Dani's Comments - EID100

To the engineers, policymakers and companies building what comes next,

We stand at a point where 5G, the Internet of Things and blockchain are no longer science fiction, they're near-horizon realities. But being possible doesn't mean being good. So here's what I hope you build toward.

Remember the digital divide first. Before we celebrate every TV and streetlight getting connected, ask who gets left out. Rural Canadians already pay double what urban households do for a fraction of the speed. If 5G rolls out only where it's profitable, the IoT just becomes another way for the privileged to get more, while everyone else falls further behind.

Build with interoperability in mind, not against it. We've already seen how network effects lock users into platforms that don't talk to each other. Don't let smart cities and smart homes become another Babel of incompatible standards owned by a handful of companies. Open APIs made the last wave of innovation possible, they should shape this one too.

Be honest about algorithmic bias. If sensors and AI are going to decide which neighborhoods get better traffic flow or which appliances get flagged for maintenance first, someone needs to check those systems for the same prejudices we've already found baked into predictive policing and hiring algorithms. Weapons of Math Destruction shouldn't get an IoT upgrade.

Use blockchain for something other than speculation. Its real promise, a transparent and tamper-proof ledger, could track supply chains, verify emergency aid distribution or protect land titles in places where corruption thrives. That's a better use of the technology than another NFT drop.

And please, learn from open source. The most resilient innovations weren't built by one company behind closed doors, they were built by communities. If the future of connected devices is going to earn public trust, it needs the same transparency.

None of this happens by accident. It takes intention, and it takes people asking hard questions before the technology ships, not after.

For this week's assignment I made a Codecademy account and picked Learn SQL, since it was the language from Module 10 that I knew the least about. I had heard of it, but I could not have told you what it actually did.

Turns out SQL stands for Structured Query Language, and it is the main language for accessing and manipulating databases. A database is just a collection of tables, and each table has rows filled with data. What SQL lets you do is ask that pile of data a question and get a clean answer back. The module gave the example of checking which hotel rooms are free on a certain date, and once I started writing queries it clicked.

I got through the Queries module and I am sitting at 60% overall. The syntax is a lot friendlier than I expected. SELECT, FROM, WHERE. You are basically saying "give me these columns, from this table, but only the rows that match this." It reads almost like a sentence, which is a nice contrast to the binary machine language example from the module.

The part that surprised me most was how directly this connects to everything else we have studied. Web analytics, Big Data, all those clickstream metrics sitting in tables somewhere. Someone has to query them to get anything useful out. SQL is the tool that does it.

Two hours in, I would not call myself a programmer. But the field feels a lot less like magic now, which I think was the point.

Hey everyone. I am the CTO of a new startup called SafeTenant, and I want to share what we are building and why it honestly keeps me up at night, in a good way.

A lot of affordable and older rental housing quietly falls apart before anyone notices. A heating system dies in January. Mould creeps behind a wall. By the time a tenant reports it, the fix is expensive, and someone has already been living in unsafe conditions. Landlords react instead of preventing, and the people who suffer most are usually the ones with the least power to complain.

Our vision is to flip that around using predictive maintenance, which is one of the classic Big Data applications we studied. We install cheap sensors that track things like temperature, humidity, and how hard the boiler is working. That gives us a constant stream of readings, and this is where the three Vs of Big Data really show up. The Volume is huge, it arrives with serious Velocity because sensors never sleep, and it comes in a messy Variety of formats from different devices.

We then feed that data into a model that learns the normal rhythm of a building and flags when something drifts. I am careful about how I pitch this to investors, because Big Data gives us correlation, not causation. Our system can tell you that a certain pattern usually shows up before a furnace failure, but it cannot explain exactly why on its own. That honesty matters when a family staying warm is on the line.

On top of that, we use forecasting and optimization to schedule repairs before the breakdown, so crews fix the right thing at the right time instead of scrambling after the fact.

The part I keep front of mind is bias. If we only train on data from shiny new buildings, the model will overfit and miss the older ones that need it most. So we are deliberately pulling data from a wide range of housing.

For this module, I played the NOVA Cybersecurity Lab and worked through the Einstagram coding challenge, reaching Level 2. The activity has you drag "Until" and "If" blocks to program a bot through a maze to a star, basically writing conditional logic like "if path is ahead, move forward, else turn." Fumbling to get the sequence right (I used 24 blocks when the goal was 5!) made three course ideas click.

