HN user
I think so too, although I’m not sure which part you mean exactly :)
My own bet is that the broader concept is “LLM access infrastructure” - privacy, policy, key management, and usage visibility in one layer instead of being scattered across apps and vendors.
Sorry for the late reply, sleep and work got in the way :)
Good question. In the current code, streaming usage is best-effort: for OpenAI-style SSE we rely on the provider’s usage chunk, and if the stream drops before that arrives we can undercount, including falling back to zero for that request. Anthropic is slightly better in our adapter because we accumulate input_tokens from message_start and output_tokens from message_delta, but premature termination can still leave usage incomplete.
That trade-off is deliberate for now: VoidLLM is optimized around a very small hot-path overhead, so we don’t run a tokenizer inline on every streaming response. The usage data is operational metadata, not a billing source of truth.
The likely next step is to improve this off the hot path: better mid-stream interruption signaling, and optional local token estimation / reconciliation as a fallback path rather than making every stream pay for it.
I built a small browser game where everyone plays the exact same sequence.
No RNG, no luck — your leaderboard position reflects only reaction time and execution.
Two modes:
KEYSTORM – hit appearing keys as fast as possible, speed ramps up until you fail
AIMSTORM – click shrinking targets before the timeout expires
On Mobile only AIMSTORM
Both modes are deterministic: every player faces identical challenges.
There’s no winning state — difficulty scales beyond human limits. You just try to survive longer than everyone else.
Playable here: https://1vsall.voidmind.io
Would love feedback.