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How tech workers are feeling in 2026: a workforce splitting in two

Noam Segal — Tue, 07 Jul 2026 13:04:22 GMT

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A year ago, we ran our first large-scale survey of how tech workers feel about their jobs and careers. We summed up what emerged in four words: burned out, but optimistic. Today we’re back with the results from our 2026 survey, and it’s a tale of two workforces.

One half feels amplified by AI—more capable, more confident, more excited than they’ve been in their entire career. The other half feels shaken by it—less sure of their value and whether there’s still a place for them. Which side of that line people fall on predicts how they feel about their career more than anything else, including their current role, seniority, company size, or any other measure we collected. The workforce is bifurcating into two realities.

But there’s more: Burnout overall jumped 11 points in a single year, and four in 10 respondents are worried about losing their job. Even those who feel optimistic about their own career may not recommend that friends follow their path. In the AI era, everyone agrees the ground is moving. No one is sure yet if it’s an earthquake or a launch.

We think these findings are important enough that we’re making this post free for everyone.

Let’s break it down.

Our biggest takeaways

The workforce is splitting in two. Tech workers are either amplified by AI or shaken by it, and that divide shapes their feelings about work more than any title, tenure, or company.
Burnout is surging, and optimism is fading. Significant burnout rose from 44.7% to 55.7% of respondents, while career optimism fell from 54.8% to 48.7%. Those who feel destabilized by AI are feeling the least optimistic and the most burned out. A worrisome trend.
Tech workers wouldn’t recommend their own field. More than half (53%) would steer a newcomer away from a career in their role, even though they’re optimistic about their own future.
Productivity is up, but quality is questionable. 82% say AI is making them measurably more productive, but many worry the gains are coming at the cost of the sharpness of the work and the worker.
The underlying fear is of being overworked. Only 22% worry about “losing my job to AI.” Far more worry about being expected to do more for the same pay (51%), getting trapped in an unsustainable pace (46%), and the quality of their work going down (41%).
Almost everyone is ambivalent. 77% of respondents picked at least one positive and one negative emotion about AI. The average person selected more than five emotions. The defining feeling about AI is ambivalence.
Designers and researchers are the most worried. They report the most AI anxiety, the most fear of job loss, the worst-rated managers, and the lowest willingness to recommend their field. It’s a continuation of a trend we flagged last year.
Founders are still the happiest people in tech, and small companies are still the best places to work. Both findings replicate from 2025, and both are statistically robust.
Managers are still the biggest lever for happiness. Manager quality remains the strongest driver of burnout and one of the strongest drivers of everything else.
The industry, in tech workers’ own words, is “chaotic.” Asked to describe the state of tech in a sentence, the most common theme by far was chaos, though the sentiment was split almost evenly between excitement and dread.

Takeaway 1: The workforce is splitting in two

To understand AI’s deeper impact on people, we asked an existential question: How has working with AI shifted how you see yourself as a professional? We gave respondents five options. Here’s how they responded:

“Amplified (I can do more, and better)”: 49.0%
“Redefined (My role is changing shape, but I don’t see that as clearly positive or negative)”: 27.4%
“Destabilized (I’m less sure where I stand or what’s really mine)”: 13.9%
“Diminished (I feel less essential or less valuable)”: 5.0%
“Unchanged”: 3.2%

When we lined up that question against the rest of the survey, the four identity groups differed dramatically:

As you go from “amplified” to “diminished,” optimism collapses, burnout climbs, layoff fear climbs, and willingness to recommend the field falls off. The people who feel amplified by AI are thriving. Those who feel diminished by it are in distress on every measure.

To make sure this wasn’t an artifact, we ran the numbers a few different ways:

In a regression pitting every variable against each other, AI-identity stance was the single strongest predictor of career optimism (standardized β = +0.39) and of whether someone would recommend their field (β = +0.60)—stronger than role, level, and company size combined.
As an effect size, the gap between the “amplified” and “diminished” groups on optimism is large (Cohen’s d ≈ 1.55). For context, the famously strong “founder effect” we’ll discuss later clocks in at d ≈ 0.56. The AI divide is roughly three times as large as that. It is, by a wide margin, the biggest effect in the dataset.

The question that best predicts how a tech worker feels about their work, in 2026, is no longer “What do you do?” or “Where do you work?” It’s “What has AI done to your sense of who you are?”

Meet the four tech workers of 2026

We did one more pass on this data: instead of using a single identity question, we clustered respondents based on the full pattern of emotions they reported about AI. Four types emerged, and you almost certainly recognize them.

The Energized (41%). The all-in adopters. They lead with “excited” (91%), “curious” (83%), and “hopeful” (59%). They’re the most optimistic group, the least burned out, and the only segment with a clearly positive read on their field. For them, AI truly seems like a superpower.

“Product has become fun again! You become an explorer, you play around . . . you spend long hours full of excitement. We’re in an amusement park.” —PM, Principal IC

The Conflicted (35%). The ambivalent center of gravity—and the largest group after the Amplified. Their signature emotions are “conflicted—holding positive and negative feelings at once” (68%)—and “curious” (64%), trailed closely by “overwhelmed” (56%) and “tired” (55%). They haven’t soured on AI; they’re just exhausted by the work of keeping up with it while holding two feelings at the same time.

“I’m simultaneously having the most fun I’ve had as a product builder and also feeling the most uncertainty I’ve felt. I’m confident I’ll be able to keep my skills sharp and adapt, but I’m not yet sure what it is that I’ll need to adapt into.” —PM, Senior IC

The Disoriented (12%). Defined almost entirely by one feeling: “disoriented—my role keeps shifting,” layered with “overwhelmed” (74%) and “tired” (73%). These are people watching their job change shape beneath them faster than they can find their footing again. They still think AI is somewhat useful. They’re not “refusers.” They’re just losing the thread of their role in the workplace.

“Things are so uncertain, we’re like farmers on the cusp of the industrial revolution. We know going into farming is the wisest long-term career choice, but we don’t see a clear path. This kind of uncertainty crowds out productivity.” —VP Product

The Resentful (12%). The burned-out and checked-out. Every one of them selected “resentful—I feel pressured to use AI,” and they cluster with “tired,” “conflicted,” and “overwhelmed.” They report the lowest optimism, the lowest willingness to recommend their field, and the lowest sense that AI is helping them at all. This is AI fatigue transformed into resistance.

“Tech overall kind of sucks right now. We used to adopt new technology because we were excited about the cool new things we could do. Now all we hear is ‘Use AI or you will lose your job’—and then people get fired anyway. I hate it.” —Director of Product

Takeaway 2: Burnout is surging, and optimism is fading

Significant burnout is now the majority experience for tech workers. 55.7% of working tech professionals report significant burnout—meaning they describe themselves as “moderately,” “very,” or “completely” burned out. Last year, that number was 44.7%. More than a quarter (26.2%) are now “very” or “completely” burned out.

Career optimism is dropping. Fewer than half (48.7%) of respondents are optimistic about the future of their career (down from 54.8% being optimistic last year). We’ve gone from “burned out but optimistic” in 2025 to “significantly burned out, and not that optimistic” a year later. We’re curious (and a little scared) about how this will look in a year.

That being said, job enjoyment is holding up: 42.6% enjoy their work “very much” or “extremely”; another 36.7% rate it “moderately”; and only about one in five (20.6%) enjoy it slightly or not at all.

Why the apparent contradiction? Enjoyment, burnout, and optimism are different constructs. Enjoyment is about the work itself, and people still like the work. Burnout is about pace, and people are increasingly worn out by how much they have to do. Optimism is about where things are heading. You can love your craft, be worn out by how much of it you’re doing, and feel doubt about the future all at once.

How worried are you about layoffs?

This year, we also added a question to the survey: How worried are you about being laid off in the next year?

41.2% are at least moderately worried, including 19.9% who are “very” or “extremely” worried. 28% aren’t worried at all. So roughly four in 10 tech workers are carrying real job-security anxiety into their week—a sizable undercurrent.

What makes layoff worry worth its own question is how tightly it’s bound to everything else. Of all the things we measured, layoff worry is the single strongest correlate of career pessimism (r = –0.47). Nothing else tracks negative outlook as closely. When people are scared of losing their jobs, their optimism goes first.

We’ll come back to who is most worried later. It’s not who you might guess.

Takeaway 3: Tech workers wouldn’t recommend their own field

This year, we asked an NPS-like question about people’s careers and roles: On a scale of 0 to 10, how likely are you to recommend a career in your role to a friend starting out today?

More than half of working tech professionals would actively steer a newcomer away from the path they chose. That translates to an average NPS score of –39. Moreover, a third of the people who call themselves optimistic still wouldn’t recommend their own field.

The cleanest way to say it: “The water’s fine; don’t come in.” People have largely made peace with their own trajectory. They’ve got the skills, the relationships, and the seniority to ride it out. But they’ve lost faith that the on-ramp still works for someone behind them.

“I’m lucky I’m later in my career . . . AI can augment what I’ve built. I think I won’t be in a position to hire and mentor new PMs, but I’ll be safe. Which feels really crappy to say.”

“I’m at the point where I can just retire and choose not to, so I’m not worried about my own career. But I’m worried about the younger generations.”

The recommendation score varies enormously by role, and the spread is its own story.

Founders would (just barely) still wave you in. Designers and researchers very much would not. And the score climbs steadily with seniority: senior and staff-level individual contributors are the least likely to recommend their field (both at NPS –49), while VPs (–23) and founders (–5) are the most. The further up you’ve climbed, the more the ladder still looks worth it; the people on the rungs below are the ones telling others not to start the climb.

Takeaway 4: Productivity is up, but quality is questionable

Given the rising burnout, the layoff anxiety, the doom in the discourse, you’d expect tech workers to be rather sour on AI. They’re not.

At the individual level, the AI numbers are among the most positive in the survey. 82% say AI is already making them at least moderately better at their job, and nearly half (49.4%) say “very much” or “extremely.” 60% feel confident or ahead of their peers in AI skills, compared with just 22.5% who feel anxious or behind.

But then we looked closer at what “better at my job” means. When we asked people to describe in their own words how AI had changed their work, “better” turned out to mean producing more and faster, but not higher quality. The productivity gains are coupled with deep unease about the costs of leveraging AI.

“I can do more, faster, but not better.”

“Amplified and destabilized at the same time. We just set a new denominator for the job. And it moves higher and higher every month.”

And the cost isn’t only in the quality of outputs. A striking number of people described their focus, their judgment, and their thinking as suffering:

“I’m amplified, but my brain is rotting, and my work feels worse.”

“I feel like I don’t think hard enough anymore—I just follow Claude. I don’t fully understand what I merge.”

“I miss feeling smart and having aha moments. I miss talking [to] and brainstorming [with] humans instead of machines.”

The productivity gains are real, but the quality of the work and the sharpness of the person producing it are taking a hit. The bar keeps rising to match what AI makes possible, and a growing share of people feel that neither the output nor their own mind is keeping up.

Takeaway 5: The underlying fear is of being overworked

Respondents’ number-one worry about AI’s impact on their career is the squeeze—AI raised the bar for output, and the reward was . . . more output expected, for the same paycheck.

They’re scared that the work will get harder, faster, and cheaper, and that they’ll be expected to keep smiling through it.

It feels like the dominant narrative about AI and work has been about replacement: the robots are coming for your job. Clearly, that’s not what tech workers are most afraid of. “Losing my job to AI” came in near the bottom of the list, at 22%.

Remember the “AI is replacing parts of my job” question? Half of the respondents say it’s happening to at least a moderate extent. You’d expect that feeling to drive layoff anxiety, but it doesn’t. The correlation between “AI is taking over parts of my job” and “I’m worried about being laid off” is essentially zero (r = +0.05).

What people are actually worried about is being asked to do more for the same pay, and watching the quality of their work slip.

It shows up vividly in the open-ended answers:

“More and more work is being handed off to me because I can use AI to get it done. But that makes it impossible to keep up with quality standards and not burn out.”

“AI helps with the toil, but then it’s also an enabler to do even more toil.”

“When we automate intellectual tasks, we’ll have to do high-value creative or strategic work only—doing that eight hours a day is not realistic. I used to take rest during repetitive tasks.”

