MiniMax M2.7, MiMo-V2-Pro, GLM-5, GLM5-turbo, Kimi K2.5, DeepSeek V3.2, Step 3.5 Flash (this last one is particularly cheap while still being powerful).
It doesn't use an extra model (so it supports every language that works with Whisper out of the box and use less memory), it works by applying Dynamic Time Warping to cross-attention weights.
Because the internet is noisy and not up to date all recent LLMs are trained using Reinforcement Learning with Verifiable Rewards, if a model has learned the wrong signature of a function for example it would be apparent when executing the code.
I'm not from Pakistan but Karachi is the only vertical city in Pakistan, most people lives in apartment buildings, I would suggest looking at other cities like Lahore.
The problems are not visual but epistemic. If the author didn't specify enough to produce a useful chart, then it's going to be the diagram equivalent of stock images thrown on a finished presentation by a lazy intern. You can't rejection-sample away this kind of systemic fault.
The simple truth we're about to realize is there is no free lunch: a tool cannot inject more intent into a piece than its author put in. It might smooth out some blemishes or highlight some alternative choices, but it can't transform the input "make me a video game" into something greater than a statistical mix-mash of the concept. And traditional tools of automation give you a much better, more precise interface for intent than natural language, which allows these vagaries.
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