Tariq Deir: Writing

Talk is cheap. Here's my September.

2026-09-27T00:00:00Z

What I worked on in September, sorted by bucket, with receipts. Plenty of building, not much writing

Games, just for fun

The Climb. Started life as a roguelite, which was way too ambitious. It's now a lane runner, as a fun experiment. Play it.
Agent Village. Built over a weekend inspried by many other sims. Ten AI residents, each one a sub-agent running on the DeepSeek API, with its own personality, memories and goals. They formed groups and made up their own games and customs.
Big Trucker. A 3D truck-delivery game in the browser. Released on 30 September. Play it.

Speed to Lead

My current product bet: helping small businesses win more work from the enquiries they already get.
Researched it before any code: eight sectors, 18 alternatives and 37 audited claims.
Built the landing page as a smoke test to gauge appetite, before writing code.
Still deciding how it ships, and in what form factor.
Will throw a couple hundred $ behind a PPC campaign to see if I can generate sign ups.

The Capture Loop (client work)

The Capture Loop is one of my service products. It's for high-ticket specialist businesses that get enquiries online but close on the phone or face to face. We take full responsibility from the ad campaign to the hand-raise sitting in the CRM, and optimise the whole loop. September was spent deep in one client's account.
This month we have reduced junk enquiries a further 5%
Reduced cost of a qualified enquiry from $33 to $23
Increased conversion by a further 22%

Empty Leg Finder V3

V3 is so close, so much backend work has been done this month building parsers, structuring data, de-duping data etc.
Front end polish coming up next.
The target was end of September; we didn't quite make it. Still building.
Habit worth stealing: a decision log the AI agents can't rewrite.

Site and X

Rebuilt tariqdeir.com on Eleventy and Netlify, and moved it off Ghost.
X is where I'm focusing. Started 100 days of posting and replying every day on 24 September. Say hello: @TariqDeir.

Learning and tools

Went back to basics on the terminal.
Dictated 95,400 words in VoiceInk.
Cut eight apps and subscriptions in one morning.

Off the clock

Teaching my kid to read, eating too many chicken parmi sandwiches, in the WoW Forever beta, and fixing my sleep.
Finished Paul Arden's It's Not How Good You Are, It's How Good You Want To Be. Five stars.

The receipts.

Number	What
95,400	words dictated
3	releases of The Climb in one day
216	tests and browser checks passing
10	AI villagers
37	claims audited before any app code
34	offers researched
8	apps cut in one morning
1	site rebuilt by hand
1	pink fluffy cat in a cat car my daughter loves

October.

Ship Empty Leg Finder V3.
Decide how Speed to Lead ships: plug-in or standalone app.
Keep playing with The Climb and Big Trucker.
Reach day 30 of 100 on X, in late October.

I'll do one of these every month. If you're building something alongside everything else, reply on X and tell me what it is. I'd genuinely love to know.

Catch you in the next one.

Tariq

Sometimes the decision is outside of your control.

2026-08-24T00:00:00Z

Three weeks ago I published the empty leg analysis behind Empty Leg Finder, the SaaS platform I'm contracted to lead.

Fifty thousand empty legs deeply analysed and cross referenced coupled with a heap of customer retention data, forming the case for a discovery led marketplace over search-first marketplace.

This week the decision came back: return to V2. Shelve V3.

And as I don't own this project and many of the decisions, here's what I've learned sitting with that and processing it.

Evidence isn't mandate.

Data tells you what's true, it doesn't tell you who decides. Those are two different jobs, and confusing them is how product people break their own hearts.

Here's the shape of it: You're hired to find the answer, talk to users, run the analysis, follow the behaviour. You do the work honestly and the evidence points somewhere unexpected, perhaps somewhere better. You bring it back, certain the data will speak for itself.

It doesn't because data never speaks for itself. Someone with capital at risk speaks for it, and they're weighing things your spreadsheet can't see: sunk investment in the existing product, appetite for mid-year risk, promises already made to people you've never met. The evidence was your job, the decision was never yours to make.

That just happened to me. Three months of build and discovery work pointed clearly away from our current product toward a new concept. The stakeholders looked at the same data and chose the current product anyway.

They have a case. The existing product is revenue; my concept was a thesis. Pivots are expensive, and the people funding the risk get to price it. If I can't argue their side convincingly, I haven't understood the decision — I've just resented it and resent doesn't get you very far in life.

