{"id":235845,"date":"2026-10-05T13:50:33","date_gmt":"2026-10-05T13:50:33","guid":{"rendered":"https:\/\/mycryptomania.com\/?p=235845"},"modified":"2026-10-05T13:50:33","modified_gmt":"2026-10-05T13:50:33","slug":"jev-is-not-a-chatbot-its-a-judgment-engine-and-i-built-a-real-time-log-guardian-with-it","status":"publish","type":"post","link":"https:\/\/mycryptomania.com\/?p=235845","title":{"rendered":"Jev Is Not a Chatbot. It\u2019s a Judgment Engine \u2014 And I Built a Real-Time Log Guardian With It"},"content":{"rendered":"

How TypeSafe\u2019s Jev\u200a\u2014\u200aa model that returns probabilities, not paragraphs\u200a\u2014\u200areplaces 500 regex rules with one API call, catches credential leaks in milliseconds, and monitors 100+ logs\/sec using a batch window architecture nobody else is talking\u00a0about.<\/em><\/p>\n

\ud83c\udf4b The Lemon Explanation: What Even Is\u00a0Jev?<\/h3>\n

Okay, let me start with the simplest possible explanation.<\/p>\n

You know how when you ask ChatGPT something, it writes you paragraphs? It generates words. Lots of words. Some useful, some not. You then have to read<\/em> those words and figure out what to do with them in your\u00a0code.<\/p>\n

Jev doesn\u2019t do\u00a0that.<\/strong><\/p>\n

Here\u2019s the lemon\u00a0version:<\/p>\n

Imagine you have a super-smart friend who you can ask yes\/no questions or \u201cpick one from this list\u201d questions\u200a\u2014\u200aand they answer instantly with a confidence score. They don\u2019t explain themselves. They don\u2019t write essays. They just say: <\/em>\u201cDatabase issue. 87% confident. Emergency page on-call.\u201d<\/em><\/strong>That\u2019s Jev.<\/em><\/p>\n

Jev is a System One model<\/strong>\u200a\u2014\u200ait\u2019s built for fast, structured judgment. Like your gut reaction, but calibrated, typed, and callable from\u00a0code.<\/p>\n

The output isn\u2019t a string of text. It\u2019s a typed JSON object<\/strong> with probabilities. Your code reads it and acts. No parsing, no prompt engineering for output format, no string regex to extract the\u00a0answer.<\/p>\n

\ud83e\udde0 Jev vs. Traditional LLMs\u200a\u2014\u200aWhat\u2019s Actually Different?<\/h3>\n

Let me make this crystal clear with a\u00a0table:<\/p>\n

FeatureGPT \/ Claude \/ GeminiJev (TypeSafe System One)Output format<\/strong>Text (you parse it)Typed JSON (probabilities + choice)What it does well<\/strong>Writing, reasoning, explanationFast structured judgmentSpeed<\/strong>Seconds (reasoning takes time)Sub-second judgmentsToken cost<\/strong>Input + output tokensInput only\u200a\u2014\u200aoutput tokens = 0How you use it<\/strong>Write a prompt, parse a responseDefine questions + criteria, consume answersGood for<\/strong>Open-ended tasksDecisions your code needs to makeUncertainty<\/strong>Hidden inside textExplicit\u200a\u2014\u200aevery answer has a probabilityDrift risk<\/strong>High (prompt changes break output format)Low (schema is your contract)<\/p>\n

The key insight: traditional LLMs are optimized to be helpful to humans reading their output. Jev is optimized to be helpful to code reading its\u00a0output.<\/strong><\/p>\n

\u2699\ufe0f How Jev Works\u200a\u2014\u200aThree Primitives, That\u2019s\u00a0It<\/h3>\n

Jev gives you three building blocks. Everything is a combination of these\u00a0three:<\/p>\n

1. choice\u200a\u2014\u200aPick one from a defined\u00a0list<\/h3>\n

{
“type”: “choice”,
“instructions”: “What is the failure domain of this log?”,
“criteria”: {
“database”: “DB queries, connection pools, Postgres errors”,
“network_dns”: “Socket timeout, DNS failure, TLS handshake”,
“auth_security”: “Auth failures, token errors, credential leaks”,
“app_logic”: “NullPointer, assertion, business rule violation”
}
}<\/p>\n

