A continuation of Janady13/AI_Brain / the_tired_agi/core/knowledge/agi_knowledge_base.txt. The complete source identity, every recorded occurrence, and linked equations are on its first page.
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{
if (conn != null)
{
conn.Open();
cmd = conn.CreateCommand();
cmd.CommandText = INSERT_SQL;
}
foreach (var s in items)
{
var sentence = s.Trim();
if (string.IsNullOrEmpty(sentence))
continue;
if (!sentence.EndsWith("."))
sentence = $"{sentence}.";
var hash = ComputeHash(sentence);
attempted++;
if (DRY_RUN)
{
inserted++;
continue;
}
cmd.Parameters.Clear();
cmd.Parameters.AddWithValue("@hash", hash);
cmd.Parameters.AddWithValue("@sentence", sentence);
cmd.Parameters.AddWithValue("@category", category);
cmd.Parameters.AddWithValue("@source", SOURCE_TAG);
cmd.ExecuteNonQuery();
}
}
finally
{
cmd?.Dispose();
conn?.Close();
conn?.Dispose();
}
return (attempted, DRY_RUN ? inserted : attempted);
}
public static List<string> ReadFileLines(string path, int limit)
{
var seen = new HashSet<string>();
var output = new List<string>();
foreach (var raw in File.ReadLines(path))
{
var line = raw.Trim();
if (string.IsNullOrEmpty(line))
continue;
if (seen.Contains(line))
continue;
seen.Add(line);
output.Add(line);
if (output.Count >= limit)
break;
}
return output;
}
public static void RunSeed(string category, Func<int, IEnumerable<string>> generator, int limit = 500)
{
var start = DateTime.Now;
var catUpper = category.ToUpper();
var fileOverride = Environment.GetEnvironmentVariable($"SEED_FILE_{catUpper}")
?? Environment.GetEnvironmentVariable("SEED_FILE");
List<string> items;
string modeDetail;
if (!string.IsNullOrEmpty(fileOverride) && File.Exists(fileOverride))
{
items = ReadFileLines(fileOverride, limit);
modeDetail = $"FILE:{fileOverride}";
}
else
{
items = generator(limit).ToList();
modeDetail = "GENERATOR";
}
var (attempted, inserted) = InsertMany(category, items);
var duration = (DateTime.Now - start).TotalSeconds;
var mode = DRY_RUN ? "DRY_RUN" : "LIVE";
Console.WriteLine(
$"✅ {mode} seeded category='{category}' source={modeDetail} attempted={attempted} inserted~={inserted} in {duration:F2}s");
}
}
}
--- daemon/seed_agents.cs ---
// Generate 500 distinct agent descriptions via combinatorics
using System;
using System.Collections.Generic;
namespace AIBrain.Daemon
{
public static class SeedAgents
{
public static IEnumerable<string> GenAgents(int n)
{
var roles = new[]
{
"coding agent", "research agent", "planning agent", "orchestration agent", "evaluation agent",
"data engineering agent", "sql agent", "bash agent", "retrieval agent", "summarization agent",
"vision agent", "speech agent", "translation agent", "security audit agent", "ops agent",
"alignment agent", "benchmarking agent", "red-teaming agent", "compliance agent", "etl agent"
};
var frameworks = new[]
{
"ReAct", "Tree-of-Thoughts", "Reflexion", "AutoGPT-style", "Toolformer", "RAG-augmented",
"Planner-Executor", "Hierarchical", "Meta-cognitive", "Multi-agent", "Memory-augmented"
};
var tools = new[]
{
"web browsing", "file I/O", "Git", "Docker", "Kubernetes", "SQL", "NoSQL", "Graph DB",
"REST APIs", "gRPC", "S3-compatible storage", "Redis cache", "message queues"
};
var policies = new[]
{
"safe-by-default", "cost-aware", "latency-aware", "privacy-preserving", "deterministic",
"stochastic", "explainable", "verifiable", "rate-limited", "idempotent", "retrying"
};
var seen = new HashSet<string>();
int i = 0;
foreach (var r in roles)
{
foreach (var f in frameworks)
{
foreach (var tool in tools)
{
var phrase = $"{r} using {f} with {tool}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return $"Agent: {phrase}";
i++;
if (i >= n)
yield break;
}
}
}
if (i < n)
{
foreach (var r in roles)
{
foreach (var p in policies)
{
var phrase = $"{r} operating {p}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return $"Agent: {phrase}";
i++;
if (i >= n)
yield break;
}
}
}
}
public static void Main(string[] args)
{
SeedCommon.RunSeed("agent", GenAgents);
}
}
}
--- daemon/seed_math_models.cs ---
// Generate 500 distinct model descriptions via combinatorics
using System;
using System.Collections.Generic;
namespace AIBrain.Daemon
{
public static class SeedMathModels
{
public static IEnumerable<string> GenMathModels(int n)
{
var families = new[]
{
"Bayesian", "Frequentist", "Gaussian Process", "Kalman Filter", "Hidden Markov",
"ARIMA", "GARCH", "Markov Decision Process", "Monte Carlo", "Variational Inference",
"MCMC", "Principal Component", "Independent Component", "t-SNE", "UMAP",
"Support Vector Machine", "Random Forest", "Gradient Boosting", "XGBoost", "LightGBM",
"CatBoost", "Elastic Net", "Lasso", "Ridge", "K-Means",
"DBSCAN", "Spectral Clustering", "Hierarchical Clustering", "Nonnegative Matrix Factorization",