Concepts die. Learnings compound.

The product was never the only output. It was the container for the output.

Let me explain.

The last months of building and discovery gave me findings that don't care which product they live in. That our best-retained customers were the ones who barely touched the product, they wanted to be tapped on the shoulder, not handed a search box. That users search where they think the market is, while the actual supply sits somewhere else entirely. That the product I should have been building was a discovery engine, not a search engine.

The concept built on those findings might be dead but the findings aren't. Every one of them now shapes how I execute the product the stakeholders chose, from where I target acquisition, to what I make the conversion event, to which users I go and find. Same evidence, different vehicle.

I wrote last week that your effort isn’t the asset the learning is. It was easy to write when it cost nothing, but this month it cost me three months of work and a concept I believed in, and I found out whether I actually meant it.

I did. But I’ll be honest, as I’m only human: it took a day or two of being annoyed first. Then you have to remind yourself, principles only held in victory aren’t principles. They’re predictions.

So the shelved work sits documented and waiting, and I execute the current product with full commitment, not grudging compliance, pure commitment.

Market data doesn't expire just because a decision did and if the thesis is right, it'll still be right in six months.

Catch you in the next one.
Tariq

Sometimes your best customers barely use your product

2026-08-20T00:00:00Z

I noticed something slightly uncomfortable in my product and customer data this week.

I have been auditing flights and speaking to existing customers nonstop whilst building out version three of Empty Leg Finder.

And this latest finding stopped me dead in my tracks.

In fact, I double and triple checked it because I wasn't entirely sure I believed what I was seeing: some of my best customers barely use the product.

At first, that sounds bad.

No, scratch that.

It sounds fucking terrible.

Surely the people getting the most value should be logging in, searching constantly and clicking around like maniacs?

Well, for a product in the empty leg space, apparently not.

Now, this would have caught me completely off guard had I not already been suspicious that search was the wrong front door, I may have even shed a few tears.

When I looked at members who had stayed subscribed for three months or more, a pattern jumped out.

These users were hardly ever searching.

Most of them signed up, told us where they fly from, set up alerts and then disappeared.

Meanwhile, many of the customers who joined, searched heavily, set no alerts and tried to find a specific flight churned within 30 days. In fact, they would often churn within the first week.

It's weird when you're effectively building a case against your own product after spending so much time building it.

But such is life.

I've been in business long enough to know that the market does not care how hard you worked on something.

It cares how valuable that thing is to them.

And perhaps, for this product, in this specific market, maybe, just maybe, I've been looking at engagement completely backwards.

If someone has to keep opening my product every day to get value from it, perhaps I haven't actually removed much work from their life? For some apps that is the correct shape, you want to create something sticky and engaging.

But it would seem my longest-standing members don't want another platform to browse.

They want me quietly watching thousands of flights and tapping them on the shoulder when something relevant appears.

The less they need to do, the better the product might actually be working.

Again, it's all so obvious in hindsight when you've got five or six months of data sitting in front of you.

I admit, that creates a strange problem for builders because we're conditioned to celebrate activity.

Sessions. Searches. Clicks. Time in product.

But those metrics only matter if activity is actually the thing the customer came for in the first place.

Sometimes the job of the product is to make the activity disappear.

I don't know yet if this observation leads to the right version of Empty Leg Finder.

But when the people who stay behave completely differently from the people who leave, you'd be an idiot not to pay attention.

So what am I going to do about it?

I've built a fresh concept on Replit that I'm deploying this week to test whether I can get 20 members paying for the service within the next 30 days.

No point burning down V2.

No point throwing away the progress we've made on V3.

And equally, no point defending either of them just because I've spent months working on them.

If this new concept runs and gains traction, it will give me a much stronger signal for how I approach finalising V3.

And that brings me to the lesson I think is probably more important than anything else in this essay.

Do not get emotionally attached to your effort.

That is how sunk cost starts creeping into your decisions.

You stop asking, is this still the right thing to build?

And start asking, how do I justify everything I've already built?

Those are very different questions.

When we start a new product, offer or concept, we have no choice but to work from assumptions.

We take risks.

We make bets.

We build things without knowing whether we're right.

That's the job.

But those assumptions should eventually be replaced by evidence, not by:

Well, I've already spent six months building this, so I might as well carry on.

