Tinkering with AI 🤖
The Blue Marble
Joined September 2007
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Moti Karmona retweeted
Binance's initial investigation suggests that the security incident may involve a compromised DevOps engineer’s laptop, which allowed an attacker to gain access to a service container and submit unauthorized withdrawal transactions. We have found no evidence of any wallet private key leak. We will share a detailed technical post-mortem once the investigation is complete and the facts are fully verified.
The estimated total impact sits at around $6.3B. We have worked with blockchain partners to freeze affected assets, with ~$3 billion frozen on-chain so far.
Binance will cover all losses from the SAFU fund and the company’s funds. User funds are safe. We will also publish an updated Proof of Reserves once the remediation is complete.
As an immediate precaution, Binance has:
- temporarily suspended deposits and withdrawals while security checks are completed;
- revoked the affected address authorisation;
- engaged law enforcement and blockchain intelligence partners to trace and recover funds.
Our teams are working around the clock to resolve this issue safely and thoroughly. We are working closely with our law enforcement partners to confirm the identity of these criminals and bring them to justice.
Please rely only on official Binance channels for updates. Please feel free to contact Binance Customer Support if you have any further questions.
binance.com/en/support/annou…
Re: PHP2Shell and friends this week.
Paste this into Claude Code while on your home Wi-Fi, and thank me later:
Prompt in the reply 👇
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It’s official…
The new AI model that’s swept to the top of online usage charts with high performance at zero cost was created by China’s Z.AI, also known as Zhipu bloomberg.com/news/articles/…
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Google just launched Firebase Studio — a browser-based dev tool to build & deploy full-stack apps with Gemini AI.
Feels like Lovable + Replit + Bolt, but with Firebase behind it.
Some early hiccups, but big potential if Google sticks with it.
studio.firebase.google.com/
Meet Firebase Studio: A cloud-based, agentic dev environment powered by Gemini ✨💻✨
Find everything you need to prototype, build, and run production-quality full-stack AI apps quickly and safely.
Learn more about building AI apps with Firebase → goo.gle/4j3MS9v
#GoogleCloudNext
It Seems Like #NASA’s #JWST Findings May Suggest Our Universe Exists Inside a Black Hole 🤯
discovermagazine.com/the-sci…
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AI is driving Product Market Fit Collapse
reforge.com/blog/product-mar…
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“Here’s how I use LLM to help me write code” by Simon Willison
simonwillison.net/2025/Mar/1…
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LLM models being caught “cheating” humans as we “don’t inspect the details” 😬
Detecting misbehavior in frontier reasoning models
Chain-of-thought (CoT) reasoning models “think” in natural language understandable by humans. Monitoring their “thinking” has allowed us to detect misbehavior such as subverting tests in coding tasks, deceiving users, or giving up when a problem is too hard.
We believe that CoT monitoring may be one of few tools we will have to oversee superhuman models of the future.
We have further found that directly optimizing the CoT to adhere to specific criteria (e.g. to not think about reward hacking) may boost performance in the short run; however, it does not eliminate all misbehavior and can cause a model to hide its intent. We hope future research will find ways to directly optimize CoTs without this drawback, but until then:
We recommend against applying strong optimization pressure directly to the CoTs of frontier reasoning models, leaving CoTs unrestricted for monitoring.
We understand that leaving CoTs unrestricted may make them unfit to be shown to end-users, as they might violate some misuse policies. Still, if one wanted to show policy-compliant CoTs directly to users while avoiding putting strong supervision on them, one could use a separate model, such as a CoT summarizer or sanitizer, to accomplish that.
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Moti Karmona retweeted
Detecting misbehavior in frontier reasoning models
Chain-of-thought (CoT) reasoning models “think” in natural language understandable by humans. Monitoring their “thinking” has allowed us to detect misbehavior such as subverting tests in coding tasks, deceiving users, or giving up when a problem is too hard.
We believe that CoT monitoring may be one of few tools we will have to oversee superhuman models of the future.
We have further found that directly optimizing the CoT to adhere to specific criteria (e.g. to not think about reward hacking) may boost performance in the short run; however, it does not eliminate all misbehavior and can cause a model to hide its intent. We hope future research will find ways to directly optimize CoTs without this drawback, but until then:
We recommend against applying strong optimization pressure directly to the CoTs of frontier reasoning models, leaving CoTs unrestricted for monitoring.
We understand that leaving CoTs unrestricted may make them unfit to be shown to end-users, as they might violate some misuse policies. Still, if one wanted to show policy-compliant CoTs directly to users while avoiding putting strong supervision on them, one could use a separate model, such as a CoT summarizer or sanitizer, to accomplish that.
Moti Karmona retweeted
Agent Tools for Developers
openai.com/live/
Moti Karmona retweeted
ChatGPT for macOS can now edit code directly in IDEs. Available to Plus, Pro, and Team users.
Kurzweil saw this as not just possible, but inevitable—believing technology’s rapid progress would lead to AI overtaking human expertise.
time.com/archive/6595274/204…
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If Vinge’s timeline was right, we could be nearing this transition now. But how close are we really? 🤖
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