songrenchu

7 ms·

1.
by songrenchu 2mo ago
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 specify capabilities it doesn't support. Third is config
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by songrenchu 2mo ago
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 long horizon goals a single harness loop struggles to close
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by songrenchu 2mo ago
Thank you for the discussion! This really nudges us to think how to make the broader white collar agent use case message more clear
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by songrenchu 2mo ago
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 when we need to package the harness agent as part of the pr
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by songrenchu 2mo ago
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 format. Each harness is treating somehow like a blackbox, an
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by songrenchu 2mo ago
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
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Show HN: HarnessRouter: Unified interface for agent harnesses(github.com)
10 points by songrenchu 2mo ago|14 comments
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Show HN: Visualizing OpenClaw runs to debug flaws and token spikes(github.com)
1 points by songrenchu 6mo ago|0 comments
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by songrenchu 2y ago
This is interesting, with e2e private chat, there are much more possibilities being unlocked without worrying about censorship
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by songrenchu 2y ago
Take a look at https://epsilla.com/ ?
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by songrenchu 2y ago
Great, does it support creating monthly newsletter like emails?
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by songrenchu 3y ago
Nice! Componentization of gen AI workflow is a new trend, and we do need an evaluation framework like this
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by songrenchu 3y ago
Thank you for pointing it out. We just removed it from README
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by songrenchu 3y ago
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, metadata, embedding, we designed in a way closer to SQL, treat vec
15.
by songrenchu 3y ago
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 vector, and in the future we will work on sparse vector su
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by songrenchu 3y ago
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 cross multiple machine cluster. And the search will become
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by songrenchu 3y ago
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't fit into memory. SpeedANN is in-memory solution
18.
by songrenchu 3y ago
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 role based / privilege based access control, authN&#x2F
19.
Show HN: Epsilla – Open-source vector database with low query latency(github.com)
111 points by songrenchu 3y ago|24 comments