Educators have a wide range of options when it comes to coding and robotics kits, workforce skills technology, and MakerSpace tools. The Building Ontario Business Initiative (BOBI) Act mandates public-sector organizations, like school boards and universities, to give preference to Ontario businesses. Other provinces and territories have given similar guidance in recent months. 

As the only Canadian STEM EdTech company that designs and manufactures computer science hardware and creates all instructional content right here in Ontario, we know how much local matters.

When school boards choose local, they’re not just meeting policy requirements—they’re investing in their communities and helping students develop the skills that matter most right here in Ontario.

Local also means relevant: content and training tailored to Ontario’s curriculum, career pathways, and workforce priorities. We’re here to ensure that choosing local never means compromising on impact.

• The BOBI Act requires public sector organizations to give preference to Ontario businesses for procurements under $121,200 CAD.

• For non-competitive procurements under $25,000, purchases should be made from Ontario businesses 

• For competitive procurements, Ontario businesses must be invited

The Act came into effect on April 1st, 2024. It aims to stimulate local economic growth, enhance supply chain resilience, and support job creation within the province. Ready more here >>

Supporting Canadian-made education products helps keep innovation, quality, and economic growth rooted in our communities. Whether you’re a teacher in British Columbia or a district leader in Nova Scotia, every Canadian purchase helps strengthen the network of companies committed to serving Canadian classrooms with purpose-built tools that reflect our shared values.

At InkSmith, we strive to be a trusted partner to schools and districts. That means understanding the realities of the classroom and using design-thinking principles to ideate, prototype, and manufacture tools that help educators hit their goals while students build durable, future-ready skills — right here in Ontario.

When you’re exploring new EdTech solutions, we'd love to connect and learn more about your needs.

Thank you for choosing to support local.

—

The InkSmith Team

In recent conversations with businesses across Kitchener-Waterloo, a common thread has emerged: teams are tired. While every workplace looks different, the undercurrent of burnout, stress, and emotional strain seems to be everywhere, from factory floors to office boardrooms.

Mental health and well-being in the workplace isn’t a new conversation. But in 2025, it’s no longer a conversation we can afford to postpone.

More than 5 million Canadians aged 15 and older met the diagnostic criteria for a mood, anxiety, or substance use disorder in the span of just one year, according to Statistics Canada. As the Ontario Chamber of Commerce notes in its 2025 Economic Report, the province is experiencing a mental health “echo pandemic”— with lasting impacts on people, workplaces, and communities.

While 71% of Ontario businesses recognize that employee mental health and well-being are key to their organization’s success, only 41% currently have a formal strategy in place. Among small businesses, that number drops to just 32%.

Add in limited access to primary care and rising reports of opioid-related harm, and it’s no surprise that employers are feeling overwhelmed.

But here’s the opportunity: workplace well-being doesn’t have to start with a formal strategy. It can start with a conversation.

A 5-minute Practice That Can Make a Difference

At OCTC, our team begins every meeting with a check-in. The first five to ten minutes are set aside to ask: How are you? What’s on your mind? We ask and we listen. This simple practice has helped foster a supportive, psychologically safe team culture despite working fully remote.

Small changes like this can make a big difference. When employees feel safe to share what they need — whether it’s time, space, or accommodation — they’re more likely to stay engaged.

Practical Tools to Support You and Your Team

If you’re looking for practical and free resources to get started or improve your approach, these tools offer strong foundations:

A free mental health program for Ontario’s healthcare sector offering live workshops, e-learning, and tools to address workplace stress and psychological safety.

A clear, actionable guide with five recommendations for business leaders committed to supporting employee well-being.

Tailored to help employers create accessible workplaces for employees who have a mental health disability. Includes interactive courses and training modules.

You can also explore the Greater Kitchener-Waterloo Chamber’s Mental Health Resource Hub for additional supports and local services.

A Final Thought

Supporting your team’s mental health doesn’t require a major overhaul. It starts with listening, being open, and with creating space for people to be human—especially when the pressures of work and life feel heavy.

This month, as we observe Mental Health Awareness Month and prepare for National Accessibility Week (May 26–June 1), we invite all business leaders to take action. A check-in, a new training or a conversation that helps strengthen their workplace.

The Ontario Corporate Training Centre (OCTC) is here to help businesses bridge the gap. Through free disability awareness and confidence training, along with support connecting to local employment service providers, OCTC offers practical, easy-to-implement solutions to help teams thrive.

The post Ontario Corporate Training Centre (OCTC): Why Mental Health and well-being matters more than ever in Ontario Workplaces appeared first on Greater KW Chamber of Commerce.

The Region of Waterloo could also win the same award for their lack of response to the ongoing decades of gamesmanship up here in Elmira, Ontario. Today's K-W Record advises us about the ongoing efforts at land assembly in Wilmot Township mostly against the wishes of local citizens. Reporter Terry Pender also wrote a separate article that described the "Code of Silence" award mentioned in the title above.

Yes this is the same reporter who wrote the article titled "50 YEARS after the Vietnam War" published on May 1 ,2025.  Yes it is the same reporter whom I've launched a complaint against for his failures to follow through with me in regards to a couple of errors in that May 1 article. Now all this being said I am forced to reconsider from Mr. Pender's position. His two articles in today's Record are both excellent and well written. They are on pages A1 and A2 and well worth reading. What I am reconsidering is Mr. Pender's workload which of course normally none of us would do. In this case I can understand that the hot, local topic is the 770 acre Wilmot land assembly not the fifty year old Vietnam War nor the thirty-six year old Elmira Water Crisis.

Is it possible that Mr. Pender's employer and supervisors have him a little overstretched at the moment? Is it possible that our print media who by all accounts are in desperate financial/readership straits are trying to squeeze more and more out of their employees including reporters? Would it be very understandable if they were? While none of this relieves the Record and its' reporters of transparency and accountability nevertheless these factors could explain the delays in their response and correction of the errors in their article. 

I am not at this time remotely interested in punitive actions against either the reporter or the Record. I can understand if they are both between a rock and a hard place and it is affecting their response time. That said as I advised  Mr. Pender two days ago, I am in my 76th year.  Thirty-six of them have been dealing with a bunch of twits/politicians and worse here in Elmira regarding Uniroyal Chemical. I have long ago burned through whatever patience I used to have dealing with those professional deceivers. Maybe a tiny bit more patience from my end combined with at least a promise of a date for both a phone call and a correction in the Record would be very helpful.  

In The Canary Code: A Guide to Neurodiversity, Dignity, and Intersectional Belonging at Work, Ludmila N. Praslova, PhD uses the metaphor of canaries in coal mines to highlight how marginalized individuals often notice harm in workplace systems before anyone else does. She writes:

“Organizational problems like the lack of fairness, bullying, and toxic cultures impact people with more intense senses and nervous systems before affecting others. Sensitive does not mean broken: it means processing the experience more fully, and intensely, just like birds process the air — the oxygen and the pollutants — more fully.”

In other words, when we listen to canaries, we hear early warnings about toxic systems and gain the opportunity to build workplaces where everyone can thrive.

So, when a colleague raises a concern — about bias in a process, exclusion in a meeting, or barriers in workplace culture — let’s listen to them and believe them. Without downplaying what they shared. Without dismissing their concern because we haven’t personally experienced it. Without trying to help them see it differently. Without saying, “I’m sure they didn’t mean to offend you.”

Let’s also ask ourselves, “What can we learn from their perspective?” Then, advocate for change and help address the systemic issue.

Share this action on Bluesky, LinkedIn, Instagram, or Threads.

A newsletter subscriber recently asked, “I find it disconcerting when people (almost always men) refer to other people (always men) by their last name. What are your thoughts, and what might be an appropriate response?”

Great question.

Using last names among men — especially in professional or male-dominated spaces — can be a subtle way of signaling in-group belonging. It can create a sense of camaraderie. But when this pattern isn’t extended to women, it sends another message: They’re not part of the inner circle.

When women are consistently referred to by their first names, while men get the “last-name treatment,” it can feel minimizing or exclusionary. Over time, those small signals can add up, chipping away at a sense of belonging.

Pay attention to naming patterns in your workplace. Are they consistent across genders and other identities? If not, disrupt the norm. Use inclusive language yourself, and model a naming convention that doesn’t reinforce “us vs. them” dynamics.

A simple “Hey, let’s stick with first names for everyone” can go a long way.

The Mansfield Rule is an inclusion strategy, primarily for law firms, that requires them to consider at least 30% of qualified underrepresented candidates for leadership roles and other key opportunities. It was inspired by a hackathon to improve gender balance in law firm leadership. Diversity Lab, the sponsor of that hackathon, now runs the Mansfield Rule certification program.

It’s important to note that the Mansfield Rule is not a quota system. It does not lead to the exclusion of any individual from consideration based on their race, gender, or any other aspect of their identity.

Despite its positive results (or perhaps because of them), the Mansfield Rule has faced criticism.

Fortunately, a recent U.S. Federal Court decision has upheld the legality of the Mansfield Rule. In a case involving President Trump’s Executive Order targeting law firm Perkins Coie, the Court affirmed that the rule is lawful and aligns with anti-discriminatory laws. Page 58 of the Court’s opinion states,

“The Mansfield Rule expressly does not establish any hiring quotas or other illegally discriminatory practices, requiring only that participating law firms consider attorneys from diverse backgrounds for certain positions.”

It’s helpful guidance for anyone who uses the Mansfield Rule or a similar strategy to diversify the hiring and promotion pipeline.

Given that May is Mental Health Awareness Month in many countries, here is one way to be an ally: Lobby for and support office accommodations that can help ease and prevent mental health challenges.

In the Harvard Business Review’s How to Be a Mental Health Ally, advocate and author Katherine Ponte wrote:

“Allies and leaders should lobby for and support office accommodations that can benefit all employees by helping prevent mental health challenges and mitigating workplace stressors that can worsen mental health. Some easy and low-cost examples of accommodations from the American Disabilities Act include offering late starts (many psychiatric medications can be sedating), breaks to attend medical appointments, flextime, quiet workspaces, office psychiatric service dogs (or emotional support animals), remote work, and part-time work. Encourage all employees to discuss accommodations for their team and suggestions for how best to incorporate them.”

Ponte’s article also contains suggestions for talking to a colleague who may be struggling with their mental health. She provides some helpful examples of what to say and not say. Be sure to check it out.

This week’s spotlight on an ally action from the Better Allies community is from newsletter subscriber Sarah Rabeda, who wrote,

“When I joined the office two years ago, no one ran the mic for questions, nor did the speakers repeat the questions before answering. I started raising my hand and, instead of asking a question, requested the mic running. It took a few meetings of raising my hand and requesting, but now it’s part of our meeting culture here.”

