10 interesting stories served every morning and every evening.
In Masters of Doom, a book about the game development company id Software and its inﬂuence on popular culture, David Kushner reﬂected on the unconventional working style of the company’s ace coder, John Carmack.
To increase his productivity and ﬁnd a break from distraction while working on his breakthrough Quake engine, Carmack adopted an aggressive tactic — he began gradually shifting the start of his workday.
Eventually, he was starting his programming in the evening and ﬁnishing just before dawn.
These uninterrupted stretches of silence, isolation, and deep work allowed Carmack to reinvent gaming with the world’s ﬁrst lightning-fast 3D game engine.
While Carmack’s schedule may have made it harder for the rest of his team to reach him at times, the value he produced while working at his full cognitive capacity far outweighed that inconvenience.
The ‘Carmacks’ of the world — those whose work involves writing code, getting creative, and problem-solving — operate on what tech investor Paul Graham refers to as the maker schedule. In his 2009 essay titled “Maker’s Schedule, Manager’s Schedule”, he argued that people who make things operate on a different schedule from those who manage things.
Managers’ days are “cut into one-hour intervals. You can block off several hours for a single task if you need to, but by default, you change what you’re doing every hour.”Makers, on the other hand, “generally prefer to use time in units of half a day at least. You can’t write or program well in units of an hour. That’s barely enough time to get started.”
For managers, interruptions in the form of meetings, phone calls, and Slack notiﬁcations are normal. For someone on the maker schedule, however, even the slightest distraction can have a disruptive effect.
Research shows that it takes as long as 30 minutes for makers to get into the ﬂow and we can’t simply switch from one task to another. Instead, it changes the whole mode in which we work and this constant context switching prevents our brains from fully engaging the task at hand. A study conducted by Gloria Marks, a Professor of Informatics at the University of California, revealed that it takes us an average of 23 minutes and 15 seconds to refocus on a task after an interruption, and even when we do, we experience a decrease in productivity.
A single standup meeting can, therefore, blow a whole afternoon by breaking it into two pieces each too small to do anything substantial in. And if you know your work is going to be interrupted, why bother starting anything ambitious?
Working in an open ofﬁce renders us even more vulnerable.
Separately, managers and makers work ﬁne. Friction happens when they meet. And since most powerful people operate on the manager schedule, they’re in a position to force everyone to adapt to their schedule, potentially wrecking the makers’ productivity.
And the predictable result is — almost no organizations today support maker schedules.
The reasons why most managers fail to accommodate the makers and their schedule are quite straightforward.
Instant messaging tools like Slack transformed the way we communicate at work, empowering the managers to collaborate with makers at their convenience. The work style these tools enable ﬁts the managers’ schedule so neatly, that they often don’t see the costs to the maker. Immediate response becomes the implicit expectation, with barely any barriers or restrictions in place.
And in the absence of barriers, convenience always wins.
The reason why many managers fail to see and address this problem is that they are used to looking at communication and assume it’s a good thing. Because they see activity. People are attending meetings, talking to each other, the online presence indicators are bright green. Clearly, a lot of work is happening!
At the same time, real work is not getting done. Meaningful work is usually done quietly and in solitude.
Most makers don’t have the levels of control and autonomy necessary to block out half a day without any calls or meetings. So instead of pushing the issue with the management, we try to compensate by attempting to multitask — unfortunately, that rarely works. Building context can take hours, and context switching between communication and creative work only kills the quality of both.
Being busy feels like work to us, but it’s not the work that needs to be done.
In many companies, the choice that the makers face is that between caving to the managers and sacriﬁcing their deep work time and productivity — or offending people.
But there are smarter compromises.
The ﬁrst technique that Paul Graham recommends to simulate the manager’s schedule within the maker’s is “ofﬁce hours”.
Ofﬁce hours are chunks of time that makers set aside for meetings, while the rest of the time they are free to go into a Do Not Disturb mode. Managers get their (brief) face time with the makers on their team, while makers get long stretches of time to get stuff done.
During his time as a technical lead at Buffer, Harrison Harnisch decided to apply this concept to his schedule, splitting his week up, and setting clear expectations about how a day should be treated. On Mondays and Fridays, he focused solely on collaborating with his team, while reserving the rest of the week for heads-down coding.
We have adopted a similar schedule at Nuclino, reserving several days per week for our “maker time” while working from home. It doesn’t mean that we ignore all messages and only look up from our work when something is on ﬁre — but the general expectation is that it’s okay to not be immediately available to your teammates when you are focusing on your work.
“It is important to note that deep work time can be interrupted by things that are both urgent and important. However, treating every question as urgent is likely to do more harm than good.”
It’s a natural knee-jerk reaction for many managers to schedule a meeting whenever a decision needs to be made. Most of the time, such meetings quickly morph into ad hoc group brainstorming sessions that may feel productive because of all the talking, but at the end of the day yield no tangible results, disrupting everyone’s work for no good reason.
On the days that are reserved for collaboration, it does not always need to happen synchronously. Nor does it have to be face-to-face for it to be meaningful and productive.
Instead, communication can happen at a quieter asynchronous frequency in the form of thoughtful, written discussions rather than soul-sucking meetings or erratic one-line-at-a-time chat messages.
“People think it’s efﬁcient to distribute information all at the same time to a bunch of people around a room. But it’s actually a lot less efﬁcient than distributing it asynchronously by writing it up and sending it out and letting people absorb it when they’re ready to so it doesn’t break their days into smaller bits.”
