cody
triton
cody | triton | |
---|---|---|
22 | 30 | |
1,942 | 11,054 | |
16.0% | 4.3% | |
9.9 | 9.9 | |
1 day ago | 2 days ago | |
TypeScript | C++ | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
cody
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Ask HN: Cheapest way to use LLM coding assistance?
checkout the cody extension https://github.com/sourcegraph/cody available for various editors like vscode
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The lifecycle of a code AI completion
I don't think it is. There is a test file which includes C#, Kotlin, etc among supported languages, which aren't included in the file you linked: https://github.com/sourcegraph/cody/blob/main/vscode/src/com...
But this test didn't seem to include TypeScript so it's obviously not comprehensive. I'm not convinced this information is actually in one place.
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Ollama is now available on Windows in preview
Cody (https://github.com/sourcegraph/cody) supports using Ollama for autocomplete in VS Code. See the release notes at https://sourcegraph.com/blog/cody-vscode-1.1.0-release for instructions. And soon it'll support Ollama for chat/refactoring as well (https://twitter.com/sqs/status/1750045006382162346/video/1).
Disclaimer: I work on Cody.
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My 2024 AI Predictions
Have you tried Cody (https://cody.dev)? Cody has a deep understanding of your codebase and generally does much better at code gen than just one-shotting GPT4 without context.
(disclaimer: I work at Sourcegraph)
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š 7 AI Tools to Improve your productivity: A Deep Dive šŖāØ
3ļøā£ Cody AI š¤
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An ex-Googler's guide to dev tools
Author of the post hereāas another commenter mentioned, this is indeed a bit dated now, someone should probably write an updated post!
There's been a ton of evolution in dev tools in the past 3 years with some old workhorses retiring (RIP Phabricator) and new ones (like Graphite, which is awesome) emerging... and of course AI-AI-AI. LLMs have created some great new tools for the developer inner loopāthat's probably the most glaring omission here. If I were to include that category today, it would mention tools like ChatGPT, GH Copilot, Cursor, and our own Sourcegraph Cody (https://cody.dev). I'm told that Google has internal AI dev tools now that generate more code than humans.
Excited to see what changes the next 3 years bringāthe pace of innovation is only accelerating!
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LocalPilot: Open-source GitHub Copilot on your MacBook
I'm sorry to hear that. We have made a lot of improvements to Cody recently. We had a big release on Oct 4 that significantly decreased latency while improving completion quality. You can read all about it here: https://about.sourcegraph.com/blog/feature-release-october-2...
We love feedback and ideas as well, and like I said are constantly iterating on the UI to improve it. I'm actually wrapping up a blog post on how to better leverage Cody w/ VS Studio, that'll be out either later today or sometime tomorrow. As far as feedback though: https://github.com/sourcegraph/cody/discussions/new?category... would be the place to share ideas :)
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Show HN: Ollama for Linux ā Run LLMs on Linux with GPU Acceleration
Ollama is awesome. I am part of a team building a code AI application[1], and we want to give devs the option to run it locally instead of only supporting external LLMs from Anthropic, OpenAI, etc. Those big remote LLMs are incredibly powerful and probably the right choice for most devs, but it's good for devs to have a local option as wellāfor security, privacy, cost, latency, simplicity, freedom, etc.
As an app dev, we have 2 choices:
(1) Build our own support for LLMs, GPU/CPU execution, model downloading, inference optimizations, etc.
(2) Just tell users "run Ollama" and have our app hit the Ollama API on localhost (or shell out to `ollama`).
Obviously choice 2 is much, much simpler. There are some things in the middle, like less polished wrappers around llama.cpp, but Ollama is the only thing that 100% of people I've told about have been able to install without any problems.
That's huge because it's finally possible to build real apps that use local LLMsāand still reach a big userbase. Your userbase is now (pretty much) "anyone who can download and run a desktop app and who has a relatively modern laptop", which is a big population.
I'm really excited to see what people build on Ollama.
(And Ollama will simplify deploying server-side LLM apps as well, but right now from participating in the community, it seems most people are only thinking of it for local apps. I expect that to change when people realize that they can ship a self-contained server app that runs on a cheap AWS/GCP instance and uses an Ollama-executed LLM for various features.)
[1] Shameless plug for the WIP PR where I'm implementing Ollama support in Cody, our code AI app: https://github.com/sourcegraph/cody/pull/905.
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Cody ā The AI that knows your entire codebase
Awesome. The repository is at https://github.com/sourcegraph/cody for anyone who hasn't seen it yet.
- Code AI with Codebase Context
triton
- OpenAI Triton: language and compiler for highly efficient Deep-Learning
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Show HN: Ollama for Linux ā Run LLMs on Linux with GPU Acceleration
There's a ton of cool opportunity in the runtime layer. I've been keeping my eye on the compiler-based approaches. From what I've gathered many of the larger "production" inference tools use compilers:
- https://github.com/openai/triton
- Core Functionality for AMD #1983
- Project name easily confused with Nvidia triton
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Nvidia's CUDA Monopoly
Does anyone have more inside knowledge from OpenAI or AMD on AMDGPU support for Triton?
I see this:
https://github.com/openai/triton/issues/1073
But it's not clear to me if we will see AMD GPUs as first class citizens for pytorch in the future?
- @soumithchintala (Cofounded and lead @PyTorch at Meta) on Twitter: I'm fairly puzzled by $NVDA skyrocketing... (cont.)
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The tiny corp raised $5.1M
I thought this was a good overview of the idea Triton can circumvent the CUDA moat: https://www.semianalysis.com/p/nvidiaopenaitritonpytorch
It also looks like they added MLIR backend to Triton though I wonder if Mojo has advantages since it was built on MLIR? https://github.com/openai/triton/pull/1004
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Anyone hosting a local LLM server
I'm pretty happy with the setup, because it allows me to keep all the AI stuff and its dozens of conda envs and repos etc. seperate from my normal setup and "portable". It may have some performance impact (although I don't personally notice any significant difference to running it "natively" on windows), and it may enable some extra functionality, such as access to OpenAi's Triton etc., but that's currently neither here nor there.
- Triton: Runtime for highly efficient custom Deep-Learning primitives
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Mojo ā a new programming language for all AI developers
Very cool development. There is too much busy work going from development to test to production. This will help to unify everything. OpenAI Triton https://github.com/openai/triton/ is going for a similar goal. But this is a more fundamental approach.
What are some alternatives?
ollama - Get up and running with Llama 3, Mistral, Gemma, and other large language models.
cuda-python - CUDA Python Low-level Bindings
zoekt - Fast trigram based code search
Halide - a language for fast, portable data-parallel computation
lsp-cody - A Client to Connect to the Cody LSP Gateway
GPU-Puzzles - Solve puzzles. Learn CUDA.
koboldcpp - A simple one-file way to run various GGML and GGUF models with KoboldAI's UI
dfdx - Deep learning in Rust, with shape checked tensors and neural networks
llm-ls - LSP server leveraging LLMs for code completion (and more?)
web-llm - Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.
localpilot
cutlass - CUDA Templates for Linear Algebra Subroutines