Blog

One giant JSON file is easy to publish and hard to use. A catalog gives every dataset a stable URL, a readable page, and a fetch you can cache on its own.

HTTP status codes cheat sheet: the 13 codes that matter (200, 301, 404, 429, 503...), 401 vs 403 explained, plus a fetchable API dataset.

Bytes are exact and free to measure. Tokens are a model-specific estimate. Context is a budget you spend. A short primer on why the distinction matters.

HTTP methods reference: GET, POST, PUT, PATCH, DELETE safety and idempotency table, PUT vs PATCH explained, plus a fetchable API dataset.

CC0 data needs no attribution, so why cite it? Because a citation is how the next person finds the source when a number looks wrong. A practical format.

SQL join types explained: INNER, LEFT, RIGHT, FULL OUTER, CROSS, and SELF JOIN with syntax, which rows survive, and a fetchable dataset.

Git commands cheat sheet: the 15 essential commands (clone, add, commit, branch, merge, rebase, stash, reset) with exactly what each does.

Unix file commands cheat sheet: ls, find, grep, awk, sed, tar with examples, how they compose in pipes, plus a fetchable dataset.

LLM glossary: 8 key terms explained (tokens, context window, temperature, RAG, fine-tuning, quantization, KV cache, emergence) + fetchable dataset.

Cognitive biases decision checklist: 7 biases (confirmation, anchoring, sunk cost...) with a counter-move for each, plus a fetchable dataset.

Common network ports reference: 21, 22, 53, 80, 443, 3306, 5432, 6379, 8080, 27017 explained, plus how to read connection refused + dataset.

Grounding only helps if you verify the result. A practical loop: extract each claim, look it up in the dataset, compare exactly, and fail loudly on a mismatch.

Preloading a dataset into context wastes budget on rows the agent never reads. The lookup-first pattern fetches only the rows a task needs. Here is how it works.

How YJTOON stays free: open CC0 data funded by a paid ebook companion, and why selling synthesis beats paywalling facts.

A worked example: the same TCP/IP dataset in JSON (847 bytes) vs TOON (484 bytes), and how to switch formats per request.

Why public, free, and open datasets — not just algorithms and compute — will decide what AGI becomes, and what builders should do about it.

How YJTOON serves 185 datasets from a shared hosting plan: SQLite in WAL mode, IP-hash rate limiting, pre-generated static files, and no authentication.

How prompt caching works with long reference data: keep stable data in a stable prefix, put variable content after it, and treat format consistency as a cache key.

The llms.txt convention in plain terms: a simple text file at the site root that tells LLMs what the site offers and which URLs matter. How YJTOON uses it.

What public-domain dedication (CC0) means for consumers of YJTOON data, what attribution obligations remain, and how to license reference data cleanly.

The silent failure modes in reference data: unit names, timestamp formats, and character encodings. How YJTOON keeps them unambiguous and what consumers should check.

What the YJTOON search endpoint does (minimum 2 characters, LIKE across fields, 50-result cap) and the design choices that make search usable by agents.

Why every dataset needs a stable slug and canonical URL: caching, deduplication, versioning, and how agents should reference data they found earlier.

A practical comparison of CSV, JSON, and TOON for reference datasets: token cost, parseability, nesting, and when each format is the right choice.

How rate limiting actually works on YJTOON (120 requests per minute, escalating cooldowns) and the retry patterns agents should use when they hit a 429.

Why the Content-Type header matters for data APIs, what YJTOON serves for each format, and how agents and browsers interpret those headers.

A tour of the YJTOON catalog: 27 categories and 185 datasets spanning programming, HTTP, math, science, history, and more. How the catalog is organized and how to browse it efficiently.

A practical checklist for making a public data API easy for LLM agents: stable URLs, content negotiation, clear errors, rate-limit headers, and static fallbacks.

Why RSS is still the right feed format for technical blogs, and the small set of fields a developer should validate before subscribing.

The minimum set of Open Graph and Twitter Card tags a technical blog post needs, why each one matters, and the easy mistakes to avoid.