I've built V2.

I'm building V3.

And now some of the data is challenging one of the assumptions sitting underneath both.

My options are pretty simple.

Ignore it and protect my ego.

Burn everything down and overreact.

Or run another small bet and let customers tell me whether there's something here.

I'm choosing the third one.

Yes, it sucks when the market challenges something you've put a huge amount of energy into.

But that's the cost of admission and, in my opinion, the privilege of getting to compete.

Your effort isn't the asset.

The learning is.

Another assumption challenged, another small bet going into the market.

We keep chipping away until we deliver something undeniably valuable for the people we're trying to serve.

Catch you in the next one.

Tariq

I analysed 50,000 empty legs and realised we were solving the wrong problem

2026-08-08T00:00:00Z

Empty Leg Finder V2 is live. People are paying for it, the product works, and I’m currently working out what V3 should become.

Today I realised we might have built the core experience around the wrong behaviour, and I've had this hunch for a while.

For anyone unfamiliar with the problem, an empty leg is created when a private jet needs to reposition without passengers. The aircraft is flying anyway, the operator would obviously rather put somebody on it, and that creates an opportunity for a traveller to fly privately for considerably less than arranging a normal charter.

In theory, that makes for a fairly obvious marketplace. Someone tells us where they want to depart from, where they want to go and roughly when they want to travel. We find an empty leg that matches.

That’s essentially how Empty Leg Finder has worked.

Except there’s one fairly significant problem: the data says those matches are incredibly unlikely to happen.

I had a hunch that something was wrong

Something about the existing search experience has been bothering me for a while.

We have supply. Quite a lot of supply, actually. But when you interact with the product like a normal travel search engine, it can still feel empty. You choose where you want to leave from, choose where you want to go and get nothing back.

From the customer's perspective, the obvious conclusion is that we simply don't have enough inventory. That’s a perfectly reasonable conclusion too, you searched for a flight and there wasn’t one.

But I wanted to understand whether that was actually what was happening. Do we genuinely have a supply problem, or have we designed a matching experience that makes perfectly good supply almost impossible to find?

So today I audited the data.

We have 50,819 raw empty-leg records collected over roughly eight months. After removing duplicates, that's 49,484 distinct opportunities. There are caveats to the dataset, which I’ll get into later, but it’s enough data to understand the basic shape of the inventory we’re trying to build a product around.

And the shape is weird.

Across those empty legs there are more than 22,500 different directional routes. Of those routes, 64.8% appeared exactly once.

Not once a month. Once in the entire dataset.

The top ten routes combined account for just 2.6% of all supply, and the top 50 only account for 7.7%. Even the single busiest directional route I found — Las Vegas to Van Nuys — appeared 282 times across the dataset.

That sounds like plenty until you remember we’re looking across roughly eight months. One of the strongest exact routes in the entire marketplace still only shows up at around once per day on average.

Now imagine you aren't searching one of the strongest routes. You're searching whatever combination happens to fit your plans next Thursday.

The odds get ugly very quickly.

Then I removed the destination

This was the test that changed how I'm thinking about V3.

Instead of asking, “How many empty legs match this departure and this destination?”, I asked a different question: “How many empty legs are available from this departure if the traveller is flexible about where they go?”

I tested it against high-, medium- and lower-volume airports.

From Teterboro, the most common exact destination produced 79 opportunities in the dataset. Remove the destination requirement and there were 1,706 departures.

That’s roughly 22 times more inventory.

At Centennial Airport near Denver, the strongest exact route produced 28 opportunities. Allow any destination and there were 348 — more than 12 times as many.

Kansas City showed the same thing. Its strongest exact route produced 11 opportunities, versus 141 when the destination was flexible. Nearly 13 times more.

The important thing here is that I'm deliberately comparing flexible discovery against the best-performing exact route from each airport. Those aren't randomly chosen destination pairs. They're the routes where traditional search has the best possible chance of succeeding.

A typical route is substantially worse.

In practical terms, requiring somebody to specify both an origin and destination can hide more than 99% of the available supply around them.

That was the penny-drop moment for me.

Maybe people aren't supposed to search empty legs.

Maybe they're supposed to discover them.

We may have built the interaction backwards

Normal travel starts with intent. I know where I am, I know where I want to go and I know roughly when I want to travel. An airline or hotel search engine then shows me the inventory that satisfies those requirements.

