#015 • 8 Apr 2025

Contents of this Newsletter

🫷🏻

Shift Left Magic

⏰ : 6 min

📓

Fresh Blogs

⏰ : 10 min

🗞️

This Week in AI

⏰ : 5 min

🎶

From Prompts to Playlists

⏰ : 2 min

MAIN STORY

Shift Left Testing: Why Fast Teams Don’t Wait To Test?

Fast-moving teams don’t have the luxury of fixing things late. That’s why more of them are betting on Shift Left Testing, where testing starts early and problems are caught before they pile up. It’s quietly being normalised for teams that want to move fast and stay sane. Shift Left Testing is not really a type of testing. It’s a development strategy, a philosophy even. And once you see it for what it really is, it’s hard to unsee. That’s also why some of the fastest-moving engineering teams we’ve met swear by it. Not because it’s well known, but because it quietly saves them time, cycles, bugs and more. If you’re building at speed, chances are the teams you admire are already doing this—and not talking about it.

In traditional SDLCs, testing tends to happen at the very end of the pipeline — right before release. Shift Left flips this on the ground. It’s about bringing testing to the left of the timeline — earlier in the dev process. It means creating test cases alongside requirements, writing code with testing front and center, and catching bugs in real time. It’s less “code now, fix later” and more “build it right the first time.”

And while we call it Shift Left Testing, what we’re talking about is a completely different way of building software.

Left vs. Right: The Real Contrast

Most teams don’t stick rigidly to just shift left or shift right, they move between both, depending on what’s needed. But the mindset difference between the two is huge. Some companies we spoke to don’t treat Shift Left as a nice-to-have instead, they quietly rely on it to stay fast without breaking things. This was a consistent pattern—fast teams were already doing this, long before it had a name. Others often hesitate because it feels time-consuming upfront. But what they’re missing is that it’s not extra work. It’s a faster path, just frontloaded.

Shift Left is like rehearsing thoroughly before a high-stakes live performance where mistakes aren’t just embarrassing, they’re costly. It’s about getting everything right before the spotlight hits, so there’s no room for last-minute scrambles. ensuring everything’s polished before the spotlight hits. Shift Right is the opposite: perform first, gather feedback, and adjust based on what the audience notices. In real terms, Shift Left is coding with guidance—thinking about edge cases as you write logic, building test cases alongside features. Shift Right is finishing the logic, shipping it, and fixing what shows up.

But there’s also a subtle psychological difference.

As humans, we’re wired for instant gratification. Shipping fast feels productive. Seeing your work live gives a dopamine hit. Shift Right feeds into this perfectly. You release quickly, you get feedback, you feel like you’re moving.

Shift Left, on the other hand, delays that gratification. It asks teams to pause, to plan, to prepare before seeing any results. That feels like a slowdown. But here’s where you should understand that it’s not. It’s an investment. One that pays off big in fewer bugs, faster rework cycles, and a more predictable delivery pipeline.

And the teams that look slow are not essentially slow because we don’t see them putting out fires later.

Shift Right Isn’t The Bad Guy Either

Let’s give Shift Right its due credit. It enables:

Dark launches, beta releases, and rapid experimentation
Real-world usage data to prioritize what actually needs fixing
Smarter decisions based on user impact, not guesswork

For instance, when a bug surfaces post-release, Shift Right helps you choose:

Should you hotfix it immediately?
Or schedule it for a future release and address it properly?

Not all bugs require alarms. Shift Right gives you the context to decide which ones do.

But it comes with a risk: relying on user feedback alone means problems can surface too late—after they’ve already hurt real users or delayed launches. You might fix visible bugs fast but let technical debt quietly pile up. Suddenly, small cracks turn into larger faults.

Shift Left is the Underdog

Shift Left might not have the same shiny experimentation vibe, but it brings:

Cleaner code with fewer regressions
Clearer direction (thanks to early test case scoping)
Lower costs (because rework is expensive)
Fewer security risks (because you’re thinking about them upfront)

Rework is expensive—both in time and context switching. Shift Left helps teams avoid that by testing earlier and catching issues before they become problems. Teams that seem slow upfront? They’re often the ones shipping faster, later.

Early testing also gives developers a clearer path. When scope, requirements, and edge cases are mapped from the beginning, there’s less thrash mid-sprint—and less cognitive fatigue. Fewer pivots mean more flow.

Shift Left isn’t limited to Test Driven Development, either. TDD is one way to get there—but not the only way. You can start small: build test cases from your PRDs, validate early with QA, or use tools that integrate testing into the design process.

Why Teams Don’t Like Shift Left (We Can Change Your Mind)

Most tech teams hesitate to fully embrace Shift Left because, on the surface, it feels time-consuming. But the irony is that the same teams that feel like they “don’t have time” for Shift Left often end up spending double the time on rework. The companies who scale smoothly already solved this problem — and they’re not doing it manually. Writing test cases upfront, aligning across product, QA, and engineering, making sure nothing slips through the cracks, all this adds pressure before the code even exists.

That’s exactly why we built Quash—to help teams get ahead of common QA slowdowns delays and bottlenecks by embedding testing at the very beginning. While others are still catching up, teams using Quash are already moving faster with cleaner, more reliable releases.

