A browser that runs directly inside your existing terminal

Rust 474 Updated Jul 31

(FYI — At the end of the page, I have linked YouTube video of my project where I have walked through the code, the technical aspects, and also a link to the literature survey paper)

Choosing where to eat sounds trivial, until you’re standing on a street with 40 options, reading reviews that contradict each other, and ending up at the nearest place anyway.

That frustration turned into a six‑month final-year project and taught me more about data, sentiment analysis, and machine learning.

This is what we built, how it works, and what actually surprised me along the way.

I tried to build an engine that recommends a restaurant to you based on the database fed into it, your personal preferences (user profile), and the restaurant's specialty (restaurant’s profile)

Platforms like Yelp and TripAdvisor are sitting on enormous amounts of useful data with millions of real reviews, ratings, and user histories. The information is there. The challenge is turning unstructured human language into something a system can reason about. Most recommendation systems take one of two approaches:

Collaborative filtering — “Recommending things based on what similar users liked.” This works well when you have enough data, but falls apart the moment a new user joins. No history means no recommendations. This is the cold start problem.

Content-based filtering — “ Recommending based on the characteristics of the item itself.” Handles new users better, but tends to suggest things too similar to what you’ve already tried.

The goal for this project was to handle both scenarios properly:

• Give good recommendations to users with history
• Give sensible recommendations to users with none

We used the Yelp Academic Dataset — business listings, user reviews, star ratings, and check-in data in JSON format.

A star rating tells you that someone liked or disliked a place. The review tells you why. That distinction matters for recommendations.

Sentiment analysis is the NLP technique that bridges that gap. It basically classifies text as positive, negative, or neutral.

But the more useful version is aspect-level sentiment analysis: figuring out that a restaurant has excellent food but slow service. Those are different opinions about different things, and a recommendation system should treat them differently.

For example, A person might have commented on a Restaurant xyz, “Loved the Pizza, it was amazing but the Beer was average and disappointing.”

This implies that they like pizza and had higher expectations for the beer’s quality/taste. It also implies that the restaurant makes great pizza, but the beer is not that great. This is Data!

Of course we can't rely on one such data point. A collection of such reviews is what creates a user profile, a profile of what they like, dislike, and don’t really care about. And many such reviews on a business give us the business profile of what they are best at and what they could improve on.

Before any model touches the text, the text has to be cleaned. This was the most time-consuming part of the project and the least glamorous.

Every review went through:

• Tokenization — splitting text into individual
• Stopword removal — filtering out words like “the”, “a”, “is” that add noise without meaning
• POS tagging — identifying each word’s grammatical role to preserve context
• Lemmatization — reducing words to their root form (“running”, “ran”, “runs” → “run”), which is more accurate than stemming

The output is a bag of words, a numerical representation the models can actually work with.

Getting this pipeline right took longer than expected. A lot of that time was spent on edge cases: emoji characters in reviews, inconsistent punctuation, non-ASCII text. None of it is interesting to fix, but all of it matters.

This is the part I found most interesting.

Latent Dirichlet Allocation (LDA) is an unsupervised technique that discovers hidden topics in a collection of documents. You don’t tell it what the topics are but it finds them by identifying which words tend to appear together.

Running LDA on restaurant reviews, it comes up with topics like:

• Food type: “crust”, “cheese”, “pepperoni”
• Service experience: “staff”, “wait”, “service”
• Atmosphere: ambience”, “cozy”, “decor”

Each restaurant ends up with a topic distribution — a kind of fingerprint describing what customers most often talk about. Two Italian restaurants with identical category labels can look very different in topic space: one might be dominated by food quality discussions, the other by atmosphere and price.

This gave the recommendations a texture that simple category matching never could. One thing LDA requires is choosing the number of topics upfront — a hyperparameter you tune manually. Too few and everything blurs together; too many and they become meaningless.

There’s no automated answer here. You run it, read through the outputs, and use judgment. That was a useful reminder that unsupervised learning always needs a human in the loop.

The Recommendation Logic
User profiles and restaurant profiles were created using the process described above and converted into 2 vectors using the LDA technique.

Matching users to restaurants used cosine similarity: representing both as vectors and measuring the angle between them. The smaller the angle, the better the match. It’s direction that matters, not magnitude — so a user who left two reviews and one who left two hundred are treated fairly.

For new users with no history, we asked for preferences directly for crude recommendations. Cuisine type, dietary needs, atmosphere preference — and used those as the starting profile. It’s a workaround, not a solution, but it gave the system something to build from.

The full pipeline — preprocessing, LDA, cosine similarity matching — produced a relatively good accuracy and precision against test data. Better than I’d expected given the noise in the dataset.

One thing worth noting: the system also surfaced the least compatible restaurants alongside the most compatible ones. That turned out to be a useful sanity check.

Data cleaning and making it useful is the majority of the project. The modeling is maybe 30% of the work. The other 70% is making the data usable.

Unsupervised models need human review. With LDA there’s no ground truth to validate against. The topics either make intuitive sense or they don’t.

The cold start problem doesn’t have a clean answer. The content-based workaround is reasonable, but in a real product you’d need to think harder about onboarding — capturing enough preference signal without making sign-up feel like a survey.

The thing this project left me with is a much more concrete understanding of what goes into building a recommendations engine. There’s real work behind those “you might also like” emails that you receive as part of promotions. It’s a pipeline built using you and the data you provide.

If you’re building something similar, the Yelp dataset is a good starting point. Start simple, get the full pipeline working end to end, then improve the pieces.

GitHub - spurthym/Restaurnt-Recommendation-System

What Your Restaurant Reviews Know About You was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

It only takes a few repeated, unsupported comments to change how people see a coworker.

Author Mita Mallick described a jealous colleague who said things like:

• “Did you notice Mita wasn’t invited to that meeting? Clearly our boss doesn’t trust her.”
• “Mita is such a micromanager and so in the weeds! How do you deal with her?”
• “Mita is completely overwhelmed. I’m happy to take on these new projects.”

None of these statements were presented as facts, yet repeated often enough, they shaped how others viewed her.

Bit by bit, day by day, this person shifted the narrative about her and negatively impacted her personal brand.

When we hear a claim about a coworker, pause before accepting or repeating it. Ask, “What makes you say that?” or “Have you talked with them about your concern?”

By challenging unsupported claims, we can build a workplace where people are evaluated on their contributions, not on rumors.

Share this action on Instagram, LinkedIn, or YouTube.

After noticing that Gemini had toned down a significant accomplishment when helping revise her résumé, Jennifer Horsburgh changed one word: her first name became Jeff. And the results were striking.

In a viral LinkedIn post, Horsburgh explained that the difference was in the framing, not the facts. Jennifer’s volunteer work was labeled as community service, and her wins were things she “assisted with” and “collaborated on.” The identical kind of work, with Jeff’s name on it, became leadership, summarizing that he had “engineered” and “architected” solutions to problems.

She pointed out “these verbs are everything, because they decide who reads as a senior director and who reads as the assistant.”

When she asked Gemini how it sorts people’s work, it explained, “I was essentially hallucinating a glass ceiling for you before you even walked into the room.”

It’s infuriating to see the bias.

Let’s learn from the cautionary tale Horsburgh shared with us, whether we’re using AI to revise our résumés, mentoring someone else to do so, or writing recommendations or performance feedback for a colleague. Look out for softening language that minimizes someone’s impact, such as “helped,” “supported,” “assisted,” or “participated.” If those words don’t accurately reflect the person’s contributions, replace them with language that better describes what they actually accomplished.

Let’s not scale the bias that was used to train these systems.

Disability rights organization The Arc reported that slurs against people with intellectual and developmental disabilities are on the rise. The R-word is showing up on social media, in schools, in entertainment, and in everyday conversation.

As The Arc explains, “The R-word comes from the Latin retardare, meaning to slow down, delay, hold back, or hinder. But where a word starts isn’t the same as what it means now.”

Today it’s an insult that demeans their worth and humanity. And it’s become normalized enough that many people, especially younger people, do not always recognize it as a slur.

If we hear someone saying the R-word, The Arc recommends saying something without making it a scene. For example, “Hey, that word is a slur. Can we not use it?”

As they point out, the goal is education, not humiliation.

A client recently asked me for suggestions on how to support autistic people, and I remembered something I had shared years ago in my newsletter: Offer a practice interview.

I came across this idea while reading How Microsoft Tapped the Autism Community for Talent. During interviews, people with autism may experience anxiety, which can cause them to freeze up and struggle to communicate their knowledge.

Yet Microsoft knows that autistic individuals can be strong at problem-solving, coding, and paying attention to detail. And the company decided to adapt its hiring processes to better meet the needs of this group.

Here’s just one of their approaches: Offer candidates a practice interview where they get feedback from recruiters before doing the official one.

I think this approach would work for anyone with pre-interview anxiety. Perhaps because they’re returning to work after taking a caregiving or medical leave. Or they have a non-traditional educational path. Or they’re a member of an underrepresented demographic.

Is this a best practice you can advocate for?

p.s. You may have noticed that I’ve used both person-first language (“people with autism”) and identity-first language (“autistic people”). I follow guidance from the National Institutes of Health (NIH): experts suggest defaulting to person-first language when writing generally about children and using a mix of person-first and identity-first language when writing about adults. (And if writing about a specific person, ask them for their preference.)

Newsletter subscriber Cody wrote,

“Soon after joining my company, I decided to reach out and ask, ‘How can I support or get involved in inclusion initiatives here?’ I was put in touch with the people who head up inclusion efforts, and they welcomed me into a role where I could help almost immediately. It really made it clear to me that sometimes all you need to do is reach out and express interest.”

Cody added,

“Being willing to raise your hand and make efforts to help is enough to signal not only that you’re a safe person, but that you’re willing and able to get involved and do work to make changes. Plus, sometimes you just get to meet some really neat people that way.”

Consider how you might help with your organization’s inclusion efforts, if you’re not already doing so. It could be as easy as reaching out to an employee resource group to ask if they need help with an upcoming event.

