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TECH & AI
The Wrong Way to Think About XRPL Event Infrastructure
jackminion 0



Most developers approach XRPL event monitoring like it’s a simple polling problem. They spin up a script that checks wallet balances every few minutes, maybe set up a cron job to scan for new transactions. When they need real-time updates, they build a WebSocket listener that connects directly to the ledger.

This approach breaks down fast. WebSocket connections drop. Rate limits kick in. Transaction parsing gets complex when you’re dealing with DEX trades, escrow releases, and payment channel updates. The script that worked fine in development starts missing events in production.

The correct frame: XRPL event monitoring is an infrastructure problem, not a feature problem.

You need reliable delivery, retry logic, signature verification, and dead-letter queues. You need to handle 22 different event types across seven categories without your application logic becoming a mess of conditional statements. Most importantly, you need this infrastructure to work when your application scales beyond a few users.

I built XRNotify because every XRPL developer was solving the same infrastructure problem independently. The results were predictable: brittle listeners, missed events, no monitoring, and hours spent debugging webhook delivery failures instead of building product features.

The Infrastructure Layer XRPL Was Missing

XRNotify sits between the XRP Ledger and your application. It maintains persistent connections to the network, parses transaction data in real-time, and delivers structured events to your endpoints via webhooks.

The delivery mechanism uses exponential backoff retry with dead-letter queues for failed deliveries. Every payload includes HMAC-SHA256 signatures for verification. When your endpoint is down for maintenance, events queue up and deliver in order once you’re back online.

The event categorization covers wallet activity, DEX operations, escrow management, payment channels, NFT transfers, network state changes, and validator updates. Instead of parsing raw XRPL transaction data, you receive structured JSON with the specific information your application needs.

Beyond Simple Notifications

The real value emerges when you consider XRNotify as part of a larger data infrastructure. Network state data flows to The Neutral Bridge for financial research. Anomaly patterns feed into H.U.N.I.E.’s intelligence layer. The same infrastructure that delivers your application webhooks also powers the circuit breaker logic in H.U.N.I.E. Sentinel.

This isn’t accidental architecture. XRPL generates massive amounts of transaction data, but most of it gets processed once and discarded. XRNotify captures that data at the moment it becomes available, then routes it to multiple systems that extract different types of value.

Your application gets the specific events it needs. The research infrastructure gets comprehensive network state data. The security layer gets real-time anomaly detection. The same infrastructure investment serves multiple purposes.

Technical Implementation

The system runs on Next.js 14 with PostgreSQL for persistence and Redis for event queuing. Node.js workers handle the XRPL connections and webhook delivery. XRPL.js provides the ledger interface, but the heavy lifting happens in the retry logic and delivery infrastructure.

The webhook endpoints receive POST requests with JSON payloads. Each payload includes event metadata, transaction details, and the HMAC signature for verification. Your application validates the signature, processes the event, and returns a 200 status code. If validation fails or processing errors occur, the retry mechanism handles redelivery.

Event filtering happens at the subscription level. You specify which wallets to monitor, which event types to receive, and which endpoint should handle each category. The infrastructure handles the complexity of maintaining multiple XRPL connections, parsing different transaction types, and routing events to the correct destinations.

The Result

Developers connect their systems to XRPL events without building listener infrastructure. Applications respond to network activity in real-time without polling delays. The webhook delivery guarantees mean events reach your system even during deployment downtime or temporary outages.

This is infrastructure that works when you need it to work. No missed events, no parsing edge cases, no webhook delivery debugging at 2 AM.

https://www.xrnotify.io



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TECH & AI
Your trycatch sucks – lets fix it
jackminion 0


You’re not handling errors. You’re hiding them.

Every app crashes. Every API fails. Every database hiccups at 2am on a Friday.The difference between a good dev and a great one? What happens next.

Let’s roast your error handling — then make it legendary.

