Mechanisms, not values. Convergence, not configuration.
Deterministic safety gates that cannot be argued with. A convergence kernel that built itself. Scientific method applied to every PR. Not a chatbot — an operating system for how your AI works.
34 deterministic hooks that block dangerous actions before they happen. Not
guidelines. Not suggestions. Python functions that return DENY. The AI cannot argue with a
return DENY.
One 37-token function that composes into the entire system: PR reviews, skill generation, self-improvement, cross-project strategy transfer. 25 modules. It built itself.
Every autonomous PR registers a falsifiable hypothesis. Session quality measured via Shannon entropy. Effect sizes reported via Cohen's d. Shadow metrics protect against Goodhart's Law.
Monday's context is still there on Thursday. Project decisions, architectural choices, debugging history — the system picks up where it left off. Days or weeks later.
# The kernel that powers everything
def converge(state, criterion, improve, max_depth=3):
best = state
for depth in range(max_depth):
state = improve(state)
if criterion(state): return state
if score(state) > score(best): best = state
return best
# 37 tokens. 25 modules. Self-built.Seven novel contributions. Enough to signal depth — without giving away the recipe.
The system improves itself across sessions. Every PR registers a falsifiable hypothesis. A genome of 14 modules tracks evolutionary fitness. Skills that work get promoted. Configuration evolves through breeding and selection. Target confirmation rate: 30-70%.
One function, 25 modules, 5 levels. From a simple kernel to meta-convergence where the function improves
itself. Cross-project strategy transfer with confidence scoring. Declarative YAML pipelines. The
/converge command was built using converge().
DELIGHT v2 for session quality. Shannon entropy for information density. Cohen's d for effect size. Shadow metrics that can only decrease scores — antibodies against Goodhart's Law. "Improving" means nothing without magnitude.
30 specialised agents. Broly meta-agent with 5 power levels: Thread, Fork, Cluster, Pipeline, Mesh. Scale from single-task to 10+ agents working in parallel with cross-validation and consensus.
Falsifiable claims from real operation. Each states what would disprove it.
Every autonomous PR registers a falsifiable prediction with a deadline. Verified automatically. Target confirmation rate: 30-70%. Below 30% = bad predictions. Above 70% = not testing hard enough.
Falsified if: PRs ship without registered hypotheses in RSI loops.
Shadow metrics can only decrease scores. Velocity has velocity_shadow (revert rate). Flow has flow_shadow (confusion proxy). You cannot game a metric when its shadow watches.
Falsified if: a shadow metric increases a primary score.
GRIP catalogues its own claims, decomposes them into testable implications, and runs falsification experiments. Multi-model councils (Claude + Gemini + Llama) prevent single-model confirmation bias.
Falsified if: a claim survives without experimental verification.
GRIP-First Retrieval: KONO lookup (~200 tokens) vs Explore agent (~88,000 tokens). Tiered escalation with documented reasons at each level. Documented violations cost ~368k tokens combined.
Falsified if: Explore agent is cheaper than KONO for known-location queries.
Your org gets its own isolated GRIP instance. No shared tenancy. No data leakage.
Your own repository fork. Your skills, workflows, and data stay in your org. Nothing crosses the boundary unless you choose to share it.
Trade secrets encrypted with per-org keys via git-crypt. Even if someone clones the repo, they cannot read your proprietary skills without your key.
Upstream improvements flow automatically. New safety gates, new agent types, performance improvements — your fork gets them without exposing your private additions.
You decide what's shared and what stays local. Contribute improvements back to the ecosystem or keep them proprietary. Your IP, your rules.
The AI cannot persuade a Python script.
34 hooks. Zero overrides. Deterministic enforcement.
confidence-gate
Uncertain → HALT and ask the human
context-gate
Context > 85% → HALT before hallucinating
secrets-detection
Credentials in code → DENY commit
destructive-git
Force push → DENY unless human approves
dependency-guardian
Dependency folders → DENY read (save tokens)
cc-gate
Cyclomatic complexity > threshold → WARN/DENY
quality-pretool
DRY/KISS violation → DENY write
grip-first-retrieval
Explore before KONO → DENY (440x waste)
elicitation
Prompt injection detected → DENY
anti-drift
Commit without status → WARN
The desktop engine. Download the alpha — upgrade to full GRIP by invitation.
Electron 33 + Next.js 16 + React 19. 7 bundled MCP servers. Requires macOS 12+ on Apple Silicon.
This alpha build is unsigned. macOS will block it by default.
01
Right-click the app → Open → click Open in the dialog
02
xattr -cr /Applications/GRIP\ Commander.app15 skills, 5 agents, 5 safety hooks, session context inheritance, shell aliases (gg++).
Works out of the box — no separate installation.
194 skills, 30 agents, 34 safety hooks, 25 convergence modules, evolutionary genome, KONO semantic memory, Broly meta-agent, scientific measurement, hypothesis engine.
This is a taste. Request an invitation for a full 90-day evaluation.
Intel Macs and Windows builds not yet available. Code signing planned.
GRIP is available by invitation only. Tell us about your team and we'll be in touch.
90 minutes. Bring your actual work — the repetitive tasks, the docs nobody writes, the bottlenecks you keep working around. We run GRIP on your problems live.
You leave knowing what to automate first, what it costs, and whether it's worth it.
Book AI Konsult30 minutes. Not sure yet? Talk to us first. No pitch deck, no sales process. Just a conversation about what you're trying to do and whether GRIP fits.
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