{"id":1820,"date":"2026-06-21T13:59:07","date_gmt":"2026-06-21T20:59:07","guid":{"rendered":"http:\/\/macdaddy4sure.ai\/?p=1820"},"modified":"2026-06-21T13:59:07","modified_gmt":"2026-06-21T20:59:07","slug":"augmented-intelligence-v11-0-a-multimodal-personal-cognitive-architecture-for-windows","status":"publish","type":"post","link":"http:\/\/macdaddy4sure.ai\/index.php\/2026\/06\/21\/augmented-intelligence-v11-0-a-multimodal-personal-cognitive-architecture-for-windows\/","title":{"rendered":"Augmented Intelligence v11.0: A Multimodal Personal Cognitive Architecture for Windows"},"content":{"rendered":"\n

Tyler Crockett | Macdaddy4sure.ai<\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

This paper describes Augmented Intelligence v11.0 \u2014 Typed Commands, a large-scale Windows-native software system that integrates multimodal perception, tiered memory, large language model (LLM) inference, desktop automation, and domain-specific knowledge processing into a unified runtime. The system comprises approximately 299 C++ translation units and 309 header modules, orchestrated through a command-driven interaction model in which natural-language-style typed commands (and optionally speech) are parsed and dispatched to hundreds of specialized handlers. Core capabilities include dual-camera vision with TensorFlow and LiteRT-based object detection, dual-microphone audio capture and Whisper transcription, MySQL-backed persistent memory, local and remote LLM backends (Ollama, llama.cpp, and multiple cloud APIs), computer-use automation with goal-directed WikiHow integration, and a U.S. Code legal corpus with full-text search. The architecture is explicitly informed by cognitive psychology constructs\u2014working memory, short-term and long-term recall, and multiple intelligences\u2014translated into concurrent software threads and database schemas. This document provides a descriptive overview of the system\u2019s design, components, and intended research trajectory rather than empirical evaluation results.<\/p>\n\n\n\n

    \n
  1. Introduction<\/li>\n<\/ol>\n\n\n\n

    Contemporary personal AI assistants are predominantly chat-centric: they accept text input, produce text output, and maintain limited session context. Augmented Intelligence pursues a broader thesis: that useful machine cognition for an individual operator requires continuous sensing, structured memory, action in the physical and digital environment, and specialized domain reasoning\u2014not merely conversational fluency.<\/p>\n\n\n\n

    Version 11.0, subtitled \u201cTyped Commands,\u201d reflects a deliberate interface choice. While speech recognition is supported, the primary control surface is a console prompt (>>) accepting tokenized command streams routed through a central interpreter (SpeechCommands.cpp). This design treats the system as an operating environment for augmented cognition rather than a single application.<\/p>\n\n\n\n

    The software is authored by Tyler Crockett under the Apache License 2.0 and is built as a monolithic executable (_AugmentedIntelligence.exe) linking OpenCV, TensorFlow, LiteRT (TensorFlow Lite C API v2.18.0), llama.cpp\/ggml (with optional CUDA), Whisper, MySQL, FFmpeg, Tesseract, Lua, Python, and numerous other native libraries on Windows x64.<\/p>\n\n\n\n

      \n
    1. Background and Motivation<\/li>\n<\/ol>\n\n\n\n

      2.1 Cognitive Foundations<\/p>\n\n\n\n

      The project\u2019s AugmentedIntelligence.cpp header commentary frames design goals using established psychological models:<\/p>\n\n\n\n

        \n
      • Cattell\u2013Horn\u2013Carroll (CHC) factors: crystallized intelligence, fluid intelligence, quantitative reasoning, reading\/writing, short-term and working memory, long-term storage and retrieval, visual and auditory processing, processing speed.<\/li>\n\n\n\n
      • Sternberg\u2019s triarchic theory: analytical, creative, and practical intelligence.<\/li>\n\n\n\n
      • Gardner\u2019s multiple intelligences: bodily-kinesthetic, musical, spatial, linguistic, logical-mathematical, interpersonal, and intrapersonal capacities.<\/li>\n<\/ul>\n\n\n\n