In our experience, the best way to prevent a useless meeting is to write up our goals and thoughts ﬁrst. Despite working in the same ofﬁce, our team at Nuclino has converted nearly all of our meetings into asynchronously written reports.
Not only does that preserve a detailed log — every meeting and project we’ve ever had is neatly documented — it also helps every team member have a say, properly express their thoughts, and absorb the input of others at a time and pace that is convenient for them.
A lot of the interruptions happen because people have repetitive questions and can’t ﬁnd the answers on their own. If the issue is a blocker, having to wait till the “ofﬁce hours” start can be frustrating.
The most straightforward way to address this is to build a team knowledge base. Not only does that minimize the number of repetitive questions bounced around the ofﬁce, it allows new team members to basically onboard themselves.
But at the end of the day, it’s a matter of culture. None of these rules would work if the management fails to see that makers need to follow a different schedule — and to make an effort to respect it.
The truth is, though there is a time and place for synchronous, instant, and face-to-face communication, that time is not all the time. In fact, very few things are urgent enough to justify the potential cost of an interruption. Most are trivial. And while the ofﬁces we work in and the collaboration tools we use may nudge us to adopt the ASAP culture, being always available and keeping busy are not sustainable substitutes for challenging, thoughtful work.
Keep calm and follow the nohello rule.
Google has begun testing their upcoming extension manifest V3 in the the latest Chrome Canary build, and with this initial ‘alpha’ release, developers can begin testing their extensions under the upcoming speciﬁcation.
In a post to the Chromium Extensions Google group, Simeon Vincent, a Google Developer Advocate for Chrome Extensions, stated that as of October 31st a developer preview of the extension manifest v3 is now available in the Chrome 80 Canary build.
“Think of it as an early alpha. The “dev preview” is the ﬁrst opportunity for extensions developers to start experimenting with a work-in-progress version of the MV3 platform.
We’re far from ﬁnished with the implementation work on the MV3 platform, so ﬁrst and foremost expect changes.
As for what’s changing, the four big-ticket items in MV3 are:
The declarativeNetRequest API has already been available for experimentation in Chrome Canary and we’re continuing to iterate on it’s capabilities
As part of this launch, Google has created a Migrating to Manifest V3 guide that developers can use to migrate their existing extensions.
The most controversial aspect of the extension manifest v3 is the upcoming changes to the webRequest API. In v3, Google has changed the API so that extensions can only monitor browser connections, but not modify any of the content before it’s displayed.
Instead Google wants developers to use the declarativeNetRequest API, which has the browser, not the extension, strip content or resources from a visited web sites. This API, though, has a limit of 30,000 rules that can be created.
Unfortunately, this change will break popular ad blockers such as uBlock Origin, which rely on the original functionality of the webRequest API and need more rules than are available in the declarativeNetRequest API.
If you are using the current Chrome Canary build you can test the new changes by creating your own extension and setting its manifest version to 3.
BrowserNative.com, who ﬁrst reported about the developer preview launch, shared with BleepingComputer a test extension that can be used to test the new changes.
For example, in the extension manifest.json ﬁle below, the version has been set to 3 and it’s using a background.scripts call.
As background.scripts is no longer supported, trying to load the extension will generate an error, as shown below, that states you need to “use the “background.service_worker” key instead”.
If you switch the extension to use a service_worker instead then the extension loads properly into Google Chrome.
All extension developers should consult the migration guide to make sure their extensions will work properly with the upcoming manifest v3 changes.
While they are now in preview, Google expects the manifest v3 to go live in 2020 with the v2 end of life to be determined in the future.
(This is a talk I gave at the last
Y Combinator dinner of the summer.
Usually we don’t have a speaker at the last dinner; it’s more of
a party. But it seemed worth spoiling the atmosphere if I could
save some of the startups from
preventable deaths. So at the last minute I cooked up this rather
grim talk. I didn’t mean this as an essay; I wrote it down
because I only had two hours before dinner and think fastest while
A couple days ago I told a reporter that we expected about a third of the companies we funded to succeed. Actually I was being conservative. I’m hoping it might be as much as a half. Wouldn’t it be amazing if we could achieve a 50% success rate?
Another way of saying that is that half of you are going to die. Phrased that way, it doesn’t sound good at all. In fact, it’s kind of weird when you think about it, because our definition of success is that the founders get rich. If half the startups we fund succeed, then half of you are going to get rich and the other half are going to get nothing.
If you can just avoid dying, you get rich. That sounds like a joke, but it’s actually a pretty good description of what happens in a typical startup. It certainly describes what happened in Viaweb. We avoided dying till we got rich.
It was really close, too. When we were visiting Yahoo to talk about being acquired, we had to interrupt everything and borrow one of their conference rooms to talk down an investor who was about to back out of a new funding round we needed to stay alive. So even in the middle of getting rich we were ﬁghting off the grim reaper.
You may have heard that quote about luck consisting of opportunity meeting preparation. You’ve now done the preparation. The work you’ve done so far has, in effect, put you in a position to get lucky: you can now get rich by not letting your company die. That’s more than most people have. So let’s talk about how not to die.
We’ve done this ﬁve times now, and we’ve seen a bunch of startups die. About 10 of them so far. We don’t know exactly what happens when they die, because they generally don’t die loudly and heroically. Mostly they crawl off somewhere and die.
