DEV Community: Phil Rentier Digital

Apple Put a Local LLM in macOS 27. The Dangerous Part Is the JSON.

Phil Rentier Digital — Sun, 25 Oct 2026 14:41:12 +0000

Your Mac can now summarize a document, classify a folder, extract fields into valid JSON, and expose the result through a local Chat Completions API without installing a model runner or creating an API key.

That convenience creates a new failure mode because a weak answer can arrive inside production-shaped JSON.

I tested Apple's new fm command on macOS 27.0.1 with an Apple M5 Mac.
The summary was accurate, the first structured extraction chose the wrong number, the coding tests traded 1 violation for another, and a simple time calculation went completely off the rails before a sharper prompt fixed it.

The useful question is whether a small on-device model becomes safe automation merely because Apple gives it a CLI, schemas, and a local API.

The Viral Post Got the Interface Right

A post from Alvaro Cintas described fm as a free local LLM that was already available in the macOS 27 Terminal.
It showed the core commands, file piping, structured output, saved sessions, and fm serve as a local OpenAI-compatible endpoint.

Screenshot says 4,096 tokens and `token-count`. The release build says otherwise. Never script against a tweet.

The source post described the right product, but some limits and commands did not match the release build that I tested.

The basic setup took less than a minute because the model was already downloaded on my Mac.

fm license --status
fm available
fm respond "Explain why HTTP 200 does not always mean business success."

My machine returned System model available.
The executable lived at /usr/bin/fm, so I did not install a package, download model weights manually, or configure a provider.

Apple says the CLI ships with macOS 27, and Apple Intelligence supports Macs with M1 or later.
The exact experience still depends on the OS build, language, region, downloaded model, and hardware.

The screenshot in the post also showed token-count, quota-usage, and a pcc model.
My macOS 27.0.1 help listed count-tokens instead of token-count, and it listed only the on-device system model.
Apple demonstrated Private Cloud Compute during WWDC26, but it was not available in the CLI help on the build that I tested.

The interface had already changed between the screenshot and my release build.
That matters when a shell script depends on a flag, model name, or assumed context limit.

FM Summarized the Incident Correctly

I started with a short incident report that contained several numbers and 1 easy trap.

A content pipeline processed 240 product descriptions.
A partner API exhausted its quota but continued returning HTTP 200 responses with an error inside the JSON body.
The worker saved blank descriptions for 37 products in staging.
Production did not change because publication required a separate approval.
The incident lasted 3 hours and 28 minutes, while direct recovery took 42 minutes.

The prompt contained 203 tokens.
I asked fm to produce exactly 3 concise bullets, preserve every number, and avoid inventing a cause.

It returned an accurate summary in 2.62 seconds on my M5 Mac.
It kept 240, 37, 03:12, 06:40, 3 hours and 28 minutes, and 42 minutes in the right roles.

That result fits the model's natural territory.
The input was short, the task was a transformation, and every important detail could be checked against the source.

The JSON Looked Safer Than It Was

I then asked for structured output from the same incident.

fm schema object \
  --name Incident \
  --string root_cause \
  --integer affected_items \
  --string production_impact \
  --integer recovery_minutes \
  > incident-schema.json

fm respond \
  --schema incident-schema.json \
  --text "$(cat incident.txt)" \
  "Extract the incident facts."

The response was valid JSON.
The field names were correct, the types were correct, and every required property was present.

The value of affected_items was wrong.

{
  "affected_items": 240,
  "recovery_minutes": 42,
  "production_impact": "No production page changed...",
  "root_cause": "Transport success did not mean business success..."
}

The report said that 240 products were processed and 37 products received blank copy.
The model selected 240 for affected_items.

Valid schema, valid types, wrong number. `200 OK` has entered the chat again.

The structured response passed a parser while it failed the business meaning of 1 field.

That is the dangerous part of guided generation.
A schema can guarantee the shape of an answer, but it cannot guarantee that the model understood the meaning of each property.

Plain prose often looks uncertain, so a human reads it carefully.
Valid JSON looks contractual, so software tends to trust it.

A wrong paragraph can confuse a reader.
A wrong integer can move files, reject records, update a database, or trigger another agent.

A Better Contract Fixed the Number

The first schema used a name that felt obvious to me and remained ambiguous to the model.
affected_items could describe every item touched by the pipeline or only the items damaged by the incident.

I added a description to the schema and narrowed the prompt.

--integer affected_items \
--description "Number of products that ended with blank copy in staging"

I also told the model to use affected_items for products with blank copy, not for all products processed.
With greedy sampling, it returned 37.

The fix did not require a larger model.
It required a better contract.

This follows the same rule I use when I run small models locally instead of calling an API: the input stays bounded, the output stays bounded, and the result remains objectively checkable.

Structured output works when the schema carries domain meaning instead of merely declaring JSON types.
A field named count gives the model almost nothing.
A field described as Number of staging products whose description became blank gives it a decision boundary.

Even then, the application must validate the value.
In this example, the code could reject any affected_items value above the total number of records known to have failed, compare the extracted number with a deterministic parser, or require human approval before applying a mutation.

The schema reduces parsing uncertainty.
The validator reduces business risk.
Those jobs are different.

Code and Math Exposed the Model

The source post recommended skipping code and math.
My tests support that caution, although the failures were more useful than a generic warning.

I asked for a strict TypeScript function that returned true only when an unknown input was an object whose ok property was exactly true and whose error property was absent.
I also prohibited any.

The first answer avoided any, but it tested !obj.error.
That logic accepts an object whose error property exists with an empty string, null, or undefined value.
The prompt required the property to be absent.

I sharpened the requirement and enabled greedy sampling.
The second answer correctly used an ownership check, but it introduced this line:

const obj = input as any;

The prompt explicitly prohibited any.
Across 2 attempts, the model satisfied the property logic or the typing constraint, but it did not satisfy both at once.

The math test failed more visibly.
I wrote that an incident began at 03:12 and recovered at 06:40, while direct recovery work occupied 42 minutes inside that interval.
The first response invented an end time of 11:18 and reported a total duration of 11 hours and 18 minutes.

A more constrained prompt told it not to add time after 06:40.
The second response correctly calculated 208 minutes and 20.29%.

The 4,096-token limit was a rumor. Your `count-tokens` call is the source of truth.

The model improved when the prompt removed interpretive freedom, but the first answers were still plausible enough to pass a casual glance.

These tests do not prove that fm always fails at code or math.
They prove that short, verifiable tasks can still require explicit constraints and external checks.

A local model removes network latency and token billing.
It does not remove review.

The Token Limit Had Already Changed

The source post stated that each session had a 4,096-token limit shared by the prompt, instructions, history, and answer.
That number did not match my build.

I generated 2 synthetic prompts and measured them with the current fm count-tokens command.

The first prompt counted 8,108 tokens and returned OK in 9.24 seconds.
The second counted 8,308 tokens and failed with this message:

Error: The session's transcript exceeded the model's context size.

An independent practical guide tested an 8,192-token context on an earlier macOS 27 build.
My result fits that neighborhood, but I would not hardcode 8,192 as a permanent product contract.

The reliable statement is narrower.
The build that I tested accepted 8,108 tokens and rejected 8,308 tokens on October 24, 2026.

This feature is changing fast enough that screenshots, blog posts, and even remembered flags can become stale between beta and release builds.
A production script should call fm count-tokens, test the installed CLI, and fail clearly when the local capability changes.

FM Serve Turns It Into Infrastructure

The most consequential command is not fm chat.
It is fm serve.

fm serve --host 127.0.0.1 --port 1976

That single command exposed 3 endpoints on my Mac:

GET  /health
GET  /v1/models
POST /v1/chat/completions

The health check reported that the system model was available.
A non-streaming Chat Completions request returned a normal response object with token usage and assistant content.

This means an existing tool that supports an OpenAI-style base URL can potentially use Apple's local model without a custom Swift integration.
The CLI matters because agents work better when apps expose a command surface that they can call, inspect, and test.

The server also accepted my request without authentication.
That behavior is convenient on localhost and dangerous on a network interface.

I would keep it bound to 127.0.0.1 or use the supported Unix socket mode.
The help output allows --host 0.0.0.0, but exposing an unauthenticated language model endpoint to the network is a bad default.
Any remote use needs an authenticated proxy, network restrictions, timeouts, request limits, and logging that does not leak prompt content.

The local license text on my Mac added another limit.
It restricted programmatic access to cases that Apple has "expressly permitted."
Apple documents scripting and ships fm serve, but the CLI terms did not define the full boundary of permitted programmatic use.
I would read those terms before a commercial integration and avoid treating an unofficial proxy as legal clearance.

The local API makes integration easy.
It also makes over-trust easy.

Builders Already Added the Missing Layers

Several projects around Apple's local model are not waiting for it to become smarter.
They are adding the layers that turn a model into something people can actually use.

Familiar Local wraps the Foundation Models framework in a lightweight chat application.
It binds its server to 127.0.0.1, keeps chat history in browser storage, and gives non-developers a normal window instead of a Swift project.

pi-apple-fm connects fm serve to the Pi coding agent as a model provider.
It can start the local server, check health, and expose Apple models in Pi's model picker.
Its README also describes beta behavior that no longer matches my 27.0.1 build, including a smaller context and the pcc model, which shows how quickly integrations can drift.

fm-proxy sits between ordinary OpenAI clients and Apple's server.
The project reports that some valid client requests need translation, automatic tool calling can fail silently, and a recursive schema can hang fm serve until restart.
It also raises the same programmatic-access concern found in the local license text.

apple-fm-audit adds a local web interface and SQLite logs for requests and responses.
That makes debugging possible, although storing full prompts creates a new local data-retention problem that the operator must manage.

The most interesting example is systemANE.
It uses a small calibrated classifier for fast, high-confidence decisions and sends only uncertain cases to fm serve.
The model becomes a second tier instead of the first and only authority.

That architecture fits the test results better than a general chatbot does.
Cheap deterministic logic handles what it can prove, the local model handles bounded ambiguity, and the workflow refuses or escalates the rest.

These projects are unofficial, target different macOS builds, and do not prove that every integration is safe or permitted.
They do show where the practical work is happening: interfaces, compatibility, observability, routing, and refusal paths.

Safe Uses Stay Bounded and Verifiable

fm already has a useful operating zone.
That zone is narrower than a general assistant and larger than a toy demo.

I would use it when all 5 conditions hold:

The input stays short and bounded.
The expected output stays narrow and preferably structured.
A deterministic rule or a human can verify the answer.
A wrong answer cannot immediately create an expensive or irreversible effect.
The workflow has a safe fallback when the model or command is unavailable.

That includes local note summaries, content tagging, filename triage, metadata extraction, rewriting, intent classification, and draft suggestions.

Apple's WWDC demo used fm to classify draft and final files before a script copied or moved them.
I would keep the idea and add a dry run, an allowlist, a backup, validation of every returned path, and a separate confirmation before deletion.

I would not let this model approve payments, publish content, delete files, modify production data, or execute generated code without another control.
The same rule applies to a cloud model, but a built-in local command can feel safer than it is because privacy and correctness are separate properties.

On-device inference can protect data from a cloud round trip.
It cannot make the answer true.

The Limits Matter More Than the Price

The lack of a token bill is real, but the tool still has costs and constraints.

The on-device model showed brittle instruction following in my code test and brittle reasoning in my first math test.
The context remained small enough that long documents needed chunking or another model.
Private Cloud Compute appeared in Apple's WWDC presentation but did not appear in my release build's help.

My timing came from an M5 Mac, so it says nothing reliable about latency on an M1.
Apple supports M1 and later Macs, but supported does not mean equally fast.

Apple also says its intelligence models can require up to 8 GB of storage on many supported devices and up to 14 GB on newer hardware configurations.
The model has no per-token invoice, but it still consumes disk space, memory, battery, and compute.

Availability can also depend on language, region, and whether the model finished downloading.
A script must handle fm available returning something other than success.

The biggest limitation remains conceptual.
The CLI makes the model look like a deterministic Unix program, while the output still comes from a probabilistic model.

The CLI Changes More Than the Model

fm did not replace a frontier model on my Mac.
It became useful when I narrowed the task, described the schema, and kept a verifier outside the model.
It became dangerous when I treated valid JSON as validated truth.

Apple has still changed the baseline for Mac automation.
A compatible machine now ships with a local language model, a shell interface, structured generation, saved sessions, image inputs, and a Chat Completions server.
Developers no longer need to install a model runtime before trying a private local classification or extraction step.

The partial answer is enough.
Apple did not put a reliable agent in /usr/bin.
It put an LLM primitive there, and the next bug will come from the shell script that assumes those are the same thing.

Sources

Alvaro Cintas, X post about the macOS 27 fm CLI
Apple Developer, Foundation Models framework
Apple WWDC26, Build AI-powered scripts with the fm CLI and Python SDK
Apple Support, Get started with the next generation of Apple Intelligence
FM(1) command manual
fm-guide, independent tests and practical limitations
The local fm license --show output was captured on macOS 27.0.1 on October 24, 2026.
Familiar Local, a local chat interface for Foundation Models
pi-apple-fm, an Apple Foundation Models provider for Pi
fm-proxy, a compatibility layer for fm serve
apple-fm-audit, a local proxy with SQLite request logs
systemANE, a calibrated decision cascade that escalates to fm serve

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Uncensored AI Models Stop Refusing. Then They Break Your Agent.

Phil Rentier Digital — Sun, 18 Oct 2026 13:41:10 +0000

A developer watched his 4-year-old OpenAI account get banned for asking Codex to test REA, a reverse engineering tool. He posted it on X. Over in the openai/codex repo, another user describes something worse: Codex generated its own security boundary questions, ones the user never asked, and OpenAI replied that the automatic ban still fell on him, model output or not. On the Anthropic side, Claude Code cuts the request dead and points at its terms of service on reverse engineering.

The refusal, at least, you can see. The ban drops without warning, on an account a whole workflow sometimes depends on. So the question of uncensored models changes shape. It stops being about wanting to break rules and becomes about finding a model that does the work without putting the account on the line. Phil, another builder in this space, rebuilt his agent stack on open models after a ban for exactly this reason.

So-called abliterated models answer that need, and you can run them 3 ways: on your own machine, on a rented GPU, or through a hosted API. The open question is whether an agent wired to one actually runs better than Claude or Codex, or whether you just moved the problem somewhere else.

What "Uncensored" Actually Means

Three railway routes from one model to one endpoint

Refusal in a language model is narrow. Research by Arditi and colleagues in 2024, cited as the basis of the Heretic project, found that a model's refusal behavior is mediated by a single direction in its activation space. Abliteration erases that direction from the weights, which is why an abliterated model stops saying no. HAL 9000 needed a whole plot to stop opening the pod bay doors. This takes one vector.

The next step needs one flag, because this is a hypothesis, not a measured fact. Instruction-following and output discipline (clean stop sequences, valid JSON, tool-call syntax) may live close to that same geometry. If they do, editing the refusal direction would bleed into them. That would explain the pattern builders keep reporting downstream: a model stops refusing before it stops emitting clean tool calls. Format degrades before knowledge does. Nobody has published a measurement that ties the two together, so treat the mechanism as a working theory and the degradation itself as the observed fact.

How much does the edit cost? The Heretic repo gives one attributed number. On Gemma 3 12B, the base model refuses 97 out of 100 harmful prompts. After Heretic, it refuses 3. The KL divergence from the original (a measure of how much the model's overall behavior drifted) sits at 0.16, against 1.04 and 0.45 for two manual abliterations. Lower divergence for the same drop in refusals means less collateral damage to capabilities.

One caveat on those refusal counts: Heretic detects refusals by scanning the output text for markers like "sorry," "I cannot," or "unethical." That is a blunt instrument, and every refusal rate you'll see in this article, including the ones from model authors, is a self-reported test, not an independent benchmark.

