kramit1288

interesting, traces goes back to 2006. thats cool :)

what happens when the semantic layer is uncertain or unavailable? For payments, “LLM could not decide” is itself a policy case. Failing open is risky, failing closed may create too much friction and routing everything uncertain to HITL can become noisy fast.

I think the valuable part here is the audit trail behind it: why this spend was allowed, blocked or escalated.

quite impressive, how did you collected? just find images online or you actually have all of these OS.

top model changes every other month between Claude, GPT and gemini. but its dominated by GPT overall. Claude has taken lead in coding task but GPT 5.5 has come stronger. gemini was good in between. but its dominated by GPT 5.5 and claude overall. Coding is the area where disruption is hardest. Opencalw early this year was a major breakthrough in agentic AI and it is still making noise and becoming more mature and going toward enterprise. Agentic coding is still in adoption phase where teams are trying it , trying to make sense out of it, running it and not beleving it and eventually it is discussion point over tea. it is still in adoption phase but needle has moved from being alient to being something real which team started discussing and using it like a champ.

thats good but difficult to identify if its pure AI generated or not. identifying itself can be hallicunation.

accurate memory estimation is key here. it will crash if that accurate and it cant be generic for all local llm. each local llm has different context estimates.

This looks really cool. feels nostalgic. it would be more fun if it can be switched into whatever desktop mode i want like unix.

I think Ads wont be impacting the the results of inference or any biasness. ads will be injected out of LLM inference.

it had to happen someday