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This blog post is created for the purposes of entering the Hack the Zero Stack with Vercel v0 and AWS Databases hackathon. #H0Hackathon Building Innward: A B2B Hospitality Operating System with Vercel and Amazon Aurora The hospitality industry is notorious for relying on "legacy" software—clunky, slow, and disconnected. For the Hack the Zero Stack hackathon, I set out to build Innward , a modern, AI-ready Property Management System (PMS) that proves you can build enterprise-grade B2B tools in record time using Vercel v0 and AWS Databases. The Vision: Moving Beyond the Spreadsheet Hotel managers don't just need a place to store "Room 101: Occupied." They need to solve the "Hidden Math" of revenue management. This means: Relational Complexity: Linking dates, room groups, and individual stays. Dynamic Pricing: Deriving rates based on occupancy and logic-based rules. Market Intelligence: Real-time benchmarking against competitors. The "Zero Stack": Vercel + Amazon Aurora To handle this complexity, I chose Amazon Aurora PostgreSQL (Serverless v2) . Why Aurora for B2B? In a B2B SaaS environment, data isolation and relational integrity are non-negotiable. Aurora provided the robust relational power needed to join complex pricing tables while scaling automatically as more hotels (tenants) join the platform. The Zero-Secret Architecture One of the most rewarding parts of this build was implementing the AWS RDS Signer . Following the "Zero Stack" philosophy, I moved away from static database passwords. Innward uses IAM-based authentication to communicate between Vercel and AWS. By utilizing the @aws-sdk/rds-signer , the application generates short-lived tokens on the fly. This means even if an environment variable were leaked, the database remains locked tight. // lib/db.ts snippet const signer = new Signer ({ credentials : awsCredentialsProvider ({ roleArn : process . env . AWS_ROLE_ARN ! , clientConfig : { region : process . env . AWS_REGION }, }), region : process . env .

By mid-afternoon, you can know where your floor will close by end of day — accurately enough to make the remaining hours a decision, not a guess. Here's how intraday performance forecasting works and what it takes to build it. The Problem With Yesterday's Numbers Most contact centers have end-of-day metrics. Dials, connects, conversion rate against target. Those numbers are accurate, useful for trend analysis, and arrive the next morning. By the time you see them, the day is already over. The decisions that drive outcomes happen during the day — in real time, when hours remain to influence the result. Do you push harder in the final stretch? Adjust campaign priority? Pull a server that's underperforming? Those decisions get made in the afternoon with one question underneath all of them: where are we going to close? If you're answering that question with yesterday's data and experienced intuition, you're working with an information deficit that compounds every day it stays open. How Intraday Forecasting Works The system records dial conversion rates at regular intervals throughout the business day. Not a snapshot at end of day. A continuous read of how the floor is performing as it performs. Every morning, before the floor opens, the model retrains. It processes the intraday conversion patterns from previous days — how conversion tends to develop through the morning, when it typically accelerates, when it softens, how afternoon performance differs from morning — and calibrates to the current operation's historical data. As the day runs, the forecast updates on a regular schedule. Each update incorporates actual conversion data that's come in, narrowing the prediction window. By mid-afternoon, with hours remaining, the model's error range has compressed enough that the closing metric is predictable within an actionable range. Not a rough estimate. A forecast with a documented accuracy track. What this changes in practice: Before the forecasting system, the afternoon c

