Rlm-codelens – Codebase intelligence with recursive language model
Graph-aware RLM decomposition beats context-window limits; but Codeium/Sourcegraph Cody solve this already.

Novel pattern-classification approach to autorouting, but unproven against real boards.
Circuit designers, EDA engineers, researchers in AI-assisted PCB routing
KiCad autorouter · Altium Designer · Traditional grid-based A* routing
One of the biggest problems in my view for training an AI to do autorouting is the traditional grid-based representation of autorouting problems which challenges spatial understanding. But we know that vision models are very good at classifying, so I wondered if we could train a model to output a path as a classification. But then how do you represent the path? This lead me down the track of trying to build an autorouter that represented paths as a bunch of patterns.
More details: https://blog.autorouting.com/p/the-recursive-pattern-pathfin...
Graph-aware RLM decomposition beats context-window limits; but Codeium/Sourcegraph Cody solve this already.
Clever Rubik's cube demo but it's educational content, not a reusable tool.
Self-generating apps from usage patterns and unified intent routing, but needs proof it outperforms Claude Projects.
SPF flattening via recursive DNS, solves real 10-lookup limit, but audience is narrow sysadmins-only.
Pretty dot grid animation with sliders, but shader toys already do this better.
Semantic primitives show up in activation patterns across Qwen, Gemma, LLaMA, SmolLM2.