Lambda Scholarship Foundation Canada is seeking a volunteer Treasurer to join our board and help lead the financial stewardship of a 40-year-old national charity at a pivotal moment in its growth.

Lambda partners with Canadian post-secondary institutions, high schools, and other eligible agencies to provide scholarships, bursaries, and financial awards to 2SLGBTQIA+ and QTBIPOC students — reducing financial barriers and increasing access to education for marginalized communities.

Established in 1985, Lambda emerged during a time of struggle, resistance, and hope. Our first endowment launched at the University of Ottawa in 1995. Today, we support 12 endowments and scholarships across Canada and have distributed over $256,645 to 182 recipients.

Our mission: Empower 2SLGBTQIA+ and QTBIPOC Individuals Through Education and Research.

To learn more about our work and impact, read our 2024/2025 Annual Report and Year in Review blog post.

Our work is grounded in lived experience and a deep understanding of the systemic barriers that queer and trans youth — especially those who are Black, Indigenous, racialized, and newcomers — face when pursuing post-secondary education.

Our outgoing Treasurer has served in the role for approximately 10 years and has been involved with Lambda for over 25. This kind of long-standing dedication — characteristic of our volunteer leaders — reflects the integrity of the organization and the depth of commitment our mission inspires. We are seeking someone ready to carry that commitment forward.

1. Advanced fundraising and financial sustainability, including new scholarships, community awards, and growing existing endowments
2. Modernizing financial data and oversight
3. Developing formal organizational policies and governance frameworks
4. Canada-wide brand awareness and event sponsorship strategy
5. Launching a Youth Scholarship Application Mentorship Service — free, one-on-one online mentorship for 2SLGBTQIA+ and QTBIPOC youth

The Treasurer is an officer of the board and a member of the Executive Committee. Key responsibilities include:

• Chair the Finance Committee, providing oversight of Lambda’s financial health and reporting regularly to the full board
• Act as a signing officer for agreements, cheques, and bank transactions
• Keep financial records and reports on file in accordance with CRA and ONCA obligations
• Speak to the budget in partnership with the Executive Director and Finance Committee
• Serve on the Executive Committee and, in the absence of the Chair and Vice-Chair, serve as acting Chair
• Ensure a smooth transition by working collaboratively with your successor upon leaving office

We are seeking a candidate with experience in one or more of the following:

• Finance, banking, investment management, or accounting
• Nonprofit financial governance or CRA charitable compliance
• Bookkeeping, financial reporting, or audit processes

Experience with QuickBooks Online or similar accounting software is an asset. Bilingualism (English and French) is an asset but not required.

Lambda is an equity-serving organization. We strongly encourage applications from those who identify as Indigenous, women, trans, non-binary, gender non-conforming, Black, people of colour, and persons with disabilities.

Our current board includes members from Montréal, Ottawa, Calgary, and Vancouver. We welcome applications from all provinces and territories.

1. Monthly board meeting (currently the third Sunday of the month)
2. Finance Committee meetings as Chair (frequency set by the committee)
3. Approximately 8–10 hours per month
4. 2-year term

New board members are typically appointed at our annual AGM in September. We encourage interested candidates to apply early, as the board may make appointments in advance of the AGM.

We hope to have finalist candidates attend a Lambda board meeting in August ahead of our September AGM.

Please send a single PDF — no later than September 15, 2026 at 9 am EST — that includes:

A cover letter describing your financial experience and your interest in Lambda’s mission, with specific reference to the Treasurer role. Please confirm your availability for the required hours per month and your commitment to a 2-year term.
Your resume.

Email: president@lambdafoundation.org 
Attention: El Chenier, President

We look forward to learning more about you and your commitment to supporting Lambda Scholarship Foundation Canada and the 2SLGBTQIA+ and QTBIPOC communities we serve.

The post Lambda Foundation appeared first on Capacity Canada.

Trick Question: How many more times can Waterloo Region lie to Wilmot citizens before they stop believing you? 

The answer is  zero for half the Township at least.

Next trick question: Why is the Region finally in September making commitments about sharing water level data with Wilmot citizens?

The answer is because we've just had one of the wettest summers in recent memory and they are hoping that water levels are higher than usual. Plus if they don't share the information, sympathetic voters in Waterloo, Kitchener and Cambridge may punish them at next month's municipal election.

Third trick question: Actually it might be a trick headline above. Do Township voters (i.e. Woolwich, Wellseley, Wilmot, North Dumphries) only get to vote for one regional councillor which happens to be their mayor? Voters in Waterloo, Kitchener and Cambridge get to vote for additional councillors specific to their cities to represent their larger populations, do they not? If this is indeed the case then the only regional bum that Wilmot can possibly throw out is their current mayor Natasha Salonen.

Hmm meanwhile today's K-W Record has two interesting articles that might affect next month's election. The one is by Luisa D'Amato titled "Greater transparency coming on water levels, council told" and the other is by Jeff Outhit titled "Put your ID in your shoe when you run". The first is a very conveniently timed regional council meeting yesterday in which the Region of Waterloo appear at long last to be responding decently, properly and honestly to local Wilmot citizens who help pay for their salaries. Is this article for real just like is yesterday's regional council meeting for real or is it all for show? Basically this year Regional Council have left mayor Salonen out to dry with their disappointing, arrogant, "we know better than you" attitudes. Both yesterday's council meeting and Louisa's gentle handling and lack of appropriate editorial content today appear to be a gift to mayor Natasha Salonen to get her reelected. 

The second article by Jeff Outhit is an incredible boost to the current Chair of Regional Council, Karen Redman. Apparently Karen Redman was in New York City as part of her duties as a Canadian federal Member of Parliament (Lib.) during the 911 attacks on the World Trade Centre etc. She literally had absolutely nothing to do responsibility wise or politically regarding the attacks but thanks to the K-W Record her profile has just received a huge boost with the electorate, again right before next month's election. Keep in mind she hopes to maintain her position as Regional Chair but thanks to Premier Doug Ford it is not up to the electorate but up to him whether she continues as Regional Chair or he appoints someone else, likely a Conservative. My respect for our media in general and the K-W Record specifically has taken a fall in recent years.      

♦

Several casual-upscale restaurants and wine bars in the heart of Ottawa and Gatineau feature menus that showcase the distinctive qualities of low-intervention natural wines.

While these wines might present a challenge to diners inclined to favour the conventional aromas, flavours and textures of a noble grape, I’ve found that, though they can be described as “wild” or even “funky,” they can also reward and re-fresh the dining-out experience. In doing so, they also have ensconced themselves in the industry, embraced by more than just a core of dedicated sommeliers.

At each of Bar Lupulus, Arlo Wine Bar (in banner photo), Fauna Food + Bar and Soif Bar à vin (above), natural wines characterized as biodynamic, relying on only indigenous-yeast fermentation and with no filtration and minimal additives exhibit what Fauna’s wine director calls an “ethos” of authenticity that foregrounds the grape, the terroir and the winemaker.

For more, visit Vine Routes …

Check out my latest post Natural-wine hotspots in the Nation’s capital from AndrewCoppolino.com.

ACO North Waterloo Region Branch closes October 31, 2026

The ACO NWR website will be available until July 2027.

The province-wide ACO will continue and current members will be able to renew, but not as branch members; branch activities will cease

We encourage those who are interested in heritage preservation to consider joining the Waterloo Historical Society (www.whs.ca/). The WHS lists heritage preservation as one of its goals/mandates and many of ACO’s local members are already members of the WHS.

ACO will continue to work toward the goals of heritage preservation at a provincial level across Ontario

We thank all members and supporters of heritage preservation, past and present!

For further information, see

Statement from Marg Rowell, President of the ACO North Waterloo Region Branch

Statement from Deb Crawford to ACO members

We acknowledge that the land on which we live was inhabited by the Haudenosauee, Anishinaabe and Attawandaron (Neutral) people before the arrival of Europeans. We recognize the enduring presence and deep traditional knowledge of the Indigenous people with whom we share this land today. This land was part of the Haldimand Tract proclamation of 1784, which promised the Haudenosaunee land six miles on both sides of the Grand River from Lake Erie to its source. We also affirm our commitment to reconciliation in our community.