I recently built a multi-agent customer support system on Azure AI Foundry and NVIDIA NIM. First time doing anything like this. Made four predictions upfront about what would happen. Three of them were wrong. Here is what I actually learned. 1. "Tokens" is not a unit of cost It is a unit of work. The price per unit of work varies by 5-10x depending on which model did the work. I was tracking total token count across both the small 9B model and the large 49B model as if they cost the same. They do not. Total tokens went up in the optimized version. Cost in dollars probably went down. I was measuring the wrong thing the whole time. 2. A verbatim hash cache on natural language traffic deflects ~0% of queries I predicted 25-40% cache deflection. The actual number was 0%. Every query in my test set was a unique string, so the hash-based cache never had a single chance to fire. A verbatim cache is not a simpler version of a semantic cache. It is a different thing entirely. If your workload is natural language, build semantic similarity caching from day one, not as an upgrade later. 3. configure_azure_monitor() does not capture OpenAI SDK calls by default You need to install and initialize opentelemetry-instrumentation-httpx explicitly: pip install opentelemetry-instrumentation-httpx==0.61b0 from opentelemetry.instrumentation.httpx import HTTPXClientInstrumentor HTTPXClientInstrumentor().instrument() Without this, your App Insights Logs will show customMetric and performanceCounter entries (CPU, memory) but nothing about what your agent actually did. 4. Pin your OpenTelemetry versions or everything breaks Installing opentelemetry-instrumentation-httpx without version pinning pulled in opentelemetry-api 1.42.1. But azure-monitor-opentelemetry-exporter needs opentelemetry-api==1.40. The conflict is silent until things start misbehaving. Pin everything to the 0.61b0 / 1.40.0 line: pip install \ "opentelemetry-api==1.40.0" \ "opentelemetry-instrumentation==0.61b0" \ "opentelem

Look at almost any piece of electronics on your desk and you will find a small light staring back at you. A router with a row of blinking status lights. A power brick with a steady green dot. A development board with a tiny red point that flickers every time it does something. We barely notice these lights anymore, but each one descends from a single laboratory breakthrough in 1962, when an engineer at General Electric coaxed a sliver of semiconductor into glowing visible red for the first time. Who invented the first visible LED The engineer was Nick Holonyak Jr., a consulting scientist at General Electric's lab in Syracuse, New York, and a former student of John Bardeen, one of the inventors of the transistor. On October 9, 1962, Holonyak demonstrated the first practical visible-spectrum light-emitting diode. It emitted red light, and it worked at room temperature, which made it genuinely useful rather than a laboratory curiosity. What made his approach different was the material. Other researchers in the early 1960s were building diodes that emitted infrared light, which is invisible to the human eye. Holonyak gambled on a different alloy, gallium arsenide phosphide, and it paid off with the first light a person could actually see coming out of a semiconductor. He was so confident in the idea that he predicted LEDs would one day replace the incandescent bulb. At the time that sounded outlandish. Today it is simply how lighting works. Why a tiny red light mattered so much The incandescent bulb that Thomas Edison commercialized makes light by heating a filament until it glows. That is wildly inefficient, because most of the energy escapes as heat rather than light, and the filament eventually burns out. An LED works on a completely different principle. When current flows across a specially engineered semiconductor junction, electrons release their energy directly as photons. There is no filament to burn out, almost no wasted heat, and the device can switch on and o

By RUGERO Tesla ( @404Saint ). There is a persistent illusion in Industrial Control Systems (ICS) security research: that high-level libraries, abstraction frameworks, or protocol tooling give you a real understanding of Operational Technology (OT) behavior. They don’t. They hide the architecture. Determined to understand what actually happens when a Programmable Logic Controller (PLC) receives a control-plane command, I built an EtherNet/IP and Common Industrial Protocol (CIP) sandbox from scratch. No Scapy. No protocol wrappers. Just raw sockets, a Linux loopback interface, a cpppo simulator, and a passive monitoring tool ( enip_monitor.py ) capturing traffic in real time. It looked clean on paper. Then I reached the application layer. And things stopped behaving like theory. The Reality of the “Industrial Abstraction Layer” If you come from Modbus or traditional IT networking, you’re used to linear memory spaces—fixed registers, predictable offsets, and flat addressing. EtherNet/IP and CIP discard that model entirely. Instead, they introduce a structured object system wrapped inside multiple encapsulation layers: +-----------------------------------------------------------+ | EtherNet/IP Encapsulation Header (24 bytes) | | → Session control, commands (0x0065, 0x006F) | +-----------------------------------------------------------+ | Common Packet Format (CPF) | | → Routing, addressing, and transport segmentation | +-----------------------------------------------------------+ | CIP Application Layer | | → Service codes (0x4C, 0x4D, 0x10, etc.) | +-----------------------------------------------------------+ To communicate with a PLC at the wire level, your code must: Establish a session using RegisterSession (0x0065) Wrap all subsequent requests in SendRRData (0x006F) Encode routing information inside CPF structures Construct symbolic or logical paths for the CIP Message Router Ensure strict byte alignment across nested payload layers A single mistake in any layer b

