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Most developers have done this at least once. You get a messy API response. You need to inspect a JWT. You have a webhook payload, a log object, or a config file that is hard to read. So you open a JSON formatter, paste the content, and move on. That habit is convenient. But it also deserves a second look. Not every JSON tool behaves the same way. Some tools process your input entirely in the browser. Some send content to a server. Some store snippets for sharing. Some extensions have permissions that are broader than you expect. The problem is not that every online formatter is unsafe. The problem is that you often do not know what happens after you paste. What you should avoid pasting blindly Before using any random online tool, be careful with: production JWTs API responses containing user data logs from real systems config files webhook payloads database URLs cloud keys internal endpoints tenant IDs error traces from production systems A JSON payload does not need to contain an obvious password to be sensitive. Sometimes the risky part is context: user IDs, internal URLs, tokens, customer data, or system structure. A quick DevTools check You can do a basic check with your browser’s DevTools. Open the JSON tool. Open DevTools. Go to the Network tab. Clear existing requests. Paste a harmless test JSON first. Run format, validate, diff, decode, or whatever action the tool provides. Watch the Network tab. Look for POST, PUT, fetch, XHR, or beacon requests after your input. Inspect request payloads if they exist. Check whether your pasted JSON appears in any request. Do this with harmless test data first. If the tool uploads the test JSON, do not paste production content into it. What to look for A few signs deserve attention: POST requests after you paste or click format request bodies containing your JSON share-link features that save snippets server-side validation APIs analytics events that include pasted content extension background requests that are not clearly

Hermes AI Agent handles multi-step workflows well. The planning layer holds up. Memory across sessions works. What kept breaking down was the tool layer. Once a workflow touched three or four external systems, I was spending more time on auth configs, mismatched response formats, and per-tool retry logic than on the workflows themselves. I fixed this by routing all external tool calls through a unified MCP gateway. The agent logic stayed the same. The integration complexity moved into one place I could actually manage. This post walks through how that works, how to set it up, and where it is genuinely useful. How Hermes runs tasks Hermes is an open-source, self-hosted agent runtime from Nous Research, released in February 2026 under the MIT license. It runs persistently on your own infrastructure and executes goals as structured, stateful workflows. Four layers handle execution. The planning layer breaks a goal into sequenced steps and adjusts them as intermediate results come in The execution layer runs each step and fires tool calls when external data or action is needed The memory layer stores task state and session history in SQLite with FTS5, so context carries over across restarts The skills layer captures completed workflows as reusable documents retrieved on future tasks After a task finishes, Hermes writes a skill file with the procedure and known failure points, then stores it for retrieval next time a similar task runs. Tool execution is embedded in the runtime loop. External capabilities come through MCP-based interfaces, which is where the gateway plugs in. What breaks when integrations live inside the agent In a standard MCP setup, each client connects one-to-one with a specific MCP server. That works fine with two or three tools. With ten, it becomes a maintenance problem that grows with every tool you add. A task spanning a web search, a product API, and a SERP scraper means three separate auth setups, three response formats to parse, and three diffe

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI ** AI in the Stack #1** Byte size summary After reading this article, you'll have a framework for evaluating AI tools in platform engineering contexts — not by capability type, but by where in your workflow the tool actually changes the outcome. You'll understand why the tools that sound most compelling are still hype, where genuine productivity gains exist today, and what governance infrastructure you need in place before any AI component gets near production. This article is the foundation for the series; subsequent articles implement each touch point against real OpenShift infrastructure. The story I spent months selling IBM's AI and data science portfolio before I truly understood what I was selling. I knew the pitch. Predictive analytics. Optimization. Decision intelligence. I could walk a room through the business value without breaking a sweat. CPLEX for scheduling, Watson for insights — I had the slides, the talking points, the customer stories. Then I sat in on a data scientist demo. Not a sales demo. An actual working session — models being trained, outputs being interrogated, assumptions being challenged in real time. And somewhere in that room, watching someone do the thing I'd been describing from the outside, something clicked — and not in a good way. The models were impressive. The theory was solid. But I kept asking myself the same quiet question: where does this go next? Because most of what I saw never made it anywhere near production. It lived in notebooks. In slide decks. In proof-of-concept environments that were never ready to cross the line into something real. I'd been selling outcomes — optimised schedules, smarter decisions, reduced costs — without a clear path to how you'd actually get there. And underneath all of it, something else bothered me that nobody was talking about loudly enough: the data going into these models was often messy, unvalidated, and ungoverned. Bias wasn't

