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When an international recruiter recently asked for my CV, I instinctively started writing it the way many developers do: A chronological list of companies, Programming languages, frameworks, Technical achievements. Then it hit me. I wasn't writing this document for a senior engineer. I was writing it for the recruiter sitting between me and the interview. If the first person reading my CV couldn't immediately understand the value I brought, I might never reach the technical interview at all. Knowing the Receivers So I rewrote it from a different perspective. Instead of simply listing technologies, I described the business context behind my work. 10,000+ emails sent a day (in addition to "Using AWS SES/SQS") 800+ restaurants / POS everyday (in additional "optimised SQL speed"). Cut down waste to 1.3% from 10 ~ 15% Critical updates often in 24 hours. Increased revenue, reduced costs, improved reliability Helped onboarded new clients I still included the languages and frameworks I used, so the CTO can understand, but they became supporting evidence rather than the headline. I also highlighted the moments that demonstrated trust: Delivering critical business updates under tight deadlines, Resolving high-priority production issues, Taking responsibility for systems the business depended on, and Taking initiatives to write a mobile app using my own time. That small shift completely changed how I viewed a CV. It's not a journal of everything I've done, and it's not a technical specification. Its job is to communicate your value clearly to the person reading it, and that person is often a recruiter before it's ever seen by an engineering manager. One lesson I keep coming back to is this: Write for my audience. Outcome (for now) After reviewing the rewritten CV, the recruiter was confident enough to forward it to Tata Consultancy Services for a role. Whether or not that particular opportunity works out, it reinforced an important lesson for me: recruiters need to understand

The café was crowded that evening, but to me, the world had fallen completely silent. An open...

I’m VikingRob—Full-Stack Dev, SaaS Builder, and Solo Survivor. Hello I Just wanted to introduce myself. I’m Robert, but most people know me as VikingRob (thanks to a long red beard and a habit of grinding through hard Jobs with a foul mouth. Down to earth guy I'm a No B.S Person. I’ve been surviving in the trenches of solo entrepreneurship and freelancing for a while now. Lately, the market feels incredibly flooded, and landing solid, consistent work has become a massive mountain to climb. I’ve managed to keep things moving with some passive income from selling front-end and back-end sites I've built, but as anyone with a family knows, "passive" rarely means "enough" when consistency drops. I’m supporting a family of five—including a wife dealing with severe mental health challenges—so the pressure to secure steady, reliable income is incredibly real right now. To adapt, I am shifting my core focus toward offering full-scale services: Custom Website Architecture (End-to-end development) Front-End & Advanced Back-End Integration SaaS Product Development A lot of my heaviest back-end work is locked away under strict NDAs, which makes traditional portfolio-sharing tough, and I don't maintain standard social media accounts. But I know how to build clean, functional, scalable software that drives results. If you're looking to collaborate, need an engineering heavy-lifter for a SaaS project, or just want to swap freelance survival stories, let’s connect! What is everyone else doing to beat the market noise right now?

Better to sacrifice a part to preserve the whole. — The 36 Stratagems, Sacrifice the Plum Tree to...

Note: Google Cloud credits are provided for this project. In this blog post, I want to demonstrate how I use Antigravity CLI to build an image analysis demo using Angular, Firebase Hybrid & On-device Inference Web SDK, and Gemini models. Users upload an image and use a Gemini model to analyze it to generate a few alternative texts, tags, recommendations, and CSS tips to enhance the image quality. When the demo is running on Chrome 148+, the Hybrid & On-device SDK leverages the Prompt API of the on-device Gemini Nano model to perform the image-to-text tasks, and the token usage is 0. When other browsers such as Safari or Firefox executes the same tasks on the demo, the SDK falls back to Cloud AI (Gemini 3.5 Flash model), and the token usage is greater than 0. Next, I will describe how I installed the skills in my Angular project, and registered the Stitch MCP server in the Antigravity CLI to develop the infrastructure, services, and UI design of my demo. 1. Skills I installed grill-with-docs , angular , and firebase skills in my project for the following reasons: grill-with-docs: Conduct a rigid Q&A session to generate a specification for a feature, refactor or a critical fix. AI is responsible for performing a thorough analysis and putting in more effort to generate code to achieve the task. Angular: Provide the best practices of Modern Angular architecture, such as using signals and signal forms. Firebase: Provide the skill for Firebase AI Logic, Firebase Remote, etc. Resources Firebase Hybrid & On-device Image Analysis App Firebase Hybrid & On-device Inference Chrome Built-in Prompt API Stitch Stitch MCP Server grill-with-docs Angular skill Firebase skill

