u/gappyvalley

I needed a secondary phone for work. I have a 17 pro max as my main personal phone and I wanted a phone that can fit both my personal and work phone in my pocket when I am out doing on site work. Got this product red SE for $100 and I have been loving it so far. I need to charge twice a day as the battery health is 80%. I am thinking of just replacing the battery

I was refreshing my youtube and found out my favourite reviewer uploaded a battery test of 78 smartphones:

https://youtu.be/MpgUFrsIWSQ

the author said they started using robotic arm to simulate a person using the phone but they wanted to further enhance it by using agentic ai. cloud ai have too high latency so local llm was a perfect solution for this. he bought RTX PRO 6000 and H20 just to run qwen3.6 27B and 35B-A3B. insane dedication for a smartphone battery test. he also built his own custom 5G tower to do a controlled testing of 5G battery usage but thats unrelated to this sub.

transcript:

Our Battery Life 5.0 model was built around that idea.
We added a vision-language model.
Images from the industrial camera go straight into it.
The model determines what is on-screen and where the buttons are, which actions are available, and what it should tap next.
It makes every decision autonomously.
It is like giving our battery-test robot a soul, and a personality.
Its phone use is now far more realistic and humanlike.
So we got to work.
The first problem to solve was compute.
How could we power an agent that operates phones?
Battery testing runs on extremely tight timing.
The robotic arm waits; if the model takes even one extra second to respond, the entire test sequence falls out of rhythm.
We also had to prevent network fluctuations from affecting results.
So cloud-based agents were ruled out immediately.
So we bit the bullet and ran inference locally.
We spent well into six figures in RMB on a centralized inference server, packing it with an NVIDIA H20 compute card and an RTX PRO 6000 Blackwell.
It runs two models.
One is Qwen3.6-35B-A3B, a mixture-of-experts model.
It is extremely fast and handles rapid, scroll-heavy browsing, such as feeds on Weibo, Taobao, and Xiaohongshu.
The other is the dense Qwen3.6-27B model.
It is larger and more accurate, and handles precise actions: locating a particular screen, finding a specific button, dismissing pop-ups, and so on.
We also made the two models work together.
If A3B loses the plot, perhaps by hallucinating or misreading information on-screen, it does not simply sit there stuck.
It immediately calls in the 27B model for a second opinion.
The more accurate 27B can spot the problem at a glance.
So even when A3B stumbles, there is a fallback.
In our tests, from the moment this phone-operating agent receives an image to calculating the robotic arm's path, the whole process takes under two seconds.
That is far less latency than a cloud model.
Deploying the models locally was clearly worthwhile.

edit:
results: https://socpk.com/batlife

really loving it!