Rabeda added,

“Recently, we had a higher-up visit our office to share strategy with a room of about 80 people. He began using the mic and, a few minutes later, said, ‘I don’t need the mic; I’m not going to use it,’ to which I immediately said loudly, ‘Please keep using the mic.’ After the meeting, I thanked him for continuing to use the mic. I explained that it’s for accessibility, as I and likely others could not hear him from the back. He graciously thanked me and explained that mics make him forget to breathe, so that is something he will work on as a leader.”

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

That’s all for this week. I wish you strength and safety as we all move forward.

Karen Catlin (she/her), Author of the Better Allies® book series
pronounced KAIR-en KAT-lin, click to hear my name

Copyright © 2025 Karen Catlin. All rights reserved.

Being an ally is a journey. Want to join us?

• Follow @BetterAllies on Bluesky, Instagram, Medium, Threads, or YouTube. Or follow Karen Catlin on LinkedIn
• This content originally appeared in our newsletter. Subscribe to “5 Ally Actions” to get it delivered to your inbox every Friday
• Read the Better Allies books
• Form a Better Allies book club
• Get your Better Allies gear
• Tell someone about these resources

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

Learn From Canaries in the Coal Mine, 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.

Dawn Gill

Dawn Gill’s recent career change might sound like a sharp departure from her long-time role as House of Friendship’s Development Officer.

Certainly, her current position as Constituency Coordinator for her Member of Parliament invites a lot of questions about the ins and outs of political life. But she simply sees it as another way to remain focused on tackling social inequality, just from a different position.

Dawn firmly believes that the only way to build strong communities is for each of us to jump in and take action, starting with the simplest acts: “Whether that means donating, volunteering, or being an authentic ambassador in the community … all those things add up,” she says.

When I talk about the advocacy work I do, people ask me what the world of politics is like. I don’t think of it as politics. It aligns with my values.

That grassroots thinking led Dawn and her husband Rodney to explore the idea of leaving a gift in their Will to House of Friendship.

They discovered how accessible legacy giving is. “To borrow a catchphrase,” Dawn says, “we realized we’re richer than we think! We decided rather than wait for the ‘wealthy’ to take action, how about a whole bunch of folks just each do a little bit?”

Leaving a Gift in her Will allows Dawn to continue to strengthen her community and keep her values alive. It’s a simple act of kindness that keeps adding up – for years to come.

“I think, if you want to say I love you – say it now. If you want to give a gift – do it now.”

If you would like to explore the idea of leaving a gift in your Will to House of Friendship, contact Development Manager Joanne Adair at joannea@houseoffriendship.org, or visit www.houseoffriendship.org/wills.

The post New Job, Same Values appeared first on House Of Friendship.

♦

Years of water damage from the leaking roof made this space in our club unusable. Now it is being used for board rooms, yoga classes, ballroom dancing etc. The City of Waterloo repaired the room, and our GM, John Thomas, ordered and helped install the furniture, blinds, TV etc. We are very appreciative of the group of volunteers that worked tirelessly to install the new floor. Huge thank you to Marty Bell, Carl Keller, Marcus Baker, Martin Rombout and Dave Zenger (absent on picture day)!

♦

KITCHENER - A rough seventh inning for the Kitchener Panthers.

The Chatham-Kent Barnstormers scored eight runs in the inning, en route to a 12-5 decision in front of over 1,000 students in Waterloo region Thursday morning. The contest was Kitchener's final exhibition game.

The Panthers stormed out to a 3-0 lead in the first, but the Barnstormers caught up by the fifth.

The game went back-and-forth, and Kitchener had a 4-3 threshold until the Barnstormers took over in the seventh.

Evan Elliott struck out four in three innings of work, and gave up two runs on three hits.

On offense, Yordan Manduley, AJ Karosas and Klaus Aplevich each picked up two hits.

Charlie Towers and Wander Santana both had a hit and an RBI.

These teams will meet again in a home-and-home this weekend in regular season play.

First, Chatham on Saturday. Then, the Panthers host its home opener Sunday at 2 p.m.

GET YOUR TICKETS NOW and #PackTheJack all summer long!

Reading Chip Huyen’s “AI Engineering”, I've learned a lot about the revolutionary foundation models which are changing software engineering and even the AI itself. I am now clear regarding how AI is going to turn the tech world upside down and evolve.

If you’re interested in the key insights of this book, this is what caught my eye. For those of you who have already read it, this should be a useful refresher.

Please note that I have focused virtually on the most interesting ideas, and skipping some background info.

I believe that foundation models have completely changed the whole scenery of AI. Nowadays, we don’t even start by designing our own models; we typically use pre-trained models. Chip Huyen explains how this move brings AI to experts, allowing them to harness powerful tools. This chapter is in itself a journey, in retrospect and through vision, with AI from its prehistory to the sophisticated systems of today. A major takeaway is the tangible impact of AI on society, just how critical the need for expertise in AI engineering has become.

The sheer versatility of foundation models is best exemplified by the Super-NaturalInstructions benchmark (Wang et al., 2022), which highlights the multiple functions the models are capable of conducting across translation, question answering, sentiment analysis, and others (see the following picture).

When I learnt AI engineering, it became obvious that comprehension of foundation models isn’t optional — it’s a requirement. In this chapter, I learnt a more detailed and digestible version of the underpinnings of these models from Chip Huyen. If you’re tasked with AI systems building or fine-tuning this chapter underlines the most essential factors.

Starting with the basics: Training these models takes a lot of resources and expertise out of reach of many developers like me. There’s no need to construct all the pieces ourselves — we have models to use. We can use existing models. But, before we can safely pick and tweak models, we need to understand a few key things: Data models used for training, structure of data models, and adjustments made after training the models.

One of the central features discussed in this chapter is the notion of training data. The performance of a model depends on the data it used while training. A model without Vietnamese training data will bomb with Vietnamese text. When most of the training data emanates from Wikipedia, these models are not very good in technical areas such as Science or law. Knowing the data used to train a model, you are able to see its strengths and its weaknesses.

Transformers occupy a considerable part of the structure topic. Chip explains why the transformers became the reflection and why they still are the winning choice now. Still, she does not hide behind the questions: what amazing progress could await us after the transformer era? While the response is not entirely clear, this is a rather important trend for observation.

Size also plays a role. The chapter explains “scaling laws,” which help developers make principled choices as to the size of the model given historical and computational limitations. I see it as a triangle of trade-offs: parameters, tokens, and FLOPS (floating point operations). Building an overly large model if you do not have enough data or computational ability may simply make it redundant and useless.

How the model selects the next word is also an important factor; it is called sampling. Chip selects this as the top neglected concept in the sphere of AI, and this revelation really does speak to me now. Sampling approaches like temperature and top-k have a huge effect on the results generated by the model. Want fewer errors? Adjust your sampling.

Once the model has been trained, it is fine-tuned to fit human needs. More often than not, this translates to additional training, which is goal-oriented and where indications of end users are heard. Though there are several benefits, there is a risk of mistreating the performance in some of the domains from time to time. Developers have to balance this.

Though this chapter does not promise to transform you into a foundational model pro, it does provide builders and tinkerers with basic strategies for decision-making.

With the growth of artificial intelligence in day-to-day tools and services, understanding the basics enables to creation of structures that are both creative and reliable, with the needs of the users at the forefront.

This chapter shifted my opinion on working with AI more than anything else. Although we could easily become excited about what they can offer, developers should check whether they accurately provide results, Chip Huyen argues. In this article, we discuss how to evaluate AI systems by taking a closer look at open-ended tools like chatbots and also creative platforms.

Assessment does not merely precede a final step in any process. It has to be used consistently and systematically from the onset to the end when establishing a system. What Chip had believed to be the key obstacle to the adoption of AI — evaluation — resonated with me after diving into this chapter. Outputs of AI systems that are unregulated pose real threats. The risks become apparent upon watching chatbots entertaining questionable guidance or AI tools creating false legal precedents.

What makes evaluation tough? Interestingly, foundation models are meant to function in an unfamiliar manner. Contrary to traditional models, which make a selection among a given set of outcomes, AI systems like the GPT generate unique variations in responses. The fact that many prompts have no single clear answer makes the process of evaluating metrics more complicated.

Chip presents a number of evaluation techniques that address the complexity of open-ended responses:

• Although it is easy to compute accuracy scores, it is best when conducting tasks that have fixed known correct answers.
• Subjective evaluation entails application of human assessors, entailing delays and high costs.
• AI-as-a-judge is an innovative method in which models of AI judges evaluate the quality of other AI generated results. Although it is fast and efficient, questions about possible biases and reliability issues between various AI judges surface.

An important lesson learned is to understand potential failure points in your system. Chip supports architectural changes that make failure mechanisms transparent so that personalised evaluation efforts can be developed.

We should also consider sampling- how models pick a word to suggest next. This aspect is more than a technicality. Although it increases a model’s ability to produce new ideas and options, it may also result in mistakes. Through polishing your sampling processes, you can significantly amplify the reliability of your product.

After reading and going through Chapter 4, it became clear that the reason why AI systems often fail in the real world is that they lack reliable evaluation. There is not much self-deception in her evaluation — the dearth of relevant evaluation is still a significant impediment to AI deployment.

Moving ahead, our fundamental problem is: open-endedness. Unlike standard ML problems with clear right or wrong answers, GPT and other generative models generate unpredictable outputs influenced by their context. This creates a challenge because metrics such as precision or accuracy aren’t sufficient; evaluation is a complicated, multi-layered challenge.

Chip introduces three primary evaluation strategies:

• Exact Evaluation: Accurate indicators like or BLEU score. Such metrics benefit structured tasks, like classification, but do not work so well when it comes to creativity.
• Subjective Evaluation: Human rating of outputs. It may be the preferred approach, but it is very resource-intensive and time-consuming.
• AI-as-a-Judge: Using automated approaches to evaluate the quality of AI-created content. It is helpful and applicable to many situations, where valid criticisms about possible biases can be raised.

One important takeaway for me was to emphasize careful tracking of experiments. We are prone to making changes to prompts or training data, without keeping track of them. Lacking systematic documentation, it will be so easy to encounter major complications later. To know what works Chip highlights the need to log all the variables — iterations of the prompt, rubric revisions, and demographics of users.

Another particularly interesting discovery that I made was: The log-priorities of the tokens generated by the model. Staring at a model’s confidence for individual tokens provides insights regarding its fluency, coherence, and possible truthfulness. I never would have guessed how much worth a logprob could add to a model output evaluation — this realization in and of itself is game-changing for the work that I do.

