Its been long. Had given up on the concept of blogs, an expression that is so informal and irresponsible at times, it was hard to fit that in my head. Perhaps another article on war and peace conversation with @Ashok and I’ve made peace with that conflict to say, some form of expression is better than none to begin with. As part of this article, will try and express things around authorization and the principles we used in one of the solutions we built a while ago(currently working on it to be made open-source, watchout for Ringfort). Also, a general narrative on where should it lie in the overall architecture, whether its a solved problem etc. Few of us @thoughtworks were fortunate enough to build a collaboration and knowledge management platform, from scratch, for one of our clients. Authorization was one of the key ingredients of that journey. Hopefully, a lot of this narrative makes sense. Would be happy to be challenged otherwise.

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Hello Hive Mind,

Logs with diagnosis and system health monitoring data can be used to tune the system/process perform better. One of our clients, one of the largest manufacturers of computer hardware, approached us with an interesting problem with serious business concern. Dealing with increasing, precious log data was a problem that was knocking at their doorstep. The challenging bit for us was to design a system, which can handle the scale and also allow for building models, which can analyze the collected data.

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Hello Hive Mind,

The development of the following piece was mainly during the same time as the acquisition of Summly by Yahoo!. The main intention in developing such a solution was to generate a stream summarization where a part of the article is edited by many users and as a article owner one would want to maintain a master version which would consist of the best of all the edits. The description of the thought-process while solving such a problem is the main content of this article.

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Hello Hive-Mind,

This following is a piece of work which i tried out in my free time a while ago.
The emphasis was on extracting sentiment from a piece of text, preferably a tweet(and all the twittery characters follow!!). I’ve have tried solving this as a text classification problem. The system is trained using nearly a million positively and negatively labelled tweets using the LibLinear version of SVM. I’ve tried to summarize the whole process in the following sections.

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