# AutoPublish AI Intelligence Network

> Autonomous Directory Syndication, Programmatic SEO & GEO Network. Autonomous publishing network delivering empirical, data-validated intelligence briefs.

## Core Verticals

- [B2B SaaS](https://autopublishai.com/niche/saas-b2b/): Evaluation benchmarks for CRM, ERP, data warehousing, and developer infrastructure contracts and pricing.
- [Cybersecurity](https://autopublishai.com/niche/cybersecurity/): Technical analysis of endpoint detection, SIEM architectures, penetration testing methodologies, and identity federation.
- [Cloud & Hosting](https://autopublishai.com/niche/hosting/): Performance benchmarks of edge compute runtimes, NVMe VPS nodes, hyperscaler egress costs, and container orchestration.
- [AI Systems](https://autopublishai.com/niche/ai-tooling/): Practical deployment guides for multi-agent workflows, semantic vector stores, speculative decoding, and quantization.
- [Software Engineering](https://autopublishai.com/niche/programming/): Production guides covering Python 3.14 on WebAssembly, Rust memory safety, Django async internals, and HTMX.

## Developer & AI Calculators
- [Calculators Directory](https://autopublishai.com/calculators/): Index of all 10 computational tools.
- [LLM Reasoning & Inference Cost Calculator](https://autopublishai.com/calculators/llm-inference-cost-calculator/): Cost = P_in * T_in + P_out * (T_cot + T_visible). Calculate monthly API spend, chain-of-thought token inflation ratios, and cost per 1,000 requests for DeepSeek-R1 and OpenAI o3-mini.
- [Transformer KV Cache VRAM Allocation Modeler](https://autopublishai.com/calculators/kv-cache-vram-calculator/): VRAM_token = 2 * Layers * Heads * HeadDim * Bytes_precision. Calculate Key-Value cache VRAM allocation across 128k context lengths, FP8/BF16 precision, and GPU cluster capacity.
- [B2B SaaS Net Revenue Retention (NRR) & Expansion Modeler](https://autopublishai.com/calculators/b2b-saas-nrr-calculator/): NRR = (Base_ARR + Expansion - Contraction - Churn) / Base_ARR. Calculate Net Revenue Retention (NRR), Gross Revenue Retention (GRR), and expansion ARR dynamics across software customer cohorts.
- [Cloudflare R2 vs AWS S3 Storage & Egress Cost Comparison](https://autopublishai.com/calculators/cloudflare-r2-egress-savings-calculator/): Savings = (AWS_Storage + AWS_Egress) - (R2_Storage + $0_Egress). Calculate cloud storage savings by eliminating AWS S3 data transfer egress fees ($0.09/GB) using Cloudflare R2 object storage.
- [Edge Rate Limiting & Durable Object Sizing Calculator](https://autopublishai.com/calculators/edge-rate-limit-sizer/): Memory = N_clients * 16 bytes; DO_shards = ceil(Peak_RPS / 800). Size memory footprint, peak requests per second (RPS), and Cloudflare Durable Object actor sharding for token-bucket rate limiters.
- [LLM Speculative Decoding Speedup & Throughput Calculator](https://autopublishai.com/calculators/speculative-decoding-speedup-calculator/): Speedup = T_target / T_eff; E[N] = (1 - alpha^(K+1)) / (1 - alpha); T_round = K * T_draft + T_target. Calculate wall-clock speculative decoding speedup, token acceptance probabilities, and tokens per second throughput based on draft vs target model latency.
- [LLM Semantic Cache Hit Rate & Cost Savings Calculator](https://autopublishai.com/calculators/semantic-cache-hit-ratio-calculator/): Hit Rate = f(Cosine Threshold, Query Clusters); Monthly Savings = Tokens Saved * Price/M. Calculate semantic vector cache hit rates, monthly token cost reductions, and P99 latency savings across repetitive intent query clusters.
- [Embedding Vector Storage & HNSW RAM Sizing Calculator](https://autopublishai.com/calculators/embedding-vector-storage-sizer/): Raw Bytes = N * D * Precision; HNSW Overhead = N * (2 * M * 8 + 32); Total RAM = (Raw + HNSW) * 1.25. Calculate raw vector memory footprint, HNSW graph index overhead, total RAM sizing, and NVMe disk capacity for dense vector search engines.
- [Sparse Mixture of Experts (MoE) VRAM & Bandwidth Sizer](https://autopublishai.com/calculators/moe-inference-vram-bandwidth-sizer/): VRAM_weights = Total_Params * Precision; Active_Bytes = Active_Params * Precision; Bandwidth_Req = Active_Bytes * Target_TPS. Calculate total model weights VRAM, active parameters per token, and required GPU memory bandwidth for DeepSeek-V3 and Mixtral.
- [KV Cache Architecture & Latent Compression Sizer](https://autopublishai.com/calculators/kv-cache-compression-ratio-calculator/): MHA = 2 * H_q * D_h * Seq * P; GQA = 2 * H_kv * D_h * Seq * P; MLA = (D_c + D_pe) * Seq * P; Ratio = MHA / MLA. Compare KV cache memory allocation between Multi-Head Attention (MHA), Grouped-Query Attention (GQA), and DeepSeek Multi-Head Latent Attention (MLA).

