PromptMan
OnPremise Prompt management for LLMs
What it does
PromptMan designed from the ground up as a REST API‑first prompt registry. PromptMan provides: - Full CRUD for prompts - Versioning, tagging, collections - Authentication and authorization - RBAC - Read‑only roles - Multiple database backends - Performance benchmarks for different DB engines - Stateless architecture - Horizontal scaling - 100% On‑Premise
Does the same job
all alternatives →- PEPrompt Engine – Auto pick LLMs based on your prompts2024 · jigsawstack.com · ▲93
Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…
- APAutomatic prompt optimizer for LLMs2023 · promptperfect.jina.ai · ▲126
- PPPromptly – Prompt Management Platform for LLM Apps2023 · trypromptly.com · ▲15
We are excited to show Promptly (https://trypromptly.com), a prompt management platform for LLM apps that makes it easy to experiment, share and manage prompts in production. With Promptly, users can: - Try out different prompts and model parameters for various providers - Quickly share prompt snippets together with parameters and generated output. Think of it as CodePen or JSFiddle for prompts - Create high level endpoints on top of provider APIs (Open AI, DreamStudio etc) with templated and versioned prompts - Use built-in caching for endpoints that will help save on Open AI…
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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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