For us the main indication of impending doom is when we don’t hear from you. When we haven’t heard from, or about, a startup for a couple months, that’s a bad sign. If we send them an email asking what’s up, and they don’t reply, that’s a really bad sign. So far that is a 100% accurate predictor of death.
Whereas if a startup regularly does new deals and releases and either sends us mail or shows up at YC events, they’re probably going to live.
I realize this will sound naive, but maybe the linkage works in both directions. Maybe if you can arrange that we keep hearing from you, you won’t die.
That may not be so naive as it sounds. You’ve probably noticed that having dinners every Tuesday with us and the other founders causes you to get more done than you would otherwise, because every dinner is a mini Demo Day. Every dinner is a kind of a deadline. So the mere constraint of staying in regular contact with us will push you to make things happen, because otherwise you’ll be embarrassed to tell us that you haven’t done anything new since the last time we talked.
If this works, it would be an amazing hack. It would be pretty cool if merely by staying in regular contact with us you could get rich. It sounds crazy, but there’s a good chance that would work.
A variant is to stay in touch with other YC-funded startups. There is now a whole neighborhood of them in San Francisco. If you move there, the peer pressure that made you work harder all summer will continue to operate.
When startups die, the ofﬁcial cause of death is always either running out of money or a critical founder bailing. Often the two occur simultaneously. But I think the underlying cause is usually that they’ve become demoralized. You rarely hear of a startup that’s working around the clock doing deals and pumping out new features, and dies because they can’t pay their bills and their ISP unplugs their server.
Startups rarely die in mid keystroke. So keep typing!
If so many startups get demoralized and fail when merely by hanging on they could get rich, you have to assume that running a startup can be demoralizing. That is certainly true. I’ve been there, and that’s why I’ve never done another startup. The low points in a startup are just unbelievably low. I bet even Google had moments where things seemed hopeless.
Knowing that should help. If you know it’s going to feel terrible sometimes, then when it feels terrible you won’t think “ouch, this feels terrible, I give up.” It feels that way for everyone. And if you just hang on, things will probably get better. The metaphor people use to describe the way a startup feels is at least a roller coaster and not drowning. You don’t just sink and sink; there are ups after the downs.
Another feeling that seems alarming but is in fact normal in a startup is the feeling that what you’re doing isn’t working. The reason you can expect to feel this is that what you do probably won’t work. Startups almost never get it right the ﬁrst time. Much more commonly you launch something, and no one cares. Don’t assume when this happens that you’ve failed. That’s normal for startups. But don’t sit around doing nothing. Iterate.
I like Paul Buchheit’s suggestion of trying to make something that at least someone really loves. As long as you’ve made something that a few users are ecstatic about, you’re on the right track. It will be good for your morale to have even a handful of users who really love you, and startups run on morale. But also it will tell you what to focus on. What is it about you that they love? Can you do more of that? Where can you ﬁnd more people who love that sort of thing? As long as you have some core of users who love you, all you have to do is expand it. It may take a while, but as long as you keep plugging away, you’ll win in the end. Both Blogger and Delicious did that. Both took years to succeed. But both began with a core of fanatically devoted users, and all Evan and Joshua had to do was grow that core incrementally.
Wufoo is on the same trajectory now.
So when you release something and it seems like no one cares, look more closely. Are there zero users who really love you, or is there at least some little group that does? It’s quite possible there will be zero. In that case, tweak your product and try again. Every one of you is working on a space that contains at least one winning permutation somewhere in it. If you just keep trying, you’ll ﬁnd it.
Let me mention some things not to do. The number one thing not to do is other things. If you ﬁnd yourself saying a sentence that ends with “but we’re going to keep working on the startup,” you are in big trouble. Bob’s going to grad school, but we’re going to keep working on the startup. We’re moving back to Minnesota, but we’re going to keep working on the startup. We’re taking on some consulting projects, but we’re going to keep working on the startup. You may as well just translate these to “we’re giving up on the startup, but we’re not willing to admit that to ourselves,” because that’s what it means most of the time. A startup is so hard that working on it can’t be preceded by “but.”
In particular, don’t go to graduate school, and don’t start other projects. Distraction is fatal to startups. Going to (or back to) school is a huge predictor of death because in addition to the distraction it gives you something to say you’re doing. If you’re only doing a startup, then if the startup fails, you fail. If you’re in grad school and your startup fails, you can say later “Oh yeah, we had this startup on the side when I was in grad school, but it didn’t go anywhere.”
You can’t use euphemisms like “didn’t go anywhere” for something that’s your only occupation. People won’t let you.
One of the most interesting things we’ve discovered from working on Y Combinator is that founders are more motivated by the fear of looking bad than by the hope of getting millions of dollars. So if you want to get millions of dollars, put yourself in a position where failure will be public and humiliating.
When we ﬁrst met the founders of
Octopart, they seemed very smart, but not a great bet to succeed, because they didn’t seem especially committed. One of the two founders was still in grad school. It was the usual story: he’d drop out if it looked like the startup was taking off. Since then he has not only dropped out of grad school, but appeared full length in
Newsweek with the word “Billionaire” printed across his chest. He just cannot fail now. Everyone he knows has seen that picture. Girls who dissed him in high school have seen it. His mom probably has it on the fridge. It would be unthinkably humiliating to fail now. At this point he is committed to ﬁght to the death.
I wish every startup we funded could appear in a Newsweek article describing them as the next generation of billionaires, because then none of them would be able to give up. The success rate would be 90%. I’m not kidding.
