AI's Goldfish Problem
We're building brilliant tools that have no memory.
AI can write code faster than we can think. The problem is, it can't remember what it wrote five minutes ago.
AI Coding Assistants Are Brilliant Goldfish
A developer's struggle on Reddit reveals the second-act problem of 'vibe coding'.
Developers are discovering that getting an AI to rapidly prototype an app is the easy part. The real challenge comes next, when you try to iterate or fix bugs, and realise the AI has no persistent context of its own work. A recent Reddit thread nailed this 'context starvation', where the magic of AI generation gives way to the messy reality of maintaining a growing codebase.
This isn't just about a faulty memory. It's about a fundamental mismatch between our development process and the AI's stateless nature. We build complex systems based on historical decisions and architectural choices, but the LLM treats each prompt like a new conversation. Without a human providing constant context, AI-generated code quickly becomes a tangled mess, creating a velocity trap where speed today means crippling maintenance debt tomorrow.
The solution isn't better prompts, it's better infrastructure. We need tools that act as a memory layer for these powerful but forgetful models. This is the real story for founders and developers: the race to build is over, and the race to build sustainably with AI has just begun.
Building the Scaffolding for AI
The most interesting tools aren't just generating things; they're adding the missing layers of observability and specialisation.
LunaRoute: A flight recorder for your AI coding assistant.
This gives you X-ray vision into what your AI is doing, turning a black box into a transparent process. It’s the infrastructure we need to debug and secure AI interactions instead of just blindly trusting the output.
Perplexity Patents: An AI legal intern that never sleeps.
This is a perfect example of AI tackling a specialised, expert-gated domain. It signals a shift from general-purpose chatbots to highly focused agents that can actually disrupt established, data-heavy industries.
Rethinking the Inbox and the To-Do List
Two new tools are tackling productivity from opposite ends: one adds AI intelligence, the other strips everything back to basics.
Superinbox: Your email gets an AI ghostwriter that sounds just like you.
Instead of forcing you into a new app, it embeds AI into your existing workflow. This is how AI wins: by being powerful but unobtrusive, making the tools we already use smarter.
Tweek: A digital zen garden for your weekly to-do list.
In a world of feature-bloated productivity apps, Tweek is a rebellion. Its success proves there's a growing market for minimalist tools that do one thing well, reducing digital noise rather than adding to it.
Quick hits
Canva's New Empire: The Affinity Suite is now completely free.
Canva just made its entire professional-grade design suite free, firing a direct shot at Adobe's subscription model and aiming to own the entire creative workflow.
Blur It: The 'undo' button for accidental screen share leaks.
This simple Chrome extension lets you blur sensitive info on the fly, solving a painfully common remote work problem with zero fuss.
Postiz: Your own open-source AI social media manager.
For anyone tired of vendor lock-in, this self-hostable AI tool gives you full control over your social media content and, more importantly, your data.
My takeaway
We're officially past the 'wow, it works' phase of AI and into the 'how do we actually live with this' era.
The initial hype was all about generation speed, creating a velocity trap where we build things we can't maintain. Now, the focus is shifting to building the necessary infrastructure for memory, observability, and specialisation. This is the unglamorous but essential work of turning a magic trick into a reliable tool.
This next wave isn't about bigger models, but smarter systems built around them. The real opportunity lies in creating the tools that manage, secure, and contextualise AI's output. What part of your own workflow is still treated like a black box, and what would it take to add a layer of real intelligence to it?
What's the biggest 'goldfish memory' problem you see in your own tools?
Drop me a reply. Till next time, this is Louis, and you are reading Louis.log().