VW's plan calls for half as many models but didn't mention closures or job cuts.

Now it's an arms race between OEMs locking down chips and tuners trying to crack them.

Last month, Polestar shocked the auto industry when it announced that it was pulling out of the US. The EV company's decision came after the federal government denied its authorization to continue selling its cars despite a rule banning vehicles with Chinese-made connected vehicle software. Polestar, which is headquartered in Sweden but majority owned by […]

Durante o desenvolvimento do Templo Digital, meu projeto de hackathon (uma vitrine 3D de cursos...

What nobody tells you about exporting your multi-agent prototype to a local workspace. Every architect who's prototyped a multi-agent app in Google AI Studio eventually hits the same wall: the prototype works, but it lives in a browser tab. At I/O 2026, Google shipped a fix — Export to Antigravity, a one-click handoff to a local production workspace, carrying "all the context" with it. I ran a real two-agent prototype through it. Here's exactly what survived the trip, what didn't, and what I had to fix by hand — including a bug that had nothing to do with the export itself. 1. The Pilot Project + The Click The project: Research Digest — a sequential two-agent app. Agent 1 (Researcher) takes a topic, uses grounded web search to gather sources. Agent 2 (Editor) synthesizes those findings into a polished digest. Persistence via Firestore, with a history archive of past digests. Built entirely from a single prompt in AI Studio's Build mode . Along the way, provisioning Firestore surfaced my first real gotcha before I even got to the export step — more on that below. Triggering the export: Code tab → Export → Export to Antigravity. The dialog is genuinely informative — it tells you upfront what's coming: all project files, conversation history, and explicitly "1 secret will be included." 2. What Actually Survives the Trip The export dialog's claims, checked one by one: Claimed to transfer What I found All project files ✅ Confirmed — full structure landed intact: .agents, .antigravity, src, config files, README.md with setup instructions Secrets (1 secret) ✅ Confirmed — GEMINI_API_KEY arrived populated in .env, worked immediately, no manual re-entry Conversation history history❌ Did not transfer. The imported "Research Digest" project showed "No conversations yet" in Antigravity's Agent Manager, despite the dialog's explicit promise. Checked twice, on two separate screens — consistent result. 3. The Gotchas Gotcha 1 — "Conversation history will carry over" is currently no

TL;DR: In 2026, the old "cheaper hourly rate vs. more control" framing is outdated. AI-assisted delivery is compressing team size, contracts are shifting from hourly to outcome-based, and onboarding windows have shrunk from months to days. Use staff augmentation when you have strong internal PM capacity and need specific skills for 3-6 months. Use a dedicated team when you're running a 2+ year product and need a self-contained unit with its own PM/QA. Below is a breakdown of the current landscape, including how providers like Toptal-style networks, 6senseHQ , Cleveroad , ScienceSoft , BairesDev , SolveIt , and Uptech fit into each model. Why this decision looks different in 2026 than it did in 2023 Three things changed the calculus this year: AI-assisted engineers ship more per head. Teams are increasingly built around a handful of seniors paired with AI coding assistants rather than a dozen mid-level developers billed by the hour — which makes the traditional "cost per hour" comparison less meaningful than "cost per shipped outcome." Contracts are moving from time-and-materials to outcome-based. Buyers are pushing vendors to tie payment to delivery milestones, not logged hours, partly because AI tooling makes hour-counting a weaker proxy for value. Onboarding windows collapsed. Several dedicated-team providers now quote 3-7 day ramp-up instead of the 2-4 week window that was standard a few years ago, which narrows the traditional "augmentation is faster to start" advantage. None of this changes the fundamental difference between the two models. It changes how much each one costs you in practice. The core difference, restated simply Staff augmentation : you hire individual engineers who join your team, use your tools, and report to your leads. You manage the work. Dedicated team : you hire a self-contained unit (engineers + QA + a PM/lead) that runs its own delivery process. You manage the roadmap, they manage the mechanics. The break-even point most guides converge

