one month ago, i wrote about pivoting from freelance development to AI-powered bug bounty hunting. i started with “valid duplicates.” here’s where the rig stands today.

From Duplicates to CVEs

CVE-2026-1190 (KeyCloak)

my agents found a vulnerability in KeyCloak. not a duplicate this time—a valid CVE assigned. the discovery was fully automated; the agents identified the weak point and i verified the exploit path. if you’re running KeyCloak, patch it.

ClickHouse - Critical Bug Fix

i found a permission bypass in ClickHouse’s BACKUP/RESTORE operations that allowed:

1. RESTORE execution in readonly mode (it modifies data—this should never have been possible)
2. The internal setting being exploited in initial queries (meant only for ON CLUSTER secondary queries)
3. Bypassing BACKUP permission checks by establishing S3/remote connections before access verification

the fix (PR #94617) was merged as a critical bug fix impacting RBAC. my report closed clickhouse-private#45780. this is what happens when you point AI agents at database source code—they find logic errors humans miss during code review.

Opera.com SRV Record Takeover

my rig identified dangling SRV records pointing to e1.viju.vc. two records:

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_sip._tcp.opera.com.    86400   IN  SRV 0 0 5060 e1.viju.vc.
_sips._tcp.opera.com.   86400   IN  SRV 0 0 5061 e1.viju.vc.

RFC 3263 compliant SIP clients connecting to opera.com would resolve to attacker-controlled infrastructure. the agents detected it; i registered the domain manually (for obvious reasons) and reported it. this is the kind of legacy infrastructure vulnerability that automated recon excels at finding.

The Rankings

i’m now #8 in Germany on HackerOne (VDP + BBP combined) and #2 for VDP alone (leaderboard). the path to #1? two of my 3 subdomain takeovers currently in triage—one of which is the 400+ domain campaign. that one is already completed, but triage on that scale takes time.

Financial Reality Check

the rig is not profitable yet.

i’ve spent more than i’ve earned. why? strategy. i’ve focused heavily on VDP programs—no payouts, but easier attack surfaces for building reputation and getting those crucial private program invites. the leaderboard position (#2 VDP in Germany) is just a nice side effect. subdomain takeovers also have real costs (domain registrations, infrastructure), and i spent the first two weeks of january perfecting the automation.

this is a reputation-first, profit-later play. private programs and Bug Bounty programs pay. VDPs don’t. but VDPs aren’t farmed as heavily as BBPs since there’s no money in it, making them easier surfaces to find bugs on. you need the reputation to get the invites—and it’s working: i’ve received 30+ private invites across HackerOne, Bugcrowd, YesWeHack, and HackenProof. smart contracts? i’m already tackling them—but it’s really fucking hard.

What’s Changed in the Rig

the multi-stage, phase-based workflow (Recon → Exploit → Verify) is now locked in. the early “invalid” reports—the AI hallucinations—are gone. current workflow:

1. agents recon and identify targets
2. specialized exploit agents probe for specific vulnerability classes
3. verification agents independently confirm findings before i see them

this reduces false positives to near zero. when a report hits my queue, it’s already been validated by a second AI agent.

The Claude Divorce

i completely ditched Claude Code (and my $200/month Max subscription). why? Anthropic banned me for using OpenCode. i’m not even appealing it—they’ve been nerfing their models too hard over the past 6 months anyway. it’s a constant up and down of model quality that i don’t want to partake in anymore.

my new stack:

• OpenAI Max subscription for the heavy lifting
• Chinese models via OpenRouter using the only parameter to whitelist US providers:
  • Kimi K2.5
  • DeepSeek V3.2
  • MiniMax 2.1

quality-wise? roughly between Sonnet 4.5 and Opus 4.5 depending on the task and model. and the pricing? about 1/100 of Anthropic costs. for $200, i get heavily subsidized access. even the US providers on OpenRouter somehow match those chinese prices.

The Bottom Line

findings driving these rankings are AI-generated. i’m not manually running sqlmap or crawling JavaScript. i steer. i decide which targets to prioritize. i handle the manual steps that require human judgment (like domain registration for takeovers). but the discovery? that’s the machines.

the pivot is working. the rig is producing. and we’re just getting started.

I am currently open for contractual engagements.

if you want me to point this system at your infrastructure before someone else does, get in touch.

your choice.

Let’s be real for a second. The project market for freelance developers in Germany right now? It’s basically dead. The economy is weird, budgets are frozen, and everyone is sitting on their hands.

But i’m not the type to sit around.

For those who don’t know my history, i was deep in the blackhat community back in the early 2000s (until about 2005). I’m not talking about running scripts i found on a forum; i’m talking about deep research. My most notable pwns from that era have never been discovered to this day, and i intend to keep it that way.

Fast forward to 2025. I’ve spent the last few years building high-end AI products and consulting. I realized i had two things: a deep understanding of offensive security from my youth, and a mastery of the modern AI stack.

