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- Sovereign Ada RTX
- What This Is
- Architecture
- Cold Boot Protocol
- RTX Sovereign Driver
- Python: VC Dimension Calculator
- C: Sovereign Entropy Runtime
- SystemVerilog: Formal Assertions
- Build & Run
- Source Layout
- Key Constants
- Design Principles
- Philosophical Foundation: Lovelace's Objection
- Related Repositories
- Sovereign Source License v1.0
- What This Is
Sovereign Ada RTX
A complete deterministic computer recreated in pure Ada β from first principles to Resonant Tensor Exchange.
What This Is
This is not a library. This is not a framework. This is a computer.
Sovereign Ada RTX is a fully deterministic, fail-closed processor recreated from first principles in pure Ada β a language designed for air-gapped, safety-critical systems where a single undefined behavior can kill. Every component is built from the ground up: no operating system dependencies, no runtime allocations, no external libraries, no realloc, no garbage collector, no OS syscalls. Just Ada, the compiler, and the hardware.
The system implements a complete computing stack:
| Layer | What It Recreates | Ada Package |
|---|---|---|
| Type System | CPU registers, word sizes, bus widths | Core_Types |
| Arithmetic | ALU with overflow detection, saturating math | Math_Utils |
| Memory | Fixed-capacity RAM with bounds checking | Memory_Structures |
| Configuration | BIOS/UEFI config registers | Config |
| Validation | Hardware fault detection, range checks | Validation |
| Integrity | CRC/ECC memory protection | Integrity |
| Serialization | Binary bus protocol, wire format | Serialization |
| Parser | Instruction decoder, opcode dispatch | Parser |
| State Machine | CPU control unit, fetch-decode-execute cycle | State_Machine |
| Dispatcher | Instruction execution engine | Dispatcher |
| Error Handling | Machine check exception, fail-closed trap | Errors |
| Self-Test | POST (Power-On Self-Test) | Self_Test |
| Benchmarks | Performance validation suite | Benchmark |
| RTX Engine | Resonant Tensor Exchange processor | RTX_Sovereign_Driver |
| CNN | Convolutional neural network forward pass | CNN_Engine |
| SVM | Support vector machine margin optimizer | SVM_Margin |
| String Utils | BIOS string operations | String_Utils |
| Status Utils | Hardware status register classification | Status_Utils |
| Buffer Stats | Memory health monitoring | Buffer_Stats |
| Math Const | Fixed-point mathematical constants | Math_Const |
| Command Utils | Opcode classification table | Command_Utils |
Every function in this system has known, bounded behavior. There are no hidden allocations. No pointer aliasing. No race conditions. No undefined behavior. The compiler enforces this at build time.
Architecture
graph TB
subgraph "Sovereign Ada RTX β Deterministic Processor"
subgraph "CPU Core"
CT[Core_Types<br/>Registers & Bus]
MU[Math_Utils<br/>ALU]
ER[Errors<br/>Fault Handler]
end
subgraph "Memory Subsystem"
MS[Memory_Structures<br/>RAM Controller]
CF[Config<br/>BIOS Registers]
BS[Buffer_Stats<br/>Health Monitor]
end
subgraph "Execution Pipeline"
PA[Parser<br/>Instruction Decoder]
SM[State_Machine<br/>Control Unit]
DI[Dispatcher<br/>Execution Engine]
VL[Validation<br/>Fault Detection]
end
subgraph "Data Integrity"
IG[Integrity<br/>ECC/CRC Engine]
SE[Serialization<br/>Wire Protocol]
SU[String_Utils<br/>String Operations]
end
subgraph "Neural Compute"
RTX[RTX_Sovereign_Driver<br/>256-Dim Tensor Engine]
CNN[CNN_Engine<br/>2D Convolution]
SVM[SVM_Margin<br/>Margin Optimizer]
end
subgraph "Self-Validation"
ST[Self_Test<br/>40-Test POST]
BM[Benchmark<br/>9-Test Suite]
CU[Command_Utils<br/>Opcode Table]
MC[Math_Const<br/>Fixed-Point Constants]
SU2[Status_Utils<br/>Status Register]
end
subgraph "REPL"
MA[main.adb<br/>Cold Boot REPL]
end
CT --> MU
CT --> ER
CT --> MS
CF --> DI
MS --> SE
PA --> SM
SM --> DI
DI --> VL
DI --> IG
DI --> RTX