If you’ve taken a step towards being a better ally, please reply to this email and tell me about it. And mention if I can quote you by name or credit you anonymously in an upcoming newsletter.

That’s all for this week. I’m glad you’re on this journey with me,

Karen Catlin (she/her), Author of the Better Allies® book series

Copyright © 2026 Karen Catlin. All rights reserved.

Together, we can make a difference with the Better Allies® approach.

• Say thanks to Karen and buy her a coffee ☕ (Need a receipt for educational reimbursement? Reply to this email, and we’ll take care of it.)
• Sponsor an edition of this newsletter
• Follow @BetterAllies on Instagram, Medium, or YouTube. Or follow Karen Catlin on LinkedIn
• Read the Better Allies books
• Tell someone about these resources

Question Unsupported Claims, and Other Actions for Allies was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

It's the deception that gets to me. It's the ingenuous choice of wording. It's the never ending gilding of the lily in order to hide ugly truths. That's what I don't like about our regional government. This deception may partially be in order to protect the guilty. It may be in order to protect the names and reputations of so called "captains" of industry and or persons who built factories and provided jobs in Kitchener-Waterloo. However it's also about protecting their own butts. Some of these politicians have been around for decades and they literally have blood on their hands from decisions they've made and decisions they've avoided. 

Anybody remember the Uniroyal Tire plant on Strange St. for decades? How many know the long and toxic list of airborne contaminants that employees there worked in, some for decades? How many know the battles some of those employees fought to get pis* as* compensation from our provincial government's Workman's Compensation Board (WCB) later renamed to something more politically correct?   

Toxins included benzene, toluene, xylenes, chlorinated solvents and a plethora of other petroleum and rubber related toxins. These toxins don't just exist as air borne contaminants. If they leak, if they are mishandled, if they are negligently stored and most especially if they are buried on site they can and do migrate through the subsurface. Hence a nearby wellfield is the perfect way to introduce some of those same toxins that the Uniroyal workers were breathing, into the water supply. Many decades ago some BTEX chemicals (listed above) were found in low concentrations in some of the Strange St. wells. Who was the most likely contributor, Uniroyal or a nearby day care or school?

The initial Strange St. Wellfield consisted of wells K10A, K11, K13. This is an old wellfield. Current wells are named K10A, K11A, K13B, K18 and K19.  We are advised that well K13B is a direct replacement for well K13A completed in 2023. Oddly both K13B and K11A appear on the maps provided in the Grand River Source Protection Area reports as further west than the original wellfield. We are not told that wells K10A, K11 and K13 were still pumping and producing water for consumption until the very early 2000s. K18 and K19 on the other hand did not appear to be contributing to the drinking water supply until 2006 several years later. Also oddly they are located even further away (westwards) from the original wellfield than K11A and K13B.  Now I do have a memory of reading years ago that several of the Strange St. wells had been closed down and replaced with wells either slightly north or mostly west of their original location. 

Nobody apparently wants to point fingers or besmirch corporate heroes hence it seems to me that instead citizens drank contaminated water for years (just like in Elmira) while regional bureaucrats and politicians decided how best not to identify contaminated wells while either relocating them, shutting them down, or "managing" their pumping rates so as to stop pumping as the plumes approach the wells. 

When asked why our cancer rates keep rising I advise that it is the air we breathe, the food we eat and the water we drink. As soon as you stop doing that you stop feeding your body carcinogens. 

WELLAND - The good news is the runs were down. The better news is it was an exciting game that required extra innings.

But the Kitchener Panthers couldn't slay the Jackfish, losing 3-2 in 10 innings Thursday in Welland.

The game was scoreless until the sixth on a James Smibert sac fly, before Welland scored again in the seventh.

But Malik Williams stepped up again, crushing his ninth home run of the season to dead centre to tie the game.

Smibert would hit a walk-off single in the 10th to finish it and hand Kitchener its seventh straight defeat.

Kitchener drops to 10-22 on the year, and have fallen to eighth place. Welland leads the league with a 23-10 record.

The Panthers continue on the road Friday night in London and Saturday in Guelph.

Kitchener isn't back home until Thursday, Aug. 6 at 7:05 p.m. against the Royals.

GET YOUR TICKETS NOW and #PackTheJack!

MongoDB-compatible database engine for cloud-native and open-source workloads. Built for scalability, performance, and developer productivity.

C 3.4k Updated Jul 31

Melina has a very personal reason for the work she does.

♦

As Integration Lead at Hospice Waterloo Region, Melina works to provide support to men in House of Friendship’s ShelterCare program who are struggling with the grief that comes with losing a loved one to an overdose. 

And Melina can provide that support to others because she has lived through it herself. 

“I lost so many people to drug poisoning,” said Melina. “It started when my son’s friend died at the tender age of 17.

“It just gave me a sense of purpose and meaning to take all this sadness, anger, and shock I was experiencing.

I refused to let them die without being able to honour them.”

Melina helps guide the men in ShelterCare who are experiencing this kind of grief. 

“I remember the first group session I held, and it was only a couple of people,” said Melina. “But I’m finding more and more the men are coming in and they want to talk.”

Men in the ShelterCare program have often experienced a series of losses by the time they become homeless.

“They have lost their sense of purpose, lost their jobs, lost their homes, lost their families, lost their friends, lost their children – all of these relationships,” said Melina. “But most importantly, they have lost a sense of who they are.”

Melina works to help them process these losses and acknowledge the emotions that they are feeling.

Melina also supports the men with writing memorial messages when a participant or friend loses their life to a drug poisoning, and gives them the space they need to have their voices heard.

“Every person has a story, and I want to give them that space to share them.”

The post Providing a Safe Space appeared first on House Of Friendship.

Many of the longtime nearby residents don't remember the "good old days".  The stench of acrid, acidic fumes leaving the site. The generally modest albeit well built homes nearby on Edwin St., Loiusa St., St. Leger etc. A company called Pannill Veneer operated on the site for decades before it closed down. Prior to Panill Veneer however was the Breithaupt Tannery. Solvents, acids and heavy metals were common and well recognized from the tannery industry. It operated approximately from the 1880s until about 1950 before shutting down. There were other tanneries in Kitchener-Waterloo and they too made a mess and left the mess for others to clean up. These industries like so many others were protected by the politicians that they helped to get elected. These same politicians minimized the negative environmental and health effects of these companies both on employees and residents while they were operating and making money as well as after they cut and ran. This is what politicians do especially if they hope to have the funds to get re-elected.

The very same politicians and their successors allowed all the anti social aspects of these heavy, stinking and loud industries, often situated beside residential areas, without proper safeguards or even reasonable mitigating efforts. Then when they packed up and departed little to no effort was made to hold them accountable to clean up the mess they left behind. These were very unhealthy messes combined with high costs to clean them up. Politicians did what they always do. They lied like dogs and passed the costs on to the public taxpayers as much as possible. The Breithaupt Tannery $ 9 million cleanup was the most expensive in Kitchener's history. It was funded by the City of Kitchener (taxpayers), The Region of Waterloo (taxpayers) and the developer (Queensgate Dev't.) who by the way complained that the Province of Ontario (taxpayers) failed to chip in.  Three levels of taxpayers but all out of the same pockets.

Never any health studies, never any environmental studies until after a developer decided they could make money on the site. Breithaupt Tannery did not just mess up people's health through noise and air emissions. They also contaminated the nearby Lancaster Wellfield which supplied water to Kitchener citizens. How many of them died prematurely and or with compromised health issues while the bast**d politicians and industrialists laughed all the way to the bank? 

The nearby Lancaster Wellfield has been shut down now for a very long time. Do you think they shut it down the day the Tannery closed? Did they shut it down before the toxic contaminants reached the drinking wells? Meanwhile Waterloo Region are recommissioning other shut down, contaminated wells so why not these? Allegedly there has been a major cleanup. Or was it? Regardless if we are so desperate for water the Region can reopen these wells with or without an Environmental Assessment. Just like local industry in Elmira and K-W,  Waterloo Region knows that Environmental Assessments are easy to "fix". Afterall it's politicians who write the legislation, warts and loopholes and all.

Written by: Ayomide Awesu, Co-op Student, Public Relations Assistant, Capacity Canada, aspiring communications and marketing professional, entrepreneur, and creative storyteller passionate about digital media and community engagement.

Youth volunteering is becoming more important in our community and more in the nonprofit sector. I believe young people bring creativity, energy and fresh perspectives to nonprofit organizations. Which helps nonprofits expand their target audience and explore new opportunities and helps them stay connected to the world.

Volunteering gives youth a chance to understand how nonprofits work and how small actions/events can make a big impact. This builds confidence, responsibility and helps us discover what we care and want to learn more about.

As a student currently completing my co-op placement with St. Mary’s High School at Capacity Canada, I’ve seen how much youth involvement matters. Working with different staff and community leaders in my own personal life has changed my perspective. I have learned how different projects come together and how we use teamwork and our collected skills to plan events.

Before this placement I volunteered at my church and summer camps. Those experiences taught me how small actions for example, helping organize an event or supporting younger kids can make a difference. Each opportunity helped me grow and be more confident and understand how to be a leader.

Even with all these different benefits, youth volunteering can be tough sometimes. Some youth don’t know where to start or worry if their opinions will be heard and acted upon. Some face barriers like transportations and balancing school. However nonprofit organizations make volunteering easier since they have more flexible roles, mentorship and clear guidance.

• Why do youth choose to volunteer?
  Is it for experience, connection to community or purpose?
  Each reason shapes how we volunteer.
• Who benefits from youth volunteering?
  Is it the organization, the community or the volunteers? This helps us discern how meaningful it is.
• Whose voice is heard in youth volunteering?
  Are youth included in decision making or only in participation? There should be full engagement and responsibility for volunteers.
• How does volunteering create an impact?

Altogether my experiences have shown me that volunteering is one of the most meaningful ways youths can and learn with their community. Working in a nonprofit for my co-op placement, helping at church and volunteering at camps and events have all shown me how to make a difference and care for the world and people around me.

