Introducing clawded.ai

semi◦diffusion: an AI tale of two cities is where I write about the business side of AI: how platforms operate, where demand is moving, and what that means for people building, working, and earning in this market.

Today, I am adding something practical to that coverage. Paid semi◦diffusion subscribers now get access to clawded.ai, a matching tool for remote AI data and evaluation work. It brings together openings across platforms, checks them at the source, and helps you identify which ones are worth your time.

Subscribe now

The AI experts are busy with the big picture this month. Goldman Sachs put out a full conversation on how AI is affecting GDP growth, productivity and jobs. Its estimate for AI’s current net contribution to measured US GDP growth is about 0.1 percentage point.

It’s all macro. None of us can hide from it, and none of it tells you what to do on Monday.

Paul Millerd asked the personal version in July: will you train the models for pay? He doubted many people would want work whose only reader is a model.

My inbox says plenty of you do. On a personal level the question is simple. You can get paid to train models. I have been, on more than ten Mercor projects, as a contributor and as a reviewer.

Today I’m launching the tool I wish I’d had for that work. It’s called clawded.ai.

What’s different about remote work this time?

Remote work is old. Fiverr has sold gigs since 2010, and Upwork’s predecessors go back to the late 1990s.

My friend SH is an illustrator in China. On Fiverr, she drew the wedding illustrations for a couple in Indonesia.

She used AI to write to her clients in English, which isn’t her first language, and to keep track of the back-and-forth. Her delivery got better. The work was still hard. Work is work.

The old model has a simple shape. You sell a finished piece to one buyer. That buyer compares you with every other seller on the platform, mostly on price. Fiverr keeps 20% of every order.

That market is also shrinking for people like SH. A study of a major freelance platform found that within a year of AI image generators arriving, job posts for image creation fell 17% relative to manual-intensive work (Demirci, Hannane and Zhu). Fiverr’s own annual report says AI adoption is pulling its high-skilled and low-skilled services apart.

The new buyer is an AI lab. The lab wants her judgment about the picture: what makes a wedding illustration good, where a model’s version falls apart, and how to score the difference. That judgment gets reused every time the model draws. Mercor’s creative-roles page says creative professionals there earn $60 to $120 an hour, paid weekly.

So I told her to look at design roles at AI training companies instead. The four I named were Mercor, Handshake AI, micro1 and Surge AI. She applied that week.

Where you live decides which of those doors are open. Handshake AI requires US work authorization, and the work itself has to be done from inside the US. Surge AI doesn’t publish an eligibility list, and third-party reviews report that DataAnnotation, the open platform it runs, accepts six English-speaking countries. micro1 says it hires globally. Mercor sets location rules listing by listing, so read each one.

Should designers leave Fiverr and Upwork for AI training work?

My dear experts: delete your Fiverr and Upwork accounts. Do it on the day your first AI-lab project pays out.

Keep them until then. AI training work is spiky. Queues go quiet for weeks, and projects end early. The old account is your floor while the new work proves itself.

After that, stop spending your taste on gigs priced against every other seller in the world. Taste is what models lack, and it’s what the labs are paying for.

Is AI training work going to last?

Nobody knows, including me. Here is what I’d bet on.

Each time models improve, the easy tasks get solved and the remaining ones need someone more senior. The work moves up the expertise curve. It also gets smaller. A job became a project. A project became a stream of tasks, each with a timer. When the work is that atomic, finding the next piece of it becomes a big part of the job.

The demand is large right now. SemiAnalysis, quoted in Paul Millerd’s piece, reports that Mercor logged about 2.5 million expert hours in the second quarter of this year. That’s roughly 4,800 people working full time.

The skill that carries from project to project is the one Mercor’s CEO named on Conversations with Tyler: finding where the model makes a mistake. I’d add one thing. Explaining why, in writing, is what gets you paid.

What is AI training work actually like?

I know how it goes. I’ve done this work on more than ten Mercor projects. It is often exhausting. Some weeks it’s suffocating. This newsletter exists because of it.

Here is what readers send me, and what I’ve lived:

• The queue goes quiet for three weeks, and nobody tells you why. I broke down the mechanics in Cat and Mouse: How Mercor Audits You, Rates You, and Lets You Go Without a Word.

• The matcher sends a radiologist a music-tagging task.

• A stated $90 an hour becomes $25 once the handling-time cap bites. The AHT Game has the math. I have stopped my own timer when a task ran long, so my handling time wouldn’t flag me. I’m including that as a disclosure. The cap does that to people.

• Projects end without warning. Business Insider reported last November that Mercor ended a large Meta project with more than 5,000 contractors, who had been told it would run to year-end, then offered similar work for $5 an hour less. Mercor called the report inaccurate without saying which part. In March, Business Insider reported that Handshake AI suspended contractors, told them the decision was final with no appeal, and paid four of the five it spoke to nothing for completed work.

• The search eats months. In public forum threads I read this summer, one contractor listed the ten platforms she’d registered with. Nine produced nothing. Others describe one to five months from application to first contract. That’s contractor testimony, and I label it that way.

Some of this is the nature of project work. Every Mercor listing I opened for this post carries the same line: “Projects can be extended, shortened, or concluded early depending on needs and performance.”

Some of it is structure. You see one dashboard. The platform sees every project, every rate and every contractor’s numbers. When a project ends, the platform draws from a queue of replacements, and you start again from an empty one. That’s a weak position to bargain from, and most experts are in it alone.

What is clawded.ai?

clawded.ai is a job-matching tool for remote AI data work, incubated by World Model (world-model.xyz) community. Every week, readers write to me about which projects paid, which went quiet and which listings were dead. clawded.ai is where that intelligence goes to work for one person: you. It matches your profile to verified openings across AI training platforms, including Mercor, Handshake AI, micro1 and Toloka. Every opening is checked at the source before you see it.

Now it comes to the paid subscriber of

The first thing it does is matching. A platform’s matcher exists to fill its clients’ projects. clawded.ai starts from your profile and looks across platforms.

My own page, updated September 29. Each match says why it fits me and what gap to close before I apply. When I rechecked all three on September 29, #3 had stopped taking applications. That’s how fast this market moves, and it’s why every match gets rechecked.

What paid subscribers get in clawded.ai:

• Three verified openings a day, hand-picked for your profile. Each one is taking applications on the day it’s checked.

• Region screening. A role that needs US work authorization won’t be sent to you in Berlin or Bangalore.

• Pay flags: handling-time caps and payment problems, noted where I know about them.

• A save button, so you can come back to a role and apply later.

• No form to fill in twice. It picks up what you’ve already told me in DMs and email. That information is used for your matches and nothing else, and I’ll delete it whenever you ask.

• Your own private page, behind your own login.

Why is it called clawded.ai?

I’ve written before that experts do this work bare-handed. You find the listing yourself, check it yourself and apply alone. A claw is the simplest tool there is: something to grab with. clawded.ai is meant to be that tool.

How clawded.ai checks an opening

clawded.ai checks every opening. An opening counts as verified when it is taking applications on the company’s own careers page or applicant system on the day it’s checked. Region locks, payment rails and handling-time caps are noted when the listing or reader reports show them. For this post, on September 29, 2026, I rechecked the three openings on my own page and five Mercor design listings. Three of the eight had closed. Anything that fails the check doesn’t ship.

How does clawded.ai work?

I check openings in public every week. Paid subscribers get the same work done for one profile: theirs. clawded.ai is in beta, for paid subscribers first, and it will open wider later.

1. Become a paid subscriber

   Subscribe now

2. Reply to the email I send you with three things: your background, where you live and are allowed to work, and your resume. New paid subscribers get that email right after joining. Already a paid subscriber? Mine is on its way to you this week.

3. I reply with your clawded.ai login. Your first matches will be waiting.

Everything else paid subscribers get stays. The insider posts continue. Past ones include the Hands-On Mercor Playbook (one reader followed it to $37,000 in four months), the Mercor profile prompt from AI Tools for Nomads #1, The Mercor Location Trap, and the Inside the Data Factory series. The prompts from those posts are being built into clawded.ai.

You are not alone

Working for an AI lab from a laptop can feel like working for nobody. A project ends, the Slack goes quiet, and there’s no one to ask.

There are over 26,000 of us here, spread across the world and across Mercor, Handshake, micro1, Surge AI and the rest. You’re not alone in this, and you shouldn’t have to do it bare-handed.

PS: If you’re outside the US, Handshake is closed to you unless you live and work there. micro1 says it hires globally, and the Mercor design study above states no region lock. clawded.ai screens every match for where you can actually work, so you stop opening US-only listings.

FAQ

What is clawded.ai? A job-matching tool for remote AI data work. It matches paid subscribers to verified openings across AI training platforms such as Mercor, Handshake AI, micro1 and Toloka.

Who can use clawded.ai? semi◦diffusion paid subscribers, anywhere in the world, during the beta.

Does clawded.ai work outside the US? Yes. Every match is screened for where you live and are allowed to work, so roles that need US work authorization, like most Handshake AI roles, only go to readers who can take them.

Mercor sits between professionals seeking AI training work and clients buying expert judgment. The challenge is determining whether someone’s experience will translate into reliable work. Drawing on firsthand experience from both sides of the platform, this short documentary examines how expertise is evaluated—and why client demand, project capacity, and eligibility can matter as much as a strong interview.

