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It's not as if the credits are worth anything, so until they do, it doesn't really matter to the powers that be.įunny, how it went from "measuring" contributed flops to meaning nothing :DĪnyways, as Retvari Zoltan says on gpugrid: That still puts it (if the methodology stands up) on a respectable par with ~#11 on - but no way is Bitcoin Utopia the second-most powerful supercomputer in the world, as its daily credit output would have you believe. If we leave out those three big integer projects, then BOINC's overall floating-point computing power comes out at 4.0 - 4.2 PetaFlops. I could see no sign of any large-scale input from the sort of Uppercase project that Jord describes. SETI and Einstein have large numbers of computers attached: GPUGrid certainly does a lot of floating point maths, but I think rather overstate the work done by their credit awards. Other high-paying projects are SETI, Einstein, and GPUGrid. Looking in particular at the 'Top 100' page, I thing we can identify visually that Collatz, DRTG and PrimeGrid are other high-paying integer math projects (I'm not sure why DRTG shows so strongly - it seems to have been offline for the last couple of months). Strip out that one project (which seems to be accounting for 80% of all credit awarded, these days), and the residual figure is a much more consistent 6.8 PetaFlops across all three sites - and pretty close to the 6.99 PetaFlops we started with here. At the time I collected the figures, the grand total computing power was reported to be between 13 PetaFlops and 32 PetaFlops on the different sites, using that RAC -> GFlops reverse calculation.īut all of that was wildly different interpretations of the Bitcoin Utopia phenomenon, which the three sites were displaying as 6.2, 18.1, and 25.3 PetaFlops respectively. The three sites all collect and aggregate their data at different times of the day, and monitor a different number of projects (between 67 and 145). I looked at three of the major statistics sites: BOINCstats (which tends to be regarded as authoritative by David Anderson) BOINC Combined Statistics (the in-house stats site - the power behind those 'Projects in which you are participating' lists on your account pages at BOINC projects) and Free-DC (the source of the graphs on BOINC's Top 100 multi-project BOINC participants page). Well, since I'm helping to look at various aspects of the credit system at the moment, I thought I'd have a scout around. All of those might be diluting the value Richard points out, I don't know.Īll of those might be diluting the value Richard points out, I don't know. ![]() There have been fan-based projects in the past, there are probably still fan-based projects this moment, that run nothing but Uppercase and give out credit for that. ![]() Now to give you an idea: even you can start a project today that pays twelve trillion credit per task done on the Uppercase application included in the package. Everything is open source, everything can be hacked, edited, reprogrammed, adjusted, tinkered with to the hearts content of the project administrator. Aside from perhaps not being included in the Projects List. Not much there can be done to force a project to follow the will of BOINC development, while users can en-masse say that they leave the place. Maybe, but then that's normally done by the users rather than the BOINC development team, for the simple reason of what to do when they would request that a project follow their credit sampling, and the project says "no"? I think a more reasonable request would be to have the project look at their credit distribution, is it as skewed for non-ASIC devices? ![]() ![]() Oh, Beekeeper Studio is also open source and free □. DB Browser for SQLite is a high quality, visual, open source tool to create, design, and edit database files compatible with SQLite. Pop open a new table and easily execute SQL, tab back and forth with your other views quickly and easily.īrowse, edit, and update your SQlite database in realtime with Beekeeper Studio, the cleanest and easiest to use SQLite DB Browser available. Simply click the (+) button at the top of the table list to create a table using the table creation UI.
