L1 Field Note
Databases From POC to Production: A Natural Language Search Migration Playbook
A staged playbook for moving from Postgres and pgvector prototypes to hybrid search, agents, and GraphRAG only when the workload requires it.
L1 Field Note
Databases GraphRAG: Use It When Relationships Matter, Not When Search Is Hard
A production decision guide for GraphRAG, entity graphs, community summaries, relationship reasoning, and where graph retrieval is overkill.
L1 Field Note
AI Engineering Agentic RAG: When Natural Language Search Becomes an Action System
A production framework for adding agents only when users need planning, tool calls, validation, retries, and governed actions.
L1 Field Note
Databases When Search Becomes a Product: OpenSearch and Elasticsearch Architecture
Why marketplace, travel, retail, local commerce, and support search often need a dedicated search platform instead of only pgvector.
L1 Field Note
Databases When Postgres and pgvector Are Enough for Natural Language Search
A database-first decision guide for using PostgreSQL full-text search and pgvector before adding a search engine or vector database.
L1 Field Note
Databases Hybrid Search Before RAG: The Production Baseline for Natural Language Search
Why production search systems should combine BM25, vector retrieval, filters, fusion, ranking, and reranking before relying on LLM answers.
L1 Field Note
Databases Natural Language Search Architecture: A Database Architect's Decision Framework
A production decision framework for choosing lexical search, vector search, hybrid retrieval, RAG, agents, or GraphRAG by workload shape.
L2 Deep Dive
Databases Weaviate Hybrid Search for DB Engineers: Schema, Objects, BM25, Vectors, and Filters
A database engineer's guide to Weaviate hybrid search, including collections, objects, BM25, vectors, filters, tenancy, schema design, and operational tradeoffs.
L2 Deep Dive
Databases Weaviate Named Vectors: One Object, Multiple Embedding Spaces, Better Retrieval Control
How Weaviate named vectors let one object carry title, body, image, code, or support-ticket embeddings, and what that means for schema evolution and backfills.
L2 Deep Dive
Databases Qdrant Hybrid Search with Dense + Sparse Vectors: Why Fusion Strategy Matters
Why dense plus sparse retrieval in Qdrant needs careful fusion, score normalization, candidate sizing, and reranking to work in production RAG.
L2 Deep Dive
Databases Qdrant for Production RAG: Dense, Sparse, Multivector, Payload Filters, and Operational Boundaries
A DBA and platform-engineering view of Qdrant for production RAG, covering collections, points, payloads, filters, dense and sparse retrieval, snapshots, scaling, and limits.
L2 Deep Dive
AI Engineering Retrieve, Constrain, Verify, Abstain: A Near-Zero Hallucination RAG Architecture, Reviewed
A breakdown of Fareed Khan's open-source RAG pipeline for large document corpora: hybrid retrieval, claim-level faithfulness verification, and calibrated abstention — with the real numbers, and the honest limits of the 10M-vector scale claim.
L2 Deep Dive
Databases OpenSearch Hybrid Search for Product Catalogs: BM25, Vectors, Filters, and Rerankers
A production architecture for product catalog hybrid search with OpenSearch, combining BM25, vector retrieval, filters, shard design, reranking, and relevance debugging.
L2 Deep Dive
Databases OpenSearch as a Vector Database: When Search Infrastructure Beats a Dedicated Vector DB
When OpenSearch is the right vector-search platform because keyword search, hybrid retrieval, relevance debugging, and search operations already matter.
L2 Deep Dive
Databases Why pgvector Returns Too Few Results: Filtered HNSW, ef_search, and Iterative Scans
Why approximate pgvector searches can under-return rows after SQL filters, and how to tune filtered HNSW with ef_search, partial indexes, partitioning, and iterative scans.
L2 Deep Dive
AI Engineering The Agentic Orchestration Layer: Scaling Database Operations from Sailor to Captain
AI agents execute tasks quickly, but managing parallel migrations, Terraform drift, and database reviews manually creates a severe cognitive bottleneck for systems architects.
L2 Deep Dive
Databases When pgvector Is Enough: The Postgres-First RAG Architecture
When a Postgres-first RAG design with pgvector is simpler, safer, and easier to operate than adding a separate vector database.
L2 Deep Dive
Databases pgvector vs OpenSearch vs Qdrant vs Weaviate: A Database Architect's Decision Matrix
A production-oriented decision matrix for choosing pgvector, OpenSearch, Qdrant, or Weaviate by workload shape, filters, hybrid search, operations, cost, tenancy, and recovery.