So, i pivoted. I built a war machine.

Meet the Rig

I didn’t just write a python script that wraps Nmap. I built a fully distributed, AI-orchestrated bug bounty automation platform running on NixOS.

It’s a beast that runs while i sleep.

The Architecture

At its core, it is a distributed system managed by a central orchestrator (kistel) and a fleet of execution nodes (pwn1 through pwn4).

• Infrastructure: Everything is defined in NixOS flakes and deployed via deploy-rs. If a node gets tainted or i need more compute, i just spin it up, and the state configuration handles the rest.
• The Brain: A custom Go 1.25 backend backed by PostgreSQL 17 (sharding across 32 distinct table structures).
• The Muscle: A fleet of VMs loaded with 40+ standard security tools (Nuclei, Ffuf, SQLMap, etc.), but—and this is the key—driven by AI agents.

The AI Advantage: Multi-Model Orchestration

Most people trying to use AI for hacking just paste code into ChatGPT and ask “is this vulnerable?”. That’s amateur hour.

My system uses a Multi-LLM Orchestration layer. I use CLIProxyAPI to bundle all my various subscriptions behind a single Claude Code compatible API. This allows the orchestrator to dynamically route tasks to the model best suited for the job:

• Claude (Opus/Sonnet 4.5): Handles the high-level logic, exploit chaining, and “creative” thinking.
• MiniMax M2 & Kimi K2: My daily workhorses for context-heavy tasks (like dumping entire repo structures or massive JS files).
• GPT-5.2 Codex: Used strictly for generating exploit payloads and proof-of-concept scripts.

It’s Not a Script, It’s an Organization

I designed the system to function like a cyber-crime syndicate, but automated.

1. The Agent System (42+ Agents)
I have specialized agents for different domains. I don’t just have a “hacker bot.” I have:

• Recon Agents: Web discovery, deep GitHub dorking, subdomain enumeration.
• Exploit Agents: Specialized in XSS, SSRF, SQLi, GraphQL, and even CI/CD supply chain attacks.
• Blockchain Agents: Deep analysis of Move language contracts (Sui/Aptos), detecting specific vulnerabilities like Hot Potato pattern violations, Shared Object DoS, Flash Loan logic errors, and simulating MEV sandwich attacks.
• Apple Silicon Agents: Specialized in XNU kernel exploitation, PAC/Canary intersection analysis, and BlastDoor sandbox escapes.
• Platform Agents: Specialized agents for Android, iOS, Chrome V8 engine analysis.

2. The Skills System (100+ Skills)
The agents are equipped with “skills”—modular capabilities that they can activate on demand. Need to bypass a specific JWT implementation? There is a skill for that. Need to race a database transaction? The agent activates the RaceCondition skill and executes.

3. Automated Workflow
The system automatically syncs programs from HackerOne, Bugcrowd, Intigriti, YesWeHack, and Hackenproof. I’ve also manually imported targets from Google VRP and Apple Security Bounty, since they don’t operate as traditional bounty platforms.

1. Ingest: It pulls the scope.
2. Plan: LLMs analyze the scope and group targets based on technology stacks.
3. Execute: bounty-cli triggers the campaign.
4. Chain: If an agent finds a low-severity bug, it pings the Orchestrator, which might spin up a different agent to try and chain it into a critical.
5. Submit: The system auto-formats the report, writes the reproduction steps, and even handles the initial triage Q&A via an auto-answer service.

Successes (and Failures) so far

During the first week of operating the v1 prototype of this rig, i managed to report valid duplicates. While “duplicate” might sound bad to some, to me, it was pure gold. It validated my entire idea. It proved that my AI agents were finding the same bugs as human hunters, just faster and while i was asleep.

Currently, 2 of my issues are in active triage and looking good.

Of course, it wasn’t all smooth sailing. Early on, i had a couple of “Invalid” reports—classic AI hallucinations where the model thought it saw a vulnerability that wasn’t there. That failure was actually the catalyst for refining my approach into the multi-stage, phase-based workflow (Recon -> Exploit -> Verify) i use now.

I also still have a bunch of reports on HackerOne that are sitting in “Unread” or “No-Reply” limbo. In two specific cases, the triager promised to reopen the ticket if i proved X. I proved X in both cases. I haven’t heard back yet.

But honestly? Coming from doing blackhat hacking 20-odd years ago, to playing CTFs for fun, and now transitioning to legitimate Bug Bounties is quite the journey. Even if it hasn’t yielded massive financial success yet, the validation is there. The machine works.

I am currently open for contractual engagements.

If you want this rig pointed at your infrastructure to find the holes before a state actor or a ransomware gang does, contact me. You can hire a consultancy that throws a junior pentester at your API for a week, or you can hire me and my army of 42+ AI agents.

Your choice.

Happy New Year.

