the real value of an AI notebook is not just getting a summary, but keeping the source trail, notes, and follow-up thinking in one place. If you want to try that kind of workflow in a browser, this page gives a pretty clear starting point: https://www.opennotebook.shop/

A lot of AI document pipelines still treat conversion as a boring preprocessing step, but it is really part of the architecture. Once files feed retrieval, agents, or automation, the conversion layer has to preserve structure, expose output for review, and respect trust boundaries. This MarkItDown page frames that idea nicely: https://www.markitdown.store/

This is exactly why I think private AI search needs to be treated as infrastructure, not just a chatbot feature. I recently came across this Onyx-focused page, and it frames the problem well: https://onyx.guru/

This framing makes sense to me. The biggest shift is treating the extension as a claim rather than evidence. I tried a few renamed files in the browser version here: https://www.magika.uk The interesting cases were not the obvious ones, but the mismatches. When the filename says one thing and the content suggests another, file type detection starts to look less like metadata cleanup and more like an early decision point in an upload pipeline.

Really enjoyed reading this! The idea of multi-agent collaboration for real workflows makes a lot of sense—it's a much-needed solution for anyone working with AI tools in a practical setting. I love the vision of moving beyond just isolated chatbots or single prompts to a more structured, parallel AI process. That kind of flexibility will make AI tools much more powerful and adaptable to different tasks. For anyone curious about this approach, you can check out Multica here: multica.uk. It's definitely worth exploring!

This is such an interesting concept! Being able to simulate decisions and see potential outcomes before committing sounds like the future of decision-making. I’ve always thought about how often we rely on guesswork, and this seems like a great way to make more informed choices. I’d love to explore how this could be used in market research or even in predicting social media trends. Excited to see how MiroFish evolves! For anyone curious about it, you can check it out here: https://www.mirofish.work/

Small disclosure: this essay came from what I am exploring with GenericAgent. The idea is simple: if long-running agents need both external action and internal adjustment, then the runtime should support real tool use, memory, and reusable skills instead of just longer prompts. I am testing that direction here: https://www.genericagent.org/