For a while, I understood neural networks mostly mechanically. Data entered the network, passed through layers, and eventually produced a prediction. Loss was calculated, gradients computed and weights updated. I could follow the sequence but the pieces still felt strangely arbitrary. Why this structure? Why did stacking layers make a network so powerful? Why were activation functions so important? I understood much of what the network was doing, step by step, without feeling like I understood the network as a whole.

Then I watched Josh Starmer’s StatQuest video on the key ideas underlying neural networks. He presented neural networks as systems that sculpt functions. Instead of following data through layers, I could zoom out and picture what those transformations were collectively constructing. That shift helped immediately.

Then I got greedy and started looking for other ways to think about neural networks. By the time I was done, I realised that different mental models made different questions easier to answer. Some let me zoom out and collapse the network into one idea. Others did the opposite, breaking it into smaller transformations or computations. Some connected neural networks to ideas I already understood; others made me reinterpret things I thought I understood already. Together, they made neural networks feel much less mysterious. Learning to switch between these mental models was the breakthrough I needed.

The mechanical understanding I started with was essentially viewing neural networks as a stack of transformations.

Each layer progressively transforms the data before passing it on:

where

• x is the input
• h’s are intermediate hidden representations, and
• y is the final output.

This was probably my default mental model before I knew to call it one. It made the forward pass easy to follow: the input is transformed step by step until the network produces an output.

This perspective helps explain why neural networks have depth at all. Each layer can transform the output of the previous layer, allowing the network to build increasingly complex computations. Earlier layers produce simpler transformations that later layers can refine further.

At the same time, this perspective kept my attention on the individual steps. I didn’t have a clear picture of what all those transformations were collectively doing. I needed to zoom out.

Function approximation gave me that zoomed-out view.

I was already comfortable thinking about models like linear and logistic regression as functions. Given some inputs, these models learn a mapping that produces an output. A neural network can be understood in exactly the same way:

This stripped away much of the apparent mystery surrounding neural networks. Underneath all that machinery, the network was still doing something familiar: learning a function that maps inputs to outputs, just like the other models I already understood. The difference was that neural networks could learn far more flexible and expressive functions.

But how do these complex functions emerge from simple neurons?

I already understood abstractly that stacking layers with nonlinear activation functions allowed neural networks to model increasingly complex relationships. But I couldn’t visualise it.

In Josh Starmer’s explanation, each neuron works with a given activation function. The weights and biases slice, flip and stretch that same activation function into new shapes. As a result, each neuron applies a small nonlinear transformation to its input. These small transformations and shapes are stitched together across layers to create yet new shapes. A complex decision boundary emerges from many local transformations.

With Softplus, a neuron can contribute a smooth bend into the function surface. With ReLU, neurons contribute piecewise linear changes. Deep networks combine huge numbers of these simple transformations, progressively sculpting a much more complex function.

Don’t take my word for it. Watch the video. His animations (and the noises he makes while calculating) make it worth your time.

What clicked for me was that the network didn’t need any individual neuron to represent a complicated function. Each neuron could contribute something simple. Complexity emerged from composing and combining many of those simple transformations.

Function approximation had helped me zoom out and see the network as one flexible function. Function sculpting let me zoom in just enough to see how that flexibility could be constructed.

One thing that distinguishes neural networks from many of the models I was used to working with is that they can also learn how to represent the input itself.

In my previous experience with econometrics and causal inference, deciding how to represent a problem happened largely before fitting the model. I thought carefully about which variables to include, what they measured, and how they should be transformed. The model then learned from the representation I had given it.

With neural networks, deciding how to represent the problem doesn’t happen entirely before training. The network also learns useful ways of representing the data. The hidden states in the stack of transformations aren’t just intermediate values but intermediate representations shaped during training to become useful for the task. This is why neural networks are often described as systems for representation learning.

This applies even to relatively simple neural networks. For example, a churn model might learn hidden behavioural patterns that help distinguish customers with different risks of leaving. In a larger language model, the learned representations can encode complex semantic relationships. The scale and complexity differ but both are forms of representation learning.

I had been thinking of learning primarily as learning a mapping from my representation of the problem to the target. Representation learning made me realise that the representation itself could be part of what was learned.

One thing that puzzled me when learning about neural networks was the amount of attention given to activation functions. I understood that without them, stacking linear layers still collapses into a linear transformation. But why did the choice of activation function matter?

I initially thought about activation functions through the function-sculpting perspective. Different activation functions give the network different shapes to work with, so I assumed that choosing between sigmoid, tanh or ReLU was primarily about the kinds of functions the network could construct.

The optimisation perspective gave me a different answer.

Unlike simpler models such as linear regression, neural networks generally do not have neat closed-form solutions for their parameters. These parameters are learned iteratively through optimisation. At first glance, optimisation can seem like a secondary implementation detail — merely the mechanism through which weights get updated during training.

But it isn’t enough for a neural network to be capable of representing a useful function; optimisation has to find the parameters that produce one. Neural networks were already highly expressive decades ago. The challenge was whether optimisation could actually find a useful solution. Gradient-based optimisation struggled to reliably train deep networks. Much of the progress in deep learning has involved making highly expressive neural networks easier to train.

Many neural network design choices are intended to solve optimisation problems. Sigmoid activation functions can saturate, while ReLU often allows gradients to propagate more effectively. Weight initialisation affects whether signals and gradients remain stable across many layers. Learning rates and optimisers determine how we move through the loss landscape.

This perspective also changed how I thought about architecture. From my original stack-of-transformations perspective, residual connections seemed strange. If each layer was supposed to progressively transform the representation, why let an earlier representation bypass a block and add it back later?

From an optimisation perspective, the question changes. Residual connections provide identity paths through deep networks, helping information and gradients flow and allowing blocks to learn changes to an existing representation. What had looked awkward from one mental model made much more sense from another.

This perspective helped explain why so much of deep learning is about more than designing expressive architectures. It is also about making those architectures trainable.

The optimisation perspective explains why gradients matter. But how are the gradients computed?

Function approximation had taught me to think of a neural network as one enormous function. But trying to imagine its derivatives gave me a headache. Differentiating an enormous, complicated function with millions of parameters sounded overwhelming.

The computational graph perspective reverses that. It breaks this gigantic function back into the small operations that produce it.

During the forward pass, data moves through the graph step by step: inputs are transformed; activations are computed; predictions are produced; and loss is calculated. Backpropagation then works backwards through this graph to compute the gradients.

The key insight is that the gradients aren’t computed as one enormous derivative. Each operation only needs to compute local derivatives with respect to its own inputs. By the magic of chain rule, these local derivatives are stitched together, and gradients are able to propagate backward through the whole network. Backpropagation no longer felt like a memorised algorithm.

Neural networks became much easier to understand once I stopped searching for a single explanation.

What I initially thought of as one complicated system could be viewed at very different scales. I could zoom out and view the system as a single function, or zoom in and think about individual transformations and computations. I could think about what representations the network was learning, or switch questions entirely and ask whether optimisation could actually find them.

What surprised me most was that a mental model that made one question easier could make another harder to reason about

• Thinking of a neural network as one enormous function connected it to more familiar machine learning models but made the idea of differentiating it overwhelming. Instead, breaking that same function into a computational graph made backpropagation much easier to understand.
• Residual connections were initially confusing when I viewed networks as stacks of transformations. However, from an optimisation perspective, their purpose made more sense.

No single perspective fully explains neural networks. Each lens makes different questions easier to reason about. The real shift was learning which mental model to reach for when something stopped making sense.

1. Stack of Transformations
   Why are there multiple layers?
   Main insight: Layers progressively reshape data into new representations.
2. Function Approximation
   What is the network fundamentally doing?
   Main insight: A neural network is ultimately learning a function from inputs to outputs.
3. Function Sculpting
   How do simple neurons create complex functions?
   Main insight: Complex functions emerge from many simple nonlinear transformations.
4. Representation Learning
   What are hidden layers learning?
   Main insight: Neural networks learn useful internal representations of the input.
5. Optimisation System
   Why do so many design choices concern training?
   Main insight: Expressiveness isn’t enough; optimisation has to find useful parameters.
6. Computational Graph
   How are gradients actually computed?
   Main insight: Derivative calculations can be decomposed into local derivatives stitched together by the chain rule.