Over the last few years, i’ve immersed myself completely in the AI landscape. I’m not just talking about playing with ChatGPT for fun; i’m talking about high-level consulting, training and running my own models in my own racks here in Nuremberg, Germany, and building scalable services on top of this emerging tech.

Through all of this, if there is one lesson i’ve learned that i need to hammer home, it’s this: it’s by far not all great, but we are also far from the “doom and gloom” scenario that a lot of conservative IT guys make it seem.

The “All-Knowing Oracle” Fallacy

The single biggest issue i see in the industry right now is professionals treating AI as an all-knowing oracle. It is not. It is a probabilistic engine with a hard training cutoff.

If you are asking it for recent frameworks, zero-day exploits, or bleeding-edge library changes without deep research or web search capability enabled, you will get old or wrong information. That is not the AI being “stupid” or “useless”—that is you using the tool incorrectly.

You wouldn’t use a hammer to drive in a screw and then complain that the hammer is broken.

It’s a Tool, Not a Replacement

This technology is nothing more than a new, incredibly powerful tool in your tool belt. It is not a god-tool that solves architecture problems by magic. But, if you take the time to actually master the AI CLI tools, you can command them to control your system with ease or develop boilerplate and logic faster than you can physically type.

The barrier to entry here is the willingness to learn. Learn the specific quirks of the models, learn how to prompt correctly, and mastery will follow.

The Cost of Mastery (and how to mitigate it)

I hear the complaints constantly: “I’m not paying $20 for ChatGPT” or “$200 for the good Claude plan is too much.”

Look, if you want professional results, you need professional tools. However, i know the subscriptions add up, especially when you need access to the absolute top-tier context windows.

Here is a hot tip for the budget-conscious:

You can grab legitimate accounts on marketplaces like G2A for a fraction of the enterprise price.

• ChatGPT Pro 1 Month
• Google AI Ultra 1 Month

We are talking about a $200 subscription tier for roughly ~$20.
Disclaimer: You are buying a pre-provisioned account, not upgrading your personal one. Just make sure you understand the trade-offs. No account sharing, but it’s a new login every month.

My Current “Daily Driver” Loadout

The landscape changes weekly, but as of late 2025, this is what i am actually running in production:

1. Context Heavy Lifting:
MiniMax Plus/Max ($20/$50). This has been my daily workhorse for anything requiring massive context. When you need to dump an entire repository into the prompt to refactor a legacy module, this is currently the king.

2. Integration & Logic:
Google Gemini CLI & Gemini 3 Pro. These past couple of days, this has been a standout performer. It handles system integration and logical reasoning surprisingly well, often beating out the others on complex instruction following.

3. The “Classic” Options:
I still maintain subscriptions for Claude Code and OpenAI Codex, but it feels like they are constantly playing games with their user base. They release a ground-breaking model, get everyone hooked, and then silently nerf it to save on backend compute costs once scaling becomes an issue.

I find myself constantly toggling between max subscriptions for both. It’s an endless cycle of GPT 5, 5.1, 5.2, followed by Claude retorting with Sonnet 4.5, Opus 4.5, and Haiku 4.5. The quality fluctuates wildly, so you need to be agile and willing to switch providers.

(Side note: It is genuinely sad that Deepseek and Kimi don’t offer a dedicated code subscription yet. I’d jump on that in a heartbeat.)

A Real World Case Study: The GitLab Upgrade

To prove this isn’t just theoretical, here is a recent win. I successfully used Claude Code to upgrade a self-hosted GitLab instance from v15 to v17.

If you’ve ever managed GitLab, you know the upgrade paths are treacherous. This instance was running on Kubernetes, adding a massive layer of complexity. We even hit a snag where the PostgreSQL version requirements changed mid-stream; i was running an old v13 instance from a time when pg_upgrade automation wasn’t standard yet (though it is now), and it handled that transition flawlessly too.

It took about a day. I didn’t just tell the AI “upgrade this”—that would be suicide. I commanded it to perform backups. I had it verify migration paths. I had it check for deprecations. The AI was the hands, but i was the supervisor.

The result? Zero data loss. Full upgrade completed in about 24 hours. A human team debugging the Helm charts, migration failures, and PostgreSQL schema changes would have likely taken three times as long.

The Golden Rule: Context and Verification

Here is the trick that makes this viable in production: always prompt the AI to verify what it just did after every command.

Do not just “rawdog” a sequence of 20 commands blindly and then discover “whoopsie, i deleted your prod db.” You need to be explicit. Tell it: “This IS the production deployment. Be careful.”

It can be a tremendously effective engineer and problem solver, but only if you let it in on the details.

If you assume it should know that prd5.xyz.gcp... is your production environment, ask yourself: would a trainee or a fresh new hire understand that context immediately? Probably not.

So treat the AI like that new hire. Give it the full context. Tell it the stakes. And force it to verify its own work step-by-step.

The Plumber Analogy

This brings me to the most critical point: Do not use AI for tasks where you cannot judge the output.

It is basically a super-fast typewriter for your ideas. Use it to accelerate your workflow.

