a next fresh new 1M tokens context window.
The most important figure is the power consumed per token generated. You can optimize for that and get to a reasonably efficient system, or you can maximize token generation speed and end up with two times the power consumption for very little gain. You also will likely need to have a way to get rid of excess heat and all those fans get loud. I stuck the system in my garage, that made the noise much more manageable.
1. Start by learning a simulation tool, e.g. Mujoco (open source) or Isaac Sim. 2. Learn basics of optimal control and reinforcement learning, reproduce papers/ideas in the simulation. 3. Get your hands dirty on a cheap robot, and try deploy your trained model on it. For mobility and manipulation. Unitree Go1/Go2 for mobility, and robotic arms for manipulation.
I recently did an Enphase system of a similar size to yours. It was fully DIY except for wiring the combiner and a roofing company to plug all the holes I drilled. Working with PG&E was truly an epic year-plus battle culminating in a CPUC complaint, but in the end it was really just a bunch of emails.
I don't have any installer recommendations, but it should be easy enough to find a local electrician, and I've found that they tend to know others in adjacent fields.
20× 455 W Canadian Solar panels (~$173 ea)
1× GridBoss MID V2 (~$2 400)
1× FlexBoss 21 (~$2 400)
4× Eco-Worthy 48 V 100 Ah LiFePO₄ batteries (~$1 500 ea)
18 U server rack (~$500) — total hardware ~$14 760
My big hang-up has been the rooftop work, permitting and inspections—almost no one I call will touch a true DIY system. If anyone here in the Bay Area has recommendations for installers or back-of-house permit-whisperers who’ll partner on a non-Tesla/Sunrun job, I’d love to hear how you made it happen. Thanks again for the inspiring guide!
> Some functions have axes arguments. Some have different versions with different names. Some have Conventions. Some have Conventions and axes arguments. And some don’t provide any vectorized version at all.
> But the biggest flaw of NumPy is this: Say you create a function that solves some problem with arrays of some given shape. Now, how do you apply it to particular dimensions of some larger arrays? The answer is: You re-write your function from scratch in a much more complex way. The basic principle of programming is abstraction—solving simple problems and then using the solutions as building blocks for more complex problems. NumPy doesn’t let you do that.
Usually when I write Matlab code, the vectorized version just works, and if there are any changes needed, they're pretty minor and intuitive. With numpy I feel like I have to look up the documentation for every single function, transposing and reshaping the array into whatever shape that particular function expects. It's not very consistent.
When the company IPOs, your vested shares would immediately vest into actual shares. At that point, you would be taxed and awarded a W-2. This is non-negotiable, and this is something that the company would be forced to handle. The idea that you have a lingering tax payment due before lockout period expires doesn't make sense to me. Your RSUs are now shares and when that conversion occurred on IPO date, you would have been taxed. You own no more tax until you sell your shares.
We did not receive a W-2, and the company has not reported the RSUs as taxable income yet.
Even though our RSUs fully vested at IPO, they are not yet settled as shares—the company has set the settlement date to March 15, 2025.
The company is requiring us to prepay withholding taxes in cash before they release the shares. If we don’t pay by the deadline, the RSUs will be forfeited entirely.
This is why we are trying to better understand how this aligns with U.S. tax laws and whether this is standard practice.
We agree that this doesn’t sound like how RSUs typically work in the U.S., which is why we are seeking advice. If you have any thoughts on how this situation might fit within U.S. tax regulations, we’d really appreciate your perspective!