RTX --> CNN
RTX --> SVM
ST --> CT
BM --> MU
MA --> PA
MA --> ST
MA --> BM
MA --> RTX
end
style CT fill:#1a1a2e,stroke:#e94560,color:#fff
style RTX fill:#0f3460,stroke:#e94560,color:#fff
style ST fill:#16213e,stroke:#e94560,color:#fff
style MA fill:#533483,stroke:#e94560,color:#fff
State Machine
stateDiagram-v2
[*] --> Idle
Idle --> Initializing : START
Initializing --> Configured : INIT_OK
Configured --> Awaiting_Input : READY
Awaiting_Input --> Parsing : INPUT
Parsing --> Validating : PARSED
Validating --> Processing : VALID
Processing --> Serializing : PROCESSED
Processing --> Checking_Integrity : CHECK
Processing --> Authorized : DIRECT
Processing --> Awaiting_Input : LOOP
Serializing --> Checking_Integrity : SERIALIZED
Checking_Integrity --> Authorized : VERIFIED
Checking_Integrity --> Awaiting_Input : RECOVER
Authorized --> Awaiting_Input : NEXT
Authorized --> Processing : PROCESS
Authorized --> Shutdown : EXIT
Authorized --> Error_State : FAIL
Error_State --> Idle : RECOVER
Error_State --> Shutdown : HALT
Error_State --> Awaiting_Input : SOFT_RESET
Shutdown --> [*]
Cold Boot Protocol
When the processor starts, it executes the following deterministic cold boot sequence:
========================================
ADA DETERMINISTIC PROCESSOR v1.0
Pure Ada, no external dependencies
========================================
Build: pure Ada standard library only
Architecture: core types + config + parser + state machine + validation
+ math utils + memory + serialization + integrity + dispatcher + self-test
+ RTX sovereign driver + benchmark suite
Initializing subsystems...
Phase 1: Subsystem Initialization
Dispatcher.Init_Context (Ctx)
βββ State_Machine.Init_Machine (Ctx.SM) β State: Idle
βββ Config.Init_Config (Ctx.Cfg) β Table: empty
βββ Config.Apply_Defaults (Ctx.Cfg) β 7 defaults loaded
β βββ Key_Max_Length = 64
β βββ Key_Timeout = 1000
β βββ Key_Auth_Level = 0 (None)
β βββ Key_Checksum_Seed = 0xADA1
β βββ Key_Enable_Strict = 1
β βββ Key_Buffer_Limit = 128
β βββ Key_Math_Mode = 0
βββ Memory_Structures.Init_Store (Ctx.Store) β 0 items
βββ Memory_Structures.Init_Buffer (Ctx.Buf) β 0 bytes
βββ Ctx.Auth := None
βββ Ctx.Seed := 0xADA1
Phase 2: Power-On Self-Test (40 Tests)
The system runs a comprehensive self-test covering every package:
=== SOVEREIGN ADA RTX SELF TEST ===
Tests: 40
Math Operations:
[PASS] Safe_Add_Normal β 10 + 20 = 30
[PASS] Safe_Add_Overflow β 2^31-1 + 1 saturates
[PASS] Safe_Sub_Under β 5 - 10 underflows to 0
[PASS] ISqrt β sqrt(100) = 10
[PASS] Power2 β 16 is power of 2, 15 is not
[PASS] GCD_LCM β gcd(12,8)=4, lcm(4,6)=12
[PASS] Align β align_up(5,4)=8, align_down(7,4)=4
[PASS] NextPow2 β next_pow2(5) = 8
[PASS] Log2 β log2(16) = 4
Memory Operations:
[PASS] Buffer_Ops β append byte, check length
[PASS] Buffer_Fill β fill buffer with value
[PASS] Buffer_Eq β compare two identical buffers
[PASS] Store_Insert β insert + find by ID
[PASS] Store_Remove β remove by ID, verify count
[PASS] Invalid_Item β reject invalid data item
Serialization:
[PASS] Header_Ser β serialize header = 7 bytes
[PASS] Header_De β deserialize + verify magic 0xADA1
[PASS] Item_Ser β serialize data item
[PASS] Item_De β deserialize + verify fields
Integrity:
[PASS] Integrity_Ver β verify correct checksum
[PASS] Integrity_Fail β detect corrupted checksum
State Machine:
[PASS] State_Trans β valid transition: Idle β Initializing
[PASS] State_Invalid β reject invalid: Initializing β Serializing
Configuration:
[PASS] Config_Set β set + get max_length = 32
[PASS] Config_Bad β reject out-of-range value
[PASS] Config_Count β count set entries = 2
Dispatch:
[PASS] Dispatch β context state = Idle
Parser:
[PASS] Parse_Help β "HELP" β Cmd_Help
[PASS] Parse_Bad β "NOCMD" β Parse_Error
Validation:
[PASS] Validate_Buf β length 100 > max 64 β Buffer_Overflow
[PASS] Validate_Item β invalid item β Invalid_Input
[PASS] Auth_Deny β Guest < Admin β Authorization_Denied
