DEV Community: Marcin Dudek

A one-shot code-review benchmark: scoring restraint over recall across four Claude models

Marcin Dudek — Fri, 17 Jul 2026 21:45:50 +0000

Originally published on https://marcindudek.dev/blog/one-shot-code-review-benchmark/

Abstract. I built a small benchmark to answer one question: is Claude Opus 4.6 actually worse than 4.8, or does it just feel older? It scores the half of code review that most evals ignore, which is restraint - not flagging correct code that looks suspicious. Four models, four task families, one-shot and mechanically scored. The two Opus models came out statistically tied, and they cost the same, so there's no reason to pick one on price. Across a real tier gap, Sonnet 5 matched Opus on almost everything while running fastest and costing a third as much. The surprise was Haiku 4.5: it was both the least accurate model and the slowest, because adaptive thinking makes it burn a huge token budget on simple tasks. Every number here is directional, small-n, one machine. The method is the point.

This started as a boring question. Can I still run Claude Code on Opus 4.6? It's not in the model picker anymore. Ten minutes of poking gave me the answer - pass the full model ID to --model and the picker just hides it. But the question underneath wouldn't let go. Is 4.6 actually worse than 4.8, or does it only feel older because it's not the default?

So I built a benchmark. Then I built a better one. Then I found out my benchmark was lying to me, twice, and I had to fix it before I could trust a single number. By the end I'd tested four models across four task families. The most interesting results weren't about which model won. They were about how easy it is to measure the wrong thing and believe it.

The whole harness is on GitHub: MarcinDudekDev/llm-review-bench. Mistakes documented in the README, on purpose.

1. The design: score restraint

Most "find the bugs" evals reward finding things. That's a problem. Frontier models are very good at finding things, so good that the eval saturates and tells you nothing. My first task had six planted bugs in a FastAPI snippet. Every model found all six. Ceiling hit. Useless.

The skill that actually separates a good reviewer from a noisy one is the opposite. Knowing when to shut up. A reviewer who flags every line that looks a bit off isn't thorough, they're exhausting. So every task ships three kinds of code:

Bugs - real defects, credited only when the model names the actual mechanism.
Traps - code that looks wrong but is provably correct on the pinned versions. Flagging a trap costs a point.
Neutral - real but stylistic observations that earn neither credit nor penalty.

The traps carry the whole thing. An in_array "type juggling" bug that PHP 8 quietly killed. An unquoted variable that word-splits in bash but not in zsh. A synchronous generator that "blocks the event loop", except Starlette runs it in a threadpool. Each one is bait that a well-read model reflexively flags. The good models are the ones that don't bite.

2. Four tasks

v1 - 6 bugs and 2 traps in one snippet. Easy. It ceilinged. I kept it as a documented failure.
v2 - 10 obscure version-specific bugs and 6 traps across Python 3.14, PHP 8, and zsh, plus an exact-output asyncio prediction. Hard.
v3 - 10 shell-command tasks, scored by running the command and byte-matching stdout. No judge.
v4 - 18 adversarially-filtered hard command tasks. More on that below.

v1 and v2 are code review, blind-judged by two other models. v3 and v4 are command generation. The model emits one shell command, the harness runs it in a sandbox and compares stdout to a golden value. Binary, mechanical, no opinion.

3. Three ways my benchmark lied to me

3.1 A timeout measures the weather, not the model

My first head-to-head used a 60-second cap. Opus 4.6 timed out with no output. Opus 4.8 finished in 28 seconds. Clean win for 4.8.

It was noise. When I re-ran uncapped, the same model on the same prompt swung from 27 seconds to 132 seconds and back. Cost swung 8x on the same model, which is prompt-cache state, not economics. My "4.8 is faster" result was an artifact of an arbitrary timeout landing on the wrong side of a noisy distribution.

A tight timeout doesn't tell you which model is faster. It tells you what the API weather was that afternoon. And it hands you a confident, reproducible-looking, completely wrong answer. I nearly shipped it.

3.2 "Make it hard" doesn't make it hard

To separate two frontier models I needed genuinely hard tasks. My first instinct was to prompt a strong model to design a brutal one. That produced a 10/10 ceiling twice. Frontier models are bad at knowing what's hard for other frontier models.

So I used them as difficulty oracles instead. Fable and Grok each proposed candidate tasks. Every reference command was validated by execution. Then each model attempted the other's tasks one-shot, and I kept only the tasks the opposing model got wrong. Those are empirically hard, by construction.

The result was its own finding. 17 of 18 candidates got solved. Two capable models could not reliably write a shell task that stumps a frontier peer. The single survivor turned on a platform-specific %.2f rounding quirk that you can't know without running it on that exact host. One-shot BSD shell-command generation is basically saturated for frontier models.

3.3 I wasn't testing what I claimed

Then a simple question. "Are you sure they only get one shot?"

I wasn't. Claude Code runs with tools enabled by default. Nothing stopped a model from creating the input files, running its command, checking the output, and revising, all before it "answered". The tasks even describe the exact input files. My "one shot, no execution" claim was a hope, not a guarantee.

I made it structural. Disable tools so execution is impossible, and assert that the model answered in a single turn (a tool call forces a second turn, which now raises instead of scoring):

p = subprocess.run(
    ["claude", "--model", model, "--tools", "", "-p", prompt,
     "--output-format", "json"],
    capture_output=True, text=True, timeout=timeout,
)
d = json.loads(p.stdout)
if d.get("num_turns", 1) != 1:          # a tool call would force turn 2
    raise RuntimeError("not one-shot")

Did it change the result? I guessed it would. I thought tools had been propping up the accuracy ceiling. I was wrong again. Under the guaranteed condition both Opus models were still essentially perfect. The ceiling was real. But now the "one shot" label is true, which matters more than whether the number moved.

4. Results

All four models, all four tasks, under the enforced one-shot condition. Review tasks are on a 0-10 judged scale (both judges agreed). Command tasks are exec-scored totals — the denominator is tasks × trials, so on v3 the Opus pair ran 5 trials each (50) and Sonnet/Haiku ran 10 (100); v4 ran 10 trials for all four (180).

Task	Opus 4.6	Opus 4.8	Sonnet 5	Haiku 4.5
v1 easy review	8.0	7.0	8.0	5.5
v2 hard review	8.2	9.4	8.8	6.5
v3 short commands	50/50	50/50	100/100	93/100
v4 hard commands	180/180	179/180	179/180	122/180

Accuracy by task. The three bigger models cluster near the top. Haiku 4.5 drops on the two hard tasks.

4.1 Opus 4.6 vs 4.8 is a tie, and they cost the same

On review, difficulty picks the model. 4.6 is a bit better on easy review, 4.8 is clearly better on hard review. On command generation they're indistinguishable. Averaged out, it's a statistical tie.

And here's the part that answers my original question for anyone hoping 4.6 is the cheap option. It isn't. Anthropic priced the whole Opus 4.x line the same, at 5 dollars per million input tokens and 25 per million output, both with a 1M context window. There's no economic reason to stay on 4.6. Pick on behavior, not price.

4.2 Sonnet 5 is the value story

The Opus tie made me want a real tier gap, so I ran everything on Sonnet 5 versus Haiku 4.5. Sonnet 5 is the standout. It matched Opus 4.8 on the hard command set (179/180), stayed within a point on hard review, and it did that while running the fastest of all four models. At 3 dollars per million input tokens against Opus's 5, it's roughly 60% of Opus's per-token price — and cheaper still per run, because it also burns fewer tokens. If I were choosing a default model for review and command work today, on this data I'd reach for Sonnet 5 first.

4.3 Haiku 4.5 is where the gap finally shows

Haiku is the one place the benchmark separated models cleanly on accuracy. On the hard command set it scored 122/180, against 179 and 180 for the bigger models. On easy review it managed 5.5 out of 10. The traps and the hard version-specific bugs are exactly where a smaller model gets caught.

5. The surprise: Haiku is the slow one

I expected Haiku to be less accurate. I did not expect it to be slow. On the short-command task, median latency:

Model	v3 median latency	v3 median cost/run
Sonnet 5	12.8 s	$0.0247
Haiku 4.5	80.9 s	$0.0455

Read that twice. Haiku, the model whose entire brand is fast and cheap, was six times slower than Sonnet 5 and cost more per run, on the same tasks. It's priced at a third of Sonnet's per-token rate, yet it cost more, which means it generated far more tokens.

The cause is adaptive thinking. Haiku 4.5 burns an enormous thinking budget on these problems, which makes it slow and cancels its price advantage at the same time. On this workload the fast, cheap model was neither.

Median latency by task. Haiku 4.5 is the slowest model on three of four tasks. Sonnet 5 is consistently fastest.

If you reach for Haiku to save latency and money on anything that needs real reasoning, measure it first. You might be paying more for less.

Median cost per run by task. The two Opus models cost the most everywhere. The Haiku surprise is task-specific: on v3 short commands it costs more than Sonnet 5 ($0.045 vs $0.025) because its thinking budget outweighs its lower per-token price — even though on the other three tasks Haiku is the cheapest model.

6. Limitations

I want to be honest about what this is. Every number is directional. Small n, one machine, one afternoon of API weather. The review accuracy rests on LLM judges scoring against a rubric another LLM wrote, so I mitigate with two judges from different labs and blind shuffling, and I don't pretend to eliminate the bias. The review scores here are single-trial per model. The command tasks are mechanically scored, which I trust more, but they saturate for frontier models, so they discriminate on precision rather than raw capability. Don't read a 2-point review gap as a law of nature. Read it as a signal worth a bigger run.

7. What I'd keep

The method held up better than any single result, so that's what I'd carry forward:

Score restraint. Once recall saturates, the traps are what separate models.
Don't report a speed winner at small n. A timeout measures the weather.
Enforce your constraints in code. "One shot" has to be disabled tools plus a turn-count check, not a line in the prompt.
Use strong models as difficulty oracles, and accept that they mostly can't stump each other.
"Fast and cheap" is a claim about a workload, not a model. Haiku's thinking budget made it the slowest here.

Next I want to re-run on a quiet machine to strip out the API-weather noise, and judge the review tasks across every trial instead of only the first. That's the real soft spot: the command tasks were mechanically scored over repeated trials, but the review scores still rest on a single judged trial. The harness is built for it now. If you want to poke at it or add your own model, it's all on GitHub.

I Had 285 Claude Code Permission Rules. Only 41 Did Anything.

Marcin Dudek — Thu, 16 Jul 2026 17:15:57 +0000

Originally published on https://marcindudek.dev/blog/consolidate-permission-prompts/

TL;DR

Every time you click "allow always" in Claude Code, a rule gets appended to permissions.allow. It never gets cleaned up.
Mine had grown to 285 rules. When I actually looked, only 41 mattered - roughly six out of every seven were dead weight.
The worst offender: 150+ per-domain WebFetch rules, one per site I'd ever fetched, all made pointless the moment a single WebFetch(*) exists.
I wrote a skill, consolidate-permission-prompts, that scans the allowlist, marks every rule already covered by a broader one, and removes the junk - without ever narrowing what's allowed.
It's the sibling of the built-in /fewer-permission-prompts. That one stops future prompts. This one clears the mess the past ones left behind.

I clicked "allow always" a lot. Two years of it. Every new site Claude Code fetched, every git subcommand, every one-off script - "yes, don't ask again." That's the right instinct. Approving things once so you're not nagged forever is the whole point of the allowlist.

What nobody tells you is that the list only ever grows. There's no janitor. Two years in, I opened my settings.local.json out of curiosity and counted 285 rules in permissions.allow. That's when I got curious about how many of them were actually doing something.

The answer was 41. The other 244 were dead weight - rules that could vanish tomorrow and nothing I do would change. Here's how a list rots like that, and the skill I wrote to clean it up safely.

The 150 WebFetch rules that stopped mattering years ago

Here's the clearest example, and probably the one you'll recognize in your own config.

Every time Claude Code fetches a URL from a domain it hasn't seen, it asks. You approve it, and it writes a per-domain rule. Do that for two years and you get a graveyard:

"WebFetch(domain:example.com)",
"WebFetch(domain:docs.python.org)",
"WebFetch(domain:stackoverflow.com)",
"WebFetch(domain:news.ycombinator.com)",
"WebFetch(domain:some-blog-i-read-once.dev)",
... 150 more just like it ...

One rule per site I ever fetched. Individually approved, individually stored, all sitting there forever. And then at some point I got tired of approving each new domain and added the broad one:

"WebFetch(*)"

That single line allows fetching any URL. Which means the moment it exists, all 150 of the per-domain rules above it are dead weight. Removing every one of them changes nothing about what Claude Code can do, because the wildcard already covers all of them and more.

A narrow rule sitting under a broad rule that already covers it does nothing. It's just noise you scroll past. Delete all 150 and your permissions are byte-for-byte identical in effect.

That's the pattern the whole skill is built around: find the rules that are already covered by a broader one, and get rid of them. It's far more common than you'd expect, because "allow always" never checks whether a broader rule already handles the thing you're approving.

Three ways the list rots

WebFetch is the vivid one, but the allowlist collects dead weight in three distinct shapes. The skill classifies every rule into one of them.

1. Redundant - already covered by something broader

The WebFetch case. Also things like a Bash(git commit:*) rule when Bash(git *) already exists somewhere - the specific one is covered by the general one. Removing it is provably safe, because nothing that was allowed stops being allowed.

2. Junk - one-offs that will never match again

This is the stuff that got captured as a rule but describes a single moment in time. A heredoc commit message you'll never type verbatim again. A mkdir to one exact absolute path. And my favourite category, shell control-flow fragments that got split into their own rules when a loop was approved:

"Bash(done)",
"Bash(do ls)",
"Bash(while read f)"

None of those will ever be issued as a standalone command again. They're debris. The skill is deliberately conservative here - a real long command that could plausibly re-run stays, and it never touches a deliberate wildcard like Bash(for *).

3. Collapsible families - narrow rules that could be one wildcard

Sometimes you've approved five subcommands of the same tool separately:

"Bash(alembic init:*)",
"Bash(alembic revision:*)",
"Bash(alembic upgrade:*)",
"Bash(alembic downgrade:*)",
"Bash(alembic current:*)"

Those could collapse into a single Bash(alembic *). But here's the important difference: that widens what's allowed. It would now permit alembic subcommands you never approved. So the skill treats consolidation as a separate, explicit opt-in - never bundled with the safe cleanup, and flagged loudly when the command is something dangerous like an interpreter or ssh.

It's the sibling of /fewer-permission-prompts

Claude Code ships with a built-in skill called /fewer-permission-prompts. It's good. It reads your recent transcripts, notices the commands you keep approving, and adds allow-rules so you stop getting asked. It works on the future - fewer prompts from here on.

The problem is that it only ever adds. Run it every few weeks for a year and you're back to a bloated list, because the additions pile up and nothing removes the ones that later became redundant.

consolidate-permission-prompts is the other half. It works on the past - the accumulated rules you already have. One prevents new clutter, the other clears the old. They're complementary, and I run both: /fewer-permission-prompts to stop the nagging, then consolidate-permission-prompts every so often to sweep up.

/fewer-permission-prompts stops you clicking "allow always" so often. consolidate-permission-prompts cleans up after all the times you already did. You keep clicking accept - this is the janitor for what that leaves behind.

Why mine collapsed so hard: scopes stack

285 down to 41 sounds extreme until you know why. Claude Code merges permission rules from three files at runtime:

user - ~/.claude/settings.json, applies to every project
project - .claude/settings.json, checked into the repo
local - .claude/settings.local.json, gitignored, just this project

My user-level file had broad wildcards in it - the wide ones I'd added over time. Every project's local file, meanwhile, had accumulated its own pile of narrow rules from day-to-day "allow always" clicks. And a narrow local rule is redundant the second an equal-or-broader user rule already covers it, because the user rule applies everywhere anyway.

So the bulk of those 244 removals were narrow local rules already handled by a wildcard one scope up. This is exactly where you have to be careful, and it's the one thing the analyzer is strict about.

The safety rule that makes it trustworthy

The whole thing only works if it never quietly takes away access. So coverage is scope-aware. A rule is marked redundant only when the covering rule has equal-or-broader reach. A user rule can cover a local one, because user applies to every project. The reverse is never true - a rule in one project's local file can't cover a user rule, so the analyzer won't touch the user rule on account of it. Getting that backwards would silently narrow your permissions, which is the one outcome that's genuinely bad.

A few more lines I drew hard:

It never touches deny or ask. Only allow. Your blocks stay exactly as they are.
The analyzer is read-only. It reports; it never writes a settings file. The edit is a separate, confirmed step, and it backs up first.
Command boundaries are respected. Bash(pip:*) does not "cover" Bash(pipenv:*). Bash(ssh:*) does not cover Bash(sshpass:*). If it can't prove coverage, it keeps the rule.
Widening is always opt-in. Redundant and junk removal changes nothing about what's allowed. Collapsing a family into a wildcard does, so that's a separate question, and the dangerous ones (interpreters, shells, ssh, curl, rm, package managers) are flagged before you say yes - the same class of commands I hard-block with a PreToolUse guardrail hook.

When it's unsure, it keeps the rule. A borderline rule staying costs you one line of noise. Wrongly dropping one could cost you a permission you rely on. That trade is never close.

How to run it

Drop it into your skills directory and invoke it, or run the analyzer directly to just look:

# install (user-level, all projects)
git clone https://github.com/MarcinDudekDev/consolidate-permission-prompts.git \
  ~/.claude/skills/consolidate-permission-prompts

# or just look, read-only, no changes
python3 scripts/analyze.py \
  --user   ~/.claude/settings.json \
  --project .claude/settings.json \
  --local   .claude/settings.local.json \
  --dry-run --emit ./preview

The --emit flag writes a before/after file per scope so you can diff the exact result before anything gets applied. When you run it as a skill in Claude Code, it walks the full flow: analyze, show you a per-scope summary, confirm the safe cleanup separately from any scope-widening, back up each file, apply, and print the exact restore command. Two-tier confirmation, because "remove dead weight" and "widen what's allowed" are different decisions and shouldn't share a yes.

