Good call out! This book was definitely useful in terms of thinking about specialization before testing the market. There was even a sequel to it I believe.
However, the general audience for "Built to Sell" is for SMBs and does not cater towards the venture-backed technology startups here in the valley (who hope for a strategic acquisition instead of a ebidta multiple).
Congrats on your exit! Your note reminded me something Ezra Roizen wrote in Magic Box Paradigm. Paraphrasing here, he said that M&A outcomes often feel miraculous, but then again, babies are miracles too, and they happen every day :)
OP here, thanks for this feedback, my workflow was to first have a draft and then feed it into a LLM to fix grammar and improve conciseness. Wished there was a tool (I think folks are already working on) that is similar to what a book editor does which suggests changes as opposed to changing the styling.
I agree that good bankers are hard to come by and unfortunately most simply forward decks to a broad list, and they won’t get founders the outcome they deserve. My point was more about readiness for a sale. Having gone through it, I’ve come to believe there are certain prerequisites for even kicking off a process, primarily business fundamentals and pre-existing relationships.
In my experience, a banker can amplify an existing market, but they can’t create one from scratch (at least not easily).
For example, in our case we had roughly six serious inbound inquiries before engaging bankers. While our bankers (and they are great) ran a broad discovery process and put us in front of many potential acquirers, the eventual buyer was one that reached out to us unsolicitedly rather than being introduced through the process.
Hey guys, cofounder of Polarr here. Now we have a AI copilot for photo editing, and hopefully this is a lot simpler to use than the cockpit panel that editors deal with everyday. Love to hear your thoughts and feedback.
If you search CoreML, TFlite or Caffe2 on GitHub, there are already a score of offline AI models on the vertical of image recognition and vision. While these guys might have gotten lucky landing a couple deals with phone makers to enable some 'AI', it's hard to imagine the real AI leaders like Apple, Google or FB making models more and more accessible in the future.
Perhaps I'm not understanding the intent of this collaboration between Apple and IBM but I would like to think that anyone who can write/publish iOS apps should have the aptitude to spend a couple hours understand the basics of deep learning. Would you honestly use an app that was contains an image-classification model that's trained using this flow. Please enlighten me. Or am I the only person who DD'd on their product? Have you tried other web interface versions of online models trainers like SageMaker, Rekognition? Do you work for IBM?
I have used both CoreML and MPSCNN in the past, the pain point is typically how to translate a cloud-based model to run directly on the phone. Caffe2 to CoreML is good so far, but issue is CoreML is a black box and crashes are often not decipherable. I'm looking more in a universal flow that can port models trained in TensorFlow or PyTorch and at the same time some way to debug intermediate results. Or better yet, if there are existing mobile models that can be used is best. Just reached out!
Just spent a few minutes playing with the Watson Custom Model for vision flow and let's just say I am totally disappointed is an understatement, few things I noticed:
1. You first need to register an account, and to my surprise there is no command line tool or REST APIs, the entire interface is written in HTML. Hmmm, are they expecting me to specify the network structure by pressing buttons
2. Okay next, after choosing the visual model, it leads you directly to a web page with a bunch of widgets where you can add classes and negatives. To a seasoned ML engineers, this whole interface is useless. The classification has to be done at a full image level, no way to define the layers, the loss function, or any knobs to play around with the network. To an amateur, this is also very confusing. What are they expecting us to drag in to the negatives, if it's a logistic classifier, I could understand but for classifying an image, what exactly do you expect us to put?
3. Btw, to upload images, they expect .zip format, and this is where i stopped. Do they seriously think I will now export this so-called "model" to CoreML and load it to my Xcode?
If they came up with this 5 years ago I might play with it a little longer, but don't the IBM engineers keep up with what's going on at GOOGL, FB or AMZN. I can't possibly imagine anyone using this to develop iPhone apps for the purpose of image recognition, even if it's an offline flow.