What happens when you strip away the corporate scripts, the rehearsed slide decks, and the standard talking points?
You get the absolute truth about where software engineering is heading.
Welcome to the launch of ๐๐ฒ๐ซ๐ฎ๐ฌ ๐๐๐ง๐๐ข๐; a brand-new series dedicated to quick, raw, and completely candid interviews with the actual folks from Qyrus.
Weโre sitting down with our engineers, leaders, and thinkers to get their unfiltered perspectives on the realities of modern engineering.
For our very first episode, Senior Platform Engineer, Harshitha N takes the interviewer's seat to have a real, pull-no-punches chat with @RaoulKumar, VP of Product Strategy.
They dive straight into the big topics:
โ๏ธ ๐๐ก๐ ๐๐จ๐ง๐ญ๐๐ฑ๐ญ ๐๐ซ๐จ๐๐ฅ๐๐ฆ: Why scaling your test automation without actual context just creates unnecessary noise.
โ๏ธ๐๐ก๐ ๐๐๐ญ๐๐ก-๐๐ฉ ๐๐ซ๐๐ฉ: Why waiting until the end of a cycle to find defects means you're already too late.
โ๏ธ๐๐๐ฌ๐ญ๐ซ๐จ๐ฒ๐ข๐ง๐ ๐๐ข๐ฅ๐จ๐ฌ: How fragmented tools and disconnected strategies slow down enterprise teams.
True quality engineering isn't about validating outcomes after the fact; itโs about actively shaping them from the start.
Check out the teaser to see what we mean, and then click the link to watch their full conversation.
๐ Watch the full interview here: https://t.co/viOaCKmbZs
#QyrusCandid #QualityEngineering #SoftwareTesting #DevOps #TechLeadership
@nvidia just dropped the blueprint for the next decade of AI at GTC Taipei.Jensen Huang took the stage and laid it all out:
โข AI factories running at industrial scale
โข Autonomous agents that donโt just chat โ they do things
โข Physical AI and robotics stepping into the real world
โข A brand-new generation of personal computers built AI-native from the ground up This isnโt hype. This is the shift from software to full-stack intelligence infrastructure.If you want to see where AI is actually headed (not the headlines, the real roadmap), watch the full keynote.
Every tool promises simplicityโฆ but brings its own set of problems.
Confusing setups, broken flows, endless tweaks โ itโs a pattern weโve all accepted.
You spend more time fixing the tool than building what actually matters.
Workarounds become workflows, and complexity becomes normal.
But what if it didnโt have to be this way?
@testwithqapi we dont add another layer โ we remove the noise.
No extra setup, no headache, no unnecessary features you never use.
It simply fits into your flow, exactly where you need it.
Clean, seamless, and built to just work.
Because the best tool isnโt the one you manage โ itโs the one you donโt have to think about.
#tool #efficiency #build #product #growth #scale #api #testing
Most teams think building an LLM product is the hard part.
Itโs not.
The hard part is knowing whether your AI actually works reliably once real users start using it.
Because in production, LLMs donโt usually fail with obvious crashes.
They fail quietly:
hallucinating information
giving inconsistent responses
generating confident but incorrect reasoning
drifting in quality over time
behaving differently after prompt or model updates
And thatโs exactly where many AI products break.
Right now, thousands of teams are rushing to ship copilots, AI agents, RAG systems, internal assistants, and GenAI workflows.
But very few are solving the most important problem:
๐ How do you continuously measure AI quality?
Thatโs why we created the qAPI LLM Evaluator.
Not just to โtest prompts.โ
But to help teams operationalize AI reliability at scale.
With qAPI LLM Evaluator, teams can:
โ Benchmark multiple LLMs side-by-side
โ Detect hallucinations automatically
โ Compare prompt versions
โ Evaluate consistency across multiple runs
โ Create custom business evaluation metrics
โ Measure reliability before production rollout
Because AI systems cannot be treated like static software anymore.
Models evolve.
Prompts drift.
Outputs change.
Context changes.
User behavior changes.
Which means evaluation canโt be a one-time activity.
It has to become a continuous feedback loop.
The companies that win with AI over the next few years wonโt simply be the ones building LLM features the fastest.
Theyโll be the ones that can:
measure AI quality consistently
detect failures early
improve outputs continuously
and build trust in production systems
Thatโs the shift happening right now.
Weโre moving from:
โCan we build with AI?โ
to:
โCan we trust what our AI produces?โ
And thatโs the future qAPI is building for.
#AI #LLM #GenAI #ArtificialIntelligence #MachineLearning #AIEvaluation #AIEngineering #RAG #LLMOps #EnterpriseAI #AIProducts #qAPI
Everyone knows the domino effectโฆ
But when itโs your APIs failing one after another, itโs not satisfying anymore.
One small failure can cascade across services:
Auth โ Payments โ Orders โ UIโฆ everything falls.
๐ Real example: In 2017, a small mistake in AWS S3 triggered a 4โhour outage that affected a huge portion of the internet, costing businesses an estimated $150M+.
Thatโs how worse untested systems can get.
๐ก At qAPI, we help teams test everything before production โ so your risk of downtime stays minimal and your users never feel the impact.
๐ Donโt wait for failure to test your APIs.
Build Test and Then Deploy, before it breaks.
#APITesting #DominoEffect #DevOps #QualityEngineering #ShiftLeft #TryqAPI #buildinpublic
When working with APIs, not all test cases are created equal. Understanding what youโre testing makes all the difference ๐
๐น Response Test Cases
These validate the entire response body. You check if the API returns the correct data structure, values, and format. Think of it as verifying the complete output โ status, payload, and correctness.
๐น Header Test Cases
Headers carry metadata about the request/response (like content-type, authorization, caching). Testing headers ensures:
โ Proper authentication
โ Correct content types (JSON, XML, etc.)
โ Security and caching behavior
These are often overlooked but critical for reliability and security ๐
๐น JSON Path Test Cases
These focus on specific fields inside the response using JSONPath expressions. Instead of checking everything, you validate targeted values like:
$.user.id == 101
Perfect for precise assertions and scalable automation โ
๐ก Why does this matter for a developer/tester?
โ Helps you write granular, maintainable tests
โ Catches bugs at different layers (data, metadata, structure)
โ Improves API reliability and debugging speed
โ Makes you more effective in test automation frameworks
In short: knowing what to validate is just as important as knowing how to validate.
#APITesting #SoftwareTesting #QA #Automation #Developers #TestingTips #dev #build #tools #product #launch #api #code #nocode #testers