That works because commercial aviation operates predictable schedules between predictable destinations.

Empty legs don't.

The aircraft isn't flying because of your travel plans. It’s flying because an operator needs the aircraft somewhere else. Your opportunity exists when your plans happen to intersect with where that aircraft is already going.

Writing that down makes it sound annoyingly obvious.

But it has fairly significant implications for the product.

V2 effectively begins with: Where do you want to go?

I'm increasingly convinced V3 should begin with: Where are you?

From there, we can show you what's actually possible.

If you're in New York, maybe there are aircraft leaving from Teterboro, White Plains, Morristown or another nearby private aviation airport over the next few days. One might be going to Miami, another to Nashville, another to the Caribbean and another somewhere you hadn't considered at all.

The important thing is that we're starting with the inventory that actually exists rather than asking the customer to guess the route on which it might exist.

That isn't really search anymore.

It's discovery.

And when I look at the supply through that lens, the marketplace suddenly looks very different.

We’ve also been making people understand private aviation geography

Another interesting problem turned up in our search data.

Users tend to search the airports they know.

Again, this is completely reasonable. Someone in Los Angeles searches LAX. Someone in New York searches JFK. Someone in Miami searches MIA.

But that's often not where the private aviation inventory sits.

In Los Angeles, significant empty-leg supply sits around Van Nuys and other business aviation airports. In New York it’s heavily concentrated around Teterboro. In South Florida, Opa-locka matters considerably more than most normal travellers would ever realise.

So you can have a situation where a customer is geographically very close to an empty leg, but because we've asked them to specify an airport rather than a location, the product decides there isn't a match.

That isn't a supply problem either. It's a product problem.

We're effectively asking customers to understand private aviation's airport network before we're willing to show them what’s available.

Which, when I put it like that, is a pretty crap user experience.

If somebody tells us they're in Los Angeles, the product should understand what "Los Angeles" means in the context of private aviation. The customer shouldn’t need to know which airport an operator prefers or what its ICAO code is.

That’s our job.

The radius matters more than I expected

Once I started thinking geographically rather than in exact airport pairs, the obvious next question was how far the product should look.

At around a 50-mile departure radius, the amount of available inventory increased by roughly 1.3 to 1.8 times in the markets I tested. Push that radius out to 150 miles and it can increase by two to four times.

Sounds great. Keep expanding the circle.

Except that eventually you start lying to people.

At 150 miles you're frequently pulling in an entirely different market. Denver starts picking up airports around Aspen and Eagle. Other cities start bleeding into neighbouring metros. Technically there may be a jet within the radius, but “private jet deals leaving near you” becomes a fairly generous interpretation of the word near when somebody has to drive for three hours.

So I'm currently leaning towards roughly 50 miles as the default interpretation of nearby, with perhaps a separate regional option for somebody willing to travel further for the right aircraft or destination.

I don't know exactly where we'll land yet, which is partly why I'm writing this now rather than six months from now with the benefit of hindsight.

The point is that geography has clearly turned out to be a better way of understanding the supply than exact airport matching.

This is also an incredibly short-fuse product

Another number from the audit that matters is 4.4 days.

That's the median time between an empty leg being listed and the aircraft departing. A quarter of the inventory appears less than a day before departure.

So this isn't just discovery-led inventory. It’s short-fuse discovery-led inventory.

That potentially changes how I think about the customer relationship too.

Perhaps the useful question isn't, “Where would you like to fly next month?”

It might be, “What's leaving near me this week?”

And if there isn't anything interesting today, the product should remember where you are and surface something when it appears.

Tell us where you live. Tell us where else you regularly spend time. Perhaps tell us how far you'd travel to catch the right aircraft. Then let Empty Leg Finder watch the market rather than requiring you to repeatedly search it.

That starts to feel much closer to the behaviour of the underlying supply.

I don't think this works equally well everywhere

There is a temptation here to turn “empty legs near you” into a universal marketing promise.

The data doesn't support that.

Within 50 miles of New York, the dataset contained 2,690 opportunities across 412 destinations. Las Vegas had 1,990. Miami had 1,706, Dallas 1,418 and Los Angeles 1,303.

Those markets have enough recurring supply to support a fairly compelling discovery experience.

Portland had 141 opportunities over the same period. Minneapolis had 189.

