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Here is the bet we made: build software memory-first, not model-first , and it will outperform. Everyone else is racing to wrap the next model. We did the opposite. We built the memory layer first, the routing first, tool-calling, now the recursive engine, then let the model be a swappable part. Today that bet has a name: Backboard Development Studio . It starts with the R-CLI , a coding harness now in open beta. The headline result? It beats frontier models using open ones. Keep reading, the numbers are below and there is a promo code at the bottom. Test it. The beta is open. Two lines and you are running. # macOS / Linux curl -fsSL https://app.backboard.io/api/cli | bash # Windows (PowerShell) irm https://app.backboard.io/api/cli/windows | iex Get your API key: https://app.backboard.io Promo code: DEVTOCLI for credit toward inference while you put it through its paces. Find the Promo submit in the top right corner of the billing page. The hypothesis, stated plainly Model-first thinking says: pick the smartest model, prompt it well, hope it remembers. Memory-first thinking says: give the system real persistence, real routing, real recall, and a "smaller" model will outwork a "smarter" one that forgets everything between turns. We believed the second one. So we built it. The R-CLI is powered by our memory algorithms (the same ones that rank #1 on LoCoMo and LongMemEval ) and runs on Backboard's unified API: memory, routing across 17,000+ models , RAG, and stateful threads behind one key. Then we tested it in public. That part did not go quietly. The numbers we're getting on internal test runs this week 92% on Terminal Bench 2.1 running Codex 5.5 70% on Terminal Bench 2.1 running GLM 5.1 , an open-source model Up to 30% fewer tokens and up to 90% lower cost than the closed harnesses 0% of your code used to train anyone's model <-- Please read the T's & C's of your fav harnesses... Read that second line again. An open model, inside our harness, posting numbers that go

At some point, a monolith starts working against you. In my case, Finovara was a single Spring Boot application handling everything (authentication, transactions, limits, piggy banks, notifications, activity logs, currency conversion) The bigger it got, the harder it was to change anything without worrying about something else breaking. So I broke it apart. This post is about the first three pieces I extracted: the API Gateway , the activity-log-backend , and a shared module called contracts-backend . The Old Structure Everything lived in one Spring Boot app. The activity log — which tracks everything a user does in the app (expenses added, limits changed, logins, account changes) — was tangled up with the core business logic. Adding a new type of activity meant touching the same codebase responsible for transactions, security, and everything else. The New Structure finovara-backend/ ├── api-gateway/ ├── activity-log-backend/ ├── contracts-backend/ └── core-backend/ Four modules (there will be more). Each with a clear responsibility. API Gateway The gateway is the single entry point for every request coming from the frontend. It runs on port 8888 with SSL enabled and uses Spring Cloud Gateway (WebFlux-based). Routing is simple and declarative: routes : - id : activity-log-backend uri : ${ACTIVITY_LOG_URL} predicates : - Path=/api/account-activity/**,/api/archive-activities/** - id : notification-backend uri : ${NOTIFICATION_BACKEND_URL} predicates : - Path=/api/notification-settings/** - id : core-backend uri : ${CORE_BACKEND_URL} predicates : - Path=/** Activity log routes go to activity-log-backend . Notification routes go to notification-backend . Everything else falls through to core-backend . The gateway also handles CORS centrally — https://localhost:5173 is the only allowed origin, with credentials support. No individual service needs to worry about CORS anymore. One thing worth noting: the gateway uses use-insecure-trust-manager: true for the HTTP client. Th

At some point, a monolith starts working against you. In my case, Finovara was a single Spring Boot application handling everything (authentication, transactions, limits, piggy banks, notifications, activity logs, currency conversion) The bigger it got, the harder it was to change anything without worrying about something else breaking. So I broke it apart. This post is about the first three pieces I extracted: the API Gateway , the activity-log-backend , and a shared module called contracts-backend . The Old Structure Everything lived in one Spring Boot app. The activity log — which tracks everything a user does in the app (expenses added, limits changed, logins, account changes) — was tangled up with the core business logic. Adding a new type of activity meant touching the same codebase responsible for transactions, security, and everything else. The New Structure finovara-backend/ ├── api-gateway/ ├── activity-log-backend/ ├── contracts-backend/ └── core-backend/ Four modules (there will be more). Each with a clear responsibility. API Gateway The gateway is the single entry point for every request coming from the frontend. It runs on port 8888 with SSL enabled and uses Spring Cloud Gateway (WebFlux-based). Routing is simple and declarative: routes : - id : activity-log-backend uri : ${ACTIVITY_LOG_URL} predicates : - Path=/api/account-activity/**,/api/archive-activities/** - id : notification-backend uri : ${NOTIFICATION_BACKEND_URL} predicates : - Path=/api/notification-settings/** - id : core-backend uri : ${CORE_BACKEND_URL} predicates : - Path=/** Activity log routes go to activity-log-backend . Notification routes go to notification-backend . Everything else falls through to core-backend . The gateway also handles CORS centrally — https://localhost:5173 is the only allowed origin, with credentials support. No individual service needs to worry about CORS anymore. One thing worth noting: the gateway uses use-insecure-trust-manager: true for the HTTP client. Th

