just wanted to share this as ive found it genuinely useful and think theres an interesting asymmetry between whats being built and the attention its getting most ai products get more expensive as they grow - @tryharness is trying to make itself cheaper ai models and agents are multiplying but context is fragmented between all of them harness is an always on local intelligence layer that sees and remembers what you do then gives that context to whichever ai or agent needs it screenpipe is probably the clearest comp and validates the category - yc s26 building persistent local context for agents harness takes a different approach - browser first with no desktop app required and a privacy focused stack using local processing and venice for inference that matters for both privacy and accessibility - especially on work machines where installing desktop software can be restricted harness could become a layer across the ai stack - one persistent context layer connecting your computer memory, personal memory and whichever models and agents you use then theres the economic difference - and this is where harness gets really interesting most ai products have inference costs that rise with usage harness is building a treasury of productive compute assets like diem which when staked generates fresh venice api capacity every day users consume that inference and anything unused is sold through surplus with the proceeds buying more inference producing assets more usage - more revenue - bigger treasury - more owned inference - cheaper ai 100m harness unlocks resident and access to treasury generated inference so the same holding can become more useful as the treasury compounds current usage = around 2k mau, 400 to 700 wau and around 200 dau with usage up roughly 75% from previous month thats around 2000 different users a month - up to 700 a week - around 200 on a typical day dev @saltorious1 works at bankr with previous experience at coinbase / opensea / dinari sam altman recently described a future ai that continuously watches your screen and maintains the context of your life and work thats remarkably close to what harness is already building the bigger bet is that as models become increasingly interchangeable context becomes increasingly valuable harness wants to own the layer that knows you across those models while progressively owning the inference needed to power them