07 Aug 2026
Django community aggregator: Community blog posts
Issue 349: Django 6.1 and a DSF Executive Director
News
Call for applicants for a Django Executive Director
The DSF is hiring its first Executive Director to run fundraising, operations, staff, and legal/compliance for the Foundation. The role is US-based (no visa sponsorship). Applications (resume, optional cover letter, and a vision statement) are due September 14, 2026.
Django 6.1 released
Django 6.1 introduces model field fetch modes, database-level delete options for ForeignKey.on_delete, and dictionary-based email settings. Django 6.0 is now out of mainstream support and receives security and data loss fixes only until April 2027, so plan your upgrade before then.
Thank you to release manager Jacob Walls and the entire Django team on another happy feature release.
Releases
Django security releases issued: 6.0.8 and 5.2.17
The releases address high-severity spatial lookup flaws that could write files or make network requests, plus denial-of-service risks in language and geometry handling and potential XSS from unsafe admin URLField values. Upgrade to Django 6.0.8 or 5.2.17 as soon as possible.
Python 3.15.0 candidate 1 is here!
Python 3.15.0 candidate 1 is available for testing, giving projects a release candidate against which to check compatibility and build wheels.
Python 3.14.7 and 3.13.15 are now available!
Python 3.14.7 and 3.13.15 are available as bug-fix releases, so update your installations.
Wagtail CMS News
An agent-heavy roadmap for 2026
A standards-based look at what agent readiness requires, without the usual hype.
Sponsored Link
Find and fix Python errors faster - for free - with Honeybadger
When something breaks in production, logs tell you something happened. Honeybadger tells you why.
Honeybadger filters out noise and transforms your Python logs into context-rich issues so you can stop guessing and ship the fix faster.
Django Fellow Reports
Django Fellow Report - Jacob
Highlights this week included clearing release blockers for 6.1 and making the parallel test runner more fault tolerant. That means lots of tickets triaged, reviewed, and authored!
Django Fellow Report - Natalia
This week I prioritized time-sensitive security work 🥷, including finalizing patches, preparing and validating backports, and sending pre-notifications ahead of the release. Alongside that, I focused on Tim's "Sprint quickstart" PR 🏃➡️ and attended meetings 🎧. Otherwise, I spent time preparing my DjangoCon US talk (it is coming together well, even if I am a bit wary of expectations around my htmx expertise 🎤).
Django Fellow Report - Sarah
Focus of the week was mostly around helping finalize the security release and reviewing the GSOC Selenium to Playwright migration (which is looking in good shape!).
Articles
Django Claude Skills
Mariatta built a set of Django house rules for Claude to follow in her own projects, covering formatting (black, isort, djlint, flake8), full test coverage as a merge requirement, has_perm() over group-membership checks for permissions, and a single Markdown email template that renders both text and HTML. She's explicit that these are personal conventions, not Django community consensus, and that a project's own style wins when contributing elsewhere. She published it as an Astro static site rather than plain Markdown, since she'd rather read the rules in a browser than as agent-only files.
What I love about Django
The best parts of Django are the ones you stop noticing - a tour of the abstractions that have given Buttondown the most leverage over the years.
Django Doesn't Have to Feel Old: Modernize Your Frontend with Vite
A walkthrough of wiring django-vite into a Django project: hot module replacement so CSS edits show up without a full page reload, explicit imports instead of global script-tag collisions, and a vite_asset template tag that resolves to the dev server or hashed production files depending on DEBUG.
Django 6 isn't a revolution. And that's exactly why I like it.
An experienced developer's take on years of building applications with Django and why the latest update continues a philosophy that many modern frameworks seem to have forgotten.
Python: how time-machine is O(1) where freezegun is O(n)
The comparison explains why time-machine avoids freezegun's O(n) slowdown when mocking dates and times, building on benchmarks that found it 100 to 200 times faster across two project sizes.
Core Dispatch #9
Python 3.15.0 release candidate 1 is out, followed by maintenance releases Python 3.14.7 and 3.13.15. The latest Core Dispatch also tracks two new PEPs entering the queue.
Devtools must be open source
Agents can now rebase your local tweaks against upstream automatically, so personalizing a tool costs a prompt instead of an ongoing maintenance burden. That's the case for why closed-source tools like Claude Code can't be personalized the way open alternatives like Codex or Pi can.
DjangoCon US
Save 10% on DjangoCon US 2026 registration
DjangoCon US 2026 runs August 24 to 28 in Chicago, now under three weeks away. Register through our link to take 10% off. Time is running out to get your ticket. Get yours before they are gone!
Events
Chicago Like a Local: Things to Do During DjangoCon US 2026 (Part 1)
Conference chair Keanya Phelps rounds up Italian beef at Mr. Beef and Al's, ramen and vinyl records at Wax in West Town, shuffleboard at Electric Shuffle, and rooftop lake views at Offshore on Navy Pier. She also flags the free Chicago House Music Festival in Millennium Park, August 27 to 30, which overlaps the last days of the conference.
