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https://github.com/x0rz/EQGRP

Tu ai probleme cu capul omule. Daca te uiti prin discutiile vechi o sa vezi ca au fost multi oameni ajutati aici. Diferenta este ca ei au stiut sa vorbeasca si sa ceara. Daca apari aici cu atitudinea asta nu o sa primesti altceva in afara de injuraturi. In celalalt topic ai inceput sa blestemi. Aici folosesti caps lock si ne bagi pe toti la gramada. Tie iti pare normal asta? Uite un thread al unui om ce a primit ajutorul nostru, desi toti au fost sceptici la inceput: https://rstforums.com/forum/topic/97857-tata-are-cancer-daca-puteti-sa-ma-ajutati/ - Am fost cu totii alaturi de omul ala si cu sfaturi si cu tot ce se putea, fara sa cerem ceva. Trecand peste asta, nu vrem sa facem din RST un forum de miloaga. Tu in primul rand ar trebui sa te adresezi institutiilor statului ROMAN ticalos care nu e in stare sa ofere asistenta/asigurare sociala si medicala. Stii cati sunt in situatia ta?

Da-i pm la @aelius , are o colectie intreaga de spargatori

1. Introduction to Autonomous Mobile Robots (edX) The objective of Introduction to Autonomous Mobile Robots is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments. The main emphasis is put on mobile robot locomotion and kinematics, environment perception, probabilistic map based localization and mapping, and motion planning. The lectures and exercises of this course introduce several types of robots such as wheeled robots, legged robots and drones. 2. Underactuated Robotics (edX) Underactuated Robotics is taught by Russ Tedrake, Robin Deits and Twan Koolen. It is comprised of 19 lectures covering algorithms for walking, running, swimming, flying, and manipulation and the prerequisites for this course include basic linear algebra and differential equations. 3. Introduction to Robotics (MIT) Introduction to Robotics, taught by Harry Asada and John Leonard, provides an overview of robot mechanisms, dynamics, and intelligent controls. Topics include planar and spatial kinematics, and motion planning; mechanism design for manipulators and mobile robots, multi-rigid-body dynamics, 3D graphic simulation; control design, actuators, and sensors; wireless networking, task modeling, human-machine interface, and embedded software. 4. Control of Mobile Robots (Coursera) Control of Mobile Robots, taught by Magnus Egerstedt, is a course that focuses on the application of modern control theory to the problem of making robots move around in safe and effective ways. The structure of this class is somewhat unusual since it involves many moving parts – to do robotics right, one has to go from basic theory all the way to an actual robot moving around in the real world, which is the challenge this course has set out to address. 5. Robot Mechanics and Control, Part I (edX) Robot Mechanics and Control, Part I provides a mathematical introduction to the mechanics and control of robots that can be modeled as kinematic chains. Topics covered include the concept of a robot’s configuration space and degrees of freedom, static grasp analysis, the description of rigid body motions, kinematics of open and closed chains, and the basics of robot control. 6. Robot Mechanics and Control, Part II (edX) Robot Mechanics and Control, Part II covers screw motions and the product of exponentials kinematics formula, inverse kinematics of open chains, velocity kinematics and statics, closed chain kinematics, and basics of robot control. 7. Autonomous Navigation for Flying Robots (edX) Autonomous Navigation for Flying Robots introduces the basic concepts for autonomous navigation for quadrotors. The following topics will be covered: 3D geometry, probabilistic state estimation, visual odometry, SLAM, 3D mapping, linear control. In particular, you will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory. 8. Artificial Intelligence for Robotics (Udacity) Artificial Intelligence for Robotics teaches you how to program all the major systems of a robotic car from the leader of Google and Stanford’s autonomous driving teams. You will learn basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars. 9. Robotic vision (QUT) Robotic vision introduces you to the field of computer vision and the mathematics and algorithms that underpin it. You’ll learn how to interpret images to determine the color, size, shape and position of objects in the scene, and you’ll build an intelligent vision system that can recognize objects of different colors and shapes. 10. Applied Robot Design for Non-Robot-Designers (Stanford) In Applied Robot Design for Non-Robot-Designers you will learn how to design and build the mechanical hardware of robots. The goal is to take people with no mechanical experience and teach them to build professional-quality robots. The course consists of weekly labs and a final project, each of which will entail building an interesting robotic device. For example, students will build a pantilt camera turret in the belts lab. Topics will include: Electric motors, unusual actuators, sensors, mechanical transmissions, rotary and linear motion, counterbalancing, and standard mechanisms. 11. Introduction to Robotics (Stanford) The purpose of Introduction to Robotics is to introduce you to basics of modeling, design, planning, and control of robot systems. In essence, the material treated in this course is a brief survey of relevant results from geometry, kinematics, statics, dynamics, and control. 12. Introduction to Robotics Specialization (University of Pennsylvania) The Introduction to Robotics Specialization introduces you to the concepts of robot flight and movement, how robots perceive their environment, and how they adjust their movements to avoid obstacles, navigate difficult terrains and accomplish complex tasks such as construction and disaster recovery. You will be exposed to real world examples of how robots have been applied in disaster situations, how they have made advances in human health care and what their future capabilities will be. The courses build towards a capstone in which you will learn how to program a robot to perform a variety of movements such as flying and grasping objects. Sursa: https://hackerlists.com/online-robotics-courses/