[PASS] Auth_Ok β Admin >= Operator β Success
Store Serialization:
[PASS] Store_Ser β serialize 5 items
[PASS] Store_De β deserialize + verify count = 5
Bit Operations:
[PASS] Rotate β rotate_left(1, 4) = 16
String Operations:
[PASS] String_Len β "HELLO" length = 5
[PASS] String_Upper β "test" β "TEST"
Status Classification:
[PASS] Status_Utils β is_success(Success)=T, is_failure(Overflow)=T
[PASS] Severity β Integrity_Failure severity = 4
Buffer Statistics:
[PASS] Buf_Occ β 64/128 = 50% occupancy
[PASS] Buf_Avg β average of four 10s = 10
Command Classification:
[PASS] Cmd_Utils β Help is query, SetConfig is mutating
Mathematical Constants:
[PASS] Math_Const β factorial(5) = 120
[PASS] Fibonacci β fibonacci(10) = 55
OVERALL: PASS
Tests executed: 40
Phase 3: Benchmark Suite (9 Tests)
After self-test passes, the system runs performance benchmarks:
=== SOVEREIGN RTX BENCHMARK SUITE ===
Benchmarks: 9
------------------------------------
[PASS] GCD_48_18 iters=1 cycles=4
[PASS] Fib_10 iters=1 cycles=177
[PASS] Fact_10 iters=1 cycles=10
[PASS] Pow_2_10 iters=1 cycles=10
[PASS] Sum_100 iters=1 cycles=100
[PASS] GCD_Large iters=1 cycles=6
[PASS] Fib_20 iters=1 cycles=21891
[PASS] Safe_Add_Bench iters=1000 cycles=1000
[PASS] ISqrt_Bench iters=100 cycles=100
RESULT: ALL BENCHMARKS PASSED
Phase 4: RTX Engine Initialization
Initializing RTX Sovereign Engine...
RTX engine initialized (256-dim tensor, cold-boot state).
Tensor weights: all 1, bias: 0
Phase 5: Ready
Self-tests passed. System ready.
Type HELP for commands, EXIT to quit.
>
RTX Sovereign Driver
The Resonant Tensor Exchange (RTX) is a 256-dimensional fixed-point tensor processor implemented in pure Ada. It performs deterministic neural network inference and margin optimization without floating-point arithmetic, memory allocation, or OS interaction.
Architecture
RTX_Sovereign_Driver
βββ Tensor_State: array (0..255) of Word32
βββ Initialize_Engine: cold-boot to all-ones weights, zero bias
βββ Execute_Forward_Pass: dot product with overflow detection
β βββ For each dimension i in 0..255:
β β βββ Safe_Mul(Features[i], Weights[i]) β check overflow
β β βββ Safe_Add(Acc, Product) β check overflow
β β βββ On overflow: set error, return Word32'Last
β βββ Return Safe_Add(Acc, Bias)
βββ Optimize_Margin: SVM hinge loss gradient update
βββ Forward pass β Projection
βββ Margin = Label Γ Projection
βββ If Margin β₯ 1000: weight decay (L2 regularization)
βββ If Margin < 1000: adjust hyperplane + bias
Forward Pass Example
-- Cold-boot state: all weights = 1, bias = 0
-- Input: features[0..255] = some 256-dim vector
-- Output: Ξ£(features[i] Γ weights[i]) + bias
Features : Tensor_State := (0 => 10, 1 => 20, 2 => 30, others => 0);
Weights : Tensor_State := (others => 1);
Bias : Word32 := 0;
-- Execute_Forward_Pass returns: 10Γ1 + 20Γ1 + 30Γ1 + 0 = 60
Result := RTX_Sovereign_Driver.Execute_Forward_Pass(Features, Weights, Bias, Log);
Margin Optimization
-- Hinge loss: if margin < 1.0 (scaled to 1000), adjust weights
-- If margin β₯ 1000: decay all weights by 1 (saturating)
-- If margin < 1000: add features to weights, add label to bias
Status := RTX_Sovereign_Driver.Optimize_Margin(
Features => Input_Vector,
Label => 1,
Weights => Model_Weights,
Bias => Model_Bias,
Log => Error_Log
);
Safety Guarantees
| Property | Mechanism |
|---|---|
| No overflow | Safe_Mul / Safe_Add with saturation |
| No allocation | Fixed 256-element arrays |
| No undefined behavior | Ada type system + range checks |
| Deterministic timing | No branches on data-dependent paths in inner loop |
| Fail-closed | Any overflow β Arithmetic_Overflow error, return Word32'Last |
Python: VC Dimension Calculator
PAC-learning VC dimension calculator for quantized neural network weight spaces. Given a number of weights d and bits per weight b, computes the number of unique weight vectors and the VC dimension of the resulting linear classifier space.