🤦 Level 0: The “Trust Me Bro” Dev

No try/catch at all. Just vibes.

const data = await fetchUserData(userId);
console.log(data.profile.name); // 💥 TypeError: Cannot read properties of undefined

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The crime: One bad response nukes the entire app. Users see a white screen. You get a 3am Slack ping.

🐣 Level 1: The Junior — “I Googled try/catch”

try {
const data = await fetchUserData(userId);
setUser(data);
} catch (err) {
console.log(err); // 👈 and… that’s it. shipped.
}

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What’s wrong here?

console.log in production helps nobody — users still see a broken UI
No distinction between a 404 and a 500 — every error is treated the same
The error disappears into the void (or a DevTools tab nobody has open)

err might be null, a string, or an Error object — you’re not checking

The mindset: “At least it won’t crash.” — Yeah, it just silently breaks instead. Cool.

📈 Level 2: The Mid-Level — “I’ve Been Burned Before”

Now we’re thinking. You’ve seen production fires. You have trust issues with APIs. Good.

2a — Typed errors, real messages

try {
const data = await fetchUserData(userId);
setUser(data);
} catch (err) {
if (err instanceof NetworkError) {
showToast(“Connection lost. Check your internet.”, “warning”);
} else if (err.status === 404) {
showToast(“User not found.”, “error”);
} else {
showToast(“Something went wrong. We’re on it.”, “error”);
logger.error(“(fetchUserData)”, { userId, err }); // 👈 goes to Sentry/Datadog
}
}

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✅ Users get useful feedback, not a frozen screen✅ Engineers get structured logs, not a haystack of console.logs

2b — Undo previous operations (the “atomic mindset”)

Imagine you’re updating a user’s profile and their avatar. Step 1 succeeds. Step 2 fails.Congrats — your user now has a corrupted half-state.

let previousProfile = null;

try {
previousProfile = await getProfile(userId); // snapshot
await updateProfile(userId, newProfileData); // step 1
await uploadAvatar(userId, newAvatar); // step 2 💥 fails here
} catch (err) {
logger.error(“Profile update failed”, { err });

// ↩️ Roll back step 1 since step 2 failed
if (previousProfile) {
await updateProfile(userId, previousProfile);
}

showToast(“Update failed. Your profile has been restored.”, “warning”);
}

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✅ Users never see broken half-state✅ Rollback is explicit, not accidental

2c — Wrap it in a clean utility (stop repeating yourself)

Tired of writing try/catch 50 times? Make a helper:

// utils/tryCatch.js
export async function tryCatch(fn, fallback = null) {
try {
const result = await fn();
return (result, null);
} catch (err) {
return (fallback, err);
}
}

// Usage — clean, flat, readable
const (user, err) = await tryCatch(() => fetchUserData(userId));

if (err) {
showToast(“Couldn’t load user.”, “error”);
return;
}

setUser(user);

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✅ No more deeply nested try/catch pyramids✅ Forces you to handle the error at call site — can’t ignore it

🧠 Level 3: The Senior — “I’ve Seen Things”

You don’t just catch errors. You anticipate them. You build systems that heal themselves.

3a — Retry queue with exponential backoff

Networks are flaky. Don’t give up on the first failure.

async function fetchWithRetry(fn, { retries = 3, delay = 500 } = {}) {
for (let attempt = 1; attempt retries; attempt++) {
try {
return await fn();
} catch (err) {
const isLast = attempt === retries;
const isRetryable = err.status >= 500 || err instanceof NetworkError;

if (isLast || !isRetryable) throw err; // don’t retry 401s or 404s

const backoff = delay * 2 ** (attempt – 1); // 500ms → 1s → 2s
logger.warn(`Attempt ${attempt} failed. Retrying in ${backoff}ms…`);
await sleep(backoff);
}
}
}

// Usage
const data = await fetchWithRetry(() => fetchUserData(userId));

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✅ Temporary blips are invisible to users✅ Smart: retries server errors, not client errors (no point retrying a 401)

3b — Circuit breaker (stop hammering a dead service)

A retry queue is great — unless the whole service is down. Then you’re just DDoS-ing a corpse.

