Latest news with #North-American
Business Journals
a day ago
- Business
- Business Journals
Near, Skilled, and Experts in AI: Latin America's Tech Edge
Firms like Glofy are helping U.S. tech leaders build high-performing distributed teams with strategic alignment. As demand for top-tier developers soars, U.S. companies are re-evaluating their outsourcing playbooks. Attention is shifting from longtime hubs such as India and Eastern Europe toward Latin America, where time-zone alignment and cultural affinity complement deep technical talent. The region now boasts a fast-growing pool of specialists in AI, machine learning, and data science, fluent in North-American business norms and English. Industries tapping that talent range from healthcare, insurance, and government to venture-backed tech startups. Glofy fills roles in mainstream stacks: .NET, Python, React and, increasingly, in enterprise platforms like SAP and Microsoft Dynamics, matching the market's appetite for large-scale system expertise. 'Glofy gives us both the speed and domain depth a high-stakes sector demands,' says Nicholas, Director of Technology at a company pioneering AI-driven analytics for high-risk patients. 'Their developers arrive trained and industry-savvy, cutting our onboarding curve and letting us execute fast where it matters most.' How Glofy Keeps Quality High Beyond simple staff augmentation, Glofy runs a four-stage talent funnel: a calibrated code challenge, live pair-programming with a senior engineer, a soft-skills and English assessment, and a cultural-fit interview focused on North-American agile rituals. Fewer than 3% of applicants clear all stages. The result is a bench of bilingual specialists ready to slot into daily stand-ups, sprint reviews, and late-night incident calls without friction. Clients report ramp-up times trimmed by 40% and turnover rates well below regional averages metrics that translate into real velocity and cost certainty on complex roadmaps. 'Companies aren't just outsourcing tasks anymore, they're seeking integration, innovation, and velocity, all powered by AI and business insight,' notes Gonzalo Rosa, CEO of Glofy. With a vibrant tech ecosystem and surging AI focus, Latin America is no longer an alternative; it's the go-to region for scaling with vision and precision. Among the companies leading this shift, Glofy stands out as a quiet force driving growth with purpose.
India.com
05-05-2025
- Business
- India.com
Vijaya Bhaskara Rao Builds Clouds that Speak through Silence
Vijaya Bhaskara Vijaya Bhaskara Rao measures cloud-transformation success by the silences it produces: incidents that never happen, critical bridges that stay empty, and product teams that focus on features instead of firefighting. Over a sixteen-year journey, he has made that quiet reliability his signature. His core formula—automate every repeatable task, expose telemetry from the first sprint, and embed rollback logic in the initial commit—proved itself when he stabilized complex WebSphere estates for a major North-American insurer. After documenting dependencies, scripting predictable build steps, and rehearsing fail-over drills, mean response time dropped by a quarter and critical incidents virtually disappeared. What seemed remarkable to observers became the opening chapter of a playbook Vijaya now applies to financial services, global development programs, and healthcare systems alike. He begins each engagement with an 'evidence sweep': CPU saturation curves, queue depths, garbage-collection pauses, and patch-level drift across hybrid estates. These metrics are codified into Terraform and Ansible so the baseline can be reproduced in minutes. With observability, security, and rollback traveling alongside business logic, inevitable surprises manifest as clear, actionable signals rather than cryptic stack traces. As deployments shift from monoliths to containers, Vijaya's guard-rails move seamlessly—blue-green templates, health probes, and admission-controller policies embed operational wisdom into every YAML file. 'My experience of implementing large-scale container platforms has taught me that reliability is engineered long before the first pod starts. Clear policies and consistent observability make scaling a routine, not a rescue,' he notes. The outcome is unambiguous: change-failure rates decline, audit findings shrink, and infrastructure spending ties directly to product velocity rather than emergency overtime. Foundations of Reliability Vijaya's respect for first principles was forged during nights dissecting heap dumps and SSL handshakes. Those sessions revealed that most outages begin as faint anomalies: a cache miss that stretches a response by fractions of a millisecond, or a thread pool that never fully drains after a processing surge. By converting such weak signals into concrete metrics, he transforms intuition into automation. At the insurer mentioned above, TLS configuration, queue-depth thresholds, and JVM parameters became version-controlled artifacts. Each pull request triggered quality-gates that validated performance budgets and encryption posture; the same dashboards satisfied security analysts and auditors, removing the traditional divide between development and compliance. Modernization projects follow a similarly deliberate rhythm. Before a workload is containerized, Vijaya ' characterizes ' it over several release cycles—capturing thread-dump signatures, database fan-in patterns, and latency histograms. Migration scripts appear only after this evidence review, unfolding in incremental stages that conclude with blue-green cutovers. Operational knowledge, thus embedded, allows a rollback to become a simple label switch rather than a high-stress intervention. 