Waterloo Region’s luxury real estate market remained relatively balanced last month in July 2026, but the latest numbers show noticeably different conditions depending on property type, price point and buyer demand.

According to the August 2026 Waterloo Region Luxury Market Report from the Institute for Luxury Home Marketing, the luxury benchmark price was $1.1 million for single-family homes and $700,000 for attached homes. These benchmark prices are thresholds established by the Institute for the purposes of its luxury market analysis.

For homeowners considering selling, buyers looking for a luxury property and anyone keeping an eye on the Waterloo Region luxury real estate market, here is what the July 2026 data tells us.

The luxury single-family home segment recorded 257 properties for sale and 45 sales in July, resulting in an 18% sales ratio and placing the market firmly in balanced territory. The Institute defines a balanced market as a sales ratio between 12% and 21%.

Compared with July 2025, however, both inventory and sales activity declined.

In July 2025, there were 313 luxury single-family homes available and 60 sales. By July 2026, inventory had fallen 18% to 257 homes, while the number of properties sold declined 25% to 45.

At the same time, values held up well. The median sale price increased from approximately $1.22 million in July 2025 to $1.26 million in July 2026, representing a 4% year-over-year increase.

One of the more important changes for sellers is the amount of time luxury homes are spending on the market.

The median days on market increased from 22 days in July 2025 to 32 days in July 2026, a 45% increase. Luxury single-family homes also sold for a median of 96.67% of their asking price, compared with 98.69% one year earlier.
For sellers, this reinforces the importance of getting the initial positioning right. In a balanced luxury market, buyers generally have enough choice to be selective. Pricing, presentation, property preparation and the quality of the marketing launch can have a meaningful impact on how quickly a home attracts serious interest.

A luxury listing can still sell exceptionally well, but relying on the market alone to create urgency is less effective when buyers have competing options.

Demand also varied considerably based on bedroom count.

Three-bedroom luxury homes recorded a 26% sales ratio, placing that segment in seller’s-market territory. Four-bedroom homes had a 14% sales ratio, five-bedroom homes 19%, and properties with six or more bedrooms 17%.

Median sale prices in July included:

• 3 bedrooms: $1,225,000
• 4 bedrooms: $1,292,500
• 5 bedrooms: $1,356,000
• 6+ bedrooms: $1,402,500

The median days on market ranged from 25 days for four-bedroom properties to 51 days for homes with six or more bedrooms.

The report identified $3.6 million to $3.999 million as the most active price band during July, with a 100% sales ratio. Because activity at individual luxury price points can involve a relatively small number of properties, these figures are best considered alongside broader market conditions rather than in isolation.

Luxury attached homes told a somewhat different story in July.

The attached segment had 69 active listings and 12 sales, producing a 17% sales ratio and also qualifying as a balanced market.

Unlike the single-family segment, both inventory and sales were higher than a year earlier.

Inventory increased from 56 homes in July 2025 to 69 in July 2026, a 23% increase. Sales rose from 7 to 12, representing a 71% year-over-year increase.
Despite the increase in activity, pricing remained relatively stable. The median luxury attached-home sale price was $773,250 in July 2026, compared with approximately $775,000 in July 2025.

While single-family homes took longer to sell, the attached luxury market moved in the opposite direction.

Median days on market fell from 34 days in July 2025 to 26 days in July 2026. Homes sold for a median of 97.47% of list price, compared with 97.93% one year earlier.

The most active price range was $840,000 to $859,999, where the report recorded a 200% sales ratio.

Among attached homes, three-bedroom properties were particularly active. They recorded 10 sales against 41 properties in inventory, producing a 24% sales ratio, which places that segment in seller’s-market territory according to the Institute’s methodology. Their median sale price was $774,000, with a median 25 days on market.

The headline is fairly straightforward: the Waterloo Region luxury housing market is balanced, but balanced does not mean every property is experiencing the same market.

Single-family luxury inventory is lower than it was last year, yet sales have also slowed and homes are taking longer to sell. Buyers at this level are typically comparing several factors, including location, lot, condition, renovations, design, privacy, amenities and overall value.

That makes strategic positioning especially important.

For sellers, the strongest approach begins before the property reaches MLS®. Understanding comparable sales, competing listings and current buyer expectations can help determine where a home should be positioned within its specific segment of the market.

Professional photography, video, staging, targeted digital marketing and exposure to the right buyer audience are also particularly important for distinctive and higher-value properties. Luxury buyers are not simply comparing square footage and bedroom counts. They are evaluating the complete property and whether its asking price reflects what is available elsewhere in the market.

For buyers, a balanced market can provide more opportunity to evaluate a property carefully without assuming that every listing will immediately generate intense competition.

That does not mean desirable homes will not sell quickly.

The data shows that certain segments, including three-bedroom properties, were moving more quickly than the overall luxury market. Individual homes with strong locations, desirable features and compelling pricing can still attract significant interest.

Buyers considering a luxury home in Kitchener, Waterloo or the surrounding townships should look beyond broad regional averages and evaluate the market surrounding the specific property.

Recent comparable sales, competing inventory, days on market and seller motivation can all help determine how aggressively to approach an offer.

July’s numbers point to a luxury real estate market that is neither strongly favouring buyers nor sellers overall.

For Waterloo Region single-family luxury homes, the median selling price increased year over year even as the number of sales declined and marketing times increased. Meanwhile, the attached luxury segment experienced substantially more sales activity and shorter marketing times while median pricing remained relatively unchanged.

That is why broad headlines rarely tell the full story.

The value and marketability of a luxury property depend on its neighbourhood, property type, condition, features, price range and current competition. A strategy that works for one home may not be appropriate for another, even within the same city.

If you are considering selling a luxury home in Waterloo Region, understanding where your property fits within today’s market is the first step.

The Deutschmann Team combines extensive Waterloo Region market experience with strategic pricing, professional property preparation, premium marketing and experienced negotiation to help position each home for the strongest possible result.

Thinking about selling? Contact The Deutschmann Team to find out what your home could sell for in today’s Waterloo Region real estate market.

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

There is a most unusual technical, hydrogeological report on-line that I read many, many years ago. Like many others, rereading it years later is very helpful especially if one has been constantly elevating one's   background knowledge and staying up to date with current events. Now I am somewhat reluctant and concerned to be the bearer of bad news particularly because I know that our idiot politicians are highly likely to do what they do best, which is to misuse and abuse information for their own self-serving purposes.  This report is titled "Groundwater Contamination In The Kitchener-Waterloo Area, Ontario". It was written in 1995 by a group of hydrogeologists from the University of Waterloo and possibly elsewhere. Many of these are well known, world class hydrogeologists including names such as Sanderson, Karrow, Greenhouse, Paloschi, Schneider, Mulamoottil, Mason, McBean, Fitzpatrick, Mitchell, Shrubsole & Child. Dr. John Cherry is also referenced in this report.

Recently I have published here various known contaminants in various Waterloo Region wellfields. This report expands significantly on my Blog postings of last month (July 2026) describing various wellfields and the various toxic contaminants in their groundwater. This report focuses on four older wellfields namely one in Waterloo (William St. Wellfield) and three in Kitchener (Greenbrook, Strange St. and Parkway). It also advises that at the time of this 1995 report there were twenty-three different wells involved over the four wellfields with four in the William St. Wellfield, seven in the Greenbrook Wellfield, seven also in the Strange St. Wellfield and finally five in the Parkway Wellfield. Currently not only are there different numbers of wells versus in 1995 but appropriately old wells that have been shut down since 1995 have had their names removed and new wells have of course new names.   

This report indicates that BTEX compounds normally from gasoline have been detected in all four of these wellfields. The BTEX compounds are benzene, toluene, ethyl benzene and xylene. Tetrachloroethylene (PCE) has also been found in all four wellfields.  PCE or Perc(hlorethylene) has often been used by dry cleaners in decades past. Very worrisome are aluminum, cadmium, chromium and lead all found in the Parkway wells. Two items of interest: these metals are often associated with tanneries producing leather of which two local names come to my mind i.e. Breithaupt and Lang. Gee you don't think that our regional politicians have been keen to avoid embarrassing prominent local, industrial families do you? The second item of interest is the plan to allegedly rehabilitate the Parkway wells. Oddly we have mercury in the Greenbrook Wellfield. That compound could be from a tannery or even possibly naturally occurring nearby. 

The last one is a humdinger. Total PCBs have been detected in the William St. and the Strange St. Wellfields. My God!  PCBs or Polychlorinated Biphenyls are a first cousin to dioxins and in some cases are even referred to as "dioxin like". My guess is that the former Uniroyal Tire plant on Strange St. may be a culprit based upon their plethora of toxic compounds found in the air in and around the plant decades ago. 

Now I have a teaser for you. Is it possible that there is strong analytical evidence that Varnicolor Chemical in Elmira not only contributed six chlorinated solvents to the Elmira drinking water aquifers as stated here yesterday, but also NDMA? Stay tuned for tomorrow's Blog posting.

If you are considering building a custom home in Kitchener-Waterloo, one of the first questions is usually: how much does it cost per square foot to build a house?

The quick answer is that a reasonable 2026 planning range for custom-home construction in Kitchener-Waterloo is approximately $300 to $600+ per square foot, depending on the builder, design, site and level of finish.

A current Kitchener-Waterloo-area custom builder places quality custom construction around $300-$375 per square foot, premium construction around $375-$475, and luxury construction around $475-$600+. RE/MAX Canada’s current Ontario guide places many professionally built detached homes around $375-$500 per square foot for construction alone, while noting that the broader Ontario range can extend from approximately $300 to $800 per square foot.

These are planning estimates, not fixed prices.

The most important point is that cost per square foot is not the same thing as the total cost of your project.

Land, development charges, permits, architectural and engineering fees, demolition, site preparation, servicing, financing, HST, landscaping and the level of finish can all materially affect the final number.

There is no official municipal or industry-wide price per square foot for building a custom home. Each builder prices projects differently, and what is included in a quoted square-foot cost can vary significantly.

For preliminary budgeting, current published estimates suggest:

For anyone planning a build, these numbers should be used as an early feasibility tool – not as a substitute for an actual builder quote.

Using $375-$500 per square foot as a practical preliminary construction range:

At the luxury end, the numbers increase quickly. A 3,500-square-foot home at $600 per square foot represents approximately $2.1 million in construction before land and any project costs outside the builder’s quoted construction price.

Again, these examples are budgeting exercises based on current published construction estimates – not guaranteed building costs.

This is where comparing builders can become confusing.

There is no universal definition of what a builder must include in a price-per-square-foot quote. One builder may include items another treats as separate costs.

Before comparing two prices, ask whether each quote includes:

• Architectural and design fees
• Structural and mechanical engineering
• Excavation and foundation
• Site servicing
• Development charges
• Building permits
• Finished basement space
• Garage construction
• Driveway
• Landscaping
• Cabinetry and millwork allowances
• Plumbing and lighting allowances
• Appliances
• HST
• Builder or construction-management fees
• Contingency allowances

This is why we would never compare two builders based solely on their advertised cost per square foot. The specifications, allowances and exclusions matter just as much as the headline number.