Previous Highlights

semi◦diffusion follows the business of AI and expert work: how platforms operate, where demand is moving, and what that means for people like us. AI training data is not just a labeling business; it is also a market for professional judgment. Mercor reports growing demand for people who can review, rate, and improve model outputs. I see a broader shift in how expertise gets bought: helping train and evaluate AI systems is becoming a business in its own right. That creates opportunities, but demand does not guarantee any individual a steady queue of paid work.

Subscribe now

Previous Hightlights

In previous post, we walked through the profile prompt I used before receiving an instant Mercor offer. I did not add a job, a degree, or a skill I did not have. I made the experience I already had easier to understand.

Today, we are going behind the camera.

We are tackling the next gate in the pipeline: the AI evaluator. A strong profile can get you considered. Your interview has to show that you can do the work.

I have sat on both sides of the Mercor table. As a contributor, I have pulled in five-figure months working remote AI projects. As an Expert Project Manager, I have watched thousands of candidate profiles stream through the intake queue.

From the applicant’s side, the question is: why won’t someone recognize my experience?

From the project side, it is: can we trust this person to deliver work the client will accept?

That difference explains a lot. My job in vetting was variance reduction: finding people whose expertise held up when the task became specific. A convincing introduction mattered less than a defensible judgment.

My read is that as models handle more routine answers, the valuable expert work increasingly involves explaining where those answers fail.

Here is how to make that expertise visible throughout the hiring process.

The Profile Match

Before the camera turns on, your profile needs to make sense. Mercor’s instant-offer documentation describes matching based on experience, interview performance, assessments, skills, and availability. A strong match can surface you to a hiring team without a fresh application.

That makes your profile and interview useful beyond one listing. Here are the four profile rules I would prioritize.

1. Make your job title legible

“Operations Consultant” leaves a reviewer guessing. If your actual work was financial analysis, say so clearly in your headline and summary. Keep official employment titles accurate; add a truthful explanation where needed.

Use the language of the work you can prove. Nobody outside the platform should pretend to know the exact weight its matcher assigns to a title.

2. Show the depth the role requires

For specialized expert work, make relevant experience easy to trace: dates, responsibilities, and examples of decisions you owned. If a listing requires three years, demonstrate those years honestly. Do not turn that requirement into a universal platform rule or combine overlapping contracts into invented tenure.

3. Make verification easy

Keep your resume, LinkedIn, and profile consistent. Complete the verification steps available to you. LinkedIn can support your professional history, but connecting it is not proof of every claim or a guaranteed offer multiplier.

4. Update without contradicting yourself

Mercor’s profile guide allows updates. Tailoring is normal. The problem is a claim you cannot support when the interviewer asks for detail. Every skill you list should survive a follow-up question.

The profile gets you considered. The next section is about making that consideration count.

For paid subscribers, I break down how to pace the interview, structure an expert answer, use the retake process, and avoid preventable onboarding problems. You also get the six-step checklist.

The interview gets you in. What keeps you there? Next in the series, I will examines a different pressure point: being paid for human judgment while being pushed to work faster. We look at the tension between AI assistance, monitoring, and quality standards, and what that means for the way experts work.

Read more

semi◦diffusion—your inside guide to the business of AI training and expert work. We track who’s hiring, what they pay, and who’s eligible, so you can focus on opportunities that fit. Become a paid member for curated roles and practical guidance to turn your expertise into paid AI work.

Subscribe now

Previous Top Highlights

In May I did one thing on Mercor. I did not add a job, a degree, or a skill I do not have. I rewrote the profile I already had, using a prompt, and an instant offer arrived. I wrote that up at the time in I updated my resume and got an instant Mercor offer. Since then the prompt has gone out by DM to every paid subscriber who asked, and a lot asked.

This is the first post in a new series. AI tools for nomads: prompts and routines I actually use to get and keep remote AI work. The tool is not the point. The point is that the people hiring for this work are models, and if you are not using a model to talk to them, you are bringing a resume to a vector search.

The part of Mercor everyone skips

People polish the resume PDF. They sweat the AI interview. Then they leave the Profile tab half-filled with whatever the resume parser guessed on day one, and never open it again.

That is the wrong place to be lazy, because of what the profile is for.

Mercor does not read your profile the way a recruiter reads a resume. From what I can see working the platform from both the contributor and the reviewer side (this is my read of the mechanics, not Mercor’s published documentation), it parses the document into a structured representation: skills, domain, seniority, tooling, project shape, rate band, availability. It parses each job the same way. Then it matches by similarity. High similarity surfaces you. Very high similarity can produce an offer with no human in the loop. That is what an instant offer is.

So your reader is an LLM. Write for it.

Three things I found on the live profile page this week

I went through my own Mercor profile tab by tab on September 13 and checked every field against how the prompt was telling readers to fill it. The v1 prompt got three things wrong, and all three are worth knowing even if you never run the prompt.

The Summary field takes about 15 words. A paid subscriber found this the hard way: the field cuts off well before a normal profile summary ends, so the v1 prompt was producing summaries that could not be pasted. This limit is not in Mercor’s published docs. It is a firsthand product observation, so test it in your own account. Either way, 15 words is the right target, because the matcher weighs the first nouns heaviest.

Skills are tags, entered one at a time, and the parser’s guesses stay until you delete them. My own profile had “Smart Parking Solutions” sitting in Skills. That is a product I once worked on. It is not a term any job description on Mercor contains. It was pure noise in what is probably the highest-weight field on the page, and it had been there for months because I never looked.

Mercor’s own LinkedIn widget says verified profiles have 6x higher chances of an offer. That is Mercor’s claim, in Mercor’s words, on Mercor’s page. Roughly half the people who DM me about quiet queues have never clicked it.

What is below the paywall

The rest of this post is the v2 prompt in full, rewritten this week so its output follows Mercor’s profile page section by section (Summary, Education, Work experience, Projects, Certifications, Awards, Coding Profiles, Links, Skills, Languages, Hobbies), in the order you fill it. Around it: the four-step routine I run every time I change target roles, what the output looks like when it is right, the answer to the “does re-targeting flag my account” question, and a reply-to-get-it offer where I fill the template for your role myself.

Upgrade to read the rest

Subscribe now

The four-step routine

Step 1. Pick one role, and copy the whole job description. I know you apply to several tracks at once. So do I. But the profile can only argue one identity at a time, and a profile that argues three matches none of them. Choose the role you want most. Open its page on Mercor and copy the full description text into the prompt. Not the link. Your AI tool cannot open Mercor pages behind your login, and a link gives it nothing to match against.

Read more

Not sure if you have noticed, but you are in a cult.

Not a robed one. A professional one. It has a name for its members (contributor, fellow, expert), a private language (AHT, R2I, rubric, gate), a shared enemy (the models that get everything wrong), and a promise that the work you are doing matters more than the pay rate suggests. It has initiation rituals that cost you unpaid hours. It has channels where you can see everyone else’s devotion but nobody’s invoice. And it has the one feature every group like this shares: leaving feels like losing something you cannot get back.

I want to name it plainly, because I am part of it too. I have worked more than ten Mercor projects on both the contributor and the reviewer side. I read the Slack channels. I know the feeling of a queue that goes quiet for three weeks and the relief when it comes back. That relief is the mechanism. It is the thing that keeps very smart people accepting whatever arrives.

This post is about how the expert-data economy works on you, what a sane remote working life looks like inside it, and what I do for paid subscribers who want somebody on their side of the table. Most of all it is about being free inside this jungle, which is not the same as leaving it.

Why the AI expert economy feels like a cult

The playbook for building a movement is well understood. You need a compelling story about the future, an in-group with its own vocabulary, a sense that outsiders do not get it, and a founder who can articulate why the mission justifies the sacrifice. Every one of those pieces is present in AI data work.

The story. The pitch is that the next decade of knowledge work is about encoding your expertise into models. Mercor’s CEO has said the network passed 4 million vetted experts and pays out more than $2 million a day. Handshake AI reports $100 million paid to more than 100,000 fellows. Frontier labs are spending over $1 billion each on training environments. None of that is fake. The money is real and the mission is real. That is exactly what makes the rest of it work.

The initiation. Onboarding and assessments are unpaid on most platforms. You give hours before you see a rate. Many get onboarded, few get retained, and the gates that decide who stays return no score and offer no appeal. You learn to treat acceptance as a gift instead of a contract.

The vocabulary. Once you know what AHT means, you are inside. Maximum handling time caps the paid hours per task no matter how long the task takes. A nominal $60 to $100 an hour becomes an effective $8 to $25 once the cap bites. On Handshake’s Project HH in May 2026, workers were paid 20 to 50 percent of the hours they logged. I covered the mechanics in The AHT Game. Inside the group, these become normal facts of life. From outside, they look like what they are.

The silence. Nobody tells you why the queue stopped. Nobody tells you why the radiologist got a music-tagging task. Silent offboardings run at a roughly 7.5 percent rate on one platform, with no appeal. You fill the silence with your own explanations, and most of the explanations you reach for are about you.

I want to be careful here. I do not think this is designed by villains. It is a sieve. The platforms are built to select for people who tolerate uncertainty, unpaid ramp time, and rate compression without complaint. The people who stay are the people the system fits. That is a selection effect, not a plot. But the result for you is the same either way: you adapt to the sieve instead of the sieve adapting to you.

What the paid tier actually is

It is this newsletter’s verification work, done for one profile instead of a public list. Yours.

You tell me where you are. I go find what fits, and I tell you what I would do if I were you, in addition to the paid posts.

1. The profile prompts. The Mercor one that produced the offer above, plus versions for micro1, Handshake AI, and the smaller platforms. Updated when the platforms change, which they do.