• Josh Starmer (StatQuest): The Essential Main Ideas of Neural Networks

This article grew out of a more detailed exploration of neural network mental models on my website.

Learning neural networks through mental models was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

ORDER OF CORPORATE ENVIRONMENTAL DAMAGE DONE IN ELMIRA, ONT.

1)     Uniroyal Chemical

2)     Varnicolor Chemical

3)     Nutrite (Yara)

Nutrite contributed ammonia to the Elmira Aquifers as also did Uniroyal. Nutrite are however not only third on the list but a long ways down that list compared to the mass on and off site dumping of liquid wastes done by both Uniroyal and Varnicolor. Uniroyal is first not based upon number of years of dumping but upon the volumes of chemical production and volumes of wastewaters they produced as a  side effect of that production. Likely a few gas stations in town (beside the Steddick Hotel, Gord's, Voisin's) also contributed a few petroleum hydrocarbons such as Benzene, Toluene, Ethyl Benzene & Xylene to our ground and drinking water as both Varnicolor and Uniroyal did as well. 

Yes there were cleanups at all three of the mentioned gas stations/car dealerships. Yes there have been Mickey Mouse cleanups at the much larger messes made by Uniroyal, Varnicolor and Nutrite. No other corporate polluters within Elmira have even remotely been brought to justice. Only Uniroyal Chemical has received even a small fraction of the media attention that their behaviour and actions deserve.

Varnicolor 's contaminants include NDMA, chlorobenzene, trichloroethylene, PCBs, Benzene, Toluene, Ethyl Benzene and Xylenes, trichloroethane, chloroform and more. They were both accidentally and purposefully dumped on both of their properties namely the former 62 Union St. (now 84 Howard Ave.) site as well as at their infamous Lot 91 at the extreme east end of Oriole Parkway.  The intentional dumping at Lot 91 included intentionally rupturing barrels and letting them leak out as well as pumping dirty solvents into two end to end 10,000 gallon tanks with holes in those ends. The first method was seen and an Affidavit given to the Ontario M.O.E. by a Uniroyal employee working at their warehouse fourty metres from Lot 91. Videotapes exist of pumping into the two 10,000 gallon tanks with witnesses noticing strong solvent odours for the next few days followed by an examination of the ends of the two tanks after the company was investigated.

Up on the main site (62 Union St.) the mass dumping was accomplished by a buried road tanker behind the main, orange administrative building connected via pipes to the floor drain inside the building. There was also a buried septic tank behind the road tanker unconnected to toilets but also connected to the same floor drain inside the orange administrative building. Affidavits and statements from multiple witnesses who were ordered to dump liquid chemical wastes down this floor drain have been in the hands of the Ont. M.O.E. for decades. 

Those liquid wastes included waste water from paint booth water curtains from major automotive manufacturers as well as dimethylamine ( precurser to NDMA)  from the production of can coatings used to inhibit rust inside food cans. These can coatings were produced inside the orange building and there are two affidavits from former employees other than myself to this fact in the hands of the Ont. Min. of Environment (M.O.E.). Minor dumping (45 gallons or less) occurred on an almost weekly basis as unsecured drums with waste solvents got dropped or tipped over regularly. Spills sometimes were "contained" with sawdust and yes the M.O.E. also has Affidavits to that fact as well. 

I believe that while Uniroyal Chemical were environmental pigs and received some of the condemnation they deserved, Varnicolor were coddled and protected immorally and illegally by the Ontario Ministry of Environment. This protection resulted in greatly reduced remediation for the entire Elmira Aquifers and now we are seeing the result of that as the 2028 "cleanup" has failed and all parties grudgingly so admit.

I spent three weekends convinced I needed a framework to build an AI agent.

I installed one, followed the docs, and eventually hit an error I couldn’t make sense of. I spent more time trying to understand the framework than building the thing I actually wanted.

So I stopped.

I opened a blank Python file and started with the simplest version I could think of: call the model, give it one tool, run the tool when it asks, send the result back, and repeat.

Forty minutes later, I had a working agent.

It wasn’t sophisticated. It didn’t have memory, multiple agents, or a fancy orchestration layer. It was a loop, a model call, and a function.

That was the part I had been missing.

Before worrying about frameworks, I needed to understand the loop underneath them.

That’s what we’ll build here.

An AI agent is a large language model running inside a loop, with access to tools it can choose to call, until it decides the task is done or a limit is hit.

At its core, that’s all an agent loop is. Frameworks add useful abstractions around it, but the underlying pattern doesn’t change. If you strip away the marketing, an agent has three parts.

• Model: the reasoning engine that reads context and decides what to do next
• Tools: functions the agent can call to take action, like a web search, a database query, or a file write
• Loop: the code that calls the model, checks if it wants to use a tool, runs that tool, feeds the result back, and repeats

A plain chat completion answers once and stops. An agent keeps going. It reads its own tool results, updates its plan, and calls the model again, sometimes five times, sometimes fifty, until the task resolves.

Here is the part most tutorials gloss over. There are two very different design choices hiding inside the word “agent,” and conflating them is where projects go sideways.

Deterministic control flow means your code decides the sequence of steps. Step one always runs, then step two, then a fixed branch based on a condition you wrote. The model does reasoning inside each step, but it does not choose what happens next.

Agent directed control flow means the model itself decides which tool to call, in what order, and when to stop. Your code just exposes options and executes whatever the model picks.

Most production systems that call themselves agents are actually a hybrid. A deterministic scaffold, wrapped around a small number of genuinely open ended decision points. That hybrid is what this article teaches you to build, because a fully autonomous loop with no rails is the fastest way to burn API credits on an infinite retry spiral.

Strip away the vocabulary differences between frameworks and every agent loop runs the same four stage cycle.

1. Observe: gather the current state, the conversation so far, and any new tool results
2. Think: send that context to the model and let it reason about the next move
3. Act: if the model requested a tool, execute it in your own code, not inside the model
4. Update: append the result to the conversation state and decide whether to loop again or stop

That is the entire mechanism behind tools like Claude Code, Cursor’s agent mode, and Gemini CLI. They are dressed up differently, but underneath, it is this same cycle running turn by turn until a stopping condition is met.

Let’s make this concrete. Below is a minimal, framework free agent loop in Python, using the Anthropic API directly. It is deliberately small enough to read top to bottom in one sitting.

import anthropic

client = anthropic.Anthropic()
def get_order_status(order_id: str) -> str:
    # In a real system this hits your database or an API.
    return f"Order {order_id} shipped yesterday, arriving in 2 days."
tools = [
    {
        "name": "get_order_status",
        "description": "Look up the shipping status of a customer order.",
        "input_schema": {
            "type": "object",
            "properties": {"order_id": {"type": "string"}},
            "required": ["order_id"],
        },
    }
]
messages = [{"role": "user", "content": "What is the status of order #5678?"}]
MAX_TURNS = 5
for turn in range(MAX_TURNS):
    response = client.messages.create(
        model="claude-sonnet-4-6",
        max_tokens=1000,
        tools=tools,
        messages=messages,
    )
    messages.append({"role": "assistant", "content": response.content})
    if response.stop_reason != "tool_use":
        final_text = next(b.text for b in response.content if b.type == "text")
        print(final_text)
        break
    tool_results = []
    for block in response.content:
        if block.type == "tool_use" and block.name == "get_order_status":
            result = get_order_status(block.input["order_id"])
            tool_results.append(
                {"type": "tool_result", "tool_use_id": block.id, "content": result}
            )
    messages.append({"role": "user", "content": tool_results})

So what makes this loop deterministic? The important part isn’t the model call. It’s the code around it.

The for turn in range(MAX_TURNS) line is a hard rail. The model cannot loop forever, because your code, not the model, owns the exit condition. That single line prevents the most common production failure: an agent that keeps calling a tool in circles because it never got a satisfying answer.

The tool execution itself, get_order_status(block.input["order_id"]), runs in your process, under your control, with your error handling. The model never touches your database directly. It only ever asks, and your code decides whether and how to comply.