class CircuitBreaker {
constructor(threshold = 5, timeout = 30_000) {
this.failures = 0;
this.threshold = threshold;
this.timeout = timeout;
this.state = “CLOSED”; // CLOSED = healthy, OPEN = tripped, HALF_OPEN = testing
this.nextAttempt = Date.now();
}

async call(fn) {
if (this.state === “OPEN”) {
if (Date.now() this.nextAttempt) {
throw new Error(“Circuit open — service unavailable”);
}
this.state = “HALF_OPEN”;
}

try {
const result = await fn();
this.reset();
return result;
} catch (err) {
this.recordFailure();
throw err;
}
}

recordFailure() {
this.failures++;
if (this.failures >= this.threshold) {
this.state = “OPEN”;
this.nextAttempt = Date.now() + this.timeout;
logger.error(“🔴 Circuit breaker TRIPPED”);
}
}

reset() {
this.failures = 0;
this.state = “CLOSED”;
}
}

// Usage
const userServiceBreaker = new CircuitBreaker();
const data = await userServiceBreaker.call(() => fetchUserData(userId));

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✅ Failing fast is kind — users get an error immediately, not after 10s of retrying✅ Gives the downstream service a breather to recover

3c — Structured error classes (errors that mean something)

Stop throwing raw strings or generic Errors. Give your errors context.

class AppError extends Error {
constructor(message, { code, statusCode = 500, context = {}, retryable = false } = {}) {
super(message);
this.name = “AppError”;
this.code = code;
this.statusCode = statusCode;
this.context = context;
this.retryable = retryable;
this.timestamp = new Date().toISOString();
}
}

// Subclass for specificity
class AuthError extends AppError {
constructor(message, context) {
super(message, { code: “AUTH_ERROR”, statusCode: 401, context, retryable: false });
}
}

class ServiceUnavailableError extends AppError {
constructor(service, context) {
super(`${service} is unavailable`, { code: “SERVICE_DOWN”, statusCode: 503, context, retryable: true });
}
}

// Throwing
throw new ServiceUnavailableError(“UserService”, { userId, attempt: 3 });

// Catching
catch (err) {
if (err instanceof AuthError) {
redirectToLogin();
} else if (err instanceof AppError && err.retryable) {
retryQueue.add(err);
} else {
logger.error(err.code, err.context);
showToast(“Unexpected error. Engineers notified.”);
}
}

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✅ Catch blocks can make decisions, not just log and pray✅ Every error carries its own context — no more guessing what happened

🗺️ The Full Picture

What you do
Junior
Mid
Senior

Catch errors
✅
✅
✅

Inform the user
❌
✅
✅

Send to a logger
❌
✅
✅

Typed/structured errors
❌
⚠️ partial
✅

Rollback on failure
❌
✅
✅

Retry transient errors
❌
❌
✅

Circuit breaker
❌
❌
✅

Errors are self-describing
❌
❌
✅

✅ The Golden Rules

Never swallow errors silently. A hidden bug is a time bomb.

Always tell the user something. Frozen UI is worse than an error message.

Log with context, not just a message — what failed, who triggered it, when.

Not all errors are equal — 404 ≠ 500 ≠ NetworkError. Handle them differently.

Retryable ≠ always retry — client errors (4xx) should fail fast.

Leave the system in a valid state. Roll back or compensate when operations are partial.

Your catch block is business logic — treat it that way.

“The mark of a great engineer isn’t writing code that never fails.It’s writing code that fails gracefully.”

Now go fix your try/catches. 🛠️



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TECH & AI
The Hidden Networking Problem Behind AI Agent Failures
jackminion 0



AI agents are being built as if the network is a perfect, low‑latency, lossless abstraction… but it isn’t. And as these systems scale, the real failures won’t come from model quality, but from latency, packet loss, protocol behavior, and the messy reality of distributed systems instead. If we want agents that actually work in production, networking has to become a first‑class design concern again.

