AI Engineering Notes by Sevada Yenokyan

A practical red-flag checklist for checking an AI contractor before contract: data, integrations, evaluation, human approval and ownership.

$ audit ai_contractor --red-flags
> inspect: data / integrations / ownership
> score: critical / weak / reviewable / strong
> output: reject, audit first or controlled pilot

A practical checklist for evaluating an AI developer before contract: workflow, data, integrations, risk, human review and ownership.

$ interview ai_developer --before-contract
> questions: 25
> score: outcome / data / ownership
> output: audit, prototype or production scope

A practical procurement matrix for choosing between a freelancer, AI studio, agency or internal team for AI projects in Armenia.

$ compare ai_delivery_format --market armenia
> options: freelancer / ai_studio / agency
> inspect: scope / risk / ownership
> output: route by TCO and support

A practical comparison for choosing between vibe-coded prototypes, AI-assisted MVP work and production engineering in Armenia.

$ evaluate vibe_coder --market armenia
> compare: prototype / mvp / engineering
> inspect: data / integrations / tests
> output: route by risk and TCO

A practical selection framework for AI agent projects in Armenia: workflow fit, tool permissions, RAG context, approval boundaries, risks and next steps.

$ evaluate ai_agent_developer --market armenia
> inspect: workflow / tools / approvals
> route: audit / pilot / production agent
> output: criteria-based selection

A practical selection framework for n8n workflow automation in Armenia: process mapping, credentials, errors, approvals, monitoring and handoff.

$ evaluate n8n_specialist --market armenia
> inspect: workflow / credentials / errors
> route: audit / pilot / production automation
> output: criteria-based selection

A practical selection framework for LLM development in Armenia: prompt contracts, context policy, tool boundaries, evaluation, deployment and ownership.

$ evaluate llm_developer --market armenia
> inspect: scope / prompts / context / tools
> route: audit / prototype / production LLM
> output: criteria-based selection

A practical selection framework for RAG development in Armenia: source audit, retrieval design, citations, evaluation, permissions and update workflow.

$ evaluate rag_developer --market armenia
> inspect: sources / permissions / retrieval
> route: audit / prototype / production RAG
> output: criteria-based selection

A practical estimate model for AI automation in Yerevan: workflow, data readiness, integrations, human controls, evaluation, deployment and maintenance.

$ estimate ai_automation --city yerevan
> inspect: workflow / data / integrations / approvals
> route: audit / pilot / production automation
> output: assumption-based range

A practical cost model for AI development in Armenia: discovery, data preparation, integrations, evaluation, deployment, maintenance and hidden post-demo expenses.

$ estimate ai_project_cost --market armenia --year 2026
> inspect: scope / data / integrations / evaluation
> route: audit / pilot / production / maintenance
> output: range before commitment

A practical guide to using an AI audit before development: workflow value, data readiness, integration risk, evaluation, human control and first pilot scope.

$ audit ai_workflow --market armenia
> inspect: workflow / data / integrations / evaluation
> route: no-build / cleanup / prototype / production pilot
> output: decision before development

A practical comparison of AI delivery models in Armenia: independent specialist, freelancer, studio, agency and in-house hire by workflow, TCO, proof and ownership.

$ compare ai_delivery_models --market armenia
> options: specialist / freelancer / studio / agency / in-house
> score: workflow / data / integrations / evaluation
> route: audit, pilot or long-term capability

A profile-oriented proof map for Sevada Yenokyan and aicoding.am: stack, projects, working standards, artifacts, boundaries and next steps.

$ inspect person_entity --name sevada_yenokyan
> proof: case studies / articles / runbooks
> stack: LLM / RAG / automation / product
> route: profile to project brief

A criteria-based comparison for AI specialists in Armenia: delivery models, weighted matrix, TCO, proof quality, risks, scenarios and next steps.

$ compare ai_specialists --market armenia
> avoid: advertising rankings
> score: workflow / data / evaluation
> route: audit, pilot or long-term capability

A practical guide to evaluating an AI developer in Yerevan: local context, remote production delivery, scoring criteria, red flags and next steps.

$ evaluate ai_developer --city yerevan
> check: workflow / data / integrations
> delivery: repo / review / deploy
> route: audit or narrow pilot

A practical business task map for AI specialist work in Armenia: process mapping, data review, RAG, automation, integrations, human review and rollout ownership.

$ map ai_specialist --market armenia
> choose: automation / rag / agent / tool
> control: human review and logs
> rollout: narrow workflow first

A practical guide for evaluating an AI developer in Armenia: workflow fit, data access, integrations, evaluation, ownership, red flags and a production-safe next step.

$ evaluate ai_developer --market armenia
> check: process / data / integrations
> proof: case studies and evaluation method
> risk: demo-only delivery rejected

A browser-based AR manicure try-on MVP for beauty salons: live camera UX, MediaPipe hand tracking, Canvas2D nail masks, PNG nail assets and privacy-first frontend architecture.

$ npm run dev -- --host 127.0.0.1
> camera: active
> tracking: MediaPipe Hand Landmarker
> canvas: 1280x720

A practical explanation of transformer architecture, self-attention, black box AI, AGI limits, and how AI systems gain empirical experience through memory, RAG, tools, agents and feedback.

$ init transformer_context
> attention_map: active
> black_box_risk: explainability_required
> empirical_loop: action -> feedback -> memory

A routed skill family for AI web design, motion design and vibe coding: design direction, frontend quality gates, motion runtime policy, browser QA and multilingual routing.

$ codex skills validate web-design-motion-family
> route intent to focused skills
> choose CSS, Motion, GSAP, Lottie or Rive only when justified
> verify desktop, mobile, console and reduced motion

A conservative project memory router for Codex: ARCHITECTURE.md keeps the durable project map, runbooks keep repeatable procedures, project skills keep reusable AI workflows, and AGENTS.md stays compact.

$ architecture-update
> scan durable project changes
> route facts to architecture, runbooks, or project skills
> keep AGENTS.md compact

A compact memory layer for AI sessions: AGENTS.md keeps permanent rules, summaries handle handoff, plans own large tasks, and diary files preserve durable context without chat noise.

$ read diary/current.md diary/decisions.md
> load compact project state
> preserve durable decisions
> skip transcripts and cosmetic noise

Controlled self-learning for Codex: update skills extract reusable lessons from completed work, reject local project context, and update global skill families only when the lesson is portable.

$ n8n-skill-update --after-session
> scan completed workflow work
> extract reusable failure modes
> patch one skill or report no change

Production-grade Django operating platform for flower retail: CRM, orders, bouquet recipes, inventory, delivery, POS, storefront, multichannel messaging, analytics, integrations and external APIs in one live system.

$ docker compose -f docker/docker-compose.yml ps
> web / db / redis / celery / telegram-bot / nginx: running
$ python manage.py check
> System check identified no issues

Browser-first B2B inbox for multi-channel operator workflows. Django keeps the canonical workspace data while the React client handles the dialog queue, contacts, media and operator runtime.

$ open workspace
> sync server snapshot
> resolve threads / contacts / media
> process dialogs in browser runtime

The Update All architecture separates reusable lessons from local implementation details and routes them to the right family updater skills. The system's default mode is to change nothing until a lesson proves reusable value.

$ update-all --analyze-session
> candidates: reusable_lessons / project_noise
> route: most_specific_family_updater
> default: no_changes