The if response.stop_reason != "tool_use" check is the loop's stopping condition. When the model has no more tool calls to make, it has produced a final answer, and the loop exits cleanly instead of continuing to poll.

That is the entire pattern. Everything a heavyweight agent framework adds on top, streaming, retries, multi agent handoffs, persistent memory, is scaffolding around this same core.

Once the basic loop runs, the real engineering work is deciding where to keep the model on a leash and where to let it improvise. A few rails that pay for themselves immediately:

• Turn limits: cap how many loop iterations a single task can consume, like the MAX_TURNS example above
• Tool allowlists: only expose the specific tools relevant to the current task, not your entire API surface
• Cost ceilings: track token spend per task and abort past a threshold, before it becomes a surprise invoice
• Human checkpoints: require explicit approval before any destructive action, like deleting data or sending money
• Schema validation: reject malformed tool calls before they ever reach your execution layer

Notice the pattern. None of these limit what the model can reason about. They limit what it can do unsupervised. That distinction is the whole discipline of building agents that are useful in production instead of merely impressive in a demo.

Not every task needs an agent. If the sequence of steps is fully known ahead of time, a deterministic workflow with the model doing reasoning inside fixed steps is simpler, cheaper, and easier to audit.

Reach for a full agent loop when the task is genuinely open ended and you cannot predict the steps in advance, like debugging an unfamiliar codebase or researching a topic where the right sources are not known upfront. Reach for a plain deterministic workflow when compliance or auditability requires a reviewable, fixed control flow, or when the steps really are always the same.

Most real systems land in between: a deterministic outer shell, with one or two agent loops embedded at the specific points where judgment genuinely cannot be scripted in advance.

A few failure patterns show up constantly in first agent builds, and all of them trace back to skipping a rail.

• Letting the loop run unbounded, so a confused model spins through dozens of turns before anyone notices
• Executing tool calls with no validation, so a malformed argument crashes the whole run instead of failing gracefully
• Feeding the entire conversation history back every turn with no trimming, until the context window fills and costs balloon
• Treating the agent as fully autonomous from day one instead of starting with tight guardrails and loosening them as trust builds

An agent is a model in a loop with tools, and the loop is yours to control. The frameworks did not invent anything the core pattern does not already contain, they just packaged it. Once you have built one from scratch, every framework you look at afterward reads like a variation on the same forty lines of code.

The next time you see an “AI agent” framework, don’t start by learning the framework. Look underneath it. Find the loop. Find the tools. Find the stopping condition.

Once you understand those pieces, the framework stops looking like magic.

AI Agents Aren’t Magic: Build One From Scratch With a Deterministic Agent Loop was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

Before ChatGPT, AI had experts. Before GPT could explain a research paper. Before AI could write your emails, generate images, or answer almost any question you throw at it.

There was another way of building intelligent machines. Give the machine the knowledge of an expert. But here's a question hiding underneath.

We now ask AI systems to explain a concept, recommend something, diagnose patterns, summarize information, or help us make decisions. The technology is very different from the rule-based systems of the 1960s and 1970s, but the underlying problem is not entirely new: How should a machine represent knowledge, reason with it, and explain the answer it produces?

So researchers tried something different. They took what experts knew: facts, experience, rules, and ways of reasoning, and turned that knowledge into something a machine could work with. And that idea gave us something called "Expert Systems."

Imagine you have a doctor who has spent decades diagnosing patients. Over those years, the doctor has learned patterns: If this symptom appears ...And this test shows this... Then this condition is present.

An expert system stores knowledge and applies rules to reason through a problem. And that's where the architecture gets interesting:

Researchers collect knowledge from experts, documents, observation, and other resources. That knowledge is then translated into facts and rules, the system can understand.

In our doctor example, this might mean asking the doctor: Which symptoms matter? Which test results change your diagnosis? What combination of evidence makes you suspect a particular infection?

That knowledge is then translated into facts and rules the system can work with.

The Memory; this is where the knowledge lives. Facts, rules, relationships, domain-specific information. If certain symptoms are present and test results match a particular pattern. Then consider a particular condition. It is essentially the system’s stored expertise.

For example:

IF the patient has symptom A
AND test result B is positive
THEN consider condition C.

The knowledge base is essentially the system’s stored expertise.

The Reasoning: having knowledge is not enough. Something still needs to reason with it. This is the part that makes an expert system feel intelligent. It takes information given by the user and compares it against the rules in the knowledge base.

Imagine the doctor enters the patient's symptoms and test results. The inference engine compares that information against the rules stored in the knowledge base and determines which rules apply.

It asks, "Given what I know, which rules apply here ?" and then it works towards a conclusion.

An expert system needs to deal with that same question, the explanation system helps to show how the system arrived at its conclusion. It can trace the rules and facts that were used during the reasoning process. This matters because users shouldn’t always have to blindly trust a machine’s answer. They should be able to understand the reasoning behind it.

If the system concludes that “condition C” is likely, the explanation system can trace the rules that led to that conclusion:

For Example:-Lets say Symptom A + Test B → Rule 17 → Condition C

Instead of simply saying “Condition C,” the system can show something about why it reached that conclusion.

Where humans meet the system, all of that knowledge and reasoning would be useless if humans could not communicate with the system. That’s where the user Interface comes in. The user provides information. The system processes it, and the result comes back. It’s the bridge between human input and machine reasoning.

1. MYCIN — at Stanford University in the 1970s, MYCIN was designed to help diagnose certain bacterial infections and recommend appropriate treatment. It wasn’t a modern machine learning model. Instead, it relied heavily on rules representing medical knowledge. The system could also explain the reasoning behind its recommendations.
2. DENDRAL — Developed at Stanford University in the 1960s, DENDRAL was designed to help scientists analyze mass-spectrometry data and determine molecular structures. The machine wasn’t simply "learning everything ", it was working within a specific domain, using specialized knowledge to solve a difficult scientific problem.
3. COMPASS — Company Office Management and Personnel Alternative Selection System, or COMPAS, was one of the earliest and most notable examples of an expert system used for industrial troubleshooting. Developed in the 1980s by NYNEX (a major telephone company that later became part of Verizon), COMPASS was specifically designed to analyze error messages and maintenance logs from large telephone switching systems.

When we hear "AI" today, we usually think about: ChatGPT, Neural Networks, Deep learning, Large language models.

Expert systems largely followed a knowledge-first approach: humans supplied the knowledge, and the system applied rules to reason over it.

Modern AI often works differently. Machine learning systems can learn patterns from large amounts of data rather than requiring humans to write every rule explicitly. Neural networks and large language models can represent complex patterns that would be extremely difficult to encode as a traditional collection of if–then rules.

The architecture and methods are different. But some of the questions remain surprisingly familiar:

What does the system know?
How does it arrive at an answer?
How do we know when we should trust it?
Can it explain its reasoning or limitations?

Those questions did not begin with ChatGPT. Expert systems forced researchers to confront them decades earlier.

Expert systems did not become the foundation of today's AI in a simple, straight line. Their limitations helped reveal something important: intelligence is not just about storing knowledge or following rules.

We also need ways to handle uncertainty, changing information, learning, context, and situations that were never explicitly programmed.

Modern AI approaches these problems differently, but the questions raised by expert systems are still with us. When an AI system gives us an answer today, we still want to know:

Where did this answer come from?
What information shaped it?
Can I trust it?
And what happens when the system encounters something it doesn't know?

Understanding expert systems gives us a glimpse into an earlier attempt to answer those questions and helps us understand why AI evolved beyond rules.

The other side of the theory is that the AI we know today didn’t appear out of nowhere. It has a history, and sometimes understanding the older ideas makes the newer ones make a lot more sense.

How We Put Human Expertise Inside a Machine was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

Every Saturday morning, one of several volunteer teams from St. Michael Roman Catholic Church in Waterloo spends part of their day offering granola bars, scones, boiled eggs, fruit cups and warm smiles to the men at ShelterCare.

♦Gai, a member of one of the volunteer teams, has been volunteering regularly for the past three years. She was drawn to this work because she has seen firsthand how great the need is in our community.

