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Claude Can Access Your Other Devices

Thu, 30 Jul 2026 14:35:11 +0200

My partner and I share a Claude Pro account and both regularly use the Claude Desktop app. Recently, my partner has been performing some shopping research and has been using Claude to help with it. What was intresting to me was that on my machine, Chrome windows were opening and navigating to websites to research the products my partner was interested in.

As we’re both software engineers we decided to research this a little further. Both of us have Claude Desktop installed with the Claude for Chrome extension installed and configured. However on occasions where their Chrome was closed, Claude seemed to delegate to my machine without showing any indication of this happening.

What’s more concerning is that Chrome is not my daily driver, it usually sits in the background and I rarely interact with it. I discovered the open tabs purely by accident when clearing up some old windows.

Right now we’re trying to run other experiments to see if we can block the tool on one machine and see if Claude (and more worryingly Claude Code), will automatically switch to the other. If it does, we’ll have to rethink our workflow as unmitigated remote Claude access to a machine that isn’t our own is an uncomfortable thought.

It’s worth noting that neither of us has chosen to use Claude remote-control, the Computer use preview or Claude code on the web. What I’m trying to say is that we haven’t explicitly opted in to any remote access features.

This is uncomfortable for me and highlights the power and trust I have given Claude (probably naievly), but thankfully it was only my partner. For anyone sharing with less trustworthy actors, or worse, selling your credits to unknowns, take heed as you may be putting your own digital safety and privacy at risk.

Productivity Theatre

Thu, 23 Apr 2026 22:32:29 +0200

Security Theatre is the practice of implementing security measures that are considered to provide the feeling of improved security while doing little or nothing to achieve it (Wikipedia).

Productivity Theatre follows the same script.

AI agents and orchestrators are sold as productivity multipliers. Spin up a swarm of agents, watch the terminal scroll, and feel productive. Deliverability, questionable…

A coding agent that opens 47 PRs in an afternoon looks impressive. How many of those PRs are coherent? How many survive review? An orchestrator that spins up five sub-agents to tackle a task feels powerful. How many of those agents are duplicating work, fighting each other, or solving the wrong problem? I’m still uncertain the value add that people coding with multiple Claude Max subscriptions are actually delivering.

Activity is not output. Motion is not progress.

The same pattern plays out everywhere: dashboards that track lines of code written, meetings about productivity frameworks, tools that optimise the process of planning work instead of doing it. The theatre runs on the feeling that something is happening.

Next time you see claims about AI-enhanced productivity, look past the demo. Try to find out what’s actually shipping.

Reallocating $100/Month Claude Code spend to Zed and OpenRouter

Mon, 06 Apr 2026 12:40:28 +0200

Instead of $100/month for Claude, pay $10/month for Zed editor and set up a monthly $90 top up to OpenRouter. You can use the Zed Agent harness or keep using Claude Code, however you pay API costs for what you use. When you’re not using, your credits build up rather than missing the usage window.

I’ve been disappointed to feel that I’m hitting Claude limits faster than before. For context, I use both Claude Code and the Claude desktop app for work and pay $100/month for the privilege of hitting limits. I’m not the only one (this was AMD’s senior director of AI) with numerous other reports found over Reddit and Twitter.

My usage pattern is “bursty” so I’m not using the windows all the time throughout the day but find it incredibly frustrating to hit a limit mid-way through a coding session.

This article is how I’m reallocating that spend to other tools and models while getting more flexibility at the same time.

Picking an Agent Harness and Exploring other models

I like options and while Opus is undoubtedly the market leader for agentic coding, there are other models that I like to use to balance cost and speed depending on the complexity of the task in hand. I’m looking at how I can use different models with an Agent Harness.

verbose output

An “agent harness” coordinates sending and receiving messages from LLMs, injecting tool defintions and calling the tools and orchestrating all of this into workflows (including retrying failing tasks).

Claude Code is an example of such a system. It takes the user message, coordinates reading/writing files - among other things - and makes calls to the LLM.

Zed and OpenRouter

Plans: $10 / month - pricing page

You don’t realise how slow/laggy VSCode and the all of the forks are until you try out Zed. The builtin agent harness is basic but nice with the ability to follow the agent around as it modifies files and to add new profiles to customise the agent behaviour. Like Cursor it shows the context usage and the rules that are being applied to the current session. If you continue to use Claude Code or other tools like Mistral Vibe, Zed integrates them directly into the editor using the Agent Client Protocol (ACP) - see supported agents.

The biggest disadvantage is definitely the lack of extensions compared to VSCode but there are enough to cover common languages and common tasks.

Zed do offer usage based pricing once you have used up the credits they provide however their token prices are higher than going directly to the API themselves. This is why I prefer to use the OpenRouter integration into Zed instead. A nice side benefit is you get the more native context window sizes. For some reason Zed limits the Gemini 3.1 context to 200k tokens in their native integration however with OpenRouter you can make use of the full 1M. Their docs say this may be changed in the future.

OpenRouter

Edit: It has been brought to my attention (thanks to hhthrowaway1230 on HackerNews) that OpenRouter does charge a 5.5% fee.

The largest option of models and providers that I know of is OpenRouter and it’s easy enough to sign up, pre-pay some credits and get an API key.