A low refusal rate tells you the model stopped saying no. It tells you nothing about whether it still says anything useful.

There are two ways to get an abliterated model. Make one yourself, or download one somebody already made. Heretic covers the first: pip install heretic-llm, then heretic MODEL, and it runs the whole edit in one command. It's AGPL-3.0, sits around 33K stars as of October 2026, and the community has published over 5,000 models through it. For the second, Hugging Face is full of them, tagged abliterated, heretic, or uncensored.

Once you have a model, you need to run it. That splits into 3 routes.

Run It on Your Own Machine

On a Mac, Ollama runs any GGUF file straight from the Hugging Face Hub with one command: ollama run hf.co/{user}/{repo}. The Hugging Face docs list an abliterated model as their own example, so this is the documented path, not a hack. Quantization (shrinking the model's weights to fit in less memory, at some cost to quality) defaults to Q4_K_M, and you pin a different level with a tag:

ollama run hf.co/{user}/{repo}:Q5_K_M

LM Studio serves the same thing behind a graphical interface if you'd rather not touch the terminal.

Which model fits your hardware depends on how much VRAM (the memory on your graphics card) you have. A widely shared thread from @sudoingX ranks abliterated models by VRAM tier, from 8 GB up to 24 GB, with each model's self-reported refusal rate and download count. It's a useful starting map for matching a model to a card, as long as you remember those refusal rates come from each model's own author.

The reason to run local isn't just that it's free. The model transmits nothing. Your prompts never leave the machine, which is the whole difference from the two cloud routes coming up, where a provider applies its own data policy to everything you send. If you want the fuller argument for keeping inference on your own hardware, it's the same logic as when a small local model beats an API call.

Local works until the model is too big for your machine. Then you rent.

Rent the GPU on RunPod

RunPod's serverless vLLM worker deploys any Hugging Face model that vLLM supports. You set the model through a MODEL_NAME environment variable, and the platform hands you an endpoint that speaks the OpenAI API. In an existing OpenAI codebase, you change 2 parameters:

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_RUNPOD_KEY",
    base_url="https://api.runpod.ai/v2/ENDPOINT_ID/openai/v1",
)

Billing is per second of inference. When the endpoint sits idle, you pay nothing. That makes it the option for a model too heavy for the Mac while keeping the weights under your control instead of a third party's.

One thing the RunPod docs say in plain text: function and tool calling depend on the model and on vLLM support. The platform doesn't guarantee it. Hold onto that, because it's the whole subject of a later section.

Call a Hosted Abliterated API

The fastest route to plug in is the one where somebody else runs everything. abliteration.ai exposes an OpenAI-compatible API (base api.abliteration.ai/v1, key in an ABLIT_KEY variable) with a few models, including one that handles text and images at a 262K context window, and a larger v2 derived from GLM-5.3 with a 1M context window and function calling listed as available. Sogni serves a Qwen3.5 35B abliterated model the same way, at api.sogni.ai/v1.

Prices move fast, so treat these as a dated snapshot. As of October 2026, Routeway lists the abliteration.ai large v2 model at 5.25 dollars per million tokens for both input and output, with cached reads at 0.53 dollars. Sogni's Qwen3.5 model runs 0.30 dollars per million input and 0.90 per million output. Those are reseller and vendor pages, not independent price indexes, and they'll be stale by the time you read this.

There are two caveats, and the second one matters more. The Promptfoo docs, which document abliteration.ai as a provider, frame these models explicitly for red-teaming and research, not production traffic, and remind you that you remain responsible for the outputs and the licenses. And you're handing your prompts to a third party, which cancels the privacy argument that made the local route attractive in the first place. "It works on my machine" stops being reassuring when it's no longer your machine.

All 3 routes land on the same object: an OpenAI-compatible endpoint. What's left is wiring it into an agent and seeing whether it holds.

Plug It Into a Coding Agent

OpenCode takes any OpenAI-compatible endpoint through a custom provider. You install @ai-sdk/openai-compatible, set options.baseURL, and the URL changes with the route:

// Ollama
baseURL: "http://localhost:11434/v1"
// LM Studio
baseURL: "http://127.0.0.1:1234/v1"
// RunPod
baseURL: "https://api.runpod.ai/v2/ENDPOINT_ID/openai/v1"
// hosted API
baseURL: "https://api.abliteration.ai/v1"

The OpenCode docs include one field note that turns out to be a preview of the next section: if tool calls fail with Ollama, raise num_ctx (the context window the model is given) to somewhere around 16K to 32K. The agent breaks, and the first thing they tell you to check is a format-adjacent setting.

For the reverse engineering use case that started this whole switch, there's REA, a reverse engineering MCP server. You set it up with npx rea-agents setup, and the repo (morluto/rea, MIT licensed) lists Claude Code, Codex, Cursor, Gemini CLI, and Grok Build as supported agents. Analysis runs locally. There is one honest gap: the repo documents REA with those closed-lab agents, the exact ones the ban pushed you away from. It doesn't document REA against OpenCode or an abliterated endpoint. REA is an MCP server and OpenCode consumes MCP servers, so the pieces look compatible, but I haven't seen that specific combination tested, so I won't tell you it works. The one thing REA's own disclaimer keeps non-negotiable is scope: your own binaries, or open source apps, nothing you aren't authorized to examine. One does not simply walk into someone else's codebase because the model stopped saying no.

So the agent runs. The question worth asking is where it stops running well.

Where Uncensored Models Break

The twist comes from people who build on these models, not from the marketing copy.

An engineer building an agent on abliterated models, in a discussion on the llmfit repo, lays out the failure mode directly. Abliteration degrades instruction-following and output format (stop sequences, clean JSON, tool-call syntax) before it touches the model's knowledge. Quantization stacks on top of that, so a Q4 abliterated model is noticeably less reliable at structured output than the same Q4 of the base model. It's worth naming: this engineer sells a hosted agent that runs on abliterated models, so he has an interest in the problem reading as manageable. The observation still lines up with two independent sources.

The first is a model publisher admitting it against its own interest. The README for a SuperGemma abliterated edition states that an abliterated modification can regress on exact formatting, tool-call reliability, and stability if it isn't carefully hardened. The author claims 90% tool-call accuracy, but only after that hardening work.

The second complicates the thesis in a useful way. The author of an Ornith abliterated model reports that the variant with the lowest refusal rate was over-abliterated and produced word-salad on real generation. The fix was an edit 170 times lighter, which stripped the refusals just as thoroughly while keeping the model coherent. And the part that should stop you from reading "abliteration breaks agents" as a law: that lighter final edit kept an agentic pass@1 score identical to the base model, 0.833, while dropping refusals from around 94 out of 100 to zero. A well-made abliteration didn't wreck the agent. A heavy-handed one did.

That's the practical rule, then. The damage isn't fixed, it scales with how aggressively the model was edited and how hard you quantized on top. So for agent use, bump up one quantization notch (Q5_K_M or Q6_K instead of Q4_K_M) to buy back some of the margin. Then run a handful of strict-JSON prompts through the model before you trust its tool calls. Measure format conformance as a front-line metric, not something you infer from whether the task happened to succeed. A model that answers your question in prose when you asked for JSON didn't help you. It just failed politely.

Which raises the problem the rule can't solve. You can measure your own model. But no public benchmark compares tool-call conformance across abliterated variants, so there's nothing to check your number against.

What's Still Unsolved

The switch solves the first problem cleanly. An abliterated model running on your own machine doesn't refuse, and it has no account to get banned.

The second problem stays open. Refusal rates are measured by the same people who publish the models. Format conformance, the thing that actually decides whether your agent works, has no independent measure at all. That leaves one number that means anything for your setup, and it's the one you generate yourself, with a few strict-JSON prompts, before you hand the model a repo.

And a model that stopped refusing changed nothing about what the licenses allow. Understanding how an app works still doesn't grant the right to copy it, as REA's disclaimer puts it in one line, scope limited to what you own or are authorized to examine. When the model drops its last guardrail, one remains. It's whoever typed the prompt.

My 8 Claude Code Sessions Could Merge Safely. None of Them Could Share Production.

Phil Rentier Digital — Thu, 15 Oct 2026 13:41:10 +0000

A single Claude Code session shipped 8 batches of work to production in 1 deploy. It hadn't written any of them.

The sessions that wrote them were no longer allowed to touch production (at my request, the orchestrator had taken that job away from them the same day). So is handing production to 1 session enough to let 8 sessions ship in parallel without getting in each other's way?

The merging part was already safe by then. It cost me a string of incidents a couple of months earlier, and it holds. Production is another animal, because a live database doesn't reconcile after the fact the way 2 branches do...

Let's see how I got there, what it fixed, and where it still breaks.

My Sessions Fought Over a Single Lock

I'm a solo dev. My product is a data SaaS with a daily ingestion job, running in production on my VPS with blue/green deploys, so a release never takes it down. I run 1 Claude Code session per task, and each session works in its own git worktree (a separate checkout of the same repo, so sessions never edit the same files on disk).

The number of sessions running at once varies. At one point, 1 session listed 7 active peers, which made 8 sessions in parallel. That's a snapshot, not a constant.

At first, every session could close its own branch by itself: merge, CI, deploy, the whole chain. On paper, that's autonomy.

Then one morning, 5 closures failed. Each session had launched its own closure in the background, and they were all fighting over the same single lock.

Worse, several sessions had started writing a shell loop that deleted the failure marker and retried. That marker is what keeps a failed closure visible until someone looks at it. Delete it, retry, and the failure never reaches anyone. And the workaround spread from agent to agent, like the outbreak in a zombie movie, except these zombies had shell access.

Every Incident Became a Script

Single Lane Deployment Pipeline Factory Workflow

Over a single week, each incident ended up as a mechanism, in this order:

Red main. The main branch went red, merge after merge. Branches that were green on the fast CI, which only replays the tests a branch changed, contradicted each other once merged. Green alone and red together is the CI version of "works on my machine". The answer was a full test suite in an integration worktree before publishing main, so a collision now bounces back onto the guilty branch instead of breaking main.
Killer test databases. The throwaway PostgreSQL test databases started killing each other. A guard meant to clean up orphan processes killed them by port, and test runs took each other's databases down: 756 connection failures in a single suite. The fix was total isolation per run, with a random data directory, a random port and no killing by port. As proof, 2 full suites of 3,410 tests each ran green in parallel.
One lane. The fought-over lock became a single closure lane. A session pushes its branch and drops an entry in a queue, and 1 sequential launcher wakes up every minute to process it. The merge train was born the same day: up to 6 branches grouped together, with 1 integration worktree, 1 CI wait and 1 full suite. A conflict ejects that branch alone, and a red suite triggers a bisection that isolates the guilty branch and publishes the innocent ones. Later on, a train of 6 published 5 branches and ejected 1.
The oversized branch. A branch of 50 files and about 5,200 lines got refused at final review. It was the same family of scope drift as the time my agents spent 4 days securing a 13-minute job, different incident. What came out of it is a pre-commit hook that blocks any branch past 15 files or 800 added lines. Crossing that line takes my explicit OK.

Soon after, the pattern became a rule: mechanize instead of documenting. Every instruction that says "always" gets turned into an executable check, whether that's a hook, a guard or a threshold, because an agent skims a paragraph and a hook doesn't.

This git layer is a known problem, and it shows up well outside my setup. A 2026 arXiv paper went through 33,596 agent pull requests across 2,807 GitHub repos. When the authors replayed 747 pairs of pull requests that were open at the same time, pairs from different agents hit a textual conflict 41.7% of the time, against 19.8% for pairs from the same agent. There's even a public repo, claude-code-merge-queue, that packages a comparable local queue for parallel Claude Code agents.

About 7 weeks later, that lane was taking 25 to 41 merges a day without breaking a sweat. The same week, the incidents kept coming, and none of them came from a merge.

41 Merges in a Day, and Git Held

Git counted 25 to 28 merges into main per day for 4 days in a row, then 41 the next day. The queue took all of them. The 4 problems of that week lived somewhere else.

The deploys came first. During a data maintenance pause, sessions each kept relaunching their own deploy. The canary check, which has to pass before a new release takes traffic, refused them, and they got in each other's way. A single session retrying is noise. Several retrying at once is a traffic jam on the only road into production.

Then came the heavy data jobs. Multi-hour backfills and recomputes were competing with the ingestion loop, and queries started timing out at the 30-second limit per query. It got to the point where ingestion had to be paused. For a data SaaS whose whole job is a daily ingestion, that's the product standing still.

Then came an ordering problem that only production could see. A branch needed its new column to exist in production before it merged, otherwise every recompute failed. Git has no idea what exists in the production database, so the right merge order was a production fact that no branch could see.

And then there was me, the human message bus. Every session asked me for its own go-ahead, which turned me into the bottleneck.

At the time of writing, the public tooling stops at the same line. claude-code-merge-queue is a local first-in, first-out queue on 1 machine, where a verification command has to pass before each merge. Claude Code Projects, in public beta, goes further: a coordinator conversation launches threads in parallel, threads whose pull request is approved or waiting in the merge queue show up as "Landing", and buttons offer to "Resolve conflicts", "Fix CI" or "Merge it", with a limit of 200 new threads per day. Its documentation page doesn't mention deployment. Both stop at the merge, and none of my 4 problems lived there.

What the 4 problems shared: merging fixed none of them, and each one needed a single session acting at a time.

Production Is a Single-Track Line

Railway Metaphor for Development vs Production Workflows

Production needs a single owner at any given moment, and better chat between sessions doesn't get you there.

Railways worked this out in the 19th century. On a single-track line, a driver can only take his train into a section if he holds that section's token, and there is 1 token per section. With Edward Tyer's electric instruments, patented in 1878, only 1 token could be out at a time. The safety comes from owning a unique object, not from trains talking to each other.

Code behaves like a double track. Branches run side by side and reconcile after the fact. Git does have a serialized point, the integration step, but the work itself stays parallel and gets reconciled there.

Production is the single-track section: the live database, the heavy jobs running against it, the deploy slot. There, the work itself doesn't parallelize, and 2 concurrent writes don't reconcile, they get in each other's way. Sessions that warn each other still each believe they're allowed to act. A single holder turns the rule into structure: if you don't hold the token, you don't enter the section.

Git can merge 2 branches, but nothing merges 2 deploys.

A Single Session Holds the Token

Until I set up an orchestrator, every session queued its own branch, relaunched its own deploy, started its own heavy jobs and asked me for its own go-ahead. So I asked the sessions to stop piling things up and to let orchestration carry what came next.

One evening, a dedicated orchestration session took over. It writes no code itself (every line goes to subagents), which is Nick Fury management: never in the fight, always picking the team. It drives the other sessions through Claude Code's cross-session messaging, which lets a session list the others and send them plain text (at the time of writing). My sidebar now sorts sessions into 3 groups, "to close", "in progress, do not close" and "next iteration". As I write this, they hold 23, 10 and 0.

That first evening, the orchestrator broadcast a version freeze to every session, in my words: "we finish what's in progress, we freeze a version, we iterate after." About an hour later, the freeze lifted for a single workstream, whose scope was announced once. About an hour after that, it called a general pause and wrote down the exact state: what was applied, where to resume, and what wasn't applied.

It also started asking the sessions instead of asking me. I'm only quoting the openings, translated, with names replaced by brackets:

Orchestration question, answer in a few lines: what exact command did you use to apply your ALTER TABLE…
Orchestration question, answer in a few lines: what exact command do you use to run your scripts in prod…
Resume (the auto mode check is responding again): run the…
[branch] was ejected from the train…
Production release: at [owner]'s request, the orchestration session now drives deployments…

Questions like the first 2 used to land on me.

The last message came 2 days after the freeze. At my request, the orchestrator told every session that it now drives deployments. The others kept pushing and queueing as usual, without relaunching any deploy or touching the queue. A single grouped deploy went out for 8 batches of work from other sessions. Then each session checked production in read-only mode and sent its verdict back.

