Over time, help desks have become complex platforms: endless configuration, per‑user pricing, feature sprawl, and workflows designed for organisations with dedicated support operations. That complexity comes with real costs — not just money, but attention, time, and cognitive load.

We wanted to take a very different approach.

Opinionated by Design

Kindly is intentionally opinionated.

That means we make decisions so you don’t have to.

• One knowledge base style
• Fixed ticket statuses
• A single, coherent workflow
• No elaborate rule engines or branching logic

This isn’t a limitation — it’s a constraint that keeps the product simple, predictable, and fast to use.

Opinionated software tends to age better. It’s easier to learn, easier to maintain, and easier to trust. Tools that try to be everything to everyone often end up being frustrating for most people.

Kindly is designed for teams who want to get on with their work, not administer their support tool.

Inspired by 37signals (and Fizzy)

Kindly draws clear inspiration from companies like 37signals, who have consistently shown that:

• Simpler products win long‑term
• Clear constraints create better outcomes
• Software should respect the user’s time

We’re also influenced by smaller, focused tools like Fizzy — products that do one job well, with clarity and confidence, without pretending to be an enterprise platform.

That philosophy runs through every decision in Kindly.

Simple Pricing, on Purpose

Support tools are often priced per user.

That sounds reasonable until you realise it actively discourages collaboration. Teams end up sharing logins, restricting access, or avoiding involving the right people — all to control cost.

Kindly is not priced per user.

You can invite your whole team without doing pricing gymnastics. Support should be a shared responsibility, not a line item you try to minimise.

The pricing is deliberately simple and cost‑effective, because predictability matters — especially for small teams and early‑stage products.

Built for Small, Focused Teams

Kindly is for teams who:

• Want a calm, reliable support setup
• Don’t need endless customisation
• Value clarity over flexibility
• Prefer tools that get out of the way

If you’re looking for deeply bespoke workflows, complex automation trees, or enterprise‑grade reporting, Kindly may not be the right fit.

And that’s okay.

We believe strongly that a smaller, opinionated product — built with care — can serve its audience far better than a bloated one trying to cover every possible use case.

The Long‑Term View

Kindly is still early, but the direction is set.

We’ll continue to say no to features that add complexity without clear value. We’ll continue to optimise for simplicity, usability, and calm. And we’ll continue to build software that we’d genuinely want to use ourselves.

That’s the why behind Kindly.

If that resonates, you’ll feel at home here.

In an earlier post, I argued that much of the recent excitement around AI in AEC has focused on outputs rather than outcomes. Beautiful images, clever concepts—but very little impact on how firms actually operate day to day.

This post builds on that idea, with a sharper focus on AI agents, why they matter now, and—just as importantly—where they don’t belong.

Two recent articles are particularly useful reference points for this discussion:

• “AI for Architecture Firm Owners” on EntreArchitect
• “AI in AEC: Agents Are Coming” by Paul O’Carroll on Medium

Both are worth reading – or listen to the EntreArchitect podcast – and both point in the same direction: AI’s real leverage in AEC is operational, not creative.

Agents are about systems, not inspiration

Paul O’Carroll’s article makes a clear distinction that’s often missing in AI conversations: agents are not single prompts or tools, but systems that act over time.

They:

• Observe state (projects, data, deadlines)
• Take action based on rules or goals
• Hand off to humans when judgment is required

That framing aligns closely with the EntreArchitect view that AI works best where processes already exist and repeat. As one of the key ideas from that article puts it:

AI is most valuable when it supports defined workflows, not when it tries to invent them.

In other words, agents don’t replace thinking. They reduce friction between moments of thinking.

The real problem: business bottlenecks

Most architectural practices don’t struggle with a lack of creativity. They struggle with flow.

Common bottlenecks include:

• Proposal and variation preparation
• Information handover between phases
• Coordination across tools and disciplines
• QA processes that rely on a few overloaded individuals
• Admin work expanding faster than fees

O’Carroll describes agents as being particularly effective in these in-between spaces—monitoring, nudging, preparing, and escalating—rather than producing final outputs.

This is where agents shine:

• Drafting before review
• Checking before approval
• Summarising before decisions
• Flagging issues before they become problems

Humans stay in control, but they arrive earlier and better informed.

Protecting creative work by automating everything else

Both articles push back—implicitly or explicitly—against the idea that AI should be aimed at design authorship.

That matters.

When AI is applied directly to creative production, firms risk:

• Homogenised outputs
• Shallow iteration
• Less time spent thinking, more time spent reacting

When AI is applied to operational drag, the opposite happens.