International Volunteer Year reminds us that every volunteer contribution matters. By creating meaningful opportunities for youth to get involved, nonprofit organizations are investing not only in their missions but also in the next generation of community leaders.

Youth have the creativity, energy and independence that nonprofits need to stay connected. When we choose to volunteer and help, we are shaping the type of world that we want to live in.

Written by:

♦Ayomide Awesu, Co-op Student & Public Relations Assistant, Capacity Canada

Ayomide is currently a Highschool student taking a co – op credit. She has plenty of volunteer experience in communication and technology from leading at her church, running her own business and volunteering in graphic design and communication camps. She has developed skills in marketing and operating design for live streams and blogs. She also has customer service skills by maintaining a good relationship with clients/customers for her business and learning how to efficiently use different creating platforms including; canva, adobe and presentations.

Email: ayo@capacitycanada.ca

The post Youth volunteering: Building the future of nonprofit leadership appeared first on Capacity Canada.

Getting someone to download a shopping app has never been easier. Getting them to open it a second time is a different problem entirely.

That gap between install numbers and actual repeat purchases is where most ecommerce app investments quietly fail. Retailers assume the hard part is getting noticed. In reality, the hard part starts the moment the app is already on someone’s phone, competing for attention against every other app they’ve ever downloaded and mostly forgotten.

Australian shoppers have shifted decisively toward mobile it’s now where they browse, compare, and pay, often without a second thought. But that shift only rewards the businesses that treat the app as a conversion system, not a digital brochure with a buy button attached.

Here’s what actually separates an ecommerce app people keep using from one that quietly disappears off their home screen.

A few things are driving Australians deeper into mobile shopping regardless of app quality:

• It’s simply faster. One-tap checkout, saved payment details, and real-time order tracking remove the friction that used to slow a purchase down.
• It feels personal. Apps that surface relevant products and offers based on past behavior keep people coming back the ones that don’t, don’t.
• Payments are safer and quicker than they used to be, which lowers the psychological barrier to buying on impulse.

None of this is optional anymore, it’s the baseline expectation. Which means the businesses that only meet the baseline are the ones getting lost in it.

A high-converting shopping app isn’t defined by its feature count. It’s defined by how little friction stands between “I’m curious” and “I bought it.” A few features do most of the work:

Search that assumes the user is impatient. Predictive autocomplete, smart category filters, price-range sliders, and rating filters all exist for one reason: cutting down the time between opening the app and finding something worth buying.

Recommendations that feel earned, not random. Suggestions based on real browsing and purchase history read as helpful. Generic “you might also like” blocks read as filler, and users learn to ignore them fast.

Checkout with the fewest possible steps. Guest checkout, saved addresses, and stored payment methods aren’t nice-to-haves they’re the difference between a completed sale and an abandoned cart.

Wishlists that come with a nudge. A saved item is only useful if the app follows up a restock alert, a price drop, a gentle reminder instead of letting it sit forgotten.

Product pages that answer objections before they’re raised. Multiple angles, honest descriptions, clear shipping details, and visible reviews all reduce the hesitation that kills a purchase at the last second.

Notifications that respect the user’s attention. Done well, they recover abandoned carts and win back lapsed shoppers. Done carelessly, they’re the reason people delete the app entirely.

AI in ecommerce apps has moved past being a novelty feature it’s now doing quiet, structural work across the shopping journey:

• Chatbots that resolve real queries and product comparisons around the clock, not just deflect them to a support queue.
• Predictive analytics that forecast demand from historical patterns, letting retailers plan campaigns and stock before demand spikes rather than reacting to it.
• Dynamic pricing that adjusts based on real-time demand signals, rather than static seasonal discounts.
• Fraud detection that reads behavioral and device patterns to catch suspicious transactions before they become chargebacks.

The businesses treating these as core infrastructure not add-ons are the ones pulling ahead on both margins and customer trust.

Most of the risk in an ecommerce app project gets decided before a single screen is designed — in who you choose to build it.

Do they have real ecommerce-specific experience, not just general app development? Retail has particular failure points — cart abandonment, checkout drop-off, retention that a team without that specific background won’t know to design around.

Can they show technical depth across the stack you’ll need Shopify, Magento, WooCommerce integrations, or fully custom builds — rather than a one-size-fits-all template approach?

Do they take compliance seriously by default? Australian Privacy Principles and PCI-DSS aren’t optional extras; a partner who treats them as an afterthought is a liability waiting to surface later.

What happens after launch? Apps fail quietly in the weeks after release without ongoing monitoring, patches, and performance tuning. A development partner without a real post-launch process is handing you a half-finished project.

Are you comparing value or just quotes? The cheapest bid rarely accounts for the cost of rebuilding what wasn’t done right the first time.

✔ Map the conversion journey before designing a single screen

✔ Prioritize checkout friction reduction over feature count

✔ Treat AI (recommendations, fraud detection, forecasting) as infrastructure, not add-ons

✔ Confirm Australian Privacy Principles and PCI-DSS compliance upfront

✔ Choose a partner with a defined post-launch support process, not just a launch date

Q1.How long does ecommerce app development typically take?

A. It depends heavily on scope. A straightforward app can take a few months; one with custom integrations, AI-driven personalization, or complex payment logic will reasonably take longer. Timeline should track complexity, not the other way around.

Q2.Should the app be built for iOS, Android, or both?

A. Most retailers benefit from cross-platform development from day one, so they’re not choosing between reach and consistency. The right call depends on where your specific customers already are.

Q3. Can a new app integrate with our existing store and systems?

A. Yes modern ecommerce apps commonly integrate with platforms like Shopify and WooCommerce, along with payment gateways, inventory systems, and CRMs, so data stays synchronized rather than living in silos.

• Mobile shopping growth in Australia rewards convenience and personalization by default apps that only meet that baseline get lost in it.
• The features that actually drive conversion (fast checkout, smart recommendations, low-friction search) matter more than raw feature count.
• AI is now doing structural work in ecommerce forecasting, fraud detection, and pricing not just customer-facing chat.
• Most project risk gets set at the vendor-selection stage, well before development starts.

Building or rebuilding a shopping app that actually converts takes more than checking feature boxes it means starting from how your specific customers browse and buy. 7 Pillars works with Australian and New Zealand retailers on exactly this kind of ecommerce app development, from planning through post-launch support.

Why Most Ecommerce Apps Get Downloaded and Then Ignored was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

OpenAI's Codex Security CLI and TypeScript SDK for finding, validating, and fixing security vulnerabilities. npm: www.npmjs.com/package/@op…

TypeScript 7.8k Updated Jul 31

Imagine two developers solving the same problem.

Both solutions return the correct answer. Both pass every test case. Both get deployed to production without any issues.

So how do you decide which one is actually better?

In software engineering, getting the correct output is only the first step. As applications grow and start handling larger amounts of data, the gap between two “correct” solutions can become enormous. One solution might continue working smoothly as traffic increases, while another might gradually slow down.

To make those decisions objectively, we need a way to measure our code. That is exactly what this episode is about.

When you’re just starting out, the first question you ask about any piece of code is:

“Does it work?”

And honestly, that’s the right place to begin.

If your solution doesn’t produce the correct result, nothing else matters. However, as you gain experience, you’ll often find yourself in situations where multiple solutions solve the same problem correctly.

That’s when a different question starts to matter:

“Which solution is more efficient?”

This is where Time Complexity and Space Complexity come into the picture.

When we talk about the complexity of an algorithm, we’re usually interested in two things.

The first is time complexity, which describes how the number of operations an algorithm performs grows as the input size increases. In simple terms, it helps us understand how much additional work the algorithm has to do when it receives more data.

The second is space complexity, which tells us how much additional memory the code needs as the input size grows.

One thing that often confuses beginners is that we are not measuring actual seconds or megabytes.

We are not asking how fast your laptop runs compared to mine. We are not comparing JavaScript against Java or Python. Those things can vary depending on hardware, operating systems, compilers, and many other factors.

Instead, we focus on something much more useful:

How does the solution scale as the input gets larger? What happens if the input doubles? What happens if it becomes ten times larger? What happens if it grows from a thousand items to a million?

That way of thinking is what makes complexity a reliable measuring tool regardless of the machine or language you are using.

Big O Notation is simply the language we use to describe that growth.

The “O” stands for “order of,” and the expression inside the brackets tells us how the algorithm grows relative to the size of the input. The input size is usually represented by the letter n.

At first glance, the notation may look mathematical and intimidating, but the underlying idea is surprisingly simple. Once you understand the common patterns, reading Big O becomes second nature.

An algorithm is considered O(1) when the amount of work stays the same no matter how large the input becomes.

For example, imagine you have an array containing thousands or even millions of names, and you want to access the first item.

function getFirstItem(arr) {
  return arr[0];
}

Whether the array contains 10 items or 10 million items, retrieving the first element still requires the same operation.

The input size has no impact on the amount of work being done, which is why this is called constant time.

With O(n), the amount of work grows in direct proportion to the size of the input.

Suppose you want to find a particular name inside an unsorted array.

function findName(arr, target) {
  for (let i = 0; i < arr.length; i++) {
    if (arr[i] === target) {
      return i;
    }
  }
  return -1;
}

Since the array isn’t sorted and doesn’t provide any shortcuts, you may have to check each item one by one.

If there are 10 items, you might inspect up to 10 elements.

If there are 1,000 items, you might inspect up to 1,000 elements.

If there are a million items, you might inspect up to a million elements.

The work increases alongside the input size, so this is O(n), also known as linear time.

Quadratic time usually appears when you have a loop running inside another loop.

function printAllPairs(arr) {
  for (let i = 0; i < arr.length; i++) {
    for (let j = 0; j < arr.length; j++) {
      console.log(arr[i], arr[j]);
    }
  }
}

Here, every element is paired with every other element.

If the array contains 10 items, the inner operation runs roughly 100 times.

If the array contains 100 items, it runs roughly 10,000 times.

The growth accelerates quickly, which is why quadratic solutions often become problematic when working with large datasets.