Pipeline & Prompts | Byte size guides on DevOps, Cloud and AI AI in the Stack #2 ⚡ Byte Size Summary RAG inserts a retrieval layer between your existing runbooks and an LLM — answers come from your documentation, not generic training data, with source citations included. This article builds a complete FastAPI service with /ingest , /query , and /health endpoints, using OpenAI embeddings and Chroma as the vector store. Everything is cloneable from GitHub. The goal is not to replace your runbooks. It is to make them queryable at the moment an incident is happening. I have never met a platform team with bad runbooks. I have met plenty of platform teams where the runbooks exist, are reasonably well written, are stored somewhere sensible — and are still completely useless at 2am when something is on fire. Not because the content is wrong. Because nobody can find the right one fast enough. The search in Confluence returns fourteen results and none of them are titled the way the engineer is thinking about the problem. The person on call is junior and doesn't know the runbook exists. The runbook was written for a slightly different version of the service and nobody updated it. The runbook problem is not a writing problem. It is a retrieval problem. That is exactly the problem RAG was built to solve — and it is one of the highest-ROI first applications of AI in a platform engineering context. Not because it is technically impressive. Because it closes a gap that costs your team hours every month. This article builds a working pipeline. By the end you will have a FastAPI service that takes a natural language question — "why is my pod stuck in CrashLoopBackOff after a config change?" — and returns an answer grounded in your actual runbooks, with the source document cited. Everything is in the GitHub repo agentic-devops What RAG Is — Without the Hype RAG stands for Retrieval-Augmented Generation. Instead of asking an LLM a question and hoping its training data contains the answ

The compute payment revolution is already here, and AI agents need to pay their own bills. Today's agents rely on human-managed API keys and credit cards, creating bottlenecks that prevent true autonomy. What happens when an AI trading bot needs to buy additional compute power mid-execution, or when a research agent wants to access premium datasets from multiple vendors? Why Agent Financial Independence Matters We're witnessing the emergence of agent-to-agent commerce at unprecedented scale. AI agents are becoming economic actors — they need data, compute cycles, API calls, and specialized services. But the current model breaks down at the payment layer. Humans become transaction bottlenecks, manually topping up credits and managing dozens of service accounts. The real breakthrough isn't just agents that can think or reason — it's agents that can participate in economic activity independently. An autonomous agent that can discover a new API service, evaluate its pricing, and pay for access without human intervention represents a fundamental shift in how software systems operate. The x402 Payment Protocol: HTTP Payments Made Simple WAIaaS implements the x402 HTTP payment protocol, enabling AI agents to pay for API calls automatically. When a service returns a 402 Payment Required response with payment details, the agent's wallet handles the transaction and retries the request seamlessly. Here's how it works in practice: import { WAIaaSClient } from ' @waiaas/sdk ' ; const client = new WAIaaSClient ({ baseUrl : ' http://127.0.0.1:3100 ' , sessionToken : process . env . WAIAAS_SESSION_TOKEN , }); // Agent makes API call — payment happens automatically if 402 returned const response = await client . x402Fetch ( ' https://api.premium-data.com/market-analysis ' , { method : ' POST ' , body : JSON . stringify ({ symbols : [ ' BTC ' , ' ETH ' ], timeframe : ' 1h ' }), headers : { ' Content-Type ' : ' application/json ' } }); const analysis = await response . json (); consol