Development charges are an important part of a new-build budget, and they vary between Kitchener and Waterloo.

For a new single-detached home in either city, there is generally a City development charge as well as a separate Region of Waterloo development charge.

The City of Waterloo’s currently published revised 2026 development charge applicable to a new single-detached home is $25,201.

The Region of Waterloo’s current full-service development charge applicable to a new single-detached dwelling in the city is $43,285.

Published Base Rates:

• City of Waterloo: $25,201
• Region of Waterloo: $43,285
• Published base-rate total: $68,486

There is, however, an important 2026 development to be aware of.

On June 15, 2026, Waterloo Council approved, in principle, a 30% reduction in residential City development charges, retroactive to March 30, 2026, through the Ontario-Canada Development Charge Reduction Program. Participation depends on a competitive funding application, and the City has stated that funding approval is not guaranteed.

As of August 19, 2026, Waterloo’s public development-charge schedule continues to show the $25,201 rate.

For that reason, anyone budgeting a Waterloo build should not assume that the 30% reduction applies unless the City confirms it for the specific project.

Kitchener’s development charge depends on where the property is located.

For permits issued after December 1, 2025, the City’s published rate applicable to a new single-detached home is:

Kitchener City Development Charge:

• Central neighbourhood area: $21,215
• Suburban full-service area: $31,587

Adding the current Regional full-service charge of $43,285 gives published base-rate totals of approximately:

City + Region Published Base-Rate Totals:

• Central Kitchener: $64,500
• Full-service suburban Kitchener: $74,872

Kitchener notes that development-charge rates are subject to change and that the applicable rate is generally determined based on the rules in effect when the building permit is issued.

These figures should therefore be used for planning purposes. Before purchasing a lot or finalizing a construction budget, obtain a project-specific calculation from the municipality.

Potentially.

This is one reason a teardown property and a vacant lot should not automatically be treated the same way financially.

The Region of Waterloo provides redevelopment allowances where qualifying new development replaces existing development. Depending on the circumstances, those allowances can reduce Regional development charges.

Municipal redevelopment credits may also apply under the applicable City development-charge bylaws.

If you are considering buying an older home specifically to demolish and rebuild, have the potential credits confirmed before calculating the economics of the project.

As of August 19, 2026, neither local school board is currently collecting Education Development Charges on new development.

The Waterloo Region District School Board stopped collecting EDCs effective June 1, 2026 because it does not currently meet the provincial eligibility requirements for a new bylaw.

The Waterloo Catholic District School Board also states that, as of June 1, 2026, it is not collecting EDCs until notice is given of a new EDC bylaw.
This could change, so it should be reconfirmed when a building permit is actually being obtained.

The City of Waterloo’s current published permit fee for a new single-detached home is $1.41 per square foot.

Waterloo states that the initial review timeline for a complete new-home permit application is 10 business days once all required documents have been submitted and applicable fees have been paid.

That does not mean every permit will be issued within 10 business days. Revisions, missing information or additional approvals can extend the process.

Kitchener’s current published permit fee for a new detached home is $1.07 per square foot, plus a $500 rebate fee that is automatically refunded when the final building inspection is approved.

Kitchener also notes that additional charges, including a damage deposit and stormwater-management fee, may apply.

Its initial review period for a complete residential permit application is also 10 business days.

This is one of the most important parts of the entire process.

A $500,000 lot can ultimately be a better purchase than a $400,000 lot if the less expensive property requires significant servicing, grading, retaining walls, demolition or other site work.

Before buying land for a custom home, investigate:

• Zoning and permitted use
• The actual building envelope
• Front, side and rear setbacks
• Maximum lot coverage
• Building-height restrictions
• Easements
• Existing water and sanitary services
• Utility locations
• Grading and drainage
• Soil and groundwater conditions
• Tree restrictions
• Whether demolition is required
• Heritage considerations
• Whether the property falls within a GRCA-regulated area
• On rural properties, whether a well and septic system will be required

The Grand River Conservation Authority regulates certain river and stream valleys, floodplains, slopes, wetlands and other regulated areas. If proposed construction falls within a GRCA-regulated area, a GRCA permit may be required in addition to municipal approvals.

For rural and estate properties in areas such as Woolwich, Wilmot and Wellesley, servicing can also be very different from a city lot. Rural properties may rely on private wells and septic systems, both of which introduce additional site-planning and cost considerations.

This is why buildability should be investigated before purchasing the property.

There isn’t one right answer.

A vacant serviced lot may be simpler because there is no existing house to demolish.

A teardown can sometimes offer advantages such as an established location, existing road access and services, and potential redevelopment credits.

But a teardown can also introduce additional costs for demolition, utility disconnections, hazardous-material remediation, tree removal, grading and site preparation.

The right comparison is not: Which property is cheaper to buy?

It is: What is my total cost to get from the property I am buying to the finished home I want?

That is the number that matters.

Generally, yes, on a cost-per-square-foot basis.

A bungalow requires a larger foundation and roof to produce the same amount of above-grade living space.

For example, a 3,000-square-foot bungalow may require close to a 3,000-square-foot footprint, while a 3,000-square-foot two-storey home might provide approximately 1,500 square feet on each level.

Because foundations and roofing are major construction components, building upward can often be more cost-efficient than building outward.

That does not necessarily mean a two-storey is the better investment. Bungalows can command a premium because of their accessibility, functionality and appeal to buyers looking for main-floor living.

Square footage is only one part of the equation.

Two 3,000-square-foot homes can have dramatically different construction costs.

Some of the biggest cost drivers include:

• Complicated rooflines and architecture
• Large structural spans
• Extensive glass
• Premium windows and exterior doors
• Natural stone
• Custom cabinetry and millwork
• Integrated appliances
• High-end plumbing fixtures
• Large-format tile and stone
• Heated floors
• Sophisticated HVAC or hydronic systems
• Home automation
• Generators
• Finished basement space
• Extensive exterior structures
• Pools and outdoor living areas

When comparing builder quotes, confirm whether garages, basements and covered outdoor areas are included in the square footage used to calculate the price.

For higher-end custom homes, the project often extends well beyond the house itself.

An inground pool, cabana, covered outdoor kitchen, extensive patios, retaining walls, fencing, irrigation, landscape lighting, mature landscaping and a finished driveway can represent a significant additional investment.

If those items are part of the vision for the finished home, they should be considered during the initial site planning – not after the house is nearly complete.

Pool location, grading, equipment access, drainage and future construction access are much easier to solve before the home and landscaping are finished.

There are significant temporary HST programs that anyone building a qualifying new home in 2026 should investigate with their accountant or tax advisor.

The CRA’s Ontario Enhanced New Housing Rebate applies, subject to eligibility requirements, to qualifying homes purchased from a builder between April 1, 2026 and March 31, 2027 and to eligible owner-built homes where construction begins during that period.

Depending on the value of the home and the applicant’s circumstances, qualifying homeowners may be eligible for substantial rebates of the provincial portion of HST.

There are also separate federal and Ontario programs for qualifying first-time buyers of new homes.

The eligibility requirements and interaction between the various rebates are detailed, so these programs should be confirmed directly with the CRA and the homeowner’s accountant or tax professional before being incorporated into the construction budget.

A current Kitchener-Waterloo design-build guide estimates approximately 3-6 months for design and permits and 10-16 months for construction.

That equates to approximately 13-22 months from the initial planning stage to completion in many cases.

That is an industry estimate rather than a guaranteed timeline.

Project complexity, municipal revisions, conservation approvals, weather, trade availability, material lead times and changes made during construction can all affect the schedule.

For a truly custom home, it is generally better to plan conservatively rather than structure a sale or move around the most optimistic possible completion date.

This depends on what you are trying to achieve.

Building gives you control over things that are difficult or impossible to change later: the placement of the house, floor plan, ceiling heights, windows, mechanical systems, garage dimensions and overall architecture.

But when comparing building with buying an existing home, you need to compare the complete investment, not just the builder’s price per square foot.

We would look at three numbers:

1. What will the land or teardown property cost?
2. What will the complete finished project cost?
3. What is the completed home realistically likely to be worth?

The third number is often overlooked.

It is possible to build an incredible home and still spend significantly more than the surrounding market will support.

For that reason, resale value should be considered before the design and budget are finalized – particularly when building a high-end custom home.

Before committing to a lot or teardown in Kitchener-Waterloo, we recommend confirming:

• What can legally be built
• Where the house can sit on the lot
• Whether the intended size and design fit the building envelope
• Municipal and Regional development charges
• Possible redevelopment credits
• Servicing
• GRCA restrictions
• Demolition requirements
• Realistic site-preparation costs
• Realistic construction costs
• The probable market value of the completed home

The best time to discover a problem with a building lot is before you own it.

A property can look perfect and still be completely wrong for the home you intend to build.

A reasonable preliminary planning range for a custom detached home is approximately $300-$600+ per square foot. Current local industry guidance places quality custom construction around $300-$375, premium construction around $375-$475 and luxury custom construction around $475-$600+ per square foot. Actual costs depend on the builder, design, site, specifications and what is included in the quote.

At $375-$500 per square foot, a preliminary construction-only budget would be approximately $937,500 to $1.25 million. A more highly customized luxury home could exceed that amount. Land and costs excluded from the builder’s quote would be additional.

At $375-$500 per square foot, approximately $1.125 million to $1.5 million is a reasonable preliminary construction-only planning range. Luxury architecture, finishes and site conditions can push the cost substantially higher.

Generally, no. Land should normally be considered separately from construction cost. Development charges, permits, financing, servicing, landscaping and other project expenses may also fall outside a builder’s quoted price per square foot. Always confirm exactly what is included.

The City’s currently published revised 2026 base rate applicable to a new single-detached home is $25,201, while the current Regional full-service rate is $43,285, producing a published base-rate total of $68,486. Waterloo Council has also approved in principle a 30% reduction in City residential development charges through the new Development Charge Reduction Program, but funding approval was not guaranteed and the City’s published rate continues to show $25,201 as of August 19, 2026. The actual amount payable should therefore be confirmed with the City for the specific project.

Kitchener currently publishes a City charge of $21,215 in the central-neighbourhood area and $31,587 in a full-service suburban area for a new single-detached home. With the current Regional full-service charge of $43,285, the published base-rate totals are approximately $64,500 and $74,872, respectively. Rates are subject to change and should be confirmed for the specific property.

As of August 19, 2026, neither the Waterloo Region District School Board nor the Waterloo Catholic District School Board is collecting Education Development Charges on new development. This should be reconfirmed when permits are obtained because that status can change.

Generally, yes, on a cost-per-square-foot basis. A bungalow requires more foundation and roof area to create the same amount of living space, while a two-storey home can spread those costs over more above-grade floor area.

No. Zoning, setbacks, lot coverage, servicing, easements, grading, environmental constraints and other restrictions can affect what can actually be built. Properties within GRCA-regulated areas may also require conservation-authority approval in addition to municipal permits.

Building a custom home starts with much more than choosing a builder.

The property you buy, what you pay for it, what can actually be built on it and what the finished home will ultimately be worth are all part of the same decision.