Many performance bottlenecks can be traced back to an unnoticed O(n²) operation.

Logarithmic complexity often feels strange when you first encounter it, but the idea is actually very intuitive.

Instead of examining every item one by one, the algorithm repeatedly cuts the problem in half.

Think about searching for a word in a physical dictionary.

You wouldn’t start from page one and move forward page by page. Instead, you would open the dictionary somewhere near the middle, check whether your word comes before or after that section, eliminate half the remaining pages, and repeat the process.

Each step removes half of the remaining work.

That is the essence of O(log n).

Because the search space shrinks so aggressively, the number of steps grows very slowly. Even when dealing with extremely large datasets, logarithmic algorithms remain remarkably efficient.

We’ll see a classic example of this when we cover Binary Search later in the series.

This complexity appears frequently in efficient sorting algorithms.

It sits somewhere between O(n) and O(n²).

Algorithms with O(n log n) complexity usually combine two ideas:

1. They process all elements.
2. They repeatedly divide the problem into smaller pieces.

Popular sorting algorithms such as Merge Sort and Quick Sort often achieve this complexity.

As datasets become larger, O(n log n) performs dramatically better than O(n²), which is why it is considered the standard target for efficient sorting.

If we arranged these complexities from most efficient to least efficient as the input grows, the order would look like this:

O(1) → O(log n) → O(n) → O(n log n) → O(n²)

You can think of them like this:

• O(1) stays flat no matter how large the input becomes.
• O(log n) grows very slowly.
• O(n) grows steadily alongside the input.
• O(n log n) grows faster than linear time but remains practical.
• O(n²) rises sharply and becomes expensive very quickly.

This ranking is something you’ll use constantly as you learn algorithms and data structures.

Whenever you encounter a new solution, one of the first things you should ask is:

“Where does this algorithm sit on that scale?”

Everything we’ve discussed so far has focused on time, meaning the amount of work being performed.

Space complexity follows the same idea, except we’re measuring memory usage instead of steps.

Consider this example:

function doubleAll(arr) {
  let result = [];

  for (let i = 0; i < arr.length; i++) {
    result.push(arr[i] * 2);
  }

  return result;
}

The new array grows alongside the input array. If the input doubles in size, the extra memory required also doubles.

Because the memory usage grows with the input, this is O(n) space.

Now compare that with:

function sumAll(arr) {
  let total = 0;

  for (let i = 0; i < arr.length; i++) {
    total += arr[i];
  }

  return total;
}

Here, we’re only using a single variable regardless of how large the input becomes.

Whether the array contains 10 elements or 10 million, the extra memory remains essentially the same.

That makes this O(1) space.

In real-world development, you’ll often encounter trade-offs between time and memory. Sometimes using extra memory can significantly reduce execution time. Other times, minimizing memory usage may require additional processing.

Understanding both time and space complexity helps you make those trade-offs consciously rather than accidentally.

When calculating Big O, we focus only on the part of the algorithm that grows the fastest.

For example:

• O(n + 50) becomes O(n)
• O(3n) becomes O(n)
• O(n² + n) becomes O(n²)

Why?

Because Big O is concerned with long-term growth.

As the input becomes very large, constant values and smaller terms become insignificant compared to the dominant term.

The goal is not to count every operation perfectly.

The goal is to understand how the algorithm behaves as the input continues to grow.

From now on, whenever you look at a piece of code, train yourself to ask a simple question:

"As the input gets larger, what happens to the amount of work being done?"

If the work stays the same, you're probably looking at O(1).

If it grows alongside the input, it's likely O(n).

If you see a loop inside another loop, there's a good chance you're dealing with O(n²).

If the problem size keeps getting cut in half, you're probably looking at O(log n).

Developing this habit is one of the most valuable steps you can take as a programmer. It changes the way you read code, write code, and evaluate solutions.

In the next episode, we'll move into Arrays. Now that you understand how to measure performance, the operations we discuss will have much more meaning because you'll be able to analyze not just what they do, but also how efficiently they do it.

Before We Compare Solutions, We Need a Way to Measure Them was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

The post Civic Holiday Monday appeared first on Grand River Rocks Climbing Gym.

A little bit of irony here. The stupid twits invent a weird pretend scientific test to prove one thing and actually prove the very last thing they intended which is that there both have been and are free phase and residual DNAPL in the Municipal Upper and Lower Aquifers beneath the town of Elmira. Keep in mind how late in the game the dirty polluter and their fellow travellors decided that the high concentrations of both NDMA and chlorobenzene needed further efforts. Not back in 1998 when the off-site groundwater pumping began but many years later they added pumping wells W8 and W9 to address specific problems on the east and west sides of Union St. not very far from Park Ave. The east side of the street was actually at the edge of the Yara (Nutrite) property and the pumping well went right down to the Municipal Lower Aquifer. High chlorobenzene concentrations were embarrassing and needed to be reduced in order to continue the DNAPL coverup which said that free phase DNAPLS had never left the Uniroyal Chemical site. The W9 pumping well also went right through to the Municipal Lower Aquifer and in fact its' well screen rested on the surface of the exposed Bedrock Aquifer.  There is no LAT (Lower Aquitard) located here thus providing a direct route for contamination. Yes this is one of the main routes that has contaminated the Bedrock Aquifer with both NDMA, chlorobenzene and everything else despite the professional liars on behalf of Lanxess Canada/Uniroyal Chemical saying otherwise.

Table 2 (CH38A  ML aquifer) gives us our first hint. This observation well located by Industrial Dr. and Oriole Parkway (Sanyo Canada right there) reacted strongly to the cessation of pumping at W3R almost two hundred metres southwards. From Non-Detect for chlorobenzene on June 25/26 before pumping stopped, it in a few days jumped to 40 and later 45 ppb. after pumping was stopped at W3R. After pumping was restarted concentrations of chlorobenzene fell to Non-Detect again. This is due to likely residual DNAPL left over from the years long heavy pumping at W4 further north and a little east of CH38 and had caused unusually high concentrations of chlorobenzene at that well for decades. W4 beside the Howard St. Water Tower and observation well OW57-32R was the location of free phase DNAPL 100 feet below ground surface originally confirmed and then denied by CRA, Uniroyal and the M.O.E..

Table 2 (OW119-27  MU aquifer) tells a somewhat similar story. Prior to pumping being stopped this well three metres north of pumping well W3R had a concentration of 68.1 ppb. of chlorobenzene and within a few days of the cessation of pumping it was at 30.7 ppb. dropping down to .98 (yes less than 1) ppb over the shutdown weeks and then jumping back up to 83.3 ppb. after pumping at W3R was restored. Clearly the core of the chlorobenzene plume has been drawn towards W3R by its' pumping and then drifts away when pumping stops. The ongoing chlorobenzene concentrations up to the drinking standard criteria of 80 ppb. after nearly thirty years of pumping are the obvious and clear  result of subsurface residual or free phase DNAPL presence which dissolves very much more slowly in water than NDMA and most other solvents. 

Table 2 (OW155-32  ML aquifer)  This observation well is located about five metres north of pumping well W5A and had a chlorobenzene concentration of Non-Detect prior to the shutdown of pumping. Within a few days its' concentration had jumped to 135 ppb. It stayed above the drinking water standard throughout the pump shutdown period and then somewhat oddly remained high (151 ppb) even more than two months after pumping restarted at W5A. I see two possibilities here. This pumping well is very close to the south-west corner of the Uniroyal/Lanxess site and either free phase or residual DNAPL  exists very nearby or since pumping well W4 to the west (Water Tower area) was shut down several years ago the remaining contaminant plume has via W5A pumping migrated eastwards towards  itself (i.e. W5A). Either way this again is the second area where DNAPL left the Uniroyal site decades ago and has permanently contaminated the Elmira Aquifers (including Bedrock).

Lastly we have Table 2 (CH44D  ML aquifer)  Here we have a monitoring well twenty metres north of pumping well W8. I guess I may have saved the best for last. Its' concentration prior to the cessation of pumping was 4.56 ppb (parts per billion). Within a few days of pumping being stopped the chlorobenzene concentration had soared to 703 ppb. !!!  It remained high throughout the shutdown period exceeding 1,000 ppb. on two occasions. Then in November two months after pumping restarted it was back down to 20.1 ppb. !!!  This tells me a couple of things. Firstly either residual or free phase DNAPL is present right there in the ML aquifer very close to W8 and CH44D and the volume of groundwater impacted is small leading to amazingly fast reactions to both stopping and restarting the pumping regime. Also based upon a similar reaction to pumping by NDMA I suspect that the NDMA may very well be mixed in with the DNAPL . 

What have I slowly learned over thirty-six years? Thin skinned and faint of heart individuals have a much higher tolerance for well dressed, well spoken liars, cheats and thieves than they do for those who would point out these character deficiencies and speak of them. Hence my apologies to the Jens, Sandys, Sebastians, Nathans and others who are far more impressed than I by the Ramin Ansaris, Allan Deals, Lou Almeidas, Steve Quigleys, Hadley Stamms, Lubnas, Jasons, and so many more. While I don't share your character priorities I try to understand them despite occasionally finding them grating. Also the term "cheats and thieves" may be interpreted as more figurative than literal.    

The post Civic Holiday Hours appeared first on Grand River Rocks Climbing Gym.

The post Civic Holiday Hours appeared first on Grand River Rocks Climbing Gym.

KITCHENER – This was a night where eight runs won a game at Jack Couch Park.

In the finale of the season series between the Kitchener Panthers and the Hamilton Cardinals, the visiting Cardinals held on to an 8-5 victory Tuesday night.

The Panthers used just two pitchers in this one.

Jorge Minyety surrendered four runs in the first inning, and another in the second, but went five innings and struck out seven batters.

Ben Hewitt entered the ballgame in the sixth inning and went a season-high four innings. He also struck out seven and didn't give up an earned run.

Sending two pitchers up to the mound alone, was a win for Kiefer’s squad, who had become accustomed to diving deep into the bullpen.