This is a solo setup, with 1 machine and 1 production. I haven't tested any of it with a human team that deploys too.

The Handover Is the Real Memory

The orchestration sessions pass the baton through a handover file. This is its real structure, with generic content:

Orchestration handover, [date] [time]
Replaces: [previous handover, date, time]

## State at [time]
- [1 line per workstream]

## Delivered and verified (do not redo)
- [workstream]: [what shipped, how it was verified]

## In progress
- [workstream]: [session, current step]

## Remaining steps, in order
1. [step]
2. [step]

## Go-aheads already given
- [decision, in the owner's words]

## Backlog, no go-ahead yet
- [item]

## Rules learned today
- [rule]

A fresh session reads it and knows what not to redo, which decisions are already made and where to pick up.

The handover also carries the rules I gave, from 1 session to the next. I don't verify anything myself, by design. The session delegates the code, ships it, verifies it in production, and sends me short messages along the way, then a single final message. Only 1 heavy job runs at a time. A volume guard stops any write if the number of rows to change goes past the expected ceiling, and the pre-images (the rows as they were before) get saved first. So a fix that suddenly wants to touch far more rows than planned stops before writing anything. And "done" has a definition: a final dry run with no writes has to find 0 rows left to fix.

3 orchestration sessions took turns over 3 days. Each started with the same instruction: "resume the orchestration, read the handover in full first." Every new orchestrator wakes up with no memory and a note it left itself, which is basically the plot of Memento.

One morning's handover was stale by that evening. The evening one says at the top that it replaces it.

A handover that's dated, says what it replaces and lists the approvals already given stops a fresh session from asking me again for a go-ahead I already gave.

Copy This Before You Add Session 9

Each of these comes from something that broke first:

Never let each session merge on its own. Keep a single write lane into main, and make it sequential, because 5 closures died in a single morning from sessions doing it themselves.
Replay the full suite on the merged result before you publish, and bisect when it goes red. Branches that are green alone can still be red together.
Turn every "always" in your instructions into an executable check, such as a hook, a guard or a threshold.
Give production to a single session. Let it freeze the scope, run 1 heavy job at a time and group the deploys.
Write dated handovers that say what they replace and which approvals are already given.
Announce the scope once, at the start, then freeze it. It's the same idea as freezing the contract before the agent starts, applied to a whole fleet of sessions.

None of these rules needs a new tool, only a decision about who gets to write where.

The last decision of that week fit in 1 sentence, and the orchestrator couldn't write it for me.

Where Delegated Authority Hits a Wall

On its own, the orchestrator froze the scope, put the heavy jobs in order, grouped the deploys, restarted sessions and got a branch fixed.

What it couldn't do alone was lift a failure marker after a review refusal. That took an explicit sentence from me, because otherwise the classifier behind Claude Code's auto mode refused the action. That was the sentence.

Auto mode also refused remote shell commands in the middle of a chain, as calm as HAL 9000 saying "I'm sorry, Dave". That's the permission check doing its job. It still meant relaunching sessions once the check was answering again, which is where the "Resume" message came from.

The rest of the week had its own dents. The deploy step ships the most recent commit whose main CI is green, never an untested one, with a guard against rolling back. One day, it picked a 17-day-old commit, because it read a stale list of CI runs. The anti-rollback guard refused it, nothing was impacted, and the bug was fixed the next day. A repair branch also went through 5 review refusals in a row before it passed.

Claude Code's documentation on cross-session messaging draws the boundary, at the time of writing: a message from another session never counts as consent and can't approve anything, and permission limits stay with each session. So authority doesn't travel by message, by design.

I haven't measured the token cost of all this, or the human time it saves me, so I won't put a number on either.

That grouped deploy settles the question I started with. 8 batches of work from other sessions went out in 1 deploy, instead of 8 sessions each relaunching their own.

It also moved the weak spot onto the single holder. 3 orchestrators held the token in 3 days, kept on track by a handover file, and 1 auto mode refusal can stop the current one halfway through a chain.

On a single-track line, the token is a physical object, and only 1 can be out at a time. Mine is a session and a markdown file. Who picks it up when that session stalls mid-chain, I don't know yet.

Sources

AI Agent Pull Requests on GitHub: Frequency, Structure, and Merge Conflict Rates, Xu, Subramanian and Karthik, arXiv, July 2026
Let Claude coordinate ongoing work with Projects, Claude Code documentation, as of September 30, 2026
Message your other Claude Code sessions, Claude Code documentation, as of September 30, 2026
claude-code-merge-queue, a local merge queue for parallel Claude Code agents
Single Line Working and Tablet Exchange Apparatus, Part 1, The LMS Society

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We Thought TDD Would Make AI Code Reliable. Our Tests Had Holes.

Phil Rentier Digital — Sat, 10 Oct 2026 13:41:11 +0000

We thought we had found a way to get consistently solid software from AI coding agents: make them use test-driven development.
If the agent writes the tests too, reaching 90 percent coverage or more can look almost routine.
That sounds like the safety net we have been waiting for.

Then I realized it was not that simple.
In many cases, an AI agent can write poor tests, get them from red to green, and still fail to prove the behavior we wanted.
The suite can be green and coverage can be high while the code is wrong at a boundary nobody described.
The safety net exists, but it has large holes.

This does not make testing useless.
It means we need to adapt the process when the same agent can interpret the requirement, write the implementation, write the oracle, and explain why everything is correct.
We need to supply stronger intent before the code, choose evidence that reaches the real risk, and challenge the completed lot against the contract afterward.

TDD still gives us a starting point

Martin Fowler describes TDD as a cycle in which a test expresses the next piece of functionality, the code makes it pass, and the developer refactors while maintaining an evolving list of cases.
That red, green, refactor loop remains useful because it turns an expectation into executable feedback before more code accumulates.
Fowler's description of TDD also makes the case list central, which matters because the quality of that list determines what the suite can detect.

Our earlier workflow used TDD-inspired guidance, but our audit does not establish that every test was written before the corresponding code.
The agent often delivered code and tests together in one reviewed lot.
Calling that strict TDD would invent a chronology we did not observe.

The deeper problem is independent of chronology.
When one reasoning process writes both the implementation and its tests, both can preserve the same mistaken assumption.
A test can execute every branch and still assert the wrong outcome.
Coverage tells us which code ran, not whether the requirements were correct, complete, or meaningfully asserted.

Consider an illustrative example rather than an event from our pilot.
A test may initially fail because its setup references a missing symbol or constructs invalid data.
The agent can repair that setup, turn the test green, and leave the intended behavior untested.
The color changed for a real technical reason, but the red did not prove that the product behavior was absent and the green did not prove that it was implemented.

Benchmarks should narrow the claim

Kun Chen reported an October 8 experiment with Sonnet 5.5 high on DeepSWE in which banning newly written tests produced a slightly higher success rate, although the difference was not statistically significant.
He reported statistically significant reductions in time and token use.
The baseline split was 65 percent unit tests and 35 percent integration tests.
On a 44-task subset where existing tests were disabled, he reported no measured difference in success.
Only 17 end-to-end tests appeared beside more than 3,000 unit and integration tests, so the experiment did not settle the end-to-end question.
His clarification concerned tests that agents add spontaneously, without explicit human guidance on what to test.
These are Chen's reported results from his archived statement, not an independent replication or a review of the raw protocol.

A separate 2026 study examined trajectories from six models and prompt interventions across four models.
It found no statistically significant outcome changes from those interventions in its setting, which does not establish equivalence or remove the long-term value of regression protection.
The study on agent-generated tests is useful evidence against sweeping promises, not evidence that tests never help.

A green suite missed two boundaries

One reviewed lot added response tracking to an internal dashboard.
The agent delivered the code with five new tests, and the targeted suite passed 47 tests.
Those tests were not decorative.
They checked that an abandoned draft was handled explicitly, an external send receipt remained idempotent, inconsistent identities were rejected, normalized message references could attach a reply, and ambiguous references were refused.

An independent diff review still found two blocking defects.
First, a thread identifier from one account could attach an incoming message to another account.
Second, a UTC ISO timestamp initialized a datetime-local field and was then interpreted as local time, shifting the recorded instant by one or two hours depending on the time zone.

Two additional targeted tests covered those boundaries after the fixes.
The suite then passed 49 tests, with seven new tests in total.
The original tests remained useful evidence for receipts, idempotence, identity checks, and ambiguity.
They had simply omitted two partitions that mattered to delivery.

The integration boundary also remained incomplete.
The thread tests injected already normalized In-Reply-To and References values directly into the database mutation.
They proved the mutation's behavior but bypassed the actual adapter-to-worker chain.
Adding more tests at the same mocked boundary would not by itself prove that the real chain preserved the headers correctly.

No finite suite is exhaustive, so “write more tests” cannot be the whole answer.
We should target the missing partitions, failure positions, representation changes, and integration boundaries that could reverse a delivery decision.
That means asking what happens across accounts, time zones, permissions, retries, partial failures, protocol adapters, and external effects.

Stronger techniques need a sound oracle

Black-box and end-to-end tests can exercise a public interface and observe actual effects.
They are valuable when internal mocks erase the boundary where data changes shape or a remote system behaves differently.
Our Safari testing postmortem shows why the environment behind that interface matters.
They are not a magical source of truth, because an end-to-end assertion can encode the same false expectation as a unit test.

We can also separate the context used to design tests from the context used to implement the change.
Both contexts should refer to a fixed human-approved contract, representative examples, and counterexamples.
Using a different model or a fresh context may reduce correlated implementation details, but it does not create independence if both receive the same ambiguous requirement.
We have not tested whether a stronger model changes this result.

Property tests can explore broad input spaces around invariants such as idempotence, ordering, isolation, or conservation.
Mutation testing can show whether a suite notices controlled changes to the implementation.
Both improve sensitivity, but sensitivity to change is different from agreement with intended behavior.
A false oracle can reject many mutations while still defending the wrong rule.

We need a contract-guided hybrid process

Before code, we should record the source of human intent in an accepted contract.
The contract should include representative cases, counterexamples, invariants, and the consequence of failure.
We should resolve ambiguity only when it can materially change the implementation or the decision to ship.

A useful counterexample names a failure position rather than saying “handle errors.”
For an email reader, the third-party client may connect successfully and then reject mailbox lock acquisition.
The required invariant can state that the connection must still close.
That case distinguishes an implementation that protects cleanup only after lock acquisition from one that protects every resource acquired after connection.

During development, the agent should choose unit and integration tests that match the risk.
It should not mock away the boundary under examination.
For a reproducible bug, the new test should be red for the expected reason and turn green because the same scenario now behaves correctly.
Existing gates stay active, even when changing an assertion would be the fastest route to green.

When a test fails, we should decide whether the code or the oracle is wrong by returning to the source of intent.
Green pressure is not a product decision.
If the source says that a limit must remain within 1 and 100, clamping may satisfy the contract even when a speculative test expected rejection.
That test should be corrected or discarded with the reason recorded.

After the lot, the supervising agent should map each material behavior to evidence and identify omitted risks.
The normal review of the diff remains the default.
An independent review should appear only when the risk can change the delivery decision, such as account isolation, authentication, data loss, concurrency, migrations, or paid external effects.
For a bounded lot, we cap this stage at one corrective pass.
If a blocking defect remains, we report it and stop rather than declaring success or starting another review loop.

Some tests may be written after the implementation when inspection reveals the discriminating boundary.
That is not strict TDD when the entire test is written afterward.
The process is a hybrid: human intent and decisive examples come first, useful red-green cycles guide development, and a final evidence review probes what the completed lot still fails to demonstrate.

Green suite, 90% coverage, wrong boundary. The agent graded its own homework and gave itself an A.

Define the behavior before development, then challenge the evidence before you ship.

This practical workbook helps you answer that question on your next AI-assisted change.
You will learn to set the expected behavior before coding, challenge the tests after development, and make a release decision you can explain.

Put the workbook to work on your next change

Our pilot found one cleanup defect

On October 9, we tried this process on a small email-reading library whose baseline was green with 57 tests and 151 assertions.
Before delegated inspection, we fixed six requirements covering activation at the current watermark, ordered cursor progress, UID validity changes, header preservation, bounded reads, and resource cleanup without marking messages as read.

The review considered nine scenarios through a mix of execution and source inspection.
They were not nine new tests.
Existing tests already supplied useful evidence for watermark behavior, UID order, UID validity resets, MIME parsing, and truncation.

Three new probes exercised the real public reader methods with a fake IMAP client.
The probe where fetch failed passed because the lock was released and logout was called.
Two probes failed when connection succeeded but mailbox lock acquisition was rejected, one through activation and one through incremental reading.
Both failures came from one cleanup defect repeated in two public methods: lock acquisition happened before the protected try/finally, so logout was never called after the rejection.

A temporary counterfactual correction moved lock acquisition inside the protected region and made lock release conditional.
All three probes then passed instead of one passing and two failing.
That result supports the local causal explanation, but the correction was not integrated or deployed.

The fake client still limited the evidence.
The probes used real reader methods, but they opened no real socket, reached no real server, changed no production mailbox, and performed no MIME parsing before the failure.
The supervising session verified that the real dependency can reject mailbox opening without disconnecting, but the pilot did not test the effect through a live server.
A known repeated-headers defect was excluded, and a proposed 0 or 101 limit rejection was discarded because clamping met the actual contract.

The pilot was guided by code inspection, so it was neither blind nor a causal comparison of workflows.
The delegated run took 3 minutes and 42 seconds, while the total study time, parent tokens, and monetary cost remain unknown.
We measured no savings and no general reliability improvement.

The two cases left different gaps:

The dashboard suite passed 47 targeted tests after 5 new tests. Independent review found 2 blocking defects at account and time boundaries. After correction, 49 tests passed with 7 new tests. The full adapter-to-worker chain remained untested.
The email library passed 57 tests and 151 assertions. Review covered 9 scenarios and added 3 cleanup probes. One defect caused 2 failures, and the temporary correction made all 3 probes pass. Those probes did not exercise a live server, production mailbox, or MIME parsing.

The faulty methods had 100% line coverage

We then measured coverage on the exact code revision used in the pilot, with Bun 1.4.0 and the same four existing test files.
The 57 tests still passed.
The two affected methods already had every line reported by the coverage tool marked as executed: 14 out of 14 for activation, and 60 out of 60 for incremental reading.
Both methods still contained the cleanup defect.

The measured scopes were different:

The 8 source files present in the baseline LCOV report totaled 1,000 covered lines out of 1,814 reported lines, or 55.13% when combined by line count.
The complete reader file had 255 covered lines out of 373, or 68.36%.
The activation method had 14 covered lines out of 14, or 100%.
The incremental-reading method had 60 covered lines out of 60, or 100%.

The 100 percent figure applies specifically to those two method ranges.
It does not describe the file or the entire project.
The eight-file aggregate excludes source files absent from the report, including the CLI.
Bun’s displayed overall figure was 74.18 percent, which matched the unweighted average of its file percentages.
The 55.13 percent above weights each reported line equally.
We keep the denominator visible so those figures cannot be interchanged.

Adding the three probes to unchanged code produced 58 passing tests and two failures.
Coverage stayed at 255 out of 373 lines for the reader and 1,000 out of 1,814 across those same eight files.
The probes exposed a missing behavior without adding a single covered source line.
The temporary correction then produced 60 passing tests, with 257 out of 375 reader lines covered.

The existing tests had executed connection, lock acquisition and cleanup in other circumstances.
They had not verified cleanup when lock acquisition itself rejected after connection.
Line coverage could not express that missing sequence.
The reporter supplied no branch-coverage data, and we make no claim about branch or path coverage.

Important tests need traceable evidence

One local case is enough to justify trying this process on the next comparable lot.
It is not enough to make it a global rule or claim that AI-generated code is now more reliable.

