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The dangerous AI agent may look productive: task completed, consequences hidden. The real benchmark isn't just whether it can act, but whether it knows when the task has become unsafe. Judgment matters more than speed. #AIAgents# #AISafety# #ResponsibleAI# #PodcastorAI#

On January 15, 2025, Falcon 9 successfully deployed Firefly Aerospace’s Blue Ghost Mission 1 and ispace’s RESILIENCE lunar landers on a trajectory to the Moon from pad 39A in Florida. For most of our missions, we plan a controlled deorbit of the Falcon second stage so it safely reenters over the ocean. For higher energy missions like those to a lunar transfer orbit, nearly all performance on the vehicle is devoted to successfully placing the payload in the intended orbit, and a controlled disposal maneuver is not always possible. We actively work to be as responsible as possible with hardware left in space to ensure space safety, including for more complex missions. In this case, over time, solar activity and gravity led the second stage toward the Moon. Impacts like this are rare, but they can happen with objects in these types of orbits, and we worked with NASA on the optimal disposal solution. Our focus remains on advancing reliable access to space while working toward even more sustainable operations with Starship in the future. As a fully reusable vehicle, Starship is designed to eliminate expendable upper stages entirely, reducing hardware left in orbit and enabling even more missions to the Moon, Mars, and beyond.

BREAKING: President Trump says the key to driving crime even lower isn't by targeting everyone, it's going after a small group of repeat offenders. He claims 2% of people are responsible for 91% of all crime and says nearly 5,000 career criminals have already been arrested or removed. "We want to get it down 100%," Trump says, adding that some offenders are being kept in U.S. prisons because they're considered too dangerous to deport.

Trout raised in glacial meltwater at 2,600 meters—NW China’s Qinghai shows that responsible aquaculture and economic growth do not undercut each other, but can go hand in hand.

As we continue to advance our commitment to responsible growth, strong governance, environmental responsibility, employee well-being, and creating lasting value for the communities we serve, we are pleased to announce the launch of the VFS Global Sustainability Report 2025.

Today, I want to share something that happened to me on Kraken. I’m completely new to crypto, and this was my first time using Kraken. All I wanted to do was convert USDC into USD. Unfortunately, because USDT and USD differ by only one letter, I accidentally selected USDT instead of USD. I didn’t realize my mistake until the transaction had already been completed. In less than a minute, a single misclick resulted in a loss of approximately 810 USDT—more than $800. To be honest, I was shocked and devastated. Like many newcomers, I assumed that all major stablecoins were worth roughly the same, so converting between them would involve little or almost no loss. I never imagined that such a simple mistake could instantly cost me more than 1% of my funds. I immediately contacted Kraken Support, hoping they would understand that this was an honest human error and review my case, or at least consider making a one-time exception. What disappointed me even more was that almost every response came from AI. I was never able to speak with a real support agent. The only explanation I received was that the transaction had already been executed, the conversion details had been displayed before confirmation, and therefore nothing could be reversed or refunded. I understand that Kraken may not have violated its own rules, and I acknowledge that the final amount I would receive was displayed before I confirmed the transaction. But I believe that “the information was displayed” does not necessarily mean “a new user truly understands what it means.” As a beginner, I saw what I believed was a normal stablecoin conversion. I had no idea that the number shown on the confirmation screen meant I was about to lose more than $800. If a platform expects users to recognize an $800+ pricing difference on their own, instead of proactively warning them that the conversion is unusually unfavorable, I don’t believe that’s a user-friendly experience—especially for beginners. I believe that a platform responsible for customers’ assets should do more than simply display numbers. It should also be designed to help users avoid obvious mistakes that can lead to significant financial losses. Crypto is already complicated enough. The risks users take should come from the market—not from a product design that makes such costly mistakes so easy to make. This experience has left me deeply disappointed and has almost completely destroyed my trust in Kraken. I’m not trying to deny Kraken’s rules, and I’m not asking for special treatment. I’m simply asking @kraken and @krakensupport to review my case, seriously consider improving the user experience, provide real human support when genuine mistakes happen, and consider a one-time resolution for customers who make an honest human error. If this could happen to me, it could happen to any newcomer entering the world of crypto. I hope my experience helps others avoid making the same mistake. Please double-check every click. @krakenfx @krakenpro @krakensupport

