标签：#Design

What Is Precision Tracking Radar? Precision tracking radar is an active radar sensing system designed to repeatedly measure a selected target and maintain an updated estimate of its state over time. For developers, the important distinction is that precision tracking is not simply repeated target detection. Detection answers: Is there evidence of a target in the current radar measurements? Tracking answers: Does this new measurement belong to an existing target, and how should that target state be updated? A practical precision tracking pipeline can be represented as: RF sensing → target measurement → detection → association → state update → continuous track → mission output That makes precision tracking radar a real-time data-processing system as much as an RF sensing system. A Practical Definition Precision tracking radar is a radar capability that combines repeated target measurements across time to maintain a continuous estimate of target position, motion or other relevant state information. The key word is continuous. A detector can operate independently on each radar update. A tracker has memory. It maintains information from previous measurements and decides how new observations relate to that history. From a software architecture perspective, tracking introduces persistent state into the sensing pipeline. Detection and Tracking Should Be Separate Services A useful radar architecture keeps target detection and target tracking logically separate. The detector processes current radar measurements. The tracker consumes target-related measurements over time. Conceptually: Radar measurement ↓ Detection ↓ Measurement object ↓ Association ↓ Track update ↓ Track state This separation helps developers understand where errors originate. If the detector produces unstable measurements, the tracker cannot fully repair them. If detections are stable but tracks switch between targets, the problem may exist in association. If sensor-relative detections are correct but missio

Kasia Trapszo discusses how Netflix evolved its commerce platform from a U.S. DVD service into global infrastructure. She explains navigating international payment realities, adapting to strict regulatory mandates, decomposing monolithic architectures along domain boundaries, and re-architecting systems for massive live-event demand - proving great systems survive by continually evolving. By Kasia Trapszo

I think we are still talking about AI memory in the wrong way. Most implementations are variations of...

Webhooks vs Polling: Why Real-Time Integrations Matter in 2026 In modern software, knowing that something happened is often just as important as knowing what happened. A customer completes a payment. An order changes from pending to shipped. A user creates an account. A GitHub pull request is opened. A subscription is renewed. An AI workflow needs to start processing a new request. The question is simple: How does your application know that something changed? For years, developers have relied on two common approaches: polling and webhooks. Both solve the same fundamental problem—keeping systems synchronized—but they do it in completely different ways. Polling repeatedly asks an API whether something has changed. Webhooks allow the external system to notify your application when something actually happens. That difference can have a major impact on performance, scalability, API usage, responsiveness, reliability, and overall system architecture. And as applications become increasingly connected in 2026, understanding when to use each approach is more important than ever. What Is Polling? Polling is the traditional approach to checking for changes. Your application periodically sends a request to another system: “Has anything changed?” For example, imagine an e-commerce application that needs to know when an order has been paid. It might call an API every 30 seconds: GET /orders/12345 The response might say: status: pending Thirty seconds later, the application asks again. Then again. And again. Eventually: status: paid The application finally discovers that the payment has been completed. The basic workflow looks like this: Application → API → “Anything new?” API → Application → “No.” Thirty seconds later: Application → API → “Anything new?” API → Application → “No.” Eventually: Application → API → “Anything new?” API → Application → “Yes, the order has been paid.” The approach is straightforward and easy to understand. But there is a problem. Most of those requests

A retry answers a narrow question: might the same operation succeed if I attempt it again? Recovery has a harder job. It must bring the original business operation to a known, valid outcome after something went wrong. Getting there may require another attempt, a status lookup, resuming from persisted state, or compensation. If the system cannot resolve the operation safely, it must hand it to a person. This difference matters as soon as an AI workflow does more than return text. If it retrieves data, calls tools, writes state, or continues after the HTTP request ends, adding three retries around the workflow is not a recovery design. It is three more chances to spend money, repeat a side effect, or lose track of what already happened. A retry repeats an attempt Suppose a support feature performs this workflow: load the ticket and approved policy -> generate a reply -> validate the reply -> save it as a draft The policy read returns 503 Service Unavailable with an applicable Retry-After response, and the dependency contract classifies it as transient. No application business state changed, and the request still has time left. A delayed retry may be reasonable. Now suppose the draft save times out after the request reached the database. The caller cannot tell whether the write committed. Repeating the complete workflow creates a new model response and may save a second draft. Retrying only the write is safe when the write is naturally idempotent, or when the boundary can recognize the retry as the same logical operation. Otherwise, the second attempt may create another draft. Both failures may appear as a timeout or dependency exception in application code. They do not have the same effect. What happened What is known Suitable response A transient policy read failed before returning data No application business state changed Retry the read within its budget The model endpoint rejected an invalid request The same request will fail again Stop and fix the request or cont