That's a very different product.

Rather than pretending otherwise, I think the right experience in those markets is probably something like: there isn't anything particularly interesting near you right now, but save your location and we'll tell you when there is.

This is one of those areas where I think marketplace products get themselves into trouble by trying to make the same promise everywhere. Our supply isn't distributed evenly, so the product experience shouldn't pretend that it is.

The stronger version of Empty Leg Finder might actually be one that knows when not to show you a load of rubbish.

There are also several things this data doesn't tell me

Having nearly 50,000 opportunities in a spreadsheet creates the dangerous feeling that you know more than you actually do.

There are some important limitations here.

Our feed coverage changed significantly over the period I analysed, particularly after a new source came online in May. That means I cannot look at the increase in listings and tell you that the empty-leg market is rapidly growing. Much of that increase is simply us seeing more of the market.

Pricing is another one. Only 5.5% of the opportunities had a usable published price. Among those that did, the median was around $5,500, which is interesting, but the priced subset is heavily skewed towards particular feeds and aircraft categories.

So I'm definitely not about to start publishing “the average empty leg costs $5,500” all over the internet.

Most importantly, our data tells us that an operator listed an empty leg. It doesn't tell us whether that aircraft was still available when a customer enquired, whether somebody eventually booked it, whether the operator repositioned it differently or whether something else happened.

Listed supply is not necessarily actionable supply.

That is probably the biggest hole in the dataset today, and it's something V3 needs to help us measure.

Such is the joy of answering one question and creating another five.

So where does that leave V3?

I started today trying to understand how we could make search better.

I'm ending it questioning whether search should be the primary experience at all.

My current thinking is that Empty Leg Finder should become a discovery-led marketplace rather than a search-led one.

Tell us where you are. We'll understand the private airports around you, surface the empty legs leaving nearby and let you explore where those aircraft could take you. If there isn't anything useful today, we'll keep watching.

There will still be search. I'm not deleting a perfectly sensible feature because I got excited by a spreadsheet.

But I increasingly think search should be secondary rather than the organising principle of the entire product.

The bigger lesson for me is that what looks like a marketplace liquidity problem can sometimes be a matching problem.

We had thousands of flights in the database and users could still quite reasonably conclude there was nothing available. The inventory wasn't necessarily missing. We were just asking customers to describe their intent in a way that had almost no chance of intersecting with it.

Destination flexibility exposes 12 to 22 times more inventory. Nearby airports add another meaningful increase. Exact routes are extraordinarily fragmented. And customers themselves are already telling us, through the airports they search, that they think geographically rather than in terms of the private aviation network.

That's enough evidence for me to continue building V3 in this direction.

It isn't enough evidence to tell me whether customers will actually behave the way I think they will.

I'll find that bit out by putting V3 in front of them.

Catch you in the next one,

Tariq

Why did I start this site?

2026-07-28T00:00:00Z

I have forgone coming up with a fancy hook to draw you in. The temptation was there, but sometimes you just want to write for writing’s sake instead of trying to optimise this or maximise that.

And as I’m constantly optimising and iterating in my day-to-day life, I’ve given myself permission to just blurt words onto a page, and it’s incredibly refreshing, in fact it's almost cathartic.

Perhaps I should end the essay there, because that is reason enough, but that would be incredibly selfish of me.

Although there is some truth in that last sentence, and If we were being completely honest with ourselves, the vast majority of what we do is a selfish pursuit to improve our own lives.

Is that not why many of us became business owners, founders, creators, makers or indie hackers in the first place?

We rejected what the world was lining up for us, or perhaps we sampled the 9-to-5 life, recoiled and thought: “Not for me, there must be another way”

That was certainly my experience.

I knew pretty early on that working for somebody else would not suit my wiring very well as I hated not being in control. I was rebellious, and I found the day-to-day experience of school lacklustre outside of socialising, chasing girls and playing sport.

Later in life, I could put much of that down to ADHD as my brain works on urgency, novelty, challenge and interest, rather than importance in the way many neurotypical brains seem to.

Already dropping fun facts.

I digress.

Founder life

The pursuit of being a founder, as painful as it can be, is incredibly rewarding, painful, but incredibly rewarding.I felt repeating that would generate more impact, you’ll have to let me know whether it worked.

There is no feeling quite like working through a challenge, figuring something out, hitting a breakthrough, failing, retrying, failing again and then finally winning.