'Over decades spent refining middleware foundations, I learned that guard-rails widen roads instead of narrowing them. When baselines are peer-reviewed code, freedom and confidence scale together,' he reflects. Governance follows the same pattern of frictionless enforcement. Policy-as-code engines intercept non-compliant images long before production, yet developers self-serve fixes by updating the very manifests that failed validation. Vijaya insists that every failure path educate rather than punish; the platform is largely invisible until it must speak, and then it does so in the objective language of actionable telemetry. Automating Trust at Enterprise Scale A premier US payments network provided a definitive test of Vijaya's rigor: 120 interdependent services required cloud modernization under intense regulatory oversight. He began not with architecture diagrams but with a Terraform module encapsulating segmentation rules, encryption defaults, and cost-allocation tags. From that seed grew a disciplined ecosystem: infrastructure changes entered exclusively by Git pull requests, SonarQube gates enforced code hygiene, and AppDynamics fed live performance heat maps directly into sprint retrospectives. Within half a year, deployment lead-time fell from multiple release cycles to a single iteration, while change-failure rate dropped nearly forty percent—figures confirmed by the organization's risk committee. Central to this acceleration is Vijaya's 'deployment health score,' a composite index blending test coverage, latency budgets, and vulnerability scan results. Displayed simultaneously on engineering monitors and executive dashboards, the score transforms disagreements into data-driven decisions. Security specialists contribute policy updates through the same Git workflow as feature developers; auditors shift from periodic freezes to perpetual attestation; release managers realize that postponement adds no safety once the score is green. 'Lessons accumulated while modernizing regulated platforms showed me that transparency is the most effective compliance strategy. Shared metrics replace theatrical risk meetings with routine, evidence-based planning,' Vijaya explains. Guard-rails, however, remain collaborative. Pipeline templates are open to revision, rollback hooks publish human-readable remediation steps, and every auto-revert tag includes guidance for re-promotion after a fix. This shared stewardship rewires organizational culture: developers treat latency anomalies as solvable puzzles rather than finger-pointing episodes, and executives link infrastructure budgets to observable product gains instead of unverified assurances. Trust becomes the platform's most scalable feature. Cultivating Continuous Improvement Technical discipline sustains performance metrics; human dynamics sustain progress. Vijaya therefore embeds psychological safety into the delivery process. Fortnightly 'architecture cafés' invite junior engineers to present anomalies such as packet-loss blips or thread-starvation events while senior architects practice active listening. Whiteboard sessions conclude with action items that feed directly into the next sprint backlog. Six months into one such program, engagement surveys showed a double-digit rise in employees who felt comfortable admitting mistakes—a signal matched by an equally strong uptick in proactive pull requests focused on operational resilience. Incident management exhibits the same empathy-infused structure. Sev-1 bridges open with a timeline of facts, hypotheses, and next experiments. Annotated post-mortems become searchable artifacts feeding capacity-planning models and onboarding curricula. Rotating on-call ownership broadens domain knowledge, while recognition of small wins—an extra unit test here, a tighter readiness probe there—compounds into more resilient code bases. As developers internalize a culture where evidence beats ego, release cadence accelerates without sacrificing stability. Budget committees notice the shift: automation investments correlate with lower rework costs and higher customer satisfaction scores. Retention charts improve as night-time alerts decline, validating Vijaya's belief that robust platforms and healthy teams reinforce one another rather than compete for attention. Engineering for an AI-Powered Landscape With guard-rails and culture firmly in place, Vijaya now pursues predictive operations. He pilots large-language-model prompts that transform plain-language governance—' encrypt all customer data in transit and at rest '—into Open Policy Agent rules automatically inserted into CI pipelines. Initial results compress compliance rollout from several iterations to a handful of days and identify configuration drift within hours. Parallel telemetry pipelines feed multivariate anomaly detectors that correlate Kubernetes events, database latencies, and CI telemetry. Pilot clusters have already flagged the majority of high-severity incidents at least two hours early, granting engineers the luxury of graceful remediation instead of crisis intervention. Reinforcement agents further refine autoscaling thresholds based on historic diurnal patterns, trimming compute expense while maintaining latency objectives. Chat-ops bots assemble incident updates by summarizing Grafana traces, linking them to the pull requests that introduced regressions, and proposing rollbacks with a single click. Vijaya cautions that algorithms amplify existing discipline rather than substitute for it, yet within ecosystems where every log line and policy rule is version-controlled, AI becomes a force multiplier of human insight. His roadmap envisions predictive insights as routine, compliance expressed conversationally, and engineering creativity redirected toward higher-order abstractions such as user experience and domain modelling. By preserving clean seams—data, policy, and remediation exposed as stable APIs—Vijaya ensures that machine reasoning integrates naturally instead of via brittle glue code. A Vision Anchored in Quiet Excellence Across continents and industries, Vijaya's blueprint remains consistent: automate the obvious, illuminate the unknown, and keep users blissfully unaware of the machinery beneath. The platforms he architects are praised not for flash but for the silence they foster—silence that signals predictability. Operational expenditures track downward because waste is measurable and pruned; release trains accelerate because guard-rails eliminate hesitation; auditors close findings swiftly because evidence is innate, not after-the-fact. A ' quiet platform,' in Vijaya's lexicon, does not hide problems—it surfaces only the right ones to the right people at the right moment. As enterprises accelerate toward an AI-shaped horizon, Vijaya's principle—evidence first, empathy always—offers a pragmatic compass. His career demonstrates that resilience and velocity are not opposing endpoints but co-products of disciplined automation and human-centric culture. By weaving those values into every line of code and every team ritual, Vijaya Bhaskara Rao ensures that the future of cloud will continue to speak in a language of calm, measurable confidence
