Thank you to everyone who joined us for our live K-Dense Web Office Hours on April 17th!
This intimate session brought a great mix of questions: from researchers looking to streamline manuscript writing, to teams navigating petabyte-scale data, to users eager to run K-Dense with open source models.
Here were the highlights from the conversation:
Open Source Model Support
| Question | Answer |
|---|---|
| Will K-Dense BYOK support open source models like those available through Ollama? | Yes, open source model support is on the roadmap. The update will include a UI option to select between models. The main challenge is that many open source models still struggle with reliable skill calling and activation, which is critical for K-Dense's agent workflows. |
| Which open source models are recommended for testing? | The team sees Qwen 3.5, Qwen 3.6 (released just yesterday), and Gemma 4 as the best current options for skill-capable open source use. |
| Can I use different models for different agent roles? | Yes. K-Dense's architecture will support this kind of model routing to allow role-based assignment of models to agents. |
Model Performance Comparison
| Question | Answer |
|---|---|
| Which models perform best for Scientific Agent Skills on the platform? | Claude Opus and GPT-5.4 are currently the top performers for Scientific Agent Skills. |
| What about OpenAI's new GPT Rosalind model for life sciences? | GPT Rosalind is a promising new life sciences–focused model from OpenAI. It's currently in closed access, but it represents a good direction with domain-specific fine-tuning. |
Platform Comparisons
| Question | Answer |
|---|---|
| How does K-Dense Web compare to Claude CoWork? | Claude CoWork is a work companion that connects to apps and summarizes emails and Slack, and it's great for day-to-day productivity. K-Dense Web is the knowledge work component for end-to-end research, providing research-backed citations, dataset analysis, and code generation with an interdisciplinary approach (as opposed to specialized tools like Kosmos). |
| What are K-Dense Web's key strengths over other platforms? | K-Dense Web handles very long context outputs and provides comprehensive analysis combining multiple disciplines. The multiagent architecture acts like a consulting firm, bringing diverse expertise to a problem. |
Enterprise and Technical Considerations
| Question | Answer |
|---|---|
| Can K-Dense Web handle petabyte-scale datasets? | Large datasets at petabyte scale remain a challenge. MCP server integration is available but introduces latency issues at that scale. For enterprise customers with this need, K-Dense offers local deployment options. |
| What does the enterprise deployment process look like? | The team provides custom cost solutions based on requirements. Implementation timelines range from weeks to months depending on complexity, and a hardware deployment option is available to reduce IT approval cycles. |
| Does K-Dense Web have memory or a knowledge base that persists between sessions? | There is no memory system between sessions at this time. The team is considering a file system–based knowledge base and is waiting for stronger memory implementations from the broader industry before committing to an approach. |
Thanks again to everyone who attended this month's office hours. We love hearing directly from the community, and your questions continue to shape the direction of K-Dense Web.
Stay tuned for details on our next Office Hours event on May 20. Register now on Luma.