Blogs

Generative AI is reshaping how customers discover brands and products. For marketing leaders and data/analytics product managers, channel influence now hinges on a fundamentally new dimension: prompt tracking to measure how often and in what contexts brands appear in AI-generated responses. This focus replaces the classic SEO era’s preoccupation with keywords and rank tracking. Why prompt visibility tracking LLMs like ChatGPT, Gemini, and Claude synthesize answers from diverse sources dynamically. Unlike deterministic search engines, their outputs vary based on input prompts, user context, conversation history, and persona. Consequently, traditional metrics like mentions, rankings, or link count fall short of capturing true AI visibility. ...

The tl;dr about Coast FIRE… Coast FIRE is when you have enough in your retirement accounts that without any additional contributions, your net worth will grow to support retirement at a traditional retirement age. I encourage everyone to read up on this very interesting personal finance concept! Load packages library(tidyverse) library(scales) CoastFIRE function coastfire_calculator <- function(retirement_spend, withdraw_rate, growth_rate, inflation_rate, retire_age, current_age){ df <- (retirement_spend) / (withdraw_rate * (1+(growth_rate - inflation_rate))^(retire_age - current_age)) df } Formula parameters Assumes 3.5% safe withdrawal rate, 8% growth, 3% inflation and retired at 67 years old. ...

A few months ago, I found myself trying to wrangle a large language model (LLM) into doing my bidding with what can only be described as a “mega-prompt.” If you’ve ever tried to fit an entire workflow, context, and all possible edge cases into a single AI prompt, you’ll know the feeling: somewhere between optimism and mild dread. The results were…mixed. Sometimes the model would spit out something useful. Other times, it would get lost, hallucinate details, or just run out of steam halfway through. The more I tried to stuff into the prompt, the less reliable things became. ...

Hot take: The term “Generative Engine Optimization” (GEO) feels like a phrase invented by a committee who couldn’t agree on what the future actually looks like. The whole thing sounds like someone threw “generative AI” and “search engine optimization” together in a blender just so they could charge their clients more. In the age of AI search, clinging to outdated or unclear terminology like GEO only muddles the waters. It’s trying to ride the coattails of Search Engine Optimization (SEO), but it fumbles the landing with language that just doesn’t track. ...

This piece originally appeared in FT Optimize on June 25th, 2020. With each passing year the utility of third-party cookies continues to decline as it faces barriers from web browsers and government regulation. The current system still works and there will be alternatives but it is always best to start preparing for the new status quo. FT Optimize believes that the best way to maximize SEM ad revenue is to use first-party data. ...

This piece originally appeared in FT Optimize on June 7th, 2019. Google has made a concerted effort to get more advertisers to switch over to their “smart bidding” platform. This technology uses machine learning to optimize max CPC at auction time by predicting future conversion rate and value based on historical data. The purpose is to relinquish bidding activities to Google so advertiser’s can divert their attention to other details of account management. ...

I came across a tweet about the potential price of Ergo five years from now when the mining block rewards are gradually reduced from 66 to 3: I don't know who needs to hear this: Currently block reward for #Ergo miners is 66 $ERG per block mined, that is approx. \(154,440 a day<br>In about 5 years block reward will be 3 <a href="https://twitter.com/search?q=%24ERG&amp;src=ctag&amp;ref_src=twsrc%5Etfw">\)ERG per block mined, to maintain \(154,440 a day in rewards that brings the price of <a href="https://twitter.com/search?q=%24ERG&amp;src=ctag&amp;ref_src=twsrc%5Etfw">\)ERG to $71.5 🐂 ...

Last updated: 2022-01-31 This is a quick “copy & paste guide” for data scientists and developers who are interested in storing the Ergo blockchain to a standardized database format using a Raspberry Pi 4. We will be using the Ergo Blockchain Explorer (backend) but focus our efforts specifically on the Chain Grabber module. A few prerequisites before we get started… Clean install of Raspberry Pi 4+ Root file system mounted to SSD (60GB+) SSH enabled with stable internet connection ...