Hey everyone, I need 12 more testers to complete the 14-day closed testing requirement on the Google Play Store for my game, Sugar Fall: A Whimsical Adventure with a Dark Twist! 🍩👻 Anyone can join immediately. Please support an indie dev! ✅ Step 1: Join the Google Group https://groups.google.com/g/cos-alpha-testing-team/ 📥 Step 2: Download the Game (Google Play Store) https://play.google.com/store/apps/details?id=com.crackorigins.sugarfall 🤝 Mutual Help: Please leave a comment below with your app links and a screenshot after downloading mine. I will test your app/game back immediately and keep it for 14+ days. Thank you so much for your support! 🙏 submitted by /u/anyboy1 [link] [留言]

As a web developer, I've noticed that many beginners spend months watching tutorials but struggle when it's time to build something from scratch. That's one reason I started building WebCoDeveloper — a place where I can share practical web development knowledge, real coding examples, and solutions to problems I've faced while working on projects. My goal isn't to create another tutorial website. It's to build a resource that helps developers move from "I watched a video about it" to "I actually built it." I'm curious: What's the biggest challenge you faced while learning web development? Understanding JavaScript? React/Next.js concepts? Building projects? Finding quality learning resources? Getting your first developer job? I'd love to hear your experiences and learn what resources have helped you the most.

Hello, I'm Shrijith Venkatramana. I'm building git-lrc, an AI code reviewer that runs on every commit. Star Us to help devs discover the project. Do give it a try and share your feedback for improving the product. Large Language Models have made it surprisingly easy to generate text. Building a reliable AI application, however, is a completely different problem. Once you move beyond a simple "send prompt, get response" demo, you quickly encounter real-world concerns: Prompt management Structured outputs Multi-step workflows Tool calling Observability Evaluation Model switching Production debugging Many teams end up creating custom frameworks around OpenAI, Anthropic, Gemini, or local models just to manage these concerns. This is where Genkit comes in. Originally developed by Google, Genkit provides a framework for building AI-powered applications with a focus on workflows, tooling, observability, evaluation, and production readiness. While most examples online focus on Node.js, Genkit now has growing support for Go, making it an interesting option for backend engineers who want AI capabilities without introducing an entirely separate application stack. In this article we'll build practical examples and explore how Genkit helps structure real-world AI systems. Why Genkit Exists Most AI applications evolve like this: Phase 1: response := callLLM ( prompt ) Everything seems simple. Phase 2: You need: Retry logic Prompt versioning JSON outputs Tool integrations Tracing Metrics Human review workflows Now your codebase starts accumulating AI-specific infrastructure. Genkit attempts to provide these building blocks from day one. Think of it as: "Spring Boot for AI workflows" rather than "an LLM SDK." Installing Genkit for Go Create a new project: mkdir genkit-demo cd genkit-demo go mod init github.com/example/genkit-demo Install Genkit: go get github.com/firebase/genkit/go/ai Depending on your provider, you'll also install provider plugins. For Gemini: go get github.com/fi

Krishnan is reportedly starting a new institution to continue shaping Trump's AI policy.

