I’ve had some feedback that my last few weekly notes (especially over the holidays) have been a bit too long and that I should try to keep them shorter and more focused on a single topic.

Well, this week I wrote a lot thanks to the inclement weather and some insomnia, so I broke most of it out into separate posts:

• Seizing The Means Of Production (Again) covers my ongoing efforts to build a minimal-friction agent management setup using agentbox and webterm.
• Vibing with the Agent Control Protocol dives into my new Slack-like web interface for chatting with AI agents via ACP, called vibes.
• Thoughts on AI-Assisted Software Development in 2026 is a quick brain dump of some of the lessons I’ve “learned” this week.

This is the age of TikTok, after all, and attention spans are shorter than ever, so I might as well try to adapt.

But there was a lot more going on this week, so here’s a quick roundup of two other things I worked on:

go-rdp Improvements

My web-based RDP client got UDP transport support—experimental, gated behind a --udp flag, but apparently functional. So far it’s going well, even if MP3 audio support is not all there yet (and yes, I know it’s a bit much for a web client).

This is one of my “things that should exist” projects that I’ve been using to experiment how to spoon-feed AI agents with highly complex protocol specs, and it’s been working out great so far, largely because RDP is so well documented.

The test suite now includes Microsoft Protocol Test Suite validation tests for RDPEUDP, RDPEMT, RDPEDISP, and RDPRFX. It’s the kind of spec compliance work that’s tedious but essential, and that I am increasingly convinced can be massively sped up with AI (after all, it took me less than a working week of continuous effort to do the whole thing, instead of the months it would have taken me otherwise).

go-ooxml from OO to… Hero?

As another data point, I decided to build my own Office Open XML library in Go this week, called go-ooxml. It too is meant to be a clean-room implementation, and I intend it to be a comprehensive replacement for the various format-specific Python libraries out there–I’ve already been using Go as an accelerator for Python in things like pysdfCAD, so I know it works.

And it also fits into the “things that I think should exist” category, because existing Go libraries for this are either abandoned, incomplete, or have APIs that make me want to cry.

And on the AI front the ECMA-376 spec is perfect:

• It is a real thing people used for other implementations, and progress has been ridiculous–agents are genuinely useful for this kind of spec-driven implementation work.
• In about two days (less than four hours of “actual work”), I went from initial project setup to enough to handle basic document creation (mostly Word, which is the most complex)

Is it production-ready? Of course not.

But it’s already more complete than many alternatives, and having the foundation right means I can add features as I need them without fighting the architecture.

Agents make the breadth possible, but they don’t remove the need for taste: deciding what the public API should look like, what’s testable, and what’s going to be maintainable six months from now is, of course, the hard part still to come…

At least I’m aware of it, I guess.

A few things I jotted down during this week’s bout of coding with AI agents–i.e., while building out my agentbox and webterm setups and other things.

Agents love specs. My go-ooxml project went from nothing to 60% spec compliance in days because I fed the agents the actual ECMA-376 spec documents and told them to implement against those. No hallucination about what XML elements should be called, no invented APIs—just spec-compliant code.

Mobile is an afterthought until it isn’t. Half the webterm fixes this week were iOS/iPad edge cases. If you’re building tools you’ll use on multiple devices, test on all of them early, because agents can only help you with things that they can test for autonomously.

The unglamorous work matters. I did a lot of CI/CD cleanup jobs, release automation, Docker pipelines, and invested quite a few hours in creating solid SKILL.md scaffolding–none of this is exciting, but it’s what separates a tool you can rely on from a tool that occasionally bites you, and right now, for me, at least, it’s what makes AI agents genuinely useful.

There’s going to be more software. With such a low barrier to entry into new languages, tools or frameworks, any decent programmer is soon going to realize that their skills are transferable to the point where they can take on work in any technology.

There’s going to be more shitty software because, well, there are a lot of overconfident people out there, for starters, and the law of averages is inevitably going to kick in at some point. I am acutely aware that I am treading a fine line between “productive developer leveraging AI” and “architecture astronaut”, but my focus is always on shipping self-contained, small tools that solve real problems (for me, at least), so I hope I can avoid that pitfall.

The number of truly gifted developers is going to stay roughly the same, because programming, like any form of engineering, is a mindset much more than it is a skill.

Some of these are debatable, of course, but they are my current take on things. Let’s see how they hold up over time.

Although most of my actual work happens inside a terminal, I have been wanting an easy way to talk to the newfangled crop of AI agents from my iPhone.

So I spent a good chunk of this week building out a Slack-like web interface for chatting with agents via the Agent Client Protocol (ACP) called vibes.

