Viewing profile — songrenchu

We added 4 things to remediate lowest common denominator. The first is task interface, it is portable cross harnesses. Second is capability discovery. Each implemenation needs to s…

Yes, these two are good use cases. With no harness lock-in, a fallback and context handoff can solve LLM vendor service unreliable. Loop engineering can also be done for achieving …

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Thank you for the discussion! This really nudges us to think how to make the broader white collar agent use case message more clear

You are right, for coding scenario, I also stick with one (CC in my case, really got disappointed at codex during gpt-5.4 time and never came back since then) We need HarnessRouter…

The router sits between application layer and the harness layer. HarnessRouter is the spec translation layer that translates the unified interface into each harness's own api forma…

Think of it as OpenRouter, but for different agent harnesses, not models. Instead of sticking to any one harness, you can route to and use any of them through a unified API

Hey HN! We are building HarnessRouter, a canonical API for running Codex, Claude Code, Hermes, and other managed agent harnesses as your product backend. Before building HarnessRou…

Built a trace view to see OpenClaw LLM / tool / subagent calls and debug it.

This is interesting, with e2e private chat, there are much more possibilities being unlocked without worrying about censorship

Take a look at https://epsilla.com/ ?

Great, does it support creating monthly newsletter like emails?

Nice! Componentization of gen AI workflow is a new trend, and we do need an evaluation framework like this

Thank you for pointing it out. We just removed it from README

Thank you for the insightful topic! By reading the question itself drive me think a lot. For the database perspective, instead of dividing the table schema into 3 parts: id, metada…

For now we didn't put a limit on the dimension of the vectors, so the machine can fit as much as #vector * #dimension * sizeof(float) into memory. For now we just support dense vec…

You are right. We designed our storage in a segment-based way, with configurable segment size, so it can horizontally scale in the future cross multiple workers in one machine, and…

Thank you for sharing! DiskANN was published in 2019 and SpeedANN in 2022. DiskANN is specialized in disk based ANNS solution, and it's focus on the scenario where the vectors don'…

You are right, there are numerous vector databases in the market. Most of them (including us) are still pretty early and a lot of enterprise readiness features to build. Including …

Hey HN! We are building Epsilla ( https://github.com/epsilla-cloud/vectordb ), an open-source, self-hostable vector database for semantic similarity search that specializes in low …