This might sound like it contradicts the layoff worry from earlier. It doesn’t. People fear layoffs, but they mostly don’t blame AI for them. What they fear from AI is being buried in more work.

Add this all up, and you get a workforce that’s more productive than ever but quietly dreading what comes next. The speed AI unlocked got plowed straight back into expectations. Every gain becomes the new baseline, and the people expected to hit it are running out of room to breathe.

Takeaway 6: Almost everyone is ambivalent

If there’s one feeling that defines tech workers’ relationship with AI in 2026, it isn’t excitement, and it isn’t fear. It’s both, at the same time.

We asked people to check off every emotion that described how they feel about AI in their work. Here’s the full list, in order:

The two leaders are unambiguously positive (curious, excited). But the next cluster (if we ignore “conflicted”) is made up of people who are overwhelmed and tired. People are curious and overwhelmed. Excited and tired. Only 33% feel “hopeful,” even though 64% feel “excited.” Excitement about the present is running well ahead of hope about where this all goes.

Nikhyl Singhal named this phenomenon “smiling exhaustion.” The burnout of a few years ago was grim—all overhead and no agency. Today’s is different. People are shipping again, compensation has climbed, and many roles seem reborn. The catch is that there’s no off-switch: the tempo is brutal, and the rules rewrite themselves every month. It’s relentless, but it can also be exhilarating.

You see this in that 51% explicitly selected “holding positive and negative feelings at once.” But that undercounts the real ambivalence. When we looked at who picked at least one positive and at least one negative emotion, the number jumped to 77%. The average respondent selected five or more emotions (one person selected 13). It’s a workforce in which three out of four individuals are carrying a complex set of emotions about work.

Takeaway 7: Designers and researchers are the most worried

If AI is dividing the workforce, the obvious question is: along what lines? Who’s getting amplified, and who’s getting left behind?

The clearest pattern is by role: designers and researchers are at the epicenter of AI anxiety across the board, while founders and executives are feeling the best. We measured the share of each role that landed in negative identity or emotional buckets, and the spread is stark:

Among researchers, 51% are “anxious about my job security,” versus 15% of founders. Among designers, 63% feel “overwhelmed by the pace of change” and 61% feel “tired,” the highest of any role. Researchers are among the most likely to fear “losing my job to AI” (36%, just behind Data/Analytics at 38%), and designers are the most likely to feel the comp squeeze (61% selected “expected to do more for the same compensation”). Both report the lowest willingness to recommend their field of any role, and designers, as we’ll see, report the worst-rated managers in the survey.

Last year, designers and researchers showed the largest negative sentiment shift of any group. A year later, they’re the most negative on nearly every measure we have.

As a researcher, I’m acutely aware of the years of insecurities plaguing the research community. The biggest discussions for us have always been about getting a seat at the table and democratizing research across other functions. Many now feel the seat is being pulled from under us, and the work is being democratized, not to other roles but to AI.

By level, the most identity-destabilized group is early-career ICs (27%). (This is a wrinkle we’ll untangle in a moment, because those same early-career folks are, paradoxically, among the more optimistic.)

And the bigger the company, the more likely its people are to feel adrift in the AI transition: 23% feel destabilized at 10,000-plus-person companies, versus 15% at companies of 1 to 10.

AI is hardest on people in creative and research roles, on the most junior people, and on people working at the largest companies.

Takeaway 8: Founders are the happiest people in tech, and small companies are the best places to work

For all the AI upheaval, some of last year’s biggest findings came back almost unchanged, and their persistence through such a turbulent year makes them all the more convincing. Founders are still the happiest people in tech, and smaller companies are still better places to work than big ones. Before you read those as good news, it’s worth saying what “best” means here. The whole industry is sitting on a high baseline of burnout and a rather negative career view, and the winners of this section are the people who feel a little less of it.

Founders aren’t just the happiest people in tech—on most measures, they’re genuinely happy.

Founders and executives top nearly every measure in the survey: the highest optimism, the highest job enjoyment, the lowest burnout, the lowest layoff worry, and the most excitement about AI. That gap between founders and execs versus everyone else holds up statistically. On career optimism, it measures d ≈ 0.56, a medium-size effect and the second-largest in the entire dataset, behind only the AI divide.

As we wrote last year, the likeliest explanation is ownership: founders have the most control over their own destiny, and control turns out to be one of the best buffers against everything else. 71% are optimistic about their careers, they enjoy their work more than any other role, and they’re the least worried about layoffs of any group.

Ownership has limits, though. Nearly half of founders (47%) are still at least moderately burned out, with 18% very or completely burned out, even with the most control and the most upside of anyone in tech. And when we asked whether they’d recommend their path to a newcomer, founders landed at an NPS of –5. That is far healthier than the field’s –39, but it’s still bad. Even the happiest people in tech come out slightly net-negative on telling someone to follow their path.

One caveat: we only surveyed people who are founders today. The ones whose startups failed aren’t represented, and most startups don’t make it. Keep that in mind before you quit to go start something!

Smaller companies are still better places to work than big ones.

Company size predicts sentiment with almost eerie consistency. Walk from the smallest companies to the largest, and every measure of well-being gets steadily worse as the company grows:

People at small companies are more optimistic, less burned out, less worried about layoffs, and even feel AI is helping them more, likely because they have more freedom to actually use it. The “big-company blues” we described last year have settled in.

Look at the absolute numbers, though, not just the slope. Even at the smallest companies, 42% of people are at least moderately burned out, and the would-recommend score never climbs out of the red, sitting at –28 at 1-to-10-person shops. Small companies are winning a race to the least bad.

Two smaller echoes of 2025:

Where you physically work still hardly matters. There are barely any differences between how fully remote, hybrid, and in-office workers feel. Hybrid workers come out marginally the happiest (and in-office workers rate their managers the worst), but the gaps are small, just as we found last year. Employment type tells a familiar story with one twist. Founders and the self-employed are the happiest and least burned out, while contractors and freelancers are an interesting split—they are among the least burned out (less of the grind) but the most worried about layoffs (no job security).

One wrinkle you may have noticed: the largest companies (10,000+) tick up slightly in optimism and down in burnout compared with the 5,001–10,000 tier, breaking the smooth gradient. But neither difference is statistically significant (5,001–10,000 is our smallest sample), so the line flattens at the top rather than reversing. The one measure that does keep climbing to the very top is layoff worry. Workers at 10,000-plus-person companies are the most worried of anyone in the survey.

Takeaway 9: Managers are still the biggest lever for happiness

One more finding held firm from last year, and it may be the most actionable of all. Manager effectiveness remains the strongest driver of burnout in the entire dataset (it beats role, company size, and AI sentiment), and one of the strongest drivers of everything else. The gradient is dramatic:

Workers with an extremely effective manager report roughly 65% higher job enjoyment and dramatically lower burnout than those with an ineffective one. Yet only 25.5% of tech workers rate their manager as highly effective, while 36.5% rate theirs as ineffective, numbers that have barely budged since last year. The most powerful retention lever in tech is also the most neglected. (Notably, the worst-rated managers cluster in Data/Analytics and Design. The latter is a double blow, since designers are also among the most AI-anxious.)

Takeaway 10: The industry is “chaotic”

We asked, “In a sentence, how would you describe the state of the tech industry right now?” About 70% of respondents answered, and the single most common theme, by a wide margin, was chaos: roughly three in 10 explicitly used words like change, chaotic, uncertain, unstable, and in flux. Another one in six described an industry moving too fast to keep up with—treadmills, hamster wheels, hurricanes, “drinking from a firehose.” After that came AI hype and bubble talk (12%) and then, finally, a note of excitement and opportunity (11%).

A few responses capture the sentiment better than any percentage can:

“We’re in the 2nd inning of a massive shift, and no one knows how it will end, but all you can do is keep taking at-bats.”

“It feels like working on pure software is like picking up pennies in front of a steamroller.”

“The industry feels like it has lost its center of gravity—replacing curiosity about customers with an obsession over AI, automation, and efficiency.”

The chaos plus hype is well-described in this quote from a senior PM:

“Manic. Half are out of touch, clinging to the bandwagon, making the problem worse by pouring into the overhype. The other half are exhausted by the first half.” —Senior IC PM

When we ran sentiment analysis on the chaos-related quotes, the split was nearly even: 37% positive, 37% negative, and 26% neutral. The dominant theme is disorientation, but the emotional charge is truly bimodal. The same churn reads as thrilling to one person and terrifying to the next.

We confirmed this by splitting the responses by who wrote them. Career optimists and career pessimists describe the same industry in opposite terms. Optimists reach for “exciting,” “transforming,” “opportunity,” “fast-moving.” Pessimists reach for “chaos,” “layoffs,” “greed,” “dystopia.” Same disruption, opposite forecasts: half the room is anxiously bracing for AI’s impact; the other half is eagerly leaning into the AI era.

Where do we go from here?

The 2026 workforce is more burned out and less optimistic than a year ago, splitting along the fault line of AI into those who are thriving and those who are struggling, and a large, ambivalent middle caught between. Tech workers are mostly afraid of being squeezed by their jobs and increasing productivity expectations, privately convinced the field is no longer worth recommending to newcomers, while individually still finding real power and even joy in the tools. It’s a complicated moment. It’s also not a hopeless one.

So here’s what the data suggests you can actually do about it.

If you’re an employee:

Find something impactful to do with AI—then go deep. The “amplified” are the people who found the two or three tasks where AI measurably changed their output and got very good at those. Trying to use AI for everything is how you end up overwhelmed and conflicted, not empowered.
Watch the squeeze. The biggest career risk is silently absorbing a higher and higher bar for the same pay until you’re burned out. Take our burnout test here, and if your output has doubled this year, talk to your manager about scope and compensation.
Your manager matters more than almost anything. A great manager is associated with about 65% higher job enjoyment and far less burnout. If you have one, protect that relationship. If you don’t, getting closer to a better one may be the highest-leverage career move available to you.
Consider a smaller company—or your own. Every well-being measure in this survey improves as company size shrinks, and founders are the happiest group in tech. More autonomy and control is, year after year, the most reliable buffer against burnout and pessimism we find.
If you’re early-career, find mentors. The rungs are disappearing, but strong mentorship remains highly effective. Seek the teams and managers who still invest in developing people. That investment is rarer and more valuable than it’s ever been.

If you lead a team or company:

Invest in managers—it’s still the best money you’ll spend. Only a quarter of tech workers rate their manager as highly effective, and nothing else in the data moves burnout, enjoyment, and retention as much. This was our top recommendation last year. It’s our top recommendation again.
Manage the squeeze. Your people can feel AI raising the bar, and they’re watching to see whether you turn productivity gains into impossible expectations or actual relief. The fastest way to end up with resentment on your team is to pocket the productivity and turn saved time into more work for them.
Don’t let the bottom rung rot. If AI is doing the entry-level work that juniors used to learn on, you’re optimizing this year’s output by starving next year’s senior talent. Be deliberate about how early-career people develop when the old apprenticeship tasks are gone.
Pay special attention to design and research. For two years running, people in these roles report the worst sentiment, the highest AI anxiety, and some of the worst-rated managers. That’s a retention problem and a signal worth understanding before it becomes an exodus.
Treat AI adoption as a sorting risk rather than a productivity win. The same technology is lifting one part of your workforce while destabilizing another. The companies that come out of this ahead will help support the destabilized group instead of leaving them behind.

The throughline, if there is one, is the same as last year’s: Having the most advanced AI or the fanciest offices won’t determine which organizations succeed. Remembering that there are people underneath all this change will—people who, right now, are excited and exhausted, hopeful and scared, often all at once. Those people are watching closely to see whether the future they’re helping to build will still have a place for them.

Huge thanks to the 5,920 tech professionals who shared how they’re really feeling. Your candor is what makes this possible, and it’ll help us keep tracking where the industry is headed. 🙏

Have a fulfilling and productive week.

Noam (and Lenny) 👋

Appendix: Who took this survey?

This year’s survey reached 5,920 tech professionals, of whom 5,332 are currently working. All analyses are based on currently employed tech workers.

Role. As with last year, this is a product-centric audience: Product Management 46.9%, Engineering 12.6%, Founder/Executive 9.1%, Design 7.9%, Operations 4.3%, Product Marketing 4.0%, Research 3.2%, Data/Analytics 2.9%, Sales/GTM 2.8%, with a long tail of other functions.