Jev returns: { choice: “database”, confidence: 0.92\u00a0}<\/p>\n

2. noul\u200a\u2014\u200aProbability of a yes\/no condition<\/h3>\n

{
“type”: “noul”,
“instructions”: “Is there risk of an imminent system outage?”,
“criteria”: {
“true”: “Cascading failure, resource exhaustion, multi-service degradation”,
“false”: “Isolated, recoverable, routine error”
}
}<\/p>\n

Jev returns: { noul: 0.87 }\u200a\u2014\u200athat’s 87% probability of\u00a0YES.<\/p>\n

3. score\u200a\u2014\u200aRate something along a defined\u00a0scale<\/h3>\n

{
“type”: “score”,
“instructions”: “Rate severity from benign (1) to catastrophic (5)”,
“criteria”: [
“Level 1: Routine trace, healthy HTTP 200”,
“Level 2: Normal operational event”,
“Level 3: Non-critical warning”,
“Level 4: Degraded subsystem, high latency”,
“Level 5: Fatal failure, OOM, credential leak”
]
}<\/p>\n

Jev returns: { score: 3.8, confidence: 0.91\u00a0}<\/p>\n

You ask multiple questions in one API call, and they all run in parallel.<\/strong> No waiting for one to finish before asking the\u00a0next.<\/p>\n

\ud83c\udfd7\ufe0f The Problem We Set Out to Solve: Log Analysis is\u00a0Broken<\/h3>\n

Here\u2019s what every engineering team does\u00a0today:<\/p>\n

Logs go into Datadog \/ CloudWatch \/\u00a0ELKYou write regex rules: if log.contains(“OOM”) \u2192\u00a0alertYou get paged at 3am because \u201cOOM\u201d appeared in a test<\/em>\u00a0logOR you miss<\/em> a real outage because the message was “heap exhausted” not\u00a0“OOM”<\/p>\n

Regex-based log alerting has three fatal\u00a0flaws:<\/p>\n

\u274c It\u2019s literal<\/strong>\u200a\u2014\u200ait only catches exact strings. \u201cDB connection failed\u201d and \u201ccould not connect to postgres\u201d are the same thing to a human but different patterns to a\u00a0regex.<\/p>\n

\u274c It\u2019s single-log blind<\/strong>\u200a\u2014\u200aeach log is evaluated in isolation. But the scary<\/em> pattern is: DB pool timeout \u2192 queue backup \u2192 memory pressure \u2192 OOM crash. Three individually \u201cfine\u201d logs that together scream \u201cwake up on-call\u00a0NOW.\u201d<\/p>\n

\u274c It exposes secrets silently<\/strong>\u200a\u2014\u200aa log like “Connecting to postgresql:\/\/admin:super_secret_123@prod-db:5432\/users” contains a plaintext credential. Regex might catch password= but it won’t catch URL-encoded credentials, JWTs in headers, or PII in custom\u00a0formats.<\/p>\n

We wanted to solve all three with one model\u00a0call.<\/strong><\/p>\n

\ud83d\udd28 How We Built the TypeSafe Log\u00a0Guardian<\/h3>\n

Let me walk you through the actual architecture. It\u2019s simpler than you\u00a0think.<\/p>\n

The Stack<\/h3>\n

Backend<\/strong>: Node.js + Express + TypeScriptAI Brain<\/strong>: TypeSafe Jev (via REST\u00a0API)Frontend<\/strong>: Vanilla HTML\/CSS\/JS (no framework needed)Pattern<\/strong>: Batch Window\u00a0Engine<\/p>\n

The Breakthrough Idea: Batch\u00a0Windows<\/h3>\n

Instead of analyzing logs one-by-one (which is slow and misses cross-log patterns), we collect ALL logs from a time window (say, 2 seconds) and send them to Jev in a single API\u00a0call<\/strong>.<\/p>\n

Logs coming in at 100\/sec
\u2193
[Buffer for 2 seconds]
\u2193
2 seconds \u00d7 100 logs\/sec = 200 logs
\u2193
ONE Jev call with all 200 logs
\u2193
Jev sees the whole picture, not just individual lines<\/p>\n