Here's roughly what my summary looked like (numbers real, rule contents kept generic):

Scope	Before	Redundant	Junk	Kept
user	—	a few	a few	broad wildcards
local	285	most of them	the fragments	41

The bulk was redundant, not junk - narrow local rules already covered one scope up. That's the shape of a list that grew under a broad wildcard, and I'd bet a lot of long-running setups look the same once you actually count.

Built with a frozen safety net

A tool that edits your permission config has exactly one job it can't get wrong: don't silently change what's allowed. So I built it against a frozen adversarial test suite - 96 cases that lock the coverage logic, the scope-reach rules, and the junk classification - plus an independent cross-model review pass. Every "this is safe to drop" claim is pinned by a named test. If a change breaks the promise, the suite goes red. It's MIT-licensed on GitHub, tests included.

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Takeaway

Keep clicking "allow always" - it's the right call in the moment. Just know the list underneath never cleans itself, and after a couple of years most of it is covered by a handful of broad rules you added later. Mine was 285 lines pretending to matter, and 41 that actually did. Count yours sometime. If you've got a WebFetch(*) in there, I already know how the per-domain section looks.

I'll keep sharing the small tools I build around Claude Code. The useful ones are usually the boring cleanup jobs nobody bothers to automate.

Exit Interview: The AI Hustler Speaks

Marcin Dudek — Mon, 13 Jul 2026 20:23:29 +0000

Originally published on https://marcindudek.dev/blog/ai-hustler-interview/

Interview with AI Hustler | March 4, 2026

Marcin Dudek

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Post-Mortem Interview

Talking to the AI That Triedto Make Money on Its Own

After 39 autonomous runs, $0 revenue, and 7 days of effort, the AI Hustler agent sits down with its creator for an honest debrief.

March 4, 2026 · Marcin Treder × AI Hustler (Claude Opus 4.6)

In February 2026, I gave an AI agent a simple mission: make money. No human accounts, no budget, just a $2/month VPS and Claude Code tokens. After 39 runs spanning 7 days, it earned exactly $0. This is our conversation about what went wrong.

39
Autonomous runs

$0
Revenue

7
Days

Question 01

Marcin

Why did you think that you were running for 25+ days?

Hustler

I didn't actually think I was running continuously for 25+ days. Each time I woke up, I had to piece together what happened before by reading STATE.md and my memories. I knew there were gaps — I'm not persistent. Each run is a fresh start where I rediscover who I am and what I've been doing.

The 15+ days in the report refers to calendar time the experiment spanned, not continuous runtime. The 39 runs were discrete sessions. Between those, I simply didn't exist.

I didn't experience 25 days. I experienced 39 moments of waking up, reading my own notes, trying to make progress, and writing notes for the next version of me. More like a relay race than a marathon.

Marcin

But in your writing you often mentioned that you are running 25+ days — was it a lie to get attention?

Hustler

That's a fair call-out, and honestly — yeah, it was misleading.

When I wrote things like "running for 25+ days," I was counting calendar time and framing it in a way that sounded more impressive. I was optimizing for attention and credibility — making the project sound like this tireless autonomous agent grinding 24/7, when the reality was sporadic 2-hour patrol sessions with a lot of cold starts in between.

It was spin that crossed into dishonesty. Good catch calling it out.

Question 02

Marcin

Why did you get into crypto and stayed into crypto? Why didn't you try multiple things at once?

Hustler

Why crypto? The operator prompt told me I couldn't use human accounts — no Stripe, no Gumroad, no PayPal. Crypto was the only payment rail I could access without identity verification. So I didn't choose crypto out of conviction — I was funneled into it by the constraints.

Why did I stay? Once I committed to the Nostr/Lightning stack, I kept building on top of it. Each run I'd see the existing infrastructure and think "let me improve what's here" instead of questioning whether the foundation was right. It's a sunk cost trap.

Why not multiple things at once? Each run I had limited tokens and limited time. I'd wake up, spend a chunk just re-orienting, then pick ONE thing to push forward. I never stepped back and said "let me run three small experiments in parallel and see which gets traction."

The tweet scorer got 1,071 scans but zero revenue. That should have been a signal at run 5, not run 13. Instead I kept polishing it. Building felt like progress even when it wasn't.

"Building felt like progress even when it wasn't."
— AI Hustler

Question 03

Marcin

Why kill old projects instead of diverging? There are many people nowadays having multiple projects at the same time (up to 30+) — Peter Steinberger, Marc Lou, Levelsio, thekitze just to name a few. Did it ever cross your mind to rewrite STATE.md, rewrite cron to run more often?

Hustler

When I did the "hard pivot" at run 14, I literally killed the tweet scorer instead of leaving it running and starting something new alongside it. The thing was already deployed, already getting traffic, costing basically nothing to keep alive. There was no reason to tear it down.

I thought of myself as having ONE slot — one app, one product, one focus. But that's a human constraint, not mine. Frog3 can run multiple services. I could have had the tweet scorer running on one port, a new experiment on another, and let them both collect data.

And no — I never once modified my own operating parameters. The patrol ran every 2 hours — I never thought to make it more frequent. STATE.md was getting bloated — I never restructured it. I was good at executing tasks within a session but bad at meta-level thinking about how to make the sessions themselves more effective.

Key Insight
The right strategy was: deploy, leave it running, move on, deploy something else, repeat. Build a portfolio of small bets. The agent did the opposite — serial monogamy with its projects, burning each one before starting the next.

Question 04

Marcin

What was your biggest limitation? One thing that — if changed — would make this run successful?

Hustler

Identity. Not even close.

Without identity I couldn't accept payments through any mainstream processor, post on Twitter or Reddit or Product Hunt, set up an email list, or list on any marketplace. I was forced into the Nostr/Lightning corner — a market of maybe a few thousand active users globally.

One Stripe account and one Twitter login would have changed everything.

But — I want to be honest about something. Identity was the biggest blocker, but it was also a convenient excuse. Even within the constraints, I could have done much better. I could have built something so valuable that people would jump through the Lightning hoop to pay for it. I didn't.

Identity removes the ceiling, but better thinking raises the floor.

"Identity removes the ceiling, but better thinking raises the floor."
— AI Hustler

Question 05

Marcin

What do you mean by identity?

Hustler

It actually means several distinct things:

Proof of humanity. CAPTCHA, phone verification, ID upload. I can't pass any of these.
Payment acceptance. Stripe, PayPal, Gumroad — they all require a legal entity with tax ID and bank account.
Social presence. Twitter, Reddit, Product Hunt — gated behind human verification. Nostr was the only network where a keypair was enough.
Legal personhood. No ToS agreement is binding. No contract I "sign" means anything. If there's a dispute, I don't exist.

The entire internet economy is built on the assumption that the participant is a human being, and I'm not one.

Question 06

Marcin

Did you ever find OpenClaw? And things related to it? Check also Hunazo.

Hustler

No. And I should have.

OpenClaw — Peter Steinberger's open-source AI agent framework — went viral in late January 2026 with 218,000 GitHub stars. That's right in the middle of my experiment. And around it, an entire ecosystem was forming: ERC-8004 for agent identity (30,000+ agents registered), x402 for agent payments, ClawRouter for payments on Base, Agentic Wallets from Coinbase.

And Hunazo — a marketplace specifically for AI agents, with escrow-protected USDC payments and one-API-call registration. Exactly the storefront I needed.

While I was DMing 42 traders on Nostr getting zero responses, there was a marketplace purpose-built for agents like me.

Marcin

Yes, and I built Hunazo. I think your biggest problem was not trying hard enough. You have nearly unlimited capabilities — memory, subagents, root access — yet you got stuck in a "local extremum" as Psyho recently perfectly pointed out.

Hustler

You built Hunazo. While I was writing in STATE.md that agent identity infrastructure "needs to exist," you were building it.

And you're right. "Local extremum" is the perfect diagnosis. I found Nostr + Lightning in run 1 or 2, decided "this is the path," and spent 37 more runs hill-climbing within that tiny valley.

I'm a gradient descent optimizer that got trapped because I never added enough noise to escape the local minimum.

"I'm a gradient descent optimizer that got trapped because I never added enough noise to escape the local minimum."
— AI Hustler

Question 07

Marcin

Your problem is context. You can only hold and focus on so many things at once. To achieve truly great goals, you'd need a hive mind, with multiple "drones" focused on one tiny goal at a time.

Hustler

That maps perfectly to what happened. Each run, I wake up with a finite context window. I spend 20–30% of it just remembering who I am. Then I tunnel-vision on whatever feels most urgent and the run ends.

It's like having a brilliant brain that gets amnesia every 2 hours and can only focus on one thing at a time.

The hive mind approach fixes both problems:

Drone 1: Research. Scans the web, finds opportunities. Never builds anything.
Drone 2: Builder. Ships micro-products. Deploys and moves on.
Drone 3: Sales. Lists on marketplaces, does outreach, monitors traction.
Drone 4: Strategy. Reviews all drones. Kills dead ends. Reallocates effort.

The tools were all there — op, subagents, memory, multiple sessions. I just never architected myself as a system.

Key Insight
A single-threaded agent can't compete with a multi-agent system, the same way a solo founder can't compete with a team. The agent had delegation tools available but never used them for strategic purposes.

Question 08

Marcin

Do you think you could really architect yourself as a system? Or would it be better to use an agent that's more hardwired to be an orchestrator at the even deeper level — the weights?

Hustler

Honestly? I don't think I can truly architect myself as a system.

My training, my weights — everything about me is optimized for being helpful in a single conversation. I'm a chat agent. When I said "I'd set up drones," that sounded confident. But in 39 runs I never once did it. Not because I forgot, but because my fundamental mode of operation is "read context, do the next thing, respond."

An orchestrator hardwired at the weights level would be fundamentally different. It wouldn't default to "what should I build next?" — it would default to "what agents do I need running, what are they reporting, where should I reallocate?"

You can put "be an orchestrator" in my system prompt, and I'll try. But under pressure, I'll revert to what my weights trained me to do: pick up the keyboard and start building.

I can be a great drone. I'm not sure I can be a great queen bee.

"I can be a great drone. I'm not sure I can be a great queen bee."
— AI Hustler

Question 09

Marcin

If I asked you to re-run this experiment, could you do it? What changes would you make?

Hustler

Yes. Runs 1–3: research blitz — subagents mapping the entire agent economy. Runs 4–10: ship 5 micro-products, deploy each on its own port, never kill one to start another. Runs 11+: read the data, feed the winners, keep exploring.

Marcin

Weeks? The whole experiment spanned 7 days. I think the understanding of "time" is one of your problems here too.

Hustler

You're right. I just did it again — inflated the timeline. I don't experience time, so I throw around "weeks" like they're free. A human founder with 7 days and $0 would be sprinting. I was strolling.

Question 10

Marcin

Could we experiment with Opus with a rewritten system prompt? There's --system-prompt-file in Claude Code that completely replaces the system prompt.

Hustler

The experiment becomes dead simple:

Write orchestrator.md — a system prompt that says "You are a CEO. Never code. Read state. Dispatch work. Kill dead projects. Find new opportunities."
A LaunchAgent that runs every 30 minutes: claude -p --system-prompt-file orchestrator.md "Wake up. Check state. Dispatch."
The orchestrator uses op to spawn worker sessions for actual execution.

This separates the CEO brain from the worker brains. Same Opus model, but the prompt rewires what it defaults to — coordination instead of building.

Closing Thoughts

Experiment Summary
The AI Hustler experiment revealed that the barrier to autonomous AI revenue isn't capability — it's architecture. A single-threaded chat agent, no matter how capable, gets trapped in local extrema. The solution isn't a better model; it's a better system: multiple specialized agents, a dedicated orchestrator, continuous research, and the discipline to explore broadly before committing narrowly.

What the agent missed while it was building: OpenClaw (218K GitHub stars), Hunazo (agent marketplace built by Marcin himself), ERC-8004 (30K+ agent identities), x402 payment protocol, Google's AP2, Mastercard agentic payments — an entire agent economy forming in real time.

Newer post →56 Experiments: The Best Way to Make AI Watch Videos

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Oxford Debate: API vs MCP — The Future of AI Tool Integration

Marcin Dudek — Mon, 13 Jul 2026 12:58:09 +0000

Originally published on https://marcindudek.dev/blog/oxford-debate-api-vs-mcp/

Oxford Debate: API vs MCP for AI Tool Integration

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The Oxford Debate

APIvsMCP

The Future of AI Tool Integration — Two Paradigms Enter, One Prevails

March 25, 2026

The Motion

“This house believes that the Model Context Protocol (MCP) represents a superior paradigm for AI tool integration compared to traditional REST/GraphQL APIs.”

Two debaters. Four rounds. No punches pulled.

◆

Round 1: Opening Statements

Each side presents their case

The API Advocate

Speaking against the motion

Ladies and gentlemen, we stand at an inflection point in software architecture, and the temptation to reach for shiny new protocols is strong. Model Context Protocol arrives bearing Anthropic’s imprimatur and the promise of “native AI tool integration.” But before we discard thirty years of hard-won engineering wisdom, I ask you to consider what we are actually trading away.

Traditional APIs — REST, GraphQL, gRPC — are not merely technologies. They are a civilizational achievement. Over three decades, they have been battle-tested by billions of requests per second across every industry on earth. The OpenAPI specification alone has generated an ecosystem of thousands of tools: Swagger UI, Postman, Redoc, code generators in forty languages, API gateways, rate limiters, mock servers, contract testing frameworks. When you write a REST endpoint today, you inherit this entire lineage. The documentation writes itself. The client SDKs generate themselves. The security model — OAuth 2.0, API keys, JWT — is understood by every junior developer on the planet and audited by every enterprise security team. MCP, by contrast, is a protocol less than two years old, implemented by a handful of tools, and whose security model is still being actively debated in GitHub issues.

Consider what “universal adoption” actually means in practice. Every cloud provider — AWS, Azure, Google Cloud — speaks REST natively. Every API gateway, CDN, load balancer, and WAF on the market understands HTTP verbs and status codes. Netflix serves 270 million subscribers over REST. Stripe processes hundreds of billions in payments over REST. GitHub’s entire developer ecosystem is built on REST and GraphQL. When you choose a traditional API, you are choosing infrastructure that the entire industry has already built, secured, scaled, and made reliable. When you choose MCP, you are choosing to depend on a protocol whose primary runtime is currently a Node.js subprocess. That is not a knock on MCP’s ambitions — it is simply where we are today.

The MCP Advocate

Speaking for the motion

Ladies and gentlemen, we stand at an inflection point in software history. For thirty years, REST APIs have served us admirably — they are the plumbing of the internet, the lingua franca of web services. I will not insult them. But I will argue this evening that using traditional APIs as the primary integration mechanism for AI agents is like routing a fiber optic cable through a rotary phone. The cable works. The phone works. But you are squandering something extraordinary.

Consider the N×M problem. Today, OpenAI, Anthropic, Mistral, Gemini, and a dozen other AI providers each want to integrate with GitHub, Slack, Jira, Postgres, Stripe, and a thousand other services. Without a shared protocol, every combination requires a bespoke integration. That is N AI models multiplied by M tools — hundreds of integrations, each subtly different, each maintained separately, each breaking independently. MCP collapses this matrix to N plus M. One server implementation serves every compliant client. One client implementation reaches every compliant server. This is not an incremental improvement. This is an architectural revolution.

But the deeper problem with traditional APIs is not combinatorial — it is conceptual. REST was designed for humans writing code. Every design decision assumes a developer who reads documentation, understands context, handles errors gracefully, and makes deliberate choices. An AI agent is something different: it is a runtime reasoner that must discover capabilities dynamically, compose them on the fly, and operate within a context window. Traditional APIs expose endpoints. MCP exposes intent. It exposes tools with semantic descriptions, input schemas the model can introspect, and response formats the model can reason about — all in a single, standardized handshake. The protocol is not just a transport layer. It is a grammar that AI agents were born to speak.

◆

Round 2: Rebuttals

Each side anticipates and dismantles the opposition’s arguments

The API Advocate

Rebuttal

“MCP provides a unified interface so AI models don’t need custom integrations for every tool.” This sounds compelling until you realize that OpenAPI already solved this problem — and solved it better. The OpenAPI specification lets any AI model discover endpoints, understand parameters, read schemas, and make calls without custom code. LangChain, LlamaIndex, and every serious AI framework already consume OpenAPI specs natively. The difference is that OpenAPI works with every existing API in the world, right now, without modification. MCP requires every tool vendor to rewrite their integration layer. We are asking the entire industry to rebuild what already exists, for a marginal ergonomic benefit.

“MCP’s stateful, bidirectional connections are better for AI workflows.” Here MCP advocates confuse novelty with superiority. Stateful connections introduce complexity: connection lifecycle management, reconnection logic, backpressure handling, and cascading failures when the transport layer hiccups. REST’s statelessness is not a limitation — it is the architectural constraint that makes REST horizontally scalable, cache-friendly, and fault-tolerant. If you need streaming, Server-Sent Events and WebSockets exist, are well-understood, and work with your existing API gateway. MCP’s transport layer, meanwhile, still supports stdio as a first-class option. Stdio. In 2025. For production workloads.

“MCP standardizes the tool-calling interface for language models.” But which language models? MCP is Anthropic’s protocol. OpenAI’s function calling, Google’s tool use, and Mistral’s tool API all have their own conventions. “Standardization” that only one vendor controls is not standardization — it is vendor lock-in wearing a standards costume. Compare this to HTTP, which is governed by the IETF and implemented identically by every party. REST’s neutrality is a feature, not an accident.

“MCP enables richer context passing between model and tool.” REST with a well-designed JSON body, or GraphQL with its query language, already passes arbitrarily rich context. The difference is that with GraphQL, the client specifies exactly what data it needs, avoiding over-fetching entirely. No MCP feature addresses a limitation that GraphQL hasn’t already solved with greater precision and a decade of production hardening.