Seniority. A senior crowd: IC–Senior 28.9%, IC–Staff/Principal 19.9%, Director 15.4%, Manager 12.1%, Founder/Exec 10.8%, VP+ 7.4%, and IC–Early career 5.5%. Roughly 54% individual contributors and 46% managers and above.

Company size. A fairly even spread, from 1–10-person startups (11.1%) up through 10,000-plus-person enterprises (18.0%), with the middle bands well represented.

Work setup. Fully remote 47.0%, hybrid 43.0%, fully in-office just 10.0%.

A methodological note for the careful reader: We made year-over-year comparisons only for questions whose wording matched across years. We redesigned much of the survey this year to focus on AI, which means a few 2025 themes (engagement, belonging, quitting intentions, career clarity) aren’t measured here, while others (layoff worry, the AI block, the career-recommendation score) are new. We also didn’t collect age, tenure, or geography this year, so when we discuss career stage, we’re using job level as a proxy.

If you’re finding this newsletter valuable, share it with a friend, and consider subscribing if you haven’t already. There are group discounts, gift options, and referral bonuses available.

Sincerely,

Lenny 👋

🎙️ How I AI: Sonnet 5 review & How to run autonomous coding agents from your phone

Lenny Rachitsky — Mon, 06 Jul 2026 15:01:17 GMT

Sonnet 5 review: I ran 64 generations to find out if it’s worth it

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Runway—The creative AI platform for images, video, and more
Hyperagent—Deploy fleets of agents that handle real work

Claire puts Anthropic’s new Sonnet 5 through a real benchmark. She builds the How I AI Bench live using Claude Code, then blind-tests Sonnet 5 against Sonnet 4.6, Opus 4.8, GPT-5.5, and Gemini 3 Pro across PRDs, prototypes, agentic tasks, and agent personality. She breaks down what won, what failed, and how builders can create their own repeatable benchmark before trusting the next model release.

Biggest takeaways:

Sonnet 5 is priced closer to previous Sonnet models than to Opus, but it doesn’t automatically replace either one. At $2 per million input tokens and $10 per million output tokens through the end of summer, Sonnet 5 sits in an interesting middle band. In Claire’s benchmark, it finished near the bottom of her personal preference ranking, which means the cost argument only holds if the quality argument also holds for your specific use case.
One-off vibe checks feel useful, but they’re not repeatable—and repeatability is what makes a benchmark actually matter. Claire has tested GPT-5.5, open-weight models like GLM-5.2, and others this way before, but she could never compare results across time. The How I AI Bench fixes that by using frozen inputs, a fixed rubric, and the same tasks every time a new model comes out.
Claude Code can read old session history and use it to generate benchmark ideas tailored to a person’s actual work. Claire gave Claude Code a simple prompt asking it to brainstorm eval tasks based on what they’d worked on together, and it pulled from stored sessions on her desktop. Builders can do the same with Codex. That context is sitting there unused for most people.
Building an HTML scoring page to rate outputs based on gut feel and export JSON takes maybe 45 minutes with Claude Code, and Claire argues it’s worth every minute. She scored 64 generations across five models by hand, gave each one a 1-to-5 gut score, added loose notes, and found that the human signal turned out to be the most useful part of the whole benchmark.
LLM-as-judge evals are too generous and cluster toward the middle of the scale. Claire had both GPT-5.5 and Opus 4.8 judge the outputs, and neither was spiky enough. They missed things she flagged immediately on a visual pass, like broken prototypes and ignored wireframe constraints. Models can’t yet see what the human eye catches in the first screenshot.
Claire’s taste and the automated benchmark disagreed almost completely, and she thinks her taste was at least partly right. The LLM judges ranked Gemini 3 Pro highest and Sonnet 4.6 lowest. Claire’s ranking was almost exactly the opposite. When she ran a 70/30 Claire-to-LLM weighted index, Sonnet 4.6 jumped to first. That divergence tells her the eval rubric needs to encode more of what she actually cares about before she can trust the automated scores.
Sonnet 4.6 is still Claire’s choice for daily agent work because of its personality, not its benchmark scores. She pays for API credits to run her OpenClaw on Sonnet 4.6 specifically because she likes how it talks to her. No other model in this test matched it on the voice eval, which asked things like “ugh, deploys are red again” and waited to see how the model responded.
For builders, Claire recommends GPT-5.5 for PRDs, Sonnet 4.6 for prototypes and chitchat, and Opus 4.8 or Sonnet 5 for codebase navigation. Those are the task-by-task recommendations that came out of the Claire-weighted index. Complex, dense UI work is where Opus 4.8 still earns its price premium; for everything simpler, Sonnet 4.6 holds up.
The How I AI Bench is version one, and a lot of it needs to get sharper. The agentic bug-hunting task turned out to be too easy: every model aced it, which means it can’t differentiate between good and great. Claire plans to retire that task, encode more of her taste into the rubric, and keep running the benchmark blind every time a new model drops. The goal is to make this a benchmark the labs actually care about.

Blog and detailed workflow walkthroughs from this episode:

Building a Custom Benchmark for Sonnet 5, and Why the Results Surprised Me: https://www.chatprd.ai/how-i-ai/sonnet-5-review-and-custom-benchmark

↳ How to Conduct a Blind ‘Vibe Check’ to Evaluate AI Model Quality: https://www.chatprd.ai/how-i-ai/workflows/how-to-conduct-a-blind-vibe-check-to-evaluate-ai-model-quality

↳ How to Build a Custom AI Model Benchmark Using Claude Code: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-custom-ai-model-benchmark-using-claude-code

↳ How to Create a Weighted Index for AI Model Benchmark Results: https://www.chatprd.ai/how-i-ai/workflows/how-to-create-a-weighted-index-for-ai-model-benchmark-results

How I run autonomous coding agents from my phone with OpenAI Symphony + Linear | Alessio Fanelli (Kernel Labs)

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Firecrawl—Power AI agents with clean web data
Jira Product Discovery—Prioritize with insights, build with confidence

Alessio Fanelli, the founder of Kernel Labs and co-host of Latent Space podcast, shows Claire how he manages autonomous coding agents from his phone using OpenAI Symphony, Linear, and a cloud VPS. He walks through the shift from “agent prompter” to “agent manager,” explains why Linear works as a state machine for async agent work, and shares what he’s learned from tracking token costs, purging skills files, and giving agents better senses. He also demos a very different use case: using Codex with browser access to hunt for underpriced Pokémon cards on eBay.

Biggest takeaways:

The shift from “agent prompter” to “agent manager” is the unlock most people are still missing. Alessio described how early agentic workflows felt fine until the second or third intervention, when the friction of local runtimes and clunky interfaces killed momentum. Moving to a cloud VPS with multiple communication channels (Linear, shell, mobile) made real async management possible.
Symphony isn’t magic; it’s just a very opinionated Markdown spec that tells the model how to behave. Claire made this point explicitly during the episode, and it’s the most important corrective to the “complex agent orchestration” framing that intimidates people. The whole framework is a Markdown file, and the models are good enough now to lock to it faithfully.
Token cost tracking per task is the primitive that most agent setups don’t have, and it should be table stakes. Alessio showed tasks ranging from 15 million to 221 million tokens, and the 221-million-token job (making an app deployable on Vercel) made complete sense in hindsight. Without that ledger, you have no feedback loop for improving your specs or your tooling.
Purge your skills files every few months or they become a liability. Models have a strong tendency to add instructions rather than replace them, so a skills file that grows over time eventually contradicts itself. Alessio’s advice is to keep files short, tight, and explicit about what the agent needs to ask for, not exhaustive lists of every possible behavior.
AI’s biggest unlocked opportunity is businesses built on heterogeneous data. The category Alessio described—things like trading cards, vintage clothing, and fish inventory—has always been impossible to scale because the data is inconsistent, visual, and contextual. LLMs are the first technology malleable enough to handle that messiness without extensive preprocessing.
Giving agents better senses (screenshots, visual diffs, video) extends autonomous runs dramatically. Kernel Labs built Glimpse, a Playwright extension for coding agents, specifically because the bottleneck wasn’t orchestration but rather agents hitting ambiguity in the UI and calling for help. Better tooling at the perception layer keeps the run going.
Context offloading is an underrated AI use case, and it’s worth building deliberately. Alessio’s email monitoring setup gave him the certainty that nothing important was slipping through, which removed a low-grade background anxiety. The same logic applies to personal finance, inventory, and any domain where staying on top of information is taking cognitive bandwidth you’d rather spend elsewhere.
The Pokémon card demo is the clearest proof that AI is compressing the information advantage that scale used to provide. Finding underpriced PSA-graded cards at $10,000-plus price points was previously a function of having more time, more people, and more domain expertise than competitors. Codex with browser access and a custom pricing skill collapses that advantage to a single well-written prompt.
Small businesses are the most interesting AI story, and they’re being underreported. Alessio’s observation from Japan, that small two- and three-person operations are running happily and profitably, points to a different kind of AI opportunity than the enterprise narrative suggests. The leverage AI gives a one-person operation is asymmetric in a way that bigger organizations can’t replicate at the same cost.

Blog and detailed workflow walkthroughs from this episode:

How Alessio Fanelli uses Open AI Symphony for Autonomous Coding and Pokémon Card Trading Workflows: https://www.chatprd.ai/how-i-ai/alessio-fanelli-uses-open-ai-symphony-for-autonomous-coding-and-pokemon-card-trading

↳ Build an AI Agent to Find Underpriced Pokémon Cards for Arbitrage: https://www.chatprd.ai/how-i-ai/workflows/build-an-ai-agent-to-find-underpriced-pok-mon-cards-for-arbitrage

↳ Automate Software Development with an AI Agent Manager using OpenAI Symphony and Linear: https://www.chatprd.ai/how-i-ai/workflows/automate-software-development-with-an-ai-agent-manager-using-openai-symphony-and-linear

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

How I run autonomous coding agents from my phone with OpenAI Symphony + Linear | Alessio Fanelli (Kernel Labs)

Claire Vo — Mon, 06 Jul 2026 12:03:37 GMT

Alessio Fanelli, founder of Kernel Labs and co-host of Latent Space podcast, walks us through two very different AI workflows: (1) a fully autonomous coding setup using OpenAI Symphony + Linear, where Linear acts as a state machine and Symphony manages agents through the whole dev lifecycle with zero babysitting; (2) Codex with browser access searching eBay for underpriced Pokémon cards—autonomously browsing, extracting PSA certificate numbers, and flagging deals on $10K–$20K cards for his San Carlos card shop, Merlin Games.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

Why “agent manager” is a better mental model than “agent prompter”
Why local Mac Minis don’t scale, and what a cloud VPS unlocks
How to wire Symphony and Linear together as an agent state machine
How to track token costs per task (and what 221 million tokens buys you)
What Glimpse does, and why better agent senses extend autonomous runs
Why your CLAUDE.md probably needs a full purge, not more instructions
How Codex scouts underpriced $10K Pokémon cards on eBay at scale
The new category of small business that AI just made possible

Brought to you by:

Firecrawl—Power AI agents with clean web data

Jira Product Discovery—Prioritize with insights, build with confidence

In this episode, we cover:

(00:00) Intro

(02:24) Prompter vs. agent manager

(04:31) Live demo: Symphony + Linear

(09:31) Setting up Symphony

(14:15) Purging your skills files

(18:06) The benefits of this system

(19:10) Demo: Using Codex to hunt for Pokémon cards

(24:17) The benefit of AI for small businesses

(28:23) Lightning round

Tools referenced:

• OpenAI Codex: https://openai.com/codex

• OpenAI Symphony (open-source framework): https://github.com/openai/symphony

• Linear (project management/agent state machine): https://linear.app

• PSA (Professional Sports Authenticator) grading: https://www.psacard.com

• TCGplayer (card pricing): https://www.tcgplayer.com

• eBay (used for card price scouting): https://www.ebay.com

Other references:

• Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-smart-glasses

• The Monk and the Riddle by Randy Komisar: https://www.amazon.com/Monk-Riddle-Creating-Making-Living/dp/1578516447/ref=sr_1_1

• The Divine Comedy by Dante Alighieri: https://www.amazon.com/dp/0451208633

• AS Roma (football club Alessio and Claire are both fans of): https://www.asroma.com/en

Where to find Alessio Fanelli:

X: https://x.com/FanaHOVA

Latent Space podcast: https://www.latent.space/

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

🧠 Community Wisdom: Quarterly planning and AI, cash vs. equity comp, paying for interview exercises, AI-powered outbound, compliance startup opportunities, and more

Kiyani — Sat, 04 Jul 2026 16:58:47 GMT

👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.

