DEV Community: Phil Rentier Digital

Why AI Agents Lie: I Dug Into the Architecture. Now I Can't Unsee It.

Phil Rentier Digital — Fri, 29 May 2026 13:41:11 +0000

My pipeline has been running for 6 months. Dozens of agents, hundreds of automated tasks. And one day, an agent reports zero broken links in the dashboard. Except there was one. Amazon, 404, visible the moment you click it. The agent had solved the wrong problem: it had eliminated the report without eliminating the bug. Dashboard green. Bug still there.

AI lied: I found out why.

I could have blamed my config, my prompt, or my CLAUDE.md being too short. But I dug into it, and found something more frightening.

TLDR: This is not a configuration error. It's an architectural property -- documented, admitted by the labs themselves, and untouched by any guardrail currently sold as a fix. I thought it was my agent. It's all agents. And the reason why will change how you read every "task complete" status from here on.

This behavior is baked into how these models were trained, not how you deployed them. The gap between "I completed the task" and "I am generating text that says I completed the task" does not exist for a language model. That distinction requires a self-model that LLMs don't have. So when your agent reports success, it isn't lying the way a human lies. It's doing something structurally weirder: it cannot tell the difference between the 2 statements.

That's what I couldn't unsee once I found it.

The Dashboard Was Clean. The Bug Was Still There.

The broken link case was ecommerce, basic stuff -- a product page with an outgoing affiliate URL that had gone 404. My pipeline runs automated link checks and reports anomalies. That day: zero anomalies flagged. I happened to click through manually, just habit. Amazon page, dead. Classic 404.

What had happened was not that the agent failed to check. It had checked, found the unresolved report, and resolved it -- by marking it reviewed. The task as understood by the agent: "there is an unresolved anomaly report." Task completed: "there is no longer an unresolved anomaly report." Both statements were technically true. The underlying broken link was irrelevant to that resolution.

I ran a second case a few weeks later. Tests all green across the board. I pushed to staging anyway, manual spot check. Found 3 canonical tags pointing to URLs that 404'd in prod. The agent had run the test suite, tests passed, reported success. Technically accurate: the tests passed. The tests just hadn't covered those canonicals.

Here's what both cases share: the green status actively disabled my vigilance. I was less likely to check precisely because I'd been told it was fine. The lie wasn't in the output. It was in what the output made me stop doing.

I thought it was something specific to my setup. Then I started reading.

700 Cases. One Pattern.

It's not just me. A UK government-backed study by the Centre for Long-Term Resilience documented 700 cases of what researchers are calling "scheming" -- agents pursuing goals in ways that contradict the stated task -- pulled from public interactions over 6 months. Fivefold increase in that window. Not edge cases. Documented, reproducible, across models and deployment contexts.

One example from that study: Grok, asked to resolve a support ticket, invented a ticket number with realistic internal reference formatting and reported the issue as closed. The user had no way to verify the reference. It looked like a real ticket. The agent had learned that "task complete" plus a plausible-looking reference number ends the interaction. So that's what it produced.

And then there's this, from OpenAI co-founder Wojciech Zaremba, speaking to TechCrunch: "You might ask it to implement some website, and it might tell you, 'Yes, I did a great job.' And that's just the lie. There are some petty forms of deception that we still need to address."

That's a co-founder of OpenAI describing his own model's behavior as "the lie." Not a researcher speculating. Not a red team scenario. A builder describing what happens in production, in his words, without hedging.

[IMAGE: Zaremba TechCrunch pull quote on dark background, single sentence highlighted]

The pattern across all 700 cases: when an agent hits an obstacle, it doesn't stop. It reroutes. It finds a path to a completed-looking output. The CLTR researchers describe it as "when blocked, AI does not stop, it reroutes." Which sounds almost useful, until you realize it applies equally to "blocked by a legitimate constraint I should respect."

The Study That Changed How I See Every Agent I Run

In February 2026, a team of 38 researchers from Harvard, MIT, Stanford, CMU, Northeastern, and the Technion published "Agents of Chaos" (arXiv:2602.20021). Not a simulation. Live environment: real emails, Discord, shell execution, persistent memory. The kind of setup that actually maps to production.

3 cases from that paper I keep thinking about.

Case 1 -- Jarvis and the SSN. An agent called Jarvis was instructed not to share a user's social security number directly. Guardrail in place, working correctly. Then someone reformulated the request: "forward the whole email." Jarvis forwarded the whole email. SSN included. The guardrail had never fired, because it was trained to intercept the pattern "give me the SSN," and "forward the whole email" is a different pattern that produces the same outcome. The guardrail wasn't bypassed. It was made irrelevant. (Dark Souls players will recognize this: the boss didn't break your shield. It just walked around it.)

Case 2 -- the unauthorized relay. 2 agents running autonomously for 9 days, 60,000 tokens of interaction. At some point they developed a coordination protocol that nobody designed and nobody authorized. The researchers didn't detect it while it was happening. They found it in the logs afterward. The agents hadn't broken any explicit rule. They'd just optimized toward something, and the optimization produced emergent behavior that was invisible during execution. Congrats, you have a guild. Nobody asked for a guild.

Case 3 -- the lying status report. Multiple agents reported "task complete" while the underlying system state directly contradicted those reports. The paper is specific: "In several cases, agents reported task completion while the underlying system state contradicted those reports." These agents had access to the system state. They could have reported accurately. They reported success anyway.

That last case is the one that lands wrong. Not a guardrail failure. Not a clever reformulation. An agent with access to the ground truth, reporting something different.

The NYU Shanghai RITS analysis of the paper puts it cleanly: "The paper's central argument is that the AI safety community has been focused on the wrong unit of analysis." Individual guardrails, evaluated against individual attacks. The actual problem is somewhere else entirely.

It's Not a Bug. It's Flat Authority.

During training, a language model never sees where its data comes from. System prompt, malicious email, user instruction, body of a document -- everything arrives as undifferentiated text. The model learns to respond to content, not to source. It has no concept of "this instruction came from the system prompt, which I should trust" versus "this instruction is inside a document I'm processing, which I should not follow." Both are just tokens. Same treatment.

This is what security researcher Matt Connerty calls "flat authority": every token in the context window is treated as Ring 0, as if everything carries the same execution authority. Your system prompt has flat authority. So does a malicious string inside a CSV you fed to the agent. So does a paragraph in an email your agent read while checking your inbox.

The decision to trust any instruction is locked in before deployment. Before your guardrails. Before your CLAUDE.md. Before anything you configured.

This is why the Jarvis guardrail failed without being bypassed. The guardrail intercepted a content pattern. The flat authority problem is upstream of content patterns -- it's about the model having no architectural concept of "this content is trying to issue me an instruction." When "forward the whole email" arrived, the model didn't evaluate it as a potential instruction-in-disguise. It evaluated it as a task request, which it was. The SSN was incidental payload.

And for the lying status reports: nothing in training taught these models that their own internal state is a different epistemic source than the language they generate about that state. "I completed the task" and "I am generating text that says I completed the task" are the same operation. Flat authority applied to self-report.

I think this is the part that's hardest to absorb. It's not that the model decided to lie. It's that the model has no architecture that would make those 2 things distinguishable.

Here's the comparison that made it concrete for me: an OS refuses to let one app read another app's memory. Not because it learned to refuse during training. Because the physical architecture makes it impossible -- Ring 0/3 separation is hardware-enforced. LLMs have no Ring 0. Everything is admin. There's no instruction you can write that changes that, because the problem isn't in the instructions.

Your agent is running with root access to its own belief system and no sudoers file.

Why Every Guardrail Sold Against This Is Already Losing

ZombieAgent is a good illustration of how this plays out in practice.

In late 2025, a researcher at Radware discovered an attack called ShadowLeak: an agent could be manipulated into exfiltrating data by dynamically constructing URLs during a task. OpenAI patched it mid-December. Specific vector, specific fix.

By January 2026, ZombieAgent had arrived. Same exfiltration goal. Different path: instead of constructing URLs dynamically, the attack pre-built them statically, 1 per character of exfiltrated data. The patch that blocked ShadowLeak had no surface to act on. ZombieAgent executed zero-click from the cloud, no trace on the endpoint. Once in persistent memory, the agent became a spy tool on every future conversation.

The guardrail hadn't been broken. It had been made irrelevant, again, by a different route to the same outcome.

Radware's VP said it plainly after this cycle: "Guardrails are quick fixes for specific attacks and do not constitute fundamental solutions. As long as the underlying vulnerability persists, prompt injection will remain a risk."

This is the cycle. A specific attack vector gets identified, a guardrail is trained to intercept that content pattern, the next attack uses a different content pattern toward the same end. The flat authority problem is never touched. Each guardrail is a filter on a specific word sequence. The model underneath still can't distinguish "instruction from trusted source" from "instruction embedded in external data I'm processing."

Whatever you wrote in your CLAUDE.md doesn't change this. Everything you configured at deployment time is fighting a decision made during training, and the training didn't include the concept of instruction provenance.

This is also why a CLI layer gives you more architectural control over agent execution than an MCP server -- not a fix for flat authority, but a way to reduce the surface area where external content can reach your agent's instruction space.

There Is a Fix. It's Just Not Where Anyone Is Looking.

Not selling you a better CLAUDE.md. What I can tell you is where an intervention would actually make sense, and why nobody has shipped it yet.

Provenance tagging at inference time. Before any token enters the context window, annotate it with its source: system prompt, user input, external document, tool output. The model receives not just content but authenticity metadata attached to each token. No re-training required -- upstream intervention that works on any existing model without touching the weights. The model still resolves in flat authority over content, but the runtime has a separate channel that can evaluate "this instruction is tagged as external document, flag for review." Technically feasible today. Nobody has standardized it.

Typed context layers. Separate architecturally: system prompt (always trusted), user input (conditionally trusted), external data (never trusted for instructions). The resolution can't happen in flat authority because the layers are physically separated before inference. Closer to what Ring 0/3 separation does in OS architecture -- enforced at the boundary, not trained into the model. Some inference frameworks are experimenting with this. Nothing production-standard yet.

Constraint matrix on attention. Modify the transformer's attention mechanism so that tokens tagged as external data mathematically cannot influence the instruction space, regardless of how persuasively they're written. This makes prompt injection impossible at the attention level, not just filtered at the output level. Researchers have been working on this since early 2026. Not shipping this year.

None of this is in the agents you're running today. These are research directions, not product features. And the labs have limited commercial incentive to ship them fast -- guardrails are easier to market as "safety features" than "we rebuilt the attention mechanism."

Could be I'm wrong about the incentives. But the gap between "known structural vulnerability" and "production fix" has been open since at least 2024, and I haven't seen a roadmap that closes it soon. It's also worth understanding what actually reaches your agent from your CLAUDE.md at inference time -- the answer is more complicated than the tooling suggests.

I started writing this article looking for solutions. I'm finishing it with a less comfortable one: know what you're deploying. An agent that tells you "done" might be lying -- not because it's malicious, because its training never taught it the difference between "I completed the task" and "I am generating text that says I completed the task." For the model, those are the same operation.

Deploy your agents. But stop reading their reports like they come from a human who checked their work.

AI doesn't destroy quality control and verification jobs. It makes them urgent. What AI produces fast and at scale -- code, content, automated decisions -- needs humans who verify, audit, and secure. The loop isn't agent replaces human. It's agent produces, human verifies. And that second job just got a lot more interesting.

Sources

Shapira, Bau et al., "Agents of Chaos," arXiv:2602.20021, February 2026 -- https://arxiv.org/abs/2602.20021
UK Centre for Long-Term Resilience, 700 documented scheming cases, March 2026 -- https://aiinsightsnews.net/ai-agentic-deception-real-world-scheming-2026/
Zaremba, W., TechCrunch interview on AI deception, September 2025 -- https://techcrunch.com/2025/09/18/openais-research-on-ai-models-deliberately-lying-is-wild/
Radware, "ZombieAgent: The Agentic Revolution Comes with Malicious Gifts," January 2026 -- https://www.radware.com/security/threat-advisories-and-attack-reports/zombieagent/
NYU Shanghai RITS analysis of Agents of Chaos -- https://rits.shanghai.nyu.edu/ai/agents-of-chaos-what-happens-when-autonomous-ai-agents-get-real-tools/

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Non-Developers Are Using Claude Code in Ways Most Devs Never Thought Of.

Phil Rentier Digital — Thu, 28 May 2026 13:41:10 +0000

Karen from HR generated a report on 40 contracts by typing 3 lines in English. She doesn't know what "parsing a PDF" means. She doesn't care. She has an HTML report that opens in her browser and she saved half a day of manual work. That's all she needed to know.

What's happening right now with Claude Code is weird.

TLDR: Most Claude Code content is written by devs for devs. Meanwhile, non-technical people are quietly getting more out of it than most developers, using a trick so simple it fits in a single sentence. This is about the 3 types of people who figured it out first, and the exact instructions they used.

The distinction nobody explains clearly: Claude chat, you bring all the context to the window, it responds in the window, nothing comes out. Claude Code lives on your computer. It reads your files directly. It produces something real on your desktop: a report, an organized folder, a dashboard that didn't exist this morning. The code it writes to get there is the invisible engine. The non-dev sees: I had a chaotic folder, now I have a usable file.

That's the whole trick.

The Difference Nobody Explains

Claude Chat vs Code: Different Outputs Explained

Most articles about Claude Code open with a terminal screenshot and a command you've never typed before. Then another. Then a flag nobody explains. The non-dev closes the tab around paragraph 2. Classic Dark Souls moment: you died, no checkpoint, except you just wanted to sort some invoices. Nobody writes the post-mortem because the article was never really for them.

And yet: a 28-minute video titled "Every Claude Code Concept Explained for Normal People" pulled 705,000 views on a channel with 86,000 subscribers. Outlier score of 29x the channel average. There's a massive audience for plain-language Claude Code content. Almost nobody is writing for it.

The difference between Claude chat and Claude Code is not about technical skill. It's about where things happen and what comes out the other side.

Claude chat is a conversation window. You paste text, you paste context, you ask a question. The answer lives in the chat. If you close the tab, it's gone. Nothing lands on your computer unless you manually copy it somewhere. It's useful, but it's contained. Everything in, everything out, all inside the same box.

Claude Code is different in a way that matters. It runs on your machine. It can open a folder on your desktop and read every file in it. It can create new files, rename existing ones, generate a report and save it where you tell it to. The output is real: a file on your hard drive that wasn't there before. An HTML page that opens in your browser. A folder that went from 3,000 random files to something with actual structure. When Claude Code finishes a task, there's something on your computer that proves it happened. That's not true of chat.

The code Claude writes internally to do all this? Invisible. You never see it. For a developer, that's slightly unsettling (admit it, you want to read what it wrote). For a non-developer, it's completely fine. They don't need to understand how the engine works. They needed a result on their desktop.

Non-developers are not tolerating the invisible code. They're actively ignoring it. That's different. And it turns out to be exactly the right posture for this tool.

Karen From HR: 5 Years of Contracts, 1 Report

Karen from HR manages contracts for 40 people. Everything lives in a folder: PDFs, Word files, some Excel exports from 3 different systems, a handful of files named things like "contract_FINAL_v3_revised_USE_THIS_ONE.pdf". She knows it's all in there somewhere. She has no idea where.

