Just listened to @cursor_ai founders on @lexfridman.
https://t.co/Rr1862ataX
They solved practical GenAI dev problems we all face.
Core insight: same technical patterns work across research tools, IDEs, and analytics platforms.
Two features are in focus this week. @Simplifine2024
1. tool to search and evaluate research papers, with detailed feedback for each result.
2. browser extension to add papers directly to projects.
We’re testing both internally, looking forward to seeing them in action.
Meta is planning up to 4 GW of new nuclear capacity by the early 2030s.
https://t.co/WTtkDaiE2M
This isn’t just about meeting energy needs. It’s about powering AI innovation and building a sustainable future.
Traditional energy generation isn’t enough to keep up with rising demand.
Nuclear provides reliable, large-scale power for data centers and surrounding communities.
Proud to announce our acceptance into the @NVIDIA Inception program!
This opportunity provides access to GPU resources and support technology, propelling our mission further.
I learned that it’s better to build the base of all these components and build up.
Adding new capabilities later can cause integration issues if you didn’t account for them upfront.
“What’s the file size limit?” That’s a question users frequently ask the chat.
I realized we need to make the chat feature clearer.
Key areas to tackle:
1. enhancing chat about the app
2. enabling chat to access the full library
3. making interactions more conversational
Spent today refining Simplifine’s literature search.
Focused on handling file access issues, improving user options, and syncing changes between the UI and backend.
Next: aligning it with the rest of the app’s functionality.
Scaling came down to two options:
1️⃣ Scale-out: multiple smaller instances
→ Easier to replace, less risk of cascading failures.
2️⃣ Scale-up: fewer bigger instances
→ More cost-efficient but harder to manage multiple workers and failures impact more workloads.
@Microsoft This is a step towards more effective extractive methods, potentially beneficial for Simplifine's complex queries where naive RAG falls short.
Implementing Graph RAG with few-shot learning might not be cost-effective for us now, but it's exciting to see progress in this area!
Recently I read a paper by @Microsoft on Graph RAG for query summarization.
They highlight the limitations of Naive RAG in handling large corpora.
Graph RAG uses iterated prompting and entity extraction.
The results are promising, albeit on preliminary data.
@Microsoft Compared to current methods, Graph RAG offers a more structured way to handle complex queries.
Thought it's really interesting:
https://t.co/EzAfN9xUwi