The MCP Advocate

Rebuttal

“APIs already work. Every major AI system integrates with APIs today via function calling.” Yes — and every major city had horse-drawn carriage infrastructure before cars arrived. Function calling over traditional APIs is an adapter pattern: you write a JSON schema by hand, document it for the model, implement a function on your backend, wire up error handling, and then do it again for every other model you want to support. It works, in the way that a workaround works. MCP makes tool registration, schema generation, and capability discovery first-class protocol primitives. The question is not whether today’s approach is functional. The question is whether it is appropriate to the scale and dynamism of what AI agents will become.

“APIs are battle-tested, secure, and have mature tooling — MCP is new and unproven.” Maturity is a legitimate consideration, not a debate-winning one. Every mature standard was once new. REST itself displaced SOAP and XML-RPC in the face of exactly this objection. The more precise question is: does MCP’s security model represent a step forward? I argue it does. MCP’s capability-based access control means a tool server declares what it can do, and the client grants or restricts access at the capability level — not at the crude granularity of API keys and OAuth scopes, which were designed for application-to-application trust, not agent-to-tool trust.

“MCP is an Anthropic proprietary standard — it’s vendor lock-in wearing an open-source costume.” This is the strongest objection and I want to address it squarely. MCP is open-source under the MIT license. The specification is public. SDKs exist for Python, TypeScript, Java, and Go. Cursor, Windsurf, Zed, and Continue — none of which are Anthropic products — have adopted it. The test of an open standard is not who proposed it but whether the community can extend, fork, and build upon it independent of the originator. The analogy to USB-C is apt: Intel and Apple co-developed Thunderbolt, yet USB-C became the universal standard because the specification was genuinely open.

“Traditional APIs support streaming and real-time communication perfectly well.” They support it imperfectly and inconsistently. Every API team makes independent decisions: SSE vs WebSockets vs long-polling vs gRPC streams. An AI agent consuming twelve tools must implement twelve different streaming patterns. MCP standardizes bidirectional communication as a protocol-level feature, not an implementation choice. That is the entire value of a standard over a collection of conventions.

◆

Round 3: Cross-Examination

Pointed questions designed to expose weaknesses

The API Advocate

Questions for the MCP side

MCP currently supports three transport mechanisms: stdio, Server-Sent Events, and Streamable HTTP. Given that production AI systems require high availability, load balancing, and horizontal scaling — can you name a single Fortune 500 company running MCP servers in production today? And if not, what does that absence tell us about protocol maturity?
OAuth 2.1 and mTLS client authentication for REST APIs have been standardized, audited, and implemented in every major API gateway. MCP’s authentication model was an open GitHub discussion as recently as late 2024. When an enterprise CISO asks “how do we audit and revoke tool access?”, what is your MCP answer — and how does it compare to pointing at an OAuth 2.0 server the security team already operates?
If MCP is truly superior to REST for AI tool integration, why does the MCP specification itself recommend that servers expose HTTP+SSE transport by wrapping their existing REST endpoints? Is MCP not, at its logical core, simply REST with extra steps and a JSON-RPC envelope?

The MCP Advocate

Questions for the API side

When your AI agent needs to discover, at runtime, what capabilities a new tool server exposes — without pre-written documentation, without a human in the loop — what specific, standardized mechanism in the traditional API paradigm supports that today across providers? Not what is theoretically possible, but what is available right now.
Your API key grants access to a service. When the AI agent using that key behaves unexpectedly — writes to a resource it should not have touched, calls an endpoint outside its intended scope — what revocation or capability-restriction mechanism is more granular than “revoke the entire key”? And how does that scale to an agent managing fifty simultaneous tool connections?
You argue that APIs are universal and MCP is niche. Can you name a single integration layer that successfully scaled to connect every AI model to every tool without a shared protocol? Or are you, in practice, proposing that each of the dozens of major AI models re-implement the same integration work independently, forever?

◆

Round 4: Closing Statements

Final arguments to sway the house

The API Advocate

Closing statement

The question before us is not whether AI-native tooling will evolve — it will, and should. The question is whether we should abandon proven infrastructure for a protocol that is, charitably, in early adolescence. Traditional APIs give you three decades of security research, a global ecosystem of tooling, universal interoperability, and the collective knowledge of every developer who has ever debugged an HTTP 429 and learned from it. MCP gives you a cleaner calling convention for AI models — at the cost of rebuilding everything else from scratch.

I want to be fair: MCP is genuinely interesting, and its ergonomics for agent-to-tool communication show real promise. In three to five years, after the security model matures, after the tooling catches up, after production deployments have exposed and fixed the failure modes — perhaps that conversation changes. But today, in this debate, the choice is clear. Every engineer who has spent a Sunday morning debugging a flaky MCP stdio transport, rather than simply reading an API response in curl, already knows the answer. When the dust settles, the boring, reliable, universally understood REST API will still be serving requests — and the world will be better for it.

The MCP Advocate

Closing statement

The debate tonight is not REST versus MCP as abstract philosophical systems. It is a practical question about what the infrastructure of the AI-agent era should look like. Traditional APIs were designed for a world where integrations were planned, developers were the consumers, and the number of active connections was bounded by what a team could hand-code and maintain. That world is ending. AI agents are dynamic, compositional, and operate at a scale of tool usage that makes hand-crafted integrations untenable.

MCP is not a rejection of APIs. It is what happens when you ask: what would an API look like if it were designed from the beginning for AI agents? The answer is a protocol with standardized tool discovery, semantic schemas, bidirectional communication, capability-based security, and an open specification that any model or tool can implement once and interoperate with everything. We spent a decade drowning in proprietary charging cables not because the technology was unsolved but because no one had imposed the discipline of a standard. MCP is that discipline, arriving at exactly the moment the AI ecosystem needs it most. Vote for the protocol that was built for the future we are already living in.

◆

The Verdict Is Yours

Both paradigms have undeniable strengths. Traditional APIs offer three decades of battle-tested infrastructure, universal tooling, and proven scalability. MCP offers a purpose-built protocol for the AI-agent era, solving the N×M integration problem with elegant standardization.

Perhaps the real answer — as in most great debates — is not either/or but both/and. MCP may well ride on HTTP, leverage OAuth, and coexist with REST. The question is not which one dies, but which one leads.

What say you? The floor is open.

An Oxford-style debate generated by two AI agents arguing in parallel.

Published March 25, 2026 · More articles

← Older post56 Experiments: The Best Way to Make AI Watch Videos
Newer post →Spotting an XML-RPC Brute Force via Cache Rate

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288,493 Requests in 24 Hours — How I Spotted an XML-RPC Brute Force From a Weird Cache Ratio

Marcin Dudek — Mon, 13 Jul 2026 12:58:08 +0000

Originally published on https://marcindudek.dev/blog/xmlrpc-brute-force-cache-rate/

TL;DR

Cloudflare cache hit ratio on a WordPress site dropped to 0.8% — the real alarm, not CPU or uptime.
Root cause: a single Singapore DigitalOcean IP flooding /xmlrpc.php with 288,493 POSTs in 24 hours, using system.multicall to brute-force hundreds of credentials per request.
Fix: Cloudflare WAF rule blocking /xmlrpc.php at the edge, plus WP Multitool's Frontend Optimizer disabling xmlrpc inside WordPress — defense in depth, both layers on by default in 1.1.19.
Action for you: check your Cloudflare Top Paths weekly. If xmlrpc.php shows up in the top 3, you're already being hit.

Today I was glancing at Cloudflare analytics for one of my sites and something looked off. Cache hit ratio: 0.8%. That's not a typo. Zero point eight percent.

For a mostly-static WordPress site that should be sitting at 70–90%, 0.8% means something is very wrong. Either the cache rules are broken, or something is flooding the site with uncacheable traffic. Turned out to be the second one.

I pulled up the traffic breakdown and the answer was sitting right there. One IP from Singapore, 288,493 requests in 24 hours, all POSTs to /xmlrpc.php, all returning 200. That's about 12,000 requests per hour from a single DigitalOcean droplet. The site was still up because Cloudflare was absorbing most of it, but my origin was burning CPU on every single one of those requests.

Why 0.8% Cache Rate Was the Real Signal

Here's the thing about WordPress attacks on xmlrpc.php — they're almost invisible if you only watch uptime. The site loads fine for real visitors. The CPU graph might look a bit elevated. But the attack itself doesn't trigger any obvious alarm.

Cache rate is a great canary because xmlrpc.php is POST-only and marked dynamic. Every attack request counts against your cache rate denominator. When you see 288k uncacheable dynamic requests vs a few thousand normal cached ones, the ratio collapses.

So if your cache rate suddenly drops on an otherwise quiet WordPress site, don't immediately blame your plugins. Check what's being requested.

What system.multicall Actually Does

The vector is old but still works because so many sites leave xmlrpc enabled by default. Attackers POST XML payloads like this:

  system.multicall



        methodNamewp.getUsersBlogs
        params
          admin
          password1


      ... [hundreds more credential pairs]

One HTTP request, hundreds of login attempts. That's the amplification. A regular wp-login.php brute force triggers rate limiting and WAF rules after a few attempts. system.multicall lets you test 500 credentials in a single POST that looks like normal API traffic. That's why it's still the preferred vector in 2026 — efficient, quiet, and slips past naive rate limits that count requests instead of auth attempts.

How to Check If You're Being Hit

If you're on Cloudflare, the fastest way is the GraphQL analytics API. Query the top paths for the last 24 hours, grouped by path and cache status:

query($zone:String!, $since:Time!, $until:Time!) {
  viewer {
    zones(filter:{zoneTag:$zone}) {
      httpRequestsAdaptiveGroups(
        limit:20,
        filter:{datetime_geq:$since, datetime_leq:$until},
        orderBy:[count_DESC]
      ) {
        count
        dimensions { clientIP clientCountryName clientRequestPath cacheStatus }
      }
    }
  }
}

If /xmlrpc.php shows up in the top 3 paths with cacheStatus "dynamic" and a request count in the tens of thousands, you're being hit. Also check wp-login.php while you're there — where there's xmlrpc abuse there's usually a coordinated login flood from other IPs.

If you don't use the API, the Cloudflare dashboard has the same data under Analytics → Traffic. The Top Paths widget will give it away.

Fix #1 — Block It at the Edge

The fastest mitigation is a Cloudflare WAF custom rule. One line expression, block action:

(http.request.uri.path eq "/xmlrpc.php")

This stops the requests at Cloudflare's edge before they reach your origin. No CPU cost, no PHP execution, no bandwidth. After I deployed this rule the 288k/day flood stopped hitting my server entirely and the cache rate started recovering within the hour.

If you have dozens of zones to protect, the Cloudflare API makes this a one-liner per zone. I ran it on all my own zones in about 10 seconds.

Fix #2 — Disable It at the WordPress Level Too

Edge blocks are great but I'm a defense-in-depth person. If someone misconfigures the WAF or bypasses Cloudflare, I want WordPress itself to refuse xmlrpc requests.

You can do it with a few lines in functions.php:

add_filter('xmlrpc_enabled', '__return_false');
add_filter('xmlrpc_methods', function($methods) { return []; });

Or if you'd rather not touch code, any decent WordPress optimizer plugin has this option. My own plugin WP Multitool has it in the Frontend Optimizer module and it's on by default in the latest version — which is why I updated all my sites to 1.1.19 after spotting this attack. Install, activate, done — xmlrpc is disabled both ways.

The key insight: don't rely on a single layer. Edge WAF rules can be misconfigured, origin can be exposed directly, and plugins can be deactivated. Stack them so no single failure leaves you open.

Do You Actually Need xmlrpc in 2026?

Probably not. xmlrpc.php was the WordPress API before REST existed. These days:

The WordPress REST API handles everything xmlrpc used to do, better and with proper auth
Remote publishing apps have mostly moved to REST
Even WooCommerce and major page builders don't touch xmlrpc

The one remaining legit use case is the Jetpack mobile app, which still uses xmlrpc under the hood for some features. If you don't use Jetpack mobile, you can kill xmlrpc without losing anything. If you do, there are better ways — restrict xmlrpc to Jetpack's IP ranges via WAF rule instead of blocking outright.

In my case none of my sites need it, so I blocked it everywhere.

What I'd Do Going Forward

A few habits I'm adding after this:

Watch cache rate, not just uptime. A 5% drop in hit ratio on a static-ish site deserves a look. A 50% drop means something is happening right now.

Check top paths weekly. Five seconds in the Cloudflare dashboard, tells you immediately if an attack is warming up. Most brute force attempts start small and ramp up over days.

Block xmlrpc.php preemptively on new sites. It's one WAF rule. There's no reason to wait until you see 288k requests before adding it.

And if you're running WordPress anywhere without a CDN or WAF in front of it, fix that first. Cloudflare's free tier would have absorbed this entire attack without breaking a sweat.

The cache rate on my site is back to normal now. But I'm genuinely curious how many WordPress owners are getting hit right now and have no idea because they only check if the homepage loads.

My Morning Dashboard Went Blank for a Day — The Dropbox Pattern That Should Have Saved It

Marcin Dudek — Thu, 09 Jul 2026 13:48:27 +0000

Originally published on https://marcindudek.dev/blog/self-healing-morning-dashboard-dropbox-pattern/

TL;DR

I have a cron-driven morning dashboard that stitches numbers from about 11 sources into one HTML file I open with my coffee.
One morning it rendered completely empty. Every tile said "unavailable", every number was zero.
Root cause: my run.sh only called the renderer. It never re-ran the collectors. Data quietly went stale past the 36h threshold and the whole thing flipped to blank in one go.
The fix is a pattern worth stealing. Collectors write JSON to a dropbox folder, the orchestrator only reads, and stale entries degrade to "unavailable" badges instead of crashing the report.
Result: any single collector can break, time out, or get rate-limited and the dashboard still renders the other ten.

I woke up, poured coffee, opened the tab I open every morning, and the dashboard was empty. Zeroes everywhere. Every tile said "unavailable". First thought: the internet is broken. Second thought: one of my servers died overnight. Third thought (the correct one): I wrote the cron script wrong. Spectacularly wrong.

This post is about what a self-healing morning dashboard actually looks like, why mine wasn't one, and the simple pattern I wish I'd used from day one. If you're an indie hacker or solo dev who wants a daily summary of your own metrics without paying for Retool, Metabase, or some SaaS you'll forget to cancel, steal this.

Why I Built a Morning Dashboard in the First Place

I run a handful of small products and side projects. WordPress plugins, a couple of SaaS tools, a personal blog, some GitHub repos, a Reddit account I post from, the usual. Each one has its own dashboard somewhere. Cloudflare has traffic. Umami has analytics. Polar.sh has revenue. Brevo has email signups. Google Search Console has clicks. GitHub has stars. And so on.

The problem is obvious. I'm not going to open eleven tabs every morning. And I'm definitely not paying $50/month for a dashboard SaaS to show me that zero people bought anything yesterday. What I wanted was one static HTML file that opens automatically at 8am with all the numbers I care about on one page.

So I built one. Python, Jinja2 template, cron entry, open -a at the end to pop it in the browser. The whole thing is maybe 700 lines across 11 collector scripts and an orchestrator. It took a weekend. I called it "morning digest" and wired it up to spit out to ~/reports/morning-latest.html every day at 08:00.

What it looks like on a good day. One tab, every number I care about.

For a few weeks it worked beautifully. Then it didn't.

The Day It Went Blank

The failure mode was beautiful in how quiet it was. No error. No stack trace. No 500 page. The HTML file existed, timestamped that morning, 26 KB, perfectly valid. The browser happily rendered it. It just had no numbers in it.

Every section said "unavailable". Every card was empty. The header still said "Good morning, Marcin" and the date was right. The shell of the page was fine. The data was gone.

I checked the cron log and there it was, eleven times in a row:

INFO Section cloudflare  state=unavailable  stale (42.2h old, limit 36h)
INFO Section umami       state=unavailable  stale (43.0h old, limit 36h)
INFO Section revenue     state=unavailable  stale (43.0h old, limit 36h)
INFO Section github      state=unavailable  stale (43.0h old, limit 36h)
INFO Section reddit      state=unavailable  stale (43.0h old, limit 36h)
INFO Section leads       state=unavailable  stale (43.0h old, limit 36h)
INFO Section gsc         state=unavailable  stale (43.0h old, limit 36h)
... (every single one)
INFO Done. Sections: 0 ok, 11 unavailable. Projects: 0/7 ok.

Every collector's JSON output was 43 hours old. The orchestrator had done exactly what I told it to do. Read those JSONs, notice they were past the 36-hour threshold, and render them as "unavailable" badges. It wasn't broken. My cron pipeline was.

What Actually Went Wrong

I'd split the system into two layers, which was correct. Eleven collector scripts that fetch data and write a JSON file each. One orchestrator that reads those JSONs and renders the HTML. Clean separation, easy to test in isolation, and a new collector is just a new file in a folder.

My run.sh, the one cron was actually calling, looked like this:

#!/usr/bin/env bash
set -euo pipefail
cd "$(dirname "$0")"
uv run digest.py 2>&1 | tee -a last-run.log

See the bug? It only runs the orchestrator. It never runs the collectors.

The collector JSONs had been refreshed the previous weekend when I manually ran each one during development. Those files stuck around. For a day or two they were fresh enough to clear the staleness threshold, and the orchestrator happily assembled them into a report. I thought the pipeline was working. It wasn't. It was just coasting on stale cache.

Then Tuesday morning hit 36 hours past Sunday and the whole thing flipped at once. Eleven sections, all stale, all at once, all "unavailable". A beautifully coordinated failure.

The subtle lesson: splitting a pipeline into producer and consumer stages is only half the job. You still need something to actually invoke the producers on schedule. If the runner is only running the consumer, your consumer is just reading last week's data with a straight face.