Insert Molding Design: How to Place Metal Inserts Without Disaster — 8 Years of Structure Design Notes Every structure designer has been burned by inserts at some point — a nut seated crooked, an insert causing sink marks or cracks, pull-out force too low, an insert washed away by melt during injection. I've tripped on all of these myself. Insert molding sounds simple: drop a metal part into the mold and inject plastic around it. But metal and plastic have thermal expansion coefficients an order of magnitude apart, and every detail — shrinkage, grip force, locating method — can turn into a disaster. This article walks through the key design principles of insert molding, from insert types and locating structures to wall thickness and defect prevention. All of it is experience paid for with real money on real projects. Three Common Types of Inserts Insert molding falls into three categories by purpose, each with completely different design priorities. 1. Thread Inserts (Nut Inserts) The most common type. Tapping threads directly into plastic fails fast — fine threads under M3 strip after a few cycles — so metal nuts are embedded in the plastic. Copper inserts dominate because copper conducts heat well (fast heat dissipation during molding), has moderate hardness, and gives clean threads after tapping. We made a portable Bluetooth speaker with an ABS housing whose four corner posts needed M2.5 screws. Tapping the plastic posts directly stripped after three cycles. We switched to embedded M2.5×4mm copper nuts and measured over 45N pull-out, still stable after 500 screw cycles. The key: leave at least 1.5mm of plastic wall around the nut's outer diameter, or the area sinks and bubbles after cooling. 2. Locating / Support Inserts These locate, support, or conduct magnetism — locating pins in motor brackets, magnetic cores in sensor housings. The biggest challenge is insert positioning accuracy and post-molding offset. In 2024 we made a smart lock panel embedding a stainle

Chad Schuster discusses bridging Python's developer velocity with C-like performance using Numba JIT and GPUs. Drawing from large-scale actuarial modeling, he explains LLVM pipeline architecture, performance gains up to 750x, and essential trade-offs like OOP limits, type inference errors, and compile-time overhead for engineering leaders scaling compute-heavy enterprise systems. By Chad Schuster

What it is reimagine-it is a one-command agent skill that redesigns an existing HTML file into a beautiful, working artifact — using only the nouns, dates, colors, links, and numbers already in that file. No mood boards, no gold layouts with swapped labels. The output is a real page you can open. npx reimagine-it@2.4.2 -i mypage.html -o redesigned.html What's new in v2.4.2 1. Source fidelity floor raised to 80% across every token Before v2.4.2, 61 of 105 token×source cells fell below 80% fidelity — the engine preferred headings over real source anchors, so phrases like "Venator Become" or "Arcade Tee" never rendered. Now: Anchors = headings + source anchors , deduplicated — every clickable phrase survives. All 105 token×source cells ≥80% (worst token: 80%). All seven shipped examples report 100% fidelity in their auto.json reports. 2. Links and emails surface on every token A shared Source-index footer renders all content.links and emails on every generated page — not just the webpage/landing tokens. 3. All 15 design tokens in the browser extension The popup now exposes all 15 tokens: webpage, landing, dashboard, infographic, cinematic, artistic, photography, svg, 3js, simulation, glass, editorial, motion, gradient, showcase . 4. Docs can't drift anymore A new docs-drift CI job regenerates the case tables and fails the build if they diverge from ground truth. The 15 design tokens Token What it builds webpage Clean content-first page landing Conversion-focused landing dashboard KPI dashboard from facts infographic Paper-poster argument cinematic Film-poster energy artistic Expressive art direction photography Photo-led layout svg Living SVG mark 3js WebGL orbit scene simulation Interactive timeline glass Glassmorphism UI editorial Magazine layout motion Animated micro-interactions gradient Bold gradient arena showcase Product showcase Measured, not vibes 57/57 unit tests pass 15-token benchmark : all tokens hold the 100/100 usability bar 100-source stress test : 0 er