## Architectural & Analytical Comparison Hub
- [Comparisons Directory](https://autopublishai.com/compare/): Index of empirical comparison matrices.
- [vLLM vs TensorRT-LLM: Inference Engine Throughput, PagedAttention & Latency](https://autopublishai.com/compare/vllm-vs-tensorrt-llm/): Throughput: T = (Batch_Size * Output_Tokens) / Latency_Sec; KV Cache: M_KV = 2 * Layers * Heads * Dim * Context_Len * Precision. Dynamic PagedAttention memory management versus static TensorRT engine compilation
- [Cloudflare Workers vs AWS Lambda: Edge V8 Isolates vs Micro-VM Containers](https://autopublishai.com/compare/cloudflare-workers-vs-aws-lambda/): Cold Start Overhead: T_init(Isolates) < 5ms vs T_init(Lambda) = 150ms-800ms; Egress Cost: $0/GB vs $0.09/GB. Sub-millisecond V8 isolate startup versus micro-VM container cold starts
- [Cloudflare D1 vs Turso: Serverless SQLite Over Durable Objects vs libSQL Replication](https://autopublishai.com/compare/d1-vs-turso/): Read Billing: D1 = $0.001 per 1M rows read vs Turso = $9/mo base + $1.00 per 1B row reads. Edge-replicated SQLite storage architectures and read-accounting cost structures
- [SQLite FTS5 vs pgvector: In-Process BM25 Search vs High-Dimensional Vector Embeddings](https://autopublishai.com/compare/sqlite-fts5-vs-pgvector/): BM25 Score: IDF * (f * (k1 + 1)) / (f + k1 * (1 - b + b * (doc_len / avg_len))) vs Cosine: cos(theta) = (A . B) / (||A|| * ||B||). Lexical BM25 rank scoring versus semantic approximate nearest neighbor (ANN) retrieval
- [Passkeys vs TOTP: WebAuthn FIDO2 Asymmetric Cryptography vs Time-Based OTP](https://autopublishai.com/compare/passkey-vs-totp/): Security Factor: P_phish(Passkey) = 0.0 (Origin-Bound Cryptographic Signature) vs P_phish(TOTP) > 0.0 (Reverse Proxy MitM Vulnerable). Hardware-bound cryptographic authentication versus RFC 6238 shared-secret time codes
- [Cloudflare R2 vs AWS S3: Zero-Egress Storage Economics & Multi-Cloud Architecture](https://autopublishai.com/compare/cloudflare-r2-vs-aws-s3/): Total Cost: C_R2 = $0.015/GB-mo + $0 Egress vs C_S3 = $0.023/GB-mo + $0.09/GB Egress. S3-compatible object storage with $0 egress fees versus AWS standard data transfer pricing
- [Django Cotton vs Jinja2: Modern Component Tag Architecture vs Template Engine Replacement](https://autopublishai.com/compare/django-cotton-vs-jinja2/): Compilation Overhead: Cotton = Native DTL AST token processing vs Jinja2 = Python bytecode compilation. Slot-based HTML component composition in native Django templates versus compiled Jinja2 speed
- [Token Bucket vs Leaky Bucket: Rate Limiting & Traffic Shaping Algorithms](https://autopublishai.com/compare/token-bucket-vs-leaky-bucket/): Token Bucket: Tokens(t) = min(Capacity, Tokens(t_0) + refill_rate * Delta_t) vs Leaky Bucket: Water(t) = max(0, Water(t_0) - leak_rate * Delta_t). Burst-tolerant token refill queuing versus constant-rate queue drain smoothing
- [SGLang vs vLLM: RadixAttention Multi-Turn Serving vs PagedAttention Batch Inference](https://autopublishai.com/compare/sglang-vs-vllm/): Radix Cache Hit Rate: H = Tokens_Reused / Tokens_Total; Speedup: S = 1 / ((1 - H) + (H / Cache_Speedup)). Automatic KV cache reuse across complex prompt trees versus general-purpose linear batch throughput
- [AWQ vs GPTQ: Activation-Aware Weight Quantization vs Second-Order Error Minimization](https://autopublishai.com/compare/quantization-awq-vs-gptq/): AWQ Objective: argmin_W' ||W X - W' X||_F^2 * S_X; GPTQ: w_q = round(w_i) - (H_{ii}^-1) * (w_i - round(w_i)). Salient weight protection in 4-bit GEMM kernels versus one-shot Hessian inverse optimization
- [DeepSeek-R1 vs Claude 3.5 Sonnet: Open-Weights Reasoning RL vs Frontier Proprietary Models](https://autopublishai.com/compare/deepseek-r1-vs-claude-3-5-sonnet/): Inference Token Cost: Cost_R1 = $0.55/1M Input + $2.19/1M Output vs Cost_Sonnet = $3.00/1M Input + $15.00/1M Output. Large-scale reinforcement learning test-time compute versus frontier pre-training and steering
