JanSchu

My personal opinion is that it will be extremely difficult in the future to monetize plain software. Either you need a very strong edge and unique angle for your distribution or you have to build a product that cannot be reproduced by agents that can build software.

This will be 2 types of products.

1. A product which requires tech that is not inside the training data distribution of the models underlying coding agents. This is usually then very cutting edge.

2. A product that uses data/insights to generate value for a customer to which a coding agent has no access.

These are the only abstract moats I can think of, the rest will be a race to the bottom

I'll add to the implementation list

Each platform has an api that you can use to post. You just have to setup a developer account for each platform

What I did was to break the development into different layers which had to be completed after another, since the functionalities build on each other. Each layer had independent work streams which run in parallel. Each work stream was one independent worktree/session in Claude code

First I triggered all work streams per layer and brought them to a level of completion I was happy with. Then you merge one after another (challenge in github with the @codex the implementation and rebases when you move to the next work stream.

This is roughly how it looked like:

Layer 0 - Project Scaffolding

Layer 1 — Core Features Stream A — Content Pipeline Stream B — Social Platform Providers Stream C — Media Library Stream D — Notification System Stream E — Settings UI

                        T-0.1 (Scaffolding)
                              │
                        T-0.2 (Core Models + Auth)
                              │
          ┌───────────────────┼───────────────────┬──────────────┐
          │                   │                   │              │
     Stream A            Stream B            Stream C       Stream D
     (Content)           (Providers)         (Media)        (Notifs)
          │                   │                   │              │
     T-1A.1 Composer    T-1B.1 FB/IG/LI    T-1C.1 Library  T-1D.1 Engine
          │              T-1B.2 Others           │              │
     T-1A.2 Calendar         │                   │         Stream E
          │                  │                   │         T-1E.1 Settings UI
     T-1A.3 Publisher ◄──────┘                   │
          │                                      │
          └──────────◄───────────────────────────┘
          (Publisher needs providers + media processing)

Layer 2 — Collaboration & Engagement Stream F — Approval & Client Portal Stream G — Inbox Stream H — Calendar & Composer Enhancements Stream I — Client Onboarding

          Layer 1 complete
                │
    ┌───────────┼───────────┬──────────────┐
    │           │           │              │
 Stream F   Stream G    Stream H       Stream I
 (Approval  (Inbox)     (Calendar+     (Onboarding)
  + Portal)              Composer
    │                    enhance)
 T-2F.1 Approval
    │
 T-2F.2 Portal

Thus I did run up to 4 agents in parallel, but o be honest this is the max level of parallelism my brain was able to handle, I really felt like the bottleneck here.

Additionally, your token usage is very high since you are having so many agent do work at the same time, hence I very often reached my claude session token limits and had to wait for the next session to begin (I do have the 5x Max plan)

yeah htmx is from 2020, it feels like yesterday

yes

The spec document was also written by Claude (over many iteration) and lots of manual additions. It took me tho 4 full days to get the specs to the level I was happy with.

One main thing I did was to use the deep research feature of Claude to get a good understanding of what other tools are offering (features, integrations etc.)

Then each feature in the specs document got refined with manual suggestions and screenshots of other tools that I took.

I did not include it yet, because you have to pay for the API. They changed their pricing model recently to pay only per request. I'll be looking into it the next weeks

I do have originally a data science background, thus python is usually my go to language, and have a lot of experience with django already. This helps a lot when reviewing AI code and if you have to judge architecture, etc.

And for hmtx I simply wanted to have something lightweight that is not very invasive to keep things simple and dependencies low.

In my head this was a good consideration to keep complexity low for my AI agents :-)

Postgres is simply a battle proven technology.