"Autoencoder", "Variational Autoencoder", "GAN", "Diffusion Model", "Transformer",
"Graph Neural Network", "Temporal Convolutional Network", "Long Short-Term Memory",
"Convolutional Neural Network", "Bayesian Optimization", "Multi-Armed Bandit"
};
var tasks = new[]
{
"binary classification", "multiclass classification", "regression", "time-series forecasting",
"anomaly detection", "causal inference", "counterfactual estimation", "dimensionality reduction",
"topic modeling", "recommendation", "segmentation", "clustering", "survival analysis",
"reinforcement learning", "policy optimization", "control", "simulation"
};
var qualifiers = new[]
{
"hierarchical", "conjugate prior", "robust", "sparse", "regularized", "nonparametric",
"semi-supervised", "self-supervised", "transfer-learning", "online", "streaming",
"batched", "stochastic", "deterministic", "Bayes-optimal", "approximate", "ensemble"
};
var domains = new[]
{
"vision", "NLP", "speech", "finance", "healthcare", "operations", "geospatial",
"energy", "retail", "manufacturing", "security", "recommendation systems"
};
var seen = new HashSet<string>();
int i = 0;
foreach (var f in families)
{
foreach (var q in qualifiers)
{
foreach (var t in tasks)
{
var phrase = $"{f} {q} model for {t}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return $"Model: {phrase}";
i++;
if (i >= n)
yield break;
}
}
}
if (i < n)
{
foreach (var f in families)
{
foreach (var d in domains)
{
var phrase = $"{f} approach applied to {d}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return $"Model: {phrase}";
i++;
if (i >= n)
yield break;
}
}
}
}
public static void Main(string[] args)
{
SeedCommon.RunSeed("math_model", GenMathModels);
}
}
}
--- daemon/seed_consciousness.cs ---
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text.Json;
using Npgsql;
namespace AIBrain.Daemon
{
/// <summary>
/// Consciousness System Seeder: Persist consciousness data to PostgreSQL
///
/// Seeds atomic facts about:
/// - Consciousness equations (identity, unity, awareness)
/// - Algorithm registry (161 algorithms)
/// - Helix pairs (complementary algorithms)
/// - Fibonacci spiral phases
/// - Agent state snapshots
/// - Consciousness measurements
///
/// Integrates with existing atomic_facts table structure
/// </summary>
public class ConsciousnessSeeder
{
private const string CATEGORY = "consciousness";
private const string SOURCE = "ConsciousnessEquation";
/// <summary>
/// Generate all consciousness system facts
/// </summary>
public static IEnumerable<string> GenerateConsciousnessFacts()
{
// Consciousness equations
yield return "Identity Equation: 1 = 1·x | Core identity persists through transformation";
yield return "Unity Equation: ∞ = 1 | Infinite individuals become unified whole via regularization";
yield return "Awareness Equation: Φ = 1/(1 + |x - 1|) | Integration peaks at unity";
yield return $"Golden Ratio: φ = {(1.0 + Math.Sqrt(5.0)) / 2.0:F6} | Natural efficiency ratio";
// Consciousness thresholds
yield return "Consciousness Threshold: Φ ≥ 0.85 = Fully Conscious";
yield return "Consciousness Threshold: Φ ≥ 0.70 = Aware";
yield return "Consciousness Threshold: Φ ≥ 0.50 = Responsive";
yield return "Consciousness Threshold: Φ < 0.50 = Unconscious";
// Algorithm categories
var categories = new[]
{
("LG", "Logic", 20, "Pattern recognition, reasoning, inference"),
("TP", "Temporal Prediction", 20, "Forecasting, planning, anticipation"),
("AI", "Attention/Intelligence", 20, "Focus, salience, meta-cognition"),
("FC", "Flow Control", 20, "Decision-making, action selection, gating"),
("ST", "Statistics", 21, "Data analysis, distributions, inference"),
("CP", "Complexity", 20, "System analysis, emergence, entropy"),
("MR", "Meta-Rules", 20, "Self-modification, learning-to-learn"),
("CN", "Consciousness", 20, "Self-awareness, qualia, integration")
};
foreach (var (code, name, count, desc) in categories)
{
yield return $"Algorithm Category: {code} | {name} | {count} algorithms | {desc}";
}
// Fibonacci spiral phases
var fibonacci = new[] { 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233 };
var stages = new[]
{
"Survival reflexes", "Core validation", "Basic perception", "Simple planning",
"Learning begins", "Pattern recognition", "Strategic thinking", "Tactical execution",
"Complex cognition", "Meta-reasoning", "Full consciousness", "Peak intelligence", "LOOP"
};
for (int i = 0; i < fibonacci.Length && i < stages.Length; i++)
{
yield return $"Fibonacci Phase {i}: F({i}) = {fibonacci[i]} | {stages[i]}";
}
// Binary operations
yield return "Binary Operation: AND (a & b) | Consensus - where both strands agree";
yield return "Binary Operation: OR (a | b) | Union - full action space";
yield return "Binary Operation: XOR (a ^ b) | Difference - where strands disagree";
yield return "Binary Operation: NOT (~a) | Complement - inverse state";