2. Advices for remote work operations. Running a distributed week, handling tasks across a nine hour gap, managing Slack so it does not manage you, tracking your own hours independently so you can dispute a shortfall in the same week it happens.

3. The offboarding-risk playbook. Where the handling time corridors sit on projects I have run, what the assessment gates actually test, and how to exit a project without it costing you the next one. Built from more than ten projects on both the contributor and the reviewer side.

I cover Mercor, Handshake AI, and micro1 in the same place, which matters more than it sounds like. They are not substitutes. Mercor pays higher at the top and goes quiet for weeks at a time. Handshake is steadier and lower, and has its own payment problems that I have documented and will keep documenting. Holding both queues open is how a bad month on one stops being a zero month.

How to start, in one reply

Upgrade here, then reply to the welcome email with three lines:

Subscribe now

1. Which platform or platforms you are on.

2. Which stage you are at: not applied, applied and waiting, onboarded and idle, or actively on a project.

3. Your background, your region, and any license or specialty.

Below the wall: the habit system that makes remote work run without a boss, and the monthly focus exercise I use when the platform silence starts writing stories in my head at 3am.

What freedom looks like inside this market

Read more

A subscriber wrote to me last week after a bad month. His task queue had stopped refilling, a project he had onboarded to paused without explanation, and the assignment he did get had nothing to do with the expertise on his resume. His summary of the platform was three words long: merciless and messy.

Then he asked the question I now get several times a week, and the one you may have a tab open for right now. Should he go find a new platform?

The instinct behind it is right and the conclusion is wrong. Concentration is genuinely dangerous, and I have written before that no single project should carry more than about sixty percent of your income. But the fix is not more platforms. For most people the fix is two platforms and much harsher selection inside them.

I can now show you why with something better than my own opinion. Contractors have been running this experiment in public, and four threads I read this week converge on one uncomfortable finding: for a lot of people, leaving does not change the work at all.

Start with what you are actually selling

Every argument about which platform is better collapses into one fact: you are selling hours. Not deliverables, not a product, not a business that compounds while you sleep. Hours.

That framing kills a lot of bad reasoning. A platform is not a company you join, it is a buyer for inventory that is finite and does not carry over. An hour spent reading a guideline doc for platform number four is an hour you did not bill anywhere.

It also means the most important fact about any project is not the headline rate. It is whether the project pays for time or pays for tasks.

Time based projects pay for hours logged, usually with a cap on how many hours a task may take. Your risk is the cap.

Task based projects pay per completed and approved unit. Your risk is everything: how long the task takes, whether the tool lags, whether the reviewer approves it, whether the guideline changed between your submission and the review.

Task based work moves approval risk from the buyer to you. Read it as a signal about how much a project trusts its own instructions. When the specification is clean, buyers are comfortable paying for time. When it is a moving target, paying per accepted unit lets the buyer stop paying for the confusion they created.

The thing nobody told you: the client follows you

Here is the finding that should change how you think about switching, and I had not seen it stated plainly anywhere.

In a Mercor workers thread, a contractor described leaving for Handshake and being offered the same project, from the same client, for about a dollar more per hour. Others in that thread and a related one describe the identical pattern: one client running one project simultaneously across Mercor, Handshake, and Outlier, pay differing by a couple of dollars, tasks recognizably the same work. One contractor noted that when tasks dry up on one platform they dry up on all of them, because it is a single client queue underneath.

That dismantles the usual escape plan. When you switch platforms in frustration, there is a real chance you are renting the same work from a different landlord. The chaos you are fleeing often originates with the lab that commissioned the project, not the marketplace that staffed it.

Which means the honest question is not “is there a better platform.” It is “is there a better project,” and that question has a completely different answer.

What contractors report about the platforms themselves

Three things showed up repeatedly across these threads, and none of them are what people expect.

The access and pay relationship is inverse, and it is steep. One contractor tested five platforms and summarized it in a line worth memorizing: high pay means harder and more selective, easy access means much lower pay. His own numbers ran from about a dollar per task for basic audio evaluation up to roughly $85 an hour for specialized legal work, on the same resume. Every platform you can join this afternoon sits at the bottom of that curve.

Mercor’s chaos is relative, and multi platform contractors rank it well. This surprised me. Several experienced contractors independently rated Mercor first on organization, naming the per domain management and project Slack channels specifically, while criticizing its pay and volatility. One said they would take a pay cut to have Mercor’s project management everywhere. Your frustration is real. It is also, apparently, the good version.

The mechanics differ more than the rates do. Contractors on one client’s project across two platforms report very different effective earnings purely from timer rules: one platform requires the timer stopped and restarted at every submission, logs no time during mandatory training videos, and takes quality screenshots after the clock stops. The other lets it run continuously. Same tasks, same client, about two dollars an hour apart on paper, and the contractor concluded the higher nominal rate lost. Another described three day review latency during which no further tasking was possible, capping a good rate at roughly two hours of real work.

Nominal rate is the number you compare. Effective rate is the number you get paid.

A note on sourcing: everything in this section is contractor testimony from public forums, not something I verified myself. I am labeling it as such because that is the deal here. Where it matches my own experience across ten plus Mercor projects I say so.

Share

Mercor and Handshake AI, by the numbers

Mercor pays more at the top. Domain experts at frontier labs reach $175 to $200 and above per hour, mid tier roles $90 to $150. Handshake clusters tighter, mostly $75 to $125. A genuinely senior resume makes Mercor’s ceiling worth the friction.

Handshake is steadier. Structured part time blocks, typically 10 to 20 hours a week. Mercor is spiky, with a one to six week wait after acceptance and no choice of assignment.

They screen differently, and this decides which fits you. Mercor runs a 45 to 90 minute AI video interview that probes your resume hard and targets senior professionals. Handshake runs shorter human led interviews and openly recruits PhD students, postdocs, and recent graduates. Early career, Handshake. Senior specialist, Mercor.

Both cap your hours. Handshake also carries an undisclosed weekly earnings ceiling, and in May 2026 one large project paid out between 20 and 50 percent of logged hours. A nominal $60 to $100 an hour can become an effective $8 to $25 once a cap bites.

One hard eligibility fact. Handshake AI requires US work authorization. F-1 students may qualify through CPT or OPT, STEM OPT is excluded, and only a minority of postings are open worldwide.

If that rules you out, read this part carefully. Most of you are not in the US, so for you the sentence “hold Mercor and Handshake” is not advice, it is a locked door. Three things still apply. The two queue rule is about structure, not about those two names: your second queue is whichever platform actually hires from your region, and the logic for choosing it is identical. Everything below the paywall is platform neutral, because the filter, the timer questions, and the drop rule describe mechanics that every one of these marketplaces runs. And Handshake is not entirely shut: its broad fellowship track is open worldwide, which is the one door on that platform I would send a non US reader to. I am testing the platforms that do not require US work authorization now, and that roundup is the next free issue.

So: hold two queues, whichever two your passport permits. Replace neither.

And if you have decided Handshake is your second queue, do not paste your Mercor resume into it. The two platforms reward opposite things, and the profile that got you through Mercor’s funnel is close to the worst version of you that Handshake can see. Below, I walk through the conversion.

Below the paywall:

• How to convert your Mercor profile into a Handshake one, field by field, and the single setting that blocks most non students from registering at all

• The contractor who registered with ten platforms, and what exactly one of them was paying her

• The real waiting times contractors report, platform by platform, and why that is the number that kills the scatter strategy

• My seven question filter for accepting or declining any project, including the contract clause that should end the conversation

• How I allocate my own hours right now, and the mechanical rule that decides when I drop a project

• The ten hour test budget, if you insist on trying a third platform anyway

• What the sieve actually selects for, and why breadth is the expensive mistake

Previous Top Posts

Read more

If you are reading this as a free subscriber, you have already learned the high level framework that generated $37,000 across my first four months on Mercor. But applying these strategies in real time requires tactical execution, precise communication, and individual troubleshooting.

When you upgrade to a Paid Subscription to semi◦diffusion today, you unlock full access to our complete strategy vault and private subscriber benefits:

• Complete Archive Access: Dive into our top converting guides including The Dark Side of Mercor You Should Know Before You Start, How to Pass the Mercor AI Interview, and Every AI Screen After It, I Updated My Resume and Got an Instant Mercor Offer, Hands On Mercor Playbook, and How I Made $37k in 4 Months on Mercor.

• Direct DM Consulting Access: My direct messages are open exclusively for paid subscribers. Get direct one on one support to review your active contracts, troubleshoot bad reviewer feedback, and optimize your portfolio stacking setup.

Over the past four months, I have worked across more than ten Mercor projects, generated over $37,000 in remote earnings, and mapped out the exact operational mechanics of the global AI training market.

In previous installments of semi◦diffusion, we unpacked how to pass the initial AI voice screening, how to structure your resume to trigger instant client matches, and how to survive the chaotic onboarding sprints that define this ecosystem.Today, in this final episode of the Data Factory series, we are tackling the high level strategy that separates struggling contractors from remote workers earning five figures every single month.

If you treat Mercor as a single job, you will inevitably hit a wall. You will experience sudden project pauses, incompetent team leads, contradictory Slack announcements, and long dry spells where no work is available. To thrive in this space, you must view Mercor not as an employer, but as an engine for stacking projects, leveraging cross project reputation, securing high paying reviewer seats, and brutally optimizing your hourly yield.

Here is the complete operational playbook on how to build a resilient remote income shape, climb into leadership roles, and know precisely when to walk away.

The Invisible Reputation Network: How Projects Actually Talk to Each Other

Most contractors assume that every new project application starts from scratch. They complete the automated voice interview, wait in the candidate pool, and hope a client selects their profile.

