Towards AI
Every summary, chronological. Filter by category, tag, or source from the rail.
GPU Bandwidth Limits LLM Speed, Not FLOPS
Generating one token from a 70B model on H100 needs 140GB weight reads—one op per byte—making memory bandwidth the inference bottleneck, not compute throughput.
Agent 365: Govern Sprawling AI Agents Securely
Microsoft Agent 365 acts as a control plane to observe, govern, and secure AI agents across Microsoft tools, local devices, multi-cloud platforms, and SaaS partners, addressing agent sprawl with discovery, policy controls, and runtime blocking—now generally available at $15/user/month.
Synthetic Data Exposes Hidden ML Bias Before Production
Real training data hides bias via underrepresentation (e.g., rural at 9%), proxies, and skewed labels; generate synthetic data with controlled segments (e.g., rural at 25%) to reveal it through disaggregated AUC drops (0.791 to 0.768) and disparate impact <0.8, then retrain on mixed data to fix.
Make Your Site an AI Answer Machine with Question Pages
Transform your website from a human brochure to an AI-citable answer machine by creating pages that directly answer client questions, using structured formats, FAQ schema, expertise signals, and internal links—boosting recommendations without redesigns.
Compliant LLM Clinical Pipelines: 85% Skip LLMs
Use constrained decoding, lossy Pydantic parsing, deterministic Python computation/validation, and conditional LLM judging to build ALCOA++/21 CFR Part 11-compliant pipelines processing clinical data at $0.15 per 1K records, with 85% records avoiding LLMs entirely.
637MB LLM Runs Offline on Base MacBook Air, Works Surprisingly Well
TinyLlama, a 637MB open-source LLM, runs instantly on a stock MacBook Air via Ollama—no internet, GPU, or API needed—handling Node.js servers and casual chats effectively, lowering the bar for useful local AI.
Claude's Agentic OS Chains Skills into Full Workflows
Claude becomes an agentic operating system by combining tool use, multi-step planning, and persistent context to orchestrate skills like file access, APIs, and sub-agents, automating business processes end-to-end without manual intervention.
AI Labs Race to Build Enterprise Deployment Layer
OpenAI and Anthropic partner with PE firms and consultancies to deploy AI in enterprises, addressing the adoption bottleneck beyond compute shortages amid explosive cloud growth (Google Cloud +63% to $20B).
Agents as Tools vs Handoffs: AI Orchestration Trade-offs
Agents as tools centralize control for multi-intent synthesis; handoffs decentralize for phased conversations. Combine both to balance consistency and adaptability in production AI systems.
8 Habits to Unlock Claude Code's Full Potential
Transform Claude Code from smart autocomplete to shipping accelerator by treating CLAUDE.md as living memory, using /btw for side queries, Chrome extension for visual verification, /sandbox to cut 84% of prompts, critiquing plans like design reviews, running multi-sessions for TDD, and /clear between tasks.
Reverse These 3 RAG Decisions to Prevent Silent Failures
RAG systems fail quietly when retrieval quality drops unnoticed—monitor document retrieval directly, not just LLM outputs, and pick databases after analyzing query patterns.
Databricks RAG: Low-Dim Qwen3 + Rerank for 89% Recall@10
Minimize embedding dims to 256 with Qwen3 MRL (self-managed path), set num_results=50, always rerank ANN top-50 candidates for +15pts recall@10 over 74% baseline.
Persist RAG Memory Across Turns with Lakebase PostgresSaver
Swap LangChain's InMemorySaver for PostgresSaver backed by Databricks Lakebase to maintain conversation history in RAG agents, enabling context-aware multi-turn responses like resolving 'it' to prior mentions across Model Serving requests.
Track One User-Feature Pair to Catch ML Pipeline Bugs
A rec model's 0.91 AUC failed in prod after 4 days due to 21-hour stale user_30d_purchases features. Track user U-9842 and this feature through every pipeline layer to expose and prevent such mismatches.
LangGraph Builds Resilient Multi-Agent LLM Debate for Drift Tests
LangGraph's stateful graphs, Pydantic schemas, and isolated memory enable adversarial multi-agent debates that run 50 rounds reliably, detecting LLM drift via self-critiquing refinement loops.
Codex /goal Autonomously Shipped 14/18 Features Overnight
OpenAI's Codex /goal CLI implemented 14 of 18 backlog features solo in 18 hours for $4.20 ($0.30/feature), running without human approvals by using soft stops and self-summarization.
5 LLM Agent Patterns for Reliable, Bloat-Free Workflows
Use prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer patterns to build production-ready LLM agents; start with simple workflows unless tasks demand adaptive reasoning, prioritizing tool interfaces, docs, and logging.
GStack: Claude Skills Pack Scales Solo Dev to Full Team
Garry Tan's open-source GStack equips one developer with 23+ Claude AI skills for code reviews, security audits, browser QA, and one-command deploys directly from terminal, exploding to 85k GitHub stars in weeks.
Yin-Yang LLM Pipeline Cuts Noise in Code Scanning
Build reliable AI code scanners by pitting a recall-focused hypothesis agent against a precision-focused evidence agent, stripping reasoning to avoid bias, and enforcing a deterministic policy gate—treating LLMs as stochastic machines, not oracles.
Agentic Pipelines: Cache Keys Cut Token Bloat 95%
Intercept tool calls with a ToolOrchestrator that swaps cache keys for large datasets, keeping LLM context to metadata only—avoids 50k-token ping-pong, slashes latency and costs by 95%, frees model for pure reasoning.
Fix AI Note Forgetting: Unlock LLM Mechanics via RAG
Structure notes in consistent Markdown, retrieve relevant chunks to fit context windows (measured in tokens), instruct model to use only provided notes to avoid hallucinations, and tune temperature for consistent explanations or varied practice questions.
AI Code Speed Trap: Become a Better Vibe Coder
AI tools generate code 10000x faster, but speed alone creates technical debt—your 'vibe coder' type, like the Demanding Child who demands magic without understanding, determines if you ship reliably.
GraphRAG and Vectorless RAG Fix Vector RAG's Silent Failures
Vector RAG structurally fails by confidently hallucinating on semantically similar but incorrect chunks with no errors logged. GraphRAG maps entity relationships via graphs; Vectorless RAG skips vectors for LLM reasoning over document structure—each excels where the other can't.
AI Agent Memory: 4 Dimensions, Benchmarks, Tool Tiers
No single tool solves agent memory's four dimensions—storage, curation, retrieval, lifecycle. ECAI benchmarks show full-context approaches hit 100% accuracy but with 9.87s median latency and 14x token costs; selective systems like Mem0 score 91.6% on LoCoMo at <7k tokens/call. Match tiers to stack and bottlenecks like temporal queries.
SageMaker Fine-Tuning: LoRA Beats QLoRA on Cost-Perf Balance
LoRA cuts trainable params by 96% vs full fine-tuning, balancing cost savings and accuracy on Llama2-7B/Mistral7B; QLoRA saves 8x memory but trains slower due to dequantization overhead.
Pipeline Beats Prompt for Reliable Trip Planning
Replace LLM text generation with a 5-layer pipeline that parses constraints, grounds in live data, validates outputs, scores quality, and regenerates low-confidence plans to deliver realistic itineraries.
AI Digital Twin Agent Simulates Warehouse Scenarios via NL Queries
Combine a simple Python inventory simulation (Poisson demand, reorder thresholds) with an LLM agent to interpret natural language questions like 'increase demand 25%', run scenarios over 30 days, and explain impacts like stockouts and replenishment frequency.