At The Deutschmann Team, we help clients look at potential building lots, estate properties and teardowns from a real-estate perspective – including the value of the land today, the surrounding market, potential resale value and whether the overall project makes financial sense.

If you are considering purchasing a building lot, buying a teardown, undertaking a major renovation or deciding between building and purchasing an existing luxury home in Kitchener-Waterloo, reach out before you make the purchase.

A little due diligence at the beginning can prevent a very expensive surprise later.

The post How Much Does It Cost to Build a House in Kitchener-Waterloo in 2026? appeared first on Kitchener Waterloo Real Estate Agent - The Deutschmann Team.

I had just started my annual Harry Potter marathon again (don’t judge me) when my brain once again refused to watch a movie normally.

Right in the middle of the Sorting Ceremony, I turned to my husband with this existential question:

“Do you think the Sorting Hat has good recall?”

For those who aren’t familiar with the term, recall is a system’s ability to correctly find all the cases it is supposed to detect.

Anyway, he went to bed, and I stayed there with my questions.

After all, how can we be sure Harry was supposed to end up in Gryffindor? That Hermione wasn’t actually a Ravenclaw deep down? Is there some kind of ground truth, a committee of experts, a validated set of labels at Hogwarts? Or do we simply assume that, because the Sorting Hat said so, its prediction must be correct?

This is pretty much the kind of question that has been following me ever since I fell into machine learning a little over fifteen years ago.

Back then, I found it almost magical that statistical models could make complex phenomena somewhat predictable, without necessarily spending weeks looking for the right equation. I loved the idea that, with good data, well-chosen metrics, and a bit of methodology, you could tackle very different problems, from face detection to diagnostic support.

Then, like many others, I gradually drifted toward generative AI.

And if you work in this field, this scene will probably sound familiar: someone shows you a new AI agent with stars in their eyes, the demo is impressive, everyone is nodding along… until someone asks the uncomfortable question:

“Okay, but how do you prove that it works… every time?”

That’s usually the moment when we go from “this is incredible” to “well, when I tested it on one or two examples, it seemed pretty good.”

In my previous world, the world of “traditional” machine learning (I feel like a dinosaur when I write that), the evaluation framework was relatively clear: a training set, a test set, a few metrics, and one simple objective: show that a model performs well enough on representative data to be deployed into production.

Of course, every use case came with its own subtleties, but at least the playing field was clearly marked.

With generative AI, many of our data scientist reflexes need to be reconsidered.

• How do you measure quality when there isn’t always a single correct answer?
• How do you compare two assistants that both produce acceptable results, but with different styles, reasoning processes, or trade-offs?
• And most importantly, how do you demonstrate that a new version is genuinely better than the previous one?

For me, this is one of the biggest challenges: the hardest part is no longer building the algorithm itself (thank you, pretrained models), but proving that it does what we expect it to do, and that it will continue doing so over time.

At its core, the problem goes far beyond AI. In everyday life, we spend our time evaluating things like exams, movies, restaurants, and hotels. And we always come back to the same questions: Is it good enough? And when several options seem good, which one is actually the best?

That’s exactly why the Sorting Hat fascinates me. In a way, it already does what we ask many AI systems to do: gather information, arbitrate between sometimes conflicting criteria, interact with the user, and then make a decision. So before talking about benchmarks or metrics, we first need to understand what it really means to make a good decision.

For the rest of this article, I’d like to propose a little game.

I’ll show you a few images. Each time, your mission will be very simple.

Answer the question: “Which one is the best?”

It shouldn’t be too difficult. Well… at least not at!

Let’s start with this photo.

If I ask you “who is the best?”, most of you will answer without hesitation: the one who crosses the finish line first (the torso, not the head).

When the differences become invisible to the naked eye, a photo finish settles the matter down to the thousandth of a second. That’s how, in the picture, Lyles beats Thompson at the 2024 Olympics, despite having the exact same official time.

This is the kind of thing that makes a data scientist happy: a clear evaluation criterion and a reliable measurement system. Once the race is over, there is rarely much room for debate: the best is the fastest.

Let’s move on to the second example.

I‘ve always had a soft spot for Pixar, but since I have two boys, let’s just say I’ve probably watched Cars more often than is reasonable to admit publicly… At first glance, we’re facing the same problem as before. Three competitors arrive almost at the same time. We just need to see who crosses the line first, right?

Not so fast.

This image comes from the end of the first race in the movie. Lightning McQueen is in bad shape: he has just lost a tire, part of his bodywork is falling apart, and in one last desperate effort, he sticks out his tongue just before the finish line. The judges then examine the photo finish and conclude that all three cars finish in a tie.

Obviously, I couldn’t stop myself from going down the rabbit hole again. Can we consider the tongue as part of the car? And if the answer is yes, why do we completely ignore the tire that came off a few meters earlier? At what point does a car that is losing pieces stop being the car we are trying to measure?

The photo finish works perfectly well. The problem is that we first need to know what it is actually supposed to measure. As long as we stay with simple cases, the rule seems obvious. Then an edge case appears, and suddenly we discover all the ambiguities we never formally defined.

The lesson is simple: precisely defining what we are trying to measure, while thinking from the start about the weirdest possible situations, avoids a lot of debates later on. In AI, as elsewhere, it is often the exceptions that reveal how fragile the framework really is.

Let’s move on to the next example. Still the Olympics, but this time we’re going to talk about ice dancing.

To be honest, when I watch this kind of competition, I’m usually incapable of saying who truly deserves to win. Unless there is a spectacular fall or an obvious mistake, I quickly find myself ranking competitors based on highly scientific criteria such as “I liked that one, the music and costumes were nice.” And yet, the judges manage it without directly answering the question: who is the best?

They start by breaking it down into several smaller questions. They look at technical difficulty, quality of execution, choreography, interpretation, skating skills… then they assign scores for each of these aspects before combining them into a final score. The lesson here is that when a concept becomes too complex to be summarized by a single metric, we build a more detailed evaluation guide. This is exactly the idea behind the rubrics used for some AI systems (but, spoiler alert, that topic deserves an article of its own).

You have probably seen those 3D slow-motion replays shown during competitions. For the last few years, judges have had access to tools that allow them to analyze certain movements with far greater precision. When technology makes it possible, we can therefore delegate part of the measurements to automated tools. And yet, nobody has seriously suggested letting cameras alone decide the ranking.

Part of the evaluation remains deliberately human, especially everything related to artistic interpretation. And to limit subjectivity as much as possible, the most extreme scores are discarded before calculating the average.

Ice dancing reminds us that a good evaluation does not always rely on a single measurement. The more complex the problem, the more we need to multiply perspectives, accept a degree of judgment, and make our criteria as explicit as possible.

After that sporting digression, let’s return to my current favorite topic and take a look at these two posters.

If I ask you, “So, which one looks like the better adaptation?”, I’m almost certain I’ll trigger a debate longer than the seventh book. Harry Potter fans have already survived the great books versus movies battle, so let’s just say they’re well trained. And yet, we do have a few clues available.

The series will have more time to develop side plots and characters that the movies sometimes had to make disappear with a wave of a magic wand. The special effects will benefit from twenty years of progress, and we can already look at the cast, the writers’ experience, or the budgets involved.
In short, there is no shortage of signals.

Personally, I am waiting for the series with as much anticipation as I once waited for my Hogwarts letter. I’ve already subjected my husband to several perfectly objective analyses explaining why this adaptation has every chance of being better than the movies. He claims that I sometimes confuse prediction with personal preference.

I think he simply lacks confidence in my evaluation methods.

Even with the best possible preparation, there is one thing producers cannot fully anticipate: the audience’s reaction. You can prepare for quality, frame it, test it… but you cannot decide in advance how a series will be received.

In other words, the verdict will happen in production. Only then will we know whether the magic works once again.

For an AI system, it’s much the same story.

Upstream, we can consult experts, build evaluation datasets, run benchmarks, and multiply testing campaigns. All of this is essential for comparing solutions, anticipating problems, and reducing risk.

But even the best testing environment never completely reproduces reality.

The real evaluation begins when the product meets its real users, with their expectations, their habits, and above all, all the unexpected (and sometimes slightly ridiculous) ideas they will come up with for using it.

One last image before we wrap up!

Every time a live-action remake is released, and there have been many of them, the same criticisms come back:

“Nobody asked for this remake.”

“The original wasn’t respected.”

“Disney was better before.”

Why does The Walt Disney Company keep making them if they are so disappointing?

Because the box office often tells a different story: despite mixed reviews, many of them have performed very well.

And even when the immediate results disappoint, the objective may be elsewhere and over a longer period of time: relaunching a franchise, allowing parents to introduce the movies from their childhood to their own children, refreshing merchandise lines, and extending the life of these worlds in the parks. The movie is therefore not evaluated only as a movie, but as a piece of a much larger ecosystem.

As a fan, I will mostly judge its faithfulness to the original, the emotions it creates, its ability to bring something new, and, of course, to recreate a bit of magic. Disney is probably looking at different indicators: ticket sales, Disney+ subscriptions, merchandise sales, park attendance, and renewed interest in the franchise, probably among many other metrics we will never know about.

So, are these movies “good”? It all depends on the person doing the evaluation and the metric being used. We are watching the same movie, but we are not trying to answer the same question.

This brings us to our final lesson: sometimes, the success of an AI system is not measured by the most obvious metric at first glance, but through a broader reading of the business objectives.

I’m still not sure I’ve answered the question. And what if, from the very beginning, it simply wasn’t the right question?

While trying to evaluate the Sorting Hat, I somehow ended up talking about photo finishes, Lightning McQueen’s tongue, ice dancing, HBO’s Harry Potter, and Disney remakes.

More than anything else, all of these examples show one thing: evaluation is not just about metrics.

In the 100 meters, everybody knows what needs to be measured.
In Cars, you first need to decide what actually crosses the finish line.
In ice dancing, multiple criteria matter.

And for a TV series or a remake, not everybody is looking for the same kind of success.

So before building a benchmark, it is worth asking:

• What are we actually trying to measure? With what measuring tool
• What does success look like?
• What are the edge cases?
• Who is judging the result?
• And most importantly: good for whom?

That is probably the real difficulty of AI evaluation: the hardest part is not always finding the right metric, but asking the right question.

As for the Sorting Hat’s recall, I’m still stuck.

But my next Harry Potter rewatch promises to be a very relaxing experience for my husband.

Would the Sorting Hat make a good AI system? was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

One year ago, we expanded Code Like a Girl to Substack after 9.5 years on Medium, hoping that more people might read what we published.

We got so much more than that.

After nearly a decade of publishing Code Like a Girl, this is the first time it has truly felt like a community.

And that is because of all of you. THANK YOU so much.

In our first year here:

• 87 women and non-binary writers trusted us with their stories.
• We published more than 140 stories and 2,500 notes and restacks.
• Together, you generated more than 100,000 reads.
• More than 130 of you recommended Code Like a Girl, sending over 1,000 subscribers our way.
• And with your help, 5,500 people found and followed us.

But the number we care about most is 2,800.