This is the killer feature. Jev can\u00a0say:<\/p>\n

\u201c3 services are showing simultaneous resource exhaustion. DB pool is full, memory is at 94%, and there are 284 requests queued. This is a coordinated cascade. Wake up on-call\u00a0NOW.\u201d<\/p>\n

No per-log rule system can ever do that. Because each log, individually, looks borderline okay.<\/p>\n

The Code: Sending a Batch to\u00a0Jev<\/h3>\n

Here\u2019s the real code from our typesafe-analyzer.ts:<\/p>\n

\/\/ Build a compact log digest for Jev
const logDigest = logs
.map((l, i) =>
`[${i + 1}] svc=${l.service} level=${l.level} msg=”${l.message.substring(0, 200)}”`
)
.join(‘n’);const state = {
analysis_mode: ‘batch_window’,
window_duration_ms: windowMs,
log_count: logs.length,
services_present: […new Set(logs.map(l => l.service))],
level_distribution: {
ERROR: logs.filter(l => l.level === ‘ERROR’).length,
WARN: logs.filter(l => l.level === ‘WARN’).length,
INFO: logs.filter(l => l.level === ‘INFO’).length,
},
log_batch: logDigest,
};const questions = {
system_health_score: {
type: ‘score’,
instructions: `Rate the overall system health from 1 (catastrophic) to 10 (perfect)`,
criteria: [
‘Level 1: Multiple simultaneous critical failures \u2014 OOM, DB pool exhausted, credential leaks’,
\/\/ … etc
],
},
highest_urgency: {
type: ‘choice’,
instructions: ‘What is the HIGHEST urgency action required?’,
criteria: {
none: ‘All logs are benign. No action needed.’,
low: ‘Minor warnings. Engineering team should be aware.’,
high: ‘Significant issues \u2014 notify DevOps immediately.’,
critical: ‘CRITICAL: Multiple severe events \u2014 wake up on-call NOW.’,
},
},
batch_outage_risk: {
type: ‘noul’,
instructions: ‘Is there a credible risk of outage in the next 5\u201315 minutes?’,
criteria: {
true: ‘Patterns indicate cascading failure heading toward outage.’,
false: ‘Errors are isolated, handled, or not systemic.’,
},
},
batch_leak_detected: {
type: ‘noul’,
instructions: ‘Do ANY logs contain exposed secrets, credentials, or PII?’,
criteria: {
true: ‘At least one log has unredacted passwords, API keys, JWTs, or PII.’,
false: ‘No sensitive data detected in any log in this batch.’,
},
},
};\/\/ ONE API call for the entire batch
const response = await fetch(‘https:\/\/api.typesafe.ai\/v1\/systemone’, {
method: ‘POST’,
headers: { ‘Authorization’: `Bearer ${API_KEY}`, ‘Content-Type’: ‘application\/json’ },
body: JSON.stringify({ state, model: ‘jev-latest’, questions }),
});<\/p>\n

That\u2019s it. One POST. Jev evaluates ALL questions against ALL logs simultaneously and returns structured JSON.<\/p>\n

What Jev\u00a0Returns<\/h3>\n

{
“answers”: {
“system_health_score”: { “score”: 2.1, “confidence”: 0.88 },
“highest_urgency”: { “choice”: “critical”, “confidence”: 0.95 },
“batch_outage_risk”: { “noul”: 0.89 },
“batch_leak_detected”: { “noul”: 0.97 }
},
“usage”: { “input_tokens”: 847 }
}<\/p>\n

Notice: output_tokens doesn’t exist.<\/strong> Jev’s structured judgments are computationally free on the output side. You only pay for what you send\u00a0in.<\/p>\n

The Single-Log Deep Inspector<\/h3>\n

For manual analysis or individual suspicious logs, we also support single-log mode with 5 parallel questions:<\/p>\n

QuestionPrimitiveWhat it tells yousensitive_data_leakchoiceIs there a password, JWT, API key, or PII?impending_outage_risknoulProbability this log signals a coming crashseverity_scorescore1-5 architectural severityrecommended_actionchoicesuppress \/ record \/ notify \/ emergency pagefailure_domainchoicedatabase \/ memory \/ network \/ auth \/ app_logic<\/p>\n

