Steve Budarz

One of the quickest ways to spot a Rust team in an interview loop is the first question they ask. It’s almost never, “What are we building next?” It’s usually, “What breaks first if this goes wrong?” Throughput. Latency. Failure modes. Edge cases. Most stacks start with features. Rust teams start with consequences. You learn a lot about an engineering culture from that alone.

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Here’s the uncomfortable truth about why junior developers can’t get hired right now. Many juniors wonder why they can’t land their first role. The reality? You’re often competing with candidates who have 10+ years of experience and advanced degrees. Employers are naturally drawn to these “safer” bets—but that doesn’t mean juniors are out of luck. Understanding this gap helps you strategize smarter, not blame yourself. My take: focus on building projects that solve problems that would mimic real problems you would likely face in the workplace. Connect with senior developers, recruiters and even directors and start a conversation. This will help you figure out what “real problems” would look like and from there you’d be able to see what projects may help you land that next role. Question: Did you have a project that helped you stand out or land a role? #softwaredevelopment #softwareengineering #jobmarket #competition #tech

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Modern software engineers don't just code, they design systems that scale. Here is how a real-world video streaming platform like YouTube or Netflix handle millions of videos and viewers seamlessly? Building a modern video streaming platform is more than just storing and playing videos. It requires a well-orchestrated system design that can handle uploads, processing, metadata, delivery, and personalized recommendations all at scale. This visual breaks it all down: 1. Upload pipeline: Videos are uploaded via SDK/UI, passed through load balancers, and stored in S3 2. Processing pipeline: Services split, filter, convert, and encode videos into various resolutions 3. Metadata & storage: Hygraph Metadata Store and Cassandra manage all video-related data 4. Messaging & coordination: Kafka coordinates between upload, processing, notification, and stats services 5. Discovery & engagement: Search, homepage, and recommendation services are powered by ElasticSearch 6. Delivery: Processed content is served to subscribers via CDN for fast and efficient streaming This is a great reference for anyone building scalable, media-heavy applications or preparing for system design interviews. 💻 Are you serious about landing your next tech offer in 2025? 📩 Want access to our exclusive real job opportunities list (from my strong network of recruiters and hiring managers)?  🔹Join our 1:1 mentorship program Everyone Who Codes (EWC) has a simple mission: helping engineers get hired! 💼   Follow Lakshmi Marikumar for tech career & job search insights.   Subscribe to our free newsletter on Substack: https://lnkd.in/gN83YA4H #systemdesign #scalability #softwareengineering #techjobs #interviews #softwareengineer #softwaredeveloper #MAANG #tier2 #jobsearch #mentorship #ai #aitools #aiagents #dsa #mockinterview #machinelearning #aiengineer #MAANGMentors #criticalthinking #problemsolving

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Before you hire an engineer, answer this: What must this person deliver in the first 90 days? If you can’t define that clearly, you’re not ready to open the role. Instead of writing this: “Senior Full-Stack Engineer with React and Node.” Write this: – Ship two production-ready features per month. – Reduce page load time by 30%. – Improve reliability with monitoring and alerts. – Clean up high-risk technical debt in the billing flow. This is performance-based hiring. Strong role definitions include: Role mission 30/60/90 measurable outcomes Core competencies required Non-negotiables (overlap hours, communication standards, etc.) When outcomes are clear: -Sourcing improves. -Screening becomes faster. -Interviews become sharper. -Onboarding becomes aligned. Most hiring mistakes start before the first interview. They start with a vague job description. What are the first 3 measurable outcomes in the next tech role you plan to open?

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✨ The #NVIDIARubin platform is engineered to master multi-step problem-solving and massive long-context reasoning workflows at scale. Built using extreme co-design, Rubin delivers more AI tokens per watt and lower cost per token compared to the previous architecture. ✔️ 50 PFLOPS of NVFP4 inference ✔️ 22 TB/s of HBM4 bandwidth ✔️ 3.6 TB/s of NVIDIA NVLink bandwidth per GPU Learn more about NVIDIA Rubin ➡️ https://bit.ly/4r1SzJ8

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Software engineers fear the AI that will take their job while the real danger I see should be in companies and solution owners themselves. Products that you spent years and tons of dollars for buildng selling it for whatever the number of subscriptions is can now be easily cloned and it costs nothing but the Gen AI subscription and some small resources. I am really curious of how the scene will look like after maybe Q2 of 2026. The development that business owners needs to follow “not to fail in the common nokia example” is now talking days not years like all used before!! #ArtificialIntelligence #GenerativeAI #SaaS #StartupLife #ProductManagement  #SoftwareEngineering #TechTrends #DigitalTransformation  #Innovation #FutureOfWork #Entrepreneurship  

