Sanjay

1/Introducing a new feature in an established product has some unique consideration in contrast to product market fit for new products. This is my framework for ‘feature’ market fit via a) Meaningful Value Add b) Cannibalization c) Competition - a 🧵...

Dube, good you bowled only one lol

Hi @wabi - build a minimalist app that tracks UV levels. User will provide the city (in US). App should notify user when UV is high, too high, low and too low.

$AMZN a story in 3 lines

Accelerating Manufacturing Innovation at Michelin With Data and AI sloanreview.mit.edu/article/accele… via @mitsmr

You’re welcome!

When you see red across the board for a week+ and then start seeing the Christmas colors, PAY ATTENTION.

@tomeghale Oura has double charged me ; hasn't delivered product + support has been moronic / useless. @ouraring on X pretended like they are responding but didn't so sh*t. Any idea how you can fix this?

When I sent @ouraring support why I didn't receive the ring, they responded with "you having battery issues?" - even if they have poor support, it is hard to believe they hire people who can't read... beginning to think they are a scam

@ouraring you have charged twice for the same Ouar 4 ring. Once when you sent the sizing thing and once when I sent the size. Your support is truly the worst for a tech company. I see your typical response is "privacy... blah blah" - knowing that already - how are you going to fix double billing me?

This is one of several ways that you can try DeepSeek R1 without downloading the app or sharing any data with a Chinese company.

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it. - Ongoing learning. AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives. Finally, AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves. The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work. The variety of valuable things we can build is nearly unlimited. What a great time to build! [Original text: deeplearning.ai/the-batch/issu… ]

@titanvest - my account is on hold and when I submitted required docs, it seems like there is an issue (on your side). I have contacted support over 4 hours ago & with no response. Can you please contact me asap?

WHY TRUMP WON While the legacy media has a meltdown searching for hitherto undiagnosed psychoses in the electorate to explain its embrace of a Hitlerian strongman, the truth is much simpler than their fictions. This election is a reminder that after all the manufactured drama and overheated rhetoric, politics is still about issues. Whether you agreed with him or not, Trump ran a substantive campaign based on issues like the border, inflation, crime, and war. Harris ran on vibes, celebrity endorsements, name-calling (“convicted felon”, “fascist”), debunked hoaxes (“very fine people”), and platitudes (“democracy”). She would neither defend the Biden-Harris record nor say what she would do differently. When she did talk about specific issues, they were often stolen from Trump (child tax credit; no tax on tips; border funding). On the one issue where Democrats had an advantage, abortion, Trump deftly got ahead of the issue by rejecting a national ban and removing problematic language from the GOP platform. Harris wore out the issue by blatantly lying about Trump’s position and by exhibiting her own party’s extremism (nobody needed to see an abortion truck at the DNC). While Trump expanded his coalition with MAHA (health) and DOGE (government efficiency), Harris concluded her ersatz campaign by going all in on demonizing her opponent, pretending Madison Square Garden was a Nazi convention. The fact that voters saw through it should be reassuring, even if you don’t agree with the result. Voters want to know how a candidate will give them a better life and, increasingly, they have learned to tune out the rest as noise. While the legacy media creates excuses and impugns the motives of voters to explain why Trump won, the reason is simple: Trump is the candidate who spoke to voters’ concerns directly. It’s the issues, stupid.

America and Americans won! A true comeback story!

@davidplouffe 😂

Kamala Harris (strong) handled an ambush Fox interview light years better than the hash Donald Trump (unstable) made of the Fox pep rally disguised as a town hall.

Why I lugged my 27-pound toddler to a rocket launch: thefp.com/p/tim-urban-da…

sometimes 7 decades happen in 7 days. an amazing time to be alive.

For decades I suffered psychological pain because the most powerful flying object ever built was a decade in the past, two, three … half a CENTURY in the past … longer than from the Sopwith Camel to Saturn V, and now finally SpaceX has eased that pain. Thank you.