Right now, it looks like this:

I blame OpenClaw for planting the seed of a chat-like interface for agents in my mind–this is not replacing my terminal-based workflow, but it’s a nice complement, especially for quick check-ins or when I want to give an agent a task and review the results later from my phone.

The web layer is “just” Preact and SSE, with enough CSS for it to work nicely in small screens and touch input, and the main timeline view shows messages from me and the agent, with support for rich content like code blocks, KaTeX formulae, images, and resource links.

But the key thing is the tool permission flow: when the agent wants to call a tool, it shows a modal with an explanation of what the tool does (fetched from the ACP server), and I can approve or deny it with a tap–that is the key part of ACP that I wanted to leverage, and that so far I’ve only seen in CLI/TUI clients.

One thing that isn’t on the diagram above is the database. I love SQLite for small projects like this, and all the more so now that I learned the tricks around using JSON columns for flexible data storage. And, of course, you get full text search support out of the box, which is perfect for searching what I intend to be an infinite timeline.

Like MCP, I am not crazy about it. ACP has many of the same flaws, except that now you also have to deal with the ambiguity of how to surface all of the interactivity you’d have in a TUI in a chat timeline.

Content parsing went through several iterations to handle all the edge cases: tool calls, thinking panels, resource links, embedded resources with annotations, live updates from the agent, etc.

And I had to test it with multiple ACP servers, since each implementation has its own quirks. Right now, vibes works reasonably well with my python-steward, Mistral’s vibe and GitHub Copilot CLI, but all of them have small differences in how they implement the spec.

If I had to do it again, I would have probably built a proper acp client library in Go or Python first, but since I was building both the client and server sides at the same time, I just kept iterating on the wire format until everything worked.

It’s not just the convenience of having a cute web app on my phone–having a low-friction review loop is essential when working with agents (which is why I was keen on leveraging ACP in the first place), but I also wanted persistent history and richer rendering than what a terminal can provide, because I want to give my agents more complex tasks that involve multiple steps and outputs.

Everyone and their dog seems to be thinking that agents only have to have bash (Armin Ronacher makes some excellent points), but I am trying to strike a balance when designing steward: Give it all the tools it should need for most use cases, a little scripting engine (QuickJS) for extensibility, and extensive SKILL.md support so I can teach it to do new things.

I am pretty sure that my endgame will eventually involve WASM (maybe tinygo in a WASM sandbox or a Cloudflare-like V8 isolate) and I’m actually hedging my bets by looking at porting a subset of busybox to WASM, but for the moment I want to keep things simple and give agents access to higher-level tools that can do complex things without needing to script them from scratch.

Because, well, I don’t want to write coding agents. There’s a special kind of myopia around their incredible success, but I think there should be some balance in the Force.

Crustaceans are cool and all, but sometimes you just want to vibe with your agent about something as prosaic as scheduling a meeting or searching your Obsidian vault.

Since last week, I’ve been hardening my agentbox and webterm setup through sheer friction. The pattern is still the same:

Small, UNIX-like, loosely coupled pieces which I then glue together with labels, WebSockets and enough duct tape to survive iOS, and the goal is still the same: to reduce friction and cognitive load when managing multiple AI agents across what is now almost a dozen development sandboxes.

My take on tooling is, again, that a good personal stack is boring. You should be able to understand it end-to-end, swap parts in and out of, and keep it running even when upstreams change direction. The constraint is always the same: my time is finite, so any complexity I add needs to pay rent.

And boy, has there been complexity this week.

I rewrote a huge chunk of webterm this week.

I started out with the excellent Textual scaffolding (i.e., xterm.js + a thin server), but I kept having weird glitches (mis-aligned double-width characters, non-working mobile input, theme handling, etc.).

So, being myself, I decided to reinvent that particular wheel, and serendipitously I stumbled onto a WASM build of Ghostty that is pretty amazing–it can render using WebGL, fixed all of my performance issues with xterm.js, and… well, it was a bit of a challenge to deal with, but only because of a few incomplete features.

In the pre-AI days, I would have stopped there, but this week it took me under an hour to create a patched fork of ghostty-web that filled in the gaps I wanted and that I could just drop into webterm.

Then came the boring part–ensuring the font stack worked properly across platforms, fixing a few rendering glitches, replacing the entire screenshot capture stack (which is what I loved about Textual) with pyte, and… a lot of mobile testing.

Still, the end result is totally worth it:

There were a lot of little quality-of-life improvements that came out of this rewrite:

• The dashboard got typeahead search, so I can quickly find the right sandbox among many.
• And the most satisfying cosmetic fix: dashboard screenshots now use each session’s actual theme palette.
• PWA support landed, so the iPad can treat it like a proper “app”.
• The WebSocket plumbing got a proper send queue so slow clients couldn’t freeze other sessions.

