The Twigg team launched a stateful LLM API that stores and assembles conversation context so developers don't rebuild the layer themselves. Developers create a chat, receive an ID, and send new prompts or tool results plus that ID; the API appends messages and assembles history. Switching model providers means changing one field in the request, avoiding vendor lock-in. The product grew out of a year of building internal AI workspaces where the same context infrastructure was repeatedly rebuilt.
A developer released ContextMemory, an open-source proxy that sits between any Ollama-compatible chat client and the LLM, adding per-session markdown memory and optional tools without changing the client code. It works by swapping the chat URL to point at ContextMemory instead of raw Ollama.
A developer released ContextMemory, an open-source proxy that sits between any Ollama-compatible chat client and the LLM, adding per-session markdown memory and optional tools without changing the client code. It works by swapping the chat URL to point at ContextMemory instead of raw Ollama.
Jylus is a backend service for developers building AI apps: send it your application's records, ask what context a question needs, and it returns a compact evidence pack with references to source records. Developers pass that pack into their existing model calls. It removes the need to build the retrieval/context-assembly layer (e.g. RAG plumbing) yourself.
Jylus is a backend service for developers building AI apps: send it your application's records, ask what context a question needs, and it returns a compact evidence pack with references to source records. Developers pass that pack into their existing model calls. It removes the need to build the retrieval/context-assembly layer (e.g. RAG plumbing) yourself.
A self-built AI gateway that manages prompts in production, allowing easy switching between models and providers while enforcing structured output. It solves the pain of prompt management, provider migration, and output validation for teams using multiple LLMs.
A self-built AI gateway that manages prompts in production, allowing easy switching between models and providers while enforcing structured output. It solves the pain of prompt management, provider migration, and output validation for teams using multiple LLMs.
A developer is seeking feedback from AI developers on a product idea: a single API for building production-ready AI assistants, aiming to solve common backend problems like chat session storage, agent memory, file attachments, and context assembly. The post is an early-stage call for input, not a product launch.
A developer is seeking feedback from AI developers on a product idea: a single API for building production-ready AI assistants, aiming to solve common backend problems like chat session storage, agent memory, file attachments, and context assembly. The post is an early-stage call for input, not a product launch.
Inkfold is a workspace that lets users interact with multiple AI providers (e.g., OpenAI, Anthropic) through a unified interface, with a shared memory system that persists context across sessions. It solves the problem of managing separate chat histories and contexts when using different AI models, providing a seamless experience for power users and developers.
Inkfold is a workspace that lets users interact with multiple AI providers (e.g., OpenAI, Anthropic) through a unified interface, with a shared memory system that persists context across sessions. It solves the problem of managing separate chat histories and contexts when using different AI models, providing a seamless experience for power users and developers.