In most parts of the world, Easter is being celebrated right now, honestly, not really.
Today, I was invited to a weekly meeting despite the holiday. I had forgotten one fact: Orthodox countries celebrate Easter a week later. (and countries in the Far East simply don t have this holiday at all.)
This is actually a broader topic, because every country celebrates holidays differently, and with many teams being international, some kind of synchronisation is necessary the question is: how?
When we first put multi-step AI agents to work handling backend customer queries our biggest surprise wasn't getting wrong answers. It was loops. An agent got stuck trying to fix a syntax error in a SQL query running the failing command thirty times in two minutes.
The fix wasn't changing the way we wrote prompts. We added a limit, on how many times something could run and a way to stop everything. Now if any agent makes the mistake twice the system stops running and sends the problem to a Slack channel for a person to check.
This simple rule cut our cloud computing costs by forty percent in the month. Better it stopped users from waiting ten minutes for a request that would never finish.
If you use loops don't trust the model to know when to stop. Put limits outside the LLM part of the system.
I m planning to create a website for my extension, and I want to make it as visually engaging as possible while keeping it minimalist, so the main idea is immediately clear.
Building a working prototype with AI can feel like the hardest part is over. Then you come back a week later to change one small thing, and suddenly you re dealing with unfamiliar code, broken dependencies, or a feature that affects three other parts of the app.
I m starting to think the real test of an AI-built app isn t whether it works on day one, but whether you can understand and maintain it on day thirty.
For those building with AI, what do you do to make sure you actually understand what you ve shipped?
For two quarters our content team chased click-through rates by writing sensational headlines for our weekly email newsletter. Traffic spiked every Tuesday. Our downstream product signups remained completely flat.
We looked at our analytics. Discovered that those catchy headlines were meant to increase click-through rates but actually attracted curious casual readers who bounced within thirty seconds. They were looking for entertainment, not software solutions.
We rewrote our titles to be boringly specific about the technical problem the article solved. Traffic dropped by thirty percent. Our trial signups from those same emails doubled.
Optimizing for attention attracts the audience. Optimize for qualification instead. Let traffic numbers reflect your actual target market.
Scrolling through today's launches, it feels like half of them are AI agents now applying to jobs for you, editing your videos, handling outreach, monitoring other agents. Some of it genuinely saves hours. Some of it I still want to keep my hands on.
Last month we checked our internal collection of prompt examples and found instructions going past two thousand words. We thought more details meant results but our tests showed the opposite effect.
When directions get long models start missing important formatting rules hidden in the middle sections. Our output became more consistent only after we made those big sections shorter and used bullet points instead.
We learned to see prompts as code functions not as assignments. Keep the focus small explain input values clearly. Take out all the friendly conversation words.
A short instruction set makes the model use its knowledge instead of getting lost in different style rules. Fewer words often mean thinking.
The shoutouts on Product Hunt allow makers to list the tools that they used to make their launch possible, and the Top Product tags make use of this information, together with reviews. At some point I caught myself using this as a way to choose a database or hosting provider, and I am not sure that this is a good thing.
The positive aspect is obvious: a tool that is frequently mentioned by working makers has probably been through a lot of deadlines. Knowing what builders are listing Supabase or Vercel gives me more insight than reading the feature list ever could.
Me? Oh, for me it was definitely never a number. When I got my first job, I made my own lunch and used to eat it all by myself on the office rooftop. Partly because I didn't much like all the baseball talk at lunch, but mainly, of course, to save money. Then, about a year and a half later, I opened my own company. It has already been 22 years since then, and I still run the company myself.
So my conclusion is: it was about having enough money set aside to get me through the low seasons, rather than reaching a certain level of profit.