| Function | Purpose |
|---|---|
count_weight_vectors(d, b) |
2^(d*b) unique quantized weight vectors |
vc_dimension(d) |
d + 1 for linear classifiers in d dimensions |
pacific_bound(d, b, epsilon) |
O(vc / epsilon) sample complexity |
cd python/
python quantized_vc_bounds.py
C: Sovereign Entropy Runtime
CUDA-Q quantum kernel implementing the Sovereign Entropy Theorem. Computes H(X) = -Ξ£ p(x)Β·logβ(p(x)) with dynamic parallelism and entanglement witnesses.
| Kernel | Purpose |
|---|---|
entropy_kernel |
Entropy computation with logβ via bit-shift + Taylor |
witness_kernel |
CHSH inequality: `E(a,b) = β¨Ο |
main |
CUDA-Q qpp::execute + cudaq::sample integration |
cd ../sovereign-entropy-theorem/
nvq++ cudaq/sovereign_entropy.cu -o sovereign_entropy
./sovereign_entropy
SystemVerilog: Formal Assertions
SystemVerilog Assertion (SVA) suite extracted from the Ada processor invariants. Targets formal verification tools (Jasper, VC Formal, OneSpin) or simulation with assertions enabled.
24 assertions across 9 invariant categories:
| Category | Assertions | What It Proves |
|---|---|---|
| Type Ranges | assert_byte_range, assert_word32_value |
All values fit Ada Integer bounds |
| Buffer Capacity | assert_buf_len, assert_buf_full, assert_buf_empty |
Length β€ 128, full/empty flags consistent |
| Data Store | assert_store_count, assert_valid_id |
Count β€ 32, valid items have non-zero ID |
| State Machine | assert_legal_trans, assert_shutdown |
Only valid transitions allowed, Shutdown is absorbing |
| Safe Arithmetic | assert_safe_add, assert_safe_sub, assert_gcd |
Saturation on overflow/underflow, GCD non-negative |
| Integrity | assert_integrity, assert_item_csum |
Checksum match required for integrity claims |
| Serial Header | assert_hdr_magic, assert_hdr_ver, assert_hdr_len |
Magic 0xADA1, version 1β2, length β€ 64 |
| Authorization | (placeholder) | Monotonic auth level lattice |
| Fail-Closed | assert_reset |
Reset returns to ST_IDLE |
cd sv/
# Formal verification
jasper sovereign_invariants.sv
# Or simulation with assertions
vcs -sverilog sovereign_invariants.sv -debug_access+all
Build & Run
Prerequisites
- GNAT Ada compiler (GPL 2022 or later)
gnatchop(comes with GNAT)gnatmake(comes with GNAT)
Build
# Option 1: Build from individual files
cd src/
gnatchop -w sovereign_core.txt # If using monolith
gnatmake main.adb rtx_sovereign_driver.adb -o sovereign_rtx_processor
# Option 2: Build all at once
gnatmake -o sovereign_rtx_processor \
main.adb \
core_types.adb errors.adb math_utils.adb \
memory_structures.adb config.adb validation.adb \
integrity.adb serialization.adb parser.adb \
state_machine.adb dispatcher.adb self_test.adb \
string_utils.adb status_utils.adb buffer_stats.adb \
math_const.adb command_utils.adb benchmark.adb \
rtx_sovereign_driver.adb cnn_engine.adb svm_margin.adb
Run
./sovereign_rtx_processor
Interactive Commands
> HELP
Commands: HELP SET PROCESS STATE SERIALIZE VERIFY TEST RESET AUTH EXIT
> SET 0 32 -- Set max_length to 32
> AUTH 3 -- Set auth level to Admin
> PROCESS 10 20 30 -- Process three bytes
> STATE -- Query state
> SERIALIZE -- Serialize store to buffer
> VERIFY -- Verify store integrity
> RESET -- Reset to idle
> EXIT -- Shutdown
Source Layout
sovereign-ada-rtx/
βββ src/
β βββ core_types.ads / .adb -- Foundational types & enums
β βββ errors.ads / .adb -- Fail-closed error handling