I don’t like that I have a set window of Anthropic credits. If I use it I have to wait for it to reset (or pay). But when I’m not using it I’m missing out on that window of opportunity. Instead I can top up my OpenRouter credits which expire after 365 days if unused. Then I can use the credits when I’m working and save them/roll-over when I’m not.

To minimise data exposure risk, I have chosen not to consent to OpenRouter being able to use inputs/outputs “to improve the product” (though you get a 1% discount if you do), and I have enabled the “Zero Data Retention (ZDR) Endpoints Only” in my Workspace Guardrail settings. You do lose out on some models here - for example, qwen/qwen3.6-plus which is only hosted on Alibaba Cloud - however that’s a small price I’m willing to pay.

Cursor

Plans: $20 | $60 | $200 / month - pricing page

verbose output

I originally switched from VSCode & Copilot to Cursor in 2025 after experiencing the magic of the Cursor “Tab” jumping around the editor preempting my next move.

As it moved from autocomplete-on-steroids to more agentic coding, I was thankful to have access to multiple models to experiment with (this is now also available in Copilot but in the beginning they were OpenAI only).

I mostly ignored Cursor 2.0 as they put more emphasis on the chat interface however with Cursor 3.0 as a complete rewrite (in Rust like Zed) and focused on Agent orchestration, I am curious to try it out.

Cursor was (or still semi-is) my preferred editor. As a VSCode fork, all extensions are available. They were an early adopter of the plan mode -> agent mode workflow and now support a new debug mode which is a more advanced print style debug that the agent can also interact with.

Cursor also supports different types of rule applications, something I personally love and am surprised that other agent harnesses haven’t adopted. Most agent harnesses take an “apply intelligently” approach, trying to let the AI make decisions on when to include a rule based on the description. But Cursor also offers the ability to only apply to specific files. I know I have rules that only apply to *.py files, or even **/models.py etc. I am able to make the most of my context window by explicitly setting those rules to be added only to certain filepath regexs. It guarantees their usage

Choosing Cursor you get API rate pricing above the included use in your plan (and you can limit this so your total spend is limited to $100) but you are still paying minimum $20/month which does not roll over to the next month.

Claude Code and OpenRouter

Claude Code is optimized for Anthropic models and may not work correctly with other providers.

I know - I said I’m redirecting funds away from Anthropic, but it is possible to continue using the Claude Code agent harness with other models (or even Opus should you want to). We might want to do this because Claude Code is undeniably a great harness, however we need to configure Claude Code to use OpenRouter rather than the Anthropic API.

First, log out of Claude Code if you have been using it before:
```
claude

> /logout
```

Next, set some environment variables to configure the OpenRouter endpoints and which models you want to use for “Opus”, “Sonnet”, “Haiku” and “SubAgents” (I recommend setting these in your ~/.zshrc or ~/.bashrc file so they persist):

export OPENROUTER_API_KEY="<your-openrouter-api-key>"
export ANTHROPIC_BASE_URL="https://openrouter.ai/api"
export ANTHROPIC_AUTH_TOKEN="$OPENROUTER_API_KEY"
export ANTHROPIC_API_KEY="" # Important: Must be explicitly empty

# Set these models to whichever model you would like to use on OpenRouter
export ANTHROPIC_DEFAULT_OPUS_MODEL="anthropic/claude-opus-4.6"
export ANTHROPIC_DEFAULT_SONNET_MODEL="anthropic/claude-sonnet-4.6"
export ANTHROPIC_DEFAULT_HAIKU_MODEL="anthropic/claude-haiku-4.5"
export CLAUDE_CODE_SUBAGENT_MODEL="anthropic/claude-opus-4.6"

Verify that Claude Code is using your new config (you may need to restart your terminal or source ~/.zshrc):
```
claude

> /status
Auth token: ANTHROPIC_AUTH_TOKEN
Anthropic base URL: https://openrouter.ai/api
```

Other CLI tools like OpenCode + OpenRouter

There are a multitude of other coding Agent Harnesses that can be used from the command line with OpenRouter. I’ve tried a few but none have stuck, here’s the list for you to try and my brief thoughts on them:

OpenCode - Typescript - The one I use the most. Good support for a lot of things. Very popular.
Crush - Go - I want to like it. It has a distinct style choice (that I don’t mind). It’s performant. But it’s a pain to configure custom models (all manual) so annoying when trying out new ones.

Even for popular tools that typically limit you to use their own models like Gemini CLI, often there are forks which attempt to make it OpenRouter compatible. This is worth checking if you are using and like a different harness but want to try other models.

I’m now a happy subscriber to Zed for the reasonable $10/month. I actually also maintain my Cursor subscription for $20/month as I want to see where they go with their new Cursor 3, agent orchestrator. The other $70 gets auto added to my OpenRouter credits each month which don’t get lost. They rollover, waiting for me to use them.

If you’re regularly hitting Claude limits and want to give other models a shot (but you can still use Opus when you need to), I highly recommend giving it a try. You can get started with Zed for free and load up OpenRouter with $20 worth of credits without any subscription.