L2 Deep Dive
Databases Migrating from Elasticsearch/OpenSearch Keyword Search to Semantic Search
A safe migration path from keyword search to semantic or hybrid OpenSearch retrieval using dual indexing, embeddings, backfill, relevance evaluation, A/B testing, fallback, rollback, and cutover.
L2 Deep Dive
Databases Building Hybrid Search in OpenSearch: BM25, k-NN, Filters, and Reranking
A production guide to OpenSearch hybrid retrieval with BM25, vector k-NN, metadata filters, score fusion, reranking, relevance debugging, and observability.
L2 Deep Dive
Databases Postgres Full-Text Search + pgvector: Cheap Hybrid Search Before Buying a Vector DB
How to combine PostgreSQL full-text search and pgvector for low-cost hybrid retrieval, including tsvector, ranking, semantic search, fusion, filters, observability, and when to outgrow it.
L1 Field Note
AI Engineering AI Token Cost Is the New Cloud Bill
Token spend behaves differently from compute and storage — it scales with usage and prompt design. Treating it like an engineering cost line, the way you treat a database bill, is how you bring it under control.
L1 Field Note
Databases Why Database Engineers Should Care About AI Cost Engineering
The skills that make a good cost-aware DBA — measuring usage, finding structural waste, balancing cost against reliability — transfer almost directly to AI workloads. Database engineers are unusually well positioned to own AI cost.
L3 Reference Guide
AI Engineering Build vs Buy: The AI Platform Architecture Decision
Evaluating the architectural tradeoffs between turnkey AI coding tools and building an internal AI gateway — with design options, failure modes, and implementation guidance.
L2 Deep Dive
AI Engineering AI Governance for Engineering Teams: Preventing Shadow AI Spend Without Blocking Innovation
How to govern LLM API spend using centralized gateways without slowing down developer velocity, drawing on established cloud cost control patterns.
L2 Deep Dive
AI Engineering AI Token Cost Overruns: Why AI Coding Assistants Are Becoming the New Cloud Bill Problem
Why AI coding assistant spend needs cloud-style FinOps controls before agent loops, context growth, and workspace credits become a surprise bill.
L2 Deep Dive
AI Engineering Agent Productivity Depends on Context Throughput
AI coding agents work better when voice, clipboard, screenshots, and MCP tools reduce context friction.
L2 Deep Dive
AI Engineering AI Cost Incident Runbook: What to Do When Monthly Token Spend Suddenly Doubles
An operational playbook for triaging and containing LLM token spend spikes — from alert fire to root cause within 30 minutes.
L2 Deep Dive
Databases The Stack for AI-Accelerated Database Operations Is Now Open Source
Three May 2026 breakout projects close the gaps that stop database teams from moving schema changes, query assistance, and operational workflows to AI: declarative Postgres migrations, local LLM inference, and a full agent platform.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: April 2026 — Production Agent Infrastructure
The highest-starred new open-source projects in April 2026 targeting production-scale AI agent memory, protocol enforcement, and Postgres environment management — what breaks when agents leave single-developer scope.
L2 Deep Dive
Databases Stop Writing Ad-Hoc Queries: Build a Skill Backbone for Your DB Engineering Workflows
How to codify repetitive DB tasks into testable, reusable Claude skills that produce consistent SQL, runbooks, and migration outputs instead of one-off chat prompts.
L2 Deep Dive
AI Engineering Agentic SRE Architecture: Skills, Agents, MCP Servers, and Human Approval Loops
The definitive 2026 reference architecture for autonomous database operations, from detection to multi-agent diagnosis to human-in-the-loop remediation.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: April 2026 — Part I
The highest-starred new open-source projects in April 2026 relevant to database engineering, infrastructure, and AI tooling — focused on eliminating manual context re-injection across system design, platform automation, and AI memory.
L2 Deep Dive
AI Engineering Prompt Caching, Context Pruning, and Model Routing: Practical Ways to Reduce LLM Cost
How to combine semantic routing, structured context pruning, and prompt caching to reduce production LLM API costs without degrading application quality.
L1 Field Note
AI Engineering AI Coding Assistant ROI: When $200/Developer/Month Is Cheap — and When It Is Waste
Why treating AI assistant seats like standard SaaS licenses obscures their true infrastructure cost profile, and how to measure ROI using cloud compute parallels.