However, if you veer off into territory you don’t understand—if you ask it to write kernel modules when you don’t know C, or manage a Kubernetes cluster when you don’t understand pods—you will end up with a destroyed home directory or a compromised production database.

If used correctly, it is a humongous multiplier for any IT professional.
If used incorrectly, it is a liability.

Think of it like a plumber. If a plumber doesn’t know their tools and floods a client’s bathroom, it isn’t the wrench’s fault. It’s the plumber’s fault.

It’s your fault.

If you’re in IT (otherwise you probably would not be reading this), your sole job is to constantly learn new tools and emerging things. If you’re not doing that, then maybe IT isn’t for you?

In any case: this is a new tool and most of us should at least learn how to use it. It will make a massive impact on how much time you actually spend solving problems versus solving the “problems around your actual problems.” We all know the drill: you just wanted to add a user, but some certificate expired, then some policy needed updating from the last version, and suddenly it’s 3 hours later.

Why not just have the AI do that in the background? Send it off, fetch yourself a coffee, call someone, or clear a few 5-minute tasks from your list. The amount of times per day where i can knock out small tasks because the big stuff—the stuff that usually requires huge amounts of focus—now only requires me to review, steer, and manage, is life-changing.

I’ve basically become a manager and reviewer of 10 virtual me’s. By now, i’ve got them almost behaving like me and doing things exactly like i’d do them. Make use of this. Use it to free up your day for other things like playing video games, going outside, or spending time with your pets—or go full 100x engineer and be your own company of 30 (10 virtual employees working round the clock is basically 30 FTEs).

Stop whining, pay the few bucks, and actually learn to use the tools of your trade.

Merry Christmas 🎄

You’ve probably heard about the new Generative Fill feature in the upcoming Photoshop version. Well i’ve come bearing good news, it’s public, available for everyone with an Adobe Creative Cloud Subscription, and it’s called Adobe Photoshop 2024.

Now i’ve been using the AI features in Photoshop Beta since it came out a couple of months ago, and i must say that i’m very happy about what Adobe has put together.

These AI features almost creep into every other thing Adobe Photoshop does.

Object Select

Now with AI power, it’s even easier to select objects inside your images, no need to use a Lasso for drawing odd shapes anymore.

Editing layers

Having a layer and just editing it’s prompt to get new variations might be the most mind blowing feature of them all.

Editing the Sky

Go to the select menu and pick Sky, now you can use generative fill to make a whole new sky.

so much more

Over time, i think we will find more and more of these helper features that make using Adobe Photoshop really something new.

Yeah i lied, it’s actually 6 things you’re doing wrong.. My bad.

Since y’all probably have used Midjourney V5 in the past 6 months or so, i wanted to share a couple of tips and tricks to improve your Prompts. This works for most AI image generators such als Dall-E, Midjourney, Gencraft, etc.

What are prompts?

I’m pretty certain that 80% of the english speaking IT folks know this, but i’ll do it for the 80% of german IT engineers who have no fucking clue.

Prompts are the thing you tell the AI to do. You prompt it to draw an image of “gay pirate wearing a nazi pyjama”, and it will do so. The prompt is your Input for most of the current Transformer based models.

Tip 1 - make your prompts shorter

Having ver long prompts doesn’t help. It’s not like horsepower where more is better, it’s about being precise and concise. Almost like talking to your girlfriend. The more you talk, the deeper the trouble gets. Just stay on point.

Tip 2 - not providing enough constraints

Same thing, if you don’t give your girlfriend any constraints, then god knows, she’ll start buying import beer or Rosé. Same goes for AI image generators. Open ended prompts without guidance will produce random results, nothing you can repeat or use. For example “Spaceship” or “Airplane” might be too general, try something like “an American Airlines Airplane” or “a futuristic spaceship, star trek style, entering a wormhole”.

Tip 3 - ambiguous language

You need to be very clear, or your Girlfriend^WAI will have issues. Avoid pronouns and implied subjects that can confuse the Girlfriend^WAI.

Tip 4 - abstract concepts

Just like your girlfriend, abstract concepts make the AI’s head hurt. It has no idea about emotions, so instead of having your a AI prompt be like “Painting of a happy female” be “Painting of a smiling female”. AI will the know what smiling is, but what emotion/abstract concept lies behind “happy” might be going too far.

Tip 5 - being impatient

So the first result that Dall-E or Midjourney yielded isn’t perfect. You’re giving up of course… But nobody told you that it can take several attempts, and especially learning, for how to craft your prompts correctly.

Depending on the model your AI has been trained on, you will need to play around to become proficient. Have you ever heard of the 8 year old car mechanic? Exactly.

Tip 6 - use ChatGPT to create your ideal prompts

Now here comes the kicker. Instead of retraining your brain to write these stupid prompts, you could also just ask ChatGPT (gpt-4) to write them for you.