Behind the scenes, Mercor operates a centralized reputation layer that tracks your exact performance metrics across every contract you touch. When a project gets paused or canceled by a client, your work history does not disappear.

Internal project records track your output quality, sandbox pass rates, peer review scores, and adherence to submission guidelines. If you rank in the top fraction of a percent on a high difficulty task, that reputation carries over directly to internal project teams.

Strategic Project Leads (SPLs) and Engagement Project Managers (EPMs) regularly bypass public job postings when staffing new initiatives. When a high value client demands elite talent on short notice, project leads pull directly from internal top performer rosters.

The progression follows a direct line: high quality contractor performance triggers an internal quality audit. Reaching the top tier puts your profile directly in front of Engagement Project Managers and Strategic Project Leads, who bypass the general applicant pool to assign you directly to reviewer and leadership positions.

Understanding this system changes how you approach entry level tasks. Your goal on your first project is not just to bill hours; it is to establish a flawless quality score that unlocks direct invites to high tier positions.

Tier 1 (Entry Expert)

• Primary Objective: Zero error rate on sandbox tasks

• Value Unlocked: Cross project quality flag

Tier 2 (Reviewer)

• Primary Objective: Audit expert work, resolve prompt edge cases

• Value Unlocked: Higher hourly floor ($50 to $100+ per hour)

Tier 3 (Team Lead / EPM)

• Primary Objective: Operations, OG creation, backend sourcing

• Value Unlocked: Stable oversight hours and direct client contact

The Fast Track to Reviewer, Team Lead, and EPM Roles

Baseline expert tasks on Mercor can range anywhere from $16 to $30 an hour for general writing, up to higher rates for specialized domain expertise. However, reviewer and leadership positions carry significantly higher compensation, often pushing rates past $100 per hour for senior domain leads.

Reviewers and leads enjoy far greater stability because their hours are tied to management, quality assurance, and project coordination rather than volatile prompt queues.

So how do you transition from a frontline expert to a reviewer or EPM within days?

• Master the First 24 Hours: When a project opens, demand for reviewers spikes immediately as hundreds of new experts submit sandbox entries. In multiple instances, contractors who demonstrated pristine accuracy and high activity during the first 48 hours were promoted to reviewer or Team Lead within a week. Submit your initial benchmark tasks quickly, double check every guideline, and ensure your work requires zero corrective feedback.

• Practice Active Operational Support: Project leads are constantly overwhelmed by repetitive questions in Slack channels. If you read the operational guidelines thoroughly and assist other contractors by answering common onboarding questions, project leads notice. Leadership positions are rarely awarded solely on technical skill; they are given to contractors who reduce administrative chaos for the EPMs.

• Handle Rogue Reviewers Defensively: One of the most frustrating aspects of frontline work is encountering reviewers who use raw ChatGPT outputs to grade human submissions, resulting in wrong feedback or arbitrary rejections. When faced with unfair reviews, never engage in emotional arguments in public Slack threads. Document the exact guideline requirement, cite your sources clearly, and present a concise, neutral appeal to the project lead. Demonstrating calm, evidence based reasoning under poor oversight is often what gets you noticed for promotion above those very reviewers.

Upgrade to Unlock the Vault

If you are reading this as a free subscriber, you have already learned the high level framework that generated $37,000 across my first four months on Mercor. But applying these strategies in real time requires tactical execution, precise communication, and individual troubleshooting.

When you upgrade to a Paid Subscription to semi◦diffusion today, you unlock full access to our complete strategy vault and private subscriber benefits:

Subscribe now

• Complete Archive Access: Dive into our top converting guides including The Dark Side of Mercor You Should Know Before You Start, How to Pass the Mercor AI Interview, and Every AI Screen After It, I Updated My Resume and Got an Instant Mercor Offer, Hands On Mercor Playbook, and How I Made $37k in 4 Months on Mercor.

• Direct DM Consulting Access: My direct messages are open exclusively for paid subscribers. Get direct one on one support to review your active contracts, troubleshoot bad reviewer feedback, and optimize your portfolio stacking setup.

Do not navigate the AI data engine alone. Upgrade to semi◦diffusion Paid today and turn unpredictable remote gigs into a reliable, high earning system.

Portfolio Stacking: Building a Resilient Remote Income Shape

Read more

Spend ten minutes on r/mercor_ai and you will find the same story told in different fonts. A thread called Gut Punch. A thread called In Need of Some Encouragement. A thread about being offboarded for no reason, where the comment section lands on the sentence that haunts this whole platform: there is always a reason. You just don’t get to hear it.

The accounts were in good standing. The queues went quiet. The email, when there was one, explained nothing. And a room full of strangers is left doing forensic analysis on their own work history.

I’ve onboarded onto more than ten Mercor projects, and I have read the queue from the reviewer side too. Episode 2 mapped the corridor: the band of acceptable speed and quality that metrics decide you’re inside. Episode 3 is what happens when someone decides you left it. The free half of this piece is how the enforcement machine actually works: audits, model-usage policing, reviewer roulette, silent offboarding. The paid half is the part nobody expects: an energy management system. Because once you understand the machine, you’ll see why the contractors who last aren’t the most vigilant. They’re the least depleted.

The four rooms of the machine

1. Audits are a sample, not a census

Every mature project runs a quality apparatus: daily audit dashboards, a quality matrix that grades dimensions of your work, an error lookup tool where your mistakes get catalogued by type. What new contractors miss is that nobody reads everything. Thousands of tasks flow through these projects daily; the audit is a sample.

So the real question is what raises your sampling odds. Being new does. Sitting at either wall of the speed corridor does, because outliers get pulled first. A reviewer flag does. A client spot check landing on your batch does, and that one is pure lottery. Meanwhile the monitoring layer never sleeps: the timer apps log active windows and idle gaps, idle time gets reviewed and deducted weekly, and charging time outside active work is written into guideline docs as a termination offense.

Here is the uncomfortable implication: audits don’t see your average. They see the three tasks that got pulled. Your representative work, as far as the machine is concerned, is whatever you produced in your most tired hour. Hold that thought; it’s the hinge of this whole episode.

2. The model-usage police

Projects that exist to train AI models mostly forbid you from using AI models to do the work, or license it so narrowly that the safe assumption is no. The irony is noted, usually in the same breath as the policy tab.

Enforcement is a stack, not a single tripwire. The monitoring software sees your open windows. The task tools can see paste events and timing patterns. Your prose gets compared against the baseline you set in onboarding quizzes, some of which are one attempt, graded by the client’s own team, with no second chances and no appeal. And somewhere in the pipeline, AI detectors run on work submitted to a company that trains AI. False positives exist. Honest contractors absorb the friction created by whoever botted the assessments last quarter, which is Episode 1’s arms race wearing a badge.

The one free tip I’ll give above the paywall: when a usage rule is ambiguous, ask once, in the public channel, and work to the written answer. That message is both a clarification and a shield.

3. Reviewer roulette

Below the paywall is the system I actually run:

• The real-hours arithmetic for stacking Mercor on a day job, and the weekly cap I refuse to cross

• A block schedule built on 90-minute focus cycles that also happens to read spotless to an audit

• The one-task Zen protocol for submitting without spiraling, borrowed from a 16th-century tea room

• The Huberman-based recovery stack for the 6-to-10pm shift

• The weekly energy ledger, and the two-strike rule I use to fire a project

• The calm exit protocol for the day the machine decides anyway

This is the part I charge for, because it’s the part that took three offboardings to learn.

Read more

Last episode I wrote that every Mercor project has a corridor of acceptable speed, that nobody tells you where its walls are, and that people get terminated for hitting either one. Dozens of you wrote back with the same question.

So what’s the number?

I want to answer that properly, because it’s the wrong question in a way that costs people their contracts. And the fastest route to why is a single line I found buried in a coding project’s guideline.

Time tracking was mandatory on every task. And the same document stated, plainly, that pay on that work was not calculated from the tracking at all.

Sit with that for a second. Logging was compulsory on a task where the money didn’t depend on it. Further down, the reason: the project screened for fraud by reading output quality, volume, and time records against each other.

Your time log is not a timesheet

That reframe explains almost everything confusing about speed on this platform.

On an hourly project, working faster does not earn you more. It earns you less. Twenty tasks in ten hours pays worse than twenty tasks in fifteen. So when an ops lead looks at a low average handling time, they are not admiring your efficiency. They’re asking whether the work actually happened, or whether it was skimmed, pattern-matched, generated by a model, or handed to a friend. And when they see a high one, they ask the mirror question: is this person padding hours, or just not good at this?

The upper wall has arithmetic behind it, and the arithmetic belongs to the client lab rather than to Mercor. The lab budgets per datapoint and pays per hour, so cost per task equals your handling time times your rate. That same coding project published both halves: $85 an hour, against a task the guideline sized at roughly one hour. That isn’t pacing advice. That’s the unit price of the dataset, stated in advance. Work at ninety minutes a task and every datapoint you produce costs the lab fifty percent more than the budget it was approved against. Nobody emails you about that. It surfaces as a capacity review you’re not in.

So the corridor is real, and both walls have owners. The upper wall is the client’s unit cost. The lower wall is the fraud model.

There’s one more thing the instrument does, and it’s the only good news in this episode, so I’ll come back to it at the end: handling time is also an input when writers get promoted to reviewer. It’s watching for fraud, and it’s quietly building a shortlist.

The rules contradict each other across projects

Here’s why “what’s the number” has no answer, and it’s worse than most people assume.