I would have rewritten this in Go, but as it happens the Go equivalent of pyte didn’t seem to be good enough yet, and running half a dozen sessions at a time for a single person isn’t a load-sensitive setup anyway.

Again, this is all about reducing friction: Color helps me recognize the project, and typehead find makes it trivial to, well… find. The less mental overhead I have to deal with when switching contexts, the more likely I am to actually use the tools I’ve built.

But getting it to work properly on mobile was a pain:

• Mobile keyboard handling was a mess. You can’t customize the iOS onscreen keyboard in the browser, and modifier keys were especially problematic.
• To make mobile usable for real work (not just htop screenshots), webterm now pops up a draggable keybar with Esc/Ctrl/Shift/Tab and arrow keys, which are “sticky” so you can tap out proper Ctrl/Shift arrow sequences–and Ctrl+C, which is kind of essential.
• Focus was a big problem. iOS is incredibly finicky about input browser input events–and if you test on an iPad with a keyboard attached, you miss half the problems. The “solution” was to monkeypatch input via a hidden textarea that captures all input events and forwards them to the terminal renderer–and that still breaks in weird, unpredictable ways.

I might have gone a bit overboard with testing–I don’t have an Android tablet, so I decided to test on my Oculus Quest 2 headset browser, which is almost Android with a head strap:

Then came even weirder rendering bugs, since, well, terminals are terminals. And for such a simple concept, the stack is surprisingly complex:

For instance, you’ll notice in the diagram above that there is a PTY layer and tmux in the mix. That means there are two layers of terminal emulation happening, and both need to be configured properly to avoid glitches.

For instance, I kept getting 1;10;0c when I connected, which led me down the weirdness of ANSI escape codes and nested terminal emulators (something I hadn’t done since running emacs to wrap VAX sessions…). tmux sends DA2 queries, but my wrapper ended up having to filter more than DA1 responses and not messing up UTF-8 sequences.

Then I realized that the Copilot CLI sends a bunch of semi-broken escape sequences that pyte couldn’t handle properly, which led to all sorts of rendering glitches in the screenshots, and another round of patches, and another…

I also spent a good chunk of time this week improving the agentbox Docker setup, adding better release automation, cleaning up old artifacts, and generally making it easier to spin up new sandboxes with the right tools and my secret weapon:

A set of starter SKILL.md files that teach the bundled agents how to manage the environment, use how I prefer to develop, and generally be useful and run through proper code/lint/test/fix cycles without me having to babysit them.

Right now I’m at a point where I can just go into any of my git repositories, run make init (or, if it’s an old project, point Copilot at the skel files and tell it to read and adapt them according to the local SPEC.md), and have a fully functional AI agent sandbox ready to go.

That I can do that and the infra for it in under a minute, with proper workspace mappings, RDP/web terminal access, and SyncThing to get the results back out, is just… chef’s kiss.

Ah well. At least now I have a pretty solid UX that even works from on my ageing iPad Mini 5 snappily enough (as long as I don’t try to open too many tabs), and I can finally start focusing on other stuff.

Which I sort of did, all at once…

Since last week, I’ve been heads-down building a coding agent setup that works for me and using it to build a bunch of projects, and I think I’ve finally nailed it. A lot more stuff has happened since then, but I wanted to jot down some notes before I forget everything, and my next weekly post will probably be about the other projects I’ve been working on.

I have now achieved coding agent nirvana–I am running several instances of my agentbox code agent container in a couple of VMs (one trusted, another untrusted), and am using my textual-webterm front-end to check in on them with zero friction:

This is all browser-based, so one click on those screenshots (which update automatically based on terminal activity) opens the respective terminal in a new tab, ready for me to review the work, pop into vim for fixes, etc. Since the agents themselves expend very little CPU or RAM and I’ve capped each container to half a CPU core, a 6-core VM can run literally dozens of agents in parallel, although the real limitation is my ability to review the code.

But it’s turned out to be a spectacularly productive setup – a very real benefit for me is having the segregated workspaces constantly active, which saves me hours of switching between them in VS Code, and another is being able to just “drop in” from my laptop, desktop, iPad, etc.

As someone who is constantly juggling dozens of projects and has to deal with hundreds of context switches a day, the less friction I have when coming back to a project the better, and this completely fixes that. Although I had this mostly working last week, getting the pty screen capture to work “right” was quite the pain, and I had to guide the LLM through various ANSI and double-width character scenarios–that would be worth a dedicated post on its own if I had the time, but anyone who’s worked with terminal emulators will know what I’m talking about.

