That’s all for the day.
✅ Revision Deep Learning
✅ Revision LLMs
✅ Prepare for an interview.
✅Applied Google DeepMind hackathon.
✅ Project research and architecture planning.
This exact tension, needing to hold growing context without paying growing memory cost, is precisely why KV caching exists.
A full breakdown of how it actually works next Monday.
What's your take? Should context windows scale via smarter software, or are we just waiting on better hardware?
So, the verdict is that software created the shape of the problem, quadratic compute growth, and trained positional limits. Hardware decides how far you're allowed to push before physically running out of memory or bandwidth.
Neither one is "the" bottleneck. They're a coupled system, software writes the check, and hardware decides if it clears.
@virattt@ycombinator@findatasets Congratulations but curious, why do we need stock market infrastructure for agents, especially apart from latency issues?