PRACTICAL TECHNOLOGY. CREATIVE THINKING.
We build useful business tools, AV/IT solutions, and creative work. Start with what gets in the way. Make something that helps.
Find your starting point01 / FIND YOUR STARTING POINT
START WITH THE PROBLEM
Describe one recurring frustration. Friction Finder turns your context and a recent example into possible patterns and questions for a first conversation.
The official process and the way work actually happens are rarely the same thing.
Start with Friction Finder Why we built Friction FinderFriction Finder sends your description for AI analysis when you request a starting point. Keep it general; leave out passwords and confidential details.
02 / WHAT WE DO
SYSTEMS THAT MAKE SENSE
Bring the equipment, software, and people into the same plan: a clearer meeting-room setup, reliable connections, and a handover your team can use.
Start with the room’s purpose, what is installed, and what keeps going wrong. We can work out what needs attention.
Describe your AV or IT setup ↗Clarify a meeting-room inquiry ↗03 / CURRENTLY ON THE WORKBENCH
SMALL TOOLS. USEFUL OUTCOMES.Bring your own notes, scope, inquiry, or project details. Use AI to prepare a draft, check its sources, and edit before sharing. Clearly labeled sample and manual modes are also available.
Scope & Change Tracker
Agreed scope + new requests → reviewed classifications and a discussion draft.
“A second display in reception”
Possible addition · Review before includingQuote Readiness Assistant
Turn an incomplete inquiry into a clearer project brief and focused follow-up questions before preparing a quote.
“We’d like better hybrid meetings.”
Room layout and compatibility still need answersProject Handoff Hub
Delivery notes → a handoff with instructions, owners and unresolved items.
Delivered doesn’t mean accepted.
Owner, open issue, next actionALSO USEFUL / DECISION & ACTION BRIEF
When meeting notes leave room for disagreement, turn them into decisions, actions and open questions. Check the source, then edit and export the brief.
AI-assisted extraction + local sample and manual modes
Make a meeting brief Why we built thisPilot the weekly report with the office team.
Riley prepares the first draft.
“next Friday” — date needs confirmationWho covers the report when Riley is away?
From the demo’s fictional meeting. Always review before sharing.
WEBSITES & INTERACTIVE EXPERIENCES
Public experiments from the workshop. Explore their approaches, and check each tool’s explanation before entering information.
Practice a difficult decision before facing it at work. A fictional decision → consequences, reasoning and a practical field note.
Choose an adventure ↗Why we built thisCheck an email or pitch that sounds impressive but says little. A vague draft → editorial feedback, plain English and a suggested revision.
Review a draft ↗Why we built thisHelp a reader who is missing the context. Source text + a reader → a clearer explanation and short version to check and copy.
Reshape an explanation ↗Why we built thisExplore a headline you want to think through. A story or question → a conversation across several fictional perspectives.
Bring a topic to the room ↗Why we built thisThe workshop print above is original BIT TEK visual work: a path from idea to use.
More tools & workshop notes ↓04 / HOW WE WORK
A useful result takes both. We agree on the task, the boundaries, and what needs checking before release.
Start with what’s getting in the way and what a useful result looks like.
Agree on access, sensitive information, and the choices that affect your work.
Test what matters. Flag uncertainty. Review it together before release.
Behind the Bench: guides to all nine highlighted tools →
There wasn't a master plan.
How the Workbench happenedNotes from the first page
This wasn't supposed to become a workshop.
There was no master plan, product roadmap, market analysis, or whiteboard covered in arrows pointing toward THE FUTURE.
It started with small problems.
Something took too long. Something was unnecessarily confusing. An idea was harder to explain than it should have been. A pile of information required far too much digging to find the one useful answer buried inside it.
And increasingly, the same question kept coming up:
Could this be better?
For a long time, answering that question with software meant the idea had to be important enough to justify actually building it. Development takes time. Time costs money. And “I wonder if this would be useful” is traditionally a somewhat flimsy business case.