All five run in one API call, in parallel<\/strong>.<\/p>\n

The Redaction Engine<\/h3>\n

When Jev detects a credential leak, we don\u2019t just alert\u200a\u2014\u200awe automatically redact:<\/p>\n

\/\/ If Jev says it’s a plaintext password:
redacted = message.replace(
\/(password|pass|pwd|secret)s*[:=]s*[“‘]?([^”‘,s]+)[“‘]?\/gi,
‘$1=”[REDACTED_SECRET]”‘
);\/\/ If Jev says it’s a JWT\/API key:
redacted = message.replace(
\/eyJ[a-zA-Z0-9_-]{10,}.eyJ[a-zA-Z0-9_-]{10,}.[a-zA-Z0-9_-]+\/g,
‘[REDACTED_JWT]’
);<\/p>\n

The detection<\/em> is AI-powered (catches anything). The redaction<\/em> is precise regex (fast, deterministic). Best of both\u00a0worlds.<\/p>\n

The Server Architecture<\/h3>\n

Express Server (port 3000)
\u2502
\u251c\u2500\u2500 POST \/api\/logs\/analyze \u2192 Single-log Jev analysis
\u251c\u2500\u2500 POST \/api\/logs\/batch \u2192 Batch window Jev analysis
\u251c\u2500\u2500 GET \/api\/stream \u2192 SSE stream (single-log results)
\u251c\u2500\u2500 GET \/api\/batch-stream \u2192 SSE stream (batch results)
\u2514\u2500\u2500 Static files \u2192 \/public \u2192 Frontend dashboard<\/p>\n

The frontend connects via Server-Sent Events (SSE)<\/strong>\u200a\u2014\u200aa lightweight WebSocket alternative. New Jev results appear in the UI in real time without\u00a0polling.<\/p>\n

\ud83c\udfaf Use Cases Beyond Log\u00a0Analysis<\/h3>\n

Once you understand the Jev pattern\u200a\u2014\u200asend structured state, ask typed questions, get calibrated answers<\/em>\u200a\u2014\u200ayou start seeing it everywhere:<\/p>\n

1. \ud83d\uded2 E-commerce: Smart Cart Abandonment<\/h3>\n

State: { user_session, cart_value, pages_viewed, time_on_site }
Questions:
– purchase_intent: noul (probability they’ll buy)
– discount_sensitivity: score (1-5 how price-sensitive)
– recommended_nudge: choice (none \/ email \/ coupon \/ live_chat)<\/p>\n

No hallucinated persuasion copy. Just: show coupon? Yes\/\u211678% confidence.<\/p>\n

2. \ud83c\udfe5 Healthcare: Triage\u00a0Routing<\/h3>\n

State: { patient_symptoms, vitals, history }
Questions:
– urgency_level: score (routine \u2192 emergency)
– likely_department: choice (cardiology \/ neurology \/ ortho \/ …)
– escalate_to_physician: noul<\/p>\n

3. \ud83d\udce7 Email: Support Ticket Classification<\/h3>\n

State: { email_subject, email_body, sender_tier }
Questions:
– department: choice (billing \/ technical \/ sales \/ abuse)
– sentiment: score (furious \u2192 delighted)
– requires_human: noul
– priority: choice (low \/ normal \/ urgent \/ critical)<\/p>\n

4. \ud83d\udd10 Security: Real-Time Threat\u00a0Scoring<\/h3>\n

State: { ip_address, request_pattern, user_agent, geo_location }
Questions:
– is_bot: noul
– attack_type: choice (sql_injection \/ xss \/ brute_force \/ legitimate)
– block_action: choice (allow \/ rate_limit \/ captcha \/ block)<\/p>\n

5. \ud83d\udcdd Content Moderation at\u00a0Scale<\/h3>\n

State: { post_content, user_history, platform_rules }
Questions:
– violates_policy: noul
– violation_category: choice (spam \/ hate \/ misinformation \/ explicit)
– action: choice (approve \/ warn \/ remove \/ ban)<\/p>\n