The Part of the AI Conversation That’s Missing

As of now, the AI world is tightly focused on bigger models, longer context windows, agent frameworks, orchestration layers, and clever prompting. That’s perfectly fine, all interesting. But none of those things matter if the network underneath can’t reliably deliver data.

AI agents all run across:

And even then, most agent architectures are designed as if the network is a solved problem, but it isn’t and never was.

The Actual Failure Modes Aren’t “AI Issues”, They’re Network Problems

Here are the patterns that continue to show up in modern distributed systems, now amplified by AI workloads:

Latency Amplification

Agents that depend on synchronous calls to remote interference endpoints collapse whenever RTT spikes. A small jump, say 40ms to 120 ms, can turn a responsive agent into a stalled one.

Retry Storms

Agents retry due to their assumption that the service is slow, not the network. Multiply that across dozens of agents, and you get a self-inflicted outage.

Partial observability

Your dashboard can say that everything is green, but your packet capture says otherwise. Retransmits, duplicate ACKs, microbursts, all the concepts that explain behavior, rarely show up in Layer-7-only observability.

Protocol mismatch

HTTP/2 and gRPC work fine until you introduce:

MTU fragmentation
middleboxes
head-of-line blocking
asymmetric routing

Then your ‘fast’ protocol becomes bottlenecked.

Edge constraints

Everyone wants ‘AI at the edge,’ but nobody talks about:

Agents can’t reliably count on shipping huge context windows or raw telemetry upstream.

Practical Advice for Anyone Deploying Agents

If you’re designing or deploying agents, this is the minimum for reliability:

Measure at the packet level, not the application level alone.
Design for variable latency, instead of just ideal latency.
Use protocols that can degrade gracefully.
Implement real backpressure instead of simple retries.
Cache intelligently, especially when it comes to embedding and model outputs.
Stream context in prioritized chunks.
Instrument NIC/PHY telemetry, rather than just HTTP metrics.
Test under real network conditions, this includes loss, jitter, and reordering.

If your agent’s architecture can’t handle the network at its worst, it won’t survive the real world.

Observability Has to Go Below Layer 7 Again

Modern observability stacks are great at, logs, traces, and service metrics. But they’re blind to the things that actually break distributed systems, which are:

What is MTU?
Maximum Transmission Unit (MTU) is the size of the largest protocol data unit that can be communicated in a single network layer transaction. If your AI’s context window data exceeds this without proper fragmentation handling, you see “mysterious” packet loss.

packet loss
bufferbloat
link flaps
retransmit storms
NIC queue saturation

If you want agents that behave predictably, you need visibility into the layers where unpredictability thrives.

This doesn’t mean you have to capture full PCAPs everywhere; even lightweight NIC counters and synthetic probes can reveal the truth just as easily.

Why Rust Keeps Showing Up in These Conversations

Rust isn’t just a “fast” language; it has you think like a systems engineer with its core concepts:

ownership
memory layout
buffer lifetimes
concurrency (without data races)

That mindset is essential whenever you’re building telemetry collectors, edge inference runtimes, protocol parsers, or agent‑side networking components.

Rust gives you the tools to build small, reliable pieces of infrastructure that agents depend on.

Where This Is All Heading

Here’s what I expect to see over the next few years:

Network‑aware agents will outperform everything else out there.
Observability will shift down the stack, closer to the packet and NIC levels.
Hybrid inference (local and remote) will become the default.
Protocol engineering will matter again, and efficiency will beat sheer force.

The teams that understand networking will create the agents that thrive.

Final Thought

If you want AI agents that are reliable and useful, make networking your primary design concern. Treat the network as a critical infrastructure. Start now, and audit your agent architecture for network assumptions and proactively engineer for real-world environments.

The future of AI belongs to those who prioritize improved networking for their product. Actively invest in understanding (and solving) your network challenges. Your agents’ success depends on it.

Have you run into an ‘AI problem’ that turned out to be a networking issue in disguise? I’d love to hear your stories (and how you debugged them) in the comments below.



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