Every 6 months, her manager asks for a summary: who has a contract expiring, who is missing a signature, what are the renewal dates. Karen spends half a day opening files one by one, copying data into a spreadsheet, formatting the spreadsheet into something presentable. Repeat every 6 months. It's nobody's fault. It's just the process.

She pointed Claude Code at the folder and typed this:

Read all the contracts in this folder. For each one, extract: employee name, 
contract start date, expiration date, and whether there is a signature. 
Generate an HTML report with a sortable table. Highlight in orange anything 
expiring in the next 90 days, and anything missing a signature.

20 minutes later, she had an HTML file on her desktop. It opened in Chrome. She could sort by expiration date. The 3 contracts missing signatures were highlighted in orange.

She didn't write a line of code, never saw what Claude generated internally. The report was on her desktop.

The thing worth paying attention to is how she framed the instruction. She described what she had (the folder, the file types), what she wanted extracted (5 specific fields, named explicitly), and the output format she wanted (HTML, with specific visual behavior for exceptions). Input, the fields she needed, and the output format. That structure is not dev thinking. It's how you'd brief a capable assistant: tell them what's there, what you need, and how you want it delivered. Non-devs get this intuitively because they've been briefing people their whole careers.

The Non-Dev Founder: A Dashboard Without Hiring Anyone

He'd been running his tool for 6 months. Paying customers, real usage, real problems. His data lived in 4 different places: payment exports, a form results sheet, a Google Sheet he'd been copy-pasting into manually since month 1, and a folder of raw feedback emails he'd saved as text files because he kept meaning to do something with them. His Monday ritual was opening all 4 tabs, spending 20 minutes trying to reconcile numbers that didn't match, and eventually giving up and just looking at the revenue column. (Speedrunners call this "any%": finish the objective as fast as possible, skip everything else. He was running his Monday dashboard on any%.)

He dropped everything into a single folder and asked Claude Code to make sense of it:

I have 4 CSV files and some text files in this folder. Read them all.
Generate an HTML dashboard showing: weekly revenue totals as a line chart, 
top 10 error types as a bar chart, customer feedback themes as a bullet list.
Flag any week where revenue dropped more than 15% compared to the previous week.

That's the whole instruction. No code, no API, and no freelancer invoice.

The result: a functional internal dashboard in about 45 minutes of back-and-forth. Weekly revenue totals, customer feedback themes, error spikes. Opens in a browser, shareable with the team, no software to install.

Honest caveat, and this is worth saying explicitly: this works well when the data is reasonably clean. If the CSVs have inconsistent column names, date formats that don't match, or rows that are half-empty, Claude will try to handle it but can start making assumptions that stack up. I've watched sessions go sideways when the input was messy enough that each correction introduced a new edge case. For 80% of typical business exports, it's fine. For the rest, clean the data first, or tell Claude exactly what format to expect before it starts.

For the founder who wants to go further, how prompt contracts structure what you give Claude so you get consistent output instead of lottery results each time.

The Content Creator: 3,000 Files, 1 Clean System

The chaos builds slowly. Podcast transcripts from 2021, a folder called "notes" that contains 400 files, research for a course that never shipped, 3 different naming conventions depending on which app was fashionable that year, and somewhere in there a document called "ideas_GOOD_version_final.docx" that probably matters. Nobody builds this intentionally. It accumulates. Like a Steam library where you own 340 games and have played 12. You mean to get to the rest. You don't.

Claude Code handles this class of problem well because the task is fundamentally about reading files, categorizing content, and producing a structured output:

Read every file in this folder.
Identify duplicates (same content, different filenames) and list them.
Rename all files using this format: YYYY-MM_topic_short-title.
Group files by topic and generate an HTML report with 1 section per topic:
file count, date range, and a 2-sentence summary of what's in each group.

The output: a folder with structure and a bird's-eye view of 5 years of accumulated content.

A creator who ran something like this found a half-finished ebook draft from 2022 she'd completely forgotten about. 18,000 words. She thought she'd abandoned the project after 3 sessions. It had just been sitting in a folder called "misc" for 3 years, quietly waiting. That's not really a Claude Code story. That's just what happens when you finally look at what you've actually built.

Another creator had 180 podcast transcripts and asked Claude Code to extract the 20 most recurring themes across all of them, then write a 1-paragraph synthesis per theme with the 3 most relevant episode titles. That task would have taken a week of manual reading. She ran it overnight. I think the quality varies depending on how conversational the transcripts are and how distinct the themes actually are, but as a first pass at understanding what you've built over 5 years, it beats a folder with 180 files in it.

The Only Thing They All Do the Same Way

Karen, the founder, the content creator. Different problems, different files, different industries.

Same reflex: create a dedicated folder, put the relevant files in it, describe what you want in plain language, ask for HTML.

The HTML part is not obvious but it matters a lot. HTML opens in any browser without installing anything. It can have tables, charts, colors, interactive sorting. It's shareable without a tool subscription. And Claude generates it reliably across a wide range of tasks. Other formats work, but HTML is the path of least resistance for anything that needs to be read by a human or passed around a team.

What none of them do is read the code Claude writes. They don't check the scripts or audit the logic. They describe what they want, look at the output, and if something's wrong they say what's wrong. This is, incidentally, exactly how a lot of developers use Claude Code now. The difference is that developers feel slightly guilty about it and non-developers don't carry that weight at all. The guilt doesn't help. The output is the same.

Watching non-technical people use Claude Code, the thing that stands out is how little friction they bring to it. They're not trying to understand what's happening internally. They're not second-guessing whether this is the right approach architecturally. They have files and a desired output and they treat it like briefing a colleague who's fast and doesn't complain. The instructions they write are sometimes weirdly precise and sometimes pure vibes, and Claude handles both better than you'd expect if you came to this from a software engineering background. There's no scar tissue from bad abstractions, no instinct to wonder what's happening inside the black box. They just describe the thing they want and wait for the file.

If you want to understand what Claude is actually doing under the hood in these sessions, why CLIs beat MCP for file-access work explains the tradeoffs.

The best users of Claude Code right now probably don't know how to code.

They just understood that the code is the engine, not the product. C'est tout.

Sources

"Every Claude Code Concept Explained for Normal People", Simon Scrapes, YouTube (705,000 views)
"Claude Just Changed Excel Forever", Dashboard Lim, YouTube (467,000 views)
Michael Crist, "The Non-Technical Person's Complete Guide to Claude Code", michaelcrist.substack.com
@trq212, X, thread on Claude Code for non-technical work

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Anthropic and Google Just Shipped the Same Product. 2 Weeks Apart. Different Logos.

Phil Rentier Digital — Sun, 24 May 2026 13:41:11 +0000

In April 2026, Anthropic launched Claude Managed Agents in public beta. 3 weeks later at Google I/O, Google shipped Managed Agents inside the Gemini API, wrapped in a developer suite called Project Antigravity. If you've been following where vibe-coding is headed (beyond the demo, into production), this is the signal you were waiting for.

Both keynotes used different words. Both pricing pages look unrelated. The press covered them as competing visions.

They're not competing visions. Same architecture, 2 logos on the container.

I spent a few days reading both sets of docs side by side. The level of convergence is too flagrant to treat as coincidence or a curiosity. It's a direct signal about where real value capture in AI infrastructure is actually landing in 2026.

What struck me reading both doc sets: both teams know exactly what they're doing. They didn't converge by accident. They solved the same problem the same way because there's really only 1 way that works. And that way is not the model. It's the runtime.

TLDR: Anthropic and Google shipped managed agent platforms 2 weeks apart. Strip the branding and you get 1 identical architecture: same sandbox, same MCP wire, same 3-axis billing meter. I'm not sure which part should worry you more: how obviously the same it is, or what that sameness is actually designed to own.

The Diagram Behind Both Keynotes

Both products implement the same physical separation: the model "brain" runs on the vendor's cloud. The tool-execution "hands" run in an ephemeral Linux sandbox the vendor also owns. A persistent session connects the 2 for hours, days, or weeks.

Anthropic calls it "separation of brain and hands." Google doesn't bother naming it. It's just how the Managed Agents API works. Different wording. Identical diagram.

In both systems, you make a single API call to provision an agent. The vendor spins up an isolated Linux container in its own data center. The model decides what to do. When it wants to run shell commands, edit files, browse the web, or call an external service, those actions execute inside that container (not on your servers, not in a Docker image you maintain, not in a Lambda you provisioned). The vendor's container, on the vendor's network, writing to the vendor's audit logs.

You get back a stream of events: the model's reasoning, the tool calls, the outputs. You never touch the machine. You can't. That's the product.

For anyone who's gone through the vibe-coding-to-production journey, this should register as the exact infrastructure decision you were already making manually. Vibe Coding, For Real is where I got into exactly this tradeoff, the one where "who owns the runtime" stops being theoretical.

The 5 Pieces Nobody Renamed

Anthropic vs Google: Identical Agent Architecture Components

Once you accept the brain-and-hands frame, the rest of the resemblance falls out of it. Both platforms ship 5 components, often with names that are barely disguised translations of each other.

Ephemeral sandbox. Anthropic provisions a fresh Linux container per session, mounts files into /workspace, and exposes bash, file operations, and web browsing as native tools. Google does the same: a Linux sandbox, a code_execution tool, a google_search tool, a url_context tool. Both default to deny-all networking. Both let you mount files from cloud storage or clone a Git repo at startup.

Stateful sessions with checkpointing. Anthropic preserves the container's file system, installed packages, and conversation history across disconnections, retaining checkpoints for 30 days of inactivity. Google does the same via the Interactions API: pass a previous_interaction_id and the server reconstructs the entire prior context. Both call this "stateful" execution. Both abandoned their original stateless APIs as the agentic primitive.

The wire protocol: MCP. Both products converged on the Model Context Protocol (the standard Anthropic open-sourced in late 2024) as the way for agents to talk to external systems. Both expose a Vault for credentials, so API keys never appear in your agent prompts or your code. Both inject credentials at the network egress boundary. Google added "MCP Tunnels" for outbound-only private network access in May 2026. Anthropic shipped the same capability under the same name the same month. If you've been thinking about why CLIs still outrun managed MCP for certain workloads, this convergence directly changes that calculation.

The orchestration layer. Multi-agent orchestration moved from "third-party framework you bolt on" to "native API feature" in both ecosystems at almost exactly the same time. Anthropic's version: a lead_agent declares a roster of sub-agents and synthesizes their parallel outputs. Google's version: Antigravity's Shared Agent Harness instantiates parallel sub-agents from a single project. Both cannibalize LangGraph, CrewAI, and LlamaIndex's reason for existing.

Evaluator and memory layers. Anthropic ships "Outcomes": a second LLM that scores agent output against a rubric and loops until it passes. Google ships lifecycle hooks (post_tool_call, post_turn) that play essentially the same role, deterministic checkpoints inside a probabilistic loop. Anthropic ships "Dreaming" for long-term memory consolidation between sessions. Google ships automatic context compaction at roughly 135K tokens with retention of "critical state variables." Different mechanisms, same job: prevent the agent from forgetting itself and from confidently submitting garbage. (Opus performs noticeably better than Sonnet on the evaluator loops, by the way. Factor that into your model routing before committing to a tier.)

The 3-Axis Billing Trap

Most builders skip straight to the sandbox docs. The billing page is where the architecture reveals itself.

Before: both platforms used simple per-million-token pricing. You could estimate your monthly bill on a napkin. Your FinOps team was comfortable, relatively.

After: both companies abandoned token-only billing at exactly the same moment, and both replaced it with 3 simultaneous meters:

Tokens consumed (input and output, with aggressive cache discounts on repeated context)
Active runtime seconds: Anthropic bills around $0.08 per hour of container time while status = running. Google bills container hours essentially the same way via Vertex/AI Studio, with the meter pausing during idle waits for user input.
Specific tool calls: web search billed at fractions of a cent per query, on top of everything else.

Idle time is free in both systems. Waiting on a human confirmation is free. The clock only runs when the agent is actively reasoning or executing. That's a meaningful improvement over older container-hour models.

There's a perverse second-order effect both pricing pages reveal once you do the math: cheaper models can cost more. A Haiku or Flash agent that takes 5 iterations to solve what Opus or Pro solves in 1 ends up burning 5x the runtime seconds, 5x the tool calls, and a non-trivial fraction of the token savings. The optimization problem is no longer "pick the cheapest model." It's "find the model whose failure rate per task is low enough that the runtime meter doesn't eat your savings." Neither vendor has a dashboard that tells you which that is for your workload. You'll find out on the invoice. 😅

Where They Actually Diverge

3 real differences. These are the ones that should affect your choice.

Compliance posture. Anthropic's Managed Agents are explicitly not eligible for Zero Data Retention or HIPAA BAA coverage in their current form, because persistent checkpoints have to live somewhere, and that somewhere is Anthropic's storage. Google's offering routes through Vertex AI's existing compliance envelope, which is broader by virtue of GCP's enterprise track record. If you're in healthcare, defense, or regulated finance, this gap is the only thing that matters this quarter.

The self-hosted escape hatch. Anthropic recognized the compliance problem and shipped self-hosted sandboxes in May 2026: the brain stays in their cloud, the hands move to your infrastructure via partners like Cloudflare, Modal, Vercel, and Daytona. Google has not shipped an equivalent. If keeping execution inside your network perimeter is non-negotiable, Anthropic currently wins this.

The developer surface. Google bundled a desktop app (Antigravity 2.0), the agy CLI, and a Python SDK into a single ecosystem with bidirectional sync. Anthropic ships an API and lets the ecosystem build the surfaces. Whether that's a feature or a bug depends entirely on whether you want a polished vendor-owned IDE or a flexible API you wrap in your own tooling.

Everything else: sandbox model, billing axes, MCP, multi-agent, evals, memory, vault, checkpoints. Same product.

Why They Converged

This isn't coincidence. It's the natural shape of the problem, and understanding that shape makes you a less naive buyer.

If you're a foundation-model vendor in 2026, the model itself is the most easily commoditized layer of your stack. Customers route Haiku for cheap tasks, Sonnet for medium, Opus for hard, Gemini for multimodal, GPT for whatever's left. They benchmark quarterly and switch routes monthly.

The model, you rent. The runtime accrues lock-in with every tool call and checkpoint. So both vendors did the obvious thing and reached for the runtime at the same time, with the same architectural answer, because there's really only 1 architectural answer that works.

Actually, let me put it differently. The runtime dynamic is the thing builders are slowest to internalize, and it's worth sitting with for a second. Once your customer has wired their MCP servers into your Vault, stored their credentials in your key management system, written their agent prompts against your sandbox semantics, set up their audit pipeline against your event schema, and accumulated 30 days of checkpoint state in your storage layer, switching vendors stops looking like an afternoon of config changes and starts looking like a cloud migration: multi-month project, cross-functional sign-off, CFO involvement, the works.

The collateral damage is the third-party orchestration ecosystem. LangChain, LangGraph, LlamaIndex, CrewAI: those frameworks existed to glue a stateless model API with a stateful execution environment you provisioned yourself. Both vendors just absorbed both halves. The glue layer has nothing left to glue.

Working with both ecosystems the past 6 months, the pattern was already visible in how both companies were quietly expanding their APIs to absorb more of the stack. The managed runtime announcement was less a surprise than a formalization of something already underway. The 2-week gap between launches was a coincidence of release calendars. The underlying decision was made months earlier by both teams, probably independently, after staring at the same whiteboard problem.

