Integrating SonarQube with JFrog's AppTrust for secure software development

🚀 Case Study: How we built OTPDock in 12 days 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 End-to-end tests at scale often fail because of flaky OTP (one-time password) flows. Every failure = wasted engineering hours, blocked releases, and stressed teams. In enterprise CI/CD, that translates to serious costs. 𝗧𝗵𝗲 𝗕𝘂𝗶𝗹𝗱 Instead of hacking Gmail or juggling disposable inboxes, we shipped OTPDock: ✅ Temporary inboxes per test environment ✅ Regex OTP extraction + custom parsing ✅ API-first delivery into Playwright & Cypress ✅ Lightweight backend (NodeJS + DynamoDB) 𝗧𝗵𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 • Day 0: Scoped the pain → “one problem, one solution” • Day 5: Working OTP inbox + regex parsing • Day 12: Live product + first external user 𝗧𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 ⚡ Cut flaky OTP test failures by ~70% (conservative benchmark) ⚡ Engineers saved hours per week → faster releases ⚡ Inbound interest from other dev teams = instant proof of demand 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻 An MVP isn’t about polish. It’s about getting a solution live, in users’ hands, before most teams even finish planning. 👉 That’s our model at ViaLabs: build investor-ready MVPs in 4–8 weeks. Focused scope. Weekly demos. Real traction. ⚡ 𝗥𝗲𝗮𝗱𝘆 𝘁𝗼 𝗴𝗲𝘁 𝘆𝗼𝘂𝗿 𝗠𝗩𝗣 𝗹𝗶𝘃𝗲 𝗶𝗻 𝘄𝗲𝗲𝗸𝘀, 𝗻𝗼𝘁 𝗺𝗼𝗻𝘁𝗵𝘀? Book a call: https://lnkd.in/d38duqBM Want to check out OTPDock? Sign-up here, it's free https://otpdock.com

Learn how to integrate the Model Context Protocol (MCP) into your Rails application to create intelligent, AI-powered tools that can interact with your data and perform complex tasks conversationally. Complete with code examples, client integrations, and real-world use cases

𝟲 𝘄𝗮𝘆𝘀 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲𝗱 𝗰𝗼𝗱𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝗲𝗻𝘀 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀: ⚠️ Hallucinations and "context rot" ⚠️ Coding velocity without oversight ⚠️ Hidden runtime assets and misconfigurations ⚠️ Ambiguous asset ownership ⚠️ Compliance and licensing violations ⚠️ Tool sprawl and alert fatigue Read if you want to understand the full impact of each of these: https://lnkd.in/gu_9R4xu #AIcoding #AIgeneratedcode #AIsecurity #securecoding

Claude Code has one problem: 👉 It's hard to review the generated code in terminal. That makes developers more likely to miss bugs, ship security holes, approve features no one asked for. The fix? CodeRabbit CLI. It was just released today. It brings intelligent, context-aware reviews straight into your terminal. Works with Claude, Cursor, Gemini, and other AI agents. It provides one-click fixes. Best part: it’s free to try → https://lnkd.in/d8jtKmT8 AI writes the code. AI reviews the code. You make the final call. This is the new era of software development.

Most code review tools tell you what changed. Almost none tell you why it matters. What if code analysis could infer developer intent from PRs and tickets, tie findings to business risk, and even simulate likely runtime impact before code is merged? Instead of a flood of generic warnings, imagine PR feedback like: “This touches payments, has high runtime exposure, and requires security signoff” with explainability and confidence scores. Under the hood, this could use an Agent reasoning over an indexed codebase combined with retrieval-augmented generation (RAG) to connect PRs, past incidents, ownership graphs, and runtime dependencies. Each finding could be scored for business impact, and the system could continuously learn from developer feedback and real incidents, turning static checks into living, risk-aware guardrails that prioritize what truly matters. Would this kind of context-aware, self-evolving approach shift code review from noise to actionable intelligence or just add a layer of complexity? PS: A basic drawio diagram below explaining the system #CodeReview #SoftwareDevelopment #DevTools #AIForDev #AgenticSystems #RAG #DevSecOps

