Kimi K3 is looking like the new OSS heavy hitter! ๐ช๐
It is beating out GLM 5.2 on our Ghidra reversing benchmarks and hanging close to the frontier closed models.
The pressure is mounting on closed model providers. Things are getting exciting!
While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected.
Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions.
Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent.
But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models?
We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem.
Read the full report: https://t.co/TZSugXi2ZE
A lot of engineers I talk to seem to think this way: โThere is no upper bound to how much software we needโ
Unfortunately, this software engineer wet dream ๐ is not the bottleneck for a lot of businesses. Especially those started by engineers themselves. Itโs been a problem before AI and will be/is a hurdle to overcome after.
I've been looking into public benchmarks and am astounded by the poor quality vs. how much we rely on them as an industry.
@xeophon pointed me to this excellent site https://t.co/xt5uUSyrcA which is an independent third party which scrutinizes benchmarks and "looks at the data" ๐ช
For example, here are some popular benchmarks we all rely on. Before trusting a benchmark its worth running them and inspecting examples the grader failed carefully, to get a sense of the quality.
I'm continuing to look more into this, and will write something up eventually once I've collected more data, but I think this is an important subject.
My wife got pregnant by another man.
It's been a challenging 9 months knowing he's not my son but I'm so excited to raise him like my own.
Here's 9 things it taught me about PHP:
๐ Introducing DeepInfer, our in-house inference framework, now fully open source!
Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. Weโre sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable.
๐ https://t.co/JGAik1kgk0
1/4
Be extremely intentional with your friendships โ not acquaintances, not business partners, actual friends
Try to pick high-integrity people โ the right people
Invest time in those relationships
Cut or downgrade folks who are transactional or who donโt show up for you
Reallocate that time and energy to the right people who do
It's ok to be cutthroat about this, as life is the memories you create with your friends and family.
Invest in making moments.
Why are we calling every new kernel release a Megakernel. This is a well written CUDA graph engine with persistent kernels and PDL.
The author didnโt even call this a megakernel
https://t.co/YUNNOvqasF
Some observations about the "total" automation of software engineering:
1. There are more software engineers now than in 2025, and demand is growing.
2. Software engineers have never worked harder than they do now.
3. They largely don't write code anymore, agents write ~100% of the code.
4. The bottom line of the companies that employs these engineers and agents (i.e. the terminal purpose of all this code) is typically not budging as a result of AI adoption.
This is why you have to think in terms of systems. Automating one aspect of a system creates its own friction and moves the bottlenecks elsewhere.
Iโm literally that weirdo who wrote (all by hand) a 15K word blog post on:
- why one cant treat models as oracles
- how models gaslight you all the time
- what it takes to build confidence in the modelโs output
https://t.co/SmnEGkhJzX
So yeah sure, I believe in magical woo-woo