By removing:

• Manual coordination
• Repetitive documentation
• Status-chasing and context-switching

You free up time and energy for:

• Design exploration
• Client dialogue
• Critical review
• Leadership and mentoring

AI becomes a buffer around creative work, not an intrusion into it.

Why agents matter now

What both articles recognise—implicitly from different angles—is that firms are under pressure:

• Fees are tighter
• Teams are leaner
• Projects are more complex
• Expectations keep rising

In that environment, incremental efficiency gains matter.

Agent-based AI is compelling not because it’s impressive, but because it:

• Reduces reliance on heroic individuals
• Smooths work through the business
• Makes practices more resilient

This isn’t about futurism. It’s about removing known pain points.

A simple test for adoption

A practical rule of thumb emerges from both pieces:

If a task is repeatable, structured, and slowing your practice down, it’s a candidate for an AI agent.

Conversely, if it’s exploratory, ambiguous, or fundamentally creative, it should remain human-led.

That distinction is critical—and it’s where much of the AI noise in AEC currently goes wrong.

For most of my career, I’ve been building and leading teams across architecture, design, and technology — in-office, hybrid, fully remote, and everything in between.

One pattern has been constant: the need to understand how a team is really doing — without drowning everyone in meetings.

That tension is why I built Team Pulse.

https://teampulse.do

Why Team Pulse exists

Managing teams well is essential.

Managing them through meetings is often inefficient.

As teams scale — or become hybrid or remote — recurring group meetings tend to expand in duration and frequency. Status updates bleed into discussion. Important signals get lost. Quieter voices disappear. And large amounts of time are consumed just synchronising information.

Yet the underlying need is valid:

• What is everyone focused on?
• What’s progressing well?
• Where are the blockers?
• What patterns are emerging across the team?

Team Pulse is built on the idea that these questions don’t require live meetings to answer.

Lessons from my own businesses

In my previous companies, I experimented extensively with lightweight, written check-ins to replace or reduce meetings.

What worked wasn’t the tooling — it was the rhythm.

Regular, predictable check-ins gave me better insight than most meetings ever did. They also created space for people to surface blockers they were uncomfortable raising in more formal or public settings.

The unexpected benefit was transparency.

When team members could see what others were working on — even information that wasn’t strictly necessary for their role — it often led to:

• Faster problem-solving
• Better cross-team empathy
• Fewer duplicated efforts
• Higher overall productivity

In practice, sharing more context — not less — made teams stronger.

Async doesn’t mean disconnected

Team Pulse isn’t about avoiding communication. It’s about choosing the right format.

Written, asynchronous check-ins allow people to respond when they’re focused, not interrupted. They encourage thoughtfulness rather than performance. And they scale far better than meetings as teams grow.

For leaders, this provides a clear, ongoing view of team health and direction.

For teams, it creates shared visibility — a sense of alignment without constant calls.

Everyone benefits from the same source of truth.

How Team Pulse works

At its core, Team Pulse is built around pulses — recurring check-ins that run at a rhythm that suits your team and business.

• Teams can create one or more pulses
• Each pulse can run daily, weekly, or on a custom cadence
• Questions are short and focused (priorities, progress, blockers, wins)

Each cycle is automatically summarised, highlighting themes, trends, and emerging issues — so you’re not reading dozens of individual updates.

The goal is simple:

clarity without noise.

Designed to stay lightweight

Team Pulse is intentionally opinionated and minimal.

It’s not a task manager.
It’s not a project tracker.
It’s a rhythm and insight tool.

The free version supports small teams getting started. The paid version unlocks richer summaries, more teams, and deeper insights — without adding unnecessary complexity.

More detail is available at: https://teampulse.do

A quiet beginning

Team Pulse has grown out of real usage, real teams, and real operational needs — not a desire to ship “another tool.” Over time, it will evolve carefully, guided by the same principles that led to its creation: clarity, calm, and respect for people’s time.

If you lead a team — or are part of one — and want alignment without constant meetings, Team Pulse may be worth exploring.

As I wrote earlier, 2025 has been a personal challenge for me following the failure of Codesign so I thought it was important to review achievements in among all the challenges. As you can see many of the “achievements” are not specifically achievements so much as trying to keep moving forward.

Running

Back in 2011 I had started running again and managed a respectable 753km. Over the last few years I have managed to run over 1,000km / year (or very close to it) and this year I set a new benchmark of 1,600km. It certainly helps when you have a lot of time on your hands.

I’ve also managed to keep my monthly running streak alive with at least one run every month since January 2017.