Boston Globe
21-03-2025
- General
- Boston Globe
I don't feel NRE (new relationship energy)
I did the work over the last year. I'm definitely in a better place than I was before. I met a woman I was interested in and we started dating. And this time, everything just feels … different? And I'm not sure if it's in a good way or a bad way. Or just a different way. Previously when I fell for someone, I fell hard . The feelings were intense and often obsessive. I thought about the person all the time. The 'new relationship energy (NRE)' was strong, hot, and compelling. But this time, its less like Old Faithful and more like a pleasantly warm bath. Let me be clear: She is easily the hottest woman I've ever dated. Smart, an athlete, a sexy job — the works. This is definitely not a problem of attraction. Advertisement But the lack of intense NRE is throwing me off. Is this warm-feeling excitement normal? It feels like I skipped right over the emotional honeymoon phase. Which is not bad, just weird for me. Related : There are a few other factors that might be at play: As a culture, generally, marriage and relationships can be more practical than emotional here. Young people try to give love relationships a try, and then sometimes marry for practicality. It's much more about alignment of values and personal goals. Indeed, that was basically her entire reason she asked me to go steady: 'We envision similar futures for ourselves and we get along really well.' After we'd been dating for a month, she asked me to move in. Which leads to the second potential factor: She discovered her sexuality late in life, and has experience with flings but not serious relationships. So when she asked me to move in, I had to be the one to explain the typical North-American dating 'timeline' — how long until people move in, marry, meet families, etc. Despite her being more than five years older, when it comes to navigating healthy emotional relationship practices, I am definitely the partner driving the bus. And to be fair, there's probably a lot of me projecting Anglo-European relationship expectations onto this relationship. Advertisement I love spending time with her. The sex is great, we make each other laugh, we enjoy going out and doing all sorts of activities together, and she's right: We do have similar visions for our future. But it just feels different than my previous relationships. Is it normal to not feel obsessive or compelled to be together? Is this the *actual* normal, and all my previous relationships were driven by unhealthy emotional dynamics? SMITTEN (AND OVERTHINKING IT??) A. There is no 'actual normal' that applies to all relationships. But sure, the feeling of being in love might feel different as you work on yourself. Drama can feel exciting. If your new relationship is stable and happy, it might seem less intense and special. But that's not necessarily the case. Related : My takeaway from your letter? You're learning about this woman while getting to know yourself. You're discovering whether the connection will fizzle or become something stronger and more important in your life. Can you just do that? Instead of trying to ask big questions about whose experience makes them more knowledgeable and whose culture is dictating the pace of the relationship? Your partnership, however it plays out, will be unique. You can do whatever you want at the pace you want. Instead of explaining how others take big steps where you're from, tell her what feels right for you . Ask her what she likes. Advertisement Trust me, in the US, there is no one path or pace. I know people who move in together at three months and others who try at three years. Also, in the US, we might frame everything as love, but rent, expenses, and family values dictate our choices, too. Geography is also key. The timeline for people in Massachusetts is different than for those in Wyoming. Instead of asking yourself 'Is this normal?,' try, 'Do I like this? Am I having a good time? Do I want more of it?' So far, it seems like your answers would be 'yes, yes, and yes.' Sounds great to me. MEREDITH READERS RESPOND: Your prior relationships that had this NRE did not end up panning out, did they? You took time to work on yourself, and now you're in a new relationship that feels different, but maybe that's because it's the right relationship. Feeling different isn't necessarily a bad thing. Also this relationship seems like it is still relatively new, and there are cultural differences you may just have to get used to, so stop overthinking it and continue to get to know your partner and see where the relationship evolves. THEGOODPLACE20 Even without cultural differences, every single person no matter where they're from is an INDIVIDUAL with their own wants/needs. BKLYNMOM Related : Every love is different. What you refer to as NRE may just be an emotional word for obsession. Now that you've worked on some of your issues, you may be obsessing less and enjoying more. Stop worrying about things that are going well. Advertisement WIZEN I'll tell you this as an example: Long ago I would only be interested in women who gave me that 'strong' feeling. That huge crush feeling. In every case the relationships were not healthy or they were never interested in me. Finally I decided I had to do something different. So I decided that I would ask women out who were attractive but also good people, people that aligned with my life, who I enjoyed spending time with. But none of them gave me that 'crush' feeling. This was on purpose. Those relationships ended up being much better relationships. So I called that 'intense crush feeling' my 'bad picker' and I never listened to it again. JSMUS Send your own relationship and dating questions to or Catch new episodes of wherever you listen to podcasts. Column and comments are edited and reprinted from .
