Why programmers work at night [This essay has been expanded into a book, you should read it, here] Image via Wikipedia A popular saying goes that Programmers are machines that turn caffeine into code. And sure enough, ask a random programmer when they do their best work and there’s a high chance they will admit to a lot of late nights. Some earlier, some later. At the gist of all this is avoiding distractions. I think it boils down to three things: the maker’s schedule, the sleepy brain and bright computer screens. The maker’s schedule Paul Graham wrote about the maker’s schedule in 2009 – basically that there are two types of schedules in this world (primarily?). On the other hand you have something PG calls the maker’s schedule – a schedule for those of us who produce stuff. This is why programmers are so annoyed when you distract them. Because of this huge mental investment, we simply can’t start working until we can expect a couple of hours without being distracted. The sleepy brain Bright computer screens Fin Fin
Beckstrom's law In economics, Beckstrom's law is a model or theorem formulated by Rod Beckstrom. It purports to answer "the decades old question of 'how valuable is a network'", and states in summary that "The value of a network equals the net value added to each user’s transactions conducted through that network, summed over all users." According to its creator, this law can be used to value any network be it social networks, electronic networks, support groups and even the Internet as a whole.[1] This new model values the network by looking from the edge of the network at all of the transactions conducted and the value added to each. It states that one way to contemplate the value the network adds to each transaction is to imagine the network being shut off and what the additional transactions costs or loss would be. As an explicit economic model[edit] where: = value of a network j to all users Vi,j = net present value of all transactions to user i with respect to network j, over any time period
Anatomy of Facebook Conductance (graph) in a graph is defined as: where the are the entries of the adjacency matrix for G, so that is the total number (or weight) of the edges incident with S. The conductance of the whole graph is the minimum conductance over all the possible cuts: Equivalently, conductance of a graph is defined as follows: For a d-regular graph, the conductance is equal to the isoperimetric number divided by d. In practical applications, one often considers the conductance only over a cut. The notion of conductance underpins the study of percolation in physics and other applied areas; thus, for example, the permeability of petroleum through porous rock can be modeled in terms of the conductance of a graph, with weights given by pore sizes. of the capacity of divided by the ergodic flow out of . for the conditional probability of leaving a set of nodes S given that we were in that set to begin with, the conductance is the minimal over sets that have a total stationary probability of at most 1/2. Béla Bollobás (1998).
Mozilla may make Flash click-to-play by default in future Firefox Firefox developers are considering making Web plugins like Adobe Flash an opt-in feature. Although there is still a long way to go before it’s ready for Firefox proper, switching to an opt-in, "click-to-play" approach for plugins could help make Firefox faster, more secure, and a bit easier on the laptop battery. A very early version of the "click-to-play" option for plugins is now available in the Firefox nightly channel. Once that’s installed you’ll need to type about:config in your URL bar and then search for and enable the plugins.click_to_play flag. Once that’s done, visit a page with Flash content and it won’t load until you click on it. While HTML5 reduces the need for Flash and other plugins, they’re still a big part of the Web today. Another popular solution is the click-to-play approach that Mozilla developers are considering. Whether or not the click-to-play approach that Mozilla is considering will ever become the default behavior for Firefox remains to be seen.
Reed's law Reed's law is the assertion of David P. Reed that the utility of large networks, particularly social networks, can scale exponentially with the size of the network. The reason for this is that the number of possible sub-groups of network participants is 2N − N − 1, where N is the number of participants. the number of participants, N, orthe number of possible pair connections, N(N − 1)/2 (which follows Metcalfe's law). so that even if the utility of groups available to be joined is very small on a peer-group basis, eventually the network effect of potential group membership can dominate the overall economics of the system. Derivation[edit] Quote[edit] From David P. "[E]ven Metcalfe's law understates the value created by a group-forming network [GFN] as it grows. Criticism[edit] Other analysts of network value functions, including Andrew Odlyzko and Eric S. See also[edit] References[edit] External links[edit]
Things You Should Never Do, Part I by Joel Spolsky Thursday, April 06, 2000 Netscape 6.0 is finally going into its first public beta. There never was a version 5.0. The last major release, version 4.0, was released almost three years ago. Three years is an awfully long time in the Internet world. It's a bit smarmy of me to criticize them for waiting so long between releases. Well, yes. They decided to rewrite the code from scratch. Netscape wasn't the first company to make this mistake. We're programmers. There's a subtle reason that programmers always want to throw away the code and start over. It’s harder to read code than to write it. This is why code reuse is so hard. As a corollary of this axiom, you can ask almost any programmer today about the code they are working on. Why is it a mess? "Well," they say, "look at this function. The idea that new code is better than old is patently absurd. Back to that two page function. Each of these bugs took weeks of real-world usage before they were found. Is there an alternative?