In addition, I set a goal back in 2020 to run a half marathon a month (or more specifically 12 in one year). While I didn’t achieve that in 2020, this year I ticked it off with a total of 12 including one “race”.

Reading

This year I have read 42 books and am in the middle of a couple more. This is down significantly on quantity from last year but not on quality.

While I mostly read novels I have – not surprisingly – read a number of books related to AI to [try] to keep abreast of developments.

Work

2025 was an unplanned and unexpected gap year (on top of 2024 which was that way as well.)

This has been the primary source of my 2025 challenges.

In total I worked (for income) for 51 hours (or 2.5% of a standard year.)

In addition I helped out at NXT BLD / NXT DEV which was great fun.

I applied for over 50+ jobs and have been rejected for all of them – aside from a couple of applications that are still open at year end. This has not been fun at all and mentally exhausting.

Web Apps

To keep my occupied and curious I have spent a lot of time developing apps.

I have developed 6 Rails Apps of various types and to varying degrees of completeness. Some have business goals in mind while others have been concepts to explore both Vibe Coding and the inclusion of AI Agents within the App.

Two (maybe three) of these apps will be launching in 2026.

Writing

I restarted my writing in 2025 with 10 Posts and plan to continue this in 2026. There is a huge amount happening in the AEC Tech space and things continue to accelerate so it will be interesting to watch developments. I think we’ll start to see some consolidation in 2026 in some areas.

Statistics

Overall these feel more like statistics then straight-out achievements but either way that’s a wrap for 2025:

• 10 Blog Posts
• 51 Hours Work
• 50+ Job Applications and rejections
• 42 Books
• 1,600km Running
• 12 Half Marathons

It’s great to see the awards continuing to thrive. The 2025 NZIA Student Design Awards showcase an impressive range of ideas and deeply thought out responses to today’s design challenges. If you want a glimpse of architecture’s future, this year’s finalists are well worth a look.

2025 Awards: https://www.nzia.co.nz/awards/student-design-awards/2025-student-design-awards/

Cursor recently began launching a set of lessons for getting the most out of their AI, and they’re genuinely excellent resources. While they have only released a handful of lessons to date they clear and easy to understand; provide working examples and set the foundations for when they get more detailed. If you’re curious: https://cursor.com/learn

But as good as this new wave of AI-assisted development is, it’s important to restate a truth that often gets lost:

And more importantly:

AI Still Makes Confident Mistakes

Even today, the most advanced coding assistants make authoritative—but incorrect—assumptions.

A very fresh example: while working with a third-party library, the AI confidently suggested intercepting a specific event. It wrote the code, wired up the handler, and explained exactly how the event would flow.

One problem:

The event didn’t exist.

Only when I explicitly asked the AI to read through the actual source code of the library did it realise that its initial suggestion was a hallucination. It guessed the event name based on patterns it had seen elsewhere—and got it wrong.

A junior developer might do something similar, but the difference is crucial: an experienced developer can quickly spot that something “smells off.” A non-developer or early junior may not.

AI Agents Are Not Autonomous Engineers

Cursor describes this dynamic particularly well in their lesson on agents:

“Think of agents like fast junior developers who need clear direction, who also can easily forget things, so they require oversight. They can get stuck in loops, repeating the same failing approach without recognizing they need a different strategy.”

— https://cursor.com/learn/agents

This is the perfect framing.

AI assistants are fast.

They’re helpful.

They can unlock productivity you simply don’t get from traditional tooling.

But they are not senior developers.

They are not architects.

And—crucially—they are not responsible for correctness.

You are.

AI Raises the Floor, Not the Ceiling

I genuinely believe AI is transforming software development, and it’s only accelerating from here. It does raise the floor—people with little experience can achieve things previously out of reach.

But the ceiling still depends on human judgment, experience, and the ability to reason about the problem space.

AI can propose code.

AI can scaffold an app.

AI can refactor, explain, and optimise.

But AI cannot yet:

• Validate architectural choices
• Detect subtle conceptual errors
• Spot when a generated solution contradicts the underlying system
• Recognise when it made up an API call or event that doesn’t exist
• Apply pragmatic tradeoff thinking

The Future: Developers Who Use AI vs Developers Who Don’t

The real divide emerging in our industry isn’t between developers and non-developers—it’s between developers who can leverage AI effectively and those who can’t.

AI is a multiplier.

If you bring experience, context, and judgment, it makes you dramatically more productive.

If you don’t, it can amplify your mistakes.

And just like a junior developer, AI needs guidance, constraints, and oversight.

The magic happens when you combine AI’s speed with human intuition and expertise.

