∎ 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.
●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