I just learned how to use VOSViewer, which I like, even though it is a bit intimidating.
Also, I love how you can use Elicit to automatically extract information from multiple papers through the use of columns!
Would you recommend any other tools?
Hi everyone!
I have to buy a new laptop, and I want to use it mainly for Bitwig. I have :
- MotuM2
- Rode NT1
- nektar Impact lx49+
- Roland drum kit that I will upgrade with eDrumin 12
I could create songs with multiple tracks and devices, even though I am not used to do this right now. The idea is to have a laptop not get in the way of any further updates for at least the next 5 years, and 10 years would be optimal.
I am getting overwhelmed by all the available options for laptops, and am looking for any recommendations regarding a specific 2026 model.
Thanks!
I currently work for an academic lab. Through Pewdiepie's video on Odysseus, I just learned that our team has the potential to run a local LLM: this means confidential data will never go through OpenAI or Anthropic's servers, which allows us to use local AI in a research context (ethical). Also, we could tune our local model to produce more accurate citations using a curated GraphRAG tailored to our field, and reduce the risk for hallucinations.
Concretely, this is the setup I am imagining:
Material: An Apple Mac Studio with 96 GB of Unified Memory with M3 Ultra
UI: Odysseus
Model(s): DeepSeek-R1-Distill-Llama-70B without fine-tuning OR DeepSeek-R1-32B with fine-tuning (other models would be involve in fine-tuning [see next paragraph])
Method:
a. Entity and relationship extraction of seminal papers in GraphRAG with Qwen 2.5 72B Instruct -> Summarization with Qwen 2.5 14B Instruct. All done offline (to avoid giving AI companies access to licensed journal articles). I want to do this with at least 100 papers, if not way more.
b. Fine-tuning (I guess for "training" a model to rely more systematically on the GraphRAG than on its pre-trained parameters). I know mlx-lm is recommended for Mac OS, but I am unsure of how to use it in practice. I know I would need to provide the model with QA pairs to "teach" the model on how to use the studies to answer prompts (only responding based on the studies in the GraphRAG), but this all feels pretty abstract right now.. I am not even sure if I should sacrifice the 70B model for a fine-tuned 32B (I won't be able to fine-tune the 70B locally with the M3 Ultra).
Machine running as a server for the lab: The Mac will be locally hosting Odysseus, and our researchers will be able to connect to it locally through Tailscale, potentially with Headscale to maintain privacy.
All of this setup could allow our lab to feed sensitive data in our own model, while staying confidential and nourishing niche ideas. However, since I am new to this (I learned about all of this 3 days ago), I wish to gain more knowledge on how to optimize our setup to fit our needs. I figured this thread could also become a resource for anyone looking to do something similar at an academic level, or as a science enthusiast.
If you think you can help with our project, feel free to join in our new AI adventure!
I am a psychology student, and I can't imagine what my life would be like today without all the knowledge I've gained from my years of study.
Attachment theory is a big one for me. Realizing that I needed to focus on being part of a stable, close-knit group of friends has helped me become more secure in my daily social interactions.
What are some concepts that have helped you and could potentially benefit society more if they were better known?