Another benefit of this approach is that none of the agents are running locally and can’t possibly harm any of my personal data.

The whole thing (minus Tailscale, which is how I connect everything securely) looks like this:

I have several levels of sandboxing in place:

• Each container is an agentbox instance with its own /workspace folder
• Containers are capped in both CPU and RAM (although that only impacts their ability to run builds and tests–but even Playwright testing works fine)
• The containers are running in a full VM inside Proxmox (capped at six cores and 16GB) and one of my ARM boards (more cores, but just 8GB of physical RAM)
• The “untrusted” agents use LiteLLM to access Azure OpenAI, so they never have production keys and can be capped in various ways
• Each setup runs a SyncThing instance that syncs the workspace contents back to my Mac so I can do final reviews, testing and commits–that’s the only way any of the code reaches my own machine.

As to the actual agent TUI inside the agent containers, I’m using the new GitHub Copilot CLI (which gives me access to both Anthropic’s Claude Opus 4.5 and OpenAI’s GPT-5.2-Codex models), Gemini (for kicks) and Mistral Vibe (which has been surprisingly capable).

After last week’s shenanigans I relegated OpenCode to the “untrusted” tier, and I also have my own toy coding assistant (based on python-steward, and focused on testing custom MCP tooling) there.

A good part of the initial effort was bootstrapping this, of course, but since I did it the UNIX way (simple tools that work well together), I’ve avoided the pitfall of doing what most agent harnesses/sandboxing tools are trying to do, which is to do full-blown, heavily integrated environments that take forever to set up and are a pain to maintain.

I don’t care about that, and prefer to keep things nice and modular. Here’s an example of my docker compose file:

---
x-env: &env
  DISPLAY: ":10"
  TERM: xterm-256color
  PUID: "${PUID:-1000}"
  PGID: "${PGID:-1000}"
  TZ: Europe/Lisbon

x-agent: &agent
  image: ghcr.io/rcarmo/agentbox:latest
  environment:
    <<: *env
  restart: unless-stopped
  deploy:
    resources:
      limits:
        cpus: "${CPU_LIMITS:-2}"
        memory: "${MEMORY_LIMITS:-4G}"
  privileged: true # Required for Docker-in-Docker
  networks:
    - the_matrix

services:
  syncthing:
    image: syncthing/syncthing:latest
    container_name: agent-syncthing
    hostname: sandbox
    environment:
      <<: *env
      HOME: /var/syncthing/config
      STGUIADDRESS: 0.0.0.0:8384
      GOMAXPROCS: "2"
    volumes:
      - ./workspaces:/workspaces
      - ./config:/var/syncthing/config
    network_mode: host
    restart: unless-stopped
    cpuset: "0"
    cpu_shares: 2
    healthcheck:
      test: curl -fkLsS -m 2 127.0.0.1:8384/rest/noauth/health | grep -o --color=never OK || exit 1
      interval: 1m
      timeout: 10s
      retries: 3

  # ... various agent containers ...

  guerite:
    <<: *agent
    container_name: agent-guerite
    hostname: guerite
    environment:
      <<: *env
      ENABLE_DOCKER: "true" # this one needs nested Docker
    labels:
      webterm-command: docker exec -u agent -it agent-guerite tmux new -As0 \; attach -d
    volumes:
      - config:/config
      - local:/home/agent/.local
      - ./workspaces/guerite:/workspace

  go-rdp:
    <<: *agent
    container_name: agent-go-rdp
    hostname: go-rdp
    ports:
      - "4000:3000" # RDP service proxy
    labels:
      webterm-command: docker exec -u agent -it agent-go-rdp tmux new -As0 \; attach -d
    volumes:
      - config:/config
      - local:/home/agent/.local
      - ./workspaces/go-rdp:/workspace

# ... more agent containers ...

volumes:
  config:
    driver: local
    driver_opts:
      type: none
      o: bind
      device: ./home
  local:
    driver: local
    driver_opts:
      type: none
      o: bind
      device: ./home/.local

networks:
  the_matrix:
    driver: bridge

You’ll notice the labels, which are what textual-webterm uses to figure out what containers to talk to.

It’s been insane. Since this setup lets me drop back into each project at the click of a link and I can guide the agents for a couple of minutes at a time, or take notes and write specs in a separate tmux window. Either of which fits well with my workflow and doesn’t require me to fire up a bloated IDE and loading a project folder (which can take quite a long time on its own).