It’s been almost two years since we made the decision to close Codesign, and the ten months that followed — navigating the actual shutdown — were some of the most challenging of my career. The weight of disappointment, loss and responsibility was immense.

There were employees who had trusted me with their careers, customers who relied on the tools we built, and investors — including myself — who believed deeply in the vision. Closing a company isn’t just an operational process; it’s the unwinding of a dream. Even so, we worked hard to make the transition as smooth as possible: our team all moved on to great roles, and we released a free version of the product that many customers still use today.

Since then, the focus has been on moving forward — finding my footing again, getting back into the job market, and figuring out what comes next. I’ve taken on some consulting and advisory work along the way, but the search for a full-time role has been a journey in itself, with all the ups and downs that come with being a founder trying to fit into more traditional boxes.

Over the past year, I’ve sent countless job applications, had interviews and calls, and received plenty of polite rejections. What I’ve learned is that being a CEO or founder doesn’t always fit neatly into the boxes recruiters and hiring managers look for.

I’m a generalist by nature — spanning business, product, finance, and operations — and my approach has always been rooted in understanding customers and solving problems. Yet when applying for a Product Manager role, for example, I don’t tick the “X years of experience” box, even though I’ve effectively lived and breathed those responsibilities for years.

It’s a strange position to be in — to have broad skills and deep experience, but to find that they don’t translate easily into traditional job descriptions. It’s humbling, and if I’m honest, mentally tough.

Running has been my outlet. This year, I’ve run more kilometres than ever before — not for medals, but for sanity. It’s become a moving meditation of sorts, a reminder that progress isn’t always measured in outcomes, but in the simple act of keeping going.

In between the job hunt and the running shoes, I’ve been exploring new ideas — tinkering with tools and applications that I believe can make a difference. A couple of those are starting to take shape, and I’ll share more about them soon.

For now, I [try to] remind myself daily that success and failure are both temporary — it’s the courage to continue that really counts.

The AEC world is evolving fast, and so are the tools and platforms that help professionals keep up. As the pace of ConTech, AI, and digital transformation accelerates, having a few reliable directories and databases in your toolkit isn’t just nice — it’s essential.

Here are three great resources to bookmark, explore, and revisit as the industry changes.

1. AEC Magazine AI Directory

What it is:

The AI Spotlight Directory, run by AEC Magazine, is a curated repository of architecture, engineering, and construction tools that are using AI in meaningful, practical ways.

Why it matters:

This directory isn’t just flashy startups — it’s a carefully maintained list, reviewed by editors who know the AEC space. It features a wide variety of AI-driven tools, from generative design to construction monitoring to smart documentation. Because it’s editorially curated, it’s a solid first stop when you want to understand which AEC AI tools are actually making progress (not just hype).

Key offerings:

• Tools for generative modelling, like Forma Building Design.

• Construction monitoring platforms, such as Buildots, which use site imagery + AI to track progress.

• Knowledge management systems (for example, Tektome) that turn unstructured design data into searchable, structured information.

• And many more, covering visualization, documentation, code compliance and more.

2. ADDD Marketplace & ConTech Database

What they are:

ADDD Marketplace and the ConTech Database (also created by ADDD) are complementary resources built to help AEC professionals navigate the increasingly crowded ConTech and design-tech landscape. Rather than functioning as generic lists, they offer structured, easy-to-navigate ways to understand who’s building what — and why it matters.

Why they exist:

As construction-tech and design-tech continue expanding, it’s become harder to track the differences between tools, assess where they fit, and stay aligned with broader trends. The team behind ADDD created both the Marketplace and the ConTech Database to address that problem from two angles: one more editorial and discovery-oriented, the other more lightweight and direct.

What they offer:

ADDD Marketplace

• A growing directory of ConTech and design tools

• Clear categorisation for filtering by need or workflow

• Editorial-style explainers and trend summaries

• ADDDitive, a newsletter that contextualises what’s happening across software and innovation

• Occasional reports capturing emerging themes in digital AEC

ConTech Database

• A simple, transparent list of ConTech companies

• Clean filters for quick scanning

• Consistent, card-based structure for easy comparison

• A fast way to get a high-level snapshot of the ConTech landscape

Together, these resources give you two useful layers:

— one for deeper discovery and understanding,

— one for quick scanning and orientation.

3. AI in AEC

What it is:

AI in AEC is a dedicated hub focused on helping AEC professionals understand how artificial intelligence is actually being applied in practice. It includes articles, guides, training materials, and a structured course that breaks down AI’s real-world impact across design, engineering, and construction workflows.