When we ﬁrst knew the Octoparts they were lighthearted, cheery guys. Now when we talk to them they seem grimly determined. The electronic parts distributors are trying to squash them to keep their monopoly pricing. (If it strikes you as odd that people still order electronic parts out of thick paper catalogs in 2007, there’s a reason for that. The distributors want to prevent the transparency that comes from having prices online.) I feel kind of bad that we’ve transformed these guys from lighthearted to grimly determined. But that comes with the territory. If a startup succeeds, you get millions of dollars, and you don’t get that kind of money just by asking for it. You have to assume it takes some amount of pain.
And however tough things get for the Octoparts, I predict they’ll succeed. They may have to morph themselves into something totally different, but they won’t just crawl off and die. They’re smart; they’re working in a promising ﬁeld; and they just cannot give up.
All of you guys already have the ﬁrst two. You’re all smart and working on promising ideas. Whether you end up among the living or the dead comes down to the third ingredient, not giving up.
So I’ll tell you now: bad shit is coming. It always is in a startup. The odds of getting from launch to liquidity without some kind of disaster happening are one in a thousand. So don’t get demoralized. When the disaster strikes, just say to yourself, ok, this was what Paul was talking about. What did he say to do? Oh, yeah. Don’t give up.
You’re writing software that processes data, and it works ﬁne when you test it on a small sample ﬁle. But when you load the real data, your program crashes.
The problem is that you don’t have enough memory—if you have 16GB of RAM, you can’t load a 100GB ﬁle. At some point the operating system will run out of memory, fail to allocate, and there goes your program.
So what can you do? You could spin up a Big Data cluster—all you’ll need to do is:
* In many cases, learn a completely new API and rewrite all your code.
This can be expensive and frustrating; luckily, in many cases it’s also unnecessary.
You need a solution that’s simple and easy: processing your data on a single computer, with minimal setup, and as much as possible using the same libraries you’re already using.
And much of the time you can actually do that, using a set of techniques that are sometimes called “out-of-core computation”.
* Why you need RAM at all.
* The easiest way to process data that doesn’t ﬁt in memory: spending some money.
* The three basic software techniques for handling too much data: compression, chunking, and indexing.
Followup articles will then show you how to apply these techniques to particular libraries like NumPy and Pandas.
Before we move on to talking about solutions, let’s clarify why the problem exists at all. Your computer’s memory (RAM) lets you read and write data, but so does your hard drive—so why does your computer need RAM at all? Disk is cheaper than RAM, so it can usually ﬁt all your data, so why can’t your code just limit itself to reading and writing from disk?
In theory, that can work. However, even the more modern and fast solid-state hard drives (SSDs) are much, much slower than RAM:
If you want fast computation, data has to ﬁt in RAM, otherwise your code may run as much as 150× times more slowly.
The easiest solution to not having enough RAM is to throw money at the problem. You can either buy a computer or rent a virtual machine (VM) in the cloud with lots more memory than most laptops. In November 2019, with minimal searching and very little price comparison, I found that you can:
* Buy a Thinkpad M720 Tower, with 6 cores and 64GB RAM, for $1074.
* Rent a VM in the cloud, with 64 cores and 432GB RAM, for $3.62/hour.
These are just numbers I found with minimal work, and with a little more research you can probably do even better.
If spending some money on hardware will make your data ﬁt into RAM, that is often the cheapest solution: your time is pretty expensive, after all.
Sometimes, however, it’s insufﬁcient.
For example, if you’re running many data processing jobs, over a period of time, cloud computing may be the natural solution, but also an expensive one. At one job the compute cost for the software I was working on would have used up all our projected revenue for the product, including the all-important revenue needed to pay my salary.
If buying/renting more RAM isn’t sufﬁcient or possible, the next step is to ﬁgure out how to reduce memory usage by changing your software.
Compression means using a different representation for your data, in a way that uses less memory. There are two forms of compression:
* Lossless: The data you’re storing has the exact same information as the original data.
* Lossy: The data you’re storing loses some of the details in the original data, but in a way that ideally doesn’t impact the results of your calculation very much.
Just to be clear, I’m not talking about a ZIP or gzip ﬁle, since those typically involve compression on disk. To process the data from a ZIP ﬁle you will typically uncompress it as part of loading the ﬁles into memory. So that’s not going to help.
What you need is compression of representation in memory.
For example, let’s say your data has two values, and will only ever have those two values: “AVAILABLE” and “UNAVAILABLE”. Instead of storing them as a string with ~10 bytes or more per entry, you could store them as a boolean, True or False, which you could store in 1 byte. You might even get the representation down to the single bit necessary to represent a boolean, reducing memory usage by another factor of 8.
Chunking is useful when you need to process all the data, but don’t need to load all the data into memory at once. Instead you can load it into memory in chunks, processing the data one chunk at time (or as we’ll discuss in a future article, multiple chunks in parallel).
Let’s say, for example, that you want to ﬁnd the largest word in a book. You could load all the data into memory at once:
But since in our case the book doesn’t ﬁt into memory, you could instead load the book page by page:
You are using much less memory, since you only have one page of the book in memory at any given time. And you still get the same answer in the end.
Indexing is useful when you only need to use a subset of the data, and you expect to be loading different subsets of the data at different times.
You could solve this use case with chunking: load all the data every time, and just ﬁlter out the data you don’t care about. But that’s slow, since you need to load lots of irrelevant data.
If you only need part of the data, instead of chunking you are better off using an index, a summary of the data that tells you where to ﬁnd the data you care about.