        These are not implemented as formal psychometric instruments; rather, they motivate parallel software subsystems\u2014visual checking threads per academic domain, listening pipelines, reading ingestion, and executive-function-style goal management on the desktop.<\/p>\n\n\n\n

        2.2 Design Goals<\/p>\n\n\n\n

          \n
        1. Ambient awareness \u2014 persistently capture and index what the system sees and hears.<\/li>\n\n\n\n
        2. Recallable memory \u2014 promote salient events from working memory through short-term into long-term storage, queryable via ranked keyword search.<\/li>\n\n\n\n
        3. Grounded action \u2014 type into applications, navigate UIs, pursue procedural goals.<\/li>\n\n\n\n
        4. Verifiable reasoning \u2014 optional ethics, fallacy, bias, abuse, and legal checking across modalities.<\/li>\n\n\n\n
        5. Extensibility \u2014 hundreds of domain modules (\u201cmodes\u201d) activatable by command.<\/li>\n\n\n\n
        6. <\/li>\n\n\n\n
        7. System Architecture<\/li>\n<\/ol>\n\n\n\n

          3.1 Runtime Model<\/p>\n\n\n\n

          The application follows a three-phase lifecycle:<\/p>\n\n\n\n

            \n
          1. Initialization \u2014 legal terms acceptance; _AI::InitDatabases() initializes MySQL schemas (including U.S. Code, book databases, and program tables).<\/li>\n\n\n\n
          2. Configuration \u2014 extensive settings menus backed by MySQL persistence and named application profiles (save\/load\/update configuration snapshots).<\/li>\n\n\n\n
          3. Execution \u2014 \u201cRun Program\u201d invokes _AI::InitThreads(), spawning detached std::thread workers according to enabled settings; a parallel typing-command loop accepts user input.<\/li>\n<\/ol>\n\n\n\n

            \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
            \u2502 Main Thread: Terms \u2192 Menu \u2192 Settings \/ Run Program \u2502
            \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
            \u2502 InitThreads()
            \u25bc
            \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
            \u2502 Background Workers (conditional on settings) \u2502
            \u2502 \u2022 TypingCommands \u2022 Vision (cameras) \u2502
            \u2502 \u2022 Sound \/ Stream \u2022 Whisper (speech) \u2502
            \u2502 \u2022 Visual checking \u2022 Listening checking \u2502
            \u2502 \u2022 Reading \u2022 Computers (desktop agent) \u2502
            \u2502 \u2022 Driving, Gaming, domain-specific analyzers \u2502
            \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518
            \u2502
            \u25bc
            \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510
            \u2502 SpeechCommands \u2014 central command router \u2502
            \u2502 \u2192 LLM APIs, memory, parsers, modes, automation, databases \u2502
            \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518<\/p>\n\n\n\n

            3.2 Command-Centric Integration<\/p>\n\n\n\n

            Nearly all functionality converges on _SpeechCommands::SpeechCommands(vector command), a large dispatch function that:<\/p>\n\n\n\n

              \n
            • Handles emergency shutdown<\/li>\n\n\n\n
            • Routes legal\/HTML parsing (parse federal law, parse html, search us code)<\/li>\n\n\n\n
            • Invokes LLM backends (simple_text, simple_image, multi-API comparison)<\/li>\n\n\n\n
            • Activates domain modes (mode engage )<\/li>\n\n\n\n
            • Delegates to Lua scripts with bindings for memory, settings, sockets, and reinforcement-learning globals<\/li>\n\n\n\n
            • Optionally executes each command in an isolated thread (ExecuteCommandThreaded) for concurrent operation and future GUI terminal visualization<\/li>\n<\/ul>\n\n\n\n