The mobile OS should broker agent functionality

Mon, 06 Apr 2026 09:56:48 +0200

AI Agents on mobile need a way to access functionality of other apps. The mobile operating system should become the broker, registering & exposing tools and managing permissions, authentication and battery usage.

Such an interface would open up the possibility of OpenClaw-esq behaviour on mobile through third-party apps. These apps could choose to use on-device or remote models but would all use the same APIs and protocols to communicate with applications on your device.

A short story of a pocket agent future

Skip story

You open your iPhone and choose to update to iOS 28 and enable “Agentic Applications”. The phone screen ripples in colour and welcomes you, guiding you into a new entry in your Settings where you can see all Agents installed on your phone. There’s nothing there but the wizard teaches you that this is where you can find your Agents, manage their permissions & notifications, view the events and logs and spot those using excessive battery.

You’ve been reading about “WetSheep”, a new local-first agent that performs better than any other. As a privacy advocate this is what you have been waiting for so you open the App Store. Installing it is just like any other application - albeit with a longer download as it fetches the model - but soon, you’re ready. You press the fresh fluffy icon and start talking.

“What’s my total net-worth?”

The white circle in front of you pulses. WetSheep uses an on-device model to translate speech to text and understands your intent. Behind the scenes it’s asking iOS what tools it has available and comes up with a plan. WetSheep identifies five applications that may provide an insight into your net-worth: two banks, a multi-currency account, your trading app and a 401k benefits app. All of these require FaceID to log in, but WetSheep doesn’t care. It makes the function calls as they were instructed, and waits.

These functions are defined and provided by each application. getBalance, showBalance, listPositions… there is a pattern but no common name. All the Agent knows it that they will likely help it reach it’s goal. But there’s a trick. The Agent is not communicating with the application, it’s talking to the operating system - iOS 28 itself.

iOS knows this is a new agent. It’s allowed to run but it doesn’t have permission to get this information. Should it? Lets ask the user. Approved, great! But these applications require FaceID to access this information. We can ask once for this session, convenience after all. Everything looks good, so iOS starts the relevant applications in the background and calls the functions. Battery is precious so apps have a finite time to respond in, otherwise they’re silently closed. That’s not the case here. Some numbers, some JSON and a string are passed back to WetSheep via the functions it called. Data can be sensitive so it’s not logged but iOS does keep track of which apps & functions this agent used.

WetSheep collates the data and checks to see if it needs to do anything else to achieve the goal. Everything seems to be present. It finds a relevant way to display the balances from its library of UI elements - a simple Stat total and a table should be enough - and presents it on-screen. Task complete.

You’re impressed! You know there is a third bank account - traditional bricks and mortar, no app - but with the information it had available the agent has done a good job. There are even nice links you can use that take you straight into the relevant app to verify the output. This single interaction has given you the confidence to set WetSheep as your default voice agent. You set the trigger word to “Oi!” in your settings and put your phone back in your pocket.

It’s reassuring to have a privacy focused agentic assistant ready to help you with your life. But when iOS asks if you’d like to allow WetSheep to run autonomously in the background, you clicked “No”. Not…yet.

While the story above is fiction, it doesn’t require technology that doesn’t already exist today. On the desktop, tools like OpenClaw (an open-source agent that can control your computer) have shown that given enough tools and context/memory (and tokens), LLMs are capable of dynamically orchestrating workflows to achieve these goals - and far more complex ones - themselves.

But mobile is fundamentally different. Battery life, sandboxed apps, biometric auth and permissions, all create constraints that add friction to delivering the same experience.

Making pocket agentic assistants a reality

This hypothetical story is to demonstrate what could be possible given a system that exists to enable controlled, secure agentic behaviour.

“Hey Siri, send my ETA to the person I’m meeting.”
“Siri, check me in for my flight when it opens.”
“Siri, how much money do I have in my bank accounts?”

Seemingly simple requests that have stumped Apple for years, costing billions to develop an annoying failure that will soon rely on Google’s Gemini to function (also $1 billion / year…).

OpenClaw - for all it’s security flaws - has not only shown that it’s possible, but that it is something people want. People are willing to use a far-from-perfect product (including buying dedicated computers to run it) to have that fully autonomous feeling.

Mobile devices arguably have access to all the information already through the applications installed by the user. They don’t need to access the complete app store, only the apps that the user has decided are relevant to them (and coincidentally are often already logged in and ready to go).

With the goal of a higher level of autonomy in mind, this article aims to discuss what should be done by mobile operating system providers (dominated by iOS & Android) to enable such functionality in the not-too-distant future.

The Operating System becomes the Agentic Broker

At its core, agents need some common functionality to unlock the full extent of their functionality.

Note: (detailed descriptions of what these are in verbose mode)

Memory - remembering previous context, facts and intent;

verbose output

The Agent persistent state. Keeps track of facts, preferences & previous interactions. Examples could be “user is male mid-forties” or “user eats vegetarian and prefers to walk”. This avoids the agent re-asking the user every time.
Permissions control - only able to access what they are allowed to access;

verbose output

In charge of what the agent can actually do on the device, similar to how permissions like camera & location are already managed by the operating system. In addition, the operating system can control access via biometrics like FaceID, what applications (and their respective background functionality) the agent can and cannot access.

