bluecoconut

11 ms·

61.
by bluecoconut 3y ago
Another recent (but not called out in this article) is the "Textbooks Are All You Need" paper [1]; the results seem to suggest that careful curation and curriculums of training data can significantly improve model capabilities (wh
62.
Feature Store Comparison(featurestore.org)
3 points by bluecoconut 3y ago|0 comments
63.
The Information: A History, a Theory, a Flood(en.wikipedia.org)
2 points by bluecoconut 3y ago|0 comments
64.
Sdf – Generate 3D meshes based on SDFs(github.com)
5 points by bluecoconut 3y ago|2 comments
65.
by bluecoconut 3y ago
> So, agents have the potential to become a central piece of the LLM app architecture (or even take over the whole stack, if you believe in recursive self-improvement). ... . There’s only one problem: agents don’t really work yet. I real
66.
A Guide to Consistent Hashing(toptal.com)
3 points by bluecoconut 3y ago|0 comments
67.
DataDM: Open-source local-LLM code-interpreter with dataset search(github.com)
5 points by bluecoconut 3y ago|0 comments
68.
by bluecoconut 3y ago
I completely agree that greatly increasing data accessibility is a huge unlock and value add. A package I open sourced recently might be useful for use cases like this, https://github.com/approximatelabs/datadm It&#x27
69.
by bluecoconut 3y ago
Hey HN, I’m excited to share dataDM! This is an open source GPT Code Interpreter with a special focus on data and privacy (by using local LLM models). It’s built on gradio with guidance as the llm wrapper, and executes code against a local
70.
Show HN: DataDM – An open source private data assistant(github.com)
9 points by bluecoconut 3y ago|1 comments
71.
by bluecoconut 3y ago
For GPT/Copilot style help for pandas, in notebooks REPL flow (without needing to install plugins), I built sketch. I genuinely use it every-time I'm working on pandas dataframes for a quick one-off analysis. Just makes the iterat
72.
Bird-SQL: A Big Bench for Large-Scale Database Grounded Text-to-SQLs(bird-bench.github.io)
2 points by bluecoconut 3y ago|0 comments
73.
by bluecoconut 3y ago
I sorta did this, feel free to check it out and let me know your thoughts! On the main langchain post (In January) that got the traction on hackernews, i left this comment: https://news.ycombinator.com/item?id=34422917 . It
74.
by bluecoconut 3y ago
Not production, but yes to scale: I pushed milvus to ~140 million vectors (768 dimension) (though only a handful of requests per second (~10)), and it faired alright once everything was up and running and relatively static on the document s
75.
by bluecoconut 3y ago
Really exciting how fast fully pre-trained new models are appearing. Here's another repo (with the same "open-llama" name) that has been available on hugging face as well for a few weeks. (different training dataset) https:&
76.
Ask HN: Recommendations for AI assistants in React apps?
1 points by bluecoconut 4y ago|0 comments
77.
by bluecoconut 4y ago
Small warning: 0 temperature still isn't deterministic with openAI endpoints. So, if you are relying on this as an absolute, it'll definitely fail at some point (this bit me at one point recently). If you just like it because it&#
78.
by bluecoconut 4y ago
Support for the ChatGPT endpoint now added to lambdaprompt[1]! (solves a similar problem as langchain, with almost no boilerplate!) Props to openai for making such a usable endpoint, was very easy to wrap. Example code using the new functio
79.
by bluecoconut 4y ago
Looks really neat! All the images on the site show mobile apps, does this also do website app design? Also, I'd love to see how it handles design consistency between pages. Can this be used to come up with a design and component system
80.
by bluecoconut 4y ago
They hallucinate the references. Both for a discussion of the topic, and for some progress on the research check this out: https://www.deepmind.com/blog/gophercite-teaching-language-m...
81.
by bluecoconut 4y ago
https://news.ycombinator.com/item?id=34102363 Another very similar tool / thread
82.
by bluecoconut 4y ago
This is great~ There's been some really rapid progress on Text2SQL in the last 6 months, and I really thinking this will have a real impact on the modern data stack ecosystem! I had similar success with lambdaprompt for solving Text2SQ
83.
by bluecoconut 4y ago
In terms of applications with it, I have made things like sketch: https://github.com/approximatelabs/sketch Raw prompt-structure ideas i've worked with: - Iterate on a prompt with another "discriminator"
84.
by bluecoconut 4y ago
This is great! I love seeing how rapidly in the past 6 months these ideas are evolving. I've been internally calling these systems "prompt machines". I'm a strong believer that chaining together language model prompts is
85.
by bluecoconut 4y ago
This is sending summary statistics to a cloud machine by default (for ease of immediate use. https://github.com/approximatelabs/sketch#sketch-currently-u... You can run using your own OpenAI key by setting 2 environmen
86.
by bluecoconut 4y ago
to get the strings of the results back out, add the kwarg `call_display=False` to the functions. so: ``` print(data_pd.sketch.ask("Is there any PII in this dataset ?", call_display=False)) ``` should work for you. Right now it b
87.
by bluecoconut 4y ago
Thanks! That is definitely a big part of it, getting to use copilot style answers without having to install any plugins to the IDE (so getting to use this in colab or jupyter notebooks directly feels great). That said, I use both copilot an
88.
by bluecoconut 4y ago
Thanks! Right now this is running off of GPT-3 (`text-davinci-003`) and via a small code change can run on codex (`code-davinci-002`) but the quality only improves a little bit with that change. That said, this is the first version to show
89.
Show HN: Sketch – AI code-writing assistant that understands data content(github.com)
252 points by bluecoconut 4y ago|49 comments
90.
by bluecoconut 4y ago
From the article: Myth 1: Quantum Computing Will Take Over HPC! Myth 2: Everything Will Be Deep Learning! Myth 3: Extreme Specialization as Seen in Smartphones Will Push Supercomputers Beyond Moore’s Law! Myth 4: Everything Will Run on Som

More ›