I learned about LLMs simulating Doom in real time via GameNGen, Val.town's dynamic image generation prompts, and Cursor's speculative edits. I also found that ChatGPT currently outperforms Flux.1 for creating consistent cartoons and comic strips.
New image models are getting much better at comic-style generation, especially with text rendering and prompt expansion, though model quality still matters more than prompt detail alone.
I researched code agent frameworks including Factory and Cognition, tested multi-modal generators like Flux and Suno, discovered DocxTemplater for document automation, and experimented with the browser's File System API using showDirectoryPicker.
I discovered E2E for cheap Indian GPU hosting, explored XML tags for better LLM prompting, and tested tools like Jupyter Lite and VoidEditor. I also learned about Ollama's concurrency and animating faces with Segmind's Hallo.
I explored using LLMs for educational games and market research, compared log storage costs across cloud providers, and tested document conversion tools. I also found that NumPy often outperforms HNSW indexing for similarity searches on datasets under 1M vectors.
I discovered Hermes 3's special reasoning tokens, Lumentis for transcript documentation, and the 27% reuse threshold for Anthropic’s prompt caching. I also noted that LLMs write better code in Markdown than JSON and explored Copilot's system prompt.