David H Bebawy

Turns out companies are reimagining the entire SDLC around agents and attempting to move toward a world where engineers might not write a single line of code directly. I was talking to an engineering leader, and it seems many companies are preparing for a fully agentic SDLC. Repositories are starting to include Claude and agent-related files, MCPs are being built for internal tools, and much more is changing. Code reviews are getting automated, among other things. What times we are living in :)

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The 2025 "AI experiment" phase is officially over. 2026 brings the Efficiency Reset and agencies built for 10% growth are about to learn they're operating in a 2% market. The US staffing industry will grow to $183.3 billion in 2026, according to Staffing Industry Analysts. That's 2% growth. Not explosive. Not forgiving. Just steady, specialized, and unforgiving to agencies with bloated overhead. The winners won't be the ones with the most recruiters. They'll be the ones with Integrated Infrastructure - systems that scale capacity without scaling problems. Here's what's changing: → Margin compression in commoditized work (temp staffing plateaus) → Margin expansion in specialization (Legal, Defense, Tech retain pricing power) → Hybrid staffing models replacing fixed headcount → Predictive analytics replacing gut-feel capacity planning The 2025 mindset was "let's try AI tools and see what sticks." The 2026 reality is "we need infrastructure that survives slow growth." Most agencies are still building for boom cycles. They hire FTEs when demand spikes, then bleed cash when it dips. The flex capacity model - where you deploy specialists on-demand - survives volatility that kills fixed-cost operations. This is why we built Blink Hire as infrastructure, not staffing. Our clients scale up in 48 hours when Q2 lateral hiring spikes, then scale down without severance when it softens. Is your agency built for 2% growth, or still hoping for 10%?

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Our very own Ainsley Castro Charles joined CEO of EQ app, Marcus Sawyerr, on the AI in Recruiting podcast, and the conversation highlights one of the biggest issues hiring teams are navigating right now. Companies are adopting tools to move faster, yet the biggest gaps still come from old job requirements, outdated filters, and processes that remove capable people long before anyone learns what they can do. She talks through these moments with a level of practicality that matches what is happening inside most organizations today. If you are rethinking your approach to skills-based hiring or trying to understand how technology can support access rather than limit it, this episode is definitely worth watching. It offers a clear look at the decisions that shape a candidate’s path and how small changes in process and judgment make a measurable difference.

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How a three-person IT Ops team can support thousands of employees... AI news can be a bit clickbaity sometimes, but here’s a genuinely impressive use case: Remote saved $500K annually and supports thousands of employees with just a three-person IT Ops team. Zapier is hosting a session with Marcus Saito, Head of IT and Automation at Remote on how they use AI and automation to deflect 28% of IT tickets, save time, and scale ops without adding headcount. Register: https://lnkd.in/gGp9u5r8

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Companies are losing the talent war because they're fighting with yesterday's weapons. While you manually source & screen resumes, competitors deployed AI reconnaissance that changes everything: Here's the framework that shocked me after 30 years in staffing: While you process 50 resumes a week... Well-planned AI systems analyze 5,000 candidates daily. They don't just find more people faster. They find better ones that traditional methods miss. But most companies automate the wrong parts and get garbage results. It's all in the expertise. The secret isn't more AI. It's knowing where humans add value and where machines dominate. Here's the tactical framework that works: 1. Mission Planning: Document Your Recruitment Intel Feed the system examples of your best hires from the past 2 years. Include specific skills, career trajectories, and must-have qualifications. The AI learns your talent DNA before executing search missions. Most companies skip this phase and wonder why they get terrible candidates. 2. Execute Systematic Candidate Reconnaissance Deploy an automated search across LinkedIn, job boards, and other talent pools. The system enriches profiles and scores against your criteria. Qualified targets flow directly into your engagement pipeline. 3. Establish Human Command and Control AI handles volume and initial screening. Humans maintain oversight at critical decision points. Assess performance outcomes and adjust the AI. This hybrid approach delivers consistent evaluation while avoiding AI bias traps. 4. Deploy Performance Intelligence Track time-to-hire, cost-per-hire, and retention data. These numbers tell you if your operation is winning or just staying busy. Modern AI recruitment stacks cost less than legacy tools while delivering exponentially better results. This isn't about replacing quality recruiters. It's giving your best and brightest force multipliers so they can focus on talent relationships instead of search strings and resume screening. Over 3 decades in staffing... I've watched companies struggle with talent acquisition while missing obvious tactical advantages. That's where we come in with an unbiased outsider's perspective. And help you create solutions that seem impossible from the inside.

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I had a backend engineer drop out on day 4 of a hiring process. Said this: “If you can’t show me code quality or your CI/CD flow, I’m out.” Dev hiring is now candidate-led. If your process: Doesn’t expose them to your real stack Can’t show how you test and deploy Feels like gatekeeping... They’ll move on. To someone who treats hiring like a preview, not a pop quiz.

