thawab

i got excited seeing the post, because i thought v3 was released. For anyone who want's to know more about v3 here is a link: https://payloadcms.com/blog/30-beta-install-payload-into-any...

I would recommend to focus on a niche like browser automation/QA with puppeter specially after they released the webdriver bidi[0]. There is a market for this kind of product if it's lighter/faster than chrome.

[0] https://developer.chrome.com/blog/webdriver-bidi

New research paper drop or go viral > create a repo with AI code > post it in social media. Users star a repo to bookmark it. The few who test the code write in the issue section and get their issue closed with no replies.

Thats why some subreddits flagged these name squatters.

followers, clicks? anyone who spends a few minutes browsing his repo will know he is a fraud. Here is an example:

https://github.com/kyegomez/AlphaFold3

most issues are people not able to run his code. These issues are closed. The repo has 700 stars.

Here is more context about OP:

https://www.reddit.com/r/MachineLearning/comments/15sq2v1/d_...

This dude has issues, the reddit post in /r/MachineLearning top comment:

> Yes, basically. Delete any kyegomez link on sight. He namesquats recent papers for the clout, though the code never actually runs, much less replicates the paper results. We've had problems in /r/mlscaling with people unwittingly linking his garbage - we haven't bothered to set up an Automod rule, though.

[0] https://github.com/princeton-nlp/tree-of-thought-llm/issues/...

[1] https://x.com/ShunyuYao12/status/1663946702754021383

Why use it if you can do RAG with openai's assistants api?

In the example folder they used qdrant as a vector database, why not use openai’s assistants api? The idea for a vendor lock solution is to make things simpler. Is it because qdrant is faster?

Meta text to image model cm3leon[0], was announced july 2023. It wasn't released yet, I think this one might take a while.

[0] https://ai.meta.com/blog/generative-ai-text-images-cm3leon/

The other cofounder is not far from him: https://x.com/secemp9/status/1840517950060855431

If you are depending on an open source project for your startup, why say “Get dat shit outta here” after you change its name to your product? If its shit then why use it?

I think it’s having an agile team focused on this. In the past it was because cursor index your code (vector search) so any question you ask the llm has context of your code. Now it’s the autocomplete feature (their own model). Next i think it will be composer (multi file edit, still in beta).

continue(yc) is an open source vscode extension. The best thing about cursor is their auto complete feature, their own fine-tuned model. It will be a while for others to build something close to it.

Srcbook with AI can do the same job faster.

Not OP but i am interested in the same idea to document components. An alternative to storybook.

that's really an interesting case, most puppeteer and playwright deployments are using chrome. If Verso will be faster they might have an advantage in crawl/scrap/QA.

I would assume any product built on llm that has 10 million MAU, openai will go after. On the LLM side the gap is close between openai, anthropic and now meta. OpenAI might start to focus on products that’s built on its technology even if it’s UI/UX frontend.

Yeah, they seen how their product is used and now expanding the use case. Most of their userbase don’t know these tools, and looking for a better search than google.

You can use this one until they have theirs up, it will be identical /s : https://linear.app/docs

Put a screenshot of Todoist, linear and Tegon. You will see which is inspired or copied. That's the point a few are making here.

Why not use Gen AI? it's built for this. Klarna partnered with OpenAI for their implementation.

Qwen MoE[0] showed promise for a small size. I hope they spend more time in them.

[0] https://qwenlm.github.io/blog/qwen-moe/

>Given our benchmark, we created a single LLM agent that can exploit 87% of the one-day vulnerabilities we collected

They tested 15 vulnerabilities, 5 of them are XSS. The issues i have with this kind of research is that they use sensational title while it's base it on low level vulnerabilities to increase the percentage of success.

It's an assumption from my side, that they will utilize GenAI in material discovery for Aramco or SABIC[0]. Even if Groq won't fit that use-case a couple of billions with another hardware vendor is nothing if this paid off.

[0]https://en.wikipedia.org/wiki/SABIC

It’s about material discovery[0], Aramco built a 250b model to help them improve efficiency [1].

[0] https://deepmind.google/discover/blog/millions-of-new-materi...

[1] https://www.aramco.com/en/news-media/speeches/2024/leap24--r...

now we use postgres, testing duckdb/md for analytics.

This is really promising, please look into embedded analytics. We are struggling with building our embedding for SAAS. Now we are doing it with cube and superset. All the alternatives are expensive or hard to implement.Being able to offload the analytics to the browser is a plus.

Open source means i have documentation to reproduce the same results. This is only true with tinyllama and this model. The other models (llama, mistral) are free to use and not open source.

Although Falcon 180B and YI-34B might lead in some benchmarks, the extensive tools and community backing Llama provide it with a significant advantage.

Why would the deepmind cofounder go for 1 model and not a MOE architecture like gpt4? 1.3 billion dollars is not enough to get you to gpt4?

with the amount they paid, how did they not get a seat in the board?