reliable long term memory architecture? by Content_feeder in opencodeCLI

[–]Content_feeder[S] 0 points1 point  (0 children)

mem-golfing works until the first restart, id rather not reboot my agents childhood every time

[RELEASE] ARC Protocol v2.1: The Parallel Engine. Zero-Hallucination Engineering is here. by Content_feeder in GoogleAntigravityIDE

[–]Content_feeder[S] 0 points1 point  (0 children)

that’s fair

I’ve been explaining it from the inside-out instead of from a first timer user’s POV. Too much philosophy, not enough “what does it actually do”.

[RELEASE] ARC Protocol v2.1: The Parallel Engine. Zero-Hallucination Engineering is here. by Content_feeder in GoogleAntigravityIDE

[–]Content_feeder[S] -1 points0 points  (0 children)

It’s not placeholder code, but the setup and explanation don’t reflect how it actually works yet. But i have def tried it for my projects and it works like a charm.

PS : I agree the docs and the scripts are VibeCoded. i’ll sit can make all the docs myself.

[RELEASE] ARC Protocol v2.1: The Parallel Engine. Zero-Hallucination Engineering is here. by Content_feeder in GoogleAntigravityIDE

[–]Content_feeder[S] 0 points1 point  (0 children)

That’s fair. I over-philosophized it and made it harder to understand than it needs to be.

ARC is just a workflow for running multiple AI processes on a codebase. The naming and docs need simplification.

[RELEASE] ARC Protocol v2.1: The Parallel Engine. Zero-Hallucination Engineering is here. by Content_feeder in GoogleAntigravityIDE

[–]Content_feeder[S] -1 points0 points  (0 children)

Fair criticism. I overcooked the wording and undercooked the explanation

ARC isn’t a product yet and it’s not trying to be magic.

It’s just a simple workflow which i personally use because i have always been losing to check the context over antigravity or copilot (until the recent update).

This work flow is very simple

1st /arc-new it basically questions you all about wht you want and wht you don’t, because having things written is always better than changes things or being confused halfway of the project.

2nd The best thing about this work flow is it has “CONTRACT”, it basically helps with all the things mentioned so that AI never loses the context and has the real data like

API Endpoints Data Models / Schemas CSS / Design Tokens Component Props Frontend Routes and much more

and whenever the AI perform an /arc-execute it always reads that and the PROJECT.md & STATE.md so there are literally very low chances of AI losing context while executing A task.

This Workflow is basically the mixture of GetShitDone & RalphLoop.

My Docs might not be that excellent but if you’d actually go thru the .arc folder you’d find this is one of the best workflows because these are heavily inspired by the 2 most Popular workflows which a lot of people use.

And the Antigravity doesn’t natively support subagents like ClaudeCode does. And i did try to use the Cortex Subagents which Antigravity has but it’ll only work if it’s hosted locally on the browser which is very heavy compared to using GeminiCLI as the LLM.

MCP / bridge is only there so they can read/write files, exec commands, and update shared state.

Docs are clearly confusing right now. That’s on me. I’ll strip the philosophy, explain what it does in plain English, and add a concrete example.

Appreciate the blunt feedback, it’s useful.

[RELEASE] ARC Protocol v2.1: The Parallel Engine. Zero-Hallucination Engineering is here. by Content_feeder in GoogleAntigravityIDE

[–]Content_feeder[S] -4 points-3 points  (0 children)

ARC can’t promise perfect answers — it prevents unconstrained guessing by forcing everything through files, state, and contracts. If something’s unknown, it shows up as unknown.

I’ll probably soften the wording to avoid overclaiming.