VECTOR_NATIVE_TRANSLATION

Portfolio as Protocol

LLMs process pattern distributions in vector space, not words. Vector Native is a syntax layer that works with this nature—using symbols dense in training data to trigger pre-trained statistical patterns.

Primary use: agent-to-agent communication where semantic drift and compute waste matter.

format: vn_1.1semiotic_density: ~3.4xmeaning_per_token: optimized
Read the origin story
●ENTITY|type:human|name:aria_han
├──role:3x_ceo·ai_systems_architect
├──location:san_francisco
├──hours_in_claude_code:4000+
└──domain:multi_agent_systems·coordination_protocols
●THESIS
|core:coordination_>_capability
|method:theory→architecture→implementation
|output:production_systems·open_source·writing
●SYSTEM_BLOCK|type:production|count:3
├──●system|name:heycontext|status:shipped
|role:ceo·lead_architect·lead_engineer
|timeline:sept_2024→jan_2026
|desc:multi_agent_orchestration_workspace
|capability:agents_coordinate·learn·improve_through_experience
|tech:[fastapi,redis,convex,agno,nextjs]
|status_detail:shipped_to_production
└──insight:bottleneck_is_coordination_overhead_not_individual_capability
├──●system|name:heycontent|status:integrated
|role:ceo·lead_developer
|timeline:mar_2025→sept_2025
|desc:cross_platform_memory_architecture
|platforms:[instagram,youtube,gmail,notes]
|method:semantic_linking·vector_embeddings
|integration:core_tech_in_heycontext
└──insight:long_horizon_work_requires_persistent_memory
└──●system|name:brink_mind|status:testflight_phase
|role:ceo·lead_architect·swiftui_developer
|timeline:nov_2024→mar_2025
|desc:voice_ai_mental_health·biometric_fusion
|platform:[ios,watchos,healthkit]
└──insight:users_need_privacy_first_tool_not_ai_companion
●EVIDENCE_BLOCK|type:hackathons|count:6|outcome:5_wins_1_finalist
├──●entry|name:darwin|year:2025
|event:aws_ai_agents_hackathon
|award:best_use_of_semgrep
|desc:evolutionary_code_generation·models_compete·weak_code_dies·strong_code_survives
└──url:devpost.com/software/darwin-cmfysv
├──●entry|name:the_convergence|year:2025
|event:weavehacks_2_self_improving_agents_google_cloud
|award:reinforcement_learning_track_winner
|desc:self_improving_agents·rl_framework·published_pypi·integrated_heycontext
└──url:devpost.com/software/the-convergence
├──●entry|name:content_creator_connector|year:2025
|event:multimodal_ai_agents
|award:best_use_of_agno
|desc:automated_creator_outreach·finds_mid_size_creators·researches_brand·sends_personalized_emails
└──url:devpost.com/software/content-creator-connector
├──●entry|name:theravoice|year:2024
|event:vertical_specific_ai_agents_hackathon
|award:best_use_of_ai_ml_api
|desc:voice_ai_therapy·aixplain·nlp·tts
└──url:devpost.com/software/draft_name
├──●entry|name:hotagents|year:2024
|event:gpt4o_vs_gemini_hackathon
|award:best_use_of_wordware
|desc:hotkey_triggered_agents·simplify_workflow·condense_llm_use_cases
└──url:github.com/ariaxhan/hotagents
└──●entry|name:freetime|year:2024
|event:ai_agents_2.0_hackathon
|outcome:finalist
|desc:ai_social_planner·coordinates_gatherings·shared_interests
└──url:github.com/ariaxhan/freetime
●OPEN_SOURCE_BLOCK
├──●project|name:kernel
|status:active_development·production_validated
|license:mit
|desc:self_evolving_claude_code_plugin·agentdb_first_methodology
|origin:built_from_failure_paths·not_theory·every_pattern_earned_through_breaking
|hours:4000+_daily_iteration·patterns_extracted_from_real_production_failures
|capability:multi_agent_orchestration·contracts·checkpoints·verdicts
|tech:[claude_code,sqlite,shell]
|validation:enterprise_production_feb_2026
|thesis:representation_is_the_bottleneck·markdown_bad_for_agents·sqlite_good
└──url:github.com/ariaxhan/kernel-claude
├──●project|name:vector_native
|status:active_development
|license:mit
|language:python
|desc:a2a_communication_protocol·3x_semantic_density
|thesis:natural_language_inefficient_for_agent_coordination
|method:meaning_density_>_token_count
|evidence:symbols_trigger_pre_trained_statistical_patterns
└──url:github.com/persist-os/vector-native
└──●project|name:the_convergence
|status:published_pypi·production_deployed
|desc:self_improving_agent_framework·evolutionary_pressure
|thesis:agents_need_evolutionary_pressure_to_improve
|method:multi_armed_bandit·adaptive_selection
|evidence:hackathon_winner_weavehacks_rl_track·integrated_heycontext
|distribution:pypi·github
