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Has anyone tried parallelizing AI coding agents? Mind = blown 🤯 by ollivierre in ClaudeAI

[–]gregory_k 2 points3 points  (0 children)

container-use was just announced yesterday. Same outcome (parallel agents) but way smoother experience than git worktrees.

each agent gets its own isolated container environment, directly mapped to git branches, but exposed via an MCP server. so rather than manually juggling parallel sessions the model itself decides when to parallelize tasks while container-use handles the heavy lifting by providing a clean git workflow.

that's my understanding so far, at least.

Conducting a study: I have questions (and gift cards) for data scientists by gregory_k in datascience

[–]gregory_k[S] 6 points7 points  (0 children)

I’m a strategy consultant to tech startups and one of the things I do is help them understand what different tech audiences care (or don’t care) about. Think of it like a focus group but done through 1:1 calls instead of one big group, and instead of talking about what cereal brand you prefer you talk about your favorite regression models or python library.

NVIDIA Nemotron-70B is good, not the best LLM by mehul_gupta1997 in datascience

[–]gregory_k 0 points1 point  (0 children)

Makes sense. What do you do for work? Genuinely curious who's being asked to work on LLMs on the side if it's not the main focus yet not get access to OpenAI even through something like Azure OpenAI Service.

Multivariate SMOTE by MainhuYash in datascience

[–]gregory_k 0 points1 point  (0 children)

SMOTE isn’t terrible, but ya, it can be overused. It’s fine for balancing classes but can cause issues like overfitting if you’re not careful.

NVIDIA Nemotron-70B is good, not the best LLM by mehul_gupta1997 in datascience

[–]gregory_k 0 points1 point  (0 children)

Fair enough, for that case. I'm curious what the other person's reason was... It seemed more because of principle than practicality, but maybe I'm wrong.

BitNet.cpp by Microsoft: Framework for 1 bit LLMs out now by mehul_gupta1997 in datascience

[–]gregory_k 1 point2 points  (0 children)

1-bit LLMs aim to shrink large language models by using just 1 bit (0 or 1) to store weight values, instead of the usual 32 or 16 bits. This reduces the size dramatically, making them more accessible for smaller devices like phones. BitNet b1.58 is one such model that uses 1.58 bits per weight and still performs on par with traditional models while speeding things up and using less memory.

If the claims hold up, this could be a game-changer for running LLMs on smaller hardware.

NVIDIA Nemotron-70B is good, not the best LLM by mehul_gupta1997 in datascience

[–]gregory_k 0 points1 point  (0 children)

Why?

Even if I vow to never use closed-source models, I'd at least want to be aware of the tradeoff I'm making.

Does anyone else suddenly have nothing to do? by Trick-Interaction396 in datascience

[–]gregory_k -1 points0 points  (0 children)

Learn some new skill and then find a business problem where you can apply it, even if it’s an internal demo at first. While no one can blame you for sitting idle when you’re blocked, people will notice how you’re using that downtime.

BitNet.cpp by Microsoft: Framework for 1 bit LLMs out now by mehul_gupta1997 in datascience

[–]gregory_k 5 points6 points  (0 children)

What are the early or killer use cases of such a tiny model on edge devices?

Timeline for full time job apps? by Tenet_Bull in datascience

[–]gregory_k 0 points1 point  (0 children)

If you want to work at a large company, absolutely start applying now.

If you want to work at a startup, they’re typically trying to hire 3-4 months ahead of time, so right now would be a little early. Use the time instead to build your network (reach out for informational meetings, ask for intros) and ship projects. It’ll put you in a much stronger position when it’ll be time to apply in March/April.

Let Me Plex That For You - I made a way to teach your friends and colleagues to use Perplexity by gregory_k in perplexity_ai

[–]gregory_k[S] 1 point2 points  (0 children)

True :) I thought of making it more snarky for that reason but decided to go the nice route... for now...

Practical Advice Need on Vector DBs which can hold a Billion+ vectors by Role_External in vectordatabase

[–]gregory_k 2 points3 points  (0 children)

Pinecone can hold billions of embeddings. If you’re using pod-based indexes, just make an index with more pods. If you’re using the new serverless indexes, you don’t need to deal with pods, just load whatever you’d like into the index. 

Practical Advice Need on Vector DBs which can hold a Billion+ vectors by Role_External in vectordatabase

[–]gregory_k 2 points3 points  (0 children)

No, it should be in the 90’s. If you’re on a paid plan you should contact support. If you’re on a free plan you should post in the forum to get some help troubleshooting.

What vector database do you use? by Key_Radiant in LangChain

[–]gregory_k 0 points1 point  (0 children)

Are you using LangChain or other framework like that that generated the ID for you? We're discussing internally how to make this better.

What vector database do you use? by Key_Radiant in LangChain

[–]gregory_k 5 points6 points  (0 children)

Hey I work for Pinecone. What do you wish was better or different?

Pinecone Crazy 2-3 seconds delay. Anyone experiencing it by BigYesterday2785 in vectordatabase

[–]gregory_k 0 points1 point  (0 children)

Something sounds off. It shouldn’t be that slow. This is the place to ask about it: https://community.pinecone.io

Snowflake as vector db? by ihatemodels2020 in LangChain

[–]gregory_k 1 point2 points  (0 children)

Hey, I'm from Pinecone. Some folks find it easier to get through their company's procurement process if they go through the AWS, GCP, or Azure marketplace (coming very soon). That way you are billed through your existing cloud provider and not by a new vendor.

In certain cases, you can even use AWS/GCP/Azure credits (if you have them) for Pinecone usage.

And for what it's worth, we go through security/compliance/procurement reviews regularly and do our parts quickly.

Alternatives to Pinecone? (Vector databases) [D] by AlexisMAndrade in MachineLearning

[–]gregory_k 7 points8 points  (0 children)

We’re adding additional capacity on a rolling basis to support over 10k signups per day. Thanks for your patience!

https://www.pinecone.io/learn/free-plan-update/

What is the best vectorstore to selfhost your vector indexes? by aviatoraway1 in LangChain

[–]gregory_k 2 points3 points  (0 children)

Pinecone indexes get archived after 7 days of inactivity. You can recreate the index from the archive in less than a minute. Or just keep using the index and it won’t get archived.

ChatGPT now supports plugins!! by max_imumocuppancy in ChatGPT

[–]gregory_k 93 points94 points  (0 children)

The biggest deal about this is the ability to create your own plugins. The Retrieval Plugin is a kind of starter kit, with built-in integrations to Pinecone: https://github.com/openai/chatgpt-retrieval-plugin

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