But it's also an incredibly lonely experience.

Very few people understand the day-to-day reality, the difficult decisions, or the fact that you often have to make those decisions with 60%, perhaps 70%, of the information available.

That is simply the risk we accept.

And I want to get back in that seat after spending the last four years of building for others.

So what do I want to get out of this?

When I combine all of that and ask myself the question again, why did I start this site? I think the answer is something like this.

I want to create leverage and expand the opportunities available to me by building a body of work around my name.
I want to own something again, something that I have total creative control over and which direction to point in next.
I want to improve my thinking, clarify my ideas and become better at expressing them.
I want to connect with other founders who, much like me, enjoy eating glass and occasionally question whether they are masochists.
I want to create a public record of my thinking, the experiments I run and how my beliefs or frameworks change over a long enough period of time.
I want to help other founders by providing a real and present view of building, running and acquiring customers for their own products.

Tired of polished BS

And I am genuinely tired of the slop all over the web and social media in addition much of what we read is polished backwards.

The result is already known, the story has been cleaned up and the difficult decisions, uncertainty and wrong turns have been edited out until the whole thing looks inevitable.

Fucking inevitable.

I want to share the present moment instead and all of the uncertainty of the present, you know, how to think about a rapidly changing environment as it's rapidly changing.

What I am doing now, what I think will happen, what actually happens and how I respond when those things are not the same.

I'm going to fuck up, I'm going to miss a publication date, I'm going to publicly make a few large mistakes while sharing the receipts with you.

And as scary as typing that out feels, there is something romantic and strangely addictive about it.

Anyway, less ranting and more focused writing will follow.

But I wanted to explain why I started this site without framing it as a perfectly optimised, entirely selfless pursuit designed to maximise your engagement and goodwill.

It is very much a selfish pursuit.

My intention is to make it useful and valuable to you too.

Catch you in the next one,
Tariq

I built an AI agent that qualifies my discovery calls automatically

2025-11-11T00:00:00Z

How I stopped checking my inbox and started showing up to calls already knowing who I'm talking to—and whether they're a fit.

TL;DR

Discovery calls are time-intensive, most founders waste hours on unqualified prospects because they don't know who they're talking to until the call starts.
I built a Lindy agent that automates qualification, it scrapes LinkedIn profiles, analyses fit against my ICP, and delivers a custom prospect report to Slack before every call.
The system saves me 3–5 hours per week and ensures I show up to every call prepared, focused, and already knowing the conversation openers that matter.

Here's how discovery calls used to work for me:

Someone books a slot on Calendly. I get a calendar invite. Maybe I glance at their email address before the call. Maybe I don't. Either way, I'm flying blind until we're five minutes into the conversation.

That approach is fine when you've got time to burn. But when you're building One Task Today alongside a full-time job, a family, and multiple ventures, every minute counts.

I needed a system that gave me the information that matters, before I ever join the call.

The problem: discovery calls are a black box

Discovery calls are supposed to be mutual exploration. But too often, they're one-sided interrogations where I'm scrambling to understand who this person is, what they do, and whether we're even a fit.

The typical flow looked like this:

Prospect books a call
I receive a calendar notification (buried in my inbox)
I join the call with zero context
First 10 minutes = "So tell me about yourself..."
Realise halfway through they're not remotely a fit

Rinse. Repeat. Hours wasted.

All the information I needed was already available. LinkedIn profiles. Company websites. Recent posts and wins. I just wasn't capturing it systematically.

The solution: an AI agent that does the work for me

I built a simple but powerful agent on Lindy that handles three critical tasks automatically:

1. Meeting details → Slack notification

As soon as someone books a discovery call, the agent sends the meeting details to a dedicated Slack channel. No more checking my inbox for calendar invites. Just one clean notification in the channel I actually monitor.

2. LinkedIn profile scraping

The agent scrapes the prospect's LinkedIn profile and pulls the key data points: current role, company, recent activity, mutual connections, and anything else that gives me context on who they are and what they're building.

3. Custom prospect report

Here's where it gets good. The agent takes all that scraped data and runs it through a qualification framework. It has complete context on:

My business
My goals
My ICP
My qualification criteria

Then it generates a custom report that includes:

Who they are: Role, company, stage of business
What they do: Core offering, target market, business model
How they do it: Team size, funding status, go-to-market approach
Recent wins: Launches, milestones, notable achievements
Conversation openers: Specific talking points based on their activity and challenges
Fit assessment: Clear recommendation on whether they match my ICP

All of this lands in Slack before I ever join the call.