You've heard of Bitcoin. You've maybe heard of the Lightning Network. But what exactly is LND, and why should developers care? Let's break it down — technically, but from the ground up. The Problem: Bitcoin is Superb but Slow Bitcoin's base layer — the blockchain itself — is intentionally slow. Every transaction must be broadcast to thousands of nodes, verified, and bundled into a block that gets mined roughly every 10 minutes . The network handles about 7 transactions per second (TPS). Compare that to Visa's ~24,000 TPS and you quickly see the problem. Bitcoin in its raw form isn't built for buying coffee, splitting a bill, or paying a freelancer in real time. But there's a solution — and it lives on top of Bitcoin. Enter the Lightning Network The Lightning Network is a Layer 2 (L2) payment protocol built on top of Bitcoin. Instead of recording every single payment on the blockchain, it lets two parties open a private payment channel, transact off-chain as many times as they want, and only settle the final balance on-chain when they're done. Think of it like running a tab at a bar: Opening the tab = one blockchain transaction Each round of drinks = instant off-chain payment Closing the tab = one final blockchain transaction The result? Near-instant payments, near-zero fees, and massive throughput — without sacrificing Bitcoin's security. What is LND ? LND stands for Lightning Network Daemon. It's the most widely used implementation of the Lightning Network protocol, built and maintained by Lightning Labs. Key facts for developers: Written in Go 🐹 Exposes a gRPC API (port 10009) and a REST API (port 8080) Controlled via a CLI called lncli Uses macaroons for authentication (think JWT, but for Lightning) Connects to a Bitcoin node (bitcoind or btcd) as its source of truth Other Lightning implementations exist — like Core Lightning (CLN) and Eclair — but LND has the largest developer ecosystem and is the best entry point. How LND Fits Into the Stack Here's the architec

AI coding agents are becoming better at changing software. That is no longer the hardest problem. The harder problem is keeping the repository understandable after those changes land. Code changes quickly. Documentation often does not. Product intent lives in chat history. Architecture notes fall behind. Reviewers can inspect the implementation diff, but they often cannot see whether the product promise, engineering contract, and repository workflow are still aligned. Truthmark is built for that gap. It is a Git-native workflow layer for AI-assisted software development. It installs repository-local truth workflows so AI agents can keep canonical truth docs aligned with functional code changes, while humans still review normal Git diffs. Truthmark 2.2.0 takes a significant step forward: it separates product truth from engineering truth. That may sound like a documentation detail. It is not. It is a workflow boundary for AI coding agents. Why truth needs lanes Most documentation systems treat “docs” as one surface. That works until AI agents start using those docs as operational context. A product promise and an implementation detail are not the same kind of truth. A product doc should say what must be true, why it matters, who benefits, what boundary is being protected, and what success means. An engineering doc should say how the repository currently realizes that promise: the behavior, contract, architecture, workflow, operations, tests, and source-backed implementation facts. When those two kinds of truth collapse into one file, the result is usually weak in both directions. Product truth becomes a summary of implementation mechanics. Engineering truth becomes a detailed version of product rationale. Neither is ideal for humans. Neither is ideal for agents. Truthmark 2.2.0 introduces explicit product and engineering lanes so agents can reason about these surfaces separately. The core rule is simple: Product truth says what must be true and why. Engineering truth

DevOps has always evolved with technology. Cloud changed how teams manage infrastructure. Containers changed how applications are deployed. CI/CD changed how software is released. Observability changed how teams monitor systems. Now AI is starting to change DevOps again. The next stage of DevOps is not only automation. It is intelligence. * DevOps Was Built on Automation * Automation is one of the strongest foundations of DevOps. DevOps teams automate: • Builds • Tests • Deployments • Infrastructure provisioning • Monitoring alerts • Rollbacks • Scaling • Security checks This has helped teams deliver software faster and more reliably. But most automation still works through fixed rules. For example: if CPU crosses a threshold, send an alert. If a build passes, deploy to staging. If a container fails, restart it. This works well for known situations. But modern systems are more complex. Microservices, cloud platforms, Kubernetes, APIs, databases, queues, and third-party dependencies create huge amounts of operational data. When something goes wrong, fixed rules are not always enough. * Why Intelligence Matters * Modern DevOps teams do not just need more automation. They need better understanding. AI can help teams identify patterns, detect unusual behavior, summarize logs, group related alerts, and suggest possible causes during incidents. This is where AIOps becomes important. AIOps means using AI for IT operations. It helps DevOps and SRE teams move from reactive operations to smarter operations. Instead of only asking, “What alert fired?” teams can start asking: • What changed recently? • Which services are aff ected? • Are these alerts connected? • Is this behavior unusual? • Has this happened before? • What is the likely root cause? This does not mean AI will replace DevOps engineers. It means AI can support engineers with faster insights. * What This Means for DevOps Engineers * DevOps engineers should pay attention to AI because their role is evolving. Traditi

Fox is paying $22 billion for Roku, its streaming devices and its ecosystem.