L2 Deep Dive
Databases Top GitHub Breakouts: March 2026 — Agent Adaptation and Production-Scale Vector Search
The second wave of March 2026 breakouts: an agent that learns from every conversation, a Rust vector index that outperforms FAISS at a fraction of the memory, and a Kubernetes-native agent control plane.
L1 Field Note
AI Engineering Token Budgeting for Engineering Teams: Daily, Weekly, Monthly Controls by Developer and Repository
How to implement token quotas, chargebacks, and spend controls for AI engineering teams, drawing parallels from cloud database cost management.
L1 Field Note
Engineering Fundamentals AI Cost Observability Dashboard: LangSmith vs Helicone
How to build an AI FinOps dashboard and choose between proxy-based and instrumentation-based observability.
L3 Reference Guide
AI Engineering GitHub Breakouts: Q1 2026 — The Quarter's Top Productivity Shifts
Six open-source projects from Q1 2026 that converged on eliminating the manual scaffolding between AI agents and production infrastructure: context management, local cloud testing, and vector retrieval.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: March 2026 — Part I
Three components AI teams still build by hand — task decomposition graphs, persistent agent workspaces, and path-scored retrieval — each got a breakout open-source release in March 2026 that replaces custom wiring with library calls.
L1 Field Note
Databases Beyond the Slow Query Log: AI-Driven Troubleshooting Across the Application-Database Boundary
How to use LLMs to holistically diagnose complex production incidents by correlating database metrics, application ORM models, and driver configurations.
L1 Field Note
AI Engineering Why Agentic AI Costs Explode: Context Size, Tool Calls, MCP Servers, Repo Size, and Retry Loops
Agentic AI systems can quietly accumulate massive API bills due to compounding context windows, retry loops, and unconstrained workspace parsing.
L1 Field Note
AI Engineering Codex Credits and Cost Controls for Business Teams
Practical strategies for managing OpenAI Codex API consumption, workspace credits, and governance across your organization.
L2 Deep Dive
AI Engineering Claude Code Cost Management for Engineering Teams
A deep dive into model routing rules, context pruning with Graphify, and governing agent API spend.
L2 Deep Dive
AI Engineering Optimizing the Complete RAG Performance Pipeline
An end-to-end performance engineering guide for production RAG: optimizing query embedding, hybrid retrieval, reranking, prompt caching, time-to-first-token, and cost per request.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: February 2026 — Local Agents and MCP Bridges
February 2026's highest-starred new open-source projects connecting AI agents to local infrastructure, Kubernetes clusters, and structured data without cloud API dependencies.
L2 Deep Dive
Databases LLM-Assisted Troubleshooting for Vector Search
A production triage guide for multi-stage vector search: isolating query embedding latency, HNSW graph degradation, hybrid BM25 fusion skew, and cross-encoder reranking bottlenecks.
L1 Field Note
AI Engineering The New AI FinOps Model: Seat Cost vs Token Cost vs Agent Runtime Cost
Why traditional SaaS spend models fail for agentic AI, and how platform teams are treating LLM compute like database provisioned IOPS.
L2 Deep Dive
Databases Why Vector Database Performance Is Different: Recall vs Latency
A systems engineering analysis of vector search performance: navigating the fundamental tradeoff between Recall@K, query latency, index memory footprint, quantization, and filtered search.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: February 2026 — Part II
The highest-starred new open-source projects in February 2026 — agent-native LLM routing, free AWS local emulation, and cross-platform semantic memory for AI coding agents.
L2 Deep Dive
AI Engineering MCP Server Observability: The New Control Plane for AI + Enterprise Tools
How the Model Context Protocol (MCP) became the networking layer for AI agents, and why monitoring these connections is critical for enterprise security.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: February 2026 — Part I
The highest-starred new open-source projects in February 2026 — eliminating the context tax that slows AI-assisted code review, infrastructure generation, and database operations.
L2 Deep Dive
Databases Why Is This Elasticsearch Query Slow? Search Profiling and LLM Analysis
A deep dive into diagnosing slow Elasticsearch queries: using the Search Profile API to separate query, fetch, and aggregation phases from thread queue delays and network transit.
L2 Deep Dive
AI Engineering LLM-Assisted Elasticsearch Performance Troubleshooting: Heap, Shards, CPU and Search
A cluster-level diagnostic guide for Elasticsearch: isolating JVM garbage collection stalls, thread pool rejections, over-sharding bloat, disk watermarks, and circuit breakers.