One of my favorite words in the startup and product-building world is pivot. For a long time, I thought a failed project meant wasted time. Today, I see it differently. Every project I worked on—even the ones that never gained users or reached the finish line—taught me something I couldn't have learned from books alone. They taught me how to validate ideas, communicate with users, make technical decisions, prioritize features, and, most importantly, when to change direction. I've come to believe that many successful founders didn't succeed because they had the perfect first idea. They succeeded because their previous attempts gave them the experience to recognize a better opportunity. In fact, I think that if many of them had started directly with the project that eventually made them successful, they might have failed. They first needed the lessons, the mistakes, and the discipline that came from building things that didn't work. I'm still on that journey. Some of my own projects didn't succeed the way I had hoped, but I don't consider them failures. They were investments in experience. Every project made me a better builder and helped me better understand what I want to create and how I should create it. One principle that keeps me moving comes from the Quran: «"Indeed, Allah will not change the condition of a people until they change what is within themselves." (Quran 13:11)» And another verse that reminds me to stay patient during difficult times: «"Allah does not burden a soul beyond what it can bear." (Quran 2:286)» If you're building something today and it isn't working, don't be afraid to pivot. Sometimes changing direction isn't giving up—it's applying everything you've learned so far. I'm curious: Have you ever pivoted a project? What did it teach you?

"Show a smile, hide the blade." — The 36 Stratagems, Conceal a Dagger in a Smile Previously on...

The 4.8 L eight-cylinder generates more than 1,000 hp and 1,000 Nm, Ruf says.

I asked AI to help me write this article. Then I sat there for a second, thinking about how ironic...

The Bezos-backed automaker building America’s cheapest electric truck is teaming up with the crayon company in a bid to brighten its rides. Make ours Razzmatazz.

State agency's delay could mean free robotaxi rides in company’s new Ojai vehicle for a few months.

NHTSA administrator Jonathan Morris called reports that self-driving cars had driven into emergency scenes and blocked ambulances and firefighters “unacceptable.”

For a long time, education and work rewarded one thing above all else: the ability to produce correct answers. School exams were built around it. Technical interviews were built around it. Even many engineering jobs were built around it. The person who could respond faster, explain better, and deliver the right output was often seen as the most valuable person in the room. But AI is changing that. Today, answers are becoming cheap. With modern AI tools, anyone can generate code, summaries, documentation, architecture drafts, and even product ideas in seconds. The scarcity is no longer in producing answers. The scarcity is in defining the right problem. That is why, in the AI era, learning how to ask better questions matters more than learning how to write better answers. The Bottleneck Has Moved The biggest shift is not that AI can answer questions. The bigger shift is that answering is no longer the hardest part. When answers can be generated instantly, the real bottleneck becomes: What exactly should be asked? What is the real problem behind the surface request? What constraints actually matter? What outcome is considered good enough? AI can generate many possible answers. But it still depends heavily on the quality of the question. A vague prompt creates vague output. A precise question creates leverage. In that sense, the person who defines the problem is now more important than the person who simply responds to it. The Problem Setter Is More Valuable Than the Problem Solver This idea may sound exaggerated at first, but it becomes obvious in practice. Suppose someone says: Optimize this system. That sounds like a reasonable task, but it is actually too weak to produce a strong result. Optimize for what? Cost? Latency? Reliability? Simplicity? Team productivity? Now compare it with this: We have a Node.js API running on AWS ECS. Under burst traffic, CPU throttling causes latency spikes. How can we reduce p95 latency without increasing infrastructure cost by more

Some of you may have noticed I disappeared a bit from the community over the last couple of weeks....