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Every year, software engineers spend hundreds of hours: - Building their skills. - Preparing for interviews. - Navigating endless rejections. - Finally, landing the job. It’s exhausting. Thankless. Sometimes demoralizing. But here’s the dirty secret: When you finally get hired, recruiters pocket tens of thousands of dollars in placement fees, while you get… nothing. That imbalance is what the founders of Taro (YC S22) decided to flip on its head. Over the last 3 years, Taro quietly built a community of 150,000+ engineers, a trusted space where developers learn, prepare, and connect. Now, they’re launching the first recruiting network that pays you to get hired. Upload your résumé → Taro calculates a hiring bonus → you get a cash reward when you land a job with one of their partner companies. The twist? Instead of recruiters hoarding fees, Taro shares a portion directly with the engineer. Most recruiting firms spend thousands on candidate acquisition, spamming inboxes and burning trust. Taro did the opposite: they built community first. By keeping acquisition costs near zero, they can flip the model and reward the actual talent driving the value: the engineers themselves. The founders, former hiring managers at Meta, Pinterest, and Robinhood, knew what top talent truly cared about: fairness, recognition, and opportunity. Recruiting is a $200B+ industry, but engineers, the ones who power it, see none of that upside. Taro’s launch signals a new era: - Where communities replace cold calls. - Where incentives are shared, not hoarded. - Where getting hired doesn’t just change your career, it puts money back in your pocket. Stop letting recruiters profit from your hard work. Start getting paid when you get hired. joinTaro.com/get-a-job ------------------------------------------------------------------------------------ Stories. Strategies. Shared Opportunities. That’s the future we’ll keep telling.

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Former GitHub CEO - Thomas Dohmke just came out of stealth with Entire : first open-source CLI called Checkpoints that captures agent context alongside code every time you commit Thomas Dohmke, former GitHub CEO, raise $60M in seed funding for his new startup Entire — now valued at $300M — to rethink how we build software in the age of AI agents. This isn’t just another dev tool — Entire is launching its first open-source CLI called Checkpoints that captures agent context alongside code every time you commit. That means the why, how, and what behind AI-generated code won’t be lost in chat logs or ephemeral sessions anymore. Why this matters for the AI-driven future of software development: Context as first-class data: Entire CLI hooks into your Git workflow and stores session history (prompts, responses, files changed, reasoning) as versioned checkpoints alongside code — making agent work traceable, reviewable, and auditable. Bridging human + agent workflows: Traditional tools assume humans write code — but with AI agents writing more every day, we need tooling that treats agent reasoning as part of the development lifecycle. Entire is setting that foundation. Open ecosystem & transparency: By launching this as open source, Entire is giving the community a way to build standards for agent context tracking rather than locking this capability in proprietary platforms. This is a huge milestone in the evolution of developer experiences — turning AI agent output from opaque artifact into traceable, collaborative process that humans can understand and improve. If you’re working with AI assistants, multi-agent systems, or just thinking about how to scale developer productivity in 2026 and beyond, this is worth a close look. https://lnkd.in/gZ8rEm9s #AI #DeveloperTools #OpenSource #SoftwareEngineering #CLI #AIagents #SoftwareContext

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The vibe coding experiment is over. Results are in. Five months ago, the narrative was simple: prompt an AI, get an app, no coding knowledge required. Software engineers were finished. By now, thousands tried it. Generated apps by the hundreds. Maintaining a single one? Disaster. The code looks clean until you need to change something. AI hallucinates nonexistent libraries. Technical debt compounds hourly. Reddit filled with cautionary tales. Here's what this means for hiring: The gap between AI-assisted developers and prompt-only builders just became visible and permanent. Real engineers use AI as a force multiplier—faster prototyping, boilerplate acceleration, debugging support. They read the output, understand the architecture, own the decisions. Prompt-only builders hit a ceiling the moment anything breaks or needs adaptation. No foundation to debug from. No systems thinking to guide the fix. What to prioritize now: fundamentals over frameworks, architectural thinking over syntax memorization, the ability to evaluate and modify AI output rather than blindly accept it. Ask candidates how they've debugged AI-generated code. Ask what they do when the AI is wrong. The tool changed. The job didn't. Engineering is still engineering. First AI bubble popped. Won't be the last.

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