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The AI Agents Hype: Loud Claims, Quiet Reality
The AI Agents Hype: Loud Claims, Quiet Reality
The hype says AI agents will replace developers. Reality is much quieter: value comes from workflow-first systems with bounded, auditable LLM steps.
This is how you avoid demo-driven engineering and ship systems that you can actually operate.
A CTO-level view, backed by DORA, METR, McKinsey, and Goldman Sachs.
CTO Architecture Review to Roadmap (Part 1)
When companies reach out to a Fractional CTO, they almost never start with a blank slate. They’ve already built something. It mostly works. It’s creaking in a few places. People are nervous about scaling, security, costs, or that “big rewrite” someone keeps lobbying for. And quite often, they’ve already done some kind of architecture review.
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Stop Reading Every Resume: Focus on Right Candidates
You already have an ATS. You already use job boards. You already spend too many evenings skimming resumes that all start to look the same after a while. The real problem isn’t getting more candidates into the funnel. The real problem is deciding who deserves your attention without burning out.
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Why ATS Filters Aren't Enough for Hiring Decisions
Applicant Tracking Systems (ATS) were supposed to make hiring simpler. They collect resumes, store candidates, and apply keyword filters so overwhelmed HR teams can quickly shortlist people who “match” the job description. The problem is that matching keywords is not the same as making good hiring decisions.
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5 Signs Resume Screening Is Burning Out Your HR Team
Resume screening is one of those tasks everyone agrees is important… and almost nobody actually enjoys doing. When it goes well, your team feels like a strategic partner to the business. When it goes badly, it turns into late evenings, rushed decisions, and a nagging fear that the best candidates slipped through the cracks.
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Surviving LLM Rate Limits: Building Backpressure
Once you move beyond a toy demo and start running real workloads on top of a large language model, rate limits stop being a theoretical concern and become a very practical constraint. At small scale you can mostly ignore them. At medium scale you start seeing occasional 429 errors and retriable failures. At larger scale your whole system can suddenly feel brittle: bursts of errors, retries piling up, and users waiting far longer than they should.
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Building Reliable AI Pipelines on Azure
Modern AI systems fail more often than most engineers expect. Not because the models are fragile, but because the infrastructure surrounding them is. Network latency, cold starts, concurrency spikes, and the notorious 429 rate-limit errors all come into play.
Anyone who builds LLM-powered systems quickly learns the same lesson: in production, retries are not optional, they're architecture.
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