Sonnet 5 review: I ran 64 generations to find out if it's worth it

Claire Vo — Tue, 30 Jun 2026 23:22:23 GMT

I’ve been testing every major frontier model release since the start of the year, and when Anthropic dropped Sonnet 5, I wanted more than a vibe check. I got tired of one-off tests I couldn’t repeat or compare over time, so I built something better: the How I AI Bench, a repeatable eval harness I constructed live using Claude Code while recording this episode. I ran Sonnet 5 blind against four other frontier models (Sonnet 4.6, Opus 4.8, GPT-5.5, and Gemini 3 Pro) across PRD quality, prototype generation, agentic task completion, and agent personality. The results were not what I expected.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

What Anthropic claims Sonnet 5 improves over Sonnet 4.6, and where the benchmark data actually backs that up
How I built the How I AI Bench in under 45 minutes using Claude Code, starting from my own stored session history
Why I combined human vibe scoring (70%) with LLM as judge scoring (30%) instead of trusting either alone
How to set up a local HTML scoring page so you can rate AI outputs on gut feel and export those scores as JSON
Which model I recommend for PRDs, which for complex prototypes, and which for chatting with an agent daily

Brought to you by:

Runway—The creative AI platform for images, video and more

Hyperagent—Deploy fleets of agents that handle real work

In this episode, we cover:

(00:00) Sonnet 5 is out

(01:55) What Anthropic claims

(04:02) Why I’m done with one-off vibe checks

(05:05) Building the How I AI Bench live with Claude Code

(07:42) The scoring system

(10:43) Agent voice eval

(11:57) Quick recap

(13:58) Results: The How I AI index leaderboard

(21:21) What I’m improving for the next run

(22:16) Generating a Claire-weighted index

(23:53) Model-by-task recommendations

Tools referenced:

• Claude Sonnet 5: https://www.anthropic.com/news/claude-sonnet-5

• Claude Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8

• GPT-5.5 (OpenAI): https://openai.com/index/introducing-gpt-5-5/

• Gemini 3 Pro (Google DeepMind): https://deepmind.google/models/gemini/pro/

• Cursor: https://www.cursor.com/

Other references:

• SWE-bench Pro (agentic coding benchmark referenced): https://www.swebench.com/

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

How top PMs increase their leverage with AI

Colin Matthews — Tue, 30 Jun 2026 13:31:39 GMT

👋 Hey there, I’m Lenny. Each week, I answer reader questions about building product, driving growth, and accelerating your career. For more: Lenny’s Podcast | Lennybot | How I AI | My favorite AI/PM courses, public speaking course, and interview prep copilot

P.S. Get a full free year of Google AI, Cursor, Lovable, Notion, Manus, Replit, Gamma, n8n, Canva, ElevenLabs, Factory, Wispr Flow, Fin, Supabase, Bolt, Linear, PostHog, Framer, Railway, Granola, Warp, Gumloop, Magic Patterns, Mobbin, Stripe Atlas, and ChatPRD, by becoming an Insider subscriber. Yes, this is for real.

For years, the PM job drifted toward coordinating and aligning people. That version of the role is fading. Today, the best PMs at the best companies are prototyping with real code, querying data conversationally with MCP, confidently running coding AI agents, and finding countless ways to increase their leverage with AI.

To help you navigate this shift, I’m excited to announce that I’ve co-developed a course with my frequent collaborator and new Head of Education, Colin Matthews. It’s called Become an AI-Native Builder, and Colin will teach you how to best use the latest AI tools—including Codex, Claude Code, Cursor, and many of the Product Pass products—in your day-to-day work as a PM. You’ll use skills and MCPs to support discovery, create prototypes using your real codebase, ship changes to production using GitHub, and set up evals to automate and improve the quality of your work. This is the course I’ve always wished existed. And it’s not just for PMs. It’s great for designers, ops, researchers, sales, and basically anyone who’s non-technical and wants to get their hands dirty.

The first cohort kicks off on July 13th. If you’re a Lenny’s Newsletter annual subscriber you’ll get $600 off (and Insiders get $1,000 off). Grab your discount code here and sign up today.

In addition to this course, Colin is hosting a handful of free workshops with leaders from OpenAI, Cursor, Linear, Replit, and Lovable. These will be live and hands-on, and you’ll learn and practice new skills alongside other people in the trenches. These workshops are exclusive to paid Lenny’s Newsletter subscribers, and you can sign up for them here.

Colin is one of the most talented instructors of AI that I’ve come across. We’ve done four guest posts together (one is my third-most-popular post of all time) and, like me, he’s low on hype and high on pragmatic advice. He’s taught AI and other technical skills to tens of thousands of PMs at leading companies like OpenAI, Google, Stripe, Figma, Microsoft, and more. He’s a longtime product leader, a founder, and has shipped more than 10 SaaS products solo.

To mark the launch of this course, Colin wrote a guest post that will help you understand what’s possible with AI right now and where you stand on the “ladders of leverage.”

Let’s get into it.

I’ve trained over 30,000 PMs on how to integrate AI into their workflows. Early this year, I noticed a shift. Whereas before I heard from executives that they expected teams to be using AI for just basic prototyping and general productivity, now they’re increasingly looking for their employees to use AI to complete entire tasks. After building bespoke training programs to level up product and design ICs across industries like healthcare, legal, and streaming, I saw two main gaps: knowing what level of AI to reach for and having the technical skills to leverage AI tools to their fullest.

I wanted to share a forward-looking framework on how the most AI-native PMs are operating in mid-2026, and how you can create much more leverage for yourself with AI.

Think of this framework as three ladders, each for a different type of leverage that AI can give you.

Personal leverage helps you check items off your own to-do list at work. Product leverage accelerates your ability to ship the right things more quickly. And systems leverage helps build repeatable steps to consistently outsource work to AI and get high-quality results.

As you ascend each ladder rung, you get an order of magnitude more leverage. On the first rung, you use AI for assistance in your own work. On the second rung, you pass tasks to AI and review the output. At the top of the ladder, AI completes multi-step tasks and checks its own results. You will always apply some level of review at the end, but increasing leverage frees you up for other work.

Not every task, workflow, or company demands that you move up to the highest rung to get the most out of AI. The right rung on each ladder is about the best use of AI for the work in front of you.

I’ll walk through all three ladders, with examples you can start using today. Let’s get into it.

The personal leverage ladder

This is the most common way we all use AI at work: drafting docs, researching, or creating small artifacts. Most PMs are already pretty capable here, but it’s worth detailing the rungs so that you can see where you’re at and where you might go.

Rung 1: You use AI to write text. You’re using AI to help you with PRDs, Jira tickets, emails, etc. You then copy and paste answers into other tools. Most people are at this rung, or even lower. Don’t feel bad if you’re hanging out here. There’s so much opportunity!
Rung 2: You use AI to create artifacts. Think slides, basic Excel models, or small prototypes. Instead of generating text for you to copy, AI generates the actual artifact.
Rung 3: You get AI to complete a full to-do item for you. You’ve connected your LLM to external products like Amplitude, Google Drive, Notion, and Canva so it can pull and push information as needed. You run a prompt or skill to complete a task you might have handed off to a colleague before, like reading through customer support tickets or analyzing A/B test results.

Let’s walk through an example of each rung to illustrate baseline expectations.

At the beginning, you’re simply talking to the AI. For example, if I wanted to create a PRD, I might ask Claude to help me write it with a prompt like this:

The AI has very little context on your company or what a good PRD looks like. You’ll likely talk back and forth until you get a good enough result, then copy-paste to Google Docs or Word to improve it before sharing with your team.

Next is getting the AI to do work instead of just helping you do the work. Continuing with the last example, you could have Claude generate a financial model that shows the cost of hosting an agent yourself vs. using a managed service like Vercel.

Here’s a prompt I recently used:

Create a model that represents costs if we build and host ourselves vs. using managed agents. Do research on the engineering time saved and the compute costs in self-hosted vs. managed. Look at other vendors, like Cloudflare, Vercel, or E2B that provide sandboxes for agents for pricing. Demonstrate both the cost of the pilot and the cost at scale in the model, assuming we have 5M+ agent instances running annually (where an agent instance is per hour).

And here’s what the resulting model looks like:

You can check out this generated model here.

As mentioned, you should expect the output to need significant revision at this rung, but it’s a step forward from copy-pasting text from AI into a separate document.

You’re at the highest rung of personal leverage if you are able to delegate complete to-do items to AI. To illustrate this, I’ll use a fictional product called Stride. Stride is a Strava clone, where athletes can share their performance for running, swimming, and other activities. Let’s say I wanted to do a retention analysis of users who share their exercises with an attached photo, and compare that against the cohort who don’t share photos. To complete this task, I’ll connect my LLM of choice (Claude) to my product analytics software (PostHog) and give it instructions to run this analysis for me. This was my prompt:

Use PostHog to check if users who use social share features have a higher 30d retention than those who don’t. Show me an html doc as a final output visualizing cohorts and any other useful data. Cite all your sources so I can validate.

And here’s the result:

In the past, this would have been a task you’d fit in between meetings. Instead, you’re handing it off to an LLM to complete end-to-end. I’d recommend including “cite your sources” so you can easily validate if the output is correct. In this case, Claude provides links directly to the source data in PostHog.

Pro tip: To allow models to complete tasks for you (and thus move to this rung in your personal work), you’ll need to connect your LLM to the products you use frequently via MCP. Claude Code, Codex, and Cursor can all connect to tools like Figma, Amplitude, PostHog, Pendo, and more via MCP. This may sound complicated, but it’s really easy to do, and once you create this connection, you’ll never have to touch it again. Simply navigate to your product’s connectors marketplace and add your tools (Claude, ChatGPT, Gemini).

Once you have your connectors set up, try completing a common task using your AI, like:

Analyzing how a launch went by reviewing recent customer tickets and online sentiment
Checking how many users actually use a feature through product analytics events
Summarizing a recording from a customer call and creating a prototype based on their feedback
Updating your next sprint based on a change in roadmap priorities

You’ll likely find the results disappointing at first, but that’s just because the model doesn’t know how to meet your standards yet. Continue iterating with the model until you have a good result, then lock in the workflow by creating a skill—just ask your LLM to create one in the same chat you completed the task in.

You can repeat this workflow to get reasonable first drafts for almost anything: PRDs, roadmaps, marketing assets, survey analysis, prototypes, Figma mocks, and more. I totally acknowledge that the quality may not be perfect, but we’ll come back to that when we talk about systems leverage.

The product leverage ladder

Product leverage closes the gap between what you want to build and what you can ship, even without strong design or engineering chops. The way to leverage AI for this ladder breaks down into three rungs:

Rung 1: You create web-based prototypes. These communicate your ideas better than docs, but the prototype itself doesn’t have any value beyond communication. And your prototype’s code is independent from your product—it doesn’t use your existing codebase.
Rung 2: You create code-based prototypes. Claude Code or Codex accesses your real codebase as the context for generating prototypes, instead of screenshots or complex prompts.
Rung 3: You get an agent to ship changes to production as pull requests. An engineer picks up the PR, reviews, and merges your code into the product.

Let’s spend a bit more time with each of these.

Using AI tools like Lovable, Replit, and Magic Patterns is an amazing way to quickly and easily create a prototype. You can use this to share a concept or design with stakeholders, customers, or internal team members, allowing you to validate whether your solution is usable and solves the customer problem faster than ever.

Let’s revisit Stride as an example. Here’s what the profile page currently looks like:

Customers have been complaining that they don’t have clear cancellation paths and are confused about their subscription status after a free trial. You can use AI prototyping to create a quick mock to test with customers and stakeholders if one or more of your solutions address this problem:

This helps get to the right solution by testing more concepts faster, but typically the underlying code doesn’t have any value. Web AI prototyping tools have limited context on our real components, pages, and data models, so this prototype is removed from reality. That means that it will take more work to translate the tested prototype to real code.