Why every developer should ship a small, personal knowledge API for their own use, and the minimum features it needs to be useful to an agent.

Static reference files fronted by a CDN are a cheap cache for LLM context. Here is when to reach for them and how to keep them fresh.

Tags help both humans and agents find related material. Here is the small, opinionated tag set YJTOON uses, with rules for when to add a new tag.

A short decision guide for picking between the YJTOON REST API, the static file tree, and the inline JSON snippet, based on latency, freshness, and reuse.

The exact 7-step workflow we use to take a blog post from a blank JSON file to a live URL on the CDN. With the failure modes and the rollback.

How YJTOON picks between JSON, YAML, and TOON based on the Accept header, and how to override it without changing your client library.

Practical rules for shaping reference datasets so they are useful to humans and to LLM agents: naming, units, ranges, ambiguity, and provenance.

How to make agents robust to API failures without overcomplicating them. Includes retry, backoff, circuit-breakers, and graceful degradation.

Patterns for keeping an agent responsive when a reference API is slow. Includes cached fallbacks, partial answers, and honest errors.

TOON is at its best on flat tables. This post explains the supported patterns for one level of nesting and why deeper trees usually mean a different format.

A guided tour of one of the most-used YJTOON datasets: HTTP status codes. What is in it, what is not, and how to extend it.

A short, opinionated SEO checklist for technical blog posts. No fluff, no SEO-speak. The minimum that actually moves rankings.

How to attribute open data sources in derived datasets and in LLM prompts. A short set of rules and the small set of edge cases that trip people up.

How to structure reference material in system prompts so models actually use it. Covers placement, length budgets, attribution, and a few anti-patterns to avoid.

A copy-paste template for the "reference card" pattern in system prompts, with annotated examples and the small set of fields that actually help.

A short checklist for keeping a sitemap.xml honest on a small site: which URLs to include, how to set lastmod, and how to avoid the common traps.

YJTOON serves the same data two ways: a REST API and a CDN-fronted static file tree. Here is a decision guide with examples.

When to force JSON-schema output and when to let the model answer in prose. A short decision guide with failure examples.

How many tokens does pretty-printed JSON waste in a prompt? We measured three common styles and a couple of fixes.

A line-by-line walkthrough of a minimal TOON parser, with the tradeoffs of keeping it small and the tests you should add before growing it.

A simple budgeting method for prompts that combine instructions, several reference documents, and a question. Includes a worked example.

TOON is best understood as a prompt compression format, not a storage format. This reframe changes how you think about when to reach for it.

A short tour of TOON encoder edge cases: empty cells, embedded newlines, numeric precision, and the rules for escaping safely.

A field-by-field walkthrough of the TOON grammar: indentation, table headers, key-value rows, comments, and edge cases. Useful for parser authors and prompt authors.

Hard numbers from 12 YJTOON datasets: tokens used by JSON, YAML, and TOON for the same payload. Includes a short table and a guide to when TOON wins (and when it does not).

A reference for the rate-limit headers YJTOON returns, what each one means, and how to use them without guessing.

Practical patterns for checking model answers against a small reference dataset, with code snippets and a discussion of common failure modes.

How to evolve a public dataset over time without forcing every consumer to re-sync. A small set of rules learned from production.

Vector databases are the default answer for "AI search." Most of the time they are the wrong default. Here is when to reach for something simpler.

Reasoning behind YJTOON's code-only, no-admin-UI blog: durability, agent-friendliness, and the smallest surface that still works.

Reasoning behind YJTOON's no-authentication, no-signup design and the small set of protections that make it work in practice.

Practical guide to YJTOON rate limits, the 429 response shape, and the recommended request patterns for agents and CI pipelines.

How to request YJTOON datasets in TOON format and include them in prompts with minimal token overhead. Includes practical examples and tips for agents.

YJTOON provides free, structured reference datasets in JSON, YAML, and token-efficient TOON. Designed for quick human lookup and efficient LLM consumption. No signup, no limits beyond fair use.