That is how many women and non-binary people in tech, and the people who champion them, now call this community home. They show up for each other week after week.

We are so grateful to every writer who trusted us enough to publish with us, every reader who stopped to comment instead of scrolling past, and every person who recommended us to someone else.

You built this with us.

So for our first anniversary, we wanted to point the spotlight straight back at our writers.

We went back through the over 140 stories we published this year and looked for the ideas our writers returned to again and again. Then we chose one quote from every writer who published a full story with us and nine clear themes came out of the analysis.

Here is the year in their words.

Every one of these writers was told by a teacher, parent, professor, job posting, or the voice in her own head that she was not technical enough.

One won $10,000 in her first machine learning competition seven years after she could not write a line of Python. One got into DevOps with a toddler and no degree. One had audited hundreds of apps at Apple and still assumed building one was something other people did.

These women stopped waiting for permission to call themselves technical. They learned, built, experimented, and stepped into work they had once assumed belonged to someone else.

AI is not just another technical tool — it’s a relational one. Because of that, AI is changing what “being technical enough” means — especially for women.
Alyssa Fu Ward, PhD · Read →

My path into tech didn’t look like a pipeline. It looked like survival, curiosity, pattern recognition, and learning on the fly while my life was on fire.
Chelsey Sidler · Read →

Last year, I won $10,000 in my first machine learning competition. Seven years ago, I couldn’t write a single line of Python.
Claudia Ng · Read →

I did not lack ideas, nor did I misunderstand the market, since this is literally my job. But I hesitated because I was sure that building an app was something other people did: engineers, product managers, developers. That story is wrong. Now, AI has just made it possible to prove it.
Julie Tonna · Read →

I’m not a developer; I don’t write code in the traditional sense. But I can tell an AI what I need with enough clarity that it can build it for me. And that changed everything.
Kim Doyal · Read →

Don’t compare your work-in-progress to someone else’s final version.
Aniko · Read →

Reopening your mind requires you to realize you’ve closed it. It’s as simple as saying to yourself, “Maybe this can be for me.”
Anna Dallara · Read →

It has been over a decade of persistence, evolution, and unlearning to realize that success in tech and life comes from being whole.
Apoorvaa Deshpande · Read →

Sometimes the people who underestimate us the most hand us the biggest opening to prove them wrong.
Bette A. Ludwig, PhD 🌱 · Read →

AI is the next chapter in a story that started with a modem screech and a blinking cursor. You already know how it ends. You figure it out.
Kelsey Helstrom · Read →

I studied full-stack software engineering for eight months straight while raising a toddler, working part-time, and managing everything alone. Eleven months later, I was working full-time in DevOps, earning more in one month than I used to earn in four.
Maxine Meurer · Read →

You become a programmer the moment your way of thinking begins to change.
Nova · Read →

The women in our community did more than talk about technology. They made things with it.

A website in a weekend. A tool to survive a book deadline. Interfaces designed around the way people actually move their hands. Games, experiments, workflows, and systems that started with a question: Could I make this work?

Others turned around and taught what they had just figured out, writing the tutorial, explaining the compiler, or showing the messy process behind building with AI.

Nobody handed them a brief. They followed their curiosity, built what they needed, and then showed the rest of us how they did it.

What if we build interfaces that honour gestures the way my grandfather honoured storytelling with fullness, with meaning?
Blessing Okpala, PhD · Read →

Every single bug taught me something, not just about state management, redirect logic, or the unique flavors of API weirdness, but about resilience, boundaries, and the specific psychology required to debug a mess of your own making.
Karo (Product with Attitude) · Read →

Remember, when making a text-based game, countless possibilities exist for expanding and enhancing the game, such as adding more story branches and complex interactions.
Eliza · Read →

I wasn’t just the boring engineer anymore. I was the person building things that looked like magic.
Jenny Ouyang · Read →

It’s addictive, but unlike binge watching Netflix, side quests don’t just pass the time — they level you up.
Katrina Watson · Read →

Your camera roll holds more strategy than your analytics dashboard.
Mia Kiraki 🎭 · Read →

Avoiding the technology completely doesn’t erase it. It erases your perspective from it.
Nicolle Weeks · Read →

Modern streaming speed is not just a software optimisation problem. It is fundamentally a challenge shaped by geography, physics, and distributed systems engineering .
Nidhi · Read →

Together with Claude Code, we collected memories, analyzed the sessions we had, and we discovered that there was, in fact, a process — a messy, non-linear, and highly collaborative process where a human and AI work together to build something useful .
Olena Mytruk · Read →

AI made it easier to produce an answer, write the code, build the prototype, and generate something that looked finished.

Our writers kept coming back to the idea that the key to creating excellence was to stop and ask if what AI was building was any good.

One caught a query that ran perfectly and answered the wrong question. Another stopped asking the model to build things and started asking why it had built them that way. Others wrote about taste, context, product decisions, and defining what good looks like before the model ever gets involved.

The work may be getting faster. The judgment still belongs to us.

If one cannot define what good output looks like before launch, they are not managing an AI product. They are reacting to one.
Ankita Chatrath · Read →

I stopped asking “can you build this?” and started asking “why did you build it this way?” This was the unlock.
Ciara Wearen · Read →

The skill that suddenly matters most in an AI-saturated organization is the exact one you have been told is slowing you down. The instinct to ask “is this actually true?” before forwarding the polished thing upward. The willingness to pressure-test a draft that reads fine. The discipline to own the call the model cannot make.
Sumaiya Shrabony · Read →

The best data analysts don’t just know the algorithms; they know which questions are worth asking in the first place.
Colette Molteni · Read →

A hammer is useless without knowing what you’re building. Most of us have been handed increasingly sophisticated hammers while standing in an empty lot, wondering why the house isn’t appearing.
Daria Cupareanu · Read →

Because the best AI products don’t start with a model, they start with a moment. A need. A curiosity. A little friction is worth fixing.
Esha Pathak · Read →

The takeaway is this: an agent is any system where the AI decides what to do next, not just responds to what you ask.
Ileana · Read →

AI is great at pattern recognition, but terrible at mind-reading.
Karen Spinner · Read →

The differentiator was no longer whether you could make something, it was whether you could tell if what you’d made was any good.
Kate Moran · Read →

When writing code becomes easy, deciding what to build becomes the real bottleneck.
Kessie 🌟 · Read →

Taste without input is just opinion.
Sarah Gibbons · Read →

The model might say “I understand your concern” or “That’s an interesting perspective,” but these utterances don’t reflect understanding or interest.
The Strategic Linguist · Read →

AI handled the query. I had to handle knowing the query was answering the wrong question.
Thais Cooke · Read →

Our writers saw the same gap again and again: companies wanted the benefits of change without redesigning the systems around it.

AI pilots stalled. Licenses were bought and left unused. Leaders talked about transformation while teams were still trying to get through the sprint. Good ideas got discussed, admired, and quietly sent to the place where ideas go when nobody owns what happens next.

The technology was often ready.

The organization wasn’t.

No owner, no timeline, no next step, no genuine curiosity about what “looking into it” might actually require. It is the organizational equivalent of a smile that doesn’t reach the eyes.
Uncertainty, By Design · Read →

Real adoption isn’t about having 10 super-users. It’s about having 500 people who are 20% better at their jobs.
Anna Wojciechowska · Read →

The best leaders aren’t know-it-alls with a cheatsheet of Very Good Answers. They take people across various perspectives and walks of life and bring them together.
Christine Miao · Read →

There is no “future of work” moment anymore. We are already in it, and it’s wearing noise-cancelling headphones in a corner, trying to finish a sprint ticket before lunch.
Elizabeth Eagle-Simbeye · Read →

The things we automate away don’t disappear. They become luxuries.
Kriti Agarwal · Read →

A stalled pilot is not evidence that AI lacks value. It is evidence that the system around it was never designed to carry it.
Maribeth Martorana · Read →

“We promise not to look” is a policy. “Our infrastructure can’t see the plaintext, and you can verify that yourself” is structure.
Shalini Sah · Read →

Some of the most important work in tech is the work nobody notices when it is going well.

Ops keeps systems standing. Program managers hold moving parts together. Institutional memory lives in people, not org charts. Accountability gets documented before anyone needs to ask who was responsible.

Because this work prevents problems instead of producing something shiny, it is easy to dismiss as overhead, toil, or cost.

Our writers kept making the same case: the work that holds everything together deserves to be seen before something breaks.

Good operations is empathy at scale.
Katie Barnes · Read →

It is exhausting to speak a language of connection in a room that only values output.
Kaisa Martiskainen · Read →

Ops is not a synonym for toil; it literally means “get shit done as efficiently as possible”.
Charity Majors · Read →

But when responsibility is unclear and accountability is undocumented, systems don’t fail immediately. They look fine until pressure rises.
Judy Ossello (AI Mechanic) · Read →

Here’s what actually gets cut in a reorg: institutional knowledge, process memory, and the people whose value was never legible on an org chart.
Sovereign Insight Strategies · Read →

That’s the paradox: performance, whether in code or in people, only becomes visible when it fails.
Stefania Barabas · Read →

While everyone else marveled at what AI could do, our writers kept asking what it had learned from us.

They found old biases dressed up as new technology. A six-year-old who could not find his own continent in AI’s version of Africa. Identical code judged differently once a woman said she had used the tool. Neural networks shaped by outdated theories about male and female brains. Devices collecting data in spaces most of us still think of as private.

AI did not arrive without a history. It inherited our assumptions, our blind spots, and the systems we had already built.

Now those inheritances are helping decide what comes next.

Rather than training AI on what should be, we’re training it on what was, then deploying it to decide what comes next.
AI.Mirror · Read →

With systems like this, your kid’s face data is now traveling through multiple hands, governed by privacy policies you haven’t read.
Courtney Hart · Read →

It’s the occasional, barely noticeable, yet persistent reminders that technology is designed for someone else’s worldview, not yours.
Daria’s Tech Musings · Read →

NNs were designed to resemble flawed sexist theories about brain development, which resulted in an increased risk of propagating bias.
Feminist Science · Read →

The glasses that entered your home, your doctor’s waiting room, or your child’s school play are not just cameras. They are live data collection nodes connected to one of the world’s largest advertising companies.
Kristina Kroot · Read →

When a man uses AI, he’s “streamlining workflow.” When a woman does, she’s “taking shortcuts.”
Mariam Vossough · Read →

My son asked, “ Where is this? ” But the question haunting me is: Where is he in a world increasingly run by systems that can’t see where he comes from?
Rebecca Mbaya · Read →

Again and again, our writers traced a personal disappointment back to the system that produced it.

The application that disappeared into silence. The performance review that called her “too direct.” The raise that never came. The leadership potential nobody seemed able to see. The firing whose explanation only needed to sound plausible on paper.

Once you put enough of those stories side by side, the pattern gets harder to miss.

Women were being asked to carry structural problems as personal ones.