Why this changes everything

The difference between showing up to a call blind and showing up prepared is massive.

Before the agent:

First 10 minutes wasted on basic discovery
No context on fit until deep into the conversation
Generic questions that don't land
Feeling reactive instead of in control

After the agent:

I know exactly who I'm talking to before I join
Conversation openers are specific and relevant
I can disqualify bad fits before the call even happens
High-quality prospects get a better experience because I'm prepared

The agent doesn't just save time. It makes me better at my job.

What this really means: focus on what matters

The beauty of this system isn't just automation for automation's sake. It's selective attention.

I don't need to check my inbox obsessively anymore. I don't need to context-switch between email, calendar, and LinkedIn. I get one notification, in one place, with all the information I need to decide whether this call is worth my time.

And if it is? I show up ready to deliver value immediately.

This is the edge that matters in 2025: knowing what deserves your attention and filtering out what doesn't.

Building this yourself

You don't need to be a developer to build something like this. Lindy is a no-code AI agent platform that lets you chain actions together with simple logic.

The basic flow looks like this:

Trigger: New calendar event created (via Calendly or Google Calendar integration)
Action 1: Extract attendee email and meeting details
Action 2: Scrape LinkedIn profile using the email (or name + company)
Action 3: Run the scraped data through an AI prompt that includes your ICP, business context, and qualification criteria
Action 4: Send the formatted report to Slack

Total setup time? Less than an hour if you know what you want.

The hard part isn't the tool. It's clarity on your qualification criteria. If you don't know what makes someone a good fit, the agent can't help you.

The broader pattern: agents as operating leverage

This isn't just about discovery calls. It's about a shift in how we work.

We're moving from "doing everything manually" to "orchestrating agents that do the repeatable work for us." The goal isn't to eliminate human judgment—it's to preserve human judgment for the decisions that actually matter.

I don't need to manually qualify every prospect. I need to show up prepared for the ones who are a fit and gracefully decline the ones who aren't.

That's leverage.

And the best part? This is just the beginning. Every founder should be asking: What am I doing repeatedly that an agent could handle better?

Action steps

Audit your discovery call process right now. How much time are you spending on unqualified prospects? How often do you show up to calls unprepared?
Define your ICP and qualification criteria clearly. Write down the specific characteristics that make someone a good fit vs. a waste of time.
Identify one repeatable task that's eating your time. Discovery call prep, email triage, client onboarding—pick one and explore how an AI agent could automate it.
Set up a Lindy account and experiment. Start with a simple automation (like calendar → Slack notifications) and build from there.
Review after two weeks. How much time did you save? What would you automate next?

The bottom line

Discovery calls don't have to be a black box. With the right automation, you can show up to every conversation already knowing who you're talking to, whether they're a fit, and exactly what to ask.

The question isn't whether AI agents can help you. It's whether you'll build the systems that let them.

Letting go of desires

2025-09-13T00:00:00Z

Originally published October 25, 2025. Part of the Archived Essays & Memos collection.

During my Sunday reflection, I revisited a quote from James Clear I’d captured earlier in the week:

It doesn’t make sense to continue wanting something if you’re not willing to do what it takes to get it. If you don’t want to live the lifestyle, then release yourself from the desire. To crave the result but not the process is to guarantee disappointment.

— James Clear

Just let that sit with you for a moment.

Take it all in.

Then think long and hard about where you are right now, and whether your behaviour and habits actually match your desires.

Often, our goals and dreams aren’t really ours.

We adopt them because they sound impressive, or because people around us chase them.

For some, it’s easier to borrow a dream than define one.

There’s an even riskier path: the goalless traveller.

Without a clear aim, you drift — and drifting means you’re quietly absorbed into other people’s desires.

It feels harmless in the moment.

It isn’t.

The cost shows up later, when regret asks why you never tried harder.

When I was younger, I lived in that first camp.

I’d say things because they sounded ambitious.

It felt good — a quick dopamine hit.

But that’s where the goal lived and died.

I didn’t want the work; I wanted the feeling.