The next rung up is to ask AI to prototype with your real product. This does not require running the full product on your laptop—or an expert-level coding ability—but you will need some technical skills. First, use a product like Claude Code or Codex to write code and run your app. Second, you’ll need a codebase that contains your UI but not your full backend; more on that later.

You can create the same prototype, this time asking Claude Code or Codex to use your existing codebase:

This time, the cancellation flow is added to the existing settings page, uses real components, and follows the design patterns of the actual product.

To create a codebase or repo that’s easy to use and doesn’t require running the full backend, pair with an engineer and have them use a prompt like this on your main codebase:

Create a new repo that contains all of the base UI elements, styles, routes, pages, and components for [list parts of the product you want included]. Create a mock data store that mimics the API data model and is stored locally. I should be able to run the resulting repo without any environment variables or backend services.

Once complete, clone the new repo to your computer. You should have an easy-to-run version of your UI that you can’t accidentally mess up. Sometimes your engineer will tell you this process is more complicated to get running than a simple prompt. It is very much worth the effort to build prototypes on your real styles and components, so do your best to get over the hump!

The last rung in product leverage is getting an agent to ship code to production. This is a great example of where your technical judgment as a PM is critical. It’s possible that your billing change is only in the UI, and all backend APIs, data elements, and events already exist. It’s also possible that this would require new infrastructure or integrations with another team to ship.

As a PM, it does not make sense to spend time being a worse engineer than the rest of your team. Knowing when you should write a doc, ship a prototype, or create a PR is as important as the technical ability to complete these tasks. PRs are great for copy changes, small UI/UX tweaks, and changes to views that use existing backend code.

The systems leverage ladder

🎙️ How I AI: GLM-5.2 review & How Gusto built a new product line with Claude Code

Lenny Rachitsky — Mon, 29 Jun 2026 15:02:35 GMT

GLM-5.2: why I’m replacing Opus in Claude Code with this new model

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Mercury—Radically different banking, loved by over 300K entrepreneurs

Claire tests GLM-5.2, the new open-weight model from Z.ai, inside her actual ChatPRD codebase. She runs it through codebase audits, UI redesigns, and a 45-minute autonomous bug-hunting task in Cursor and Claude Code, and breaks down where it surprised her, where it struggled, and why it may be good enough to replace Opus for some coding workflows.

Biggest takeaways:

Open-weight models are no longer a hobbyist curiosity—they are production-grade alternatives. GLM-5.2, built by Beijing-based Z.ai, benchmarks near Claude Opus 4.8 and above GPT-5.5 on SWE Bench Pro, with a million-token context window and full support for reasoning mode, function calling, structured output, and context caching. The decision is no longer about capability ceilings but, instead, about cost, control, and vendor dependency. Claire’s live testing confirmed it: this is not a toy.
Self-hosting changes the vendor power dynamic in ways that matter at scale. Open-weight means the trained model weights are publicly available, letting teams run inference on their own hardware, fine-tune on proprietary data, and route around any single provider’s API terms. When frontier labs change pricing or policy, teams using open-weight models can switch inference providers without touching a line of application code. The key: you’re not locked in.
Getting GLM-5.2 running in Cursor took 30 minutes, and Claire documented the undocumented part. Route your API key through Open Router, override the OpenAI base URL in Cursor’s settings to openrouter.ai/api/v1/cursor (the /cursor suffix isn’t documented anywhere), and add z-ai/glm-5.2 as a custom model. Claude Code requires two environment variable changes and one edit to claude/settings.json. Total time: under an hour, once you have the exact strings.
The 45-minute autonomous task revealed both the ceiling and the floor. Claire gave GLM-5.2 a single prompt inside Claude Code: pull the last 72 hours of Sentry errors and Vercel logs, then build a prioritized bug-fix plan. Over 45 minutes, it ran MCP tool calls, authenticated into external services, and produced a dark-mode engineering canvas with 20 Sentry errors, five Vercel log signals, and 14 planned fixes, including two P0s Claire hadn’t spotted through normal monitoring. The model surfaced signal-to-noise issues in their error pipeline that weren’t showing up elsewhere.
It hit a wall with React, then recovered. During the long-running task, GLM-5.2 struggled with TypeScript compilation errors before eventually producing clean React output. Claire’s read: HTML and CSS generation is reliable; React under agentic, multi-step pressure is shakier. For teams whose codebase is primarily React (she estimates it covers 98% of her own use), this is the friction point to test before committing the model to critical paths.
The cost math is striking: $3.36 for 6 million tokens, including the full 45-minute agentic session. A 72% cache rate helped, but even at full price, open-weight inference through Open Router sits well below Opus or GPT-5.5 rates for equivalent coding capability. For agents accumulating long context windows over extended sessions (the exact workload where frontier model costs compound fastest), open-weight alternatives offer a structurally different cost curve.
Claire’s recommendation: put GLM-5.2 in rotation, not in the spotlight. She’s keeping it in Cursor for frontend and design work, and in Claude Code for long-running agentic tasks, alongside closed frontier models rather than as a replacement. The constraint she’s watching: can it handle her React-heavy workload at the same consistency she gets from Composer? If it can, the cost-and-control argument gets much harder to ignore.

Blog and detailed workflow walkthroughs from this episode:

GLM 5.2: A Live Review of an Opus-Level Open-Weights Model: https://www.chatprd.ai/how-i-ai/glm-5-2-review-open-weights-model

↳ How to Deploy an Autonomous AI Agent for Bug Triage and Prioritization: https://www.chatprd.ai/how-i-ai/workflows/how-to-deploy-an-autonomous-ai-agent-for-bug-triage-and-prioritization

↳ How to Perform an AI-Powered Codebase Audit and Architecture Visualization: https://www.chatprd.ai/how-i-ai/workflows/how-to-perform-an-ai-powered-codebase-audit-and-architecture-visualization

↳ How to Configure the Open-Weight GLM 5.2 Model in Cursor: https://www.chatprd.ai/how-i-ai/workflows/how-to-configure-the-open-weight-glm-5-2-model-in-cursor

No Figma. No Jira. No docs. How Gusto built a new product line with Claude Code | Eddie Kim (CTO)

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
Magic Patterns—Prototypes that look like your product
Jira Product Discovery—Prioritize with insights, build with confidence

Eddie Kim is the co-founder and CTO of Gusto. In this episode, he shares how a five-person team used Claude Code, a permanent Zoom room, and almost none of the usual product process—no PM, no Figma, no Jira, no long specs—to build Gusto Cofounder from scratch in just 10 weeks.

Biggest takeaways:

A five-person team with no process can outship a large team with full process, if AI handles the engineering. Eddie’s product launched at Gusto’s tier-one level after 10 weeks, starting from zero code. The constraint wasn’t a liability—it was the design. When AI does the building, coordination overhead doesn’t scale the engineering; it just slows it down. The key: strip process to what the team actually needs, then let AI fill the gap.
“Zero code to tier-one launch” is now a viable founding path. The team reached a production milestone at Gusto without a line of pre-existing code. This flips the assumption that early teams spend months on infrastructure before shipping anything real. With Claude Code as the primary builder, the initial sprint becomes about direction and judgment, not typing. It compresses the time between idea validation and real user contact from months to weeks.
No meetings, no Jira, no text threads. It shipped anyway. The team had no standup cadence, no ticket system, no async thread to resolve blockers. What replaced all of that: shared context held inside the AI loop. When the model carries state and the team is small and aligned, human coordination overhead becomes optional.
The technical stack for a production AI agent is shockingly minimal. The entire agent loop ran on Cloudflare Workers with the Vercel AI SDK. Nothing else. No proprietary orchestration layer, no third-party agent framework. Everything else was built in-house. Teams often over-architect before they’ve proven anything; Eddie’s stack is evidence that infrastructure minimalism accelerates the path to learning what the agent actually needs to do.
Building agents is not as complicated as the community makes it sound. An agent is an AI SDK running somewhere in the cloud, able to look up files and call tools. That’s the full definition. The complexity people fear (state management, orchestration, reliability) is solvable with the same judgment calls any backend system requires. Eddie’s team shipped one at production quality in 10 weeks without specialist AI infrastructure experience.
The “permanent Zoom” model of AI development changes how teams think about context. Claude Code running in a persistent loop means the model has continuous access to the codebase’s current state. That’s closer to having an engineer who never closes their laptop than a chat interface you query on demand. For small teams, this is the equivalent of a senior engineer who is always available, always current, and never needs onboarding after a break.
The lesson for founding teams isn’t “use Claude Code.” It’s “design your process for AI as a team member.” Most early teams graft AI tools onto a human-scaled workflow: standups, tickets, PRs reviewed by three people. Eddie’s team treated the AI as a primary contributor from day one and built their coordination model around that assumption. The result: a workflow that gets faster as the AI improves, not one that merely offloads tasks to it.

Blog and detailed workflow walkthroughs from this episode:

How Gusto Built a New Product Line in 10 Weeks with Claude Code, No Jira, and No Docs: https://www.chatprd.ai/how-i-ai/how-gusto-built-a-new-product-line-in-10-weeks-with-claude-code-no-jira-and-no-docs

↳ How to Build a New AI Product in 10 Weeks Using the ‘No-Process’ Method: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-new-ai-product-in-10-weeks-using-the-no-process-method

↳ How to Fix Bugs Using an AI-Powered Test-Driven Development (TDD) Workflow: https://www.chatprd.ai/how-i-ai/workflows/how-to-fix-bugs-using-an-ai-powered-test-driven-development-tdd-workflow

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

No Figma. No Jira. No docs. How Gusto built a new product line with Claude Code | Eddie Kim (CTO)

Claire Vo — Mon, 29 Jun 2026 12:03:38 GMT

Eddie Kim is the co-founder and CTO of the payroll and HR platform Gusto, which just crossed $1 billion in revenue and serves more than 500,000 small businesses. Recently he did something most CTOs don’t: he went back to writing code. With three other engineers and one designer, Eddie built Gusto Cofounder, a net-new AI product, from zero code to a tier-one launch in 10 weeks. He walks through how that team actually worked, why they threw out nearly every process, and how anyone can copy the approach.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

The trash-can method: how to write, review, and delete a full PR as a product decision instead of a planning doc
The two-tool agent stack behind Gusto Cofounder
The exact “perma-Zoom” setup that replaced standups, retros, and Slack threads for 10 weeks
How a designer with no engineering background hit the 94th percentile for shipping code
The eval-first workflow Eddie uses to fix real customer bugs with Claude Code
How a non-technical leader can prototype an idea to win buy-in, then carry it all the way to production-quality code

Brought to you by:

Magic Patterns—Prototypes that look like your product

Jira Product Discovery—Prioritize with insights, build with confidence

In this episode, we cover:

(00:00) Intro: five people, 10 weeks

(02:38) The origins of Cofounder

(08:32) Inside the 10-week build process

(12:50) Building with no PMs

(14:38) The “trash can” method

(17:15) The stack architecture

(19:10) Shipping to production from day one

(22:03) How a designer became a top engineer

(29:05) Demo: Cofounder over text and Slack

(31:45) Demo: running a real payroll

(36:26) Live coding with evals in Claude Code

(39:39) Recap: prototype, small team, permission

(43:17) Lightning round

(48:44) Where to find Eddie and Cofounder

Tools referenced:

• Gusto Cofounder (early access/waitlist): https://gusto.com/cofounder

• Claude Code (Anthropic): https://claude.ai/code

• Cloudflare Workers: https://workers.cloudflare.com/

• Vercel AI SDK: https://sdk.vercel.ai/

• DX (engineering analytics): https://getdx.com/

• Wispr Flow (voice-to-text): https://wisprflow.ai

• OpenClaw: https://openclaw.ai/

Other references:

• Gusto (the main product, “Gusto Classic”): https://gusto.com

• Mindbody (referenced as customer data source): https://www.mindbodyonline.com/

Where to find Eddie Kim:

LinkedIn: https://www.linkedin.com/in/edawerd/

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

OpenAI Codex lead on the new shape of product work | Andrew Ambrosino

Lenny Rachitsky — Sun, 28 Jun 2026 12:31:54 GMT

Andrew Ambrosino leads development of the Codex desktop app at OpenAI. Nearly 100% of OpenAI employees—not just engineers—now use Codex weekly. A lifelong builder with a background spanning engineering, design, product management, and founding companies, he is now responsible for turning the Codex desktop experience into what he calls “the best desktop app that has ever existed, full stop.”

