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I love making diagrams to help understand, plan, etc. However, the options are (A) auto-placement languages like Mermaid or Graphviz (which don&#x27;t let me decide how the diagram looks), or (B) software like Draw.io which are powerful but are very time consuming (and inefficient for agents to manipulate).I wanted to have the benefits of both, where you can define a diagram in a diagram language, but also retain a high degree of control over what the diagram looks like.I also wanted this to work well for humans and agents.On the Github link, there&#x27;s a playground where you can try it out without installation. There&#x27;s also instructions for a simple npm install and for installing a skill you can use with Claude or other agents.

Show HN: Reladraw – A diagram language where you decide where to place things

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We found an approach to get Jev-like properties from standard LLMs like GLM-5.3-Flash.The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against Jev and Laya. We find that our setup is on-par with Jev in terms of accuracy and speed and that it substantially outperforms Laya.Still, in terms of costs per decision, Jev is several x better than our setup. In turn, our setup supports vision inputs.

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