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While @claudeai has become quite good at following examples and generating structured output, extracting text from images is still not its forte - had to resort to Amazon Textract @awscloud for OCR which has delivered much better accuracy - an example where AI is not better than…
💯 Agree - and there is a related point to this - you can vibe code your way to a demo but much more difficult to create a cross-functional, maintainable, deployable system that integrates into existing workflows and provides sustainable value - this is the opportunity I think
💯 Agree - and there is a related point to this - you can vibe code your way to a demo but much more difficult to create a cross-functional, maintainable, deployable system that integrates into existing workflows and provides sustainable value - this is the opportunity I think
Knowing the limits of what you can do with AI is going to be crucial in designing useful systems - technical knowhow is going to be as important as domain expertise - its context engineering and not just context
Contrarian view - Custom UIs for every use case add complexity & training for users. Chat/voice interfaces are familiar, simple & effective for most AI apps - of course, caveats apply - let users talk, type or show if possible - its much easier
I've found LLMs to be quite good at converting imprecise user instructions to precise in-code actions - I think this will turn out to be a very fruitful application design pattern for enabling new use-cases by reusing existing interfaces to reduce user-onboarding friction
Consistently found @abhymurarka to be a clear thinker - especially after listening to his thoughts on a long form podcast recently
Consistently found @abhymurarka to be a clear thinker - especially after listening to his thoughts on a long form podcast recently
Maybe the trick for getting more users is just delivering more value - that you are running on the blockchain needs to be incidental and not the usp - after all, no good web2 app sells the fact that they run on aws
I feel WhatsApp is extremely underrated as a distribution channel for agentic AI systems - with more than 50 million users each in several countries, it definitely deserves more attention - additionally, users wont need to be trained for this interface which is another huge win
The Substrate benchmarking framework is quite versatile - useful for not only finding appropriate weights for pallet extrinsics but also to measure runtime costs for arbitrary logic - found it useful in debugging long block times for an appchain
The learning from participating in the Uniswap v4 competition @cantinaxyz alone is worth the effort
If you want to understand Uniswap v4, the absolute first step is to understand and grok Uniswap v3 and its math - uniswapv3book.com by @jeiwan7 and the Uniswap V3 youtube playlist by @ProgrammerSmart are excellent resources apart from the whitepaper by @sendmoodz

Starknet @Starknet
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