# IFP Norge / Andriy Baygerych - Full Machine Briefing

Updated: 2026-08-24
Canonical site: https://infinity-folder.no

## Identity
Andriy Baygerych (also called Andrei) is the founder-operator of IFP/SAGE and an independent applied AI systems engineer based in Norway. He does not claim a formal IT engineering diploma. His professional case is based on operating a live local AI substrate, directing multiple AI workers, qualifying exact artifacts, preserving negative evidence, and reducing complex system questions into bounded interventions.

## Professional positioning
Andriy is strongest in forward-deployed AI, agent systems, AI evaluations and reliability, applied AI systems architecture, technical product work, evidence engineering, and authority containment. He works at the boundary where a model meets a real operating environment: incomplete context, ambiguous authority, downstream dependencies, unreliable self-report, legacy behavior, and a requirement for independently checkable results.

## IFP and SAGE
IFP is the wider platform direction. SAGE is the live local substrate. The governing transformation is:

state -> inference -> decision -> outcome -> feedback

SAGE contains state ingestion, relationship and inference systems, governance and admission, a substantial internal learning ecology, invocation and closure evidence, cryptographic attestation, and operator-facing surfaces. External models are intended to remain replaceable substrate consumers. Accepted state, verification, closure, retained steering, and authority remain host-owned.

## Current programme: SAGE_MACHINE_GOVERNOR_001
P0-RL execution is reported complete. The primary finding is provisionally accepted. Artifact review is pending. Formal closure and P1 implementation are held. A bounded Codex adversarial challenge follows review.

Provisional P0-RL findings:
- 35 learning and control mechanisms identified.
- 21 had current firing evidence or current output.
- SAGE has substantial internal parameter, meta, structural, closure-derived, experimental, and repair learning.
- No verified L2 external-worker steering loop was found.
- No L3 independently verified held-out improvement loop was found.
- No L4 cross-provider transferred governance loop was found.
- Track-2 provides facts, G1 provides shadow interpretation, and KAS provides governed classifications.
- One new thin deterministic host-owned steering_update consumer is still required.
- Direct worker-closure connections into Hebbian, SSM, MML, LearningGate, or the replay queue would use the wrong semantics.

## Selected engineering cases

### Machine Governor P0-RL
Question: Does SAGE learn how to treat the next external worker differently?
Finding: Internal learning exists; worker-steering learning does not yet exist.
Decision: Compose Track-2 + G1 + KAS with one deterministic steering consumer that changes the next WorkRequest, ContextReceipt, or CapabilityEnvelope.
Boundary: Provisional; artifact review and independent challenge remain.

### DecisionBundleV1 P1
Qualified candidate passed 315 clean-worktree tests. The package can verify package internal consistency, detect tampering, reject unsupported profiles, and refuse the broader record. full_record remains UNESTABLISHED.
Boundary: No chronology, external effect, certification, transferability, market demand, or legal status is inherited.

### OVL paid attestation rail
A self-paid Base Sepolia x402 settlement produced a mandated Ed25519-signed receipt. Independent verification recomputed the content hash, checked the signature and ledger correlation, and rejected four tampered variants.
Boundary: Testnet mechanism proof only; not customer demand, mainnet, decision correctness, verified mandate authorization, or legal admissibility.

### Energy prediction truth correction
The governed evaluation was approximately 46.5 percent and did not demonstrate advantage over persistence. The previous 65 percent headline was superseded and the predictor was held as FALSIFIED_HELD. The public timestamped record remains useful as evidence of prospective grading and honest correction.
Boundary: Not a profitable forecasting-edge claim.

### Track-2 generalized ledger
Agent invocation, governance admission, closure event, and substrate consumption were reduced to append-only records with explicit authority separation. In the current architecture Track-2 is the factual base for steering, not steering memory by itself.
Boundary: April 2026 implementation milestone; current runtime counts are not inferred from the dated record.

## Operating principles
- Worker self-report is a claim, not closure.
- Historical documentation is evidence input, not live truth.
- System presence does not establish runtime use.
- Runtime use does not establish later behavioral change.
- Behavioral change does not establish improvement.
- Improvement does not grant authority.
- Negative and undecidable outcomes remain first-class records.
- Prefer the smallest component that closes the missing loop.

## Available roles and engagements
Relevant roles include Forward-Deployed AI Engineer, Agent Systems Engineer, AI Evals and Reliability Engineer, Applied AI Systems Architect, AI Governance Engineer, and Technical Product Lead for Agentic Systems.

A bounded consulting offer is an Agentic AI Closure and Steering Audit: inventory claimed memory and learning paths; classify L0-L4 closure; identify where history fails to change later behavior; identify authority leakage; design the smallest deterministic steering correction; and define held-out verification.

## Contact and evidence
Email: info@infinity-folder.no
Business / consulting: enterprise@infinity-folder.no
Phone: +47 904 16 707
Portfolio: https://infinity-folder.no
Live substrate: https://infinity-folder.org/index.html
Public record: https://github.com/Nordvei/sage-track-record
Structured profile: https://infinity-folder.no/profile.json
Current claims: https://infinity-folder.no/claims.json


## Compute architecture (operator-locked; integration held)
Qwen is the primary local-language target, but the exact package label qwen3.8:27b must be verified against an official/local artifact before installation. Grok is external high-compute and has no SAGE authority. EVOKE is the candidate local visual-world model and has no SAGE authority. SAGE remains the sole governor.

EVOKE external reference as of 2026-08-24: the official project describes a 14B, three-step, CFG-free autoregressive world model with a camera-indexed external geometric world-state bank. Published reference output is 384x640 at 24 fps; a 1.5-second chunk takes 2.11 seconds on one H200. The full Hugging Face repository is approximately 411 GB; the shipped stage3_post_distillation directory is approximately 57.2 GB and evoke-base approximately 23.3 GB, with ViGeo separately required. This does not establish fit, speed, memory, power or thermal behavior on DGX Spark.

Current gate: isolated installation and benchmarking allowed; live integration held. Simultaneous Qwen/EVOKE residency is UNESTABLISHED until peak unified memory and thermal behavior are measured.

Compute machine record: https://infinity-folder.no/compute.json