Our platform is designed to make Shift Left not just possible, but effortless. We generate test cases for you automatically based on your product requirements and Figma designs, even before a single line of code is written. These test cases evolve as your requirements or designs change, keeping everything up to date without manual overhead.

What you get is comprehensive coverage: edge cases, happy flows, alternate paths. All mapped out clearly so your developers know exactly what to build. It’s like giving your devs a test-driven blueprint, without needing to write the tests yourself.

So instead of shifting left feeling like a chore, it becomes second nature. With Quash, you can move smoothly from scoping → requirements → test cases → development, without slowing down or breaking focus.

And suddenly, Shift Left isn’t just doable —it’s faster, cleaner, and way more scalable.

The Hybrid Approach (But Heavier on the Left)

We’re not saying ditch Shift Right entirely. A hybrid model makes sense for most modern teams to test early and learn from real usage. But Shift Left needs to be non-negotiable. Especially if you’re aiming to scale without chaos. And now, with AI on your side, Shift Left doesn’t need to slow you down. In fact, it can be your team’s competitive advantage.

At Quash, our tools embed Shift Left into your workflows so naturally, you won’t even notice it’s there. From scoping and intelligent test generation to automated tracking of edge cases—it’s Shift Left, without the extra lift.

Let your developers focus on what they do best, which is building great software. Leave the early detection and alignment to us. Because the future of testing isn’t left or right. It’s smart and we’re here to help you build it.

We’ve launched a free tool which is already being used by early-stage teams to make early testing easier than ever. Our PRD to Test Case Generator turns your product requirements into test cases, instantly. No manual writing or back-and-forth. It’s a hack to test-driven development, and it’s available now for teams who want to stay ahead of the curve. To make early testing easier than ever, it’s live now for you to try.

Check out our Recent Blogs

Manual vs Automated Testing: When and How to Use Different Types of Testing

Explore the critical differences between...

Read article

15 Best AI-Powered QA Tools Transforming Software Testing in 2025

This blog highlights 15 cutting-edge AI QA platforms that are reshaping...

Read article

news

This Week in AI

OpenAI Plans First Open-Weight Model Since GPT-2

Sam Altman confirms OpenAI will release a new open-weight model with reasoning abilities, similar to o3-mini. Not fully open-source, but modifiable. The move follows pressure from rivals like DeepSeek and Meta. Developer feedback sessions are kicking off globally, starting in SF soon.

Meta Debuts Llama 4 Models

Meta drops the first open-source Llama 4 models—Scout and Maverick—with a preview of Behemoth.

Scout: 17B MoE model, ultra-fast, 10M+ context, single-GPU ready
Maverick: Beats GPT-4o in multimodal tasks, top-tier ELO score, efficient and powerful
Behemoth: Still training, but already outperforms GPT-4.5 and Claude 3.7 in STEM benchmarks

Google's Gemini 2.5 PRO available for all

Now free for all via the Gemini app and AI Studio—no subscription needed. Canvas mode introduces live coding, UI editing, and "Open in Colab" support. Topped AI performance charts but still trails GPT-4 in multi-turn tasks.

Google's AI Mode Adds Multilmodal Search for Everyone

AI Mode now supports image + text search using Gemini + Lens. Users can upload images, ask complex questions, and get deep, contextual results. Rolling out to millions more U.S. users after positive feedback from Google One Premium testers.

OpenAI Academy is Live

A practical AI learning hub with 10+ hands-on sessions. Covers prompt engineering, RAG, and GPT-4 fine-tuning—built for real-world devs, not theory lovers.

Claude for Education Launches

Anthropic partners with top universities. Features include “learning mode” for guided reasoning, full-campus access, and new student programs with API credits.

Mureka Drops Music Composition Transformer

China’s music AI scene heats up with MusiCoT, enabling structured, customizable song generation.

DeepMind predicts AGI by 2030

Google DeepMind says AGI—AI with human-level cognitive skills could arrive within 5 years. It envisions massive gains in healthcare, education, and climate solutions, but stresses the need for safety protocols, risk planning, and global collaboration.

Tools

From Prompts to Playlists!

We’ve seen AI write test cases, generate ghibli styled images, and even predict weather. But this week, we saw it do something totally different—drop a banger.

We came across Suno, an AI tool that generates full-fledged songs from just a text prompt. Naturally, we gave it a developer-inspired prompt. We asked it to generate a song about the developer who is vibe coding and the result was amazing. It didn’t just generate lyrics and melody, it nailed it with the headphones on, code flowing, the classic dev-in-the-zone energy. Of course, we couldn’t stop there. We tried different genres, languages (including Hindi rap), and increasingly weird prompts. Each time, Suno kept delivering. That got us thinking.

Creative tools like Suno are hitting a point where they don’t just mimic creativity. And while it’s fun on the surface, it also signals a shift: AI isn't just good at logic-heavy tasks; it's getting better at navigating nuance, mood, and context. Exploring tools like Suno reminds us that the future of AI isn't just fast—it’s weird, wild, and expressive. If you’re curious. you can find the track here.

Do check out this tool if you haven’t!

Inside the Shift Left Revolution: Faster QA + AI’s Song-Generating Magic 🚀