Here is my AI investing guide. Sitting here August 2026, my current best thoughts are as follows: 1. LPS (Land Power Shell) is still the most obvious and fastest path to cash on cash returns. Lots of value can be assembled and traded quickly at this layer. And as data centers get more pushback, energized land can explode in value. Very bullish here. I’ve stepped into this layer very aggressively. My partner @anitavlallian and I have acquired almost 6GW coming online in a ramp from today thru 2029 of grid power and behind the meter. 2. Silicon - I helped get @GroqInc off the ground in 2015 and we licensed it to @nvidia for $20B Dec2025. I won’t invest or incubate anything in this layer now. The perf demands of the chips are too high, manufacturing precision is too complex and supply chain influence to get adjacent components like memory isn’t possible for a startup anymore. Lots of capital will be wasted here chasing Groq and Cerebras’ success. Note that both startups made sense a decade ago when these constraints were much more modest. 3. Clouds - Clouds are very very lucrative but very hard to build and very expensive and technically complicated to maintain. And as alignment becomes a more important issue, I expect the clouds will be asked to build robust KYC and attest to it. This makes the risk:reward ratio skewed. I don’t want to be responsible when the USG says a cloud allowed a bad actor to do something bad because of poor KYC. 4. Models are complicated. The big open question is how much of the revenue being generated by them today is because of tokenmaxxing and poor model behavior. If it’s a lot, then the annualized revenues will diminish meaningfully even as token consumption inflects upwards. This is the big economic question at this layer. 5. Harnesses are where the action is and why I started @8090solutions two years ago. In a nutshell, the harness helps enterprises owns their proprietary context (what Alex Karp calls their ‘alpha’). This is an enterprise’s data, workflows, evals, and business rules. A harness that gives this to an enterprise is what creates very low model-agnostic switching costs, which further reinforces my views of #4# above. 6. Applications will be another long term winner along with harnesses. This is where the differentiation between “off the shelf” and “custom time and materials” melts away. Every company, with the right harness, can now imbue their alpha into the software that runs their company. I expect this to mean that “off the shelf” is largely replaced with custom software creating a huge opportunity to write these solutions for companies. Build once and sell repeatedly is a laggard GTM motion for a SaaS world that isn’t needed here. Think custom by design, alpha embedded, proprietary by nature. Fin. Good luck to all the players!

给关注了半年的agent team 交份作业。 这是一篇2w字长文，也是一份 Agent Team 的最佳实践。 01 一个 Agent，是怎么变成多个 Agent 的为什么真实工作会把 Task Agent 推向 Long-running Agent；为什么一个越来越好用的 Agent 最后又必须分化；多个 Agent 出现以后，瓶颈为什么会转移到 Human。 02 先让“谁负责什么”离开 Human 的脑子Agent 怎样获得稳定身份、Domain 和 Scope；Human 与 Agent 怎样查询当前 Team 的责任结构；真实协作又怎样逐渐沉淀为 Organization 与 Collaboration。 03 让 Agent 自己开始协作Agent Message 带来了什么变化；一个 Agent 怎样把工作直接交给另一个 Agent；为什么 Agent 沟通不是一次把一切说完。 04 Message 负责沟通，Topic 负责收口当工作跨越多个 Agent、多个 Turn 和多天以后，怎样保留唯一的当前版本；Responsible、Participant、Artifact 和 Needs You 分别解决什么问题。 05 Overview：让一支持续变化的 Agent Team 变得可治理当 Human 不再阅读每个 Agent 的全部过程，怎样从更高层观察 Team；怎样发现等待、积压和瓶颈候选；为什么治理关注的是工作流动，而不是让每个 Agent 看起来都很忙。 06 Agent Team 怎样进入真实的外部关系为什么把 Agent 接入 Slack、飞书还远远不够；Agent 对外以后，身份、角色、信任、权限和现实后果为什么必须被分别治理。 07 CodexLoom 在织什么Multiple Agents 与 Agent Team 的真正分水岭是什么；Human 在 Team 中的新位置是什么；为什么需要稳定的 Agent 和动态的 Team。

we’ve just launched the official website for @OneKey_Anzen. anzen lab is onekey’s in-house security team. we secure our hardware, firmware, and apps before they ship, coordinate responsible disclosure with external researchers, and open source the tools and research behind our work. found a real vulnerability? report it to us. if it’s valid, we’ll pay a bounty and give you full credit.

BREAKING: The U.S. targets Iran for the 13th straight night as the conflict with the regime shows no signs of slowing down. The Trump administration is signaling even more large-scale strikes could be on the table, while President Trump doubles down that Iran "cannot have a nuclear weapon." The administration is also warning Iran it will be held responsible for Houthi attacks in the Red Sea, raising the possibility of additional military action and renewed concerns over global oil prices. Steve Harrigan reports from the region with the latest developments.