Django Job Board
A few openings this week, including Django and Python Software Foundation roles.
Executive Director at Django Software Foundation 🆕
Security Developer at Python Software Foundation
Senior Full Stack Engineer at Hive Collective
Senior Backend Engineer at MyOme
Python + TypeScript Engineers at Fusionbox
Videos
PyCon US 2026
PyCon US 2026 videos are up.
Projects
wemake-services/django-modern-rest
Modern REST framework for Django with types and async support!
wsvincent/django-skills
Unofficial Django skills based on Django docs and community best-practices.
07 Aug 2026 3:00pm GMT
06 Aug 2026
Planet Python
Django Weblog: Call for applicants for a Django Executive Director
The Django Software Foundation is announcing a call for an Executive Director. The Executive Director is the operational leader of the Django Software Foundation, a paid position reporting to the Board of Directors, responsible for setting the Foundation's strategic direction and turning it into action, while managing day-to-day operations. They serve as the main connector between the Board, staff, community, and sponsors.
The Django Software Foundation (DSF) is a 501(c)(3) nonprofit that develops and maintains Django, a free and open-source web application framework. The Foundation exists to support the development of Django by sponsoring sprints, meetups, gatherings and community events; to promote the use of Django among the web development community; to protect the framework's intellectual property and long-term viability; and to advance the state of the art in web development.
This is a new role for the Foundation. Django itself has been around since 2005, but the DSF wasn't founded until 2008, and the person who takes on this role will play a key part in maturing the Foundation's internal structure, helping ensure the DSF can properly support and sustain this important ecosystem going forward. The position is initially for a period of one year, renewable subject to an annual performance evaluation. Depending on the candidate, the role may be part-time or full-time.
Beyond running the Foundation, the Executive Director is a representative of the DSF itself. They embody Django's welcoming culture and help the community sustain the framework's home. The Executive Director is often called upon to represent the Foundation publicly, including at Django conferences and events, and to grow awareness of the DSF as an organization, distinct from the framework it supports.
Responsibilities
Executive Director duties include (but are not limited to):
- Fundraising: leading sponsorship development, corporate and individual membership growth, and partner relationships, including support for the jump from our current 300K USD annual fundraising goal to 500K USD. At the current funding level (around 300K per year), a full-time Executive Director isn't yet sustainable. We'd like to fix that, and we want you to lead that change.
- Admin and operations management: day-to-day operations and administration of the DSF, financial reporting, grant management, and the general running of the organization. Over time, helping grow the DSF into a more mature organization by establishing the operational foundations that support the nonprofit's growth.
- Managing the DSF Assistant and Fellows: overseeing the DSF Assistant and the Django Fellows program, the paid maintainers funded by the DSF.
- Marketing and outreach: community outreach and communications, representing the DSF publicly (for example, conference representation), and growing awareness of the Foundation as distinct from the framework.
- Legal, trademark, and follow-ups: overseeing international trademark policy enforcement, creating, signing, and renewing contracts, handling legal correspondence, and the unglamorous administrative follow-through that keeps a 501(c)(3) compliant. First-hand legal knowledge isn't required here; you'll work with counsel.
- Working group check-ins: regular coordination with the DSF working groups, the volunteer committees handling events, AI, accessibility, fundraising, and more.
- Working with our Django events and conferences like our DjangoCons.
Requirements
An Executive Director is responsible for fundraising, operations, communications, and community coordination. This is a broad remit, and it isn't our expectation that you come into the job an expert in every part of it. We hope you'll have solid experience in a few of these areas, particularly the ones most central to the role (fundraising and partnership development, nonprofit operations, and stakeholder communication). A willingness to learn and a demonstrated history of doing so are more important than comprehensive knowledge.
The areas you can expect to work across include (and are not limited to):
- Fundraising, sponsorship, and partnership development
- Nonprofit operations, financial reporting, and grant management
- Contracts, trademark, and 501(c)(3) compliance (in coordination with counsel)
- Public representation, marketing, and communications
- Coordinating staff, volunteers, and working groups
- Technical knowledge is not required, but is a nice-to-have:
- Knowledge of, or familiarity with, the Django and Python community
- Familiarity with open source licenses and communities
And required professional skills such as:
- Conflict resolution
- Time management and prioritization expertise
- Ability to focus in short periods of time and do substantial context switches
- Self-awareness to recognize their own limits and reach out for help
- Relationship-building and coordination with the Board, staff, working groups, sponsors, and external parties
- Tenacity, patience, compassion and empathy are essential
Therefore, a Django Executive Director requires the skills and judgment of an experienced nonprofit leader who is comfortable with fundraising, operations, and coordination with community stakeholders. Open-source experience and familiarity with the Django or Python community in particular are a big plus.
Being part of the Django community isn't a prerequisite for this position. We'll consider applications from anyone with a proven history of nonprofit leadership or comparable experience in an open-source or mission-driven community, but this is a remote position based in the United States, and unfortunately we are not able to offer visa sponsorship for this role.