𝗔𝗿𝗲 𝘆𝗼𝘂𝗿 𝗝𝗮𝘃𝗮 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝘁𝗮𝘀𝗸𝘀 𝗮 𝗰𝗵𝗮𝗼𝘁𝗶𝗰 𝗺𝗲𝘀𝘀? If you've ever dealt with 𝙞𝙣𝙙𝙚𝙥𝙚𝙣𝙙𝙚𝙣𝙩 𝙩𝙝𝙧𝙚𝙖𝙙𝙨 or the "𝙛𝙞𝙧𝙚-𝙖𝙣𝙙-𝙛𝙤𝙧𝙜𝙚𝙩" model, you know how messy concurrency can get. You're left with: ⇒ Complex error handling ⇒ Difficult cancellations ⇒ No clear way to manage the lifecycle of your tasks. 𝙄𝙩'𝙨 𝙖 𝙧𝙚𝙘𝙞𝙥𝙚 𝙛𝙤𝙧 𝙗𝙪𝙜𝙨 𝙖𝙣𝙙 𝙬𝙖𝙨𝙩𝙚𝙙 𝙚𝙛𝙛𝙤𝙧𝙩. 𝗕𝘂𝘁 𝘄𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗮𝗹𝗹 𝗼𝗳 𝗶𝘁? In this carousel, we'll dive into 𝙎𝙩𝙧𝙪𝙘𝙩𝙪𝙧𝙚𝙙 𝘾𝙤𝙣𝙘𝙪𝙧𝙧𝙚𝙣𝙘𝙮 and show how this modern approach solves the biggest problems of traditional threading models. You'll see how it makes your code: → cleaner  → easier to debug → much safer. 𝘚𝘸𝘪𝘱𝘦 𝘭𝘦𝘧𝘵 👉 𝘵𝘰 𝘭𝘦𝘢𝘳𝘯 𝘩𝘰𝘸 𝘵𝘰 𝘮𝘢𝘴𝘵𝘦𝘳 𝘤𝘰𝘯𝘤𝘶𝘳𝘳𝘦𝘯𝘤𝘺 𝘢𝘯𝘥 𝘸𝘳𝘪𝘵𝘦 𝘴𝘢𝘧𝘦𝘳, 𝘮𝘰𝘳𝘦 𝘱𝘳𝘦𝘥𝘪𝘤𝘵𝘢𝘣𝘭𝘦 𝘤𝘰𝘥𝘦.

Most developers handle HTTP redirects manually, burning cycles on repetitive code that should be automatic. The curl -L flag eliminates redirect handling overhead entirely. Instead of writing custom logic to parse Location headers and manage 301/302 responses, curl follows redirects natively — up to 50 hops by default. This becomes critical when you're running large-scale operations through proxy infrastructure. Modern web scraping and API automation workflows hit redirect chains constantly, especially when dealing with CDN routing and geo-specific endpoints. Manual redirect handling adds latency and introduces failure points that kill throughput. Smart operators combine curl's native redirect handling with IPv6 proxy rotation to maintain session persistence across redirect chains without triggering rate limits. Read the technical breakdown: https://lnkd.in/dxaA8tUX #WebScraping #IPv6Proxies #DevOps #NetworkEngineering #APIAutomation

📝 𝐁𝐲𝐭𝐞 #2:  We tend to architect the war-ground of distributed systems to solve business problems of scale w/o proper setup of weapons one of those is distributed tracing from the world of 𝐨𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: In one of the orgs we have seen 18% of production issues failed to Identify the regressing service, Taking 2.2x Longer to Resolve. 𝐏𝐫𝐚𝐭𝐢𝐜𝐚𝐥 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧:  - Implement *request-scoped distributed tracing* with 100+ error samples within the first x minutes of an incident.   - Use *flow-instance-based sampling* (sample entire request flows, not fragments) to get complete end-to-end visibility.   - Build *automated root cause detection* that analyses trace patterns to identify which backend service regression caused frontend errors.  𝐆𝐞𝐧𝐞𝐫𝐚𝐥 𝐆𝐮𝐢𝐝𝐚𝐧𝐜𝐞:  - Correlate frontend errors to backend service calls using trace lineage. Look for latency/error rate spikes in downstream dependencies.   - Set trace sampling rates dynamically based on error rates—increase sampling during incidents.  𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬: - https://opentelemetry.io/ for standardized tracing instrumentation #knowledge_bytes #observability

🚨 𝗬𝗼𝘂𝗿 𝘀𝗰𝗮𝗻𝗻𝗲𝗿 𝗹𝗶𝗲𝘀 𝘁𝗼 𝘆𝗼𝘂 🚨 Traditional dependency scanning tools analyze 𝗶𝗻𝘁𝗲𝗻𝘁𝗶𝗼𝗻 (manifest files) but entirely miss 𝗿𝗲𝗮𝗹𝗶𝘁𝘆 (what the compiler actually puts in the final binary). The compiler makes autonomous decisions that fundamentally shape your attack surface. 𝗪𝗵𝘆 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: This discrepancy means vulnerable legacy code is being "𝗯𝗮𝗸𝗲𝗱 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝘆𝗼𝘂𝗿 𝗯𝗶𝗻𝗮𝗿𝗶𝗲𝘀" through static linking and hidden liabilities. The tools show you one thing, but the reality of what's running in production is entirely different. 𝗧𝗵𝗲 𝗯𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: You must analyze the compiled binaries themselves. NetRise provides the scalable, automated Binary Composition Analysis (BCA) that reveals what's actually running in production, so you can stop guessing and start securing. 👀 Read more in comments: ⬇️

Make developers more efficient by using the tools that allow them to do the work in the least steps - Fernando Villalba tells John Crickett