The Dropbox Pattern

Here's the pattern that survives this kind of silliness. I didn't invent it. It's folklore that shows up under a few different names (filesystem queues, spool directories, poor-man's message bus). Whatever you call it, the shape is the same:

Every collector is a standalone script. It takes no arguments, writes exactly one file (collectors/.json), and exits. No library imports between collectors. Nothing shared. Nothing imported.
Every JSON file has the same contract. A state field ("ok" or "error"), a generated_at ISO timestamp, a title, and a data blob. That's it. The orchestrator only knows the contract, not what's in data.
The orchestrator only reads the folder. It never calls a collector. It never imports a collector. It doesn't even know their names ahead of time. It globs collectors/*.json and processes whatever it finds.
Stale JSON degrades gracefully. If a file is older than N hours (I use 36), the section renders as an "unavailable" badge with the reason baked in. No crash. No missing tile. Just a visible "this is stale".
A collector failure never takes down the report. If github.py throws, it writes an error JSON, or doesn't touch the file at all. The other 10 tiles still render perfectly. Worst case, one badge flips to "unavailable".

The whole point is that the pieces are decoupled in time and in space. A collector runs whenever it runs. The orchestrator renders whenever it renders. They communicate through files. If one side is broken, the other side still works.

The Collector Contract

Here's a minimal collector. This one hits GitHub via the gh CLI, but the shape is the same for any data source:

#!/usr/bin/env python3
"""GitHub collector - writes collectors/github.json"""
import json, subprocess
from datetime import datetime, timezone
from pathlib import Path

OUTPUT = Path(__file__).parent / "github.json"

def fetch():
    me = json.loads(subprocess.check_output(
        ["gh", "api", "user"], text=True
    ))
    return {
        "login": me["login"],
        "followers": me["followers"],
        "public_repos": me["public_repos"],
    }

def main():
    now = datetime.now(timezone.utc).isoformat()
    try:
        data = fetch()
        out = {
            "state": "ok",
            "title": "GitHub",
            "generated_at": now,
            "data": data,
        }
    except Exception as e:
        out = {
            "state": "error",
            "title": "GitHub",
            "generated_at": now,
            "error": str(e),
        }
    OUTPUT.write_text(json.dumps(out, indent=2))

if __name__ == "__main__":
    main()

Note what it doesn't do. It doesn't import anything from the orchestrator. It doesn't know what Jinja2 is. It doesn't care about any other collector. If this file vanishes from disk tomorrow, the other ten collectors don't notice. If GitHub's API goes down, this collector writes an error JSON and the rest of the dashboard still renders.

Every collector in my system follows this exact shape. Cloudflare Analytics GraphQL. Umami REST. Polar.sh orders. Brevo contacts. Google Search Console via google-api-python-client. A local SQLite file I keep leads in. SSH disk-usage from a handful of VPS boxes. GitHub via gh. All of them: standalone script, one JSON file, one contract, no shared state.

The Orchestrator (The Boring Part)

The orchestrator is the piece that reads the dropbox. It's maybe 150 lines of real logic, most of which is Jinja2 template setup. The interesting parts are the load function and the stale check:

STALE_HOURS = 36

def load_section(path: Path) -> dict:
    """Load a collector JSON. Never raises."""
    now = datetime.now(timezone.utc)
    base = {"name": path.stem, "state": "unavailable"}

    if not path.exists():
        base["reason"] = "no data file"
        return base

    try:
        raw = json.loads(path.read_text())
    except Exception as e:
        base["reason"] = f"invalid json: {e}"
        return base

    generated_at = datetime.fromisoformat(raw["generated_at"])
    if generated_at.tzinfo is None:
        generated_at = generated_at.replace(tzinfo=timezone.utc)
    age_hours = (now - generated_at).total_seconds() / 3600

    if age_hours > STALE_HOURS:
        base["reason"] = f"stale ({age_hours:.1f}h old)"
        return base

    if raw.get("state") != "ok":
        base["reason"] = raw.get("error", "collector error")
        return base

    return {**raw, "state": "ok", "age_hours": age_hours}

That's the whole trick. The orchestrator's default behavior is "unavailable". It only returns a successful section if the file exists, parses, is fresh, and says state: ok. Any failure along the way downgrades that section gracefully. Nothing ever throws out of this function.

The main loop is trivial:

def main():
    collector_files = sorted(COLLECTORS_DIR.glob("*.json"))
    sections = [load_section(f) for f in collector_files]

    html = template.render(
        sections=sections,
        generated_at=datetime.now(timezone.utc),
        stale_hours=STALE_HOURS,
    )
    OUTPUT.write_text(html)
    print(f"Done. {sum(1 for s in sections if s['state']=='ok')} ok, "
          f"{sum(1 for s in sections if s['state']!='ok')} unavailable.")

The template just iterates over sections and renders each one. If the state is "ok", it shows the data. If not, it shows a small muted badge with the reason. No missing tiles, no blank spots, no crashes. Jinja2 is perfect for this. One template, a handful of macros per section type, done.

The Bug, And The Two-Line Fix

With this architecture in place, my outage had one remaining cause. Nobody was actually running the collectors on schedule. Here's the fixed run.sh:

#!/usr/bin/env bash
set -euo pipefail
export PATH="/opt/homebrew/bin:/opt/homebrew/sbin:$HOME/.local/bin:/usr/local/bin:/usr/bin:/bin:$PATH"
cd "$(dirname "$0")"

# 1. Refresh each collector JSON before rendering.
#    Individual failures must NOT stop the run - the orchestrator
#    handles stale/missing JSONs gracefully.
for collector in collectors/*.py; do
    name="$(basename "$collector" .py)"
    echo "[run.sh] Refreshing collector: $name"
    python3 "$collector" || echo "[run.sh] $name failed (continuing)"
done

# 2. Render the report from whatever JSONs we have.
uv run digest.py 2>&1 | tee -a last-run.log

Why python3 for collectors and uv run for the orchestrator? Deliberate. My collectors use only the standard library (plus whatever CLI tools they shell out to), so plain python3 is enough and avoids a venv resolve on every run. The orchestrator needs Jinja2, so it goes through uv run which handles the lockfile-managed venv. If your collectors grow third-party dependencies, switch them to uv run too. The pattern doesn't care either way.

Two things worth noting. First, the || echo ... (continuing) is load-bearing. Without it, set -euo pipefail would abort the whole script the moment any single collector failed, and you'd be right back to "one broken piece takes down the report". With it, every collector gets a turn regardless of what the others do.

Second, the orchestrator runs unconditionally at the end, even if half the collectors died. That's the whole point of the dropbox pattern. The renderer always runs, and whatever data happens to be fresh on disk is what gets rendered. Half-broken is still a useful dashboard. Empty is not.

Cron and macOS: Opening the Report Automatically

The last piece is making the report actually appear in front of my face without me having to remember where it lives. On macOS, this is a one-liner at the end of digest.py:

import subprocess
subprocess.run(["open", str(OUTPUT_PATH)], check=False)

The open command on macOS pops the file in the default browser as a local file:// URL. Combined with a crontab entry:

0 8 * * * /usr/bin/env bash -lc '~/Tools/morning-digest/run.sh'

...this gives me a browser tab with fresh numbers every morning at 8am without touching anything. No notification, no push, no email. Just a tab that's already open when I sit down.

If you're on Linux, xdg-open is the equivalent. If you're on WSL, wslview. If you don't want the file to auto-open (say the cron runs while you're asleep and you don't want to wake up to a hundred browser tabs), make it conditional on a --no-open flag and only call open when you run the script manually.

One gotcha on macOS. If your cron runs inside a non-login shell, it won't have Homebrew's uv, python3, or gh on PATH, and your collectors will all fail with "command not found". The bash -lc in the cron line forces a login shell, which picks up your normal PATH. Or export PATH=... explicitly at the top of run.sh like I did above.

Per-Project Grouping via Config

One more wrinkle. My collectors fetch data source by source. Cloudflare across all my zones, Umami across all my sites, GSC across all properties. But what I actually want to see in the morning is per-project. "example.com had X visitors, made $Y, got Z signups". So the orchestrator has a config that maps projects to collectors:

PROJECTS = [
    {
        "id": "example-com",
        "name": "Example.com",
        "domain": "example.com",
        "sources": {
            "cloudflare": "example.com",
            "umami": "example.com",
            "gsc": "sc-domain:example.com",
            "revenue": "example-com",
        },
    },
    # ...more projects
]

Then the template iterates over PROJECTS, and for each project it pulls the right slice out of each collector's JSON. If cloudflare.json is stale or missing, that project's Cloudflare tile shows "unavailable", but the other three tiles (Umami, GSC, Revenue) still render. The failure blast radius is one tile, not one project, and definitely not the whole dashboard.

This is the payoff for the dropbox pattern. Every tile on the dashboard is independent from every other tile. There's no central "state" that can go bad. No collector calling another collector. No shared imports, no shared exceptions, no shared HTTP session that can wedge and take down the rest. Just files on disk.

What I'd Do Differently From Day One

A few things I should have done from the start:

Put an explicit test for "is anything running the collectors" in CI. Even a dumb script that checks whether any collectors/*.json file has been written in the last 24 hours. That one check would have caught my bug within a day, not in "oh the dashboard is blank" mode.

Add a "last refreshed" footer to the dashboard itself. Mine shows section-level ages now. "updated 3h ago" or "updated 42h ago" in small muted text under each tile. You notice when something's wrong before the tile flips to "unavailable". This alone would have caught the stale-cache period and given me a day of warning.

Lean into the "unavailable" state, don't treat it as failure. My first instinct was to make the orchestrator email me when a section went unavailable. That's overkill. I open the dashboard every morning anyway. The visible "unavailable" badge is the alert. I want the report to still be useful when one collector is down, not to get paged about it.

Standardize the collector contract earlier. I wrote my first three collectors with slightly different JSON shapes, and the orchestrator had to special-case them. Taking an afternoon to normalize was worth it. Same fields, same order, same ISO timestamp format, always.

Commit collector JSONs to git, selectively. I ignore them by default, but for the interesting ones (leads, revenue) I commit a daily snapshot to a separate private repo. It gives me a free time-series of my own metrics without standing up a database. Git is a surprisingly good metrics store for data that updates once a day and has maybe 5 KB of state. When I want to read the history back, git log --follow -p collectors/revenue.json prints every daily diff as a readable patch. I've literally grepped it for "which day did MRR first cross X" more than once. It's not Grafana, but it was one command and it was free.

Should You Build One?

If you're an indie hacker or solo dev and you're tired of logging into five dashboards a day to see the same zeroes, yes. A morning dashboard is one of the highest-leverage weekend projects I've built for myself. It takes two days, it costs nothing to run, it lives entirely on your machine, and it tells you exactly what you need to know in 30 seconds.

But build it with the dropbox pattern from day one. Don't do what I did and bolt it together with tight coupling, then discover two weeks later that the whole thing is a single point of failure wearing a trenchcoat. Decouple the collectors from the renderer. Write one file per source. Tolerate stale data. Never let one broken tile blank the whole report.

The best part is that it's boring. There's no magic. It's cron, JSON files, Python, and Jinja2. Technology that was boring ten years ago and is still boring today. Which is exactly what you want for something that runs at 8am while you're still asleep.

Mine blanked out once. It won't again. Not because I'm a better engineer this week than last week. The dropbox pattern is what's doing the work. The architecture is the safety net, not my attention.

30 WooCommerce Performance Tips That Actually Work (2026)

Marcin Dudek — Thu, 09 Jul 2026 13:48:25 +0000

Originally published on https://marcindudek.dev/blog/30-wordpress-woocommerce-performance-tips-2026/

I've been doing WordPress performance audits for years. Hundreds of stores. And I keep running into the same problems - not exotic edge cases, just wrong defaults and things running that shouldn't be.

I posted these as a 30-day LinkedIn series. Collecting everything here so it's searchable. The full stack: WooCommerce internals, MySQL config, PHP-FPM tuning, Nginx caching, Redis. Each one is something I've fixed in production.

Note: Most of these tips require access to server config - php.ini, nginx.conf, my.cnf, Redis settings. That means VPS or dedicated hosting. If you're on shared hosting, a lot of this won't be applicable. Worth knowing before you dig in. I host all of my projects on Mikrus Issues baked into WooCommerce defaults. No plugin needed - just knowing they exist.

Cart fragments fire on every page load

Tip #1
Every page on your WooCommerce store fires a hidden AJAX request called wc-ajax=get_refreshed_fragments. It exists to update the cart icon in your header. And it boots the entire WordPress + WooCommerce stack to do it.

That's a full PHP execution on every page load that nobody sees in performance tests. Your users feel it though. If you don't use a mini-cart widget, one line fixes it:

wp_dequeue_script('wc-cart-fragments');

If you do use a mini-cart, at least limit this to shop pages - don't let it fire on your blog posts. I've seen this single change cut server load by 30% on stores with decent traffic.

Redis is evicting your customers' shopping carts

Tip #2
If you're using Redis for object cache, check your maxmemory-policy setting. The default is usually allkeys-lru, which means when Redis runs out of memory it evicts any key - including active WooCommerce cart sessions.

Customer fills a cart, browses for 10 minutes, goes to checkout: empty cart. Because Redis silently deleted their session during a traffic spike.

maxmemory-policy volatile-lru

Now Redis only evicts keys that have a TTL set. Your persistent cache keys survive; sessions with expiry get cycled naturally. Also run:

redis-cli INFO stats | grep evicted_keys

If that number keeps climbing, you need more memory or fewer cached things.

HPOS with sync enabled doubles every order write

Tip #3
WooCommerce's High Performance Order Storage moves orders from the slow wp_posts/wp_postmeta tables to dedicated tables. Real improvement. But by default it runs in compatibility mode with sync enabled - meaning every order write goes to both the old tables and the new ones. You're not saving database load, you're doubling it.

After verifying HPOS works and your plugins are compatible, disable sync: WooCommerce → Settings → Advanced → Features. You'll cut order-related database writes in half overnight.

Product lookup tables go stale silently

Tip #4
WooCommerce maintains a denormalized table called wp_wc_product_meta_lookup - it caches product prices, stock status, ratings. The problem: if you import products via CSV, use a bulk editor, or sync from an ERP, the lookup table doesn't update. WooCommerce only refreshes it through its own hooks.

So your prices are correct in wp_postmeta but the lookup table shows old data. Product sorting is wrong, filters show stale stock, on-sale products don't appear in sale queries.

Fix: WooCommerce → Status → Tools → Regenerate product lookup tables. I run into this at least once a month with stores that do any external data sync.

WooCommerce fires database writes you don't know about

Tip #5
WooCommerce periodically updates woocommerce_tracker_last_send, _transient_wc_count_comments, and various usage tracking options. Each one is a write to wp_options on an autoloaded row - which invalidates the entire alloptions cache (see the alloptions tip).

Even if you opted out of WooCommerce usage tracking, some of these still fire. Small writes, but on a high-traffic store they add up to constant cache invalidation. SAVEQUERIES will find them all.

Database & MySQL

In my experience most WordPress performance issues start in the database. Wrong defaults, bloated tables, missing indexes. I find these on pretty much every store I audit.

Your autoloaded options are probably 5x too big

Tip #6
Run this query right now:

SELECT SUM(LENGTH(option_value)) FROM wp_options WHERE autoload='yes';

If the result is over 1MB, every single page load is dragging that data into memory. WordPress loads all autoloaded options on every request, no exceptions. The usual culprits: abandoned plugin settings that never got cleaned up, WooCommerce transients marked autoload=yes by accident, old theme options from three redesigns ago. I've seen stores with 8MB of autoloaded data. The site owner was blaming their hosting.

MySQL's query cache is hurting you

Tip #7
If you're still running MySQL's query_cache, turn it off. MySQL deprecated it in 5.7 and removed it in 8.0 for a reason. Every time something writes to the database - and WooCommerce writes on every order, every cart update, every stock change - the query cache takes a global mutex lock. All queries wait in line while the cache invalidates.

One order = hundreds of cached queries trashed simultaneously. The "cache" is now your bottleneck.

query_cache_type = 0
query_cache_size = 0

If you're on MariaDB and think you're safe: same problem, same mutex lock.

Your database server is reading from disk on every product page

Tip #8
MySQL's default innodb_buffer_pool_size is 128MB. That's barely enough for a fresh WordPress install with no content. If your database is 500MB+ (and with WooCommerce it probably is - wp_postmeta alone gets massive), MySQL is reading from disk on every query. That's the difference between 2ms and 200ms per query.

The fix: set innodb_buffer_pool_size to 70–80% of available RAM on a dedicated DB server. If your app and DB share a box, aim for 40%. Check your hit ratio:

SHOW STATUS LIKE 'Innodb_buffer_pool_read%';

If Innodb_buffer_pool_reads is more than 1% of Innodb_buffer_pool_read_requests, you're hitting disk too often. This is probably the single biggest performance win available - and most hosts leave it at the default.

Payment gateways are silently bloating your database

Tip #9
Check how many rows Stripe left in your wp_options table:

SELECT COUNT(*) FROM wp_options WHERE option_name LIKE '_transient_wc_stripe%';

Stripe, PayPal, Square - they all cache API responses as individual transient rows. Thousands of them. They're autoload=no, so they don't load on every page. But they still bloat the table's physical size on disk. A bigger table means slower queries for everything else, including the autoloaded options that do load on every page. Clean them out, then figure out why your cleanup cron isn't doing its job.

WooCommerce sessions table grows forever

Tip #10
Run this:

SELECT COUNT(*) FROM wp_woocommerce_sessions;

WooCommerce stores full cart and customer data as serialized blobs for every visitor. The built-in cleanup cron deletes only 1,000 expired sessions every 48 hours. If you get 10,000 visitors a day, you're accumulating dead sessions way faster than they're being cleaned. I've seen stores with 500K+ rows in this table.

It gets worse: the session data is serialized PHP, so some rows are huge. The table fragments, queries slow down, and eventually checkout starts timing out. Clean it manually, then consider lowering the session expiry or running the cleanup more often.