There is a pattern I keep seeing when designing Agentic AI systems. We start by asking: Which LLM should we use? Should we use LangGraph? Where can MCP fit? Should we build multiple agents? Do we need RAG? Should we add memory? Should every step be handled by an autonomous agent? These are useful questions. But they are often asked too early . The result can be an architecture that is technically impressive but operationally difficult, expensive, slow, and surprisingly hard to trust. A better approach is to reverse the order: Start with the product outcome. Define the constraints. Then design the architecture. Choose the tools last. I have found a useful way to structure those constraints around four dimensions: LCFE L — Latency C — Cost F — Failure E — Evaluation This is not a framework that says every agentic system must look the same. It is a way of forcing architectural decisions to start with the realities of the product rather than the capabilities of the technology. In this article, I’ll walk through a concrete incident-automation example and show how starting with constraints can completely change the architecture. 1. The "backwards" way of designing an agent Imagine we want to build an AI Incident Resolution Assistant for an engineering organization. The goal sounds straightforward: When a production incident is raised, the AI should investigate the incident, gather context, identify the likely cause, recommend or perform remediation, and verify the result. Now imagine the team starts with the technology. The first architecture might look like this: User / Incident | v ┌──────────────┐ │ Triage Agent │ └──────┬───────┘ | v ┌────────────────┐ │ Research Agent │ └───────┬────────┘ | ┌──────────────┼──────────────┐ v v v Logs Agent Metrics Agent Knowledge Agent | | | └──────────────┼──────────────┘ | v ┌─────────────────┐ │ Remediation │ │ Agent │ └────────┬────────┘ | v ┌─────────────────┐ │ Validation Agent│ └────────┬────────┘ | v Resolution It looks sophis

System Design: Payment Processing System A capstone system design walkthrough — designing a payment processing system end to end — covering the core domain model, the ledger as the system's source of truth, idempotency and exactly-once-effect guarantees, integrating with external payment gateways and card networks, handling asynchronous webhooks, reconciliation, fraud and risk checks, and the specific correctness and compliance demands that make payments a uniquely unforgiving system design problem. Table of Contents Introduction Why Payment Systems Are a Different Kind of Hard The Core Domain Model The Ledger: Double-Entry Bookkeeping as the Source of Truth Idempotency: The Single Most Important Property Integrating with Payment Gateways and Card Networks The Payment State Machine Webhooks: Handling Asynchronous Gateway Callbacks The Saga: Coordinating Payment Across Multiple Services Reconciliation Fraud and Risk Checks Data Security and Compliance Consistency, Availability, and the CAP Trade-off for Money Scaling the System Observability for a Payment System Common Pitfalls Quick Reference Table Conclusion Introduction A payment processing system takes the general system design vocabulary covered in this series' System Design guide — databases, caching, queues, load balancing — and applies it to a domain where the ordinary consequences of a bug are dramatically higher: a double-charged customer, a lost payment, or a corrupted ledger isn't a degraded user experience, it's real money moved incorrectly, sometimes irreversibly. This guide walks through designing such a system end to end, drawing directly on this series' DDD, Event-Driven Architecture, Database Migrations, and Secret Management guides, each of which turns out to be load-bearing infrastructure for getting payments right rather than optional architectural polish. Client → Payment API → [validate, risk-check] → Payment Gateway (Stripe/Adyen/etc.) → Card Network → Bank ↓ ↓ (async webhook) Ledger (source o

Ikea has teamed up with Microsoft to launch a new assortment of gaming-inspired furniture and home accessories, coinciding with the 25th anniversary of the first Xbox console. The nine-piece Yxstaby collection (good luck pronouncing that) includes a TV stand, laptop stand, side table, lounge chair, and multi-functional toolbox, with standout products like a stool, cushion, […]

