meticulously tracks conversations as a content-addressed chain, seamlessly compressing any repeated context along the way. Developers can easily integrate this system by directing their SDK to the Condense provider route, adding a Condense key, and retaining their existing provider key without needing to make any additional changes. Compatibly, it supports routes for both Anthropic and OpenAI, and also offers pass-through functionalities for other provider pathways, including model lists and embeddings, ensuring a versatile integration. This makes it an invaluable tool for optimizing interactions with language models while enhancing overall efficiency in processing and managing session data.
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