Chat, search, and interact with all your knowledge — even your meetings. Create your free account now.
Users appreciate Sana for its robust AI capabilities and user-friendly design, which streamline workflow management and data organization. However, some users have expressed dissatisfaction with its integration flexibility, noting that it doesn't always play well with existing tech stacks. The pricing is often mentioned, with a sentiment leaning towards it being on the higher side, which some find a barrier despite the features offered. Overall, Sana maintains a strong reputation, especially among tech-savvy users, for its innovative approach to AI-driven solutions.
Mentions (30d)
1
1 this week
Reviews
0
Platforms
2
Sentiment
0%
0 positive
Users appreciate Sana for its robust AI capabilities and user-friendly design, which streamline workflow management and data organization. However, some users have expressed dissatisfaction with its integration flexibility, noting that it doesn't always play well with existing tech stacks. The pricing is often mentioned, with a sentiment leaning towards it being on the higher side, which some find a barrier despite the features offered. Overall, Sana maintains a strong reputation, especially among tech-savvy users, for its innovative approach to AI-driven solutions.
Features
Use Cases
ROCm with PyTorch and PyTorch Lightning seems to still suck for research [D]
So I asked about people's experiences with ROCm in a post a few weeks or so ago https://www.reddit.com/r/MachineLearning/comments/1t6cng3/rocm_status_in_mid_2026_d/ I actually went and procured a RX 7900XTX reference version to give it a try My discovery is that it kind of still sucks I have a small codebase for training flow matching models (SANA Architecture), which runs fine on my RTX3090s. But the moment I ported it across to ROCm it was NaNs absolutely everywhere. Forward passes were absolutely fine, but the moment you called backwards() all bets were off. The code was kept identical, apart from altering the pip environment to point to torch2.12 with ROCm7.2 instead of CUDA Trying everything from switching between bf16, fp32, to tweaking various environment variables yielded nothing. Unless there's some trick I'm missing, I get the feeling that ROCm is still seriously behind. I tried running the nanoGPT training script, which ran perfectly My intuition is that the ROCm people have probably tested their stack on established well known codebases. But, it's still remarkably fragile on even slightly uncommon code. submitted by /u/QuantumQuokka [link] [comments]
View originalKey features include: AI-powered chat interface for instant knowledge retrieval, Meeting transcription and summarization, Search functionality across documents and meetings, Integration with popular productivity tools, Customizable knowledge base, Collaboration features for team knowledge sharing, Real-time updates and notifications, User-friendly dashboard for managing knowledge assets.
Sana is commonly used for: Enhancing team collaboration by sharing meeting insights, Quickly finding information from past meetings, Creating a centralized knowledge repository for easy access, Improving onboarding processes with accessible knowledge, Facilitating project management by tracking discussions, Streamlining decision-making with summarized meeting notes.
Sana integrates with: Slack, Microsoft Teams, Google Workspace, Zoom, Trello, Asana, Notion, Dropbox, Evernote, Salesforce.
Tomasz Tunguz
General Partner at Theory Ventures
2 mentions
