Founder @ https://xitter.cf/t.co/73ZP71hlt9 - building benevolent Skynet of Construction
Toronto, Ontario
Joined July 2017
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We just raised $2M to build intelligence layer for construction.
My last startup failed. We built a product and got users, but we couldn’t fundraise and couldn’t close sales. For months afterwards, I felt like a failure.
When I decided to try again, I started with what we’d gotten wrong: sales. As a technical founder, I knew how to build, not how to sell. So I talked to 30+ potential co-founders until I met Lylia Djait-Paulien, who spent years at IBM leading complex enterprise deals.
Then we went looking for a problem expensive enough that a business would pay to solve it. Neither of us had ever worked in construction. Cold calls turned into meetings, meetings turned into pilots, and pilots turned into annual contracts.
It took 18 months and a pivot to get here. Today, Lylia and I are announcing a US$2M pre-seed round for Tyce, backed by BDC Thrive Fund and MaRS Investment Accelerator Fund.
Construction is a $2T+ industry in North America, yet AI has barely touched how it works. We’re changing that.
Tyce is building the intelligence layer for the built world, giving teams grounded answers from their own project data in seconds, so they can catch multi-million-dollar mistakes before construction starts.
We’re hiring founding engineers in Toronto (full-stack and AI agents). If you want to build AI that will shape the physical world, DM me or tag someone below 👇
People stopped talking about a role of prompt engineer, turns out every knowledge works is now prompt engineering.
Anuar retweeted
.@sama says the advice to pick one thing to build and pour all your focus into it no longer applies.
"The canonical advice that was correct two years ago was, 'Pick one, and put all your effort into it, because you can't build all 30 things.'"
"You can now build all 30 things. And you can get people to use them all. And you can work more on the ones that work."
"So I think this mindset of lots of ideas, quick feedback, and being open to a lot of things not working is great for this new era for how people build."
Great AI engineering requires understanding where the uncertainty and failure points could be and decomposing it into verifiable agent runs.
Something I've seen that's different about how AI works with code and how I work with code is that AI reads documentation about how to do something. It just assumes that everything works, builds out the whole entire workflow or the integration, and then tries to run it and test it. When something breaks, it's not always obvious where it breaks or how it breaks. With smarter models, we've been able to figure out where it's breaking, but it tries to guess where something's breaking and then tries to fix that. Sometimes the fix works, and sometimes it doesn't. It'll show up as a problem somewhere else, and it'll do a hacky fix there and a hacky fix somewhere else. Sometimes a fix that you're doing upstream means you need to rearchitect or change everything downstream.
What do you do? This is very, very different from how I work and what I've seen most developers work. I think we verify each step to see whether it works exactly like the documentation says it should work. Usually, this approach means that if step number 3 fails in a subtle way, we will fix that step, and then we'll devise the downstream steps. That usually ends up in a much better-quality integration.
I'm not really sure. Maybe this just needs to be a skill, especially when working with external tools, and there could be a skill or something to put in your agents.md. I don't really know, but just something observed.
All the AI features getting commoditized.
The moat is now in the hands of founders who put this all together into a product that produces other types of moats: data, network effects, economies of scale…
every major company is arriving at roughly the same offering:
persistent memory, email/calendar/messages, browser + computer use, background tasks, proactive notifications, voice, app/tool execution, ambient context, & some notion of a personal agent sitting above everything.
remarkable levels of convergence with very little differentiation whatsoever.
Anuar retweeted
huge morning for Canadian Investment Summit
- Effective today Canada will allow immediate expensing for most new capital investments
- Reducing federal employee count by 10%, same level as 2015
- Cutting spending on consultants by 20%
- "our capital markets host 40% of world's mining companies"
- Canada unlocking airport competitiveness through privatization of operating companies. taking those profits helping less profitable regional airports
- Canadian pensions already own airports in Europe & Oceania, have expertise
- Lufthansa can fly Frankfurt to Toronto, but it's banned from flying Toronto to Vancouver. Foreign competition is illegal here, and we wonder why fares are so high.
- Budget airlines have failed in Canada partly because economics at federal operated airports are higher than anywhere in world
- Ottawa's own review said Canada's big airports need private-sector discipline and should be sold. That was a decade ago. Today we are acting. $AC.TO $ONEX.TO
BREAKING NEWS: CANADA EXPANDS TAX BREAK TO BROAD RANGE OF INVESTMENTS INCLUDING PIPELINES — BLOOMBERG.COM
Anuar retweeted
We just announced Canada’s new Productivity Mega Deduction.
This cuts Canada’s marginal effective tax rate from 13% to 6.4% on new business investment — the lowest of any major economy in the world.
Canada is building big — and becoming the best place in the world to build.
More here: pm.gc.ca/en/news/news-releas…
Anuar retweeted
BREAKING: People aged 65 and older now outnumber children aged 5 and younger worldwide for the first time in recorded history, per Axios
Anuar retweeted
JUST IN: Garry Tan says 2 startups just broke the oldest math in business.
▫️ Emergent: 9 figures in 8 months, 15 people
▫️ Retell: $60M ARR, ~40 people
Revenue-per-head like this never existed before.
Check the full breakdown here 👇
the-ai-corner.com/p/garry-ta…
Anuar retweeted
Introducing GLM-5.3-Flash
- Leading capabilities at a highly competitive price
- Natively multimodal with a 1M-token context window
- A 320B-A18B model released under the MIT License
- Previously previewed as Ox Alpha, running entirely on Chinese AI chips
Blog: z.ai/blog/glm-5.3-flash
Available now across all official platforms:
Weights: huggingface.co/zai-org/GLM-5…
API: docs.z.ai/guides/llm/glm-5.3…
Coding Plan: z.ai/subscribe
ZCode: zcode.z.ai/en
Chat: chat.z.ai
AutoClaw: autoclaw.z.ai
A lot of engineers who claim to have crazy outcomes of AI coding agents like full web apps in a single run, are not running it in production with users. Once you do, you would realize that all the work is going 95->99.99% of completion - improving UX, fixing bugs, etc...
Engineering mind cannot comprehend this
Can confirm this is accurate.
Know tons of reps that have made life-changing money here.
cc @ryan_c_walsh
Anuar retweeted
Since the launch of ChatGPT, the Philippines offshore industry (8% of country’s GDP) has actually grown much bigger.
Employment in IT and business outsourcing is up by +20% to 1.9m workers and industry revenue is up +30% to $42B.
“AI is helping offshore workers in the Philippines do new, more complex jobs. They are picking up work training AI models or supervising AI agents. Hospitals in America are increasingly outsourcing the checking of insurance eligibility and filing of medical records to Filipinos packing AI tools. Mr Gallimore says more “high-value” work, such as accountancy or engineering, is going offshore in part because “AI is a leveller. You can now teach someone complex stuff quickly” and still get it done more cheaply than in America or Europe.”
***
More here: economist.com/asia/2026/08/0…