6. \ud83d\udcb0 FinTech: Transaction Risk<\/h3>\n

State: { transaction, account_history, device_fingerprint }
Questions:
– fraud_probability: noul
– risk_tier: score (low \u2192 high)
– action: choice (approve \/ step_up_auth \/ decline \/ freeze_account)<\/p>\n

The pattern is always the\u00a0same:<\/p>\n

Describe your context as structured stateAsk your questions using the three primitivesConsume the typed answers in\u00a0codeAct deterministically based on probabilities<\/p>\n

\ud83d\udcca Why This Beats Traditional Approaches for Log\u00a0Analysis<\/h3>\n

Let me be concrete about the problem our app\u00a0solves:<\/p>\n

ScenarioRegex \/ RulesJev Batch Mode”password=abc123″ in log\u2705 Caught (keyword match)\u2705 Caught”my DB creds are admin\/secret”\u274c Missed\u2705 Caught (understands context)JWT token in a URL param\u274c Depends on regex\u2705 Caught3 services showing simultaneous exhaustion\u274c Each log looks fine individually\u2705 Cross-log cascade detected”50 active connections”\u200a\u2014\u200acritical or not?\u274c No context\u2705 Context-aware (depends on max pool size, other logs)New log format you’ve never seen\u274c New rules needed\u2705 Works out of the boxCostFree~$0.05\u20130.10 per 100-log\u00a0batch<\/p>\n

The $0.05\u20130.10 cost per 100-log batch vs. a security breach that costs $100,000+? The math is\u00a0obvious.<\/p>\n

\ud83d\ude80 Running It Yourself\u200a\u2014\u200a5 Minutes to a Running\u00a0System<\/h3>\n

Prerequisites<\/h3>\n

Node.js 18+A TypeSafe API key (get one at typesafe.ai<\/a>)<\/p>\n

Clone &\u00a0Run<\/h3>\n

# Clone the repo
git clone https:\/\/github.com\/padmarajkore\/AI-Log-Analyzer.git
cd log-analyzer# Install dependencies
npm install# Add your API key
echo “TYPESAFE_API_KEY=your_key_here” > .env# Start the server
npm run dev<\/p>\n

Open http:\/\/localhost:3000<\/a>\u200a\u2014\u200ayou\u2019ll see the dashboard.<\/p>\n

Try the Simulator<\/h3>\n

# In a second terminal, start the log traffic generator
npm run simulate<\/p>\n

This fires realistic microservice logs at 1 log\/sec by default. Crank it up in the UI to 100+\/sec to see the batch engine in\u00a0action.<\/p>\n

Send Your Own\u00a0Logs<\/h3>\n

# Test a credential leak
curl -X POST http:\/\/localhost:3000\/api\/logs\/batch
-H “Content-Type: application\/json”
-d ‘{
“windowMs”: 2000,
“logs”: [
{
“service”: “auth-service”,
“level”: “ERROR”,
“message”: “DB connect failed: postgresql:\/\/admin:super_secret_prod_123@db.prod:5432\/users”
},
{
“service”: “payment-gateway”,
“level”: “WARN”,
“message”: “Connection pool: active=50 idle=0 waiting=284”
}
]
}’<\/p>\n

Watch the dashboard light up with a CRITICAL alert. Jev will catch the credential leak AND the cascading connection pool exhaustion\u200a\u2014\u200ain one\u00a0call.<\/p>\n

\ud83c\udfc1 What We Learned Building\u00a0This<\/h3>\n

1. The batch window idea is underrated.<\/strong> The shift from per-log analysis to window-based batch analysis is a paradigm shift. It\u2019s what lets you catch cascading failures that look invisible at the individual log\u00a0level.<\/p>\n

2. Output tokens = 0 is a superpower.<\/strong> Traditional LLM calls cost you input + output. Jev\u2019s structured judgments have effectively zero output tokens. For a high-volume use case like log analysis, this cost model is orders of magnitude better.<\/p>\n

3. Calibrated uncertainty is actually useful.<\/strong> When the outage probability is 0.51 vs 0.89, you want to know the difference. An LLM saying \u201cthere might be an issue\u201d vs. Jev saying \u201c87% outage probability\u201d leads to completely different actions.<\/p>\n