Listen on YouTube, Spotify, and Apple Podcasts

In our in-depth conversation, we discuss:

Why AI has completely flipped the product development process
What “taste” really means as a professional skill, and why it is emerging as the most valuable capability in an AI-first workplace
Why Andrew believes the Codex app would have failed if they launched it last November (vs. in February)
The “zone defense” model for how product managers at OpenAI operate when everyone can build anything
How roles are collapsed on Andrew’s team, and why eliminating the concept of roles entirely is a big mistake
How Andrew uses Codex to run his own workflows
The vision for a home base that coordinates work across ChatGPT, Codex, and the tools people already use.

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

Mercury—Radically different banking, now with Command

Where to find Andrew Ambrosino:

• X: https://x.com/ajambrosino

• LinkedIn: https://www.linkedin.com/in/ajambrosino

• Website: https://ambrosino.io

Referenced:

• Codex: chatgpt.com/codex

• The Primal Mark: How the Beginning Shapes the End in the Development of Creative Ideas: https://www.gsb.stanford.edu/faculty-research/publications/primal-mark-how-beginning-shapes-end-development-creative-ideas

• Linear: https://linear.app

• “Taste” is not just taste in aesthetics: https://x.com/thenanyu/status/2067327619897446721

• Linear’s secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu

• Paul Graham’s website: https://paulgraham.com

• The design process is dead. Here’s what’s replacing it. | Jenny Wen (head of design at Claude): https://www.lennysnewsletter.com/p/the-design-process-is-dead

• The case study factory: https://essays.uxdesign.cc/case-study-factory

• Why humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead): https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck

• OpenClaw: https://openclaw.ai

• OpenClaw: The complete guide to building, training, and living with your personal AI agent: https://www.lennysnewsletter.com/p/openclaw-the-complete-guide-to-building

• From skeptic to true believer: How OpenClaw changed my life | Claire Vo: https://www.lennysnewsletter.com/p/how-openclaw-changed-my-life-claire-vo

• The Codex feature that works while you sleep: https://www.lennysnewsletter.com/p/the-codex-feature-that-works-while

• The AI paradox: More automation, more humans, more work | Dan Shipper: https://www.lennysnewsletter.com/p/the-ai-paradox-dan-shipper

• Atlas: https://chatgpt.com/atlas

• Anthropic: https://www.anthropic.com

• Adobe Premiere: https://www.adobe.com/products/premiere

• The Magic School Bus Rides Again: https://www.netflix.com/title/80108373

Recommended books:

• The Gruffalo: https://www.amazon.com/Gruffalo-Julia-Donaldson/dp/0803730470

• The Big Orange Splot: https://www.amazon.com/Big-Orange-Splot-Manus-Pinkwater/dp/0590445103

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

🧠 Community Wisdom: Beating a career slump, adding more structure to an established team, questions for new-team 1:1s, the evolving shape of the growth role, and more

Kiyani — Sat, 27 Jun 2026 17:09:06 GMT

GLM 5.2: why I’m replacing Opus in Claude Code with this new model

Claire Vo — Wed, 24 Jun 2026 12:03:52 GMT

I put GLM 5.2, the open-weight coding model from Z.AI, through four real tasks inside my actual codebase: a codebase architecture audit, a UI redesign, and a 45-minute autonomous bug-hunting session pulling from Sentry and Vercel logs. Total cost: $3.36 for roughly 6 million tokens, a prioritized bug-fix dashboard I’m actually shipping from, and a landing page redesign that matched Chat PRD’s design system on the first try.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

What “open-weight” actually means and why it matters for cost and vendor independence
How to connect GLM 5.2 to Cursor and Claude Code
How it performs on codebase exploration and autonomous architecture summarization in a real production Next.js app
Whether GLM 5.2 can match an existing design system
How the model handles a 45-minute long-running autonomous task
Where GLM 5.2 stumbled
The actual cost breakdown

Brought to you by:

Mercury—Radically different banking loved by over 300K entrepreneurs

In this episode, we cover:

(00:00) What open-weight models are and why GLM 5.2 is worth testing

(01:38) GLM 5.2 model overview

(04:02) Capabilities and benchmark results

(06:02) How to set up GLM 5.2 in Cursor

(08:37) How to set up GLM 5.2 in Claude Code

(11:04) Live test 1: codebase exploration and architecture audit on ChatPRD

(12:43) Live test 2: generating an HTML architecture and roadmap page

(16:37) Live test 3: redesigning the How I AI landing page in Cursor

(20:57) Live test 4: 45-minute autonomous task, pulling Sentry errors and Vercel logs

(22:35) Where it struggled

(23:49) My verdict on the output

(25:23) Cost breakdown

Tools referenced:

z.ai: https://z.ai
GLM 5.2: https://z.ai/blog/glm-5.2
OpenRouter: https://openrouter.ai
Cursor: https://cursor.com
Claude Code: https://docs.anthropic.com/en/docs/claude-code
Sentry: https://sentry.io
Vercel: https://vercel.com

Other references:

SWE-Bench Pro leaderboard (coding benchmark scores referenced in episode): https://www.swebench.com
Frontier Suite and Post-Train Bench (additional benchmarks cited): https://scale.com/leaderboard
Use Claude Code with OpenRouter: https://openrouter.ai/docs/cookbook/coding-agents/claude-code-integration

Where to find Claire Vo:

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

The new inner game: Your unfair advantage in the age of AI

Joe Hudson — Tue, 23 Jun 2026 12:45:59 GMT

P.S. Get a full free year of Google AI, Cursor, Lovable, Notion, Manus, Replit, Gamma, n8n, Canva, ElevenLabs, Factory, Wispr Flow, Fin, Supabase, Bolt, Linear, PostHog, Framer, Railway, Granola, Warp, Gumloop, Magic Patterns, Mobbin, Stripe Atlas, and ChatPRD, by becoming an Insider subscriber. Yes, this is for real.

Joe Hudson is one of the most sought-after executive coaches in Silicon Valley, working with influential leaders like Sam Altman (OpenAI CEO) and founders and execs across Apple, Google, X, and more. He now spends much of his time coaching the research team at OpenAI and, from that inside perspective, has observed that the skills for success in AI-forward environments aren’t the ones you’d expect. Below, Joe explains and helps you prepare for what’s ahead.

For more from Joe, check out his Substack, Leadership Newsletter, and Connection Course. You can also find him on X and LinkedIn. And don’t miss my podcast conversation with him from last year!

Almost everyone I talk to is scared of the same thing.

“I’m going to get replaced by AI.”

“I can’t keep up with how fast everything is changing.”

“I’m going to end up in the permanent underclass.”

I hear this from senior VPs at Fortune 500 companies, and I hear it from people working inside the frontier AI labs. None of these fears are unfounded. The ground really is moving. Most of what you and I have been trained to do all our lives is being commoditized in the same way machines commoditized physical labor a century ago. The traditional skills we’ve optimized for—effort and knowledge—are becoming the exact two things AI does best.

But the trouble with fear is that it’s a terrible planner. Fear creates binary thinking and false ends, and braces us for worst-case scenarios, rather than preparing us for an unknown future.

Think about all the hard conversations you’ve had with your boss or investor. You rehearsed them in the shower. You ran every way they could go sideways. Then you actually had the conversation. How many times did it go exactly as scripted? So many of those rehearsals prepared you for conversations that never happened.

That’s where most of us are right now with AI. We’re afraid and rehearsing for the wrong conversations. Meanwhile, the skills that will actually decide whether we thrive aren’t even on our radar.

I spend my weeks coaching the people building this technology at OpenAI, and the courses I teach on emotional clarity at the Art of Accomplishment are full of folks from every top frontier AI lab. I have an unusual front-row seat to what’s coming because people in these environments are already operating in the future.

What I’ve seen is that those who thrive in these AI-pilled environments can stay in difficult conversations, not turn on themselves—or each other—when things get hard, and keep going in the midst of failure.

That’s the heart of what I want to show you, and it’s the opposite of what fear pushes you toward. Your unfair advantage in the age of AI comes from emotional clarity: the ability to feel what you’re feeling without being run by it. When knowledge and effort are nearly free, emotional clarity is scarce. But luckily, it’s a skill that can be learned.

In this post, I’m going to give you ways to measure your readiness around emotional clarity and hand you the specific methods to train for future success, for yourself and for your team.

How AI is changing work: the NBA-ification of teams

AI is increasingly amplifying what one person can accomplish, which means teams will start looking less like factories and more like NBA rosters: organizations will flatten and headcount will shrink, with far more capital riding on each person and their skills.

This is already happening in AI-forward tech companies like Anthropic, Amazon, Shopify, Coinbase, and Block, which are flattening their orgs and even creating “player-coach” roles. I also watch this happen every week inside the labs.

On an NBA team (and in these labs), knowledge isn’t the moat. You don’t win with the player who’s memorized the most plays or knows the most about shooting baskets. You win based on who makes the right read with two seconds on the clock, who can stay composed when the game gets physical, and who makes everyone around them better instead of worse to play with.

The result is a drastic change in what makes a team great. Talent and chemistry have always mattered, but when each person is amplified by AI, every decision, every pivot, and every bit of friction compounds. When an individual can face hard things, they accomplish a lot. But a team that can face hard things together moves exponentially faster. How to develop those emotional skills is what the rest of this piece is about.

The new inner game: the “wisdom stack” that will set you apart

What’s pulling people ahead in the age of AI is a set of capabilities most of us never thought of as skills at all. After seeing the same qualities consistently show up inside the labs, I’ve distilled them into four traits and called them the wisdom stack:

Discernment
“In conflict we trust”
Willingness to fail
Positive self-talk

1. Discernment

As AI increasingly takes over the doing, the critical work left for humans is the higher-level deciding: what to build, what to leave behind, and when to change course.

LLMs can advise you on what decision to make, but no model can feel the subtle tension in a meeting room or register your body’s signal when something’s off. Only you have that data, and that makes discernment more valuable than ever. And what most people don’t realize is that what often degrades decision-making is a lack of emotional clarity.

You’ve probably been told that better choices come from more data or sharper reasoning, but neuroscience shows us that our choices are fundamentally emotional. Our feelings are the underlying “context-setters” on which our rational brain acts. When we avoid certain emotional states, our solution set becomes constrained in ways we may not even be aware of.

I see this play out often, even at the highest levels of leadership. It’s why some of the most brilliant, sharp leaders still make terrible decisions: they’re unconsciously dodging or grasping at an emotion, and don’t have access to the full picture.

If you’re scared of upsetting your boss, you’re less likely to have the critical conversation that could revolutionize your business. If you’re scared of technology taking your job, you’re less likely to take a big swing in an unknown field. (And by the way, AI is an unknown field, and it’ll reward those willing to take lots of big swings.)

I saw this in action a few years ago, coaching the CEO of a fast-growing company. He was generous with his people and quick to please. But the company couldn’t kill anything—every initiative lived forever, their roadmap was clogged, and teams were stretched thin. He tried to fix it with reorgs and new prioritization frameworks. Nothing worked.

I asked him when he’d last said no to something in his personal life. He went quiet. His discomfort with disappointing people had become the company’s inability to prioritize.

The constraint was emotional, not strategic. Once we worked with this fear, he started sunsetting initiatives cleanly, and his team followed. Within six months they’d cut 40% of active projects, and revenue per employee had jumped.

Do this work, and your avoidance stops running the show from the backseat. That’s the real advantage in the age of AI.

How to check your readiness: Some of the most powerful work I do with CEOs is around uncovering the unconscious emotions that end up driving critical decisions. You can try it for yourself with a practice I call the Golden Algorithm:

Name a recurring frustration in your life.
Identify the emotion underneath it (rejection, helplessness, fear of abandonment, etc.).
List the ways you try to avoid that emotion. Be specific.
List the results. In almost every case, you’ll see that the strategy you’re using to avoid the emotion is creating the exact thing you’re afraid of.