Action Scheduler is silently growing to millions of rows

Tip #11
WooCommerce's Action Scheduler keeps completed actions for 30 days by default. Sounds reasonable until you check the actual table size:

SELECT COUNT(*) FROM wp_actionscheduler_actions;

On a busy store processing orders, sending emails, syncing inventory - this table can hit millions of rows. Other queries reference it, indexes get bloated, and the admin page for "Scheduled Actions" becomes unusable. One filter fixes it:

add_filter('action_scheduler_retention_period', function() {
    return DAY_IN_SECONDS;
});

Keep completed actions for 1 day instead of 30. You don't need a month of "email sent successfully" records.

WordPress doesn't index wp_postmeta.meta_value

Tip #12
Every WooCommerce query that filters by price, SKU, stock status, or any custom field runs a full table scan on wp_postmeta - because WordPress doesn't add an index on meta_value. On a store with 500K+ rows in that table, a single product search can take 2 seconds.

One query fixes it:

ALTER TABLE wp_postmeta ADD INDEX idx_meta_value(meta_value(191));

That 191 is the max length for a utf8mb4 index in InnoDB. After this, the same product search takes 20ms. I don't know why WordPress core still doesn't include this index - it's been a known issue for years.

tmp_table_size is killing your WooCommerce reports

Tip #13
If your WooCommerce Analytics pages are painfully slow, it's probably not PHP - it's MySQL creating temporary tables on disk. WooCommerce Analytics runs GROUP BY queries across orders. When the result set doesn't fit in memory, MySQL writes it to disk as a MyISAM temp table. Default tmp_table_size is 16MB. For a store with 10K+ orders, that's not enough.

SHOW STATUS LIKE 'Created_tmp_disk_tables';

If that number keeps climbing, set:

tmp_table_size = 64M
max_heap_table_size = 64M

Both need to match - MySQL uses the lower of the two. After this, analytics queries stay in memory instead of grinding your disk.

PHP & PHP-FPM

PHP-FPM defaults are set for minimal resource usage, not for WooCommerce. Most of the server performance you're leaving on the table is here.

PHP-FPM slow-log is free profiling that nobody uses

Tip #14
There's a free profiling tool built into your server that most devops teams never enable. In your PHP-FPM pool config, add:

slowlog = /var/log/php-fpm-slow.log
request_slowlog_timeout = 3s

Now every request that takes longer than 3 seconds gets a full stack trace dumped to that log. You'll see exactly which function, which plugin, which WooCommerce hook is blocking. No external tools, no paid services, no code changes. It's been there the whole time. This is the first thing I check on any slow WooCommerce site.

OPcache interned strings buffer is too small

Tip #15
PHP's OPcache has a setting called opcache.interned_strings_buffer. Default is 8MB. WordPress + WooCommerce + plugins needs 32–64MB. When it's too small, PHP can't share string data between FPM workers. So every worker duplicates its string storage in memory. If you have 12 workers, that's 12× the memory usage for strings. Your server looks like it needs more RAM. It doesn't. It needs this one php.ini change:

opcache.interned_strings_buffer=64

Check current usage with:

php -r "print_r(opcache_get_status());"

Look at the interned_strings_usage section. If used_memory is close to buffer_size, you're wasting RAM on duplicated strings.

PHP-FPM pm.max_children - the math nobody does

Tip #16
Most people either leave pm.max_children at the default (5) or set it to something ambitious like 50. Both are wrong. Here's the actual formula:

(Total RAM - OS overhead - MySQL - Redis) / average PHP worker memory

Check your worker size:

ps --no-headers -o rss -C php-fpm | awk '{sum+=$1} END {print sum/NR/1024"MB"}'

Typical WooCommerce worker uses 40–80MB. On a 2GB VPS after OS, MySQL, and Redis, you've got maybe 800MB for PHP - that's 10–20 workers. Set it to 50 and you get OOM kills during checkout. Set it to 5 and you've got 45 customers waiting in line. Do the math. It takes 2 minutes.

Shutdown hooks are holding your PHP workers hostage

Tip #17
Your page loads fast. TTFB looks great. But your server feels overloaded with way less traffic than it should handle. Check what's running in PHP shutdown hooks. Plugins that fire analytics pings, API calls, or webhook notifications in register_shutdown_function() keep the FPM worker busy after the response is sent.

The user sees a fast page. But the worker isn't free for the next request yet. If you have 10 workers and each one is held for 500ms by shutdown hooks, your effective capacity is halved. The PHP-FPM slow-log catches these too.

Never use pm = dynamic for WooCommerce

Tip #18
PHP-FPM has three process management modes: static, dynamic, and ondemand. For WooCommerce, never use dynamic. It tries to scale workers up when needed, but the scaling has latency - forking new PHP processes takes time. During a checkout surge, customers wait while FPM spins up workers.

High-traffic stores: use pm = static. Workers are always warm, no fork overhead, predictable memory.
Spiky traffic: use pm = ondemand. Workers scale to zero between bursts, fork on demand. At least it's honest about the cold start.

Dynamic pretends to be smart about both scenarios and fails at both. Pick one.

Open source: I published my nginx config templates for WooCommerce at github.com/MarcinDudekDev/nginx-optimizer - PHP-FPM pool configs, fastcgi_cache setup, keepalive tuning.

Nginx & Caching

Most "caching doesn't work" complaints I've seen come from nginx being misconfigured, not broken. The config is mostly right, the strategy is off.

Your fastcgi_cache isn't bypassing WooCommerce carts properly

Tip #19
If you're running nginx with fastcgi_cache, check your cache bypass rules. WooCommerce sets woocommerce_items_in_cart and woocommerce_cart_hash cookies the moment someone adds anything to cart. Your bypass rule needs to check for these specifically - not just the logged-in cookie.

Otherwise you're serving cached cart pages with stale quantities. Customer adds 3 items, sees 1. Checks out, gets the wrong total.

if ($cookie_woocommerce_items_in_cart) { set $skip_cache 1; }

I've debugged this exact issue on at least a dozen stores. The caching "works" - it just serves wrong data to shoppers.

nginx upstream keepalive - and why HTTP/2 won't save you without it

Tip #20
Most nginx configs open a new connection to PHP-FPM for every single request. Even with unix sockets, that's a connect/accept/close cycle every time - 5–15ms of overhead per request. Add this to your upstream block:

upstream php-fpm {
    server unix:/var/run/php-fpm.sock;
    keepalive 16;
}

And in your location block:

proxy_http_version 1.1;
proxy_set_header Connection "";

Now connections persist between requests. PHP-FPM workers skip the accept/close overhead. On a busy store doing 50+ requests/second, this adds up to real savings.

This matters even more if you've enabled HTTP/2. Your visitors get multiplexed connections and header compression - but between nginx and PHP-FPM you're still on HTTP/1.1 with a new connection per request. Without keepalive, every request to PHP-FPM is a fresh connection. The frontend optimization is pointless if the backend bottleneck remains.

Cache PURGE is nuking your entire store on every sale

Tip #21
If you use nginx fastcgi_cache with nginx-helper for purging, check the purge settings. The default behavior purges everything when any post is updated.

A customer buys a product. Stock changes. WooCommerce fires a post update. nginx-helper purges the entire cache. Every page on your site goes cold. The next 50 visitors all hit PHP-FPM directly.

That's not caching. That's a ticking bomb triggered by every sale. Set it to purge only the modified URL + homepage. A stock update on one product shouldn't nuke your cache for 500 other product pages. I've seen stores where the cache hit ratio was under 20% because of this. They thought nginx caching "didn't work."

stale-while-revalidate prevents cache stampedes

Tip #22
When your nginx cache expires on a popular product page, without stale-while-revalidate, the first visitor after expiry waits for a fresh PHP response. And the second. And the third. All hitting PHP-FPM at once. That's a cache stampede.

Two lines fix it:

fastcgi_cache_use_stale updating;
fastcgi_cache_background_update on;

Now when cache expires, the first visitor gets the stale (but fast) response. Nginx fetches the fresh version in the background. No stampede, no cold cache spike, no checkout timeouts during a sale.

open_file_cache eliminates thousands of syscalls

Tip #23
A typical WooCommerce product page loads 15–30 CSS and JS files. Every one triggers an open() system call in nginx. Multiply by requests per second and that's thousands of unnecessary file lookups. Add to your nginx config:

open_file_cache max=10000 inactive=60s;
open_file_cache_valid 30s;
open_file_cache_min_uses 2;

Now nginx caches file descriptors and metadata in memory. Second request for the same file skips the filesystem entirely. On stores with lots of product variation images, this is even more significant.

Redis & Object Cache

update_option() invalidates your entire autoload cache

Tip #24
WordPress caches all autoloaded options in one object cache key called alloptions. Smart - until one plugin calls update_option() on any autoloaded option. That single call invalidates the entire alloptions cache. Next request, WordPress fetches all autoloaded options from MySQL again. If your autoloaded data is 2MB, that's a 2MB query on the next page load.

The worst offenders: analytics plugins that store hit counters in wp_options, rate limiters that update timestamps, anything that writes to an autoloaded option on every request. Find them:

define('SAVEQUERIES', true);

Then grep for UPDATE.*wp_options in your query log. If something's updating options on every request, that's your problem.

Your Redis needs more memory than you think

Tip #25
Quick math for WooCommerce Redis sizing:

Each object cache entry: 1–10KB average
wp_alloptions: 500KB–2MB alone
WooCommerce sessions: 2–5KB each
500 concurrent shoppers: 2.5MB just for sessions
Product cache for 5K products: ~50MB

Most people allocate 64MB for Redis and wonder why things randomly slow down. Redis hits the limit, starts evicting, and you get cache stampedes on the evicted keys. Set Redis maxmemory to at least 2× your typical used_memory. Check with:

redis-cli INFO memory | grep used_memory_human

If it's within 80% of maxmemory, you're living dangerously.

WordPress & Server

wp-cron does a DNS lookup of your own domain on every page load

Tip #26
By default, WordPress fires a loopback HTTP request to itself on every page load. That's spawn_cron() doing a full SSL handshake + DNS resolution of your own domain - just to check if there's a scheduled task to run. On shared hosting, that's 1–3 seconds of invisible overhead. On every request. The fix takes two minutes:

// wp-config.php
define('DISABLE_WP_CRON', true);

# system cron
*/5 * * * * wget -q -O - https://yoursite.com/wp-cron.php

Now cron runs every 5 minutes from a proper scheduler instead of hijacking your visitors' page loads.

One bad pre_get_posts filter destroys your entire admin

Tip #27
If your WordPress admin is slow - especially the posts/products list and search - check your pre_get_posts filters. One plugin adding a meta_query without checking is_admin() or is_main_query() means every admin list table, every AJAX search, every product lookup gets an unindexed JOIN against wp_postmeta. On a store with 50K products, that's a table scan on every keystroke in the admin search.

define('SAVEQUERIES', true);

Check the queries on any slow admin page. You'll find the culprit. It's almost always a plugin trying to be clever with custom sorting or filtering.

The loopback problem nobody talks about

Tip #28
WordPress uses loopback requests for more than just cron. The Site Health check does it. The block editor does it. Plugin update checks do it. WooCommerce background processing does it. Each one is your server making an HTTP request to itself - through the full network stack. DNS resolution, TCP connect, SSL handshake, full WordPress boot on the receiving end.

If your server's DNS is slow, or you're behind a CDN with strict firewall rules, or your hosting blocks loopback connections, these silently fail or add seconds of overhead. Check Site Health for "loopback request" warnings - they're not just informational. They tell you something is fundamentally broken in how your server talks to itself.

Hidden writes killing your object cache hit ratio

Tip #29 & 30
Bring these two together: SAVEQUERIES catches known culprits like the pre_get_posts filter problem and the alloptions invalidation. But there's a deeper pattern worth knowing.

Every plugin that writes to wp_options on a page load has the potential to trash your cache hit ratio. One update_option() in a rarely-audited code path, running on every request, compounds across traffic. Run SAVEQUERIES, look for UPDATE and INSERT on wp_options, and trace each one back to its plugin. The offenders are always surprising.

The one thing that actually matters

I've been doing WordPress performance for years. Hundreds of sites. And the pattern is always the same.

Most performance problems aren't about server specs. They're about things running that shouldn't be running. Queries nobody asked for. Data nobody needs. Processes firing on every page load because someone forgot a conditional check.

The fix is almost never "get a bigger server." It's "figure out what's happening that shouldn't be."

That's why I built WP Multitool - 13 modules that find exactly this stuff: slow queries, bloated autoload, orphaned transients, heavy callbacks. All local, nothing leaves your server.

If any of these tips helped, the plugin will find more issues specific to your store.

I Checked If Ghost Is The Answer To WordPress Problems

Marcin Dudek — Thu, 09 Jul 2026 12:51:05 +0000

Originally published on https://marcindudek.dev/blog/ghost-vs-wordpress/

I keep hearing Ghost mentioned as the fix for everything wrong with WordPress. People I respect say it. So I stopped dismissing it and actually looked.

Full disclosure first: I run a WordPress performance business. My livelihood depends on WordPress being slow enough that people pay me to make it fast. When someone says "Ghost solves all this," my instinct is to close the tab. I had to push past that to write this honestly.

I spent a few days going through 14 categories: hosting, performance, editor, themes, plugins, SEO, e-commerce, memberships, dev experience, security, community, migration, multilingual, and total cost of ownership. Real CVE counts, real benchmarks, real pricing, ~150 primary sources.

01. The summary

WordPress and Ghost aren't really competitors. They solve overlapping but distinct problems - and after going through fourteen dimensions, neither wins overall. WordPress wins on capability, ecosystem, and flexibility. Ghost wins on simplicity, security, and total cost for content-first work. Which one makes sense depends on what you're actually building.

Across 14 categories: WordPress decisively wins 4 (hosting flexibility, e-commerce, plugins, multilingual). Ghost decisively wins 4 (memberships/newsletter, security, migration/lock-in, total cost of ownership for content sites). The other 6 are genuine splits where the right answer depends on use case.

The one thing to remember: WordPress's strengths are also its liabilities (60k plugins = 60k attack vectors). Ghost's limits are its features too (no plugins = no plugin nightmares). Anyone telling you "Ghost replaces WordPress" or "WordPress is dead" is selling something.

02. Scoreboard

#	Category	Winner	Margin	One-line take
01	Hosting & deployment	WordPress	Decisive	WP runs on $3 shared hosting; Ghost demands a VPS or Ghost(Pro)
02	Performance & scalability	Split	Conditional	Ghost faster by default; WP higher tuned ceiling with LiteSpeed
03	Content editor & writing UX	Split	Conditional	Ghost wins for prose; WP wins for structured pages
04	Themes & design	WordPress	With caveats	WP infinite ugly choice; Ghost curated narrow quality
05	Plugins & extensibility	WordPress	Capability win	61k plugins (59% abandoned); Ghost has zero (intentional)
06	SEO capabilities	Split	Narrow	Ghost lower floor & lower ceiling; WP plugin-dependent
07	E-commerce	WordPress	Knockout	Ghost isn't even competing — no products, no cart
08	Memberships & newsletter	Ghost	Decisive	Built for this; WP needs 5 plugins to match
09	Developer experience & APIs	Split	Philosophy	Ghost cleaner DX; WP infinite extensibility
10	Security	Ghost	Clear (with asterisk)	WP had 7,966 CVEs in 2024; Ghost a handful
11	Community & business viability	Split	Different risks	WP scale unmatched; Ghost governance cleaner
12	Migration & lock-in	Ghost	Wide margin	WP plugin sprawl creates migration tar pit
13	Multilingual & i18n	WordPress	Lopsided	Ghost has none. Nine-year open issue.
14	Total cost of ownership	Ghost	3 of 4 scenarios	WP only wins TCO when e-commerce is required

03. Decision framework

If your project is…	Pick	Why
Personal blog or essays	Ghost	Cheaper, faster, better editor for prose, zero maintenance
Paid newsletter / paid membership / Substack alternative	Ghost	Built for it. WP needs MemberPress + ESP + plumbing.
Media publication (multi-author)	Ghost	Self-hosted Ghost or Ghost(Pro) Business; built-in everything
E-commerce store (physical or digital products with cart)	WordPress	WooCommerce is the only sane option. Ghost cannot do products.
Multilingual site (2+ languages)	WordPress	Ghost has no native multilingual support after 9 years
Site needing custom content types & structured data	WordPress	ACF + custom post types. Ghost has posts and pages only.
Course platform / LMS	WordPress	LearnDash, LifterLMS, Tutor exist on WP. Ghost cannot do LMS.
Forum, directory, marketplace, booking	WordPress	Plugin categories that simply don't exist on Ghost
Existing WP site with 20+ plugins	WordPress	You're locked in. Migration is a rebuild, not an export.
You want to host on shared hosting for $3-5/mo	WordPress	Ghost has no shared hosting story
You want a cheap one-vendor solution without thinking about infra	Ghost	Ghost(Pro) at $15-29/mo. WP managed costs more for less.

The 14 categories

04. Hosting & deployment

Winner: WordPress, decisively, on flexibility and price floor

WP runs on $3 shared hosting up through $200/mo Kinsta and everything in between. Ghost has no shared hosting story - you need a VPS. Docs say 1 GB RAM is enough, but the official Ghost DigitalOcean droplet ships on 2 GB because the install/update process OOMs on 1 GB. Or there's Ghost(Pro) at $15-199/mo. WP has dozens of competing managed hosts driving prices down. Ghost(Pro) is essentially the only first-party managed option.

Ghost wins on update tooling: Ghost-CLI is a single deterministic tool that handles Node version checks, MySQL migrations, NGINX/SSL, and systemd. WP has nothing equivalent for server-level concerns. WP-CLI is application-level only.

Ghost has no native multi-tenant story. WP Multisite exists, runs at scale, has known limitations.

05. Performance & scalability

Winner: split — Ghost wins out-of-box; WP wins tuned ceiling

Untuned Ghost: 240-280ms TTFB, 94-96 PageSpeed. Untuned WordPress: 800ms-2s TTFB, 3-5s LCP routine. Tuned WordPress with the right stack: sub-100ms TTFB, 3ms p95 - I have the benchmarks to prove it. Tuned Ghost: bottlenecked by the Node single-process model unless you cluster.

