So many models coming out these days, so many developments happening in the AI space in general, it's kinda hard to keep up with it all. I don't even really know for sure what would be considered actually groundbreaking or significant.
I try to generally keep up with the overall trends, but I’m an engineer at a resource-constrained startup, not a research scientist. I want to see real-world application, at least mid-term value, minimum lock-in, and strong supportability. Until then, I just don’t have time to think about it.
I did scoff a bit when the response to "it's hard to keep up with what's actually important in AI" with "just read this summary of the 10 most relevant papers every week".
Unless you are really working on the bleeding edge (or trying to make money by predicting the hype machine) you probably need to know about one or two developments every 6 months. The summary of 60 papers in that time might not be what everyone needs.
To be clear, I didn't downvote here and I have no issue with you promoting a blog!
6 months is way too infrequent. If last time you checked the state of AI was 6 months ago, you'd miss - among other things - NotebookLM's podcast generator, the rise of "reasoning models", Deepseek-R1 debacle, Claude 3.7 Sonnet and "deep research" - all of which are broadly useful to end-users.
The focus of your link appears to be papers and research. I would imagine somebody with less time for these developments is looking for more practical "here's how you can use this cool new AI" style articles instead.
For me nothing has been groundbreaking nor significant. What we are seeing is the same in every new innovation, a suite of micro-innovations which improves efficiency and reduces cost.
But LLMs are still fundamentally a stochastic parrot that depends heavily on source data to produce useful results. So we will go through a lull until there is some new groundbreaking research which moves everything forward. And then the cycle repeats.
