Author shares method for filtering AI information: prioritize community-ranked papers, skeptical forums, and careful research over engagement-optimized platforms

The author argues that platforms optimizing for engagement produce noise rather than signal, with sensational content rewarded over accurate analysis. Instead, they use HuggingFace Daily Papers (ranked by upvotes from readers), Hacker News (a self-correcting technical community), and selectively Twitter for researcher threads.

For navigating research papers, the author recommends starting with a recent review paper, following citations forward and backward, reading 10–15 papers to build context, then going deep on topics of genuine interest rather than trending subjects. This process takes weeks, not days.

The author distinguishes between old problems with existing solutions in the literature (optimization instability, data distribution shift, latency under load) and genuinely new problems unique to large-scale language models or diffusion architectures, requiring different research strategies for each.