Why it matters:

While the broader conversation about “AI in AEC” has surged recently, it has been developing steadily for years. Stjepan — the creator behind the platform — has played a significant role in shaping that discussion. Beyond building the course, he regularly attends and speaks at conferences worldwide, giving him ongoing exposure to what’s happening at the coal face: the successes, the failures, and the emerging patterns among early adopters.

What it offers:

• Clear explanations of where AI adds value today — and where it doesn’t

• Practical use cases drawn from real project experiences

• Training that helps teams build confidence in integrating AI tools into workflows

• A curated, practitioner-informed perspective grounded in firsthand stories from across the industry

AI in AEC is a useful resource for anyone wanting to go beyond the hype and understand how AI is meaningfully shaping day-to-day professional practice — not just future predictions, but the reality unfolding inside firms right now.

But over the past year, something has shifted. AI has quietly grown up in the AEC world. It’s no longer a curiosity; it’s becoming an actual workflow partner. Tools are moving beyond the novelty stage and beginning to rethink how design, iteration, and communication really happen.

A few platforms in particular show where things are heading.

Snaptrude is leaning fully into the idea that language—not geometry—is the new starting point for design. Their homepage is now literally a prompt box, which says everything you need to know about the direction they’re taking. Ask for a building, a layout, or a change and the software delivers something usable, not just a rough mass. The shift isn’t about AI drawing rectangles; it’s about design intent becoming the primary input. That’s a deep philosophical change.

Arcol, on the other hand, is framing the future around agents rather than features. Instead of adding another set of AI buttons, they’re asking what it would look like to have a competent assistant sitting inside the tool—someone you can tell, in plain language, to reorganise the floorplate, check a condition, or bring the model up to a certain level of detail. It’s a workflow built around conversation rather than command sequences, which aligns much more naturally with how architects think and iterate.

Then there’s Motif, pushing the visual boundary with not just AI renderings but full AI-generated videos. This might sound like a cosmetic upgrade, but it fundamentally changes how early-stage design is communicated. Clients don’t think in terms of sections and exploded axonometrics; they think in motion. The ability to produce cinematic walkthroughs early in the process removes an enormous amount of friction. Visualisation moves from a specialist bottleneck to an integrated part of the design conversation.

Across all of these tools, the common shift is clear: we’re moving from geometry-first software to intent-first software. Instead of coaxing a design out of a string of clicks, you describe what you want and let the system handle the starting point. The loop becomes faster, cleaner, and more collaborative.

AI isn’t replacing architects or designers. But it is reshaping the rhythm of the work—how we explore options, how we communicate ideas, and how much time gets spent on the mechanics rather than the thinking. The tools are beginning to differentiate their philosophies too: Snaptrude leaning into language, Arcol championing agents, Motif pushing visual storytelling. It feels like the hype cycle is finally giving way to practical, meaningful divergence.

And this is just the beginning…

A few months on, that pace hasn’t dropped. If anything, it’s increased.

One clear example is Motif, who have adopted a simple but surprisingly impactful approach: a regular monthly release drop on their releases page. Each update on its own is modest, but together they create a sense of steady, deliberate progress — something the AEC industry hasn’t always been known for. It’s a rhythm that keeps momentum visible (and one we’re quietly thinking about mirroring with Team Pulse).

The same pattern shows up elsewhere. Arcol continues to roll out updates as they refine their browser-based modelling approach, and their blog reflects a team iterating quickly rather than waiting for big milestones.

Snaptrude is doing something similar, shipping frequent improvements across early-stage design and their AI-assisted workflows — their release notes read like a team in constant forward motion.

Qonic is also regularly publishing updates, steadily expanding what their cloud BIM platform can do, as shown in their release notes.

Giraffe, in a more specialised niche, has been openly sharing ongoing R&D through their R&D updates, showing the same “keep things moving” mentality.

That Open Company has just pushed a major release, with details on their release page, adding another marker to a year that feels unusually active.

None of these teams are standing still, and taken together, they reinforce the same point: AEC innovation is not slowing down — if anything, we’re entering a phase where monthly progress is becoming the norm.

It’s an encouraging trend, especially after years where change often felt sporadic or heavily dependent on a small group of incumbents. The industry is now being pushed from multiple angles, and the cumulative effect is noticeable.

Over a decade ago when I was still involved with Archicad I was a strong advocate for increasing the number of releases while reducing the overall impact of each release. Many customers were overwhelmed with the number of new features to learn and a more regular release cycle would have allowed them to adopt these features at a more sustainable pace.

I’ll keep tracking these developments, but for now the takeaway is simple: the pace is healthy, the energy is there, and AEC continues to move in the right direction.