Hi HN, I’m the solo founder of MapLoud.
I originally started building it as an AI mind-mapping tool, but I kept running into a bigger problem: context fragmentation. Ideas, research, AI conversations, analyses, and drafts tend to live in different places, and we repeatedly have to reconstruct the context.
MapLoud is my attempt to build around that problem.
You can start with a thought and develop it in Idea Studio, visualize it as a mind map, and apply structured Thinking Tools like 5 Whys, First Principles, SWOT, and Roadmaps to the existing project context.
Or you can start with existing information in Workbench—documents, notes, PDFs, screenshots, and images—and ask questions across those sources with supporting evidence, then carry what you learn into the rest of the project.
One of the things I’m particularly interested in is contextual continuity. Insights discovered while working on a project can become part of that project’s context, so subsequent work can build on what you’ve already learned instead of starting from another blank AI conversation.
I’m also beginning to build MapLoud Teams around the same idea: shared research, thinking, and eventually decisions that retain the context behind them.
I’m a project/program manager rather than a traditional software engineer, and I’ve built MapLoud solo from concept through the current web and iOS products.
I’d really value feedback from HN—particularly on whether the persistent-context approach feels meaningfully different from your current AI workflow, what you think is unnecessary, and where you think the model breaks down.
Hi HN, I’m the solo founder of MapLoud. I originally started building it as an AI mind-mapping tool, but I kept running into a bigger problem: context fragmentation. Ideas, research, AI conversations, analyses, and drafts tend to live in different places, and we repeatedly have to reconstruct the context. MapLoud is my attempt to build around that problem. You can start with a thought and develop it in Idea Studio, visualize it as a mind map, and apply structured Thinking Tools like 5 Whys, First Principles, SWOT, and Roadmaps to the existing project context. Or you can start with existing information in Workbench—documents, notes, PDFs, screenshots, and images—and ask questions across those sources with supporting evidence, then carry what you learn into the rest of the project. One of the things I’m particularly interested in is contextual continuity. Insights discovered while working on a project can become part of that project’s context, so subsequent work can build on what you’ve already learned instead of starting from another blank AI conversation. I’m also beginning to build MapLoud Teams around the same idea: shared research, thinking, and eventually decisions that retain the context behind them. I’m a project/program manager rather than a traditional software engineer, and I’ve built MapLoud solo from concept through the current web and iOS products. I’d really value feedback from HN—particularly on whether the persistent-context approach feels meaningfully different from your current AI workflow, what you think is unnecessary, and where you think the model breaks down.