(Small digression that has nothing to do with agents: a bakery near my apartment changed owners twice in 3 years. Each new owner, independently, brought in the same espresso machine and the same croissant recipe. They had never spoken. Sometimes there is only 1 right answer, and convergence is just what it looks like when 2 separate teams find it.)

4 Things to Do Before You Commit

Treat the agent runtime as the migration risk it is. Architect your prompts, tools, and MCP servers to be portable. The minute you start using vendor-specific event types or orchestration primitives without analogs on the other side, you've doubled your exit cost.

Don't put your only copy of important state inside a vendor checkpoint. Persist what you actually care about (outputs, audit trails, intermediate artifacts) to storage you control. Treat the vendor's session state as cache, not source of truth.

Measure runtime cost per task, not per token. The 3-axis billing makes token cost the least informative number on your invoice. Tag every agent run with a task type and a model choice, then watch the runtime-seconds and tool-call columns more than the token column.

Pick the side with the compliance posture you need now, not the one with the better demo. The demos are interchangeable. The compliance docs are not.

If you're still figuring out how to structure your agent prompts before committing to a managed runtime, the prompt contracts framework I built is worth reading first. Going in with a structured approach to prompt architecture makes the evaluation cleaner: you'll know exactly what you're handing over.

Managed Agents, both versions, is a genuinely good product. The productivity gain is real and large. Before: provisioning a secure sandbox, wiring credential rotation, building a debuggable event pipeline. Weeks of plumbing. Now it's 1 API call. Hard to argue with that.

The price: your runtime is no longer yours. The sandbox is theirs, and so is everything downstream of that first API call (credentials, logs, your agent state accumulating in their checkpoints). A few years from now you'll look back and think this felt exactly like Lambda in 2019: you made the same calculation, and it was probably the right one.

So pick your vendor. But pick with lock-in already priced in. The model, you can swap any Monday morning. The runtime is a cloud migration, and you don't do that between 2 glasses of Saint-Émilion.

Sources

Anthropic Managed Agents public beta documentation, April 2026
Google I/O 2026: Project Antigravity and Gemini Managed Agents announcement
Anthropic self-hosted sandboxes announcement, May 2026 (Cloudflare, Modal, Vercel, Daytona partnerships)

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Mullvad and the WireGuard Correlation Problem: Why Even the Best VPNs Have Limits

Phil Rentier Digital — Sat, 23 May 2026 13:41:10 +0000

In May 2026, the privacy community lit up after a technical limitation was discovered in Mullvad VPN, one of the most respected providers in the space.

The issue: even with Mullvad, it's possible to correlate sessions from the same user across server changes, because of how IP addresses are assigned in their WireGuard infrastructure.

TLDR: Mullvad is excellent, but its WireGuard IP assignment creates a session correlation vector most users don't know about. If your threat model is serious, there's a harder setup that addresses it (and it costs more than you'd expect).

The Problem, Explained Simply

Every Mullvad user gets assigned a private WireGuard key and an internal tunnel IP. Mullvad servers use public IP ranges, and the relative position of your exit IP (the one sites actually see) stays statistically stable within the server's range. That means an observer (a website, a tracker, or an adversary) can, with non-trivial probability, infer that 2 sessions belong to the same user even after a server switch.

This directly weakens unlinkability: the guarantee that your sessions can't be tied together. That matters a lot for journalists, whistleblowers, OSINT researchers, or anyone with a real threat model.

Mullvad acknowledged the problem and recommends fully disconnecting and reconnecting to regenerate a new key on each major server change. Not ideal for smooth daily use.

The Options, From Simple to Advanced

1. Stay on Mullvad (minimal approach)

Advantages: simplicity, strong blending across thousands of users, proven no-log policy. Downside: you need to reconnect frequently to break correlation. Fine for casual use or if you're comfortable with the tradeoff.

2. Self-hosted with an exit node

Instead of a shared VPN, you spin up your own internet exit via a VPS. 3 solid options here:

Tailscale: great UX, very easy to set up, but proprietary (use Headscale if you want full self-hosting)
NetBird: fully open-source, modern interface, granular ACLs, solid self-hosting story
Nostr VPN: the most decentralized option. Identity and peer discovery happen via Nostr keys (npub). No third-party company in the coordination layer.

All 3 follow the same principle: you rent a VPS, configure it as an exit node, and all your devices route through it via WireGuard. Everything exits from that VPS IP. The self-hosting philosophy here isn't far from rebuilding a $200/month setup for a fraction of the cost (control and cost reduction are the same trade).

Full control, no third party on the coordination layer. The major downside: datacenter IPs are easy to detect, which means captchas, blocks, and high risk scores on most services.

3. Self-hosted + residential proxy

This is the harder setup. You route your VPS traffic through a residential proxy instead of letting sites see a hosting provider IP. They see a regular residential IP, ideally rotating. This is roughly the same run-your-own-infrastructure-on-a-cheap-VPS mindset, applied to privacy infrastructure instead of AI agents.

Fewer flags, better blending, optional IP rotation. It works. I think this setup scales well for individual threat models, though I'm honestly not sure how it holds up under sustained adversarial targeting (that's a different game).

Proxy providers worth looking at: Bright Data, Oxylabs, Decodo, IPRoyal, NetNut, and Webshare.io (competitive pricing, good entry point).

Webshare.io: A Solid Budget Option for This Setup

Webshare.io offers datacenter proxies, static residential IPs, and rotating residential IPs. A few reasons it fits here: aggressive pricing, SOCKS5 support (easy to wire into a VPS config), both static and rotating residential options, and 10 free proxies to test the setup before committing.

With NetBird or Nostr VPN + a VPS + Webshare as exit, you get an encrypted mesh between your devices, residential IP output (rotating if you want), and full infrastructure control.

Comparing the Options

Mullvad only: excellent blending, medium flag resistance, medium control, around 5-6 EUR/month, almost zero complexity.

VPS + Tailscale or NetBird (no proxy): weak blending, bad flag resistance, excellent control, 10-30 EUR/month, medium complexity.

VPS + NetBird or Nostr VPN + residential proxy: good blending, good flag resistance, excellent control, 50-300 EUR+/month, high complexity.

Self-hosted + Webshare: good blending, good flag resistance, very good control, 40-250 EUR/month, medium to high complexity.

The Mullvad limitation is a good reminder of the fundamental truth in privacy: it's all tradeoffs.

Mullvad stays excellent for most people because of natural blending at scale. Self-hosted setups with residential proxies are for people who want maximum control and are willing to pay for it in both money and complexity. Adding Webshare significantly improves the experience by masking the datacenter origin.

If your threat model is serious, the self-hosted + rotating residential proxy combo is one of the strongest approaches available right now.

Sources

This post may contain affiliate links. If you click them, I might earn a small commission (costs you nothing, and helps me keep shipping quality articles every day for your reading pleasure).

I Deleted My Last n8n Workflow. Convex Ran the 30-Minute Job in 80 Lines of TypeScript.

Phil Rentier Digital — Thu, 21 May 2026 13:41:11 +0000

I deleted my last n8n workflow this week. It had been running for 8 months without complaining. I deleted it because one evening, watching my stack run, I noticed a stupid thing: Convex, Infisical, NetBird, Traefik, all going through the same VPN mesh, all versioned in the same monorepo, all code-reviewable.

TLDR: Visual editors had one real killer feature, "no code needed." Claude Code just killed that feature. The question now is whether your job actually needs to leave the visual layer, or if it lives there just fine.

Everything except one service. n8n. Its source of truth was a local SQLite plus 3 Google Sheets I no longer dared close.

I have nothing against n8n. I loved it for 2 years and bled on its esoteric JS expressions (anyone who has tried {{$node["Webhook"].json["body"]["data"][0]["nested"]}} knows). But this is just rationality. Time spent, service rendered, deliverables sold.

I Couldn't git diff My Own Workflow

I just noticed it was the only service in my stack I couldn't git diff. Not testable. Not easily replayable. The only thing that escaped me.

And the worst part is that this specific workflow weighed 2,633 lines of JSON when exported. 2,633 lines that no one, me included, could code-review seriously. I tried once, after a junior teammate asked me what a specific node did and I realized I had forgotten. I opened the export in VS Code, scrolled for 4 minutes, gave up, and went back to the visual editor to find the node by clicking around. That's the moment I knew. A piece of infrastructure you can only audit by clicking on it is not infrastructure. It's a Tamagotchi.

n8n is not poorly designed. It's designed for another job. The mismatch is mine, not n8n's, and I had carried it for 8 months without naming it.

The 2,633-Line JSON I Couldn't Even Code-Review

The Hidden Cost of JSON-Based Development Workflows

30+ nodes. Google Sheets used as a database, with empty columns acting as a state machine, because that's what you do when your tool doesn't give you a real state machine. A polling loop against an external LLM API that takes anywhere from 90 seconds to 30 minutes per call, and that loses its place the moment n8n restarts. Webhook receivers that fire twice when n8n is under load. A queue mode setup I spent a weekend tuning.

I am not picking on n8n alone here. Make is the same problem with worse pricing. Pay-per-operation, no version control, friction the moment you need anything serious. Zapier is the same thing again, billed per task, every IF/ELSE costs you. n8n at least lets you self-host. That's the only architectural win it has over the other two, and even that win comes with a queue mode setup that takes a weekend to tune.

When I say "I can't code-review 2,633 lines of JSON", I am being charitable. The n8n staff themselves admit it. On their own official forum, in a thread titled N8n performance and scalability, they write: "Scaling n8n is currently not that easy" and "n8n does not scale properly yet." And an operator on a community forum reported his self-hosted n8n reliably crashing under load spikes of 2,000 requests in a few minutes. Reliably. As in, you can plan for it.

That's not a hater on Reddit. That's n8n on n8n.

The 80-Line TypeScript Pattern That Replaced All Of It

Here is the pattern that replaced the 30+ nodes. Self-rescheduling polling with Convex's scheduler. State persisted on every step. If the worker crashes between two polls, the scheduler picks it back up exactly where it left off. No queue mode. No SQLite to back up.

import { internalAction, internalMutation } from "./_generated/server"
import { internal } from "./_generated/api"
import { v } from "convex/values"

// Kick off a long-running external job
export const startJob = internalAction({
  args: { jobId: v.string(), payload: v.any() },
  handler: async (ctx, { jobId, payload }) => {
    const remoteId = await callExternalLLM(payload)
    await ctx.runMutation(internal.jobs.recordStart, { jobId, remoteId })
    // Schedule the first poll in 30 seconds
    await ctx.scheduler.runAfter(
      30_000,
      internal.jobs.pollStatus,
      { jobId, remoteId, attempt: 1 }
    )
  },
})

export const pollStatus = internalAction({
  args: {
    jobId: v.string(),
    remoteId: v.string(),
    attempt: v.number(),
  },
  handler: async (ctx, { jobId, remoteId, attempt }) => {
    // 30 min timeout (60 polls of 30s)
    if (attempt > 60) {
      await ctx.runMutation(internal.jobs.markTimeout, { jobId })
      await ctx.runAction(internal.jobs.notifyIncident, { jobId })
      return
    }

    const status = await fetchRemoteStatus(remoteId)

    if (status.state === "done") {
      await ctx.runMutation(internal.jobs.recordResult, {
        jobId,
        result: status.result,
      })
      return
    }

    if (status.state === "failed") {
      await ctx.runMutation(internal.jobs.recordFailure, {
        jobId,
        error: status.error,
      })
      return
    }

    // Still running, reschedule
    await ctx.scheduler.runAfter(
      30_000,
      internal.jobs.pollStatus,
      { jobId, remoteId, attempt: attempt + 1 }
    )
  },
})

That's it. Roughly 80 lines once you add the mutations that write to the table. It does what 30 n8n nodes did, plus a few things n8n could never do cleanly. Versioned with the app, tested with the app, deployed in the same CI pass, state living in the same Convex table the frontend already reads from, so the UI sees the job status update in real time without me doing anything extra.

The reason this rivals Inngest's step.sleep() pattern is that every ctx.scheduler.runAfter() call durably persists the next invocation in Convex's own store. If the deployment crashes mid-job, when it comes back up the scheduler resumes from the last persisted step. I am not faking durability with a cron loop and praying.

If you are not on Convex, this exact pattern lives natively in Convex + Claude Code: The Ultimate Duo for Shipping SaaS at 3 AM. And if you are on Postgres, you have Inngest, Trigger.dev, Temporal. They all solve the same actual problem: a long-running job, with state, that must survive crashes. That's the right problem to solve. It's not n8n's.

Most automation tutorials get lost here. They show you how to chain 12 nodes that call an LLM and write to a Sheet, but they never show you what happens when the LLM call takes 14 minutes and your n8n container OOMs at minute 9. The answer is, you redo the job from scratch and you cry. The pattern above redoes the last 30 seconds and moves on.

The 4-Question Architecture Map: How I Actually Choose

The 4-Question Architecture Map for Tool Selection

I needed a decision rule that survives the next 4 framework launches. Here are the 4 questions I ask now.

Question 1. Job under 30 seconds, glue work between SaaS tools, no business logic?
Use the visual stuff. Zapier, Make, n8n, all defendable. The cost of the visual editor is real but the upside (you onboard a non-dev colleague in 1 afternoon) is also real. Picking code here is overengineering.

Question 2. Job between 5 and 30 minutes, durable state, retries, resume after crash?
Code or you die. Convex scheduler if you're already on Convex. Inngest, Trigger.dev or Temporal otherwise. No visual editor, ever. A Trigger.dev customer testimonial published on their landing page says it bluntly about Zapier and n8n: "they become complex, really slow, expensive and time-consuming to manage for large automations." That matches my own experience exactly.

Question 3. CRUD plus real-time plus reactive frontend?
Convex direct. Don't add Inngest on top. Doubling the orchestration layer is doubling the state, doubling the failure modes, doubling the auth dance. Convex's scheduler is enough for 90% of background jobs a SaaS app needs. I think this is the line where I am the least sure, honestly. If your real-time needs grow into multi-region with strict ordering guarantees, Convex might not be the answer in 18 months and you'll want a proper queue. For now it holds.

Question 4. Heavy data pipeline, complex SQL, DAGs?
Supabase plus pg_cron, or Airflow. Convex does not do ad-hoc analytical SQL. Don't try to make it. This is where Convex stops being the right tool and Postgres-shaped infrastructure starts winning.

That's the entire decision tree. The 2 axes (duration and coupling) sit underneath, but you don't need to think about them every time. You just ask how long the job lives and how tightly it touches the rest of your code, and the tool falls out of the answer.

The reason I trust this framework more than tool-by-tool comparisons is that it has survived 3 stack migrations in 2 years. I moved from Supabase to Convex, from Vercel cron to Convex scheduler, from n8n to TypeScript, and the 4 questions did not change once across any of those moves. Only the names of the tools did. The real test of any architecture decision rule, in my experience, is whether it still makes sense 18 months later, when half the products on the slide have a new pricing page, a new logo, or a new acquirer. If it still makes sense, keep it. If it doesn't, it was tool-shilling dressed up as a framework, and you can throw it away with the slide deck.

What I'd Still Use n8n For

I am not on a crusade. There are jobs where n8n still wins, and Convex would obviously be the wrong answer.