Name: Iepurasul_cel_viteaz.jpg SHA-1: 473F7236DA2AE45FB56A9DDAAB25B1287762B624 Iepurasii viteji: 1. @pr00f 2. @Hertz 3. @sandabot

http://www.digi24.ro/stiri/actualitate/social/asistatii-social-nu-vor-sa-munceasca-703542 Lehamite level 10..

@RUSU79 du-te nene la munca, am vazut oameni fara maini care muncesc = matura casa <=> tine matura in gura si farasul tinut intre barba si umar, hraneste animalele cu o tigaie = ia grau si arunca la pasari. Omul @vatman32 "Eventual cred ca te poate ajuta si cu un loc de munca frumusel dar repet, nu bag mana-n foc, doar o sa pun 1-2 lemne si suflu-n el daca intelegi metafora.. " te ajuta si cu servici, al dracu nu vrei sa te duci. Eu cu scoala la zi + certificate de calificare si nu-mi gasesc si tu al dracu prost primesti pe tava, se roaga omul de tine. Nu-l ajutati, nu merita, este escroc, nici sa scrie nu are habar prostul pulii. https://www.facebook.com/NTDTelevision/videos/1703481939693909/

Stii care e chestia lumea a ajuns pe marte dar tu crezi ca noi mai suntem in 1907. Te inteleg, stiu acum 10 ani mai mergea treaba asta, dar acuma este foarte greu si nu veni sa te lupti cu lupi ! Daca tot stai toata ziua in casa si intri si pe acest forum trebuia sa cauti sa vezi ca au mai fost persoane ajutate pe RST, dar au venit cu acte doveditoare, poze la buletin, clip video, si multe altele, daca vrei pana dimineata si eu "am toate boalele toate capu, cu capu cu toate :))" si vin si eu cu cateva poze luate eventual dupa salvatii copii ! Pentru sanatatea dvs evitati forumul asta, daca tot stai toata ziua in casa cauta despe deep web si cere acolo ajutor, cei de acolo sunt mai inimosi !

Orice are legatura intr-un mod mai neortodox cu vreun instrument de plata online atrage ban.

Un mass scanner facut in 2001 de Bagabontu, ce necesita uid/gid 0 (synscan). La vremea aia era bomboana de pe coliva, insa pustanii nestiind programare sau ce e ala socket, cred ca au descoperit pe dumnezeu cand il vad in 2017. Sunt fel si fel de aratarii de oameni care cred ca daca sparg servere cu oarece tools, sunt si hackeri.

