I stumbled upon this quite bonkers article about building a web search engine from scratch as a solo developer with relatively modest resources, i absolutely can recommend reading it:
Building a web search engine from scratch in two months with 3 billion neural embeddings
-->This is a super cool leaderboard for lossless text compression via NLP (and yes, that includes AI)! The top solution manages to compress the first GB of the English Wikipedia to a whopping 10.7% of its original size, including the compression program itself!
-->This is a article that explores the capabilities of OpenAI’s o3 model. HN
The impacts of AI reasoning are getting closer and closer to surpassing human capabilities.
But running it to solve a problem is extremely expensive.
I stumbled on HN over this very interesting article about a new kind of context memory system that, is able to remove information that is “unhelpful or redundant details”.
Thinking further, i think this would be super helpful for semantic search, that is currently not very performant due to the missing filters that extract importance. I have tried to counter this problem until now via summarization through small LLMs, but as one might guess turns out as not very precise and super expensive. There are other ideas one could post process text with LLMs but they are not very efficient either.
-->Opportunities for AI in Accessibility
I think this article falls short in capturing the full impact AI will have on accessibility, not only for people with disabilities but also for those who, despite not having recognized disabilities, face accessibility issues. This includes tasks ranging from reading cursive handwriting to understanding non-native speakers who use dialects.
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