The _Ext(CursesView) subclass in pipeline_app.py:941 owns the getch() loop and inserts preprocess_key(vm, ch) before the base dispatch_key. This single seam unblocks everything else — custom chords without touching glyfi's base, bracketed paste without losing newlines, multi-line input.
The seam (pipeline_app.py:1197–1199):
viewmodel.bus.emit(KeyPressed(key=pch, mode_ui=viewmodel.mode_ui))
if not preprocess_key_fn(viewmodel, pch): # True = consumed; skip base
A continuation of "The acraflow middleware worker apparatus" with a deep inventory of token-conservation features — the implemented mechanisms that realize the middleware's design. This enhanced gist catalogs the full repertoire of levers, handlers, components, and directives that make token-efficient solving possible: from cached grounding to deterministic routing to synthetic pre-staging, every feature that saves tokens (or ensures they're spent wisely).
Once a problem is solved, re-encountering it should cost no model call.
A "worker" here is not one model call. It is a pipeline of roles, each a middleware handler that grounds, disambiguates, or rewrites the step around the core model. The worker model never sees the machinery; from its point of view it simply produced a clean, correct turn. These are the fundamentals of that middleware: how a worker is steered and grounded by preamble, how its request and response are disambiguated, how the policy layer wraps/alters steps and sends synthetic ones back up — with every step driven by a specific role — and how the worker is kept coherent across a multistep flow: maintaining its place in the flow, holding both near and long-horizon goals, and being forced to follow through to completion.
acraflow is an invisible steering layer between an agentic LLM and its provider. The agent works normally; the apparatus catalogs what it solves, keeps its context lean by construction, and enforces correctness deterministically. This note covers the context fundamentals it's built on and the tenets it recognizes and enforces — the why, not a code tour.
waddup nerds
This document introduces a collection of mathematical formalizations applying Residue Number System (RNS) theory to large language model computation. The work presents seven novel contributions at the intersection of number theory and neural network architecture, each with rigorous definitions, theorem statements, and verified implementations across multiple configuration scales.