Hamilton had won the game by getting to Minyety early. The four-spot in the first set the tone for what turned out to be another tough go for Kitchener.

Two home runs from Evan Magill did not give the home side much confidence.

The Panther offence picked up late. A three-run rally in the eighth made things interesting, but they could only get to within three runs of a comeback.

Kitchener drops to 10-21 on the season, while Hamilton improves to 18-11.

Kitchener now heads on the road for four games. Their next two? A rematch against the Welland Jackfish Thursday, and then into London Friday.

The Panthers don't return home until Thursday, Aug. 6 against Guelph at 7:05 p.m.

GET YOUR TICKETS NOW and #PackTheJack!

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♦

Join the KWMP Dance Club for another session of weekly dance classes designed to foster community, fitness, and fun through beginner-to-advanced level dance instruction.

Beginner/Intermediate – 7:00pm to 8:00pm
Intermediate/Advanced – 8:00pm to 9:30pm

KWMP Arts Centre (14 Shaftsbury Drive, Kitchener)

Participants must be at least 16 years old.

Tailored to people who are new to dance or have basic training, this class is perfect for those who want to explore dance and/or hone their skills in a casual and low-pressure environment. Build confidence in movement with slower-paced instruction, a break down of technique and the introduction of basic choreography – and have fun while you’re doing it!

Class Structure: (1 hour total) Approx. 0.5hr Warm/Technique, 0.5hr Choreography

For more experienced dancers who want to challenge themselves, continue to push their skills and are comfortable picking up choreography. This class will be less focused on the basics and more about exploring musicality, style and more complex moves and combinations. It will include faster-paced choreography, transitions and a focus on performance quality. Great for audition training and taking your dance skills to the next level!

Class Structure: (1.5 Hour total) Approx. 0.5hr Warm Up, 1hr Choreography

$20 per class ($15 for KWMP Members). Sign up for a KWMP Membership.

Register Now

NO REFUNDS WILL BE PROVIDED DUE TO CANCELLATIONS.

The post KWMP Dance Club – Summer 2026 appeared first on K-W Musical Productions.

When it comes to decorating a home, inspiration can come from unexpected places. While Pinterest boards, Instagram, and design magazines are great starting points, some of the most unique ideas can be found right here in Waterloo Region’s vibrant arts community.

Local art shows and exhibits offer more than an afternoon outing; they provide fresh perspectives on colour, texture, storytelling, and personal style. Whether you’re settling into a new home, refreshing a room, or preparing a property for sale, exploring local art can spark ideas that help transform your space into something truly personal.

1. Discover New Colour Palettes

Artists often combine colours in ways that wouldn’t normally occur to us. A landscape painting, abstract canvas, or mixed-media piece may inspire a fresh colour scheme for a living room, bedroom, or home office.

Look for:

• Unexpected colour combinations
• Accent colours that could work in pillows, rugs or accessories
• Mood and atmosphere created through colour

2. Create a Statement Wall

A single piece of local artwork can become the focal point of an entire room. Instead of designing a room first and adding art later, try doing the opposite.

Ideas include:

• Oversized paintings above a sofa or fireplace
• A gallery wall featuring works from local artists
• Framed prints that complement your home’s architectural style

3. Add Texture and Dimension

Art isn’t limited to paintings. Ceramics, glasswork, textiles, and sculpture can add depth and visual interest throughout your home.

Consider incorporating:

• Handmade pottery on shelves
• Glass art that catches natural light
• Textile pieces for warmth and softness
• Sculptural accents for entryways and built-ins

4. Reflect the Character of Waterloo Region

Many local artists draw inspiration from the landscapes, architecture, and communities that make our region unique. Displaying local artwork creates a stronger connection to the place you call home.

Popular themes include:

• Rural Waterloo Region landscapes
• Urban scenes from Kitchener and Uptown Waterloo
• Local landmarks and historic buildings
• Nature-inspired works featuring trails, forests, and rivers

5. Support Local Artists While Decorating

Purchasing art from local creators allows you to build a home that feels authentic while supporting the creative community.

Benefits include:

• Unique pieces you won’t find in big box stores
• Opportunities to meet the artist behind the work
• Custom commissions tailored to your space
• A meaningful story behind every piece

6. Bring Personality Into New Construction Homes

Newer homes often feature neutral finishes and clean lines. Artwork is one of the easiest ways to add warmth and personality without undertaking major renovations.

Art can:

• Soften minimalist spaces
• Add colour to neutral rooms
• Create conversation pieces for guests
• Reflect your hobbies, interests, and travels

Waterloo Region offers plenty of opportunities to discover local and Canadian artists throughout the year. Here are some galleries, outdoor spaces, and artists to inspire your home decor!

• Kitchener-Waterloo Art Gallery
• Homer Watson House & Gallery
• Canadian Clay and Glass Gallery
• Button Factory Arts Centre
• Kitchener Art Walk
• Kitchener-Waterloo Society of Artists

Discover more local art galleries here.

@stephboutari

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Your home should tell your story, and local art is one of the most meaningful ways to make a space feel personal. Whether you’re drawn to bold contemporary works, handcrafted ceramics, or landscapes inspired by Waterloo Region, local galleries offer endless inspiration.

The next time you’re looking to refresh a room, consider spending an afternoon exploring a local exhibit. You may discover the perfect piece and the perfect idea to bring new life to your home.

Back-to-School Home Organization Ideas for Busy Families.

Every September, families seem to organize the same things: backpacks, lunch containers, calendars, homework stations, and shoe racks.

But what if the secret to a smoother school year isn’t more storage? The solution is identifying the moments where your home creates the most friction.

Instead of organizing room by room, organize around the daily routines that tend to slow everyone down.

By creating simple “zones” for the busiest moments of the day, your home can work with your family instead of against it.

Whether you’re juggling kindergarten drop-offs, high school schedules, extracurriculars, or working from home, these ideas can help make mornings calmer and evenings more manageable.

Instead of a traditional mudroom, think of your entryway as a launch pad. Everything needed to leave the house should be within arm’s reach.

This could include:

• Individual hooks for each family member
• A basket for hats and mittens (seasonally swapped)
• Backpack parking spots
• Charging station for phones and smartwatches
• Small mirror for last-minute checks

Rather than buying matching bins immediately, work with what you have at home. After a few weeks, you’ll know what storage you actually need.

School paperwork has a way of multiplying overnight. Instead of carrying everything to the kitchen counter, create one dedicated paper station near the entrance of your home.

Give every paper a dedicated spot with a next step:

• “Needs action”
• “To sign”
• “To be kept/ filed”
• Recycling (Add a small recycling bin directly underneath so papers don’t migrate through the house)

Tip: Snap photos of permission slips or artwork before filing (or recycling) them to reduce paper clutter.

One of the easiest ways to recuce after-school chaos is making snacks accessible. Choose one lower cabinet or pantry shelf and stock it with:

• Granola bars
• Fruit pouches
• Crackers
• Trail mix
• Apples
• Refillable water bottles

Younger children can help pack lunches in the morning, and older kids can grab after-school snacks without emptying the pantry.

A homework station doesn’t need an entire room. Instead, think about a space that reduces distractions.

Helpful additions to a homework space could include:

• A small desk lamp
• Pencil cup
• Charging dock
• Noise-reducing headphones

If multiple children share the homework space, consider using rolling carts to make it easier for everyone to keep their supplies separate.

Here’s an organizing trick borrowed from the professionals: Keep one basket in your main living space. Throughout the day, toss in items that belong elsewhere, such as:

• Toys
• Chargers
• Books
• Water bottles
• Socks

Spend five minutes before bedtime returning everything to its proper place. Older kids can be responsible for returning their own items to their room or a dedicated space in your home, i.e. a reading nook. You could even consider giving each family member their own small basket.

Instead of constantly carrying items around the house throughout the day, this approach enables you to contain clutter until your “reset” time.

This may become your family’s favourite organization habit. Choose one shelf, cabinet, or bench where everything for tomorrow sits for the night. Think: anything that has already been prepped for the next day.

This could include:

• Unrefrigerated packed lunches or snacks
• Library books
• Sports equipment
• Musical instruments
• Signed permission forms
• Jackets
• Shoes

In the morning, there’s no searching because your “tomorrow” items are already waiting for you.

One reason homes tend to feel cluttered is that supplies for one activity end up scattered throughout the house. Instead, group items by how they’re used. For example, if you have avid swimmers in your home, rather than sending each bathing suit to a bedroom, towels in the linen closet, goggles in the garage, keep each child’s swimming items together in a bag in the mudroom, all ready to go.

Other examples:

• Soccer bin | Cleats, shin guards, water bottle, sunscreen
• Art bin | Markers, construction paper, glue, paint
• Homework kit | Calculator, highlighters, chargers, notebook

Everything leaves to get used, and then is returned together

Many command centres become decorative instead of functional.

Keep yours simple.

Include only:

• Weekly calendar
• Meal plan
• Important appointments
• Grocery list
• School reminders

Avoid covering every inch of your command centre with sticky notes and paper. White space makes important information easier to spot!

Back-to-school organization isn’t about creating a picture-perfect home. It’s about reducing the number of decisions your family has to make every day.

When backpacks always have a place, snacks are easy to grab, paperwork has a destination, and tomorrow is prepared tonight, routines become smoother and stress levels naturally decrease.

The best organizational systems aren’t the most elaborate. The best systems are the ones your family can actually stick with.

As life evolves through each school year, don’t be afraid to adjust your systems. The goal isn’t perfection; it’s creating a home that supports your family’s routines and gives you more time to enjoy what matters most.

How To Get Your Family & Home Back Into a Routine After Summer (checklist included)

From First House to Forever Home: How KW Families Grow Into Their Space

♦

Nothing against parsley, sage and thyme.

But I love rosemary. The woody, shrubby herb that has a crisp, slightly astringent quality and those warming overtones of pine aroma is versatile and should be front and centre in your battery of herbs and spices in order to play a significant role in your cooking. 

I especially love it as a flavouring for onions, such as the cipollini pictured above.