The DSF is an equal opportunity employer. We welcome applicants of every background and don't discriminate on the basis of race, color, religion, gender, gender identity or expression, sexual orientation, national origin, disability, age, or veteran status.
How to apply
If you're interested in applying for the position, please submit your application via hiring@djangoproject.com. Your application should include:
- A cover letter (optional)
- A resume or CV
- A brief vision statement (500 to 1000 words) addressing your understanding of the Foundation's current position, the key opportunities and challenges you see for the Foundation, and your approach to the role
References may be requested during the interview process.
The compensation for this role is a base salary of $90,000 to $120,000, plus a bonus of up to $60,000 tied to our progress toward the $500,000 fundraising goal, which we'll tier as we work toward it. Depending on the candidate, the DSF will consider a part-time position and adjust the salary accordingly.
Applicants will be evaluated based on the following criteria:
- Relevant nonprofit leadership and operational experience
- Track record in fundraising and partnership development
- Understanding of the position and of the DSF's current stage
- Clarity, formality, and precision of communications
- Familiarity with open source and/or the Django and Python community
- Strength of reference(s)
Applications will be open until midnight Central Time, September 14, 2026, with the expectation that the successful candidate will start around November 1, 2026 (to be confirmed).
Reference: Announcing the Search for a DSF Executive Director (Django Project blog, June 17, 2026).
06 Aug 2026 2:45pm GMT
Planet Twisted
Hynek Schlawack: Production-ready Python Docker Containers with uv
Starting with 0.3.0, Astral's uv brought many great features, including support for cross-platform lock files uv.lock. Together with subsequent fixes, it has become Python's finest workflow tool for my (non-scientific) use cases. Here's how I build production-ready containers, as fast as possible.
06 Aug 2026 12:00am GMT
Planet Python
Hynek Schlawack: Production-ready Python Docker Containers with uv
Starting with 0.3.0, Astral's uv brought many great features, including support for cross-platform lock files uv.lock. Together with subsequent fixes, it has become Python's finest workflow tool for my (non-scientific) use cases. Here's how I build production-ready containers, as fast as possible.
06 Aug 2026 12:00am GMT
05 Aug 2026
Planet Python
Django Weblog: Django 6.1 released
The Django team is happy to announce the release of Django 6.1.
The release notes offer a harmonious mélange of new features and usability improvements. A few highlights are:
-
Model field fetch modes for configuring on-demand fetching behavior
-
Database-level delete options for
ForeignKey.on_delete -
Dictionary-based email settings
You can get Django 6.1 from our downloads page or from the Python Package Index.
The PGP key ID used for this release is Jacob Walls: 131403F4D16D8DC7
With the release of Django 6.1, Django 6.0 has reached the end of mainstream support. The final minor bug fix release, 6.0.8, which was also a security release, was issued yesterday, Aug. 4, 2026. Django 6.0 will receive security and data loss fixes until April 2027. All users are encouraged to upgrade before then to continue receiving fixes for security issues.
See the downloads page for a table of supported versions and the future release schedule.
05 Aug 2026 7:30pm GMT
03 Aug 2026
Django community aggregator: Community blog posts
Running Headscale on my own infra (and finally killing my WireGuard setup)
Hello everyone 👋
This one started in the dumbest possible way: I wanted to check on my 3D printer from outside my house.
I have a Bambu printer running in LAN-only mode, and I use OctoApp to control it from my phone. Works great at home. Useless the moment I leave. The usual answer is OctoEverywhere, which is a lovely project, but it means my printer traffic goes through someone else's servers, and I already run enough infrastructure that this felt silly.
So I went looking for a way to just… have my home network with me. And I ended up rebuilding my entire remote access setup in an afternoon.
What I had before
My old setup was, in hindsight, a bit of a Rube Goldberg machine:
- A Hetzner VPS running WireGuard
- A node at home connected to that WireGuard tunnel
- Nginx Proxy Manager on that node
- A Pi-hole for the WireGuard side, resolving things to
10.0.0.xaddresses - Another Pi-hole for my actual home network, resolving to
192.168.0.x
Two Pi-holes. Two address ranges. Two mental models of "where am I right now". It worked, but every time I added a service I had to think about which side of the tunnel it lived on.
What I actually wanted was much simpler: when I'm away, I want my phone to behave exactly like it's sitting on my home WiFi. Same IPs, same DNS, no difference at all.
That's a mesh VPN, and the nicest one is Tailscale.
I don't trust free
Tailscale is excellent. I want to say that first, because what follows is going to sound like criticism and it isn't. The client is great, the free tier is generous, and for most people it's the right answer.
But every time I look at a free tier this good, I catch myself doing the same mental arithmetic: someone is paying for this, and it isn't me. Tailscale is a venture-funded company. Free tiers built on top of venture funding have a well-documented lifecycle, and the last chapter is rarely "and it stayed free and generous forever". The limits shrink, or a device cap appears that you're already over, or the company gets acquired by someone with different ideas.
