When I wrote LPWAN Meshes: Reticulum, Where I Landed earlier this year, I covered Reticulum as Mark Qvist’s reference Python implementation: rnsd, Sideband, NomadNet, all built on RNS. That was accurate at the time, and it undersold what was already starting to happen underneath it. Reticulum was never meant to be a Python project. It is a protocol, and a protocol with only one implementation isn’t decentralised yet, it’s just a single point of failure with good branding.

Part one and part two of this series covered how Reticulum and MeshCore actually move a sensor reading: LXMF’s delivery methods and Propagation Nodes on one side, a purpose-built sensor role and CayenneLPP encoding on the other. Both are genuinely decentralised, no shared infrastructure required beyond the nodes themselves. This closing part asks the question that decides whether that matters for a given deployment: what does the alternative, LoRaWAN, actually require to do the same job?

Part one of this series looked at sensor telemetry over Reticulum, and landed on a specific trade-off: Reticulum gives you cryptographic primitives (Destinations, Links, LXMF’s delivery methods) and leaves the shape of a telemetry deployment up to you. MeshCore takes the opposite approach. It has an actual sensor node role, a standard payload encoding, and a query model built around telemetry specifically, not general-purpose messaging repurposed for it.

LPWAN Meshes: Reticulum, Where I Landed covered Reticulum as a communications stack: transport-agnostic, cryptographically addressed, built for chat and file transfer over LoRa, packet radio, or whatever medium is available. This post is narrower on purpose: it breaks down the specific mechanics of moving a sensor reading, not a conversation, through Reticulum: which delivery method to pick, what a reading actually costs in packets and bits per second, and what already exists in the open-source community to build on rather than reinvent.

Earlier this year, in The Hidden World of Corporate IoT Spying, I looked at how consumer gadgets quietly turn personal spaces into corporate data collection nodes: robot vacuums mapping living rooms for foreign servers, smart TVs cataloguing every frame you watch, and Eufy security cameras quietly beaming unencrypted thumbnails and facial identifiers to the cloud despite marketing slogans promising “local storage for your eyes only.”

Seven months later, that trajectory has only accelerated. The news cycle around domestic cameras and automated surveillance has moved from occasional security quirks to systemic betrayal.

Starting 24 August, a popup village of builders, programmers, and ecological economists is settling into the Austrian mountains south of Vienna for a month. They are meeting in the Höllental, a valley of steep limestone cliffs and cold rivers, to run the Valley of the Commons: four consecutive weeks, each built around a different theme, commons economics, cosmo-local production, community living, governance and funding, tested against each other in the same physical place rather than argued about in the abstract.

Farmers are accustomed to operating under pressure from both ends of their business. Upstream, four companies, Bayer, Corteva, Syngenta, and BASF, collectively control more than half of global commercial seed sales. Downstream, in Australia, Coles and Woolworths alone account for around 67% of grocery retail volume. The farmer sits in the middle, bearing the operational and climatic risks while having almost zero bargaining power at either end of the transaction. The Treadmill Nobody Chose covered this squeeze in detail. This piece is about the third pressure point layered on top of it.

Ask most people when farmers started cooperating and they will picture the grain co-op down the road, or a barn raising in a period drama: something quaint, something that grew up alongside the tractor and the telephone. The real answer is older than writing. For most of the roughly nine thousand years since people first settled down to grow food, cooperation was not a value farmers held. It was the machinery. A shared irrigation channel, a shared plough team, a shared calendar for who grazes which pasture and when: none of it worked without a standing agreement among neighbours about who did what, when, and what happened if someone broke the rule. Take the agreement away and the technology stops functioning, not because anyone lacked the tools, but because nobody had a way to run them past the scale of a single household.

I upgraded to Ubuntu 26.04 LTS recently, the first jump I’d made since 24.04, sticking to LTS releases the way I always have. Within days I was fighting AppArmor denials in my logs, snaps behaving differently to their .deb equivalents for no obvious reason, and the general low-grade friction of debugging the packaging layer instead of the software I actually wanted to run. I’d already documented one of these fights in public: getting Zoom’s camera working again on Wayland meant ripping the Snap out entirely and installing Mozilla’s own .deb instead, because the sandbox was standing between the app and the hardware and no amount of granted permissions fixed it.

A three-part series on licensing and the economics of digital sovereignty was planned: how the commons got built, how Europe is choosing to defend it, how the AI stack could be governed as one. Then Martin Owens, an Inkscape developer and member of its project leadership committee, replied to part one on Mastodon, in two posts stitched together by the character limit, and pointed straight at something the series had missed entirely.

A vocabulary has been metastasising through environmental policy. It did not arrive as a position to be argued with. It crept in quietly, one term at a time, through grant applications, government white papers, and conference panels, until it had colonised the language before most people noticed there was anything to resist. We are told that the central challenge of our warming, degrading world is to make nature investable. We hear about biodiversity credits, green bonds, and nature-based financial instruments. The premise is simple: public funds are scarce, the cost of ecological restoration is vast, and therefore we must design financial structures that allow private institutional capital to flow into conservation.

There are two dominant ways to think about who should govern artificial intelligence.

The market frame says: the data, the models, and the compute are private goods, owned by the organisations that assembled them, exchanged through contract and property rights. Governance follows ownership. OpenAI owns GPT-4. Google owns Gemini. These are products. Markets will sort it out.

The state frame says: AI is too consequential to leave to markets, so regulators must impose rules from above. The EU’s AI Act is the most developed version of this: binding requirements, conformity assessments, registration databases, penalties for violations.