A native species of Asia and the Mediterranean, rosemary certainly helps highlight those latter cuisines. It is used extensively in southern France and Italy to season grilled meats such as lamb, beef, chicken and pork, but it works just about anywhere, even sweet dishes. 

On a trip to cottage country recently, I picked a few evergreen spruce tips and compared them to some fresh rosemary I had in the kitchen. The similarities were remarkable. 

Rosemary can be used as a strong and appetizing marinade for meats – just combine a few of the whole fresh sprigs and let it do its work for a couple of hours in the fridge. You can also simply drop a couple sprigs into a sauté  pan when you are frying some mushrooms or onions and let the flavours blend together.  

Use rosemary to infuse flavour: chop some fresh leaves finely and work them into softened butter for spreading on warm bread; or, wash a few sprigs of rosemary and pat dry with a towel before letting them air dry for a few days then slip them into a bottle of good quality olive oil.  

Finally, this weekend plan a roasted chicken with rosemary seasoning that is just about the simplest way to achieve terrific flavour that will fill the house with an inviting aroma and hearty woodsy flavour that is sure to satisfy the table. 

Check out my latest post Rosemary — a favourite from AndrewCoppolino.com.

I replaced the word "consumption" with the word "corruption". Both are accurate. Basically TRAC just like previous iterations of Woolwich Council controlled CPAC and UPAC are out of their depth, corrupt bodies by intention. Their mandates are typical Dave Brenneman (CAO) BS. CAO stands for Chief Executive Officer and BS does not stand for Brotherhood of Sisters. Some of the TRAC members, especially the newer ones,  may believe the propaganda whereas it is difficult to believe that the longer  running TRAC (previously TAG) members don't know any better. Susan Bryant in particular has known what's been going on for decades and has enjoyed herself playing the game with the local bigshots, polluters, bureaucrats and politicians. 

Today I'm looking at the Figures and Tables presented in the extremely recent "Lanxess Canada Municipal Aquifer Steady State Conditions Trial Report".  As mentioned yesterday there actually is no steady state being examined. Not only is pumping well E7 doing its' thing at the south end of Elmira but the two Municipal Upper Aquifer wells are pumping at the north end on the Uniroyal/Lanxess site. Add to that the failure of pumping well W5A at the crucial moment and you've really got very little science going on. This report is recommending further similar tests down the road albeit of longer duration which is a very bad idea. Honest brokers doing experiments on contaminated aquifers is one thing but long term, client driven, for profit consultants is something entirely different. My and everybody else who are knowledgeable and honest (barely a handful locally) legitimately can suspect not only junk science and nonsensical conclusions but also no more than a financial holiday from pumping for Lanxess Canada.  

The Figures allegedly show groundwater elevation changes as well as some difference in flow direction during the shutdown and later startup of off-site pumping underneath parts of Elmira.  The Tables allegedly show groundwater contamination concentrations for numerous known Uniroyal Chemical contaminants over the course of the test. Except they don't! They show a grand total of seven monitoring well results including double counting the observation wells screened in both the Municipal Upper and Municipal Lower aquifers namely CH38, OW155 and then three others namely OW 119, OW175 and CH44D screened in one aquifer. That's it which hardly gives a full picture of the levels of contamination throughout Elmira's subsurface. 

Getting back to the number of contaminants tested there are allegedly sixteen VOCs and SVOCs (volatile organics/semi volatile organics) in total including NDMA and chlorobenzene. Even here the company cheaped out and worse by not  testing them on a weekly basis before, during and after the shutdown and startup as they did their favourite two namely NDMA and chlorobenzene. Unbelievably they tested the majority (14 of 16) just prior to the shutdown and then just prior to the restart of pumping. A couple of extra contaminants also got tested in November 2025 two to three months after the restart of off-site pumping. Despite less than surprising results at most of the specifically chosen well locations there were a few surprises of both higher and lower results than expected with NDMA and chlorobenzene. This is why all the listed Uniroyal contaminants should have been fully tested equally with NDMA and chlorobenzene. Of course doing so just might have surprised them particularly with the other (than chlorobenzene) four chlorinated solvents including chlorophenols, dichlorophenols and even perhaps non chlorinated aniline. 

This report is just one more in a long litany of self-serving, public relations tools by Uniroyal/Lanxess to build and maintain the facade that they and their fellow travellors are and have been doing everything possible to remediate the Elmira Aquifers. Unfortunately sweet talk, puffery, psuedo science and phony public consultation  does not clean aquifers. Good science, honest intentions and communication and money do.

The Waterloo Region luxury home market remained active in June 2026, although growing inventory gave buyers more options and made strategic pricing increasingly important for sellers.

According to the Institute for Luxury Home Marketing’s July 2026 report, the region’s luxury single-family market recorded a 22% sales-to-inventory ratio in June. Under the Institute’s methodology, that places the market within seller’s-market territory.

However, the overall classification does not tell the entire story. Inventory increased compared with last year, the number of completed sales declined and activity varied considerably between price ranges.

For homeowners considering selling a luxury home in Waterloo Region, success continues to depend on property-specific pricing, presentation and marketing rather than broad market conditions alone.

For the purposes of its June 2026 analysis, the Institute for Luxury Home Marketing established a luxury benchmark price of $1.1 million for single-family homes in Waterloo Region.

This benchmark is used to define the segment included in the report. It should not be interpreted as an appraisal or an indication that every home priced above $1.1 million offers the same features, quality or market appeal.

In June 2026, the luxury single-family home market recorded:

• 273 active listings
• 60 completed sales
• A 22% sales-to-inventory ratio
• A median sale price of $1,205,000
• A median of 18 days on market
• A median sale-to-list price ratio of 98.31%

The Institute classifies a sales ratio of 21% or higher as a seller’s market. At 22%, Waterloo Region sat only slightly above that threshold, suggesting that conditions remained favourable for some sellers without creating uniform competition across the entire luxury market.

There were 273 luxury single-family homes available in June 2026, compared with 226 in June 2025. This represented a 21% year-over-year increase in inventory.

At the same time, completed sales declined from 78 in June 2025 to 60 in June 2026, a decrease of approximately 23%.

This combination gave luxury buyers more choice than they had one year earlier. When more comparable homes are available, buyers can be more selective about location, renovations, lot size, layout and overall condition.

For sellers, increased competition makes the initial launch particularly important. A property that enters the market with an unsupported asking price or underwhelming presentation may lose buyer attention to better-positioned alternatives.

The median sale price for a luxury single-family home was $1,205,000 in June 2026. This was approximately 5% lower than the June 2025 median of $1,271,675.

The median was also lower than the $1,271,200 recorded in May 2026.

Monthly luxury-market prices can shift based on the composition of the homes sold. Because the luxury segment records fewer transactions than the overall residential market, several high-value or entry-level luxury sales can have a noticeable effect on the median.

The monthly median is therefore useful for understanding market direction, but it should not be used by itself to estimate the value of an individual property.

Neighbourhood, lot characteristics, architectural quality, renovations, privacy, finished living space and competing listings all influence a luxury home’s market value.

Luxury single-family homes sold for a median of 98.31% of their asking price in June 2026. This was nearly unchanged from the 98.20% recorded in June 2025.

Although the market technically met the Institute’s seller’s-market criteria, the sale-to-list ratio suggests that buyers were still negotiating on many properties.

A seller’s-market label does not mean every home will sell above asking price. It means sales activity is relatively strong compared with available inventory under the report’s methodology.

Property-specific factors still determine whether a home sells quickly, requires negotiation or remains available longer than competing listings.

The most active price range in June was $1.1 million to $1.149 million.

This segment recorded 20 sales against 27 available listings, producing a 74% sales-to-inventory ratio. That was considerably stronger than the overall luxury market ratio of 22%.

Activity varied significantly in higher price ranges:

• $1.15 million to $1.199 million recorded 9 sales and 39 listings.
• $1.2 million to $1.249 million recorded 2 sales and 23 listings.
• $1.3 million to $1.399 million recorded 8 sales and 34 listings.
• $1.5 million to $1.699 million recorded 7 sales and 24 listings.
• $1.7 million to $1.899 million recorded 6 sales and 12 listings.
• No sales were reported between $1.9 million and $2.399 million.
• Four sales were recorded between $2.4 million and $2.799 million.

The results demonstrate why the luxury market should not be treated as one uniform category. A home priced slightly above $1.1 million faced very different conditions from a property priced near $2 million.

The higher the price point, the smaller the qualified buyer pool generally becomes. This makes accurate positioning and targeted exposure increasingly important.

Five-bedroom luxury homes accounted for 24 of the 60 single-family sales completed in June. Their median sale price was $1,275,000, and they sold in a median of 18 days.

Four-bedroom homes recorded 19 sales at a median price of $1,260,000. This category also had the greatest amount of competition, with 112 available listings and a 17% sales ratio.

Three-bedroom homes recorded 11 sales at a median price of $1,150,000. They sold in a median of 10 days and produced a 31% sales ratio.

Homes with 6 or more bedrooms recorded 5 sales against 40 listings, resulting in a 13% sales ratio.

Bedroom count provides some context, but it does not explain buyer demand on its own. Buyers also consider functionality, bedroom sizes, bathroom access, office space, finished basements and whether the overall layout meets their needs.

The luxury benchmark for attached homes in Waterloo Region was $700,000 in June 2026. The market recorded 72 active listings and 15 completed sales, resulting in a 21% sales-to-inventory ratio. Under the Institute for Luxury Home Marketing’s methodology, this placed the attached luxury segment within seller’s-market territory.

Inventory rose from 38 listings in June 2025 to 72 in June 2026, representing an 89% year-over-year increase. Sales increased more modestly, from 14 to 15.

While buyer demand remained active, the larger supply of available properties gave buyers considerably more choice and increased competition among sellers.

The median sale price was $725,000, down approximately 6% from $775,000 one year earlier.

However, the homes that sold moved quickly, spending a median of just 12 days on the market compared with 44 days in June 2025. Attached luxury homes also sold for a median of 101.23% of their asking price, suggesting that well-positioned properties continued to attract strong buyer interest.