Imagine you want to only read the parts of the book that talk about aardvarks. If you used chunking, you would read the whole book, page by page, looking for aardvarks—but that would take quite a while.
Or, you can go to the end of the book, where the book’s index is, and ﬁnd the entry for “Aardvarks”. It might tell you to read pages 7, 19, and 120-123. So now you can read those pages, and those pages only, which is much faster.
This works because the index is much smaller than the full book, so loading the index into memory to lookup the relevant data is much easier.
The simplest and most common way to implement indexing is by naming ﬁles in a directory:
If you want the data for March 2019, you just load 2019-Mar.csv—no need to load data for February, July, or any other month.
The easiest solution to lack of RAM is spending money to get more RAM. But if that isn’t possible or sufﬁcient in your case, you will one way or another ﬁnding yourself using compression, chunking, or indexing.
These same techniques appear in many different software packages and tools.
Even Big Data systems are built on these techniques: using multiple computers to process chunks of the data, for example.
In follow-up articles I will show you to how to apply these techniques with speciﬁc libraries and tools: NumPy, Pandas, and even ZIP ﬁles. If you want to read these articles as they come out, sign up for my newsletter in the form below.
Google is engaged with one of the U. S.’s largest health-care systems on a project to collect and crunch the detailed personal-health information of millions of people across 21 states.
The initiative, code-named “Project Nightingale,” appears to be the biggest effort yet by a Silicon Valley giant to gain a toehold in the health-care industry through the handling of patients’ medical data. Amazon.com Inc., Apple Inc. and Microsoft Corp. are also aggressively pushing into health care, though they haven’t yet struck deals of…
Another year, another predicted new-car reliability ranking from non-proﬁt consumer products researchers at Consumer Reports. And this year, the domestic automakers aren’t looking so good, taking up 11 of the bottom 12 spots. Here’s a look at reliability rankings for all the brands.
Each year, Consumer Reports sends a questionnaire to its members to learn about issues that they’ve had with their cars over the past year, and the severity of those issues. In their latest survey, CR got info on over 500,000 vehicles from model years 2000 to 2018, gathering information about problems that owners have had in twelve key areas, including engine internals, accessory drive, cooling system, transmission internals, drivetrain, fuel system, electrical system, brakes, climate control, exhaust, in-car electronics, and others.
With typically 200 to 400 samples from each model, Consumer Reports’ latest data shows every American automaker in the bottom half of the predicted new-car reliability rankings.
At the top are Lexus and Toyota once again, with the GX as the most reliable Lexus and the Prius C as the most reliable Toyota. Subaru, Kia, and Inﬁniti have also stayed in the top six spots, though Mazda made a huge leap from 12 to number three. That leap, CR says, comes courtesy of Mazda “[working] out problems that plagued the CX-9 SUV and MX-5 Miata roadster.”
German brands are in the middle, with Audi, BMW, and Mini coming it at numbers seven through nine, Porsche at 11, VW at 16, and Mercedes at 17. Korean brands Hyundai and Genesis—as well as Japanese brands Acura, Nissan, and Honda—are also in the middle there, ranked at 10, 12, 13, 14, and 15, respectively.
Honda’s reliability ranking dropped the most of these brands—down by six thanks to an Odyssey that allegedly has infotainment and door lock issues, yielding “much-worse-than-average reliability.” In addition, the CR-V and Accord’s ratings are down to just “average” thanks in part to “infotainment system and interior rattles,” and the Clarity line of cars apparently has “electronic glitches” that bring it to “much-worse-than-average.”
But on the plus side for Honda, its luxury sister brand, Acura, is up six spots thanks to recently worked-out transmission and infotainment problem, CR says.
Left at the bottom are the Americans, with the exception of Swedish brand Volvo, which is literally at the very bottom, ranked 29, in part because of alleged infotainment issues with the XC60 and XC90, as well as “complaints about engine knocking or pinging” on the S90
Ford sits at number 18 and the once-mighty Buick brand dropped a whopping 11 spots to 19. Consumer Reports attributes the giant drop of GM’s mid-level luxury brand to the Enclave’s nine-speed transmission woes which contributed to its “much-worse-than-average rating.” Other Buicks like the LaCrosse, Encore, and Envision were rated average, CR says.
Lincoln, Dodge, Jeep, Chevy, and Chrysler are ranked between 20 and 24, while GMC, Ram, Tesla, and Cadillac are in positions 25 to 28. The American brands that dropped the most were Chrysler (down seven), Tesla (down six) and Chevrolet (down ﬁve).
CR says Chrysler’s Paciﬁca was one that contributed to the drop in rankings thanks to the minivan’s infotainment and transmission issues.
Update Oct. 24, 2018 8:25 P. M: A Fiat Chrysler spokesperson provided the following comment on the new rankings:
“The quality and reliability of our vehicles is of the greatest importance to all of us here at FCA US. We are in constant communication with our customers, addressing their feedback in an effort to continuously improve the quality of our vehicles. In addition, our teams are aggressively working toward solutions to any concerns to ensure complete customer satisfaction. We encourage people to experience our lineup for themselves, and we thank our loyal customers who continue to love their vehicles as they recommend them to their family and friends.”
As for why Tesla dropped six spots, CR notes the Model S’s reported suspension and door handle issues, as well as the Model X remaining at “much worse than average” thanks in part to the infotainment screen and the Falcon Doors.