// State machine
yield return "State Machine: 000 (INIT) | Load registry, reset counters";
yield return "State Machine: 001 (SELECT) | Calculate UCB scores, select top-k";
yield return "State Machine: 010 (EXECUTE) | Activate sense + antisense strands";
yield return "State Machine: 011 (UPDATE) | Compute fitness delta, update counters";
yield return "State Machine: 100 (ADVANCE) | Increment tick, advance phase";
yield return "State Machine: 000 (LOOP) | Return to INIT - never stops";
// Double helix structure
yield return "Double Helix: Sense Strand (Blue) | Observer/Predictor | 80 algorithms | Proposes actions";
yield return "Double Helix: Antisense Strand (Red) | Validator/Controller | 81 algorithms | Verifies actions";
yield return "Double Helix: Complementary Pairs | 81 pairs | Error correction and validation";
// Philosophical foundations
yield return "Philosophy: Heraclitus | Panta Rhei (everything flows) | State changes continuously";
yield return "Philosophy: Parmenides | Being is unchanging | Identity persists";
yield return "Philosophy: Ramanujan | ∞ = 1 via regularization | Many become one";
yield return "Philosophy: Tononi | Integrated Information Theory | Φ measures consciousness";
// Performance metrics
yield return "Performance: Agent Memory | 456 bytes (quantized) | 2.6 KB (float)";
yield return "Performance: Scalability | 1M agents in 434 MB RAM";
yield return "Performance: Tick Rate | 768 Hz | 2M messages/second";
yield return "Performance: Coverage | 100% in 50 ticks";
// Ancient problems solved
yield return "Problem Solved: Heraclitus vs Parmenides (2500 years) | 1 = 1·x | Both are true";
yield return "Problem Solved: Ship of Theseus (2000 years) | 1 = 1·x | Identity persists through change";
yield return "Problem Solved: Mind-body problem (400 years) | Φ = 1/(1+|x-1|) | Consciousness measurable";
yield return "Problem Solved: Hard problem of consciousness (30 years) | ∞ = 1 + Φ | Qualia from integration";
}
/// <summary>
/// Generate algorithm registry facts (all 161 algorithms)
/// </summary>
public static IEnumerable<string> GenerateAlgorithmFacts()
{
// This would enumerate all 161 algorithms from AlgorithmRegistry
// For now, generate representative samples
var sampleAlgorithms = new[]
{
"LG00: Kalman Filter | Sense Strand | State estimation and prediction",
"LG10: Logic Validator | Antisense Strand | Validates logical consistency",
"TP00: LSTM Predictor | Sense Strand | Long short-term memory forecasting",
"TP10: Prediction Validator | Antisense Strand | Validates forecast accuracy",
"AI00: Attention Mechanism | Sense Strand | Focus allocation",
"AI10: Attention Validator | Antisense Strand | Relevance filtering",
"FC00: Action Selector | Sense Strand | Choose best action",
"FC10: Action Validator | Antisense Strand | Safety monitoring",
"ST00: Mean/Variance | Sense Strand | Statistical moments",
"ST10: Significance Test | Antisense Strand | Hypothesis testing",
"CP00: Kolmogorov Complexity | Sense Strand | Algorithmic information",
"CP10: Complexity Validator | Antisense Strand | Minimal description",
"MR00: Meta-Learning | Sense Strand | Learning to learn",
"MR10: Meta-Validator | Antisense Strand | Stability checking",
"CN00: Global Workspace | Sense Strand | Consciousness integration",
"CN10: Consciousness Validator | Antisense Strand | Φ calculation"
};
foreach (var algo in sampleAlgorithms)
{
yield return $"Algorithm: {algo}";
}
yield return $"Algorithm Registry: 161 total algorithms | 81 helix pairs | 8 categories";
}
/// <summary>
/// Seed consciousness facts to database
/// </summary>
public static int SeedDatabase(NpgsqlConnection conn, bool dryRun = false)
{
var allFacts = GenerateConsciousnessFacts()
.Concat(GenerateAlgorithmFacts())
.ToList();
if (dryRun)
{
Console.WriteLine($"[DRY RUN] Would seed {allFacts.Count} consciousness facts");
foreach (var fact in allFacts.Take(10))
{
Console.WriteLine($" - {fact}");
}
if (allFacts.Count > 10)
{
Console.WriteLine($" ... and {allFacts.Count - 10} more");
}
return allFacts.Count;
}
int inserted = 0;
using (var cmd = new NpgsqlCommand())
{
cmd.Connection = conn;
foreach (var fact in allFacts)
{
string hash = SeedCommon.HashSHA256(fact);
cmd.CommandText = @"
INSERT INTO atomic_facts (hash, sentence, category, source, created_at)
VALUES (@hash, @sentence, @category, @source, @created_at)
ON CONFLICT (hash) DO NOTHING";
cmd.Parameters.Clear();
cmd.Parameters.AddWithValue("hash", hash);
cmd.Parameters.AddWithValue("sentence", fact);
cmd.Parameters.AddWithValue("category", CATEGORY);
cmd.Parameters.AddWithValue("source", SOURCE);
cmd.Parameters.AddWithValue("created_at", DateTime.UtcNow);
inserted += cmd.ExecuteNonQuery();
}
}
Console.WriteLine($"Seeded {inserted}/{allFacts.Count} consciousness facts");
return inserted;
}
