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AI 资讯

Introducing Flame IDE 🔥: Multiple Projects, Parallel Branches, and AI Agents in One Workspace

Hi DEV! 👋 I’m the developer behind Flame IDE , a free desktop IDE for macOS, Windows, and Linux. I built Flame to bring my everyday development workflow into one place: multiple repositories, Git worktrees, AI agents, browser previews, terminals, debugging, databases, and API testing. I wanted to spend less time moving between tools and more time building. That also meant making everyday tasks easier, from browsing folders visually and editing images to fixing a failing test or resolving a merge conflict with AI. Multiple projects should feel like one workspace A frontend, an API, and a shared package often belong to the same piece of work. In Flame, I can group them, switch between them, and keep their branches and changes visible without opening a separate IDE window for every repository. The Broadcast terminal runs a command across selected projects with separate output for each. Saved run configurations handle the scripts, servers, and browser previews I regularly start together. Less repeated setup, especially when working across the whole stack. Parallel work should be easy to start and review Git worktrees are incredibly useful, but preparing each checkout can become a chore: missing .env files, local configuration, dependencies, and another window to manage. Flame automates worktree creation and local configuration copying, with initialization steps to prepare the checkout. Features, experiments, and agent tasks can live side by side in one window. The Agents Manager lets me create, monitor, schedule, stop, retry, and review AI tasks across projects, each with its own permissions and landing strategy. Point at the problem. Let AI see what you see. I got tired of screenshotting bugs, drawing red circles, and describing my UI to an AI chat. In Flame’s built-in browser, I select an element and tell the agent what to fix. It can inspect the DOM, screenshots, console errors, and page state, then interact with the page to check its changes. I can follow the fix in

2026-09-08 原文 →
AI 资讯

How Figma Uses AI Agents for Security

The engineering team at software company Figma recently documented how they built AI agents to help their security team investigate alerts, search past incidents, check company systems, and even prepare code fixes. The agents learn from previous investigations, reducing repetitive work and helping engineers resolve complex alerts about 70% faster. By Renato Losio

2026-09-06 原文 →
AI 资讯

Before and After: Measuring Security Posture Improvement With Real Metrics

188 vulnerabilities. That's where we started. After the modernisation, the jjwt migration, the targeted remediations, and the documented suppressions, here's where we ended up: 6 open findings. All suppressed with documented reasons. 0 unaddressed. But "188 to 6" is a headline, not a measurement. This article is about what meaningful security posture measurement actually looks like — the metrics that tell a real story versus the ones that just make a dashboard look good. Why Raw Finding Count Is a Weak Metric The most common way teams measure SCA progress is finding count. Before: 188. After: 6. Improvement: 182 findings resolved. 97% reduction. That number is real but it's also misleading in isolation. Here's why. If I had suppressed all 188 findings without fixing anything, my finding count would also be 6. The dashboard would look identical. The actual security posture would be unchanged. Finding count measures activity, not outcomes. What you need to measure is: What risk was actually reduced — not just what got closed What risk remains and why — the documented residual risk How the remediation was achieved — fix vs. suppress breakdown What the exploit exposure looks like — before and after exploit maturity These four dimensions together tell a story that a single number can't. Metric 1: Risk Reduction by Severity The most important before/after comparison is severity distribution, not total count. Severity Before After Fixed After Suppressed Remaining Critical 10 10 0 0 High 99 93 6 0 Medium 59 29 23 0 (7 in 4.x backlog) Low 20 0 20 0 Total 188 132 49 0 unaddressed Every Critical finding was fixed — not suppressed, fixed. That's the number that matters most. No Critical vulnerability was accepted as residual risk. The 6 suppressed High findings are all in the "no known exploit" category with documented unreachability justifications. The 23 suppressed Medium findings are split between test-scope dependencies and unreachable code paths. What to say when presentin

