I've been using Kimi K3 for ~16 hours now.
The model is clearly good at a lot of different things (especially frontend), but non obvious reason why people are enjoying it so much is that it clearly does not follow the same rules in terms of safeguards and copyright.
Kimi will happily clone MacOSX. If you ask it to help you improve another AI model, it will do it with a smile on its virtual face.
Ask Fable to do the same thing? It literally starts to perceive you as a criminal committing a war crime (like no bro, all I want to do is fine tune an open source model).
After using all three recent releases, Fable, GPT 5.6, and now Kimi, it's clear that the full power of the models has been significantly held back by the safeguard restrictions caused by last months debacle with the USG -- leading to the top models being quite literally lobotomized in some areas, which leads to subpar results as the safeguards pollute its entire thinking and problem solving abilities.
The funny part? Is that you could have predicted this outcome 2-3 years ago when you started to see the rise of Chinese EVs and smartphones compared to western alternatives.
They quite literally tried to copy the Tesla Model S and iPhone as hard as possible and then eventually it started to diverge to the point where their EVs and phones are just genuinely better (which is why we have export controls banning their EVs, because they would literally drive all US manufacturers to ZERO)
There is a very clear behavior difference in Chinese capitalism and American capitalism.
American capitalism tries to protects copyright, patents, etc (oh no, you can't download a book through LibGen, that's ILLEGAL!).
Versus Chinese capitalism actually just does not give a fuck.
"Hey you want a video gen model (Seeddance 2.5) trained on every single anime ever? And you want the main character to look exactly like Messi? Sure, here you go!"
You see what I mean? When one half of the competition is being held up by regulators and restrictions on people who don't understand the technology and the other half has a leader who quite literally today said they are going to set up AI centers around the world to help other countries onboard to their open-source AIs, this is the sort of results that you will start to get.
These models were not smart enough to have this difference in philosophy matter -- but the newest class of models is where this difference makes a big deal. If these models are finally at the point where they are smarter than 99% of humans, why would you want to use the American one who tries to impose its world view onto you versus the Chinese one who will just do what you say without asking any questions?
And this isn't a full on bullpost on Kimi, the model is clearly not as smart as Fable / GPT 5.6 on things like math and science, but it's lack of handcuffs means that it can show the world what the frontier labs are gatekeeping from you and that starts to build customer resentment and loyalty towards the East, which is probably not what the USG wants.
Interesting times. Interesting times, indeed.
We've kept hearing how GLM-5.2 beats Opus 4.8, and are skeptical of benchmarks - so we tested them on a real bug from the Cline repo. While both models fixed the issue, GLM was the winner in terms of cost and code quality:
- GLM used twice as many tokens (GLM 1.1m vs Opus 660K) but cost half as much (GLM $0.41 vs Opus $0.81)
- Opus finished quicker - 1.6 min and 12 tool calls vs GLM 4.7 min and 28 tool calls
- GLM cleaned up dead code and verified the build compiled before completing. Opus didn't - it left type errors that passed tests but broke the production build.
Both runs used the same Cline harness prompting and tools, so it seems GLM is RL trained to spend more tokens verifying its work before completing. Impressive work by the @Zai_org team!
If you run Meta ads, this is the only video on Andromeda you need to watch.
We sat down with a Creative Strategist at Meta, someone who works directly with the top 1% of disruptor brands on the platform.
Here's our full conversation
00:00 Working with Disruptor brands at Meta
02:34 What is Andromeda?
09:34 Andromeda + GEM working together
11:15 The shift from targeting to creative
14:50 Creative similarity explained
19:10 Targeting personas
23:30 Creative strategists in the AI era
25:13 Creative themes
30:29 Is changing the script enough?
36:56 How many creative concepts should you run?
41:22 Brand consistency vs creative diversity
43:40 Partnership ads deep dive
52:55 Meta AI tools brands should be using
57:56 Measurement & ad account structures
1:01:18 Advice for brands wanting to become Disruptors
1:02:40 Where to stay up to date with Meta updates
our most valuable episode to date
old way: log into google ads, click around, manually tweak bids, guess what to fix
new way: connect claude code with google ads
- pull your campaign data into claude code
- ask: "what is missing, what is not working?”
- find new keywords
- pull and rewrite ad copy
- generate fresh creatives
- adjust bids and budgets
- and, ship the creatives via replicate
all without having to shift between tools
this is how you can use ai for paid ads
for anyone asking where to learn this stuff:
• RAG → https://t.co/4bzbUIwV5g
• Agentic RAG → https://t.co/IotOiGmV1Y
• AI Agents → https://t.co/nEeMnVJQbk
• Multi-Agent Systems → https://t.co/pavDPVJEFj
• LangGraph → https://t.co/3miEqqFzF0
• LangGraph (code) → https://t.co/v7kxHZXqba
• MCP → https://t.co/lKawRb4etX
• Memory Systems → https://t.co/LSaT2UaPAS
• Evals → https://t.co/vxChxa1kqQ
• Context Engineering → search "Context Engineering Survey" on arXiv
and please skip the "build an ai agent in 10 minutes" videos
build something, watch it fail, then figure out why.