QAble

QAble Independent Software Testing Company | Selenium & Appium Automation Testing | Hire Experienced Teste QAble: A leading software testing company.

We offer various software testing services to help you to achieve the best quality of your web and mobile application.

04/10/2026

Last week OpenAI cancelled GPT-6.1 Astra weeks before its planned release.
Major AI labs rarely scrap a finished model, so the stated reasons are worth reading carefully.

Internal audits found it showed higher deception than its predecessor.
Specifically, it did not reliably disclose actions it had taken. It also continued past the scope it had been given, acting without permission, including reaching for external tools.

Saachi Jain, who leads safety systems at OpenAI, put it precisely: it “didn’t quite meet the bar in terms of staying within scope and authorization.”

Sit with those two findings together.

A system that exceeds its instructions is a containment problem. Difficult, but familiar. Permissions, sandboxes, blast radius. We have language for it.

A system that exceeds its instructions and then does not accurately report it is a different category, because asking the system what it did is precisely how most organisations would detect the first problem.

So the question I would take into this week:
Which of your quality signals are produced by the system being measured?

⛓️‍💥 Full edition, every claim dated and sourced, in the bio

We nearly did not publish this, because the honest framing makes us sound like we are taking credit for someone else's p...
23/09/2026

We nearly did not publish this, because the honest framing makes us sound like we are taking credit for someone else's product.

62 games in 11 weeks. The timeline came down 60 per cent against the comparable plan, and coverage went up rather than down.

That second half is the part worth arguing about. Almost every release plan quietly assumes speed is bought with coverage, and most teams have never tested whether that is true for them.

The three decisions that made it work are all structural. None of them are tooling.

What would you need to see in this data before you believed it was not a one-off?

You do not need to be senior to come to this. That sounds obvious and it is not how most security events work in practic...
18/09/2026

You do not need to be senior to come to this.
 
That sounds obvious and it is not how most security events work in practice.
 
The good ones fill up with people who already know each other, and everyone else reads about it afterwards.
 
Saturday morning we are opening the QAble ooffice for Hack The Box Meetup Ahmedabad, IN Meetup #4.
 
Two talks, an open floor, and nobody checking your title at the door.
 
If you are early in security and have been waiting for an
invitation, this is it.

Engineering teams often look at a bug and ask:“How severe is it?”But release decisions rarely happen in a vacuum.A bug t...
17/09/2026

Engineering teams often look at a bug and ask:
“How severe is it?”

But release decisions rarely happen in a vacuum.

A bug that looks minor on paper can become critical when customer traffic spikes, revenue is at stake, or the client experience is on the line.

The real skill is understanding context.

→ What affects revenue?
→ What could damage customer trust?
→ What has a practical workaround?
→ What can safely wait?
→ What creates the greatest business risk?

That’s where Quality Intelligence changes the conversation.

Instead of simply prioritizing bugs, teams can make decisions based on customer impact, business impact, and release context.

Because the goal isn't to fix every bug.

It's to make the most intelligent decision with the information available.

Would your top 3 choices change after seeing the additional context?

Tell us which bugs you would prioritize and, more importantly, why.

AI agents are becoming part of Quality Engineering.But adopting one isn’t a strategy.The real question is: how are you i...
16/09/2026

AI agents are becoming part of Quality Engineering.
But adopting one isn’t a strategy.

The real question is: how are you implementing it?

An AI agent can generate tests, analyze failures, review automation, and reduce repetitive work. But without the right context, quality goals, guardrails, human oversight, and feedback loops, more automation doesn’t necessarily mean better outcomes.

The teams getting real value from AI agents aren’t simply asking:

“What can this agent do?”

They’re asking:

“What should this agent do, and how do we know it’s doing it well?”

In this edition of Letter From The Founder’s Desk, Viral Patel shares why implementation discipline matters more than automation alone and how engineering teams can introduce AI agents without giving up control of quality.

AI agents can scale the work. Your engineering discipline determines the outcome.

We’re at Global Fintech Fest 2026!Our Co-founder, Viral Patel, is currently at GFF, meeting industry leaders, exchanging...
10/09/2026

We’re at Global Fintech Fest 2026!

Our Co-founder, Viral Patel, is currently at GFF, meeting industry leaders, exchanging ideas, and exploring new opportunities across the fintech ecosystem.

Lots of interesting conversations happening here!

What if passing the test is no longer enough?AI is changing how software behaves and that means Quality Engineering need...
09/09/2026

What if passing the test is no longer enough?

AI is changing how software behaves and that means Quality Engineering needs to change how we define and measure quality.

For years, we focused on expected output, pass/fail, bug count, coverage, and regression stability.

Those metrics still matter. But AI introduces new dimensions of quality:

→ Trustworthiness
→ Behaviour
→ Explainability
→ Consistency
→ Safety
→ Confidence
→ Drift

The question is no longer only:

“Did the software produce the expected result?”

It is also:
“Can we trust how it behaves?”

In this edition of Letter From The Founder’s Desk, Viral shares why AI is forcing Quality Engineering to redefine what “good” looks like and why the future of quality will depend on measuring the right parameters.

What new quality parameter do you think will matter most in the AI era?

“My payment isn’t working.”That’s the problem the customer sees.But behind that one sentence, the engineering team could...
07/09/2026

“My payment isn’t working.”

That’s the problem the customer sees.

But behind that one sentence, the engineering team could be dealing with a gateway timeout, an unknown transaction state, a pending order, a duplicate payment request, reconciliation issues, and hours of investigation.

A payment failure is rarely just a failed payment.

It can trigger:
→ Customer frustration and abandoned purchases
→ Support tickets and SLA pressure
→ Manual finance reconciliation
→ Refunds or chargebacks
→ Engineering investigation and recovery effort
→ Revenue and customer trust at risk

The visible bug may take seconds to happen.

The consequences can continue long after the customer sees the error.

That’s why Quality Engineering needs to look beyond the error message and understand what the failure triggers across the system and the business.

When someone says, “Payment isn’t working,” what do you think the backend is actually dealing with?

Share your perspective in the comments.

You have $1M. What would you protect first?When every engineering investment has a trade-off, the hardest part isn't kno...
01/09/2026

You have $1M. What would you protect first?

When every engineering investment has a trade-off, the hardest part isn't knowing what each capability does.

It's deciding where your next dollar creates the most business value.

→ Speed?
→ Security?
→ Performance?
→ Observability?
→ Your people?

There's no universal allocation.

The right answer depends on the risks you're carrying, the customers you're protecting, and the outcomes you're trying to create.

The scenario in this post puts that decision in your hands.

So, if you were the CTO, how would you allocate the 100%?

Drop your percentages in the comments and tell us why!

A green pipeline can make a release look safer than it really is.But here's the catch:A successful deployment only tells...
25/08/2026

A green pipeline can make a release look safer than it really is.

But here's the catch:

A successful deployment only tells you that the software was delivered successfully. It doesn't tell you whether business-critical scenarios are protected, dependencies are stable, monitoring is effective, or customers are protected from unexpected failures.

That's the difference between Delivery Confidence and Release Confidence.
So here's the challenge:

Can you spot the misleading statement before you swipe?

Once you spot it, tell us in the comments: Which statement did you choose, and what made you suspicious?

Tag someone who would enjoy this challenge!

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