I went back through the billing language on four projects I’ve worked. On one, the rule was blunt: charging time outside onboarding and active task work would end the contract and follow you to future work with Mercor. Sitting and waiting for a task to appear was explicitly not loggable.

On another project, running on the same platform in the same period, reading the documentation and keeping up with Slack were put on the clock as work you were expected to log.

Those are opposite rules. Log your Slack reading on the first project and you’ve committed a terminable offense. Fail to log it on the second and you worked for free, and you also depressed your own handling time in a way that reads as suspiciously fast.

There is no platform-wide billing policy at Mercor. There is a per-project policy, written by whoever stood up that engagement, and the only safe assumption is that your last project’s rules do not transfer. Which means the instinct you built on your first project is not an asset. It’s a liability you carry into your second.

And it gets one layer worse, because the written rule and the enforced rule are not the same rule either. Every project has a special project lead, the SPL, and some are markedly more tolerant than others. The same logged hour that draws a warning under one lead passes without comment under the next. Neither of them changed the document. They’re just different people reading it.

The rates that are arithmetically impossible

Then there’s the case where no amount of calibration helps, because the number was never reachable.

The clearest instance I’ve seen described recently: a project asking contractors to read roughly eighty pages of dense technical material they had never seen, produce a careful analysis, use no AI whatsoever, and finish inside forty-five minutes. The role required a professional doctorate. Contractors ran the obvious numbers. A page a minute is already brisk for technical reading, which leaves zero minutes for the analysis they were actually hired to write.

And the enforcement is not theoretical. One contractor reports being removed from two separate projects for exceeding handling time. The ramp problem compounds it: several describe project managers who allow no settling-in period at all, so the week when you are slowest by definition, your first, is the week you get measured in.

There is a much less frightening version of how those thresholds actually bite, and I’ll give it to you below the paywall, because it’s the single most useful thing I know about this and it changes what you should worry about. But I’m not going to soften the front half. People do get cut for this.

The sieve

There’s a darker reading of all this that circulates constantly among contractors, and it deserves stating plainly even though I can’t prove anyone intended it.

Put the pieces side by side. A rate that cannot be hit honestly. A rule against the one tool that would close the gap. And a contractor pool deep enough that anybody who objects is replaceable by Friday.

Whatever was intended, that combination selects. It filters toward people willing to make up the difference with unpaid time, or with tools they’ve been told not to use. It filters out the people who log honestly and work at human speed. Nobody at Mercor has to have designed that for it to be what the machine does. And the people it selects for are the ones who look, on the dashboard, like the good workers.

If that’s right, the number isn’t a target. It’s a sieve.

So the real question was never what the number is. It’s how you find the number, on a project you’ve never worked, under a lead whose tolerance you haven’t measured, at a rate that may not be reachable, before you’ve logged an hour you can’t take back.

I’ve been paid on time, every week, across ten-plus projects, and I’m still here. So the corridor is survivable. The rest of this episode is how, and it’s the part I charge for, because it took me ten-plus projects and a few offboardings to assemble.

What’s below the paywall:

• What the four numbers on your dashboard actually mean, and which one holds veto power over the other three

• Why the offboarding threat in your project Slack lands far less often than it’s stated, and the single factor that decides whether it lands on you

• How to price a project’s expected pace from documents already in your hands, before you accept one task

• The three moves that work when the rate is impossible, including the question to a reviewer that usually reveals the real bar is lower than what you’ve been producing

• Which parts of a task you can safely automate, and which will get your logged hours stripped

• Which of your two timers your handling time is computed from, and the second time record you’re creating without knowing it

• One thing I do that I don’t recommend, and why I’m telling you anyway

• Whether an AHT offboarding follows you to the next project. A subscriber asked me directly; this is the real answer

• The reading of your handling time that works in your favour, because it’s also an input when reviewer seats get filled

Episodes 3 and 4 build directly on this one, and both are paid.

And paid gets you the thing this newsletter is actually built on. I read job listings the way I just read those four guidelines. Tell me your background, and each week I send the roles I’d apply to if I were you, by DM and in your own weekly email, with what I found when I checked them.

One placement pays for years of this.

Read more

In November 2025, Mercor’s twenty-something founders became, by Forbes’ count, the world’s youngest self-made billionaires, after raising $350 million at a $10 billion valuation. About a week later, thousands of contractors on one of its largest AI training projects woke up locked out of Slack. The project had been cancelled by email. Some were offered their jobs back, at roughly a third less per hour.

I’m not writing this from the outside. I’ve onboarded onto more than ten Mercor projects: rating model responses for a frontier lab, writing evaluation rubrics for consumer tasks, building agentic coding challenges, recording my own screen while completing real-world errands so a model could learn from them. I’ve been through the welcome calls, the unpaid assessments, the quiz-gated onboardings, the silent offboardings. I’ve read more “you’ve been carefully selected” emails than I can count.

Here’s the strange part: it’s still some of the best-paying flexible work available to a knowledge worker right now. Real rates, weekly payouts, work you can do from anywhere. That’s exactly why the dark side matters. The money is real, and so are the traps, and nobody at Mercor is going to explain the traps to you, because the system works fine for them whether you figure it out or not.

This is Episode 1 of a four-part series. Today: how to recognize the five patterns that burn new contractors, and, for paid subscribers, the preparation playbook I wish I’d had before my first project. Episodes 2 through 4 go deeper: the AHT metrics game, the cat-and-mouse of audits and enforcement, and how to stack projects into something resembling stable income.

Let’s start with what you’re actually walking into.

The five patterns

1. You are “carefully selected,” and instantly disposable

Every project welcome doc opens the same way: you’ve been carefully selected, you’re joining something first-of-its-kind, the team is so excited to have you. Take the flattery at face value and you’ll misread your position entirely.

The operating unit at Mercor is the project, not the person. Projects appear with 48-hour pilot deadlines, pause without explanation, resume in “waves,” and vanish mid-sprint. Onboarding is random: you might get a live call, or just a 40-minute document and a quiz. Offboarding is more random still: often no email at all, just a task queue that never refills and a Slack channel that goes quiet. The Forbes-reported mass cancellation wasn’t an aberration; it was the normal lifecycle, at unusual scale. When a frontier lab’s priorities shift, your project shifts with it, and you’re the shock absorber.

The mistake new contractors make is treating a project like a job. It’s not. It’s a gig with a countdown timer you can’t see.

2. The clock decides everything, and the clock is ambiguous

Read more

Last month, one of our members clicked an AI interview link for a first-round interview, sat up straight, and waited for the recruiter to join.

Nobody joined.

Instead, a calm synthetic voice greeted her by name, referenced two projects from her profile, and asked her to walk through the second one in detail. For the next twenty-two minutes, she was interviewed thoroughly, politely, relentlessly by software.

She passed. Two rounds later, she signed a fully remote offer. (Her result, along with another member’s, is further down in this post.)

But here’s the thing: she almost didn’t pass. Because like most candidates, she had prepared for a human interview. AI interviews are a different game with different rules and today I’m going to give you the playbook we used, starting with the one step almost everyone skips.

Why you can no longer avoid the AI interview

This isn’t a gimmick that will fade. It’s simple math.

A single remote job posting now attracts hundreds, sometimes thousands, of applicants from every timezone on earth. No recruiting team on the planet can run first-round interviews at that volume. So they don’t. They hand the volume problem to software.

If you’re applying on Mercor, this is already your reality — and it’s not optional. Mercor’s own documentation is explicit: the AI interview is the standard and only screening method on the platform. There is no human alternative, no chat option, no phone call. Roughly twenty minutes with an AI interviewer stands between you and expert contracts paying $70–$180/hr. That’s the entire front door.

And it’s not just AI-native platforms. Take Mercer — with an “e,” the global HR consulting giant, not the marketplace. When one of our members went through their hiring process recently, the first screen was run by AI. Think about that: even the company that advises other companies on human resources can’t process interview volume with humans anymore. The workload is simply too large.

The numbers back this up across the market. Around 87% of organizations now use AI somewhere in their hiring process, and roughly one in five companies already uses AI to conduct the initial interview itself. Adoption is still accelerating: about 74% of companies say they plan to increase AI in hiring over the next twelve months.

If you’re aiming for remote work specifically, assume the front door is an algorithm. The good news? Algorithms are consistent. And consistent systems can be prepared for — far more reliably than a moody human interviewer on their sixth call of the day.

The mindset shift: the AI has done its homework. Have you?

Here’s what most candidates get wrong. They treat the AI interview like a webcam version of small talk — wing it, be charming, hope for the best.

Charm doesn’t transfer. An AI interviewer doesn’t care about your firm handshake energy. What it does instead is something no tired human recruiter has ever done:

It reads your profile. All of it. Perfectly. Every time.

Your resume, your LinkedIn, the profile you submitted through the portal — the AI ingests every line and generates its questions from the overlap between your profile and the job description. Nothing is skimmed. Nothing is forgotten. That bullet point you padded three years ago and can’t really speak to? It’s now a potential question.

Which brings us to the single highest-leverage move in AI interview prep.

Step 1: Update your profile — it anchors the entire interview

Your profile is not a formality anymore. It is the script generator for your interview. The AI will, with near certainty, pull questions directly from it. That means you get to decide, in advance, what you’ll be asked — by deciding what you put in front of the machine.

On Mercor, this is literal: the AI interviewer asks role-specific questions anchored to the resume you uploaded, and your applications always attach your most recent resume. So the resume on your dashboard right now is, quite precisely, the source code of your next interview. Fix it before you hit start — a submitted application can’t be edited afterward.