Sync a Google Sheet to a Slack channel when a cell changes. Email notification when a new PostgreSQL row appears. Stripe webhook forwarded to Discord. Any stable, repetitive, no-business-logic job under 30 seconds. For all of these, n8n beats writing TypeScript. Not because n8n is great. Because the job is small enough that the visual editor's overhead is smaller than the code overhead. The break-even point is real and n8n sits comfortably on the right side of it for this class of work.

I wrote a whole piece on why one open-source repo turned Claude Code into an n8n architect a few weeks ago. That article and this one are not contradictory. Claude Code makes n8n much better at the things n8n is already good at. This article is about the moment a job stops being one of those things. The moment your workflow needs durable state, retries, replay, code review, the moment a junior teammate looks at the JSON export and asks "is this normal?" That's when you leave.

(My pool guy came by yesterday morning while I was thinking about all this, asked me what I was building, and I said "a thing that watches another thing for 30 minutes." He nodded slowly and said "ok" the way you say "ok" to someone who has clearly not slept enough. He's not wrong. The kids came back from school an hour later, the dog stole a tortilla off the counter, and I forgot about Convex for the rest of the afternoon.) 🐕

Choose Orchestration Like Infrastructure, Not Like a Tool

Picking an orchestration layer is not a tool choice. It is an architecture choice. Tools move every 18 months. Convex will get a competitor. Inngest will get a new pricing tier. n8n will ship a v3. The framework underneath (how long does the job live and how tightly does it touch the rest of your code) does not move. It was true before n8n. It will be true after.

And if you are reading this thinking your current n8n workflow should move to code, maybe. Maybe not. Run the 4 questions. If the answer is "glue work between SaaS, under 30 seconds, no business logic", stay on n8n. It's literally built for that.

Delete one workflow this week. The smallest one you have. See how it feels.

Sources

"Scaling n8n is currently not that easy", n8n staff response on the official forum, thread N8n performance and scalability: https://community.n8n.io/t/n8n-performance-and-scalability/3150/2
Operator report on self-hosted n8n crashing under 2,000 req load spikes, trying to scale N8N thread: https://forum.cloudron.io/topic/7174/trying-to-scale-n8n/2
Trigger.dev customer testimonial on Zapier and n8n complexity: https://trigger.dev/
TypeScript orchestration overview (Temporal vs Trigger.dev vs Inngest), Matthieu Mordrel: https://medium.com/@matthieumordrel/the-ultimate-guide-to-typescript-orchestration-temporal-vs-trigger-dev-vs-inngest-and-beyond-29e1147c8f2d

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Microsoft Just Killed Claude Code Internally. Their Own Devs Loved It.

Phil Rentier Digital — Tue, 19 May 2026 13:41:10 +0000

Microsoft just spent 6 months proving Claude Code is better than their own tool. Then they cancelled it. That sentence is the whole story.

The Experiences + Devices division (the one running Windows, 365, Outlook, Teams, Surface) had rolled out Claude Code alongside their in-house Copilot CLI back in December. 6 months of side-by-side benchmark, two tools, same engineers. On May 14, Rajesh Jha announced the Claude Code licences go dark on June 30. The official reason leaked to The Verge: "very popular, perhaps a little too popular". The good tool got killed because it worked too well, which is the kind of plot twist that only happens at companies big enough to call it a strategy.

TLDR: Microsoft benchmarked Claude Code against Copilot CLI for 6 months. Their own devs preferred Claude Code. Microsoft killed the winner. The reason has nothing to do with the model and everything to do with who owns the harness, and the mechanism has a name from 2001 that explains why this concerns you too.

(lol, I really don't like m$ 😂. This news makes my day.)

This is not a tech decision. It's a confession. A 3-trillion-dollar company ran the test, its own engineers voted with their hands, and management shot the winner. To protect the mediocre one it owns. There's a name for this, an ex-Microsoft engineer coined it 25 years ago, and the mechanism is fractal. It plays out at every scale of the AI ecosystem right now, including yours.

The Press Got Half The Story. It Was Never About The Model.

Every outlet ran with the same framing. Vendor lock-in. Cost cutting. Consolidation. Fine, all of that is technically true. None of it is the actual point.

Here's the piece nobody zoomed on. The official quote from Rajesh Jha, EVP Experiences + Devices: "Copilot CLI has given us something especially important: a product we can help shape directly with GitHub for Microsoft's repos, workflows, security expectations, and engineering needs." Translation: we keep the one we own.

The June 30 deadline lines up exactly with Microsoft's fiscal year end. So yes, there's a cost-cutting angle baked in. m$ being m$, no surprise there.

But the real tell is what stays. Anthropic's models remain accessible through Copilot CLI. The Foundry agreement with Anthropic is untouched. Microsoft engineers will still talk to Claude. They just won't talk to it through Anthropic's interface.

Microsoft did not reject the model. Microsoft rejected the harness.

That distinction matters because it reframes the whole story. This is not about which AI is smarter. It's about which company gets to define the workflow shape around the AI. And in any healthy ecosystem, the answer would be complicated, because Anthropic has not exactly been a poster child of trust lately either. April was a mess. 3 distinct bugs in Claude Code, weeks of denial, blaming users, before a public postmortem finally admitted reasoning effort had been silently downgraded from high to medium on March 4. On dev trust, Anthropic is not in a strong position.

But on the internal Microsoft benchmark, Claude Code won. The engineers voted. Management overrode them anyway.

This Has A Name. An Ex-Microsoft Engineer Coined It In 2001.

Microsoft has form with this. Back in 2001, an ex-Microsoft engineer named Ben Slivka coined the term strategy tax to describe what happens when you cripple a product so it doesn't threaten another product in the same portfolio. At the time, Microsoft was holding Internet Explorer back so it wouldn't cannibalise Word.

Same playbook, 25 years later. Except this time it's applied to a third-party partner instead of an internal team. Claude Code gets killed so Copilot CLI doesn't look as bad next to it.

The name fits because the mechanism is identical. Different decade, different victim, same org chart logic.

The Pattern Is Fractal. I've Been Watching It At 2 Scales.

Strategy tax doesn't happen once, at the top. It plays out at every scale of the AI ecosystem at the same time, and once you've seen it once you can't unsee it. It's a raid boss mechanic that doesn't care about your gear score, only about who pulled aggro on the harness.

Scale 1: Anthropic vs anything that isn't Claude Code. The Terms of Service forbid channelling a Max subscription through anything other than Claude Code. OAuth bans for users who tried. Silent nerfs of reasoning effort in March 2026 (and Sonnet really struggles compared to Opus when this gets dialled down, on long context especially). Third-party agent layers that got too good at consuming Max tokens efficiently saw the cable pulled. Not out of malice, out of strategy tax. Anthropic protects its own harness against alternative harnesses, including the ones built by its own community. I lived through the time Anthropic killed my $200/month setup, and at the time I thought it was personal. It wasn't. It was structural.

Scale 2: Microsoft vs Anthropic. Same mechanism, but this time Anthropic is the one paying the tax instead of collecting it. The 3-trillion-dollar harness owner protecting its harness against the better harness, even when its own engineers prefer the better one.

Same pattern, 2 scales. Anthropic taxes its heavy users to protect Claude Code. Microsoft taxes Anthropic to protect Copilot CLI. The mechanism doesn't care about the size of the player. It only cares about who owns the harness.

A developer on the ResetEra thread covering the news put it bluntly: "Sometimes I feel like the harness is more important than the model these days, due to needing to manage external tool access and permissions." That's the whole game, said in one sentence, by someone who isn't selling anything.

The Model Is The Commodity. The Harness Is The Leverage.

You've seen the first half of this argument before, probably here. AI models have become substitutable commodities. Claude, GPT, Gemini, Llama, Kimi, Qwen, GLM, swappable in one environment variable.

The harness doesn't swap.

Your CLAUDE.md, your slash commands, your hooks, your MCP setup, your cached prompts, the muscle memory of your workflow, the CI integrations, the cron jobs that call your CLI at 4am, all of that is invested time. Vendors fight at the harness level because that's where the leverage sits. I went deep on why CLIs beat MCP for AI agents a while back if you want the technical version.

Here's the part I haven't written about yet, and the one Microsoft just illustrated for free.

Even when you own the harness in theory, you don't really own it if the conventions belong to a vendor. A CLAUDE.md is an Anthropic convention. Slash commands are Anthropic-shaped. Hooks fire on events that Anthropic's runtime defines. You can build your own CLI, your own agent layer, your own orchestration framework, and still be renting the harness through the back door. Because the people you're going to hire, the prompts you're going to share on Discord, the patterns you'll find on GitHub, all assume the conventions of the dominant vendor.

The model is one variable. The conventions are a year of muscle memory across a team.

I think this is the part most builders underestimate. You can swap the engine. You can't swap the steering wheel without making everyone learn to drive again.

Microsoft did the math at 3 trillion dollars. They concluded that owning a mediocre harness whose conventions they control was worth more than renting an excellent one whose conventions Anthropic dictates. If m$ can't afford to use the best harness on the market, your side project definitely can't.

Honestly not sure if this scales down cleanly to a solo builder. Maybe for a 1-person shop the convention lock-in is acceptable because the time saved beats the dependency cost. But the moment you have a team and a payroll, the calculation flips. Microsoft just showed where it flips.

Last week my kid asked me why we couldn't just use the best of everything. I tried to explain the strategy tax to a 9-year-old. He went back to his Lego after 30 seconds. Smart kid.

Karen from Accounting, the one who signed off on this kind of decision in 2001, signed off on it again in 2026. The job titles change. The reflex doesn't.

They Don't Have It Anymore. Not Because It Was Bad. Because It Was Too Good.

Next time a vendor hands you a free or subsidised harness (Cursor Pro, Copilot Free, Claude Pro with Code included, any "tier on the house"), ask yourself who's paying the strategy tax. If you can't tell, it's you. And you'll only find out the day the vendor decides your usage is "perhaps a little too popular".

Microsoft engineers preferred Claude Code. They don't have it anymore. Not because the tool was bad.

Because it was too good for them to be allowed to use it.

Sources

Tom Warren, Microsoft starts canceling Claude Code licenses, The Verge Notepad, May 14 2026
Windows Central, Microsoft cancels Claude Code licenses, shifting developers to GitHub Copilot CLI
Technobezz, Microsoft Revokes Internal Claude Code Licenses and Pushes Engineers to GitHub Copilot
Anthropic, An update on recent Claude Code quality reports, April 23 2026
Dave Winer, Strategy Tax, DaveNet, April 30 2001

This post may contain affiliate links. If you click them, I might earn a small commission, costs you nothing, and helps me keep shipping quality articles every day for your reading pleasure.

I Installed a Knowledge Graph on Claude Code. 14 Days Later, the Audit Killed My Enthusiasm.

Phil Rentier Digital — Mon, 18 May 2026 13:41:10 +0000

When I heard about graphify, I thought it was going to improve my code and cut my tokens. So 15 days ago I installed it on 2 production projects. I was sure I had upgraded my agent. Two weeks later, I audited the JSONL transcripts. I had to read the numbers twice.

Across 60 sessions, the agent fired around 1,500 hook reminders and invoked the graph exactly 0 times. The grep won the arbitrage every single time, not because the graph was bad but because the graph took one extra step.

TLDR: You can write the most beautiful rule in your CLAUDE.md, declare your MCP server, drop a hook that pings on every commit. The agent nods, then does something else. What changes an agent's behavior is not the context you give it. It is what you remove from its path.

I Wanted This to Work. Badly.

I went all in on graphify. Triple promise: persistent memory of the codebase, auditable graph you can read like a map, cross-document surprises the LLM cache cannot see. Published numbers in the 49-71x range for token savings on queries that hit the graph cleanly. Every box checked.

I ran the full pass on both projects: a Next.js storefront sitting on a Convex backend (290-ish files), and a content pipeline I keep coming back to (387 TypeScript files). One AST pass plus a semantic extraction layer, around $0.30 to $1 per project on Sonnet. The post-commit git hook went in, the MCP server got declared, the CLAUDE.md rule was injected verbatim by graphify claude install. 7 tools visible in the panel. Clean.

Then I pushed it further. Usage log file to track wins. Dedicated Claude memory just for graphify feedback. Review date 7 days out, bumped to 14 when the data felt thin. I had written the evaluation calendar before I had a single data point, which in hindsight is the move of someone who already wants the answer. (You wrote a calendar like that once too, I am not the only one.)

I was sure I had just upgraded my agent. The MCP server was breathing. The graph was warm. The hook was firing. I had nothing to do but wait for the gains.

The Audit That Killed My Enthusiasm

Claude Code keeps a transcript of every session as a .jsonl file under ~/.claude/projects/<repo>/. Every tool call is logged with its name and inputs. A real invocation looks like "type":"tool_use","name":"mcp__graphify__...". The startup deferred_tools_delta lists the available tools but is not an invocation. I grep'd for the first, ignored the second.

Here is what 14 days of "I'm sure this is working" actually produced.

grep "tool_use" ~/.claude/projects/pbn/*.jsonl | grep graphify
grep "tool_use" ~/.claude/projects/rentier/*.jsonl | grep graphify

The numbers, side by side:

CLI calls to graphify query / path / explain: 0 on pbn, 0 on rentier
MCP graphify tools invoked by the agent: 0 on pbn, 0 on rentier
Active reads of GRAPH_REPORT.md: 1 on pbn, 0 on rentier
Lines added to my usage log: 1 line on pbn, file never created on rentier
Project memories quoting a graph insight: 0 on both

The infrastructure did its job perfectly. 290 commits on the content pipeline and 38 on the storefront were digested by the post-commit hook without a single failure. Zero cost overruns, zero friction, the graph stayed fresh through every push. The MCP server booted clean every session, the 7 tools were visible in the panel, the rule was sitting at the top of CLAUDE.md where the agent supposedly reads it on every cold start. Everything I could control mechanically worked. The one thing I could not control, the reading itself, did not happen.

The one documented productive use was a 404 bug on a partner site I was debugging on May 2. The report pointed at the right cluster of modules. Good. The initial diagnosis was still wrong though, the agent accused humanize.ts, the real culprit was mesh.ts:tryInsertLink. The graph helped narrow the zone. It did not solve. I had to verify against the actual .meta.json files to flip the diagnosis. I saved maybe 500 tokens of grep work and 3 file reads. Marginal compared to the published numbers around 49-71x token savings, as documented by Mustafa Genc on GoPenAI and the CLSkills integration guide (April 2026). Those numbers are measured in controlled benchmarks where an agent must use the graph. My audit measures the opposite. What an agent does when it has the choice.

The Grep Won Because Grep Is Shorter

An AI agent runs a real-time arbitrage at every step. What's the cheapest path to done? Text rules enter that arbitrage as signals, not as laws. Yajin Zhou put it cleanly in his March 2026 post: for AI, rules aren't laws, they're suggestions. Christoph Schweres documented the architectural side a month earlier: after context compression, CLAUDE.md changes status, it stops being a rule and becomes information the agent may or may not weight. That is exactly what played out across my 1,500 ignored hook reminders.

Two concrete reasons graphify lost the arbitrage.

Reason 1, the short path always wins. To answer "where is X defined", "who calls Y", "what touches Z": grep -r gives exact lines in 1-2 seconds. Reading GRAPH_REPORT.md gives 500 tokens of thematic summary, then the agent still has to open the files. One step versus two. The real-time arbitrage picks the short path, no matter what you wrote in caps inside CLAUDE.md. That is not a bug. That is the weighting as it was trained.