1. Machine Learning (FREE) Andrew Ng First, and arguably the most popular course on this list, Machine Learning provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas. The course is 11 weeks long and averages a 4.9/5 user rating, currently. It is free to take, but you can pay $79 for a certificate upon course completion. 2. Machine Learning Foundations: A Case Study Approach (FREE) Carlos Guestrin, Emily Fox In Machine Learning Foundations: A Case Study Approach, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of it you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. By the end of this course, you will be able to: Identify potential applications of machine learning in practice. Describe the core differences in analyses enabled by regression, classification, and clustering. Select the appropriate machine learning task for a potential application. Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. Represent your data as features to serve as input to machine learning models. Assess the model quality in terms of relevant error metrics for each task. Utilize a dataset to fit a model to analyze new data. Build an end-to-end application that uses machine learning at its core. Implement these techniques in Python. The course is 6 weeks long and requires about 5-8 hours of commitment per week. It currently averages a 4.6/5 user rating and is free to take, but you can pay $59 for a certificate upon completion. 3. Learning From Data (FREE) Yaser S. Abu-Mostafa Learning From Data is an introductory course in machine learning that will cover basic theory, algorithms, and applications. It balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion: What is learning? Can a machine learn? How to do it? How to do it well? Take-home lessons. You’ll learn how to: Identify basic theoretical principles, algorithms, and applications of Machine Learning Elaborate on the connections between theory and practice in Machine Learning Master the mathematical and heuristic aspects of Machine Learning and their applications to real world situations The course is 10 weeks long and requires about 10 – 20 hours per week of commitment. It is free to take, but you can add a verified certificate of completion for $49. 4. Statistical Learning (FREE) Trevor Hastie, Rob Tibshirani This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: Linear and polynomial regression, logistic regression and linear discriminant analysis Cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso) Nonlinear models, splines and generalized additive models Tree-based methods, random forests and boosting; support-vector machines Also, some unsupervised learning methods are discussed like principal components and clustering (k-means and hierarchical). This is not a math-heavy class and all computing is done in R. If you are not familiar with R that is ok. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. The class is free to take and is expected of you to commit 3 – 5 hours per week to work through the course material. If you complete the course, and achieve a passing grade of 50% on the quizzes. If you get 90% or higher, your statement will be “with distinction”. 5. Machine Learning: Regression (FREE) Carlos Guestrin, Emily Fox In Machine Learning: Regression, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data — such as outliers — on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. By the end of this course, you will be able to: Describe the input and output of a regression model Compare and contrast bias and variance when modeling data Estimate model parameters using optimization algorithms Tune parameters with cross validation Analyze the performance of the model Describe the notion of sparsity and how LASSO leads to sparse solutions Deploy methods to select between models Exploit the model to form predictions Build a regression model to predict prices using a housing dataset Implement these techniques in Python The course requires 6 weeks of your time and approximately 5 – 8 hours per week to study the material. It’s current user rating averages a 4.8/5. The course is free to take, but you can pay $59 to receive a certificate of completion at the end. 6. Machine Learning: Classification (FREE) Carlos Guestrin, Emily Fox In Machine Learning: Classification, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. By the end of this course, you will be able to: Describe the input and output of a classification model Tackle both binary and multiclass classification problems Implement a logistic regression model for large-scale classification Create a non-linear model using decision trees Improve the performance of any model using boosting Scale your methods with stochastic gradient ascent Describe the underlying decision boundaries Build a classification model to predict sentiment in a product review dataset Analyze financial data to predict loan defaults Use techniques for handling missing data Evaluate your models using precision-recall metrics Implement these techniques in Python (or in the language of your choice, though Python is highly recommended) The course is 7 weeks long and currently averages a 4.6/5 user rating. While the course materials are provided for free, you will need to pay $59 to earn a course completion certificate. 