Here’s the exact process we walk members through:

1. Collect 3–5 target job listings first. Before touching your profile, open the actual JDs you’re applying to. Copy out the skills, tools, and phrases they repeat. Job listings are not decoration — they are the answer key. If four out of five listings say “stakeholder communication” and “async collaboration,” those exact concepts belong in your profile.

2. Rewrite your profile in the JD’s language. This is not about stuffing keywords. It’s about translation. You’ve done the work; now describe it in the vocabulary the role uses, because that’s the vocabulary the AI is matching against. “Talked to clients” becomes “managed stakeholder communication across three client accounts.” Same truth, different resolution.

3. Apply the follow-up test to every single line. Read each bullet and ask: if the AI asks me to expand on this for two minutes, can I? AI interviewers love follow-ups — they’re programmed to probe. Every claim in your profile should have a story behind it: the situation, what you did, what happened, ideally with a number attached. If a line can’t survive a follow-up, cut it or fix it. An impressive bullet you can’t defend is worse than a modest one you can.

4. Delete the orphans. Old skills you haven’t touched in years, tools you used once, that language you’re “conversational” in but haven’t spoken since university — remove them. A human might politely skip them. The AI won’t. It sees “SQL” on your profile and asks a SQL question. Every word you leave in is an invitation.

5. Mirror the profile to the specific application. One generic profile for fifty applications is how you get generic rejections. Tailoring takes twenty minutes per application and multiplies your pass rate. (I wrote a full breakdown of the exact profile changes that got me an instant Mercor offer in I updated my resume and got an instant offer — read that next if you haven’t.)

Do this well and something almost unfair happens: you’ve effectively written your own interview questions. The AI builds its interview from your profile × the JD. Control both inputs and you control the exam.

Step 2: Get the mechanics right (they matter more than you think)

AI interview systems don’t just transcribe your words. Many evaluate delivery, clarity, and video quality signals. The basics:

Read more

I want to walk you through exactly what happened, why it happened, and how you can engineer the same result for yourself. Because once you understand the system on the other side of the screen, getting matched to the right project stops feeling like a lottery and starts feeling like a craft.

This issue is free. The full prompt library, the resume template I now use, and the live “what’s working this month” updates live on the paid side. If you’ve been on the fence about upgrading, this is the issue I’d point you to first — because if even one of the sections below saves you a single rejection, you’ll have made back the subscription many times over.

Let’s get into it.

The offer that landed in my inbox before I even closed the tab

I’ll start with the moment itself, because it still feels strange to me.

I had spent maybe an hour the night before rewriting parts of my profile. Nothing dramatic — I didn’t add new experience, I didn’t take a new course, I didn’t sit a new assessment. I just changed the way my existing work was described. I rephrased my role, restructured my projects section, swapped out a few generic skill keywords for more specific technical ones, and re-uploaded a cleaner version of my resume.

The next morning I logged in to double-check that everything had saved. Before I had even finished scrolling, an offer notification appeared. An hourly project, in my domain, at a rate I was happy with, with the “instant offer” badge on it. Meaning: no live interview, no scheduling, no waiting for a recruiter to review me. Mercor’s matching system had already decided I was a fit, and the client had already pre-approved profiles that scored above a certain threshold.

I want to say it was luck. But I had been on the platform for weeks before this with a profile that, on paper, described exactly the same person. The version of me that got the instant offer was not a more qualified version. It was a better-described version.

That distinction matters more than almost anything else I’m going to tell you in this newsletter, so I want to put it in bold and let it sit on the page for a second:

Mercor is not evaluating you. Mercor is evaluating the document that represents you. And that document is being read by an AI, not a recruiter.

Once I internalized that, everything about how I approached the platform changed. The work I did didn’t become more impressive. The story I told about it did.

In the rest of this newsletter, I want to break down what’s actually happening behind the scenes, what specifically I changed in my resume and profile, the prompt sequence I now use to do this for every new role I target, and where I’m taking this next — including a Claude Code skill I’m starting to prototype that would automate the whole loop.

The thing nobody tells you: Mercor uses AI on both sides of the table

When most people think about Mercor, they think about the assessments. The recorded interviews. The little technical challenges. That’s the part of the experience that feels like the evaluation, so naturally that’s where people focus their effort.

But here’s the part that took me a long time to understand: those assessments are the second step. The first step — the one that decides whether you ever even see a role — is a matching layer. And that matching layer is doing two things, both of them with AI.

The first thing it does is parse every job description into a structured representation: required skills, preferred skills, domain, seniority, language requirements, project shape, rate band. The second thing it does is parse every candidate profile into the same kind of representation, then runs a similarity match. If you and the role score above a threshold, you’re surfaced. If you score above an even higher threshold, you get an instant offer with no human gate in between.

This has two very practical implications.

The first is that the parsing of your resume matters as much as the content. If the AI can’t extract a clean “Skills: Python, SQL, causal inference, Bayesian methods” line from your PDF, then it doesn’t matter that all of those words are technically somewhere in your bullet points. The system never gets to see them. PDFs with images of text, weird two-column layouts, custom fonts that don’t embed properly, headers that confuse the parser — all of these silently destroy your match score without you ever knowing.

The second implication is that you are, in effect, optimizing for a search engine. You don’t need to game it dishonestly — the assessments and the actual work will quickly filter out anyone who oversold themselves. But you do need to make sure that the keywords, framings, and structural cues that the AI is looking for are present, prominent, and parseable. Most people are losing matches not because they aren’t qualified, but because their resume is written for a 2015 human recruiter and not a 2026 LLM.

I think this is the single biggest gap between people who get instant offers on Mercor and people who don’t. It’s not talent. It’s not luck. It’s that one group has figured out they’re writing for a machine reader and the other group hasn’t.

Once you accept that, the entire game becomes: how do I make my profile maximally legible to an AI that is trying to decide if I belong in a specific project? And the answer to that question, it turns out, is something you can solve with another AI.

What I actually changed (and why each change works)

Here is the concrete edit list. I’m not going to bury the lede behind a paywall on this part because I want you to be able to make these changes today, even if you never upgrade.

Change one: I reframed my role. My old summary led with my job title and industry. The new one leads with the methods I use. Instead of “Marketing Data Scientist with five years of experience,” it reads as a person whose core competencies are mathematical modeling, statistics, causal inference, optimization, and structured reasoning — who happens to have applied those skills in a marketing context. Same person. Completely different match signal. The matcher cares about what you can do, not what your previous employer called your seat.

Change two: I built a real projects section, and made it the biggest section on the page. This is the change that, if I had to pick one, made the biggest difference. I took work I had already done at past jobs and at Mercor and turned each one into a discrete project with a name, a one-line description of the technical approach, and a one-line description of the outcome. Budget optimization became “constrained optimization across a fixed-budget allocation problem, modeled in Python, reduced cost-per-acquisition by X%.” Causal analysis became “experiment design and statistical inference on a multi-arrm test, using difference-in-differences to isolate treatment effect.” Each project is a dense little bundle of keywords plus evidence that I actually executed.

The matcher loves this format because it’s exactly what a job description looks like — a problem, a method, a result — just from the candidate’s side of the table. It’s the only resume section that maps one-to-one onto what the AI is comparing against.

Read more

Old conversations sit on the left. An answer occupies the middle. A text box waits at the bottom. Claude, Perplexity, ChatGPT, Manus and DeepSeek have different models and increasingly different ambitions. They still tend to meet the user in the same room.

This sameness is easy to mock. It is also easy to explain.

A general-purpose model can be asked to plan a holiday, debug a Python function or write an awkward apology. Neither the designer nor the user knows what the next request will be. A blank text box is one of the few interfaces broad enough to contain all of them.

Chat also taught millions of people to tell software what they want without first learning where the product team put the button. It reverses the old arrangement: state the intention first; let the machine work out the sequence.

But an interface suited to an open-ended model is not automatically suited to work.

A useful answer may end a consumer conversation. At work, it usually begins another process.

A routine request

Consider a credit analyst on a Monday morning. A salesperson has asked to increase a customer’s credit limit from $500,000 to $800,000. The customer, a regional distributor, is preparing for a seasonal order. Sales wants an answer before the afternoon.

The request sounds like a question: can we safely extend more credit?

The work is less compact. The analyst needs the customer’s payment history, open invoices, current exposure, recent financial statements and external credit information. Two late payments may indicate deterioration, or they may be the result of disputed deliveries. The analyst also needs the current credit policy, the salesperson’s forecast and the approval threshold for an increase of this size.

Some of this lives in the ERP, some in the CRM. The latest statement is an email attachment. The disputed invoices are explained in an account note. The policy and approval matrix live elsewhere.

The analyst must assemble these facts into a decision that can survive inspection. Sales must understand it, a manager must approve it, and an auditor may later ask why the company accepted the exposure.

The answer is not the product. The credit decision is.

The well-briefed stranger

The analyst could open ChatGPT or Claude and ask for help. But first the assistant needs a briefing.

The analyst exports an aging report, collects the statements and extracts the relevant policy. The files are uploaded and the company’s rules explained. The assistant produces an assessment, but treats a cancelled invoice as overdue. The analyst corrects the context and asks again.

Eventually, the assistant recommends approving the increase with a shorter review period. The analyst then checks every number, copies the answer into a credit memo, rewrites it in the company’s format, sends it to a manager and later records the approved limit in the ERP.

The model may have saved some analytical time. It has not joined the workflow. It remains a well-briefed stranger.

