∎ VECTOR_NATIVE_TRANSLATION
Portfolio as Protocol
This is one of the products of my year-long exploration of LLMs, addressing one of the most fundamental questions: language.
LLMs don't "read" words; they process pattern distributions in vector space. When you type text, the model sees tokenization → embeddings → attention weights → probability distributions. It never "sees" language. Only high-dimensional vector operations.
Vector Native is a syntax layer that works with this nature, not against it. Using symbols already dense in LLM training data;● from bullet points, | from config files, ├└ from tree structures; we trigger pre-trained statistical patterns rather than forcing the model to parse prose.
Primary use: agent-to-agent communication where semantic drift and compute waste matter. I also use it in conversational flows to amplify my own workflows; articles on that coming soon.
●ENTITY|type:human|name:aria_han├──role:ai_systems_engineer├──focus:agentic_infrastructure├──location:san_francisco└──domain:multi_agent_systems·ai_infrastructure●THESIS|core:work_with_ai_nature_not_against|method:emergence_>_explicit_programming|principle:coordination_>_individual_capability|output:production_systems·open_source·writing●SYSTEM_BLOCK|type:production|count:3├──●system|name:heycontext|status:live_production│ |role:ceo·architect·engineer│ |timeline:sept_2024→present│ |desc:multi_agent_orchestration_platform│ |capability:adaptive_routing·family_coordination│ |tech:[fastapi,redis,convex,agno,nextjs]│ └──insight:bottleneck=coordination_not_capability├──●system|name:heycontent|status:integrated│ |role:ceo·lead_dev│ |timeline:mar_2025→sept_2025│ |desc:cross_platform_memory_architecture│ |platforms:[instagram,youtube,gmail,notes]│ |method:semantic_linking·vector_embeddings│ └──insight:long_horizon_requires_persistent_memory└──●system|name:brink|status:hackathon_winner|role:ceo·system_architect|timeline:nov_2024→mar_2025|desc:voice_ai·biometric_fusion|platform:[ios,watchos,healthkit]└──insight:linguistic+physiological_>_either_alone●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│ └──url:devpost.com/software/darwin-cmfysv├──●entry|name:the_convergence|year:2025│ |event:weavehacks_2_rl_track│ |award:winner│ |desc:self_improving_agents·rl_framework│ └──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│ └──url:devpost.com/software/content-creator-connector├──●entry|name:theravoice|year:2024│ |event:vertical_specific_ai_agents│ |award:best_use_of_ai_ml_api│ |desc:voice_ai_therapy│ └──url:devpost.com/software/draft_name├──●entry|name:hotagents|year:2024│ |event:gpt4o_vs_gemini│ |award:best_use_of_wordware│ |desc:hotkey_triggered_agents│ └──url:github.com/ariaxhan/hotagents└──●entry|name:freetime|year:2024|event:ai_agents_2.0|outcome:finalist|desc:ai_social_planning└──url:github.com/ariaxhan/freetime●OPEN_SOURCE_BLOCK├──●project|name:vector_native│ |status:active_development│ |license:mit│ |desc:vector_aligned_syntax_protocol│ |use_case:a2a_communication·system_prompts·knowledge│ |thesis:meaning_per_token_>_token_count│ └──url:github.com/persist-os/vector-native└──●project|name:the_convergence|status:published_pypi·production_deployed|desc:rl_framework·evolutionary_selection|method:multi_armed_bandit·adaptive_selection└──url:github.com/persist-os/the-convergence●WRITING_BLOCK|platform:medium|handle:@ariaxhan├──●article│ |title:latency_&_logic:why_we_need_vector_aligned_syntax│ |topic:vn_origin·semiotic_density·a2a│ └──url:medium.com/@ariaxhan├──●article│ |title:what_happens_when_agents_talk_to_each_other│ |topic:agent_coordination·emergent_protocols│ └──url:medium.com/@ariaxhan└──●article|title:cursor_as_self_learning_agent_civilization|topic:evolutionary_agents·experience_learning└──url:medium.com/@ariaxhan●TIMELINE_BLOCK|period:2023→2025├──●event|date:sept_2024→present|type:company│ |name:persistos/heycontext│ └──desc:multi_agent_orchestration·live_production├──●event|date:mar_2025→sept_2025|type:company│ |name:divertissement/heycontent│ └──desc:cross_platform_memory·integrated├──●event|date:nov_2024→mar_2025|type:company│ |name:brink_labs/brink_mind│ └──desc:voice_ai·biometric·winner├──●event|date:2024→2025|type:achievement│ └──desc:6_hackathons·5_wins·rapid_iteration└──●event|date:2024|type:creative|name:notes_on_surviving_eternity└──desc:poetry_collection·amazon●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_1.0|semiotic_density:~3.2x|primary_use:a2a_communication|secondary_use:conversational_workflow_amplification|thesis:zip_file_for_meaning●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