Here is my open proposal to @garrytan@snowmaker@daltonc@mwseibel@ycombinator :
Hey Garry and the YC Team,
My name is Amrit Shenava and I run a housing platform @Flashmateshq based out of Mangalore in India. I understand that founders who get accepted into YC have to move to SF for the program and in the process, housing can be challenging process. Over the last two batches, there were over 80+ YC founders who have used Flashmates for their housing needs. We even have a platform called Subletter, where users can find flexible living housing options and even have a bot for it @SubletterSF. Subletter is a product that is built to address housing needs in a city like SF and given that we have YC founders using the platform, I would love to partner with YC to help their founders find a place. Flashmates is proudly built on top of @Replit, a notable YC startup. I did not apply to YC as I found it to not be the right fit for me however want to help and partner up with YC as I want founders to focus on building and not be worried about housing. If this is something you are interested in, please do let me know in the replies and even if you are not interested, that's okay however would still love to help founders with their housing needs.
Hey @_shikharsaxena, now that I expect you have less people asking for access. Can you pls give access to superdm to me too? (Have filled the google form thing too)
@tibo_maker World's not ready to witness the amount of alpha I can become if I actually open and read my bookmarked tweets. Some banger tweets there lying since years.
@prasann_pandya@myreaderai Mad respect g. Been following you just cos of your tweets but now knowing your background and what you building, super pumped to see what you ship.
Godspeed.
Autonomous driving through very dense dynamic traffic, with extremely tight-complex-stochastic traffic-dynamics on sub-urban roads, connecting to an open ground, with absolutely zero traffic-rules.
This is the most heavily cluttered environment where we have tested our #autonomousdriving technology, presenting many of the adversarial negotiation scenarios as well, throughout the autonomous navigation task.
This demo was done at the Mata Baglamukhi Madir campus in the city of Nalkheda, in MP, India, and was done in the presence of heavy police forces deployed that day on the ground, as can be seen in our demo.
Our autonomous vehicle starts from the temple with a generic open environment, with zero traffic rules, with very narrow corridors created out of barricades for vehicles movement by the security forces.
In the corridor no two vehicles can pass through at the same time, and our vehicle was tasked with driving through this corridor, while negotiating its way from any traffic, two-wheelers, or pedestrians it faces, with dense presence of bikes and cars on either side, presenting a very challenging environment for #autonomousvehicles.
The vehicle exits the open area, and then assumes generic dual lane navigation, avoiding both static and dynamic obstacles, before encountering a police check-post, where the vehicle is supposed to wait if the barricade is closed, and proceed if open.
Upon exiting the checkpost, the vehicle negotiates a traffic-intersection with stochastic and adversarial driving behaviour of other vehicles on the road.
Our vehicle continuously faced heavily cluttered traffic scene, where entities on the road can execute a random driving pattern, making the decision making task very challenging.
We did the demo over a period of two days, successfully executing multiple (30+) trials in this setting. This demo was again a culmination of our prior works and demos: Kankali Kali Mata demo, on-roads, bidirectional negotiation capability on single lane roads, and open environment Level-5 negotiation capability as showcased in our Toll-Plaza demo.
We again scaled up classical decision making and motion planning algorithmic framework, to adapt to such a level of density of obstacles on the road. This framework is further being scaled up with #reinforcementlearning and unsupervised #deeplearning at @swaayatt.
We will again do a demo in the month of June here, showcasing autonomously acquired skills to pave the way for Level-5 autonomous driving, and to solve the Level-4 autonomy problem by the end of 2024.
#MachineLearning