For over a decade, the standard approach to bot mitigation has relied on a fundamentally flawed premise: tracking every micro-movement a user makes. The industry standard "invisible" CAPTCHAs ingest your mouse curves, touch pressure, scrolling behavior, and browser history to calculate a "human score." At Conversion.business , we took the opposite approach. We built a zero-telemetry, privacy-first Gamified CAPTCHA platform that tracks no behavioral interactions. Instead, we rely on a mathematically rigorous Game Randomization Strategy and a strict cryptographic handshake. Here is the architectural teardown of why standard machine learning fails against our engine, and why discarding behavioral telemetry actually increases security. The Flaw in "Invisible" Telemetry Standard CAPTCHA systems rely on security through obscurity. They collect massive amounts of user telemetry and run it through proprietary risk-analysis models. This creates two massive problems: The Privacy Tax: You are forcing your users to surrender behavioral biometric data just to log in. The ML Training Loop: If an attacker can reverse-engineer the "human" mouse-curve threshold, they can train a bot to inject fake cursor paths. Once the model is trained, the security layer is completely compromised until the vendor updates their algorithm. The Conversion.business Approach: Zero Telemetry Our OopsSDK does not track mouse movements, touch pressure, or cross-site cookies. We rely on a lean, transparent Verification Signature that collects only: solveTimeMs : The exact duration from puzzle initialization to completion. webglFingerprint : Hashed hardware renderer info. userAgent : To identify known headless browsers. How do we stop bots without tracking behavior? By attacking the core requirement of machine learning: predictability . The Game Randomization Strategy Machine learning models, specifically reinforcement learning and computer vision bots, require a predictable environment to train effectively

Quark’s Outlines: Python User-Defined Methods Overview, Historical Timeline, Problems & Solutions An Overview of Python User-Defined Methods What is a Python user-defined method? When you define a function inside a class, Python does not treat it as just a function. When you call it from an instance, Python changes it into a method. This method knows which object it was called from. It adds that object as the first argument when the function runs. A Python user-defined method joins a function, a class, and a class instance (or None ). It is created when you get a function from a class or an instance. Python binds the instance to the function and forms a method. Python lets you bind a class function to an instance as a method. class Box : def show ( self , word ): print ( " Box says: " , word ) x = Box () x . show ( " hi " ) # prints: # Box says: hi The method x.show is bound to the instance x . Python passes x as the first argument. What does "bound method" mean in Python? When a method is bound, it remembers the instance that called it. A bound method is created when you get a method from an object. It holds a reference to both the function and the instance. Python will pass the instance automatically when you call the method. If you get the same method from the class, Python gives you an unbound method. That means the function is not tied to any one object. Python uses bound methods to remember which object to call with. class Lamp : def turn_on ( self ): print ( " The lamp is now on. " ) l = Lamp () m = Lamp () a = l . turn_on b = m . turn_on a () b () # prints: # The lamp is now on. # The lamp is now on. Each bound method remembers which Lamp it came from. A Historical Timeline of Python User-Defined Methods Where do Python user-defined methods come from? Python user-defined methods grew from early ideas in object-oriented design. In many languages, methods are just functions that get special treatment when called from an object. Python made this clear by lettin

Hello, my name is Greg. About six months ago I started using AI chatbots like ChatGPT and Claude at work for small tasks — proofreading emails, summarizing meeting notes, that kind of thing. But also some technical stuff too. One thing that really came in handy was analyzing packet captures from Wireshark. I work with VoIP phone systems, and when things go wrong, feeding a PCAP file into an AI chatbot speeds up the troubleshooting process dramatically. Before long I was asking AI to write code. First simple HTML pages, then Python, then C#. I was amazed by the results. These weren't big projects — just small experiments — but they came to life in minutes instead of days. I found out there was already a term for this: vibe coding . Perfect, I thought. I made project after project and wanted to share the excitement with other people who were surely doing the same thing. I created a free learning website, published a book on Kindle Unlimited, and went looking for a community. I landed on Reddit. There were already vibe coding subreddits. I thought — this is great, I've found my people. Then reality hit. These communities had "vibe coding" in the name, but they weren't exactly vibe coding friendly. The term had already been claimed by people focused on monetizing their creations fast, with little interest in actually learning to code. That created a massive anti-vibe-coding crowd on the other side, and honestly there was an all-out war going on between them. Not really the place for someone just looking to share cool stuff they made. I came to a realization: I wasn't really a vibe coder — at least not the kind people were arguing about. I wasn't in it for the money. I was in it for the fun. I didn't mind learning programming concepts along the way. I wasn't trying to sell anything or launch a startup. I just liked making things and solving problems. So I retreated and regrouped. That's when I found a better description: Play-First Programmers . People who start by playi