              This hub-and-spoke pattern trades modularity at the linker level for unified observability and consistent memory logging at the command layer.<\/p>\n\n\n\n

                \n
              1. Memory Architecture<\/li>\n<\/ol>\n\n\n\n

                The system implements a four-tier memory model synchronized between in-process buffers and MySQL databases.<\/p>\n\n\n\n

                Tier<\/strong><\/td>Role<\/strong><\/td>Representative globals \/ tables<\/strong><\/td><\/tr>
                Working memory<\/td>Live scratchpad for current cognition<\/td>wm_simple_text, wm_simple_image, vision paths, sound paths, LLM history<\/td><\/tr>
                Short-term memory<\/td>Rolling buffer; promoted to working memory on schedule<\/td>stm_simple_text, stm_simple_image, etc.<\/td><\/tr>
                Long-term memory<\/td>Cross-session persistence<\/td>MySQL long-term memory database<\/td><\/tr>
                Specialized corpora<\/td>Domain-specific structured knowledge<\/td>US Code (us_code_sections), WikiHow goals, passwords, vision\/sound metadata<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                _DatabaseFunctions provides:<\/p>\n\n\n\n

                  \n
                • Ranked keyword search (MemorySearchRanked) across memory tables<\/li>\n\n\n\n
                • U.S. Code FULLTEXT search with SQL LIMIT (not full table scans)<\/li>\n\n\n\n
                • WikiHow article matching for computer goals<\/li>\n\n\n\n
                • Password storage with encryption integration<\/li>\n\n\n\n
                • Music metadata and lyrics search<\/li>\n<\/ul>\n\n\n\n

                  Settings control enable flags and capacity limits (e.g., working_memory_limit, long_term_memory_limit defaulting to 1000 slots).<\/p>\n\n\n\n

                    \n
                  1. Perception Subsystems<\/li>\n<\/ol>\n\n\n\n

                    5.1 Vision<\/p>\n\n\n\n

                    The _Vision module manages up to two configurable cameras (e.g., Depstech webcam, GoPro Hero9) with independent FPS and resolution settings. Capabilities include:<\/p>\n\n\n\n

                      \n
                    • Object detection via TensorFlow SavedModel graphs and\/or LiteRT\/TFLite interpreters<\/li>\n\n\n\n
                    • Specialized detectors for domain-specific classes (ants, birds, vessels, balls, trash, etc.)<\/li>\n\n\n\n
                    • Vision memory \u2014 frames written to D:\/vision_memory and indexed in MySQL<\/li>\n\n\n\n
                    • Visual analysis \u2014 LLM vision models via Ollama (e.g., llama3.2-vision:11b)<\/li>\n\n\n\n
                    • OCR and text pipelines \u2014 Tesseract plus TFLite text detection\/recognition models<\/li>\n\n\n\n
                    • Visual checking \u2014 when enabled, spawns parallel per-domain analysis threads (algebra, biology, ethics, law, engineering, and dozens more) that continuously interpret camera input through subject-specific lenses<\/li>\n<\/ul>\n\n\n\n

                      5.2 Audio and Speech<\/p>\n\n\n\n

                        \n
                      • Microphone 1 \u2014 archival environmental recording (Sound.cpp \u2192 D:\/sound_memory)<\/li>\n\n\n\n
                      • Microphone 2 \u2014 low-latency streaming for speech pipeline (Sound_Streaming)<\/li>\n\n\n\n
                      • Whisper \u2014 local (whisper.lib) or remote server transcription<\/li>\n\n\n\n
                      • Speech commands \u2014 transcribed utterances fed into the same SpeechCommands router as typed input<\/li>\n\n\n\n
                      • Listening checking \u2014 optional parallel domain analyzers on audio (mirroring visual checking architecture)<\/li>\n<\/ul>\n\n\n\n

