i have many social network ids, many more email ids and even more chat software ids. as an individual completely connected and spending almost two to four hours on the internet, i had to keep open many windows and login to even keep track of my emails and chat messages and social networks. that is when i wanted to develop an integrated software which is desktop, mobile, internet and gaming console based. i wanted it to fill the gaps left by other  providers in the market. the architecture was to be plugin based – where any developer can download and implement their own connectors to be used on any of the media. we wanted to support 10 or 25 each of the most import email, social networks, blogging and chat software. the features which we would support would be same across all the similar range of type of networks (like same for gmail or hotmail and same for gtalk or yahoo! chat). i did no initial market research and no technology analysis of the available open api’s – as i wanted to build a highly pluggable and generic architecture, which is first agnostic of the technology and second of the specific feature of that type of network. i also planned to provide a programmer api and widget approach for developer and website integration.

once i had to make a very important decision and needed very vital information regarding the actual need of one of my product. i wanted to conduct an online survey through one of the tools – to understand the need of my product from real users. i later understood that a black and white result of the survey is of no or little use to start-up or business to find the real depth of the need. also, we are mostly unable to get the real geographic distribution with depth. this is when i hit upon the idea of adaptive survey, which also requires some amount of skill in setting up of the survey. the execution should be in such a way that there is a definite breakpoint to get survey results even without taking up the whole survey. as also, a clear cut geographic distribution is available at various levels of hierarchy. there is also a recommendation or suggestion engine that takes care to give the remedial measures based upon a data mine. this mine is built from manual data or search engine retrieved information. also, we can suggest some steps from previous surveys that we have collected. this type of survey and related result is most useful for launch of new products, new features, strategy, research and development and any new market evaluation.

i started building upon my initial job search engine idea, careerguru and renamed it jobber and then jobbing. as i always wanted to search and reach out to a dream job J i understand everybody who is looking out for a change – is always looking to reach their dream role as per their level. the idea behind jobbing is to use context driven behavioural search in repeated applications and take the candidate to shortlisted dream jobs. apart from this, we want to be able to provide candidate database to other or partner job sites (with a single face for registration). also, we want to be a cheap job publishing site for jobs – with the best return for the money spent. there are host of other features which we want to wow both the user and the corporate. final goal is to be a partner and consultant (as jobbing) for the premium jobs. also, we plan to provide a host of lifecycle management and history services for candidate recruitment – right from short listing, then unto chat, phone calls, video interview, feedbacks, skill levels and upto managing documents and then sending or storing the entire interview lifecycle in the corporate database.

lutsu! has been developed to an alpha version, during some of my most challenging and testing times. i decided the area of url management and analytics – which I could later integrate into the bigger picture of techarmy product suite. the target was a one month or a new year release for alpha version. so i started working on url shortening, packeting, protection, information and redirection. in one week’s time, i had thought out how the end product would look like about six months down the line. i envisioned the most comprehensive url analytics api, ever on the internet. and in another week’s time – i understood how we can build a layer three to maximize advertising performance and also provide advertising acceleration (which is independent of the product that you use). so, the final goal was an ad performance and ad accelerator software. also, having carefully understood product pricing details of other competitors, i thought of building same features and provide an entire package at competitive rates (especially personalized domains, api and then brand awareness). also, i wanted to utilize my developer skills to the maximum by providing technology neutral api for developers, website widgets, official shortener integration and web services.