So I now have the ability to create a bunch of things that I think should exist:

• I now have my web-based RDP client working with a Go back-end that uses tinygo and WASM to do high-performance decoding in the browser (which is something I’ve always wanted), and I decided to push it to the limit against the public test suites because I think a Go-based RDP client is something that should exist.
• I took the existing pysdfCAD implementation of signed distance functions and replaced the slow marching cubes implementation it was using to render STL meshes with a Go-based backend that renders meshes much faster and with better quality (when it works–I need to sort out some bugs).
• I built two (for now) Visual Studio Code extensions for mind-mapping and Kanban that match what I currently need from Obsidian (and will be looking at enhancing Foam to match the Obsidian editor soon)
• I’m taking a couple of years of hacky scripts and building a writing agent that is going to help me do the automated conversion and re-tagging of the 4000+ legacy pages of this site that are still in Textile format (the name editor is taken for building a WYSIWYG Markdown editor to replace Obsidian with VS Code)
• I ported a bunch of my own stuff (and a few fun things, like Salvatore Sanfilippo’s embedding model) to Go.
• I started packaging my own MCP servers as Azure App Services, so I can use the basic techniques to accelerate my day job.

I’ve read about the Ralph Wiggum Loop, and it’s not for me (I find it to be the kind of thing you’d do if you’re an irresponsible adolescent rich kid with an inexhaustible supply of money/tokens and don’t really care about the quality of the results, and that’s all I’m going to say about it for now).

• My key workflow (write a SPEC.md, instruct the agents to run full lint/test cycles and aim for 80% test coverage, then go back, review and write TODO.md files for them to base their internal planning tools out of and work in batches) still works the best as far as final code quality is concerned. I still have to ask for significant refactorings now and then, but since my specs are usually very detailed (what libraries to use, which should be vendored, what code organization I want, and what specific test scenarios I believe to be the quality bar) things mostly work out fine.
• Switching between models for coding and auditing/testing is still key. Claude (even Opus) has a tendency to be overly creative in tests, so I typically ask for test and security audits with GPT-5.2 that catch dozens of obviously stupid things that the Anthropic models did. Gemini is still a grey area, since I’m just using the free tier for it (although it seems unsurprisingly good at architecting Go packages).
• Switching between frontier and small(ish) models for coding and testing also works great. gpt-5-mini, sonnet, haiku, mistral and gemini flash do a very adequate job of running and fixing most test cases, as well as front-end coding.
• SyncThing really doesn’t like when agents create virtualenvs or install npm packages, so I routinely have to tell the agents that they are in a containerized environment and that it’s fine to install pip and npm packages globally (i.e., outside the workspace mount point).
• Like I wrote a little while back, MCP is still the way to go for deterministic results with tools. Support for MCP (and SKILL.md) is very uneven across all the current agentic TUIs, but with a few strategically placed symlinks I can have a workspace setup that works well across VS Code and the remote agents.
• Having a shared set of MCP tooling and skills across as many of your agents as possible really cuts down on the amount of prompting and scaffolding agents need to create per project. In that regard, umcp has probably been the best bang for the buck (or line of code) that I wrote in 2025, because I use it all the time.
• Claude Code and Gemini have a bunch of teething issues with tmux. Fortunately both Mistral Vibe and the new Copilot CLI work pretty well, and clipboard support is flawless even when using them inside both tmux and textual-webterm.

And, finally, coding agents are like crack. My current setup is so addictive I find myself reviewing work and crafting TODOs for the agents from my iPad before I go to bed instead of easing myself into sleep with a nice book, which is something I really need to put some work into.

But I have a huge, decades-long list of ideas and projects I think should exist, and after three years of hallucinations and false starts, we’re finally at an inflection point where for someone with my particular set of skills and experience LLMs are a tremendous force multiplier for getting (some) stuff done, provided you have the right setup and workflow.

They’re still very far from perfect, still very unreliable without the right guardrails and guidance, and still unable to replace a skilled programmer (let alone an accountant, a program manager or even your average call center agent), but in the right hands, they’re not a bicycle for the mind–they’re a motorcycle.

Or a wingsuit. Just mind the jagged cliffs zipping past you at horrendous speeds, and make sure you carry a parachute.

Return to work happened mostly as expected–my personal productivity instantly tanked, but I still managed to finish a few things I’d started during the holiday break–and started entirely new ones, which certainly didn’t help my ever-growing backlog.

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Like everyone else, I’ve been looking at SKILL.md files and tried converting some of my MCP tooling into that format. While it’s an interesting approach, I’ve found that it doesn’t quite work for me as well as MCP does, which is… intriguing.

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OK, this was an intense few days, for sure. I ended up going down around a dozen different rabbit holes and staying up until 3AM doing all sorts of debatably fun things, but here’s the most notable successes and failures.

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