Your code has been hiding things from you.

LucidCode makes it confess — in first person — verified by a 3-engine anti-hallucination ensemble. Deterministic AST re-verifier, sandboxed fuzzer, adversarial LLM devil. Every claim anchored, every hallucination rejected.

How it works

01 — DETECT

Twelve psychiatric syndromes

An AST Surgeon scans for Suppression, Amnesia, Despair, Insomnia, Hoarding, Deafness, Selective_Mutism, and more. Deterministic. Line-anchored. Zero LLM in this step.

02 — CONFESS

Code speaks in first person

An LLM voices each trauma as the code itself: "I confessed at line 6: I called out without a clock. I hang forever if the server stalls."

03 — VERIFY

Three engines vote

A deterministic AST re-verifier (0.90 trust), a sandboxed fuzzer (0.70), and a diversified LLM devil (0.40) vote in parallel. Bayesian aggregation produces a calibrated verdict. Hallucinations are logged and never shown.

Why it's different

LucidCodeSemgrepCopilotCriticGPT
Approach Confession + ensemble Static rule match Autocomplete Self-critique
Anti-hallucination Diverse 3-engine n/a n/a Single LLM
Output style First-person confession Rule ID + line Code suggestion Chat critique
Runs offline Yes (subprocess mode) Partial No No

Request access

LucidCode and Laundry are available by direct engagement. Every deployment is scoped to your codebase, your security posture, and your team's review flow.

TO REQUEST THE SERVICE

abdarahman10555@gmail.com

Response within one business day.