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Industry 4.0 data collection sounds simple until you look closely at the factory floor. In theory, the flow is clean: machine → gateway → cloud → dashboard In practice, it is usually less tidy. Factories may have PLCs, CNC machines, sensors, meters, inspection systems, production lines, and older equipment all working together. Some devices use Ethernet. Some still rely on serial interfaces. Some data is useful every second. Some data only matters when a machine changes state, crosses a threshold, or triggers an alarm. This is where an industrial edge gateway becomes useful. A gateway such as Robustel EG5120 can sit between factory equipment and upper-layer systems, helping collect selected machine or PLC data, handle it locally where needed, and forward useful information toward cloud or enterprise platforms. That does not mean the gateway replaces PLCs, SCADA, MES, or the cloud. It simply means factory data often needs a practical middle layer before it becomes useful somewhere else. Factory data is not one clean data stream One thing that gets underestimated in Industry 4.0 projects is how mixed the data sources can be. A PLC may provide equipment status, alarms, and process values. A CNC machine may expose cycle information or maintenance indicators. Sensors and meters may generate temperature, vibration, energy, or environmental data. Inspection systems may produce quality-related events or selected result data. A production line may generate throughput signals, downtime events, or operating states. These are all “factory data,” but they do not behave the same way. A machine fault may need quick attention. An energy reading may only need periodic reporting. A repeated sensor value may not need to be sent upstream every time. A quality inspection output may be useful as metadata, but not every raw file is practical to upload continuously.So the first question is not only: Can we connect this machine? A better question is: What data do we actually need, where sho

Hello Devs 👋 When teams talk about engineering metrics, the conversation usually moves toward speed....

Nine major suspected cargo thefts happened at Tesla’s Nevada battery factory in January alone, according to sheriff’s records obtained by WIRED.

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The AI Engineer World's Fair has attracted a lot of adults, and they brought their kids too, for...

GitLab's 2026 AI Accountability Report highlights an AI Paradox: although 78% of developers say they code faster, overall software delivery has not accelerated due to downstream testing and review bottlenecks and new challenges for enterprise governance and traceability. By Sergio De Simone

How I ended up running a local LLM to generate my git commit messages

Protocol adapters are one of the easiest places for agent-commerce architecture to drift. An adapter begins with the narrow responsibility of translating an external protocol request into something the commerce platform understands. For example, an MCP-style tool may ask for return terms, an ACP-style interaction may ask whether checkout can be prepared, an AP2-related flow may carry payment authority information, and an internal feed may publish product capabilities. Those are adapter concerns at the boundary. The problem starts when the adapter does more than translate. It checks product availability from catalog fields. It interprets policy text. It decides whether checkout is ready. It treats a payment artifact as authority. It turns a domain blocker into a softer protocol response. Each shortcut may solve an integration problem locally, but it also creates a second place where commercial meaning is decided. When several adapters exist, those local decisions begin to diverge. The MCP tool may block return-policy quotation, the ACP adapter may expose the product as purchasable, the feed may publish it as checkout-ready, and the AP2-related flow may reject delegated payment. At that point, the platform does not only have multiple integrations. It has multiple interpretations of the same commercial state. This is the adapter problem in agent-ready commerce: semantic drift at the protocol boundary. The adapter should know how to speak the protocol. It should not decide product truth, policy meaning, eligibility, checkout validity, or payment authority. Those decisions belong inside the commerce platform, where they can be shared, tested, evidenced, and audited. This is the fifth article in the Agent-Ready Commerce series. Part 1 introduced the broader architecture model: Facts → Eligibility → Authority → State transition → Evidence → Audit Part 2 focused on commercial truth. It argued that catalog data is not enough. A platform needs source-backed, freshness-aware p