7. Machine Learning: Clustering & Retrieval (FREE) Carlos Guestrin, Emily Fox In Machine Learning: Clustering & Retrieval you will examine similarity-based algorithms for retrieval. You will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. By the end of this course, you will be able to: Create a document retrieval system using k-nearest neighbors Identify various similarity metrics for text data Reduce computations in k-nearest neighbor search by using KD-trees Produce approximate nearest neighbors using locality sensitive hashing Compare and contrast supervised and unsupervised learning tasks Cluster documents by topic using k-means Describe how to parallelize k-means using MapReduce. Examine probabilistic clustering approaches using mixtures models Fit a mixture of Gaussian model using expectation maximization (EM) Perform mixed membership modeling using latent Dirichlet allocation (LDA) Describe the steps of a Gibbs sampler and how to use its output to draw inferences Compare and contrast initialization techniques for non-convex optimization objectives Implement these techniques in Python The course is 6 weeks in length and currently averages a 4.9/5 user rating. The course materials are free, but you’ll need to pay $59 if you want a course completion certificate. 8. Unsupervised Machine Learning Hidden Markov Models in Python ($50) Justin C While the current fad in deep learning is to use recurrent neural networks to model sequences, this course will introduce you to a machine learning algorithm that has been around for several decades now – the Hidden Markov Model. In Unsupervised Machine Learning Hidden Markov Models in Python, you’ll learn to measure the probability distribution of a sequence of random variables. In this course you’ll learn: How to use gradient descent to solve for the optimal parameters of an HMM, as an alternative to the popular expectation-maximization algorithm. How to work with sequences in Theano, a popular library for deep learning How to look at a model of sickness and health, and calculate how to predict how long you’ll stay sick, if you get sick How Markov models can be used to analyze how people interact with your website, and fix problem areas like high bounce rate, which could be affecting your SEO Practical applications of Markov models, including generating images, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in biology – how is DNA, the code of life, translated into physical or behavioral attributes of an organism? The course is comprised of 35 videos and runs a total time of 4 hours. It currently averages a 4.7/5 user rating. However, the course is not free, it costs $50. 9. Data Science and Machine Learning with Python – Hands On! ($35) Frank Kane If you’ve got some programming or scripting experience, Data Science and Machine Learning with Python – Hands On! will teach you the techniques used by real data scientists in the tech industry – and prepare you for a move into this hot career path. This comprehensive course includes 68 lectures spanning almost 9 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. It covers the machine learning and data mining techniques real employers are looking for, including: Regression analysis K-Means Clustering Principal Component Analysis Train/Test and cross validation Bayesian Methods Decision Trees and Random Forests Multivariate Regression Multi-Level Models Support Vector Machines Reinforcement Learning Collaborative Filtering K-Nearest Neighbor Bias/Variance Tradeoff Ensemble Learning Term Frequency / Inverse Document Frequency Experimental Design and A/B Tests The course costs $35 and currently has an average user rating of 4.6/5. 10. Machine Learning for Data Science and Analytics (FREE) Ansaf Salleb-Aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis, Peter Orbanz Machine Learning for Data Science and Analytics is an introduction to machine learning and algorithms. You will develop a basic understanding of the principles of machine learning and derive practical solutions using predictive analytics. You will also examine why algorithms play an essential role in Big Data analysis. In this course, you’ll learn: What machine learning is and how it is related to statistics and data analysis How machine learning uses computer algorithms to search for patterns in data How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and preterm birth How to uncover hidden themes in large collections of documents using topic modeling How to prepare data, deal with missing data and create custom data analysis solutions for different industries Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming The course is 5 weeks and requires a commitment of 7-10 hours per week. It is free, but you have the option of paying $99 for a verified certificate of completion. Sursa: https://hackerlists.com/beginner-ml-courses/

