Last May, I wrote a blog post titled As an Experienced LLM User, I Actually Don’t Use Generative LLMs Often as a contrasting response to the hype around the rising popularity of agentic coding. In that post, I noted that while LLMs are most definitely not useless and they can answer simple coding questions faster than it would take for me to write it myself with sufficient accuracy, agents are a tougher sell: they are unpredictable, expensive, and the hype around it was wildly disproportionate given the results I had seen in personal usage. However, I concluded that I was open to agents if LLMs improved enough such that all my concerns were addressed and agents were more dependable.
Hello. I have recently been spending a lot of time dithering. In image processing, dithering is the act of applying intentional perturbations or noise in order to compensate for the information lost during colour reduction, also known as quantisation. In particular, I’ve become very interested in a specific type of dithering and its application to colour palettes of arbitrary or irregular colour distributions. Before going into detail however, let’s briefly review the basics of image dithering.
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