                        5.3 Reading<\/p>\n\n\n\n

                        The _Reading and _Computers modules support screen and document text ingestion, integrating with working memory and LLM analysis commands (simple_image, reading reasoning flags).<\/p>\n\n\n\n

                          \n
                        1. Reasoning and Language Models<\/li>\n<\/ol>\n\n\n\n

                          6.1 Backend Diversity<\/p>\n\n\n\n

                          Backend<\/strong><\/td>Integration<\/strong><\/td>Notes<\/strong><\/td><\/tr>
                          Ollama<\/td>_LLM3::OllamaAPI<\/td>Primary local\/remote path; supports image models<\/td><\/tr>
                          llama.cpp<\/td>LlamaCppIntegration.cpp<\/td>Direct GGUF inference; llama_cpp_cuda_enabled gates GPU layers<\/td><\/tr>
                          Cloud APIs<\/td>Settings per provider<\/td>ChatGPT, Grok, Gemini, Llama API, Mistral, Claude<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                          Responses can be logged to working memory, stored in MySQL, and printed comparatively across providers. Optional ethics_check_enabled gates generation.<\/p>\n\n\n\n

                          6.2 Command Patterns<\/p>\n\n\n\n

                            \n
                          • simple_text \u2014 text-only LLM inference<\/li>\n\n\n\n
                          • simple_image \u2014 vision-conditioned inference<\/li>\n\n\n\n
                          • Multi-API mode \u2014 parallel requests with unified output formatting (PrintOutput)<\/li>\n<\/ul>\n\n\n\n

                            Threading is configurable (typing_commands_threads) to avoid blocking the command prompt during long inference.<\/p>\n\n\n\n

                              \n
                            1. Action and Computer Use<\/li>\n<\/ol>\n\n\n\n

                              7.1 Desktop Agent (_Computers)<\/p>\n\n\n\n

                              The computer-use subsystem treats the Windows desktop as an observable, actionable environment:<\/p>\n\n\n\n

                                \n
                              • Desktop capture (CaptureDesktop), cursor\/window tracking<\/li>\n\n\n\n
                              • Active window identification and task logging to MySQL<\/li>\n\n\n\n
                              • Goal management \u2014 SetGoal, WikiHow solution matching, step-indexed procedure execution<\/li>\n\n\n\n
                              • On-screen text detection (Tesseract + TFLite) and object-detection assist during UI navigation<\/li>\n\n\n\n
                              • AES-backed password retrieval for automated login flows<\/li>\n\n\n\n
                              • Speech-command handlers for enabling\/disabling \u201ccomputer aids\u201d<\/li>\n<\/ul>\n\n\n\n

                                7.2 Keyboard Automation (_Typing)<\/p>\n\n\n\n

                                A queued typing service uses Win32 SendInput for character-by-character injection, with optional vision-based virtual keyboard detection. This bridges LLM outputs and procedural plans into real application interaction.<\/p>\n\n\n\n

                                7.3 Creative Application Modules<\/p>\n\n\n\n

                                Dedicated handlers exist for Adobe Creative Cloud applications (Photoshop, Premiere, Illustrator, After Effects, Audition, Bridge, Lightroom, InDesign, Dreamweaver, Flash, Animate, Media Encoder), indicating intent to automate professional media workflows.<\/p>\n\n\n\n

                                  \n
                                1. Domain Knowledge Modules<\/li>\n<\/ol>\n\n\n\n

                                  Beyond core infrastructure, the project contains hundreds of paired .hpp\/.cpp modules, each encapsulating a subject domain. Representative categories:<\/p>\n\n\n\n

