标签：#ai

A blunt playbook for devs who don’t want to turn into autocomplete zombies. The first time an AI wrote code for me, I felt like I had unlocked cheat codes for real life. I typed a half-baked function name, hit enter, and suddenly I had a block of code that looked legit. It was magical. The second time, though? It suggested something so catastrophic basically the programming equivalent of pulling the fire alarm that I realized: this thing is less “mentor” and more “overconfident intern who thinks they know pointers but actually just broke prod.” That’s where most of us are right now. AI is everywhere: in our IDEs, our docs, even sneaking into PR reviews. Some days it feels like rocket fuel; other days it feels like an autocomplete with a drinking problem. The tricky part isn’t whether AI is “good” or “bad.” The tricky part is how we, as developers, use it without becoming lazy, dependent, or worse complacent. Because here’s the uncomfortable truth: AI won’t replace you, but bad AI habits absolutely will. TLDR : This article is a survival guide for developers in the AI era. We’ll break down why AI feels both magical and mid, the five switches that make AI actually useful, when to trust and when to verify, how to use AI as a research assistant (not a code monkey), the dangers of autocomplete brain, and a playbook for building a healthy workflow. Why AI feels both magical and mid Every dev I know has had that moment with AI. The first time it autocompleted a function and nailed it, you probably thought: “Wow… this thing just saved me half an hour.” It’s the same dopamine hit as discovering ctrl+r in bash or realizing you can pipe grep into less . Pure wizardry. But the honeymoon ends quickly. The same tool that wrote a clean utility function also happily hallucinates imports that don’t exist, invents APIs, and will confidently explain things that are flat-out wrong. It’s like pair programming with someone who sounds senior but has never actually shipped code. The magic-

Notes from building a memory layer that forgets on purpose. Most "memory-enabled" agents don't remember anything. They re-read. Every turn, the whole conversation gets pasted back into the prompt, and we call that memory because the model can answer questions about earlier turns. It's a good trick. I used it for months. It also falls apart the moment real people start using the thing, and it falls apart in three separate ways. The first is the one everyone notices: it's expensive and noisy. You re-send every prior turn on every request. The single line you actually care about - "I'm allergic to peanuts" - is buried under a thousand lines of small talk, and you pay for all of it, every time. The second is quieter and worse. Transcript-stuffing has no idea what stale means. If someone told your agent "I'm vegetarian" in March and "I eat fish now" in May, you've just handed the model both facts with equal weight. Now it has to guess which one is current. Sometimes it guesses wrong, and there's nothing in the system that even thinks that's a problem. The third one is the reason I stopped treating this as a side quest. When you finally add summarization to control the cost from problem one, the summarizer is free to drop whatever it wants to save tokens. Including the allergy. I spent years around fintech, where the wrong record surviving (or the right one quietly vanishing) is how people get hurt, so this landed hard: forgetting an allergy to save 40 tokens isn't a cost bug. It's a safety bug wearing a cost bug's clothes. So the question I actually wanted to answer wasn't "how do I make my agent remember more." It was: how do I build something where acting on a fact the user already retracted and silently dropping a fact that must survive are impossible by construction, not just unlikely if the prompt is good that day. Everyone has already solved one third of this The encouraging part is that you don't have to invent much. The discouraging part is that every existing sy

A story about an AI alert model that cut false alarms from 60% to 7% — and what happened when...

Honestly, bootcamp Grad Dives Into Google vs OpenAI API Pricing When I finished my coding bootcamp three months ago, I thought I understood what an API did. I mean, you send a request, you get a response back, right? What I did not understand was how dramatically the cost could vary depending on which model you picked. I had no idea that a single line of code change could mean the difference between paying pennies and paying hundreds of dollars at scale. That is the rabbit hole I fell down last week, and I want to walk you through everything I learned. This is the post I wish I had read before I burned through my first $50 in API credits. Why I Started Looking At Pricing In The First Place I was building a small app that takes user reviews and summarizes them. Pretty straightforward. I figured I would just plug in the most popular model and call it a day. That model, if you have been paying attention to the news, is GPT-4o. So I wired it up, ran a few tests, and everything looked great. Then I did the math. GPT-4o charges $2.50 per million tokens on input and $10.00 per million tokens on output. I did not even know what a "million tokens" really meant in practice. So I tested my app with maybe 50 reviews and watched my credit balance drop. It was not catastrophic, but it was enough that I started wondering if there was a cheaper way. I was shocked when I found out how big the gap actually is. The Pricing Table That Changed My Whole Plan I stumbled onto a platform called Global API, and honestly, the pricing chart there blew my mind. They give you access to 184 different AI models, with prices ranging all the way from $0.01 to $3.50 per million tokens. Compare that to the GPT-4o output price of $10.00 per million tokens, and you start to understand why I panicked a little when I saw my early numbers. Here are the five models I ended up comparing side by side: Model Input Cost Output Cost Context Window DeepSeek V4 Flash $0.27 $1.10 128K DeepSeek V4 Pro $0.55 $2.20 20

A group made up of dozens of cybersecurity experts urged the White House to remove export control restrictions on Anthropic’s models Fable and Mythos, arguing that the order is going to limit the ability of cybersecurity defenders to secure their software and products.

Armed with a ton of new upgrades, Ferrari came to Spain full of confidence.

Salesforce says it wants to use Fin's team and technology to improve Agentforce, its existing enterprise platform that businesses can use to build custom AI agents that automate tasks.

Today, I’m talking with Adam Bry, who is CEO of Skydio, the leading US maker of autonomous drones. Before we recorded this episode, I actually got to remotely operate one of Skydio’s drones in the Bay Area from Adam’s laptop in our podcast studio in New York and fly an indoor drone around our office. […]

Indian IT services company HCLTech is investing $150 million in the Bengaluru startup.

Fox says the deal will create the third-largest television company in the United States.

ArrowJS, developed by Justin Schroeder, is a reactive UI library that has reached its 1.0 release after three years in development. It utilizes core web technologies, avoids JSX and compilers. Notable features include an optional WASM sandbox for executing untrusted code. The framework's minimalism is highlighted by its reliance on three main functions: reactive, html, and component. By Daniel Curtis

NewCore argues the next challenge in enterprise security will be managing AI agents, not people.

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