The topic also includes using the perplexity to rate fluency and the return of traditional NLG metrics such as relevance and faithfulness. However, such approaches are applicable only in special situations. Open-ended tasks frequently require customised scoring metrics that adapt to your very specific application.

A big eye-opener for me: Although helpful, MCQs do not make for an ideal metric to compare generative models. They test recognition, not generation. Still, there are a few research that remain with MMLU-style metrics ignoring the areas where models shine or struggle catastrophically in generation tasks.

Chip emphasizes multi-layered evaluation pipelines. You can utilize naive classifiers for detecting overall performance and then rely on more refined human or machine arbiters for all details. By dividing evaluation data according to user types or input categories, you might discover unanticipated biases or performance problems.

And don’t underestimate hallucination. It’s not random — it arises from models believing generated information; is accurate. In a case study from the book, it is shown how a model (with misinformation) judged a shampoo bottle to be a milk container. Why? It told lies to itself and treated them as actual facts.

Primarily, Chip argues for the evaluation to be embedded at every stage, from model selection, throughout development cycles, or even post-production. Establishing robust sets of annotations for tests should be an integral part of the process, not just nice-to-have frills and not an afterthought.

This chapter reshaped my approach. For AI applications where trust becomes a key factor, evaluation needs to be a continuous process, targeted, and core to our procedures. Measuring it as a QA checklist is a shallow approach: it’s the foundation on which real-world success is built.

Prompt engineering was the very first model adaptation technique I ever discovered — and the one I might underestimate. In chapter 5 of AI Engineering, it turns out prompt engineering is less about crafting witty questions and more about excelling at communicating with smart machines. Chip Huyen leads us on a technical and philosophical odyssey and makes “playing with prompts” an engineering practice.

We begin with the fundamentals: a prompt is a direction to a model. That might be a straightforward question, such as, “What is the capital of France?” or a multi-action assignment like, “Break down the following sales report and summarise its insights in bullet form.” Prompts may involve:

• Task specification (what is to be done),
• Examples of desired input-output behaviour (few-shot),
• Context or previous conversation (particularly for chat interfaces).

What is distinctive about prompt engineering is the fact that it doesn’t modify the model’s weights — it is completely input-centric. This makes it the best and least expensive method to fine-tune a foundation model.

Don’t let its simplicity fool you. Prompting has its nuances. The location of your instructions — beginning or end — can significantly impact performance. Chip has empirical data: models such as GPT-4 respond better to instructions at the start but others like LLaMA-3 might like them at the end.

We also learn to test robustness. If replacing “five” with “5” causes your model’s response to break, it’s not robust. Red flag. More robust models are less fragile to such perturbations to prompts, and so fiddling with prompts is a good proxy for model quality.

Chip believes in approaching prompt engineering scientifically. Keep track of experiments, test variations, and systematically optimise. It’s quite like A/B testing in product development.

One of the most relevant sections of interest here is prompt security. While prompts can also mislead models as much as they direct models, malicious users can cause models to ignore instructions by fooling them through prompt injection attacks. This is most dangerous in environments with multiple users like in finance or customer support. Some defensive methods are:

• Input/output filtering
• Escaping unsafe tokens
• Utilizing system prompts to constrain model behavior
• And more broadly, treating prompts as if they’re code: structured, vetted, and protected.

The chapter also discusses in-context learning:

• Zero-shot prompting
• Few-shot prompting
• Chain-of-Thought (CoT) prompting, which requires the model to reason in a step-by-step

Chip demonstrates tangible benchmarks: by utilising CoT, Gemini Ultra’s MMLU, score was boosted from 83.7% to 90.04%. That’s a testament to how influential structured prompting can be, even when contrasted with fine-tuning.

Ultimately, this chapter transformed the way I perceive prompt engineering. And when executed optimally, it enables us to harness a model’s full power without incurring a single cent on training costs.

I had not yet realised how much context informs the behaviour of AI systems before reading this chapter. Chapter 6 of Chip’s AI Engineering is a technical deep dive into two of the most influential patterns used to scale AI applications: Retrieval-Augmented Generation (RAG) and agentic systems.

Foundation models are strong but forgetful. They are unable to recall document-length text or maintain a track of changing conversation. To solve this problem, we have to equip them with tools to retrieve and engage with related information — welcome to RAG and agents.

RAG alters the paradigm by allowing models to retrieve useful external information prior to generating a response. Rather than providing the model with a behemoth prompt containing all possible data, you can:

• Pull out only the relevant parts using techniques such as vector search.
• Pass it on to the prompt.

The consequence? More relevance and less hallucination.

RAG is analogous to feature engineering for foundation models. While traditional ML had you hand-engineer features, RAG has you engineer the appropriate context. Chip notes that this pattern excels when your app relies on private knowledge bases or domain-specific material.

• An embedding model which transforms documents and queries into vectors.
• A search system (e.g., Weaviate, FAISS) to retrieve the most relevant documents.
• A composer who combines all these into a request to the model.

One interesting real-life example I discovered: a user queries, “Can the Printer-A300 print 100 pages per second?” The system pulls in the manual section with specifications and includes it in the model’s prompt — providing grounded and accurate responses.

Agents: Equipping Models with Tools and Autonomy Agents bring it to the next level. Not merely fetching static information, agents are able to utilize tools, plan and execute actions. They’re how you transition from “static chatbot” to “AI assistant scheduling appointments, checking the weather, and following up.”

Chip refers to agents as models implemented using APIs or plugins — such as web search engines, scheduling applications, or CRM applications. The intrigue lies in the dynamic interactivity. Agents may:

• Think about their actions
• Make Multi-Step Decisions
• Use buffer memory
• Call outsource functions

But it’s not completely rosy. Agents are fragile. They call the wrong tool, don’t plan properly, or delude themselves into taking fictional steps. Managing their own memory is vital. Strategies are:

• FIFO memory: Store the most recent turns only
• Summary Memory: Recap past conversations
• Long-term vector memory: Retrieve by similarity

The architectural considerations are enormous. Your app architecture becomes a modular, dynamic system with layers of search, planning, and decision-making when using RAG and agents.

This chapter made me rethink the architecture of AI. Whether you are designing systems that require reliability, context comprehension, or autonomy, RAG and agents aren’t “nice to have” but are a necessity.

I used to believe finetuning was a matter of having a large GPU and lots of data. That was all blown out of the water by chapter 7 of AI Engineering. Chip Huyen takes us through why and when and how to finetune foundation models — and how to do it without breaking the bank.

The chapter begins with a fundamental fact: you might never have to finetune at all. Prompt engineering and retrieval-augmented generation (RAG) already yield strong customization as it is, and finetuning is more a last resort than a first action. Chip provides a framework to guide you to decide: if your system requires behavioral change greater than prompting and RAG, and if you have good data, finetuning is the optimal choice.

Traditional finetuning finetuned all model weights, which was possible when models were tiny. But when models grew out of control, it became infeasible. Finetuning all parameters of a multi-billion-parameter model takes up too much memory and compute, something most practitioners do not even have.

The industry moved to more parameter-efficient fine-tuning (PEFT) methods instead.

Chip discusses several PEFT approaches, beginning with LoRA (Low-Rank Adaptation).

LoRA is a star among finetuning methods because it:

• Keeps the base model on hold.
• Injects light adapter modules
• Needs fewer resources,
• And enables modular deployment (e.g., changing adapters to accommodate different scenarios).

She even deconstructs the architecture of LoRA and how it differs from partial finetuning when a subset of layers are updated. Surprisingly, LoRA consistently beats partial finetuning on both sample and memory efficiency, but at the expense of a slightly greater inference latency.

This modularity also creates a system referred to as model merging — taking multiple specially fine-tuned adapters and merging them into a single model. This is particularly beneficial to deploy on edge devices or scenarios in which multiple features are to be packaged into a single model.

The Bottleneck Isn’t Finetuning. The real bottleneck is data. There’s a single thing to learn from this chapter: Finetuning is not difficult. Good data is.

Finetuning relies on instruction data to work properly. High-quality and neatly labeled data is costly and time-consuming to collect. This sets up a paradox: you can simply add a LoRA adapter to your model, but if your data aren’t clean, the output will be trash.

Throughout the chapter, Chip highlights trade-offs:

• Parameter efficiency vs. latency
• Data Quality vs. Scale
• Customization versus generalizability

She also discusses quantized training and distillation, and how both of these methods complement the larger toolkit of tuning.

At the end of the chapter, I realized that finetuning is not about brute force anymore. Finetuning is about precision, minimalism, and informed decisions — that’s a mindset any modern AI engineer should adopt.

If I’ve taken anything away from working with AI, it’s this: however advanced your model is, it’s as good as the data. Chapter 8 of AI Engineering by Chip Huyen delves deep into the fundamental reality and demonstrates why dataset engineering is the most underappreciated but essential skill in AI.

As models become commoditized, firms are unable to count on model innovation anymore. Chip maintains that if it contains high-quality domain-specific or proprietary data, your dataset becomes your differentiator. Since compute is readily available and open-source models abound in the market, data is the moat now.

Still, dataset work is unglamorous. Chip is refreshingly blunt about it here: “Data will mostly just be toil, tears, and sweat.” She is correct. Good data work involves tedious and iterative work, but it’s also what distinguishes toy prototypes from solid AI products.

The chapter splits dataset work into three key activities:

• Curation — What data do I require? In how much quantity? Where do I get it? How do I maintain quality?
• Synthesis — Leveraging AI itself to produce annotated examples, particularly when it is too expensive or time-consuming to get it from humans.
• Processing — Cleaning and de-duplicating and formatting data to make it usable.

I was glad to see Chip’s emphasis on dataset life cycle. She discusses how pre-training and post-training call for varying data approaches:

• Pretraining: Emphasizes on breadth (expressed in tokens).
• Posttraining: Emphasizes depth and clarity (measured in examples).

And it’s nonlinear — you’ll constantly go back and forth between curation, synthesis, and cleanup. Chip recommends approaching dataset creation as software development: version control, documentation, reproducibility.

Human vs. Synthetic Data Human-labeled data, particularly instructions and conversation, is still gold. It’s costly, however, Chip estimates a good (prompt, response) pair to cost $10 and thousands to train instruction-following models. No wonder firms like OpenAI hire graduate-degree-carrying professional annotators.

On the other hand, synthetic data (data created by AI itself) is taking hold. Faster, scalable, and inexpensive — but dangerous. Unless it’s carefully filtered, you’re left with self-reinforcing biases or poor quality signals. Nonetheless, lots of start-ups are using it to bootstrap new models successfully.