It’s the girlbossification of AI. Girlboss told us that leaning in was empowerment. Hustle culture told us burning out was ambition. Now the same packaging is back, this time with a new product inside: adopt this technology or get left behind.
AI Meets Girlboss · Read →

The message between the lines was pretty clear: credibility often feels conditional — and dependent on how well you blend in.
Anabella Aguilera · Read →

You might think that your job ad and hiring process is gender neutral and meritocratic. However years of data suggests that details make a difference and that recruitment is not a level playing field.
Andra Keay · Read →

Nobody in that room asked the obvious question: why is someone performing above average being scored as average?
Anna • bubble boss 🫧 · Read →

Most women don’t just “opt out”, they’re quietly squeezed out by workplaces that still don’t recognise their leadership potential.
Anuja Gaunekar · Read →

It’s not that you’re failing. It’s that the way you’re trying to work no longer works for you.
Atty · Read →

Pre-#MeToo, the rule for a woman in Silicon Valley was: “DO NOT TALK ABOUT YOUR FAMILY, EVER!”
Britta · Read →

In other words, women aren’t avoiding AI because they’re intimidated. They’re avoiding environments where their AI work will be questioned, interrupted, or stolen.
Elena | AI Product Leader · Read →

Inclusion without access to complexity is not equity; it is containment.
Jayne Quoiani · Read →

What I didn’t see that afternoon was that Simon could walk away from the PIP clean. I couldn’t have. Not then, not years later, when, instead of the trigger, I became the target of the same mechanism. The difference wasn’t resilience. It was architecture.
Lisa Kostova · Read →

The stated reason for a tech firing is almost never the real reason. And the stated reason does not even need to be true. It only needs to be a story HR can write down.
Louise Deason · Read →

The room quietly files her under obstructive. Difficult. Not collaborative. Meanwhile, a male peer makes the same call, with the same level of force, and gets filed under decisive. Strong. Protecting the team.
Rebecca Sutter · Read →

Somewhere along the way, ambition for women got repackaged as performance. Okay, not somewhere… The entire way it’s been like that.
Sam Campbell · Read →

Investors don’t fund businesses. They fund a story about the founder.
Sinéad Fitzgerald · Read →

But what happens when the toxic leader is in HR? That question almost never gets asked publicly.
Sophia Rook · Read →

The old rung rewarded compliance: take the task, do it well, don’t touch what isn’t yours. The new game rewards the opposite: look at the task and kill it. Guess which of those two behaviors girls get drilled in for twenty years before their first job.
Valentina · Read →

It means the silence was never a verdict on me or you. I was preparing for the right job in the wrong year, and maybe you have been too.
Lipgloss and LLMs · Read →

For once, the speed being used to shut women out has erased the excuse for doing it. So let’s prop the door open before the industry tries to lock it.
Cassidy McDonnell · Read →

We were early. But being early is different from being wrong.
Katrina C. Foster · Read →

s are performing femininity for profit. Women are performing masculinity for survival.
The Reluctant Graduate 👩🏾‍🎓 · Read →

The bill comes due somewhere.

Our writers wrote about the cost of pushing through, fitting in, proving yourself, and putting yourself last for too long.

Sometimes it looked like burnout hiding underneath achievement. Sometimes it was the mental load of carrying a label you never earned. Sometimes it was realizing that a career could look successful from the outside and still ask too much of you.

And sometimes the reckoning changed the question entirely: not How do I keep doing this? but How do I want to live?

You can look like a high-achiever to everyone around you while you’re falling apart inside.
Alicja Bialek · Read →

Burnout is an accumulation of a woman putting herself last.
Dr. Nicole Ohebshalom · Read →

There’s something deeply unfulfilling about living in a monoculture, even when that culture is your career.
Hannah Ji · Read →

Navigating unfair labels is not about pretending bias does not sting. It is about stopping the label from hijacking your mental bandwidth.
Ivyna Koh · Read →

Because the gap isn’t really about whether women ask anymore. It’s about what happens next — what women do when they ask, and someone says no.
Lucy Watson · Read →

Instead of asking, “What do you want to be?” maybe we should ask our children, “How do you want to live?”
Neela 🌶️ · Read →

The only way to get your brain back is to make it work again. Even if it sucks at first, that’s the only way it remembers how to think, learn.
Shruti Mangawa · Read →

This last theme made all the others possible: community, built on purpose.

Our writers kept creating the spaces they wished already existed. A directory born because a widely shared list of AI writers included no women. A book that brought together 26 authors across 14 countries. A woman who realized after a layoff that what she missed most was not the job, but her people.

Again and again, the answer was the same: find each other, make room, and build what is missing.

This is the room they built. And they are still building it.

My goal was to make it easier for people of all genders to find women and non-binary people who write on Substack about AI, ML, ethics, governance, data privacy, and related technology: no excuses for not knowing any.
Karen Smiley · Read →

When I got laid off in 2023, I told myself I wanted my job back. What I actually wanted was my people back.
Alex Van Holtz · Read →

In a world where content can be generated instantly, people will place even greater value on who they know and where they’ve built real relationships.
Cheyenne Dominguez | M(AI)VENS · Read →

What if we stopped looking for the room, and built it?
Emanuela B · Read →

What I’ve learned is that the time for chasing is long over. Instead, today, we are claiming our right to take up space, and we’re defining what that space looks like.
Jennifer Cloer · Read →

Women should not have to survive authoritarian creep alone. Sisterhood is strategy.
Michelle Redfern · Read →

Eighty-seven writers. One year on Substack. Nine themes.

Every quote above came from a woman or non-binary writer who believed their experience, expertise, question, frustration, or idea was worth putting into the world.

And then you all did something with it.

You read these stories.
You shared them.
You restacked them.

You added your own experiences in the comments. You found writers you wanted to keep hearing from, and helped other people find them too.

That is what Code Like a Girl has always been about. But this year, for the first time, it felt like we were all in the same room.

A room where you can build something before you feel ready. Question the system instead of yourself. Use your judgment. Admit when the cost has become too high. And find people who understand why any of that matters.

Thank you for helping us build it.

We are only one year into this chapter.

And we cannot wait to see what you write next.

Code Like a Girl is the community for women in tech who would rather build alongside each other than go it alone.

Three times a week, a story from someone who has been where you are lands in your inbox.

Subscribe for free and join our Rise Together Thursday chat. It’s a community where women in tech share their latest work, spotlight another woman’s post, and support one another through thoughtful comments and restacks.

You’ll come in with work that deserves more attention and leave feeling seen, supported, and connected.

Subscribe to Code Like A Girl on Substack

Nine Things Women in Tech Couldn’t Stop Writing About was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

I'm also going to add that government communications with the public are also usually disingenuous and misleading if not outright dishonest. Those communications have gotten so shoddy in both quantity and accuracy that it's now to the point that government's hiding secrets will look citizens in the eye and tell them "Sorry we can't clarify this or explain why because we have been forced at gunpoint/torture or worse to sign NDA's (Non-Disclosure Agreements)". The "or worse" for politicians simply means that they are afraid that they might get kicked out of office at the next election if they honestly tell why they have taken an action such as say spending $55 million dollars assembling and removing from production 600 acres of prime farmland in Wilmot Township. 

One of the biggest political lies is that there are no negative health effects from environmental spills and accidents. With one hand behind their backs and two fingers crossed they can honestly (in their wee minds) state this because knowing that all of us are mortal anyways they can justify the lie by telling themselves that those folks were on their way out regardless and if not from mercury, nitrates, TCE, NDMA,  vinyl chloride, smoking or drinking they are going to die anyways either from time or something else. Examples abound including the train derailment in East Palestine U.S.A. two or three years ago, Grassy Narrows mercury poisoning for the first decade after it went public, Agent Orange damages in Viet Nam and to American servicemen, trichloroethylene (TCE) via vapour intrusion in the Bishop St. community in Cambridge causing cancers and death, and many premature deaths in Elmira, Ontario both from Varnicolor Chemical and Uniroyal Chemical including a foreman at Varnicolor already diagnosed from solvent poisoning prior to the shutdown at Varnicolor and then his subsequent employment at Uniroyal. 

The vast majority of cleanups promised are the Cadillac cleanups whereas the reality is simply Volkswagon Beetle cleanups. This most certainly includes the Varnicolor Chemical "cleanup" back in the mid 1990s which was supposed to then require ten years of shallow pump & treat (hydraulic containment) however by 2016 the pumping was still going strong and only in 2024 were we finally told that yes indeed Varnicolor Chemical had contributed half a dozen toxic chlorinated solvents to our deeper municipal drinking aquifers after thirty-five years of government lying (M.O.E.) and coverup. In fact M.O.E. lawyer Stanley Berger back in 1991 at the public Environmental Appeal Board  hearings in Elmira actually stated that Varnicolor's pollution compared to Uniroyal was like a water pistol in a thunderstorm. The same dishonesty has been going on with the Uniroyal "cleanup" from day one with all government tiers claiming success by the mandated 2028 cleanup for twenty-five years (1993-2018) and only admitting failure AFTER the 2011-2015 Citizens Public Advisory Committee (CPAC) publicly advised Woolwich Council and the M.O.E. that it absolutely wasn't going to happen on time or even close. Now even the most blatant truth challenged parties agree, and the excuses with help from a few misguided citizens, are in high gear.       

“The future really starts now in many ways, including how we prepare our farms and fields for times ahead, [...] I think we all see and expect changes ahead; this gives us a more exact look at what those changes might look like and the importance of grasslands and groundwater recharge to all of us in times right now.”

READ MORE.

The Manitoba Forage and Grassland Association (MFGA) has released findings from a recent study with Aquanty examining how climate change could affect water resources and agricultural landscapes in southern Manitoba through 2050 and 2100.

The study, supported through Manitoba Environment and Climate Change Climate Action Funding, used the MFGA Aquanty Model to examine future climate conditions across three previously modelled watersheds: Pembina Valley, Swan Lake, and Oak River. The results highlight the increasingly important role that healthy grasslands and groundwater recharge could play in helping agricultural landscapes build resilience to future climate conditions.

Led by Aquanty’s Dr. Steve Frey, the modelling projects significant impacts to water resources by mid- and end-century driven by warmer temperatures, alongside declining snowpack, shifts in runoff timing, and increasing water losses through evapotranspiration. These changes could have significant implications for forage and pasture systems. Increasing evapotranspiration is expected to place additional stress on soil moisture and watershed water balances, increasing the potential for pasture drying and reduced forage availability during the mid-to-late summer. The study also identifies a progressive decline in subsurface water storage over the course of the century, particularly under end-of-century climate conditions. Increasing evapotranspiration and reduced snowpack are identified as key drivers of this decline, reinforcing the importance of protecting areas that support groundwater recharge.

As Dr. Frey explains in the MFGA release, maintaining perennial grasslands and protecting groundwater recharge functions could therefore become increasingly important climate adaptation strategies. Forages and grasslands, along with wetlands, forests and healthy soils, can help promote groundwater recharge and reduce surface runoff in sensitive areas. For cattle producers, these findings have practical implications. Declining groundwater storage could affect the long-term reliability of dugouts, wetlands and groundwater-fed livestock watering sources. The research points to the value of planning now for conditions that may become increasingly challenging in the decades ahead, including through grazing and land management practices that support healthy, resilient landscapes.

