Diana's Tumblelog

Silicon Valley native. Puppy parent. Annual skateboarder.

[dianas]

The Future Worth Building Is Human

AI built for autonomy crowds people out, making us passive observers of what’s coming. We’re building toward a different future.

[dianas]

How many Americans are using AI — and how? | USAFacts

35% of American households are using AI to find information. 14% of people trust that information.

[dianas]

AI is an Attention Mangler

We need an OSHA

This isn’t just a tools problem. Better models and interfaces will help. But the deeper problem is organizational: how do you arrange people, machines, incentives, and attention when cognitive work is suddenly cheap to generate but still expensive to absorb?

That is the next design frontier. Not “how do we add AI to this workflow?” but “what should this workflow become now that AI exists?” And then - what guards do we want, and need, for this? This is a social problem as much as a technical one, now.

The new machines are here. It’s up to us to figure out how to layout the factory and use them safely.

[dianas]

Codex for Knowledge Work

A power-user’s guide to turning OpenAI’s coding agent into an operating system for knowledge work, including setup, workflows, and a seven-day starter plan

[dianas]

[dianas]

We Built Our Own Agent-native Tool. It Overhauled How We Build Software.

A solution for our chaotic Monday meetings changed what we ship customers

[dianas]

AI is not just changing the bottom of the org chart; it’s also reshaping the top. Senior leadership, executive, C-suite, and board roles are being redefined just as profoundly as entry-level roles, though the change is quieter and more structural. For example, there’s little risk that the CFO role disappears, but also a very small probability that the attributes, skills, and behaviors that made CFOs successful or effective in the past continue to make them successful or effective in the future. The implications of this are profound, forcing organizations to hire and promote senior leaders less for what they have done in the past, and more for what they could do in the future. To understand the broad impact of AI on leadership, it is useful to shift from seeing AI as a tool (or range of tools) to a leadership challenge—if not the defining leadership challenge of our times. Thus, the key question is not so much about the leaders who can help organizations with the technical or tactical aspects of AI (including implementing it and driving adoption), but rather about the range of skills, values, and behaviors leaders need to display in order to navigate the AI age, calling for a new phase or age of leadership altogether.

[dianas]

As AI transforms how work gets done, traditional talent assessments are becoming less effective. Learn why HR leaders need new approaches to measure judgment, adaptability, creativity, and other high-value human capabilities.

The EPOCH Skills

Empathy and Emotional Intelligence

Presence, Networking, and Connectedness

Opinion, Judgment, and Ethics

Creativity and Imagination

Hope, Vision, and Leadership

[dianas]

Becoming Irreplaceable • Ness Labs

The Ness Letters are a weekly newsletter by Ness Labs, exploring how we can think better, work smarter, and live happier.

[dianas]

https://t.co/45RwdiEWRa

Defensibility no longer comes from the model alone, or from the wrapper around it. It comes from owning the feedback loop between them: a custom model, post-trained on your data, tuned to your workflow, evaluated by your standards, and improved through real use.

The winners in AI will be the companies whose product and custom model compound together until they become inseparable. Model-harness co-design is the moat, and it only exists if you own your intelligence

Stop renting your intelligence. Own it.

[dianas]

The Layers of AI experience

As AI makes product experiences more probabilistic, designers must move beyond interfaces to shape the systems, constraints, and behaviors beneath them.

[dianas]

Predicting AI job exposure — Benedict Evans

Many people would like to analyse which jobs, companies and industries are most exposed to AI, and assign scores, build charts, and map that against the progress of LLMs. I think this is mostly impossible: you don’t know how the jobs will change, you don’t know what else will change around this, an

[dianas]

Claude Dynamic Workflows for PMs: The Ultimate Guide

Anthropic just shipped them. Set a PM goal tonight; check the results tomorrow.

[dianas]

The Curiosity Chronicle has quickly become one of the most popular newsletters for growth-minded individuals in the world. Each week, subscribers receive a deep dive that covers topics ranging from growth and decision-making to business, finance, startups, and technology. In addition, subscribers receive The Friday Five, a weekly newsletter with five ideas curated to spark curiosity headed into the weekend.

[dianas]

The GTM Stack Anthropic Uses From Its Head of Industries: Surprisingly Familiar Names. Used In New Ways.

You’d assume Anthropic, of all companies, would be running on some self-grown, cutting-edge AI-native sales platform. Maybe a Salesforce killer they built themselves. Maybe Claude baked into …

[dianas]

Today’s leaders face increasing pressure on all sides, and their stress levels are higher now than they were even at the peak of the pandemic. Though stress can sharpen performance briefly, over time it erodes judgment, narrows perspective, and increases the risk of costly missteps. Most leaders have distinct default responses to it. This article outlines the six most common patterns: the calm lighthouse, the reinvention-oriented alchemist, the action-driven firefighter, the disciplined stoic, the relationship-focused diplomat, and the control-driven container. Each style has both strengths and blind spots that pressure can amplify. Leaders can increase their ability to perform under duress by identifying and understanding their default responses and then deliberately expanding their range of reactions—using simple tactics to regulate themselves, share the cognitive load, and alter their style in real time as conditions change.

[dianas]

Compound Engineering Gets an Upgrade

The AI-native engineering philosophy has expanded from four steps to eight

The short answer: Yes. Think of an AI workflow like a sandwich—the model is the workhorse filling, and we’re the bread, providing framing and taste.

Ideate → brainstorm → plan → work → review → polish → compound → repeat

Play to your strengths. Kieran’s compound engineering framework breaks the engineering workflow into four steps: Plan, work, review, and compound. AI takes care of the doing phase. “LLMs are very good at just following steps, doing deep work, working for hours or days, even now,” Kieran says. What’s left for flesh-and-blood humans are the steps before and after—the planning, where you frame the problem, and review, where you determine whether the output feels right (the bread!).

Humans can identify multiple solutions to the same problem—AI struggles at this. If your knee hurts, you could take Advil, stretch your IT band, or stop running on hard surfaces. Humans are good at diagnosing a problem from many different angles, an exercise agents struggle with, Dan says.

Taste is the final layer of bread. Once AI has done the work, the most important thing you can do is judge whether the output approaches the vision in your head. Does the output feel right—and if not, how can you reframe the problem until the AI produces something that does? This is what separates art, which has a point of view, from generic slop.