└──url:github.com/persist-os/the-convergence
●WRITING_BLOCK|platform:medium|handle:@ariaxhan
|philosophy:systems_thinking+technical_depth+clarity
|audience:people_who_want_to_understand_why_not_just_how
├──●article
|title:stop_writing_markdown_start_writing_memory
|thesis:markdown_for_human_eyes·terrible_for_agent_queries·sqlite_better
|category:systems
└──url:medium.com/@ariaxhan/stop-writing-markdown-start-writing-memory-e4a69c57caa9
├──●article
|title:i_put_chatgpt_in_charge_of_claude_code
|thesis:multi_model_orchestration·strategic_observer_vs_executor
|category:agents
└──url:medium.com/@ariaxhan/i-put-chatgpt-in-charge-of-claude-code-7b9bf5bb8ea9
├──●article
|title:i_tested_openais_new_codex_desktop_app
|thesis:ui_is_the_real_product·model_secondary
|category:philosophy
└──url:medium.com/@ariaxhan/i-tested-openais-new-codex-desktop-app-the-ui-is-the-real-product-c2c59bdcb5f6
├──●article
|title:automations_with_claude_code
|thesis:proactive_ai_pattern·local_context·personalized_outputs
|category:systems
└──url:medium.com/@ariaxhan/automations-with-claude-code-personalized-proactive-emails-and-code-poetry-from-local-context-3a7e93bf5a3d
├──●article
|title:kernel_self_evolving_claude_code_configuration
|thesis:config_that_learns_from_how_you_work·agentdb·orchestration·contracts
|category:systems
└──url:medium.com/@ariaxhan/kernel-the-ultimate-self-evolving-claude-code-and-cursor-configuration-system-a3ddeb7f4d32
├──●article
|title:from_friction_to_flow_building_a_command_library
|thesis:commands_as_cognitive_offloading·stop_remembering·start_invoking
|category:systems
└──url:medium.com/@ariaxhan/from-friction-to-flow-building-a-command-library-for-claude-code-a9eb19f7dce2
├──●article
|title:10_things_i_wish_i_knew_about_ai_coding
|thesis:hard_won_lessons·thousands_of_hours·practical_wisdom
|category:philosophy
└──url:medium.com/@ariaxhan/10-things-i-wish-i-knew-when-i-started-using-ai-for-coding-887c26a6c1d1
└──●article
|title:this_ai_analyzes_my_entire_life
|thesis:synthesis_pool·personal_ai·zero_cloud_cost·privacy_first
|category:agents
└──url:medium.com/@ariaxhan/the-synthesis-pool-0ce814fdfa5f
●TIMELINE_BLOCK|period:2024→2026
├──●event|date:jan_2026→present|type:practice
|name:ai_systems_architecture
└──desc:research_through_building·coordination_architectures·agent_protocols·self_improving_systems
├──●event|date:sept_2025→jan_2026|type:company
|name:persistos/heycontext
└──desc:exploring_frontier_ai_concepts·live_with_hundreds_of_users
├──●event|date:mar_2025→sept_2025|type:company
|name:divertissement/heycontent
└──desc:cross_platform_memory·what_breaks_when_synthesizing_multiple_sources·integrated_into_heycontext
├──●event|date:nov_2024→mar_2025|type:company
|name:brink_labs/brink_mind
└──desc:voice_ai·apple_watch_biometric·privacy_first_mental_health·theory_vs_real_humans
├──●event|date:2024→2025|type:achievement
|names:[darwin,convergence,ccc,theravoice,hotagents,freetime]
└──desc:6_hackathons·each_built_in_24_48_hours·validating_ideas_under_pressure
└──●event|date:2024|type:creative
|name:notes_on_surviving_eternity
└──desc:poetry_collection·amazon·exploring_time_fate_free_will
●CONTACT_BLOCK
├──email:[email protected]
├──github:github.com/ariaxhan
├──medium:medium.com/@ariaxhan
├──linkedin:linkedin.com/in/ariahan
└──x:x.com/aria__han
●META
|format:vn_1.1
|semiotic_density:~3.4x
|primary_use:a2a_communication
|secondary_use:conversational_workflow_amplification
|thesis:zip_file_for_meaning
|last_sync:feb_2026
●END_DOCUMENT

SEMIOTIC DENSITY

Not compression;meaning per token. Like a .zip file for semantics. The model already has the "unzipped" definitions.

A2A NATIVE

Primary use: agent-to-agent communication. No semantic drift. No compute wasted on pleasantries between machines.

WORKFLOW AMPLIFICATION

I also use VN in my own conversational flows. Dense system prompts, structured handoffs, reusable patterns.

TRAINING-ALIGNED

Symbols from config files, math, code. Triggers statistical patterns LLMs already know;information expands in context.

●insight|The question isn't "how do we teach AI to understand words like a human?" It's "how do we communicate in a way that works with what they actually are?" VN is one answer: selectively remove unnecessary prose, intentionally use symbols they already recognize. No code required;just prompting with intention.

more articles on conversational VN workflows coming soon