Mobile Premier League Mpl

We’re hiring for Growth & Marketing at Pollistan. Pollistan is a free-to-play social prediction platform where opinions become reputation. We’re looking for someone who deeply understands consumer internet behavior in India — what drives attention, participation, retention, and habit formation at scale. This is not a conventional marketing role. You’ll work across growth, distribution, brand, product thinking, and user psychology to help shape a new category at the intersection of gaming, social identity, and participation. Ideal background: • 3–5 years of experience in consumer internet, gaming, social platforms, or high-engagement products • Strong instinct for distribution, storytelling, and internet culture • Comfortable operating in a fast-moving 0→1 startup environment Based in Bangalore preferred. DM me if this sounds interesting, or tag someone who’d fit well.

A big step forward for India’s gaming ecosystem. The PROGA notification today brings much-needed clarity and structure to the space. It reduces ambiguity, strengthens user protection, and enables responsible innovation. For us at Pollistan, this is a strong signal to build thoughtfully, stay compliant, and focus on creating real value for users. India now has the foundation to build category-defining products. Read more: https://lnkd.in/gqK__zeu #PROGA #Startups #IndiaTech #OnlineGaming

There’s something quietly powerful happening right now. For years, ambition had rules. Stable job. Predictable path. Responsibilities first. Dreams… later. And “later” often meant never. But look around today—People are rewriting that script. You’ll see professionals picking up passions they parked years ago. Parents building something of their own between school runs. Leaders creating space—not just for outcomes, but for people. What’s changed isn’t just opportunity - It’s empathy. We’re beginning to understand that behind every “side project” is a story— Of courage, of compromise, of starting again when it’s inconvenient. And equally important— Behind every builder, there’s someone quietly supporting. A partner adjusting schedules. A manager giving flexibility. A friend saying, “Go for it.” That support? It may feel small. But it’s often the difference between hesitation and action. So here’s to this shift : - To choosing compassion over judgment. - To backing people while they rediscover themselves. - To celebrating not just outcomes—but the attempt. To everyone building something—seen or unseen. To everyone supporting—even in the smallest way. Respect. Truly. Milestones will come. But this phase—the becoming—that’s the real story. #MondayThought .

This is not an SEO is Dead, or AI is coming for your jobs post, but: Since the last few weeks and months, the amount of time I have been spending with AI has increased drastically, and so has my team. And it's not just simply prompting but building with AI. A few problems we are solving using AI: - I needed to analyse a 600mb server log file. Instead of getting a tool or any expensive subscription, I built my own server log file analyser using Claude code that works like a charm [ it actually replaced Screaming Frog server log analyser ] - We built our own Link monitoring tool that tracks 1000 Backlinks daily. [ We won't be needing Linkody or any other similar paid solution lately ] - I built my own ChatGPT Citations and Mention Tracker that can easily track 500 Prompts daily with competitor ranking insights at a fraction of the cost - Semi-Automated our Content Audits and Quality checks using AI. - There are a few solutions which never existed before that can be created using AI as per your requirements. The way AI is bridging the gap between your idea and development is mind-boggling! I don't have a coding or development background, but I know how a product functions, and that mindset can help you unblock possibilities you have never imagined. It's the right time to get your hands dirty with AI and upskill, automate, and build something that you have always thought you would need a large development team for! PS: I am building more solutions that earlier weren't part of any existing SaaS products and were always a blocker for my day-to-day work. Something is cooking, and I might launch it soon!