“I worked for the Catholic school board, near St. Mary’s Church and the Tiny Home Takeout in downtown Kitchener,” said Gai. “I could see the number of homeless people increase, and my heart went out to them.

“I’ve always felt a calling to extend grace to those less fortunate,” said Gai. “I have two sons, and I always thought, ‘this could be one of my children.’”

At ShelterCare, the 100 men in the program receive meals throughout the week from a combination of paid staff and volunteers. On weekends, having committed volunteers hand out breakfast food makes a big difference for the kitchen team.

“While they are handing out the food, I’m able to prepare for the lunch meal,” said ShelterCare cook Anita. “It makes a big difference.”

St. Michael Parish has long been supportive of the ShelterCare program. Back in 2023, when our emergency shelter first opened, the church provided welcome packages for the men who were the first to move into the new facility.

Dean, another volunteer with the program, said that he likes having the chance to give back.

“In my life, I’ve been close to this community,” said Dean. “I realized that if things were different, I could be one of these guys.”

For Gai, it didn’t take her long to see just how meaningful it can be to hand out breakfast food to men struggling with the stigma of homelessness.

“My fourth week here, I met a young man who thanked me for smiling at him. It was a simple thing, but it was a big thing,” said Gai. “If we can give something positive to someone like him who really needs it, that’s what it’s all about.”

To learn more about volunteering opportunities at House of Friendship, visit www.houseoffriendship.org/volunteer

The post Sharing Breakfast and a Smile appeared first on House Of Friendship.

A large part of it requires absolutely zero shame or concern for things like ethics, morality and honesty. If one is self-entitled enough then one can simply ignore the standards of society around them. Most of us do care that we have a reputation for honesty and truth telling. Corporations and governments on the other hand in particular simply don't care if the knowledgeable people know they lie like dogs because the knowledgeable people are in the minority. For the ethically impoverished it's all about plausible deniability. As Donald Trump has stated and people (& corporations) of his ilk prefer; he could commit murder in broad daylight in Times Square and never get convicted. To then accuse him of murder after being acquitted by a jury would only make him laugh. 

Uniroyal Chemical and corporate successors have learned how to play the environmental game. They solidify the power and authority of local politicians behind themselves via bribes, gifts, donations, political support or whatever is necessary. They have most likely attended talks, speeches, conferences and private tutoring sessions telling them how to capture the hearts and minds of most of the public. This includes always talking the language of decency, honesty, environmental concern etc. regardless of their actual behavior and actions behind the scenes. In the early days Uniroyal Chemical allegedly refused to attend meetings of CEAC (Citizens Environmental Advisory Committee) according to one  source (Ewa Dabrowska). Uniroyal and successors learned. Now they not only attend but they run the whole show which works far better for them. They own the Woolwich Township TRAC committee through Woolwich Council as they have since 2000 ( UPAC, CPAC, RAC & TAG) when Pat McLean and Susan Bryant sweet talked CPAC into agreeing to the change. Esther Thur, Henry Regier and I all argued against that change to no avail. We had not forgotten how useless Woolwich Council had been in the 1970s, 80s and 90s whereas the others had or never knew. 

Uniroyal/Lanxess will not attend any meeting in which they are outnumbered. They expect and receive deference from all authorities or they simply won't play. Part of their game plan also includes looking for opponents who can be turned for a price. In other words they want and are comfortable dealing with people who switch allegiances once well motivated. Of course those people had better never have a second change of heart if they know what is good for them. Hell hath no fury like a corporation wooed and then scorned.  

Between financially struggling media, dishonest politicians, laws riddled with loopholes, corporate money and influence; our environment, health and futures are in grave jeopardy. 

Setting up your Eyedro Electricity Monitor over WiFi is usually a smooth process. However, if you run into setup bumps, there is a fast, reliable backdoor method: temporarily provisioning WiFi using an Ethernet connection.

Connecting your Eyedro device via Ethernet bypasses local WiFi credential errors, establishes a stable connection to the MyEyedro cloud, and lets you reconfigure your WiFi settings hassle-free.

This troubleshooting approach is ideal if you encounter any of the following:

• Changed WiFi Credentials: You updated your router’s SSID or password, and the Eyedro module can no longer connect.

• Failed Initial Provisioning: Mobile devices keep dropping the Eyedro [xxx-yyyyy] setup network during initial configuration.

• Tricky Network Settings: You are dealing with complex router configurations, ad-blocking DNS setups, or dual-band (2.4GHz / 5GHz) steering issues.

1) Disconnect Power from the Eyedro Unit:

Ensures the device registers hardware changes upon startup.

2) Connect to Your Router via Ethernet:

No sensors required for network configuration.

Run a standard Ethernet patch cable from an active LAN port on your router (or switch) directly into the Eyedro module’s Ethernet port.

The Ethernet cable must be plugged in before power is applied so the Eyedro recognizes the wired interface on boot.

Note: You do not need to move your current CT sensor clamps for this setup—only the Eyedro module and its power supply need to be plugged in near the router.

3) Power Cycle the Device:

Forces the device to bypass old WiFi settings, and also stops the device from broadcasting its WiFi network in the case of first time setup.

Plug the power adapter back into the Eyedro unit. When powered up with an active Ethernet cable attached, the unit automatically ignores existing Wi-Fi configuration settings and routes traffic over the wired LAN connection via DHCP.

4) Add Device to MyEyedro Account or Verify Device is Active if Already Claimed on account:

Verify active communication.

Wait approximately 10 to 15 seconds for startup diagnostic tests to complete:

• Watch the status LEDs: A solid or toggling green light indicates successful internet communication.
• Log into your account at My.Eyedro.com. Your device should now display as Active.

5) Reconfigure or Clear Old WiFi Credentials:

Apply clean 2.4GHz network parameters.

Navigate to the device settings menu in MyEyedro:

1. Select Forget / Clear existing WiFi credentials.
2. Enter your new 2.4GHz WiFi SSID (Network Name) and Password.
3. Double-check for case sensitivity—incorrect capital letters or spaces are the most common cause of setup failures.
4. Save the new parameters.

6) Disconnect Ethernet and Return to Normal WiFi Operation:

Final transition back to wireless mode.

Unplug the power cord, remove the Ethernet cable, and move the Eyedro module back to its permanent installation location. Reapply power; the unit will now connect automatically to your updated WiFi network.

• Verify 2.4GHz Support: Eyedro modules operate exclusively on the 2.4GHz frequency band for better wall penetration. Ensure your router does not force 5GHz-only connections on your smart home hardware.
• Hardware Generation Differences:
  • Gen 6 Devices (Serials starting with A14-1): Support Ethernet hot-swapping (plug and play at any time).
  • Gen 5 Devices (Serials starting with A14-0): Require the Ethernet cable to be connected before powering on the device.
• Firewall & DNS Issues: If the status light fails to turn green even over Ethernet, check if custom router DNS rules (like Pi-hole or AdGuard) are blocking cloud traffic. Switching temporarily to Google DNS (8.8.8.8 / 8.8.4.4) often resolves stubborn connectivity blocks.

For detailed Eyedro product specifications, installation and MyEyedro cloud software information please refer to Eyedro product guides and MyEyedro user guides.

The Canadian Mental Health Association, Brant Haldimand Norfolk Branch (CMHA), is now accepting applications for membership to our Board of Directors. CMHA provides a wide range of community mental health services for people 16 years or older who are experiencing a serious or persistent mental health concern. Volunteers are asked to provide 3-4 hours of their time per month, and meetings always offer a virtual option.

We are committed to reflecting the diversity of the communities we serve, so we strongly encourage candidates with the following skills and life experiences to apply:

• Individuals from the TS & LGBTQIA+ community.
• Indigenous – First Nation, Metis, Inuit community members.
• Individuals from Racialized or Black communities.
• People with Lived Experience or their care partners
• Individuals with financial expertise.
• Individuals with fundraising expertise.
• Individuals with legal expertise.
• Gen Z/Zoomers
• People with Lived Experience or their care partners

www.bhn.cmha.ca

Please direct any enquiries to Jane Eyers from the Governance and Nominations committee at jeyers70@gmail.com.

The post Canadian Mental Health Association, Brant Haldimand Norfolk Branch (CMHA) appeared first on Capacity Canada.