I'm not predicting that Tailscale does any of this. I have no reason to think they will. But I built my remote access on a WireGuard tunnel I control, and moving to something where a company I don't control holds the keys to my entire home network felt like a downgrade, even if the software is better.
I've also been burned recently enough that I'm not in a trusting mood. I wrote a whole angry post a few months ago about paying $100 a month for a service and getting less than 24 hours of notice before it changed underneath me. That was a paid tier. If that's what happens when I'm a customer, I'm not going to build my house on a free one.
So: I like the idea, I don't trust the arrangement. Which is exactly the situation self-hosting exists for.
Enter Headscale.
What Headscale actually is
Headscale is an open source implementation of the Tailscale control server. Your devices still run the official Tailscale client, they just point at your server instead of Tailscale's.
The important thing to understand is what the control server does and doesn't do. It handles key exchange, device registration, ACLs, and DNS settings. It does not sit in the middle of your traffic. Once two devices know about each other, they talk directly. So Headscale being a small container on a cheap VPS is completely fine.
I put it on the same Hetzner box that was already running WireGuard, because it already has a public IP and I was going to be deleting the WireGuard side anyway.
The Docker Compose setup
Headscale is CLI-first, which is fine, but I got tired of typing docker exec headscale headscale ... within about four minutes. So I also added Headplane, a web UI for it.
Directory structure first:
mkdir -p ~/headscale/config/{headscale,headplane}
cd ~/headscale
wget -O config/headscale/config.yaml https://raw.githubusercontent.com/juanfont/headscale/main/config-example.yaml
wget -O config/headplane/config.yaml https://raw.githubusercontent.com/tale/headplane/main/config.example.yaml
And the compose file:
services:
headplane:
image: ghcr.io/tale/headplane:latest
container_name: headplane
restart: unless-stopped
ports:
- "3003:3000"
volumes:
- ./config/headplane/config.yaml:/etc/headplane/config.yaml
- ./config/headplane/lib:/var/lib/headplane
# Shared path to the Headscale config. This has to match
# `headscale.config_path` in the Headplane config.
- ./config/headscale/config.yaml:/etc/headscale/config.yaml
- /var/run/docker.sock:/var/run/docker.sock:ro
headscale:
image: headscale/headscale:latest
container_name: headscale
restart: unless-stopped
command: serve
labels:
# Absolutely necessary for Headplane to find Headscale.
me.tale.headplane.target: headscale
ports:
- "8083:8080"
volumes:
# Same host path in both containers. This matters.
- ./config/headscale/config.yaml:/etc/headscale/config.yaml
- ./config/headscale/lib:/var/lib/headscale
Nginx handles TLS and public exposure, same as every other service on that box. If you're not putting a firewall in front of these, bind the ports to 127.0.0.1 instead ("127.0.0.1:8083:8080"), otherwise Docker happily opens them to the internet and walks around UFW while it's at it.
Note the me.tale.headplane.target label on the Headscale container. That's how Headplane finds it, and it's how Headplane restarts Headscale when you change DNS settings from the UI. Note also that both containers mount the Headscale config from the same host path. Headplane reads it directly, so if the paths drift you get a UI that shows you stale settings.
Headscale config
The parts of config/headscale/config.yaml that matter:
server_url: https://headscale.example.com
listen_addr: 0.0.0.0:8080
metrics_listen_addr: 127.0.0.1:9090
database:
type: sqlite3
sqlite:
path: /var/lib/headscale/db.sqlite
dns:
magic_dns: true
base_domain: ts.example.com
override_local_dns: true
nameservers:
global:
- 1.1.1.1
Leave nameservers.global pointing at a public resolver for now. We'll swap it for the Pi-hole later, once the Pi-hole has joined the network and has an address to point at.
base_domain has to be different from your server_url domain, otherwise Headscale refuses to start.
Headplane config
In config/headplane/config.yaml:
server:
host: "0.0.0.0"
port: 3000
base_url: "https://headplane.example.com"
cookie_secret: "<32 char random string>"
cookie_secure: true
data_path: "/var/lib/headplane"
headscale:
url: "http://headscale:8080"
config_path: "/etc/headscale/config.yaml"
api_key: ""
Generate the cookie secret with openssl rand -hex 32.
The url is the internal Docker address: container name, container port. Not whatever port you mapped on the host. The two containers talk over the compose network.
Then start Headscale on its own, create a user and an API key:
docker compose up -d headscale
docker exec headscale headscale users create myuser
docker exec headscale headscale apikeys create --expiration 90d
Paste that key into api_key and bring up Headplane. The key is only shown once, so if you lose it, just make another one. apikeys list only shows the prefix.
Nginx
Two vhosts, nothing exotic. But the Headscale one needs WebSocket passthrough, and this is the first place I lost time (more on that below):
location / {
proxy_pass http://127.0.0.1:8083;
proxy_http_version 1.1;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $connection_upgrade;
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Forwarded-Proto https;
proxy_redirect off;
proxy_read_timeout 5m;
}
Headplane is a boring proxy block, no special headers needed. Certbot for certs, as usual.