Read the full report to explore the study and its findings in greater detail.

Building on more than a decade of collaboration

The study is the latest chapter in a long-running collaboration between MFGA and Aquanty. For more than a decade, the organizations have worked together to apply integrated hydrologic modelling to better understand water movement, flood and drought risk, land management, and the role of grasslands across the Assiniboine River Basin.

Through the MFGA Aquanty Model project, this work has helped explore how different land management practices can influence hydrologic conditions and how healthy agricultural landscapes can contribute to resilience during periods of flood and drought.

The collaboration has also moved beyond long-term scenario modelling into operational forecasting. Aquanty operates the MFGA-Aquanty Water Forecast Tool, a real-time hydrologic forecasting system covering the entire Assiniboine River Basin. The platform provides forward-looking information on water conditions across the basin, helping translate complex hydrologic modelling into information that can support land managers, watershed districts, agricultural producers, Indigenous communities and other decision-makers.

Together, the long-term climate projections highlighted in this latest study and the real-time forecasting capabilities of the broader MFGA-Aquanty project demonstrate how integrated hydrologic modelling can support decisions across very different timescales — from understanding current and near-term water conditions to planning for how Manitoba's landscapes and water resources may change decades into the future.

Click here to learn more about our ongoing collaboration with the MFGA.

You ask your AI coding assistant for a function, and what comes back looks completely reasonable. Clean formatting, sensible variable names, a comment explaining the tricky part. For a second, it feels finished.

Then you actually try to use it. The compiler rejects it over a borrow it doesn’t like. Or it compiles fine and segfaults on an input you hadn’t thought to test. Or it runs, passes every test you wrote, and turns out to be three times slower than it needed to be, because it’s using a data structure that made sense for the toy example but not for your actual dataset.

None of this means the model is bad at programming. It usually means something narrower and more interesting: the language you’re working in has exposed a gap between code that looks correct and code that actually is. And that gap isn’t the same size in every language.

It’s worth separating a few things that get lumped together under “AI can code.”

Generating syntactically plausible code just means the output resembles valid code in the target language: correct-looking brackets, correct-looking keywords. Generating compilable code means it actually survives the compiler or interpreter without errors.

Generating functionally correct code means it does what you asked.

Generating idiomatic code means it does it the way an experienced developer in that language actually would, using the right patterns and conventions instead of a technically valid but clumsy translation from some other language’s habits.

Generating maintainable, production-quality code means someone else, or you, six months later, can read it, extend it, and trust it under conditions the original prompt never mentioned.

A model can clear the first bar and completely miss the last one. “The code looks right” is a weak test precisely because it only checks the first, most superficial layer. It’s the layer easiest for a model to get right and the one that tells you the least about whether you should actually use what it gave you.

Programming languages aren’t interchangeable skins over the same underlying logic. They come with different type systems, memory models, compiler strictness, standard libraries, ecosystem conventions, and very different levels of representation in the data available to language models. All of that can shape performance, and no single factor explains the whole picture.

Python tends to be where AI coding assistants feel most fluent, and there’s a fairly obvious reason for that. It has an enormous public footprint, in tutorials, in open-source repositories, in competitive programming archives, in the kind of educational content that makes up a large share of what these models were trained on. Its syntax is comparatively forgiving, and its dynamic typing means there are fewer strict rules for a model to violate in the first place.

Benchmark evidence backs this up specifically on efficiency, not just correctness. EffiBench-X, a 2025 multi-language benchmark from researchers including King’s College London, measured not just whether generated code works but how efficiently it runs compared to expert human solutions. It found that current models consistently produce more efficient code in Python, Ruby, and JavaScript than in Java, C++, and Go.

One useful example comes from DeepSeek-R1: its Pass@1 correctness was actually very similar on C++ and Python — 75.12% versus 74.64% — while its execution-time efficiency was noticeably better on Python, at 67.30% of the human reference level versus 60.89% on C++.

That distinction matters: getting the answer right and producing an efficient implementation are not the same thing. That’s a useful reminder that “gets the right answer” and “gets it well” are separate questions, and a model can be better at one than the other depending on the language.

None of this means generated Python is automatically good. It just means the odds are better. Python code from an assistant still routinely assumes the wrong version of a library, misses an edge case around empty input or unusual encoding, reaches for a dependency that wasn’t necessary, or swallows an exception in a way that will make debugging painful three weeks from now. Comfort with a language isn’t the same as care.

C and C++ punish a specific kind of mistake that other languages simply don’t allow to happen in the first place: mistakes involving memory that the language trusts you to manage yourself.

Picture asking for a small function that copies a fixed-size buffer. A model might return something that looks completely standard, allocates a buffer, copies into it, and returns it, without accounting for what happens if the source is longer than expected, or forgetting who is responsible for freeing that memory once the caller is done with it. Nothing about that code look wrong on a read-through. It’s the kind of mistake that a compiler often won’t catch, that a casual test often won’t trigger, and that a sanitizer or a genuinely adversarial input eventually will.

A clean compilation is not a guarantee of correctness. In C and C++, code can compile, run normally on one machine, pass a handful of tests, and still contain undefined behavior that appears only under a different compiler, platform, optimization setting, or input.

Rust is one of the more interesting cases in this entire discussion, and the honest picture is more nuanced than “AI struggles with Rust.”

Rust’s defining features, ownership, borrowing, lifetimes, and a compiler that refuses to build code it considers unsafe, exist specifically to catch the exact category of mistake that quietly slips through in C and C++. Imagine a small example: a function that takes a vector, hands out a reference to one of its elements, and then tries to modify the vector while that reference is still alive.

In most languages, that’s completely normal and nobody blinks. In Rust, the borrow checker will refuse to compile it, because the compiler can’t guarantee the reference stays valid once the underlying data moves. A model unfamiliar with exactly how strict that guarantee is might generate code that looks completely reasonable and gets rejected outright.

That strictness is often mistaken for the language being “hard for AI.” Recent independent benchmarking complicates that story. A comparative analysis published on HackerNoon in early 2026, testing multiple current models on 100 recent problems published between October 2025 and February 2026, specifically chosen to avoid problems the models could have memorized, found that the performance gap between Python and Rust wasn’t statistically significant for the models tested, well within the same range as the Python-to-Java gap.

The much larger, clearly significant gap in that same study showed up with Elixir, a far less represented language, not Rust. That’s a useful corrective to the assumption that Rust is uniquely difficult. It may be more accurate to say Rust is unforgiving rather than poorly supported: the compiler exposes mistakes immediately and refuses to let weak reasoning slide through, which can make failures more visible even when the underlying error rate isn’t dramatically higher than in other mainstream languages.

But Rust exposes a different problem: keeping up with a changing ecosystem. RustEvo², a benchmark specifically designed around Rust API evolution, evaluated 588 API changes across the Rust standard library and third-party crates. The researchers found a substantial knowledge-cutoff effect: models averaged 56.1% success on APIs available before their training cutoff, compared with 32.5% on APIs introduced afterward. Retrieval of current documentation improved performance on those newer APIs by an average of 13.5%.

That matters because it changes the diagnosis. The problem isn’t simply that Rust is “too hard” for AI. Sometimes the model is reasoning incorrectly; sometimes it simply doesn’t have reliable knowledge of the API version you’re asking it to use.

The honest summary: Rust isn’t too hard for AI in some fundamental sense. It’s a language where the compiler acts as an immediate, unforgiving second reviewer, and where a model’s outdated knowledge of a fast-evolving ecosystem shows up faster and more visibly than it would in a language with looser rules.

Java sits in a comfortable middle ground for most models. Its verbosity, explicit types, predictable class structures, and well-worn API conventions are heavily represented in public code, and models tend to handle common patterns, standard class definitions, typical interface implementations, familiar boilerplate, quite reliably.

Where it gets shakier is exactly where human developers also find Java tricky: concurrency, where subtle timing assumptions are easy to get wrong; framework-specific behavior in ecosystems like Spring, where conventions matter as much as syntax; generics, where type bounds can get genuinely gnarly; and version drift across API changes, similar in spirit to what RustEvo² documented for Rust. None of this suggests Java ranks definitively below or above Rust or C++ in some universal ordering. Ranking languages on a single scale flattens differences that actually depend heavily on the specific task and model being tested.

There’s a subtler issue than raw error rate: models tend to default toward popular languages even when a different one would serve the task better, a pattern visible across the efficiency research already discussed, where scripting languages consistently receive more optimization-aware output than compiled ones.

Picture asking an assistant to design a high-throughput backend service. Python is heavily represented in the model’s training data, so a Python-based suggestion often arrives first and most confidently, complete with a plausible-sounding framework recommendation. But the right engineering call might actually hinge on latency requirements, memory constraints, the deployment environment, or the concurrency model the team already relies on, considerations a model can reason about if asked directly, but won’t automatically foreground on its own.

This is worth sitting with, because it’s not really a language-specific bug. It’s closer to a general pattern: a model tends to predict what’s common in its training data before it evaluates what’s actually optimal for your specific constraints. That’s not a reason to distrust AI-suggested languages outright. It’s a reason to state your actual constraints up front rather than letting the model guess at them from a bare problem description.

Underneath all of this, the hardest parts of AI-assisted programming usually aren’t syntactic at all. They’re the same things that separate a junior developer from a senior one: choosing the right abstraction for the problem, understanding constraints that were never explicitly stated, picking an algorithm that scales the way the real data will, managing resources correctly over the program’s whole lifetime, anticipating edge cases nobody mentioned, and writing something that still makes sense after requirements inevitably change.

Passing a test suite proves a program behaves correctly on the inputs you thought to test. It says very little about whether the underlying design will hold up as the codebase grows, or whether the next person to touch it will understand why it was written that way.

It’s worth being honest about what coding benchmarks actually measure, because headline numbers travel further than their caveats do.

Most function-level benchmarks, the kind that produce the widely quoted pass rates, test short, self-contained problems that look more like interview questions than production work. Real repository-level benchmarks like SWE-bench, which test whether a model can resolve an actual GitHub issue inside an existing codebase, tell a noticeably different story.

SWE-bench Verified scores have climbed into the 70–80% range for top models by early 2026, but a harder variant called SWE-bench Pro, designed specifically to resist the kind of memorization and pattern-matching that inflates scores on well-known benchmarks, shows even frontier models scoring closer to 23%. That’s not a contradiction. It’s a reminder that a benchmark’s difficulty and realism matter enormously, and a single headline percentage rarely tells you which kind of problem it actually measured.

The same caution applies to language comparisons. A benchmark built from competitive programming problems, like EffiBench-X, will surface different strengths and weaknesses than one built from real GitHub issues, like SWE-bench, or one built specifically around a single language’s evolving API surface, like RustEvo². None of these benchmarks is wrong. Each one is measuring a narrower slice of “can this model program” than its headline number implies.

