Evidence-backed Growth & GTM engineering systems for direct opportunity acquisition.

DirectGTM-Labs is an independent public laboratory exploring how public signals, verifiable capability evidence, respectful direct routes, automation, measurement, and experimentation can be combined into repeatable Growth and Go-to-Market systems.

Our flagship project is DirectGTM-OS.

Identify → Research → Detect Signals → Qualify → Engage → Automate → Measure → Learn

This operating model treats opportunity acquisition as an engineered, measurable system rather than a purely passive application or distribution process.

Gatekeeper-Bypass means reducing unnecessary dependence on low-context screening, opaque intermediary decisions, and single-channel acquisition.

It uses:

• verifiable capability proofs;
• public and consented signals;
• qualified decision-maker research;
• multiple legitimate acquisition routes;
• respectful, low-volume engagement;
• explicit evidence and stop rules;
• measurable experiments and learning loops.

It does not mean unauthorised access, deception, fabricated evidence, identity manipulation, data poisoning, harassment, or bypassing an explicit refusal.

The first DirectGTM-OS reference implementation is career acquisition.

It explores how a Growth Engineer can:

1. identify suitable roles and organisations;
2. research problems, requirements and decision contexts;
3. detect evidence-backed opportunity signals;
4. qualify fit, constraints and negative ICP conditions;
5. map requirements to verifiable capability proofs;
6. select legitimate direct and intermediary routes;
7. measure progression and outcomes;
8. turn results into reusable learning.

The underlying system is designed to generalise later to consulting, partnership and B2B opportunity acquisition.

Public repositories may include:

• reusable schemas and code;
• documented methodologies;
• synthetic fixtures;
• anonymised or aggregated results;
• reproducible SQL and engineering proofs;
• public-source research with clear attribution;
• explicit distinctions between fact, inference, hypothesis and unknown.

Public repositories do not contain:

• private target lists or personal contact data;
• confidential client or employer information;
• private salary limits or negotiation positions;
• unpublished outreach messages;
• credentials, tokens or infrastructure secrets;
• unsupported performance claims;
• private prompts, heuristics or proprietary operating knowledge.

LLM-generated output is never treated as evidence by itself.

• Evidence before assertion
• Reproducibility before performance theatre
• Explicit assumptions and boundaries
• Human review for consequential decisions
• Respectful routes and enforceable stop rules
• Synthetic data before exposing private data
• Measurement before scaling automation
• Learning loops before premature optimisation

DirectGTM-Labs is currently maintained by mingmingcodes.

Website: mingmingcodes.com
Contact: contact@mingmingcodes.com