tanglearncode

4 ms·

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by tanglearncode 2mo ago
This is exactly for this: https://github.com/XTSoftwareLabs/neatcontext-plugins Turn conversations to structured domain knowledge. Across sessions. Across AI agents. The stored domain knowledge is human readable and ca
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by tanglearncode 2mo ago
A claude code plugin is now open sourced: https://github.com/XTSoftwareLabs/neatcontext-plugins It extracts domain knowledge and save useful work from a Claude Code conversation as structured, reusable context that you
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by tanglearncode 2mo ago
Hi, I recently researched the hard problem for the inaccurate answers from SRE agent. I have a different opinion for the SRE agent in the market. TBH, what an oncall engineer only needs context +AI client to handle incidents. I don't s
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by tanglearncode 2mo ago
I just made a video to show how the domain knowledge issue can be solved: https://youtu.be/lh5ZgP6CHss
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by tanglearncode 3mo ago
A little more context on this. I have tried quite a few of SRE agents or AI incident handling assistants in the market. LLMs can help on incident handling if the domain knowledge and context are clear. However, in most of time, they are mis
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Show HN: A lightweight app to let LLM work for oncall(neatcontext.com)
3 points by tanglearncode 3mo ago|4 comments
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by tanglearncode 3mo ago
Yeah the context is the key to make the LLM really useful for oncall. But honestly the SRE agent companies today are trying to keep users in their platforms, not focusing on solving the context problem. That's what I saw.
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Why we stopped using an automated SRE agent(blog.neatcontext.com)
4 points by tanglearncode 3mo ago|3 comments
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by tanglearncode 3mo ago
Some lessons learned when using the SRE agents to handle incidents. We eventually ended up to a semi automation way to get more accurate LLM results by providing domain knowledge and context to LLM. Wondering if you encountered the similar