jakegold

It's hard to get much more accurate than Chrony with NTP. Even real-world PTP implementations don't often aim for more accuracy than is possible with Chrony.

Because it turns out NTP can be much more accurate than most people realize.

From the Chrony FAQ[1]:

When combined with local hardware timestamping, good network switches, and even shorter polling intervals, a sub-microsecond accuracy and stability of a few tens of nanoseconds might be possible

Good network switches and NICs with hardware timestamping support are commonplace now in server environments. NTP with Chrony is pretty hard to beat in terms of simplicity, reliability, and accuracy.

1. https://chrony.tuxfamily.org/faq.html

They mention it, but Chrony is really amazing as a time sync solution for most environments. I've used most open source (and some proprietary) implementations of NTP and PTP over the years. Chrony is more reliable, simpler to configure, and better documented than all of them.

These days, I would always choose to use Chrony when possible.

Quoting the Chrony FAQ [1]:

When combined with local hardware timestamping, good network switches, and even shorter polling intervals, a sub-microsecond accuracy and stability of a few tens of nanoseconds might be possible.

This is a level of accuracy many people think is only achievable using PTP, but Chrony can do it using NTP. Not that most people need this level of accuracy, but some do, and it's just plain cool.

Once you set up time sync, the next step is to make sure you have sufficient monitoring in place, so that you can stop worrying about all the subtle issues caused by out of sync systems.

1. https://chrony.tuxfamily.org/faq.html

In the secondary market, securities are sold by and transferred from one investor or speculator to another. It is therefore important that the secondary market be highly liquid (originally, the only way to create this liquidity was for investors and speculators to meet at a fixed place regularly; this is how stock exchanges originated, see History of the Stock Exchange). As a general rule, the greater the number of investors that participate in a given marketplace, and the greater the centralization of that marketplace, the more liquid the market.

Fundamentally, secondary markets mesh the investor's preference for liquidity (i.e., the investor's desire not to tie up his or her money for a long period of time, in case the investor needs it to deal with unforeseen circumstances) with the capital user's preference to be able to use the capital for an extended period of time.[2]

Accurate share price allocates scarce capital more efficiently when new projects are financed through a new primary market offering, but accuracy may also matter in the secondary market because: 1) price accuracy can reduce the agency costs of management, and make hostile takeover a less risky proposition and thus move capital into the hands of better managers; and 2) accurate share price aids the efficient allocation of debt finance whether debt offerings or institutional borrowing.

Humans tend to think we’re adept at the games we create, but computers have proven time and time again that we’re just not fast enough to stay on top. Machines have defeated us in chess, Jeopardy!, and even the deviously complex board game Go. Google-owned DeepMind gets credit for that last one, and now it’s dominating another game: StarCraft II. After just 18 months, DeepMind has an AI that beats the world’s best StarCraft II players, and it’s not even close.

DeepMind called its Go-dominating AI “AlphaGo,” and the StarCraft-playing bot got a similar moniker. It’s called AlphaStar, and it has more than 200 years of practice under its belt. Back at Blizzcon in November, DeepMind said its machine learning platform had managed to beat the “Insane” difficulty in-game AI about half the time. Well, it’s gotten much better since then.

AlphaStar is a convolutional neural network. The team started with replays of pro matches, giving AlphaStar a starting point to begin playing the game. Through intensive training with competing models, DeepMind was able to teach AlphaStar how to play the game as well as the best human players. Over time, it whittled the AI down to the five best “agents,” and that’s what it deployed against some of the most skilled StarCraft II players in the world.

The matches actually took place in December, so today’s internet broadcast mostly featured replays of those matches. First, AlphaStar battled a player known as TLO, who primarily plays Zerg in StarCraft. However, he had to play Protoss as that’s the only race AlphaStar trains with right now. This competition wasn’t even close — despite TLO’s best efforts, AlphaStar beat him five games to zero. Next, a different AlphaStar agent went up against a seasoned Protoss player called MaNa. Some of these matches were closer, but AlphaStar still won five games to zero. MaNa also competed against a new AlphaStar agent live on the stream, and this time MaNa finally pulled out a win.

AlphaStar demonstrated impressive micromanagement of units throughout the matches. It was quick to move damaged units back, cycling stronger ones into the front line of battles. AlphaStar also controlled the pace of battle by bringing units forward and dropping back at just the right times to inflict damage while taking less fire itself. This isn’t just a function of brute force actions per minute (APM) — AlphaStar has substantially lower APM compared with the human players, but it’s making smarter choices.

The AI also had some interesting strategic quirks. It often rushed units up ramps, which is dangerous in StarCraft II as you can’t see what’s up there until you move in. Still, it somehow worked. AlphaStar also eschewed the tried-and-true tactic of blocking off the base ramp with a wall of buildings. That’s StarCraft 101, but the AI didn’t bother with it and still managed to defend its bases.