Three-bedroom attached homes accounted for 10 of the 15 sales and recorded a median sale price of $717,500.

The $700,000 to $719,999 price range saw the highest number of transactions, with 6 sales, followed by the $720,000 to $739,999 range with 4 sales.

The June results point to a selective market rather than one where every luxury property benefits equally.

Demand remained particularly strong near the $1.1 million luxury threshold. Above that range, performance became more dependent on the home’s price, condition, location and competition.

For sellers, several factors are especially important:

A previous neighbourhood sale may no longer represent today’s conditions. Sellers need to evaluate recent sales, current listings and properties that failed to sell.

Luxury buyers expect a high level of preparation. Professional photography, thoughtful staging, video, floor plans and polished listing materials help buyers understand the property’s value.

A higher asking price often means a smaller group of qualified buyers. Marketing should be designed around the property’s likely audience rather than relying solely on general online exposure.

Showing activity, repeat visits and buyer comments can reveal whether the market is responding positively to the home’s price and presentation. A lack of engagement should not be ignored simply because the broader market is classified as favourable to sellers.

There is still active demand for luxury both single-family and attached homes in Waterloo Region, particularly within certain price ranges. However, increased inventory means sellers are competing more directly for buyer attention.

A homeowner with a well-maintained, properly positioned property may still benefit from current conditions. A home that requires extensive work, has a more specialized layout or enters the market at an aggressive price may face a longer sales process.

The decision to sell should be based on the homeowner’s circumstances and a detailed review of their specific property, neighbourhood and price category.

Selling a luxury property requires more than selecting an asking price and placing the home on MLS®. Every part of the launch should work together to communicate the home’s value and reach qualified buyers.

The Deutschmann Team provides full-service representation for Waterloo Region homeowners, including strategic pricing, professional property preparation, premium marketing and extensive market exposure.

Thinking about selling a luxury home in Waterloo Region? Contact our team for a confidential consultation and a property-specific review of current market conditions.

The post Waterloo Region Luxury Real Estate Market Update | July 2026 appeared first on Kitchener Waterloo Real Estate Agent - The Deutschmann Team.

Over the past three years, HR leaders have repeatedly been offered similar solutions: implement a chatbot or copilot and expect increased productivity. However, these tools have largely delivered incremental improvements in drafting rather than true transformation.

A significant change is now underway that will fundamentally reshape the HR function.

Agentic AI does not wait for a prompt. It watches for a trigger, decides what needs to happen next, and carries out the work across the tools your team already uses, from the applicant tracking system to payroll to Slack. Instead of a single smart assistant, you get a small workforce of specialized agents handling recruiting, onboarding, compliance, and employee service in parallel. That is the shift this article is about: from AI that helps a person do a task, to AI that runs the task on its own and simply reports back.

The easiest way to see the difference is to compare what each one does when nothing prompts them.

A generative AI assistant sits idle until someone types a question. Ask it to draft an offer letter, and it drafts one. Ask it nothing, and it does nothing.

An agentic system behaves more like a quiet coworker who never clocks out. It notices that a candidate pipeline has stalled for eight days, checks why, and either nudges the recruiter or reschedules interviews on its own. It notices a payroll anomaly before finance does. It notices that engagement scores dropped in one region and flags it to the HR business partner with a summary of what changed.

This is not automation in the old sense, where a system follows a fixed script. Agentic AI reasons through exceptions. It decides which of several possible actions makes sense given the situation, then takes that action, and only loops in a human when the stakes or ambiguity call for it.

This is not a five-year-out concept. It is running in production at companies right now, and the results are concrete enough to plan around.

Recruiting has become an orchestrated pipeline rather than a series of manual steps. An agent can draft a job description from a workforce plan, post it across boards, screen incoming resumes against a skills rubric, rank the shortlist, and negotiate interview times directly with candidates and hiring managers. Recruiters step in for the conversations that actually need a human voice, not the calendar tetris around them. Healthcare organization Wellstar reported that an agentic assistant automated thousands of routine account unlocks and cut internal approval times from several days down to about ninety minutes, freeing staff for higher-value work.

Onboarding has quietly become a coordination problem that agents are well suited to solve. When an offer is accepted, one trigger can set off a chain: provisioning system access, ordering equipment, enrolling the new hire in the right learning paths, and notifying IT, facilities, and the hiring manager, all without someone manually checking five different systems.

Compliance monitoring is moving from a once-a-quarter audit to something closer to continuous coverage. Picture a state changing its family leave rules. An agent tracking regulatory feeds can catch the update within hours, compare it against the company’s current policy, flag the exact clause that needs to change, and hand HR and legal a summary along with a drafted revision, rather than someone discovering the gap during an audit months later.

Employee service is where the volume gains are hardest to ignore. Gartner has tracked HR’s AI adoption climbing from about 19 percent in 2023 to roughly 61 percent by 2025, and a large share of that growth is in service delivery: agents triaging tickets, answering policy questions, updating records, and escalating only the cases that genuinely need a person.

PwC’s analysis of HR sub-processes across the full hire-to-retire lifecycle found that agents can now handle or assist more than half of day-to-day HR work, and closer to 88 percent of purely administrative tasks like forms and routine transactions. Strategy work, culture building, and judgment calls on people decisions remain firmly human territory, and that split is unlikely to move much even as the tools get better.

The phrase sounds abstract until you see it in practice. It simply means multiple agents, each built for a narrow job, working off shared data and shared goals instead of operating as isolated tools.

Think of a recruiting agent, an onboarding agent, and a compliance agent all reading from the same employee record. When a candidate accepts an offer, the recruiting agent’s job ends and it hands context, not just a status update, to the onboarding agent, which already knows the start date, the role, the manager, and the equipment needs. Nobody re-enters that information. Nothing gets lost between systems.

This only works if the underlying data is actually connected. ADP has pointed out that agents can only reason well when they can see across the HRIS, payroll, ATS, and ticketing systems at once. An agent boxed into a single platform behaves like a smart tool. An agent with access across the stack behaves like an orchestrator. The technology is rarely the bottleneck here. Messy, siloed data usually is.

Platforms like Sana, Moveworks, and ServiceNow have built their pitch around exactly this orchestration layer, connecting HR, IT, and finance systems so agents can complete a request end to end instead of stalling out at the edge of one tool.

Microsoft’s research on what it calls “Frontier Firms” describes organizations moving through three stages: people using AI assistants, then human-agent teams working side by side, and eventually human-led operations where agents run day-to-day execution under human direction. That last stage has produced a new kind of job title inside some companies: the “agent boss,” someone whose real work is managing a portfolio of AI agents the way a manager once managed a team of people.

That is not the same as HR disappearing. If anything, the roles that remain get more human, not less. When agents absorb the scheduling, the data entry, and the first-pass screening, HR professionals get their time back for the parts of the job that were always the hardest to automate: coaching a manager through a tough conversation, mediating a conflict, deciding how to handle a genuinely ambiguous case, and shaping the culture that no dashboard can capture.

Josh Bersin’s research on this shift makes a similar point. Companies that redesign the work itself around human-agent partnership, rather than just bolting AI onto existing processes, see the largest gains, sometimes several times the output of teams that never rethought how the work is structured in the first place.

None of this works without guardrails, and the companies getting agentic AI right are the ones treating governance as part of the design, not an afterthought bolted on after something goes wrong.

A few things matter more than the rest:

Clear escalation rules. Scheduling an interview is low stakes. Deciding on a pay adjustment or a termination is not. The best agentic systems are built with explicit boundaries around which decisions an agent can finalize and which ones must land on a person’s desk before anything happens.

Audit trails. Every action an agent takes, and the reasoning behind it, needs to be logged and reviewable. This matters for compliance, and it matters just as much for building trust with employees who want to know a real person is accountable for decisions that affect their work life.

Data quality as a first step, not a side project. An agent is only as good as what it can see. Before scaling agents across the HR stack, the harder and less glamorous work is making sure the HRIS, ATS, payroll, and benefits systems can actually talk to each other.

Regulatory awareness. Laws around pay equity, hiring bias, and algorithmic decision-making are catching up to this technology fast. Explainability is likely to become a requirement rather than a nice-to-have, so building agents that can show their reasoning now will save a lot of retrofitting later.

PwC’s own survey found that while nearly 8 in 10 executives say their companies are already adopting AI agents somewhere in the business, only about 4 in 10 have brought agents into HR specifically. That gap is an opportunity for anyone willing to move now rather than wait for the space to get crowded.

A sensible starting point looks like this:

• Pick one workflow with a clear, measurable pain point, usually recruiting screening or onboarding coordination, and pilot an agent there before attempting anything enterprise-wide.
• Fix the data connections between your core systems before adding more tools on top of a fragmented stack.
• Define escalation and approval rules before the agent goes live, not after the first edge case surfaces.
• Treat the rollout as iterative. Expect to adjust the agent’s rules as you see how it behaves against real cases, rather than expecting it to be right on day one.
• Keep people at the center of anything involving pay, termination, promotion, or other decisions with real consequences for someone’s livelihood.

Agentic AI is not another item on the HR tech shopping list. It is a different operating model, one where routine, cross-system work runs largely on its own and human attention shifts toward judgment, empathy, and the decisions that genuinely need a person behind them.

The organizations that treat this as a chance to redesign how HR work gets done, rather than just automating the same broken process faster, are the ones that will see the biggest gains. The technology is ready sooner than most HR teams expect. The real work now is getting the data, the governance, and the people ready to work alongside it.

What is agentic AI in HR? Agentic AI in HR refers to AI systems that act autonomously across HR workflows. Rather than waiting for a person to ask a question, these agents monitor signals like a stalled hiring pipeline or a compliance change, decide on the right next step, and carry it out across connected systems like the HRIS, ATS, and payroll platform.

How is agentic AI different from a generative AI chatbot? A generative AI chatbot responds when prompted and stops there. An agentic system initiates action on its own, plans a sequence of steps, executes them across multiple tools, and only involves a human when a decision requires judgment or carries real consequences.