AI changed that equation.
Suddenly, the distance between:
“I wonder if this would work…”
“Well, let's find out.”
became remarkably small.
So we started finding out.
One experiment explored whether AI could help someone think through a problem instead of simply handing over an answer.
Another asked whether learning might stick better if you had to make decisions instead of memorize them.
Another started looking at the same news story through several very different minds.
Bullshit Detector came from the considerably less academic question:
Can we please just say what we actually mean?
Some ideas worked.
Some didn't.
Some worked just well enough to reveal a much better idea hiding underneath them.
And a few went from “this is kind of ridiculous” to “wait... this is actually useful” surprisingly quickly.
Eventually, a pattern became hard to ignore.
The experiments we kept coming back to weren't really about AI.
They were about friction.
The friction between information and understanding.
Between knowing something and explaining it.
Between having choices and knowing what to do next.
Between what someone meant and what somebody else heard.
Between curiosity and the amount of effort traditionally required to follow it somewhere interesting.
So the experiments got a bench.
Everything here uses AI.
AI also helped build a lot of it.
The code, research, design iterations, testing, writing, debugging, and plenty of the product thinking have been developed collaboratively with increasingly capable AI systems.
We're not trying to build the next foundation model.
The companies spending billions of dollars on that problem appear to have it adequately covered.
We're interested in something much smaller—and, to us, much more fun:
What happens when those models become building materials?
What can a curious person or a small team make when the distance between an idea and a working prototype suddenly collapses?
What becomes worth trying when you don't need a six-month development cycle just to discover that your clever idea was, in fact, terrible?
And what happens when a prototype is cheap enough to throw away...
…but useful enough that sometimes you don't?
That's where the Workbench gets interesting.
There isn't a fixed roadmap for what belongs here.
That's not an oversight.
An experiment earns its place by being useful, interesting, surprising, or by helping someone see something a little more clearly than they did five minutes ago.
And the things on the bench aren't monuments.
People change them.
Someone gets confused on mobile. We change it.
Someone expects an analysis to go deeper. We dig deeper.
Someone points out that two modes supposedly behave differently but don't actually feel different. We test them.
Sometimes we discover a prompt problem.
Sometimes we discover a design problem.
And occasionally we spend an unreasonable amount of time fixing something only to discover the problem was three layers earlier.
This is apparently what progress looks like now.
But that's also the point.
The Workbench isn't meant to demonstrate that AI can generate things.
We already know that.
It's meant to explore what happens when curiosity, judgment, real feedback, and increasingly capable tools can work together fast enough that an idea doesn't have to remain an idea for very long.
The process is pretty simple:
Then see what deserves a place on the bench next.
There was no master plan.
Depending on how much you value master plans, this is either reassuring or deeply concerning.
Also in the workshop
A couple more experiments, just off the main bench. Take a look around—no email needed.
A guided way to record tasks and time. Explore the timecard interface in this demo.
Stuck in a game? Explore help with your next move, a tricky fight, or a character build.
Notes from the workshop
Every experiment on this bench started with something that quietly wasted time, created confusion, or made good thinking harder than it needed to be. These notes explain what bothered us enough to build something about it.
Most AI tries to impress people.
We build tools that help people think more clearly.
We kept reading the same event described in ways that seemed to belong to different worlds. The facts might overlap, yet each account decided what deserved emphasis, what sounded absurd, what needed skepticism, and what could pass without comment. Even careful reporting carries a frame; it is simply easy to forget when the frame feels familiar.
We wanted a clearer way to feel the distance between an event and the stories people build around it—without pretending that one more authoritative voice would settle everything.
We asked: what happens if several distinct personalities occupy the newsroom at once? One could stay close to the facts, another could challenge the premise, another could look for an unexpected connection, and another could use humor to expose the strange part everyone else had learned to accept.
The idea was not to replace reporting with opinions. It was to make perspective visible so the reader could compare it rather than unconsciously inherit it.
