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262k tokens. 5 novels of context. But here is what nobody is asking...
DeepSeek recently dropped a coding assistant with a 262k token context window. Impressive? Sure. Useful? That depends. More context does not mean better code. It means more room for the AI to get lost in irrelevant details. The real unlock is not bigger windows. It’s better targeting. Knowing what 5% of the codebase actually matters for the task at hand.
We are solving the wrong problem.
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I wanted to create something that practical designers could use for the #FigmaMakeathon.
Meet Depth, a new design tool that reimagines the "zoom” (pinch to zoom/Ctrl+scroll to zoom) as a progressive design inspection tool.
Each level reveals a different layer of your design. Structure. System. Intent. Three levels. One gesture.
Seeing the “why” behind each design has never been easier, even for stakeholders.
Inspect, copy tokens directly, and leave annotations anywhere.
View the live site here: https://zebra-chat-30501410.figma.site/
Support my on LinkedIn post here: https://tinyurl.com/42npc256
View in the Figma Community here: https://www.figma.com/community/file/1609710921120524425
P.S. You can also use hot keys:
“D” for toggling zoom or depth mode
“1”, “2”, and “3” for changing each separate depth mode.
Designed a high-converting UI/UX for Deepware, a Munich-based agency providing AI, cloud, and e-commerce solutions to B2B companies across Europe. Built a scalable 30-page system that turns complex technical services into clear, accessible information for enterprise decision-makers.:
AI coding tools have come a long way. I use them daily and they've made me more productive.
But a lot of us don't work on new repos. We work on codebases that have been around for years. Functions 3000+ lines long. Code from 2015 still running in production.
When you point AI at that kind of code, it struggles. The context fills up. It loses the bigger picture. Suggests a fix that works in isolation but breaks something three files away.
The tools work great on fresh projects. On older codebases, the experience is very different.
The context memory problem? Nobody has solved it yet. Once the conversation gets long, the AI loses track. Basically type and hope.
If you're on legacy code and AI isn't the magic wand it seems in demos you're not doing it wrong.
What's your experience using AI on older codebases?
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