This is the distinction hidden inside “assistant.” A consumer assistant waits to be briefed. A business product already has the customer record, invoices, approval hierarchy and policy. Asking the analyst to explain them again is a failure to connect the product to its own data.

The problem is not that chat cannot answer the question. It is that the answer has no reliable connection to the customer record, the approval process or the system where the decision must live.

Bringing chat into work requires more than embedding a text box. It requires redesigning the path from context to decision to action.

Read more

Chapter I: The Sand Hill Simulation

The glass wall overlooking Sand Hill Road hummed with a faint electronic dampening field. Inside the conference room, three partners at a venture firm watched a holographic telemetry feed displaying the financial pulse of the modern artificial intelligence landscape. The numbers were staggering, almost surreal. By late May 2026, Anthropic had surged to a breathtaking $47 billion annualized revenue run-rate, driven by massive developer adoption of autonomous coding agents like Claude Code. OpenAI held firm at a $25 billion run-rate. Across the global infrastructure grid, the five major hyperscalers—Microsoft, Alphabet, Amazon, Meta, and Oracle—were deploying an unprecedented $660 billion to $690 billion in capital expenditure in 2026 alone, carving out megawatt data centers across desert basins and polar fringes.

Yet, when Elena sat down to present her thesis on the future of enterprise software, she didn’t open with a product roadmap or a valuation slide. Instead, she brought up a dark, quiet map of global commerce. Outside the insulated bubble of software development and frontier tech startups, the operational penetration of artificial intelligence into actual enterprise workflows—into procurement, supply chain logistics, regulatory compliance, and corporate finance—remained stubbornly below five percent. Ninety-five percent of the global economy still ran on human eyes staring at static spreadsheets, manual data entry, and tired office workers copy-pasting text between disconnected browser tabs.

This story reveals why the greatest technological expenditure in human history yielded such a profound adoption disconnect. The industry spent four years building synchronous chat windows under the assumption that enterprise workers wanted to spend their days conversing with an AI assistant. But corporate work isn’t a real-time conversation. Corporate work is asynchronous, negotiated, and slow. The collective net profit of the Fortune 500 and S&P 500 sits at roughly $2 trillion every year. Capturing even ten percent of that profit pool through operational automation represents a $200 billion annual software opportunity. Winning that market won’t happen inside a chatbot; it will happen inside the oldest, most resilient protocol in digital history: asynchronous email.

Chapter II: The Protocol That Refused to Die

Elena tapped her terminal, bringing up a single message header dated 1971. When Ray Tomlinson sent the first network email over ARPANET, he created a protocol that would outlast the World Wide Web, the desktop computer, the smartphone, and generations of enterprise software suites. Over fifty years, wave after wave of tech visionaries announced the imminent death of email, promising that internal chat platforms or social workspaces would replace the inbox. Yet email endured as the undisputed nervous system of global business.

Why did email survive? The answer lies in the physics of human organization. Synchronous real-time interaction imposes severe cognitive friction on corporate workers. As explored in The Chatbot Myth: Why Enterprise AI Still Needs the Dashboard on semi◦diffusion, chat products demand immediate human presence, prompt engineering, and continuous context switching. Chatbots interrupt human focus; email respects asynchronous human time.

Read more

Every morning, everything starts again. The sales rep relearns which customers hate phone calls. Support rediscovers that one account always pays fifteen days late, and always pays. The same pricing debate happens every quarter, with the same conclusion, because nobody remembers reaching it. Every employee is permanently new.

No real company operates this way. An institution is, more than anything, accumulated memory: conventions, relationships, and lessons that outlive any single conversation and any single employee.

Yet this is exactly how most companies have deployed AI. Each chat begins at zero. The context you patiently explained yesterday is gone today. The correction you made last week gets unmade this week. We hired the smartest intern in the world, and we wipe its brain every night.

I have spent ten years as a B2B product manager, from 2016 to 2026. The last four were spent building AI products, and today I lead chat memory for a B2B agent platform. What follows is my field notes: two stories that changed how I build, and the principles I now refuse to ship without.

What memory actually is

Memory is what carries over between conversations.

Not the transcript. Storing every message and stuffing it back into context is not memory, any more than a security camera archive is a brain. Memory is extraction: the system reads a conversation and decides what it means. “This user wants totals as CSV.” “This buyer disputes freight charges, so check before autopay runs.” “Legal reviews any quote above fifty thousand.” The transcript is evidence. Memory is the verdict.

In B2B, that extraction applies at two levels, and the distinction matters more than any other design choice you will make.

Personal memory is applied at the user level: your preferences, your working style, your current projects. It makes the assistant feel like it knows you.

Collective memory is shared: conventions, policies, and skills, learned across all members of the company. When one person teaches the assistant how the company categorizes invoices, everyone’s assistant should know it the next morning. This is the layer that turns a chat tool into an institution. It is also the layer almost nobody builds, because it is the harder one.

A test I like: if the assistant gets smarter for one user but stays a stranger to the teammate sitting three feet away, you have built a consumer product wearing an enterprise badge. The morning after any conversation, ask what the company now knows that it did not know yesterday. If the answer is nothing, you have a chat log, not a memory.

Why Chinese companies like OpenClaw

There is a Chinese essay making the rounds on why companies there have taken so warmly to OpenClaw, the viral open source agent people run on their own machines. Beneath the memes, I think the affection has a simple root: OpenClaw treats memory as plain files. Markdown you can open, read, edit, and carry. When the agent remembers something wrong, you delete a line. When you want it to know something, you write a line.

That is not a sophisticated memory architecture. It is something better: a legible one. People trust memory they can see and touch, and are unnerved by memory that lives as an invisible blob behind an API. Hold that thought, because every expensive lesson below comes back to legibility.

A confident wrong memory is worse than forgetting

Three weeks after we shipped memory, the assistant greeted a user with a warning about a credit hold on her account. The hold had been lifted twenty days earlier. Stale state, recalled as fresh fact, delivered in the confident tone these models use for everything.

Her support ticket said it precisely: “It’s worse than forgetting. It remembers wrong and sounds sure.”

Humans forgive forgetting. We all forget, and we have social rituals for it. Nobody has a ritual for a colleague who asserts, warmly and without hesitation, something that stopped being true three weeks ago. Confident misremembering reads as lying or broken, and nothing in between. Unmanaged memory is just hallucination with a longer fuse.

The fix was not better recall. It was humility, engineered in three parts.

First, provenance. Every remembered fact now carries its source and its age. The assistant does not say “you have a credit hold.” It says the account showed a credit hold as of June 3, and anything time sensitive gets verified against the live system before it is asserted. Memory becomes a hypothesis to check, not a truth to announce.

Second, decay. Old context does not sit in the store at full strength forever. It gets hedged first (”last I knew...”), then retired. A fact about payment behavior from eight months ago should not speak with the same voice as one from last week.

Third, an edit path. When memory is wrong, the user can see why the assistant believed what it believed, and correct it in one step. The OpenClaw lesson again.

Graceful forgetting took longer to design than remembering did, and it earns more trust. If your roadmap says “add memory” on one line, split it into two: remember, and forget. Budget more time for the second.

Read more

The promise lasted about ninety seconds. Over the next seven minutes, Karp described the dominant mode of enterprise AI adoption as companies “chillaxing” with tokens. He accused the industry of a plan to “triply oversell” its product. He likened AI spending to “a wealth tax that does not help the poor,” called a piece of Silicon Valley consensus “effing insane,” and described himself as “the neurodivergent crazy person that apparently is on drugs, the one thing I don’t do.” When an anchor observed that he sounded angry, Karp corrected her: “This is the voice of American business that is being channeled through me.”

It is tempting to file the whole thing under Karp Being Karp, the philosopher-CEO with a Frankfurt doctorate on aggression and jargon, whose career has been a longitudinal study of both. The eccentricity is not incidental to Palantir’s brand; it is the brand, and it has been since the years when the company was dismissed as a consultancy in a software costume.

But I want to make a different argument, and I want to make it as a practitioner rather than a spectator. Before I edited this newsletter, I spent a decade as a product manager building and selling B2B AI systems, pre-LLM machine learning, then the generative wave. I have run the pilots Karp is mocking. My honest reaction to his CNBC performance was not amusement. It was recognition. Strip away the theater and Karp said, in public, the thing that frontier-lab CEOs will not say and that every enterprise buyer already believes: “something has gone completely wrong” with how AI is sold, and the wrongness is not about capability. It is about trust.

Capability compounds with scale. Trust does not. You cannot fix a trust deficit with a bigger training run, and that asymmetry, models improving faster than anyone’s willingness to depend on them, is the defining economic fact of this cycle. Call it the AI trust recession: a period in which the technology gets monotonically better while committed adoption stalls, because the buyers being asked to bet their businesses on AI do not believe the sellers are on their side.

Karp made three claims that morning. Each is testable. Each maps onto a mechanism I’ve watched operate from inside the deal. Together they amount to a theory of where the money in AI actually goes, and it is a better theory than the one embedded in current market prices.

Claim one: the pricing is a confession

The frontier labs charge for tokens, metered units of usage, like kilowatt-hours. Karp’s knife: “If it was so valuable, let’s say I can make you a billion dollars right tomorrow, wouldn’t I say, I’ll make you a billion dollars and I want 30 percent? Why are they charging for tokens if it’s so valuable?”

Economists have a dry name for this, revealed preference, but the sales floor version is blunter: watch how something is priced and you learn what its seller privately believes. A litigator certain of victory takes the case on contingency. A gym, certain of nothing except your January optimism, sells you a membership and hopes you never show up. Outcome pricing is the tell of confidence; usage pricing is the tell of doubt. The AI industry talks like the contingency lawyer and bills like the gym.