L1 Field Note
AI Engineering Context Anxiety and Harness Decay
Why agent harnesses become stale when they overfit today's model weaknesses instead of stable execution contracts.
L1 Field Note
AI Engineering Programmatic Tool Calling for DB Automation
A reference pattern for keeping large database outputs out of model context by using scripts that summarize evidence before the agent sees it.
L1 Field Note
AI Engineering Tool Search vs Loading Every MCP Tool
Why production agents need discoverable tools and context budgets instead of one giant always-loaded MCP surface.
L2 Deep Dive
AI Engineering Securing LLM-Assisted Valkey Diagnostics: Key Privacy, Audit Evidence and Safe Operations
An operational security guide for LLM-assisted Valkey and ElastiCache diagnostics: protecting key privacy, configuring least-privilege ACLs, handling audit trails, and enforcing change guardrails.
L2 Deep Dive
Databases ElastiCache for Valkey Performance Engineering: Nodes, Shards, Replicas and Failover
A production triage and performance engineering guide for Amazon ElastiCache for Valkey: diagnosing shard skew, replication lag, cluster-mode failover, and managed-service boundaries.
L1 Field Note
AI Engineering Token-Efficient Tool Use
How to design agent tool surfaces that preserve context budget for reasoning instead of wasting it on tool metadata and raw output.
L1 Field Note
AI Engineering Application Legibility for Agents
A reference architecture for making logs, metrics, test output, schemas, and deployment history readable by coding agents.
L2 Deep Dive
Databases Finding Expensive Valkey Commands and Data Structures
A production engineering guide to identifying slow commands, hot keys, big collections, unbounded pipelines, and blocking Lua scripts in Valkey without impacting live traffic.
L2 Deep Dive
AI Engineering LLM-Assisted Valkey Performance Triage on EC2: Memory, CPU, Latency and Evictions
A production triage workflow for diagnosing Valkey on EC2: separating engine event loop delays from host memory fragmentation, CPU credit exhaustion, eviction storms, and network PPS limits.
L1 Field Note
AI Engineering Agent-to-Agent Review Loops
A practical review pattern where one agent creates a change and specialized agents review risk, rollback, security, and observability.
L1 Field Note
AI Engineering Harness Engineering: The 2026 Breakthrough Concept
Why the real engineering surface around agents is the harness of tools, scripts, context, review, and telemetry.
L1 Field Note
Databases Database Runbooks as Agent Contracts
A reference operating model for turning human database runbooks into machine-usable agent contracts.
L3 Reference Guide
AI Engineering GitHub Year in Review: 2025 — What Open Source Changed in the Engineering Stack
Nine breakout repos across four themes — MCP protocol adoption, agent memory infrastructure, AI-native platform ops, and database automation — that eliminated the hand-built glue code between AI agents and production systems.
L1 Field Note
AI Engineering The New Engineer Role: Implementer to Orchestrator
Why agentic coding shifts senior engineering work toward decomposition, verification, and operating-model design.
L1 Field Note
Databases Repo-Embedded Skills for Database Teams
Why database teams should store agent instructions, runbook contracts, and review policies in the repository instead of in memory.
L2 Deep Dive
Databases Securing LLM-Assisted MongoDB Diagnostics: Profiler Data, Audit Logs and Operational Guardrails
A self-managed MongoDB control plane for minimizing diagnostic data, separating access, auditing decisions and actions, and preventing LLM-driven production changes.
L1 Field Note
Databases Agentic Code Review for Database Repositories
Database repositories contain hidden rules human reviewers know: never add a blocking index at peak hours, never widen IAM without owner approval. Agent review surfaces these violations before merge — without displacing the human judgment that set the rules.
L2 Deep Dive
AI Engineering AI Agent Observability: Monitor Tool Calls, Token Spend, Latency, and Failure Loops
Why monitoring autonomous SRE agents requires tracking tool-call hallucinations, context window saturation, and recursive retry loops, rather than just basic CPU metrics.
L2 Deep Dive
Databases MongoDB Shard Hotspots, Balancing and Replica Performance
A self-managed MongoDB workflow for distinguishing routing fan-out, uneven ownership, range-migration overhead, cleanup debt, and replica-set pressure.
L1 Field Note
AI Engineering Agent Autonomy Ladder: Manual, Confirm, Auto-Approve, Supervised
A governance model for deciding which database and cloud agent actions require approval and which can run automatically.
