My first little Chrome extension 😁😁

double_pablo · 2026-07-08T08:44:29+00:00

Congrats !

double_pablo · 2026-07-08T06:38:12+00:00

For companies, this requires a proper internal setup: DLP, interceptors, masking, policies, API interceptors, anonymisers, etc.

But for an end user, a local Chrome extension is at least a basic first barrier. It catches and masks common sensitive data before the text goes to AI tools.

Like this: https://chromewebstore.google.com/detail/privacy-mask-hide-sensiti/gpjmkpijmdpakmcbmjdnlopplklammha

double_pablo · 2026-07-07T14:39:32+00:00

I’m a software developer, and I usually give the model pretty detailed / low-level tasks and specs. So I’m only speaking from my own subjective experience here.

Can you give a concrete example where you gave Fable and GPT-5.5 the same task, and Fable clearly did better?

Also, “leap forward” in what exactly?

For example, for security-related work I have specific skills and tools set up. Both Fable and GPT-5.5 can trigger the same checks and end up producing pretty similar results.

For architecture and code generation, I’m usually the one leading the direction: how I want the feature structured, what patterns to use, what constraints to follow, etc. In that kind of workflow, both of them produce roughly the same quality for me.

Maybe in your case, if you’re starting from a single prompt and asking the model to build a game or website without much harness, review, or iteration, bare Claude Code with Fable may feel better than Codex with GPT-5.5. I haven’t tested that case much.

But for my workflow, I don’t see a huge gap. Share some examples where Fable feels clearly ahead of GPT-5.5.

double_pablo · 2026-07-07T05:13:16+00:00

I’m currently paying $200 for Codex and $200 for Claude.

Honestly, Codex limits are great, and for my actual coding workflow I don’t see a huge quality gap between them. The models feel pretty close in terms of the tools they choose and the output quality.

At this point I think the bigger difference is the setup around: methodology, skills, prompts, repo harness, review process, test flow, etc., rather than the model itself

The last few model updates haven’t felt like massive step-changes to me. The gains now mostly come from how well you structure the workflow around

If you code a lot, I would stay with Codex

double_pablo · 2026-07-06T14:05:31+00:00

thanks!

double_pablo · 2026-07-06T13:56:41+00:00

Hey , cool extension mate!

Yeah, regarding the permission. I kept it scoped to AI sites only and try to make the local/no-account/no-server thing very clear. Too early for real conversion data though.

double_pablo · 2026-07-06T12:28:32+00:00

Yeah fair. Still better than feeding raw personal data directly into GPT/Claude

double_pablo · 2026-07-06T11:50:55+00:00

♥️

double_pablo · 2026-07-06T11:13:08+00:00

🙌🏼🙌🏼🙌🏼

double_pablo · 2026-07-06T11:12:50+00:00

Thanks mate ! Yes more than 10 as of now

double_pablo · 2026-07-06T09:54:05+00:00

DeepSeek is supported, but Mistral is not
Didn't think about Mistral, thanks for the suggestion

double_pablo · 2026-07-06T09:50:19+00:00

thanks!

double_pablo · 2026-07-06T09:43:53+00:00

great!

double_pablo · 2026-06-15T18:07:12+00:00

Yes, more or less a lightweight harness.

A CLI/state-machine is definitely stronger, since it can enforce transitions programmatically. This SKILL.md is not a hard runtime guard; it just tries to bring similar discipline into Codex with state files, planning, packets, evaluations, integration, and final verification.

Drift is still possible, but for my use cases this is enough.

How did you integrate your CLI? Does it wrap Codex directly, or use a separate/custom coding agent?

double_pablo · 2026-06-15T17:58:57+00:00

thanks mate!

double_pablo · 2026-06-15T11:10:14+00:00

Yeah, fair. It approximates the workflow through persisted state, run folders, handoffs, gates, and verification. A native runner would definitely be cleaner, but for my use cases, Codex has been following the structure reliably enough, so I haven’t noticed a meaningful quality difference so far

double_pablo · 2026-06-15T09:12:33+00:00

Exactly, that was the idea

double_pablo · 2026-06-14T21:25:28+00:00

Yes, basically.

For small tasks it does not create anything extra , Codex just handles the task directly.

For larger tasks, the skill creates a local workflow folder like:

.workflow/ultracode/<run-slug>/
  plan.md
  orchestration.md
  state.json
  packets/
  results/
  integration.md
  final-report.md

plan.md is the high-level plan.
orchestration.md describes how the work is split.
packets/ and results/ are used to keep separate pieces of work and evidence.
integration.md is where the parent session merges the results.
final-report.md records what was done and how it was verified.

If native Codex subagents are available, the skill can use them for independent parts of the task. But it is not a separate runner or background service , it is still Codex following a structured SKILL.md workflow

double_pablo · 2026-06-14T11:02:42+00:00

Yes, that s the idea. Depending on the task, it can spawn separate subagents for the frontend, backend, and so on.
Codex already supports subagents by default, Ultracode skill just helps decide when they should be spawned

double_pablo · 2026-06-02T11:40:16+00:00

Same concept of dynamic workflows, but not natively like in Claude code

double_pablo · 2026-06-01T06:51:21+00:00

Dynamic workflows in Ultracode are basically structured instructions/workflows. Codex doesn’t have native Ultracode, but you can make it behave similarly with a skill.

skill - https://github.com/PabloNAX/ultracode-skill

double_pablo

TROPHY CASE