hiroakiaizawa

7 ms·

1.
by hiroakiaizawa 4mo ago
Good reminder that raw tokens/sec numbers can be misleading without latency and context-window considerations.
2.
by hiroakiaizawa 4mo ago
Interesting approach. I like that the implementation focuses on scalability rather than only visualization.
3.
by hiroakiaizawa 4mo ago
The notebook is intentionally minimal. Not a prediction model or causal explanation — just a reproducible concentration check with fixed ex-ante definitions and minimal outputs. Runs in ~30 sec on Colab.
4.
Executable notebook for testing earthquake-event concentration (Colab)(colab.research.google.com)
1 points by hiroakiaizawa 4mo ago|1 comments
5.
by hiroakiaizawa 4mo ago
One thing I've started appreciating with LLM-assisted workflows is how important fixed evaluation protocols are. Without pre-defined definitions and locked procedures, it's extremely easy to mistake iterative adaptation for genuin
6.
by hiroakiaizawa 5mo ago
Curious what domains people would try this on. Would love to see other datasets.
7.
by hiroakiaizawa 5mo ago
Tested on finance / power / earthquakes. Minimal version only. Happy to adapt to other datasets.
8.
Collapse is not random – run a minimal test in 30 seconds (Colab)(colab.research.google.com)
3 points by hiroakiaizawa 5mo ago|2 comments
9.
by hiroakiaizawa 5mo ago
Interesting. What are the main latency bottlenecks in practice?
10.
by hiroakiaizawa 5mo ago
Nice. What scale does this realistically reach on a single machine?
11.
by hiroakiaizawa 5mo ago
Interesting. What are the main trade-offs they expect from the switch?