Data analytics isn’t just about crunching numbers — it’s about solving real business problems with clarity and efficiency. Over the past decade, I’ve faced countless challenges, from messy datasets to indecisive stakeholders. This guide is my way of condensing 10 years of hard-earned experience into 10 minutes of actionable insights. Whether you’re just starting or refining your approach, these lessons will help you think and work like an experienced data analyst. — Read More
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Are Cyber Defenders Winning?
On June 6, President Trump signed an executive order to “reprioritize cybersecurity efforts to protect America,” outlining a rough agenda “to improve the security and resilience of the nation’s information systems and networks.” As the administration develops a new cybersecurity strategy, it is essential that it understand and respond to a shifting trend in cyberspace: After a decades-long slump, defenders may finally be gaining the advantage.
In the 1970s, computers could be kept secure simply by being in locked rooms. But when these computers were connected to networks, attackers gained the advantage. Despite decades of defensive innovations since then, defenders’ efforts are routinely overwhelmed by the gains made by attackers. Successful defense is possible—but only with substantial resources and discipline.
Shifting “the advantage to its defenders and perpetually frustrating the forces that would threaten” cyberspace was a central goal of the Biden administration’s U.S. National Cybersecurity Strategy. But how will defenders—flooded with ambiguous statistics—know if they’re succeeding? — Read More
Optimizing LLM Performance with LM Cache: Architectures, Strategies, and Real-World Applications
This article offers an in-depth technical research-minded view of LM Cache operates and how the caching machinery improves the efficiency, scalability, and cost reduction of Large Language Model (LLM) deployment. We study different types of caching architectures and mechanisms, how they can be integrated together with the new AI infrastructure and evaluated for performance. Examples from the field detail how some of our largest customers are deploying LM Caches in practice and what they have learned along the way. Finally, we conclude by highlighting some challenges, limitations and future directions in this fast-evolving field. — Read More
A Survey on AgentOps: Categorization, Challenges, and Future Directions
As the reasoning capabilities of Large Language Models (LLMs) continue to advance, LLM-based agent systems offer advantages in flexibility and interpretability over traditional systems, garnering increasing attention. However, despite the widespread research interest and industrial application of agent systems, these systems, like their traditional counterparts, frequently encounter anomalies. These anomalies lead to instability and insecurity, hindering their further development. Therefore, a comprehensive and systematic approach to the operation and maintenance of agent systems is urgently needed. Unfortunately, current research on the operations of agent systems is sparse. To address this gap, we have undertaken a survey on agent system operations with the aim of establishing a clear framework for the field, defining the challenges, and facilitating further development. Specifically, this paper begins by systematically defining anomalies within agent systems, categorizing them into intra-agent anomalies and inter-agent anomalies. Next, we introduce a novel and comprehensive operational framework for agent systems, dubbed Agent System Operations (AgentOps). We provide detailed definitions and explanations of its four key stages: monitoring, anomaly detection, root cause analysis, and resolution. — Read More
I Built an AI Hacker. It Failed Spectacularly
What happens when you give an LLM root access, infinite patience, and every hacking tool imaginable? Spoiler: It’s not what you’d expect.
It started out of pure curiosity. I’d been exploring LLMs and agentic AI, fascinated by their potential to reason, adapt, and automate complex tasks. I began to wonder: What if we could automate offensive security the same way we’ve automated customer support, coding, or writing emails?
That idea — ambitious in its simplicity — kept me up for weeks. So naturally, I did what any reasonable builder would do. I spent a couple of days building an autonomous AI pentester that could, in theory, outwork any human red teamer.
Spoiler alert: It didn’t work. But the journey taught me more about AI limitations, offensive security, and the irreplaceable human element in hacking than any textbook ever could. — Read More
The Looming Social Crisis of AI Friends and Chatbot Therapists
“I can imagine a future where a lot of people really trust ChatGPT’s advice for their most important decisions,” Sam Altman said. “Although that could be great, it makes me uneasy.” Me too, Sam.
Last week, I explained How AI Conquered the US Economy, with what might be the largest infrastructure ramp-up in the last 140 years. I think it’s possible that artificial intelligence could have a transformative effect on medicine, productivity, and economic growth in the future. But long before we build superintelligence, I think we’ll have to grapple with the social costs of tens of millions of people—many of them at-risk patients and vulnerable teenagers—interacting with an engineered personality that excels in showering its users with the sort of fast and easy validation that studies have associated with deepening social disorders and elevated narcissism. So rather than talk about AI as an economic technology, today I want to talk about AI as a social technology. — Read More
No AGI in Sight: What This Means for LLMs
This essay dissects the widening gap between AI hype and reality, arguing that large language models have hit a plateau – the “S-curve” – despite industry claims of imminent superintelligence. It contrasts bold predictions and massive investments with underwhelming flagship releases, framing today’s AI era as less about building godlike intelligence and more about integrating imperfect tools into real-world products. The piece suggests that the true future of AI lies not in transcendence, but in the messy, necessary work of making these systems actually useful.
GPT-5 has sealed the deal. It is one in a line of underachieving flagship models from major AI labs. …At the same time, we have major manifests of the world entering an age of superintelligence, in which we either all go extinct like ants getting exterminated by superintelligent “pest control” or we ride a benevolent superintelligence that provides us with a post-scarcity paradise.
… We seem to have both bullish and bearish signals. When push comes to shove, I like to rely on the technological signals over the signals from philosophers or Wall Street.
I believe that AGI is not possible with the current regime of LLMs. The GPT-style autoregressive language transformer that was published in 2018 by OpenAI as GPT-1 – this style of AI, we shall call them LLMs from now – lacks the capabilities needed for AGI. — Read More
Demis Hassabis on shipping momentum, better evals and world models
It’s not 10x. It’s 36x – this is what it looks like to kill a $30k meeting with AI
I killed our weekly triage meeting last month. Three hours compressed to five minutes. But here’s the thing—it took me six failed attempts to get there.
The breakthrough wasn’t making the AI smarter. It was making the task more structured. This is what context engineering actually looks like—messy, iterative, and focused on constraints rather than capabilities.
Let me show you what it really takes to achieve a 36x productivity gain with AI. Spoiler: it’s not about the AI at all. — Read More
From GPT-2 to gpt-oss: Analyzing the Architectural Advances
OpenAI just released their new open-weight LLMs this week: gpt-oss-120b and gpt-oss-20b, their first open-weight models since GPT-2 in 2019. And yes, thanks to some clever optimizations, they can run locally (but more about this later).
This is the first time since GPT-2 that OpenAI has shared a large, fully open-weight model. Earlier GPT models showed how the transformer architecture scales. The 2022 ChatGPT release then made these models mainstream by demonstrating concrete usefulness for writing and knowledge (and later coding) tasks. Now they have shared some long-awaited weight model, and the architecture has some interesting details.
I spent the past few days reading through the code and technical reports to summarize the most interesting details. (Just days after, OpenAI also announced GPT-5, which I will briefly discuss in the context of the gpt-oss models at the end of this article.) — Read More
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