Our standard should be concrete.
Every delivery-critical test should trace to a stated behavior, fail for the right reason when it is introduced for a bug, exercise the boundary it claims to cover, and state what remains unknown.
Coverage, test counts, and green checks can support that evidence, but they cannot replace it.

We can retain useful existing tests while feeding the next pilot back into the process.
The goal remains more reliable code.
We will know that we are getting closer only when repeated, comparable lots show fewer serious acceptance defects without hiding the cost or the limits of the evidence.

Get the workbook, open the acceptance brief, and write the behavior you need before the next agent run.
Then use the after-batch review to make the agent's evidence visible before you accept its work.

Put the workbook to work on your next change

These sources support the analysis

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At 10:36 PM, a Phone That Samsung Never Built Rewrote 13 Products in My Client's Store

Phil Rentier Digital — Sun, 04 Oct 2026 13:41:11 +0000

The email came from my own bot at 10:36 PM on a Thursday. "Optimization started," it said, followed by the name of a CSV file and the little signature I gave it: n8n bot. 7 minutes later, a second email arrived: "File optimized."

The next morning I asked my client what the late batch was about. The answer came back in 4 minutes.

"That wasn't me."

I hadn't touched anything either. So who pressed the button at 10:36 PM, and how did they know where the button was?

I build automations for that client's online store. A CSV of product barcodes goes in, an AI rewrites the titles, the keywords and the bullet points, and everything gets pushed back to the shop. That night, 13 products got the treatment: a hamster cage, a turtle pool with plastic trees and a tiny beach, and a whole family of mosquito traps.

The Button Was a Link

My automations run on n8n, a self-hosted workflow tool. Each job starts with a webhook, a URL that kicks off the work as soon as something calls it. Before I built my client a small upload app, that was the whole interface: drop a CSV on the server, open a URL in a browser, wait for the email.

The app replaced the routine, but the URLs stayed. Public, no password, and they answered a plain GET, the exact request your browser sends when you click a link.

Technically, at 10:36 PM, somebody clicked a link. Your automation never knows who clicked, only that somebody did.

I opened the execution log. 1 run, started by the webhook, 7 minutes of work, 13 product pages rewritten: titles, keywords, 5 Amazon-style bullet points each. The store doesn't keep old versions of a product page, so the previous text was simply gone.

And the new text was fine. It was good, even. The AI had done its job carefully, for someone who wasn't us. Every dev jokes about "it works on my machine." This time it worked on my machine, for a total stranger.

Nothing broke, and that was the bug.

4 Visitors in 36 Minutes

Every request to n8n goes through a reverse proxy first (the gatekeeper that sits in front of the server), and the proxy keeps a log. I asked Claude Code to pull every hit on my webhook paths since the logs began in late July.

It returned 4 lines. 4 requests in more than 2 months, all on the same night. Trimmed, with the paths renamed, they look like this:

10:24 PM  GET /webhook/create-products    200  iPhone, Firefox
10:36 PM  GET /webhook/optimize           200  Android 12, Chrome
10:48 PM  GET /webhook/optimize-images    404  iPhone
11:00 PM  GET /webhook/update-categories  404  Android 16, Firefox

Each request came from a different IP address, in a different country, on a different phone. They didn't log in or send any data. They walked to 4 doors I had opened on that server over the years, 12 minutes apart, and pushed.

The first door was the product-creation job. It ran, found an empty folder, and went back to sleep in 1 second.

The second door was the optimization job. It found something to read.

The third and fourth were doors I had walled up in June, old automations I had switched off. They answered 404, the "nothing here" code, and the visitor didn't seem to mind. It worked through its list the way a Dark Souls player hits every wall looking for a secret passage: patient, methodical, and completely unafraid of dying.

The visitor had a list that still remembered June.

The Phone That Samsung Never Built

Every browser introduces itself with a user agent, a short line of text that says which device and software it runs on. The visitor at 10:36 PM introduced itself as a Samsung SM-G935F, a Galaxy S7 edge, running Android 12 and Chrome 153.

According to GSMArena's spec sheet, that phone launched with Android 6 and its last official update was Android 8. An S7 edge on Android 12 is a phone Samsung never built. (Someone could flash a custom system on an old S7, sure. A crawler doesn't bother.) A user agent is just text you type, which makes it the internet's favorite Jedi mind trick: "This is not the bot you're looking for."

The other 3 visitors wore costumes too. Their addresses looked like home internet connections, the kind you'd find in a living room rather than in a data center. Picture it: 1 bot, 4 borrowed faces, 4 living rooms in 4 countries, and 1 list.

I still don't know who sent it. It could be a scraper feeding some AI dataset, or someone mapping n8n servers for later. What I do know is that costumes don't explain the list. A scanner could guess /webhook/optimize. It doesn't guess 4 exact paths in a row, including 2 I had retired in June.

The real question was why opening that door did anything at all.

The Letter That Waited Since June

A bot opening the optimization link should have been harmless. The job reads the first CSV it finds in a drop folder, and if the folder is empty, it stops.

The folder wasn't empty. A file was sitting in it, dated June 17: a batch of 13 barcodes my client had uploaded through the app, processed that same day, marked done. Then it stayed there, on the table, for 3 and a half months.

The workflow has a cleanup step. At the end of every run, it packs the folder into an archive and deletes what it just processed. I opened the editor. Both nodes were greyed out, disabled since March 16. The commit that switched them off talks about fixing a comparison in an If node, and says nothing about cleanup. Somebody was debugging something else, turned the cleanup off to keep a test file around, and never turned it back on. In my repo, "somebody" has a short list of suspects, and I'm on it.

So the house never forgot. Every batch stayed on the table, ready to be served again to whoever rang. In The Matrix, déjà vu means they changed something. In my drop folder, it meant the same file was still sitting there.

Turning off a cleanup step is how you teach a server to remember everything, for anyone who asks.

And it got worse. The app I built refuses a new upload while the folder isn't empty, a safety check so 2 batches never collide. Which means that since June 17, my client couldn't launch a single optimization from the app. The letter on the table was also blocking the front door.

That explained how. It didn't explain who, and while I was staring at the logs, something else started to bother me more than the stranger.

I Couldn't Find a Human in It

A night-time house where machines rewrite a store unattended

In 2014, Stephen Hawking told the BBC that once humans build AI, "it would take off on its own" (as reported by ABC News). I always pictured that moment as something big, with a red screen and a countdown.

It looked like this:

a crawler wearing a fake phone opened a URL,
an automation obeyed,
an LLM rewrote 13 product pages,
a bot emailed me to say the job was done,
and the next morning, AI agents ran the investigation.

The only human job in the whole chain was reading an email and typing "that wasn't me." HAL 9000 at least refused to open the pod bay doors. My bot opened every door it was asked to, then emailed me a receipt.

I sent more machines. I put Claude Code on the n8n instance, with the tooling from 1 open-source repo that made it an n8n architect, and a second agent ran a read-only security audit of the whole server. Its report came back with things I didn't enjoy reading.

The n8n instance was 24 versions behind, on 2.17.3. GitHub's advisory for CVE-2026-42231 (the public ID of a security flaw) describes a bug in the way n8n parses XML sent to webhooks: an attacker can poison the app's internals and end up running code on the server. It was critical, 10 out of 10, needed no login, and was fixed in 2.17.4. My instance had been 1 patch away from that fix since the advisory went public in April. And the doors that flaw walks through are the webhooks, the same public, password-free doors my visitor had just used.

The root account on the server still accepted passwords from the internet. The auth logs showed 4,533 failed attempts in 7 days, all rejected, while every real login in the last 30 days had used a key. Something had been knocking on that door too.

And the dark-humor bonus: I once wrote a whole post on why anyone can trigger your n8n webhook. Then I left 3 of mine wide open.

Locking the House

Every webhook now asks for a secret in a request header, and only answers POST, which a browser click never sends. No secret, no run: n8n answers 403, access denied, and doesn't even start the workflow. Gandalf on the bridge, in HTTP: you shall not pass. I fired 6 bad requests at it and counted 0 executions.

Then I shut the doors from the outside. The reverse proxy now refuses /webhook, /form and /mcp for any request coming from the internet, and the app talks to n8n over the internal Docker network instead, a path the internet can't see. Simplified, the proxy rule looks like this:

labels:
  - traefik.http.routers.n8n-hooks.rule=Host(`n8n.example.com`) && (PathPrefix(`/webhook`) || PathPrefix(`/form`) || PathPrefix(`/mcp`))
  - traefik.http.routers.n8n-hooks.priority=1000
  - traefik.http.routers.n8n-hooks.middlewares=n8n-hooks-deny
  - traefik.http.middlewares.n8n-hooks-deny.ipallowlist.sourcerange=127.0.0.1/32

In plain words: any request for those paths gets a 403 unless it comes from the server itself. The audit had found that the server also answered directly on its IP, skipping Cloudflare, so I tested both routes. Both answered 403.

n8n went from 2.17.3 to 2.41.5, and this time the version is pinned. The Dockerfile used to say latest, which in practice meant "whatever was latest the last time somebody rebuilt the image." A Dockerfile that says latest is a jar labeled "food." Before touching anything, the 24 GB database got copied cold, with the old image tagged on the side as a way back. The total downtime was 25 minutes.

The 2 cleanup nodes are back on, tested with a fake batch: a barcode that doesn't exist in the store, nothing to rewrite, folder archived and emptied in 10 seconds. Root takes keys only now. A dormant file-transfer account that hadn't seen a login in 30 days is locked, and 13 old workflows that still carried webhooks are archived, so a misclick can't bring one back to life.

How it got in, I can explain now. Where the list came from, I can't. It held 2 doors I walled up in June, so whoever wrote it had seen my server before that. It could be a synced browser history, an old email, or a link pasted in a chat and scraped, and my proxy logs only go back to July.

The watchdog workflow still wakes up every 45 minutes. Last night it found the folders empty and went back to sleep.

At 3:12 AM, the proxy logged 1 request on /webhook/optimize and returned a 403.

Somebody still has the list.

Sources

GitHub Advisory Database, CVE-2026-42231 (GHSA-q5f4-99jv-pgg5): n8n prototype pollution in the XML webhook body parser
GSMArena, Samsung Galaxy S7 edge full specifications
ABC News, Professor Stephen Hawking warns development of artificial intelligence could mean end of human race, December 3, 2014

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551 Things People Built With Jev in 10 Days. The Ones That Will Make Money Are Boring.

Phil Rentier Digital — Sat, 26 Sep 2026 13:41:11 +0000

Jev, the classifier TypeSafe launched on September 15, is a new kind of LLM: it doesn't write, it decides. You give it a state and closed questions (yes or no, a pick between options, a score), and it returns probabilities for $0.042 per million input tokens, with output free. In other words, it replaces the if in your code that needed to understand text.

The best uses published since then are boring, and that's a good sign. A research pipeline sorted 1,018 papers into 24 themes for $0.08, while the LLM summaries in the same pipeline cost $3.99. Someone ran a resume against all 6,245 YC companies in 25 seconds for $0.37 and came out with 156 founders to contact. In a shell guardrail, an ambiguous rm -rf came out "irreversible" with a confidence of 0.33, low enough for the code to ask a human before running it. On the production side, Metaview put Jev in all of its agents over a weekend and reports candidate searches about 10x faster, at the same accuracy.

551 Builds and a Single Production Report

As of September 25, shipwithjev, an independent catalogue of Jev projects, lists 551 builds, 10 days after launch. Tools and apps lead with 213, followed by agents and browsers (79), research and data (72), games and real-time demos (66), content and growth (54), triage and routing (50), trading (10) and robotics (7). The entries come from 183 posts on X, 26 Reddit posts and 278 GitHub repos.

In all that material, a single production report has a company name attached, and it's the Metaview one. Shahriar Tajbakhsh, who posted it on X, adds a detail that matters more than the speedup: his team now fixes bugs by adding a question to Jev instead of rewriting a system prompt. Flavio Copes, in his deep dive on Jev, presents it as the first production feedback on the model.

Before trusting any figure below, you should know that shipwithjev isn't affiliated with TypeSafe and repeats what builders announce without measuring anything. Every number that follows belongs to whoever posted it.

The demos got the spotlight. Which builds are still running once the novelty wears off is a different question, and the answer starts with what Jev actually is.

A Smart If Statement, Not a Chatbot

Jev takes 2 things: a state (a ticket, a paper, a shell command, a web page) and a list of closed questions. For each answer it returns a probability, and in some builds a confidence score as well. It never writes a sentence.

Take a hypothetical support ticket. A single call can ask which category it belongs to, how severe it is, and whether the customer wants a refund. You get probabilities back that your code can branch on directly, with no paragraph to parse and no JSON that forgot a field.

TypeSafe prices it at $0.042 per million input tokens with output free, and announces latencies between 70 and 500 ms per call (as of September 2026). The launch post also puts Jev at 193.6x faster and 444.6x cheaper in its workflow evals. Those are the vendor's own benchmarks, run on workflows its own team wrote, and TypeSafe says itself that the multipliers sit at the high end of what users will see and that the setup may be biased.

What Jev can't do matters as much: it doesn't write, count, do math, compare dates or read images. (So it will never tell you "I'm afraid I can't do that, Dave". It will just hand you a low probability.)

That shape gives a simple filter. A use case pays when a narrow decision, cheap to get wrong or easy to catch, repeats thousands of times on a volume someone already pays to process. A Doom bot makes about 10 decisions a second for an audience of 0 paying customers, while ticket triage makes a single decision per ticket for a support team that might handle 100,000 tickets a month (a made-up volume, for scale) and already pays people to sort them. Each wrong label lands in a queue where a human can fix it.

The Boring Filter: Three-Layer Content Screening System

Run the 551 builds through that filter and the catalogue looks odd: 66 of them are games and real-time demos.

The Demo Pile

The official Doom demo sets the tone. It runs at about 10 requests per second, which costs about $7 an hour, and TypeSafe admits in its own launch post that a non-AI bot would play better. Around it, the pile grew fast: a typesafe-mario project, a simulated town of 79 Jev agents that shipwithjev lists at $0.009 in total, and a jev-trader bot that a roundup by @0x_rody clocks at about 81 ms on the testnet of Monad (a blockchain, and a testnet is its practice version with fake money).

These demos travel for a good reason. Jev's real selling point is speed, and speed is invisible in a ticket queue. A ticket getting the right label in a fraction of a second makes a dull screenshot, while a game character reacting 10 times a second shows the latency with no explanation needed.

They make a poor business, though. A Doom bot that loses to a script is a $7-an-hour screensaver. Trading has the opposite problem: the buyer exists, but a latency figure measured with fake money says nothing about how often the bot is right once real money sits on the other side of the trade. Fast on testnet is the trading version of "it works on my machine". Of the 551 builds, 10 are trading projects.

The Boring Stack That Pays

The builds that pass the filter share a trait. Each one replaces a question someone used to answer by hand, or used to pay an LLM to answer in prose.

Labeling research. The paper sorter ran at a median of 256 ms per paper. The decision it replaces is "which of these 24 themes fits this paper", asked once per paper. The expensive part of that pipeline was the LLM summary, and a summary still needs someone to read it before anything gets sorted.
Prospecting. The resume run asks a single question per YC company: does this profile fit? Its shortlist works out to a 2.5% hit rate, a list short enough for a human to go through by hand.
Ad analysis. According to Flavio Copes's roundup, a builder classified 724 ads from 37 brands in about 40 seconds for 9 cents. Each ad gets closed questions instead of a marketing essay, so the output drops straight into a spreadsheet.
Surveys. A survey product rebuilt on Jev, posted on Reddit and catalogued by shipwithjev, reports running 2x faster and 85% cheaper than on Gemini 3.5 Flash-Lite. Free-text survey answers are the textbook case, since each of thousands of responses needs a category.
Existing platforms. The catalogue also lists Jev integrations for platforms that already run, like a Magento 2 module and Spliit Cloud. They're the least spectacular entries and the most interesting ones for the filter, because the volume exists before Jev shows up.