Forests Canada is on a mission to conserve, restore and grow Canada’s forests to sustain life and community. For over 75 years, our ambitious, large-scale tree planting programs, landowner forest stewardship outreach, extensive education resources and programs, and decades of community outreach have resulted in millions of trees being planted each year.

Forests Canada works with a trusted network of partners to ensure the long-term health and resiliency of our forests today, and for future generations. From seed to survival to sustainability, we have proudly built the infrastructure and network needed to deliver forest restoration programs focused on improving forest health and advocating for healthy ecosystems, while also inspiring students, connecting communities with their forests, and supporting job growth and local economies.

As a nonprofit registered charity, Forests Canada is governed by a Board of Directors who represent a diversity of experiences, skills, regions and perspectives. We are actively seeking new board members that can bring new perspectives to the board and contribute to our mission. Our Board of Directors provides strategic leadership, vision, and maintains high ethical standards of governance. They engage in effective planning to respond to the needs of our communities and act as stewards of our resources.

We are currently seeking qualified candidates to serve on our volunteer board. Forests Canada’s current recruitment priorities include those individuals with experience in any of the following areas:

• Fundraising – Recognizing funding opportunities and identifying strategic individual and corporate contributors to establish long-term fundraising relationships; experience in developing brands, building and implementing strategic marketing and fundraising plans.
• Professional working experience in the government sector (all levels of government).
• Financial/Accounting/Audit – Certified Public Accountant, experience in corporate finance, audit and financial accounting and reporting standards.

Forests Canada welcomes all applicants. We believe that diversity and inclusion are key to innovation through the sharing of ideas, experiences, and abilities. All qualified applicants will receive consideration without regard to race, colour, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

• Attend 3-4 Board meetings per year (1 in-person (full-day), 3 virtual (180-minute meetings)).
• Participate in standing and ad hoc committees (Nominations, Governance, Human Resources (NGHR) Committee, Audit and Finance Committee).
• Attend organizational events or meetings as a representative.
• Provide governance over the following key areas: strategic plan, by-laws, board recruitment and development, selection and evaluation of CEO, financial management, fundraising strategy, and ensure that services and programs meet the needs of the people that are served by the organization.
• Advocate among colleagues and peers for our organization.

Directors shall hold office for a term of 3 years and shall be eligible for re-election for two further terms of 3 years for a total of 9 consecutive years. The candidate will be presented to the board for approval in January 2027 with the first board meeting taking place in April 2027. An honorarium may be available.

Applicants should submit:

• A résumé / CV, and
• A letter of interest describing relevant skills, experience, and personal attributes.

Applications will be reviewed by the NGHR Committee. Selected candidates will be notified and interviewed. The application period will close October 15, 2026.

For more information and to submit your interest, please contact, Sylvia Nelson at snelson@forestscanada.ca. More about Forests Canada can be found at www.ForestsCanada.ca

The post Forests Canada appeared first on Capacity Canada.

The Davenport Community Land Trust (DCLT) is looking for five people to join our first volunteer Board of Directors. We’re looking for people who know and have connections with this neighbourhood. No board experience required, training provided.

DCLT’s purpose is to provide affordable, stable, and sustainable housing and community assets in Davenport and surrounding areas by acquiring, developing, and stewarding land and buildings for lasting community benefit and well-being.

DCLT envisions a Davenport where no one is under threat of displacement, where community members are housed with dignity, free from violence, and have access to spaces where they feel a sense of belonging.

DCLT incorporates as a non-profit in fall 2026. The Founding Board is DCLT’s first Board of Directors. The Founding Board, with the membership, will collaboratively set the vision, direction and operations of DCLT: bylaws, policies, compliance, financial systems, and membership. The Board serves until the first AGM (within 18 months of incorporation).

• Establish policies, bylaws and oversight
• Collaboratively set and uphold DCLT’s vision, mission, and strategy.
• Develop and set strategic and financial plans
• Be accountable to members and meet legal, ethical, and fiduciary obligations.

• Attend monthly Board meetings
• Ensure legal and regulatory compliance
• Steward financial planning and health
• Develop membership and community engagement
• Promote and grow DCLT’s assets and reputation
• Collaborate with DCLT’s committees and working group

Previous board experience not required. Board training is provided.

We want a board that reflects the community it serves. No one person needs every skill. As a whole, we are looking for coverage across:

• Knowledge of the Davenport neighbourhood, its history and its people
• Lived experience of housing insecurity, displacement, or tenancy
• Indigenous representation and knowledge
• Community organizing
• Real estate, development, or mortgage experience
• Architecture or urban planning
• Legal knowledge
• Fundraising
• Financial management, bookkeeping, or accounting
• Operational and organizational experience

• Be eligible under ONCA: 18+, never bankrupt, not deemed incapable under the law.
• Will become a member of DCLT upon incorporation.
• Has shown commitment to, or experience relevant to, DCLT’s mission or the community land trust sector.
• Lives, works, volunteers, or was previously displaced from within DCLT boundaries/community. Candidates with a meaningful connection to DCLTʼs area, who do not meet this criterion, may make a case through the application process.
• Aware, available and willing to take on Board responsibilities.
• Willing to contribute approximately 5-8 hours per month:
  • Monthly: 1-hour board meeting and 4–5 hours preparatory work. Periodic 2-hour working group meetings

An independent panel creates a shortlist. The Working Group elects five directors from it.

Application: Deadline Sept 15, 2026, 5:00pm EST

Link takes you to a Google Form; no google account is needed to apply.

If writing isn’t accessible for you, get in touch and we’ll take your answers by phone. Questions? Contact Bria Cole at bria.rogers.cole@gmail.com

The post Davenport Community Land Trust appeared first on Capacity Canada.

♦

Position Title: Volunteer Board Member (various positions)

Organization: Franklin Carmichael Art Group (FCAG)

Location: 34 Riverdale Drive Etobicoke, ON M9V 2T3

Term of Service: 3 years, renewable once Time Commitment: up to 15 hours/week

Founded by the late Dr. Ann Curtin, Franklin Carmichael Art Group (FCAG) is a charitable organization whose mission is to provide visual art opportunities in North Etobicoke.

FCAG serves the community by cultivating art appreciation and engagement by:

1. Providing classes, workshops and instruction in art and encouraging artistic talent.
2. Arranging art exhibitions, juried shows, establishing and granting prizes, awards and distinctions.

As a volunteer run organization, our Board members are much more than advisors. Board Directors are actively involved in bringing plans to life—leading initiatives, engaging the community and ensuring FCAG’s mission is realized in everything we do.

We are looking for passionate leaders to step into vital board roles this Fall. Positions include:
– President
– Secretary
– Director At Large

Directors are responsible for all business related to the day-to-day management and operation of the Corporation including, without limiting the generality of the following:

1. The establishment of policies and procedures
2. The supervision of the financial stability of the Corporation
3. All contractual obligations which have been authorized by the Corporation,
4. Ensuring that the objectives and management of the Corporation are properly carried out; and the management of the Corporation’s affairs.
5. Admission of members to the Corporation.
6. Attend monthly or quarterly board meetings and actively participate in shaping organizational goals.
7. Fiduciary Oversight: Review annual budgets, financial statements, and audit reports.
8. Committee Participation: Serve on at least one board committee (e.g., Fundraising, Governance, Finance).
9. Advocacy & Support: Act as an ambassador for the organization in the community and assist with resource development or fundraising.

Responsibilities of the President

1. The president shall, when present, preside at all meetings with the Board and members.
2. The president shall supervise the affairs and operations of the Corporation, sign all documents, except committee reports, requiring signature according to the Corporation’s Act, and have the powers and duties from time to time prescribed by the Board or incident to the office.
3. The president shall also act as a liaison with staff and volunteer committees.

Responsibilities of the Secretary

The Secretary is responsible for giving notice of all meetings of the Board and members and shall perform such other duties relative to the office as prescribed by the Board including:

1. Recording the minutes and reports and attending to correspondence pertaining to FCAG business;
2. Providing safe custody of the minutes of all meetings and supervising the safe keeping of the Corporate Seal, the letters patent. All books, papers, records, and correspondence belonging to FCAG;
3. Co-signing with the President, all by-laws, resolutions, Policies, Contracts and agreements of FCAG.

