I’ve been experimenting with different ways to organize and accelerate my research workflows, and it feels like the old “Google + Notion + random PDFs” method is hitting a wall.
Has anyone else tried AI-first research tools that don’t just summarize but actually guide your process?
I came across a piece that really challenged the way I think about this → How Manus Just Reinvented The Way You Should Do Research.
Curious what others are using in 2025 for research productivity.
I heard that Kimi is excellent for research purposes, but I haven’t tried it because the model is too large…
feel free to go to this link to read 
I collaborate with a variety of LLMs like ChatGPT, Claude and MLX (qwen) mostly for coding and summarizing progress or creating readme. I like to stay in charge of the overall process and don’t use AI much for structure or guidance since domain expertise is needed to stay on track. I use my kaggle notebooks and my repos on github as scaffold as well. 📌 Orientation Notebook for Kaggle Profile
All commercial LLMs seem to have a kind of “superpower” when it comes to Differential Topology—almost like having eyes that can see the underlying geometry. In my personal experience (apologies for the anecdotal note), this instinct for Differential Topology often surpasses their performance on tasks like Math Olympiad grading.
This “superpower” enables them to uncover hidden topological structures that connect seemingly unrelated matters, phenomena, events, and sequences—provided the user knows how to frame the question and activate this capability.
You can copy the above message and ask ChatGPT, Gemini, Grok or Claude. I feel they are 50% to 100% agree to this point.