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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'm making this Arduino project for my maths class and column 4 on the keypad isn't working. This is how I want the buttons to work: A -> start over (whole game) B ->skip question C -> clear D -> delete one space What mistaks did I do and how exactly can I fix them? Thank youu <3 submitted by /u/livie4lifexx [link] [留言]

If you've ever hit Unexpected token in JSON at position 42 or Unterminated string , there's a good chance an unescaped character broke your payload. JSON is strict about what's allowed inside a string, and the fix is almost always escaping . Here's the practical version. What does escaping a JSON string mean? A JSON string is wrapped in double quotes. Any character that would confuse the parser must be replaced with a backslash escape sequence. Escaping doesn't change the meaning of your text — it just makes the string valid JSON so parsers can read it. Unescaping is the reverse: turning those sequences back into readable characters (handy when you copy a value out of logs or an API response). The characters you must escape JSON defines exactly seven characters that must be escaped inside a string: Character Escaped as Double quote " \" Backslash \ \\ Newline \n Carriage return \r Tab \t Backspace \b Form feed \f The forward slash / may optionally be escaped as \/ , but it isn't required. Unicode can be written as \uXXXX (four hex digits). JSON escape examples Double quotes — He said "hello" becomes: "He said \" hello \" " Backslashes (Windows paths) — C:\temp\file.txt becomes: "C: \\ temp \\ file.txt" Newlines and tabs — a two-line, tabbed string becomes: "Line 1 \n Line 2 \t Tabbed" How to escape and unescape JSON in code In production you rarely escape by hand — every language has it built in. JavaScript const escaped = JSON . stringify ( text ); // escape const back = JSON . parse ( escaped ); // unescape Python import json escaped = json . dumps ( text ) # escape back = json . loads ( escaped ) # unescape Java (Jackson) ObjectMapper mapper = new ObjectMapper (); String escaped = mapper . writeValueAsString ( text ); String back = mapper . readValue ( escaped , String . class ); C# (.NET) using System.Text.Json ; string escaped = JsonSerializer . Serialize ( text ); string back = JsonSerializer . Deserialize < string >( escaped ); Common escaping mistakes (and f

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Six months ago, I sat down with a YouTube playlist, a blank notebook, and one goal: learn Python. What I did not expect was how hard it would be, not the Python itself, but figuring out how to actually learn it. I started with a YouTube playlist. Simple enough. Except nobody tells you what to do after you watch a video. Do you rewatch it? Take notes? Jump straight to code? I had no system. I'd watch a concept, feel like I understood it, open VS Code, and stare at a blank file. That's when I realized I had fallen into passive learning. And passive learning in the age of AI is a particularly dangerous trap, because it's so easy to confuse activity with progress. I could watch a video, feel good. I could ask Claude to explain a concept, feel good. I could even ask AI to write code, read it, nod along, and feel like I'd learned something. I hadn't. I'd just consumed. There's a difference. The real moment of honesty came when I was stuck on a coding problem. My instinct, everyone's instinct now is to open ChatGPT or Claude immediately. And I knew, sitting there with the cursor blinking, that if I did that every single time I got stuck, I was building nothing. My brain would never develop the muscle of working through problems. I would be someone who can prompt AI to code, not someone who can think in code. And in a world where AI can already write decent code, the person who can't think independently isn't valuable. They're replaceable. So I had to build a system that forced me to actually learn. After a lot of trial and failure, I landed on a 5-phase checklist that I wrote out by hand and kept next to my laptop. Phase 1: is what I call First Contact — watch one focused video, then write a summary purely from memory, then discuss it with an LLM not to get answers but to pressure-test what I thought I understood. Phase 2: is Deep Understanding — read a written source, write proper notes, map the concept visually, and list every edge case and exception I can find. Phase 3:

This is the third and final part of my first React project for the Frontend Mentor's Digital Bank Landing Page Challenge . I'm excited to say that I finally finished it. Live Demo: https://bank-landing-page-react-gmtz.vercel.app/ Github Repo: https://github.com/ayra-baet/bank-landing-page-react Learning Component Reusability Beyond Small Elements At first, I thought this final part would mostly involve finishing the Articles and Footer. But while building, I realized something more important: React's reusability isn't limited to small UI elements like buttons or cards; entire sections can be reusable too. Earlier in this project, I reused a single Button component across the header, hero, and footer. This time, I noticed that the Features and Articles sections shared almost the same structure: both had an h2 heading both used a grid layout both wrapped child components The only real difference was that the Features section included a description paragraph. That immediately felt like a perfect use case for a reusable component with conditional rendering. So I created a reusable Section component: function Section ({ backgroundColor , title , description , children }) { return ( < section className = { backgroundColor } aria-labelledby = { ` ${ title } -heading` } > < div className = "container section__container" > < div className = "section__header" > < h2 id = { ` ${ title } -heading` } > { title } </ h2 > { description && < p > { description } </ p > } </ div > < div className = "section__grid" > { children } </ div > </ div > </ section > ); } Then I reused it inside my LandingPage component: function LandingPage () { return ( <> { /* other LandingPage JSX */ } < section id = "features" > < Section backgroundColor = "section--gray-100" title = "Why choose Digitalbank?" description = "We leverage Open Banking to turn your bank account into your financial hub. Control your finances like never before." > < Features /> </ Section > </ section > < section id = "articl

This story was shared by a fellow developer on DEV who asked to remain anonymous. If you've got a story to tell — come find me. Your name won't appear anywhere. Based on real microservice security design patterns. About an engineer whose PR got blocked by an AI security system — he thought he was fixing a vulnerability. Turns out, someone had a vested interest in that vulnerability staying open. 1. $1,400,000 All-hands meeting. CTO James stood at the front, a number on the screen: $1,400,000 "This is what we're spending on security this year." He pointed at the number. "The biggest piece — right here." He clicked the remote. VoidSentinel's architecture topology appeared on screen. "VoidSentinel — an AI security platform. Integrated into our CI/CD pipeline. Starting today, every PR involving internal service-to-service calls — it reviews them automatically." The CEO didn't show up today. James didn't mention it. He looked straight at Mark — VP of Security. Mark took the mic. "VoidSentinel has been running in our pre-production environment for three weeks. It's caught 47 high-risk patterns. Zero false positives." He paused. " — Of course, some people might feel uncomfortable when their PR gets blocked. But this isn't personal. This is the security standard. " He wasn't looking at me. But I knew who he was talking about. 2. High Risk. Denied. The story started three weeks earlier. We had a payment service and a user service that talked to each other internally. They shared an old API key — one key across thirty-plus services, unchanged for five years. It wasn't that nobody knew. It just never made it to the top of the backlog. On Day 1, I opened a PR: add independent service-to-service auth between the payment and user services. Not much code — a new token exchange module, three call sites modified. Five minutes later, VoidSentinel's automated comment hit: "High-risk alert: Unauthorized internal access pattern change detected. This PR has been automatically rejected. C

Key Use Cases Power BI Visual Monitoring can be used for: power bi visual monitoring power bi report visual monitoring visual regression testing for Power BI power bi screenshot monitoring monitoring Power BI visuals visual monitoring for Power BI Report Server automated Power BI dashboard validation visual correctness control for BI reports Power BI Visual Monitoring: Automatically Detecting Broken Visuals in Power BI Reports In large Power BI environments, analytics teams often face the problem of silent regressions : even minor changes in data or models can break individual visuals without any obvious errors. Report owners frequently don’t notice that a visual has stopped rendering or is showing incorrect data — this can happen due to changes in data source structure, access rights, deleted fields, broken measures, or refresh failures. Manually checking hundreds of report pages across multiple dashboards in such conditions is extremely inefficient and nearly impossible. We, a team of BI developers and analysts, encountered this pain point during a large analytics implementation project and decided to create a solution for automated Power BI visual monitoring . Project Source Code: GitHub: https://github.com/svergio/Power-bi-report-visual-monitoring Documentation: https://svergio.github.io/Power-bi-report-visual-monitoring/ Wiki: https://github.com/svergio/Power-bi-report-visual-monitoring/wiki Why Standard Power BI Tools Don’t Solve the Problem Standard Power BI tools such as Usage Metrics and Performance Analyzer help analyze report usage and performance but do not detect visual issues. For example, built-in usage metrics show “how those dashboards and reports are being used” — number of views, popular reports, and who is viewing them. These metrics are important for assessing analytics adoption, but they say nothing about whether the visuals themselves are displaying correctly. Similarly, Performance Analyzer shows load times for each visual, helping identify s