                                    \n
                                  • Formal sciences: algebra, calculus, geometry, logic, graph theory, cryptography<\/li>\n\n\n\n
                                  • Natural sciences: biology, chemistry, physics, ecology, geology, astronomy<\/li>\n\n\n\n
                                  • Engineering: aerospace, civil, electrical, mechanical, structural, computer hardware<\/li>\n\n\n\n
                                  • Social sciences: economics, political science, psychology, ethics, game theory<\/li>\n\n\n\n
                                  • Applied skills: cooking, camping, driving, farming, navigation<\/li>\n\n\n\n
                                  • Sports and games: baseball, basketball, football, golf, chess (with ChessBot), billiards; League of Legends vision pipeline (GamingLoL with TFLite champion\/skin classifiers); RL policy server integration (GamingRL)<\/li>\n<\/ul>\n\n\n\n

                                    Domains are engaged via mode engage commands and may spawn physics, geometry, or simulation state tracked for a planned Dear ImGui visualization layer.<\/p>\n\n\n\n

                                      \n
                                    1. Data Infrastructure and Legal Corpus<\/li>\n<\/ol>\n\n\n\n

                                      9.1 MySQL as System of Record<\/p>\n\n\n\n

                                      Settings, memory tiers, vision\/sound metadata, computer logs, passwords, and legal corpora reside in MySQL (ai_settings database and domain-specific databases). The SettingsMySQL layer provides:<\/p>\n\n\n\n

                                        \n
                                      • SaveAll \/ LoadAll across categorized settings tables (llm_settings, whisper_settings, driving_settings, etc.)<\/li>\n\n\n\n
                                      • Application profile CRUD (application_profiles, application_profile_data)<\/li>\n\n\n\n
                                      • Seeding of empty tables on first run<\/li>\n<\/ul>\n\n\n\n

                                        Recent work added llama_cpp_cuda_enabled to llm_settings and profile snapshots.<\/p>\n\n\n\n

                                        9.2 U.S. Code Integration<\/p>\n\n\n\n

                                        HtmlParser ingests federal law HTML (recursively through subdirectories). Sections are stored in us_code.us_code_sections with a FULLTEXT index (ft_us_code_search). Commands such as search us code invoke ranked SQL search (keywordSearchUsCodeRanked) rather than exhaustive scans.<\/p>\n\n\n\n

                                        9.3 Remote Storage<\/p>\n\n\n\n

                                        FTP settings support offloading vision and sound archives to remote hosts (e.g., ftp:\/\/macdaddy4sure.ai), with configurable storage limits.<\/p>\n\n\n\n

                                          \n
                                        1. Machine Learning and Acceleration Stack<\/li>\n<\/ol>\n\n\n\n
                                          Component<\/strong><\/td>Version \/ Path<\/strong><\/td>Function<\/strong><\/td><\/tr>
                                          LiteRT (TFLite C API)<\/td>v2.18.0 in third_party\/litert\/<\/td>Object detection, text OCR, LoL classifiers<\/td><\/tr>
                                          TensorFlow C API<\/td>SavedModel graphs<\/td>COCO detection, legacy pipelines<\/td><\/tr>
                                          llama.cpp \/ ggml<\/td>CUDA 12.5 build<\/td>Local GGUF LLM inference<\/td><\/tr>
                                          Whisper<\/td>whisper.lib<\/td>Speech-to-text<\/td><\/tr>
                                          OpenCV 4.9<\/td>opencv_world490<\/td>Capture, processing, DNN<\/td><\/tr>
                                          CUDA<\/td>12.5 (aligned with ggml-cuda)<\/td>GPU matrix ops, LLM layers<\/td><\/tr>
                                          Tesseract 5.5<\/td>OCR<\/td>Screen and document text<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                                          Post-build deployment copies runtime DLLs (tensorflowlite_c.dll, cudart64_12.dll, cublas64_12.dll, etc.) adjacent to the executable.<\/p>\n\n\n\n

                                            \n
                                          1. User Interface and Observability<\/li>\n<\/ol>\n\n\n\n

                                            11.1 Console Interface<\/p>\n\n\n\n

                                            The primary UI is a text console: main menu, nested settings wizards, and the >> command prompt. A help command prints the command catalog, organized into topics (emergency, knowledge, socket, gaming, computer, driving, reading, simple modes, chat, HTML, analysis, memory, settings, navigation, Lua, domains).<\/p>\n\n\n\n

