AI Builder Notes - Week of June 14, 2026

My thoughts and my twitter’s feeds thoughts

This week was all about the ‘loop’ and Fable.

The Loop

The best way I can describe it is: design the flowchart. Think of the deterministic flowchart on how you want your agents to work.

Aim to have:

• more deterministic bits - this keeps things more predictable

• more verification bits - this is agent feedback

• more agent tool calls - this, on a frontier LLM, makes it perform better.

The ‘loop’ is essentially:

goal -> agent acts -> verifier checks -> state/memory updates -> policy decides next action -> repeat/stop/escalate

now the specific implementation of this - will differ based on what you’re working on.

Fable

Fable capabilities are absolutely insane, I tried it myself and it is entirely worth it for you to spend 2 minutes looking at this.

There are a few projects that I fire up a new model into to see what’s it gonna do.
A project I wanted to build was a way to teach and demonstrate ‘spin’ in table tennis, every frontier model before Fable fumbled hard. But Fable outshined them with ease: https://srijanshukla.com/artifacts/spin-lab/

If you personally did not experience a big shift in capability, you are probably not asking it a complex enough or ambitious enough task.

Fable came, and Fable was taken away. The United States Government(USG) was reported with a jailbreak or sorts - which Anthropic considers not significant. The USG anyway banned Fable just after few days of release. Big drama.

Fable was very pricey $$$$
Hence, people developed some patterns of work on those few golden days of Fable being available.
- use Fable as planner/architect/taste/spatial/front-end judge.
- use GPT-5.5/DeepSeek/Kimi as executor/worker.

Other things

• Openrouter released their Fusion feature as a model on their platform, accessible via API. Fusion is basically council-of-LLMs pattern - providing results that can rival the frontier Fable 5 solo.

• Google Open Knowledge Format - https://github.com/GoogleCloudPlatform/knowledge-catalog/blob/main/okf/SPEC.md - the next iteration I think of LLMWiki.
  This is “curated reusable context”

• Code is a DAG of decisions and dependencies.
  Dynamic Workflows let the model write that DAG for you.
  That is fine for exploratory, reversible work.
  For production software, the DAG is the product: you write the stages, checks, stop conditions, retries, and review gates yourself. The model can fill nodes but it should not own the graph.

I seem to have forgotten where I saved this from, but a great way to think about how much trust can be given out to your friendly neighbourhood model,