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Voice interfaces are rapidly becoming the next major interaction layer after mobile and web UI. Instead of clicking, users will increasingly talk to systems that understand intent, context, and can execute actions in real time. In this article, we’ll build a production-grade architecture for a real-time AI voice system using modern web technologies such as Next.js, WebRTC, and OpenAI’s streaming capabilities. We’ll also explore how this architecture powers modern conversational systems like an AI Voice Agent platform, where AI can handle real-time interactions for business use cases like bookings, support, and sales automation. 1. Why Voice AI is the Next Interface Shift Text-based chatbots solved the first wave of automation. But voice introduces: Faster interaction (no typing) Higher emotional expressiveness Better accessibility Natural multitasking Businesses are now adopting systems like Voice AI for Business to replace traditional call centers and static IVR menus. The key challenge is not just speech-to-text, but building a low-latency conversational loop that feels human. 2. System Architecture Overview A production-ready AI voice system typically consists of: Frontend (Next.js) Audio capture via Web Audio API Streaming audio chunks UI for conversation state Backend (Node.js / Edge Functions) Session management Authentication Tool execution layer AI Layer OpenAI Realtime API (streaming) Function calling Context memory Audio Pipeline Speech-to-text streaming Text-to-speech streaming Optional noise cancellation 3. Core Concept: Real-Time Streaming Loop The core of a voice agent is a continuous loop: User speaks Audio is streamed to server Model transcribes in real time Model generates response token-by-token Response is converted to audio instantly Audio is played back with minimal delay The goal is to keep latency under ~800ms for a natural experience. 4. Building the Frontend (Next.js + Web Audio API) We start by capturing microphone input: const stream = awa

Artificial Intelligence has brought the term "AI Agent" into almost every technology conversation. As a result, many people now use the words agent and automation interchangeably. While both are designed to reduce manual work and improve efficiency, they solve problems in fundamentally different ways. Understanding this distinction is essential if you're building software, automating business processes, or deciding where AI fits into your organization. What Is Automation? Automation is designed to execute predefined instructions. You tell the system exactly what to do, in what order, and under what conditions. Every time those conditions are met, it performs the same sequence of actions. For example: A customer submits a form. An email is automatically sent. A record is created in the database. A notification is sent to the sales team. Every step is predetermined. If the process changes, the workflow must be updated. Automation excels at repetitive, predictable tasks where consistency is more important than decision-making. What Is an AI Agent? An AI agent is not focused on following instructions. It is focused on achieving a goal. Instead of executing a rigid sequence of steps, an agent observes its environment, evaluates available information, makes decisions, and adjusts its actions as circumstances change. If one approach fails, it can try another. If new information becomes available, it can revise its strategy without requiring a developer to define every possible scenario in advance. In simple terms: Automation asks: "What steps should I execute?" An agent asks: "What is the best way to accomplish this objective?" This ability to reason and adapt is what makes agents fundamentally different from traditional automation. A Simple Example Imagine you're booking a business trip. An automated workflow might: Book the airline you specified. Reserve the hotel you selected. Email you the itinerary. It completes exactly what it was programmed to do. An AI agent, howev

How to Create an AI Agent: A Production Walkthrough The first agent I shipped to production failed at 3am on a Sunday. It looped on a tool call, burned through $40 in tokens before my budget alarm fired, and left a half-written draft in the database with no way to resume. That night taught me more about agent design than any framework tutorial. Since then I have built a pattern I trust enough to leave running unattended for weeks at BizFlowAI, where agents research, write, optimize and publish content without me touching them. This is that pattern, stripped down to what actually matters. Start with the job spec, not the framework Before you pick LangGraph, CrewAI, or roll your own, write the agent's job spec like you would for a junior engineer. One paragraph. What it owns, what it must never do, what "done" looks like, and which signals tell you it failed. Here is the spec for one of my production agents: The Topic Researcher owns generating a ranked list of 20 content topics per site per week. It reads from keyword_pool and search_console_perf , writes to topic_queue . It must never publish, never call paid APIs more than 8 times per run, and must finish in under 6 minutes. Done = 20 topics with score >= 0.6 and zero duplicates against the last 90 days. Failure signal = empty queue after a run, or any topic flagged by the dedupe check. If you cannot write this paragraph, do not build the agent. You will end up with a "do everything" prompt that hallucinates its way through ambiguous tasks. The job spec becomes your evaluation rubric later, so write it carefully. Rule of thumb I use : if the spec needs more than 5 tools or more than 3 decision branches, it is two agents, not one. Design the tools before you write the prompt Most agent failures I have debugged were not prompt failures. They were tool failures. The model called a tool with wrong arguments, the tool returned a 4MB JSON blob, or two tools had overlapping responsibilities and the model picked the wrong