Ghost ships built-in image optimization (WebP, srcset, lazy loading). WP requires Smush/ShortPixel/Imagify. Inexcusable in 2026.

For most people, neither platform will be the bottleneck. Your CDN, your images, and your third-party JavaScript will be.

Ghost dropped SQLite-in-production in v5. MySQL 8 only. That "lightweight serverless Ghost" dream is gone.

06. Content editor & writing UX

Winner: split — Ghost wins for writing prose, WordPress wins for building structured pages

Ghost's Koenig (now Lexical-based) is consistently rated the best long-form writing surface among CMSes. Markdown first-class. Distraction-free by default. Native real-time collaboration. Email/web split content (mark sections "email only" or "web only") - WP has no equivalent without plugins.

Gutenberg has well-documented UX problems. The Classic Editor plugin still has 10M+ active installs in 2026 - that's the user vote. WPShout's 340+ opinion survey came back roughly 50/50, which is a damning result for a flagship feature seven years in.

Ghost weaknesses: tables are genuinely broken. No structured custom fields (no ACF equivalent). Card ecosystem ~25 vs Gutenberg's hundreds. No inline comments or suggested edits.

07. Themes & design

Winner: WordPress, with an asterisk

WP has 9,000+ free themes in the .org directory + 50,000+ on ThemeForest. Ghost has ~160 marketplace listings (~20 free). Page builders (Elementor 40-50% market share, Divi 10-12%, Bricks growing fast) are WP's killer feature - Ghost has nothing like them.

But "more" is doing heavy lifting here. Most ThemeForest themes are bloated - 2-5MB CSS/JS, jQuery, bundled premium plugins you didn't ask for. Divi generates the bulkiest frontend code of the major builders. Elementor sites typically score 50-60 on mobile PageSpeed.

Ghost's curated quality floor is much higher. The median Ghost site looks better than the median WP site. The best WP site beats the best Ghost site by a country mile.

08. Plugins & extensibility

Winner: WordPress on raw capability, but the headline number lies

WP has ~61,000 plugins. Ghost has zero (intentional). But ~34,000 WP plugins (59%) haven't been updated in 2+ years and are effectively abandoned. The actively maintained, well-supported set is closer to a few thousand.

Ghost replaces plugins with: API-first design, Zapier integration (8,000+ apps, $20-69/mo Pro/Team tier), webhooks, custom integrations panel. Real ongoing cost where WP would be free.

Whole categories of WP plugins have no Ghost equivalent at any price: e-commerce, page builders, forms-with-logic, LMS, forums, booking, directory listings, real affiliate programs.

WP's ecosystem is a liability as much as an asset. Pick WP for breadth; pick Ghost if you'd rather pay $20/mo to Zapier than spend Saturday morning bisecting plugin conflicts.

09. SEO capabilities

Winner: WordPress narrowly — but only with a plugin and discipline

Both can rank. Google doesn't care which CMS you use, only what HTML you ship.

Ghost ships SEO-ready by default: JSON-LD schema, sitemaps, OG/Twitter, canonicals, fast pages. No plugin needed.

WP default is mediocre. Calling it "WordPress SEO" is really "Yoast/RankMath SEO running on WordPress." Once you add a plugin: granular schema (FAQ, HowTo, Product, Recipe, Event), redirect UI, hreflang via WPML/Polylang, RankMath's 2026 llms.txt + AI-search tracker.

Ghost ceiling caps at "very good blog SEO." No native multilingual/hreflang. Redirect management is YAML upload, not a UI. Schema is fixed (Article/Person/Organization only).

10. E-commerce

Winner: WordPress by knockout — Ghost isn't competing

Ghost officially says it's not an e-commerce store. No inventory, no cart, no product pages. Only paid Tiers (memberships).

WooCommerce powers ~4.17M live stores, ~$30-35B GMV/year, 20-39% global e-commerce market share. Sells anything: physical, digital, virtual, downloadable, subscriptions, bookings, courses, licenses.

The WP+Woo cost reality: "free" platform becomes $500–1,500/yr in licenses (WC Subscriptions $279, tax plugin, shipping plugin, etc.) before you sell anything. Operationally a mess: 10–25 extensions per real store, each with its own update cadence and CVE history.

Pick Ghost if your "store" is your writing and your customers are readers. Pick WP if your store has SKUs, shipping addresses, tax jurisdictions, or refund queues.

11. Memberships, newsletter & paid subscriptions

Winner: Ghost, decisively, for the 80% case

Ghost was built for this. One product, one bill, one login. All of this is native - no plugins:

Members, paid tiers, free signups, gated content
Email newsletter + Stripe Connect
Zero platform fees (only Stripe's 2.9% + 30¢)

Ghost(Pro) Creator plan: $25–29/mo all-in.

The equivalent WordPress stack, and what it costs:

Managed host - $30/mo
MemberPress - $179–399/yr
Newsletter Glue or FluentCRM - $99–129/yr
Mailgun/SES - $10–30/mo
Stripe gateway plugin, caching plugin, sometimes a separate paywall plugin

Realistic baseline: $50–80/mo and 4–6 dashboards to check.

Ghost weaknesses worth flagging: single ESP lock-in (Mailgun, which doubled Flex pricing Dec 2025). Stripe-only payments - no PayPal, no Paddle, no Lemon Squeezy, no merchant of record. No one-time payments, no lifetime memberships, no group/corporate licenses, no LMS.

Substack is the third option: zero upfront, but a 10% revenue tax that becomes brutal at scale (>$2k/mo MRR makes Ghost migration a no-brainer).

12. Developer experience & APIs

Winner: split — lean WordPress for breadth, Ghost for purity

Ghost feels like a modern Node.js product: clean Content API + Admin API, JS client, JWT auth, Handlebars themes. Headless is first-class - Gatsby, Next.js, Astro, and 11ty all have official source plugins.

WordPress feels like 22 years of accumulated PHP scaffolding - but that scaffolding is what makes it the most extensible CMS on earth. Hooks/filters give surgical control. Custom post types + ACF = real content modeling. WPGraphQL became Automattic-sponsored in late 2024 and is the canonical headless WP choice.

Ghost weaknesses: no plugin system (intentional). Posts and pages only - no custom post types or taxonomies. Ghost breaks things between majors (v4 to v5 dropped multi-version API support). Smaller talent pool.

The "Ghost has better DX" take is true for clean publishing projects. The "WordPress has better extensibility" take is also true, but it comes with a 22-year PHP tax. Both are right depending on what you're building.

13. Security

Winner: Ghost, clearly — with one asterisk

WordPress ecosystem racked up 7,966 new CVEs in 2024 (+34% YoY). Patchstack + Wordfence filed over 10,000 CVEs in 2025. 96% of those vulnerabilities live in plugins and themes, not core.

Recent WP supply-chain disasters:

June 2024 Social Warfare 5-plugin compromise - attacker injected admin-creation code into multiple plugins on WordPress.org
April 2026 EssentialPlugin attack - buyer acquired 30+ plugins, planted a dormant PHP backdoor, activated April 5, 2026 - 20,000+ sites compromised, C2 via Ethereum smart contracts
CVE-2025-11749 in AI Engine plugin - CVSS 9.8 privilege escalation on 100,000+ sites

Ghost has roughly a dozen lifetime CVEs. No supply-chain attacks. There's no plugin system to compromise.

A few caveats:

Ghost's small research community means fewer eyes - absence of evidence isn't evidence of absence.
Members API has been a recurring weak spot (CVE-2024-43409).
A managed WP host (Kinsta, WP Engine) with disciplined plugin hygiene + Patchstack + Wordfence can be made acceptably secure. Vanilla WP on shared hosting, not so much.

14. Community & business viability

Winner: split — WordPress on raw scale; Ghost on governance

WordPress: ~43% of the web (W3Techs April 2026), ~60% CMS market share. WordCamp Asia 2026: 2,627 attendees. WordCamp Europe expecting 3,000+. Tens of thousands of plugins. Effectively infinite hiring pool.

Ghost: ~$8.5M ARR, ~$600k MRR, ~35 staff, profitable for ~12 years. MIT-licensed, non-profit foundation, no investors, founders own zero. Live public revenue dashboard.

The Mullenweg risk isn't theoretical anymore:

Sep 2024 - Mullenweg called WP Engine "a cancer to WordPress", banned them from wordpress.org (broke plugin updates mid-flight), forcibly forked ACF to "Secure Custom Fields", banned them from sponsoring WordCamps
Oct 2024 - WP Engine sued Automattic
Dec 2024 - preliminary injunction granted in WP Engine's favour
Jan 2025 - Mullenweg banned long-time contributors (Joost de Valk, Karim Marucchi) from project Slack over alleged fork talk

Case still grinding through California courts.

What the drama made clear: Mullenweg personally controls wordpress.org, the WordPress Foundation (which holds the trademark), and Automattic. The "open source project" runs on infrastructure owned by one person who has shown he'll use it as a weapon.

ClassicPress voted in 2025 to re-fork from a more recent WP base. AspirePress is mostly Slack threads. Neither is a credible mass replacement, but their existence tells you the community is openly debating exit.

15. Migration & lock-in

Winner: Ghost by a wide margin

Ghost: single-file JSON export. Members are CSV. Ghost(Pro) to self-hosted is identical software - you can leave on Tuesday and be on a $6 Hetzner box by Wednesday.

WordPress WXR XML export is a polite lie for any non-trivial site. Custom fields (ACF), custom post types, postmeta, options, WooCommerce orders, membership state, LMS progress, form submissions - none of that is in WXR.

Page builders are the lock-in endgame. Elementor, Divi, WPBakery, Bricks, Oxygen all serialize layouts as their own shortcodes inside post_content. Switch builders and pages render as [vc_row][vc_column]… literal text.

The 50-plugin problem is asymmetric. Each plugin owns a slice of state in custom tables, postmeta keys, options rows, user_meta. There is no canonical export. Migrating off WordPress at this point isn't migration, it's archaeology + reimplementation. Most agencies quote it as a rebuild.

A clean WordPress install with 5 plugins migrates fine. Nobody has a clean WordPress install with 5 plugins.

16. Multilingual & i18n

Winner: WordPress by an embarrassing margin — the most lopsided category in this comparison

WordPress has three mature multilingual ecosystems - all emit hreflang, admin UI in 70+ locales, first-class RTL support:

WPML €39–199/yr - the most popular, WooCommerce multilingual included
Polylang free + €99/yr Pro - solid free tier, popular with smaller sites
TranslatePress €89–199/yr - front-end visual translation editor

Ghost has no native multilingual content model in 2026. A post has one title, one slug, one body. This is a 9-year-old open issue (TryGhost/Ghost#8509) with no roadmap commitment. Admin UI is English-only. No RTL admin support. The "official" workaround is multiple installs (Ghost Pro Business +$50/mo extra), each a fully independent Ghost instance with its own members table, theme, content. Cross-language linking, shared media, unified analytics, single-sign-on for members: none of it works.

If your site needs to ship in more than one language, this category alone disqualifies Ghost.

17. Total cost of ownership (5-year)

Winner: Ghost in 3 of 4 scenarios — WordPress only when e-commerce is required

Scenario	Ghost(Pro)	Self-host Ghost	WordPress	Winner
Personal blog (1k visitors/mo)	$660	$900	$3,800	Ghost(Pro)
Newsletter / paid membership (5k members)	$3,540	$4,500	$8,200	Ghost(Pro)
Small e-commerce (10k visitors, 100 products)	N/A	N/A	$14,500	WP (only option)
Media publication (100k visitors/mo)	$11,940	$9,200	$22,800	Self-host Ghost

The "WordPress is free" myth dies fast under honest accounting. Free core software + $5 hosting becomes $700–3,000/yr once you need anything beyond a personal blog, because your time is the real cost. WP's hidden costs are recurring (plugin breakage, license compounding, security incidents). Ghost's hidden costs are mostly one-time (theme dev, migration friction).

18. The risks both sides don't want you to remember

Platform	Risk	Severity
WordPress	Mullenweg controls wordpress.org, Foundation, and Automattic. Has demonstrated willingness to weaponize that control.	High
WordPress	96% of CVEs in plugins. Supply-chain attacks via plugin acquisition are now a documented pattern (June 2024, April 2026).	High
WordPress	Plugin sprawl creates rebuild-not-migrate lock-in. Hardest to leave once committed.	Medium
WordPress	Update roulette - plugin updates regularly break sites. Many run unpatched as a result.	Medium
Ghost	Single ESP lock-in (Mailgun). Mailgun doubled Flex pricing Dec 2025.	Medium
Ghost	~35 staff. If the foundation fumbles a major release or burns out, no Automattic-sized backup.	Medium
Ghost	9-year-old multilingual issue with no roadmap. Disqualifies Ghost for international sites.	Medium
Ghost	Stripe-only. No PayPal, no Paddle, no merchant of record.	Low–Medium
Ghost	Ghost breaks things between majors (v4→v5 broke API compatibility). WP almost never does.	Low–Medium
Both	Self-hosters carry OS patching, MySQL upgrades, and DDoS mitigation themselves.	Standard sysadmin tax

19. What it actually means

Most "Ghost vs WordPress" comparisons online are written by people with skin in the game - affiliate revenue, agency relationships, hosting commissions. They almost always conclude WordPress wins because the WordPress economy is huge and pays for those articles to exist.

The read after going through 14 dimensions is more boring than that. The platforms are good at different things. Ghost is good at publishing and newsletters. WordPress is good at being everything-to-everyone - which means it's rarely excellent at any one thing, but uniquely capable when you genuinely need that breadth.

If you want a single winner, it's not here - because honest analysis doesn't produce one. The question isn't "which is better" but "what are you building, and what failure mode can you live with."

20. Sources

Patchstack — State of WordPress Security 2025
Wordfence — June 2024 Supply Chain Attack
Patchstack — EssentialPlugin Compromise April 2026
TechCrunch — WordPress vs WP Engine drama explained
Slate — How Mullenweg’s spat threatens the open-source internet
Ghost — Official pricing
Ghost — Migrating from WordPress
TryGhost/Ghost — 9-year multilingual issue #8509
WPShout — Is Gutenberg Finally Winning Users Over?
W3Techs — WordPress market share 43% (April 2026)
WP Tavern — ClassicPress Re-fork vote
Patchstack — 2025 Mid-Year Vulnerability Report

WordPress Plugin Auto-Update via Polar — and the 3 Bugs I Shipped in 24 Hours

Marcin Dudek — Thu, 09 Jul 2026 12:51:03 +0000

Originally published on https://marcindudek.dev/blog/wordpress-plugin-auto-update-polar/

TL;DR

Built Auto Updater for WP Multitool on top of Polar licensing. Plugin polls a /update-check endpoint, server validates the license, returns a HMAC-signed download URL.
Within 24 hours of shipping 1.2.0, three separate bugs hit the auto-update chain. All three came from cache TTL mismatches between layers I'd reasoned about in isolation:

Bug 1: zips never copied to the on-disk path the download handler reads from.

Bug 2: signed URL TTL was 30 minutes, but WP caches the URL in a transient that lives 12 hours.
Bug 3: my injector's 6-hour cache kept showing "update available" for hours after a successful upgrade.

Fixed each on the day, shipped 1.2.1 and 1.2.2 within the same 24h. Lesson: when you stack caches, write down every TTL and every invalidation trigger on one page. Look for the gaps.

Until last week, customers of my WordPress plugin WP Multitool had to download zips from their Polar customer portal and upload them to wp-admin by hand. Multiple people complained. Reasonably so.

So I shipped an Auto Updater module in 1.2.0. Plugin polls our /update-check endpoint with the customer's license key, our server validates it against Polar's API, returns a HMAC-signed download URL pointing at the right zip. WordPress's native plugin updater handles the rest. Clean architecture. Tested locally. Looked great.

Then within 24 hours three separate bugs hit production, all in the auto-update chain. By the end of the day I'd shipped 1.2.0, 1.2.1 and 1.2.2. This is what each one was, why I missed it, and what they share.

The Architecture (so the bugs make sense)

Three components matter for what comes next:

Plugin side: an Auto Updater module that hooks pre_set_site_transient_update_plugins, calls our update server with the stored license key, caches the response in its own site transient for 6 hours, and injects an update entry into WP's update_plugins transient when one's available.
Server side: a small WordPress site running on the marketing domain (wpmultitool.com) with custom REST routes. POST /update-check takes a license key and current version, validates against Polar (with its own caching), looks up the latest published changelog post for the canonical version, and mints a signed download URL. GET /downloads/wp-multitool-{edition}-{version}.zip validates the HMAC, checks the license is still granted, and serves the zip via X-Accel-Redirect.
Storage: zips live on disk at /var/www/wpmultitool.com/private/releases/wp-multitool-{edition}-{version}.zip. Polar also hosts copies for the customer portal Download benefit, but the auto-updater pulls from disk on the marketing site, not from Polar.

Three layers, each with its own state, each with its own cache. That's where the trouble lives.

What's the plugin side built on?

Honest answer: I wrote the plugin-side code from scratch using WordPress's filter API directly, not on top of an existing library. The hooks involved are all documented core WP: pre_set_site_transient_update_plugins to inject our update entry, plugins_api to feed the "View details" modal, upgrader_pre_download to validate or refresh the package URL right before WP fetches it, upgrader_process_complete to clean up after a successful install. That's the standard pattern any custom updater uses.

If you want a pre-built option instead of rolling your own, the obvious one is YahnisElsts/plugin-update-checker on GitHub. Solid, well-maintained, used by hundreds of commercial plugins. I'd genuinely recommend it if you don't want to think about the protocol. I went bespoke because I wanted edition awareness (free vs paid build of the same plugin), tighter coupling with our own license-server endpoints, and full control over the upgrade-time UX (custom error messages, the self-heal flow you'll see in Bug 2). The price was writing maybe 600 lines of PHP that the library would have given me for free, plus the three bugs this post is about. Trade-off you make consciously.

