The AI Magic Trick Got Boring
We're past the magic show. It's time to build the plumbing.
The AI magic trick is getting old. Now we have to build the plumbing that makes it actually work.
Your AI Pair Programmer Just Got a Memory
Why developers are building memory layers for brilliant goldfish.
AI pair programmers are impressive until you ask them about a file they saw three prompts ago. They are brilliant goldfish, with almost no working memory of your project's history or structure. This lack of context is why they often provide generic advice that is useless in large, complex codebases.
The fix isn't a bigger AI model, it's smarter plumbing. Developers are building 'layered context systems' that feed the AI only the most relevant information, precisely when it is needed. This mimics how a human gets up to speed, moving from high-level architecture to specific functions without needing to memorise the entire monorepo at once. It turns a firehose of data into a focused conversation.
This is the unsexy but essential work of the current AI era. We're moving beyond the magic trick and into the hard engineering of making these tools reliable. The real advantage won't come from having the biggest model, but from building the most intelligent and persistent interface to it.
The New Toolbox
While some fix AI's core problems, others are shipping tools that change how we build and sell.
HMPL.js: Less JavaScript, more sanity.
This server-oriented template language lets you build dynamic UIs with minimal client-side code. It’s part of a growing movement away from bloated JavaScript frameworks and towards leaner, server-driven architectures.
VibrantSnap: Your AI-powered video editor.
AI-powered editing that turns raw screen recordings into polished product demos automatically. This is another example of AI democratising creative work that was previously time-consuming and expensive.
Photo Studio AI: A battle royale for your prompts.
Instead of testing ten different AI image models, this runs your prompt through all of them at once. It solves the very real problem of choice paralysis in the crowded AI tool market.
The Productivity Overhaul
The tools we use are also changing how we think about work itself.
Meetings Wrapped: Spotify Wrapped for your calendar.
Get a 'Spotify Wrapped' style summary of your year in meetings to see just how much time was wasted. It's a fun, data-driven way to confront our collective meeting fatigue and push for change.
Tweny: A focus timer that saves your eyes.
This app merges the Pomodoro technique with the 20-20-20 eye-care rule. It points to a broader trend of holistic productivity, where sustaining your well-being isn't a distraction from work, but a core part of it.
Quick hits
MarketAlerts.ai: Your personal AI hedge fund analyst.
An AI co-pilot that promises to level the playing field between retail investors and the pros by delivering institutional-grade market analysis.
Monee: Your money, your business.
In a world of data-hungry apps, this manual budgeting tool is a refreshing bet on privacy, simplicity, and control.
Revlify for Shopify: Fix your terrible product photos.
This AI tool transforms your clumsy, angled phone pictures into perfectly front-facing, professional product shots for Shopify with a single click.
My takeaway
The real story isn't the AI model, but the interface between that model and our messy reality.
We are moving past the initial shock and awe of generative AI and into the practical, unglamorous work of integration. The focus is now on building the memory layers, context engines, and user interfaces that make these powerful tools genuinely useful. This is where the hard engineering happens and where lasting value is created.
It is less about which model is 'smarter' and more about who builds the best bridge to its intelligence. This foundational work will determine whether AI remains a clever novelty or becomes an indispensable partner in complex work. The next breakthroughs won't come from a model, they'll come from the plumbing.
What happens when the AI's memory of your project becomes better than your own?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().