Pi-hole as the tailnet DNS
This is my favourite part of the whole setup, and the reason I bothered.
Join your home Pi-hole to the network like any other device:
curl -fsSL https://tailscale.com/install.sh | sh
sudo tailscale up --login-server https://headscale.example.com
tailscale ip -4
Take that 100.x.x.x address, put it in Headscale's dns.nameservers.global, and restart the container.
Now every device on the tailnet uses my home Pi-hole for all DNS. Which means:
sonarr.example.casaand every other internal domain resolves from anywhere, because Pi-hole's Local DNS Records answer for them- My phone gets Pi-hole ad blocking on cellular data, not just at home
Neither of those is new, I had both with the old WireGuard setup. The difference is that I now get them from one Pi-hole instead of two, and I didn't have to think about it. The Pi-hole joined the tailnet, I pointed Headscale at it, and every device I add from now on inherits both for free.
override_local_dns: true is what forces this. Without it, your phone keeps using whatever DNS the carrier hands it, the tunnel works fine, and your internal domains silently fail to resolve.
The subnet router
A mesh VPN only connects the devices that are on it. My printer can't run a Tailscale client. Neither can most of the stuff I care about.
The fix is a subnet router: one device on your LAN that advertises the whole network to everyone else. I used the Pi-hole box, since it was already joined:
sudo tailscale up --login-server https://headscale.example.com \
--advertise-routes=192.168.0.0/24
Two things have to happen after this or nothing works, and I hit both.
First, IP forwarding:
echo 'net.ipv4.ip_forward = 1' | sudo tee -a /etc/sysctl.d/99-tailscale.conf
echo 'net.ipv6.conf.all.forwarding = 1' | sudo tee -a /etc/sysctl.d/99-tailscale.conf
sudo sysctl -p /etc/sysctl.d/99-tailscale.conf
Second, and this is the one that got me: routes are double opt-in. The node advertises, and then the control server has to approve. Advertising alone does nothing.
docker exec headscale headscale nodes list-routes
docker exec headscale headscale nodes approve-routes --identifier 1 --routes 192.168.0.0/24
The moment I ran that second command, everything worked. Printer, internal domains, random machines on my LAN, all reachable from my phone on cellular.
Clients
You don't need a special app. The official Tailscale clients all support custom control servers.
On Android, install from Play Store or F-Droid, go to "Accounts", tap the kebab menu, and select "Use an alternate server". Enter your Headscale URL and log in.
On iOS it's slightly more buried: install from the App Store, tap the account icon, "Log in", then use the options menu to pick "Use custom coordination server".
macOS is the one with a real trap. Use the standalone build, not the Mac App Store one, because the App Store version is sandboxed and fights you on custom control servers. brew install tailscale works, or grab the standalone package from Tailscale's download page. Then:
sudo tailscale up --login-server https://headscale.example.com
tailscale set --accept-routes
That --accept-routes is easy to forget. Without it the client joins fine and then can't see anything on your LAN.
The gotchas that cost me the most time
Four things ate most of my afternoon. All of them had error messages that pointed somewhere other than the actual problem.
Headscale listening on 127.0.0.1 inside its own container
Headplane kept logging this:
Error while validating API key: [object Object]
I regenerated that API key three times. The key was fine. The real problem was one line above in the Headscale logs:
INF listening and serving HTTP on: 127.0.0.1:8080
127.0.0.1 inside a container means that container only. Not the compose network, not the other container sitting right next to it. Headplane literally could not open a connection, so it couldn't validate anything, and reported that as an auth failure.
Set listen_addr: 0.0.0.0:8080 in the Headscale config. Your host port mapping is a separate concern and keeps working the same.
Nginx eating the WebSocket upgrade
Clients wouldn't connect, and Headscale said:
WRN no upgrade header in TS2021 request. If headscale is behind a reverse proxy,
make sure it is configured to pass WebSockets through.
At least this error tells you exactly what's wrong. My nginx config was a copy-paste of the same reverse proxy block I use for every other service on that box, which has no Upgrade handling because nothing else needs it.
The commonly recommended fix uses a map block in the http context:
map $http_upgrade $connection_upgrade {
default upgrade;
'' close;
}
The idea is that plain HTTP requests to the same vhost get Connection: close instead of a bogus Connection: upgrade. If you put that map somewhere nginx doesn't load it into the http context, you get unknown "connection_upgrade" variable and nothing starts.
I'll be honest: I got that error, commented the Connection line out entirely to keep moving, and it worked. It's on my list to go back and do properly, because "it worked when I removed the correctness" is not a state I enjoy leaving things in.
The routes CLI moved
Every guide and blog post out there tells you to run:
headscale routes list
headscale routes enable -r 1
On 0.29 that gives you unknown command "routes". It's now under nodes:
headscale nodes list-routes
headscale nodes approve-routes --identifier 1 --routes 192.168.0.0/24
Related: --user wants a numeric ID now, not a username. headscale users list gives you the number.
When in doubt, headscale --help is more current than anything you'll find in a search result. I lost a few minutes to blog posts written against older versions before I just asked the binary.