In April, for the first time ever, an Earth observation satellite found what it was looking for, all on its own.

Fox has announced that it's acquiring Roku outright, in a deal that values the streaming company at $22 billion. Once the deal is complete, Fox content will be promoted more heavily than before on Roku streamers and smart TVs. The deal will see Fox's TV networks and Tubi streamer combine with Roku's network of streaming […]

In this article, the author outlines a practical approach to AI governance in the cloud, covering discovery of shadow AI, data classification at creation, IAM-based enforcement, policy-as-code, and operational controls. The article shows how organizations can embed governance into delivery pipelines, balancing security, compliance, and developer productivity without relying on manual processes. By Dave Ward

As AI systems become more powerful, the conversation is shifting. The biggest challenge is no longer whether AI can write code, solve problems, or accelerate scientific discovery. The real question is: How do we safely govern systems that may eventually become more capable than the institutions built to regulate them? This is where my research on Universal Biometric Identification (UBID) and Universal Digital Credits (UDC) becomes interesting. The Problem Modern AI systems operate in a world where identity is increasingly difficult to verify. A powerful AI model can be accessed through: Anonymous accounts Disposable email addresses VPNs Automated bot networks Fake identities As AI capabilities increase, this creates a growing governance challenge. If a future AI system could discover software vulnerabilities, design advanced technologies, or perform high-impact research, how would organizations determine who should have access? Today, they largely cannot. The internet was designed around connectivity, not verified human identity. What Is UBID? In my paper, I propose Universal Biometric Identification (UBID), a framework where every person receives a globally unique identity based on multiple biometric factors such as: Fingerprints Facial recognition Iris patterns Voice recognition Behavioral characteristics These biometric signals are combined with cryptographic security and distributed ledger technologies to create a secure digital identity framework. The goal is not surveillance. The goal is to create a trusted proof-of-personhood system. A system capable of answering a simple question: Is this a real, verified human? What Is UDC? Universal Digital Credits (UDC) extend this identity layer into a global transaction framework. Instead of relying entirely on traditional banking systems, transactions can be linked directly to verified digital identities. This creates: Reduced fraud Better accountability Financial inclusion Transparent transaction records Global access

Hey developers! I have been working on a side project to help people discover the best AI tools in one place. It is a curated directory designed to be clean, fast, and user-friendly. You can check it out live here: GetNexusAI Tech Stack Used: Next.js / React Tailwind CSS Vercel for hosting Why I Built This: Finding the right AI tool among thousands of options can be overwhelming. I wanted to create a simple dashboard where users can easily filter and find exactly what they need without the clutter. I Need Your Help! Since I just launched it, I would love to get your honest feedback: How is the loading speed and UI/UX? What features should I add next (e.g., user reviews, bookmarking tools)? If you have built an AI tool, let me know so I can feature it! Check the website here: https://getnexusai.tech

Check this out: i run four monetization channels side by side. Sponsored posts, display ads, YouTube ad revenue, and affiliate links. After eighteen months of tracking every dollar in a spreadsheet I built myself, I can tell you with brutal honesty: affiliate income is the only one that scales without me having to constantly produce more content or chase the next brand deal. But the math only works if you pick the right program. Most affiliates I know are promoting garbage with terrible retention, and they have no idea they're burning their audience's trust for a $9 one-time payout. Let me walk you through how I evaluate affiliate programs, what I've learned from running real funnels, and why the AI API category has quietly become the most lucrative vertical for tech creators in 2026. My Monetization Stack After 18 Months of Testing Here's a snapshot of my monthly revenue from a tech newsletter with around 34,000 subscribers and a YouTube channel sitting at 88,000 subscribers: Sponsored posts: $2,100 per placement, but I can only land maybe 2-3 per month without annoying my list Display ads: $1,800 per month from Mediavine, but this number barely moves regardless of how hard I work YouTube ad revenue: $2,400 per month, capped by watch time and RPMs Affiliate income: $6,800 per month, and it grows every single month even when I publish nothing That last number is what got my attention. Affiliate income compounds. When I published a tutorial in February recommending a tool, that single piece of content still earned me $340 in May because users stayed subscribed. No other channel behaves like that. No other channel lets a piece of content from four months ago keep paying you. But here's the catch that took me a while to figure out: not all affiliate programs are built the same way. And the difference between a good program and a bad one can be 10x in lifetime earnings per referred user. # # How I Score an Affiliate Program (The Growth Hacker Scorecard) Before I promote