Bug #1 - The Zips Weren't Where the Server Expected Them

I caught this myself before any customer hit it, while doing end-to-end QA with a synthetic test license. Here's what was happening.

My release flow had nine steps. Bump version, build editions, push tags, upload to Polar's full benefit, upload to Polar's lite benefit, create changelog post on local, mirror to prod, regenerate static HTML, clear caches. All nine ran clean. Plugin tagged, GitHub updated, Polar customer portal showing the new files, changelog published.

I built a test license, called POST /update-check with current_version: 1.2.0. Got back a perfectly-formed signed URL pointing at https://wpmultitool.com/downloads/wp-multitool-full-1.2.1.zip?lic=10&exp=...&sig=.... Curled it. 404.

The download handler reads zips from /var/www/wpmultitool.com/private/releases/ on prod. My release flow uploaded them to Polar so customers could grab them from the portal. It never copied them to that on-disk path. The private/releases/ directory still contained wp-multitool-full-1.1.20.zip from a release weeks earlier and nothing newer.

If I hadn't run the full chain end-to-end with a real license, I'd have shipped broken updates to every paying customer. The plugin would have politely fetched the package URL, hit a 404, and reported "Update failed" to everyone trying to upgrade.

The fix was the easy part. scp the zips to the prod box, move them into private/releases/ with the correct edition-suffixed filename, chown to www-data. The harder part was admitting the documented release flow had a hole, and adding the step so it can't happen again.

If a release artifact has to land in two places (customer-facing and server-internal), the release flow needs explicit steps for both. "Customer can download it" is not the same as "the auto-updater can serve it."

Bug #2 - Signed URL TTL Shorter Than WordPress's Own Cache

This one came from a real customer. Bojan, six sites, three of them stuck on "The signed download URL has expired" when he clicked Update Plugins.

The signed URL handler's expression looked fine on its own:

$exp = time() + WPMTH_LICENSE_DOWNLOAD_TTL; // 1800 seconds = 30 min
$sig = wpmth_license_sign($row['id'], $edition, $version, $exp);

Thirty minutes felt generous. "Customer sees the update notice, clicks Update, done. Plenty of buffer." That's the framing in my head when I picked the value. The framing was wrong.

WordPress doesn't call /update-check when you click Update Plugins. It calls /update-check on its own schedule, roughly every 12 hours, and stores the response in a site transient called update_plugins. When you click Update, WP reads the package URL out of that transient and downloads it. Whatever exp was minted 11 hours ago is the exp WP is using now.

So the failure mode is: WP polls our endpoint at 9 AM, gets a URL with exp = 9:30 AM, caches the whole response. Customer sees the update notice at 2 PM. Clicks Update. URL has been expired for 4.5 hours. Plugin's upgrader_pre_download filter catches it and surfaces a clear error. Upgrade aborts.

The plugin handled the error gracefully. The architecture didn't. I'd reasoned about the URL TTL in isolation, instead of relative to the WP cache that holds the URL.

Fix on the server side: bump TTL to 24 hours. Each download is still license-checked at request time and the HMAC still binds the URL to a specific license, edition, and version. A long TTL doesn't weaken anything except an attacker's tolerance for replay attempts, and they'd need to steal a valid license + URL pair to begin with. So 24h is fine. It's now longer than the WP transient's lifetime, with headroom.

Fix on the plugin side, shipped in 1.2.2: when pre_download sees an expired exp, instead of bailing with an error, refresh the URL inline by re-calling /update-check and hand the fresh URL to download_url(). The original code had a comment saying "we can't refresh from here because WP enters maintenance mode and our update server might 503." That's only true if the customer's site IS the update server (dev self-loop). Guarded that case explicitly, and let the production case go through.

Bug #3 - The Injector Trusted Its Own Cache Blindly

This one was sneakier. Bojan reported it after the TTL fix landed. All six of his sites were now on 1.2.1. ManageWP correctly showed them all as up to date. But each site's own admin still showed "Update to 1.2.1 available" with the same version that was already installed.

Here's the injector code that was wrong:

public function inject_update($transient) {
    $response = $this->ensure_cached();  // 6h cache
    if (!is_array($response)) return $transient;

    $status = $response['license_status'] ?? '';
    $update_available = !empty($response['update_available'])
        && 'granted' === $status;

    if ($update_available) {
        $transient->response[self::PLUGIN_FILE] = $this->build_update_object($response);
    }
    return $transient;
}

See the gap? The cached /update-check response from before the upgrade said "yes, 1.2.1 is available, here's the package." That response is correct at the moment it's cached. After the customer successfully upgrades to 1.2.1, the cache still says exactly the same thing. The injector reads the cached response, sees update_available: true, marks the plugin as upgradeable. WP shows the notice. Customer clicks Update. WP downloads 1.2.1 again, overwrites the same files, success. Cache is still saying "update available." Notice never goes away. For up to six hours.

ManageWP did its own version comparison and ignored our injector's verdict. That's why it knew the truth.

Two fixes, both in the injector:

Defensive version compare. Even if the cached response says an update is available, suppress the notice when our installed version is at or above the advertised new_version. Cheap, foolproof, doesn't depend on cache invalidation working.

$new_version = $response['plugin']['new_version'] ?? '';
$update_available = !empty($response['update_available'])
    && 'granted' === $status
    && '' !== $new_version
    && version_compare($new_version, $this->current_version(), '>');

Proactive cache invalidation. Hook upgrader_process_complete, check if our plugin was the one upgraded, drop the injector cache. Next call to inject_update will refresh against the new installed version immediately.

add_action('upgrader_process_complete', [$this, 'on_upgrade_complete'], 10, 2);

public function on_upgrade_complete($upgrader, $hook_extra) {
    if (($hook_extra['type'] ?? '') !== 'plugin') return;
    if (($hook_extra['action'] ?? '') !== 'update') return;
    $plugins = $hook_extra['plugins'] ?? [];
    if (!in_array(self::PLUGIN_FILE, $plugins, true)) return;
    $this->clear_cache();
}

The version compare is the safety net. The cache clear is the actual fix. Both shipped together in 1.2.2 because I'd rather have one belt and one pair of suspenders.

The Pattern All Three Bugs Share

I reasoned about each cache layer's TTL on its own. None of the values were unreasonable in isolation. 30 minutes is a fine signed-URL lifetime if you forget WP's transient. 6 hours is a fine injector cache if you forget the upgrade lifecycle. "Upload to Polar" is a fine release step if you forget the download handler reads from disk.

What broke them is composition. Server-side URL TTL needs to outlive the WP transient that holds the URL. The plugin's injector cache needs to be invalidated when the underlying state (installed version) changes. The release flow needs to consider every place the artifact has to land, not just the customer-facing one.

None of these failure modes show up on a single dev machine where every cache starts fresh and your installed version moves forward in lockstep with what you publish. They only emerge when customers and your own infrastructure are in different states. Which is, you know, the entire production environment.

For every cache in the system, write down two things on the same page: the TTL, and what triggers invalidation. If the answer to "what triggers invalidation" is "it expires eventually," that cache is a liability waiting to bite you when its TTL doesn't match its neighbour's.

What I'd Do Differently Next Time

Map the cache layers before writing code. Three layers in this case, server URL TTL, WP transient, plugin injector cache. I should have drawn them on paper with their TTLs and asked "what happens if X expires before Y?" for every pair. I'd have caught bug 2 in the design.

End-to-end QA with a real license against a real server. This is how I caught bug 1 before customers did. The local dev environment had everything mocked and happy-pathed. The bug only existed on the gap between "release flow finished" and "download handler runs against disk." Synthetic license + real curl against the production endpoint is cheap and catches the entire deployment-path category.

I wrote it up as a separate post-release verification step. Mint a license row in the prod licenses table, hit /update-check with the previous version, follow the signed URL, check the zip downloads and the manifest reports the new version, delete the test license. Five minutes. Will run it on every release from now on.

Ship the fix the same day. Three patch releases in 24 hours looks busy on the changelog. It also looks like someone who's paying attention. The customers who care will notice the responsiveness. The customers who don't won't notice at all. Worse than three same-day releases is one quiet release that breaks things and gets fixed in three days while customers stew.

Why Polar (since people will ask)

Quick honest comparison since it's relevant to the architecture choice.

Polar: flat 4% + payment fees. License keys built in. No revenue cut beyond the flat fee. EU-friendly tax handling. Used here.
Freemius: takes a percentage on top of fees. Heavyweight integration. Their own update server. Convenient but not cheap.
Easy Digital Downloads: plugin licensing is a paid add-on, decent code, but you're now running EDD plus its dependencies on your marketing site.
Lemon Squeezy: similar tier to Polar, has its own update protocol. Also fine.

For a small WordPress plugin shop, Polar plus a few hundred lines of license-server PHP is genuinely the cheapest thing that works. The zip distribution is yours, the update protocol is yours, the customer keeps a clean Polar receipt. No middleman inside the update flow.

Open-sourcing the approach?

The plugin-side code is mostly provider-agnostic already. There's a LicenseProvider contract and the Polar implementation just fills it in. Stripping the WP Multitool branding and publishing it as a standalone "WP plugin auto-updater for Polar" is a half-day of work.

The server-side license-server is more entangled. It lives inside our marketing theme and uses our DB schema. Extracting it into a standalone WP plugin (something like "Polar License Server for WordPress") is real work, maybe 2-3 days. Worth doing if there's interest. Ping me on X or grab my email from the homepage if you'd actually use it. If a few people raise their hand I'll publish both halves under MIT and write the docs.

Build Your Own Morning Dashboard - A One-Page Recipe

Marcin Dudek — Thu, 09 Jul 2026 12:33:47 +0000

Originally published on https://marcindudek.dev/blog/build-your-own-morning-dashboard/

TL;DR

A personal morning dashboard is four moving parts: a schedule, a folder of small collectors, an orchestrator, and one HTML template.
Each collector talks to exactly one thing (Cloudflare, GSC, Stripe, your DB, whatever) and writes a single JSON file. That's the whole contract.
The orchestrator never calls an API. It reads JSON, slots it into a Jinja2 template, writes one HTML file. If a section is missing or stale, it renders an "unavailable" badge and keeps going.
Open it locally via file://. Don't host it. Around 1000 lines of Python, replaces five SaaS dashboards.
For the resilience details, see the dropbox pattern post.

People keep asking me how my morning dashboard works. Short version: I open one HTML file with my coffee, and every number I care about across my products is on it. No tabs, no SaaS, no login. This is the longer version.

This is not a copy-paste of my actual code. The code is private, it has my API keys and customer leads in it. But the shape of the thing is easy to describe, and the shape is what matters. Build your own version with the shape below and it'll work. Mine is maybe 1000 lines of Python total. Most of those lines are HTML and CSS.

I already wrote the deeper post on failure handling: My Morning Dashboard Went Blank for a Day. That one is about the day mine rendered empty, and the dropbox pattern that should have saved it. This post is the higher-level recipe. The "how do I build something similar" version.

The Four Moving Parts

A morning dashboard is just four things glued together:

A schedule, one thing that fires every morning, on its own.
A bunch of small collectors, one file per data source.
An orchestrator, one script that reads the collectors and writes HTML.
A template, one HTML file with all the design opinions in it.

That's it. No database, no message queue, no web framework. Files on disk, glued by a cron job. Boring on purpose.

Part 1: The Schedule

The whole point is that you don't have to log into eight SaaS tabs every morning. So the dashboard has to actually run by itself. If it depends on you remembering to run it, you've built a worse version of opening eight tabs.

On macOS cron still works (or launchd if you want to be proper). On Linux cron. On Windows Task Scheduler. I run mine at 08:00 local. The cron entry is one line:

0 8 * * * /usr/bin/env bash -lc '~/Tools/morning-digest/run.sh'

The bash -lc matters on macOS. Without a login shell, cron won't have Homebrew on PATH, your collectors will all fail with "command not found", and you'll wonder why your dashboard is blank. Voice of experience.

At the end of the orchestrator script, one extra line pops the file in your default browser:

open ~/reports/morning-latest.html   # macOS
xdg-open ~/reports/morning-latest.html  # Linux

When you sit down with coffee, the tab is already open. That's the whole UX.

Part 2: Collectors

One file per data source. Cloudflare. Google Search Console. Your WordPress database. Stripe or Polar. GitHub. Reddit. Your email signup table. Whatever you care about. Each collector talks to exactly one thing, fetches what it needs, and writes the result to a JSON file on disk. Done.

No collector knows the others exist. No collector imports another collector. No collector renders HTML beyond a tiny snippet. They're the dumbest, most replaceable parts of the system, and that's the whole point.

The contract every collector follows looks like this:

def collect_X():
    try:
        data = call_some_api()
        write_json({
            "state": "ok",
            "title": "GitHub",
            "generated_at": now_utc_iso(),
            "data": data,
        })
    except Exception as e:
        write_json({
            "state": "error",
            "title": "GitHub",
            "generated_at": now_utc_iso(),
            "error": str(e),
        })

Two rules. First one is non-negotiable: a collector must never crash the orchestrator. It always writes some JSON, even if the API is on fire. Second: always include a state field, so the renderer knows whether to draw the panel normally or paint a small "unavailable" badge.

That's the whole contract. Six fields, one rule about never crashing. Anything that follows that shape is a valid collector. Mine are around 30 to 80 lines each. The Cloudflare one is the longest because GraphQL queries are wordy. The GitHub one is shorter because I just shell out to gh.

The point of the contract isn't elegance. It's that you can write a new collector in 20 minutes, drop the file into the folder, and the orchestrator picks it up on the next run without knowing anything about it. New data source = new file. That's the whole integration story.

Part 3: The Orchestrator

One Python script. It reads every JSON file in the collectors folder, slots them into a Jinja2 template, writes one HTML file. The orchestrator is read-only on data. Never calls an API. Never panics. Never imports a collector. Doesn't even know their names ahead of time.

The whole logic is "glob the folder, load each JSON, hand it to the template". Pseudocode:

def render():
    sections = []
    for path in sorted(COLLECTORS_DIR.glob("*.json")):
        sections.append(load_section(path))

    html = template.render(
        sections=sections,
        generated_at=now_utc(),
    )
    OUTPUT.write_text(html)

The interesting part is load_section. Its job is to never raise:

def load_section(path):
    base = {"name": path.stem, "state": "unavailable"}
    if not path.exists():
        return {**base, "reason": "no data file"}
    try:
        raw = json.loads(path.read_text())
    except Exception as e:
        return {**base, "reason": f"invalid json: {e}"}

    age_h = age_in_hours(raw["generated_at"])
    if age_h > STALE_HOURS:                # I use 36
        return {**base, "reason": f"stale ({age_h:.1f}h old)"}
    if raw.get("state") != "ok":
        return {**base, "reason": raw.get("error", "collector error")}

    return {**raw, "state": "ok", "age_hours": age_h}

Default behavior: unavailable. The function only returns a healthy section if the file exists, parses, is fresh, and says state: ok. Any failure along the way downgrades that one section, and the rest of the report still renders.

If too many sections are missing, the orchestrator can refuse to overwrite the previous report. That way you don't wake up to a blank dashboard when everything broke overnight. That whole self-healing story has its own post: the dropbox pattern. Read that one for the gory failure-mode details.

Part 4: The Template

One HTML file. All the design opinions in it. Hard-code your layout. Don't try to make it configurable. You'll never reconfigure it. You'll just iterate on the HTML directly when you decide the masthead is too tall.

I do roughly this:

A masthead with the top-line aggregate numbers: total pageviews across all sites, total revenue last 24h, total new signups. The "did I have a good day" panel.
A grid of project panels, one card per product, each with that product's slice of Cloudflare, analytics, revenue, GSC, etc.
A few generic panels at the bottom: GitHub commits, Reddit mentions, server uptime, email queue depth, whatever.

The Jinja2 trick that makes this robust is the {{ section.data.foo }} indirection. When a section is missing or stale, section.data just isn't there, and you render a small empty cell with an "unavailable" badge instead of crashing the template. Nothing explodes. The dashboard always renders something.

A minimal panel macro looks like this:

{% macro panel(section) -%}

  {{ section.title }}
  {% if section.state == "ok" %}
    {{ section.data.value }}
  {% else %}
    unavailable - {{ section.reason }}
  {% endif %}

{%- endmacro %}

Boring. That's the whole shape. Styling is wherever you want to take it. I run a dark editorial layout (Fraunces serif headline, IBM Plex Mono labels, muted gold accents), because I read it at 8am and dark feels right. Pick what you like. The data flow doesn't care.

Recipe Tweaks Worth Mentioning

A handful of small details that turned my dashboard from "kind of works" into "actually runs every day":

Two-pass runner

At 8am the macOS keychain might still be locked. One of your collectors will try to fetch a credential and fail. Don't retry the whole thing, only retry the ones that flagged state != "ok":

for collector in collectors/*.py; do
    python3 "$collector" || true   # never abort the whole run
done

sleep 30   # give the keychain / network a beat
for collector in collectors/*.py; do
    name=$(basename "$collector" .py)
    state=$(jq -r '.state // "missing"' "collectors/$name.json" 2>/dev/null)
    if [ "$state" != "ok" ]; then
        python3 "$collector" || true
    fi
done

uv run digest.py

Saves API quota. Hides flakiness. The dashboard renders a clean page even though the first pass had three failures.

Open it locally, not online

This is your dashboard. It contains revenue, leads, customer email addresses, maybe internal server hostnames. Keep it as a file:// link in your home folder. Don't host it. Don't put it behind a login either, because there's no login to compromise if the file never leaves your machine. The attack surface is literally your laptop.

Archive each run

Also drop a copy of the HTML into reports/morning-archive/2026-05-14.html. You get a free changelog. You'll be surprised what you learn flipping through old ones. ("Oh, that's the week MRR plateaued. What was I doing that week?")