It wasn’t until the final live match that the human challenger spotted a flaw in one of the agents. That version of AlphaStar committed to moving almost its entire army as one with the intention of swarming MaNa’s base. However, MaNa was able to repeatedly warp in a few units at the back of AlphaStar’s base. Each time, AlphaStar would turn its army around to deal with the threat. That gave MaNa enough time to build up a more powerful force and take the fight to the AI.

At the end of the day, AlphaStar won 10 matches against pro players and lost just one. If AlphaStar learned from that last match, it might be unbeatable next time.

In January 2019, DeepMind introduced AlphaStar, a program playing the real-time strategy game StarCraft II. AlphaStar uses a reinforced learning to learn the basics of the Protoss race based on replays from human players, and later played against itself to enhance its skills. At the time of the presentation, AlphaStar had knowledge equivalent to 200 years of playing time; it won 10 consecutive matches against professional players, and lost just one.

FUSE is particularly useful for writing virtual file systems. Unlike traditional file systems that essentially work with data on mass storage, virtual filesystems don't actually store data themselves. They act as a view or translation of an existing file system or storage device.

In principle, any resource available to a FUSE implementation can be exported as a file system.

There are many uses for podcasting for the classroom. They can be used to convey instructional information from the teacher or trainer, motivational stories, and auditory case studies. Podcasts can also be used by the learners as artifacts and evidence of learning; for example, a student might prepare a brief podcast as a summary of a concept in lieu of writing an essay. Podcasts can also be used as a means of self-reflection on the learning processes or products.[11] Podcasts can help keep students on the same page, including those that are absent. Absent students can use podcasts to see class lectures, daily activities, homework assignments, handouts, and more.[citation needed] A review of literature that reports the use of audio podcasts in K-12 and higher education found that individuals (1) use existing podcasts and/or (2) create their own podcasts. Students can create their own podcast to share their learning experiences with each other and also with other students from other schools.

The onboard passive hydrogen maser and rubidium clocks are very stable over a few hours. If they were left to run indefinitely, though, their timekeeping would drift, so they need to be synchronized regularly with a network of even more stable ground-based reference clocks. These include active hydrogen maser clocks and clocks based on the caesium frequency standard, which show a far better medium and long-term stability than rubidium or passive hydrogen maser clocks. These clocks on the ground are gathered together within the parallel functioning Precise Timing Facilities in the Fucino and Oberpfaffenhofen Galileo Control Centres. The ground based clocks also generate a worldwide time reference called Galileo System Time (GST), the standard for the Galileo system and are routinely compared to the local realizations of UTC, the UTC(k) of the European frequency and time laboratories.

The EU's stance is that Galileo is a neutral technology, available to all countries and everyone. At first, EU officials did not want to change their original plans for Galileo, but have since reached the compromise that Galileo is to use a different frequency. This allows the blocking or jamming of either GNSS without affecting the other.

Thanks, I gave up too early.

There was a mistake with acceptance/rejection emails for Startup School (not YC core) and so they accepted everyone. We were one of those that was meant to be rejected.

Congrats to Geoff. He, along with others, has done a great job with Startup School. I would suggest that the YC core program could learn a lot from the Startup School program.

For an example of why: my startup was initially rejected from Startup School, then accidentally accepted, and then we kicked butt and won the YC Startup School Grant out of 10,000+ startups. Then we got a YC interview and were rejected for what (I think objectively) was a pretty random reason. And I understand that the stated reason isn't necessarily the full reason.

It was still a very enjoyable and super helpful experience overall. The advice, $10k cash, and cloud credits have helped tremendously and it's still helping a lot.

I just wish I could have competed against other startups for acceptance into the core YC program, rather than have a few people attempt to judge how much of an "animal" I am in 10 minutes as they groggily try to wake themselves up with coffee. Not a knock against them at all (still a very long-term PB fan!), just the process.

Because I know I'm much better at competing with hard sustained effort over the long-term than I am at seeming super impressive on first impression.

Anyway, that's my suggestion.

We will hopefully be ramen profitable soon, and may not try for YC again because it is mentally exhausting. But whatever we do, Startup School was a huge boost for us and YC asked nothing in return, so thank you very, very much.

Momentum!

At least for me, productivity requires momentum and being at a standstill feels like quite a hurdle. So at least at first, work on something you're really excited about. Something that won't feel like work. It could be a small project you couldn't otherwise really justify spending time on, but do it anyway. Visualize the end result, get yourself excited, and finish it.

Once you've gained some speed you should be able to tackle other tasks and projects. Social media will start to seem less compelling than it does now.

Very exciting stuff. I hope this experiment is successful and it paves a way towards a completely open model. It seems only logical that any startup should be able to enter the arena and fail or succeed on their actual merit itself, and not some kind of proxy or guess.

This really does seem like one of those magical "listen to your users" moments in a startup's life. Startup School teaching by example and practicing what it preaches.