SEO is DEAD ❌

For the past 2 weeks I’ve been extremely focused on dev work for BerryNews.org, OBSESSIVELY. I’m going to be brutally honest and transparent in everything I’m going to share.

Generally speaking, if we do not count users that have ad blockers, threat protection or utilize browsers such as Brave, there is quite little traffic going to my website, quite unfortunate but then again this is a tech hobby and I’m not one bit upset about it. Side note: although I have tried GoogleAds, they proved unsuccessful and I had 2-3 paid visit at a massive cost of 50c/click.

My goal has been to learn, to develop myself and to challenge what you can do with a SBC — single board computer. Side note: So far, I’m genuinely impressed, a bit afraid that the SD card will get corrupted so I regularly back-up. There is nothing better than proper planning.

Below you will see a snapshot of roughly 28 days of website visits. At the end of October I started my intense work, and funny enough it was on SEO —Search Engine Optimization, in short for those that do not know what it means I’m making the webpages more attractive for web crawlers so that search engines will display them first. As you can see, around 5th of November a massive spike, I was so happy, after all my hard work, I’ll be getting more traffic, more exposure and more chances for feedback, which is ultimately what would push any real growth. The victory was short lived and I returned to the normal trends, but why? I was furious, it does not make sense, now I score 98/100 for SEO on any auditing tool (ex. Lighthouse), then I started researching on reddit and I stumbled upon an adjacent SEO article for web3/blockchain/crypto. “62% of Funded Blockchain and Web3 Companies Attract Fewer Than 100 Monthly Organic Visitors”

So, real companies, with funding money get 100 organic visitors per month, same as me, wow. You can read the whole article on their website: https://guerrillabuzz.com/62-of-funded-blockchain-and-web3-companies-attract-fewer-than-100-monthly-organic-visitors/#Research-Objectives

But in summary:

The analysis centers on a dataset comprising 6,734 blockchain and web3 companies, encompassing both emerging startups and well-established industry entities that have secured funding at different stages of their development. The primary emphasis is on evaluating their web traffic, particularly organic visitors, to gauge post-funding growth and consumer interest.

Although the article focuses only on niche tech, I believe the results to be universally applicable:

🔵 The market is simply oversaturated
🔵 There are so many apps which are fighting for your attention
🔵 There are so many websites to read, follow not to mention social media and their teams of behavioral psychologists
🔵 Launching a website with almost 0 skills is a days work, don't get me stated with low-code/no-code.

Generally speaking, you need to either pay for views or have a massive following.

In the end, I think the snapshot is a testament of each and every one of you who have been following me in my journey, the 5 minutes you spared in your busy schedule, reading this post, visiting the website, to liking or commenting. Every one of your interactions fuels my journey.

Thank you!

Handbooks

ML models for User Recognition using Keystroke Dynamics

The keystroke dynamics that are used in this article’s machine learning models for user recognition are behavioral biometrics. Keystroke dynamics uses the distinctive way that each person types to confirm their identity. This is accomplished by analyzing the 2 keystroke events on Key-Press and Key-Release — that make up a keystroke on computer keyboards to extract typing patterns. The article will examine how these patterns can be applied to create 3 precise machine learning models for user recognition.

Keystroke Dynamics — Predicting the User — Lambda App

The keystroke dynamics that are used in this article’s machine learning models for user recognition are behavioral biometrics. Keystroke dynamics uses the distinctive way that each person types to confirm their identity. This is accomplished by analyzing the 2 keystroke events on Key-Press and Key-Release — that make up a keystroke on computer keyboards to extract typing patterns. The article will examine how these ML Models can be used in real-life situations to predict an user.

RaspberryPi: from OS to webserver

Unlock the full potential of your Raspberry Pi 4B as I guide you through transforming it into a versatile coding machine or even a web server. In this concise handbook, I'll punctually and minimalistically outline the steps to install Ubuntu Desktop, essential software, packages, and applications, while also exploring fine-tuning options. Get ready to unleash the power of your Raspberry Pi!

PiHole ad-blocker

Discover the power of Pi-hole, your ultimate tool for reclaiming control over your internet browsing experience. In this comprehensive handbook, I'll take you through each step of the process, from understanding the basics to setting up your very own Pi-hole ad blocker.

Twitter Bot

In this tutorial, I'll show you how to create a Twitter bot that can intelligently reply to mentions. Whether you're a developer looking to explore the possibilities of automation or just curious about Twitter bots, I've got you covered.