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“I threw in one or two applications for big tech companies… and realized this is not the game and I don’t even know what the game is.” That line came from one of our cohort members last week. And it connects directly to something we talked about on day one: the integrity gap. The integrity gap is the distance between who you want to become and who you are today. It’s the space between: – The career you say you want – And the effort, clarity, and strategy you’re actually bringing For many people in a job search, the integrity gap shows up like this: <> They want a high-performance role. <> They want to be taken seriously. <> They want to compete at a high level. But they’re playing a game they don’t understand. Submitting applications without knowing how hiring decisions are made. Talking about themselves in interviews without understanding what the company actually needs. Listing skills and hoping something lands. That’s not a talent issue. It’s a clarity issue. If you don’t understand the game, you can’t close the gap. When you walk into an interview and start describing your accomplishments without first getting context, you’re throwing darts in the dark. You’re assuming what matters. A better approach is slower and more disciplined: -- Ask what success looks like in the role. ---- Ask what problems the team is trying to solve. ------ Ask what separates top performers from average ones. Now you’re not guessing. You’re aligning. And alignment is how the integrity gap closes. The same principle applies beyond interviews. You can’t say you want to be excellent and then avoid uncomfortable conversations. You can’t say you want a different life and then default to the easiest path. The long way — the deliberate way — is often the real shortcut. It builds capability instead of just outcomes. None of this is about hype or intensity. It’s about coherence. Does your behavior match your ambition? If you’re in a transition right now, that’s the real question. Where is your integrity gap — and what would it look like to close it intentionally? Highlight reel of the Week-1 Pursuit Cohort Lesson

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The irony of being a Principal Platform/DevOps Engineer is it isn’t about being better “technically”. It’s about: 1. Better planning 2. Figuring out how to prototype something new 3. “Telling the future” of why something won’t work 4. Being confident, but staying humble in your analysis It’s really about figuring out how something should be done vs doing the thing. #kubernetes #devops #platformengineering

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💡 The difference between mediocre and exceptional AI results comes down to one skill: prompt engineering. Peachtree VA developed a comprehensive blog with a foolproof prompt engineering framework and ready-to-use prompts that eliminate the guesswork. This resource delivers immediate value to small businesses and virtual assistants seeking to integrate AI into their workflows without sacrificing quality. Access the complete blog in the comments. ⬇️ #AI #PromptEngineering #Blog #PeachtreeVA #ClientSuccess

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The EnFi, Inc team just launched another major customer! I’m still amazed seeing in action what production-level Agentic Architecture unlocks for both customers and platforms. What it means in practice: No more months of ETL. Connect, understand, act, day one. Immediate value: Immediate ~30%+ time and efficiency compression through automation across the process. Step-function jump: Application processing from 1–2 days ➜ ~7–10 minutes. In software terms, that’s wild. Flexes to new use cases. We’re no longer stuck in the rinse-and-repeat of onboarding ➜ product ➜ engineering to ship value. Feedback is built-in. Agents and users surface issues in flow; improvements feed straight into the pipeline. Fixes in days or weeks, not months or release cycles. That’s PLG, the way it should be. Super excited for what’s next, and incredibly grateful to our earliest customers for seeing (and shaping) what’s possible. We are live, this is real, and we are scaling a digital workforce for credit. And it makes me realize, Agentic is the new paradigm for software and product led growth. #AI #Agentic #Fintech #Lending #PLG #Automation #TimeToValue

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Ignition K.K. 𝗛𝗼𝘄 𝘄𝗶𝗹𝗹 𝗔𝗜 𝗿𝗲𝗮𝗹𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗲 𝗿𝗲𝗰𝗿𝘂𝗶𝘁𝗺𝗲𝗻𝘁 𝗮𝗴𝗲𝗻𝗰𝗶𝗲𝘀? There’s a lot of noise about AI replacing recruiters. The reality is probably simpler — and more practical. I see two big shifts coming for large agencies. 𝟭. 𝗠𝗶𝗱𝗱𝗹𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗴𝗲𝘁𝘀 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 Many middle layers today focus on reporting, KPIs, and internal oversight. But once AI has access to company data, it can instantly generate reports, forecasts, and performance insights. Much of that operational work becomes automated. So the value of managers has to shift. Less internal control. More external impact. Future leaders will need to win clients, open doors, and create opportunities — not manage spreadsheets. Rainmakers, not administrators. 𝟮. 𝗧𝗵𝗲 𝗹𝗼𝘄𝗲𝗿 𝗲𝗻𝗱 𝗼𝗳 𝘁𝗵𝗲 𝘃𝗮𝗹𝘂𝗲 𝗰𝗵𝗮𝗶𝗻 𝘀𝗵𝗿𝗶𝗻𝗸𝘀 Sourcing, screening, and admin are increasingly automated. That likely means fewer junior and mid-tier “process” roles, and more focus on higher-value work: senior hiring, niche searches, and trusted advisory relationships. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲 Recruitment won’t disappear — it moves up the value chain. The transactional parts get automated. The human, trust-based parts become more valuable. And if AI ever truly replaced all recruitment? It probably means it replaced most jobs too — and then there’d be nothing left to recruit for anyway. AI disruption will have essentially eaten itself and caused its own extinction...;) More likely, the industry just evolves. Agencies that adapt will thrive. Those that don’t will struggle. If you’d like to better understand the recruitment market in Japan and the options available to you, please feel free to get in touch Michael Craven #rec2rec #japanjobs #ignitionkk

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Most people think contractors want software. They don’t. They want time. They want control. They want to look their customer in the eye and say the price with confidence—without needing 6 tabs open, a spreadsheet, and a prayer. One of the biggest lessons I’ve learned building Handoff is this: 🟡 Software that saves time is not enough. It has to save time in the moment that matters. Not on Sundays. Not at the office. But in the driveway, right after the walkthrough—before the homeowner gets three more bids. That’s when trust is won or lost. So we stopped optimizing for “feature parity” with legacy platforms. We stopped chasing checklists. Instead, we asked: Where does time actually matter most for a remodeler’s business? 👉 We found it’s the 10 minutes after the job walk and before they drive off. If we can generate a clear, confident estimate in that moment—without extra taps or context switching—we change the entire sales game for them. It sounds obvious now, but it took us years to realize that timing—not just speed—is the killer feature. — Curious: If you’re building for SMBs or blue-collar pros, where have you seen timing trump features? Let’s compare notes.

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