The AI Implementation Process I Use With Every Client Most AI projects do not fail at the model. They fail in the six weeks before anyone writes a prompt, and in the six weeks after the demo lands in a Slack channel and nobody knows who owns it. I have run enough of these now (from one-off automations to multi-agent content systems running unattended) that the process has converged into something stable. This is the version I actually use. It has five phases: scoping, POC, integration, evaluation, operations. Each phase has an exit criterion. If we cannot meet the exit criterion, we do not move forward. That single rule has saved more projects than any clever architecture choice. Phase 1: Scoping (1 to 2 weeks, fixed price) Scoping ends with a written document that names the workflow being automated, the system of record it touches, the success metric in hours or dollars, the data we have access to, and the smallest possible first slice. No model is chosen yet. No code is written. If we cannot produce that document, the engagement stops here and the client keeps the document. The hardest part of scoping is resisting the urge to solve the interesting problem. Clients almost always describe the AI-shaped fantasy ("an agent that handles all support tickets") when the real opportunity is narrower and uglier ("triage tier-1 tickets that mention billing, route to the right queue, draft a reply for human approval"). The narrower version ships. The fantasy does not. I run scoping as three sessions: Workflow walkthrough. Someone who actually does the work shows me their screen for an hour. I record it. I take timestamps. The point is to find the moments where a human is doing pattern matching that an LLM can do, and to find the moments where they are doing judgment that an LLM should not do. Data audit. Where does the input live? Where does the output need to go? What is the auth story? If the data is locked inside a SaaS product with no API and no export, that is the projec

Originally published on bckinfo.com Redis with Docker Compose: Persistence, Security, and Production-Ready Configuration Table of Contents Why Redis Containers Lose Data RDB vs AOF: Choosing a Persistence Strategy Basic Setup with Docker Compose Full Persistence Configuration Securing Redis in Docker Setting Resource Limits Redis in a Multi-Service Stack Backing Up and Restoring a Redis Volume Common Issues and Quick Fixes Closing Notes Redis is one of the most common services to run in Docker — it's fast to spin up, lightweight, and perfect for caching, session storage, and queues. But that same simplicity hides a trap: by default, Redis in Docker stores everything in memory, and the moment a container is removed, all of that data disappears . This guide walks through setting up Redis with Docker Compose the right way — covering persistence, authentication, resource limits, and the health checks you need before putting it anywhere near production. Why Redis Containers Lose Data A Docker container is meant to be disposable. That's a feature for stateless services, but it's a liability for a database like Redis. If you start a plain Redis container without a mounted volume, here's what happens: The container writes its dataset only inside its own writable layer. docker compose down or docker rm removes that layer entirely. The next time the container starts, Redis initializes with an empty dataset. This single oversight accounts for a large share of "we lost our session data" incidents in small teams running Redis in containers for the first time. The fix is straightforward once you understand the two persistence mechanisms Redis offers. RDB vs AOF: Choosing a Persistence Strategy Redis supports two persistence models, and production setups typically combine both: RDB (Redis Database snapshots) Point-in-time snapshots of the dataset, saved at intervals you define. Fast to restore, but you can lose any writes that happened after the last snapshot. AOF (Append Only Fil