Will agentic AI replace HR jobs? Most research points to a shift in focus rather than a wholesale replacement. Agents are taking over administrative and repetitive tasks, while strategic work, culture building, coaching, and sensitive people decisions remain led by humans, often with more time to devote to them than before.

What should HR teams do first before adopting agentic AI? Start by connecting the data across core HR systems, since agents can only reason well with access to a full picture. From there, pilot agents on one well-defined workflow, set clear rules for what requires human approval, and build in audit trails from day one.

Is agentic AI in HR safe from a compliance standpoint? It can be, provided governance is built in from the start. That means explicit escalation rules for sensitive decisions, complete audit logs of agent actions and reasoning, and ongoing attention to evolving regulations around algorithmic decision-making in hiring, pay, and promotion.

AI Assistants Are Old News;Agentic AI Is Transforming HR was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

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Colonial Acres continues to be one of Waterloo’s most established and desirable neighbourhoods, but not every home is receiving the same response from buyers.

During the 90 days ending July 22, 2026, 21 detached homes had firm sales or completed transactions in Colonial Acres. Sale prices ranged from $705,000 to $2,650,000, reflecting the neighbourhood’s diverse mix of bungalows, side-splits and larger executive properties on mature lots.

Nine homes sold above their final asking price, one sold at asking and 11 sold below asking.

The takeaway for Colonial Acres homeowners is clear: buyers are active, but increasingly selective. The homes achieving the strongest results are generally the ones that offer the right combination of preparation, presentation, pricing and value.

Request a confidential, property-specific home valuation and selling strategy.

As of July 22, 2026:

• Active listings: 5
• Conditionally sold: 1
• Firm sales awaiting closing: 15
• Recently closed sales: 6
• Median sale price: $950,000
• Average sale price: $1,239,036
• Median days on market: 17
• Average days on market: 28
• Sold above asking: 9
• Sold at asking: 1
• Sold below asking: 11

The median price is generally the more useful measure because the average was influenced by several luxury sales above $1.5 million.

Even the median should not be treated as an estimate of every Colonial Acres home. Lot size, street, renovations, layout, condition, square footage, pool, privacy and overall presentation can create substantial differences in value.

The five active detached homes were listed from $875,000 to $2,050,000.

Only one active property was priced below $1 million, while three of the five were listed above $1.5 million. This means the amount and type of competition facing a seller depends greatly on the home’s expected price range.

Several active listings had already accumulated extended market time, experienced a price adjustment or been exposed to the market through a previous listing.

Even in a highly desirable neighbourhood, buyers will resist an asking price that is not supported by the home’s condition, features and current competition.

Looking at all firm and closed sales together does not tell the entire story. Buyer behaviour changed considerably across the different price ranges.

Colonial Acres homes below $1 million

Eleven detached homes sold below $1 million during the 90-day period.

• Median sale price: $859,500
• Median time to sell: 18 days
• Seven sold above asking
• Four sold below asking

This was the most consistently competitive part of the Colonial Acres market. With only one active detached listing below $1 million as of July 22, a well-prepared and accurately priced home in this range may attract strong attention.

That does not mean every property should be intentionally underpriced or held for offers. The right approach depends on the home, current competition and the seller’s priorities.

Colonial Acres homes from $1 million to $1.5 million

Four homes sold within this range.

• Median sale price: $1,255,000
• Median time to sell: 26 days
• None sold above asking
• One sold at asking
• Three sold below asking

Buyers in this segment were more price-sensitive. They closely compared renovation quality, square footage, lot, layout, mechanical updates and overall condition before deciding what they were prepared to pay.

A Colonial Acres address remains valuable, but the neighbourhood alone will not support an asking price that exceeds the complete property’s current market value.

Colonial Acres homes above $1.5 million

Six homes sold for $1.5 million or more.

• Median sale price: $1,820,000
• Two sold above asking
• Four sold below asking
• Selling times ranged from immediate sales to 129 days

This was the most property-specific portion of the market.

Two standout homes attracted immediate competition and sold above asking within two days. Other higher-end properties required considerably more time or sold below their final asking prices.

Luxury buyers will still pay a premium for an exceptional Colonial Acres home, but they have high expectations. They are evaluating design, renovation quality, lot, privacy, outdoor living, architectural features and overall value as a complete package.

More detached homes sold during the most recent 90-day period than during the same period in 2025: 21 compared with 12.

However, the median selling time increased from approximately 10 days to 17 days. The market was busier, but buyers generally took longer to make decisions.

The types of homes sold also differed substantially between the two periods, particularly at the luxury end of the market. For that reason, a simple year-over-year change in the average price would not provide a reliable indication of how an individual Colonial Acres home’s value has changed.

A sold-over-asking headline can be impressive, but it does not always explain how a property actually performed.

A home may sell above its final asking price after being reduced or repositioned. Another may sell slightly below asking while still producing an excellent outcome based on the property’s market value, closing terms and the seller’s goals.

To properly assess a sale, we consider:

• The original and final asking prices
• Previous listings, relaunches and price adjustments
• Total market exposure
• Condition and level of renovation
• Lot size, privacy and street location
• Competing inventory at the time
• Offer conditions, deposit and closing date
• The seller’s overall priorities

A home does not have to be completely renovated to sell well.

In many cases, the best return comes from targeted preparation: completing necessary repairs, refreshing paint, improving lighting, editing furnishings, addressing visible maintenance concerns and professionally staging the property.

The key is knowing which improvements will matter to buyers—and which projects are unlikely to produce a meaningful return.

Based on recent sales and the feedback we are receiving, buyers are responding most favourably to homes offering a strong combination of:

• Mature, private or unusually large lots
• Updated kitchens and bathrooms
• Functional layouts and generous living space
• Finished lower levels
• Flexible space for working from home or extended family
• Pools and well-designed outdoor living areas
• Updated mechanical systems
• Natural light and architectural character
• Professional staging and presentation
• Pricing supported by current market evidence

A home does not have to be completely renovated to sell well.

In many cases, the best return comes from targeted preparation: completing necessary repairs, refreshing paint, improving lighting, editing furnishings, addressing visible maintenance concerns and professionally staging the property.

The key is knowing which improvements will matter to buyers—and which projects are unlikely to produce a meaningful return.

Our team represented the sellers of four Colonial Acres homes during this 90-day period, with sale prices ranging from $850,000 to $1,830,000.

Those properties included a mature bungalow, a substantial family home and executive residences at the higher end of the market. This gave us direct, current insight into buyer behaviour across several Colonial Acres price ranges.

Two of the homes sold above asking within two days:

• 303 Castlefield Avenue sold for $1,810,000 after being listed at $1,599,900.
• 612 Stonebury Crescent sold for $1,525,000 after being listed at $1,495,000.

Both were strategically priced based on their condition, features and current competition—not artificially underpriced simply to create a sold-over-asking result. The sellers fully committed to the recommended preparation, staging, pricing, marketing and launch plans.

Our other recent Colonial Acres sales required different strategies because every property and seller’s circumstances were different. What remained consistent was the importance of understanding the competition, preparing carefully and positioning the home for the buyers most likely to appreciate it.

There is meaningful demand for the right Colonial Acres home, but a desirable address alone does not guarantee a quick or competitive sale.

Begin preparing before you are ready to list

Allow enough time to determine which repairs, improvements and presentation changes are worthwhile. Rushing to market before the property is ready can weaken the initial launch.

Compare your home with the right properties

A bungalow on a deep lot should not be valued in the same way as a newer executive home with a pool on an established crescent. The most relevant comparisons depend on lot, style, size, condition and features—not simply proximity.

Price for the current market

Overpricing to leave room for negotiation can reduce showing activity and cause buyers to question the property’s value. The first days of a listing often bring the greatest attention, and that opportunity can be difficult to recreate after a property sits or requires a price reduction.

Prepare for selective buyers

Buyers are noticing unfinished repairs, dated mechanical systems, poor presentation and renovation requirements. Not every concern needs to be corrected, but the price and marketing strategy must account for them.

Treat the launch as one coordinated plan

Preparation, staging, pricing, photography, video, marketing and timing should work together. The strongest results rarely come from treating these elements as separate or optional decisions.

The average sale price during the 90 days ending July 22, 2026 was approximately $1.24 million, while the median was $950,000.
The difference is significant because several luxury sales raised the average. Neither number should be used alone to estimate a specific home’s value.

Yes. Nine of the 21 firm or closed sales sold above their final asking prices.

However, 11 sold below asking, showing that multiple offers and above-list sales are not occurring equally across every price range or property type.

The median selling time was 17 days, but individual results ranged from immediate sales to more than four months.

Condition, pricing, competition and presentation all affect how quickly a property sells.

Not necessarily. Many sellers benefit more from selective repairs, paint, lighting improvements, editing and staging than from completing a major renovation.

A pre-listing consultation can help identify which improvements are likely to influence buyers and which ones may not be worth the cost or disruption.

Colonial Acres sale prices during this period ranged from the $700,000s to more than $2.6 million. A neighbourhood average cannot account for the differences between those properties—or accurately determine the value of your home.

Our confidential, property-specific market assessment will explain:

• Which recent sales are genuinely comparable
• What buyers would compare your home against today
• How your street, lot, layout, condition and renovations affect value
• Which preparation or improvements may be worthwhile
• How staging and presentation could influence the result
• The pricing, marketing and launch strategy we would recommend

Considering selling in Colonial Acres? Contact The Deutschmann Team for a confidential home valuation and customized selling strategy.

This report examines detached-home activity in Colonial Acres during the 90 days ending July 22, 2026. Active, conditional and pending listings reflect their MLS® status as of July 22. Pending transactions had firm agreements but had not yet closed. Listing statuses and sale details may change. Market statistics describe general activity and should not be used to determine the value of a specific property without a detailed assessment. Source: ITSO MLS® data.

The post Colonial Acres Waterloo Real Estate Market Update | July 2026 appeared first on Kitchener Waterloo Real Estate Agent - The Deutschmann Team.