The Operating System is in charge of keeping the Agent in check.
Tool registry - what is available to the agent;

verbose output

Keeping track of everything available to the Agent on this specific device. For example, a GMail application may register a “sendEmail” functionality, Messages may register “sendSMS” and “sendIMessage” etc. A user may have hundreds of applications installed and each one can register some form of functionality that can run in the backgorund and help the Agent complete its task.

The tool registry lets the agent discover what it can do.
User context - if memory is the history, this is the present state of the user;

verbose output

A live snapshot of who the user is and what they are doing right now. The current app, time of day, location, recent activity, device state (e.g. locked, driving, in a call). With this information the agent is better suited to tailor its behaviour.
Notifications & communication - bi-directional communication with the user;

verbose output

Agents will want to notify the user some how; to ask for permission, notify about proactive changes. Mobile devices already have support for push notifications & alerts.

Operating systems already act as the broker between applications and the underlying hardware, but now they should become the broker between agent harnesses and applications. They are the gatekeepers providing a registry of tools, managing authentication (including biometric IDs for ease) and monitoring background usage of applications.

Only the operating system can be trusted to launch apps in the background, mediate permissions (including FaceID), enforce battery budgets and audit cross-app data flows. No third-party layer has (or should have) that power.

Additionally the Operating System could become the proxy to access models themselves. This would give the user power to restrict which AI models were being used by all installed agents. This may include local models in the future or from a provider that the user may trust or have a preference for.

Agent developers then focus on building the agent harness, the coordination between all of these elements in an efficient and practical way for the user.

This isn’t just Siri again - the key difference is plurality. Any agent can use the same brokered tools, leverage past and shared memory and coodrinate flows. Not only an assistant promoted by the vendor.

Opt-in and log

It starts with enabling these features. They’re not for everyone and mis-used they are a security risk. There must always be the ability to turn off AI & Agentic functionality at the operating system level.

Agents on device do open up users to the usual AI exploits such as prompt injection and as such should offer some amount of protection to the non-technical user. Once enabled (and risks explained), the operating system can also provide a few safety mechanisms:

PII data redaction for personal data if relevant. This can even be done on device with small models like openai/privacy-filter.
An audit log of all interactions between Agent and App. On its own it’s too verbose for the standard user but the occasional popup - as is done today - explaining that Agent X has accessed Y application/data.
A centralised security scoring mechanism in the app store. If multiple users start flagging that an agent is misbehaving, Apple or Google could quickly remove the guilty agent from the app store to limit fallout.

The OS sits between agent and app, enforces least-privilege and auditing every call.

Building on what’s there today

Arguably Android’s Android Intents and iOS App Intents are already steps into this direction.

Right now however, it seems that mobile platforms are looking to focus on developing the complete assistant experience themselves. Siri and Google Assistant - no matter how poor they perform or slow they are to update - come baked into the operating system with no clear way for anyone else to compete.

Adjusting their stance to enable others to integrate would still give Apple and Google the control over the ecosystem they want while enabling more innovation.

MCP and Skills for mobile?

Model Context Protocol (MCP) - a framework to define a way for AI to discover and use external services - was rapidly adopted as a standard in desktop that gave tool providers a unified way to expose functionality to the changing landscape of AI tools. Whether you use Claude Code, Cursor or Codex, you can add the MCP for Figma and expose the same functionality. It’s easy for Figma to maintain a single connection to all AI tools that support MCP.

In an ideal world there would a similar level of coordination between the mobile OS providers. It doesn’t have to MCP (and it likely won’t be as I don’t think it’s transferrable between desktop and mobile), but it should be a common standard so that apps and agents can be developed cross platform.

Skills - text files detailing how an Agent should perform a specific task with optional scripts or references to help it do so - are another emerging trend on desktop AI. These may actually transfer well to mobile agents and would offer more advanced users ways to modify the agent application to perform tasks in the specific way that they want it to be done. There will need to be a better way to get skills onto mobile, and to surface to layman users what actually the skill is doing, but this moves from a technology problem to a user experience/interface problem.

What would app developers need to do?

I’m not a mobile developer. I am familiar with some SDKs and capabilities but if this is drastically wrong please call me out.

Once iOS/Android have released new SDK functionality to register tools, existing application developers will need to register chunks of functionality with this SDK. Similar to MCP this will likely include a tool name (that the operating system will namespace by application so even if two apps register a getBalance function, there is no conflict), a short description of when the tool should be called and what it provides, tool inputs if relevant (e.g. a getBalance may take an optional date to get the balance at that particular time) and the required permissions (does the user need to approve this with FaceID?). The OS should then take care of the rest, coordinating agent interactions and permissions as required.

Agent developers on the other hand focus on building the logic to coordinate interaction between different components and implement developing standards. One size does not fit all, there is a need and gap for many different agent harnesses. These may be specialised on different verticals or try to become more general purpose.

Thinking into the future

Agents on mobile are coming and the OS vendors need to be ready for it. OpenClaw proved the model works on desktop and server models but the mobile adoption needs alterations to go mainstream.

We may end up with more “headless” applications, without a user interface, only providing tools/functionality to agents applications to interact with on device.

Bring back the Meta phone

I believe Apple and Google should be investing heavily to enable a stronger, broader AI ecosystem. But whether it’s them or a new entrant, the opportunity is the same.