Humor worked best when it behaved like analysis. A joke could reveal a contradiction faster than a solemn paragraph, but only when it remained attached to something true. Skepticism also became more useful beside other voices; it stopped sounding like the final verdict and became one instrument in the room.
Seeing the voices disagree made the hidden assumptions in each interpretation easier to notice.
Workshop note: we kept asking, “What if the news wasn’t written by one voice?” Things became much more interesting after that.
It removes the friction between information and perspective.
We kept seeing people read the chapter, remember the framework, and repeat the correct answer—then hesitate when a real decision arrived without an answer key. That kind of learning can transfer information, but it does not always build judgment. Uncertainty, tradeoffs, and consequences often get flattened into a lesson after the fact.
We kept returning to the gap between knowing what a good decision looks like and recognizing one while the outcome is still unknown.
We kept asking: what happens when learning behaves more like a guided story than a lecture? The experiment puts a person inside a situation, gives them meaningful choices, and lets an AI mentor respond to the reasoning behind those choices. The point is not to gamify a worksheet. It is to make curiosity and judgment part of the learning material.
A story gives context to an idea. A decision makes the learner do something with it.
The most useful moments were not always the dramatic branches. Small decisions revealed how someone was framing the whole problem: what they noticed, what they ignored, and which risk felt acceptable. A thoughtful response to that reasoning could teach more than simply revealing whether the choice was “right.”
We also learned that a mentor does not need to dominate the story. A well-timed question can create more movement than another paragraph of explanation.
The learner’s explanation of a choice often became more revealing than the choice itself.
It removes the friction between reading about decisions and making them.
We kept watching people explain something accurately and still leave the listener behind. Expertise compresses ideas: familiar terms carry years of context, missing steps feel obvious, and the expert can no longer see which parts are invisible to someone arriving fresh. Understanding something and explaining it are different kinds of work.
We were bothered by how often communication tools polished the original words without asking who was supposed to understand them. A cleaner sentence is not necessarily a clearer explanation.
We wondered whether the audience could become part of the material. Instead of merely shortening, simplifying, or changing tone, the tool reshapes an explanation around what a particular person already knows, what they care about, and what they need to do next.
The idea is that good explanation is an act of translation. The destination matters as much as the source.
The strongest revisions often changed the structure before changing the vocabulary. Starting with the reader’s actual question, moving an example earlier, or naming the consequence could do more than replacing every technical word. Sometimes precision required more detail, not less; the trick was choosing detail that helped this reader build the right mental model.
We also learned that “make it simple” is an incomplete instruction. Simple for a new employee, a customer, and a specialist under time pressure can mean three very different things, especially when the stakes are real.
It removes the friction between understanding something and helping someone else understand it.
We kept reading drafts that began with a useful idea and ended in language that sounded important but said very little. The idea got padded with abstractions, borrowed phrases, and words designed to survive a meeting. By the end, the writer still knew what they meant, but the reader had been handed fog.
The problem is not that people are foolish or dishonest. Often they are trying to sound credible, soften a difficult point, or write in the dialect their workplace expects. The sentence gets safer while the idea gets harder to find.
We wondered whether a tool could point at vague language without turning into a smug red pen. The useful move is not merely saying, “This is jargon.” It is showing what the sentence appears to mean, where confidence outruns evidence, and how the same thought might be written clearly.
The experiment is really about trust: can critique rescue the idea instead of embarrassing the writer?
The worst phrases were rarely the whole problem. Usually they were symptoms of a missing decision: who is doing what, for whom, and why it matters. Once those questions were answered, much of the inflated language became unnecessary on its own.
Clarity turned out to be less about shrinking every sentence and more about making each sentence accountable for a real claim.
Workshop note: one sentence managed to include AI, synergy, transformational, and paradigm—and still forgot to explain what the software did.
It removes the friction between what you meant and what your reader understood.
A note for the bench