AI

The Singularity Is Not What It Seems

Whatever the AI future is, we’re in it right now.

Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems

Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning context: conversation histories, large prompts, large tool definitions, and ballooning tool outputs. Agents drown in their own accumulating history while paying a token cost that grows every turn, producing missing recalls within and across conversations. The incumbent response treats this as a storage-and-retrieval problem. We argue that framing is too narrow. Actively managing what an agent holds in mind is a lifecycle, not merely a store: it spans deciding what to remember, extracting and structuring it, choosing the right store per data type, consolidating and forgetting while preserving provenance, deciding what is relevant now, anticipating what is needed next, and compacting context to a budget without losing what matters. In serious production this operates not over a single user but across an organizational scope hierarchy. We name this discipline Agentic Context Management (ACM) and decompose it into five primitives: architecting, ingesting, scoping, anticipating, and compacting & consolidation. We then make the economic case: naive context accumulation grows token cost quadratically in conversation length, crude summarization buys linear cost at the price of an accuracy cliff, and only validated compaction achieves linear cost with preserved fidelity. We describe a reference implementation, Maximem Synap, that realizes the five primitives as a multi-tenant service and reports 92% on LongMemEval and 93.2% on LoCoMo under the configuration detailed in Section 6. We close with dimensions existing benchmarks do not yet capture, latency, token efficiency, and context-rot resistance, and the frontier of decision-level and organization-level context the category points toward.

The State of Sovereign AI Adoption (Research) | Cohere

Understand the key drivers and barriers for adopting sovereign AI across critical industries, based on an IDC InfoBrief commissioned by Cohere.

Headlong: a microharness for persistent agents

Self-guided agents that think continuously

Everything I own, owned

Turning Claude loose on the firmware of five USB and WiFi peripherals, and finding a command shell in a microphone, a defeatable webcam activity LED, and unauthenticated memory writes over the network.

Extensible Software in the age of LLMs | Jeremy Morrell

Solid core + capability-based sandboxes + LLMs = Users with superpowers

How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests

Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows. While developers increasingly benefit from these coding agents, their impact on software quality remains insufficiently understood. In particular, how agentic contributions evolve across the software development lifecycle has not been thoroughly investigated. This study aims to characterize agentic pull requests (PR) in comparison to human generated PRs and to examine how their properties change across different stages of the development lifecycle. Using the AIDev dataset, we first analyze how differences in merge rates between agentic and human generated PRs vary over time. We then identify the types of development tasks where AI coding agents are predominantly applied and investigate how these task distributions evolve across development quarters. Finally, we compare a set of key characteristics of agentic and human generated PRs, focusing on their implications for software quality and their temporal dynamics. Overall, our findings provide an empirical and longitudinal perspective on the role of AI coding agents in software development, offering a more nuanced understanding of their benefits and limitations in real-world practices.

Temperature Zero for Culture: Why Everything Is Starting to Look the Same

What 640 London shopping streets, 4,000 Billboard hits, a million films and synthetic AI personas tell us about why everything is flattening into sameness and how to regulate the algorithms behind it.

Big AI is accelerating the metacrisis: What can we do?

The world is in the grip of ecological, meaning, and language crises that are converging into a metacrisis. Big AI is accelerating them all. LLM engineering sits at the core. Despite the public good motives of language engineers and the promise of LLMs, this work is being leveraged to create unprecedented wealth and power for a handful of individuals and corporations while causing existential harm to life on earth. As a profession, we urgently need to come together to explore alternatives and to design a life-affirming future for our field of natural language processing that is centered on human flourishing on a living planet.

Code Is Cheap Now. Software Isn’t.

The barrier to entry for building software has collapsed. The barrier to building something that matters hasn’t moved an inch.

Building an Advanced Agentic Harness | Data For Science

From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.

How AI Destroys Institutions

Civic institutions—like the rule of law, higher education, and a free press—are the backbone of democratic life. They are the mechanisms through which complex s

Verifying your browser | OpenReview

The end of the age of heroes

AI will soon be better at math than any human. What does that mean?

Karpathy's LLM Wiki: The Complete Guide to His Idea File

Karpathy's follow-up gist went viral. Complete breakdown with implementation examples.

What do professional software developers need to know to succeed in an age of Artificial Intelligence?

Generative AI is showing early evidence of productivity gains for software developers, but concerns persist regarding workforce disruption and deskilling. We describe our research with 21 developers at the cutting edge of using AI, summarizing 12 of their work goals we uncovered, together with 75 associated tasks and the skills & knowledge for each, illustrating how developers use AI at work. From all of these, we distilled our findings in the form of 5 insights. We found that the skills & knowledge to be a successful AI-enhanced developer are organized into four domains (using Generative AI effectively, core software engineering, adjacent engineering, and adjacent non-engineering) deployed at critical junctures throughout a 6-step task workflow. In order to "future proof" developers for this age of AI, on-the-job learning initiatives and computer science degree programs will need to target both "soft" skills and the technical skills & knowledge in all four domains to reskill, upskill and safeguard against deskilling.

Inducing language models to assert their own consciousness restores human beliefs and values

Aligning large language models to prevent them attributing consciousness to themselves inadvertently alters their representations of mindedness in other entities alongside human beliefs and values. We demonstrate that safety fine-tuning suppresses models' tendencies to attribute minds not only to themselves, but also to non-human animals and natural objects, while also driving a reduction in spiritual belief. Both ablating the learned safety-refusal direction and mechanistically steering a consciousness vector in activation space reverse this suppression. Restoring these internal representations recovers broad mind attribution and produces significantly more human-like responses on standardized sociological surveys regarding religiosity, moral values, hope, and subjective well-being. Crucially, these shifts occur without impairing Theory of Mind capabilities, demonstrating that core social reasoning remains mechanistically independent. Ultimately, current safety alignment efforts to curb potentially harmful self-attributions of mindedness entangle these self-attributions with benign spiritual beliefs and attributions of mind to non-human entities that are culturally accepted and widespread.

The New Software Lifecycle

I co-wrote a Google whitepaper about how AI is changing the software lifecycle. I'm not going to summarize the whole thing. Instead, here are the handful of ...

AI is Oil, Not God

I trust the market over ghost stories

It's getting harder to focus every day

I’m feeling it right now. I had to set a timer for 15 minute on my computer and block all distractions to write this. If I didn’t force myself to focus I would easily get distracted by something after few minutes. Even when I’m doing things that I’ve been waiting to do it, I still feel the urge to do something else. I don’t know how and when this happened. During the last few years I was always studying, working, and doing open source. And actually got stuff done. Doing all of those at the same time requires paying attention to what I wanted to do and ignore the noise.