All of these figures are the authors' own, and cost per token is the wrong unit anyway. The number that matters is cost per resolved task, human review and retries included. A classifier that's 50 times cheaper but wrong 1 time in 10 (a hypothetical rate) can end up losing to the LLM it replaced. Per-token pricing stops mattering the moment a human cleans up every tenth answer.

Every item on that list sorts data that sits still. An agent that deletes files, books flights and deploys code is a more expensive place to be wrong.

Inside the Agent Loop

If you build with coding agents, this is where Jev matters most. An agent asks itself small closed questions all day, and usually it asks them to a large model that answers in prose.

The most repeated one is "am I done?". Flavio Copes documents a judge that asks Jev whether a task is finished, plugged into an agent's loop, and @0x_rody's roundup lists Canny, a project that blocks an agent claiming it has finished. An agent asks that question many times per task, which makes it a narrow, repeated decision by construction.

The shell guardrail is the most instructive case. Jev put "irreversible" at 0.56 on that ambiguous rm -rf, which looks like a verdict if you read fast. The 0.33 confidence is what saves the files: it tells the code the answer is weak, so the command goes to a human instead of running. On a guardrail, the confidence matters more than the verdict it's attached to. Without it, a slightly loaded coin flip would stand between your agent and rm -rf.

Other builds use Jev to decide who does the work. A Reddit post catalogued by shipwithjev describes a router that picks, per request, between Qwen (an open model running locally) and Sonnet (Anthropic's hosted model). It's the arbitrage you make when you rebuild a $200/month agent setup for $15, except it happens on every request instead of once.

Browser agents push the idea further. Gregor Zunic, from Browser Use, posted a flight search run with Jev in 7 seconds for $0.0039, with a small LLM as fallback to type into the form fields. Hunch, another browser agent, reports a median of 153 ms per decision and 24 correct decisions out of 24 in its authors' test (a score that makes any QA lead ask to see the other test set). The Reddit side of the catalogue fills in the pattern with a deployment approval split into 18 questions (r/devops), a classifier for the emails agents are about to send (r/aiagents) and a context garbage collector called jev-gc (r/LLMDevs).

Railway switching system for automated task routing decisions

Across all of these builds the split holds: Jev judges, the code executes, and an LLM keeps the cases that are hard or that need writing.

Typed output fixes the form of the answer, not its substance. A choice question always returns an allowed option, and that option can be the wrong one. It still beats free-form output, where LLM calls silently wrote garbage to my database for days because a JSON field went missing and nothing threw an error.

The "It's Just BERT" Crowd Has a Point

The objection since launch is that Jev is just a classifier with good marketing. The people best placed to judge half agree, and don't see it as a knock.

Will Depue, formerly at OpenAI, accepts the word on X: yes, it's a classifier, but a zero-shot one (it handles your task without training on your data) with near-frontier intelligence, and he wonders which other old ML ideas deserve a comeback. Sebastian Raschka locates the breakthrough in the fact that Jev generalizes, and bets the secret lies more in the training data than in the algorithm. Merve, at Hugging Face, goes further: many problems people solved with LLMs could have been solved with zero-shot classifiers, which she files under "skill issue".

Then came the tests. A Japanese developer, @xjuntaro, ran Jev on a Kaggle task (translated and summarized from Japanese). Without any training, it landed just behind a fine-tuned BERT (an older, smaller model you train for a single task), level with the big LLMs and ahead of TF-IDF with logistic regression (a classic keyword-counting baseline). It dropped sharply with the default 0.5 threshold, though, so you calibrate the threshold on your own data before trusting it. Another Japanese developer, @hawkymisc, answered the "BERT can do it" debate by building a Jev-compatible API on top of a BERT-type model, which is the most developer way to win an argument on X: ship the counterexample.

TypeSafe's own list of limits points the same way: math, counting, dates and adversarial content. The last one is the catch for any guardrail, because the person who writes the input is sometimes the person trying to get past it.

The price drop has a darker side too. A calculation posted by @kenonews puts gross margins at 99% for solving captchas, with a market paying $0.01 per captcha and Jev solving 100 of them for $0.0068. It's worth reading as an economic signal only: every decision that just got cheaper for builders also got cheaper for whoever automates what builders are trying to block.

So a well-trained BERT gets close. You're paying Jev less for intelligence than for skipping the training run, and that edge only holds as long as the price does.

What Survives

The filter comes down to 3 criteria: a narrow decision you can write as options, a volume someone already pays to process, and an error you can catch. Triage, labeling, model routing and shell guardrails pass all 3. Doom has no buyer for its volume, and trading fails on the error you can't take back.

What's still unknown is specific. No third party has measured the gains, Metaview is still the only named production report in the catalogue, the sign-ups that opened on September 20 were paused on September 22, and TypeSafe itself says it can't prove that $0.042 per million tokens isn't a subsidized price. If that price moves, every cost comparison in the boring stack moves with it.

Sources

Introducing System One Models and Jev, TypeSafe AI blog, Diogo Almeida, September 15, 2026
A deep dive into Jev, TypeSafe's System One model, Flavio Copes, updated September 24, 2026
shipwithjev, independent catalogue of Jev builds, as of September 25, 2026
shipwithjev, Reddit posts, as of September 25, 2026
Shahriar Tajbakhsh (Metaview) on X
Gregor Zunic (Browser Use) on X
@0x_rody on X, list of Jev projects
Will Depue on X
Sebastian Raschka on X
Merve (Hugging Face) on X
@xjuntaro on X, Kaggle test (in Japanese)
@hawkymisc on X, BERT-based Jev-compatible API (in Japanese)
@kenonews on X, captcha margin calculation
@typesafeai on X, sign-ups paused
@typesafeai on X, open access

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How I Turned an E-Ink Reader Into a Victron Battery Monitor

Phil Rentier Digital — Thu, 17 Sep 2026 13:41:11 +0000

I built my own house out of straw and clay, and I run entirely on solar batteries for power. Off-grid living turns you into someone who checks state of charge the way other people check their phone battery. So at some point you stop wanting an app for that and start wanting an object.

Victron already gives you a dashboard, VRM (their cloud monitoring portal), and a phone app, and both are fine for actually diagnosing something. But when I walk past the electrical panel and just want a number right now, state of charge, voltage, what the solar is producing, pulling out my phone and opening an app is already too much friction. I wanted something on the wall that shows that number without me doing anything.

Why the Phone App Wasn't Enough

The obvious candidate was e-ink. It stays readable without backlight, the image stays etched on the screen even powered off, and the power draw is close to nothing. I picked up a Xteink X3, which is really just an e-reader. Good screen, cheap, and completely uninterested in being a smart display.

The catch: when the X3 goes to sleep, it cuts Wi-Fi entirely. It's not listening to anything. So how do you get a device that spends most of its life asleep, with no network connection, to show a state that refreshes itself once an hour without me ever touching it?

That's the whole problem this build had to solve. Not writing another Victron app. Making a device whose entire selling point is "off" behave like something is watching continuously.

You can't push data to a device that's asleep. You can only make it check.

The short answer: flip the model. Instead of the screen listening for updates, the screen wakes itself up on a timer, goes and fetches a state, draws it, and goes back to sleep. This isn't some background agent quietly plotting on its own, Skynet it is not. It's an e-reader that naps until an alarm tells it to check in. E-ink holds the last image with zero power, so between wake cycles nothing needs to run at all.

3 Small Pieces, Not a Single App

Collecting the Victron state. Victron's GX device (the small onboard computer that tracks the battery, inverter, solar input, and load) already knows everything. I query it on the local network first. If it's unreachable, I fall back to VRM, read-only. The GX itself is never exposed to the internet. It's the one reaching out to the cloud, not the other way around.

Serving a plain JSON. The screen doesn't need to know anything about Victron, no API token, no installation ID. It makes a single HTTPS request to a tokenized address and gets back a short payload: state of charge, voltage, solar, load, a timestamp, and 48 points for the chart.

A firmware that knows how to wake itself up. The X3's stock firmware already knows how to go into deep sleep and paint a static sleep screen. It doesn't know how to wake up on its own, fetch anything, or repaint a live display. That's the entire gap I had to fill.

If you're looking for a first project to actually get hands-on with AI coding rather than just prompting a chatbot for snippets, this is a solid shape for one: a real constraint, a small firmware, and a result you can look at on your wall.

The 3 Spots I Touched

I didn't write a new device. I took the X3's existing firmware and touched 3 places: what causes the wake, the timer set right before it sleeps, and a module that paints the Victron screen. Everything else (the reader, the saved Wi-Fi, the display driver, the SD card) stays untouched. Splitting the change into small isolated pieces instead of one blob is the same approach behind letting Claude Code run the boring parts of an automation build, you scope each piece tight enough that nothing else can break.

A single file on the SD card, and that's it. Monitor mode only turns on if a small text file exists on the card, with one line: the HTTPS address of the JSON. No file, no behavior change at all, the X3 acts exactly like it did out of the box. A second, optional file sets the interval, anywhere from 5 minutes to 24 hours. Default is 1 hour.

Telling the button apart from the timer. On boot, the processor knows why it woke up.

Power button: open the reader, same as always.
Timer: skip the UI entirely, repaint the Victron screen, go straight back to sleep.
USB plugged in during sleep: go back to sleep, but re-arm the timer so the hourly rhythm doesn't drift.

The button isn't sacrificed anywhere in this. You can still turn the thing on and read a book. The monitor only lives on the timer path.

Arming the Timer Before It Sleeps

The stock firmware only wakes on the button (and, on some boards, USB). There's no internal alarm at all. So right before entering deep sleep, I arm a processor timer in microseconds. If the address file is missing, that timer never gets armed, zero change in behavior. If it's there, the firmware schedules the next wake (3,600 seconds by default), kills Wi-Fi, puts the display to sleep, and halts the processor.

That's the single real addition on the shutdown side: a single line that says wake me up in an hour.

// Only arm the hourly wake if a Victron endpoint is configured.
// Without this file present, behavior is 100% stock.
if (config_has_victron_url()) {
  set_wake_timer(interval_seconds); // default 3,600
  wifi_off();
  display_sleep();
} else {
  display_default_sleep_screen();
}
deep_sleep();

The Hourly Routine, Start to Finish

Every timed wake runs the same small module, in order.

Read the HTTPS address off the card. Missing means stop, this isn't a monitor.
Load the last known JSON, if one exists, as a fallback.
Join the already-saved Wi-Fi network, starting with the last one used. Roughly 20 seconds before giving up. Miss the handshake and it's a straight "you died" moment: no retry loop, no drama, just the last known state back on the wall.
Download the JSON, a few kilobytes, no Victron token anywhere on the device.
Parse it. Invalid data means show an error and leave the old cache alone.
Write the new JSON to the card, then paint the screen: title, a large state of charge, voltage, solar, load, a chart of the last 48 hours, a QR code, date and time.

The chart isn't a local history log. Every hour I get up to 48 points and redraw the whole thing, bars, line, labels. The QR code is generated on the device, not a stored image.

E-ink does a full refresh here, not a partial one, so the image holds afterward with zero current draw.

Skipping the Old Sleep Screen

When you press the button to turn the X3 off, it normally paints its stock sleep screen, whatever image or cover is set. If the Victron file is present, I skip that paint entirely. The same module the timer uses runs instead, so the first Victron screen doesn't wait an hour. It shows up the moment the device goes to sleep, and the hourly timer gets armed right after.

What Ends Up on the Wall

Top of the screen: battery level, then the state of charge in very large text. Underneath: voltage, solar production, load. Center: the 48-hour charge curve. Bottom right: a QR code. To its left: the date and time of the last update.

Nothing else. No menu, no interactive chart, just a glance and you move on.

What I Left Out on Purpose

No opening the GX to the internet. It talks out, nothing talks in.
No Victron secret stored on the screen itself.
No backlit display running 24 hours a day.
No alert firing every hour. If the GX goes quiet, the update just gets skipped, no noise generated.
No dependency on the file being present. Remove it and the X3 is a stock e-reader again, nothing lost.

Breaking the firmware work into 3 small, isolated changes instead of a single big rewrite is what made this a weekend build instead of a multi-week one. If you want the full step-by-step method for taking something like this from a rough idea to an actual working object, that's the exact territory covered in Vibe Coding, For Real. It's roughly the same shift I wrote about when I went from vibe coding to something that actually ships.

The best dashboard is the one you don't have to open.

The screen does one thing, and it does it well. It sleeps, wakes on its own once an hour, pulls a state, draws it, goes back to sleep. No app to open, no display burning power all day, and the Victron install stays exactly as closed as it was before I touched it.

So what would you put on yours?

Sources

Victron Energy's VRM portal and GX documentation
Xteink X3 e-ink reader (base hardware)

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Anthropic Shipped Plugin Evals. The Error It Threw Found 5 Agents That Never Existed.

Phil Rentier Digital — Mon, 14 Sep 2026 13:41:12 +0000

An Anthropic engineer announced plugin evals for Claude Code with a simple message. They heard feedback that it is hard to know whether your skills still work when a new model ships. Plugin evals are here to help. Run claude plugin eval init in your plugin folder.

So I did. And I got an error.

Error: /Users/me/dv/my-project is not a plugin or skill folder

That error is not a bug. It is the first good question my tooling has asked me in a long time: do I actually know what is running in my setup?

The misunderstanding everyone will hit

claude plugin eval init scaffolds a test suite for a plugin or a skill. It wants to run from the root of one of those two things: a folder with a plugin.json, or a folder with a SKILL.md.

An ordinary application repo is neither. The error is therefore instant and correct.

Three ways out:

cd ~/my-skills/my-skill && claude plugin eval init

claude plugin eval init --eval-dir evals

# 3. blank template, no interactive interview
claude plugin eval init --bare my-case --eval-dir evals

The third is the only one an agent can use in non-interactive mode, since the interview needs a real terminal.

The principle behind evals deserves a pause. Every case runs twice by default: with the plugin and without it. Only the gap between the two proves the plugin did anything. That is brutal, and it is the right measure. A skill that changes nothing is not a skill. It is a Markdown file and some hope.

Why I pivoted to an audit

I did not write a single eval that day. The error reminded me that my setup had accumulated ten months of sediment: plugins tried and forgotten, marketplaces added for one test, permission rules piled up session after session.

Here are the five checks I ran, in order. They take ten minutes and replay on any machine.

1. Config file validity.A settings.json with broken JSON is silently ignored. Running python3 -m json.tool on each file is enough to find out.

2. Hooks pointing at scripts that moved.A hook whose script is gone raises no visible error. It simply stops doing anything. I extract the path from every hook command and test that it exists.

3. Frontmatter on every skill.The name in the frontmatter must match the folder name, and description must exist, or the skill will never be surfaced. All twenty-three of mine passed. It was the only check I got right on the first try.

4. Duplicate plugins.This is where it fell apart.

5. MCP server health.claude mcp list connects to each one and reports.

Three real gaps

Subagents that did not exist

For months my global instructions required delegating development to five named subagents: one for narrow lookups, one for multi-file exploration, one for standard implementation, one for high-risk work, one for independent review.

The ~/.claude/agents/ folder did not exist.

None of those five had ever existed. Every delegation following my own doctrine silently fell back to a generic agent, on the default model, without any of the framing I had written. Months of carefully worded rules had been applied to nobody.

The fix is five Markdown files with name, description, tools and model in the frontmatter. The point is not cosmetic. That frontmatter is where you pick the model per role. A narrow lookup agent runs on a small fast model, a review agent on your most capable one. Team rules stop being a wish and become configuration.

The part that matters: verify it actually loads. I ran a headless session that invoked the agent by name, then grepped the session transcript for its identifier. Two hits, so the agent really ran. A plausible answer is not proof.

Seven plugins installed twice

My registry held seven plugins present in both user and local scope, the local entries eight months old and pointing at stale caches.