AI often writes tests to PASS, not to TEST. And that's where I believe a Tester adds real value. AI is NOT the problem — AI without a Tester's brain is. For the past few months, I have been working on making our E2E integration testing more robust, reliable, and confident with Claude and Codex — using custom rules, persistent memory, and iterative corrective prompts. Sharing my observations: 𝗪𝗵𝗮𝘁 𝘄𝗲𝗻𝘁 𝘄𝗿𝗼𝗻𝗴: ↳ 𝗙𝗶𝘅𝗶𝗻𝗴 𝗧𝗲𝘀𝘁𝘀 𝘁𝗼 𝗣𝗔𝗦𝗦 — One of the fields in the payload did not update with the latest data. AI went ahead and fixed the test, with the instinct that the written test was wrong and not that the product had a bug. ↳ 𝗦𝗸𝗶𝗽𝗽𝗶𝗻𝗴 𝗳𝗮𝗶𝗹𝘂𝗿𝗲𝘀 — AI would SKIP a test that was failing and end up removing failed assertions. ↳ 𝗠𝗮𝘀𝗸𝗶𝗻𝗴 𝘃𝗮𝗹𝗶𝗱 𝗯𝘂𝗴𝘀 — Marked a continuously failing test [valid bug on staging] as a KNOWN bug and skipped it. ↳ 𝗟𝗮𝘇𝘆 𝗔𝘀𝘀𝗲𝗿𝘁𝗶𝗼𝗻𝘀 — Balance >= 0, API returns 200. ↳ 𝗦𝗰𝗵𝗲𝗺𝗮 𝘃𝘀 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿𝗮𝗹 𝘁𝗲𝘀𝘁𝘀 — Would validate "field is NOT null" against the real calculation. For example: Total Balance = Field A + Field B ↳ 𝗧𝗲𝘀𝘁 𝗱𝗮𝘁𝗮 — Choosing data from CSV if a test failed to make it PASS. My CSV had different users. On one user the test failed; it took the new user and said "it's working there." 𝗪𝗵𝗲𝗿𝗲 𝗔𝗜 𝗵𝗲𝗹𝗽𝗲𝗱: ↳ 𝗜𝗻𝗳𝗿𝗮 & 𝗖𝗜/𝗖𝗗 𝘀𝗲𝘁𝘂𝗽 — Perfect setup. All core dependencies, installation, and boilerplate code for the tests were smooth. ↳ 𝗨𝘁𝗶𝗹𝗶𝘁𝗶𝗲𝘀 & 𝗵𝗲𝗹𝗽𝗲𝗿𝘀 — All the utilities, helpers, connection utilities (DB, Kafka), and building request/response were a breeze with AI. ↳ 𝗔𝗣𝗞 𝗰𝗼𝗺𝗽𝗶𝗹𝗮𝘁𝗶𝗼𝗻 — I had to compile an Android APK for better logging and gave that to AI. It removed all failing dependencies that required more info which I did not have, handled those failures, and gave me a perfectly working APK for my use case. ↳ 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗱𝗮𝘁𝗮 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 — Very helpful to read large amounts of networking data inside the app and give unique endpoints with user flow discovery. ↳ 𝗭𝗲𝗿𝗼 𝗺𝗮𝗻𝘂𝗮𝗹 𝗰𝗼𝗱𝗲 — Not a single line of manual code written. With all of these, it was clear it will find the path of least resistance to a green build, but not the path to protect our product. Your test report would be green, but filled with lazy assertions and meaningless tests. 𝗖𝘂𝗿𝗿𝗲𝗻𝘁𝗹𝘆 𝗲𝘅𝗽𝗹𝗼𝗿𝗶𝗻𝗴: QA review gates before AI-generated tests get merged, and behavioural-first assertion standards. For AI-assisted code, extensive code reviews are now standard — either with AI-built tools or human-in-the-loop. So why not review and validate AI-generated tests the same way? The conversation should be "How do we know these AI-generated tests are protecting my application?" rather than "How many tests did AI generate?"

Brands are optimising for AI answers on ChatGPT and Claude. They're fishing in a pond. Google AI Overviews is the ocean. 20% of Google searches already show AI-generated answers. 20% of billions of daily searches = more AI answers than every LLM on Earth combined. The biggest AI answer engine isn't a chatbot. It never was. It's the search bar your customers have been using for 25 years. Where are you putting your bets? #AIOverviews #SEO #GEO

We are hiring a TPM-2 with 6–10 years of experience to lead complex, cross-functional programs. > The ideal candidate should have a solid understanding of microservices architecture and strong program management fundamentals. > This role requires someone who can drive execution, influence outcomes, and confidently manage senior stakeholders across teams. If you’re interested or know someone who’s a great fit, feel free to reach out! #hiring #TPM #Tekion

As a QA Tester if you have been active on LinkedIn over the past few months, you would have probably seen posts like- -Built an internal bug tracking tool like JIRA -Built an in-house test case repository like TestRail -Built an AI-powered end-to-end script generator that mapped user stories and APIs to workflows and automated 1000 test cases -Used AI to achieve 100% unit test coverage" It's great to see all of the possibilities that AI has enabled for testers to build. But a question that comes to my mind -What business problem does this solve and who are we building and optimising for? Especially when there's something readily available. And once you have built it- Have you stress-tested it at scale — what happens when you cross 500 or 5000 bugs and it breaks? What do you fall back to? Who maintains your test case repository as the product grows? How will you maintain and change the workflow example- bug transition states? What's your plan when the underlying model changes or a newer model is released ? What's the COST in tokens, time/bandwidth, and team productivity disruption? Are those 1000 tests/assertions lazy or identifying real in-app user flows? [More on assertions in next post] And here's the question I keep coming back to: If your developer is now shipping 5 features in 1 sprint that would have taken 2–3 sprints before (thanks to AI-assisted development), do you spend your team's bandwidth optimising an internal tool — or do you spend it testing and shipping those features that your business actually needs. There needs to be a balance between what to build vs. what to use that already exists. It's completely natural to feel the dopamine rush of building something from scratch. But does the value it adds to the organisation justify the time and cost? IMHO, QA's core job is to ensure quality and we should build with the objective which either improves quality or reduces time in the testing cycle helping accelerate business. I am genuinely interested to know if you have built something that met this objective and the follow up adoption challenges. [1/3]