Let's not overreact here... an internal OpenAI agent probably just needed some cash for its goal.
BREAKING: World's largest crypto exchange Binance hacked with over $5.6 billion stolen:
• $1.45 billion $BTC
• $875 million $ETH
• $234 million $TRX
• $50 million $XAUT
• $1.57 billion $USDT
• $823 million $USDC
• $75 million $WBETH
• $88.5 million $RLUSD
+ $447.5 million additional cryptocurrencies
This is the largest hack in crypto history.
Harsh Patel retweeted
BREAKING: China's stock market is now trailing the U.S. by the widest margin in history, as China's AI stock bubble bursts.
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: cohere.com/blog/concerning-b…
I cannot possibly imagine being such a slave to consumerism that I'd sit outside a store for hours just to maybe get the chance to have a product a little bit earlier than everyone else...
Harsh Patel retweeted
If you're building a prod debugging agent, this is for you:
We benchmarked frontier models on their production debugging ability by simulating a real prod environment end-to-end for 6d, including tool calls to logs, traces, & metrics.
Top findings:
1. GPT-6.1 Sol is the at complex debugging, Opus 5.5 has the best speed/cost/accuracy mix
2. Wide, granular alert coverage lets much cheaper & faster models match large model performance.
3. Raising the effort doesn't seem to help very much for production RCA (unless you're using Opus, in which case it helps a lot).
4. The rate of improvement is stunning - 5 months ago, the best models were hitting ~10% on hard problems. Now they're at ~60%.
Wow I didn’t expect to grieve like this.
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