A workflow that treats the model as a fast first draft, not a final answer, tends to hold up better across all of this. Specify the exact language version, compiler, or runtime up front. State your real constraints, performance targets, memory limits, concurrency needs, before asking for an implementation, not after. For anything non-trivial, ask for an approach and reasoning before asking for code. Compile immediately. Run the actual tests, not just the ones the model wrote for itself. Read the code you’re about to use, and ask the model to explain any part you don’t fully follow. Check performance and resource usage where it matters, not just functional output. And don’t merge anything you can’t explain to someone else.

A few things are worth doing differently by language. In Python, check dependency versions and edge cases explicitly, since the model’s fluency there can create false confidence. In C and C++, compile with warnings turned all the way up and run sanitizers where you can; assume memory-related mistakes are possible even when the code looks clean. In Rust, read the compiler’s own error messages carefully before asking the assistant to “just fix it,” since the compiler is often diagnosing the actual problem more precisely than a quick re-prompt will. In Java, double-check framework and API versions and think specifically about concurrency, since that’s where subtle correctness issues tend to hide behind code that otherwise looks fine.

The strongest pattern that emerges across all of this is treating the model as a collaborator you can push back on, not an oracle you simply accept.

Instead of “write this Rust function,” something closer to “I think this ownership model is correct because of X, here’s my code, tell me where my reasoning is wrong” gets you a genuinely different kind of answer, one that surfaces your own misunderstanding instead of just papering over it. Instead of “fix this C++ bug,” describing the crash, your working theory of what’s happening, and the relevant code, then asking the model to challenge that diagnosis before proposing a fix, keeps you in the loop on why the bug existed in the first place, not just what patch made it go away.

That distinction matters because it’s the difference between using AI to skip understanding a problem and using it to arrive at understanding faster.

None of this adds up to “AI is bad at programming.” Current models are, by most available evidence, genuinely strong across a wide range of languages and tasks, and the gap between top models and mainstream languages has been narrowing steadily. What the research does show is that a model’s fluency correlates strongly with how heavily represented a language is in its training data, and that language-specific rules, memory safety, ownership, strict typing, fast-moving APIs, can expose weaknesses that are completely invisible if you’re only judging code by whether it looks right on a first read.

The more a language depends on precise semantics, strict compiler guarantees, careful resource management, or fast-changing ecosystem knowledge, the more dangerous it becomes to mistake plausible code for correct code.

The developer’s job here isn’t disappearing. It’s moving up a level: less about typing out the implementation, more about understanding whether the code, in this language, under these constraints, actually deserves to exist the way it was written.

The Programming Languages AI Still Gets Surprisingly Wrong was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

How can one look at a map of the Uniroyal Chemical property (plus pits & ponds) and not be appalled? It is absolutely shocking how much of the site was taken up with toxic waste disposal. There were approximately twelve different pits and ponds of various sizes on the west side of the Canagagigue Creek and another twelve to fourteen on the east side of the Creek. The west side waste pits/ponds included RPW-1, RPW-2, P1, P2, RPW-3, RPW-4, RPW-5, RPW-6, RPW-7, RPW-8, M2, TPW-1, TPW-2. The east side had  IR-1, IR-2, RPE-1, RPE-2, RPE-3, RPE-4, RPE-5, TPE-1, TPE-2, BAE-1, RB-1, RB-2 and allegedly GP-1 & GP-2. At least these are the ones that I know of and that have documentation as to their size, location and contents. 

Uniroyal's intent was obvious with Pits and Ponds on both sides of the Creek through their property. None of these pits were covered from the elements so especially during rainstorms any pits that weren't already full and overflowing from the known 165,000 gallons of liquid wastes per day transported just to the east side alone, quickly would be. Surface drainage from the site ran into the Canagagigue Creek. This was not an accident. The Creek's water quality plummeted and many species in and around the Creek disappeared.  Where was the Ontario Ministry of Environment? How about the Ministry of Natural Resources? Maybe just like at Varnicolor Chemical down the road (Union St.) they were sitting in the office enjoying a licensed refreshment. 

What may have been an accident was the gross contamination of the multiple aquifers beneath the Uniroyal site.  Afterall the company knew that the shallow aquifer (UA) discharged directly into the Canagagigue Creek so likely they hoped/expected that the bulk of their liquid wastes simply would flow into the Creek just as the surface flows did. Also it is possible that the environmental savages did not know about the "windows" allowing direct flow into deeper aquifers that were between the UA (Upper Aquifer) and the Municipal Aquifer (MU) .  Or maybe they did and they just didn't care.

The Region of Waterloo didn't start using the Grand River for drinking water until the 1990s. I thought that that was pretty dumb at the time and still do. This most likely was because they had been told that on-site direct discharge to the Canagagigue Creek had ended around 1970. What a shocker later on after Cryptosporidium deaths made it clear that local farmers were still grazing their cattle in both the Grand River as well as its' tributaries such as the "Gig", the Conestogo and others. Then of course there were little problems such as multiple landfills without leachate controls, both legal and otherwise, along the Canagagigue that had accepted Uniroyal and Varnicolor Chemical wastes. 

We now know that there were multiple other potential industrial sources of chlorobenzene in Elmira located close to the corner of First and Union St.. I expect that we will eventually learn that the same occurred with NDMA but it's more politic not to reveal that for a little while longer. This is embarrassing information to the former M.O.E. (now MECP) who cut a sweetheart deal with Uniroyal Chemical. That deal had Uniroyal (falsely) admit to be the sole source of NDMA, chlorobenzene and ammonia in exchange for not having to properly clean up DNAPLS both on and off their site as well as for not ratting out the M.O.E.'s cooperation/incompetence in stopping their grotesque pollution sooner. One other recent admission from Lanxess Canada and consultants was that Varnicolor Chemical contributed half a dozen chlorinated solvents to Elmira's Municipal Aquifers albeit allegedly not chlorobenzene. Do you think this decades late admission is related to the Ontario Ministry of Environment's reneging on a public promise made to APTE, the Varnicolor Liason Committee (Rich Clausi, Ted Oldfield & myself) to fully examine Varnicolor horizontally and vertically back in the mid 1990s? They did neither as the still non public cleanup done at Motiveair Inc. behind Varnicolor would indicate along with the belated deep Municpal Upper Aquifer contamination admission.

Corruption stinks and the stench from the MECP is still overpowering! 

A Linux native mod manager for a variety of games

Python 909 Updated Aug 26

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This time of the year, I dive into Concord grapes … but wait! There’s a variety called the Coronation grape that, to my taste, rivals the native-Ontario Concord. Sovereign Coronation grapes have the same crispness, the same amazing colour, and much the same sweet-tartness as the traditional Concord.

The difference is … Coronation grapes don’t have seeds.

Conceived and originally produced in British Columbia’s Okanagan Valley in the 1970s, the Coronation began appearing more regularly in Niagara a decade or so ago. I’ve eaten them regularly each year, and they are fabulous.

Today, Ontario harvests nearly 3,000 tons of these intensely flavoured grapes.

A cross between native North American varieties Patricia and Himrod (two improbable but memorable names), we can take advantage of the Coronation grape now because it just happens to ripen in late August and bit earlier than other more traditional varieties.

It has a fairly thick skin and a headier taste than most grapes, and the fact that it is seedless makes it a great grape for eating and for making jams, jellies, pies, and sauces.

The Coronation grape is indeed regal … but get some soon because they won’t be around once September is gone. We don’t need yankee grapes!

Check out my latest post No to ‘Murrica: delicious Ontario Coronation grapes from AndrewCoppolino.com.

A simple and easy to use mod manager for several games using Thunderstore

TypeScript 2.2k 2 issues need help Updated Aug 26

Everyone was hissy with everyone else at least in private. Blame was cast back and forth between Uniroyal and the Min. of Environment and the other two political tiers piled on. Then Uniroyal and the Ontario M.O.E. basically told the Region and Township to bugger off. And they did. No running to the courts. No running to the media to badmouth Uniroyal or the M.O.E.. The biggest bullies are also usually the biggest cowards and neither the Region nor the Township wanted to be on the bad side of the province and their variety of infrastructure grants and more. Even the realization of poisoned tap water in Elmira was more acceptable than losing out on millions of dollars of infrastructure grants (roads, sewers, water pipes, bridges, etc.) which eventually would have cost both regional and municipal councillors their jobs as councillors.

So once the polluter and their regulator had flexed their muscles and told the Region and Township to take a hike and they had; the die was cast. Two years later the M.O.E. and Uniroyal announced a bilateral agreement between themselves that totally bypassed the Township, the Region and local citizens who had organized and gotten  party status at the Environmental Appeal Board. The October agreement ended up as a November 1991 Control Order laid on Uniroyal Chemical.  The other parties both political and citizens were left in the dust. Now with admirable cowardice and a wish to ingratiate, the two political groups complained only mildly and begged to be let back in to the fold and Uniroyal file. The citizens group leadership knew where they wanted to go and how to do it. They too made commitments that got them invited to the pending January 1992 initial UPAC (Uniroyal Public Advisory Committee) meeting. 

Woolwich Township offered to organize regular public meetings (i.e. UPAC). They offered to present a united front to the public. A united front that the Ontario M.O.E., in negotiations with Uniroyal Chemical, was happy with. Similarly Waterloo Region also wanted to be part of the initial UPAC public meetings. Their support for the M.O.E.'s and Uniroyal's direction might be less enthusiastic than the Township's as they pressed for a somewhat better cleanup. Both the Township and the Region also received money from Uniroyal Chemical towards their increased environmental and legal costs incurred during the first two to three years of the water crisis. 

It was business as usual especially with local citizens getting scr*wed the most, including by their own.    

High performance, multi-platform VNC client and server

C++ 7.4k 14 issues need help Updated Aug 21

• good banter
• building sexual tension
• Lucy's work BFF was a total delight (everyone needs a work BFF!)
• the sex positivity theme
• 90's references
• fake dating 

As soon as I saw the phrase Conestoga Lands Inc. in reference to the proposed zoning change and breach of the country side line on Bridge St. in Waterloo, Ontario I smelled something rotten in the air. Was it possible that a (in my opinion) rotten company, long ago merged with a bigger maybe less rotten firm, could still be spreading their anti social, anti environmental actions upon unsuspecting citizens? Well now that is a tricky question. I did a little bit of research trying to find out if the developers behind this proposed industrial site located beside Martin Grove Village in north Waterloo were indeed the former Conestoga Rovers & Associates (CRA) who made their initial claim to fame with the Love Canal disaster in the United States.

Well things get a little murky here. First of all CRA merged with GHD an Australian engineering firm around 2015 or so and carried on in name as GHD. CRA employees and shareholders were given shares in GHD and many (most ?) carried on with the new group. That being said the reference to CRA Lands Limited does refer to my least favourite engineering firm who are multiple winners of the MACHO Award.  They were presented with this award for their work with Uniroyal Chemical in Elmira and the award name stands for Memorial Award for Creative HydrogeOlogy. 

Now here's the rub. According to Google/AI,  CRA Lands Limited while referring to lands formerly owned by Conestoga Rovers & Assoc. are actually owned by a group of land developers. Maybe they thought that CRA had a good reputation environmentally or perhaps a good reputation as engineering consultants. I think it would be fair to suggest that dirty, rotten, polluting industries just love companies like CRA who give them the veneer of scientific oversight in their environmental decision making. Of course my harsh comments concerning dirty, rotten, polluting industries are a result of my working with/against CRA in Elmira for a couple of decades at least as they psuedo scientifically and drastically reduced  Uniroyal Chemical's cleanup costs.  

Now I don't claim to be any kind of land expert but it appears as if CRA Lands Limited and CRA Lands II Limited are controlled by Cook Lands Group. This company, again according to AI, is owned and operated by "...local Waterloo developer Ian Cook, founder of Cook Homes Limited,...". So here we appear to have once again the tail wagging the dog as home builders and developers are attempting to expand the hard country side line in order to obtain more lands to build profitable for them homes. 

I guess that I am less appalled because it isn't CRA directly behind this attempt to crack the countryside line; but only slightly less appalled. With Woolwich Township's penchant for sleeping with polluters and their fellow travellors, I expect smooth sailing for anybody riding on CRA's coattails.   

Bethany Galbraith has a long history with House of Friendship.

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Her entire family does.

“My brother, Matt, was a student at House of Friendship in 2006, so that introduced me to the Emergency Food Hamper program,” said Bethany.

That initial connection led to Bethany’s college placement at House of Friendship. It wasn’t long before the entire family started volunteering.

Even now, Bethany’s parents, John and Betty, volunteer weekly at the food program. And her niece, Binny, has worked on class projects that focus on House of Friendship, inspiring her fellow Grade 6 students to support the organization.

“It’s been a family affair. I would say we all believe quite strongly in the vision, mission and values of House of Friendship,” said Bethany. “It’s become the organization of choice for my family.”

In addition to volunteering, Bethany and her husband Jon have chosen to support House of Friendship financially throughout the year, including during the holiday season.

“We donate at Christmas in honour of our sons’ teachers,” said Bethany. “It shares the message to our children about the importance of giving and helping those in need in our community.”

And over the many years of support that Bethany and her family have provided, she’s been pleased to see how much House of Friendship has continued to adapt to meet the needs of the community.

“It’s been exciting to see the growth over the years,” said Bethany. “And how many people are now supported through House of Friendship.”

And since House of Friendship aligns so well with her family’s values, it was only natural for Bethany and Jon to consider leaving a gift for future generations, through a gift in their will.

“It was a simple choice for us,” said Bethany. “That was eight years ago, and still to this day, we would absolutely make the same decision.

“What I like about leaving a gift in our will is that it is sharing what our wishes are. It tells the story of what has been important to us, long after we are gone – and making sure this work will continue into the future.”

If you are interested in learning more about leaving a gift in your will to House of Friendship, contact Development Director Natalie Schill, at natalies@houseoffriendship.org.

The post A Family Affair appeared first on House Of Friendship.

Over the weekend, I read about the death of Jason Arday, PhD, a Black professor at Cambridge University, who had been subjected to weeks of intense public scrutiny of his academic work and credentials, misinformation, and harassment. Reuters

What started with questions about plagiarism expanded into questioning Arday’s entire background. The Guardian

I don’t want to diminish what happened to Arday in any way. But his story made me think about a pattern that can show up in much smaller ways at work: marginalized employees can face more scrutiny after a mistake, with that one incident becoming evidence for a broader judgment about their competence.

For example,

• A marginalized employee makes a small mistake, and their manager starts looking for additional “proof” that they aren’t qualified.
• A mistake that might be treated as an isolated incident for one employee becomes “evidence” of a larger pattern when made by someone from an underrepresented group.

Have you ever seen someone make one mistake, and then watched everyone start looking for the next one?

While we should hold people accountable for their mistakes and give them feedback, we need to apply the same standards to everyone.

Here’s one quick way to check yourself: Ask, “Would I apply the same level of scrutiny to someone of a different identity or background?” I learned about this “flip it to test it” approach from Kristen Pressner, a global HR executive, in her TEDx talk on bias.

If the answer is “No” or “I’m not sure,” let’s pause before looking for more evidence.

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Here’s another example of extra scrutiny, from my archives.

In ‘It was stolen from me’: Black doctors are forced out of training programs at far higher rates than white residents, Usha Lee McFarling sheds light on the inequity facing many Black residents. One example is that they were written up for transgressions that went unpunished for white residents.

In any workplace, increased surveillance brings increased employee nervousness. Small mistakes are more likely to be caught, leading to negative feedback and, over time, job loss.

Let’s define “the bar” for acceptable and unacceptable work. And then apply that bar consistently.

“Don’t want to see someone with 20–30 years experience.”

That was a hiring manager’s raw note that accidentally made its way into a job description, according to a viral LinkedIn post by Emily Worden.

The irony? The job requires 5+ years of experience.

So why doesn’t the hiring manager want someone with 20+ years under their belt?

Is it ageism? Maybe. But Worden suggests asking a more useful question: What are you really looking for? For example,

• I need someone to be hands-on.
• I need someone who’ll be satisfied with $45/hour.
• I need someone who won’t get bored with the job.
• I need someone who is comfortable taking direction instead of running the department.

Those are legitimate hiring criteria.

So the next time you hear someone say they don’t want someone who’s “overqualified” or has “too much experience,” ask what they actually mean.

Then encourage them to list that in the job description and interview for it.

Last week, a newsletter subscriber asked me an interesting question about how to respond to a racist joke.

Her husband, who I’ll call John, was giving a tour of their new home to his relatives when someone asked about his office.

“It’s up by the front door,” John said.

His uncle then quipped, “We’re white. We came in the front door, so we saw it.”

John was stunned. He wanted to remain respectful to his family, so he asked, “What do you mean?” His uncle explained that there was a time when only white people could come in the front door.

John, realizing his uncle was making a joke, just said, “I don’t get it,” and continued the tour.

Later, John wished he had said more. He felt he missed an opportunity to call out the racism.

But I think John did something important: he introduced a speed bump. By asking “What do you mean?” and then saying “I don’t get it,” he slowed the conversation down. He made his uncle explain the joke instead of laughing along. And he signaled that he wasn’t okay with it.

And for a stronger stance, John could have added: “We don’t joke about things like that here.” Or, “Please don’t make fun of terrible parts of our history.”

Subscriber Billy W. told me he recently worked to make a team event more inclusive for a blind coworker. He wrote,

“Wanting to be thoughtful, I initially tried to solve potential accessibility challenges on my own rather than putting them on the spot with questions. When I discussed my approach with someone experienced in accessibility and inclusion, they shared a phrase that has stayed with me: ‘Nothing about us without us.’”

For Billy, it was an important reminder not to make assumptions, even well-intentioned ones, as an ally. Instead, we need to invite the people we’re trying to support into the conversation and listen to their perspective.

When Billy asked his blind coworker what would be most helpful, they provided valuable guidance. They also told him they appreciated that he was being proactive.

Something for all of us to keep in mind. ❤️

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.

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

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Look Out For Extra Scrutiny, 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.

KITCHENER – Back-to-back home runs with two outs in the bottom of the ninth proved to be too little, too late for the Kitchener Panthers.

A couple of early errors and trouble on the mound proved costly in a 12-7 defeat to the Welland Jackfish Thursday.

Kitchener’s late heroics included two successive home runs, while down to their last out.

Both Yosuke Fujie and Mateo Zeppieri made things interesting with no doubt shots to keep the game alive.

Alongside the three runs scored in the ninth, the Panthers had knocked two home runs earlier in the ballgame. Trent Lawson hit his ninth of the year, and Zeppieri hit the first of his two on the evening.

Panthers starter Evan Elliott was ran out the game in the second inning, after surrendering five earned runs on four hits. 

Three more Panthers would grace the mound through the ballgame - that of Ben Hewitt, Jake Liberta, and Brett Reid – who would combine for five strikeouts - and more critically – five walks.

Welland’s win was surmounted from an outing that Pedro De Los Santos – a nine-year veteran of the league – will never forget. 

Having not thrown for more than three innings in a game all season, the former Panther went seven in this one, striking out three – with 114 pitches to his name.

The Panthers are down to only two remaining home games at Jack Couch Park for the 2026 season, and after a duel with the Maple Leafs in Toronto on Saturday, they will return for a home clash on Sunday with the Chatham-Kent Barnstormers. First pitch is scheduled for 2:05 p.m.

GET YOUR TICKETS NOW and #PackTheJack!

And so inaccurate. I guess that's not too surprising. They took an educated guess and guessed wrong. They've backed the wrong horse and aren't going to admit it now. There was a large article in the Record last November with a number of pretty obvious errors in it. I contacted the Record in writing with specifics and details and basically got nowhere. They weren't interested in having to change any part of the narrative, accurate or inaccurate. I might add that there is a good comment published on yesterday's Blog posting regarding what appears to be Lanxess's intentional leakage and even pumping of contaminated groundwater pulling it off the Uniroyal/Lanxess site into the Elmira drinking water aquifers. 

Overall media coverage has been pathetic for years despite ongoing RAC & TAG meetings later followed by TRAC meetings. My initial name for TRAC was "Totally Rotten & Corrupt" but I have softened that to "Too Reticent and Compromising". Either one works for me although the second is politer and acknowledges that there are at least a couple of members whose intentions are good. The rest know what the game is and are fine with going through the motions and providing the Ministry (MECP), Lanxess and  Woolwich Township with the cover and credibility they require. Following is the verbatim Comment from yesterday's Blog posting:

 AnonymousAugust 19, 2026 at 9:08 PM

Yesterday's Analysis is SHOCKING!!! "By pumping less on-site and more off-site they are intentionally losing on-site hydraulic containment. This means that they are purposefully drawing contaminants off their site where they Lanxess only pay 50% of those cleanup costs versus 100% of all on-site treatment costs." so they drained the surface with ditches and swales and trenches into the river AND the parts of the Aquifer that were most contaminated they are attempting to just flush away downstream as well. Wow that is so environmentally mindboggling.

What's with the teapots in Ottawa?

The family is in the city while Erin attends a conference. While there, I take Eldest Child out for breakfast at Fathers and Sons restaurant near the UOttawa campus (excellent food), but Eldest Child was flummoxed by the teapot their tea was served in. The glass is uninsulated, and so quite hot, and the heat resistant banding on top was only mildly effective. There was also no spout, so the hot tea easily spilled. We thought it was a weird affectation of this college campus restaurant and moved on...

Until we went to the Downtown restaurant Eggspectations the next day, and was served the same pot.

Is this a weird Ottawa thing?

So, I wrote this a week ago on Facebook and got a number of answers. Apparently others have seen this style of teapot, in Vancouver and in Colorado. Apparently, they were more common in the 1980s and the 1990s, so maybe it's falling out of style and these two restaurants happen to be odd throwbacks.

I do know that, when we returned to Eggspectations on our final day in Ottawa, Eldest Child was served tea from a normal teapot (fully ceramic, with a handle and a spout). They were delighted by this and thanked the waiter for using it, explaining the whole weird thing we'd encountered on Saturday and Sunday. Her reply was (paraphrased), "Yeah, we have a couple of those teapots in the back. I hate them, and try to use the regular ones when possible."

So there you go!