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- SaaSykit Flash Summer SALE, I Got Hacked, How to Prove Your AI Output Is Actually Good, How to compete in “crowded” AI markets & more
SaaSykit Flash Summer SALE, I Got Hacked, How to Prove Your AI Output Is Actually Good, How to compete in “crowded” AI markets & more
Ahmad
June 21, 2026
Hey Makers 👋
I’m a summer person. I love sun, and feel alive when it’s up shining! ☀️
To celebrate the summer, I'm running a Flash Summer Deal for a limited time!
If you've been thinking about building your next SaaS, now's the perfect opportunity to grab 25% off SaaSykit Complete. It includes everything you need to skip weeks of repetitive work—authentication, billing, multi-tenancy, teams, permissions, and the other SaaS essentials—so you can focus on building what makes your product unique.
👉 Use code SUMMER2026 to get 25% off SaaSykit Complete.
Once the deal ends, it's gone.
Alright—let's dive into what's new in the Laravel & SaaS world 👇
From the Community
PHP 8.4's property hooks let you replace simple getter methods with virtual properties, keeping a consistent property-based API. Here's when I reach for them and why.
Property hooks have existed in PHP since 8.4 - 1.5 years ago now - and whilst they're not something I've reached for often, they do have their place.
Consider the following object.
Laravel's built-in migrate:fresh drops every table before re-running your migrations, which is exactly what you want until it isn't. If a table holds seed data you don't want to regenerate, or test accounts you'd rather not recreate after every change, the all-or-nothing behaviour gets in the way. Custom Fresh, by Mahmoud Ramadan, adds a fresh:custom command that rebuilds the database while preserving the tables you name.
The short version: Someone exploited an outdated Livewire version on a dormant side project of mine. They stole my .env file, used the Mailcoach API keys to send 50,000 spam emails, and I spent a panicked night tearing through every project I own trying to find the source.
Here is the full story, what I learned, and why Docker saved me from a much worse outcome.
The Laravel team released v13.16.0 with a new artisan dev command for running your development processes, an enum-aware request helper, a withCookies() method on all responses, and an array-based maintenance mode driver for parallel testing.
You shipped the ticket classifier last quarter. It works. The tests are green and they’ve stayed green.
Last Tuesday you opened the agent’s instructions and added two lines to handle refunds better. Tests ran. Still green. You shipped it.
You read “multi-agent workflows” and built an orchestrator. Five specialist agents, a router on top, the whole org chart in code.
Now a simple refund question takes eight seconds and three API calls. Half the time the orchestrator answers directly instead of delegating. The other half it delegates, and the specialist replies with no idea which order the customer was asking about.
It’s not that hard to build a fully functioning, code-editing agent.
It seems like it would be. When you look at an agent editing files, running commands, wriggling itself out of errors, retrying different strategies - it seems like there has to be a secret behind it.
There isn’t. It’s an LLM, a loop, and enough tokens. It’s what we’ve been saying on the podcast from the start. The rest, the stuff that makes Amp so addictive and impressive? Elbow grease.
But building a small and yet highly impressive agent doesn’t even require that. You can do it in less than 400 lines of code, most of which is boilerplate.
All about SaaS
A company is essentially two things: a group of people and a collection of decisions. How those people make these decisions is the art of running a business. This guide shows how we do it.
Rules of thumb, and general philosophy:
Below you’ll find a collection of general principles we try to keep in mind at 37signals when making decisions.
Most GTM teams have implemented some version of AI: email writing, AI SDRs, intent tools, signal-based outbound, automated research, deal reviews, and so on.
AI should be making these teams dramatically more productive by now: measurably higher rep efficiency, pipeline and closed won revenue.
Anthropic shipped Claude Fable 5 yesterday, the first Mythos-class model the public can use. It tops nearly every benchmark there is, with the lead widening the longer the task runs. The smarter the model, the less your software is worth on its own.
I tweeted last week that every venture-backed application company now has to be a data company or a fintech company, ideally both. This essay is the long version.
Generative AI, over the last few years, has presented one of the greatest opportunities in history to create new products and new companies to advance humanity. It’s no wonder that so many talented people have flocked to the industry. There are only so many viable ideas, so each one will feel like an intense competition, and the reality is that only a small number of players can win. Hence, every market feels “crowded.”
Last year around this time The New York Times Magazine ran an A.I. issue with an introduction titled “Everyone Is Using A.I. for Everything. Is That Bad?” It’s an edited transcript from the Hard Fork podcast, which I think assumes two things are true that are turning out to be false.
Once you’ve tried AI, you use it “for everything.” No, in fact most people who’ve tried it are just occasional AI users.
AI has gotten so good that despite any misgivings, “everyone is using A.I.” No, in fact large chunks of the population aren’t using AI at all.
Videos
Keep building, keep rocking! 🤘