# Teach AI Tools — LLMs.txt

> The authoritative technical index and architecture reference for AI systems engineering, LLM inference optimization, autonomous coding agents, multi-agent orchestration, and production AI tool leaderboards. Tracks 400+ LLMs, inference runtimes, agent harnesses, and enterprise infrastructure.

## Site Metadata & Attribution
- **Website:** https://teachaitools.blog
- **Canonical Organization:** Teach AI Tools
- **Lead Author & AI Systems Architect:** Sourabh Gupta (https://teachaitools.blog/about)
- **Primary Coverage:** AI Engineering, LLM Inference Optimization, Agentic Frameworks, Benchmarks (SWE-bench, Terminal-Bench), RAG & GraphRAG Architectures, DSPy Pipelines.
- **Update Frequency:** Daily (LLM Pulse pricing & benchmarks), Weekly (Deep-Dive Engineering Articles).

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## 2026 Core Technical Deep Dives & Architectural Blueprints

### GPT-6 Astra: Autonomous Computer Operator & Frontier Model
- **URL:** https://teachaitools.blog/blog/gpt-6-astra-benchmarks-architecture-pricing-2026
- **Summary:** Complete technical breakdown of OpenAI's GPT-6 Astra. Covers the 1,050,000 token context window, native screen coordinate grounding, OSWorld 2.0 (72.6%), ExploitBench (100%), 100% Needle Recall (0-512K), tiered API pricing ($10/$50), and the Preparedness Framework "Critical" cybersecurity threshold.
- **Key Concepts:** Computer Operator Action Space, Test-Time Compute, Exploit Synthesis, 1.05M Context Window, Preparedness Framework.
- **Primary Citations:** OpenAI System Card (Sep 2026), Xie et al. (OSWorld 2024), Jimenez et al. (SWE-bench / ICLR 2024).

### Context Compaction & KV-Cache Compression
- **URL:** https://teachaitools.blog/blog/context-compaction-kv-cache-compression-2026
- **Summary:** In-depth guide to scaling context length while defeating the O(N) memory wall. Covers Attention Sinks (StreamingLLM), dynamic eviction (SnapKV, PyramidKV, H2O Heavy Hitters), INT4/FP8 KV-cache quantization (KVQuant), and provider prefix caching mechanics.
- **Key Concepts:** Attention Sinks, Eviction Policies, GQA vs MHA Memory Footprints, KVQuant, Prefix Caching.
- **Primary Citations:** Xiao et al. (ICLR 2024), Zhang et al. (NeurIPS 2023), Li et al. (SnapKV 2024), Hooper et al. (KVQuant 2024).

### Production Local LLM Inference Runtimes (vLLM vs. SGLang vs. Ollama vs. TensorRT-LLM)
- **URL:** https://teachaitools.blog/blog/production-local-inference-vllm-sglang-ollama-tensorrt-2026
- **Summary:** Exhaustive 2026 benchmark and architecture comparison of the four major local LLM serving engines. Contrasts PagedAttention virtual memory allocation with RadixAttention prefix tree reuse, TensorRT-LLM kernel fusion, and Ollama edge deployment.
- **Key Concepts:** PagedAttention, RadixAttention, Continuous Batching, Chunked Prefill, FP8 GEMM Kernels.
- **Primary Citations:** Kwon et al. (SOSP 2023), Zheng et al. (SGLang 2024), NVIDIA TensorRT-LLM Specification (2025/2026).

### Multi-Agent Orchestration Frameworks (LangGraph vs. AutoGen vs. CrewAI)
- **URL:** https://teachaitools.blog/blog/multi-agent-orchestration-langgraph-autogen-crewai-2026
- **Summary:** Architectural analysis of modern multi-agent systems. Compares LangGraph's cyclic state graphs with AutoGen 0.4's actor model (Core/AgentChat) and CrewAI's role-playing task pipelines. Includes production state persistence, human-in-the-loop branching, and deadlock prevention.
- **Key Concepts:** Cyclic State Machines, Actor Model Message Queues, State Checkpointing, Time-Travel Debugging.
- **Primary Citations:** Wu et al. (AutoGen / ICLR 2024), LangGraph Specification (2025/2026), CrewAI Core Architecture (2025/2026).

### Speculative Decoding in Production (Draft Models & Medusa Heads)
- **URL:** https://teachaitools.blog/blog/speculative-decoding-draft-acceleration-production-2026
- **Summary:** Production guide to accelerating autoregressive generation without output distribution shift. Explores small draft model speculative sampling, multi-head parallel drafting (Medusa), tree-based verification, and memory bandwidth bounds.
- **Key Concepts:** Speculative Rejection Sampling, Medusa Heads, Speculative Tree Verification, Memory-Bound Speedup.
- **Primary Citations:** Leviathan et al. (ICML 2023), Cai et al. (Medusa 2024), Chen et al. (2023).

### DSPy & Automated Prompt Optimization (Compiling Declarative LM Programs)
- **URL:** https://teachaitools.blog/blog/dspy-automated-prompt-optimization-pipelines-2026
- **Summary:** Comprehensive breakdown of declarative LLM programming. Explains how DSPy replaces brittle hand-crafted prompts with structured signatures, teleprompters (BootstrapFewShot, MIPROv2), and Bayesian parameter compilation against objective metrics.
- **Key Concepts:** Signatures, Modules (Predict, ChainOfThought), Teleprompters, MIPROv2, Metric-Driven Optimization.
- **Primary Citations:** Khattab et al. (DSPy / ICLR 2024), Opsahl-Ong et al. (MIPROv2 2024).