Denormalization has been the standard approach to analytical data modeling for good reason. Moving joins, lookups, and business rules out of query time and into ingestion gives you the fastest possible reads for a known access pattern. For most of the past decade, it was often the practical default for latency-sensitive analytics. Earlier columnar engines and distributed query processors could execute joins, but many workloads paid for them through higher latency, higher compute cost, spill-to-disk, or distributed coordination overhead. That constraint has loosened. Modern columnar databases with advanced join algorithms have reduced the cost of runtime joins enough that normalization is now a genuinely viable option for many analytical workloads. Denormalization still delivers faster reads, but normalization can bring operational benefits: simpler pipelines, flexible schemas, and cleaner governance. Engineers can now make the decision based on their actual workload characteristics, rather than being forced into one approach by engine limitations. This guide is a decision framework for making that choice in ClickHouse. It starts with why denormalization became the default, explains what has changed in join performance, then compares the tradeoffs on both sides so you can decide where to denormalize, where to join, and where to use ClickHouse primitives that bridge the gap. For a broader evaluation framework covering latency, concurrency, ingest throughput, SQL flexibility, and cost across real-time OLAP options, see our guide to choosing a database for real-time analytics in 2026 . For a deeper comparison of how ClickHouse executes star schema joins against Druid, Pinot, and cloud DWHs, see our star schema and fast joins guide . TL;DR Denormalization and normalization are both valid modeling strategies. The right choice depends on your workload. Denormalization's tradeoffs are primarily operational : pipeline complexity, write-path overhead, data freshness lag, back

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Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

I want to talk about why philosophy is actually far more important than people think, especially when it comes to software engineering, systems design, and AI. When most people hear the word "philosophy," they roll their eyes. They think of abstract, circular arguments that don't matter in the real world. But true philosophy, good philosophy, is more like base mathematics. It is base physics. It is the raw understanding of the essence of a concept and how that translates into real-world action. If you don't understand the origin of a thing, you are left playing a game of perceptions. You will circle around a problem, coming up with endless rationalizations, but you will be completely unable to predict where it is going to go next. The origin of something is it fundamental nature. This origin is actually its bounding box. It dictates the absolute limits of its trajectory. Knowing this gives you predictive capability before you execute. It is the a priori knowledge that separates actual engineers from people who just copy-paste solutions. (When should and how should you copy paste, for example, 'it depends'.) The Gun Analogy and Inherent Limitations Imagine you are at a shooting range, and you point a gun downrange. As long as you point that gun in the general direction of the targets, it is not going to shoot directly behind you, or 90 degrees to the left. The inherent nature of the gun, and the velocity of the bullet, give it strict limitations. Because of those limitations, you can heavily rely on the fact that the bullet won't leave that bounding box. Therefore, shooting on a range is actually very safe. It only becomes unsafe when you turn the gun in a different direction. You have to understand that you cannot ask a tool to do more than its inherent nature allows. If you are firing an M16, it is not going to act like a guided missile and hit a target in another country hundreds of miles away. It does not have that capability. * Furthermore, a gun cannot read you

Last month, I had a problem with a popular mobile banking app in Southeast Asia. Nothing exotic. A transaction didn't go through, and my support ticket had been sitting untouched for two weeks. So I opened the app's chatbot. It greeted me warmly, asked how it could help, and then couldn't do a single useful thing. It couldn't look up my transaction. It couldn't check the status of my ticket. It couldn't tell me why my issue was unresolved. It could answer FAQ questions, and that was it. I called the hotline instead. Spent an hour navigating prompts, got bounced between menus, and every path ended the same way: "Please contact our chatbot or check your existing ticket." The system was built for deflection, not resolution. The ticket that nobody had touched for fourteen days. I gave up. And somewhere in that company's dashboard, my interaction counted as a successful AI chatbot deflection. The uncomfortable part: if you shipped a deflection-optimized bot this quarter, a customer somewhere is living this exact loop right now. Your dashboard is calling it a win. The Deflection Metric Everyone Loves (and Nobody Questions) Deflection rate measures the percentage of customer contacts handled without a human agent. It's cheap to track, easy to celebrate, and it maps directly to cost savings. Industry benchmarks citing McKinsey's 2026 service operations data put AI resolutions at $0.62 per ticket versus $7.40 for human agents. That's a 12x cost difference. Of course executives love this number. But deflection doesn't measure whether the customer's problem got solved. It measures whether the customer stopped asking. Those are very different things. This is Goodhart's Law applied to customer experience: when a measure becomes a target, it ceases to be a good measure. Deflection is cheap and easy to optimize. Resolution is hard and expensive to track. So companies optimize the proxy and stop looking at the goal. Gartner data, as reported by Forbes , confirms the gap: only 14% o