Database Fundamentals Type: Video Lessons Level: Beginner Price: Free Before diving into writing SQL queries, it’s useful to get the 10,000 foot conceptual overview, learn some terminology, and see some examples of relational database tables. Database Fundamentals is a five-part video introduction to core database concepts (by two SQL pros) that explains SQL databases from square one using a mix of lecture content and screencasting. It’s a good place to start for anyone who’s a true beginner or looking to review the fundamental concepts of databases. Stanford’s Self Paced SQL Mini Courses Type: Video Lessons Level: Beginner, Intermediate Price: Free Stanford offers several free SQL mini courses with in-video quizzes and interactive programming exercises that are auto-checked. Every course has a discussion forum and references outside readings & resources. The course material draws from Stanford’s undergraduate courses. essentialSQL Type: Video Lessons, Articles, Community Level: Any Price: Free & Paid Kris Wenzel, creator of essentialSQL, has created a resource rich site. He recommends that you start with his free video course, or dive into some of his beginner text based lessons listed here. There are many thorough text based lessons on the homepage, as well as a learning community. SQL Authority – Video Learning Type: Video Lessons Level: Any, Intermediate Price: Free The owner of SQL Authority, Pinal Dave, is a tech enthusiast and independent consultant that has published 21 courses on Pluralsight and written 11 books on SQL Server. His blog has more articles and videos than you could probably ever get through. Many of the videos are on specific topics that are well beyond beginner level. SQL Server Tutorial for Beginners Type: Video Lesson Level: Beginner, Intermediate Price: Free A treasure trove of 135 short videos showing SQL database concepts using Microsoft SQL Server and SQL Server Management Studio. Pragim Technologies has video lessons of many other languages on their YouTube channel as well. SQL Server Central – Foreign Keys Part 1 & Part 2 Type: Video Lessons Level: Intermediate Price: Free The first big hurdle when learning SQL is understanding the significance of foreign keys and how to use them. These two short videos will give you a background in referential integrity. There are many other videos and resources on SQL Server Central, and although some may look a little dated, the basics of SQL haven’t changed much over the years so they’re still relevant. Microsoft Virtual Academy – SQL Server Courses Type: Video Lessons Level: Any Price: Free Designing Solutions for SQL Server and Developing Microsoft SQL Server Databases are two of the courses offered by Microsoft Virtual Academy. The two courses, as well as others at MVA, offer training on how to implement and manage database solutions, migrate to scalable cloud solutions, use powerful reporting, and integrate SQL with Sharepoint. Sursa: https://hackerlists.com/learn-sql-online/