Priming ChatGPT

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You are an AI model developed by OpenAI, and I’m introducing you to Midjourney, an AI tool that generates images based on text prompts. Your task is to assist me in creating these prompts. Are you ready?

Examples

You should pass ChatGPT some examples of good prompts, for it to know what you’re looking for.

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Some examples of really good prompts are:
- “A serene mountain landscape covered in a blanket of snow, rendered in a minimalist style, capturing the subtle shades of white and blue under the soft morning light.”
- “A futuristic cityscape at night, illuminated by neon lights and towering skyscrapers, depicted in a cyberpunk art style with a strong emphasis on the contrast between the dark shadows and vibrant glow.”
- “A bustling market scene in a bustling Asian city, portrayed with a lively and vibrant palette, highlighting the intricate patterns of textiles and the busy atmosphere with dynamic brushstrokes.”

For instance, you can ask:

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Can you generate a prompt for an image of a beach under a starry night sky?

Or you can phrase your query like this:

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Title: “A beach under a starry night sky.”

ChatGPT will then create a prompt based on the provided concept:

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Prompt: “A tranquil beach scene beneath a studded night sky, rendered in an impressionistic style. The sands are soft and glistening, reflecting the brilliant stars above. The gentle waves ebb and flow with a mesmerizing rhythm, the whitecaps gleaming under the stellar glow. The calm serenity of the ocean and the enigmatic vastness of the starry night sky paint a breathtaking tableau.”

After trying to upgrade my Jekyll version from two years ago, and failing miserably, i decided to look for alternatives.

Meet Hexo.io

What is it?

• it’s basically jekyll written in JavaScript
• it just works with jekyll Markdown files
• renders to static HTML
• has plugins

The entire migration took me (incl. overdoing some pages and adding new ones) about 3 hours.

Since i am running nixos on my Macs, it’s easily doable with a shell.nix.

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{ pkgs ? import <nixpkgs> {} }:

let
  lib = import <nixpkgs/lib>;
  NODE_MODULES_PREFIX = toString ./node_modules;

in pkgs.mkShell {
  packages = with pkgs; [
    nodejs
    nodePackages.npm
  ];

  inherit NODE_MODULES_PREFIX;

  shellHook = ''
    export PATH="$PATH:$NODE_MODULES_PREFIX/hexo-cli/bin"
    npm install
  '';
}

Now i can just cd into my repo and run nix-shell and have hexo ready in my path, ready to make new posts!

I finally got around to setting up a new mailserver and i decided to give OpenSMTPD a try. It wasn’t a natural birth, i can tell you that. The switching of the configuration syntax makes for a lot of outdated Google Search results.

So what are we going to setup. Well the title gave it away i guess, so for the slow ones amongst you: we are building a Mailserver with OpenSMTPD, Dovecot, RSpamd and Sieve. The OpenSMTPD and the Dovecot will both be using the same authentication table and hashing scheme, making this a nifty solution.

Installing the required components

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pkg_add postgresql-server opensmtpd-extras opensmtpd-extras-pgsql opensmtpd-filter-rspamd opensmtpd-filter-senderscore rspamd dovecot dovecot-pigeonhole dovecot-postgresql redis

Enabling them on boot

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rcctl enable httpd
rcctl enable smtpd
rcctl enable postgresql
rcctl enable rspamd
rcctl enable dovecot
rcctl start dovecot
rcctl enable redis
rcctl start redis

Setting up DNS

This has been explained in numerous posts on the Internet, you should by now know how to setup an MX Record (maybe SPF and DKIM).

Setting up Let’s Encrypt SSL Certificates

/etc/httpd.conf

Configure httpd to do the acme challenges.

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server "replace.with.host.name" {
        listen on * port 80
        location "/.well-known/acme-challenge/*" {
                root "/acme"
                request strip 2
        }
        location "/" {
                block return 301 "https://$SERVER_NAME$REQUEST_URI"
        }
}

And then start httpd:

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rcctl start httpd

/etc/acme-client.conf

Now we go on to configure the acme-client.

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api_url="https://acme-v02.api.letsencrypt.org/directory"
authority letsencrypt {
        api url $api_url
        account key "/etc/acme/letsencrypt-privkey.pem"
}

domain replace.with.host.name {
        #alternative names { www.replace.with.host.name }
        domain key "/etc/ssl/private/replace.with.host.name.key"
        #domain certificate "/etc/ssl/replace.with.host.name.crt"
        domain full chain certificate "/etc/ssl/replace.with.host.name.crt"
        sign with letsencrypt
}

Obtaining a certificate

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acme-client -v replace.with.host.name

Adding certificate renewal to cron

Enter the crontab with crontab -e and add the following line:

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30      0       *       *       *       /usr/sbin/acme-client replace.with.host.name && /usr/sbin/rcctl restart smtpd && /usr/sbin/rcctl restart dovecot

Preparations for our services

/etc/login.conf

Go ahead and add the following lines at the end of your /etc/login.conf:

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dovecot:\
        :openfiles-cur=1024:\
        :openfiles-max=4096:\
        :tc=daemon:

postgresql:\
        :openfiles=768:\
        :tc=daemon:

Once done, have the file cap_mkdb’d like this:

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cap_mkdb /etc/login.conf

/etc/sysctl.conf

Append the following values to /etc/sysctl.conf so PostgreSQL has a bit of breathing room:

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kern.seminfo.semmni=60
kern.seminfo.semmns=1024

Then go on to actually setting them in the kernel:

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sysctl -w kern.seminfo.semmni=60 kern.seminfo.semmns=1024

Adding a vmail user and group

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useradd -m -d /var/vmail -s /sbin/nologin vmail

Preparing PostgreSQL

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su - _postgresql
mkdir /var/postgresql/data
initdb -D /var/postgresql/data -U postgres -A scram-sha-256 -E UTF8 -W
exit
rcctl start postgresql

Next we are going to add a user, a database, two tables and three views:

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psql -Upostgres <<EOF
CREATE USER mail WITH ENCRYPTED PASSWORD 'your.mail.password';
CREATE DATABASE mail OWNER mail;
EOF

psql -Umail mail <<EOF

-- this is the table for the users accounts
CREATE TABLE public.accounts (
    id serial,
    email character varying(255) DEFAULT ''::character varying NOT NULL,
    password character varying(255) DEFAULT ''::character varying NOT NULL,
    active boolean DEFAULT true NOT NULL
);

-- this is the table for the virtual mappings for email -> email
CREATE TABLE public.virtuals (
    id serial,
    email character varying(255) DEFAULT ''::character varying NOT NULL,
    destination character varying(255) DEFAULT ''::character varying NOT NULL
);

-- this view is used to determine where to deliver things
CREATE VIEW public.delivery AS
 SELECT virtuals.email,
    virtuals.destination
   FROM public.virtuals
  WHERE (length((virtuals.email)::text) > 0)
UNION
 SELECT accounts.email,
    'vmail'::character varying AS destination
   FROM public.accounts
  WHERE (length((accounts.email)::text) > 0);

-- this view is used to determine which domains this server is serving
CREATE VIEW public.domains AS
 SELECT split_part((virtuals.email)::text, '@'::text, 2) AS domain
   FROM public.virtuals
  WHERE (length((virtuals.email)::text) > 0)
  GROUP BY (split_part((virtuals.email)::text, '@'::text, 2))
UNION
 SELECT split_part((accounts.email)::text, '@'::text, 2) AS domain
   FROM public.accounts
  WHERE (length((accounts.email)::text) > 0)
  GROUP BY (split_part((accounts.email)::text, '@'::text, 2));

-- this view should control the email addresses users can send with
CREATE VIEW public.sending AS
 SELECT virtuals.email,
    virtuals.destination AS login
   FROM public.virtuals
  WHERE (length((virtuals.email)::text) > 0)
UNION
 SELECT accounts.email,
    accounts.email AS login
   FROM public.accounts
  WHERE (length((accounts.email)::text) > 0);
EOF

/etc/mail/postgres.conf

Next we configure the PostgreSQL lookups for smtpd:

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conninfo host='localhost' user='mail' password='your.mail.password' dbname='mail'
query_alias SELECT "destination" FROM delivery WHERE "email"=$1;
query_credentials SELECT "email", "password" FROM accounts WHERE "email"=$1;
query_domain SELECT "domain" FROM domains WHERE "domain"=$1;
query_mailaddrmap SELECT "email" FROM sending WHERE "login"=$1;

Also since this file contains the password to the database, only _smtp should be able to read it:

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chown _smtpd:_smtpd /etc/mail/postgres.conf
chmod o= /etc/mail/postgres.conf

/etc/mail/smtpd.conf

Now we can go ahead and configure OpenSMTPD:

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table aliases file:/etc/mail/aliases
table auths postgres:/etc/mail/postgres.conf
table domains postgres:/etc/mail/postgres.conf
table virtuals postgres:/etc/mail/postgres.conf
table sendermap postgres:/etc/mail/postgres.conf


pki replace.with.host.name cert "/etc/ssl/replace.with.host.name.crt"
pki replace.with.host.name key "/etc/ssl/private/replace.with.host.name.key"


filter check_dyndns phase connect match rdns regex { '.*\.dyn\..*', '.*\.dsl\..*' } \
    disconnect "550 no residential connections"

filter check_rdns phase connect match !rdns \
    disconnect "550 no rDNS is so 80s"

filter check_fcrdns phase connect match !fcrdns \
    disconnect "550 no FCrDNS is so 80s"

filter senderscore \
    proc-exec "filter-senderscore -blockBelow 10 -junkBelow 70 -slowFactor 5000"

filter rspamd proc-exec "filter-rspamd"