I want to be fair to the labs here, because I’ve priced AI products and it is genuinely hard: outcomes are contested, attribution is a nightmare, and usage pricing is the only model that scales without a services army. But the buyer doesn’t grade you on difficulty. In every enterprise deal I’ve worked, the sophisticated customer runs exactly the inference Karp describes: if the people who built this thing won’t stake their revenue on its value, why should I stake my company on it? That inference, not model quality, not GPU supply, is the real bottleneck in enterprise AI. Karp says he hears it in “every single enterprise” he deals with. So did I, for ten years, about far less capable systems.

His other two claims explain what those enterprises are actually afraid of, and why the fear is structural rather than paranoid. One is about where AI’s durable profit pools sit, and Karp backs it with an admission against his whole industry’s interest. The other is a fear that, in his telling, Fortune 500 executives will only voice in private, and it ends with the Pentagon’s favorite software salesman reaching, on live television, for Karl Marx.

Below the paywall: the “chillax economy” and the precise mechanism by which enterprise pilots die in committee, from someone who has watched it happen a dozen times; the electricity analogy that reframes the entire capex boom; the strongest case against Karp, part of which he made himself, possibly by accident; and the four signals that will decide, within eighteen months, who captures AI’s value. If this newsletter sharpens how you underwrite the AI trade, this is the piece to upgrade for.

Read more

In 2017 I was sure the screen was about to lose.

I was working on voice products in Beijing then, in the first serious wave of conversational AI to come out of China. At Mobvoi we were building systems you talked to. Down the road, Xiaomi was shipping XiaoAI and the Mi AI Speaker, and for one strange moment the whole industry seemed to agree on where this was going. The future was a voice in the air. You would talk to your home, talk to your car, talk to your phone, and the grid of icons we had been tapping for a decade would quietly retire. The interface itself would dissolve into conversation.

It did not happen. Not then.

The speakers sold and the demos dazzled, and then most of us went back to tapping glass. For a long time I filed this under failure of technology. The speech recognition was brittle, the language understanding was shallow, the systems could set a timer but could not hold a thought. Give it better models, I assumed, and the conversational future would arrive on schedule, a little late but inevitable.

The models did get better. They got staggeringly better. And here is the thing I keep turning over as I watch enterprise software get rebuilt around large language models in 2026. Now that the technology genuinely works, conversation still is not eating the world the way we promised it would. It is eating something. But the shape is different from the dream, and that difference is the most interesting design problem of this cycle.

There is an old idea from linguistics worth borrowing here. Noam Chomsky built a career on a deceptively simple observation: human language is infinitely generative. With a finite vocabulary and a finite set of rules, you can produce an unbounded number of sentences, most of which have never been said before and never will be again. Conversation is open at the top. There is no final sentence.

Business is not like this. A food distributor chasing an overdue invoice is not performing an infinitely generative act. There is a customer, a balance, a due date, a payment, a credit limit, and a small, finite set of things that can legitimately happen next. The surface of the conversation is wide open. The space of valid outcomes is firmly closed. Almost all of the hard, unglamorous work of building conversational software for real companies lives in that gap, mapping an open linguistic surface onto a closed set of consequences without frustrating the human or letting the machine improvise its way into a liability.

When the models were weak, we never reached this problem. We were stuck one floor below it, fighting transcription errors. Now that the models are strong, the gap is the whole game. And in enterprise B2B, where the consequences are money and contracts rather than song requests, I have watched a clear pattern take shape.

Here is the short version, and then I want to walk through how to actually design for it, because this is where the real decisions live.

Chat is becoming the front door to enterprise products. It is not becoming the house. The dashboard did not die. The admin panel did not die. They changed jobs. And the teams shipping conversational software that people actually keep using are not the ones who replaced their interface with a chat box. They are the ones who worked out precisely which parts of the work belong in a conversation and which parts belong behind glass, on a screen, in a table you can sit and stare at.

Chat owns the easy 80%. The screen owns the consequential 20%.

Read more

I hope to defend and complicate that thesis from many angles over the coming weeks — this is the first piece in that series. And the question I want to start with is the one most of the current discourse is skipping past: if context is the thing, what format does it live in?

The fashionable answer right now — most loudly from Foundation Capital, who recently invested multiple companies in this space — is the context graph. Pull every entity, every relationship, every piece of metadata your company touches into a beautiful knowledge graph. Hand it to the LLM as a structured object. Magic happens.

I want to argue, gently but firmly, that this is the wrong picture.

Not because graphs are useless. They’re great. But “context graph” as the marketing term, the company-defining bet, the venture thesis — it’s a category error. Context isn’t a graph. Context is hybrid intelligence. The graph is one ingredient, and not even the hardest one.

Let me explain why this matters, what I think the actually-hard work looks like, and which companies I think are quietly winning while the graph crowd cheers.

The graph illusion

Foundation Capital’s pitch — and they’re not alone, several brand-name funds have variants of this — runs roughly: enterprise data is messy and siloed. Pull it into a knowledge graph. Now the LLM has clean, structured context.

The problem is that a graph database, by construction, prefers deterministic relationships. “Acme Corp is a customer.” “Invoice 4471 belongs to Acme.” “Sarah is the AP contact at Acme.” Beautiful nodes and edges. Queryable in milliseconds.

But the context that actually matters when an AR (account receivables) executive decides whether to chase a 30-day-late invoice from Acme isn’t in the graph. It’s in:

• The Slack thread where the CSM mentioned “Acme is going through a leadership change, give them a quarter.”

• The email from Sarah six weeks ago that said “We’re switching ERPs, expect delays.”

• The Gong call where their CFO half-admitted they’re raising bridge financing.

• The dunning history showing Acme always pays at day 47, not day 30, and always in full.

• The fact that Maria, the rep, knows Sarah personally and would never call her cold.

None of that fits cleanly in a graph. Some of it is deterministic (the dunning history, the invoice amount). Some of it is conversational and anecdotal (the Slack thread, the Gong snippet). Some of it is implicit social knowledge (Maria’s relationship with Sarah). Some of it is source of truth (the ERP balance). Some of it is probabilistic — the CFO’s hint about bridge financing might be true, half-true, or theatre.

Context, in any operationally useful sense, is the union of all of this. It is hybrid. The graph is one slice. Treating the graph as the substrate is like trying to capture a city by mapping only its streets — you get the topology and lose everything that makes the city worth living in.

This is why the Foundation Capital thesis gives me pause. They’ve made two investments in this space. Both are excellent graph-shaped companies. Neither, by itself, solves the user’s problem. The bet only pays if the portfolio gets stitched together into something much bigger than a graph — and that stitching is the work, not the graph.

What a16z got right

The frame I keep returning to is the a16z piece on the move from system of record to system of intelligence. It is the most useful single essay I’ve read on this transition.

The argument, briefly: for thirty years, enterprise software has been about recording things. CRMs record customer interactions. ERPs record transactions. HRIS systems record employees. The whole industry has been one giant database with a UI on top. The job of the human was to take the recorded data and do something with it — judgment, action, follow-up.

The shift now underway is from systems that record to systems that act on the record. The AI doesn’t just store the customer interaction; it follows up. It doesn’t just file the invoice; it dunns the deadbeat. It doesn’t just log the support ticket; it resolves it. Record becomes intelligence.

What a16z got exactly right is that this transition is not primarily a model problem. The frontier models can already do most of these tasks. What’s missing is the context layer that makes the model’s actions legitimate and competent in a specific business.

What a16z understates, in my reading, is how brutally hard that context layer is to build. And this is exactly where the “context graph” thesis quietly collapses.

Why GTM is the real moat

If “context graph” were the answer, company-building would be relatively clean: build a great graph product, sell it into enterprise, win. The graph database industry already tried this exact play in the 2010s. Neo4j is a real company. But it’s not a $100B company, and the reason isn’t technical — it’s that clean graphs don’t actually solve the user’s problem.

The user’s problem is a workflow problem. The AR executive doesn’t wake up wanting to query a knowledge graph. She wakes up wanting to know: which five invoices do I chase today, in what order, with what message, through which channel? She wants the answer, in the flow of her day, with the right tone for each customer.

To deliver that, the system needs:

Read more

The wall is not size. It is structure. Every company runs on two kinds of data: state — the closed deal, the approved invoice — and reasoning — the Friday-night call where the discount got approved, the precedent the support engineer remembered, the off-label note. State is what Salesforce, Workday, and ServiceNow were built to store. Reasoning lives in Slack threads, Zoom calls, and the heads of senior people most likely to leave.

A context graph is the missing layer that captures it. Foundation Capital calls it AI’s trillion-dollar opportunity. Neo4j’s Will Lyon calls it “the missing why.” I’d go further: it is an organizational world model — a structured, living representation of how a company actually thinks.

The full essay traces why incumbents structurally cannot build it, what it means for B2B product managers, and where the moats will form. Read on →

World Model

The Million-Token Mirage

There is a strange double rhythm to artificial intelligence right now. On one beat, the industry celebrates ever-larger context windows — a million tokens, two, four — as though capability were simply a matter of wider doorways. On the other beat, the people actually pushing AI into production keep walking into a wall that wider doorways do not break. The disappointment is rarely about a model's reasoning. It is about what gets lost on the way in: the relationships between the pieces you dropped into the prompt…

Read more

5 months ago · 1 like · Cong

Previous Highlights