Most of the work that needs a durable engine is not glamorous. It is an expense waiting for a signature. A job that fails at 3 a.m. because a network cable sneezed. An order message that arrives twice. A batch whose size you only know when it starts.
These are the problems I had in mind while building Packtrail. Here are six ordinary workflows, each based on a runnable example in the repository, and what Packtrail does with them.
When I first wrote about Packtrail, the pitch was a single sentence: a durable workflow engine that needs nothing but NATS. No database, no coordinator, no second thing to keep alive.
Today that sentence gets a version number. Packtrail 1.0.0 is out.
Here is the part I don’t usually put in a release post: version 1.0.0 is not the first draft with a nicer tag. It is the second attempt.
A single LLM answer has a neat, confident shape. Public opinion does not.
When a policy changes, a product launches, or a company announces something unpopular, the interesting part is rarely the first reaction. It is what happens after people see each other reacting. Arguments harden. Coalitions form. A practical objection becomes the sentence everyone repeats. A weak point disappears because nobody picks it up.
That is hard to study with one prompt.
After a long string of 0.0.x releases, Phero finally reaches v1.0.0.
There’s a particular feeling that comes with cutting a 1.0.0 tag. The 0.0.x versions are a workshop: you tear walls down, you move the staircase, you sleep on it and rebuild it the next morning. 1.0.0 is the moment you finally open the door and say: this is ready, and I stand behind it.
Now that anyone can build the prototype, the rarest skill is knowing which prototypes deserve to live.
In two previous articles I argued that AI does the easy 20% of software, writing code, and leaves the hard 80% untouched. The first traced the pattern back forty years, the second catalogued the 80% in nine parts a demo never shows. Both pieces did the same thing: they pointed a finger. At the PM who builds something in an afternoon and declares engineering obsolete. At the leap from “it works in the demo” to “we don’t need them anymore.”
A working demo is a promise the system hasn’t agreed to keep yet.
In a previous article I argued that writing code is, by Pareto, about 20% of the job, and that the other 80% is the part AI doesn’t replace. I listed that 80% as a string of bullet points and moved on. That was a cheat. Those bullet points are the whole argument, and they deserve more than a list.