Ash Maurya: Do You Really Have Product/Market Fit? The reason it’s critical to determine whether a given product is pre-PMF or post-PMF is because there is a marked flip in 80/20 focus after this transition — from getting the product right to growing the product. (more)

Ash Maurya: As a founder, you are Investor #1 in your startup. As a founder, you are Investor #1 in your startup. And, you need to be even harsher on your numbers than a professional investor. (more)

[Dario Amodei]] on Machines of Loving Grace (more)

Ash Maurya: The Early Adopter Paradox When you try to market to everyone, you reach no one. This is why you should start with early adopters. “Niche,” “beachhead market,” and “ideal customer profile (ICP)” are all synonyms of early adopters. This is sound advice. But, when defining early adopters, it’s easy to stack a bunch of defining traits because you’re trying to narrow down on a smaller sub-segment of your ideal customers. (more)

Ash Maurya: 10X your odds of success with shorter and better feedback loops. I averaged about two years between ideas, which is the typical time window to achieve product/market fit. (more)

It's not the same for all of them. But the point is, despite all the business books (case study), most have just 1 Golden Goose (ok maybe 2): a key capability, or a key decision. (more)

Peter Drucker on The Purpose and Objectives of a Business, from The Essential Drucker. (more)

There are 3 Games of Product Management, and each of these has variations based on the context. (more)

Researchers say an AI-powered transcription tool used in hospitals invents things no one ever said. OpenAI has touted its artificial intelligence-powered transcription tool Whisper as having near “human level robustness and accuracy.” (more)

Craig Mod on New York City. Blossoms in the Big Apple (more)

I think that because of my Highlighting And Annotating habit, it's harder for me to write long paragraphs. I write single-sentence paragraphs, often bullet points. (more)

Joe Procopio: OpenAI Is Building 'Her' and Big Tech Is Still Defending SaaS Canned Reports. I don’t have any great love for this current version of genAI. (more)

Joe Procopio: The Problem With GenAI? It's Too Much 'Gen' Being Sold as 'AI' I’ve been neither a proponent nor an opponent of genAI, but I have pulled some of the threads on either side of the pro/con argument. (more)

Joe Procopio is the founder of TeachingStartup.com and chief product officer at Get Spiffy, an on-demand vehicle care and maintenance startup. He was chief product officer at Automated Insights, which was acquired by private equity firm Vista Equity Partners in 2015. In 2013, Joe sold his startup, ExitEvent, to Capitol Broadcasting. Before that, he founded and built Intrepid Media, a social network for writers. https://www.inc.com/author/joe-procopio https://joeprocopio.com/

John Cutler: TBM 3/52: Real-World Learning Review. This week I will try something new, and I hope you like it. As some of you know, I am an investor in DoubleLoop.com, an early-stage startup based here in the United States. I was chatting recently with Daniel Schmidt (the founder/CEO), and he said he wanted to do a learning review session for DoubleLoop and share it with the world. (more)

Automated Insights (AI) is an American-based technology company specializing in natural language generation (NLG) software that turns big data into readable narratives.[1] Automated Insights is a company that specializes in natural language generation (NLG) technology. Their flagship product, Wordsmith, transforms structured data into written narratives, allowing businesses to automate the creation of reports, articles, and other content. This technology is used by organizations like the Associated Press to automate the generation of news stories from financial data. https://en.wikipedia.org/wiki/Automated_Insights

Retrieval augmented generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data. This allows LLMs to use domain-specific and/or updated information.[1] Use cases include providing chatbot access to internal company data, or giving factual information only from an authoritative source. https://en.wikipedia.org/wiki/Retrieval-augmented_generation

An epistemic community is a network of professionals with recognized knowledge and skill in a particular issue-area. They share a set of beliefs, which provide a value-based foundation for the actions of members. Members of an also share causal beliefs, which result from their analysis of practices that contribute to set of problems in their issue-area that then allow them to see the multiple links between policy and outcomes (doctrine?). Third, they share notions of validity, or internationally defined criteria for validating knowledge in their area of know-how. However, the members are from all different professions. https://en.wikipedia.org/wiki/Epistemic_community (cf Shared Understanding, Scenes, Collaborations, Inventions, And Progress; different from Community of Practice) (more)

Joe Procopio: Dear Startup Investors: It's Not Us, It's You. There are essentially two questions that infuriate most serious entrepreneurs. (more)

Joe Procopio: Um, Hi. This Is What Generative AI Is For. It's for When SaaS Is Dead. Oracle and SAP aren’t going bankrupt tomorrow. But we’re quickly approaching the inflection point where the user expectations and the tech are converging on a Do-It-for-Me brand of data analysis that’s going to eclipse the current Do-It-Yourself model. (more)