It’s a tool with a bent towards GTD but also general good piece of software for handling tasks, projects and goals. For more on that, see “The GTD Way: Managing Your Tasks and Information with Evernote”.Ībout a year ago, I decided to switch to Todoist, a multiplatform task and project manager. By adapting Evernote, a flexible tool for handling notes and media, I created my own GTD task manager. Personally, in my first adaptation of GTD I used Evernote. A comprehensive review of checklist and task managers is beyond this post. Most of the book looks at a non-digital approach of using paper, folders, file cabinets, and a calendar.įor the more digitally inclined, there are a lot options for managing your checklist, todo list and projects. Allen’s website lists several options from software (like Wunderlist, OneNote and Evernote) to physical notebooks. The book itself is fairly agnostic about a preferred tool and approach. When it comes to implementing David Allen’s “Getting Things Done” or GTD methodology, there are a lot of options. In turn, this product of productivity (completed tasks) become a great measurement of your productivity too. The point is to get tasks done and projects advanced. The GTD system leads us to a better “product” of productivity: getting things done or, to put it another way, completed tasks, project and goals. His book and methodology pushed me get dominion over the “stuff” and create a system to get it out of my head and into a process. This is situation is the challenge of most modern digital workers, as Allen points out. Figuring out your priorities and managing all of the stuff is hard. Like a lot of modern digital worker, I have struggled with all of the work and information I have to deal with. It’s a book I highly recommend and have gifted the most. I’ve read a lot of books about work and productivity, but arguably the most influential on me (and a lot of other high functioning executives and entrepreneurs) is David Allen’s “Getting Things Done.” It was a game changer for me personally. You need to process, priorize and execute. ![]() You have to deal with a world of too much information and endless distractions. In order to get tasks, projects and important goals done, you have to be organized. ![]() It’s about completing the tasks that drive value to your projects, business and life. ![]() Even after that the tool has a more palatable basic tier of $20/mo.The true goal of productivity is to get your most important shit done. My initial thought would be to add a label in Todoist called something like “Migrate” and have Zoho Flow or Zapier watch for tasks with that label and move them into Asana.Īlso, I noticed that Import2 has a stand alone CSV tool now ( ) that offers a 14 day free trial…so as long as you were doing a complete migration maybe it could be done within that 14 day time period. We use Zoho One so I’m tempted to try and figure out some sort of Zoho Flow (just like Zapier but not as robust) or even a Zapier integration maybe…I guess to make anything like that work you’d have to do “something” to trigger those integrations, like editing tasks in mass. I’d love to know if anyone has a more up to date suggestion for those of us who don’t have bottomless wallets. To be fair though, when this was originally written Import2 had a free tier…used it for some Hubspot export stuff and then soon after it was insanely expensive! 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The book Classical Topology and Combinatorial Group Theory champions this point of view, giving a proof of the Jordan Curve Theorem which is much easier than the standard one.Īs you can see, most interesting mathematics takes place in computable sets, and the topics that don't are also dealt with with finistic reasoning (the proofs in functional analysis and set theory fit inside books after all). Topology is precisely the work of taming uncountable spaces into chunks with invariants in such a way we can use finitistic arguments to prove theorems. Every single function and procedure treated in classical calculus is computable. ![]() The $\epsilon-\delta$ definiton of limit is algorithmic: if you give me an $\epsilon$, I must have a procedure for finding a $\delta$ that approximates the limit well enough. This could be a closed form, an integral, or some numerical method. To solve a differential equation is to find an algorithm which approximates the solution arbitrarily. Regarding the impossibility of working with differential equations, this is just not the case. To me there couldn't be a stronger sign that uncountable sets are inadequate when our system of reasoning simply can't filter them unambiguously. Mathematicians are not limited by computation, much like Turing machines are not bounded by $2^$. Without this, demonstrating the existence of a point relevant to the problem in hand may require a more complex case-by-case analysis.Ī similar enriching of possibilities occurs in measure theory, where countably additive measures make a huge difference. It is particularly useful to know that the real numbers are essentially a unique model for the key defining properties, so when we prove theorems we know we are all talking about the same thing.Īdmitting countable limits of rationals, rather than simply finite limits, turns out to have far-reaching consequences - for example, we can prove the intermediate value theorem. These numbers are packaged in a convenient form which enables results to be proved for all the numbers we might encounter on our mathematical travels. It is true that only countably many of these can ever be defined, but we don't know which we might want in advance. Real numbers include all the numbers you might want to use - including limits of convergent sequences of rational numbers. Then again it is crucial to have a system where you can use lots of tools and the reals provide that. Sometimes what one needs to know is whether or not an integral converges, but the actual value where it converges to is immaterial, and whether or not it is a computable number in any sense is irrelevant. Again, this is a small price to pay for having a fantastic accompanying mathematical theory.įinally, don't forget that it is not always a single number that is sought as the result of a computation. Now, without that property most of analysis fails, so we really need the reals to be complete, and that necessarily means lots of numbers out of our reach. Perhaps most importantly is that they form a complete metric space. The fact that you only use a fraction of those numbers is not particularly relevant.Īnother reason for introducing the real numbers is that they exhibit pleasant computational properties. That's not a big deal, and its a rather small price to pay for having a really convenient system of numbers of great relevance to physics to work with. Now, when you do that you find that are actually created lots and lots of new real numbers, most of which can't ever be obtained as the outcome of anything. Then the outcome of an experiment really is a real number. One way to do so is define the real numbers. Now, mathematically it is convenient to be able to speak of this 'actual outcome' as a single object in a nice system of measurements. 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