We reached out to Tesla, and their spokesperson provided the following comment on about the Model S suspension complaints:
The suspension issues that some Model S customers experienced primarily in 2017 were due to a supplier-related issue that did not pose any threat to vehicle safety or drivability, and presented itself only when the car was parked. The issue has already been addressed for customer vehicles in the ﬁeld and resolved at the source with fundamental design improvements. In addition, there was an unrelated false service alert that some customers received regarding their suspension in 2018, and it was ﬁxed for all customers via an over-the-air software update within two weeks of being reported. Suspension issues for Model S have improved 65% since last year, and we continue to make further improvements.
As for the reported issues with the Model X, Tesla says it’s made changes to ﬁx them:
While the earliest production Model X cars encountered some quality inconsistencies, this is simply not a concern for Model X cars being built today, and it hasn’t been one for quite a while. In fact, the quality of brand-new Model X vehicles today is 3.5 times better than the quality of brand new Model X cars from 2015. And, we continue to improve the reliability of cars already on the road via over-the-air software updates and proactive service bulletins. This proactive approach to improved reliability is one of the reasons why Tesla is the highest rated car brand among consumers, according to Consumer Reports.”
The rest of the company’s statement reads:
“Not only are our cars the safest and best performing vehicles available today, but we take feedback from our customers very seriously and quickly implement improvements any time we hear about issues. That’s just one of the reasons why Model S has been ranked number one on Consumer Reports’ owner satisfaction survey every year since 2013, which was the ﬁrst year Tesla was included in their report.
As for Chevy, it looks like the new Traverse—with the same trans issues as the Enclave—didn’t help any with its “much-worse-than-average reliability.” A General Motors representative provided the following statement:
We are committed to providing our customers high-quality products and remain focused on launching with excellence. Most of our brands continue to maintain or improve relative to industry average. As always, we are interested in obtaining the survey data to better understand our performance and where we can improve.
You can learn more about how Consumer Reports compiles this list here, and you can watch the video above. You can also check out CR’s press release for analysis on the new rankings. Whether you agree with the non-profit’s methodology, the reality is that Consumer Reports’ ﬁndings play a major role in shaping car buyers’ opinions, and also in how automakers actually go about designing their vehicles.
This post has been updated with comment from a Fiat Chrysler spokesperson.
Renaissance Technologies LLC is an American hedge fund ﬁrm based in East Setauket, New York, on Long Island, which specializes in systematic trading using quantitative models derived from mathematical and statistical analyses. The company was founded in 1982 by James Simons, an award-winning mathematician and former Cold War code breaker.
In 1988, the ﬁrm established its most profitable portfolio, the Medallion Fund, which used an improved and expanded form of Leonard Baum’s mathematical models, improved by algebraist James Ax, to explore correlations from which they could proﬁt. Simons and Ax started a hedge fund and named it Medallion in honor of the math awards that they had won.
Renaissance’s ﬂagship Medallion fund, which is run mostly for fund employees, is famed for the best record in investing history, returning more than 66 percent annualised before fees and 39 percent after fees over a 30-year span from 1988 to 2018. Renaissance offers two portfolios to outside investors—Renaissance Institutional Equities Fund (RIEF) and Renaissance Institutional Diversiﬁed Alpha (RIDA).
Simons ran Renaissance until his retirement in late 2009. The company is now run by Peter Brown (after Robert Mercer resigned), both of them were computer scientists specializing in computational linguistics who joined Renaissance in 1993 from IBM Research. Simons continues to play a role at the ﬁrm as non-executive chairman and remains invested in its funds, particularly the secretive and consistently profitable black-box strategy known as Medallion. Because of the success of Renaissance in general and Medallion in particular, Simons has been described as the best money manager on earth. By October 2015, Renaissance had roughly $65 billion worth of assets under management, most of which belong to employees of the ﬁrm.
James Simons founded Renaissance Technologies following a decade as the Chair of the Department of Mathematics at Stony Brook University. Simons is a 1976 recipient of the Oswald Veblen Prize of the American Mathematical Society, which is geometry’s highest honor. He is known in the scientiﬁc community for his work, Chern–Simons theory, which is fundamental in modern theoretical physics, including advanced theories of how invisible ﬁelds like those of gravity interact with matter to produce everything from superstrings to black holes.
The ﬁrm uses quantitative trading, where staff tap data in its petabyte-scale data warehouse to assess statistical probabilities for the direction of securities prices in any given market. Staff attribute the breadth of data on events peripheral to ﬁnancial and economic phenomena that Renaissance takes into account, and the ﬁrm’s ability to manipulate amounts of data by deploying scalable technological architectures for computation and execution. In many ways, Renaissance Technologies, along with a few other ﬁrms, has been synthesizing terabytes of data daily and extracting information signals from petabytes of data for almost two decades now, well before big data and data analytics caught the imagination of mainstream technology.
For more than twenty years, the ﬁrm’s Renaissance Technologies hedge fund, which trades in markets around the world, has employed complex mathematical models to analyze and execute trades, many of them automated. The ﬁrm uses computer-based models to predict price changes in easily traded ﬁnancial instruments. These models are based on analyzing as much data as can be gathered, then looking for non-random movements to make predictions. Some also attribute the ﬁrm’s performance to employing ﬁnancial signal processing techniques such as pattern recognition. The book The Quants describes the hiring of speech recognition experts, many from IBM, including the current leaders of the ﬁrm.