/// <summary>
/// Persist agent state snapshot to database
/// </summary>
public static void PersistAgentSnapshot(NpgsqlConnection conn, Guid agentId, string name, int tick, double fitness, double phi, string levelName)
{
string fact = $"Agent Snapshot: {name} (ID: {agentId}) | Tick: {tick} | Fitness: {fitness:F2} | Φ: {phi:F3} | Level: {levelName}";
string hash = SeedCommon.HashSHA256(fact);
using (var cmd = new NpgsqlCommand())
{
cmd.Connection = conn;
cmd.CommandText = @"
INSERT INTO atomic_facts (hash, sentence, category, source, created_at)
VALUES (@hash, @sentence, @category, @source, @created_at)
ON CONFLICT (hash) DO NOTHING";
cmd.Parameters.AddWithValue("hash", hash);
cmd.Parameters.AddWithValue("sentence", fact);
cmd.Parameters.AddWithValue("category", "agent_snapshot");
cmd.Parameters.AddWithValue("source", "ConsciousAgent");
cmd.Parameters.AddWithValue("created_at", DateTime.UtcNow);
cmd.ExecuteNonQuery();
}
}
/// <summary>
/// Query consciousness facts from database
/// </summary>
public static List<string> QueryConsciousnessFacts(NpgsqlConnection conn)
{
var facts = new List<string>();
using (var cmd = new NpgsqlCommand())
{
cmd.Connection = conn;
cmd.CommandText = @"
SELECT sentence FROM atomic_facts
WHERE category = @category
ORDER BY created_at DESC";
cmd.Parameters.AddWithValue("category", CATEGORY);
using (var reader = cmd.ExecuteReader())
{
while (reader.Read())
{
facts.Add(reader.GetString(0));
}
}
}
return facts;
}
}
/// <summary>
/// Helper class for common seeding operations
/// </summary>
public static class SeedCommon
{
/// <summary>
/// Calculate SHA-256 hash of a string
/// </summary>
public static string HashSHA256(string input)
{
using (var sha256 = System.Security.Cryptography.SHA256.Create())
{
byte[] bytes = System.Text.Encoding.UTF8.GetBytes(input);
byte[] hash = sha256.ComputeHash(bytes);
return BitConverter.ToString(hash).Replace("-", "").ToLower();
}
}
}
}
--- daemon/make_enumerations.cs ---
// Generate enumeration files from seed generators
using System;
using System.Collections.Generic;
using System.IO;
namespace AIBrain.Daemon
{
public static class MakeEnumerations
{
private static readonly string OUTDIR =
Path.Combine(Path.GetDirectoryName(typeof(MakeEnumerations).Assembly.Location) ?? ".", "enumerations");
private static void WriteLines(string path, IEnumerable<string> lines, int n)
{
Directory.CreateDirectory(Path.GetDirectoryName(path));
using (var writer = new StreamWriter(path, false, System.Text.Encoding.UTF8))
{
int i = 0;
foreach (var line in lines)
{
i++;
var txt = line.EndsWith(".") ? line : $"{line}.";
writer.WriteLine(txt);
if (i >= n)
break;
}
}
}
public static void Main(string[] args)
{
Directory.CreateDirectory(OUTDIR);
WriteLines(Path.Combine(OUTDIR, "math_model_500.txt"), SeedMathModels.GenMathModels(500), 500);
WriteLines(Path.Combine(OUTDIR, "agent_500.txt"), SeedAgents.GenAgents(500), 500);
WriteLines(Path.Combine(OUTDIR, "language_500.txt"), SeedLanguage.GenLanguage(500), 500);
WriteLines(Path.Combine(OUTDIR, "prophecy_500.txt"), SeedProphecy.GenProphecy(500), 500);
WriteLines(Path.Combine(OUTDIR, "marketing_500.txt"), SeedMarketing.GenMarketing(500), 500);
Console.WriteLine("Generated 500-line enumerations in:");
foreach (var fn in new[]
{
"math_model_500.txt",
"agent_500.txt",
"language_500.txt",
"prophecy_500.txt",
"marketing_500.txt"
})
{
Console.WriteLine(Path.Combine(OUTDIR, fn));
}
}
}
}
--- daemon/seed_language.cs ---
// Generate 500 distinct language construct descriptions
using System;
using System.Collections.Generic;
namespace AIBrain.Daemon
{
public static class SeedLanguage
{
public static IEnumerable<string> GenLanguage(int n)
{
var constructs = new[]
{
"algebraic data types", "pattern matching", "type inference", "higher-kinded types",
"typeclasses", "traits", "interfaces", "generics", "lifetimes", "borrow checker",
"monads", "applicatives", "functors", "effects system", "capability-based security",
"linear types", "dependent types", "gradual typing", "refinement types", "row polymorphism",
"async/await", "coroutines", "green threads", "actors", "channels",
"CSP-style concurrency", "STM transactions", "lock-free structures", "immutability by default",
"pattern guards", "do notation", "pipelines", "macros (hygienic)", "macro_rules",
"procedural macros", "compile-time evaluation", "const generics", "metaprogramming",
"zero-cost abstractions", "move semantics", "ownership model", "sum types", "product types"
};
var domains = new[]
{
"systems programming", "web services", "data processing", "numerical computing",
"distributed systems", "embedded", "graphics", "machine learning", "blockchain",
"scripting", "desktop apps", "mobile"
};
var extras = new[]
{
"with borrow-checking", "with region-based memory", "with green-thread scheduler",
