Mitch Kosowski’s Post

A quick poem about the current investment climate ✍ Crypto once was all the rage, Now AI’s taken center stage. Coins forgotten, bots embraced, Investors love the AI space. Who will pick the next big trend? A GPT may choose where to spend.

Have you ever felt like a scientist at the start of a horror movie when giving technical advice to leadership? "The guidance I give is always ignored until it's too late to implement!" It can be challenging to communicate effectively with upper management but here are some guidelines that might save us all from a zombie outbreak 🧟♀️ 🧟♂️ • Present multiple solutions: Clearly and concisely outline different approaches to technical problems. Typically there’s at least a quick fix that leads to more work later and a more thorough solution that takes more initial work that’s easier to build on later. Make sure all these options are understood along with a quick summary of advantages and disadvantages. • Translate complexity into time: Developers often discuss engineering complexity but upper management understands timelines. Break down tasks into how much time each will take to provide a clearer picture. • Explain the "what" not the "how": Focus on what will be delivered and its value to the customer. The intricacies of how it’s done can stay within the engineering team. While developers love discussing technology keep it brief with non-technical audiences! How do you ensure your technical advice is heard and acted upon? Share your strategies!

We are quickly entering the "trough of disillusionment" in the hype cycle for generative AI. While GPTs excel in automating certain tasks (and can even automate away large portions of some jobs) they often fall short in others. Like all new technology, the issue lies in misapplying generative AI for tasks it wasn't designed for. This is a problem compounded by the GPTs themselves suggesting they can handle these tasks (often cheerfully!). This misalignment creates frustration and unmet expectations which we're starting to see more of these days. As the dust settles I firmly believe we'll recognize GPTs as a transformative technology across many industries. However it's crucial to understand their limitations and apply them appropriately. What are your thoughts on the current state of generative AI and its future applications?

🚨 Forced to Upgrade: My Microsoft Teams Experience on Mac 🚨 Microsoft Teams finally pushed me to update to the new version. Here’s a quick rundown of my experience: 🔄 Infinite Loop Nightmare: The new Teams opened without closing the old one causing both versions to open and crash repeatedly. This endless loop forced a full restart on my part. 🚫 Missed Notifications: None of my notifications carried over leading to missed urgent messages until I manually updated them. ⚙️ Reconfigured Settings: All my Teams hardware settings were lost so I had to reconfigure everything to get my video calls working again. I get that a lot of effort went into the new Teams but for those of us upgrading this is really just the same app. So why all this hassle just to end up where we started? Microsoft is this really the best we can do in 2024? #MicrosoftTeams #TechUpdate #UserExperience #TechHassles

How often does your engineering team release software? The right answer varies depending on your team, industry, and ultimately your end users' appetite for change. However if you feel your team is releasing too slowly here's a useful guideline: aim to release at least as often as your sprint cycle. For example if you have a two-week sprint but are releasing every month, aligning your releases with your sprints can lead to significant productivity gains. By synchronizing sprints and releases, you can lower the average ticket size, reduce carry-over tasks, and minimize merge conflicts which all result in smoother and more efficient development. Releasing more frequently than your sprint cycle can also boost productivity even further especially with CI/CD that can allow for multiple releases per week or even day. But the most substantial value I've seen is in that initial alignment of making releases at least as often as sprints. This eliminates so many inefficiencies. What release strategy does your team follow and how has it impacted your productivity?

While the long-term effects of ChatGPT on various industries are still unfolding, Google's recent $60M purchase of Reddit user content offers some interesting insights into the future of search. This move suggests that even Google, the long-time authority on search, is struggling to understand the current landscape and how to serve up the best search results. Google now is experimenting with utilizing user-curated content to direct traffic to mixed reception but a human-in-the-loop search could be the future. Personally my recent experiences with Google and YouTube search can be summed up in one word: rough. To me feels like we're at a crossroads and while I don't have a crystal ball I do know that the current search status quo won't continue much longer. What are your recent experiences with search engines and also LLMs? And do you think we're on the brink of a major shift in how we find information online?

Grindr has announced it is going "AI-first". Grindr has always been a tech pioneer and was one of the first apps to utilize GPS functionality as a key feature less than a year after Apple introduced it to the iPhone. It’s a common misconception that Grindr copied Tinder when actually Grindr launched three years before Tinder and paved the way for location-based dating apps. How do you think AI will transform the user experience in dating apps?