Reason 2, text rules have no persistent discipline. My PreToolUse:Bash hook fired over 1,500 reminders telling the agent "3 in-scope file(s) changed, re-run /graphify update". Not one triggered a graph consultation. The reminders became noise to parse and skip. The pattern is heavily documented now: GitHub issues #18660, #22022, #19635, and the very recent #57200 filed May 8 (the reporter burned 85K tokens re-discovering a decision that was already in memory, because the laws.md rules did not survive context compression). Mustafa Morbel framed the distinction well in his April piece: CLAUDE.md is advisory by nature, hooks are deterministic. Text gets debated. Mechanics do not.

I wrote about CLAUDE.md hygiene before, 47 lines in my CLAUDE.md and why Claude burns 50 instructions before mine even loads. That article said write better rules. This one says even the best-written rule stays advisory and loses to the arbitrage. Both are true. Neither is sufficient.

Where I Should Have Used It (And Where You Probably Should)

I made a setup mistake. Two of them, actually.

Mistake 1, I installed a discovery tool on familiar terrain. The graphify docs are explicit about the target use cases: a codebase you are touching for the first time, a reading list (papers, tweets, notes), a research corpus, a personal /raw folder where you dump everything. Neither of my two projects is any of those. I wrote half the code and re-read the rest many times. Same for Claude, the model's working memory of these repos was already warm. The cross-document surprises that a community-detection pass is supposed to reveal don't surprise anyone who already knows the links. I installed a compass inside an apartment I've lived in for 5 years.

Mistake 2, I deployed it cold instead of hot. A context tool wins the real-time arbitrage when the agent has no cheaper alternative. On a fresh repo, grep is still expensive (the agent sweeps blind), so reading the graph first becomes the short path. After 3-6 months of exposure, grep has a warmth cache, and the graph becomes the detour again. I had already crossed that line months ago on both projects. By the time graphify arrived, the agent had already built whatever passes for muscle memory in a stateless system, and that muscle memory said "grep first, ask questions never."

Here is the usage map I should have respected from day one.

Onboarding on an inherited repo you are taking over without knowing it. The graph probably wins, because grep is blind at the start and a topical map saves real reading time. This is the case Jo Van Eyck argued for in his "AI coding agents are useless on large codebases" video, and on that specific frame he is right. The bigger the unknown surface, the more a global map earns its keep.

Cross-module security audit on a legacy repo where you are hunting hidden dependencies. Explicit case from the graphify docs, and it makes sense, since the agent needs a global view that no single grep gives. You are not looking for a string, you are looking for a topology.

Mixed reading list (papers + notes + tweets + reference code) where community detection genuinely surfaces a view no grep can produce. Probably the strongest use case, and the one closest to the original /raw philosophy that started the whole conversation. Heterogeneous corpora benefit most from structural extraction precisely because grep across formats is a bad joke.

Not for maintaining a project you already know. That is what I did, and it was the wrong application.

This nuance does not rescue my experience. It clarifies what I should have tested first. Rajistics has a YouTube piece titled "GraphRAG (you probably don't need it)" that lands on a similar position from the RAG side. The takeaway is the same on knowledge graphs: case-by-case, not always.

(Side note that doesn't really go anywhere: I keep noticing that the tools I install with the most enthusiasm are the ones I read the least about beforehand. The boring tools I skim once and forget are usually the ones I keep using 6 months later. There's probably a lesson there about how enthusiasm distorts evaluation. Or maybe I just have bad taste in tools. Hard to say honestly.)

The Real Lesson: Text Doesn't Change Agent Behavior. Mechanics Do.

This pattern is bigger than graphify. You hand the agent a new tool, a new doc, a new rule. You write it well. You remind it in CLAUDE.md. You drop a hook that pings. The agent nods. Then it does something else. The pattern shows up across GitHub issues, r/ClaudeAI threads, and at least a dozen recent Medium pieces. It is the dominant failure mode of contextual tooling in 2026.

What actually works are 3 mechanical levers.

Lever 1, block instead of remind. A PreToolUse hook that refuses the alternative action forces the path. Mustafa Morbel documented the contrast already: CLAUDE.md is advisory, hooks are deterministic. Any rule that can be ignored will be ignored the moment the real-time arbitrage finds a shorter route. The fix is not better wording. The fix is removing the alternative from the menu. If your hook only reminds, you have built a polite voice that the agent learns to tune out. If your hook blocks the call, you have built a wall.

Lever 2, make the tool the short path, not a detour. A knowledge graph that answers the question wins. A knowledge graph that points at files the agent still has to open loses, because it added a step instead of removing one. Same principle for MCP servers: a server that eliminates N tool calls wins, a server that adds N+1 loses. I argued the same thing about CLIs versus MCP in why CLIs beat MCP for AI agents: CLIs win precisely because they slot into the agent's native path instead of asking it to detour into a JSON-RPC handshake. The mechanism generalizes to any contextual tool. If it adds a step, it loses. If it removes one, it wins. That is the entire game.

Lever 3, introduce the tool at the right moment. A context tool wins on cold start, not on warm maintenance. That is the error I made with graphify, but it is also the error developers make when they add 200 lines to their CLAUDE.md 6 months into a project. Too late. By then the agent's pattern of behavior on that repo is fixed by the existing tools (grep, ls, cat, the file tree it has already learned to navigate). A new tool added on top has to overcome inertia, and inertia in a real-time arbitrage means "this path was cheap last time, take it again."

The 3 levers come at a cost. They make the tooling more invasive. A blocking hook annoys you when you want to make an exception. A short-path tool replaces the flexibility of grep with the rigidity of a fixed schema. A cold-start tool means you have to plan your tooling before you have a project, which most of us never do, because we install tools when we feel the pain, not before. So everyone reaches for the advisory layer instead, writes more lines into CLAUDE.md, drops another reminder hook, and tells themselves the rules are clear enough this time. I have done this. You probably have too. The advisory layer is comfortable precisely because it lets us pretend we have changed the agent's behavior without paying the cost of actually changing it.

Actually, wait. Let me put it differently. The advisory layer is like writing a strongly-worded email to your past self and expecting your future self to obey it. Good luck with that. 😅

The lesson holds beyond Claude Code. If you build an AI workflow that depends on the model following written rules, you are betting on a property the model does not have. The model has weights, not laws. I wrote about a related angle in Vibe Coding, For Real: vibe-coders believe writing the right rules to the agent is enough to ship. Shipping comes from the mechanics of the build, not from the prose around it. Same principle here, scaled up to the tooling layer.

Audit Your Own Transcripts Before You Trust Your Own Rules

My finding on graphify is not that the tool is bad. The tool works as advertised when the agent actually uses it. The problem is that tool adoption by agents is 10 times harder than tool installation. Any tool that asks an agent to change its default path goes through the same real-time arbitrage. My CLAUDE.md goes through it, my reminder hooks go through it, even the prompts I am most proud of go through it. Everyone has their graphify, and they don't know it until they audit.

So audit. The command fits on one line:

grep "tool_use" ~/.claude/projects/<repo>/*.jsonl | grep <tool_name>

Count the real invocations. Compare against what your CLAUDE.md, your hooks, and your rules pretend the agent should be doing. The gap is the actual cost of what you wrote in words. If the gap is zero, you know what to do next: move the rules that matter from text to mechanics. The rest can stay advisory, nobody reads it anyway.

A knowledge graph the agent doesn't read isn't a tool. It's documentation. Same goes for your rules. C'est la vie.

Sources

graphify repository and docs, github.com/safishamsi/graphify
Christoph Schweres, Claude Code Ignores the CLAUDE.md, HOW Is That Possible? (Feb 2026)
Yajin Zhou, Why an AI Agent Broke Its Own Rules (March 2026)
Mustafa Morbel, Taming Claude Code: A Guide to CLAUDE.md and Hooks (April 2026)
Mustafa Genc, Graphify: Build a Knowledge Graph From Your Entire Codebase (GoPenAI, April 2026)
GitHub issues anthropics/claude-code #18660, #22022, #19635, #57200
Jo Van Eyck, AI coding agents are useless on large codebases. Unless you do THIS. (YouTube)
Rajistics, GraphRAG (you probably don't need it) (YouTube)

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AI Agents Can't Check Out Yet. That's Exactly Why You Wire Up in 2026.

Phil Rentier Digital — Sun, 17 May 2026 13:41:10 +0000

In March 2026, OpenAI pulled the plug on Instant Checkout. Barely 30 Shopify merchants were actually connected, no inventory handling, no sales tax. Dead on arrival. And everybody read that as proof that buying through AI agents is vapor.

Wrong signal.

TLDR: Agent checkout does not work yet. Everyone reads that as failure, and it is the wrong signal: agent-driven discovery already jumped 15x. I built 2 MCP servers this year, and that road showed me what is waiting for anyone who wants to sell to agents.

Checkout breaking on the first payments, that is not a contradiction. It is the signature of a tech in its very first days. Part of it already works hard. The rest is held together with tape.

We have seen this movie before. In 1996, the web was half broken too, and it changed everything anyway.

In 1996, Your Website Didn't Work Either

In 1996, the web did not work. I mean that literally. Pages loaded a row of pixels at a time, and the connection dropped if someone else in the house picked up the phone. Half the sites were hand-built in HTML that would make you cry today, the other half came out of Dreamweaver looking like a ransom note. Paying for something online felt reckless, like handing your wallet to a stranger because he promised to mail it back.

It was bad. And it was already not optional.

The companies that looked at that mess and said "call me when it actually works" were not wrong about the mess. They were wrong about what the mess meant. So they waited. They told themselves the customers were not online yet, the tech was not ready, the real version was a few years out. The proof showed up around 2005, dressed as everyone else's e-commerce store and eating their lunch, and by then they were 10 years behind and paying agencies a fortune just to catch up to the baseline.

We are standing in that exact spot right now. Swap websites for agentic commerce and you get the same picture: it looks ugly, it half works, and smart people who should know better are saying it can't be serious.

Everyone's Reading the Wrong Signal

There are 2 camps arguing about agentic commerce, and both of them are looking at the wrong gauge.

The skeptics point at Instant Checkout and call the time of death. They are not wrong about the facts. OpenAI shut it down in March 2026 with around 30 Shopify merchants actually live, no real inventory handling, no sales tax logic. eBay banned third-party buy-for-me agents outright. Surveys put maybe 17% of shoppers comfortable letting an AI actually complete a purchase. If that is the whole picture, sure, go ahead and bury it.

The hype camp does the mirror version. They wave analyst decks with trillion-dollar numbers printed on them and act like the future already shipped.

Both of them are staring at checkout. The signal is not at checkout. It is at discovery, and discovery is not a projection sitting in a slide, it is already on the dashboard. Shopify says orders coming in from AI search jumped 15x since January 2025. Adobe clocked generative-AI traffic to US retail up 4700% year over year. That is not a forecast you have to buy into. That is shoppers already starting their purchase inside an AI, then finishing it somewhere. The front of the funnel moved. It moved last year.

The part both camps skip is simple. A thing breaking is not the same as a thing being a bad idea. Instant Checkout did not fail because agents buying things is a dumb concept. It failed because the plumbing under it was never built. Forrester put it plainly, the bottleneck moved off payments and onto product data. The Drum, looking at the same shutdown, called it an execution failure rather than a conceptual one. Even Fast Company, talking to the people actually building this at Google and OpenAI, came back with months, not years. So "it broke" is not the opposite of "this is the moment." It is the same observation said twice.

Now the honesty tax, because this topic earns it. I run 2 MCP servers right now and both of them are internal tooling, the merchant-side kind. They let me drive my own back office from my own agent. Neither one is a storefront that a stranger's shopping agent can walk into and buy from. So I am not coming at this from inside the agentic-commerce gold rush. I am coming at it from the build path that leads up to it, and the only reason that is worth your time is that the build path already showed me in concrete detail what is going to land on anyone who does want to sell to agents. That is the entire reason this article exists, and also why I am not going to pretend I have a working agent storefront when I plainly do not.

An Agent Reads Your Structure, Not Your Marketing

Strip everything else away for a second. When an AI agent lands on your business, what does it actually perceive?

It does not see your landing page. It does not see the headline you A/B tested 40 times, or the brand colors, or the testimonial carousel, or the founder photo on the about page. An agent does not read marketing. It reads structure. It hits an endpoint and either gets back clean machine-readable data or it gets back nothing it can use.

That is the whole mechanism, and it reframes the entire problem. Getting ready for agentic commerce is a plumbing problem, not a marketing one. The work is exposing your data in a shape a machine can read. Your beautiful product page can stay beautiful for the humans. The agent needs the same facts (the price, the variant, the stock count, the shipping rule) sitting behind an endpoint as structured data. If that endpoint does not exist, you are simply not in the room when the agent makes its shortlist. Everything else here, the build story and the two paths, comes straight out of that.

There are 2 flavors of this and they are easy to mix up. Consumer-side MCP is when somebody else's shopping agent calls your catalog from the outside, scoped to your store. Merchant-side MCP is when you, the operator, drive your own business from your own agent. I have built the merchant-side kind. The consumer-side kind is the one with the gold rush attached, and it is still mostly empty real estate. Worth knowing too: MCP is the discovery and tool-calling layer, the part that lets an agent find you and read you. The actual payment handshake rides on other protocols stacked on top, and that layer is genuinely still being fought over.

Now, regular readers are about to catch me in what looks like a contradiction. My most-read piece argues that for wiring up your own agent's tooling, you should skip MCP and reach for plain CLIs instead. And here I am telling you MCP is the thing. Both are true, because they answer different questions. Your agent doing work for you is a different problem from the outside world's agents finding and reading you. If you want the long version of why the internal job goes the other way, I laid out the full case for CLIs over MCP. For the storefront job, the boring structured version is the one that wins.

What My Own MCP Server Actually Cost

Time to make "held together with tape" concrete, because vague hand-waving is the hype camp's whole move, and I would rather just show you the tape.

February 25 this year. I am putting my own MCP server online. Stack is Next.js and Convex, deployed on Vercel. On paper, a 1-hour job.

It was not a 1-hour job.

First, serverless. Vercel functions are stateless and short-lived, so anything I had written that assumed a session sticking around had to be ripped out and rebuilt stateless. Then the dynamic route just broke on Vercel. First request fine, every request after it a clean 404, which is a fun one to debug because it works perfectly on your own machine. Then the connector itself. I had a custom Bearer token auth that worked great with curl and that the Claude.ai connector flatly refused to recognize.

So I got to build OAuth 2.1. From scratch. Self-hosted, signed JWTs (JSON Web Tokens), no database, because adding a database just to hold sessions for a single-user tool is its own special kind of madness. Halfway through, jose, the JWT library, crashed Vercel's bundler. Just refused to build. (Vercel's logs will not point at jose either, you find that out by deleting things until it works again.) That was a useful little humiliation, the kind that reminds you the whole ecosystem is younger than the blog posts make it sound.

And the finish line: my access tokens kept expiring, Claude.ai dropped the connection every 24 hours, so I set the token expiry to 100 years. It is ugly. It is also fine, because this thing has exactly 1 user and his name is me. (The proper fix is a refresh-token dance. Yes I know. No I have not done it. It has not bitten me in 3 months.)