Responsibilities of a Director at Large

1. Active member and representative of the Board of Directors;
2. Supports and/or participation in special events, programs, and initiatives specified by the Board of Directors;
3. Undertakes special one-time projects or ongoing projects with the approval of the Board of Directors;
4. Fulfils other general Board duties as required.

1. Commitment to FCAG mission, vision, and values.
2. Able to commit the needed time to the position.
3. Ability to build strong relationships with members, volunteers and the Board.
4. Tech-savvy with access to a personal computer.
5. Experience in specialized fields like technology, law, operations, or non-profits is a strong advantage.
6. Prior board experience is an asset, but not required (we welcome first-time board members).
7. Strong communication and collaborative teamwork skills.

Additional Skills and Experience: Experience or knowledge in areas such as IT and website management, digital platforms, people resources, volunteer and member management, marketing and communications, partnerships and fundraising, sponsorship, community outreach, historic building facilities and operations, arts administration, cultural management, nonprofit and charitable organizations, or municipal services would be an asset.

Director-at-Large: Prior Board experience is an asset, but not required. We welcome first-time Board members.
Please submit a brief statement of interest or resume to The Franklin Board of Directors at fcag.art@gmail.com by Friday, October 23, 2026.

The post Franklin Carmichael Art Group appeared first on Capacity Canada.

The post BRAWL IN THE FALL appeared first on Grand River Rocks Climbing Gym.

 TRAC - Technical, Remediation & Advisory Committee

You have been and continue to be led by the nose by the above parties exactly like the original UPAC, followed by CPAC, RAC & TAG. There was a 4 1/2 year period  (2011- Aug. 2015) when CPAC wagged the dog versus Chemtura and the M.O.E.. Your colleague Sebastian Seibel-Achenbach was on that CPAC and for a very short while we had Chemtura and the M.O.E. on their heels, actually listening in trepidation to what we told them they had to do. And along came Sandy and Mark Bauman and Chemtura/M.O.E. breathed a sigh of relief as corporate order and control was restored. Under their first 21 years and last 11 years of total control over the cleanup, it has totally failed. Neither NDMA nor chlorobenzene much less DDT, dioxins and lots more solvents, chlorinated or otherwise, have been reduced to Ontario drinking water standards. Nor will they be and those parties knew that full well in 1990 and have lied to we the citizens ever since. Much greater expenditure of funds including specific DNAPL removal was needed as well as simply even following through on Target pumping rates of wells which they grossly failed to do.

Stand up and take their power, their credibility and their legitimacy away from them! They are not and have never been honest brokers. 

I sat across from a young woman last month who couldn’t have been more than 24, who told me she was thinking about leaving engineering. She was brilliant, Technical, sharp, and thoughtful.

But somewhere between her entry into the tech sector and her early days, someone made her feel like she didn’t belong there.

I recognized that feeling immediately.

Because I started as an engineer, too. Back when that world wasn’t exactly rolling out the welcome mat for women. I remember the meetings where my ideas landed differently when I said them versus when a male colleague presented something similar.

I remember the networking events where I was often the only woman in the room, and not in a powerful way, but in a way that made you hyperaware of every word you said. I remember thinking, more than once, that maybe this wasn’t built for people like me.

But here’s what changed everything: I found my tribe.

Not everyone discouraged me. Yes, there were voices that questioned whether women belonged in STEM, voices that made the path steeper than it needed to be.

But there were also people who were thoughtful and cooperative, who believed differently. Who saw my work, not my gender. Who invited me into conversations, opened doors I didn’t even know existed, and most importantly, treated me like I belonged there.

That support wasn’t loud. It was quiet, consistent, and reassuring.

These people became more than just colleagues. They became my tribe.

The kind of people who knew my strengths before I fully believed in them myself.

They pushed back on the assumptions in the room. They made space for different perspectives. They built something I didn’t see coming: they built the evidence that a woman’s voice mattered in engineering.

What I didn’t realize then was that I was learning something far more valuable than any technical skill.

I was learning what it looked like when someone believed in you enough to create space for you. I was learning how support actually works, not through grand gestures, but through small, consistent choices. Through showing up. Through believing first, and letting their belief become your foundation until you could build your own.

Years into building my career, I realized something: the crack in the system, the place where the assumption that women aren’t suited for STEM lives, wasn’t something to accept. It was somewhere I could shine my light.

I started asking myself a question: What if I became the person for someone else that my tribe had been for me?

What if every woman I met who was thinking about leaving or maybe doesn’t feel like she belongs here didn’t have to feel alone in that decision?

That’s when CodeGirls began.

CodeGirls is a project close to my heart, a place where young women can learn real, industry-ready tech skills that will help them in their careers.

It’s a transformative women’s empowerment initiative that equips young women with industry-ready coding and business skills to help them build careers and achieve financial independence. Since its inception, CodeGirls has delivered 2,000+ trainings, achieved a 70% placement rate, and helped graduates reach an average monthly salary of 55K+, with its work spanning Karachi, Skardu, and South Africa.

And alongside that, something even more essential: a community that believed they belonged there from day one.

Because here’s what I know now: the missing piece was never whether women could code.

Women can code.

Women have always been able to code.

The missing piece was the tribe.

The people around you who make space for your growth. Who see your potential before you’re finished. Who believe in you when the system doesn’t.

Through CodeGirls, we’ve watched this transformation happen over and over. We’ve seen women come in uncertain, often having been told by culture, by family, by the world that this path wasn’t for them. And then they work anyway. They build. They lead. They are breaking the stereotypes and proving most men wrong about women’s inclusion in tech.

I think about that 24-year-old woman across from me, and I think about thousands like her. Every time a young woman decides not to leave engineering because she found her people, that’s everything.

Every time a woman realizes that belonging isn’t something she needs to earn but something she gets to build with others, that changes the trajectory of her entire life.

The system hasn’t changed overnight. There are still cracks, still assumptions, still voices that question whether women belong in spaces built for technical thinking and leadership. But what has changed is this: more of us now know better. Who know what it feels like to be believed in.

Who are determined to be that belief for someone else.

Networking isn’t about collecting business cards or climbing the right ladder. Real networking is about finding your tribe. The people who see you. The ones who make space. The ones who believe in you enough to pull you into conversations you didn’t know existed.

And once you find them, the next step is simple but profound: become that person for someone else.

That’s the work CodeGirls does. That’s the work we’re committed to at Genetech Solutions, too.

That’s the work that happens every time a woman who made it decides to open the door for another woman who’s just beginning.

The cracks in the system are also the places where light gets in. I hope you can benefit from my learnings.

I’d love to hear about your experience when you started, struggles you encountered, the mentors who believed in you, the tribes you’re building, the spaces you’re creating, and the doors you’re opening.

And if you’re looking for a way to open a door for another woman, consider sponsoring a CodeGirl. Your support helps a young woman gain industry-ready tech skills and move toward a career in the technology industry.

You can also hire a CodeGirl and give a talented woman the opportunity to put her skills to work in the industry.

If you’d like to chat about how we can support more women in tech and entrepreneurship, I’d be glad to help.

If you made it this far, we’re probably thinking about the same things. Follow me on Medium so you don’t miss what comes next: medium.com/@shamim_rajani

Shamim Rajani, COO @ Genetech Solutions | 20+ years in tech | Sharing insights on AI, tech, business, and empowerment.

Let’s connect on LinkedIn, Substack, and Twitter

When Women Believe in Women, Everything Changes was originally published in Code Like A Girl on Medium, where people are continuing the conversation by highlighting and responding to this story.

Seong, M., Kim, H. G., Yun, B., Kim, M., Kim, J.-W., Jeong, H., Kim, S., Kim, J. H., & Cho, K. H. (2026). Hybrid deep learning-numerical modeling framework for long-term prediction of groundwater discharge and radionuclide transport. Journal of Hazardous Materials, 504, 141346. doi.org/10.1016/j.jhazmat.2026.141346

CLICK HERE TO READ THE ARTICLE.