Pi-hole ignoring the tailnet
Tunnel up, subnet routes approved, ping 1.1.1.1 working, and google.com resolving to nothing.
Pi-hole's default interface listening behaviour doesn't answer queries arriving on the tailscale0 interface. Settings, then DNS, then set it to listen on all interfaces and permit all origins. Or scope it to the tailnet CIDR if you want to be tidier about it than I was.
The bash alias
Small thing, big quality of life improvement:
alias headscale='docker exec headscale headscale'
Now every command in every tutorial you find online works verbatim, without mentally prefixing the docker part every time. Just remember it's there if you ever install the real binary on the same box, or you'll spend an entertaining twenty minutes wondering why the local install ignores your config file.
What I still need to do
I'm not going to pretend this is finished.
The old WireGuard tunnel and its dedicated Pi-hole are still running. Headscale does everything they did, but I'm leaving them up until I've gone through the Nginx Proxy Manager config and confirmed nothing still points at a 10.0.0.x address. Deleting infrastructure at 2 AM after a successful afternoon is how you learn what depended on it.
Was it worth it?
Yes, and for reasons beyond the printer.
The setup collapsed two Pi-holes and two IP ranges into one mental model. My phone now behaves identically at home and on cellular. Every internal service I add from now on works remotely for free, without port forwarding or a new proxy host or any thought about which side of a tunnel it lives on.
And no ports are open to my LAN. The only publicly reachable thing is the Headscale control server, which hands out keys and doesn't carry traffic.
The total cost was one afternoon, most of which was spent on four error messages that pointed at the wrong thing. Hopefully this post saves you those.
If you want to check the printer thing specifically: Bambu printers in LAN-only mode expose MQTT on 8883 and FTPS on 990. There's no web UI, so don't waste time typing the IP into a browser like I did. Once you're on the tailnet, OctoApp just takes the same local IP and access code you use at home.
See you in the next one!
03 Aug 2026 5:00am GMT
31 Jul 2026
Django community aggregator: Community blog posts
Chicago Like a Local: Things to Do During DjangoCon US 2026 (Part 1)
DjangoCon US 2026 is coming back to Chicago from August 23-28, and we at Caktus couldn't be more thrilled, not only to be returning as a Silver Sponsor, but also because I (Keanya) have the privilege of chairing the conference once again!
31 Jul 2026 11:35pm GMT
23 Jun 2026
Planet Twisted
Glyph Lefkowitz: Adversarial Communication
As I have discussed in previous posts, "AIs" can make mistakes. In fact, they do make mistakes, and their mistake-making patterns are such that where and how they will make mistakes is both uncertain and constantly changing.
Thus, in any scenario where you want to attempt to make "productive" use of "AI", you must have a system in place for checking every result. Not checking some results; checking every result. If each result might have a consequence for you (and if it didn't have a consequence, why bother automating it?) and you cannot predict in advance which kinds of results will need verification, then verification is always required.
The verification often ends up being just as expensive as doing the work in the first place, which means that if you want your usage of "AI" to be personally profitable, you have to find someone else to externalize the cost of verification onto. This person becomes your adversary, and, if you are successful, your "AI's" victim.
The Ladder-Climber And Their Reverse-Centaur Rungs
One way that this constellation of facts can straightforwardly assemble themselves into a dystopian nightmare is the phenomenon, described by Cory Doctorow, of the reverse centaur. This is when your employer non-consensually turns you into the verification system. The "AI" does the fun part of initially performing the work, and then you do the boring part where you check if the robot is right and clean up its messes, even if everyone already knows that it would, in aggregate, be cheaper for you to do the work in the first place.
Reverse centaurs can be made from any automation, not only "AI" automation. I think that there is a reason that this term happens to have emerged in the "age of AI", though, and not with earlier automation technologies (even those which were considerably more viscerally horrific). That reason is: the wrongness of "AI" output is not merely a technical feature that must be compensated for, it is a generalized externality.
As I mentioned above, if you are responsible for the entirety of the work, both extruding the "AI" output and checking it, it's usually cheaper to have humans do the entirety of the work to begin with. When humans do the writing directly, we can check as we go, and thus verification doesn't need to be as comprehensive.
When "AI" coding advocates say "code review is the bottleneck", what they are observing is that the LLM is still rolling the dice for each PR, and a human is still necessary to verify that each of those rolls is a winner. But calling this process "code review" is a bit of a misnomer; it's not really "code review" in the traditional sense, it's human understanding.
Before the advent of "AI", the human understanding was implicit in the process of writing the code in the first place1, and the code review was a way of diffusing and extending that understanding. Now that the code can be authored with no initial understanding taking place, that cost has not gone away, it has moved.
Human understanding was always the bottleneck.
However, this is taking a collaborative view of a software project, where satisfying the needs and solving the problems of your customers are the goals. We can see that "AI" is a bad tool to satisfy those goals, because all it's doing is converting the first half of the work, that of understanding the code as you write it, to understanding the agent's output as you read it.