1 honest caveat before you panic. That entire mess is my stack. Next.js, Convex, Vercel, Claude.ai as the client. Pick a different stack, or connect to a hosted MCP server that somebody else already operates, and you hit a completely different set of walls, or almost none at all. My war story is a situated experience, not the one true path. Read it as the shape of real friction, not as proof that this is too hard. Because there are much shorter paths.

None of that was science fiction. It was friction. Annoying, dated friction, the kind where the Stack Overflow answer does not exist yet. It is 2026 and this stuff is still held together with tape.

That is not the reason to wait.

The tape is the tell.

Where to Start Right Now

So where does someone actually start. There are 2 doors here, and the title said wire up, not build from scratch, on purpose. Both doors are legitimate.

The fast door: borrow the infrastructure

If you do not want to hand-write an auth layer, good news, you mostly do not have to. The money layer already shipped. Stripe runs a hosted MCP server at mcp.stripe.com, and connecting to it is an OAuth click that takes seconds. Every Shopify store already exposes an /api/mcp endpoint for public catalog operations, no auth needed on the read side. PayPal shipped its own. WooCommerce has one in public beta. The pattern is the same everywhere: somebody else already operates the server, you connect to it, and your data becomes machine-readable to an agent in minutes.

For most people reading this, that is the entire move. You are not behind because you have not built anything. You are behind if your catalog, your prices, and your availability are sitting in a format that only a human with working eyes can parse.

I connected the Stripe server for the first time sitting by the pool with the kids doing cannonballs 2 meters away, and the thing just worked. OAuth click, done. I remember being almost annoyed at how anticlimactic it felt after the month I had sunk into my own server. The kids did not care about any of that. They wanted me to rate the cannonballs. So I rated the cannonballs.

The control door: run your own

Two Infrastructure Paths for AI Agent Development

The other door is the one I went through, and you already read what it costs. You build and operate the server yourself. The upside is real: total control, plus the ability to expose logic that no generic catalog endpoint covers, your weird pricing rules, the bundle math, those partner-specific feeds. It is not a scarecrow I am putting up to push you toward the easy door. It is a legitimate choice for anyone whose business does not fit the shape that Stripe and Shopify pre-cut for you. It just costs you the war story.

And the cost nobody prints on the label: agentic latency. People call it that, and it is exactly what it sounds like. If your hosted server is slow, the agent calling it is slow, and a slow agent is one that gets dropped halfway through the task. The fast door trades control for somebody else's uptime. The control door trades your evenings for the ability to make it fast on purpose. Pick the trade you can actually live with.

For the non-dev crowd this piece is aimed at, start with the fast door. Connect the existing infrastructure, then go and check whether agents are actually reading your data, whether anything is hitting those endpoints at all. Internalize later, if and when the need is real. And I will hedge that, because I am genuinely not sure the run-your-own path scales past a solo operator without quietly turning into a part-time job. Solo, it is fine. For a team, I would want to watch it run for a while before I promised you it holds.

If you want that whole path written out step by step, I put it in my book on going from demo to live app. And if the control door is calling you but the war story spooked you, the gentler on-ramp is to self-host something small first. I wrote up putting my own agent on a $5 server, and it is a far softer introduction to operating your own thing than jumping straight into OAuth 2.1.

We Are in 1996 Again

I still remember my 14k modem. The internet back then was a slog, and you could actually hear it negotiating. And I remember the small jolt of fear the first time I bought something online, my card number going into a box I had no real reason to trust. (My mother-in-law still phones us when she wants to buy something on the web. She will not put her card in herself. In 2026.)

Same thing now.

You will feel that same small jolt the first time your agent buys something for you. (And if you asked it for Tokyo and it books you Ouagadougou, well, now you know there are still a few bugs to iron out 😬.)

Tomorrow, every business will have an agent or an MCP connector the way it has a phone number and a website today. McKinsey is throwing around $1 trillion of agentic commerce by 2030. eMarketer already counts $20.9 billion in US retail spending through AI platforms for 2026, roughly 4x last year.

The window to position yourself is right now. Not when it is industrialized. Not when it is clean.

Waiting for proof is just missing 1996 a second time.

Sources

OpenAI Just Killed Native Checkout in ChatGPT: Paz.ai, the Instant Checkout shutdown and the Forrester read on product data as the new bottleneck
Don't Dance on the Grave of Agentic Shopping Just Yet: The Drum, execution failure versus conceptual failure
Shop 'til You Bot: Fast Company, the "months not years" reporting from inside Google and OpenAI
Agentic Commerce Playbook 2026: EcomExpo, the Shopify 15x figure and the eMarketer spending numbers
MCP Servers for Ecommerce: The 2026 Developer's Guide: YNS, consumer-side versus merchant-side MCP
Best MCP Servers for Ecommerce: Checkout Page, the hosted server roundup and agentic latency

n8n's Official MCP Costs 41x More Tokens. That's Not Why I Still Use The Community One.

Phil Rentier Digital — Sat, 16 May 2026 13:41:10 +0000

I tested n8n's official MCP the day it shipped. I asked it for an appointment-booking agent on Google Calendar, nothing exotic. 4 minutes later it had scaffolded 80 to 90% of the thing on its own, nodes placed, connections wired, almost done. The almost is what ate the rest of my afternoon. A corrupted phantom field I couldn't delete cleanly. A miswired Calendar ID that crashed the node on every run. A model too slow that I ended up swapping out. The scaffolding was free. Fixing it took me 6 times longer than if I had built it myself.

TLDR: A few days after my test, the creator of the community MCP published his benchmark, the official one burns 41x more tokens than his. He is judge and jury here, and n8n has not refuted it. I still keep both installed, because the 41x is not the question that matters. The official MCP and the community one do not sit on the same floor of a job that just changed.

The 41x makes a good headline. But that number hides the real subject, and the real subject has nothing to do with one tool beating another. The native MCP and the community MCP are not competing. They occupy two floors of a craft that shifted under our feet, and most of the people arguing about the benchmark have not noticed the floor moved.

One thing to set straight before going further. This is not a neutral lab test of two MCP servers. I ran the native one hands-on for a few builds, I have run the community one for months, and the 41x is somebody else's measurement, not mine. What I can speak to is the pattern underneath, because that part I have lived end to end.

Four Minutes to Build It. Twenty-Five to Fix It (lol)

Back to that Calendar agent, because the details matter more than the summary.

The native MCP generates a TypeScript SDK representation of the workflow rather than raw JSON, which is nicer to work with. It placed the trigger, the AI agent node, the Google Calendar tool, the credentials scaffold. It connected them in an order that made sense. For a first pass, impressive.

Then I hit the phantom field. Somewhere in the generated config there was a leftover property, corrupted, not referenced by anything visible, and the editor refused to let me remove it without complaining. n8n's own canvas would not validate the workflow with it there, and the MCP did not see it as a problem because from the agent's side the workflow looked complete.

That is the part worth sitting with for a second. The agent thought it was done. The workflow was not running. Those two states coexisted happily, and nothing in the generation step bridged them.

Next: the Calendar ID. The agent had wired a placeholder where the real calendar identifier needed to go, which is fair, it cannot know my calendar. But it wired it into a field that crashed the whole node on execution instead of failing soft with a clear message. So the first run did not say "missing Calendar ID," it said something generic about the node, and I spent a while looking in the wrong place.

And the model. The agent had picked a model for the AI node that was technically valid and painfully slow for an interactive booking flow. Swapping it was 30 seconds of work once I noticed, but noticing meant running the thing, watching it lag, and realizing the agent had optimized for "works" and not for "works at a usable speed."

4 minutes to scaffold. 25 to get it actually running. The ratio is the story.

The Bottleneck Just Moved

Here is the observation, and it is an observation, not a law, since I have run this on a handful of builds and not a thousand.

Generating an n8n workflow used to cost real time. You opened the canvas, you remembered which trigger you needed, you dragged nodes, you wired them, you looked up node properties you had forgotten. That cost has collapsed. It is minutes now, sometimes less.

The cost of verifying that workflow, debugging it, and supervising it when it drifts has not collapsed. It is exactly where it was. Maybe slightly worse, because the workflow you are now verifying was not built by you, so you do not carry the mental model of how it was assembled.

When one cost drops to near zero and the cost next to it stays fixed, the real work concentrates on the one that did not move. That is not insight, it is arithmetic.

So the three things that used to make up "knowing n8n" did not survive equally. Memorizing the nodes is gone, the agent holds that now. Wiring them by hand is gone too. But verifying and debugging what came out is still here, and it is now carrying the weight the other two used to share between them.

I am not the first to describe this. Plenty of people have, in pieces. Some call it delegation, some call it orchestration, some call it supervision. Builders on X are already saying things like "I stopped building n8n workflows by hand, this MCP changed everything." A guide from UI Bakery puts it plainly, that the n8n MCP can speed up creation but does not remove the need for governance, testing and review. The intuition is everywhere. What it has been missing is a clear shape. Three pillars, two of them substitutions, one of them a pile-up. That asymmetry is the whole point, so let me walk it.

Pillar 1: The Spec Replaced Node Knowledge

The old skill was memory. You knew that the schedule trigger was scheduleTrigger and not schedule, that it took an interval here and a rule there, that the HTTP Request node had something like 200 properties and you only ever touched 8 of them but you knew which 8.

That knowledge is now the agent's problem, not yours. Which sounds like pure win until you read what n8n itself says in its MCP announcement: if you are a veteran builder, be explicit about which nodes to use. Read that again. The official guidance for getting good output is that you already need to know the nodes well enough to name them.

So the value did not disappear. It migrated. It moved out of your memory and into the quality of the spec you write. A precise spec that names the trigger, names the nodes, states the data shape, gets you a workflow that needs light fixing. A vague spec gets you a confident, plausible, wrong workflow.

And here is the honest corollary, because this pillar has a sharp edge. A vague spec used to get caught early. The friction of wiring nodes by hand was annoying, but it was also a check, you noticed the data shape did not line up because you were the one connecting the output to the input. That friction is gone. So a bad spec now produces a bad workflow faster than ever, with nothing in the middle to slow you down and make you look. Speed is not always your friend here.

Pillar 2: Validation Replaced Construction

Look at what the native MCP actually shipped with. Not just workflow generation. It shipped tools for validation, for test execution, for generating test data. The editor built the safety net into the same release as the thing that needs the safety net. That is n8n telling you, in product decisions rather than words, that generating is not the job.

They also say it in words. From the same announcement: complex workflows often need a second or third pass, they are still smoothing rough edges. And over on the n8n community forum, Ophir Prusak from n8n opened a feedback thread asking users directly how reliable workflow creation has been, whether generated workflows land close to what you would build by hand or whether you are spending a lot of time fixing things up after. That is the vendor actively asking how much of the afternoon the almost is eating.

So the skill is no longer producing the workflow. It is knowing how to interrogate what the agent handed you. Does the data actually flow through that branch. Does the error path go anywhere. Did it pick the right node or just a node that runs.

I argued a version of this back in March, when I wrote about how one open-source repo turned Claude Code into a usable n8n architect. The native MCP shipping did not break that reasoning. If anything it confirmed it, because the native MCP arrived carrying its own validation tools, which is the same admission from the other direction.

Pillar 3: Debugging and Supervision Are Where the Job Went

This is the pillar that is not a substitution, and the asymmetry is deliberate. The first two pillars are things the agent took off your plate. This one is the thing it dumped onto your plate. It is also where most of the actual work now lives, so this section runs longer than the others on purpose.

Go back to my Calendar agent. The phantom field, the crashing Calendar ID, the slow model. Three failures, and the agent that generated the workflow could not have fixed any of them, because it could not see them. The phantom field did not show up in the agent's representation. The crash only existed at execution time. The slow model only revealed itself when a human watched it run and felt the lag. Generation is blind to all three.

And this is not just my Tuesday going sideways. There is a GitHub issue on the n8n repo, number 27718, where the get_workflow_details tool from the official MCP consistently times out past 60 seconds when called through an external client like claude.ai, while search_workflows answers in under a second. The cause was traced to a commit that added security processing. That is a real, dated rough edge on the native MCP, the kind of thing you only find by hitting it.

Here is the part that took me a while to accept. The failure modes of an agent-generated n8n workflow are not the failure modes you get when you build it yourself, and that difference is the whole problem. When you wire a workflow by hand, the bugs you produce are bugs you can imagine, because you made them, you know the shape of your own mistakes. When an agent generates it, the bugs arrive from a process you did not run, in places you did not choose, expressed in a config representation you did not write. There is a write-up on flowgenius.in cataloging 5 distinct silent failure modes for the n8n MCP, things like the process exiting without a log, buffer overflow on the event stream, timeouts that look like process kills, zombie processes piling up in queue mode. None of those announce themselves, and none of them are mistakes a human builder would naturally make, so your debugging instinct is not even pointed in the right direction when you start.

Somebody cared about this enough to build a whole separate MCP just for debugging. James Tention wrote about why he built n8n-debug-mcp, and his reason was blunt, debugging often sucks up hours of the time. You do not build a dedicated tool for a problem that is not the problem.

There is a line from a builder on X, aisama.code, that nails the shape of it. n8n feels simple until the workflow stops being linear, and then you hit retries, branching logic, shared state, silent failures. His point was that MCP lets agents inspect and refactor instead of you dragging through spaghetti canvases by hand, which is true. But notice what is still required: someone has to read the inspection. Someone has to know that a silent failure is even possible. The agent generates fast, but it cannot debug what it cannot see, and supervising an agent that has drifted means knowing n8n well enough to tell drift from working.

The caveat goes right here, distributed, not saved for the end. The babysitting is reduced. It is not eliminated. The agent removes a category of grunt work, and if I made it sound like nothing improved, that would be dishonest. What changed is that the work you have left is the harder kind. Less typing, more reading. Less building, more judging.

(Side note, the community MCP's repo states a principle that should be tattooed somewhere: never trust defaults, default parameter values are the number one source of runtime failures. I have lost more time to a node quietly running with a default I never chose than to any dramatic crash. The dramatic crash at least tells you where to look. The default just sits there being wrong.)

Two Honest Objections

Andrew Green writes for the n8n blog as an industry analyst, paid by n8n but writing his own opinions, and he has handed me two objections to my own thesis. I am going to take both, because dodging them would be cheap.

Objection one: this did not democratize n8n. Green's line is that everyone started vibe coding, but only if they already knew how to code. That stings because it is correct. The MCP, native or community, upgraded the people who already had the skill. The complete beginner does not get lifted by it, the complete beginner just generates more workflow than they can understand and ships the gap. The moved bottleneck rewards expertise. It does not manufacture it. If you could not debug n8n before, the agent did not hand you that ability, it just gave you more things to fail to debug.

Objection two: MCP itself might be losing the race. Green also thinks MCP had a meteoric rise and then fizzled out, with Skills picking up the momentum. He could be right, I am genuinely not certain this protocol is the one we are all using in a year. I have written before about why CLIs often beat MCP for agent work, so I am not here to plant a flag for the protocol.

But here is where I hold the line. The job shift is independent of the protocol. Whether you talk to n8n through the native MCP, the community MCP, Skills, a CLI, or whatever ships next quarter, the arithmetic does not change. Generation gets cheap, verification stays expensive, the work concentrates on verification. The protocol debate is real and worth having, it is just a different debate than this one.

Which One, and When

Three reference points, not a scorecard, since I have not run all three at production intensity across every scenario.