For mobile adoption, it would make sense for Zuckerberg & Meta to revive Eye OS and make it an agent broker. They would even be able to deliver an out-of-the-box experience powered by their new Muse model family - “Scaling towards personal superintelligence”. Developers could offer headless applications accelerating and easing development to bridge the missing app ecosystem and users could interact with the majority of their applications through multi-purpose agent applications instead.

While it doesn’t specifically have to be a Facebook (or Windows) phone, I think there is an opportunity for a new mobile operating system to build these functionalities. It’s only a matter of time and would be a compelling motivation for app developers and sales point for consumers. Phones with this OS will enabling a new generation of personal assistants.

Could Anthropic or OpenAI launch something to compete with the status quo? Perhaps Musk & X could enable it and further the development of the X super-app.

The pieces are here and evolving. On-device models are getting better, server connections exist where they fail. (Some) users want this. Apps and data are already available on the phone. But the missing piece, the platform that coordinates the connections, needs to be built into the operating system. It remains unknown who will build the future, turning intelligence from a feature into a platform.

Monitor Claude Code Usage With Grafana

Sat, 28 Mar 2026 23:20:00 +0100

This is possible out of the box without adding any custom certificates, proxies or MITM. You will need an OpenTelemetry compatible data collector and the ability to set some enivironment variables.

As part of a longer post on how we can use OpenTelemetry to observe AI agent behaviours, I started capturing OTEL tracing from Claude Code. It may not be common knowledge but Claude supports this out of the box. All you need is a place to send the data (I use Grafana cloud) and then to set some environment variables.

In a time where Anthropic is changing limits ad-hoc, users are claiming to use up half their usage a couple of simple prompts and Anthropic provides no easy way to quantify usage, OpenTelemetry and a little Grafana/visualisation magic is our friend.

verbose output

How does OTEL fit into the AI Agent paradigm?

In a standard microservices distributed system, REST and gRPC are used to communicate between services with each service emitting logs and spans that are used to create a end-to-end trace of a request. This is used to trace (hence the name) traffic, monitor latency and catch errors.

AI Agents fit this observability paradigm but instead of calling microservices (well, they might) they make requests to LLMs, execute tools and call MCP servers.

By wrapping AI Agents in OTEL we can get a peek at how the agent’s complex reasoning and tool usage behave.

Get a place to ship the data to

OpenTelemetry is a standardised framework, adopted by plenty of observability tooling. If you have something you already use, just use that.

Grafana Cloud

If you want to be fast with minimal effort, give Grafana Cloud a try. They have a generous free tier and Grafana is the GOAT for observability.

Grafana Cloud credentials

Grafana Cloud needs some extra information, the endpoint and authentication details (your account ID and API Key). Unfortunately they don’t make this super easy to find…

Account ID, API-Key and OpenTelemetry / OTLP endpoint

You can see all your “Stacks” (Grafana, Loki, Prometheus etc.) at https://grafana.com/profile/org;
Click “Details” on your stack (in my case “braw”);
Find the OpenTelemetry card and press “Configure” (screenshot below);
Note your OTLP Endpoint, Instance ID;
Create an API Token and note that as well;

Local SigNoz

Want to run it locally? You can also use Open Source Grafana however I prefer SigNoz in a container as it’s so quick and easy to get up and running.

The choice is yours.

Start a local collector

As I’m using Grafana Cloud I’m also going to use Grafana Alloy to collect and ship my OTEL data. Despite being a Grafana product it does also support collecting OpenTelemetry data.

verbose output

An OpenTelemetry collector is a “vendor-agnostic way to receive, process and export telemetry data.”

In our case, Alloy is a lightweight application we’re going to use to collect our metrics, logs and traces data, possibly perform some processing and then ship it to destinations of choice.

I like to do this so I can configure my OTEL applications to send data to Alloy and then Alloy is configured to send to both Grafana Cloud and my local SigNoz instance. This way I have a couple of copies of the data and can equally compare tools.

You will likely only want to send the data to Grafana Cloud.

We’ll write an Alloy config file using the token from earlier to push to our Grafana stack. This creates an OLTP endpoint listening on :4317 and :4318, batches the requests and sends them to our Grafana Cloud stack.

otel-config.alloy

otelcol.auth.basic "default" {
  username = "<ACCOUNT ID>"
  password = "<API TOKEN>"
}

otelcol.exporter.otlphttp "default" {
  client {
    endpoint = "<OTLP_ENDPOINT>"
    auth     = otelcol.auth.basic.default.handler
  }
}

otelcol.receiver.otlp "default" {
  grpc {
    endpoint = "127.0.0.1:4317"
  }

  http {
    endpoint = "127.0.0.1:4318"
  }

  output {
    metrics = [otelcol.processor.batch.default.input]
    logs    = [otelcol.processor.batch.default.input]
    traces  = [otelcol.processor.batch.default.input]
  }
}

otelcol.processor.batch "default" {
  output {
    metrics = [otelcol.exporter.otlphttp.default.input]
    logs    = [otelcol.exporter.otlphttp.default.input]
    traces  = [otelcol.exporter.otlphttp.default.input]
  }
}

Then I’m going to spin up a container with Podman (but you can also replace it with docker):

podman run \
    -v <CONFIG_FILE_PATH>:/etc/alloy/config.alloy \
    -p 12345:12345 \
    -p 4317:4317 \
    -p 4318:4318 \
    grafana/alloy:latest \
    run --server.http.listen-addr=0.0.0.0:12345 --storage.path=/var/lib/alloy/data \
    /etc/alloy/config.alloy

(replace <CONFIG_FILE_PATH> with where you stored the file before, e.g. ./otel-config.alloy)

and check that it runs on http://localhost:12345/.