2026-09-06 原文 →
AI 资讯

DevSecOps Career Path: From DevOps to Secure Pipelines

Security Bolted on at the End Is Not DevSecOps The most common failure pattern in teams that claim to "do DevSecOps" looks like this: build the pipeline, ship the feature, and run a security scan right before release — treating security as a checkbox at the end of the process instead of something built into every stage of it. That's not DevSecOps. That's a security review with extra steps. Real DevSecOps means security is woven into the pipeline itself — scanning dependencies on every commit, catching misconfigurations before they're deployed, and treating a vulnerability the same way you'd treat a failing test: something that blocks the pipeline, not something reviewed manually after the fact. This guide is for people who already have some DevOps or backend foundation and want to understand what it actually takes to move into a DevSecOps-focused role — not just "add security" to an existing skillset, but understand the mindset shift that makes the discipline distinct. This post originally appeared on the Ciphemic Academia blog . What "DevSecOps" Actually Requires DevSecOps sits at the intersection of three skill areas, and a real role expects working competence across all three, not deep expertise in just one: DevOps fundamentals — CI/CD pipelines, infrastructure-as-code, containers, the same core skills a cloud/DevOps engineer needs Application security — understanding common vulnerability classes, how to find them, and how to actually fix them, not just recognize their names Security automation — the specific skill of embedding security checks into a pipeline so they run automatically, consistently, on every change That third point is what actually distinguishes DevSecOps from "a DevOps engineer who also cares about security." It's specifically about automation and process — making secure practices the default path, not an extra manual step someone has to remember to do. Step 1: Confirm Your DevOps Foundation Is Solid DevSecOps is not an entry point into DevOps —

2026-09-06 原文 →
AI 资讯

I Built a Full IT Ticket System in Power Apps — Here's the SLA Engine That Runs Without Power Automate

I recently built a complete IT ticket management system in Power Apps — 9 screens, role-based access, live SLA tracking, and automatic email notifications. The part I want to actually talk about here isn't the UI, it's the SLA engine, because I built it to work without Power Automate , and the trick is simpler than it looks. The problem SLA tracking normally means: a ticket is "Critical" → 60 minute target → somebody needs to know if it's about to breach or already has. The obvious way to do this is a scheduled Power Automate flow that checks every ticket on a timer and flags the ones in trouble. I wanted this app to run on Power Apps collections only — no flow, no external data source — so a scheduled flow wasn't an option. The question was: can you get "live" SLA status without a background job? The trick: recalculate on every read, not on a timer Instead of a flow updating a SLAStatus field periodically, I recalculate it every time the app or a screen is opened, using Now() against the stored due date: \ UpdateIf( colTickets, Status <> "Resolved" && Status <> "Closed", { SLAStatus: If( Now() > DueDate, "Breached", DateDiff(Now(), DueDate, TimeUnit.Minutes) <= SLAMinutes * 0.2, "At Risk", "On Track" ) } ); UpdateIf(colTickets, Status = "Resolved" || Status = "Closed", {SLAStatus: "Met"}) \ \ This runs in App.OnStart , at the top of every screen's OnVisible , and behind a manual "Refresh SLA" button. The At Risk threshold is 20% of the SLA window remaining — so a Critical ticket (60 min target) goes At Risk with 12 minutes left; a Low ticket (1440 min / 24 hrs) goes At Risk with 4.8 hours left. The honest tradeoff: this only updates when someone has the app open. A ticket breaching at 2am with nobody looking won't trigger anything until the next visit. For a real production deployment I'd pair this with a scheduled flow for after-hours detection — but for a demo, an internal tool with regular traffic, or anything where "eventually consistent within the next visit"

2026-09-03 原文 →
AI 资讯

Here’s the first 4K 120Hz UST projector with optical lens shift

Detail-preserving optical lens shift is not something you expect to find on an ultra-short throw projector running Google TV, but that's exactly what you'll find inside of Awol Vision's new LuxVision. It also promises to produce an incredibly bright (6000 ISO Lumens) 4K 120Hz image measuring 120 inches when placed just 7.5 inches from the […]

2026-09-03 原文 →