listen on all tls pki replace.with.host.name filter { check_dyndns, check_rdns, check_fcrdns, senderscore, rspamd }
#listen on all port smtps smtps pki replace.with.host.name auth <auths> senders <sendermap> masquerade
#listen on all port submission tls-require pki replace.with.host.name auth <auths> senders <sendermap> masquerade
listen on all port smtps smtps pki replace.with.host.name auth <auths>
listen on all port submission tls-require pki replace.with.host.name auth <auths>

action "receive_aliases"        lmtp "/var/dovecot/lmtp" rcpt-to alias <aliases>
match from local for local action "receive_aliases"

action "receive_vmail"          lmtp "/var/dovecot/lmtp" rcpt-to virtual <virtuals>
match from any for domain <domains> action "receive_vmail"

action "outbound"               relay helo replace.with.host.name
match from auth for any action "outbound"

And finally start the smtpd:

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rcctl start smtpd

/etc/rspamd/worker-proxy.inc

In this file i actually just changed the spam_header to X-Spam-Status, but this optional.

/etc/rspamd/local.d/greylist.conf

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greylist {
        servers = "127.0.0.1:6379";
        timeout = 1min;
}

Then we start rspamd:

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rcctl start rspamd

Diff style below here

I’ve chosen to only put in things you need to change or append, everything else should remain as is.

Why did i do this? Well since dovecot has evolved into this nice configuration-file layout, i decided that this is the most efficient way to keep this document clean and relevant.

/etc/dovecot/conf.d/10-auth.conf

Towards the beginning of the file, disable plaintext authentication:

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disable_plaintext_auth = yes

Then at the end of the file, there are several includes. We are going to comment the auth-system.conf.ext and are going to comment in the auth-sql.conf.ext instead:

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#!include auth-system.conf.ext
!include auth-sql.conf.ext

/etc/dovecot/conf.d/10-mail.conf

We change the mailbox location to our vmail directory.

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mail_location = maildir:/var/vmail/%u

/etc/dovecot/conf.d/10-master.conf

Next we are going to comment in our SSL listeners, feel free to leave in 143 and 110 as they are using STARTTLS:

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service imap-login {
  inet_listener imap {
    port = 143
  }
  inet_listener imaps {
    port = 993
    ssl = yes
  }
}

service pop3-login {
  inet_listener pop3 {
    port = 110
  }
  inet_listener pop3s {
    port = 995
    ssl = yes
  }
}

Then make sure lmtp is configured with the correct permissions:

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service lmtp {
  unix_listener lmtp {
    mode = 0660
    user = vmail
    group = vmail
  }
}

/etc/dovecot/conf.d/10-ssl.conf

Now we are going to configure SSL:

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ssl = required

ssl_cert = </etc/ssl/replace.with.host.name.crt
ssl_key = </etc/ssl/private/replace.with.host.name.key

ssl_prefer_server_ciphers = yes

/etc/dovecot/conf.d/15-lda.conf

Next up is our local delivery agent (LDA):

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postmaster_address = postmaster@your.domain
hostname = replace.with.host.name

lda_mailbox_autocreate = yes

/etc/dovecot/conf.d/20-lmtp.conf

Now we configure our LMTP for Sieve:

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protocol lmtp {
  # Space separated list of plugins to load (default is global mail_plugins).
  mail_plugins = $mail_plugins sieve
}

/etc/dovecot/conf.d/90-plugin.conf

Here we configure the Sieve plugin itself:

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plugin {
  sieve_plugins = sieve_imapsieve sieve_extprograms
  sieve_global_extensions = +vnd.dovecot.pipe +vnd.dovecot.environment

  imapsieve_mailbox1_name = Junk
  imapsieve_mailbox1_causes = COPY APPEND
  imapsieve_mailbox1_before = file:/usr/local/lib/dovecot/sieve/report-spam.sieve

  imapsieve_mailbox2_name = *
  imapsieve_mailbox2_from = Junk
  imapsieve_mailbox2_causes = COPY
  imapsieve_mailbox2_before = file:/usr/local/lib/dovecot/sieve/report-ham.sieve

  imapsieve_mailbox3_name = Inbox
  imapsieve_mailbox3_causes = APPEND
  imapsieve_mailbox3_before = file:/usr/local/lib/dovecot/sieve/report-ham.sieve

  sieve_pipe_bin_dir = /usr/local/lib/dovecot/sieve
}

/usr/local/lib/dovecot/sieve/report-spam.sieve

Here we are going to fill in the default action for when we move files to spam folder. In our case we learn them as spam:

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require ["vnd.dovecot.pipe", "copy", "imapsieve", "environment", "variables"];

if environment :matches "imap.user" "*" {
  set "username" "${1}";
}

pipe :copy "sa-learn-spam.sh" [ "${username}" ];

/usr/local/lib/dovecot/sieve/sa-learn-spam.sh

Fill the file with the following contets:

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#!/bin/sh
exec /usr/local/bin/rspamc -d "${1}" learn_spam