Renaissance employs specialists with non-ﬁnancial backgrounds, including mathematicians, physicists, signal processing experts and statisticians. The ﬁrm’s latest fund is the Renaissance Institutional Equities Fund (RIEF). RIEF has historically trailed the ﬁrm’s better-known Medallion fund, a separate fund that contains only the personal money of the ﬁrm’s executives.
In a 2013 article in The Daily Telegraph, journalist Sarfraz Manzoor described Renaissance staff as math geniuses running Wall Street.
“Of his 200 employees, ensconced in a fortress-like building in unfashionable Long Island, New York, a third have PhDs, not in ﬁnance, but in ﬁelds like physics, mathematics and statistics. Renaissance has been called “the best physics and mathematics department in the world” and, according to Weatherall, “avoids hiring anyone with even the slightest whiff of Wall Street bona ﬁdes.
Renaissance is a ﬁrm run by and for scientists, employing preferably those with non-ﬁnancial backgrounds for quantitative ﬁnance research like mathematicians, statisticians, pure and experimental physicists, astronomers, and computer scientists. Wall Street experience is frowned on and a ﬂair for science is prized. It is a widely held belief within Renaissance that the herdlike mentality among business school graduates is to blame for poor investor returns. Renaissance engages roughly 150 researchers and computer programmers, half of whom have PhDs in scientiﬁc disciplines, at its 50-acre East Setauket campus in Long Island, New York, which is near the State University of New York at Stony Brook. Mathematician Isadore Singer referred to Renaissance’s East Setauket ofﬁce as the best physics and mathematics department in the world.
The ﬁrm’s administrative and back-ofﬁce functions are handled from its Manhattan ofﬁce in New York City. The ﬁrm is secretive about the workings of its business and very little is known about them. The ﬁrm is known for its ability to recruit and retain scientiﬁc types, for having a personnel turnover that is nearly non-existent, and for requiring its researchers to agree to intellectual property obligations by signing non-compete and non-disclosure agreements.
In 1978 Simons left academia and started a hedge fund management ﬁrm called Monemetrics in a Long Island strip mall. The ﬁrm primarily traded currencies at the start. It did not occur to Simons at ﬁrst to apply mathematics to his business, but he gradually realized that it should be possible to make mathematical models of the data he was collecting.
Monemetrics’ name was changed to Renaissance Technologies in 1982. Simons started recruiting some of the mathematicians and data-modeling types from his days at the Institute for Defense Analyses (IDA) and Stony Brook University. His ﬁrst recruit was Leonard Baum, a cryptanalyst from IDA who was also the co-author of the Baum–Welch algorithm. When Baum abandoned the idea of trading with mathematical models and took to fundamental trading, Simons brought in algebraist James Ax from Cornell University. Ax expanded Baum’s models for trading currencies to cover any commodity future and subsequently Simons set up Ax with his own trading account, Axcom Ltd., which eventually gave birth to the profitable fund — Medallion. During the 1980s, Ax and his researchers improved on Baum’s models and used them to explore correlations from which they could proﬁt.
From 2001 through 2013, the fund’s worst year was a 21 percent gain, after subtracting fees. Medallion reaped a 98.2 percent gain in 2008, the year the Standard & Poor’s 500 Index lost 38.5 percent.
In 1988 Renaissance established its most famous and profitable portfolio, the Medallion fund, which used an improved and expanded form of Leonard Baum’s mathematical models improved by pioneering algebraist James Ax to explore correlations from which they could proﬁt. Simons and Ax started a hedge fund and named it Medallion in honor of the math awards that they had won. The mathematical models the company developed worked better and better each year, and by 1988, Simons had decided to base the company’s trades entirely on the models.
By April 1989, peak-to-trough losses had mounted to about 30%. Ax had accounted for such a drawdown in his models and pushed to keep trading. Simons wanted to stop to research what was going on. After a brief standoff, Simons pulled rank and Ax left. Simons turned to Elwyn Berlekamp to run Medallion from Berkeley, California. A consultant for Axcom whom Simons had ﬁrst met at the IDA, Berlekamp had bought out most of Ax’s stake in Axcom and became its CEO. He worked with Sandor Straus, Jim Simons and another consultant, Henry Laufer, to overhaul Medallion’s trading system during a six-month stretch. In 1990, Berlekamp led Medallion to a 55.9% gain, net of fees — and then returned to teaching math at University of California, Berkeley after selling out to Jim Simons at six times the price for which he had bought his Axcom interests 16 months earlier. Straus took the reins of Medallion’s revamped trading system and Medallion returned 39.4% in 1991, 34% in 1992 and 39.1% in 1993, according to Medallion annual reports.
The Medallion fund is considered to be one of the most successful hedge funds ever. It has averaged a 71.8% annual return, before fees, from 1994 through mid-2014. The fund has been closed to outside investors since 1993 and is available only to current and past employees and their families. The ﬁrm bought out the last investor in the Medallion fund in 2005 and the investor community has not seen its returns since then. About 100 of Renaissance’s 275 or so employees are what it calls “qualiﬁed purchasers”, meaning they generally have at least $5 million in assets to invest. The remaining are “accredited investors”, generally worth at least $1 million.