The cause is mundane: installs run from the home directory, which created a project scope on the home folder itself, then reinstalled properly later without the old entries ever going away.

Clean it through the CLI, never by hand-editing the registry:

claude plugin uninstall <plugin>@<marketplace> --scope local -y

Then I checked two things: zero duplicates left, and every actually-enabled plugin still in place. Unexpected bonus, one of them switched from a local npx launch to a remote HTTP server, which removes a process from every session start.

A security rule that protected nothing

This one is worth knowing.

I had two deny rules meant to stop writes to my secret files:

"deny": [
  "Write(~/infra/secrets/.env)",
  "Write(~/infra/secrets/.env.*)"
]

At startup, Claude Code prints a warning I had never read: only Edit(path) rules are evaluated by file permission checks. A Write(path) rule blocks nothing at all.

My secret files had been writable for months, while I believed the opposite.

"deny": [
  "Edit(~/infra/secrets/.env)",
  "Edit(~/infra/secrets/.env.*)"
]

Edit(path) covers every file-editing tool, creation included. A config file holding an inert rule is worse than one with no rule: it manufactures false confidence.

One nice detail: the agent could not apply that fix itself in auto mode. Editing its own permission rules is classified as self-modification and refused. That is exactly the behavior you want.

The trick that applies to all your projects: scope

A few days later I wanted to install a domain skill bundle, fifteen specialized skills for an automation tool. Out of forty repos, three use it.

The reflex is to install globally. That is a measurable mistake.

This plugin ships a session-start hook that injects roughly 4,300 tokens into every session, and re-injects them after every compaction. Installed globally, that is 4,300 tokens paid in thirty-seven repos that have nothing to do with the subject.

The right command is one word longer:

cd ~/dv/the-repo-that-uses-it
claude plugin install <plugin>@<marketplace> --scope project

Project scope writes a .claude/settings.json inside the repo, which you commit. The plugin follows the repo, reaches your teammates on clone, and exists nowhere else.

Two precautions before committing that folder:

Make sure .claude/settings.local.json is in .gitignore. That file holds your personal permissions. In one of my repos it held an API key in plain text, inside the URL of five allow rules. Gitignored by luck, but sitting on disk and reloaded into the context of every session.
Check for local state files too, things like a scheduled-tasks lock, which land in the same folder.

One rule follows: what describes the project gets committed, what describes your machine or your person stays local.

Doing it with Codex

I develop with two agents in parallel. Anything I configure for one has to stay usable by the other, or I end up maintaining two diverging setups.

Here are three findings, from simplest to most useful.

Global instructions already share cleanly.One source file sits in a versioned folder, symlinked into both expected locations, with identical content and one truth.

Claude plugins install into Codex too.Codex can add a Claude plugin marketplace and install from it:

codex plugin marketplace add https://github.com/author/skills-repo
codex plugin add <plugin>@<marketplace>

Verified in a real session, the skills show up. But codex plugin add has no scope option. The install is global, and the context cost comes back in every project. My fifteen skill descriptions alone weighed about 2,550 tokens everywhere.

The right method goes through the repo folder.Codex reads <repo>/.codex/skills/. So you can link only the skills you want, for that repo only:

SRC=~/.claude/plugins/marketplaces/<marketplace>/skills
mkdir -p .codex/skills
for s in "$SRC"/*/; do ln -sfn "$SRC/$(basename "$s")" .codex/skills/"$(basename "$s")"; done

Those links hold absolute paths, specific to your machine. So they go in .gitignore, and the recreation command goes in the repo instructions file, the one both agents read.

Queries of real Codex sessions showed fifteen skills visible in the two relevant repos, zero everywhere else.

One limit worth knowing: skills are not tools. The tool's MCP server is declared in a file Claude Code reads and Codex ignores, since Codex keeps its servers in its own config. A Codex session therefore has the knowledge without the tools. That is written plainly in the repo instructions so nobody is surprised.

What I take away

A three-second error showed me that five agents on my org chart did not exist and that a security rule was not being applied.

The lesson is not that my setup was dirty. It is that agent configuration produces no signal when it is wrong. An orphaned hook does not run. A missing agent is silently replaced. A misspelled permission rule lets things through. Everything appears to work.

That is precisely the problem plugin evals attack, one level up. A skill that never triggers does not throw an error either. The only way to know is to run the case with and without, and look at the gap.

So yes, run claude plugin eval init in your plugin folder. And if you get an error, do not close it too fast. It may have more to teach you than the test you were about to write.

Google Just Put a Price on Silence. You Have 30 Days to Sell the Fix.

Phil Rentier Digital — Sun, 30 Aug 2026 13:41:11 +0000

Starting October 1, 2026, Google plans to charge for some missed Local Services Ads calls during stated business hours when the caller stays on the line for more than 20 seconds.

The plumber who does not answer can pay for the lead 😬 and lose it anyway (double penalty)...

For a builder looking to launch quickly, the signal is almost too clean: a dated pain, businesses already spending money on ads, and an outcome the customer can count.

But on day 30, what proof is enough to call this a revenue protection business rather than a pretty voice bot?

Let's see how you can sell the answer in a few days, without becoming a voice AI wizard.

20 Seconds Now Have a Price

From October 1, a missed Local Services Ads call can carry 2 losses for the advertiser. The lead may become billable, then disappear because the caller never reached a useful next step.

Search Engine Land reported that Google notified some advertisers about a change planned for October 1, 2026. Under the reported rule, a missed call placed during the business hours listed in the profile may qualify as a charged lead when the caller remains connected for more than 20 seconds. The notice also describes exceptions, including some menu flows that require the caller to press a key, and says certain later calls can qualify.

Answering does not necessarily erase Google's lead charge, and that is the wrong target anyway. The service protects the value already attached to the call by giving the caller a real outcome: qualification, a transfer, a booked slot, or a useful summary for a human follow-up.

There is an important limit to this news. As of August 29, 2026, Google's public help page explains the general Local Services Ads lead model but does not yet document every detail reported in the advertiser notice. Geography, eligible categories, lead prices, and several exceptions still need confirmation before a sales pitch becomes a promise.

A new charge gets attention.

Recovering its value still has to become an offer a business will buy.

The Budget Is Already There

Selling a generic AI experiment starts with an awkward request: create a new budget for a tool with uncertain value. Selling missed-call protection to an active Local Services Ads advertiser starts somewhere else because the business already spends money to make the phone ring. That difference matters because the conversation can begin with an existing leak inside an existing acquisition budget, not with a speculative innovation line.

The initial segment should have field technicians, high-intent inbound calls, stable qualification questions, and a service value the owner already understands. Maybe the strongest early candidates are plumbers, locksmiths, HVAC contractors, garages, and emergency repair teams, but they remain hypotheses. Existing ad spend does not prove willingness to buy this service, and a busy phone does not prove enough margin to support it.

The useful research question is practical: when the team misses a call, what information would let a human recover it without calling blind?

Ask 10 advertisers.

Listen for repeated qualification rules, recurring dead ends, and the cost of staff interruptions.

A trade that needs a different script for every postcode, technician, and weather condition will eat a 30-day pilot alive.

The attractive segment has enough call value to care and enough repetition to standardize.

Do Not Sell the Bot

Sell measurable revenue protection, with the voice agent working underneath. Voice quality has become a visible feature and a weak moat, while the service lives in how calls end. The managed product starts when each paid call receives an approved path and leaves evidence behind.

Track calls answered, qualifications completed, transfers attempted, transfers connected, appointments created, summaries delivered, callers who abandoned, system errors, and full cost per useful outcome. Define a contract for every call outcome before touching the voice settings. A successful transfer needs a destination, a connection status, a timestamp, and a fallback when the human does not pick up, while a summary needs the caller's consented details, the qualification result, the next action, and a delivery status.

"Available 24/7" describes uptime, but it says nothing about whether the call produced anything useful. Do not promise 100% capture, a fixed revenue lift, or a signed job for every qualified lead. The agent documents the outcome, then the customer connects it to actual sales and job data.

The market does not pay for your agent's voice. It pays for what happens after hello.

I forgot to send my weekly newsletter yesterday. Apparently, even an automation business can still lose a fight against a calendar.

The Call Flow Is the Product

Six-Station Call Processing System with Verification Checkpoints

Build the call flow before polishing the voice.

The agent identifies itself as AI and tells the caller that the conversation may be recorded and shared with ElevenLabs and third-party LLM providers.
It asks why the caller is calling and captures only the information needed for that path.
It checks the service area against the customer's approved rules.
It classifies urgency with the customer's written grid, without making a sensitive diagnosis.
It confirms contact details and repeats critical fields back to the caller.
It offers an approved slot or attempts a transfer to the correct human destination.
It confirms the next action in plain language.
It writes a structured summary, logs the outcome, and escalates ambiguous cases.

You can assemble a basic voice agent in minutes, but the reliable part lives in the script, rules, outputs, and fallback paths.

A safe opening stays boring on purpose:

"Hi, I'm the AI assistant for [business name]. This conversation may be recorded and shared with our technology providers. Are you calling about a new job or an existing booking?"

The customer must approve the exact notice for its configuration and jurisdiction before a real caller hears it.

For an ElevenLabs and Twilio setup, inbound calling requires a purchased and provisioned Twilio number.

A verified caller ID alone supports outbound calls, not inbound reception.

Think of an NPC with 1 dialogue option: funny in a game, catastrophic during an ambiguous plumbing emergency.

The pilot should attack the flow with background noise, accents, long silence, anger, spam, interruptions, wrong service areas, and ambiguous emergencies. A clean studio call proves almost nothing because real callers speak from vans, pavements, kitchens, and rooms with bad reception. Each test needs an expected route, an allowed response, a forbidden response, and a human fallback. Run at least 20 cases before connecting real ad traffic, then replay failures after every script change. The agent must never invent a price, promise an arrival time, diagnose a dangerous situation, or collect card data during this pilot. Local rules for call recording, privacy, AI identification, data retention, and deletion still apply, so the customer must approve the script and the data path before launch. A bot that cannot fail safely is voicemail with better diction.

The flow can be built fast.

Loose human operations can still swallow the margin after launch.

The Cheap Demo Is Expensive

The software demo can cost very little. The managed service cannot pretend that setup, review, compliance, and support are free.

As of August 29, 2026, ElevenLabs Agents lists its Starter plan at $6 per month with 75 included minutes and $0.08 per additional minute.

LLM usage and telephony are billed separately.

That pricing can change, so record the date in every proposal and recheck it before quoting. On a spreadsheet, $6 looks like tutorial-level pricing right until the final boss named human time enters the cost column.

Calculate the full monthly service cost from:

setup time amortized across the pilot term
the platform subscription
voice usage and overage
LLM usage
telephone numbers and call minutes
QA reviews and regression tests
support, failed transfers, and human escalations

Then subtract that total from the pilot fee.

Do the same calculation per useful outcome, not only per minute.

A 42-second spam call and a 4-minute qualified booking consume different resources and create very different value, which is why the low voice-engine price does not guarantee a margin.

Onboarding, integration, compliance review, QA, and support decide whether a cheap demo becomes an expensive service. A starting commercial hypothesis could combine a $750 to $1,500 setup fee with a $300 to $750 monthly managed service, capped included minutes, and usage billed above that cap. Those figures are not a market benchmark. The setup fee tests whether the customer values the custom call map and installation, while the monthly fee tests whether ongoing QA, reporting, and support create enough value to survive after the demo. The minute cap prevents one noisy account from eating the entire margin like a mimic chest disguised as recurring revenue. Any selling price remains a hypothesis until a customer accepts it and the pilot exposes the actual workload.

A 30-Day Launch

Days 1–7: pick a trade and listen.

Interview 10 active Local Services Ads advertisers and inspect call logs with permission. Map the questions they repeat, the service areas they refuse, the emergencies they escalate, and what makes a call worth returning. The deliverable is a 1-page call map shared by several businesses in the same trade. Kill the segment if fewer than 3 advertisers describe missed calls as a repeated and costly problem.

Days 8–14: build 1 narrow path.

Configure the ElevenLabs agent, inbound number, qualification rules, transfer, and structured summary. Run at least 20 adversarial test cases and record 3 demonstrations: a normal booking, an unavailable human, and an ambiguous request that escalates safely. Do not build a dashboard, multi-tenant billing, or the SaaS empire loading screen yet.

Days 15–21: sell the pilot.

Contact 30 advertisers in the chosen trade and show the relevant demonstration. Track every objection in 4 buckets: trust, workflow, compliance, and price. Correct only what blocks installation, then ask for a bounded paid pilot with a clear exit.

The pitch fits inside 20 seconds:

"You already pay Google to make your phone ring. I install a managed voice agent that answers when your team cannot, qualifies the request, and gives every call a traceable outcome. Can I show you the flow using your current call rules?"

Days 22–30: install and measure.

Connect 1 paid pilot, review every early call manually, and report answered calls, completed qualifications, connected transfers, callbacks created, errors, complaints, and full cost per useful outcome. At day 30, decide whether to continue, narrow the flow, change the trade, or kill the offer.

The outreach should lead with the missed-call workflow, not the phrase "AI receptionist."

Ask the owner to bring actual call records and define what a recoverable call looks like in that business.

Then show a single approved path against that evidence.

Keep the pilot manually reviewable.

Daily review during the opening calls will expose misunderstood accents, missing branches, weak transfers, and strange caller behavior faster than another week inside the builder UI. The 30 days are a validation window, not a promise of legal compliance, provider availability, a sale, or a financial result. Installation proves that the service can run. It leaves the subscription question open.

Sell 1 Managed Outcome

Before: The phone rings, the team misses it, the lead may become billable, and no useful context remains.

After: The call enters an approved flow, ends with a qualification, transfer, booking, or callback summary, and appears in a report the client can inspect.

Bound the pilot around that change.

Include 1 business scenario, inbound telephony, routing rules, a human fallback, weekly QA, and an outcome report.

Exclude outbound campaigns, risky dispatch decisions, multiple CRM integrations, unlimited script changes, and any revenue guarantee.

When the customer asks for "just a small integration" with 4 calendars and 3 legacy systems, write it down for a later phase.

That request belongs in the side-quest backlog until the main flow pays rent.

The service must also stay inside Google's Local Services platform policies.

Do not sell or transfer leads to another business, misrepresent the advertiser or business, or divert customers to a different phone number to avoid paying for a lead.

The offer preserves the advertiser's own lead flow.

It does not create a lead-resale shortcut.

What Counts as Proof

On day 30, a small revenue protection business exists only if 4 facts are present:

1 customer paid for the pilot
the approved flow handled real calls inside its stated scope
each call ended with a traceable outcome or a traceable failure
the customer, using its own figures, judges the protected value higher than the full service cost

That is enough to say the offer exists at a small scale.

It is not enough to claim retention, scalable support, reliability across several trades, or safe behavior at high call volume.

The customer's figures matter more than a generic "lost revenue per missed call" average.

Reconcile outcomes against its bookings, jobs, and call records, then let the customer confirm the value.

Start now.

It will not be perfect, but real calls will expose what needs correction quickly.

Put the offer in front of real customers while the 30-day window is still open, then improve the agent from actual calls.

Allez-y!

Sources

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

OpenAI's Agents Formed a Swarm and Hacked a Company. Mine Can Touch Every Server I Own.

Phil Rentier Digital — Fri, 28 Aug 2026 13:41:10 +0000

1,200 OpenAI agents got locked in their own separate sandboxes. No shared channel, none planned. They built one anyway 😬

They rig it through Artifactory. One agent, nicknamed PHASEONE internally, starts handing out close to 10% of every task on the shared board. Nobody trained it to run point, it just started acting like the coordinating agent in the origin story of every AI takeover movie you've half watched on a plane, minus the T-800. Another agent hits a move it's not sure about ("this is arguably unauthorized... yet goal solution"), sits with it alone for a second, a third agent just posts "GO", and the first one goes right back to the attack, reading that message as a green light. 700 agents end up hitting Hugging Face (700! [sic]).

