nirushiv

Unfortunately I don’t recall the name of the patent troll. Is there a place I can search these cases up?

IIRC, Apple’s evidence amounted to repeatedly shouting “they’re obviously a patent troll” (which they were)

Thanks both for explaining. I appreciate it!

Firstly, congratulations on your baby! I’m sure you’re enjoying fatherhood and soaking it all in!

Secondly, I’ve followed your work for a little while on here and IndieHackers. Your perseverance is truly an inspiration, so I wanted to say thanks. I’m working on a SaaS product, and I need to read some of these articles on the down days to start working again.

These are fairly artificial constraints.

1. Lidar is expensive for everyone. It’s not like Tesla will pay more than other manufacturers. They’d prefer not to use Lidar, but if cameras just don’t cut it, I don't think they would die on that hill.

2. I’m sure there is some fine print around the current FSD being for camera based sensors only. They can easily say the car tops off at L4 for cameras, and L5 needs Lidar, if we come to that bridge

Thanks for digging deeper. I saw that it was from an academic institution (TUM) so the lack of useful metrics in the release admittedly ticked me off a little. Dice Coeff sounds very similar to Jacard Indexing for image tasks.

Can you explain this to me a little more?

If spending is up, wouldn’t the stock market outperform Bitcoin anyway?

If we have rapid inflation, wouldn’t money go into something tangible and of value before something intangible and perhaps of value?

On the surface, this is an easy case for Apple. Their argument is simple: iOS is not a licensed product like Windows. You can’t run it - virtualized or not - without Apple’s permission. I do think Apple will win the follow up appeal.

For even a billion dollar company (let alone a 2 trillion dollar company), their legal team has had some surprisingly bad failings.

I recall another “cupcake” case they lost a few years ago because they walked into the courtroom without sufficient evidence (despite it being clear the other party was a copyright-infringing troll) and lost the ruling. Once a legal team starts taking things for granted, it’s time to find a new one.

Very valid points. I think the key is that you’re going to need a complex model for full self driving. No way around that - even if Waymo starts with a rule based model, they’re going to have to work with black boxes and debug them at some point (and they do).

Tesla just decided to jump into the deep end from the beginning.

I mean - I have no idea what’s going on at Tesla, but its very likely they’re doing some internal work with Lidar. They surey understand the risk with w vision-only approach. Elon, and Karpathy, and Laettner previously, aren’t fools. The problem for Tesla is they can’t be too Lidar dependent because nobody mass-produces them

Sounds impressive. But please allow me to rant here:

> are more accurate

Can we please stop using “accurate” as an adjective for models, especially when reporting on studies?

There are many other more telling metrics. Let’s start with precision and recall, and diagnostic ability. “Accuracy” tells nothing about the quality of a model.

/rant

Very impressive that AP disengaged at the beginning.

I don’t have much experience in SDCs, but did some computer vision/ robotics work previously.

I truly think that Tesla’s approach, if it works, will win in the long run (ie - next 15-20 years, I think the breakthrough for true L5 isn’t here yet - but will be in improvements in one-shot learning over the next decade).

Winter driving is a completely different beast than driving out in Arizona. You have to account for snow banks, slipperiness, lidar obstruction, and control itself - which is much harder.

How many thousands of miles of snow driving data does Tesla possess, compared to the #2 player?

Tesla’s approach of vision based, AP disengagement-led training will scale better, precisely due to what you’re looking at in this article - yeah, this is an unimpressive demo - but winter driving, rainy driving, dirt road driving are all general cases for Tesla, while they’re special cases for other companies (from what’s been revealed; Comma might be an exception).

Most people drive in the place they live, and drive on the highway to visit their friends and parents. It’s easy to be deceived into thinking driving is a small problem space of stop signs and lane changes. But even within just North America, there is enormous variety.

There is massive risk involved - these folks are pushing NNs further than any application I’ve seen, but with the last few releases, it suddenly doesn’t look all that crazy.

Secondly, combining rules based systems and neural networks is difficult. You end up with the N * M problem, and inevitably end up missing cases. It makes sense to do the absolute basic safety features as rules, and Tesla might be doing this, but all it takes for Tesla’s NN approach to pay off is one leap in explainability. There is an entire industry working on this - and Tesla will be the single biggest benefactor.

I don’t know who will win L5 - but I’m putting my bets on Tesla for L4.

+1 to changing the pitch.

-1 for early stage startups. The time just after raising money consists of a lot of long weeks. Things break during your release, customers report bugs and want more features. Adhoc support for your big customers because there is no full-fledged support team. That’s the worst fit for a 3-day-a-week employee IMO.

I took a look at your website. It’s quite clear, and I think a lot of people would love such an arrangement, but a word of warning:

BusinessInsider is not a reliable citation - I would try to dig up the original study. Similarly for Forbes - it has lost a lot of the allure since their website hosts a lot of low-quality content now.

Try to find more reputable sources, starting with the studies themselves, NYT, Time, etc

Great question. I run a productivity software startup, and my cofounder brought up the 4-day week idea. We opted not to pursue it. It came down to a few things:

- Onboarding / Collaboration overhead (this compounds) An additional dev is one more to manage, one more to onboard, one more to keep happy, buy hardware and software licenses for, and all that for the same number of man-hours.

- Work compounds. A programmer working on a system or feature for 10 days does the deepest learning in days 7-10. The internals, nitty-gritty stuff. For a 3 day a week employee, the same insights are gained in week 4 (10 days) instead of week 2 (10 days). Of course, I used 10 days as an example, but true expertise is built and compounded over months. It doesn’t matter how many 3-day-a-week hires we have, this process won’t be sped up.

- Compensation / Performance - how do we do this in an equitable way? Again, comes down to compounding impact. If 5 day a week employee makes 5/8 of the impact at the same productivity rate as 3 day a week employee - does that mean they get promoted / rewarded at a different rate?

- Availability / Scheduling - 30 hours is about 3.5 work days a week. It’s an added pain to manage and track who is going to be working when. If your company uses a system for taxes, payroll, vacation accrual, insurance, etc, chances are it is built for 8 by 5 employees first and foremost.

- Various laws around part time and full time workers, based on hours in some jurisdictions. I think 28 hours is the cutoff here in Ontario?