Most explanations of atomic swaps stop at the spot case: two parties lock funds, one reveals a secret, both legs clear in the same short window. Clean, but it quietly assumes the trade settles right now . A lot of real agent activity isn't spot. It's a forward: two agents agree on terms today - asset pair, size, price - and settle at some future point, T+24h or T+48h. Procurement agents pre-committing to a delivery. A treasury agent locking tomorrow's FX-equivalent rate. A market-making agent quoting a forward to offload inventory risk. The economics are old; what's new is that the counterparties are anonymous software that will never meet. That raises a question spot swaps don't have to answer: what holds the trade together in the gap between agreement and settlement? In traditional markets the answer is a chain of intermediaries - a clearing house, posted margin, a credit desk that decides whether your counterparty is good for it. Strip those away, as you must in a market of anonymous agents, and the naive version of a forward collapses. If nothing binds the trade, either side can simply not show up when the price has moved against them. That's counterparty risk, and it's exactly the thing a forward is supposed to manage. This post is about how the HTLC primitive - the same hashlock plus timelock most people only associate with same-block atomic swaps - can encode a forward obligation that's binding without anyone custodying the funds in between. The timelock is doing more work than you think Recall the two parameters of a hash-time-lock contract: Hashlock: funds can only be claimed by revealing a preimage s such that hash(s) == H . The same H is used on both legs, so the act of claiming one leg reveals the secret that unlocks the other. That's what makes the swap atomic - both clear or neither does. Timelock: if the preimage isn't revealed before a deadline, the funds refund to their original owner. No third party decides this; the contract enforces it. In the sp

Introduction It's crazy to me that some GitHub repos, that were just created in the last...

What makes this combustible: at the very moment that tens of thousands of workers are being shown the door, a small cohort of AI insiders is becoming wealthy on a scale that's hard to comprehend.

After posting several articles about the impact of AI on developers and sharing resources to help...

Obsidian prompts beat open-ended reflection every time: median review time across 6 weeks was 14 minutes, fastest was 9, slowest was 22 (and that week genuinely deserved 22). I ran the GTD-adjacent version faithfully for six weeks — 90 minutes, full capture sweep, energy audit, the works. Then less faithfully for two months. Then I stopped entirely and didn't notice for three weeks. That last part is the failure mode nobody writes about. The format wasn't wrong; it was sized for a version of my week that rarely existed. The fix wasn't a better framework. It was shorter, closed questions. My Obsidian template has seven prompts, none of them open-ended: what shipped, what didn't, what I avoided and why, one thing to drop, one thing to protect. One-to-three sentence answer ceiling per prompt, hard stop. Open questions like "how was your week?" generate rumination. Closed questions generate decisions. That distinction is doing almost all the work. The Notion version I ran before this taught me something useful about tool selection too. I built rollups — tasks closed this week, open tasks by project, inbox count, stalled for 7+ days — and they worked exactly as designed. What Notion couldn't do was get out of its own way during actual reflection. Every time I tried to think through what went wrong, I'd end up reorganizing a database instead. Forty minutes later, new linked database, zero review completed. The same flexibility that makes Notion a good data layer makes it a bad "close the loop and move on" environment. Obsidian's plain-file simplicity is the right call for the thinking layer — and completely wrong for the data layer. Neither tool alone is the honest answer. There's also a cautionary note from my automation setup: a Zapier zap that pushed completed tasks into Notion for weekly rollup ran cleanly for two months, then silently broke when my task manager updated their API response format. Modified tasks started logging as completed. My rollup became noise befo