β βββ math_utils.ads / .adb -- Safe arithmetic (30+ functions)
β βββ memory_structures.ads / .adb -- Fixed buffers & stores
β βββ config.ads / .adb -- BIOS-style configuration
β βββ validation.ads / .adb -- Range & transition validation
β βββ integrity.ads / .adb -- Checksum (items, buffers, headers, stores)
β βββ serialization.ads / .adb -- Binary wire protocol
β βββ parser.ads / .adb -- Instruction decoder
β βββ state_machine.ads / .adb -- CPU control unit
β βββ dispatcher.ads / .adb -- Execution engine
β βββ self_test.ads / .adb -- 40-test POST suite
β βββ benchmark.ads / .adb -- 9-test performance suite
β βββ rtx_sovereign_driver.ads / .adb -- 256-dim tensor engine
β βββ cnn_engine.ads / .adb -- 2D convolution + ReLU
β βββ svm_margin.ads / .adb -- SVM margin optimizer
β βββ string_utils.ads / .adb -- Bounded string ops
β βββ status_utils.ads / .adb -- Status classification
β βββ buffer_stats.ads / .adb -- Buffer health monitoring
β βββ math_const.ads / .adb -- Fixed-point constants
β βββ command_utils.ads / .adb -- Opcode classification
β βββ main.adb -- Cold boot REPL
βββ sv/
β βββ sovereign_invariants.sv -- 24 SVA formal assertions
βββ python/
β βββ quantized_vc_bounds.py -- VC dimension calculator
βββ images/
β βββ lovelace_infographic.png -- Lovelace portrait & Note G quote
β βββ menabrea_lovelace_publication.jpg -- 1843 original publication
βββ LICENSE -- Sovereign Source License v1.0
βββ README.md -- This file
Key Constants
| Constant | Value | Purpose |
|---|---|---|
| Magic | 0xADA1 |
Serialization header magic number |
| Default Seed | 0xADA1 |
Checksum seed for integrity verification |
| Max_Buffer_Size | 128 bytes | Fixed RAM capacity |
| Max_Config_Entries | 16 | Configuration register count |
| Max_Serial_Record_Size | 64 bytes | Max serialized record |
| Max_Command_Args | 8 | Max args per instruction |
| Max_Dimensions | 256 | RTX tensor dimensionality |
| Max_Tests | 40 | Self-test count |
| Max_Benches | 16 | Benchmark count |
Design Principles
- Fail-Closed: Every error path terminates or transitions to
Error_State. No silent failures. - Zero Allocation: All memory is statically sized at compile time. No
malloc, no heap. - Deterministic Timing: No dynamic dispatch, no recursion in hot paths, no OS calls.
- Type Safety: Ada's type system prevents buffer overflows, range violations, and uninitialized reads at compile time.
- Saturating Arithmetic: Integer overflow saturates to
Word32'Lastinstead of wrapping. - Verified Integrity: Every data item carries a checksum. Every deserialization verifies the checksum.
- Authenticated Operations: State transitions and config changes require authorization levels.
Philosophical Foundation: Lovelace's Objection
This project is named after Ada Lovelace β not as tribute, but as design constraint. Her 1843 statement about Babbage's Analytical Engine is the specification this processor was built to satisfy.
The Original Text (Note G, 1843)
"The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths. Its province is to assist us in making available what we are already acquainted with."
β Ada Lovelace, Note G, 1843 Translated from Menabrea's Sketch of the Analytical Engine
L.F. Menabrea, "Sketch of the Analytical Engine Invented by Charles Babbage, Esq.," with Notes by the Translator Ada Augusta, Countess of Lovelace. Originally published 1843 in Scientific Memoirs, vol. iii.