L2 Deep Dive
Databases Finding Expensive MongoDB Query Shapes with LLM-Assisted Analysis
A self-managed MongoDB workflow for ranking expensive query shapes, interpreting explain evidence, diagnosing aggregation fan-out, and validating reversible index changes.
L3 Reference Guide
AI Engineering GitHub Breakouts: Q4 2025 — The Quarter's Top Productivity Shifts
Six open-source projects that collectively delivered the missing infrastructure layer for production AI agents: secure sandboxes, deployment platforms, persistent memory, token-efficient encoding, and AI-native storage.
L2 Deep Dive
Databases LLM-Assisted MongoDB Performance Triage: Cache, Replicas and Workload
A self-managed MongoDB incident workflow for correlating WiredTiger cache, host pressure, connections, workload, replication progress, and topology evidence without mistaking symptoms for causes.
L1 Field Note
AI Engineering Outcome-Based Agent Evaluation vs Transcript Review
A field note on why agent evaluation should measure verified state changes instead of polished reasoning traces.
L1 Field Note
AI Engineering Evals Are the New Unit Tests for Agents
Why database and cloud teams need agent eval harnesses that grade outcomes, not persuasive transcripts.
L2 Deep Dive
AI Engineering Agent Loop Anatomy for DB and Cloud Engineers
A practical mental model for how coding agents plan, call tools, observe results, and complete infrastructure work without treating the model response as the whole system.
L2 Deep Dive
Databases Automated Reliability Across the Stack: Database Backups, Platform Observability, and SQL Quality (November 2025)
Three November 2025 open-source releases eliminate manual work from three engineering reliability tasks — multi-database backup verification, self-hosted log and trace collection, and SQL static analysis in CI pipelines.
L2 Deep Dive
Databases Securing LLM-Assisted Oracle Diagnostics: Licensing, Audit Trails and Change Guardrails
An Oracle control-plane design for licensed evidence collection, least-privilege diagnostics, deterministic redaction, end-to-end audit lineage, and human-approved production changes.
L2 Deep Dive
Databases When SQL Is Innocent: Concurrency, Redo, RAC, Storage and Cloud
An Oracle incident workflow for proving whether blocking, commit processing, RAC block transfer, storage, or cloud infrastructure—not SQL efficiency—caused the slowdown.
L2 Deep Dive
Databases From SQL ID to Root Cause: Plans, Cardinality and SQL Regression
An Oracle investigation workflow for proving SQL regressions with child-cursor history, normalized runtime evidence, actual row counts, bind behavior, and reversible plan control.
L2 Deep Dive
Databases LLM-Assisted Oracle Performance Triage: From DB Time to Wait Events
An Oracle incident workflow for reconciling application symptoms, DB time, average active sessions, CPU, non-idle waits, host pressure, and licensed diagnostic evidence.
L2 Deep Dive
AI Engineering Telemetry Cost Control: Why Observability Data Itself Needs Governance
If you log everything and monitor every dimension, your observability bill will eventually exceed your database infrastructure bill. Here is how to fix it.
L2 Deep Dive
AI Engineering The AI-Native Engineering Stack: Agents, Inference, and Knowledge Graphs in Production (November 2025)
Three November 2025 breakout projects eliminate the manual infrastructure build that blocks teams from running AI agents in production — covering agent backends, Kubernetes LLM inference, and SQL-driven knowledge retrieval.
L2 Deep Dive
Databases Securing LLM-Assisted PostgreSQL Diagnostics: Roles, Audit Evidence and Safe Recommendations
A PostgreSQL and Aurora control plane for collecting performance evidence without leaking SQL, overloading production, confusing audit sources, or granting an LLM change authority.
L2 Deep Dive
Databases Aurora PostgreSQL Performance Engineering: Storage, WAL, Replicas and Failover
An Aurora PostgreSQL incident workflow for separating writer pressure, shared-storage activity, local temporary I/O, WAL retention, replica lag, and application recovery after failover.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: October 2025 (Part 2)
October's memory and retrieval breakouts: a structured agent memory framework with benchmarks, a self-hosted cognitive memory engine, and sub-10ms semantic search without a vector database cluster.
L2 Deep Dive
Databases From pg_stat_statements to the Execution Plan: LLM-Assisted PostgreSQL Diagnosis
A safe PostgreSQL workflow for ranking query regressions from interval deltas, capturing the right execution plan, and using an LLM without mistaking correlation for proof.