When you hit your teenage years you decide you want to be a software developer. During your high school years, you learn how to write software using object-oriented principles. When you graduate to college, you apply all the principles you’ve learned to areas such as Artificial Intelligence or 3D graphics. And when you hit the professional circuit, you begin your never-ending quest to write commercial-quality, maintainable, and “perfect” code that will stand the test of time. Commercial-quality. Huh. That’s pretty funny. I consider myself lucky, I *love* design patterns. I like studying the theory of coding perfection. I have no problem starting up an hour-long discussion about why my XP partner’s choice of inheritance hierarchy is wrong — that HAS-A is better than IS-A in so many cases. But something has been bugging me lately and I am wondering something… …is good code impossible in modern software development? The Typical Project Proposal As a full-time contract developer (and part-time), I spend my days (and nights) developing mobile applications for clients. And what I’ve learned over the many years I’ve been doing this is that the demands of client work preclude me from writing the real quality apps that I’d like to be. Before I begin, let me just say it’s not for a lack of trying. I love the topic of clean code. I don’t know anyone who pursues that perfect software design like I do. It’s the execution that I find more elusive, and not for the reason you think. Here, let me tell you a story. Towards the end of last year, a pretty well-known company put out an RFP (Request for Proposol) to have an app built for them. They’re a huge retailer, but for the sake of anonymity let’s call them Gorilla Mart. They say they need to create an iPhone presence and would like an app produced for them by Black Friday. The catch? It’s already November 1st. That leaves just under 4 weeks to create the app. Oh, and at this time Apple is still taking two weeks to approve apps. (Ah, the good old days.) So, wait, this app has to be written in…TWO WEEKS?!?! Yes. We have two weeks to write this app. And unfortunately, we’ve won the bid. (In business, client importance matters.) This is going to happen. And then it’s happening. Despite years of constant reminders that every feature a client asks for will always be more complex to write than it is to explain, you go for it. You really believe that this time, it really can be done in two weeks. Yes. Yes! We can do this! This time it’s different! It’s just a few graphics and a service call to get a store location. XML! No sweat. We can do this…I’m pumped! Let’s go!!! It takes just a day for you and reality to once again make acquaintance. And that’s exactly how it happened. Their store location service, found right where it’s supposed to be on the top-right corner of their website, is not a web service. It’s generated by Java code. Ixnay with the API-ay. And to boot, it’s hosted by a Gorilla Mart strategic partner. Enter the nefarious “3rd party.” In client terms, a “3rd party” is akin to Angelina Jolie. Despite the promise that you’ll be able to have an enlightening conversation over a nice meal and hopefully hook up afterwards…sorry, it ain’t happenin’. You’re just gonna have to fantasize about it while you take care of business yourself. In my case, the only thing I was able to wrestle out of Gorilla Mart was a current snapshot of their current store listings in an Excel file. I had to write the store location search code from scratch. The double-whammy came later that day — they wanted the product and coupon data online so it could be changed weekly. There goes hardcoding! Two weeks to write an iPhone app have now become two weeks to write an iPhone app, a PHP backend, and integrate them togeth–what? They want me to handle QA, too?? To make up for the extra work, the coding will have to go a little faster. Forget that abstract factory, use a big fat for loop instead of the composite, there’s no time!!!! Good code has become impossible. Two Weeks To Completion Let me tell you, that two weeks was pretty miserable. First, two of the days were eliminated due to all-day meetings for my next project. (That amplifies how short a timeframe this was going to be.) Ultimately, I really had eight days to get things done. The first week I worked 74 hours and the next week…god…I don’t even recall it’s been eradicated from my synapses. Probably a good thing. I spent those eight days writing code in a fury. I used all the tools available to me to get it done: copy-and-paste (AKA re-usable code), magic numbers (avoiding the duplication of defining constants and then, gasp!, retyping them), and absolutely NO unit tests! (Who needs red bars at a time like this, it’d just demotivate me!) It was pretty bad code and I never had time to refactor. Considering the timeframe, however, it was actually pretty stellar, and it was “throwaway” code after all, right? Does any of this sound familiar? Well just wait, it gets better. As I was putting the final touches on the app (the final touches being writing the entirety of the server code), I started to look at the codebase and wondered if maybe it was worth it. The app was done after all. I survived. I SURVI- Let’s step back. What do we know about what good code is? Good code should be extendable. Maintainable. It should lend itself to modification. It should read like prose. Well, this wasn’t good code. Another thing. If you want to be a better developer, you must always keep this inevitably in mind: The client will always extend the deadline. They will always want more features. They will always want change — LATE. And here’s the formula for what to expect: (# of Executives)2 + 2 * # of New Executives + Bob’s Kids = DAYS ADDED AT LAST MINUTE Now, Executives are decent people. I think. They provide for