What if, instead, we were to take the view that every software company is a Hobbesian nightmare, red in tooth and claw? In this view, the only goal of a software project is for the individual developers to make their promo cycles and get their bonuses. Given that there is only a certain amount of money to go around, this is a zero-sum game where each programmer wants to look more productive than their colleagues.
Pretty much every organization finds it easy to reward "productivity" as expressed by lines of code emitted, but the benefits of doing thorough and thoughtful design, analysis, and code review very difficult to reward. In this world, an LLM is an invaluable tool for the sociopathic ladder-climber, particularly if your legacy organization is still structuring their workflows as if the person prompting the bot is "writing" the code, and then they get to foist off the act of "reviewing" the code onto someone else.
Here, the prompter effectively externalizes the cost of the LLM's failures but internalizes any benefits. The prompter will vibe-code a big feature, so large that the assigned reviewer can't possibly comprehend it all effectively. When this happens, the reviewer will, eventually, be pressured to approve it, even if they can try to spot a few problems along the way. The reviewer has their own work to get back to, after all, the obligation to review the prompter's (read: the bot's) code is a drain on their time that they are not going to get rewarded for.
If this feature is a big success, the prompter gets a promotion. If it causes a big issue, well, the reviewer must not have been careful enough.
This is why LLMs are "good for coding", and also why their biggest promoters keep having outages.
The Generative Gish Galloper
Coding is the biggest "success story" of this type of adversarial communication, but it is by far not the only instance of such a thing. LLMs create a new form of leverage that can turn Brandolini's law from a linear advantage into an exponential one. If you are engaged in a political debate where you want to overwhelm the other side in nonsense, an LLM can generate bullshit faster than it is physically possible for a human being to type, let alone respond thoughtfully. There is an asymmetry to the utility of this weapon as well: only one side of the political spectrum wants to flood the zone and destroy trust in institutions and the concept of truth. There's a good reason that the fascists love it.
Straightforward Spam and Fraud
This is kind of obvious, but LLMs can generate lightly-customized, plausible-looking text much more quickly than any human being. This facilitates their use in fraud, spam, and scams. In a spamming or fraudulent interaction, once again, the costs are externalized onto the victim: the recipient of a spam message has to do all the work of "checking" the LLM's output. Spammers already expect very low hit rates from boilerplate, and if the LLM can increase those percentages from 1% to 5% the technology will pay for itself; they don't need anything like reliable accuracy.
Customer "Support"
If you have any kind of commercial relationship with a company, I probably don't even need to mention this: customer "support" bots are a misery. Everybody knows it at this point. But customer support is usually conceptualized by businesses as an adversarial interaction, because it is a cost center. They maintain internal metrics on time-to-resolution and try to optimize them. Implicitly, this creates a dynamic where the goal of the customer service agent's job is not to solve your problem, but to emit noise that will cause you to think your problem is resolved, or to give up, as fast as possible. Unsurprisingly, LLMs can emit this noise faster than humans can, getting those customers off the phone. But those customers will remember those interactions, and the story outside the TTR metrics is horrible.
Similarly to the situation in software development, LLMs can look very good on paper for customer support, but mostly what they are doing is illuminating the problems with the industry's existing metrics, by turning "winning the metrics battle against the customer" into a more obvious and immediate defeat for the company's long term reputation.
"Education"
In 2026 it is sadly a fact of life that students cheat all the time using "AI", and that this cheating is very successful, in that the teachers find it very hard to detect.
LLMs are great for cheating on schoolwork because the student is externalizing the work of the checking onto the teachers, who are often starting at a disadvantage to begin with, at least in the US.
My view is that this is happening because of a divergence in the way that students vs. teachers (or, more accurately, "the broader educational system") view grading.
When a student is asked to write an essay, the teachers see the effort as both intrinsically worthwhile for the student, as well as useful as a pedagogical tool to evaluate and react to the student's progress. The student, by contrast, sees a stumbling block designed to knock them off the path to success and into a permanent underclass. It is no wonder that the student sees "AI" as useful to their own goals and has no compunction about deploying it.
There is a bitter irony that the ability to understand the inherent value of actually writing the essay on their own is the sort of thing that students can really only learn by writing a bunch of essays. There's no way that I can think of which makes the benefit legible as long as a shortcut is available.
The net effect here is a downward spiral, where the already-wobbling educational system is sustaining an attack that it doesn't have the resources to recover from. The individual students' attacks against their teachers and their schools' grading systems might appear to momentarily succeed, but they will win the battle and lose the war.
Spamming "For Good"?
Usually when we talk about someone unilaterally choosing to enter into an adversarial relationship, that's an "attack" and for good reasons we have a negative impression of the attacker. However, I would be remiss if I did not point out that there are some cases where the relationship was already adversarial; just because you're the attacker doesn't mean that you are evil.
For example we might imagine use-cases like automatically filing appeals for prior authorizations against health insurance. It's relatively well-known at this point that the main way for-profit insurers maintain their margins is by denying claims right up to the line of the policies themselves being fraud, so using a spamming tool to fight them might be entirely justifiable2 in that case.