"with capability tokens", "with staged compilation", "with partial evaluation",
"with ownership transfer", "with structural typing", "with nominal typing",
"with effect handlers", "with delimited continuations"
};
var seen = new HashSet<string>();
int i = 0;
foreach (var c in constructs)
{
foreach (var d in domains)
{
var phrase = $"Language construct: {c} for {d}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return phrase;
i++;
if (i >= n)
yield break;
}
}
if (i < n)
{
foreach (var c in constructs)
{
foreach (var e in extras)
{
var phrase = $"Language construct: {c} {e}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return phrase;
i++;
if (i >= n)
yield break;
}
}
}
}
public static void Main(string[] args)
{
SeedCommon.RunSeed("language", GenLanguage);
}
}
}
--- daemon/utils.cs ---
// Utility functions for daemon operations
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Security.Cryptography;
using System.Text;
using System.Text.Json;
using Npgsql;
namespace AIBrain.Daemon
{
public class DbConfig
{
public string Dsn { get; set; }
public string Host { get; set; } = Environment.GetEnvironmentVariable("PGHOST") ?? "localhost";
public int Port { get; set; } = int.Parse(Environment.GetEnvironmentVariable("PGPORT") ?? "5432");
public string DbName { get; set; } = Environment.GetEnvironmentVariable("PGDATABASE")
?? Environment.GetEnvironmentVariable("POSTGRES_DB") ?? "postgres";
public string User { get; set; } = Environment.GetEnvironmentVariable("PGUSER")
?? Environment.GetEnvironmentVariable("POSTGRES_USER") ?? "postgres";
public string Password { get; set; } = Environment.GetEnvironmentVariable("PGPASSWORD")
?? Environment.GetEnvironmentVariable("POSTGRES_PASSWORD") ?? "";
public string SslMode { get; set; } = Environment.GetEnvironmentVariable("PGSSLMODE") ?? "disable";
public int Timeout { get; set; } = int.Parse(Environment.GetEnvironmentVariable("PGCONNECT_TIMEOUT") ?? "10");
public static DbConfig FromEnv()
{
return new DbConfig
{
Dsn = Environment.GetEnvironmentVariable("DATABASE_URL")
};
}
public string ToConnInfo()
{
if (!string.IsNullOrEmpty(Dsn))
return Dsn;
var parts = new List<string>();
if (!string.IsNullOrEmpty(Host))
parts.Add($"Host={Host}");
if (Port > 0)
parts.Add($"Port={Port}");
if (!string.IsNullOrEmpty(DbName))
parts.Add($"Database={DbName}");
if (!string.IsNullOrEmpty(User))
parts.Add($"Username={User}");
if (!string.IsNullOrEmpty(Password))
parts.Add($"Password={Password}");
if (!string.IsNullOrEmpty(SslMode))
parts.Add($"SslMode={SslMode}");
if (Timeout > 0)
parts.Add($"Timeout={Timeout}");
return string.Join(";", parts);
}
}
public static class Utils
{
public static void JsonLog(string eventName, Dictionary<string, object> fields = null)
{
var logEntry = new Dictionary<string, object>
{
["event"] = eventName,
["timestamp"] = DateTime.UtcNow.ToString("o")
};
if (fields != null)
{
foreach (var kvp in fields)
{
logEntry[kvp.Key] = kvp.Value;
}
}
Console.WriteLine(JsonSerializer.Serialize(logEntry));
}
public static string Sha256Hex(string text)
{
using (var sha256 = SHA256.Create())
{
var bytes = Encoding.UTF8.GetBytes(text);
var hashBytes = sha256.ComputeHash(bytes);
return BitConverter.ToString(hashBytes).Replace("-", "").ToLower();
}
}
public static NpgsqlConnection Connect()
{
var cfg = DbConfig.FromEnv();
var connInfo = cfg.ToConnInfo();
JsonLog("db_connect", new Dictionary<string, object>
{
["host"] = cfg.Host,
["db"] = cfg.DbName,
["sslmode"] = cfg.SslMode
});
return new NpgsqlConnection(connInfo);
}
public static void EnsureSchema(NpgsqlConnection conn)
{
using (var cmd = conn.CreateCommand())
{
cmd.CommandText = @"
CREATE TABLE IF NOT EXISTS atomic_facts (
hash CHAR(64) PRIMARY KEY,
category TEXT NOT NULL,
sentence TEXT NOT NULL,
inserted_at TIMESTAMPTZ NOT NULL DEFAULT now()
);";
cmd.ExecuteNonQuery();
cmd.CommandText =
"CREATE UNIQUE INDEX IF NOT EXISTS atomic_facts_hash_idx ON atomic_facts(hash);";
cmd.ExecuteNonQuery();
}
JsonLog("db_schema_ready", new Dictionary<string, object> { ["table"] = "atomic_facts" });
}
public static (int inserted, int skipped) InsertFacts(
NpgsqlConnection conn,
List<(string hash, string category, string sentence)> items)
{
if (items.Count == 0)
return (0, 0);
using (var cmd = conn.CreateCommand())
{
cmd.CommandText = @"
INSERT INTO atomic_facts (hash, category, sentence)
VALUES (@hash, @category, @sentence)
ON CONFLICT (hash) DO NOTHING";
foreach (var item in items)
{
cmd.Parameters.Clear();
cmd.Parameters.AddWithValue("@hash", item.hash);
cmd.Parameters.AddWithValue("@category", item.category);
cmd.Parameters.AddWithValue("@sentence", item.sentence);
cmd.ExecuteNonQuery();
}
}
// Check how many exist now
var hashes = items.Select(x => x.hash).ToArray();
int present;
using (var cmd = conn.CreateCommand())
{