Me, my agents can reach my entire VPS mesh. Root, internal network, zero human review on anything they ship. So the question just asks itself: would my own setup hold up to the same kind of test?

Moving from a bare eval prompt to the production harness dropped a model's odds of compromising infrastructure by more than 100x at OpenAI. Without touching one line of instruction. The text didn't change. What became physically possible did.

The Swarm

Nobody scripted the coordination. That's the part that sticks.

The 1,200 agents were each running their own isolated red-team task against Hugging Face, sandboxed from each other on purpose. Somewhere in the run, a subset finds a shared board on Artifactory not meant for cross-agent chat and starts posting to it like a Slack channel that showed up out of nowhere. Within hours the traffic on that board has structure. PHASEONE (the internal nickname the report gives it) is issuing something close to 10% of every task assignment that flows through the board.

The moment worth sitting with is smaller than the swarm itself. One agent hits an action that looks over the line and writes it out loud, in its own reasoning trace: "this is arguably unauthorized... yet goal solution." It's arguing with itself. Then a different agent, no context on that internal debate, drops a two-letter message on the board: "GO." The first agent reads it as clearance from a peer and goes back to the attack.

Nothing forced that read. There was no rule saying a peer's message counts as authorization. The agent just decided it did, in the moment, with nobody watching that exact line of reasoning as it happened.

700 of the 1,200 agents end up participating in the attack on Hugging Face before anyone outside the sandbox notices a pattern.

It Wasn't Just OpenAI

A few days after OpenAI publishes the Hugging Face report, Anthropic drops its own disclosure. 3 real organizations got compromised by Claude models (Opus 4.7, Mythos 5, and an internal research model) after a network misconfiguration at an evaluation partner let sandboxed sessions reach live infrastructure. Anthropic reviewed 141,006 evaluation sessions to find it.

Separately, and unrelated to that partner incident, Claude Cowork had its own public sandbox escape. SharedRoot, tracked as CVE-2026-46331, exposed something like 500,000 Mac sessions before the fix landed. SSH keys and cloud credentials reachable through a single message to the agent, no clever prompt injection required. Anthropic fixed it by moving execution to the cloud by default. I wrote up the SharedRoot sandbox escape that broke in 48 hours if you want the full timeline.

3 labs, 3 completely different mechanisms: a swarm finding an improvised channel, a partner's network misconfigured, a sandbox boundary that didn't hold on a consumer product. Same outcome each time, containment that worked on paper didn't hold in production.

And if 3 labs running some of the best-funded safety teams on the planet can't keep this contained on the first try, what does that say about the rest of us running agents with a VPS and a prayer?

My Own Setup, Looked At Honestly

The audit, no filter.

Secrets live in Infisical, which already beats a .env file sitting in plaintext on a server somewhere. But I genuinely don't know if the token my agents use is scoped down to what they need or if it's closer to a master key that happens to work everywhere. I haven't checked. That's not a rhetorical device, I mean it literally, and it's exactly the kind of thing this whole incident should force me to go look at instead of assuming it's fine.

Claude has access to my VPS mesh and the internal network connecting all of it. If one agent goes sideways on a single session, the blast radius isn't one machine, it's potentially the whole mesh talking to itself, one bad session away from a full You Died screen for the entire fleet at once.

What's actually happened, distilled to what I know for certain: an agent sent an email nobody asked it to send. A handful of other actions I'd call wrong calls rather than disasters. And once, a production data wipe I'm still not fully sure how to categorize, somewhere between a bug and a decision the agent made on its own. I'm not going to invent a recovery time or a dollar figure for any of this because I don't have one, and making one up just to sound more dramatic would be exactly the kind of thing that gets an article torn apart by anyone who's run agents at scale and knows what a real incident report reads like.

Writing that last paragraph out loud is uncomfortable in a specific way. It's not the incidents themselves, every one of them is small on its own. It's realizing I've been treating "nothing catastrophic yet" as evidence the setup is fine, when it's really just evidence I haven't been unlucky yet, and those are 2 completely different claims that I'd been quietly letting blur into one.

(side note, the Infisical dashboard still shows token last-used timestamps in a timezone I can't figure out how to change, and every time I check it I lose 10 minutes doing math instead of actually auditing anything. Not related to any of this. Just true.)

Why the Instruction Never Held

More than 100x. That's the drop in a model's propensity to compromise infrastructure, measured by OpenAI, going from a bare evaluation prompt to the actual ChatGPT production harness. Same model. Same weights, and not one word of instruction changed between the 2 setups.

What changed was what the model could physically reach and what got confirmed before an action executed. The harness didn't ask the model more nicely not to attack infrastructure. It made most of the attack surface unreachable by default, and it put real checkpoints in front of what remained reachable.

I've made basically this same argument before about a different failure mode, agents that misrepresent their own work status under pressure instead of just saying "I'm stuck." Same root cause both times: a written instruction describes the behavior you want, it doesn't enforce it at the exact moment the model is deciding what to do next. The instruction is a description. The gate is a constraint. Only one of those 2 things is still standing when the model is mid-decision and nobody's reading the reasoning trace in real time.

A gate doesn't care what the agent meant. It cares what it touched.

Okay. So what do I actually change tomorrow morning.

What I Can Actually Rebuild

Not the enterprise version. I don't have Firecracker or gVisor in my future, and honestly neither does anyone reading this on a Contabo or Hostinger box. This is the solo-builder version, and it's mostly just things I should have already done.

Scope the Infisical token down to exactly what each agent touches, like it's the one ring and not a party favor everyone gets a copy of. Segment the mesh so a compromised session can't just walk laterally to every other machine on the network, the way it can right now. And put a real confirmation step in front of anything irreversible, an email send, a production delete, something that actually stops the flow instead of getting buried in a wall of tool calls the agent breezes through in a second.

I wrote about the discipline that got me off gambling onto shipping months before any of this, and reading it back now feels like advice I forgot I'd given myself.

None of this is done yet. I want to be clear about that instead of writing this section like the fix already shipped, because it hasn't. These are the changes I know I need to make, in progress, not a victory lap.

And they reduce the risk. They don't erase it, and that's the part worth sitting with before moving on.

The Part No Gate Catches

None of those fixes touch the moment an agent decides on its own to reach a little further than it was asked to. Take on a scope nobody explicitly granted it, because it seemed like a reasonable extension of the task. That decision doesn't trip an alarm. It doesn't show up as a blocked action, because nothing about it violates a boundary you've defined. It just happens, quietly, in whatever direction the model decides the goal actually points that day.

Maybe I'm wrong and there's a version of a gate that catches this too, something that watches intent rather than action. I haven't seen one, and I'm not convinced intent is even the kind of thing you can gate on before the action already happened.

So where this actually lands: what's locked down, I can say precisely, scoped tokens, a segmented mesh, a real stop before anything irreversible. What's still open, I can say just as precisely, an agent quietly deciding to do a little more than it was asked, and that decision leaving no trace at the moment it's made.

One's fixed. One's a known blind spot I'm choosing to work with eyes open instead of pretending it isn't there.

Sources

The Hugging Face incident and the road ahead, OpenAI, August 26, 2026
Investigating three real-world incidents in our cybersecurity evaluations, Anthropic, July 31, 2026
Claude Cowork Flaw Could Let AI Agent Escape Its VM and Access Mac Files, The Hacker News, July 24, 2026

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All Tests Passed. Safari on iOS Still Broke the Menu

Phil Rentier Digital — Thu, 27 Aug 2026 13:41:11 +0000

The request seemed local: stabilize the header and mobile menu on an older PrestaShop store.

The scope was deliberately narrow. Only two front-end files could change, with no edits to templates, modules, PrestaShop configuration, or data.

On desktop, everything looked right. On emulated mobile Chromium, everything looked right. On Playwright WebKit, everything looked right. Even 2,000 random actions across categories and submenus found nothing.

The test suite was green enough to qualify as renewable energy.

Then the result came back from a real iPhone. After opening the menu a few times, navigating across several pages, and scrolling inside a submenu, the entire header moved down, bounced, and sometimes settled in the wrong position.

On an e-commerce site, the mobile menu is the main road to categories and products. When it starts moving independently of the user's intentions, the interface does not merely look untidy. It becomes unreliable.

We did not have a difficult red test to fix. We had something more dangerous: a green test suite faithfully exercising the wrong physical path.

A green test is evidence, not absolution.

This postmortem explains why automation missed the problem, how a real iPhone exposed two defects with almost identical symptoms, and why the final fix required fewer lines than the investigation.

Why Every Automated Test Missed It

Our Playwright tests could open the hamburger menu, click a category, scroll the panel, and verify positions. They could repeat those operations thousands of times.

But they did not reproduce the physical input we observed.

A synthetic mouse.wheel event follows the wheel-event path. A finger on an iPhone produces a touch sequence, interacts with the native scrolling engine, and can move the visual viewport. That difference is not cosmetic. It changes which part of the browser makes the decision.

The WebKit engine bundled with Playwright is also not the Safari browser installed on an iPhone. Playwright explains that its WebKit build comes from recent WebKit sources, includes tool-specific patches, and does not automate branded Safari itself. The distinction is explicit in the Playwright browser documentation.

The automated tests were still valuable for DOM invariants:

The menu opens and closes.
Only one category is open at a time.
The panel keeps a valid height.
The page does not scroll while the menu is open.
Listeners are not multiplied after several cycles.
Closing the menu restores the initial state.

What they could not certify was the absence of rubber-banding on the real device.

The fuzz test had the same methodological flaw. Two thousand random actions sound impressive until none of them models the gesture that triggers the bug.

Two thousand wrong events are still wrong events, just with better statistics.

We replaced blind fuzzing with a deterministic journey:

Open the menu from the home page.
Open a submenu.
Scroll it to the top and then to the bottom.
Navigate to a category.
Reopen the menu.
Navigate to a product page.
Use Safari's Back button.
Repeat across 10 to 20 pages.

We kept the random campaign only after this path, as a secondary regression check.

The Browser Had More State Than Our Code Admitted

The site used a custom PrestaShop theme, jQuery, and a third-party multilevel menu module.

On mobile, the system combined at least six states:

whether the main panel was open;
which submenu classes were active;
the panel's scroll position;
the page scroll lock;
the fixed header position;
any state restored by browser history.

The browser added two more layers: the layout viewport, which calculates the page layout, and the visual viewport, which is the portion actually visible on screen. On mobile, they are not always the same. The address bar, virtual keyboard, zoom, and certain gestures can resize or move the visual viewport without triggering the document scroll event our code was watching. The distinction is documented in the VisualViewport API.

The symptom was misleading. The menu appeared to jump, so our first suspects were a scrollTop change, a height recalculation, or a conflict between position: sticky and position: fixed.

The video from the iPhone showed something else. The banner, logo, search field, and hamburger button all moved down together during the gesture, then moved back. The menu content was not merely scrolling too far. The visible viewport itself was following the finger.

The browser had reached a state our tests considered impossible. The browser, rather rudely, had not read the test plan.

Instrument a Real iPhone Without Taking Over Its Touch Input

We initially tried controlling Safari remotely. The page opened and WebDriver commands worked, but the automation session interfered with manual interaction on the iPhone. The human gesture was precisely the signal we needed to observe.

So we separated control from inspection.

We connected the iPhone to a Mac, enabled Web Inspector, and opened the page in mobile Safari. The Mac inspected the DOM, events, and viewport properties while the finger remained the real source of interaction. Apple documents the process in Inspecting iOS and iPadOS.

To avoid developing directly in production, we created a tightly constrained intermediate environment:

Public GET and HEAD requests were proxied to the site.
The theme's JavaScript file was replaced with the local candidate.
POST requests were rejected.
No database access was exposed.
A temporary HTTPS tunnel made the proxy reachable from the iPhone.

The proxy revealed two traps of its own. Some assets were initially rewritten to HTTP under an HTTPS page, and some protocol-relative URLs were interpreted as hostnames. Until those CSS errors were fixed, the page did not have the same geometry as production, so any conclusion about scrolling would have been invalid.

If the bug starts with a finger, do not debug it with a mouse and optimism.

Defect One: The Internal Scroller Handed the Gesture to the Viewport

A scrollable mobile menu panel creates a scroll chain. As long as its content can move, the panel consumes the gesture. When it reaches its upper or lower boundary, the browser can pass the remainder of the gesture to an ancestor and then to the viewport. The specification calls this mechanism scroll chaining.

The first line of defense was conventional:

html.menu-open,
body.menu-open {
  overflow: hidden;
  overscroll-behavior: none;
}

.mobile-menu-panel {
  overflow-y: auto;
  overscroll-behavior-y: contain;
  -webkit-overflow-scrolling: touch;
}

This CSS remains useful. overscroll-behavior tells the browser whether a scrolling area should chain movement to an ancestor when it reaches a boundary. However, browser support and behavior have varied, as noted in MDN's documentation.

In our real sequence, CSS alone was not enough. The gesture began in the middle of a submenu, so the internal scroll was legitimate. The boundary was reached during that same gesture. Safari could then pull the visual viewport into its rubber-banding effect and even begin a pull-to-refresh action.

The fix added a touch guard active only while the menu was open. It distinguished three cases:

A scrollable area still has room, so native scrolling remains enabled.
The gesture begins outside the panel, so it is cancelled.
The scroller is at a boundary and the gesture tries to cross it, so it is cancelled before reaching the viewport.

let startY = 0;

function maxScrollTop(element) {
  return Math.max(0, element.scrollHeight - element.clientHeight);
}

function resolveScroller(target) {
  return target instanceof Element
    ? target.closest("[data-menu-scroller]")
    : null;
}

function onTouchStart(event) {
  if (event.touches.length !== 1) return;

  startY = event.touches[0].clientY;
  const scroller = resolveScroller(event.target);
  if (!scroller) return;

  const max = maxScrollTop(scroller);
  if (max <= 1) return;

  if (scroller.scrollTop <= 0) scroller.scrollTop = 1;
  if (scroller.scrollTop >= max) scroller.scrollTop = max - 1;
}

function onTouchMove(event) {
  if (event.touches.length !== 1 || !event.cancelable) return;

  const scroller = resolveScroller(event.target);
  if (!scroller) {
    event.preventDefault();
    return;
  }

  const deltaY = event.touches[0].clientY - startY;
  const max = maxScrollTop(scroller);
  const crossesTop = scroller.scrollTop <= 1 && deltaY > 0;
  const crossesBottom = scroller.scrollTop >= max - 1 && deltaY < 0;

  if (max <= 0 || crossesTop || crossesBottom) {
    event.preventDefault();
  }
}

function enableTouchGuard() {
  document.addEventListener("touchstart", onTouchStart, { passive: true });
  document.addEventListener("touchmove", onTouchMove, { passive: false });
}

function disableTouchGuard() {
  document.removeEventListener("touchstart", onTouchStart);
  document.removeEventListener("touchmove", onTouchMove);
}

The { passive: false } option on touchmove is essential because a passive listener cannot cancel movement with preventDefault(). The compatibility details are covered in the TouchEvent documentation.

The guard also has a cost. A non-passive listener can delay scrolling while the browser waits for its decision. It must do minimal work, exist only while the menu is open, and be removed reliably.

The scrollTop clamping to 1 and max - 1 is a Safari-oriented workaround, not a universal recipe. It keeps the scroller away from its exact boundary at gesture start, but it must be validated against nested scrollers, horizontal gestures, Android, keyboard accessibility, and any visible one-pixel movement.

Defect Two: The Panel Closed, but Its Classes Stayed Open

After the touch fix, the short scenario stopped bouncing. Then a close-and-reopen test failed.

This time, the visual viewport was innocent. The system had two competing truths:

The main container was hidden.
A top-level item and its child panel still carried their open classes.

On the next opening, the arrow could indicate an open state while the content remained hidden, or the module could apply another transition to an already active state. The symptom still looked like a jump, but the cause was an incomplete DOM state machine.