It’s a brilliant quote, one worth revisiting every six months, or at least before you start declaring things you have no real intention of pursuing.

I'll leave you with this question:

Which desire do you need to release because you won’t live the process it requires?

When execution becomes cheap

2025-09-10T00:00:00Z

Originally published October 14th, 2025. Part of the Archived Essays collection.

“Holy Shit.”

Those two words flew out of my mouth when I revisited Replit after Agent 3’s release last month.

From planning to building to debugging, I had a team of agents working in synchronicity. The whole process unfolded like a relay race: my Fullstack Agent wrote the backend, handed it off to the Architect for review, and finally to the Tester for debugging.

In under two hours, I had a working mvp that would have cost five figures to develop just three years ago.

I’ve always been into tech but I only started building it in 2021 and experimenting with AI tools in early 2023. But this felt different, like watching the future assemble itself in real time.

The speed of improvement is absurd; every week new use cases appear, and it just keeps getting easier to build and ship.

AI isn’t a silver bullet. And yes, the industry is full of hyperbole, but dismissing what’s happening would be as foolish as taking part in the party, plus we’ve never been good at grasping exponential change or escape velocity as a species.

It’s not a matter of if, it’s a matter of when execution is commoditised. And that moment changed how I think about building. It forced me to ask the question every founder will soon face:

When execution becomes a commodity, what becomes our edge?

The Pace of Change

In my 17 years in business, the last two have outpaced the previous fifteen combined. We’ve lived through the dot-com boom, Web 2.0, smartphones, and social media — but nothing matches the velocity of what’s happening right now.

So what does all this change mean for both us and our businesses?

Let’s start with the basic building blocks of creation: ideas, knowledge, and execution.

Ideas

Ideas have always been cheap. Everyone has them, good, bad, amazing, but few are brave enough to face rejection in pursuit of validation.

We all have that one uncle who says, “I had that idea years ago.”

Cool, but you did nothing about it.

Ideas aren’t the problem. They never were.

Knowledge

Knowledge used to be a moat.

In the early 2000s, it lived behind university walls and paywalled journals. By 2010, it was free and everywhere, YouTube, blogs, MOOCs. Knowledge went from privilege to commodity, and the internet handed everyone a master’s degree in whatever they wanted to learn.

Thank you, Internet.

Execution

For the longest time, execution was a huge differentiator.

You could have a great idea and abundant knowledge, but you still needed capital, talent, and operational skill to bring it to life.

That was the founder’s edge. That edge is disintegrating.

As I saw first-hand, it cost me a total of $25 of compute, an hour of planning and one hour of tinkering to build a functional MVP using AI agents, something that would have cost between £30k–£60k just three years ago.

No team. Minimal risk. Just a few prompts of instruction and an agent network doing the work. Yes, a human still needs to be in the loop, perhaps a few, if the project is technically demanding. That in itself is both empowering and unsettling.

Execution is on a fast track to join knowledge and ideas in the commodity bin.

The next question writes itself:

When everyone can build anything, what still matters?

Discernment

The edge perhaps is moving from execution to discernment.

When building and writing code is close to free, your advantage is knowing what not to build.

It’s your ability to:

Spot patterns and opportunities others miss.
Understand the problem better than your users do.
Translate that understanding into clarity for your agents.
Build environments that feel stable, meaningful, human.

In a world where everyone can make, the only thing that matters is whether anyone cares.

So What Comes Next?

The implications of this shift are enormous.

When execution becomes cheap, the real value moves elsewhere, into meaning, trust, and discernment.

We’ll need to redefine what it means to create value when anyone can build anything. To rediscover what it means to contribute when machines can do almost everything faster, and to rebuild identity in a world that no longer measures worth by output alone.

That won’t be easy. It will challenge how we work, how we learn, and how we define purpose itself.

But that’s the work worth doing.

Because as the tools become more intelligent, the humans who thrive will be the ones who stay intentional — those who build with empathy, curiosity, and care.

You might be wondering, as am I, where’s the edge now?

It’s probably still too early to tell but I believe it belongs to those who can move across disciplines fluidly, who can see problems clearly, design elegantly, tell stories persuasively, and distribute effectively.

Well-rounded generalists & multi-skilled developers will rise and so will building communities around those same experiences and products.

Maybe the bigger question we’ll all have to face is:

What does it mean to be human when our identity has been tied to productivity for so long?