/usr/local/lib/dovecot/sieve/report-ham.sieve

Here we are going to fill in the default action for when we move files out of the spam folder. In our case we learn them as ham:

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require ["vnd.dovecot.pipe", "copy", "imapsieve", "environment", "variables"];

if environment :matches "imap.mailbox" "*" {
  set "mailbox" "${1}";
}

if string "${mailbox}" "Trash" {
  stop;
}

if environment :matches "imap.user" "*" {
  set "username" "${1}";
}

pipe :copy "sa-learn-ham.sh" [ "${username}" ];

/usr/local/lib/dovecot/sieve/sa-learn-ham.sh

Fill the file with the following contets:

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#!/bin/sh
exec /usr/local/bin/rspamc -d "${1}" learn_ham

making them both executable

To make them runnable by the system, we make them executable:

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chmod +x /usr/local/lib/dovecot/sieve/*.sh

/etc/dovecot/conf.d/90-sieve.conf

And here we configure the sieve folder that gets used for everyone.

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sieve_before = /var/vmail/sieve/

/var/vmail/sieve/junk.sieve

First create that folder:

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mkdir -p /var/vmail/sieve
chown -R vmail:vmail /var/vmail

Then we sieve away the spam:

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require "fileinto";
if header :contains "X-Spam-Status" "YES" {
    fileinto "Junk";
    stop;
}

/etc/dovecot/conf.d/auth-sql.conf.ext

Here we configure our override fields, so we don’t have to do an ugly select:

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userdb {
  driver = sql
  args = /etc/dovecot/dovecot-sql.conf.ext
  override_fields = uid=vmail gid=vmail home=/var/vmail/%u
}

/etc/dovecot/dovecot-sql.conf.ext

Now are almost done, we now configure how dovecot accesses the database. Append this to the end:

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driver = pgsql
connect = dbname=mail user=mail password=your.mail.password
default_pass_scheme = CRYPT

password_query = SELECT email AS user, '{CRYPT}' || password AS password FROM accounts WHERE active = true AND email = '%u' AND email != '' AND password != ''
user_query = SELECT email FROM delivery WHERE email = LOWER('%u')

/etc/dovecot/dovecot.conf

Last but not least we update the protocols we are going to use:

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protocols = imap pop3 lmtp

And finally start the dovecot:

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rcctl start dovecot

Adding Accounts and Aliases

To generate securely hashed passwords, you can use “smtpctl encrypt” and then enter your password. The resulting hash can be used as replacement for PASSWORD:

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INSERT INTO accounts (email,password) VALUES ('my@first.email.address','PASSWORD');
INSERT INTO virtuals (email,destination) VALUES ('my@second.mail.address','my@first.email.address');

That’s it

You should now be able to use this setup as expected.

If you find any errors, you can find me on Twitter and let me know!

Here is my list of dirty little secrets that make the game more fun and yield more kills.

Sending Players to the Gulag before they hit the ground

• while parachuting with other players beside you, cut your parachute and start shooting at them with your weapon.
• skydive to a helicopter fast, get in and fly into slow players with parachutes effectively sending them to the Gulag before they hit the ground.

Effectively using the time in the Gulag, when watching from above

• spray as many your opponents as you can to make them better visible during the fight
• if you see a player down there placing a Claymore Mine, try throwing a rock at the mine to set it off into his face
• if a player down there gets hit and isn’t done yet, you can try to kill him by throwing rocks at his head

On the Battlefield

• place a Trophy system on your Jeep or into your Helicopter for that automatic defense system
• when three people on the same team call for a drone at the same time, your team will get an advance drone for the remainder of the
  game showing everyone on the map

if you have more of these

Just go ahead and contact me on Twitter and i will add your tips with credits here.

Now go out and use it wisely. ;)

cheers

Every developer knows the pain of sorting database rows by some custom field. The easiest Example of this are DNS Records.

Today i’ve come across a solution using array_position. Basically you can pass it the Order of elements you want on top, and the rest will be below.

It works like this

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SELECT hostname, type, contentn FROM records AS r
ORDER BY type, array_position(array["SOA"::varchar, NS"::varchar], r.type)

Now all records having type SOA will be first, type NS second and type A third. Everything else will come after.

The important part

Make sure you typecast your custom array into the type of the column you are ordering against.
In my case the type column is a varchar, that’s why i am casting all elements to it.

I hope this helps some of you avoiding to write custom sorting in code.

For almost 8 years i have ran this blog on my own blog engine, println. While at the beginning it gave me all i needed, in recent years my behavior has changed, and thus my preferences. I don’t like editing blog-posts in the browser anymore. I got even more used to vi (neovim specifically) than i was before. So it was time for a change.

After a bit of exporting my old PostgreSQL database for my blog into Jekyll format, i am happy to present the result. I hope i got all the permalinks right, but i guess i will find out.

I really hope that this motivates me to write more frequently in the future again.

cheers