Token burn and business outcomes are not correlated. More burn means more inefficiency, not more value. The electricity problem Imagine you walk into a dark room. Turning on a light helps you see. Turning on every light in the building does not help you see better. It's still the same room. Now every surface is equally lit, the contrast is gone, and you're paying for power you didn't use. Tokens work the same way. A focused prompt with clear scope is the single overhead light over your desk. A sprawling prompt with unlimited exploration is every light in the building — you're burning power, not producing insight. Tokens are electricity, not output. More throughput doesn't mean more value. I've had weeks where I burned through my allocation and looked back at the end to find nothing concrete. Code that worked but went unused. Exploratory branches that dead-ended. Agents that generated plausible-looking output that didn't survive first review. A lot of motion. Not much progress. The ceiling stops you from doing that indefinitely. It forces a moment of reflection: did this burn produce anything real? If the answer is no, more capacity isn't the fix. More discipline is. Three patterns I now use instead I started paying attention to what actually ships versus what just burns context. I gave the patterns names so I could catch myself faster: RTK — Read The Knowledgebase. A focused 15-minute read of the codebase, identifying the exact files and exact changes, saves 200K+ tokens of exploratory waste. The agent doesn't discover the shape of the task — it executes against a known one. Caveman — compress before you prompt. Strip greetings, filler words ("I think", "basically", "Let me know if that makes sense"), and closing courtesies. Every word in your prompt multiplies across every response token. Less fluff in means less fluff out. Ponytail — spec the minimum viable solution. "Robust", "scalable", "enterprise-grade", "comprehensive" — these words invite scope creep. Specif

South Korea targets physical AI lead and commercial humanoid robots by 2028.

Earlier today, T-Mobile started notifying customers that it will be retiring many legacy plans and moving subscribers onto one of its current rate plans. This move includes plans that date back to the 3G era, and it's going about as well as you'd expect. Affected customers began sharing screenshots of the text on reddit and […]

WIRED has tested 100-plus bed-in-a-box mattresses for a week each. Our top pick, the Helix Midnight Luxe hybrid, is the best bed you can buy online.

While Kara Zor-El's appearance at the end of James Gunn's Superman was a very pleasant surprise, Warner Bros. Discovery's plan to fast-track a standalone Supergirl feature always felt a little dubious. It seemed odd that, after Superman, the studio wanted to flesh out its new cinematic universe with films about another Kryptonian and one of […]

VCs remain thirsty to fund AI coding startups. This one, founded by investor Chamath Palihapitiya, is no exception.

Rather than external partnerships, Uber may start leaning on its own robotaxis for driverless options.

Google is expanding Gemini’s personalized AI image generation to eligible free users in the U.S., allowing the chatbot to create images based on your interests and data from connected Google apps.

Small-scale solar helped renewables nearly triple coal generation on the US grid.

SCOTUS falls short of deeming geofence warrants unconstitutional, though.

Tidal shared its new policies regarding AI-generated music today and how the platform plans to "protect artists" and "inform listeners." Instead of banning it outright, starting on July 15th Tidal will label tracks it has identified as being 100 percent AI-generated with an icon. But starting today those tracks will no longer be monetizable. "Tidal's […]

Sony hinted in a recent Q&A with investors that the next generation PlayStation will offer some kind of experience that lets you play games outside of your living room. Here's the relevant portion from the transcript, emphasis mine: Q: How can you bring back to the PlayStation platform users who migrated to gaming PCs during […]

The first app dedicated to 1980s tape-edit effects. Discussion | Link

Sony has been scaling down its digitial store for a few years.

Xbox union members argue for transparency and good-faith bargaining.