Systems | Development | Analytics | API | Testing

bugfender

Debugging in Android Studio: Tools, Techniques, and Workflow

Jul 20, 2026 By Aleix Ventayol In Bugfender
Debugging Android apps can feel daunting given the sheer size of the ecosystem. However the suite of Android Studio debugging tools allows us to find, fix and prevent problems across our entire user base. Wherever they are, whichever devices they’re using. This guide will give you a practical workflow to maximize the functionality of Android Studio, and empower you to debug Android apps from first breakpoint to production issues.
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From Prompt to Report: Artifacts in Databox

Jul 17, 2026 By Databox: AI-powered analytics that answers back In Databox
Ask Genie a question. Get a report back, ready to share, with real data already in it. This is Genie Artifacts, Databox’s AI analyst turning a prompt into a finished document. Watch it build a report from scratch, convert that report into a slide deck with a single follow-up prompt, then get shared as a public link anyone can open, no Databox login needed. What you’ll see: Genie pulls from 130+ connected data sources, so every report and deck is built on numbers your team already trusts. No manual formatting, no rebuilding for a different format, no dashboard for the recipient to log into.
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kong

How to Proxy Every AI Traffic Pattern Through One Gateway

Jul 17, 2026 By Kong In Kong
Production AI no longer generates one kind of traffic. It generates four patterns, and most teams govern only one. **AI traffic management** starts with a single decision: **proxy AI traffic** through one control point instead of letting it flow straight from application code to model providers. Skip that step and security teams have no policy chokepoint, token spend climbs with no meter, and every new provider adds an integration nobody owns.
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hitachi vantara

The Reason Behind Stalled AI Projects

Jul 17, 2026 By Andy Gremett In Hitachi Vantara
As enterprises race to adopt AI, weak data foundations are preventing more than half (58%) of organizations in the United States and Canada from realizing value and contributing to an estimated $108 billion in wasted global AI investment each year, according to a report from Hitachi Vantara. The reason is rarely bad models or lack of ambition.
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qlik

Why It Matters: Data and AI Literacy Is Now a Business-Critical Skill

Jul 16, 2026 By Angelika Klidas In Qlik
One thing has become increasingly clear to me: the businesses that thrive in the AI era won't be the ones with the most data, they'll be the ones where every employee knows how to use it. The World Economic Forum's 2025 Future of Jobs Report names analytical thinking as the top core skill companies need today, and a 2024 Gartner survey found poor data literacy to be one of the top five obstacles to analytics success.
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bugraptors

Enterprise AI Testing Checklist: From Pre-Deployment Evaluation to Live Runtime Guardrails

Jul 16, 2026 By Kanika Vatsyayan In BugRaptors
While the benefits of LLM orchestration layers and autonomous agents are clear, they also bring a new set of non-deterministic failure modes that traditional unit testing cannot detect. A study by RAND Corporation found that 80.3% of AI projects fail to achieve the desired business outcomes, and this is because of issues in the data pipeline and model integration, not algorithmic problems.Using traditional software, you will get predictable results from known inputs.
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kong

Announcing Kong AI Gateway 2.0: Built for the Pace of Agentic AI

Jul 16, 2026 By Alex Drag In Kong
We have big news for platform and AI infra teams: *Kong AI Gateway 2.0 is available today in private beta*. It runs on its own dedicated runtime, ships on its own release cadence, and carries a completely reimagined user experience designed around the way teams actually build with AI: models, MCP servers, and agents as first-class citizens, not plugins bolted onto an API gateway.
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InsightSoftware

AI to Write Rules, or AI to Make Decisions?

Jul 16, 2026 By InsightSoftware In InsightSoftware
Last April FloQast, an American maker of accounting software, published something unusual: a detailed engineering post on Amazon Web Services’ machine-learning blog, co-authored with AWS personnel, explaining precisely how its AI-powered transaction-matching feature works under the hood. The post described cloud infrastructure, model selection, and the specific technique (generating matching rules from user-supplied examples) that powers its AutoRec product.
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bitrise

How we built a coding agent that lives in Slack, and the recipe to build your own

Jul 15, 2026 By Balazs Rostas In Bitrise
One of the busiest engineers at Bitrise doesn't have a laptop. It lives in Slack. Assign it a Jira ticket or @mention it in a thread, and it reads the requirements, posts a plan, spins up a cloud dev machine, writes and tests the code, opens a pull request, watches CI, reacts to review comments as they land and once the pull request merges, cleans up after itself and closes the ticket.
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