What This Means for Software
This is not a claim that the machine is limited to fixed arithmetic sums. It is a claim about origination: the machine executes processes that humans have specified. The processes may be complex, conditional, iterative, or even self-modifying in limited mechanical ways that Babbage contemplated; they remain ordered by us.
Ordering an engine "to adjust its own inner mechanisms based on the patterns it observes" is still an order we issue. In modern terms this is training:
- We define an architecture (layers, connectivity, activation functions).
- We define a loss / objective function.
- We define an optimization procedure (gradient descent and its variants, with fixed update rules).
- We supply data.
- The machine then executes the prescribed update rules millions or billions of times, changing numerical parameters (weights).
The resulting system can generate outputs that are novel combinations or interpolations never explicitly present in the training set. That is real and useful. It is still the execution of a process we designed and ordered. The "emergence" is the behavior of a high-dimensional function approximator under those rules, not an independent origination of analytical relations or concepts outside the space we structured.
Turing's Response
Alan Turing addressed "Lady Lovelace's Objection" directly in "Computing Machinery and Intelligence" (1950). He argued that machines can surprise us because we do not always foresee all consequences of the instructions we give, and that the ability to produce unexpected results does not require the machine to "originate" in a stronger sense. Surprise and statistical novelty are not the same as the kind of origination Lovelace denied.
How Sovereign Ada RTX Embodies This
| Lovelace's Principle | Implementation in This System |
|---|---|
| "No pretensions to originate" | No ML training, no gradient descent, no learned weights β the RTX engine executes fixed-point arithmetic on values we supply |
| "Whatever we know how to order it" | 21 packages, 43 source files, every function has known bounded behavior |
| "Can follow analysis" | State machine enforces valid transitions β the processor cannot leap to an undocumented state |
| "Make available what we are already acquainted with" | The parser, dispatcher, and serialization layer expose data we structured |
| "No power of anticipating" | Fail-closed: any overflow, any invalid state, any checksum mismatch β Error_State. The machine cannot "guess" its way out |
Cognition vs. Sophisticated Calculation
Parallel "mills" (SMs) and specialized matrix engines (Tensor Cores) accelerate the same arithmetic that any Turing-complete machine can perform, only far faster and in greater volume. High-bandwidth memory simply reduces latency for the large parameter tensors required by current models.
None of these features supplies intentionality, understanding, or the capacity to form genuinely new conceptual frameworks independent of the training regime and objective we imposed. They make large-scale statistical learning practical. They do not convert calculation into cognition.
Human thought involves (at minimum) semantic understanding, grounded reference, counterfactual reasoning that is not merely statistical pattern completion, and the capacity to form and revise goals and concepts in ways that are not fully captured by minimizing a fixed loss on a fixed data distribution. Current systems excel at next-token prediction, image synthesis, and other high-dimensional regression/classification tasks. They do not possess the former properties in any demonstrated sense.
The Design Spec
Lovelace's caution remains sound: we should neither overrate nor underrate what these machines do. They follow the analysis (including the meta-analysis of gradient-based learning) that we know how to order them to perform. They assist us in making available patterns latent in the data we supply. They do not originate in the stronger sense she denied.
The design spec for Sovereign Ada RTX is: a machine that can do whatever we know how to order it to perform, and nothing more.
Related Repositories
| Repository | Description |
|---|---|
| clay-institute-p-vs-np | P vs NP formal verification in Lean 4 |
| nvidia-stack | CUDA kernel inventions #7β#11 |
| sovereign-cuda-kernels | HyperKitty loader + CUDA kernels |
| historical-linguistics-agda | Proto-Language reconstruction in Agda |
| sovereign-entropy-theorem | Sovereign Entropy Theorem (Lean 4 + CUDA-Q) |
| perplexity-macro-vm | Ollama Cloud AI chat frontend |
| sovereign-ada-rtx | This repository |
Sovereign Source License v1.0
Copyright (c) 2025 Ahmad Ali Parr, Bel Esprit D'Accord Irrevocable Trust EIN 42-697643
Licensed under the Sovereign Source License v1.0. See LICENSE for full terms.
Author: Ahmad Ali Parr, Jessica Westerhoff Trust: Bel Esprit D'Accord Irrevocable Trust

