I'm planning to buy the ASUS Vivobook 16 for college and productivity work. My main use cases are Data Analytics, Python, SQL, Power BI, coding, and heavy multitasking.
Before I pull the trigger, I'd love to hear some honest opinions. Is the Vivobook 16 a good choice in this price range, or is there another Windows laptop.
Would appreciate any real-world experiences.
I'm considering buying the ASUS Vivobook 16 as my first laptop for college. My work will mainly involve Data Analytics (Excel, SQL, Power BI, Python), some coding in Java and VS Code, research, assignments, and general productivity.
On paper, the specs look good for the price, but I'd like to hear from people who have actually used ASUS laptops, especially the Vivobook series.
How has your experience been with performance, battery life, thermals, build quality, display, and long-term reliability? Have you faced any issues with ASUS service or durability after a year or two?
Would you buy the Vivobook 16 again, or would you choose something else in the ₹50–60K range?
I recently enabled the 80% charging limit because I wanted to preserve battery health and assumed my MacBook would stop charging once it reached that limit.
However, I just noticed that the battery is currently at 94%, while it still says “Charging to 80% Limit.” What’s making this more confusing is that it's also still plugged in and appears to be charging.
I'm trying to understand whether I'm misunderstanding how this feature works, or if macOS occasionally allows charging beyond the set limit for some reason.
Has anyone experienced this before or knows why this happens? As you can see in the SS below.
I don’t mind the model taking time to respond, but seeing the whole thinking/reasoning process on screen gets distracting really fast.
Is there a clean way to hide it while still letting the model think normally in the background?
I’ve been testing local models for retrieval augmented generation, document querying, and structured outputs, but I keep running into tradeoffs between reasoning, context handling, schema reliability, and hardware efficiency.
So far I’ve tried Gemma, Minimax, and a bit of Command-R, and I’m now looking into Qwen and LFM2. Gemma felt solid overall, but schema outputs became inconsistent under heavier workloads. Minimax felt weaker than I expected, though that might’ve been my setup.
Curious what models people are actually sticking with for serious local workflows.
I’m trying to run a local setup for retrieval augmented generation and some machine learning work.
Curious what models people are actually using right now and how they’re performing.
Tried a longer run with Ollama and got:
System:
Feels solid, but not sure if this is expected or if I’m hitting the ceiling.
Anyone getting better numbers on similar hardware? Any Ollama tweaks worth trying?