4. AI detection + deterministic redaction = best of both worlds.<\/strong> Don\u2019t try to make AI do the redaction (slow, expensive, unpredictable). Use Jev to detect<\/em> the category, then apply a precise regex to redact<\/em>. Fast and reliable.<\/p>\n

5. TypeScript + Jev = type safety all the way.<\/strong> Because Jev returns structured JSON with known shapes, you get end-to-end type safety. No any casting after parsing model\u00a0output.<\/p>\n

\ud83d\udd17 Try It\u00a0Yourself<\/h3>\n

The full source code is available on\u00a0GitHub:<\/p>\n

\ud83d\udc49 <\/strong>github.com\/YOUR_GITHUB_LINK_HERE<\/strong><\/a><\/p>\n

It includes:<\/p>\n

src\/typesafe-analyzer.ts\u200a\u2014\u200aThe Jev integration (single + batch\u00a0modes)src\/server.ts\u200a\u2014\u200aExpress API + SSE streamingsrc\/log-simulator.ts\u200a\u2014\u200a12 microservices, 80+ realistic log templatespublic\/\u200a\u2014\u200aThe real-time dashboard UItest\/test-scenarios.ts\u200a\u2014\u200aReady-made test cases (credential leaks, OOM, cascades)<\/p>\n

Clone it, drop in your TypeSafe API key, and have a live AI log guardian running in under 5\u00a0minutes.<\/p>\n

\ud83d\udcad Final\u00a0Thought<\/h3>\n

We\u2019re at an interesting moment in AI tooling. There are two very different things AI can do for software:<\/p>\n

Generate content for humans to read<\/strong>\u200a\u2014\u200aChatGPT, Claude, writing assistantsMake decisions for code to act on<\/strong>\u200a\u2014\u200aJev, structured judgment\u00a0models<\/p>\n

Most of the AI hype focuses on #1. But #2\u200a\u2014\u200aprogrammable AI decisions embedded directly in your application logic\u200a\u2014\u200ais where I think the real engineering leverage\u00a0is.<\/p>\n

Jev is the first model I\u2019ve used that genuinely feels like a programming primitive rather than a human assistant. You write code that uses<\/em> its judgments the same way you\u2019d use a return value from any function\u200a\u2014\u200atype-safe, deterministic, predictable.<\/p>\n

And for log analysis specifically? It catches the things regex can never catch, sees patterns across logs that per-log rules can never see, and runs at production scale for a cost that\u2019s negligible compared to the incidents it prevents.<\/p>\n

That feels like the future of AI in production software.<\/strong><\/p>\n

Built with TypeSafe Jev \u00b7 Node.js \u00b7 TypeScript \u00b7 Express \u00b7 Vanilla\u00a0JS<\/em><\/p>\n

\ud83d\udc49 GitHub: <\/em>https:\/\/github.com\/padmarajkore\/AI-Log-Analyzer<\/a>
\ud83d\udc49 TypeSafe docs: <\/em>docs.typesafe.ai<\/em><\/a><\/p>\n

Jev Is Not a Chatbot. It\u2019s a Judgment Engine \u2014 And I Built a Real-Time Log Guardian With It<\/a> was originally published in Coinmonks<\/a> on Medium, where people are continuing the conversation by highlighting and responding to this story.<\/p>","protected":false},"excerpt":{"rendered":"

How TypeSafe\u2019s Jev\u200a\u2014\u200aa model that returns probabilities, not paragraphs\u200a\u2014\u200areplaces 500 regex rules with one API call, catches credential leaks in milliseconds, and monitors 100+ logs\/sec using a batch window architecture nobody else is talking\u00a0about. \ud83c\udf4b The Lemon Explanation: What Even Is\u00a0Jev? Okay, let me start with the simplest possible explanation. You know how when you […]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-235845","post","type-post","status-publish","format-standard","hentry","category-interesting"],"_links":{"self":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/235845"}],"collection":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=235845"}],"version-history":[{"count":0,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=\/wp\/v2\/posts\/235845\/revisions"}],"wp:attachment":[{"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=235845"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=235845"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mycryptomania.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=235845"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}