qAPI

Simplifying End-to-End API Testing.

[qAPI @testwithqapi]

@OpenAGIxd Yes our users do!

[qAPI @testwithqapi]

@vivoplt and plans to do a series A next month

[qAPI @testwithqapi]

@RohdeAli Yes our customers spent time earlier (before integrating qapi) fact checking and still manually debugging.

[qAPI @testwithqapi]

@heynavtoor PewDiePie always does things unconventionally

[qAPI @testwithqapi]

@chaosengineerr Coding with AI seems like a sweet spot

[qAPI @testwithqapi]

@neerajjj6785 Seems inaccurate

[qAPI @testwithqapi]

@MetaMask You should always backtest and set SLs the basic 101 for any trade

[qAPI @testwithqapi]

@elonmusk Keep testing and evolving

[qAPI @testwithqapi]

What are the 5 major API Testing problems you face today?

#API #APITesting #QualityEngineering #SoftwareTesting #DevOps #DeveloperTools #Microservices #AutomationTesting #APIMonitoring #qAPI

[qAPI @testwithqapi]

@NVIDIARTXSpark We'll look forward to using this

[Qyrus @QyrusAI]

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 𝐐𝐲𝐫𝐮𝐬 𝐂𝐚𝐧𝐝𝐢𝐝; a brand-new series dedicated to quick, raw, and completely candid interviews with the actual folks from Qyrus.

We’re 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:
☑️ 𝐓𝐡𝐞 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐏𝐫𝐨𝐛𝐥𝐞𝐦: Why scaling your test automation without actual context just creates unnecessary noise.
☑️𝐓𝐡𝐞 𝐂𝐚𝐭𝐜𝐡-𝐔𝐩 𝐓𝐫𝐚𝐩: Why waiting until the end of a cycle to find defects means you're already too late.
☑️𝐃𝐞𝐬𝐭𝐫𝐨𝐲𝐢𝐧𝐠 𝐒𝐢𝐥𝐨𝐬: How fragmented tools and disconnected strategies slow down enterprise teams.

True quality engineering isn't about validating outcomes after the fact; it’s 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.

👉 Watch the full interview here: https://t.co/viOaCKmbZs

#QyrusCandid #QualityEngineering #SoftwareTesting #DevOps #TechLeadership

[qAPI @testwithqapi]

@RoundtableSpace For all the right reasons!

[qAPI @testwithqapi]

@nvidia just dropped the blueprint for the next decade of AI at GTC Taipei.Jensen Huang took the stage and laid it all out:
• AI factories running at industrial scale
• Autonomous agents that don’t just chat — they do things
• Physical AI and robotics stepping into the real world
• A brand-new generation of personal computers built AI-native from the ground up This isn’t 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.

[qAPI @testwithqapi]

Every tool promises simplicity… but brings its own set of problems.

Confusing setups, broken flows, endless tweaks — it’s a pattern we’ve 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 didn’t have to be this way?

@testwithqapi we dont add another layer — 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 isn’t the one you manage — it’s the one you don’t have to think about.

#tool #efficiency #build #product #growth #scale #api #testing

[qAPI @testwithqapi]

@nvidia Exciting times for everyone

[qAPI @testwithqapi]

#thread

[qAPI @testwithqapi]

Most teams think building an LLM product is the hard part.

It’s not.

The hard part is knowing whether your AI actually works reliably once real users start using it.

Because in production, LLMs don’t 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 that’s 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:

👉 How do you continuously measure AI quality?

That’s why we created the qAPI LLM Evaluator.

Not just to “test prompts.”

But to help teams operationalize AI reliability at scale.

With qAPI LLM Evaluator, teams can:
✅ Benchmark multiple LLMs side-by-side
✅ Detect hallucinations automatically
✅ Compare prompt versions
✅ Evaluate consistency across multiple runs
✅ Create custom business evaluation metrics
✅ 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 can’t be a one-time activity.
It has to become a continuous feedback loop.
The companies that win with AI over the next few years won’t simply be the ones building LLM features the fastest.
They’ll be the ones that can:

measure AI quality consistently

detect failures early
improve outputs continuously
and build trust in production systems
That’s the shift happening right now.
We’re moving from:
“Can we build with AI?”
to:

“Can we trust what our AI produces?”

And that’s the future qAPI is building for.

#AI #LLM #GenAI #ArtificialIntelligence #MachineLearning #AIEvaluation #AIEngineering #RAG #LLMOps #EnterpriseAI #AIProducts #qAPI

[qAPI @testwithqapi]

#Thread

[qAPI @testwithqapi]

Everyone knows the domino effect…

But when it’s your APIs failing one after another, it’s not satisfying anymore.

One small failure can cascade across services:

Auth → Payments → Orders → UI… everything falls.

📉 Real example: In 2017, a small mistake in AWS S3 triggered a 4‑hour outage that affected a huge portion of the internet, costing businesses an estimated $150M+.

That’s how worse untested systems can get.

💡 At qAPI, we help teams test everything before production — so your risk of downtime stays minimal and your users never feel the impact.

👉 Don’t wait for failure to test your APIs.

Build Test and Then Deploy, before it breaks.

#APITesting #DominoEffect #DevOps #QualityEngineering #ShiftLeft #TryqAPI #buildinpublic

[qAPI @testwithqapi]

When working with APIs, not all test cases are created equal. Understanding what you’re testing makes all the difference 👇

🔹 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 — status, payload, and correctness.

🔹 Header Test Cases

Headers carry metadata about the request/response (like content-type, authorization, caching). Testing headers ensures:

✔ Proper authentication

✔ Correct content types (JSON, XML, etc.)

✔ Security and caching behavior

These are often overlooked but critical for reliability and security 🔐

🔹 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 ✅

💡 Why does this matter for a developer/tester?

✅ Helps you write granular, maintainable tests

✅ Catches bugs at different layers (data, metadata, structure)

✅ Improves API reliability and debugging speed

✅ 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