it is fair to say llms are arguably a giant compression of what humanity chose to write down, which is already a heavily filtered subset.
but think about everything humans knew but didn’t actually textualize like tacit knowledge, felt experience, etc. all of this is structurally absent from the training distribution.
which means the silence in ai outputs might be even more signal dense than in human ones, because the filter is double.
First competition 🧵5/5
The first competition will focus on Context Compression - one of the fundamental bottlenecks in modern agent architectures.
➡️ Why this topic?
Tokens directly translate into compute usage, latency, and price. In multi-step reasoning workflows the context grows exponentially. Without careful management, agents begin to suffer from context scaling limits:
-degraded reasoning quality
-higher hallucination risk
-escalating operational costs.
Effective context compression is a structured transformation process that selectively preserves high-signal information while removing redundancy, low-value narrative, and already-consumed reasoning traces.
on days like this, you can feel yourself tempted to stop being optimistic
but reality is
i was raised on food stamps
beaten up by an addict
slept through most of my childhood on a blanket on the floor, no bed
i didn’t go to college
started working at 15 to put food on the table for my mum and my siblings
i was pronounced dead for a couple of minutes at 27 my daughter was 3
never fit into any organisation
didn’t have real friends until very recently
never had a safety net no backup ever
and yet i’m here pushing forward running through every single fucking door in front of me
the only reason i’m still here typing this is optimism
because the only thing that truly matters is surviving long enough for the odds to turn in your favour
so yeah
touch grass
keep building