The gap between investment and measurable impact is definitely something. From what I've seen in day to day work though, the productivity gains are real for individuals (for me and my team at least) even if GDP doesn't show it yet.
@whats_ai
-
Self-Improving AI Skills Gaining Momentum
By
–
Super cool to see self-improving skills getting more attention! Will play around when it's out π
-
Continued Pretraining and Scaled RL for Specialized AI Models
By
–
Continued pretraining + scaled RL is a combo I keep seeing more of. The niche specialization angle is underrated!
-
Full-Stack Vibe Coding: Multi-File Project Competition Intensifies
By
–
The competition for full-stack vibe coding is getting intense. Curious how it handles multi-file projects compared to Claude Code on its own?
-
Security Whitepaper Breaks Down Data Protection by Type
By
–
Their security whitepaper is worth a read too. The part I liked most is that they break down how different data types are protected instead of flattening everything into one generic security claim: β¦
https://244
051090.fs1.hubspotusercontent-na2.net/hubfs/24405109
0/KiloClawSecurityWhitePaper.pdf
β¦ -
Digital Infinity Increases Value of Physical Reality
By
–
This is something I think about a lot. The more digital content becomes infinite, the more we'll value the physical and real.
-
Unit Economics of Inference in AI Models
By
–
Unit economics of inference is something I wish more people talked about. Will give this a listen!
-
Xiaomi Launches Reasoning Model with Efficient Parameter Activation
By
–
Xiaomi entering the reasoning model space is interesting. 15B active out of 309B is a nice efficiency ratio, curious how it performs on real-world coding tasks.
-
Scaling AI to Thousands of Languages for Underrepresented Communities
By
–
Scaling to thousands of languages is incredible for underrepresented communities. Congrats on the internship work!
-
Reliable Results Matter More Than AGI Labels
By
–
AGI or not, what matters is whether it reliably does the thing you need. Labels matter less than results in day to day work.