L2 Deep Dive
Databases LLM-Assisted PostgreSQL Triage on EC2: Sessions, Wait Events and I/O
A PostgreSQL-on-EC2 incident workflow for correlating backend state, wait events, locks, cumulative I/O, Linux pressure, and EBS limits before investigating SQL plans.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: October 2025 (Part 1)
Three October breakouts targeting LLM prompt verbosity, parallel agent orchestration, and fragmented hybrid search stacks — all reducing coordination overhead in AI engineering.
L2 Deep Dive
Databases Securing LLM-Assisted MySQL Diagnostics: Audit Logs, Access Boundaries and Production Guardrails
A MySQL and Aurora security architecture for collecting useful performance evidence without exposing raw SQL, granting production authority, or losing auditability.
L2 Deep Dive
Databases Aurora MySQL Performance Engineering: Waits, Storage, Replicas and Failover
An Aurora-native investigation method for correlating per-instance database load, distributed storage, reader lag, endpoint behavior, and failover readiness with LLM assistance.
L2 Deep Dive
Databases Finding the SQL Behind a MySQL Performance Incident
A MySQL 8.4 investigation method for using statement-digest deltas, latency distributions, execution plans, and LLM correlation to prove which workload changed.
L2 Deep Dive
Databases LLM-Assisted MySQL Performance Triage on EC2: From Host Saturation to Database Waits
A layered MySQL 8.4 triage method for distinguishing EC2 compute, memory, EBS, connection, lock, and engine-wait failures before investigating individual SQL statements.
L3 Reference Guide
Databases Torn Page Protection Belongs Off the Foreground Path
A PostgreSQL kernel experiment shows why moving torn-page protection from WAL to background flush can change write latency.
L1 Field Note
AI Engineering Can an LLM Safely Recommend Database Performance Changes?
A security and change-control architecture for turning LLM-generated database performance hypotheses into auditable proposals without giving the model production authority.
L3 Reference Guide
AI Engineering GitHub Breakouts: Q3 2025 — The Quarter's Top Productivity Shifts
Six open-source tools from Q3 2025 that closed the infrastructure gaps blocking AI agents in production: persistent memory, intelligent model routing, and natural language database access.
L2 Deep Dive
AI Engineering AI Agents in Platform Automation: Useful Assistant or Unreviewed Change Engine
When AI agents accelerate platform operations versus when they generate unreviewed changes — the permission boundary and audit design that separates useful from risky.
L1 Field Note
AI Engineering Finding Performance Anomalies Before Asking the LLM
A deterministic anomaly-detection layer that turns normalized database telemetry into attributable, operationally meaningful evidence before LLM correlation begins.
L1 Field Note
AI Engineering Building the Database Incident Evidence Pack for an LLM
A practical evidence contract for collecting, normalizing, redacting, storing, and presenting time-bounded database telemetry to an LLM without sending an uncontrolled production dump.
L1 Field Note
AI Engineering From Metrics to Root Cause: Why Database Troubleshooting Needs Evidence Correlation
Dashboards expose database symptoms one layer at a time. This architecture turns time-bounded telemetry into ranked, testable root-cause hypotheses without giving an LLM production control.
L2 Deep Dive
Databases Top GitHub Breakouts: August 2025 — Part II
The highest-starred new open-source projects in August 2025 where AI takes over cloud operations, infrastructure provisioning, and production Postgres coding.
L2 Deep Dive
Databases Top GitHub Breakouts: August 2025 — Part I
The gap between AI prototype and production system is routing tables, deployment YAML, and observability scaffolding. August 2025's top breakouts targeted exactly the code engineers keep rewriting: model routing logic, agent deployment manifests, and PostgreSQL diagnostics.
L3 Reference Guide
Databases The Semantics AI Misses When Porting Storage Designs
Why a PostgreSQL double write buffer prototype failed despite compiling, and what it reveals about AI-assisted systems design.
L2 Deep Dive
AI Engineering FinOps Observability: Tie Cloud Cost to Workload, Team, Product, and Customer
How to connect engineering telemetry with cost telemetry to achieve granular cloud unit economics using FinOps principles and FOCUS standards.
L3 Reference Guide
Databases Natural Language SQL Agents Need Database Guardrails
The risk in a natural-language SQL agent is not bad SQL — it is authority compilation: a user sentence becomes a database operation unless the control plane proves, before execution, which role, rows, cost, and columns the query is allowed to touch.