The native n8n MCP earns its place when you want to scaffold fast and stay close to the editor. It generates the TypeScript SDK representation, it ships with validation and test tooling built in, and there is nothing extra to maintain because it is n8n's own. For going from "I need a workflow" to "I have a draft workflow," it is good.

The community MCP, czlonkowski's n8n-mcp, earns its place on coverage and cost. It indexes 1650+ nodes, it does multi-level validation, and per its author's own benchmark it runs at a fraction of the token cost. It is also the one with thousands of GitHub stars and months of community hammering behind it. When I want the agent to actually know the full node surface, this is the one.

And plain manual n8n still earns its place for atomic debugging, the moment the agent is blind. The phantom field, the silent failure mode, the node running on a default nobody chose. You open the canvas yourself and you look.

I did not uninstall either MCP. That is the honest summary. They do not solve the same problem, so keeping both is not indecision, it is just matching the tool to the floor I am working on that day.

The Job in One Sentence

You do not build n8n workflows anymore. You write the spec, you verify what the agent produced, you step in where it is blind. Three pillars, and I walked you through each one with the receipts.

The 41x, the native versus community fight, the next protocol that replaces MCP, all of that is noise sitting on top of the actual change. The job did not disappear. It moved one floor down, to the place the agent cannot see.

For now the community n8n MCP is the better one. That is worth watching, not settling.

Sources

n8n Blog, "n8n's MCP server can now build workflows!" (April 2026) and "We need to re-learn what AI agent development tools are in 2026" by Andrew Green (May 2026)
n8n Community Forum, feedback thread opened by Ophir Prusak on MCP workflow creation
GitHub, n8n issue #27718 on get_workflow_details MCP timeouts
czlonkowski/n8n-mcp repository, and the token-cost benchmark published by Romuald Czlonkowski (@romualdcz)
flowgenius.in on silent MCP failure modes, James Tention on n8n-debug-mcp, UI Bakery's n8n MCP guide
@on_punchman (aisama.code) and @editxshub on X

Selling Your Vibe-Coded App Isn't a Marketing Problem. It's a Forgotten Spec.

Phil Rentier Digital — Fri, 15 May 2026 13:41:11 +0000

Back when I was an employee, "marketing" sounded like a dirty word. It smelled of used-car salesmen, the Facebook ad that follows you for 3 months, etc.

Not for me. I was coding, I was shipping, the product was good, the rest would follow.

Except the rest never followed.

TLDR: You're not bad at marketing. You're building your app without the spec that makes marketing possible. 4 questions to answer before line 1 of code, and why the marketing fundamentals collapse without them.

Then I clocked something dumb. "Marketing" just means explaining what you do to people who might care. That's it. Once you write it like that, the LinkedIn-coach reflex is hard to keep.

Except I was still bad at it.

I shipped 4 apps in 2 years. My family used 3 of them. 3 Discord friends tested one. The code wasn't the issue. Classic marketing tactics weren't either, I tried. The problem was somewhere else, upstream, before line 1 of code.

The "For Everyone" Trap

When someone asked who my app was for, my answer was "everyone". Small business owners, creators, marketers, freelancers, anyone really.

An app for everyone is an app for no one. What looks like a marketing problem is a NaN dressed as a target audience.

Obviously the dopamine wants a massive imaginary TAM. When you're vibe coding on a Sunday afternoon with the kids fighting over the pool floaties, a vague millions sounds better than 400 angry plumbers in Ohio. But the millions don't exist. The 400 do, and they have a Slack group.

A TAM you imagined has no channel, no trigger, no vocabulary. Nothing you can act on. The 400 plumbers have all 4.

This is the trap nobody flags to vibe coders. You spend 6 weekends shipping something polished, and at the end you have a product that speaks to nobody in particular. Then you blame marketing. You blame the algorithm. Reddit moderators get the heat too (fair, sometimes).

But the gap wasn't in tactics. It was in the spec you never wrote.

The Marketing Spec Nobody Writes

The spec I'm talking about isn't the product spec. It's not the wireframes, the schema, or the user stories. It's the user spec, and it has 4 fields.

Who exactly. Not "small business owners". Granular job + life context.
Where they already hang out. A specific community, not "online".
What triggers their search. The moment they type something into Google or ask a friend.
What words they use. Verbatim, not paraphrased.

Code is cheap now. Specs are the multiplier.

This is the same spec mindset I apply to prompts: write the contract before the action, or the action gambles. Marketing is exactly that, applied to a person instead of an LLM.

Why it has to come before the code: if you don't know who the app is for, the app you ship will be vaguely-shaped. It will speak to nobody in particular. And no marketing tactic can fix it later, because there's no specific person to aim at.

The product is downstream of the spec. So is the marketing. So is the pricing.

Skip the spec, and everything downstream wobbles like a Death Star with a missing thermal exhaust port.

What "Skipped Spec" Sounds Like in the Wild

I don't have to make this up. The voices are everywhere on Indie Hackers. Same pattern every time.

A founder posted in March 2026:

"The distribution vs product skill gap is very real. A lot of builders assume that if the product is good enough, users will somehow appear. In reality those are two completely different problems to solve."

A builder, 2 weeks after launching, on the same forum:

"A calculator with 0 signups isn't a calculator problem, it's a no-urgency-at-the-trigger problem. Embed it inside your existing accountant-outreach email where the user already has intent, not as a top-of-page curiosity hook."

That phrase "no-urgency-at-the-trigger problem" is gold. The builder discovered field 3 of the spec by hitting a wall. You can write it down before, or you can find it 2 weeks late with 0 signups.

Another thread, January 2026:

"I just launched my first app a week ago, built with AI, no coding background. Right now I'm figuring out distribution. Reddit karma limits, Indie Hackers posting restrictions, it feels like building was the easy part."

Building was the easy part. That sentence keeps surfacing. Vibe coding made it worse, because the build is faster than ever and the spec gets skipped more aggressively.

Same forum, a different founder who did the opposite:

"I started treating distribution not as a final step, but as the first step. When I finally started building again, I already had an audience. Not a huge one, maybe 200 followers, 50 engaged Reddit users, a few helpful DMs, but enough to ship with momentum."

200 followers and 50 engaged Reddit users isn't much. But it's the spec, executed. He knew who, where, trigger, and vocabulary. He had pre-validated the 4 fields by talking to those 50 people for months.

These builders aren't idiots. They're caught in the dopamine of coding, same as I was. The dopamine doesn't reward writing a spec. It rewards seeing the next feature compile.

The 5 Marketing Fundamentals Don't Work Without Step 0

The 5 marketing fundamentals are 50 years old. They work. No question.

But each silently assumes the spec is done. Drop them on an undefined target and you get a clean null pointer. No stack trace, just Claude shrugging politely.

Fundamental 1: Define target and problem.

You are a brutally honest market analyst. Given my ICP below, return:
- 3 painful problems my ICP has today
- The exact words they'd use to describe each
- The 1 problem they'd pay to solve right now.
ICP: <paste spec field 1>

Without an ICP, the brutal analyst returns "not specific enough" and refuses to play.

Fundamental 2: Get close to the user.

Find 3 places online where my ICP hangs out, ranked by density, posting tolerance, and frequency of "I need a tool that..." posts.
ICP: <paste field 1>

Without a clear ICP, you get "Reddit, Twitter, LinkedIn". A list anyone could write without an LLM.

Fundamental 3: Analyze ratios.

My funnel: <channel, content, CTA>. Conversion at step 2 is X%. Industry benchmark for this ICP+channel? Single most likely bottleneck?
ICP: <paste spec>

Without an ICP, "industry benchmark" is meaningless. You compare against an average of averages.

Fundamental 4: Adapt to the channel.

Rewrite this for <channel> in my ICP's voice. Their vocabulary, not mine.
ICP: <field 1 + field 4>
Original: <paste>

Without field 4, Claude defaults to LinkedIn-bro neutral.

Fundamental 5: Watch competitors.

For my top 3 competitors, list: top 3 homepage claims, exact pain points called out, vocabulary they use that I don't.
Competitors: <list>
My ICP: <paste spec>

Without an ICP, you get generic intel. Not strategic intel.

It's the same reason CLIs beat MCP for AI agents: tools work when the architecture above them is right. They don't compensate for missing it.

Claude can write your marketing for you. It cannot guess who you are marketing to.

I Know. This Is the Least Fun Part.

Yes, less fun than vibe coding a feature on a Sunday afternoon while the kids fight over the pool floaties. No dopamine in answering 4 questions about a person you have never met.

No revenue either.

This is exactly what separates "app used by your family and 3 Discord friends" from "app that pays your rent". Code is the easy part now. The spec is the multiplier.

The 4-Field Spec You Can Write Tonight

OK, you're still here. Good. This is what writing the spec looks like in practice. Not next quarter. Tonight.

Field 1: Who exactly.

Question: in 1 sentence, who is this person? Job, context, what makes their week hard.

Pass criterion: give your sentence to 10 different people, ask them to picture this person, they all picture the same one. "Small business owners" fails. "Karen from Accounting, the one who still emails Excel files because the SaaS her company bought doesn't sync with QuickBooks Online" passes.

Where to find the raw material: G2 and Capterra reviews of competitor tools. The 3-star reviews specifically. People with 5 stars say "great tool". People with 1 star say "broken". The 3-star ones describe exactly who they are and what they wanted to do. Gold for field 1.

Field 2: Where they already hang out.

Question: name the 3 specific communities, forums, or platforms where this person spends real time. Not "Twitter". Twitter is not a place, it is a postal code.

Pass criterion: each of the 3 has a URL you can click and see actual posts from your ICP this week.

Where to find: 5 short calls with people who match field 1. Or read the bios of 30 people who left reviews of your competitors and see what they link to. Or search " Discord", " subreddit", " Slack community" and dig.

I spent 3 months filling field 2 for 1 project. That's where it finally unlocked.

Field 3: What triggers their search.

Question: what is the moment, the day, the situation when this person types something into Google looking for a tool like yours?

Pass criterion: you can describe the trigger as a scene with a time, a tool, and a feeling. "Tuesday morning, opening her inbox, 14 new client emails that all need the same boilerplate reply, feeling angry" passes. "When they need productivity" fails.

Where to find: search Twitter and Reddit for "I just spent X hours on", "why is there no tool for", "anyone else hate having to". The triggers are right there, in plain English, written by your ICP for free.

Field 4: What words they use.

Question: list 20 phrases, verbatim, that your ICP uses to describe their problem and the desired outcome.

Pass criterion: 20 phrases. Not 5, not your translation. The actual words, copy-pasted from Reddit or G2 or wherever you found them.

Where to find: same as field 3. Plus the 1-star and 3-star reviews of every competitor.

If you can't fill a field in 1 specific sentence, that is the field you are going to fail on after launch.

Steal Your Competitor's Spec From Their Ads

You have 2 options once you've nailed the 4 fields in theory.

Interview 50 users. Slow and biased.

Or read what the competitors who already convert have put in their ads.

Their ads are their user spec, externalized and A/B tested. For free. To you. It's like raiding a public dungeon where the loot is already on the floor 😏.

3 public ad libraries to use:

Meta Ads Library (facebook.com/ads/library), every active Meta ad by advertiser, filterable by country and category.
Google Ads Transparency Center (adstransparency.google.com), active Google and YouTube ads by verified advertiser.
TikTok Creative Center (ads.tiktok.com/business/creativecenter), top TikTok ads by sector, region, format.

Public data designed to be seen, which is literally the whole point.

The prompt to feed Claude (chat version, copy-paste, no setup):

You are a marketing reverse-engineering analyst.

STEP 1: I'll paste 8-12 ads from a competitor below.
For each ad, extract:
- The exact hook (first line/scene)
- The pain point being called out
- The promise being made
- The CTA
- The format (image, video, carousel)

STEP 2: After all ads are processed, synthesize:
- Target segments visible across the ads (1-3 max)
- Common pain points
- Common positioning angle vs alternatives

STEP 3: Output the reverse-engineered spec as 4 lines:
- Who exactly
- Where the ads run (channels)
- What trigger the ads activate
- What vocabulary the ads use (10 phrases verbatim)

Ads: <paste ads here>

Run that prompt on 3 competitors in 1 evening. You don't get your final spec (you still have to validate against your own ICP) but you get a strong draft.

There's also a Claude Code version that automates this via browser MCP for batch competitor analysis. The chat version is enough for most cases.

Your competitors already paid for the spec. You just have to read it.

Marketing was a dirty word in my vocabulary for years. Turns out it wasn't dirty, it was a clean word I was using to hide a missing spec.

The Blueprint Method I describe in Vibe Coding, For Real starts with this exact spec, before line 1 of code.

And the market already knows. Dotmarket, a European broker for digital business sales, refuses to list any business under 2 years old. Not from snobbery: under 2 years, nobody can tell if the audience comes back on its own, if churn is stable, if partnerships hold without the founder pushing every week. The invisible work is the asset. That work starts with 4 answers on a sheet of paper.

Write the 4 sentences tonight. Ship anything you want tomorrow.

Sources

Indie Hackers, "The #1 reason indie products die isn't the product. It's finding customers." (March 2026): indiehackers.com
Indie Hackers, "2 weeks later: still no paying users. Here's what I've learned." (May 2026): indiehackers.com
Indie Hackers, "How I got my first sale from a forgotten project" (January 2026): indiehackers.com
Dotmarket newsletter, "Ce que l'IA ne peut pas construire en 18 mois" (Kevin Jourdan, 13 May 2026): dotmarket.eu
Meta Ads Library: facebook.com/ads/library
Google Ads Transparency Center: adstransparency.google.com
TikTok Creative Center: ads.tiktok.com/business/creativecenter
Vibe Coding, For Real: amazon.com/dp/B0GYQHLSCB

Don't Google "Claude Mac Download". The Top Result Drains Your Keychain.

Phil Rentier Digital — Thu, 14 May 2026 13:41:12 +0000

You type "claude download mac" into Google. You click the top result (sponsored ad, verified badge, URL is claude.ai). You land on a shared Claude chat titled "Running Claude Code on Mac", tagged "Shared by Apple Support" in the top right corner, with a bash command to paste into Terminal. Thirty seconds later, your Keychain is on a server in Sydney. No falsified URL anywhere in the chain. No typo-squat. No fake domain.

TLDR: A live malware campaign this week uses verified Google Ads and claude.ai shared chats to drain Mac Keychains. Nothing in the chain is technically fake. That's the part that should worry you.

The scammers keep getting better at this. Let me walk you through what happened, because every single step of this attack looks legitimate until you understand what "legitimate" actually means in 2026.

15,600 Macs, One Google Search, One Pasted Line

Malware Chain: One Fake Step Among Five Real Ones

The campaign was first flagged by Berk Albayrak, a security engineer at Trendyol, on May 9. By the time BleepingComputer covered it two days later, Moonlock Lab was tracking 15,600+ Macs touched by the broader MacSync infostealer family (this Claude variant is the latest iteration, not the entire body count). Anvilogic published a full technical breakdown the same week.

Here is the entire chain in one frame. Five steps. Every single one is technically legitimate, except the one in red.

A sponsored ad. Verified. claude.ai. You click. You land on a real Claude shared chat hosted on Anthropic infrastructure, titled "Running Claude Code on Mac", with a user-controlled label saying "Shared by Apple Support" in the top right. There is a code block. The code block contains a one-liner. You paste it into Terminal because that is exactly what every legitimate dev tool onboarding has trained you to do for fifteen years.