First, it reinforced why cybersecurity demands understanding the code beneath our devices. Defending a system means knowing how its logic actually works, which is exactly what this block-based puzzle trains.

Second, the constant debugging mirrored how 0-day vulnerabilities and backdoors emerge: one misplaced instruction breaks the whole path, just as a flaw in real software hands criminals an undocumented way in.

Third, the "if/else" rule structure is essentially how a firewall works in network security, monitoring data flow and deciding, condition by condition, what to allow or block.

The good news: protecting yourself is simple. Accept only necessary cookies, check your browser's privacy settings now and then, and look for the padlock (https) before you type anything personal.

30 seconds, all original visuals and sound. Watch here 👇

Misinformation

This news report exposes a deepfake video of an election official, serving as a textbook example of disinformation. The digital creators intentionally manufactured a false video to manipulate public perception and maliciously damage democratic trust. An attentive reader can identify this pathology by noticing a lack of primary source links or checking third-party fact-checkers. Furthermore, closely examining the visual elements, like unnatural lip movements and synthetic audio anomalies, reveals that the footage is simulated. A critical reader can pause and verify the source before taking it as truth if they are aware that the topic provokes strong emotional anger.

Misinformation

This historical analysis reveals that the famous "nationwide hysteria" surrounding the 1938 War of the Worlds broadcast was actually a media-generated myth, perfectly illustrating misinformation. In the broadcast's aftermath, newspapers compiled scattered anecdotes and hastily published sensationalized headlines about widespread panic. While these claims were historically inaccurate, publishers disseminated them in good faith based on unverified police logs. An attentive reader can identify this misinformation by checking secondary historical consensus and primary data. Pausing to cross-reference sensational claims with established research prevents readers from accepting and spreading exaggerated historical narratives as fact.

Malinformation

For this week's tutorial assignment, I installed the Firefox browser along with the Disconnect privacy add-on to visualize just how many third-party trackers follow us around while we surf the web.

I visited 8 different sites across four distinct categories (civic, e-commerce, news, and social) to see how they compared. Here are the exact URLs I tested:

🌐 The 8 Sites Visited

Civic / Non-Commercial:

https://www.toronto.ca/

2. https://tpl.ca/

E-Commerce:

3. https://www.indigo.ca/

4. https://www.shopify.com/ca

News Outlets:

5. https://www.theglobeandmail.com/

6. https://www.thestar.com/

Social Networks:

7. https://www.facebook.com/marketplace/item/1445494186852341/?ref=search&referral_code=null&referral_story_type=post&tracking=browse_serp%3Abcf22491-92e7-4d64-86c5-859b87fc5536

8. https://www.linkedin.com/feed/

After reviewing the Disconnect extension icon and the "visualize" page function for each site, there was a massive difference between civic spaces and commercial sites. Non-commercial sites had barely any background tracking, while news sites were packed with them.

The absolute highest number of trackers came from The Toronto Star, which racked up an astonishing 99+ trackers.

For my first blog post, I chose to highlight two very different examples of social media communication. The first is an AI-generated image posted by Donald Trump on X, depicting himself as Jesus. This post sparked significant controversy, particularly because it was shared shortly after he published a lengthy message criticizing Pope Leo XIV. Trump later acknowledged the post, telling reporters he believed it showed him “as a doctor.”

This example clearly violates a key principle of responsible social media use: never post anything you wouldn’t want to see on the front page of major news outlets such as The Globe and Mail or The New York Times. In this case, the image was widely circulated across both and many other platforms. Even though the post was eventually removed, it remains easily accessible through a simple online search. As discussed in Module 1, “Internet content is like a tattoo – it will stick with you for a very long time, it is very expensive to get rid of, and there will always be traces left after it is removed.” This highlights the permanence and potential consequences of online actions.

In contrast, the second example comes from the TMU Women’s Basketball team’s Instagram account. The account administrator shared a celebratory banner recognizing the team’s OUA championship win. In the comments, users responded with supportive messages and encouraging images, reflecting a positive and respectful online environment. These interactions demonstrate how users are engaging in ways that align with offline social norms—they are sharing content they would feel comfortable expressing in person.