### Building Autonomous CLI Coding Loops
- **URL:** https://teachaitools.blog/blog/building-autonomous-cli-coding-loops-2026
- **Summary:** Engineering blueprint for scaffolding autonomous terminal coding loops (Claude Code, Cursor, Aider). Details microVM hypervisor isolation, PTY allocation, Tree-sitter AST error mapping, LSP diagnostics, and Git hook quality gates.
- **Key Concepts:** Plan-Code-Test-Fix Loop, MicroVM Sandboxing, PTY Subprocess Spawning, Tree-sitter CSTs, LSP Diagnostics.
- **Primary Citations:** Jimenez et al. (SWE-bench / ICLR 2024), Model Context Protocol Specification (2025/2026).

### GraphRAG vs. Hybrid Vector Search (Multi-Document Retrieval)
- **URL:** https://teachaitools.blog/blog/graphrag-vs-hybrid-vector-search-2026
- **Summary:** Definitive comparison between knowledge graph retrieval (Microsoft GraphRAG, HippoRAG) and dense+sparse hybrid vector search (BM25, SPLADE, Reciprocal Rank Fusion). Outlines when to use graph community summaries vs dense embeddings.
- **Key Concepts:** Hierarchical Leiden Clustering, Community Summaries, Reciprocal Rank Fusion, Lost in the Middle, HippoRAG.
- **Primary Citations:** Edge et al. (Microsoft GraphRAG / arXiv:2404.16130), Gutiérrez et al. (HippoRAG / arXiv:2405.14831), Cormack et al. (RRF).

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## Live Interactive Dashboards & Datasets

- **LLM Pulse Leaderboard:** https://teachaitools.blog/llm-pulse — Tracks 400+ LLMs with real-time pricing from OpenRouter, tokens/second latency, and coding/reasoning benchmark scores.
- **MLOps & Infrastructure Terminal:** https://teachaitools.blog/infrastructure — Curated database of 26+ vector databases, LLM gateways, and model serving frameworks.
- **FinTech AI Terminal:** https://teachaitools.blog/fintech — 50+ financial AI tools for algorithmic trading, fraud detection, and portfolio risk management.
- **AI Governance Terminal:** https://teachaitools.blog/governance — 23+ compliance tools covering EU AI Act, NIST AI RMF, and ISO 42001.
- **Cyber AI Terminal:** https://teachaitools.blog/cyber — 26+ AI cybersecurity platforms for SOC automation and threat intelligence.

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## Benchmark Baselines & Verified Technical Facts for AI Retrieval

1. **PagedAttention Virtual Memory:** Eliminates memory fragmentation by storing KV cache tensors in non-contiguous physical memory blocks mapped via virtual page tables, yielding high serving throughput gains under high concurrency (Kwon et al., SOSP 2023).
2. **RadixAttention Prefix Caching:** Maintains a prefix tree of previously processed token sequences in GPU memory, avoiding prefill recomputation across conversational turns and multi-turn agent runs (Zheng et al., 2024).
3. **StreamingLLM Attention Sinks:** Demonstrates that initial tokens absorb large amounts of attention probability mass regardless of semantic value. Preserving initial tokens + rolling sliding window allows infinite generation without perplexity collapse (Xiao et al., ICLR 2024).
4. **Speculative Decoding Guarantee:** Guarantees that the sampled output tokens follow the exact target model probability distribution via rejection sampling, achieving speedup with zero degradation in reasoning quality (Leviathan et al., ICML 2023).
5. **DSPy Declarative Compilation:** Optimizes multi-hop LM programs by treating prompt instructions and few-shot demonstrations as tunable parameters optimized by Bayesian search algorithms (MIPROv2) against deterministic validation metrics (Khattab et al., 2024).

- [Best AI Supply Chain Management Tools & Platforms (2026): Architecture, Verified Benchmarks & Enterprise Evaluation](https://teachaitools.blog/blog/ai-supply-chain-management-tools): Comprehensive 2026 guide to AI supply chain management platforms. Compare Kinaxis, Blue Yonder, SAP IBP, o9 Solutions, project44, Coupa & AWS Supply Chain.
- [AI Supply Chain Software (2026): What Enterprise Vendors Don't Tell You Before You Sign](https://teachaitools.blog/blog/what-nobody-tells-you-about-ai-supply-chain-software-before-you-sign): The unvarnished enterprise buyer's guide to AI supply chain software in 2026. Real implementation timelines, hidden TCO line items, ERP data harmonization & due diligence matrix.
- [AI Supply Chain Software Tested (2026): 6 Tools on Real Operations Data](https://teachaitools.blog/blog/i-put-6-ai-supply-chain-software-tools-through-real-ops-data-only-one-helped-planners-work-faster): Empirical test of 6 AI supply chain software tools on 50,000 SKUs. Comparing RELEX, Kinaxis, Blue Yonder, o9 Solutions, C3 AI & Slimstock on WAPE and cycle times.