Small-world network Small-world network exampleHubs are bigger than other nodes Average vertex degree = 1,917 Average shortest path length = 1.803. Clusterization coefficient = 0.522 Random graph Average vertex degree = 1,417 Average shortest path length = 2.109. In the context of a social network, this results in the small world phenomenon of strangers being linked by a mutual acquaintance. Properties of small-world networks[edit] This property is often analyzed by considering the fraction of nodes in the network that have a particular number of connections going into them (the degree distribution of the network). ) is defined as R. Examples of small-world networks[edit] Small-world properties are found in many real-world phenomena, including road maps, food chains, electric power grids, metabolite processing networks, networks of brain neurons, voter networks, telephone call graphs, and social influence networks. Examples of non-small-world networks[edit] Network robustness[edit] See also[edit] References[edit]
Expérience de Milgram Un article de Wikipédia, l'encyclopédie libre. Reconstitution de l'expérience de Milgram (extrait)[1] L’expérimentateur (E) amène le sujet (S) à infliger des chocs électriques à un autre participant, l’apprenant (A), qui est en fait un acteur. La majorité des participants continuent à infliger les chocs jusqu'au maximum prévu (450 V) en dépit des plaintes de l'acteur. L'expérience de Milgram est une expérience de psychologie réalisée entre 1960 et 1963 par le psychologue américain Stanley Milgram. Les résultats ont suscité beaucoup de commentaires dans l’opinion publique, mais la méthode utilisée a fait naître critiques et controverses chez les psychologues et les philosophes des sciences. Déroulement de l'expérience[modifier | modifier le code] Fac-similé de l'annonce L'objectif réel de l'expérience est de mesurer le niveau d'obéissance à un ordre même contraire à la morale de celui qui l'exécute. La majorité des variantes de l'expérience a eu lieu dans les locaux de l'université Yale.
Centrality Definition and characterization of centrality indices[edit] Next to the following classic centrality indices, there are dozens of other more specialized centrality indices. Despite its intuitive notion there is not yet a definition or characterization of centrality indices which captures all of them.[2] A very loose definition of a centrality index is the following: A centrality index is a real-valued function on the nodes of a graph. It is a structural index, i.e., if and are two isomorphic graphs and is the mapping from the vertex set of to , then the centrality of a vertex must be the same as the centrality of in . Borgatti and Everett summarize that centrality indices measure the position of a node along a predefined set of walks. Categorization[edit] Reachability, Amount of flow, Vitality, Feedback; see.[5] Degree centrality[edit] The degree centrality of a vertex , for a given graph with vertices and edges, is defined as Calculating degree centrality for all the nodes in a graph takes . be the where
Don't give away your product for free... …unless you plan to keep it free forever. The Cost of Free Doughnuts, a story on NPR shows what happens when you make something paid that was once free. During WWII, the Red Cross, which used to give away free coffee and doughnuts started charging servicemen for doughnuts at the behest of the US Government. Veterans still remember this 70 years later! The reporter goes and finds some veterans and asks about the Red Cross and they go OFF about having to pay for doughnuts. The reason that it sticks in someone’s mind is because going from free to paid is a “Categorical Change”. The psychological difference between $1 and $2 is minuscule compared to the difference between ZERO and $1. Zero isn’t a pricing strategy, it’s a different category A popular strategy for SaaS products is to be free until you figure out pricing. Here’s why: Charging money establishes the value of your product When starting something new, you should try to find out if it’s valuable. Just think about your own behaviors?
Maximum flow problem An example of a flow network with a maximum flow. The source is s, and the sink t. The numbers denote flow and capacity. History[edit] Over the years, various improved solutions to the maximum flow problem were discovered, notably the shortest augmenting path algorithm of Edmonds and Karp and independently Dinitz; the blocking flow algorithm of Dinitz; the push-relabel algorithm of Goldberg and Tarjan; and the binary blocking flow algorithm of Goldberg and Rao. Definition[edit] A flow network, with source s and sink t. Let be a network with being the source and the sink of respectively. The capacity of an edge is a mapping , denoted by or . A flow is a mapping , subject to the following two constraints: , for each (capacity constraint: the flow of an edge cannot exceed its capacity) (conservation of flows: the sum of the flows entering a node must equal the sum of the flows exiting a node, except for the source and the sink nodes) The value of flow is defined by , where is the source of . to , and a flow
styleguide - Style guides for Google-originated open-source projects Kirchhoff's theorem Kirchhoff's theorem[edit] Kirchhoff's theorem relies on the notion of the Laplacian matrix of a graph that is equal to the difference between the graph's degree matrix (a diagonal matrix with vertex degrees on the diagonals) and its adjacency matrix (a (0,1)-matrix with 1's at places corresponding to entries where the vertices are adjacent and 0's otherwise). Equivalently the number of spanning trees is equal to any cofactor of the Laplacian matrix of G. An example using the matrix-tree theorem[edit] The Matrix-Tree Theorem can be used to compute the number of labeled spanning trees of this graph. First, construct the Laplacian matrix Q for the example kite graph G (see image at right): Next, construct a matrix Q* by deleting any row and any column from Q. Finally, take the determinant of Q* to obtain t(G), which is 8 for the kite graph. Proof outline[edit] First notice that the Laplacian has the property that the sum of its entries across any row and any column is 0. is an n-by-m matrix.