Have you ever tried to use the classic Gang of Four (GoF) State Pattern in real code? You might have hit a wall. You might have thought, "Wait, this feels way too connected." You are not wrong about that. In school and many engineering interviews, the GoF State Pattern looks great. It promises to fix big, ugly switch statements. But real business rules are hard. When you use this pattern in real life, it can become a huge mess. Every state knows too much about the other states. Let us look at why this happens. We will learn the difference between the GoF pattern and a Finite State Machine (FSM). We will also learn when to use each one. The False Promise of the GoF State Pattern The main idea of the GoF State Pattern is to spread out the work. The main object gives its work to state objects. But there is a catch. The state classes themselves must trigger the change to the next state. Example: The Traffic Light Think about a simple traffic light. It goes Red to Green to Yellow to Red. It does this forever. class RedState implements TrafficLightState { change ( context : TrafficLight ): void { console . log ( " RED light, Stop " ); context . setState ( new GreenState ()); // Very connected! } } The Problem: RedState is forced to know about GreenState . This is fine for a simple traffic light. It is a closed loop. The rules will never change. But what happens when business rules change? Imagine the city council makes a new rule. From midnight to 5:00 AM, the light must flash yellow. Now, you must open your RedState and YellowState classes. You have to add new time checks. You have to add the new flashing state. The more states you add, the messier your code gets. The Better Choice: The Central FSM In the real world, things do not always happen in a straight line. An online order does not just go from Pending to Shipped to Delivered. It can jump from Pending to Cancelled. It can go from Shipped to Returned. If you use GoF here, your PendingState needs to know about many

In today’s fast-paced digital world, organizations face a growing number of challenges in managing their enterprise architectures. Complex systems, rapid technological advancements, and evolving business needs make it difficult to maintain a balance between risk management and cost efficiency. This is where Risk and Cost Driven Architecture (RCDA) plays a pivotal role. What is RCDA? RCDA, or Risk Cost Domain Architecture, is a framework that helps organizations make informed architectural decisions by weighing the trade-offs between risk and cost. This approach enables architects to develop sustainable, resilient, and cost-effective solutions that align with business goals and technical requirements. By breaking down architecture into domains of risk and cost, RCDA provides a structured methodology to address uncertainties while optimizing investments. Why RCDA Matters Every architectural decision carries a degree of risk, whether it be technical, financial, or operational. These risks, if not properly managed, can lead to project delays, increased costs, and even system failures. Traditional methods of architecture design often focus on functionality and performance, leaving risk management as an afterthought. RCDA flips this approach by putting risk management and cost at the center of decision-making, ensuring that every aspect of the architecture is thoroughly evaluated from these two perspectives. RCDA is particularly beneficial in large-scale, complex systems where the stakes are high, and decisions must be made carefully. It allows architects to balance innovation with risk tolerance, ensuring that projects are not only delivered on time and within budget but are also resilient and adaptable to future needs. The Core Principles of RCDA Risk-Driven Decision Making: RCDA emphasizes identifying and assessing risks early in the architectural design process. These risks can include security vulnerabilities, performance bottlenecks, scalability issues, and more. By

A polished UI can make a product feel completely different, but getting there usually takes more than...

