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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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For anyone who might be interested in an open source, terminal-based approach to using AI on larger tasks and real-world projects, I'm building Plandex: https://github.com/plandex-ai/plandex
I've tried to create a very tight feedback loop between the developer and the LLM. I wanted something that feels similar to git.
Apart from the planning and code generation itself, Plandex is also strongly focused on version control. Every interaction with the model is versioned so that it's easy to try out different strategies and backtrack/revise when needed.
I just released a new update (literally a few minutes ago) that includes some major improvements to reliability, as well as support for all kinds of models (previously it had been OpenAI-only). It's been fun trying out Claude Opus and Mixtral especially.
This is a laughable definition of large-scale. It's also a misrepresentation of that situation: It was 12% of issues in a dataset for the top 5000 repositories pypy packages. Further "solves" is a incredibly generous definition, so I'm assuming you didn't read the source or any of the attempts to use this service. Here's one where it deletes half the code and replaces network handling with a comment to handle network handling: https://github.com/TBD54566975/tbdex-example-android/pull/14...
"this will get much better" is the statement I've been hearing for the past year and a half. I heard it 2 years ago about the metaverse. I heard it 3 years ago about DAOs. I heard it 5 years about block chains...
What I do see is a lot more lies.
Link btw since it’s on a org: https://github.com/CopilotC-Nvim/CopilotChat.nvim
Re: official version
There was some discussion but the answer is that it’s just too difficult. Our version was reversed from a bunch of MITM and guesswork. The VSCode implementation has a “@workspace” command which involves complex tree-sitter integration, sending off all your code to be vectorized, and uses RAG to get the most relevant snippets. We obviously weren’t able to implement all features. They can get away with this in JavaScript because you can get just about anything via npm. In Lua, most things have to be from scratch. I had to spend a few hours just getting tiktoken working in Lua and even then it requires manual installation. The package management system with Lazy.nvim is very lacking.