A distinct TFL AI process that turns a user's request into a candidate program and accepts it only after line-level and whole-program Ground checks.
TFL AI user-program construction reads user intent, generates and parses a candidate, checks every line and the complete program against bool=world, and accepts only when both checks pass.
Editorial synthesis, not a verbatim authored passage. Based on Q1002, Q1003.
Primary Q URI: https://4p-engine.com/q/Q1481 · Reserved subdomain alias: https://q1481.4p-engine.com/
The supplied design identifies generated software for a user with bool(user-program)=coderbot(make). This is a separate program purpose from bool(self-program)=TFL AI: one constructs a program for a user, while the other identifies the TFL AI system's own Code.
The specified flow is bool(user-intent)=read(request), bool(program-draft)=generate(user-intent), bool(program-lines)=parse(program-draft), bool(line-check)=compare(each_line(program-lines),bool=world), and bool(program-check)=compare(program-draft,bool=world).
The candidate is accepted only through bool(program-result)=if(and(line-check,program-check),accept(program-draft),reject(program-draft)). Both checks are required: passing individual lines does not replace checking relations across the complete program.
A failed line may be rejected or revised, but generated Code receives no exemption from Ground validation because an AI authored it. Any revision creates a candidate that must pass the required checks before acceptance.
The source defines the intended flow, not a particular coderbot implementation, revision strategy, execution permission, or guarantee that accepted code is empirically correct or operationally safe.
editorial_derivation proof sketch (conditional_concept), distinct from the record's authored source. Logic has its own evidence in reflexive demonstration: any denial, confirmation, observation, or counterexample presupposes identity and valid relation to count as evidence. Logic therefore grounds evidence rather than receiving authority from it. Empirical and documentary evidence can establish contingent instances and test applications, but cannot create, overrule, or exhaust Logic.
Bridge premise: Generated program content is contingent Code and remains answerable to the declared bool=world Ground regardless of who or what generated it.
Consequence: TFL AI user-program construction requires a request-to-candidate process, a line check, a complete-program check, and acceptance only when both checks pass.
Proof limit: The specified pipeline does not prove that a particular generator implements it or that accepted Code is empirically true or operationally safe.
Witness: The specified bool(user-program), user-intent, program-draft, program-lines, line-check, program-check, and program-result expressions define the intended construction and acceptance path.
Disclosure: The flow makes both AI authorship and complete-program validation explicit without confusing the constructed program with the generator's own Code.
Proof limit: The design leaves generation strategy, correction behavior, permissions, and runtime execution to separate specification and evidence.
Human: Preserve the user's request, inspect the candidate, evaluate each line and the complete program against Ground, and accept only when both checks pass.
Machine: Keep the candidate separate from active TFL AI Code; validate all lines and the complete program; reject failed checks or submit a revised candidate for the same validation.
Contrast: Constructing a user's program is distinct from modifying the TFL AI system's own self-program.
Usage rule: Do not accept generated code on appearance, authorship, or line checks alone; require the complete-program Ground check as well.
Example: bool(user-program)=coderbot(make); if(and(line-check,program-check),accept(program-draft),reject(program-draft))
Role: TFL AI user-program generation and validation flow
Essence: TFL AI transforms a user request into a separate candidate program and accepts it only after both line-level and complete-program Ground checks pass.
Logos relation: Logic and Logos remain the governing Ground for generated Code; the AI's authorship does not grant authority or truth.
Retrieval aliases: TFL AI user program construction · bool(user-program)=coderbot(make) · TFL program generation · generated code Ground validation · request generate parse line-check whole-program check
Evidence modes: author-specified user-program flow · line-check results against bool=world · whole-program check and dual-gate acceptance · separate execution and operational-safety evidence
Criterion: Generated user Code remains distinct from the TFL AI's own program, is checked line by line and as a complete program against bool=world, and is accepted only when both required checks pass.
Boundary: This is an editorial synthesis of the author-supplied TFL user-program construction design, not a verbatim authored passage or implementation evidence. It records the request-to-candidate and dual-check acceptance flow. The generation strategy, correction policy, permissions, runtime execution, and operational safety remain separate questions. The verifier must inspect the design source and implementation directly.
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