                                            11.2 Planned GUI (Gui.hpp)<\/p>\n\n\n\n

                                            A Dear ImGui + DirectX 11 dashboard is scaffolded for:<\/p>\n\n\n\n

                                              \n
                                            • Live camera feeds with object-detection overlays<\/li>\n\n\n\n
                                            • Audio waveform and spectrogram panes<\/li>\n\n\n\n
                                            • Per-thread terminal windows (integrating with ExecuteCommandThreaded logging)<\/li>\n\n\n\n
                                            • Mode-specific physics\/geometry visualizations<\/li>\n<\/ul>\n\n\n\n

                                              The GUI is designed for hybrid operation alongside the existing console.<\/p>\n\n\n\n

                                                \n
                                              1. Security, Ethics, and Legal Compliance<\/li>\n<\/ol>\n\n\n\n
                                                  \n
                                                • Recording disclaimer at startup documents U.S. state wiretap consent regimes (one-party vs. all-party).<\/li>\n\n\n\n
                                                • Ethics module can gate LLM outputs.<\/li>\n\n\n\n
                                                • Multi-modal checking \u2014 fallacy, bias, abuse, and law analyzers across visual, listening, and reading pipelines.<\/li>\n\n\n\n
                                                • Encryption for stored credentials.<\/li>\n\n\n\n
                                                • Private computer and hostname-based access settings.<\/li>\n\n\n\n
                                                • <\/li>\n<\/ul>\n\n\n\n
                                                    \n
                                                  1. Implementation Status and Engineering Notes<\/li>\n<\/ol>\n\n\n\n

                                                    During recent development sessions, several integration issues were addressed:<\/p>\n\n\n\n

                                                      \n
                                                    1. llama.cpp CUDA \u2014 ggml-cuda.lib built with shared CUDA runtime; project pinned to CUDA 12.5 (resolving cudaGetDeviceProperties_v2 link errors caused by CUDA 13.0 cudart.lib mismatch).<\/li>\n\n\n\n
                                                    2. LiteRT upgrade \u2014 TensorFlow Lite upgraded to v2.18.0; binaries installed under third_party\/litert\/ with post-build DLL copy to x64\/Release\/.<\/li>\n\n\n\n
                                                    3. Settings persistence \u2014 llama_cpp_cuda_enabled wired through Settings menu, MySQL llm_settings, and application profiles.<\/li>\n\n\n\n
                                                    4. U.S. Code search \u2014 FULLTEXT indexing and bounded SQL queries; recursive HTML folder parsing.<\/li>\n<\/ol>\n\n\n\n

                                                      The codebase exhibits organic growth characteristics: an extremely large SpeechCommands.cpp, hundreds of domain files, and settings menus numbering thousands of lines. This reflects iterative feature accretion over multiple versions (v4.5 through v11.0).<\/p>\n\n\n\n

                                                        \n
                                                      1. Discussion<\/li>\n<\/ol>\n\n\n\n

                                                        14.1 Strengths<\/p>\n\n\n\n

                                                          \n
                                                        • Unusually broad integration surface for a personal system: perception, memory, action, and reasoning in one process.<\/li>\n\n\n\n
                                                        • Explicit memory hierarchy with database backing, enabling longitudinal study of an operator\u2019s digital life.<\/li>\n\n\n\n
                                                        • Backend-agnostic LLM layer supporting local privacy-preserving inference and cloud augmentation.<\/li>\n\n\n\n
                                                        • Legal and procedural knowledge (U.S. Code, WikiHow goals) moves beyond generic chat into structured corpora.<\/li>\n<\/ul>\n\n\n\n

