To Code or Not to Code

Low-code and no-code solutions are growing in appealability. Let’s dive into the topic and try to understand it’s implications.


One commonality across all of our apps, software and web pages? The hours upon hours poured into software languages like Python, C++ and JavaScript to name a few. These languages offer the syntax to create commands that add layer upon layer creating functional tools that make up computer programmes, video games and streaming platforms.  


By this token; to create a digital environment that interfaces with any form of software a team of developers will be required. Disrupting that bureaucratic and lengthy process are platform providers we associate with the adage “low-code” or “no-code”.


What can a new business or enterprise gain from low code and no code tools?


Simply, it affords enterprises, businesses and citizen developers the ability to create bespoke applications with minimal technical knowledge. By drag-and-dropping pre-written blocks of functionality into a workflow they can choose how an application will serve to remediate a problem area, accelerate development and reduce costs. The average cost of a software developer can range anywhere from $100 – $300 per hour. Freelance projects can reach $1000 – $5000. Larger enterprises, the fortune 500’s of the world are estimated to be paying on average $385 – $850 per hour. Projects in this realm can rise to the cost of $500,000 – $1 Million.


Using a platform service that offers a low-code or no-code option would save any business significant financial and time resources. For the younger, leaner teams this is an unbelievable saving that could make the difference between drowning or remaining afloat. Similarly this offers established enterprises an alternative to pumping finances into a reduced portfolio; allowing a greater breadth of market development.


Where is the no code revolution growing?


Platforms offering a no-code solution for website development, advertising and business use. Ranging from sole traders to unicorns include platforms like;


Wix, who have an unbelievable catalogue of clients, for example, SAPis one of the world’s leading producers of software for the management of business processes, developing solutions that facilitate effective data processing and information flow across organisations.” 


Even global consultant Deloitte, “who provides industry-leading audit, consulting, tax, and advisory services to many of the world’s most admired brands, including 80 percent of the Fortune 500.” use Wix as a web platform provider.


WordPress is also considered one of the go to no code web builders for global solutions, with clients ranging from TechCrunch, which “is a very popular tech website covering the latest news about startups, gadgets, events, and other related topics”


Continuing along the tech theme, Wiredis a long running monthly magazine and website that focuses on how technology impacts politics, culture, and the economy.”


Considering these global brands are utilising a no code solution the validation and future projections of the movement are staggering. Imagine the possibilities of empowering the market share of citizen developers with the tools to create their own AI models. 


How can this be applied to AI?


Creating an AI is a laborious and time exhaustive task, even broken into simplistic steps as described in an article published by the project rapidly grows in scale and requirements. In layman’s terms, more time and effort = more money.


Within the article it breaks the process down to half a dozen headings. Delving into those creates a cascading workload involved with the project.


Identify a problem, now, prepare the data, a  fairly innocuous heading. As a task, this could arguably be the largest time expenditure. Primarily, where does the data come from and how valid is it? In previous articles I’ve used the phrase Garbage In, Garbage Out. This is paramount at this stage, if the data isn’t prepared correctly it creates the foundations for a poor and potentially unethical model riddled with bias.


Months have gone into collating, preparing and cleaning the data. Now what? Decide on what learning method and algorithms for the model to use, there’s 10 prominent archetypes to choose from, each providing a unique assessment to the data set. How is the most optimal scenario exploited?


At this stage the platform, software language, hosting and interoperability hasn’t been taken into account, the mounting costs of bringing in data scientists, software developers, data analysts. Small wonder the application of AI isn’t widely considered a viable option to SME’s.


However,’s grand vision is to disrupt this process and offer a low-code, no-code alternative that reduces the time cost, physical input to the project and offering a pathway to democratising AI for all.


For more information please contact:

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Mike Cornell

Gunslinger and absolute bad man.

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