The AI Magic Trick Is Over

We got mesmerised by the AI magic trick. But building real products is less about summoning genies and more about laying plumbing.

Building AI Products Is 80% Boring Engineering

A developer’s 100-hour journey reveals that the "AI" part is a tiny fraction of the work.

A refreshingly honest Reddit post is making the rounds. After spending over 100 hours building a voice AI agent, the developer found the "AI" part—prompt engineering and model tuning—was only 20% of the job. The other 80% was spent on unglamorous but essential backend engineering, integrations, and error handling.

This isn't just one person's experience; it's the uncomfortable truth of production AI. The real challenge isn't the prompt, it's managing stateful conversations, handling errors, and mitigating latency. The "AI product" isn't the model, it's the entire orchestration layer that stops it from collapsing under real-world pressure and actually doing something useful.

This is a reality check for anyone building with AI. Stop chasing prompt perfection at the expense of system stability. The most durable products will be built on robust infrastructure, not just clever conversations that fall apart when poked too hard.

Read more →

AI That Does the Grunt Work

While everyone is distracted by generative magic, the smartest tools are automating the jobs nobody wants.

Sliq: Your AI data janitor.

Sliq uses AI to clean messy datasets automatically, tackling the soul-destroying grunt work that consumes most of an analyst's time. This isn't just a time-saver; it’s a sanity-saver that makes good analysis possible.

Shadow AI: The meeting assistant that actually pays attention.

Instead of just transcribing audio, Shadow AI also captures your screen, understanding the visual context most bots miss. Its bot-free, local-first approach is a clear jab at the privacy and intrusion problems of its competitors.

Voice Mate: Your AI receptionist for missed calls.

This AI screens your calls, summarises them, and schedules callbacks, turning interruptions into an organised to-do list. It’s a simple, powerful filter for the noise of constant communication.

Utility Gets an Aesthetic Upgrade

The new standard is that even the most functional tools should have a point of view.

TimeTuna: Finally, a scheduling link with a personality.

TimeTuna turns a boring booking page into a branded experience with video backgrounds. It’s a reminder that every customer touchpoint, no matter how small, is a chance to make an impression.

Screenhance: Stop shipping ugly screenshots.

This tool makes it ridiculously easy to create polished, animated screenshots for your product launch. It democratises design, allowing non-designers to create assets that look professional and compelling.

Yestoday: A weather app for the vibes.

Yestoday answers one question—is it warmer or colder than yesterday?—and pairs it with a beautiful oil painting. It’s a perfect example of a single-purpose app that wins with simplicity and aesthetics.

Quick hits

Brill: Learn a language on your lock screen.
Brill uses your iPhone's lock screen for repeated vocabulary exposure, turning idle glances into passive learning moments.

Monocle 3.0: Noise-cancelling for your eyes.
This macOS app blurs inactive windows to keep you focused, and even responds to a cursor shake to recentre your attention.

LightBuddy: Your screen is now a ring light.
LightBuddy turns your Mac display into a customisable light source, fixing terrible video call lighting without any extra hardware.

My takeaway

The real AI revolution isn't happening in the model; it's being built in the plumbing.

We were all captivated by the initial magic of generation, but that was just the technology demo. The real, difficult work is building the boring but reliable infrastructure that makes AI useful in the world. This means focusing on error handling, data pipelines, and system orchestration, not just clever prompts.

The next wave of successful AI companies won't be defined by the slickest demo, but by the most robust and reliable engineering. We need to shift our focus from "what can it say?" to "what can it do, consistently and at scale?". The magic trick is over; now we have to build a real product.

Are we spending too much time on the 20% of AI that feels like magic, and not enough on the 80% that makes it work?

Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().