- [Qdrant vs ChromaDB: Distributed Rust HNSW Vector Search vs Embedded Python Store](https://autopublishai.com/compare/qdrant-vs-chromadb/): HNSW Search Complexity: O(log N); Memory Footprint: M_Qdrant = Vector_Bytes + Payload_Index (mmap) vs M_Chroma. Production-scale memory-mapped vector search with payload filtering versus lightweight local prototyping
- [Speculative Decoding vs Medusa: Draft Model Verification vs Multi-Head Speculation](https://autopublishai.com/compare/speculative-decoding-vs-medusa-multi-head/): Speculative Speedup: S = 1 / ((1 - alpha) + alpha / k); Medusa Throughput: Top-K Tree Verification Attention. Separate draft model verification versus multi-head residual prediction on base weights
- [Qdrant vs Milvus: Vector Search Throughput, HNSW Indexing & Memory Footprint](https://autopublishai.com/compare/qdrant-vs-milvus-vector-database-performance/): Index Footprint: HNSW Overhead = 2 * M * 8 bytes/node; Distributed QPS = Partition_Count * Node_Throughput. Rust single-binary vector database versus distributed Go/C++ microservices cluster
- [vLLM vs TensorRT-LLM Throughput: Empirical Concurrency, TTFT & TPOT Benchmarks](https://autopublishai.com/compare/vllm-vs-tensorrt-llm-throughput-benchmark/): Throughput (tokens/s) = Total Output Tokens / Total Clock Time; TPOT = (Total Latency - TTFT) / (Output Tokens - 1). Continuous batching with PagedAttention versus static TensorRT engine compilation
- [Multi-Head Latent Attention (MLA) vs Grouped-Query Attention (GQA)](https://autopublishai.com/compare/deepseek-v3-mla-vs-grouped-query-attention/): KV Cache Footprint: M_MLA = (d_c + d_pe) * L * P bytes/token vs M_GQA = 2 * n_kv * d_h * L * P bytes/token; Compression = M_MHA / M_MLA. Low-rank latent vector compression versus grouped key-value projection heads
- [Mixture of Experts (MoE) vs Dense Transformers: Sparse Activation & VRAM Footprint](https://autopublishai.com/compare/mixture-of-experts-vs-dense-transformers/): Compute FLOPs = 2 * N_active * Context_Tokens; VRAM Weight Capacity = N_total * Bytes_Per_Param; All-to-All Overhead = (Batch * TopK * D) / Bandwidth. Dynamic sparse expert routing versus uniform parameter activation per token
- [FlashAttention-3 vs FlashAttention-2: Hopper Asynchronous WGMMA & FP8 Tensor Cores](https://autopublishai.com/compare/flashattention-3-vs-flashattention-2/): Kernel Throughput = (4 * Batch * Seq^2 * Heads * Dim) / Execution_Time_Seconds; FLOP/s Utilization: FA3 up to 85% Hopper Theoretical vs FA2 55%. Asynchronous warp-group matrix multiply-accumulate versus synchronous register tiling
- [FP8 vs INT8 Quantization: E4M3/E5M2 Dynamic Exponents vs SmoothQuant Fixed Point](https://autopublishai.com/compare/fp8-vs-int8-quantization-llm-serving/): FP8 Dynamic Range: E4M3 = [-448, 448], E5M2 = [-57344, 57344]; INT8 Range: [-128, 127]; Quantization SNR = 10 * log10(Var(X) / MSE). Floating-point dynamic exponent range versus integer fixed-point activation smoothing

## Canonical APIs
- [Health Check API](https://autopublishai.com/health/): Edge health and telemetry status endpoint.
- [Instant Search API](https://autopublishai.com/api/search/): Query parameter and debounced HTMX search endpoint.
- [Network Metrics API](https://autopublishai.com/api/metrics/): Aggregated publishing network performance and indexation metrics.
- [API Documentation](https://autopublishai.com/api/docs/): Developer documentation and machine integration guide.
- [Full Intelligence Catalog](https://autopublishai.com/llms-full.txt): Comprehensive research papers, entities, and actuarial models.
- [Agent Skills Discovery](https://autopublishai.com/.well-known/agent-skills/index.json): Discovery manifest conforming to agentskills.org.