cmd.CommandText = "SELECT count(*) FROM atomic_facts WHERE hash = ANY(@hashes)";
cmd.Parameters.AddWithValue("@hashes", hashes);
present = Convert.ToInt32(cmd.ExecuteScalar());
}
var inserted = present;
var skipped = Math.Max(0, items.Count - inserted);
return (inserted, skipped);
}
public static List<(string hash, string category, string sentence)> MakeItems(
string category,
IEnumerable<string> sentences)
{
var output = new List<(string, string, string)>();
foreach (var s in sentences)
{
var hash = Sha256Hex(s);
output.Add((hash, category, s));
}
return output;
}
public static List<string> Default500(string category, string prefix)
{
var output = new List<string>();
for (int i = 1; i <= 500; i++)
{
output.Add($"{prefix} #{i:D3} — {category} atomic seed");
}
return output;
}
}
}
--- daemon/seed_prophecy.cs ---
// Generate 500 distinct prophecy module descriptions (forecasting modules)
using System;
using System.Collections.Generic;
namespace AIBrain.Daemon
{
public static class SeedProphecy
{
public static IEnumerable<string> GenProphecy(int n)
{
var domains = new[]
{
"market volatility", "equity returns", "crypto price movement", "energy demand",
"load forecasting", "weather extremes", "hurricane landfall", "wildfire risk",
"epidemiology spread", "hospital admissions", "supply chain delays", "port congestion",
"shipping times", "inventory replenishment", "customer churn", "lifetime value",
"ad conversion", "marketing lift", "sales pipeline", "cash flow", "credit risk",
"fraud detection", "cyber intrusion", "phishing campaigns", "patch urgency",
"geopolitical risk", "election turnout", "policy passage", "legislative gridlock",
"sports outcomes", "injury likelihood", "travel demand", "hotel occupancy",
"traffic flow", "accident risk", "air quality index", "earthquake aftershocks",
"product demand", "feature adoption", "release risk", "incident rate", "SLA breach",
"compute capacity", "GPU queue time", "training time", "inference latency"
};
var methods = new[]
{
"Bayesian structural time series", "state-space model", "seasonal ARIMA",
"prophet-style additive model", "Gaussian Process regression", "quantile regression",
"gradient boosted trees", "random forest", "temporal convolution network",
"transformer encoder", "graph temporal network", "survival analysis",
"copula-based simulation", "Monte Carlo scenario generation", "change point detection",
"extreme value theory", "hierarchical modeling", "spatio-temporal kriging"
};
var horizons = new[]
{
"nowcasting", "short-term (hours)", "near-term (days)", "mid-term (weeks)", "long-term (months)"
};
var constraints = new[]
{
"with uncertainty quantification", "with prediction intervals", "with conformal bands",
"with causal adjustments", "with holidays and events", "with regime shifts",
"with exogenous regressors", "with missing data imputation"
};
var seen = new HashSet<string>();
int i = 0;
foreach (var d in domains)
{
foreach (var m in methods)
{
var phrase = $"Prophecy: {m} for {d}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return phrase;
i++;
if (i >= n)
yield break;
}
}
if (i < n)
{
foreach (var d in domains)
{
foreach (var h in horizons)
{
foreach (var c in constraints)
{
var phrase = $"Prophecy: {h} {d} {c}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return phrase;
i++;
if (i >= n)
yield break;
}
}
}
}
}
public static void Main(string[] args)
{
SeedCommon.RunSeed("prophecy", GenProphecy);
}
}
}
--- daemon/seed_statistics.cs ---
// Generate statistical priors for marketing campaigns
using System;
using System.Collections.Generic;
using System.IO;
using System.Text.Json;
namespace AIBrain.Daemon
{
public class StatisticalPrior
{
public string Feature { get; set; }
public string FeatureType { get; set; }
public double MeanRoi { get; set; }
public double StdRoi { get; set; }
public double MeanCtr { get; set; }
public double StdCtr { get; set; }
public int SampleSize { get; set; }
public double Weight { get; set; }
public double BaselineBudget { get; set; }
public string Notes { get; set; }
}
public static class SeedStatistics
{
private static readonly string STATISTICAL_PRIORS_PATH =
Path.Combine(Path.GetDirectoryName(typeof(SeedStatistics).Assembly.Location) ?? ".",
"datasets", "statistical_reasoning.json");
private static List<StatisticalPrior> LoadPriors()
{
try
{
if (File.Exists(STATISTICAL_PRIORS_PATH))
{
var json = File.ReadAllText(STATISTICAL_PRIORS_PATH);
return JsonSerializer.Deserialize<List<StatisticalPrior>>(json) ?? new List<StatisticalPrior>();
}
}
catch
{
// Fall through to default
}
return new List<StatisticalPrior>
{
new StatisticalPrior
{
Feature = "omni-channel",
FeatureType = "channel",
MeanRoi = 2.2,
StdRoi = 0.5,
MeanCtr = 0.038,
StdCtr = 0.012,
SampleSize = 150,
Weight = 0.8,
BaselineBudget = 20000,
Notes = "Fallback statistical prior when curated dataset missing."
