MISSION BRIEFING · STN-001
AI-assisted software delivery,
tested in public.
A working field laboratory for shipping real systems with AI — and proving they work. Experiments, evaluations, validation frameworks, and the evidence trail behind every claim.
STATION STATUS
DEMO4/4 UP
Signal DeckSTREAMING
Delivery OSNOMINAL
Hallucination LabEVAL RUN
Validation GateARMED
LAST SYNC
TELEMETRYDEMO DATAillustrative dashboard values, not live counts
EXPERIMENTS_LOGGED
147
illustrative
EVALS_EXECUTED
2,318
illustrative
PUBLIC_REPOS
2
live · real
HALLUCINATIONS_CAUGHT
412
illustrative
MEAN_TIME_TO_VALID
4.2h
illustrative
DELIVERY_CYCLES
38
illustrative
01
OPEN →SIGNAL DECK
SIG-0147FIELD NOTE
Spec-first prompting cut rework across repeated build cycles
SIG-0146EVAL
Long-context retrieval degrades silently past ~80% window fill
SIG-0145LESSON
Agent loops need a hard validation gate, not a confidence threshold
02
OPEN →DELIVERY OS
01Idea
02Requirements
03Design
04Build
05Validation
06Release
07Learn
7-STAGE GATED LIFECYCLE
03
OPEN →HALLUCINATION LAB
DEMO DATA
Model A2.1%
Model B4.8%
Model C9.4%
Baseline18.4%
HALLUCINATION RATE · LOWER IS BETTER
06
ALL REPOS →Proof Wall — featured evidence
hallucination-hunterACTIVE
AI EVALUATION
A runnable LLM hallucination (groundedness) detector: an `hh` CLI plus Python API, pluggable detectors and model backends.
ai-delivery-engineeringACTIVE
SYSTEM
A methodology repo — docs, templates, checklists, and worked examples — for shipping software reliably with AI-assisted workflows.
spec-lintPLANNED
TOOLING
A linter that flags untestable acceptance criteria before any code is generated.