Similarly, using an LLM could be justified in a fight against a company refusing to honor a warranty. One could imagine using an LLM to immediately generate replies and escalations.
However, even in imagined cases like these, the underlying problem is that the insurers and the vendors already have a tremendous amount of structural power, so it is more likely that they will have the advantage in deploying a communications weapon like an LLM, as well as enacting policies to simply ignore any LLM-based communication that you might submit. Worse, if these strategies were to become widespread, they might provide an excuse to reject any communications by feeding them into an unreliable "LLM detector" and issuing an automated "computer says no" even to hand-written correspondence.
It is also worth stressing that these cases are imagined, as compared to the very real coworker-abuse, spam, scam, fraud, and disinformation campaigns being waged in real life today.
Therefore, while legitimate uses might exist, it's hard to imagine that there's anywhere they would be genuinely valuable and sustainable. In the best case "AI" will provide a temporary advantage for underdogs that will provoke an arms race which the resource-advantaged adversaries will win in the long run, in the worst case the arms race itself will cement permanent structural change that will make things worse.
"Search" By Stealing
Most of the adversarial utility of "AI" is on the "write" side, since write-amplification is more obviously aggressive than reading. But the "read" side of LLMs - summarization and question-answering - can be a form of attack as well.
To begin with, the act of reading itself is currently enormously destructive, but that's arguably not a fundamental aspect of this technology. They could set reasonable rate-limits and respect things like robots.txt, as search engines have for decades now. They could also refrain from committing criminal levels of copyright infringement. But, today, using "AI" tools does suborn this sort of out-of-control crawling.
More insidiously, consider the scenario described in this YouTube video. The LTT Bros decided to try Linux again, and in the course of so doing, they had problems. When trying to solve these problems, they were faced with a choice: they could consult Reddit, or they could ask an LLM. Asking an LLM would "gaslight the heck out of" them, but they still found it preferable, because they would at least get an answer without getting yelled at.
Initially this sounds great. But it also means that you want to extract knowledge from a community, while mechanically eliding any values or norms that the community may want to impart as part of offering that knowledge. As someone who spent many years in a community tech support role, this is worrying. Many requests for support are people asking how to do things that will momentarily solve a superficial problem but create a long-term reliability problem or even an immediate security risk, that the question-asker doesn't want to hear about. Consider the question "I'm tired of entering my password so much, how do I make it so my laptop unlocks automatically". An obsequious chatbot will helpfully tell you how to do this without pushback.
But, this is also a sort of ethically murky area. The Linux community is somewhat famously, for many years now, a toxic cesspool of general hostility, misogyny, etc. It is certainly a good thing that people can get access to this knowledge without subjecting themselves to abuse. But it also means that the people with the power and the privilege to change the community for the better can just quietly withdraw, rather than fixing the problems. It also means that the positive elements of culture cannot be transmitted, and people will have no opportunity to learn about unknown unknowns.
In this case, the "adversarial" communication is with society. The thing that using an LLM for search lets you do is withdraw from society and avoid forming any personal connections. There are some personal connections which are painful and annoying, and so that can feel like a momentary balm. But the need to make connections in general is, like, the concept of society itself.
Who Am I Hurting?
LLMs are good at adversarial communication. They are so good at it, relative to their other benefits, that they will tend to make communications adversarial if you are not remaining vigilant about the possibility that it might do so. My request to you, dear reader, if you are going to use such tools, is to always ask yourself, "who might I be hurting, if I use an LLM for this?"
If you're using an "AI", who is its adversary? If you haven't given it one yet, who might the "AI" turn into an adversary? Who might you overwhelm with an asymmetric amount of output, or, if you're receiving information and not sending it, who are you taking that information from without consulting?
Figure out the answers to these questions and conduct yourself accordingly; the answer might be "yourself".
Acknowledgments
Thank you to my patrons who are supporting my writing on this blog. If you like what you've read here and you'd like to read more of it, or you'd like to support my various open-source endeavors, you can support my work as a sponsor!
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One of the reasons that software developers tend to prefer greenfield development is that when you are given a blank page, you can project your own specific understanding onto it. You can structure the codebase in a way that works for your brain, down to the variable naming conventions and the module layouts. LLM-assisted development makes everything into instant brownfield work, which makes developers instantly miserable; even those who are excited about the technology will frequently complain about how it feels like their agency has been stolen and their joy in the work has been diminished. But I digress. ↩
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Modulo the massive amount of other externalities involved in using LLMs, of course, but I don't have the time or energy to get into those here. ↩
23 Jun 2026 8:06pm GMT
09 Jun 2026
Planet Twisted
Hynek Schlawack: How to Ditch Codecov for Python Projects
Codecov's unreliability breaking CI on my open source projects has been a constant source of frustration for me for years. I have found a way to enforce coverage over a whole GitHub Actions build matrix that doesn't rely on third-party services.
09 Jun 2026 12:00am GMT