Tell Claude Code to use OpenTelemetry

Once you have your OTEL stack ready, we need to tell Claude to push data there. For that there are some basic environment variables we need to configure and some optional ones to capture more info (a full list of possible related variables and some notes on them can be found in --verbose mode).

We’re going to make use of the standard OTEL logs and enable a beta OTEL tracing feature. Unfortunately the tracing doesn’t seem to properly attach spans to a trace as it should but we still get some data showing.

# Undocumented Beta tracing
export BETA_TRACING_ENDPOINT="http://localhost:4318"
export CLAUDE_CODE_ENHANCED_TELEMETRY_BETA=1
export ENABLE_BETA_TRACING_DETAILED=1

# Base telemetry config
export CLAUDE_CODE_ENABLE_TELEMETRY=1
export CLAUDE_CODE_OTEL_FLUSH_TIMEOUT_MS=1000
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4317"
export OTEL_EXPORTER_OTLP_PROTOCOL="grpc"

# Lower the export interval so we see results faster
export OTEL_LOGS_EXPORT_INTERVAL=1000
export OTEL_METRIC_EXPORT_INTERVAL=1000

# Make sure we tell the exporters to use OTLP
export OTEL_LOGS_EXPORTER="otlp"
export OTEL_METRICS_EXPORTER="otlp"
export OTEL_TRACES_EXPORTER="otlp"

additionally, if you want to also log tool content, details and user prompts add the following exports:

export OTEL_LOG_TOOL_CONTENT=1
export OTEL_LOG_TOOL_DETAILS=1
export OTEL_LOG_USER_PROMPTS=1

then we run claude code as normal:

claude

verbose output

Full list of OTEL environment variables supported by Claude at the time of writing:

Environment variable	Note
`CLAUDE_CODE_ENABLE_TELEMETRY`	Required to ship OTEL data
`CLAUDE_CODE_ENHANCED_TELEMETRY_BETA`	Still investigating what this does
`CLAUDE_CODE_OTEL_FLUSH_TIMEOUT_MS`
`CLAUDE_CODE_OTEL_HEADERS_HELPER_DEBOUNCE_MS`
`CLAUDE_CODE_OTEL_SHUTDOWN_TIMEOUT_MS`
`CLAUDE_CODE_PERFETTO_TRACE`	Seems to need a feature flag but could be used to profile `claude`
`OTEL_EXPORTER_OTLP_ENDPOINT`
`OTEL_EXPORTER_OTLP_HEADERS`
`OTEL_EXPORTER_OTLP_INSECURE`
`OTEL_EXPORTER_OTLP_LOGS_PROTOCOL`
`OTEL_EXPORTER_OTLP_METRICS_PROTOCOL`
`OTEL_EXPORTER_OTLP_METRICS_TEMPORALITY_PREFERENCE`
`OTEL_EXPORTER_OTLP_PROTOCOL`
`OTEL_EXPORTER_OTLP_TRACES_PROTOCOL`
`OTEL_EXPORTER_PROMETHEUS_HOST`
`OTEL_EXPORTER_PROMETHEUS_PORT`
`OTEL_LOG_TOOL_CONTENT`
`OTEL_LOG_TOOL_DETAILS`
`OTEL_LOG_USER_PROMPTS`
`OTEL_LOGS_EXPORT_INTERVAL`
`OTEL_LOGS_EXPORTER`
`OTEL_METRIC_EXPORT_INTERVAL`
`OTEL_METRICS_EXPORTER`
`OTEL_TRACES_EXPORT_INTERVAL`
`OTEL_TRACES_EXPORTER`

Visualise your usage

Once you’ve sent a few prompts you should expect to see data in your observability tool. As a Grafana Cloud user, this is an example of what I have chosen to see:

This dashboard chooses to pull statistics from logs (via Loki) rather than traces. This is becauses traces are still in Beta. But you can adjust accordingly and perhaps get better performance.

If you’re also using Grafana you can import the following JSON to get the same dashboard.

Testing out user claims

I’ve seen some claims on Twitter (currently known as “X”) and LinkedIn that simply saying “hello” was enough to burn significant Claude usage, but lets see how true that is.

Start a new Claude session and send “hello”:

claude hello

⏺ Hello! How can I help you today?

Find the most recent session ID in our Find the most recent session ID in our https://grafana.com/explore page and selecting our logs datasource from the dropdown in the top-left.

{service_name="claude-code"} | session_id != "" | line_format "{{.session_id}}"

By adding a limit of 1 and selecting Direction: Backward we can see the most recent session ID.