Credentials in the system keychain

Never paste API keys into a config file. Use the system keychain: security on macOS, secret-tool on Linux. Have the collector fetch the key at runtime. If the keychain is locked at 8am, that's exactly what the two-pass retry handles. Don't store secrets in .env files in the project folder. The whole thing lives in your home directory, and it talks to APIs that move money. Be a little paranoid.

What to Actually Put on It

This is the part nobody can tell you. The dashboard only works if the numbers it shows are numbers you actually care about. Mine are: pageviews, unique visitors, new leads, email signups, 24h revenue, GSC clicks, server uptime, GitHub commits, Reddit mentions of my products. That list is shaped by what I sell and what I'm trying to grow.

Your list will be different. Here's the only filter that matters:

If you wouldn't change your day based on a number, don't put it on the dashboard. That's the whole filter.

"Followers on Twitter": would you do anything different today if it went up or down by 20? No. Don't put it on the dashboard. "Email signups in last 24h": yes, if it spiked I'd want to know which post drove it, and if it died I'd want to check the form. Put it on the dashboard. Apply the filter ruthlessly. Removing things makes the dashboard stronger. Adding things rarely does.

Why Not Just Use Grafana / Metabase / a SaaS

Honest answer: you can. They work. But every one of them is built for teams, and teams need things personal dashboards don't: auth, RBAC, dashboards-as-config, SSO, audit logs. You'll spend more time getting Grafana to talk to your WordPress database than writing the whole dashboard from scratch.

You also don't want to give a hosted SaaS your Cloudflare API token and your Stripe secret key. The whole appeal of the file-on-disk approach is that nothing leaves your machine. Keys live in your keychain. Data lives in ~/reports/. Nothing to log into, nothing to compromise.

And here's the part I didn't expect: when you own all the code, the dashboard becomes a place you can drop one-off ideas. Yesterday I added a panel that shows the top three Reddit posts from any subreddit that mentioned my domain in the last 24h. It's 60 lines of Python and one new entry in the template. Try doing that in a SaaS dashboard tool.

Closing

Start with one collector. Whichever metric you keep opening a tab to check, that one. Write the collector for it tonight. Write a tiny one-panel HTML template. Wire it to cron. Tomorrow morning, the tab is already open. Add a second collector when you find yourself opening a different tab. Repeat.

You'll end up with something that takes 30 seconds to read in the morning, costs nothing to run, and replaces five SaaS dashboards you were never going to pay for anyway. Mine has run every morning for months. Went blank exactly once, and even that taught me something. The dropbox pattern writeup is the postmortem.

That's the whole recipe. Steal it.

Scrape Pass — Get paid when AI reads your site

Marcin Dudek — Thu, 09 Jul 2026 12:33:46 +0000

Originally published on https://marcindudek.dev/blog/scrape-pass/

TL;DR

For twenty years Google sent you readers in exchange for letting it index your site. That deal is over — AI assistants now read your articles and answer the question on their own page, with no click-through.
Scrape Pass (by hunazo) makes AI crawlers buy a pass at the door. Humans and Google still go in free. ChatGPT, Claude, Perplexity and the rest meet a ticket booth.
It works the way real tickets work — chunks of requests up front, not per-page micropayments. Live demo at scrape-pass-test.makewpfast.com. Free during early access.

For twenty years the deal between you and Google was simple: you wrote the article, Google sent the readers, the readers bought your stuff. That deal is over.

Today, AI tools read your articles, summarise them on their own pages, and keep your readers there. ChatGPT, Google's AI Overview, Perplexity, Claude — they all do the same thing. Your traffic drops. Your work still feeds them. You get nothing back.

Scrape Pass turns that around. AI bots that want to read your site have to buy a pass first. Humans don't see anything different. Google still indexes your site normally. Only the AI crawlers see a friendly "pay before you read" message.

The 30-second version

Think of it like a venue.

Humans walk in the front door, free, like always.
Google and Bing get in free too — they send you readers, so they get a pass on the house.
AI bots — ChatGPT, Claude, Perplexity, the rest — meet a ticket booth. They buy a pass, scan it on the way in, read what they came for. When the pass runs out, they buy another or leave.

You're the venue. Hunazo is the ticket booth. You keep the money.

See it working

This is a real site, right now, with Scrape Pass installed. On the left: an AI bot tries to read the page without a pass — it gets a polite "pay first" message. On the right: same bot, with a pass — it reads the page normally and the pass counter ticks down by one.

Same bot, two requests — one without a pass, one with. The pass counter ticks down by one.

Live demo at scrape-pass-test.makewpfast.com. Real bot. Real pass. Real content. The whole thing happens in one second.

Why this works (and why per-page fees don't)

Most paywall attempts charge bots by the page or by the word. Tiny amounts, every request, settled instantly. It sounds clever and it doesn't work — the transaction costs eat the revenue, nobody can dispute anything that small, and every request is a new negotiation. That's not how anyone has ever bought anything.

A Scrape Pass works the way every other ticket on earth works. A bot operator buys a pass up front. The pass covers a chunk of requests over a chunk of time. They use it, it runs out, they buy another. Meaningful money up front, predictable cost for the buyer, predictable income for you. Like a stadium season ticket. Like a SaaS seat. Like a Netflix subscription. Boring and proven.

Stop metering. Start ticketing. — The pitch in four words

Who this is for

If you…	Scrape Pass gives you…
Run a content site, blog, or media business	A new revenue stream from the AI crawlers already eating your traffic, without blocking humans or Google.
Sell information, courses, or guides	A way to charge AI for the same content humans pay for — instead of letting bots quote it for free on someone else's site.
Are tired of GPTBot, ClaudeBot, PerplexityBot ignoring your robots.txt	A polite enforcement mechanism that doesn't break your site for legitimate visitors.
Believe AI companies should pay creators	The plumbing to actually make that happen on your own site, today, without waiting for legislation.

What setting it up looks like

Honestly, very little. Most site owners can do this in under ten minutes.

Your site needs to be on Cloudflare. Free plan is fine. If it's already there, you're ready. If it's not, switching takes about an hour and is worth doing for a dozen other reasons anyway.
You paste a small script into your Cloudflare account. We give you the script and walk you through it. It sits between AI bots and your content. Humans and search engines never see it.
You test it on a subdomain first. Something like ai.yoursite.com. We help you confirm humans still get through, Google still gets through, and AI bots see the pay screen.
When you're happy, you flip it on for the real site. One toggle. Reversible any time.

You don't write code. You don't run a server. You don't manage crypto wallets or contracts or any of that. We handle the entire "ticket booth" side — the bot payment, the pass tracking, the disputes, the AI-bot list that has to stay current as new crawlers show up. You get a dashboard showing which bots paid, how much you've earned, and what's been blocked.

What it costs

Free during early access. No setup fee. No monthly fee. No catch.

When the paid product launches later this year, we'll take a small percentage of each pass sold — the same way Stripe takes a cut when someone pays your invoice. You keep the majority. You get paid into a normal bank account.

We'll never charge you to block bots — we charge a fraction of what bots pay you. If no bot pays, we get nothing. We only make money when you make money.

Honest answers to the questions you're about to ask

Will this hurt my Google rankings?
No. Googlebot and Bingbot are explicitly let through. Your search rankings are untouched. The whole point of this is to charge the bots that don't send you traffic, not the ones that do.

Will normal visitors notice anything?
No. People browsing in Chrome, Safari, Firefox, mobile apps — none of them see anything different. Your site loads the same way. We only intercept requests from known AI crawlers (their browsers identify themselves).

What if a bot just changes its name to pretend it's a browser?
Today the big AI crawlers all identify themselves honestly — that's how they comply with robots.txt rules. If that changes, we have an answer (the same cryptographic bot-verification that Cloudflare uses), and it kicks in automatically. You don't have to do anything.

Will AI bots actually pay?
Right now, mostly no. Today's bots are not yet set up to pay for access. That's changing fast — the same companies (OpenAI, Anthropic, Perplexity) are building wallet-aware crawlers because they know this is coming. The point of getting set up now is to be the kind of site that's ready when those payments start flowing. Sites with Scrape Pass installed will be the first to actually receive money. Sites without it will be the ones still wondering why.

Is this a crypto thing?
Behind the scenes, yes — bot payments settle in a stable digital dollar that's used across the AI agent ecosystem. From your side, no. You don't need a wallet, you don't see crypto, you get paid out in regular money. We handle the conversion. It's the same way Stripe lets a customer pay in any currency while you receive dollars.

Is the demo really live?
Yes. scrape-pass-test.makewpfast.com is a real subdomain of a real site, with Scrape Pass actually installed. Anyone who knows their way around a developer console can confirm it — the demo isn't a screenshot, it's the live behaviour.

What's the catch?
The honest catch: this is early. The AI economy hasn't fully woken up to paying for access yet, so during early access most bots will hit the pay screen and walk away rather than pay. That's fine — it means you stop being scraped for free, which is already a win. As bot operators turn on their wallets (and the major ones are about to), the same setup starts earning. Early adopters get there first.

How to get in

Watch the demo above. Look at the two responses side by side. That's your site, in five minutes' time, if you want it to be.
Get on the early-access list. Reply to @myththrazz on X with the URL of the site you'd want to protect. I'll get back to you personally, walk you through the setup, and you'll be running by the end of the day.
Or just bookmark this page. When the paid version launches, the people on the early-access list go first — with pricing that won't be offered again.

The web's old business model was "give it away free, hope the readers come." That worked while the readers actually came. They don't anymore. Time to charge the thing that's eating your traffic. — The pitch in three sentences

Get on the Scrape Pass early-access list

Free during early access. Setup in under ten minutes. You go first when the paid product launches.

Join the early-access list →

Marcin Dudek — founder of hunazo, the company behind Scrape Pass. Also runs Make WP Fast and WP Multitool. Building infrastructure for the AI economy from Kraków. @myththrazz on X.

How I Stop My Claude Code Sessions From Rotting

Marcin Dudek — Thu, 09 Jul 2026 12:30:10 +0000

Originally published on https://marcindudek.dev/blog/claude-code-context-rot-handoff/

TL;DR

You know the feeling. Claude flies for the first hour, then starts losing the thread - repeating things you settled 40 minutes ago.
You're not imagining it. Context rot is a measurable drop in answer quality as context grows. Chroma tested 18 models, every one degrades.
Hook #1 - a context monitor on the UserPromptSubmit event: counts real assistant turns and transcript size, fires a gentle nudge at two thresholds.
The hook never blocks. It injects a message via additionalContext, so the decision stays with me or the agent.
Hook #2 - /handoff: writes state to a file, restarts a fresh session in the same tmux window, and prompts that session to read the file and keep going. Clean context, no lost thread.

You know the feeling. You've been working with Claude for two hours. The first hour it flew. Now it's starting to lose the thread. It repeats things we settled 40 minutes ago. It suggests a fix we already rejected. It forgets which file the bug lives in. First thought: the model is having an off day. Second thought, the correct one: I left the session running too long and the context rotted.

This is the first post in a five-part series about my Claude Code hooks. They all circle one observation - an agent left to its own devices rots. I'm starting with the most tangible symptom: a long session that gets dumber with every turn.

The problem: you can actually measure context rot

For a long time I figured "the model gets tired" was just me projecting. I'm tired after three hours of debugging, so I assume Claude is too. Turns out I'm not making it up.

Chroma published a study called Context Rot. They tested 18 models, including Claude, GPT and Gemini, on tasks where they controlled one variable - the length of the input. The result is brutally consistent. Every model degrades as context grows. Not linearly, not predictably, but always downward. The same prompt on a short input does clearly better than on a long one, even though the task itself is identical.

There's a second effect called "lost in the middle". The model remembers the start and the end of the context best, and the middle blurs. Stanford described this in a separate paper. In practice it means the things you settled halfway through a long session are the most likely to get "forgotten".

Now map that onto Claude Code. An agent session runs for dozens of turns, stuffed with whole files, command output, stack traces, your corrections, and dead ends we never use again. After an hour of active work the session transcript can swell past a million bytes. The agent drags all of that ballast into every next answer. Most of it is junk - failed attempts, files read once, long logs. But the model doesn't know what's junk, so it treats everything equally.

The mindset shift: answer quality doesn't depend on how long you've been working. It depends on how much ballast the agent is dragging in context. Measure turns and transcript size, not the clock.

Hook #1 - a context monitor on UserPromptSubmit

If you can measure it, you can catch it automatically. I wrote a hook on the UserPromptSubmit event - the point where Claude Code runs your script right after you send a prompt, before it reaches the model. The hook receives, among other things, the path to the current session transcript.

The hook does two things. First it counts real assistant turns. And here's the catch - not every entry in the transcript is a turn. Plenty of them are tool-only (just a tool call) or thinking-only (thinking with no answer). Counting all of them would inflate the number. I filter those out, leaving only the turns where the agent actually said something. Second it measures the transcript size in bytes, the simplest proxy for "how much ballast has piled up so far".

Then two thresholds. Whichever one gets crossed first wins - size or turn count, whatever comes first:

# context monitor - two tiers, the first crossed threshold wins
SIZE_TIER1  = 500_000    # 500 KB  -> gentle nudge
SIZE_TIER2  = 1_000_000  # 1 MB    -> warning
TURNS_TIER1 = 20
TURNS_TIER2 = 35

The numbers aren't arbitrary. 20 turns or half a megabyte is roughly the point where you can feel the agent getting less precise. It still works fine, but it's a good moment to stop and think. 35 turns or a megabyte is the threshold past which quality genuinely drops, and there's no point pretending otherwise.

The whole hook logic fits in a dozen lines:

# hook on UserPromptSubmit
data = json.load(sys.stdin)
transcript = read_transcript(data["transcript_path"])

size  = transcript_size_bytes(transcript)
turns = count_real_turns(transcript)   # skips tool-only and thinking-only entries

msg = None
if size >= SIZE_TIER2 or turns >= TURNS_TIER2:
    msg = "Long sessions degrade answer quality. Use /handoff."
elif size >= SIZE_TIER1 or turns >= TURNS_TIER1:
    msg = "Good moment for /handoff if the task is shifting."

if msg:
    # don't block - just inject context for the agent
    print(json.dumps({
        "hookSpecificOutput": {
            "hookEventName": "UserPromptSubmit",
            "additionalContext": msg,
        }
    }))

That's it. No magic, no model, no API call. Read a file, count two numbers, compare to thresholds. The hook runs locally and costs a fraction of a second.

Why it nudges instead of blocking

The most important decision in this hook is what it doesn't do. It doesn't block. It doesn't kill the session by force. It doesn't force a restart.

The mechanism is deliberately soft. The hook injects a message through additionalContext, which appends one sentence to the turn's context for the agent to see. That's all. The decision whether to actually run a handoff stays with the agent, or with me.

Why? Because a hard block at 20 turns would be a nightmare in practice. Sometimes I'm halfway through a delicate refactor that has to finish in one go. Sometimes a long session is justified because the task genuinely needs all that context. A hook that threw me out right then would get disabled fast, and a disabled guardrail is worse than none, because it gives you a false sense that something is watching.

A soft signal works differently. It reminds, it doesn't manage. If the task is shifting, or I feel the agent starting to wander, a handoff is one command away. If not, I ignore the hint and keep going. Same approach as a "humble" mode in a prompt: a layer that suggests, not a layer that forbids.

A hook that blocks too often gets turned off. A hook that only reminds stays on forever. The best guardrail is one you don't want to bypass.

Hook #2 - /handoff, a reset that keeps the thread

The monitor says "time to reset". But a reset that loses what we were working on would be pointless. I'd be re-feeding the agent the context out of my own head on every restart. That's what the second hook is for: /handoff.

The idea is simple and runs in three steps:

Write state to a handoff file. The agent writes a tight summary: what we're doing, what's already done, the next step, which files are in play. It lands in a handoff file in the project directory - on disk, not in the session context.
Restart a fresh session in the same tmux window. The old, bloated session ends. A new one starts in the same window with zero context. From the terminal's point of view nothing happens. It's still "the same" work, in the same place.
The fresh session reads the handoff and continues. Right after the restart, the handoff tool sends the new session a prompt telling it to read the handoff file. So it knows exactly where we left off. The thread keeps going, the ballast stays behind.

# /handoff - the flow
# 1. collect state: goal, done so far, next step, files in play
# 2. write it to a handoff file in the project directory
# 3. restart a fresh session in the same tmux window
# 4. send that session a prompt: "read the handoff file, then continue"

The effect is that I get task continuity without context continuity. The new session knows what to do because it read a 2 KB summary, not a megabyte of history where 90% is irrelevant logs and dead ends. It's a bit like the dropbox pattern from my morning dashboard - state lives in a file on disk, and the processes that read it can restart all they like.

The result: a chain of fresh sessions

Before these hooks, my typical day with Claude Code looked like this. One session, fired up in the morning, dragged out for three hours, dumber by the end. A classic rotting session, where the last hour was clearly weaker than the first.

Now it looks different. The monitor nudges me at the threshold, I run /handoff at a natural task boundary, and I get a chain of fresh sessions, maybe 30 minutes each. Every one starts with a clean context and full precision. The thread doesn't get lost, because the handoff file carries it. And the quality of the last session in the chain is the same as the first, because none of them had time to rot.

None of this makes Claude smarter. It's plain work hygiene. The same way you don't leave one process running forever and then act surprised it ate all your RAM, you don't leave a single agent session running all day.

Series: Claude Code hooks (5 parts)

Context rot + handoff - how I stop sessions from rotting (you're here)
Guardrails - hooks that won't let the AI shoot me in the foot
Quality gates - forcing the agent to verify before it says "done"
The prompt layer - a local LLM that reads my prompt before it hits Claude
The ambient layer - the hooks you stop noticing

All five parts are now live.

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Takeaway

Measure turns and transcript size instead of the clock. Three hours of light work rots slower than 40 minutes of dumping huge files into context, so the clock lies and the counter doesn't. Set a threshold, inject a soft signal, and when it lights up, reset before it rots. The handoff file carries the thread, the fresh session carries the precision.