Systems | Development | Analytics | API | Testing

zymr

AI for Treatment Personalization: Use Cases, Benefits, and Implementation Guide (2026)

Apr 7, 2026 By Suhas Phartale In Zymr
Healthcare still runs on generalized treatment protocols, even though every patient is biologically and clinically different. Clinicians often make decisions under time pressure using fragmented data from EHRs, labs, and patient history. That leads to gaps such as delayed diagnoses, trial-and-error treatments, and inconsistent outcomes. At the same time, expectations have changed. Patients now expect healthcare to be as personalized as the rest of their digital experiences.
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bugfender

iOS App Clips: What They Are and How to Create One

Apr 7, 2026 By Aleix Ventayol In Bugfender
App Clips are one of the most under-appreciated parts of the iOS universe. Introduced with iOS 14 back in 2020, they allow users to sample the best features of an app without having to download it in full. Users explore the Apple ecosystem. Developers broaden their audience. Win-win, right? Well, bizarrely few devs are actually using App Clips right now. A lot of folks think they’re going to be overly complex and full of friction.
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speedscale

From Datadog to CI Tests: Catch Regressions Before Deploy

Apr 6, 2026 By Ken Ahrens In Speedscale
I worked in observability for years, and the same pattern showed up across teams. An alert fired, the on-call rotation scrambled, and everyone did what they had to do to stabilize production. Then came the retrospective. Once the immediate pressure was gone, the conversation shifted to one question: how do we make sure this never happens again? My friend Jade Rubick coined a name for that principle: DRI, “don’t repeat the incident”.
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Hevo's Next Evolution

Apr 6, 2026 By Hevo Data In Hevo
Every company has an AI roadmap. Very few have the data infrastructure to execute it. At Hevo Data, we've spent 8 years building pipelines that are reliable, simple, and transparent so 2,000+ data teams can build without second-guessing their data. We sat down with Manish Jethani, Amit Gupta, and Scott Husband to talk about what comes next. If your data isn't AI-ready, your roadmap stays a roadmap. We've re-engineered the platform to serve as the context engine your AI vision actually runs on. Because the models are only as good as the data underneath them.
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keploy

API Testing Strategies: A Complete Guide (2026)

Apr 6, 2026 By Keploy In Keploy
API testing strategies directly impact your release cycle. With 83% of web traffic flowing through APIs, even a single failure can break payments, dashboards, and user experience. Teams that invest in automated API testing do not slow down, they ship faster with confidence. A strong strategy goes beyond checklists. It defines what success looks like, where tests run, how data stays consistent, and how testing fits into CI/CD.
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integrate

Best Excel to CSV Converter Tools in 2026 (No Data Loss)

Apr 6, 2026 By Donal Tobin In Integrate
If you need to convert Excel spreadsheets to CSV formats without data loss, the answer depends heavily on your pipeline complexity, data volume, and transformation requirements. For teams moving structured data at scale, Integrate.io stands out as the most reliable platform, combining enterprise-grade ETL, low-code transformation, and robust file-handling to ensure full data integrity from source to destination.
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perforce

Understanding ISO/PAS 8800 for AI in Automotive Safety

Apr 6, 2026 By Perforce In Perforce
With the rise of AI use in vehicle software development, concerns arise around its presence in safety-critical applications, especially when it comes to functional safety and regulatory compliance. ISO 26262, the essential standard for automotive development that requires processes for managing, designing, and verifying safety-critical systems, still applies. However, it can fall short when applied to AI models, which are inherently non-deterministic and continuously evolving.
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kong

LLM Cost Management: How to Implement AI Showback and Chargeback

Apr 6, 2026 By Alex Drag In Kong
Every enterprise moving AI into production is about to face a familiar problem in an unfamiliar form: the cost explosion, but for LLMs. This is *very *similar to what happened with cloud. In the early days of cloud, teams spun up infrastructure with no visibility into who was consuming what. Finance got the bill. Engineering got the blame. No one had the data to make good decisions. It took years of hard-won FinOps discipline to fix that. LLM spend is on the same trajectory *and moving faster*.
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