                                                          14.2 Limitations<\/p>\n\n\n\n

                                                            \n
                                                          • Monolithic architecture complicates testing, packaging, and modular deployment.<\/li>\n\n\n\n
                                                          • Windows-only with heavy native dependencies.<\/li>\n\n\n\n
                                                          • Empirical evaluation \u2014 no standardized benchmarks, user studies, or accuracy metrics are embedded in the repository.<\/li>\n\n\n\n
                                                          • Command discoverability \u2014 the vast command surface requires help and experience; no graphical command builder is complete.<\/li>\n\n\n\n
                                                          • Resource intensity \u2014 simultaneous vision, listening, checking, and LLM threads can stress CPU, GPU, and memory.<\/li>\n<\/ul>\n\n\n\n
                                                              \n
                                                            1. Future Work<\/li>\n\n\n\n
                                                            2. Complete Dear ImGui dashboard and thread-terminal observability.<\/li>\n\n\n\n
                                                            3. Formalize command grammar and publish machine-readable specification.<\/li>\n\n\n\n
                                                            4. Empirical evaluation of memory recall, legal search precision, and computer-use goal completion.<\/li>\n\n\n\n
                                                            5. Modularize SpeechCommands into plugin-style dispatch tables.<\/li>\n\n\n\n
                                                            6. Cross-platform abstraction layer (currently Windows-specific: Win32, DXGI, SendInput).<\/li>\n\n\n\n
                                                            7. Unified model registry for TensorFlow, LiteRT, and ONNX interchangeability.<\/li>\n\n\n\n
                                                            8. Federated memory sync across machines via existing networking\/socket infrastructure.<\/li>\n\n\n\n
                                                            9. <\/li>\n<\/ol>\n\n\n\n
                                                                \n
                                                              1. Conclusion<\/li>\n<\/ol>\n\n\n\n

                                                                Augmented Intelligence v11.0 represents an ambitious attempt to build a personal cognitive architecture on commodity PC hardware. By coupling continuous multimodal sensing, tiered MySQL-backed memory, diverse LLM backends, desktop automation, and hundreds of domain-specific analyzers under a unified typed-command interface, the system pursues a vision of AI as an ambient, actionable, and memory-rich partner rather than a stateless chat endpoint. While formal evaluation remains future work, the codebase documents a comprehensive research prototype spanning computer science, cognitive science, law, robotics-adjacent perception, and human-computer interaction\u2014a descriptive foundation for continued empirical and engineering study.<\/p>\n\n\n\n

                                                                References (Conceptual)<\/p>\n\n\n\n

                                                                  \n
                                                                • Cattell\u2013Horn\u2013Carroll theory of cognitive abilities<\/li>\n\n\n\n
                                                                • Sternberg\u2019s triarchic theory of intelligence<\/li>\n\n\n\n
                                                                • Gardner\u2019s theory of multiple intelligences<\/li>\n\n\n\n
                                                                • Google LiteRT (formerly TensorFlow Lite) C API documentation<\/li>\n\n\n\n
                                                                • llama.cpp \/ ggml GPU backend documentation<\/li>\n\n\n\n
                                                                • OpenAI Whisper speech recognition<\/li>\n\n\n\n
                                                                • U.S. Code corpus (House Office of the Law Revision Counsel)<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

                                                                  Tyler Crockett | Macdaddy4sure.ai Abstract This paper describes Augmented Intelligence v11.0 \u2014 Typed Commands, a large-scale Windows-native software system that integrates multimodal perception, tiered memory, large language model (LLM) inference, desktop automation, and domain-specific knowledge processing into a unified runtime. The system comprises approximately 299 C++ translation units and 309 header modules, orchestrated through a […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-1820","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1820","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/comments?post=1820"}],"version-history":[{"count":3,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1820\/revisions"}],"predecessor-version":[{"id":1823,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/posts\/1820\/revisions\/1823"}],"wp:attachment":[{"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/media?parent=1820"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/categories?post=1820"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/macdaddy4sure.ai\/index.php\/wp-json\/wp\/v2\/tags?post=1820"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}