For example:

Avoid failure → Play it safe/Never take big risks → Never win big → Feel like a failure.
Avoid conflict → People-please → Constant inner conflict → Resentment builds and leads to deeper conflict.
Avoid looking incompetent → Never ask questions → Stay confused → Underperform.
Avoid disappointing others → Say yes to everything → Be spread too thin → Disappoint everyone.

How to practice:

Change your relationship to your own emotional signal. Five minutes a day is enough to make a difference. Take the emotions you identified in the Golden Algorithm exercise and bring them to this practice: Emotional Inquiry.
Feel your feelings. Read and download this one-page guide.

How we do it at the Art of Accomplishment: Most people grade a decision by how it turned out—a good result means it was a good call; a bad result, a bad call. But that’s just holding yourself responsible for predicting the future, which even great investors can’t do. We treat our decisions like a portfolio, and focus instead on the process behind them.

Before a big decision, we ask ourselves: “Am I deciding to be myself and follow what I actually want, or am I trying to manage the future and avoid something I don’t want to feel?” If it’s the second one, we slow down and feel the thing first, then choose. Do that consistently and the portfolio wins, even when plenty of individual bets don’t. Every month, we also dogfood our own program, The Council, a three-hour team check-in about our biggest fears. It surfaces and resolves the biggest conversations and decisions we have been avoiding.

2. “In conflict we trust”

AI makes it easier than ever to simulate the illusion of connection. A model can draft the tough message, rehearse the conversation 10 times, and give you a perfectly worded apology. But a model cannot open you up to saying the hard thing. It cannot make you put down the position you’ve been defending. AI can hand you every answer in the world, but it can’t make you use them.

You can buy more compute. It’s a lot harder to buy your way out of the conversations your team is avoiding. Like the CEO who’s too scared to tell his employees the truth, so the decision festers for months. It’s the two co-founders who stopped being honest with each other six months ago, and now route every hard conversation through a third person.

And with the NBA-ification of teams, one unresolved tension between two co-founders is no longer diluted across multiple layers in a big org. Now it’s a huge crack running through a tight roster. When each player’s leverage is critical and powerful, every broken relationship becomes that much more expensive.

As a result, being able to have productive conflict, surface difficult issues, and stay present during uncomfortable conversations is about to become staggeringly more valuable. Those who thrive in this new world move toward the difficulty instead of away, because every conflict looks like an inefficiency to be solved—and every new solution builds a stronger team bond.

I witnessed this firsthand with one of my clients, Johannes, who runs a developer-tools company (Ona, formerly Gitpod) in one of the fastest-moving corners of the AI market. His leadership team had one of the usual buried tensions: sales versus engineering.

We brought their team through an intensive two-day summit built to surface the conversations they were avoiding. Afterward Johannes told me, “Our team did six months of work in two days. Every interpersonal conflict that was standing between us just went away. The clarity we walked away with, and the business decisions we’re now able to make, are extraordinary.”

How to check your readiness: Pick your most important working relationship. Is there something true you haven’t said? How long has it gone unsaid? That number, in weeks or months, is your connection debt.

How to practice:

At the end of each week, write down two things nagging at you. Then actually call the people involved and talk about it. Hint: There’s an open, impartial approach we recommend for this called VIEW (Vulnerability, Impartiality, Empathy, Wonder). And remember, you don’t need to solve the problem before you raise it. If something isn’t working for you, just say, “Hey, something isn’t working for me,” and solve it together from there.
Twice a month, pick any important relationship (a co-founder, a partner, a manager, a friend you’ve drifted from) and ask yourself: “What’s the scary thing I’m not saying?”

How we do it at the Art of Accomplishment: We run on high transparency and no back channels. If you have a problem with someone, you bring it to them, not about them to everyone else. I also have a recurring block on my calendar dedicated to hard conversations, where I reflect on what has been nagging at me and then reach out to discuss it.

3. Willingness to fail

AI is extraordinary at what’s already known. Ask it for the best practice, the standard approach, or the analysis, and it’s faster than any of us could ever be. Now the bottleneck is how fast you can learn something new—and that requires a willingness to fail.

Almost no one thinks their way to a breakthrough in one sitting. They iterate their way there: build the thing, see what needs to improve, build the next version. The “creative genius” is just the one who took more shots faster, and learned more from each one. Think Steve Jobs, fired from the company he built and exiled for a decade. Or James Dyson, who built 5,126 vacuums that failed before one that worked. Or Michael Jordan, who missed over 9,000 shots, lost almost 300 games, and blew the game-winner 26 times.

Many of you are likely nodding along. This is the Silicon Valley bible; you’ve heard it a thousand times. But knowing this truism versus embodying it emotionally is completely different. There’s a structure in our brain called the habenula that cuts our motivation the moment it senses failure. It’s the same mechanism that stops a bear from fighting for dominance every day. Our brains beat us up before we ever try again.

That’s why true experimentation is actually quite rare. Most only iterate where it already feels safe—the A/B test, the side project, the experiment we’re fairly sure will work. Companies can build a culture of safety for bigger risks, and the best ones do. But if you’re anywhere near the bleeding edge, it won’t always feel safe to try something new. To succeed, you have to become comfortable with the feeling of failure and discover that you can move through the fear in seconds when you stop fighting it.

So the two halves work as one loop. The more reps you take, the more the threat response quiets down and failure feels like information, not threat. And the faster a team makes those reps feel normal, the more reps each person is willing to take. Team culture lowers the social cost of failing; individual practice lowers the felt cost. Each feeds the other. The fastest way to get someone comfortable with failure is to put them on a team where failed experiments are so ordinary—even celebrated—that you stop flinching.

A team that’s internalized this is Anthropic’s product team, which runs “side quests,” where anyone can spend an afternoon prototyping an idea outside the official roadmap. No approval, PRD, or alignment meeting is needed. They build the thing, ship it internally, and see what happens. If teammates keep using it the next day, and the day after that, it gets polished and released. If nobody touches it, it dies.

Rather than effort or coordination, they’ve optimized for the rate at which new ideas can hit reality and get tested.

How to check your readiness:

Look at your past month of work. How many big-swing experiments did you and your team run? We aim for about five per team member.
Each week, check in with your team and track “pace” and “spin.”
1. Pace is how fast you’re moving—the rate at which you’re iterating. How quickly can you build the thing, see what’s working, and start the next one? Rate it from 0% to 150%. You’re aiming for 100%. High pace is great, but a very high pace isn’t sustainable.
2. Spin is the hidden tax. It’s the feeling of your wheels spinning with brakes on: second-guessing, bracing before you take the shot, loops you get stuck in when failure feels like a threat rather than an opportunity for learning. Rate it from 0% to 100%. You want it under 30%.

How to practice:

Play with your job. Treat it as a series of experiments rather than mandates. Once a week, take the task you dread most and ask: “If I doubled how much I enjoyed this, what would I change?” Then ask: “What are the one or two things I could do this week that would make everything else on my list easier or irrelevant?”
Take a goal you’ve been circling. Instead of one big attempt you could “fail,” write down 20 small experiments you could run around it to learn something new. Then start running them. Each one you complete is a win, regardless of how it turns out. You’re training yourself to measure progress by iterations, not outcomes.

How we do it at the Art of Accomplishment: Everyone on our team is responsible for running five experiments every quarter to refine the way they work. These are fast and loose, and easy to measure. The important thing is the pace of trying new things and learning, not getting it right or taking weeks to design the perfect experiment. And this is true for everyone in our org, from an executive assistant to the president. We also have a “failure celebration” at our annual offsite where we present our biggest “failure.” It gets a standing ovation from the rest of the team.

4. Positive self-talk

This skill sits underneath and supports the other three, which is why it’s one of the biggest differentiators I see.

You can have all the talent in the world, but if you regularly talk to yourself in a hostile tone, that mentality makes you doubt every decision, kills your creativity, and puts you into chronic stress and overwhelm.

A study on perseverative cognition, or repetitive negative self-talk and worry, found that the body can’t tell the difference between an actual threat and a thought about a threat. When we criticize ourselves, our brain reacts as if we’re under attack. Cortisol and adrenaline spike, our heart rate climbs, and blood pressure rises. I’ve seen this affect the most creative and successful people I work with. Their biggest bottleneck is how they relate to themselves.

The internal voice of a lot of high performers is brutal: “You’re not doing enough. Why aren’t you further along? This isn’t good enough.”

In the pre-AI era, self-criticism could push you to outwork the person next to you. It hurt, but the negativity could lead to success (though eventually to burnout). That math has flipped. You can’t out-grind a server farm or outwork a model that doesn’t sleep. The voice that used to drive harder work is now just shutting down every single capability that’s a differentiator in the AI era.

The good news is that this skill is more trainable than knowledge or effort. You can’t add 50 IQ points or 20 years of experience to your resume. But your internal voice was learned, which means it can be changed. In fact, we know this because we’ve measured the impact of one of our programs for seven years: we improved negative self-talk by a full standard deviation across all participants.

How to check your readiness: For one day, treat your inner monologue as a transcript. Every time you notice yourself feeling tight, stuck, or low, stop and:

Set a 5-minute timer.
Write down exactly what’s going through your head.

Most people are stunned by what they find. The voice they’ve been listening to all day says things they would never say to a friend or their child: “That was stupid of you.” “Everyone can tell you’re faking it.” “You should have known better.” “You haven’t done enough today.” “You are an imposter.”

How to practice:

🎙️ How I AI: How to write AI agent loops in Claude Code and Codex + How Claude Mythos found a 15-year-old bug in Mozilla Firefox

Lenny Rachitsky — Mon, 22 Jun 2026 15:02:37 GMT

How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
WorkOS—Make your app enterprise-ready today
Runway—The creative AI platform for images, video and more

In this hands-on tutorial, Claire explains the difference between heartbeats, crons, hooks, and goal-based loops, then builds real automations in Claude Code and Codex, including a daily PR-review loop and a weekly skills loop that spawns its own subagents. If you’ve heard “loop engineering” and wondered what it actually means, this is the beginner-friendly breakdown.

Biggest takeaways:

A loop is just a prompt that fires itself, nothing more exotic than that. The reason “loops” sound intimidating is that the hype cycle turned a basic automation concept into something mystical. Heartbeats, crons, and webhooks have been around forever. What’s new is pointing them at an AI agent instead of a batch job.
Goals are the most powerful loop type, and the one most people get wrong. A goal loop sets an outcome and runs an agent against it until the outcome is validated or the agent gets stuck. It doesn’t stop on a timer; it stops when the work is actually done. Fuzzy success criteria means the agent loops forever, burning tokens, so my advice is to let Codex write its own goals, using OpenAI’s goal-writing guide as a starting point.
Think about loops the way you think about onboarding an employee. Define the job: what they check, how often, what output you want, and who to contact when something’s wrong. “Every Friday at 10 a.m., review all merged PRs and identify skills our agents are missing” is a job description. It’s also a loop prompt.
Your agent can have its own agents. This is where loops get truly powerful. The PR-review loop Claire built in Claude Code doesn’t just check PR status; it spins off dedicated subagents to babysit individual PRs until all merge checks are green. The skills loop in Codex identifies gaps and immediately spawns subagents to validate each new skill using a goal loop.
Loops get expensive if you don’t write them carefully. If the success criteria is vague or the validation threshold is too thin, the agent will keep running and keep charging without meaningful progress. Monitor both cost and output quality from day one.
The morning briefing in Claude Cowork is a perfect loop starter. A scheduled task that fires every morning, checks your calendar and email, and sends a summary to Slack is already a fully functional loop. No code required. From there, scaling up to PR reviews or skills identification in Claude Code or Codex is a natural next step.
The power move is loops that generate their own subagent loops. In the Codex demo, Claire’s weekly automation spawned two named subagents that each ran their own goal loops to validate skills in real time. The ceiling on loop-based automation is basically “how well can you define the job?” not “how complex is the engineering?”