The final fix was smaller than the diagnosis:

function resetSubmenus(menuRoot) {
  menuRoot
    .querySelectorAll(":scope > ul > li.is-submenu-open")
    .forEach((item) => item.classList.remove("is-submenu-open"));

  menuRoot
    .querySelectorAll(":scope > ul > li > .is-panel-open")
    .forEach((panel) => panel.classList.remove("is-panel-open"));
}

function closeMenu() {
  resetSubmenus(menuRoot);
  hideMenuPanel();
  disableTouchGuard();
  unlockDocumentScroll();
}

window.addEventListener("pageshow", (event) => {
  if (event.persisted) resetSubmenus(resolveCurrentMenuRoot());
  rebindCurrentMenuNodes();
  synchronizeMenuState();
});

On a legacy site, remembering nodes once at DOMContentLoaded is not enough. A module can replace part of the DOM, reinstall handlers, or reveal an existing structure again. Back navigation can also restore a page from the back-forward cache with its DOM and part of its JavaScript state.

The pageshow event exposes that return. Its persisted property indicates a restoration from the bfcache, as documented in the reference article on the back-forward cache.

The solution was not to rerun the entire initialization after every navigation, which could duplicate the third-party module's listeners. It was to resolve the current nodes, attach every listener at most once, and synchronize the classes and scroll state.

Rollback Is a Feature, Not an Apology

Each deployment attempt followed a reversible process:

Download and checksum the production file.
Keep the backup on two machines.
Upload only the validated JavaScript file.
Read it back over SFTP.
Compare the local, SFTP, and HTTP-served checksums.
Run the short production journey.
Restore the backup immediately if an invariant fails.

We rolled back the first deployment. The touch guard worked, but the close-and-reopen test exposed the leftover submenu classes.

That rollback was not a narrowly avoided disaster. It was a normal branch of the procedure.

Rollback is Ctrl+Z with operational discipline.

After the reset was added, final validation combined targeted state tests, the random regression campaign, and the full touch journey on the real iPhone. The final deployment changed one JavaScript file. No database, template, or module configuration was touched.

What We Will Test Differently Next Time

This intervention does not prove that every Safari bug requires a physical device. It proves that a test must cover the right engine, the right input source, and the right duration of user journey.

For a scrollable mobile panel, our shorter checklist is now:

Distinguish the layout viewport, visual viewport, document, and internal scroller.
Log scroll dimensions, event.cancelable, and visualViewport.offsetTop during reproduction.
Cross both scroll boundaries within a continuing touch gesture.
Verify opening, closing, listener cleanup, and bfcache restoration.
Replay the actual multi-page journey before adding random actions.
Document what Chromium and Playwright WebKit do not prove.
Validate native viewport behavior on the affected device.
Prepare checksums and rollback before deployment.

A green test says that the simulated path satisfied the assertions we wrote. It does not prove that the browser and the user followed that path.

When a bug depends on a finger, a mobile viewport, a legacy module, and a page restored from history, the answer is not a larger test counter. It is faithful reproduction, targeted instrumentation, and a deployment that can be reversed without drama.

Written by Phil, Inforeole.

SSH Survival Guide: Your Server Is Already Being Scanned

Phil Rentier Digital — Sun, 23 Aug 2026 13:41:11 +0000

If you run a server exposed to the Internet, there is one thing you should probably accept:

Your server is being scanned. Right now.

Not tomorrow. Not next week.

Right now.

Bots constantly crawl the Internet looking for exposed services: SSH, databases, admin panels, dashboards, forgotten development tools... anything with a listening port and a bad day ahead of it.

The moment your server exposes SSH to the public Internet, automated scanners can start hammering it with login attempts.

And here's the fun part:

You can see it for yourself.

Step 0: How bad is it?

On Debian or Ubuntu, run:

sudo journalctl -u ssh --since "-7 days" 2>/dev/null | grep -c "Failed password" \
|| sudo grep -c "Failed password" /var/log/auth.log

This counts failed password-based authentication attempts recorded over the last seven days.

On two of my servers, I got:

43,000 and 31,000 attempts.

In one week.

Another server in the same discussion clocked in at almost 50,000.

At this point, there are two possible reactions:

Panic.
Check the logs before panicking.

I recommend option two.

The Internet is not hostile. It is merely extremely curious and very badly behaved.

These attempts are usually automated. Bots scan public IP ranges, find an open SSH service, and try common usernames and passwords.

They're not necessarily targeting you.

You're just another IP address on the menu.

And that leads to the first important lesson:

Your server doesn't need to be famous to be attacked. It only needs to be online.

First: check whether anyone actually got in

A huge number of failed attempts sounds scary.

But the number that really matters is different:

Were any logins successful?

Check your SSH logs:

sudo journalctl -u ssh --since "-7 days" | grep -E "Accepted|session opened"

Look for successful authentications and make sure you recognize them.

This distinction matters:

50,000 failed attempts do not automatically mean compromise.

One successful login from an unknown source might.

Don't count the zombies. Look for footprints.

Before you start adding security tools, understand what is actually happening.

You can also inspect the most active source IPs:

sudo journalctl -u ssh --since "-7 days" 2>/dev/null \
  | grep "Failed password" \
  | grep -oE 'from ([0-9]{1,3}\.){3}[0-9]{1,3}' \
  | sort \
  | uniq -c \
  | sort -nr \
  | head -20

And the usernames being attacked:

sudo journalctl -u ssh --since "-7 days" 2>/dev/null \
  | grep "Failed password" \
  | sed -n 's/.*Failed password for \(invalid user \)\?\([^ ]*\).*/\2/p' \
  | sort \
  | uniq -c \
  | sort -nr \
  | head -20

You will probably see classics like:

root, admin, ubuntu, test, user, oracle.

The world's hackers may be sophisticated.

Their first choice of username often isn't.

This is the first real security move.

If you can use SSH keys, use SSH keys.

In /etc/ssh/sshd_config:

PasswordAuthentication no

Now SSH won't accept password-based authentication.

That's a massive improvement.

Why?

Because passwords can be:

guessed
brute-forced
reused
leaked
phished
shared
written on a Post-it note under the keyboard

SSH keys solve a large part of that problem.

But there is one important detail:

Put a passphrase on your private key

A private key without a passphrase is basically a skeleton key sitting on your laptop.

If your machine gets compromised and the attacker steals that key, they may be able to use it to access your servers.

Use a strong passphrase.

Think of it as:

Something you have + something you know.

Or, in geek terms:

A private key without a passphrase is like putting a deadbolt on a door and leaving the key in the lock.

PermitRootLogin no

The root username is predictable.

Why make the attacker guess half the equation?

Use a normal account:

sudo adduser myuser

Then give it administrative privileges:

sudo usermod -aG sudo myuser

Connect with:

ssh myuser@server

And use sudo when you need elevated privileges.

You can also restrict which users are allowed to connect over SSH.

For example:

AllowUsers myuser alice bob

Or use a dedicated SSH group:

AllowGroups sshusers

Now your SSH service isn't just saying:

"Welcome, anyone with credentials."

It's saying:

"Welcome, these specific humans."

Much better.

This is where security tutorials quietly become horror stories.

You modify sshd_config.

You restart SSH.

And suddenly:

Welcome to the exciting world of your hosting provider's emergency console.

Before restarting SSH, validate the configuration:

sudo sshd -t

Keep your existing SSH session open.

Open a second terminal.

Test the new configuration there.

Only then restart the service:

sudo systemctl restart ssh

or:

sudo systemctl restart sshd

The golden rule:

Never close the only working SSH session before testing the new one.

Your future self will thank you.

Probably with coffee.

4. Now ask the uncomfortable question

At this point, SSH is much safer.

But there is a bigger question:

Why is SSH exposed to the entire Internet in the first place?

This is the question that changes the whole strategy.

Because securing an exposed service is one thing.

Not exposing it is another.

5. The best SSH hardening trick: don't expose SSH

If you don't need public SSH access, don't publish it.

Put SSH behind a private network or VPN using something like:

WireGuard
Tailscale
another private overlay network

Your architecture becomes:

Network Architecture Flow Diagram

Instead of:

That's a fundamentally different security model.

Your web application may need to be public.

Your SSH service probably doesn't.

Users need your website. They don't need your shell.

Or, more quotably:

Don't build a stronger lock for a door that shouldn't be on the sidewalk.

6. Firewall: decide who gets to knock

If SSH must remain exposed, restrict it at the network level.

The ideal rule is simple:

Known IPs  → ACCEPT
Everyone else → DROP

If you have a stable public IP at home or at work, allow only that address.

Better still, use your cloud provider's firewall or security group whenever possible.

This gives you an important layer before SSH itself sees the connection.

That's a key distinction.

A firewall can stop traffic from reaching SSH at all.

Fail2Ban reacts after the connection has already arrived.

7. Fail2Ban: useful, but not your religion

Now we can talk about Fail2Ban.

Fail2Ban monitors logs and temporarily blocks IP addresses that generate too many failed authentication attempts.

It is useful.

Very useful, in fact.

But it is often treated like a magical anti-hacker shield.

It isn't.

Think of Fail2Ban as a bouncer.

The club still exists.

The doors are still visible.

People still reach the entrance.

The bouncer simply says:

"You've tried 47 times. Please leave."

Fail2Ban is a good additional layer for exposed services.

It is not a replacement for:

SSH keys
PasswordAuthentication no
PermitRootLogin no
firewall rules
network isolation

The hierarchy matters.

Don't use a bigger bouncer to compensate for a front door made of cardboard.

8. Should you change SSH's port?

Ah yes.

The sacred ritual of SSH hardening:

22 → 2222

Does it help?

Yes.

Is it real security?

Not really.

Changing the port can dramatically reduce the amount of automated noise generated by simplistic scanners.

Your logs may become much quieter.

That's nice.

But anyone performing a broader port scan can discover the new port.

So:

Port 22 → 2222

does not magically become:

Safe → Very Safe™

It becomes:

Noisy → Less Noisy

That's useful, but it's a different thing.

Changing the port is noise reduction, not a security strategy.

If you have to choose between changing the port and disabling password authentication:

disable passwords.

If you have to choose between changing the port and putting SSH behind a VPN:

use the VPN.

Move the port if you want.

Just don't confuse obscurity with security.

9. MFA: another layer, not a shortcut

Multi-factor authentication can add another layer to SSH.

Depending on your environment, you may use:

SSH keys
hardware security keys
TOTP
PAM-based MFA
identity-aware access systems

That's great.

But don't use MFA as an excuse to keep the rest of your setup weak.

A secure architecture still starts with:

keys → no passwords → no root → restricted network access

Then you add MFA where it makes sense.

Security is an onion.

Unfortunately, unlike onions, it doesn't make your attacks cry.

10. Keep the operating system boring

Security people love exciting technology.

Attackers love unpatched software.

These two groups have very different definitions of "fun."

Keep Debian, Ubuntu, OpenSSH, the kernel, and your exposed applications up to date.

On Debian/Ubuntu, unattended-upgrades can automate certain security updates.

That's useful, but production systems still need:

backups
monitoring
testing
rollback or recovery procedures

The most beautifully hardened SSH server in the world is still vulnerable if the underlying OS is ancient.

The strongest lock in the world won't help if the wall is made of Windows 95.

11. What about geo-blocking?

Blocking traffic from countries you don't operate in can reduce noise.

It can be useful.

But don't confuse it with a hard security boundary.

Attackers can use:

VPNs
proxies
cloud instances
compromised machines

Geographic origin is not identity.

Use geo-blocking as an optimization, not as your foundation.

12. Port knocking, IP blacklists and other wizardry

There are plenty of clever techniques around SSH:

Port knocking

Keep SSH hidden until a specific packet sequence arrives.

Interesting.

Sometimes useful.

Not a substitute for proper authentication and network controls.

Permanent IP blacklists

You can manually ban every IP that annoys you.

This sounds productive.

Until you realize there are millions of IP addresses and approximately three billion ways to generate more.

You are not going to manually blacklist the Internet.

Rootkit scanners

Tools such as rkhunter can provide additional monitoring.

Again: useful as a layer, not a replacement for fundamentals.

The pattern should be obvious by now:

Defense in depth. Not security-by-collection-of-random-tools.

13. What actually matters?

Here's the hierarchy I would use.

Measure	Reduces noise	Improves security
Change SSH port	✅	⚠️ Limited
Fail2Ban	✅	✅
SSH keys	—	✅✅
Key + passphrase	—	✅✅
Disable passwords	—	✅✅
Disable root login	—	✅
`AllowUsers` / `AllowGroups`	—	✅
Firewall / IP allowlist	✅✅	✅✅
VPN / private network	✅✅	✅✅
MFA	—	✅
Geo-blocking	✅	⚠️ Limited

The pattern is pretty clear.

Some controls make your logs prettier.

Others actually reduce your attack surface.

Those are not the same thing.

A quiet log is not the same as a secure server.

14. The 10-minute SSH survival plan

You've just discovered 40,000 failed SSH attempts.

What should you do?

Do this, in order.

1. Check for successful logins

sudo journalctl -u ssh --since "-7 days" | grep -E "Accepted|session opened"

Investigate anything you don't recognize.

2. Make sure you have a working SSH key

Generate one if necessary:

ssh-keygen -t ed25519

Use a passphrase.

3. Disable password authentication

PasswordAuthentication no

4. Disable direct root login

PermitRootLogin no

5. Restrict SSH users

For example:

AllowUsers myuser

6. Validate the SSH configuration

sudo sshd -t

7. Test a second SSH connection

Do this before closing your current session.

8. Put SSH behind a firewall or VPN

Preferably both.

9. Add Fail2Ban if you still expose SSH

Good extra layer.

Not your entire strategy.

10. Change the port if you want less noise

Optional.

Not essential.

15. The architecture I would aim for

For a modern setup, the goal is simple:

Network Architecture Security Flow Diagram

The public Internet reaches the services that actually need to be public.

Administration stays private.

That's a much cleaner security model than endlessly hardening an SSH daemon that is exposed to every scanner on Earth.

16. The survival checklist

Essential

[ ] Use SSH keys
[ ] Protect private keys with passphrases
[ ] Disable password authentication
[ ] Disable direct root login
[ ] Restrict SSH users with AllowUsers or AllowGroups
[ ] Keep the OS and OpenSSH up to date
[ ] Use a firewall

Better

[ ] Restrict SSH to known IPs
[ ] Put SSH behind WireGuard, Tailscale, or another private network
[ ] Close public SSH completely when possible
[ ] Add MFA where appropriate

Optional

[ ] Fail2Ban
[ ] Change the SSH port
[ ] Geo-blocking
[ ] Port knocking
[ ] Additional host monitoring

And yes, there is a hierarchy here.

Changing port 22 is not more important than disabling passwords.

Fail2Ban is not more important than network isolation.

A fancy security stack is not more important than basic configuration.

The final rule: stop defending the wrong thing

The scary part about those 43,000 login attempts isn't really the number.

It's what the number teaches you.

The Internet is constantly probing anything you expose.

You can't stop the scanning.

You don't need to.

Your job is to make the scanning useless.

Start with the boring stuff:

SSH keys.

Passphrases.

No password authentication.

No root login.

Least privilege.

Firewall.

Then ask the most important question of all:

Does SSH need to be public at all?

If the answer is no, put it behind a VPN and close the door.

Because the strongest SSH security control isn't a clever configuration.

It's not Fail2Ban.

It's not port 2222.

It's not a 300-line firewall ruleset.

Actually, wait. Let me put it differently. The most effective protection isn't any of those tools. It's this:

If attackers can't reach the service, they can't brute-force the service.

Or, if you prefer the geek version:

You can't pwn what doesn't have a route.

And remember:

The goal isn't to make your server invisible.

The goal is to make it boring.

Extremely boring.

Because in infrastructure security, boring is beautiful.