“The model was trained to predict monthly groundwater discharge (GWD) and radionuclide transport (RNT), and its performance was evaluated against the results from HGS, which serves as a physically rigorous reference model.”

“Because real-world data for the hypothetical ADioS watershed were unavailable, the HGS model, which is a fully distributed, physically based, integrated surface–subsurface hydrological model, was used to simulate groundwater flow and radionuclide transport for target feature extraction.”

Graphical Abstract

We're pleased to highlight this publication by Minkyeong Seong and colleagues, which presents a hybrid deep learning–numerical modelling framework for improving long-term predictions of groundwater discharge and radionuclide transport. Using HydroGeoSphere (HGS) as the physics-based reference model, the researchers combined process-based numerical modelling with a graph convolutional long short-term memory (GC-LSTM) deep learning model to produce long-term predictions while dramatically reducing computational costs.

Long-term prediction of groundwater flow and radionuclide transport is essential for evaluating the safety of deep geological repositories used to store radioactive waste. While fully integrated numerical models such as HydroGeoSphere provide highly accurate simulations of coupled surface water, groundwater, and contaminant transport processes, these simulations can become computationally demanding for assessments spanning decades, centuries, millenia or even millions of years. Simpler models offer faster runtimes but often sacrifice accuracy by neglecting important processes such as unsaturated flow. This study addresses that challenge by combining the strengths of physics-based modelling with artificial intelligence to improve both efficiency and predictive performance.

Fig 1. Overview of the hybrid modeling framework: (1) data preprocessing from two numerical models: adaptive process-based total system performance assessment framework (APro-BIO) and HydroGeoSphere (HGS); (2) graph construction for the graph construction for the graph convolutional long short-term memory (GC-LSTM) model; (3) model development for predicting groundwater discharge (GWD) and radionuclide transport (RNT); (4) model training and eval uation; and (5) model interpretation using GNNExplainer.

The researchers developed a hybrid modelling framework using outputs from the Adaptive Process-Based Total System Performance Assessment Framework (APro-BIO), together with HydroGeoSphere simulations, to train a graph convolutional long short-term memory (GC-LSTM) model. HydroGeoSphere served as the benchmark model by generating groundwater discharge and radionuclide transport data that the deep learning model learned to reproduce. By incorporating hydrologic variables alongside key unsaturated soil properties, including van Genuchten parameters, the GC-LSTM captured the complex spatial and temporal interactions governing groundwater flow and contaminant transport throughout a hypothetical watershed representing a deep geological disposal system.

The hybrid framework demonstrated strong predictive performance, achieving Kling-Gupta Efficiency (KGE) values ranging from 0.67–0.85 for groundwater discharge and 0.60–0.81 for radionuclide transport. Compared to the simplified APro-BIO model, the GC-LSTM reduced discrepancies with HydroGeoSphere by up to 99%, closely reproducing both the timing and magnitude of groundwater discharge and radionuclide transport across the watershed. Once trained, the deep learning model generated predictions in less than one minute, providing more than a 3,000-fold improvement in inference speed compared to running new HydroGeoSphere simulations while maintaining excellent agreement with the physics-based model.

HydroGeoSphere played a central role in this research by providing the physically rigorous benchmark required to train and validate the hybrid modelling framework. Its fully integrated simulation of saturated and unsaturated groundwater flow, surface water interactions, and radionuclide transport enabled the researchers to accurately represent complex hydrologic processes that simplified models cannot capture. HydroGeoSphere also demonstrated the importance of unsaturated zone processes, showing that accounting for soil hydraulic properties significantly improves predictions of groundwater recharge, groundwater discharge, and radionuclide migration.

This research demonstrates how HydroGeoSphere can serve as the foundation for next-generation hybrid modelling approaches that combine physics-based simulations with artificial intelligence. By leveraging HGS to generate high-quality training data, the researchers developed a framework capable of maintaining the accuracy of fully integrated hydrologic modelling while dramatically reducing computational costs. The approach offers significant potential for long-term groundwater safety assessments, radionuclide transport studies, and other computationally intensive environmental modelling applications where both speed and reliability are essential.

Abstract:

Accurate long-term prediction of groundwater flow and radionuclide transport is critical for assessing the safety of deep geological disposal systems. This study proposes a hybrid modeling framework that integrates a numerical model with a deep learning approach to improve the predictive accuracy and computational efficiency. Outputs from the adaptive process-based total system performance assessment framework (APro-BIO) model, including water level, surface water flow, groundwater recharge, groundwater discharge (GWD), groundwater flow velocity, groundwater level, and radionuclide transport (RNT), together with van Genuchten parameters, were used as input features. Monthly groundwater discharge and radionuclide transport simulated by HydroGeoSphere (HGS) served as target variables, and a graph convolutional long short-term memory (GC-LSTM) model was trained to capture spatial and temporal dependencies. Model performance was evaluated against that of HGS, representing coupled saturated–unsaturated flow. The GC-LSTM achieved Kling–Gupta Efficiency values of 0.67–0.85 for GWD and 0.60–0.81 for RNT and reduced discrepancies relative to that of APro-BIO by up to 99 %. The model effectively reproduced temporal variability while reducing computational cost. Explainable AI analysis identified the van Genuchten β parameter as the most influential feature. These results demonstrate that the proposed framework provides an efficient and reliable alternative for long-term GWD and RNT prediction under computational constraints.

CLICK HERE TO READ THE ARTICLE.

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First of all is it fair to call a compulsive, pathological thief by any such name each and every time they walk into a store? Is it fair to call CRA/GHD intellectual prostitutes each and every time they write an alleged scientific report. I described here a little bit about their latest (Sept. 3, 2026) effort titled "Bench Scale Study for Biodegradation of Chlorobenzene and NDMA" yesterday.  

I have confirmed with a past APTE coordinator that we had all been told decades ago by Uniroyal Chemical and their consultants that chlorinated solvents such as trichloroethylene, chloroform, chlorobenzene etc. do not easily degrade or break down on their own in soils. It turns out however that chlorobenzene is the exception to that rule. Now according to this latest report by GHD, chlorobenzene actually degrades very well on its' own in shallower, aerated soils and less so in deeper anaerobic soils as per pages 7 & 8. The Figures section, particularly Figures 1 and 5 also confirms this. This of course speaks very badly of Uniroyal and CRA because the inaccurate information was used to deny chlorobenzene's presence on the Varnicolor Chemical site when zero chlorobenzene was found in Varnicolor's shallow aquifer also known as the Surficial Aquifer (SA).  

Other problems exist including the premature thawing of frozen groundwater samples meant to be used in the Bench Scale Biodegradation Study. While I appreciate the admission of this unfortunate incident that occurred at U.S. customs I am less comfortable with GHD proceeding with their experiments using compromised groundwater samples rather than starting over. Another problem might be GHD's repeatedly advising that their Biodegradation Rates may very well be biased on the high side due to possible volatilization of compounds from the groundwater samples. I wonder if this so called volatilization didn't occur and they spiked the samples according to page 3 with 150 ppb. chlorobenzene and 2.4 ppb. NDMA, if their Biodegradation rates might actually be biased on the high side. Either way again I find their protocols surprising and disturbing. It seems to be a vey peculiar way to be conducting scientific experiments. It's almost seems to be a "seat of the pants" scientific protocol. Something goes wrong so instead of really fixing it or starting over they just make a change and continue on.

Assuming for the moment that their experiments have some validity what I conclude is that dissolved chlorobenzene degrades in both aerobic (oxygen) and anaerobic (low oxygen) environments. NDMA on the other hand seems much less capable of degrading via human intervention such as amendments and nutrients both in shallow and deeper soils. Of course as mentioned yesterday chlorobenzene has other characteristics and the issue is not what the dissolved chlorobenzene will do as much as what the Dense Non Aqueous Phase Liquid (DNAPL) chlorobenzene will do. The "Non Aqueous Phase" part of that acronym makes it clear that DNAPLS are NOT dissolved in water chlorobenzene but thicker, heavier pools or puddles probably residing in depressions of lesser permeable soils such as silts and clays. Likely some of these have dissolved over the last thirty years but any claims by Lanxess and fellow travellors that they are fully dissolved is nonsense, wishful thinking and maybe even intellectual prostitution.