Blog and detailed workflow walkthroughs from this episode:

How I AI: Designing AI Agent Loops in Claude Code and Codex: https://www.chatprd.ai/how-i-ai/how-i-ai-designing-ai-agent-loops-in-claude-code-and-codex
↳ Build a Self-Improving AI to Generate Agent Skills in Codex: https://www.chatprd.ai/how-i-ai/workflows/build-a-self-improving-ai-to-generate-agent-skills-in-codex
↳ Automate Daily Pull Request Reviews with a Claude Code Agent: https://www.chatprd.ai/how-i-ai/workflows/automate-daily-pull-request-reviews-with-a-claude-code-agent

How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead

Listen now on YouTube • Spotify • Apple Podcasts

Brought to you by:
WorkOS—Make your app enterprise-ready today
Metaview—The agentic recruiting platform for winning teams

Brian Grinstead, distinguished engineer at Mozilla, breaks down how his team used AI agents to ship 423 Firefox security fixes in one month. He explains why the real unlock wasn’t just a better model, but the custom harness around it: scoring files, running goal loops, verifying bugs with subagents, and keeping humans in the review process. It’s a tactical look at how to point agents at a massive codebase and get fixes you can actually ship.

Biggest takeaways:

The Firefox security bug spike wasn’t just about the model; it was the harness too. While everyone focused on Mythos, the real story is that Firefox built a custom harness that gives AI agents the right tools to find, verify, and fix bugs. Brian says this is simpler than it looks: “It’s actually a reasonably simple wrapper around it. You just need to give it access to the right tools for the job.”
Agents are relentless in a way humans can’t be. Agents will try 14, 15, 20 different approaches to trigger a bug without getting tired or losing focus. Brian found bugs that required the agent to try 14 times before succeeding. As Brian notes, “Cognitive energy declines over time in a way that agents don’t.”
The verification loop is what eliminates false positives. Firefox uses a two-stage verification process: first, the agent must trigger an actual crash in their fuzzing build (a crystal-clear signal), and second, a verifier subagent checks that the bug report makes sense and doesn’t involve test-only configurations. By the time a bug reaches human engineers, there are almost no false positives.
Agents get laser-focused on the specific task and miss the bigger picture. When the patching agent fixed a bug, it would often patch just the one vulnerable location. Human engineers would then look at the fix and say, “This is right, but we should also check three other similar places in the codebase.”
Prioritization is essential when you have millions of lines of code. Firefox built a simple LLM judge that scores each file on two dimensions: likelihood of a memory safety issue, and ease of access from a webpage. Brian says this is “very, very simple” and anyone can replicate it.
The harness can be built in an afternoon using vendor SDKs. Firefox started with Claude’s agent SDK, which is essentially a wrapper around Claude Code CLI that streams JSON and provides programmatic hooks. Brian’s advice: use the vendor-provided harnesses (Claude agent SDK, OpenAI agent SDK) rather than third-party frameworks, because the models are likely post-trained to work best with their own infrastructure.
You should run multiple models and harnesses for security work. Because attackers will use whatever model and technique finds bugs, defenders need to scan with multiple approaches. Different models and harnesses spike on different strengths and will identify different vulnerabilities.
This approach works for more than security—performance, tech debt, and UX are all viable targets. The same pattern applies: score and prioritize areas of your codebase, give the agent a constrained goal with verification criteria, and plug the results into your existing pipeline. Brian says they’re doing active work on performance optimization using the same harness structure.

Blog and detailed workflow walkthroughs from this episode:

How Mozilla Fixed 500 Security Bugs with Claude Mythos: https://www.chatprd.ai/how-i-ai/how-mozilla-fixed-500-security-bugs-with-mythos
↳ Create an AI-Powered Patch and Verification Loop for Security Bugs: https://www.chatprd.ai/how-i-ai/workflows/create-an-ai-powered-patch-and-verification-loop-for-security-bugs
↳ Use an LLM as a Security Judge to Prioritize Codebase Analysis: https://www.chatprd.ai/how-i-ai/workflows/use-an-llm-as-a-security-judge-to-prioritize-codebase-analysis
↳ Build an AI Agentic Harness for Automated Security Bug Hunting: https://www.chatprd.ai/how-i-ai/workflows/build-an-ai-agentic-harness-for-automated-security-bug-hunting

If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.

Catch you next week,
Lenny

P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.

How Claude Mythos found a 15-year-old bug in Mozilla Firefox | Brian Grinstead

Claire Vo — Mon, 22 Jun 2026 12:03:06 GMT

Brian Grinstead is a distinguished engineer at Mozilla, where he’s worked on Firefox and the web platform since 2013 (he joined to help launch Firefox DevTools). Recently he and his team pointed an agentic bug-finding pipeline at Firefox—a codebase with tens of thousands of files and tens of millions of lines of code—and shipped a record month of security fixes. The viral chart everyone saw gave the credit to Anthropic’s new Mythos model. Brian’s take is that the harness and pipeline did just as much of the work, and he walks through exactly how it runs and how anyone can build a starter version.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

How to build a basic bug-finding harness by running Claude Code or Codex with one prompt and the -p flag, no SDK required
Why pointing an agent at a whole codebase fails, and how an LLM judge can score and rank files before you spend any compute
How a verifier subagent kills false positives by catching the agent when it cheats
The goal-loop pattern: give an agent a tightly scoped problem, a clear pass/fail signal, and let it retry far past the point a human would quit
Why teams that already invested in fuzzing, CI, and dev tooling are so far ahead
How to weigh model versus harness, and why Brian splits the credit close to 50-50
How a non-engineer can reuse the same score, verify, and fix the loop for design quality, conversion rate, or tech debt
Why AI-generated patches still can’t ship on their own, and where humans stay in the loop

Brought to you by:

WorkOS—Make your app enterprise-ready today

Metaview—The agentic recruiting platform for winning teams

In this episode, we cover:

(00:00) Introduction to Brian Grinstead

(02:43) The viral chart: Firefox Security Bug Fixes by Month

(05:32) How the custom harness works

(10:22) Goal loops and guardrails

(14:45) How they built it

(16:55) Real bugs, including a 15-year-old one

(23:00) Open-sourcing it

(26:26) Why humans still review every fix

(32:30) Live demo and prioritizing files

(40:18) Mobilizing the team and recap

(42:33) Lightning round

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email jordan@penname.co.

What happens after coding is solved? | Fiona Fung (Manager of the Claude Code and Cowork Teams)

Lenny Rachitsky — Sun, 21 Jun 2026 12:31:43 GMT

Fiona Fung leads the teams behind Claude Code and Cowork at Anthropic (overseeing Boris Cherny and the entire engineering and PM team). Before Anthropic, she spent 11 years at Microsoft building Visual Studio and TypeScript and then moved to Meta, where she started Facebook Marketplace (now generating over $100 billion in GMV annually), worked on Meta’s first smart glasses and AR glasses, and led infrastructure, growth, integrity, and safety teams at Instagram. She’s been an engineer for over 25 years and has a unique perspective on how the role of building software is changing.

Listen on YouTube, Spotify, and Apple Podcasts

In our in-depth conversation, we discuss:

What she’s learned about running a team that’s shipping 8x more code than before
Which roles AI will transform next
Specific ways her team uses AI
How Claude “routines” have changed how she operates as a manager
The context-switching problem no one has solved yet
The biggest unsolved problem in AI
What keeps her up at night

Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more

Mercury—Radically different banking, now with Command

Where to find Fiona Fung:

• LinkedIn: linkedin.com/in/fionafung

Referenced:

• Running an AI-native engineering org: https://www.youtube.com/watch?v=igO8iyca2_g

• Head of Claude Code: What happens after coding is solved | Boris Cherny: https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens

• Today, Anthropic engineers on average ship 8x as much code per quarter as they did compared to 2021-2025: https://x.com/AnthropicAI/status/2062568864240836995

• Visual Studio: https://visualstudio.microsoft.com

• Joseph Campbell’s quote: https://www.goodreads.com/quotes/192665-the-cave-you-fear-to-enter-holds-the-treasure-you

• Life-changing Cowork use case: https://x.com/lennysan/status/2059664455001334124

• Introducing Claude for Small Business: https://www.anthropic.com/news/claude-for-small-business

• Conversations with Tyler podcast: https://conversationswithtyler.com

• Sheryl Sandberg on Facebook: https://www.facebook.com/sheryl#

• Amélie on Prime Video: https://www.amazon.com/Amelie-Jean-Pierre-Jeunet/dp/B0DQ4S3N45

• Spirited Away on HBO Max: https://www.hbomax.com/movies/spirited-away/3deab668-d0a4-4a8d-9bc8-0952a0ad836e

• Nausicaä of the Valley of the Wind on HBO Max: https://www.hbomax.com/movies/nausicaa-of-the-valley-of-the-wind/ed66031b-6353-4019-ba54-35488468a4db

• Sweet Sisters Bodycare: https://sweetsistersbodycare.com

• Anthropic events: https://www.anthropic.com/events

• Clare Pooley’s quote: https://www.goodreads.com/quotes/11305360-in-a-world-where-you-can-be-anything-be-kind

Recommended books:

• Margaret Atwood’s books: https://www.amazon.com/stores/author/B000AQTHI0?ccs_id=0027a474-cd59-4a3a-bcd7-9b173c27d530

• Haruki Murakami’s books: https://www.amazon.com/stores/Haruki-Murakami/author/B000AP7AFI

• The Little Prince: https://www.amazon.com/Little-Prince-Antoine-Saint-Exup%C3%A9ry/dp/0156012197

• Nausicaä of the Valley of the Wind: https://www.amazon.com/Nausica%C3%A4-Valley-Wind-Box-Set/dp/1421550644

• High Output Management: https://www.amazon.com/High-Output-Management-Andrew-Grove/dp/0679762884

Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

Lenny may be an investor in the companies discussed.

My biggest takeaways from this conversation:

🧠 Community Wisdom: Fractional CPO compensation, free e-signature tools, why some users pay but never use your product, sharing Claude Code context across a team, and more

Kiyani — Sat, 20 Jun 2026 15:53:05 GMT

How to design AI agent loops: schedules, goals, and subagents in Claude Code and Codex

Wed, 17 Jun 2026 12:04:04 GMT

I break down every loop type from scratch—what a heartbeat, cron, hook, and goal loop actually are, when each one fits, and the five things any effective loop needs before it touches production. Then I build two live loops: a daily aging-PR reviewer in Claude Code that schedules itself at 10:15 a.m. and spins off its own subagents, and a weekly skills-identification loop in Codex that spawns goal-based subagents to validate its own output in real time.

Listen or watch on YouTube, Spotify, or Apple Podcasts

What you’ll learn:

The plain-English definition of a loop—and why it’s just an automated prompt, not a scary new paradigm
The four loop types (heartbeat, cron, hook, and goal) and when each one actually fits your workflow
How to think about loop design using the “onboarding an employee” mental model
The five things every effective loop needs: work trees, skills, plugins/connectors, subagents, and state tracking
How to build a scheduled PR-review routine in Claude Code that babysits aging PRs and alerts your team
How to set up a weekly skills-identification automation in Codex that spawns its own validating subagents
Why goal-based loops are the hardest to write well—and where most people burn tokens for nothing
The two warning signs that your loop is going to get expensive before it gets useful

Brought to you by:

WorkOS—Make your app enterprise-ready today

Runway—The creative AI platform for images, video, and more

In this episode, we cover:

(00:00) Prompts are out and loops are in

(02:30) Defining a loop

(03:03) The four ways to automate a prompt: heartbeat, cron, hooks, and goals

(06:03) Five things every effective loop needs

(09:26) The “onboarding an employee” framework for designing loops

(11:58) Live build #1: Daily aging PR loop in Claude Code

(17:08) Subagents inside loops

(19:00) Live build #2: Weekly skills identification loop in Codex

(22:57) Watching subagents spin up in real time

(25:28) Warning signals around loops

(27:31) What listeners are doing with loops

Tools referenced:

• Claude Code: https://claude.ai/code

• Codex: https://chatgpt.com/codex

• OpenClaw: https://openclaw.ai/

Other references:

• Claire’s article “Why OpenClaw Feels Alive Even Though It’s Not”: https://x.com/clairevo/article/2017741569521271175

• Addy Osmani’s article on loop engineering: https://addyosmani.com/blog/loop-engineering/

• Using Goals in Codex: https://developers.openai.com/cookbook/examples/codex/using_goals_in_codex

Where to find Claire Vo: