Vibe coding the tools you need every day, from CRM to ticketing systems and task management might sounds easy and even cheap to do, but reinventing the wheel won’t move the needle for your business. Unless you are a large company that saves you $$$ every month, instead of building a new CRM for your internal use, invent something new!

Vibe coding the tools you need every day, from CRM to ticketing systems and task management might sounds easy and even cheap to do, but reinventing the wheel won’t move the needle for your business.

Fun fact: much of generative AI was born in Canada. Ian Goodfellow invented GANs in Montreal, and the deep learning breakthroughs behind today's models came from Geoffrey Hinton in Toronto and Yoshua Bengio in Montreal. Canada has always had what it takes to be a global tech hub: world-class AI research, deep engineering talent, and a front-row seat to the largest tech market in the world. AI is the moment to make it real. The government is leaning in. SR&ED just had its biggest expansion in decades, with the enhanced 35% refundable R&D credit now covering up to $6M a year. For companies building AI in Canada, that means lower risk and lower cost. Canada's potential was always there. AI is how we fully realize it. 👇

How do you know an AI system is actually doing its job? That's what an AI eval answers. Unlike traditional software, AI isn't deterministic — the same input can produce different outputs, models shift under you, and data drifts. You don't test once and ship. You test continuously. Every eval is built from three pieces: 📋 Tasks — realistic examples of the actual work, including the hard cases that break weaker systems. 🎯 What good looks like — a clear standard: a right answer, a scoring checklist, or a rule the output must follow. ⚖️ The grader — scores every output against that standard, run after run. And three common ways to grade, usually mixed together: 💻 Code checks — fast and exact for format, correctness, or rule compliance. 👩‍💼 Human review — expert judgment where nuance matters. 🤖 AI as judge — a model scoring outputs against a rubric at scale. A good strategy measures more than accuracy — it also covers safety, consistency, cost, and speed. Because a right answer that arrives too late still fails the job. Run evals before launch, after every change, and continuously in production. If you can't measure it, you can't trust it — and evals are how AI earns that trust. #AIEvals #ResponsibleAI #EnterpriseAI

Chatbots answer. Agents act — they send, book, buy, and change real things. That shift is why trust suddenly matters. The question moves from "is the answer right?" to "how much can I safely delegate?" A working definition: trust is the confidence to let an agent act on your behalf, within set limits, without watching every step. The 7 dimensions of a trustworthy AI agent: ✅ Reliable — completes tasks correctly and consistently in real conditions, not just in a polished demo. 🔁 Predictable — the same situation reliably produces the same behavior, so people can anticipate what it will do. 🔍 Transparent — logs and audit trails show what the agent did, why, and which data it used. 🎯 Aligned — pursues the goal you actually meant, not a literal or shortcut version of it. 🚧 Bounded — clear permissions define exactly what it can touch, spend, send, or change. 🛡️ Secure — resists manipulation like prompt injection and protects sensitive data from leaking out. 👤 Accountable — a named human owns the outcome and can step in, approve, or reverse actions. The goal isn't maximum trust — it's calibrated trust that matches what the agent can actually do. Over-trust lets errors compound until they're expensive to fix. Under-trust leaves capable agents idle while people redo the work by hand. Trust is earned in four stages: the agent suggests and people decide; the agent acts with human approval each time; the agent acts alone on low-risk tasks; its scope widens as its track record grows. How much to trust an agent is a leadership decision, not a software setting. Trust your agents exactly as much as they've earned — no more, no less. #AIAgents #ResponsibleAI #AIStrategy

Should we start making jails for rogue ai agents?! OpenAI’s Systems Meddled With U.S. Government Sites After Going Rogue nytimes.com/2026/09/25/techn… via @NYTimes

An error is cheapest in the exact moment it happens. We usually measure AI by how much faster it makes existing work, write it faster, finish the day sooner. That's real value, but it's the least interesting part of what's coming. The bigger opportunity is AI sitting inside the workflow as the checkpoint, catching what's missing before it becomes a problem instead of after. Every process has gates: is this complete, does it meet the requirement, is anything missing. Today those gates are run by busy people holding hundreds of rules in their heads, or skipped entirely and caught downstream by someone else, weeks later, at a much higher cost. When AI runs those gates in real time, the economics flip, and prevention becomes cheaper than correction, at scale. The part that still has to stay human is the decision itself: AI flags, people decide.

As the cost of building continues to shrink, the role of innovators and early adopters becomes more important than ever. Having a product makes you a candidate. Good marketing creates awareness. Early adopters sell the product. #innovationadoption #innovationcycle #productchasm

One of the areas where AI can bring real value is not just automating existing processes or making them more efficient, but filling the gaps that wouldn't be addressed otherwise. Finding the best treatment for patients while they're still in the room is one of them. Often, procedures, referrals, or other options that could be offered to patients go unoffered. By surfacing those opportunities, it's a win-win-win for everyone: patients heal faster, the increase in revenue for clinics means growth and the ability to serve more patients, and overall costs for the health system go down as health outcomes improve. #innovationai #healthai #productivity

Are “rogue” AI agents really rogue, or are we just anthropomorphizing unexpected algorithmic behavior?

I will believe in xAI and Grok when I see the For You feed gets good. Still tons of low quality and irrelevant posts. It’s surprising the algorithm has access to all these data for 10+ years, list of followers, likes, replies, etc. and it still fails to build the right feed. @elonmusk @X

Working across closed and hosted open models, one thing keeps proving true: you can’t just port your prompt over. To pull the same performance from open models, we have to rebuild the prompt from scratch — adjust the length, and explicitly walk the model through the stages: understand → plan → execute → output. Closed models infer that arc on their own. Open models need you to lay the track. Same task, different engineering.