Another wake-up call: web data is less open than it was before. Chip mentioned earlier in the book how sites such as Reddit and Stack Overflow have imposed restrictions on data access and how copyright wars are intensifying. This has caused firms to enter into licensing arrangements with publishers or dig up internal corpora — emails, contracts, help tickets — to produce private datasets.

She also refers to a chilling trend: the web is filling up with content created by AI. Future models learn from this “echo chamber” and may perform worse as a consequence. Having a human-originated, high-quality dataset may become a luxury most firms are no longer in a position to afford.

I read Chapter 9 and discovered the pulse of production AI: inference optimisation. Not how much it does — how fast and affordably it does it. No consumer will wait five seconds to hear back from a chatbot. No firm will pay $10,000 per day to avoid it. Chip Huyen puts this problem into stark relief and shows a toolkit of methods to make foundation models feasible at scale.

Chip first defines the distinction between training and inference. While training refers to the process of educating a model, inference refers to applying it to make predictions in real time. Most AI engineers work with inference more than training, particularly if you are using pre-trained models.

The inference server executes the model, dispatches user requests, manages hardware allocation, and returns responses. Speed is not the issue — it’s a coordination problem mixing model design, systems engineering, and hardware planning.

Bottlenecks and Metrics Inference are prone to fail because of two fundamental bottlenecks:

• Compute-bound operations: hampered by math-intensive operations (such as matrix multiplication).
• Tasks bound on memory bandwidth: hampered by data transfer between CPU/GPU.

They guide you in selecting the correct hardware or model settings. Chip takes us through latency measurements such as:

• Time to first token
• Time by token
• Total query latency

Understanding which of the following is most important to your user flow is vital. You may accept more verbose responses if the first token appears immediately, say.

Now to the point: how to make inference faster and more affordable.

• Quantization — Lower model precision (e.g., float32 → int8). Saves space, compute and money.
• Distillation — Train a smaller “student” model to approximate a large “teacher.” Faster but a bit less precise.
• Caching — Store results to avoid multiple querying. Straightforward and efficient.
• Batching — Run multiple requests together. Improves GPU utilization but adds waiting time.
• Early stopping — Implement constraints on how many tokens to produce, or when to halt on certain criteria.

They all have trade-offs: you may gain speed at the expense of small performance losses. Chip forces us to strike a balance between latency, price, and quality, particularly on user-facing products.

If a model is too large to fit on a single GPU, model parallelism divides it across devices:

• Tensor parallelism: divides math operations
• Pipeline parallelism: splits model stages
• Split the computation or input by function.

This applies more to teams having their own models. For others who are using APIs (OpenAI and Anthropic), the lesson is to understand what is underneath the hood — so in case it’s necessary to scale, you’ll make informed decisions.

Business Impact Chip ends with the harsh reality: optimisation is not a choice. Inferencing costs increase linearly as a function of usage. Every 10,000 users means a potential daily spend of thousands if you don’t optimise.

What hit me most was her framing: inference is not simply a back-end issue — it’s a product feature. Users experience it. Companies pay for it. And good engineers learn it.

As I wrapped up AI Engineering, Chapter 10 pulled everything together. It’s not just about clever prompts or smart finetuning — it’s about how all these parts interact in a real system. This chapter walks us through the evolving architecture of AI applications and why user feedback is the backbone of iteration and trust.

Building Your AI Stack, One Layer At A Time

Chip starts with the simplest version of an AI app: a user sends a query, the model generates a response, and that’s it. But as any developer knows, this doesn’t scale. Real-world AI apps need guardrails, memory, context injection, and optimization. So the chapter introduces a modular architecture that evolves over time:

• Basic Model API Call — No augmentation or caching.
• Context Construction — Add external knowledge via RAG or tool use.
• Guardrails — Protect against harmful inputs and outputs.
• Routing and Gateways — Support multiple models and APIs.
• Caching — Speed up frequent queries.
• Write Actions and Agent Patterns — Let AI perform actions like booking or writing.

Each addition boosts capability — but also introduces complexity and failure points. Chip stresses the need for observability, with logging and metrics across all layers.

As models get smarter, the risks increase, especially with tools and write permissions. Guardrails are the protective layer:

• Input Guardrails detect and block harmful prompts (e.g., prompt injection).
• Output Guardrails check for toxic or unsafe model outputs.
• PII Handling masks sensitive data before sending it to external APIs.

She also dives into real-world risks: what if a user pastes private info into a prompt? What if an AI agent triggers a bank transfer? These aren’t hypotheticals anymore.

Optimizer AI outputs don’t improve on their own. Feedback is how we learn what’s working and what’s not. But here’s the twist: natural language feedback is easy to give and hard to parse. Chip outlines methods to extract structured signals from conversations, like:

• Explicit thumbs-up/down
• Implicit signals (time spent, follow-up questions)
• Flagging problematic behaviour

She urges us to design feedback systems upfront, not as an afterthought. It should be a core part of your data pipeline and evaluation loop.

This chapter highlights a broader shift — AI engineering is merging with full-stack development. Thanks to APIs like OpenAI’s Node SDK and frameworks like LangChain.js, more frontend developers are entering AI. Those who can build fast, iterate fast, and collect feedback fast are the ones who’ll win.

What really stuck with me?

You don’t need a giant model or deep ML expertise. You need good systems thinking, strong UX, and a feedback loop. That’s the new stack.

I hope this summary gives you a good overview of what to expect from “AI Engineering.” The book taught me valuable skills in AI, especially in understanding the full engineering lifecycle, not just building models. I’m still reading and learning the concepts in this book, but I wanted to share these insights as they’ve already transformed my understanding of the field. Whether you’re a developer, product manager, or just an AI learner, I hope my summary gives you a short explanation for why this book is worth your time.

Building Real AI Systems was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

As you may already know, the Children and Youth Planning Table has Support Teams that work year-round to improve child and youth well-being by leveraging expertise in their specific focus area. The CYPT currently has two active Support Teams:

1. Data, Research, and Evaluation Team (DRE)
2. Communications Action Team (CAT)

CAT was originally created because the CYPT had no dedicated Communications staff. It was a group of communications professionals from CYPT member organizations who advised and did communications work for the CYPT. After the CYPT Communications Coordinator position was created, CAT evolved into a space focused on networking, sharing updates, and professional development.

In Summer 2024, a very similar group to CAT began forming in the community called REACH. They are a community of practice for communications professionals working in the non-profit space. Their meetings are structured the same as our CAT meetings have been – with community updates, networking, and professional development. 

With the new 2024-2027 CYPT Strategic Plan, and the formation of the REACH Communications Group in the community, the CYPT’s Communications Action Team is shifting its focus. CAT will become more action oriented – with the goal of producing work for the CYPT and contributing to the CYPT’s Knowledge Mobilization and Influence efforts. Please stay tuned for updates about what that will look like and how you can get involved.

At this time, Stacey McCormick, the previous Co-Chair of the CAT group, will be stepping out of the role. We would like to send her a huge thank you for all the work she’s done over the years with CAT! Stacey will remain on the CYPT Steering Committee as she is the Co-Chair of CYPT’s Early Years Community of Practice. 

The post CYPT Communications Action Team Update appeared first on Children and Youth Planning Table.

Well why wouldn't it be? They sure as hell don't want to spend the appropriate amount of money to either 1) clean up the Elmira Aquifers to drinking water standards 2) clean up the embedded Uniroyal toxins (DDT, dioxins etc.) in the Canagagigue Creek sediments and soils 3) clean up the sub-surface pig pen that is their site. After a tour of their east side property  Luis and Hadley bulls*itted Sebastian and others stating that nothing flows eastwards off their site. This is despite topographical contours showing off-site eastwards flow from their property onto the Stroh property which Lanxess and GHD have refused to discuss intelligently or honestly.

So the most recent PR crap from Lanxess is a light meal and a plant tour for selected individuals. Some of the individuals invited are woefully out of their depths for the very obvious reasons that they've never seriously applied themselves to studying the issues in depth. Many have known for decades that their bread is buttered by Woolwich Township blind loyalty hence what need is there for them to actually know and understand the facts? Others such as Pat Mclean no longer even live in Elmira or Woolwich Township but continue to attend more to socialize than anything else. Seriously though she never did have a serious grasp on the details as she left the reading of technical reports to others such as myself.

Others are attending and sucking up mostly to get appointed to TRAC or whatever the next iteration of a tame, deferential "citizens'" committee will turn out to be. All in all more of the same as talk is so much cheaper than cleanup and always has been. 

Thankfully I was not invited to their dog and pony show so I don't need to formally tell them to p** off. If and when they are dragged kicking and screaming to real discussions/negotiations then maybe, just maybe depending on all the circumstances and conditions I might have an interest. Until then just like Vladimir Putin they are merely liars and obfuscators wasting everybody's time and resources for their own purposes.

♦This image is entitled MRSS Year 12 Steiner last day 2007 by Gavin Anderson. It is used in accordance with his Creative Commons license.

They still have school assemblies with visiting performers these days, don't they? I know they have author visits, and I know there are public safety mesages, talent shows, school plays and general seasonal celebrations, but were they like some of the presentations I saw in my childhood?

I remember one presentation in particular where we were hauled down to the gymnasium at Lord Lansdowne Public School and treated to a story about gas -- natural gas, to be exact. It featured a mad scientist (white coat, funny hair, glasses, the lot) in his mad laboratory, working with natural gas. And by some science magic, he manages to resurrect a dinosaur from the fossil fuel, performed by another performer in a large felt-covered suit (which must have been extremely hot under the lights).

The presentation went over all of the safety issues around natural gas, including how to smell a leak and what to do then, all told in such a way as to make the kids respect, but not fear, natural gas. They end with a song and dance number where the nerdy mad scientist dances comically badly.

Looking back, one thing that struck me about this presentation is that they went beyond safety to talk about the future of gas. The mad scientist pulled down a projection map of Canada which showed where our active gas reserves were, and he confidently told us that there was enough gas here (in and around the Rockies and in Ontario) to last us thirty years.

"Ahem," says the dinosaur. "I don't know about you, but I think these kids here plan to be around for longer than thirty years."

"Ah, well, you see," said the mad scientist, and he proceeds to highlight possible but untapped resources through the maritimes, and across the Arctic, boasting, "these should last us well into the next century!"

So...

Up to this point, this presentation was like the other public safety presentations where we were taught to respect but not fear a particular item, like police officers talking about drugs (and hauling out the incredibly creepy Blinky, the doe-eyed police car), and how prescription drugs were okay but you still had to be careful around them, or Hydro officers telling us to watch out for downed power lines after storms and not play with them like Indiana Jones and his whip. But none of the other presentations got into the subject of the future of Canada's resources.

Which makes me wonder who actually put on this presentation. A local gas company would be expected to tell kids about how to smell a gas leak and what to do if you do, but I doubt they'd have any interest in encouraging kids to support gas extraction in the Arctic. That smells like an industry public relations organization.

Well, as I'm still remembering this almost forty years down the line, all I can say is that their PR department got their money's worth.

(UPDATE): Doing a quick bit of research, I see that the gas company Enbridge is quite happy to speak to schools in this day and age but, again, more about being safe around gas rather than where to drill for it.

I’ve always worked with JavaScript, mostly focusing on front-end development, where everything is dynamic and flexible. But I knew that if I wanted to level up as a developer, I needed to understand object-oriented programming (OOP).

Unlike JavaScript, where you can get away with a more functional or prototype-based approach, OOP is a core part of languages like Kotlin/Java. It helps structure code better, making it more scalable and maintainable — something I realized I couldn’t ignore.

So, I decided to learn Kotlin with Jetpack Compose. I took a course, followed the videos, and practiced by building small projects. But I didn’t just want to copy tutorials, I wanted to build something real. I had a specific need for an app that didn’t seem to exist, so I figured, why not make it myself? That’s when the real learning began.

When I decided to build my own Android app, it all started with a problem I needed to solve. I’m currently learning Spanish, and while there are plenty of language-learning apps out there, none of them worked the way I wanted.

Spanish has a lot of irregularities, verbs that don’t follow patterns, prepositions that change meaning depending on context, and reflexive pronouns that can be confusing. I needed a tool that would help me organize these rules in a way that made sense to me. Something that would let me break things down, add explanations, include conjugation tables, and store examples that I could easily reference whenever I needed.

Since I couldn’t find an app that did exactly this, I figured — why not build one myself? That’s why I decided to create my own custom Spanish-learning app.

Once I knew what I wanted to build, the next step was figuring out how to organize everything. I didn’t want a messy collection of notes. I needed a structured system that made studying easier.

I decided to separate topics into clear categories. For example, the Past Tense section wouldn’t just be one big list; it would be divided into Past Simple, Past Progressive, and Past Perfect, each with its own explanations, conjugation tables, and example sentences. I applied the same logic to other grammar points, grouping them based on their rules and usage. This way, I could quickly find what I needed without scrolling through endless notes.

To ensure everything was organized and easy to manage, I began by creating data classes in Kotlin. These classes served as the blueprint for my desired data structure, defining the rules for how each section (like verb tenses, conjugations, and examples) would be organized.

Why was this step important?

Well, using data classes gave me a clear structure from the start. Instead of just working with random variables or arrays, I could create well-defined objects that represented specific parts of my language data. It made my code much cleaner and more maintainable.

Once I had my data classes set up, I could easily import them into my app and use them to form the actual content. This meant I could follow the predefined structure, ensuring that every section of the app adhered to the same format. It made adding, updating, and managing data a lot easier as the project grew, and it gave the app a consistent and logical flow that was critical for my study goals.

With the structure and data in place, the next step was building the actual components that would make up the app’s interface. One of the key things I focused on was creating reusable components — UI elements that could be used across multiple sections of the app, making the code more efficient and easier to maintain.

For example, I built a conjugation table UI. This component could take conjugation data from any topic — whether it was for the past tense, future tense, or any other section — and display it accordingly. By designing the table to accept parameters (based on my predefined data classes), I could easily plug in different data for each topic.

Once the reusable components were set up, it was time to build out the individual pages of the app. I used the components I had already created, along with their corresponding data files, to construct each page.

For each topic, I pulled in the relevant data constructed from the predefined data classes and displayed it using the reusable UI components. For example, the conjugation table component would pull the conjugation data for verbs in the past tense and display it on the corresponding screen along with explanations, key points, and examples.

This allowed me to make quick updates. If I needed to tweak the UI or add new content, I could adjust the components or data files, and the changes would automatically reflect across all the pages that used them.

At this point, the app was starting to come together, and I could see how the components and data were working together to form something I could actually use.

Once the individual pages were created, the next challenge was establishing navigation between screens. I wanted users to easily transition from one topic to another without feeling lost or confused.

To start, I created a main page that displayed all the topics, organized by categories. From this page, users can click on any category and be taken to the corresponding page. To make navigation even more intuitive, I added a back button on each page, allowing users to return to the main page or the previous screen they were on, giving them full control over their study flow.

Now, whenever I need to add a new topic or page, I can simply define a new route, and the app will take care of the rest. This was a key step in transforming a collection of isolated screens into a smooth, interconnected app.

To make the learning experience even more engaging, I added a quiz page where users can test their skills. This page is also divided into categories, allowing users to choose which area they want to be quizzed on, whether it’s verb conjugations, prepositions, or other grammar topics. The app then selects random quiz questions within the chosen category to test their knowledge.

The quiz consists of multiple-choice questions, and as users progress through the questions, a progress bar shows how much they’ve completed. Once all the questions are answered, the app displays the result, giving users a sense of accomplishment and a clear view of their performance.

Each question is interactive. Once you select an answer, the app immediately informs you whether it’s correct or not. If you answer incorrectly, the app provides an explanation to help you understand the reason behind the correct answer. This feature not only reinforces your learning but also offers instant feedback, assisting you in improving as you progress. It’s a fun and interactive way to ensure that what you’re learning truly sticks!

By following the same principles of clear data structures, reusable components, and separate screens for each section, I was able to integrate the quiz seamlessly into the app. This kept the overall flow and design uniform, making it easy to manage and update the quiz content as needed.

Looking back, I can honestly say this project was a great experience for me. Not only did it reinforce my understanding of Kotlin, but it also taught me how to structure an app with a clean, logical layout. The most rewarding part is knowing that, even if I come back to this project months later, I’ll still be able to understand what’s going on and make updates or improvements without feeling lost.

I’m really happy with how the app turned out. Throughout the process, I’ve learned a ton about app development, especially when it comes to creating reusable components, organizing data efficiently, and using Jetpack Compose to build a smooth, functional interface. There’s still a lot I can add and improve, and I’m excited to continue developing the app and sharing it with others. It’s been a valuable learning experience, and I’m looking forward to seeing where I can take it next.

Building an Android App from Scratch — Here’s What Happened was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

TLDR; Canada’s Q1 2025 CVCA data confirm a seed-stage freeze — only 116 deals and a spike in venture debt — validating warnings I’ve raised since 2021 that momentum and trust are eroding. Some founders still raise off-radar via U.S. safes and angel syndicates, but the bleak headline dampens risk and drains talent. Early seed capital is essential for validating ideas, keeping IP in Canada, and kick-starting the growth loop. The remedy: revive micro-tickets from angels, form operator flash-syndicates, launch corporate founder-in-residence seed pots, pilot first-loss guarantees, and double-down on trust-driven demo communities — pouring concrete now so we can hit 250+ seed cheques per quarter by 2026.

BetaKit recap of the CVCA Q1 2025 report hit hard: $1.26 billion spread across just 116 venture deals, the lightest quarter in five years; seed‑stage activity slumped to its lowest count since 2020, and pre‑seed dollars are back at 2021 troughs . Founders plugged gaps with $283 million in venture debt across only 14 deals — triple a typical Q1 . CVCA CEO Kim Furlong didn’t mince words: “A weakening at the foundation threatens the innovation economy we’ve worked hard to build.”

Those cracks didn’t appear overnight; this blog has mapped them since early 2021.

What we know is most important: momentum + trust. Fundraising is a trailing indicator of health in an ecosystem. Once you notice it is happening you are many years behind the problem.

After years of seeing there was a decline and slowly seeing the community in Waterloo start to bounce back.

“When builders feel seen and supported, they create the momentum everything else depends on. That’s how we win.” — Builders Are Everywhere, Apr 6 2025

“Progress isn’t about having the perfect plan; it’s about building momentum. And momentum starts with action.” — Just Do the Thing, Nov 25 2024

“Trust is the invisible currency that makes everything possible. And yet, like actual currency, it can be printed irresponsibly, spent recklessly, and devalued over time.” — Trust: The Currency That Holds Everything Together, Mar 13 2025

Momentum + trust is the only durable fly‑wheel in an ecosystem. Remove either and deal flow stalls — as Q1’s numbers confirm. The trend line says it started years ago which suggests that trust was lost before covid.

Lost in the frenzy of an economy fueled by low interest rates was that the slow down started before this time frame. Likely started around 2016 when BDC (the largest CDN investor in early stage at the time) changed its focus.

2021 — Parts of Canada’s Tech Scene Aren’t Working…

“Programs and milestones don’t kill startups, a lack of customer demand kills startups.”

“It is time to rethink the infrastructure and build for global scale.”

2022 — Early‑Stage Funding Is Missing in Canada

“Canada lacks a vibrant early‑stage funding network. Unless you have friends and family with wealth, raising the first $25‑$750 k is extremely difficult.”

“There always exists an opportunity to focus on early founders — mentor and fund them, then match their momentum with pre‑seed capital.”

We said the sub‑$2 million layer would decide the whole house’s stability. Q1 2025 is the tremor we predicted.

The CVCA pulls primarily from its member firms and disclosed rounds. That leaves blind spots:

• U.S.‑led safes and uncapped notes. Founders often close these quietly, especially when the lead isn’t Canadian.
• Angel‑only syndicates and rolling funds that don’t report to the CVCA.
• Community crowdfunds and alternative financing that sit outside traditional VC classification.

So yes — it’s possible the number of Canadians raising seed rounds hasn’t fallen as sharply; the deals may simply be happening off the CVCA radar. But two facts remain:

1. Signals drive behaviour. When the headline index sags, risk‑tolerant capital becomes even more cautious.
2. Domestic ownership still matters. Whether the money comes from Palo Alto or Main Street, we need enough Canadian patrons at the table to keep IP, decision‑making, and talent anchored here.

Until we collect better, broader data — or founders make a habit of announcing every cheque — we should treat the CVCA dip as a fire alarm, not a footnote.

1. Validates the leap of faith. A local $500 k seed round tells both talent and the founder that the idea is worth quitting for.
2. Locks IP at home. Clearpath, Avidbots, and Faire anchored R&D in Waterloo because early cheques arrived locally.
3. Creates the feedback loop. Seed investors double as first customers, evangelists, and connectors. Break that loop and Series A happens elsewhere.

This is what could help turn things around quickly and meet the ecosystem where it now. Ready to take off.

The above isn’t meant to be perscriptive. The details are less important than simply shifting focus back to those who build and away from instititonal controls.

If things are fixed, it might look like this. Assuming the investments are coming from CVCA members.

Back in 2022 I wrote, “Investing in the early stage will pay off when the economic cycle shifts.” The cycle has now shifted — against us. Maybe some of the action has simply gone underground, but the optics still hurt founders raising their first cheque today.

The optics increase the founder drain.

The repair kit has been sitting on the shelf since 2021. It’s time to mix the concrete, steady the forms, and pour.

Momentum over permission — always.

From Seed Freeze to Solid Footing: Canada’s Moment to Pour Concrete was originally published in Who You Calling a Jesse? on Medium, where people are continuing the conversation by highlighting and responding to this story.

I know we are way further into the year for me to be saying “Happy New Year,” but I wanted to pick up where I left off last year with my writing on Medium and reflect a little bit on the previous year.

Last year was all about learning, trying, and figuring things out as I first started as a beginner in technical writing. I didn’t come from a design background or have years of writing experience. But I had some level of curiosity that pushed me into a lot of discovery. From taking the technical writing Course on Coursera to my mentor saying something I didn’t expect: “Take the Google UX Design course.” Which was worth it!

At the time, I did not see the relation between the two; they felt like two completely different worlds. But now, as I keep writing and learning, I’m noticing something: the more I dive into technical writing, the more UX principles are quietly guiding me. From how I structure information to how I think about the reader’s experience, UX is showing up everywhere.

So here’s what I’ve learned so far, and how UX is helping me become a better writer.

Before my writing journey, “design” to me meant colors, shapes, maybe a fancy app interface. Writing, on the other hand, seemed like the thing you’d sprinkle on top.

But over time, with the help of my mentor, who helped me with my practice writing, I got to realise that sometimes, the words are the design. They guide the user. They shape the experience.

I wasn’t just writing, I was trying to build a map that someone could follow, without getting lost.

Every sentence became a tiny decision:

• Is this too technical?
• Would I understand this if I were new?
• Is there a better way to say this?

One of my early drafts looked great to me. I was proud of it. I had spent hours researching, drafting, and editing. And of course, making use of big fancy words. I felt accomplished. But then a friend read it and said, “I got lost in the middle. What were you trying to say exactly?” or “I do not understand the meaning of this word.”

The defeat that came after that, I dreaded the feeling. My mentor had a look at my piece and left excellent comments for me to follow through and make the article better. Which I later did and ended up publishing it here on Medium.

It’s easy to assume that once we’ve explained something, we’ve done our job. But good writing isn’t just about saying the right things. It’s about saying them in a way that makes someone else get it, without them needing to try so hard.

So I tried a new approach and started doing things differently:

• I cut out all the techy-sounding words I thought made me sound “smart.”
• I broke paragraphs up, added white space, and used bullet points.
• I read my work out loud to hear where it stumbled.
• Allowed my mentor to review it and give feedback or criticism.

I didn’t understand how powerful empathy could be until I needed it myself.

I remember thinking that writing a “how-to guide” would be a simple task on using the features for my imaginary app. Sounded easy! until I sat down to actually do it.

I stared at the screen, blinking. Do I start with a welcome message? Should I explain what each of the features does, or just tell the user how to use the app? Am I assuming too much? Too little? What if they do not understand me at all?

Those questions taught me my first real UX lesson:
Good writing isn’t all about accuracy. It’s about seeing the person on the other side of the screen.

I remembered what it felt like to be stuck with unclear instructions, flipping between tabs, and watching YouTube tutorials just to figure out what a button did. That confusion, that frustration, that was the teacher.

And now, every time I write, I do a little mental time travel. I go back to the girl who just wanted someone to walk her through things like a friend, not overwhelm her with big words and mismatched steps.

That’s what “thinking like a user” means to me now. It’s not a UX buzzword. It’s a quiet, daily practice to keep writing as simply as possible.

This beginner mindset is definitely something I want to keep harnessing, even as I grow. Because the more experienced you become, the easier it is to forget how it feels to be lost. And in technical writing, forgetting that is the quickest way to lose your reader.

UX isn’t just how things look — it’s how they flow.

You can write a guide that has all the right information, but if it’s out of order or hard to follow, people will still struggle.

Flow matters. It’s the difference between a smooth ride and a bumpy one. Between a reader moving effortlessly through your guide… or giving up halfway in pure frustration.

Good flow means:

• Putting steps in the right order
• Grouping related ideas together
• Answering the next question before the reader even asks

I started paying attention to when I read, too. I noticed when I felt confused, or when I had to scroll back up. Or when frustration boiled over to giving up.

Now, when I write, I try to walk through it like a first-time user would. Does it feel natural? Do things build on each other? Or does it feel like there are missing steps somewhere?

I’m learning that flow is not just for designers or developers; it’s something every writer, especially technical writers, needs to care about because flow builds trust. The readers' trust. If a reader can follow your writing easily, they trust you. They trust the product. They stay.

What Technical Writing is Teaching Me About UX was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

I suspect that it was a step by step process over at least the last 25 to 30 years, maybe longer. This morning I sent a pretty gentle e-mail to Terry Pender of the Waterloo Region Record basically asking him to either confirm his claim about fighting between workers, companies and the province of Ontario regarding cancers caused by exposure to Agent Orange in Elmira or to publicly correct it if it's totally false. I also explained the ramifications of the situation regarding the decades delayed remediation of the downstream Canagagigue Creek between the former Uniroyal Chemical (now Lanxess Canada) and the Grand River.

Is it possible that after 36 years of lies, obfuscations, deceit and delay from almost everybody that I may have lost my patience? I should certainly hope so. The e-mail to Mr. Pender included three names of various editors and managers listed on page two of the Record as well as the e-mail address of the National Newsmedia Council. This last one is a self-regulatory body who allegedly investigates themselves when there is a complaint. The whole process has the odour of self-serving protectionism from legitimate complaints and if so it seems to be the future of almost all public services. 

I also sent copies of my complaints to the Woolwich Observer and a couple of other media outlets. I was about to send it on to the Waterloo Chronicle until I recognized a name common between them and the Record. Other copies have gone elsewhere and time will tell although I repeat it shouldn't be necessary to file a complaint, get little to no basic feedback at all and then have to expand one's search for accountability. 

After all do we the public not have the right to see accurate information in our media and if human error does occur not have the right to insist upon public corrections thereof? Maybe not.

♦The Grand Indian Food Fair 2025
Saturday May 24 and Sunday May 25
12:00 PM – 10:00 PM
FREE Entry
Kitchener City Hall – 200 King St. W, Kitchener

Enjoy Indian street food, cultural dance, music, shopping & a IPL T20 Cricket Final watch party on the big screen! Indulge your taste buds with a mouthwatering array of Indian delicacies, from spicy Indian street foods to rich and aromatic curries. Immerse yourself in the vibrant culture with traditional dances like Garba and Bhangra, and sway to the beats of our energetic DJ. Explore our arts and craft store for unique treasures and souvenirs, and experience the essence of India’s rich artistic heritage. With live music, dance performances, and a lively atmosphere, the Indian Food Carnival promises a weekend of fun, flavor, and festivities for everyone!

Click here for more info

♦(dis)Ability: An Art Showcase – Meet the Artists
Tuesday, May 20
6:30 PM – 8:00 PM
FREE
Central Library – 85 Queen St. N, Kitchener

Join us to meet the artists who have submitted artwork for this exhibit held in the Central Library Art Gallery, lower level. The exhibit features work by people of all ages with lived experience with disability, residing in or with a connection to Waterloo Region. It has been coordinated by the library’s volunteer based Accessibility Advisory Group. Participants were invited to submit up to three pieces each.

Click here for more info

♦Plant Swap
Saturday, May 24
10:00 AM – 12:00 PM
FREE
Pioneer Park Library – 150 Pioneer Park Dr. Kitchener

Take a plant, leaf a plant! Come join in the fun and meet some of your gardening neighbours. We will have space for you to chat about plants with others who enjoy gardening. An avid local gardener, Evelyn, will be here to answer some of your questions about gardening.

Click here for more info

♦Queer Conscious Moving Bodies♦
Tuesday, May 20
7:00 PM – 8:30 PM
FREE
The Registry Theatre – 122 Frederick St. Kitchener

This free movement workshop explores Queer, Trans, and Gender Non-Conforming embodiment through curiosity, creativity, and community. Using movement, sound, writing, image-making, and relational exercises, the practice fosters agency, presence, and celebration. The approach centers both individual and collective experience, supporting deeper embodiment and community connection.

Click here for more info

Spring Coffee Club National Tea Day
Wednesday, May 21
1:00 PM – 3:00 PM
$38
LEG Up! Classroom 109 Ottawa Street South, Unit D

Come for tea and let’s have a tea party. Enjoy some different teas, treats and listen to some stories about tea.

Click here for more info

Campfire Concerts
Wednesday, May 21
6:00 PM – 8:00 PM
FREE
Waterloo Public Square – 75 King St. S, Waterloo

Grab a seat on a nearby patio or gather around the fire in Waterloo Public Square and listen to live music performed by different local musicians. Bring a lawn chair if you want to cozy up close to the fire. This event will be weather permitting. You can follow Create Waterloo on social media for cancellation information.

Click here for more info

“Daisy” KW Humane Society Fundraiser
Thursday, May 22
6:30 PM – 8:30 PM
$45
Kitchener Waterloo Humane Society – 250 Riverbend Dr. Kitchener

Love animals? Then join the Kitchener Waterloo Humane Society for a fun time of painting “Daisy”. $10 from each ticket is donated to the shelter! Create your own masterpiece, no experience necessary. This painting comes with a traceable if you like. They will take you through step by step.  Come a little early and see some cute cuddly friends.

Click here for more info

Dance Mix 90s Vol. 7
Friday, May 23
8:00 PM – 1:00 AM
$20 admission
TheMuseum – 10 King St. W, Kitchener

​​Dance Mix 90’s is back for Volume 7! Join us for a slammin’ night of nostalgia, featuring an old school Music Video Dance, 90’s trivia, throwback activities, themed drinks & more THEMUSEUM Members receive a 10% discount.

Click here for more info

♦

Free Bread Pickup
Tuesdays
8:30 AM – 12:00 PM
The Hangout at Grant’s Cafe – 99 Ottawa St. S, Kitchener
Click here for more info

Board Game and Card Night
Thursdays
6:30 PM – 8:00 PM
FREE
Pioneer Park Library – 150 Pioneer Park Dr. Kitchener
Click here for more info

Open Space
Fridays
10:00AM – 12:00 PM
FREE
Conestoga Gall Food Court – 550 King St. N, Waterloo
Click here for more info

The post May 14, 2025: What’s Happening in Your Neighbourhood? appeared first on KW Habilitation.

What: Why Hope Matters: The Practice of Evidence- Based Hope in a World of Climate Doom
When: 7:00pm to 9:00pm on Thursday 15 May 2025
Where: Online Webinar
Register: actionnetwork.org/events/how-to-be-hopeful-in-a-world-of-climate-doom
Website: ♦Nith Valley Ecoboosters
E-mail: nvecoboosters@gmail.com

When it comes to the ongoing fight against climate change, it is easy to lose heart as stories about the disastrous impacts of global warming seem to make headlines every day. To flip the script on this “climate fatigue”, the Nith Valley EcoBoosters are hosting a free webinar on May 15th at 7:00 p.m.

Why Hope Matters: The Practice of Evidence- Based Hope in a World of Climate Doom, will feature guest speaker Dr. Elin Kelsey, an international thought leader, scholar and science communicator on climate emotions and the role of evidence-based hope, and the author of ♦Hope Matters: Why Changing the Way We Think is Critical to Solving the Environmental Crisis.  Dr. Kelsey will share positive climate success stories and where to find them, and discuss how they help us to be more enthusiastic, optimistic and hopeful with respect to our capacity to tackle the climate crisis.

You can register for this free webinar at Action Network  or Nith Valley Ecoboosters

♦Elin KelseyA leader in the solutions-focused environmental and climate justice
movement.

Award-winning author of more than a dozen books for children and adults including: HOPE MATTERS: Why Changing the Way We Think is Critical to Solving the Environmental Crisis.

• Learn about evidence-based hope, a concept based on real progress and effective solutions that shifts the focus from fear to empowerment. While there are reasons to be anxious about the climate crisis, there are also reasons to be optimistic due to meaningful accomplishments.
• Q&A will follow Dr. Kelsey’s presentation.

Centre In The Square was bursting with rhythm, colour, and pure excitement as SAMAJAM hit the stage for two unforgettable performances as part of our Education Series. More than 3,500 elementary students from across Waterloo Region filled the theatre and became part of the show in a fully interactive musical experience.

From the moment the lights went up, students were clapping, drumming, singing, and dancing along. With instruments in hand and smiles on their faces, the young audience helped bring the performance to life in true SAMAJAM fashion.

For many, this was their first time attending a live performance in a professional theatre setting. The energy in the room was electric, and the joy was contagious. Teachers, chaperones, and Centre staff alike were thrilled to see the theatre buzzing with enthusiasm and participation.

Thank you to all the schools that joined us and helped make this event such a vibrant success. Moments like these remind us why live arts matter — they inspire, educate, and bring communities together.

Check out some highlights from the shows below!

Learn more about Centre In The Square’s Education Series

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The world faces many challenges so the Early Learning and Literacy Alliance (ELLA) decided to reach across the pond in professional discourse to exchange ideas, access practices and shared visions most especially for little humans and their caregivers. In early May our Activator, Kathilee, attended a UK conference, followed by a number of meetings across the country.

With an eye towards Dr. Li’s Generator provocation ELLA is in pursuit of considering ‘universality without uniformity’. They want to start a conversation to compare purpose, pedagogical assumptions, assess ideology, compare research and practices around policy, programs, curriculum and services.

They hope that CYPT Voting Members will join them for one, or all of the ‘Breakfast with Brits – Connecting with Canucks’ virtual morning sessions starting May 27th. View this invitation/poster for details.

When it comes to little humans and those who care for them ELLA wants to ask:

• What do our two countries have in common?
• What systems and practices work well, and which are not working?
• What lessons are we learning?
• How might we consider structures, systems and accomplishments of other nations to assess the process and practicality of our systems and structures

And they are eager to co-learn and co-create responses together so be sure to bring your questions/ideas to the sessions.

Register here

The post You’re Invited: ELLA’s “Breakfast with Brits” Series appeared first on Children and Youth Planning Table.

That is quite a strategy. I wonder if there actually is a book titled "Playing with Democracy and how to Fool most of the People Most of the Time". It's all about lying constantly to the public while either buying, mollifying or intimidating the media. You want the media on side and if that means destroying them first well all good and well. The media 36 years ago appropriately were all over Uniroyal Chemical and the Ontario Ministry of Environment. They made them pay for their decades of "trust us, we know what we are doing". Alleged state of the art waste handling plus following all provincial rules/legislation were their shields from public scrutiny and or condemnation. In fact it all turned out to be a fantasy. The company basically threw their weight around in a small community and no one wanted to oppose them including our authorities who were elected and paid to protect all of us, not just the wealthy and influential.

Now I will say that the efforts since the 1989 Elmira Crisis have been considerably increased. Not only have they included Pump & Treat remediation but also fake Public Consultation. The Township literally took over that with the aid of Pat Mclean and Susan Bryant and my fellow twits on UPAC went along with it with the exception Of Dr. Henry Regier and Esther Thur. Even then mind you the Township made sure they had two councillors on UPAC (Grace Sudden & Quentin Martin ).  

This is what Democracy has become. Rule by the smoothest liars.  Make sure that the party anoints only those with the "right stuff" to begin with. That right stuff could be a penchant for believing that the ends justify the means or it could be that they are "true believers" in the Party's ideology. At the very worst the two ruling party's don't want candidates who are likely to leave them mid term if they become disillusioned with the Party. Other than that they want candidates who are motivated by being in the public eye and who know how to behave thusly. There are of course exceptions not only in the Party's least likely to form a government but also occasionally in the Liberals or P.C.s.

Pachepsky, Y. A., Yakirevich, A., Widmer, J., Stocker, M., Hill, R. L., Coffin, A., & Dunn, L. (2024). Modeling fate and transport of E. coli in a small watershed with grazing lands around a pond. agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1622936

“The non-calibrated [HydroGeoSphere] model could tolerably simulate E. coli concentration patterns and time of peak events in most of the pond’s sampling locations.”

Pachepsky, Y. A., Yakirevich, A., Widmer, J., Stocker, M., Hill, R. L., Coffin, A., & Dunn, L. (2024). Modeling fate and transport of E. coli in a small watershed with grazing lands around a pond. agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1622936

CLICK HERE TO VIEW THE FULL POSTER.

Summary:

This research investigates the fate and transport of E. coli in a small watershed with grazing lands surrounding a pond, using HydroGeoSphere (HGS) to develop a mechanistic numerical model. Understanding how E. coli moves through surface and subsurface water systems is crucial for managing microbial contamination risks in agricultural watersheds, where livestock activities can significantly impact water quality.

The study was conducted in a 0.45 km² watershed in Georgia, USA, which is used for cow grazing. Researchers collected data from July 2021 to October 2023, monitoring E. coli concentrations in cowpats and pond water. Observations included cow movements and time spent in the pond, as these behaviors directly influence microbial loading into the water system. By integrating real-world data with numerical simulations, the study examined E. coli release, transport, and inactivation under varying environmental conditions.

HGS played a central role in modelling the watershed system, utilizing its fully integrated surface-subsurface flow capabilities. The model incorporated the Richards equation for three-dimensional transient subsurface flow and a two-dimensional diffusive wave equation for surface water flow. Microbial transport was simulated through advection-dispersion equations, incorporating linear sorption and temperature-dependent inactivation. The researchers also implemented boundary conditions reflecting precipitation, evapotranspiration, and bacterial loading from cowpats, ensuring a realistic representation of watershed hydrology and microbial dynamics.

The results indicated that the non-calibrated model effectively captured E. coli concentration patterns and peak events in most sampling locations. However, seasonal variations affected model performance, with discrepancies noted during autumn months. One key finding was that direct E. coli loading from cows in the pond was two orders of magnitude greater than contributions from surface runoff, highlighting the significant role of livestock behavior in microbial contamination. Simulated scenarios demonstrated that E. coli concentrations in the pond varied based on hydrological conditions, microbial decay rates, and bacterial loading from grazing areas.

By leveraging HGS’s advanced modelling capabilities, the study provides critical insights into microbial contamination processes in agricultural watersheds. The research underscores the importance of accounting for both surface and subsurface transport mechanisms when assessing microbial water quality risks. These findings can inform watershed management strategies, helping to develop targeted interventions that minimize microbial contamination from livestock and improve water quality in similar environments.

Research Motivation:

Grazing lands are sources of fecal microorganisms that can reach various water bodies, impacting their quality and adversely affecting their potential uses. Water runoff during and after rainfall events is a key factor in microbial transport, carrying animal waste from pastures into water sources used for irrigation and recreation. Public health concerns regarding the fate and transport of pathogenic microorganisms, as well as indicator organisms like Escherichia coli, highlight the importance of understanding microbial contamination in these systems.

Ponds are essential water sources in rural agricultural environments, with an estimated 2.5 to 4 million ponds used in the United States for irrigation, recreation, livestock watering, and post-harvest processing. Cattle ponds, in particular, serve as drinking and cooling stations for livestock during hot days, ensuring a perennial water supply. The microbiological quality of these water bodies is a critical concern since they are directly linked to animal drinking water and irrigation. Contaminated water raises concerns about potential microbial exposure for both livestock and crops, yet the microbial quality of cattle ponds and the factors influencing it remain poorly understood.

Despite the importance of microbial water quality in agricultural ponds, little attention has been given to modelling these systems. Mechanistic mathematical modelling provides a valuable tool for predicting surface water quality and assessing various sources of environmental contamination. Given the complexity of hydrogeological and hydrochemical processes, the researchers selected HydroGeoSphere (HGS) as the foundation for the site-specific model. HGS employs a fully coupled numerical approach, allowing for the simultaneous simulation of surface and variably saturated subsurface flow, solute transport, and heat transfer.

The results of this work provide valuable insights for consultants and environmental management professionals. They demonstrate an effective approach for obtaining moderately accurate forecasts of microbial water quality in cattle ponds without requiring extensive and resource-intensive data collection for model calibration.

Abstract:

Microbial contamination of surface water is a concern for public health. Grazing lands are sources of fecal microorganisms that can infect water bodies, impact water quality, and adversely affect potential water use. Water runoff during and after rainfall events are essential factors causing microbial migration from animal waste on pastures. Understanding the mechanisms of microbial transport with surface/subsurface flow is imperative to predict surface water contamination and to assign management strategies.