L3 Reference Guide
AI Engineering GitHub Breakouts: Q2 2025 — The Quarter's Top Productivity Shifts
Six Q2 2025 open-source breakouts that closed the gap between AI agents and engineering infrastructure across system design, platform operations, and database tooling.
L2 Deep Dive
AI Engineering Personal AI Agents Fail in the Last 20 Percent of Integration
Self-hosted AI agents become useful only when model quality, tool access, memory, and setup completeness line up.
L2 Deep Dive
AI Engineering Parallel AI Agents Need an Operating Model
Running many coding agents only works when git isolation, shared memory, permissions, hooks, and verification are designed as a system.
L2 Deep Dive
Databases Top GitHub Breakouts: May 2025 — Operational Baseline in a Config File
Three May 2025 open-source projects replace multi-tool assembly in document ingestion, deployment governance, and PostgreSQL backup with single-binary or configuration-first alternatives.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: May 2025 — Agent Infrastructure Without Boilerplate
Three May 2025 open-source projects eliminate the manual scaffolding that blocks every AI agent deployment: orchestration glue, vector database setup, and MCP gateway configuration.
L2 Deep Dive
Databases Three Open-Source Tools Filling the Gaps in Database Operations (May 2025)
May 2025's most-starred new projects solve three specific database team problems: backup restores that are never verified, internal knowledge that can't be retrieved, and AI agents blind to your schema history.
L2 Deep Dive
AI Engineering The Three-Layer Agent Infrastructure Stack for Database Operations (April 2025)
Building a database operations agent requires a workflow framework, production observability, and scalable inference — April 2025 shipped open-source solutions for all three layers simultaneously.
L2 Deep Dive
AI Engineering The Architecture of Natural Language Database Interfaces
Replacing the translation overhead between business questions and SQL queries requires an architecture that bridges LLM intent parsing with strict execution validation and schema retrieval.
L2 Deep Dive
AI Engineering Datadog Bits AI SRE: What an AI On-Call Teammate Changes for DBAs
How autonomous AI agents like Bits AI SRE are shifting the database incident workflow from manual dashboard hunting to conversational investigation.
L3 Reference Guide
Databases GitHub Breakouts: Q1 2025 — The Quarter's Top Productivity Shifts
Six high-traction open-source projects from Q1 2025 converged on eliminating the manual integration layer between AI assistants and production systems across databases, platform operations, and developer tooling.
L2 Deep Dive
AI Engineering Top GitHub Breakouts: February 2025
The highest-starred new open-source projects in February 2025 eliminating manual iteration in prompt engineering, infrastructure monitoring, and private data retrieval.
L2 Deep Dive
AI Engineering Evaluate AI Agents by Completed Work, Not Token Price
Production AI agent selection should measure quality, retries, tokens, latency, and verification cost per completed task.
L2 Deep Dive
Databases Natural Language SQL Agents Need Guardrails Before Orchestration
How Postgres chat agents turn intent into SQL, and why production systems need schema controls, validation, and auditability.
L2 Deep Dive
Databases Double Write Buffers Fail at the I/O Boundary
Why porting InnoDB’s double write buffer to PostgreSQL breaks on buffered I/O, fsync semantics, and background writer design.
L2 Deep Dive
AI Engineering AI-Assisted Incident Triage: From Alert Noise to Root-Cause Hypotheses
How generative AI tools like CloudWatch Investigations shift the operational burden from reading raw dashboards to validating machine-generated hypotheses.
L3 Reference Guide
AI Engineering GitHub Year in Review: 2024 — What Open Source Changed in the Engineering Stack
Nine breakout repositories across three themes — agents that operated computers, RAG that grew a graph spine, and databases that finally spoke natively to LLMs — define what actually shifted in the engineering stack in 2024.
L2 Deep Dive
AI Engineering Remote Agents Need Deployment, Permissions, and Feedback Loops
Codex mobile turns local agents into remote workflows, but production value depends on deployment, access control, and observability.
L3 Reference Guide
AI Engineering Prompt Architecture Needs Load Boundaries
The default AI coding setup loads everything into one always-on instruction file. The production alternative is a layered architecture — project memory, task skills, commands, and MCP servers each with a defined load boundary — so context bloat and stale policy stop reaching the model on every turn.