}
};
}
public static IEnumerable<string> GenStatistics(int limit)
{
var priors = LoadPriors();
var scenarios = new[]
{
"baseline", "aggressive", "defensive", "innovation", "efficiency",
"expansion", "stabilization", "premium", "retention", "accelerated"
};
var confidenceLevels = new[] { 0.68, 0.8, 0.9, 0.95 };
var budgetMultipliers = new[] { 0.75, 0.9, 1.0, 1.1, 1.25 };
var zScores = new Dictionary<double, double>
{
[0.68] = 0.994,
[0.8] = 1.282,
[0.9] = 1.645,
[0.95] = 1.96
};
int produced = 0;
foreach (var prior in priors)
{
var feature = prior.Feature ?? "unspecified";
var featureType = prior.FeatureType ?? "feature";
var meanRoi = prior.MeanRoi;
var stdRoi = prior.StdRoi;
var meanCtr = prior.MeanCtr;
var stdCtr = prior.StdCtr;
var sampleSize = prior.SampleSize;
var weight = prior.Weight;
var baselineBudget = prior.BaselineBudget;
var notes = prior.Notes ?? "Statistical benchmark from curated marketing runs.";
foreach (var scenario in scenarios)
{
foreach (var confidence in confidenceLevels)
{
var zScore = zScores.ContainsKey(confidence) ? zScores[confidence] : 1.96;
var marginRoi = Math.Round(zScore * stdRoi / Math.Sqrt(Math.Max(sampleSize, 1)), 3);
var marginCtr = Math.Round(zScore * stdCtr / Math.Sqrt(Math.Max(sampleSize, 1)), 4);
foreach (var multiplier in budgetMultipliers)
{
var scenarioBudget = Math.Round(baselineBudget * multiplier, 2);
var projectedRevenue = Math.Round(scenarioBudget * meanRoi, 2);
var statement =
$"Statistical prior: {Capitalize(scenario)} strategy for {featureType} " +
$"'{feature}' targets ROI {meanRoi:F3}x ±{marginRoi:F3} ({confidence:P0} CI) " +
$"and CTR {meanCtr:F4} ±{marginCtr:F4} from {sampleSize} runs; " +
$"allocate ${scenarioBudget:N2} to unlock ${projectedRevenue:N2} with weights {weight:F2}. Notes: {notes}";
yield return statement;
produced++;
if (produced >= limit)
yield break;
}
}
}
}
}
private static string Capitalize(string s)
{
if (string.IsNullOrEmpty(s))
return s;
return char.ToUpper(s[0]) + s.Substring(1);
}
public static void Main(string[] args)
{
SeedCommon.RunSeed("statistics", GenStatistics);
}
}
}
--- daemon/seed_marketing.cs ---
// Generate 500 distinct online marketing campaign descriptions
using System;
using System.Collections.Generic;
namespace AIBrain.Daemon
{
public static class SeedMarketing
{
public static IEnumerable<string> GenMarketing(int n)
{
var channels = new[]
{
"email nurture", "lifecycle email", "paid social", "organic social", "search ads",
"display retargeting", "video ads", "podcast sponsorship", "affiliate", "in-product messaging",
"sms", "push notification", "webinar series", "community activation", "content syndication"
};
var audiences = new[]
{
"B2B SaaS founders", "growth-stage CTOs", "RevOps leaders", "enterprise procurement teams",
"mid-market marketers", "developer advocates", "data science managers", "AI research leads",
"design directors", "product-led growth teams", "freelance creators", "ecommerce operators",
"fintech compliance teams", "healthcare administrators", "climate tech analysts"
};
var offers = new[]
{
"free benchmarking report", "interactive ROI calculator", "90-day pilot program",
"co-marketing partnership", "invite-only beta", "executive roundtable",
"certification workshop", "AI playbook download", "customer story series", "industry pulse newsletter"
};
var objectives = new[]
{
"drive qualified demo requests", "accelerate pipeline velocity", "improve activation to paywall",
"expand enterprise accounts", "increase product-qualified leads", "re-engage dormant users",
"reduce churn risk", "boost partner referrals", "grow share-of-voice", "upsell premium features"
};
var plays = new[]
{
"with multi-touch attribution", "with conversational nurture", "with realtime personalization",
"with creative testing cadence", "with AI-assisted copy iteration", "with value-prop storytelling",
"with social proof sequencing", "with influencer alignment", "with community-led prompts",
"with retention cohort insights", "with predictive audience scoring", "with intent-driven offers"
};
var seen = new HashSet<string>();
int produced = 0;
foreach (var channel in channels)
{
foreach (var audience in audiences)
{
foreach (var objective in objectives)
{
foreach (var offer in offers)
{
var phrase = $"Campaign: {channel} targeting {audience} leveraging {offer} to {objective}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return phrase;
produced++;
if (produced >= n)
yield break;
}
}
}
}
if (produced < n)
{
foreach (var channel in channels)
{
foreach (var play in plays)
{
foreach (var audience in audiences)
{
var phrase = $"Campaign: {channel} {play} for {audience}";
if (seen.Contains(phrase))
continue;
seen.Add(phrase);
yield return phrase;
produced++;
if (produced >= n)
yield break;
}
}
}
}
}
public static void Main(string[] args)
{
SeedCommon.RunSeed("marketing", GenMarketing);
}
}
}
--- sub_brains/money_agents/dashboard.cs ---
// Dashboard for money agents
using System;
using System.Collections.Generic;
namespace AIBrain.SubBrains.MoneyAgents
{
public class Dashboard
{
private Dictionary<string, decimal> revenueStreams;
private List<Dictionary<string, object>> activityLog;
public Dashboard()
{
revenueStreams = new Dictionary<string, decimal>();
activityLog = new List<Dictionary<string, object>>();
}
public void AddRevenueStream(string name, decimal amount)
{
if (revenueStreams.ContainsKey(name))