Since 1988, his ﬂagship Medallion fund has generated average annual returns of 66% before charging hefty investor fees—39% after fees—racking up trading gains of more than $100 billion. No one in the investment world comes close. Warren Buffett, George Soros, Peter Lynch, Steve Cohen, and Ray Dalio all fall short.— ‘The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution’ by Gregory Zuckerman 2019
By the year 2000, the computer-driven Medallion fund had made an average of 34% a year after fees since 1988. Simons ran Renaissance until his retirement in late 2009. Between January 1993 and April 2005, Medallion only had 17 monthly losses and out of 49 quarters in the same time period, Medallion only posted three quarterly losses. Between 1989-2005 Medallion had only one year showing a loss: 1989.
[Renaissance] won the [Labor Department]’s permission to put pieces of Medallion inside Roth IRAs. That means no taxes — ever — on the future earnings of a fund that averaged a 71.8 percent annual return, before fees, from 1994 through mid-2014.
Renaissance Technologies terminated its 401(k) retirement plan in 2010 and employees account balances were put into Individual Retirement Accounts. Contributions could be made to a standard Individual Retirement Accounts and then converted to a Roth IRA regardless of income. By 2012 Renaissance was granted a special exemption by the United States Labor Department allowing employees to invest their retirement money in Medallion arguing that Medallion had consistently outperformed their old 401(k) plan. In 2013 Renaissance’s IRA plans had 259 participants whose $86.6 million contribution grew to $153 million that year without fees or annual taxes. Renaissance set up a new 401(k) plan and in November 2014 the Labor Department allowed that plan to be invested in Medallion as well.
In 2005 Renaissance Institutional Equities Fund (RIEF) was created. RIEF has historically trailed the ﬁrm’s better-known Medallion fund, a separate fund that only contains the personal money of the ﬁrm’s executives. Renaissance also offers two Renaissance Institutional Diversiﬁed Alpha (RIDA) to outsiders. Simons ran Renaissance until his retirement in late 2009. Renaissance Institutional Equities Fund had difﬁculty with the higher volatility environment that persisted throughout the end of the summer of 2007. According to an article in Bloomberg in August 2007,
James Simons’s $29 billion Renaissance Institutional Equities Fund fell 8.7% in August 2007 when his computer models used to buy and sell stocks were overwhelmed by securities’ price swings. The two-year-old quantitative, or ‘quant’, hedge fund now has declined 7.4 percent for the year. Simons said other hedge funds have been forced to sell positions, short-circuiting statistical models based on the relationships among securities.
On 25 September 2008, Renaissance wrote a comment letter to the Securities and Exchange Commission, discouraging them from implementing a rule change that would have permitted the public to access information regarding institutional investors’ short positions, as they can currently do with long positions. The company cited a number of reasons for this, including the fact that “institutional investors may alter their trading activity to avoid public disclosure”.
In July 2014 Renaissance Technologies was included in a larger investigation undertaken by Carl Levin and the Permanent Subcommittee on Investigations on tax evasion by wealthy individuals. The focus of the tax avoidance investigation was Renaissance’s trading strategy — which involved transactions with banks such as Barclays Plc and Deutsche Bank AG — through which profits converted from rapid trading were converted into lower-taxed, long-term capital gains. The strategy was also questioned by the Internal Revenue Service (IRS). The higher rates for the ﬁve years under investigation would have been 44.4 percent, as compared to 35 percent, whereas the lower rate was 15 percent, as compared to 23.8 percent.
The IRS contend[ed] that the arrangement Renaissance’s Medallion fund had with the banks, in which the fund owned option contracts rather than the underlying ﬁnancial instruments, is a ruse and that the fund investors owe taxes at the higher rate. Because Medallion could claim that it owned just one asset — the option — and held it for more than a year, investors could declare their gains to be long-term investments.
According to the Center for Responsive Politics, Renaissance is the top ﬁnancial ﬁrm contributing to federal campaigns in the 2016 election cycle, donating $33,108,000 by July. By comparison, over that same period sixth ranked Soros Fund Management has contributed $13,238,551. Renaissance’s managers have also been active in the 2016 cycle, contributing nearly $30 million by June, with Mercer ranking as the #1 individual federal donor, largely to Republicans, and Simons ranked #5, largely to Democrats. They were top donors to the presidential campaigns of Hillary Clinton and Donald Trump.
During the 2016 campaign cycle Simons contributed $26,277,450, ranking as the 5th largest individual contributor. Simons directed all but $25,000 of his funds towards liberal candidates. Robert Mercer contributed $25,059,300, ranking as the 7th largest individual contributor. Robert Mercer directed all funds contributed towards conservative candidates.
Since 1990 Renaissance has contributed $59,081,152 to federal campaigns and since 2001 has spent $3,730,000 on lobbying.
These devices allow you to connect, control and monitor Live with a range of innovative technologies and communication protocols. Use LEGO® MINDSTORMS® EV3, Arduino, or littleBits™ to connect up sensors, lights or motors, open your sound world up to the web through JSON-based APIs, or convert OSC data to MIDI data. The list of input and output possibilities for music & sound creation with Live is almost endless.
These devices allow you to connect, control and monitor Live with a range of innovative technologies and communication protocols. Use LEGO® MINDSTORMS® EV3, Arduino, or littleBits™ to connect up sensors, lights or motors, open your sound world up to the web through JSON-based APIs, or convert OSC data to MIDI data. The list of input and output possibilities for music & sound creation with Live is almost endless.
The Pack consists of 11 Max for Live devices: a toolkit for exploration, or to open up in Max and adapt to your own needs (Max programming knowledge is required for this!). Some devices demonstrate how you can use each protocol to capture different types of data.
You can get the pack using the download button to the right or by forking our repository on GitHub.
Here’s more on how you can use each device.