Qwen and Alibaba are the biggest competitor for basically every model out there. They're beating the benchmarks like top-frontier models, focused on open-source and much cheaper than the competitors.

Excited to see what the future holds for them!

Great work by the author. Both Laya and Jev showcase how a different class of models can be efficient on tasks that don't require a 'generated output artifact'. I believe the same is true for VLMs where you're not always generating an image, but rather trying to understand more about the input image.

Token consumptions are flying through the roof and optimisation is the way forward.

Nice read. I believe that traditional software is a great way to showcase the proofs when it comes to physics and mathematics. You can easily code up a theorem in a language of your choice and justify that 'Okay, the output matches the expected value'.

I am particularly fascinated by labs like DeepMind [https://deepmind.google/science/]. The recent advances in their frontier models that are able to predict diseases before they're diagnosed is incredible. This is what AI should be built for and actually do!

Thanks! I appreciate that. Your point about DeepMind and frontier models is spot on. When they "embody"/build on the science done before them we get absolutely mindblowing synergies. But I wonder what happens when the LLMs become way smarter that us: why even loop us in? I guess that's related to the recent field medalist letter https://mathandai.org/

Looks interesting for a small team that's working on a single project. I am curious to understand how this would work out for a large org that has multiple projects under the belt.

PRs may look old fashioned, but they're a very clear way of tracking an issue that may need multiple reviews. With Delta, things can get confusing after more than two reviews + RBAC is another challenge if the agent has similar context for every category.

Thanks for pointing this out. I used PHP for one of my professional projects and never came through this - maybe because the library was not a part of our codebase.

This article will be very useful for people who might shift back to older PHP versions for compatibility and face it.

Absolutely love it and the details speak loads about the hard work that you've put in. I love the part where we can actually get up and experience the complete airplane -- felt like I was actually in a flight from Boston to Seattle!

I don't know if Google Maps/Earth expose the layout via external APIs, but if you could find a way to get the outer world to match the real view - that makes it 100/100.

thank you for your comment!

I had looked a bit into doing the realtime views and actually had it working. I could get the real aircraft heading and orientation. I was able to get it for free with community ADS-B aggregators (though data was incomplete), but doing long-haul flights was difficult because there's no ground coverage. Even sites like flightaware I believe estimate a lot of things when over oceans. But, Maybe I will look back into it!

I really appreciate your comment.. thank you for playing!

Great way of detecting AI slop.

Claude is pretty good at adding focused changes and if a fix is already present, then it correctly points it out rather than adding unnecessary refactors.

I'm not looking at building prototypes. Looking at ways to manage code bases after they've gone through hundreds if not thousands of changes.

Amazing work by the team! Looks like they've traded accuracy for speed and this is most likely going to be the case with the next class of models.

This is a valid tradeoff for one-off responses but if we're dealing with a distributed system (eg: Kafka), then only the high-confidence responses (>0.8) should move forward as input to the next service. If a low confidence output is propagated, then it can break the entire chain.

Every codebase that is being actively worked on (closed/open source) will contain code that's AI generated. With the rising abilities of agents, expectations are sky rocketing in terms of productivity.

If you're as productive as an engineer in 2016, you're not at the level that's expected. A 7 day workflow back then should take you maybe a day or less to work on today.

I've worked with "engineers" that make one commit in 2 months and it's a spelling correction. I'm pretty sure that being 7 times faster than that is still quite slow compared to me :) I hear a lot of breathless enthusiasm, but this article is possibly the first time I'm seeing the question answered "If AI is so great, where are all the applications?"

> AI does not care if you are anti-AI.

This >>>