L3 Reference Guide
AI Engineering AI Agents Need Database Guardrails Below the Prompt
Prompt-level guardrails fail open when the agent misinterprets context. The only boundary that mechanically rejects destructive SQL is the database — dedicated read-only roles, sanitized view schemas, and a network path that application credentials never touch.
L1 Field Note
AI Engineering The Agent Should Not Have Your App Credentials
Giving an AI coding agent your application's Postgres credentials is the default mistake — the agent inherits every permission the app has. Database-enforced read-only roles, replica routing, query limits, and project-scoped MCP config are the alternative that actually fails closed.
L2 Deep Dive
AI Engineering Runtime Boundaries for Agentic App Builders
A hosted AI app generator fails when the mobile chat becomes the platform — API keys end up in binaries, execution state blurs with chat, and previews break without artifact handoff. The control-plane architecture that keeps these concerns separated.
L2 Deep Dive
Databases pgvector Basics: Embeddings Inside PostgreSQL
How pgvector adds vector storage and similarity search to PostgreSQL, what the three distance operators do, and the index you must create before you hit 100K rows.
L2 Deep Dive
AI Engineering AI Agents Need a Control Plane, Not More Interfaces
Production AI agents work best when coding, files, tools, and knowledge workflows share one governed execution model.
L2 Deep Dive
Databases Top GitHub Breakouts: March 2025 (Part 2)
Three March 2025 open-source projects that eliminate the iteration pauses engineers manually bridge — research review loops, vector index calibration, and agent provisioning YAML.
L1 Field Note
AI Engineering Database Security Review for AI Access
Granting an autonomous AI agent access to your database breaks every assumption of traditional RBAC. How to secure databases against unpredictable, unbounded AI queries.
L2 Deep Dive
AI Engineering The Harness Around the Agent: How Stripe Runs 1,000 Unattended Code Reviews per Week
Stripe's Minions system runs over a thousand AI code reviews weekly using a fork of an open-source agent. The reliability comes from the deterministic pipeline around it, not the model inside.
L2 Deep Dive
AI Engineering Use Coding Agents as a Toolchain, Not a Vendor Bet
A production-minded workflow for running Cursor and Aider together without locking engineering practice to one agent.
L2 Deep Dive
Databases Vectorless RAG Patterns for Database Knowledge Systems
How tree-based retrieval can improve DB runbooks, schema docs, and incident knowledge over chunked vector search.
L2 Deep Dive
AI Engineering Durable State for Long-Running LLM Coding Sessions
A practical workflow for separating planning from execution, checkpointing progress in GitHub issues, and resuming multi-phase LLM implementation without context collapse.
L2 Deep Dive
AI Engineering Independent Parallel Agents Don't Cancel Errors — They Amplify Them
Google Research found that independent parallel agents amplify errors 17x compared to centralized orchestrator topologies. Adding more agents to a system with a shared context defect makes it worse, not more resilient.
L2 Deep Dive
AI Engineering From Chat to Agents: Designing Goal-to-Result Systems for Real Work
Chat is request-response; agents are task systems that plan, call tools, iterate, and stop when done. The minimum architecture — loop, tools, bounded memory, stopping conditions — required to make the transition from chat reliable.
L3 Reference Guide
AI Engineering How Paperclip Is Redefining AI Agent Orchestration for the Zero-Human Company
Paperclip's zero-human orchestration model — goal-directed agent teams instead of task-by-task prompting — and what that architecture requires from the software and data systems beneath it.
L2 Deep Dive
AI Engineering Why Long-Running AI Coding Sessions Fail
A practical control plane for keeping AI coding sessions on track: separate planning from execution, validate deterministically, reset context aggressively, and isolate parallel work.
L1 Field Note
Databases How a 10 Billion Row SQL Query Runs in 200ms on a GPU Database
A DBA-friendly walkthrough of how modern GPU databases execute large analytical SQL queries using columnar storage, parallel scans, and GPU aggregation.
L1 Field Note
Databases Why Databases Are Moving Toward GPU Execution Engines
A practical, DBA-friendly explanation of why modern analytical databases are increasingly using GPUs for scans, joins, aggregations, and AI-adjacent workloads.
L1 Field Note
Engineering Fundamentals CPU vs GPU vs TPU Explained for Database Engineers
How CPU, GPU, and TPU architectures differ in ways that matter for databases and AI workloads — and which compute class to reach for when adding vector search, embedding generation, or GPU-accelerated analytics.