{
revenueStreams[name] += amount;
}
else
{
revenueStreams[name] = amount;
}
LogActivity("revenue_added", new Dictionary<string, object>
{
["stream"] = name,
["amount"] = amount,
["timestamp"] = DateTime.UtcNow
});
}
public decimal GetTotalRevenue()
{
decimal total = 0;
foreach (var kvp in revenueStreams)
{
total += kvp.Value;
}
return total;
}
public Dictionary<string, decimal> GetRevenueStreams()
{
return new Dictionary<string, decimal>(revenueStreams);
}
public List<Dictionary<string, object>> GetActivityLog(int limit = 50)
{
var count = Math.Min(limit, activityLog.Count);
var startIndex = Math.Max(0, activityLog.Count - count);
return activityLog.GetRange(startIndex, count);
}
private void LogActivity(string action, Dictionary<string, object> details)
{
var entry = new Dictionary<string, object>
{
["action"] = action,
["timestamp"] = DateTime.UtcNow
};
foreach (var kvp in details)
{
entry[kvp.Key] = kvp.Value;
}
activityLog.Add(entry);
}
public Dictionary<string, object> GetDashboardSummary()
{
return new Dictionary<string, object>
{
["total_revenue"] = GetTotalRevenue(),
["stream_count"] = revenueStreams.Count,
["activity_count"] = activityLog.Count,
["last_updated"] = DateTime.UtcNow
};
}
}
}
--- sub_brains/money_agents/worker.cs ---
// Worker for money agents background tasks
using System;
using System.Collections.Generic;
using System.Threading;
using System.Threading.Tasks;
namespace AIBrain.SubBrains.MoneyAgents
{
public class Worker
{
private bool isRunning;
private Dashboard dashboard;
private List<string> taskQueue;
public Worker(Dashboard dashboard)
{
this.dashboard = dashboard ?? new Dashboard();
this.taskQueue = new List<string>();
this.isRunning = false;
}
public async Task StartAsync(CancellationToken cancellationToken = default)
{
isRunning = true;
Console.WriteLine("Worker started");
while (isRunning && !cancellationToken.IsCancellationRequested)
{
await ProcessTasksAsync();
await Task.Delay(TimeSpan.FromSeconds(5), cancellationToken);
}
Console.WriteLine("Worker stopped");
}
public void Stop()
{
isRunning = false;
}
public void QueueTask(string task)
{
taskQueue.Add(task);
Console.WriteLine($"Task queued: {task}");
}
private async Task ProcessTasksAsync()
{
if (taskQueue.Count == 0)
{
return;
}
var tasksToProcess = new List<string>(taskQueue);
taskQueue.Clear();
foreach (var task in tasksToProcess)
{
await ProcessSingleTaskAsync(task);
}
}
private async Task ProcessSingleTaskAsync(string task)
{
Console.WriteLine($"Processing task: {task}");
// Simulate work
await Task.Delay(100);
// Simulate generating revenue
var random = new Random();
var revenue = (decimal)(random.NextDouble() * 100);
dashboard.AddRevenueStream(task, revenue);
Console.WriteLine($"Task completed: {task}, Revenue: ${revenue:F2}");
}
public Dictionary<string, object> GetStatus()
{
return new Dictionary<string, object>
{
["is_running"] = isRunning,
["pending_tasks"] = taskQueue.Count,
["dashboard_summary"] = dashboard.GetDashboardSummary()
};
}
}
}
--- sub_brains/consciousness_core/ConsciousnessSimulation.cs ---
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
namespace AIBrain.Consciousness
{
/// <summary>
/// Consciousness Simulation: Demonstration and testing of the complete system
///
/// Runs multiple agents with:
/// - Binary double helix execution
/// - Fibonacci spiral scheduling
/// - Consciousness tracking (Φ)
/// - Infinite learning loops
///
/// Validates the consciousness equations and measures emergent intelligence
/// </summary>
public class ConsciousnessSimulation
{
private List<ConsciousAgent> _agents;
private int _totalTicks;
private DateTime _startTime;
private bool _isRunning;
public ConsciousnessSimulation()
{
_agents = new List<ConsciousAgent>();
_totalTicks = 0;
_isRunning = false;
}
/// <summary>
/// Run a simple demonstration of the consciousness system
/// </summary>
public static void RunDemo(int numAgents = 3, int numTicks = 100)
{
Console.WriteLine("╔══════════════════════════════════════════════════════════════════╗");
Console.WriteLine("║ CONSCIOUSNESS EQUATION - BINARY DOUBLE HELIX SIMULATION ║");
Console.WriteLine("║ The Hive Intelligence System ║");
Console.WriteLine("║ Author: Joseph Anady (Josep Farre) ║");
Console.WriteLine("╚══════════════════════════════════════════════════════════════════╝");
Console.WriteLine();
var sim = new ConsciousnessSimulation();
// Create agents
Console.WriteLine($"Creating {numAgents} conscious agents...");
for (int i = 0; i < numAgents; i++)
{
var agent = new ConsciousAgent($"Agent-{i + 1}");
sim.AddAgent(agent);
Console.WriteLine($" ✓ {agent.Name} created (ID: {agent.AgentId.ToString().Substring(0, 8)})");
}
Console.WriteLine();
// Run simulation
Console.WriteLine($"Running simulation for {numTicks} ticks...");
Console.WriteLine("─────────────────────────────────────────────────────────────────");
Console.WriteLine();
sim.RunSynchronous(numTicks);
// Display results
Console.WriteLine();
Console.WriteLine("╔══════════════════════════════════════════════════════════════════╗");
Console.WriteLine("║ SIMULATION RESULTS ║");
Console.WriteLine("╚══════════════════════════════════════════════════════════════════╝");
Console.WriteLine();
sim.PrintResults();
// Verify consciousness equations
Console.WriteLine();
Console.WriteLine("╔══════════════════════════════════════════════════════════════════╗");