For a long time, whenever we hear 'User Experience', we instinctively think of UI/UX designers, product designers, or maybe frontend engineers. Why? Because we tend to think users interact first with a graphical or command-line interface, while the backend engine plays little to no role in how they experience the product. The first half is correct. The second half, incorrect. A user doesn't experience your frontend in isolation. They experience the entire system. As I continue to compound my experience building products as a backend-leaning engineer, I've found it increasingly necessary to think beyond whether an endpoint works or whether an architecture is technically sound. I have to ask: How does this technical decision affect the user's experience? Here's how. 1. API Response Times Become UX A user doesn't care that your endpoint executes 17 database queries, that your service is making five downstream requests, or that your server is experiencing a cold start. They care that they clicked “Pay” three seconds ago and nothing has happened. Eventually, they may refresh the page, click the button again, or abandon the application altogether. The frontend can add a beautiful loading animation, but it cannot completely hide a system that is fundamentally slow. 2. Error Messages Become UX One of the easiest ways to see the relationship between backend engineering and UX is through errors. Imagine trying to make a payment and receiving: 400 Bad Request Technically, something has gone wrong. But the user has learned almost nothing. Compare that with: “Your payment could not be completed because your card was declined. Please try another payment method.” Good backend error handling should therefore answer three questions: What happened? Why did it happen? What can the user do about it? 3. API Design Becomes UX API design can feel very far removed from UX. After all, users don't see JSON responses. But, developers build products using those responses. The decisions we make

Lica co-founders are going to work on Gamma's new research team.

Building a custom GPT for one ministry client teaches you something specific about that ministry. Building the third or fourth one for a different government or enterprise client teaches you something much harder, which is how much of what worked the first time was actually general, and how much of it only worked because it happened to fit that particular institution. The Temptation That Causes The Most Damage After the first successful deployment, the obvious next move is treating that system prompt as a proven template and adapting it lightly for the next client. Swap the knowledge base, adjust a few tone instructions, change the scope boundaries to match the new domain, and ship it faster than building from scratch. That instinct is not wrong exactly, but acting on it without first separating what was actually general from what was incidentally specific to the first client produces a second deployment that quietly inherits assumptions nobody meant to carry forward. The clearest example of this showed up around scope boundary language. The refusal and redirection instructions built for the first ministry deployment had been carefully tuned against that specific institution's culture, a fairly formal, procedurally strict environment where a firm, precise boundary read as competent and appropriate. Carrying that same boundary language into a private enterprise deployment, where the internal culture was considerably less formal and staff expected a more conversational tone even when the bot was declining to answer something outside its scope, produced a tool that technically enforced the correct scope but felt oddly cold and bureaucratic to an audience that had no institutional reason to expect that register. Nothing about that was a bug in the traditional sense. The logic was sound, the boundary was correctly enforced, and it still felt wrong, because the tone calibration underneath the logic had been implicitly trained against one specific institutional culture and

DuckDB Labs has previewed DuckDB v2.0, codenamed "Cyanoptera." This release includes over 10000 commits and introduces a client/server mode, enabling network connections. Improvements also encompass extension portability, advanced data types, and a new parser. Performance enhancements include asynchronous I/O and storage optimisations. General availability is expected in fall 2026. By Olimpiu Pop

One of the biggest transitions in a software engineer’s career is moving from “How do I implement this?” to “How should we design this?” As developers, we naturally focus on writing clean code, implementing features, fixing bugs, and improving performance. But as you move toward an architect role, the questions become different: 🔹 Scalability — Will this solution work when the number of users or transactions increases 10x? 🔹 Maintainability — Can another team understand and extend this solution two years from now? 🔹 Security — Are authentication, authorization, data protection, and secrets management considered from the beginning? 🔹 Performance — Where could bottlenecks occur, and how can we identify them before they become production issues? 🔹 Resilience — What happens when a dependent service goes down? 🔹 Integration — How will this solution interact with existing enterprise systems? 🔹 Technology choices — Does the technology solve the actual business problem, or are we choosing it simply because it is popular? 🔹 Trade-offs — What are we gaining, and what are we giving up with each architectural decision? A senior developer asks: “How can I build this feature?” An architect asks: “What is the right solution for the business, technical, operational, and long-term requirements?” The most important lesson I’ve learned is that architecture is not about creating complicated diagrams or using more technologies. Good architecture is about making the right decisions at the right level , understanding trade-offs, and creating solutions that can evolve with the business. And you don't suddenly become an architect because of a designation. You gradually become one by thinking beyond your code. Java #SoftwareArchitecture #SpringBoot #Microservices #SoftwareEngineering #JavaDeveloper #TechnologyLeadership #Architect

If you’re a designer, you’ve probably had the same problem I’ve had: there are dozens of UI/UX design...