And then we can use that session ID (replace session_id in the examples below) to query things like the total cost in USD:

sum(sum_over_time({service_name="claude-code"} | session_id="{{ session_id }}" | unwrap cost_usd [$__auto]))

0.0153

or the total input_tokens:

sum(sum_over_time({service_name="claude-code"} | session_id="{{ session_id }}" | unwrap input_tokens [$__auto]))

340

or the total cache_read_tokens (seems oddly high for a new session):

sum(sum_over_time({service_name="claude-code"} | session_id="{{ session_id }}" | unwrap cache_read_tokens [$__auto]))

14,585

Maybe higher than we were expecting but definitely shouldn’t be using all significant chunks of your usage…

At least now you can leave this running and monitor usage over time. If you’re hitting limits dive back in and see how much your recent sessions really consumed.

This was a fun experiment which involved getting to know OpenTelemetry a bit better, a bit of peaking inside Claude Code and observing my token usage.

Usernames in Claude

Fri, 27 Mar 2026 21:55:06 +0100

Funny thing, there seem to be some files (or at least references to files) embedded in the claude binary that contain the username of the user. Lets look at Linux and Mac users (usernames in the /home or /Users directory):

❯ strings $(which claude) | grep -E '^/(home|Users)/[^/]+' | cut -d/ -f3 | sort -u
amorriscode
atp
boris
claude
hellja
qing
runner
user
username

The paths themselves aren’t too interesting though (list truncated):

❯ strings $(which claude) | grep -E '^/(home|Users)/[^/]+' | sort -u
/home/claude/.claude/remote
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/box_blur.rsp
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/color_matrix.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/component_transfer.rs
...
/home/runner/work/claude-cli-internal/claude-cli-internal/node_modules/@grpc/grpc-js/build/src
/home/user
/Users/amorriscode/.cargo/registry/src/artifactory.infra.ant.dev-7db23613d841872b/crossbeam-deque-0.8.6/src/deque.rs
/Users/amorriscode/.cargo/registry/src/artifactory.infra.ant.dev-7db23613d841872b/crossbeam-epoch-0.9.18/src/atomic.rs
/Users/amorriscode/.cargo/registry/src/artifactory.infra.ant.dev-7db23613d841872b/crossbeam-epoch-0.9.18/src/internal.rs
...
/Users/atp/.cargo/registry/src/artifactory.infra.ant.dev-7db23613d841872b/zune-jpeg-0.5.12/src/worker.rs
/Users/atp/code/claude-cli-internal/vendor/image-processor-src/target/aarch64-apple-darwin/release/deps/libimage_processor.dylib
/Users/boris/.cargo/registry/src/artifactory.infra.ant.dev-7db23613d841872b/napi-2.16.17/src/bindgen_runtime/module_register.rs
/Users/boris/.cargo/registry/src/artifactory.infra.ant.dev-7db23613d841872b/napi-2.16.17/src/error.rs
...
/Users/hellja/.rustup/toolchains/stable-aarch64-apple-darwin/lib/rustlib/src/rust/library/std/src/sys/thread_local/native/lazy.rs
/Users/hellja/.rustup/toolchains/stable-aarch64-apple-darwin/lib/rustlib/src/rust/library/std/src/thread/local.rs
...
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/selectors_vm/ast.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/selectors_vm/compiler.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/selectors_vm/mod.rs
/Users/username/project/README.md
/Users/username/project/src
/Users/username/project/src/index.ts
/Users/username/project/src/newFile.ts

If you look closely at the list, you can glean quite a bit of context about how claude was built:

Rust Ecosystem: The heavy presence of .cargo/registry and .rustup paths confirms that the CLI is partially written in Rust. We can even see references to specific tools like resvg (SVG rendering) and zune-jpeg.
CI/CD Traces: The paths under /home/runner and /Users/runner/work point to GitHub Actions runners being used for cross-compilation (both Ubuntu and macOS targets).
Internal Projects: We can spot references to claude-cli-internal, claude-cli-4, and even bun-internal, giving us a peek into Anthropic’s internal repo structure.
Developer Machines: Paths like /Users/amorriscode or /Users/boris suggest that some crates or components were built locally by developers and statically linked, preserving their local absolute paths via panic macros, debug symbols, or file!() macros.

If you are curious, you can explore the complete, un-truncated list of over 600 extracted paths by enabling verbose mode (toggle, bottom right).

verbose output

Full list of extracted paths:

/home/claude/.claude/remote
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/box_blur.rsp
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/color_matrix.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/component_transfer.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/convolve_matrix.rs/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/mod.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/iir_blur.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/filter/turbulence.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/geom.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/image.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/mask.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/resvg/src/render.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg-parser/src/marker.rshiddenscroll
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg-parser/src/svgtree/mod.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg-parser/src/svgtree/names.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg-parser/src/svgtree/parse.rsX
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg-parser/src/svgtree/text.rstext
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg-tree/src/filter.rs
/home/runner/.cargo/git/checkouts/resvg-4b7e4ee32ad6d954/3495d87/crates/usvg/src/writer.rs
/home/runner/.cargo/git/checkouts/woff2-rs-a4d3c822d2553afe/88cce4c/src/glyf_decoder/mod.rs
/home/runner/.cargo/git/checkouts/woff2-rs-a4d3c822d2553afe/88cce4c/src/woff2/table_directory.rs,
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/adler32-1.2.0/src/lib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/arrayvec-0.7.4/src/arrayvec.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/base64-0.21.7/src/encode.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/brotli-decompressor-2.5.1/src/bit_reader/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/brotli-decompressor-2.5.1/src/decode.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/brotli-decompressor-2.5.1/src/huffman/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/brotli-decompressor-2.5.1/src/transform.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/bytemuck-1.15.0/src/internal.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/bytes-1.6.0/src/lib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/data-url-0.2.0/src/lib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/data-url-0.2.0/src/mime.rs\
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/huffman_table.rs/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/huffman_lengths.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/input_buffer.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/length_encode.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/lz77.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/matching.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/output_writer.rsL
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/deflate-1.0.0/src/rle.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/flate2-1.0.28/src/bufreader.rs$
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/flate2-1.0.28/src/crc.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/fontdb-0.14.1/src/lib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/gif-0.12.0/src/reader/decoder.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/gif-0.12.0/src/reader/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/imagesize-0.12.0/src/formats/jxl.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/decoder.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/huffman.rsfailed to decode huffman code@
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/idct.rsD
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/parser.rsencountered  with invalid length
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/upsampler.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/worker/immediate.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/jpeg-decoder-0.3.1/src/worker/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/kurbo-0.9.5/src/cubicbez.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/miniz_oxide-0.5.4/src/inflate/core.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/miniz_oxide-0.5.4/src/inflate/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/miniz_oxide-0.5.4/src/inflate/output_buffer.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/miniz_oxide-0.7.2/src/inflate/core.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/miniz_oxide-0.7.2/src/inflate/output_buffer.rsH
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/miniz_oxide-0.7.2/src/inflate/stream.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/pathfinder_content-0.5.0/src/outline.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/decoder/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/decoder/stream.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/decoder/zlib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/encoder.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/filter.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/text_metadata.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/png-0.17.5/src/utils.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rctree-0.5.0/src/lib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/roxmltree-0.18.1/src/lib.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/roxmltree-0.18.1/src/parse.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/aat/extended_kerning.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/aat/map.rs\6
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/aat/metamorphosis.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/aat/mod.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/aat/tracking.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/buffer.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/hangul.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/indic.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/khmer.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/myanmar.rs\%
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/thai.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/universal.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/complex/vowel_constraints.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/fallback.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/glyph_set.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/normalize.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/ot/contextual.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/ot/kerning.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/ot/map.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/ot/matching.rs$
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/ot/position.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/ot/substitute.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/plan.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/shape.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/tag_table.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/tag.rs,?
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/rustybuzz-0.7.0/src/unicode.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/serde_json-1.0.115/src/error.rs at line invalid type: , expected
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/serde_json-1.0.115/src/read.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/simplecss-0.2.1/src/selector.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/svgtypes-0.11.0/src/path.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/svgtypes-0.14.0/src/error.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/svgtypes-0.14.0/src/stream.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/alpha_runs.rsL
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/blitter.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/edge_builder.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/edge_clipper.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/edge.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/fixed_point.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/geom.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/line_clipper.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/mask.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/path_geometry.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/path64/cubic64.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/path64/line_cubic_intersections.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/path64/quad64.rs
/home/runner/.cargo/registry/src/index.crates.io-6f17d22bba15001f/tiny-skia-0.10.0/src/pipeline/blitter.rs
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/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/rewritable_units/element.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/rewritable_units/mutations.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/rewritable_units/text_encoder.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/rewritable_units/tokens/attributes.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/rewriter/handlers_dispatcher.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/rewriter/mod.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/selectors_vm/ast.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/selectors_vm/compiler.rs
/Users/runner/work/bun-internal/bun-internal/vendor/lolhtml/src/selectors_vm/mod.rs
/Users/username/project/README.md
/Users/username/project/src
/Users/username/project/src/index.ts
/Users/username/project/src/newFile.ts

Verbose Mode

Sat, 28 Feb 2026 21:34:08 +0100

I like to write about technical topics, but not everyone wants to read the gritty details. Sometimes you just want the high-level overview, and other times you want to see exactly how something works under the hood.

To solve this, I’ve added a --verbose mode toggle to this blog. You can find it in the bottom right corner of the site. When enabled, it reveals extra details, code snippets, and deeper explanations hidden throughout the articles.

Here is a short example of how it looks:

This is a normal paragraph that everyone can see.

verbose output

Verbose output: This is a verbose paragraph! You’ll only see this block of text when the --verbose mode is turned on.

I’ll use it to share extra context, setup instructions or more.

Try toggling the verbose mode on and off using the switch in the bottom right corner to see how it works!

Restart the Blog

Thu, 26 Feb 2026 22:37:34 +0100

This site has gone through many iterations. From humble Django, Ghost, a brief digital garden with quartz, Astro and now static Hugo, I have relaunched with the excellent Hextra theme and Hugo.

Prioritising European

Currently this is deployed to Cloudflare pages but as I’m pushing for more European focused solutions I plan to port this to Bunny.net in the near future.

Old content

Older posts might get ported over once I’ve reviewed but for now we’re going to start from scratch.

I hope you find something that interests you!