This research aimed to develop and test a comprehensive numerical model to simulate watershed-scale surface/subsurface water flow, bacteria release from cowpats, their fate, and transport to a pond. Accounting for the complexity of hydrogeological and microbiological processes, we used components of the HydroGeoSphere (HGS) software as a basis for our model. The Richards equation simulates three-dimensional transient subsurface flow in a variably saturated porous medium. The two-dimensional depth-averaged diffusive wave equation describes surface water flow. The subsurface and surface flow equations are fully coupled. Microbial transport is described by 3D and 2D coupled advection-dispersion equations in the subsurface and surface, respectively. Linear sorption and inactivation of bacteria are taken into account.

The model was used to simulate bacterial transport at a small watershed (area ~0.45 km2) within a commercial pond in the Tifton (Georgia, USA) area. The pond was extensively monitored for three years. The bacteria concentrations remained at relatively high levels throughout the study. Simulations accounted for the possible effects of the extended lag phase, concentration-dependent E. coli die-off in waters with high dissolved organic matter content, and the presence of indigenous E. coli within the clayey bottom sediment functioning as a satisfactory bacterium habitat.

CLICK HERE TO VIEW THE FULL POSTER.

What’s crime like in Waterloo Region? How do Kitchener, Cambridge and Waterloo compare?

As an agent it’s expected that I’d have a solid understanding of the neighbourhoods I serve, and I do. This comes from experience and also from paying attention to the news. Living in the area for more than 50 years really helps too.

But I don’t know everything and it would be arrogant to pretend otherwise. For instance, I’ve never lived in Cambridge. Instead I spent half my life in Kitchener and half in Waterloo.

Knowing about crime in Waterloo Region is part of my job description. My buyers expect that. And hard data is always better than anecdotal stories and gut feelings that can be vague and even incorrect.

What if I told you that I have access to that data, and you do as well? If you were looking for a home or somewhere to rent, having a map of criminality would be a pretty handy thing to have. The Waterloo Regional Police Service provides exactly that.

Their ‘crime map’ tool gives access to historic data going back a little over a decade. Here’s the Crime Map link on the Waterloo Regional Police Service statistics page. You can select from any number of different crimes (and service calls) and input up to six of them simultaneously.

The mapping tool displays occurrences across the 28 patrol zones in Waterloo Region that the Waterloo Regional Police Service covers.

The map used to show various shades of blue depending on the frequency of reporting which was quite easy to visualize. But sometime since my last update of this post, the map now shows three shades of gray for high, medium and low levels of crime, and it’s quite a bit harder to pinpoint problematic areas.

Regardless of my quibbles about data visualization, it’s obvious that the vast majority of criminality occurs in Cambridge, Kitchener and Waterloo simply because that’s where most of the population lives.

I’ve looked up 9 crime types that most people would want to know about. They are Assault, Break & Enter, Car Theft, Drugs, Robbery, Sex Crimes, Theft Under $5000, Theft Over $5000 and Weapons Offences. The current version of my report uses crime data from 2021 to 2025.  Click on the picture below and I’ll send you a copy.

I’m not going to single-out cities and neighbourhoods today. I work with buyers and with sellers and that brings up some ethical considerations that I need to be careful of.

Providing info on crime and neighbourhood safety as a buyer’s agent is definitely my responsibility. And I can tell you that I have dug very deeply into the ‘crime map’ database. Performing due diligence for clients is part of the job.

But making that knowledge public is not the right thing to do. It would hurt homeowners and the reputations of the communities they live in. The problem with ‘problem neighbourhoods’ isn’t the vast majority of people living there, it’s the criminals.

Any public disclosures I make would be an issue professionally and ethically if I work with a seller client in a higher crime area. As someone representing my seller’s interests it’s not my role to throw shade on their neighbourhood.

Talking about the three patrol zones in Waterloo Region with the lowest crime levels is perfectly fine. But first, a caveat; I am going to exclude rural areas due to low population numbers, as well as Riverside Park in Preston simply because no one really lives there.

Just like my crime report, I used Assault, Break & Enter, Car Theft, Drugs, Robbery, Sex Crimes, Theft Under $5000, Theft Over $5000 and Weapons Offences for my rankings. My findings are unchanged from the previous data set I used and I would still recommend the following 3 areas.

♦
In Waterloo, the medium sized North 3 zone had the outright lowest numbers but only 20% of this zone is actually in Waterloo and represents at most 250 homes. North 7, covering the north-west end of the city, has almost identical crime numbers. But it covers a huge area by comparison making the crime rate per resident far lower, and making North 7 my number one choice for Waterloo.

♦

Central 7 in the south-west end is the clear winner in Kitchener. This area is a mix of brand new construction and older suburbia situated west of Homer Watson.

♦

In Cambridge, South 3 comes in on top. This area covers the Southwood area of Galt to the west side of the Grand River. I’ve had 2 clients buy here over the years.

You should know that the doctrine of caveat emptor applies when buying a home. Crime statistics in Waterloo Region are public knowledge, and a potential buyer must rely on themselves or their buyer agent to research a property thoroughly.

Chose wisely when picking your agent and your home.

The post What Is Crime Like In Waterloo Region? appeared first on Andrew Shackleton.

The Youth Impact Project is back again!! The Children and Youth Planning Table and Smart Waterloo Region have $50,000 to give out to youth ideas for positive change in our communities!

Young people living in Waterloo Region can submit their project pitch online for up to $5,000 in funding. CYPT & SWR’s Youth Decision Making Team will be evaluating applications until November 9 – or until the money runs out!

2025 is a “Challenge Year!” That means that we’re challenging youth to submit their project pitches, then take us along on the journey as they tell their story. We’re looking for project ideas that are smaller scale and less intensive.

• Application must include:
  • a 3-4 minute video pitch
  • a project outline
  • a project budget
• The project idea must address data/challenges seen in the CYPT’s Youth Impact Survey results
• Youth can apply as individuals (for up to $2000) or as a group (for up to $5000)
• Every application must include the contact information for a supportive adult who works at a community organization

Help Us Spread the Word!

We’ve created a promotional package so that this message reaches as many youth as possible. If you’re a CYPT Voting Member Organization, please share this promotional package with your communications staff and/or share this content in your social media, newsletters, and other communications. 

If you’re a young person with an idea and you want to apply, check out this “Application Guidelines” page. It’s full of information, templates, and guidelines to make your application as strong as possible!

If you have any questions at any point, please reach out to the Social Planning Associate on the CYPT Backbone Team.

Huge shoutout to United Way Waterloo Region Communities and the Region of Waterloo for giving us the funding to make this project possible. Thank you!

If you want to support this project, the United Way Waterloo Region Communities invites you to donate to their Youth Impact Project campaign.

The post Accepting Applications – 2025 Youth Impact Project appeared first on Children and Youth Planning Table.

For over 30 years, Project 10 has been dedicated to creating a world in which 2SLGBTQIA+ youth aged 14 to 25 are safe, empowered, and equipped to become agents of change. Through access to resources, support, and meaningful relationships, we help young people explore their identities on their own terms. Our services aim to promote the personal, social, sexual, and mental well-being of youth, while advocating for inclusivity and equality within the broader community.

Project 10 aims to enhance the well-being of lesbian, gay, bisexual, transgender, transsexual, two-spirit, intersex, and questioning (2SLGBTQIA+) youth and adults between the ages of 14 and 25. We achieve this through harm reduction approaches, education, and advocacy, helping youth to empower themselves both individually and collectively. Our focus is particularly on supporting individuals and groups who experience multiple, intersecting forms of oppression.

• Project 10 provides free, confidential, and anonymous services for youth aged 14 to 25.
• Our services are available in both English and French.
• We serve the Greater Montreal area, particularly focusing on anglophone, racialized, and socio-economically marginalized populations.

Our Values: We are committed to creating an open, inclusive, and accessible environment for all youth. Through advocacy, education, and direct support, we strive to improve the lives of 2SLGBTQIA+ youth and empower them to become leaders in their communities.

Project 10 is currently in the process of internal restructuring to strengthen the governance of the organization. As part of this transition, we are seeking passionate and committed individuals to join our Board of Directors and help guide the organization toward its future goals.

The board will play a critical role in overseeing the direction of the organization and ensuring its sustainability. Members will work closely with the leadership team to guide decision-making, particularly as Project 10 moves toward a more governance-focused board. Key responsibilities include:

• Strategic Decision-Making: Help shape the organization’s mission, vision, and programs.
• Committee Participation: Serve on committees, including the Finance Committee and Human Resources Committee. Board members are expected to contribute to the committee work and the overall direction of the organization.
• Time Commitment: Board members should expect to dedicate approximately 15 hours per month, which will include attending meetings and participating in committees.
• Board Term: The term length for board members is two years.

We are looking for individuals with expertise in one or more of the following areas:

1. Finance: Experience in nonprofit finance, budgeting, and fundraising.
2. Fundraising: Knowledge of strategies for securing financial support for nonprofit organizations.
3. Legal Expertise: Background in law, particularly in areas such as human rights, nonprofit governance, and equality legislation.

We are also open to individuals with other relevant expertise. If you have experience that could contribute to the success of our board and organization, we encourage you to apply.

At Project 10, we are committed to building a diverse and inclusive board that reflects the communities we serve. We have a predominantly racialized and diverse board and are looking to ensure that our leadership is representative of the youth and populations we support. We particularly encourage individuals from the 2SLGBTQIA+ community to apply, though all qualified candidates are welcome.

We value a wide range of perspectives, and our board is open to individuals who are committed to supporting the sustainability of Project 10 and its mission.

To apply for a position on the board, please send your resume/CV and a cover letter outlining your qualifications and interest in contributing to Project 10’s mission. In your cover letter, please highlight your experience in governance, finance, fundraising, law, or any other relevant areas.

• Application Deadline: 13 June 2025. Informal Q&A meeting before the deadline, with Sandra Mouaffo available upon request.
• Interview Process: Shortlisted candidates will be contacted for an interview.
• Interview Timeline: Formal candidate approval occurs at 26 June 2025 AGM
• Meeting Format: Hybrid (online + occasional in-person gatherings). Ability to travel to Montréal or speak French is preferred but not required.

• Board members will receive training and ongoing support to understand their roles and responsibilities. A comprehensive manual for board members is being developed to guide new members in their transition.
• There is an annual governance bootcamp offered to help improve governance practices and understanding of board responsibilities.

For more information or to submit your application, please contact Sandra Mouafo, Executive Director at direction@p10.qc.ca. If you have any questions about the board position or the application process, feel free to reach out.

The post Project 10 appeared first on Capacity Canada.