✦ This isn't a glossary anymore

A curated knowledge graph with editorial governance

A typical glossary gives you terms, definitions, and a search box. This goes further.

Terms are connected by reviewed, typed relationships, making it possible to follow how ideas relate across the entire corpus. Every connection is sourced, reviewed, and tracked, while the system continuously checks for missing links, dead references, and gaps in coverage.

The result is less a glossary and more a curated knowledge graph — designed not just to define concepts, but to show how they connect.

Honestly, we've stopped trying to pin down exactly what this is — and it's still being built. Depending on the day it reads like an Engineering Knowledge Operating System, a Curated Engineering Knowledge Graph, or an AI-Native Developer Knowledge Base, with a Blast Radius risk layer wiring in now. It's quietly becoming all of them — tap a ? to see what each means.

We're not quite sure what to call it — but it outgrew “glossary” a while ago.

The most ambitious framing: not a reference you read, but a substrate other tools run on. 819 reviewed, typed relationships bind the corpus into one coherent layer — alongside four-axis risk scores and editorial governance — that people browse and machines query. Engineering knowledge that's stored, connected, checked, and acted on all in the same place.

At its core it's a graph: 1,519 engineering concepts joined by reviewed, typed relationships. Every edge is sourced and tracked, every term scored on a four-axis risk model, and the whole corpus is continuously audited for missing links, dead references and gaps in coverage — closer to a maintained dataset than a wiki.

Built to be read by models as much as by people. The corpus is machine-readable end to end — typed edges, structured fields, an agent-sync layer, a deterministic assistant that answers from the graph instead of guessing, and 18 mediated narratives that walk through how distant concepts connect — so an LLM can traverse it the way a developer would.

The layer wiring in now. Every term already carries a four-axis risk score; Blast Radius teaches the graph to trace how that risk travels along its connections — turning isolated scores into a blast radius that shows how far a concept's risk actually reaches. Risk-graph readiness is at 40% and climbing as more connections are reviewed.

Glossary

1519 terms · page 1 of 76

Written for developers. Structured for AI citation.

A developer glossary, written to teach — built to be cited.

1.5k terms across 34 categories — definitions, examples, common mistakes and fixes. Authored under one editorial standard. Cross-linked, searchable, and openly crawled by AI agents.

+3 added in last 7 days ✎ 4 edited in last 7 days ↑ 125.8k term views

🧠 Logic graph

819 live edges

20 verbs in use

692 terms touched

18 narratives

✦ Latest typed relationships

The graph is growing

Each edge is LLM-proposed, gate-filtered, and human-reviewed.

URL & URLSearchParams API Often seen in AJAX Patterns for PHP Backends 1d
URL & URLSearchParams API Often seen in Axios — HTTP Client for PHP APIs 1d
Attack Surface Often seen in Penetration Testing 1d
URL & URLSearchParams API Often seen in History API — SPA Routing with PHP Backend 1d
URL & URLSearchParams API Often seen in Fetch API & HTTP Requests 1d

Garbage collection is the concrete piece of memory management that touches memory leaks directly: as a specialization of the heap-management side of the discipline, it's the runtime mechanism responsible for reclaiming unreachable objects. A leak is precisely what happens when that reclamation fails — most acutely in PHP when reference counting can't free circular references and the cycle collector is what stands between you and unbounded process growth. So the story from memory_management to memory_leak runs through GC because GC is the part of memory management whose success or failure determines whether allocations are ever returned.

Memory Management — Stack vs Heap · through · 🪢 Garbage Collection → Memory Leak

Binary trees connect to pagination through database indexing, which is the structure that actually carries the benefit between them. The index is typically a B-tree variant of the binary tree concept, and its ordered, logarithmic-lookup structure is exactly what lets efficient pagination seek directly to a page's starting key (keyset pagination) instead of scanning and counting rows. Without the index sitting in the middle, the tree's ordering properties would never reach pagination at all — it's the index that translates tree structure into fast page boundaries.

Binary Trees · by means of · 🪢 Database Indexing → Pagination Strategies

Exactly-once delivery connects to domain events through the outbox pattern, which atomically writes those events to a database table in the same transaction as the business data that produced them. The pattern is what carries the events safely to the broker without loss or duplication, so the delivery guarantee is meaningfully about the domain events being relayed — remove the outbox and there's no mechanism tying the messaging guarantee to the architectural event records. That atomic write is the load-bearing piece: it's the bridge between 'something happened in the domain' and 'the broker delivers it exactly once'.

Exactly-Once Delivery · by means of · 🪢 Transactional Outbox Pattern → Domain Events

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Last 30 days

Agents 249

PetalBot 83 Google 74 Bing 23 SEMrush 22 ChatGPT 18 Brave Search 9 Common Crawl 9 Ahrefs 7 Amazonbot 2 Applebot 2

Google 251 PetalBot 148 SEMrush 57 Bing 56 ChatGPT 42 Ahrefs 34 Brave Search 31 DuckDuckGo 4 Applebot 1 Amazonbot 1

ChatGPT 13k Amazonbot 12.8k Scrapy 11.7k Perplexity 9.8k Google 7.7k Ahrefs 7.6k SEMrush 6.1k PetalBot 5.3k Claude 3.4k Unknown AI 3k Bing 2.8k Twitter/X 2k Brave Search 1.9k Meta AI 1.7k Applebot 1.6k Majestic 891 Sogou 439 Qwen 190 Common Crawl 167 You.com 99 ShapBot 36 DuckDuckGo 31 Yandex 15 NotebookLM 8 LinkedIn 2 Backlinks.com 1 Manus AI 1

Most referenced terms