At Hey Famm, we’ve gone from a manual, multi-step process for web, API, and mobile releases to a streamlined, automatic one.
We’ve also created agent skills to review code and flag issues before they become security or performance risks.
Tame the agents and scale your startup
AI promises miracles, but is it truly delivering your vision?
Are you happy with settling for the 80/20 rule?
Is your startup standing out in a sea of vibe coded competitors?
(If the answer to any of these is “no”, read on.)
Every new model feels like a fresh box of crayons
We get excited about the possibilities, dash off a prompt, then watch the agent work its magic.
What comes out is often impressive, but not exactly what we wanted.
So we take another turn with the model.
And another.
And another.
Hours later, the result seems kind of like what we had in mind, but there are problems:
Missing features
Broken functionality
Generic designs
Repetitive, error prone processes
What if there were a way of getting what we want, while iterating quickly?
Agents getting it right on the first prompt, not the fourth
Auditable tests confirm the code isn’t broken
Software as unique as your customers
Reliable automation that gets out of the way
Fortunately, there is! I call it “Agentic Reduction”.
It’s the secret sauce that keeps LLMs in line so you can spend more time growing your startup, while spending less time chatting with robots.
With Agentic Reduction, you get better results by using LESS AI:
Skills keep models focused on your business
Tests catch bugs before they go to production
Style guides enforce branding and voice
Repeated tasks become reliable scripts
Don’t take my word for it.
And at Pink Wallet, I now have a re-runnable, agent-driven process assembling a weekly guide without blowing up context windows.