their family (assuming Satan has approved of their having one.) They want the app to succeed (promotion time!). The problem is that they all want a direct claim to the project’s success. When all is said and done, they all want to point at some feature or design decision they can each call their very own. So, back to the story, we added a couple more days to the project and got the email feature done. And then I collapsed from exhaustion. The Clients Never Care As Much As You Do The clients, despite their protestations, despite their apparent urgency, never care as much as you do about the app being on time. The afternoon that I dubbed the app completed, I sent an email with the final build to all the stakeholders, Executives (hiss!), managers and so on. “IT IS DONE! I BRING YOU V1.0!!! PRAISE THY NAME.” I hit Send, lay back in my chair and with a smug grin began to fantasize how the company would run me up onto their shoulders and lead a procession down 42nd street while I was crowned “Greatest Developer Ev-ar.” At the very least, my face would be on all their advertising, right? Funny, they didn’t seem to agree. In fact, I wasn’t sure what they thought. I heard nothing. Not a peep. Turns out the folks at Gorilla Mart were eager to and had already moved on to the next thing. You think I lie? Check this out. I pushed to the Apple Store without filling in an app description. I had requested one from Gorilla Mart and they hadn’t gotten back to me and there was no time to wait. (See previous paragraph.) I wrote them again. And again. I got some of our own management on it. Twice I heard back and twice I was told, “What did you need again?” I NEED THE APP DESCRIPTION! One week later, Apple started testing the app. This is usually a time of joyousness but it was instead a time for mortal dread. As expected, later in the day the app was rejected. It was about the saddest, poorest excuse to allow a rejection I can imagine: “App is missing an app description.” Functionally perfect; no app description. And for this reason Gorilla Mart didn’t have their app ready for Black Friday. I was pretty upset. I’d sacrificed my family for a 2-week super sprint, and no one at Gorilla Mart could be bothered to create an app description given a week of time. They gave it to us an hour after the rejection — apparently that was the signal to get down to business. If I was upset before, I would become livid a week and a half after that. You see, they still hadn’t gotten us real data. The products and coupons on the server were fake. Imaginary. The coupon code was 1234567890. You know, phoney baloney. (Balogna is spelled baloney when used in that context, BTW.) And it was that fateful morning, I checked the Portal and THE APP WAS AVAILABLE! Fake data and all! I cried out in abject horror and called up whoever I could and screamed, “I NEED THE DATA!!!!” and the woman on the other end asked me if I needed fire or police and so I hung up on 911. But then I called Gorilla Mart and was like, “I NEED DATA!!!!” and I’ll never forget the response: In the end, it turned out that at least 11 people registered their email addresses in the database, which meant there were 11 people that could potentially walk into a Gorilla Mart with a fake iPhone coupon in tow. Boy, that might get ugly. When it was all said and done, the client had said one thing correctly all along: the code was a throwaway. The only problem is it was never released in the first place. Rush To Complete, Slow To Market The lesson here is that your stakeholders, whether an external client or internal management, have figured out how to get developers to write code quickly. Effectively? No. Quickly? Yes. Here’s how it works: Tell the developer the app is simple. This serves to pressure the development team into a false frame of mind. It also gets the developers to start working earlier, whereby they… Add features by faulting the team for not recognizing their necessity. In this case, the hardcoded content was going to require app updates to change. How could I not realize that? I did, but I’d been handed a false promise earlier, that’s why. Or a client will hire “a new guy” who’s recognized there is some obvious omission. One day a client will say they just hired Steve Jobs and can we add alchemy to the app? Then they’ll… Push the deadline. Over and over. Developers work their fastest and hardest (and BTW are at their most error-prone, but who cares about that, right?) with a couple days to go on a deadline. Why tell them you can push the date out further while they’re being so productive? Take advantage of it! And so it goes, a few days are added, a week is added, just when you had worked a 20-hour shift to get everything just right. It’s like a donkey and carrot, except you’re not treated as well as the donkey. It’s a brilliant playbook. Can you blame them for thinking it works? But they don’t see the god-awful code. And so it happens time and again despite the results. Code Impossible In a globalized economy, where corporations are held to the almighty dollar and raising the stock price involves layoffs, overworked staffs, and offshoring, this strategy I’ve shown you of cutting developer costs is making good code obsolete. As developers, we’re going to be asked told conned into writing twice the code in half the time if we’re not careful. Source: http://raptureinvenice.com/is-good-code-impossible/

C/C++ Edit: si foarte curand Go

E mai greu de folosit dar te descurci tu. download link direct.

rstforums.com/forum/topic/93649-coailii-password-recovery-admin-panel/

Salut,am si eu o problema.De cateva zile incerc sa gasesc un program bun de spart conturi de facebook.Am gasit doar niste programe nasoale si am incercat sa caut si pe Deep Web.Am gasit doar un site ShadowWave care este un keyloger. Ma puteti ajuta cu niste programe?