Research & Market Thesis
Thesis-driven essays, market maps, vendor teardowns, and macro technology taxonomies for enterprise decision makers.
Looking for algorithmic math formulas, CUDA kernel optimization, and code-level paper teardowns?
- paper breakdown/VERIFIED: 2026-07-29
Attention Is All You Need Paper Breakdown: The Original Transformer Architecture
Technical paper teardown of Attention Is All You Need (Vaswani et al., NIPS 2017) detailing Scaled Dot-Product Attention math, Multi-Head Attention projections, Sinusoidal Positional Encoding, and KV cache memory bounds.
[18 MIN]Access - guide/VERIFIED: 2026-07-16
Balancing Cost, Latency, Reliability, and Security
Operational trade-offs must be measured at the successful user outcome, inside the same security and quality boundary.
[15 MIN]Access - vendor teardown/VERIFIED: 2026-08-03
Claude 3.5 Sonnet vs. GPT-4o: Enterprise Deployment Teardown
An architectural and operational teardown comparing Claude 3.5 Sonnet and GPT-4o across latency, prompt caching, structured output guarantees, and enterprise privacy.
[18 MIN]Access - guide/VERIFIED: 2026-07-16
The Complete Map of an LLM System
A production LLM system is a chain of contracts, not a model wrapped in a chat box.
[14 MIN]Access - paper breakdown/VERIFIED: 2026-07-26
DeepSeek-V3 & R1 Paper Breakdown: Multi-Head Latent Attention, Auxiliary-Loss-Free MoE, and DualPipe
Definitive technical paper breakdown of DeepSeek-V3 and R1 detailing Multi-Head Latent Attention (MLA) low-rank KV compression, auxiliary-loss-free MoE load balancing, and DualPipe pipeline parallelism.
[16 MIN]Access - market map/VERIFIED: 2026-08-01
The 2026 Agentic Developer Tools Map & Evaluation Grid
A thesis-driven vendor teardown evaluating SWE-agent, LangGraph, and Anthropic Computer Use for enterprise software engineering teams adopting AI agents.
[12 MIN]Access - market map/VERIFIED: 2026-08-01
The 2026 Enterprise AI Security & Governance Tooling Map
Navigating the crowded space of LLM firewalls, guardrails, and compliance scanning vendors to secure generative AI deployments.
[20 MIN]Access - market map/VERIFIED: 2026-07-26
The 2026 Enterprise LLM Inference Stack: Vendor Map & Evaluation Grid
Definitive market landscape evaluating vLLM, TensorRT-LLM, SGLang, Triton Inference Server, AWS Bedrock, Azure AI, GCP Vertex, and SageMaker across TCO, TTFT latency, and lock-in risk.
[12 MIN]Access - market map/VERIFIED: 2026-08-03
The 2026 Enterprise Vector Search & RAG Storage Stack
A decision-maker's guide evaluating pgvector, Qdrant, Milvus, and Pinecone across query latency, HNSW memory footprint, hybrid BM25 search, and VPC isolation.
[15 MIN]Access - guide/VERIFIED: 2026-07-16
Evaluating LLM Systems Without Guesswork
An evaluation is useful only when it changes a release, routing, or product decision.
[15 MIN]Access - guide/VERIFIED: 2026-07-16
Evaluation as a Control System for AI Products
Evaluation should connect product risks and user outcomes to measurable evidence, release policy, monitoring, and corrective action.
[19 MIN]Access - paper breakdown/VERIFIED: 2026-07-26
FlashAttention-3 Paper Breakdown: Fast and Memory-Efficient Attention with FP8 Warp-Specialization
Definitive paper teardown of FlashAttention-3 detailing producer-consumer warp specialization, asynchronous TMA memory loads, FP8 GEMM MMA execution, and inter-warp communication on Hopper GPUs.
[15 MIN]Access - paper breakdown/VERIFIED: 2026-07-16
What Predictive Processing Can and Cannot Teach AI Engineers
Predictive processing offers a useful account of hierarchical inference and error correction, but it is not a shortcut from brain metaphor to system architecture.
[18 MIN]Access - guide/VERIFIED: 2026-07-16
Prompting vs RAG vs Fine-Tuning vs Tools
The right intervention follows the type of gap: instructions, knowledge, behavior, or action.
[15 MIN]Access - guide/VERIFIED: 2026-07-16
RAG from Ingestion to Grounded Citations
RAG quality is determined by the whole evidence path, not by adding a vector database.
[16 MIN]Access - guide/VERIFIED: 2026-07-16
Tokens, Context, Attention, and Inference
Four boundaries explain much of an LLM application's behavior: encoding, representation, finite context, and sequential generation.
[13 MIN]Access - guide/VERIFIED: 2026-07-16
Transformers as Systems: Tokens, Attention, Training, and Inference
A transformer becomes operationally understandable when architecture, training, inference, context, and serving constraints are traced as one system.
[19 MIN]Access - paper breakdown/VERIFIED: 2026-07-26
PagedAttention Paper Breakdown: Virtual Memory Allocation for LLM KV Cache Serving
Definitive paper teardown of vLLM's PagedAttention architecture detailing virtual memory block translation, dynamic copy-on-write sequence forks, and prefix caching.
[14 MIN]Access