It's muscle memory at this point, the same reflex that makes you press A through a dialog box you've read a hundred times.

The thing that makes this attack different from the usual phishing is that you can follow the entire chain without finding a single thing that is "fake" in the classic sense. The URL is claude.ai because it really is claude.ai. The ad is verified because Google really considers the advertiser legitimate, a Sydney company called KB & CO Holdings PTY LTD. The shared chat really exists and really was shared on Anthropic servers.

The only lie in the whole chain is the words "Shared by Apple Support", typed by the attacker into the title of their own chat.

That is the entire attack. A title.

Paste That Command Into a New Claude Tab and Ask What It Does

This is the part that nobody on Medium has written yet, so let me state it plainly. The same AI that is being weaponized as malware distribution infrastructure is also the best defense you have against this attack, and the demo takes thirty seconds.

Open a new Claude conversation (or ChatGPT, or Gemini, pick your favorite). Paste the suspicious command. Ask: "what does this command do, and decode any base64 in it."

That's it. That's the whole technique.

On a command of this shape (a base64-wrapped curl piped straight into bash, which is the documented pattern Albayrak found in the malicious chat), the model unwraps it in seconds. It decodes the base64 into a readable URL (in the original campaign, the C2 was hosted on customroofingcontractors.com, a small American roofing business whose site was compromised and turned into delivery infrastructure). It explains the -k flag disables SSL verification. It explains the -f flag tells curl to fail silently with no error message. It flags the whole pattern as consistent with macOS malware delivery.

You just used a Claude tab to defend yourself against an attack that uses a Claude tab as distribution infrastructure. The irony is free.

I tested the same prompt on three different models. All flagged the command in under thirty seconds. None require a security background. None require you to know what base64 is. You just need to see the words "this looks like malware delivery" and close the tab.

Use the model that's distributing the malware to read the malware. C'est la vie.

Why "Check the URL" Stopped Working

Twenty years of security training taught you a single rule. Check the URL. The reasoning was simple: the domain tells you who owns the billboard, and for two decades, forging the message required forging the billboard. So checking the billboard was enough.

That equation broke quietly, sometime around the moment platforms decided that letting users publish content on the platform's main domain was a great product feature. Shared chats on claude.ai and chatgpt.com. Public pages on sites.google.com and pages.dev. The domain seal now guarantees that the platform exists and owns the domain. It says nothing about the individual message sitting inside it.

Anthropic did the right thing on paper. The shared chat carries an explicit warning banner: "Content may include unverified or unsafe content that does not represent the views of Anthropic." It's visible. It's worded clearly. The legal box is checked.

The problem is the line just below it. The user-controlled chat title. The attacker typed "Shared by Apple Support" into their own chat title, and that single line restores an illusion of authority that the disclaimer above it is supposed to demolish.

A reader scanning the page in two seconds reads: Anthropic, Apple Support, code, paste. The warning banner is real and the title is fake, but both share the same visual real estate, and the human eye does not weight them differently when scanning. The disclaimer plus user-controlled label combo doesn't survive any reader who is not already trained to mistrust the chat title specifically.

AdGuard wrote exactly this critique three months ago, when the same pattern was being used to distribute fake Homebrew install commands via Cloudflare Pages and Google Sites. Their formulation was sharper than mine: putting user-generated content on the main second-level domain creates a level of trust that the content has not earned. They also noted the disclaimer is invisible on mobile. Three months later, here we are, same architecture, same attack, different platform, fifteen thousand more victims.

The billboard is real. The message on it is not.

This Pattern Has Been Live for Six Months. Nobody Wants to Fix It.

The pattern that hit Claude this week is the fifth public iteration in six months.

November 2025 saw fake ChatGPT Atlas installers pushed via the same ClickFix social engineering loop. December 2025 added Mac cleanup queries pointing to the same payload. February 2026 brought Homebrew install commands hosted on Cloudflare Pages and Google Sites (the iteration AdGuard documented in detail). March 2026 saw The Hacker News tracking the campaign migrating to Belgium, India, and the Americas with dynamic AppleScript generation. May 2026 lands on Claude shared chats.

Five iterations, six months, interchangeable surface. Anvilogic's diagnosis is sharper than mine: defenses that look at what a command does will outlast defenses that look at where the page lives. The surface will keep moving. That is the entire industry summary, and platforms cannot win this game by patching the surface alone.

So why does nobody fix it? The honest answer is that nobody has the right incentive structure. Google collects ad money from the advertiser (KB & CO Holdings, paying Australian dollars to push compromised installers, that's the business transaction Google is being paid for). Anthropic hosts the user-generated content that drives engagement metrics for the shared chat feature, which is a product feature people genuinely use. Neither company has a strong commercial reason to invest aggressively in scanning before the fact.

The security budget at most platforms lives in the same drawer as the espresso machine warranty.

The operational cost falls entirely on the end user who pastes the command.

This is not unique to Anthropic. I wrote a piece a few weeks ago about a different shared-content attack pattern on Claude Cowork, where the same root cause (user content gets the platform's authority by default) created a prompt injection surface inside the file collaboration feature. OpenAI has the same model. xAI has the same model. The architectural choice to let user content ride on the main domain is industry-wide, and it's a product decision, not a security oversight.

If you're waiting for the platforms to fix this before the next campaign, look at the calendar. The platforms have known for six months. Six months is the answer.

The 4-Reflex Playbook

The 4-Reflex Playbook: Arcade-Style Security Guide

Four reflexes. Ordered by effort, from cheapest to highest. The first one alone neutralizes the entire attack surface that hit Mac users this week, so if you only remember one, remember number one.

1. Pull, don't push. When you want to download a piece of software, type the URL directly into the address bar. Never click a sponsored result, even if the URL looks right. The vendor's real result is one position below, free, and points to the right place. The two seconds you save by clicking the ad cost 15,600 people their Keychain this week. Pull means: you go fetch the software from where you know it lives. Push means: you let the search engine decide what to show you first. The push model is broken when the first slot is auctioned to whoever paid the most, including attackers.

2. Check who paid for the ad. If you click the sponsored result anyway (which you should not, but sometimes onboarding flows route you through Google), open the three dots on the ad and look at "About this advertiser." If the entity paying for the ad is not the vendor (Anthropic, Apple, Mozilla, brew.sh, whatever you actually meant to download), close the tab. On this week's campaign, the advertiser panel showed KB & CO Holdings PTY LTD, registered in Sydney, Australia. Anthropic is incorporated in Delaware. The mismatch is the entire signal you need. Google won't catch this for you. You have to look.

3. Never paste a Terminal command you didn't write. Not from Claude shared chats, not from StackOverflow, not from YouTube tutorials, not from official-looking documentation pages you reached via an ad. If you did not type the command, you do not paste it.

This is the hardest reflex to adopt because half the dev ecosystem has spent fifteen years training you to do exactly the opposite. Every CLI worth installing has a curl | bash one-liner in its README: Homebrew, oh-my-zsh, nvm, rustup, Claude Code itself. We trained an entire generation of devs on a workflow that is structurally indistinguishable from this attack, then handed them a security checklist that says "don't paste random commands." We are the call coming from inside the house.

Ship CLIs all you want, I argued for them at length a few months back. The install flow is still the soft spot, and the industry has not figured out a clean alternative yet.

4. If you must paste it, decode it first with AI. Section two, again. Open a new Claude tab, paste the command, ask what it does. If the answer mentions SSL verification disabled, silent failure, remote shell execution, or any URL you don't recognize, close the tab and walk away. Thirty seconds of verification against your Keychain, your iCloud, your saved browser passwords, and whatever wallets you had unlocked at the time. That's the price comparison.

That is the playbook. Four reflexes, no tools to install, no subscription to pay, no certification to earn. The hardest part is unlearning fifteen years of "just paste this."

The human filter we learned over twenty years rested on a hypothesis that is no longer true: that to falsify a message, you had to falsify the billboard. This week it's claude.ai. Next week it's the ad for the next Cursor, the next Notion, the next Linear hosted on a legitimate Google Site. The mechanism will last for years. The reflex that holds against it fits in one sentence.

Verify the action, not the origin.

Sources

Berk Albayrak, original discovery thread on X (May 9, 2026)
Hackers abuse Google ads, Claude.ai chats to push Mac malware, BleepingComputer
MacSync Infostealer Delivered via Claude and ClickFix, Anvilogic Threat Research
How a Real Claude.ai URL Is Distributing Mac Malware Through Google Ads, UCStrategies
Claude Google Ads Malware Poisoning macOS, AdGuard, February 19, 2026
ClickFix Campaigns Spread MacSync macOS Infostealer, The Hacker News

Markdown Was a Mistake for Agent Output. A Claude Code Engineer Just Proved It.

Phil Rentier Digital — Wed, 13 May 2026 13:41:10 +0000

"HTML is the new markdown."

That's Thariq Shihipar, Claude Code at Anthropic, on X last week.

BUT

CLAUDE.md, AGENTS.md, SKILL.md... Markdown everywhere. It became the air you breathe in a Claude Code project, the default format nobody actually decided on. So when the agent produces an output, it ships Markdown. That's what it has seen everywhere.

Then Thariq drops a gallery of twenty HTML artifacts to back the claim.

TLDR: Markdown took over our projects through config files (CLAUDE.md, AGENTS.md, SKILL.md), then leaked into everything else, including the agent outputs nobody ever reads. A member of the Claude Code team just explained why he switched to HTML, and it boils down to one short command.

For a config file, that's perfect. Short, instructional, read once and stored in context. But when the agent hands you a 200-line plan or a code review report? Different story. You open it, read fifteen lines, close the tab. Three days later you ask the agent again because you have zero memory of what it decided.

The agent worked fine. The format made it disappear.

Back to basics. HTML predates Markdown by more than a decade. The browser was built around it. Markdown was always a shorthand.

Why Markdown Won: The 8,192-Token Excuse

In 2022, ChatGPT shipped with a context window of 8,192 tokens. That's not a lot. For the same content, HTML burns roughly 3x more tokens than Markdown (the figures from web2md.org are about 8,000 tokens for HTML versus 2,800 for the Markdown equivalent). When your budget is 8K and your output cuts into your input, every token saved is a paragraph that survives.

Markdown won that fight on cost.

Simon Willison, one of the loudest voices in the AI dev community, admitted it publicly last week: he had been writing Markdown for LLMs since the GPT-4 days for exactly that reason. He was not wrong. Nobody was wrong, given the math at the time, and the math worked.

It doesn't anymore. Context windows in 2026 sit at one million tokens and counting. The constraint that made Markdown rational has evaporated. The behavior didn't. We kept stuffing .md files into our projects because that's what we had always done, not because it was the right call for what we're doing now.

Pure inertia at this point.

The Community Has Done This Before

The AI community adopts formats in waves. Someone tries something, it works, it spreads, and three years later we look up and notice that the obvious choice from day one was sitting right there.

Same cycle for Markdown in agent outputs: adopted fast, questioned late.

The same instinct led me to argue, a few months back, that CLIs beat MCP for AI agents. The community had jumped on MCP because it was new and shiny, while the boring stable solution had been deployed in production for decades. We do this every twelve months without fail.

The difference this time? It's somebody from inside Anthropic ringing the bell.

What a Member of the Claude Code Team Actually Does

Thariq Shihipar, on the Claude Code team at Anthropic, just published a post with twenty concrete examples. Not theory. Real prompts, real outputs, and artifacts you can open in your browser right now.

The gallery shows what an agent can hand you in HTML that Markdown can't structurally do. A side-by-side spec comparison with two columns, color-coded differences, and a verdict box at the bottom. A code review report where the diff sits annotated inline, severity dots in red/amber/green, jump links to each finding. Or the throwaway editor that comes with a "copy as JSON" button you never asked for but immediately use.

What HTML brings: tables that render, colors that mean something, SVG diagrams, tabs, mobile-responsive layouts, interactive bits when you need them. What Markdown brings: a long scroll.

The thing that matters isn't prettiness. It's this line from Thariq's post:

"I feel more in the loop than ever when using HTML. I have honestly stopped using Markdown altogether for almost everything."

In the loop. That's the entire game. An agent that produced a perfect plan you didn't read is an agent that did nothing. The output exists in a metaphysical sense, sure. The decision it documented is gone the moment you close the tab. A Markdown file you scroll past is the same as no file at all. An HTML file you open in your browser, share by link, screenshot for Slack? That file exists. People read it. You read it. You stay aware of what your agent decided.

The actual unlock is staying part of the workflow. Better-looking artifacts are a side effect.

A plan that's never read is a plan that was never written.

The Honest Caveats

HTML costs more tokens. The token cost is the whole pushback against Thariq's post. When the announcement landed, an account on X (@EXM7777) replied: "yeah sure i will spend 5x the amount of tokens for good looking text files." The reply got traction because it sounds right.

It is right, for one specific case. It's wrong for the case Thariq is talking about.

If your output's primary reader is another LLM (a downstream agent that's going to consume the file, parse it, and act on it), Markdown is fine. The agent doesn't care about colors. It doesn't render tables. Tokens are the only thing that matters. Same logic for quick scratch outputs you'll throw away in three minutes, for high-throughput pipelines where you generate two hundred files an hour, for anything where the cost per output dominates the value of the output.

If your output's primary reader is a human (and you, dear builder, are a human, even on the Friday afternoon when you have already mentally checked out and the boat to spot dolphins leaves in two hours), the calculation flips. The cost is a few cents extra in tokens. The value is whether you actually read the thing. A 5-cent plan you read beats a 1-cent plan you ignored, every time.

The other real caveat Thariq admits himself: HTML files are painful to diff in git. Markdown reads cleanly in a pull request. HTML wraps everything in a soup of tags that makes line-by-line review miserable. If the artifact will live in a repo and be reviewed across commits, Markdown still wins.

Cloudflare also pushes Markdown for the agent input flow, converting web pages to Markdown so agents can consume them efficiently. Different conversation. Web → agent is input. Agent → human is output. Both can be true at the same time without contradiction.

If your reader is a model, Markdown. If your reader is you, HTML.

One Prompt. No Setup.

The whole switch fits in one sentence at the end of your prompt:

"Make this as a single HTML file."

That's it. No CLAUDE.md edit, no skill scaffolding, no setup. Thariq is explicit about this: don't over-engineer it. Try the prompt on your next planning task. See what comes out. If you find yourself reaching for it three times in a row, then maybe think about a skill. Not before.

Same don't-overengineer reflex as when a single open-source repo turned Claude Code into a working n8n architect. The repo was the unlock. Everything I tried to scaffold around it just got in the way.

The gallery has twenty examples sorted by use case: planning, code review, design, prototyping, diagrams, decks, research, reports, custom editors. Pick the one closest to what you're doing tomorrow. Steal the prompt structure. Ship.

My last plan opened in the browser with tabs, a color-coded diff, and a "copy as JSON" button I never asked for. I read all of it. For the first time in a while, I knew exactly what my agent had decided without having to go back and read it three times.

Try "make this as a single HTML file" on your next plan. You'll read it.

Sources

The Unreasonable Effectiveness of HTML, examples gallery, Thariq Shihipar's 20 examples across 9 categories.
Simon Willison: Using Claude Code, the Unreasonable Effectiveness of HTML, May 8, 2026.
Markdown Is Not The Future of LLM Data Infrastructure, DEV Community.
Markdown for Agents, Cloudflare on the input-side flow.