I'm a bit skeptical about sending all my code to a cloud-based LLM, so I built a command-line tool that does code reviews using local LLMs via Ollama. It can also hook into OpenAI if you prefer (that gives better results tbh).
I've also had fun piping the results into other command-line tools, such as Simon Willison's llm
Fair point - but the whole idea is to create a light-weight CRM system without the tabs (and indeed without deals, pipeline etc). We keep it lightweight enough to be wizard-friendly which means that it's usable without investing too much time in setup. And we still use a traditional CRM for rev. forecasting etc.
The log-in screen is sparse - that's true. We're getting a (possibly slightly biased) 30% conversion rate from it, so it does push people to give it a spin. Same idea as the Twitter landing page. What you get as soon as you log in is a wizard which takes you through the first steps to maximise the probability of engagement.
That's a fair point. Customer segmentation can be overdone.
There was an interesting article on HBR about research Clayton innovator's-dilemma Christensen was doing into customer segmentation vs. a job-to-be-done approach (http://hbswk.hbs.edu/item/6496.html).
Once someone clicks on a link that calls out to them as a customer, you need to tell them pretty quickly what you can do and how you can do, i.e. what job you can help them do.
On brandregard.com we simply moved the discussion about features down one level, behind a customer segment link. We also discuss features in length below the fold on the landing page.
I agree. We're pretty sure who are customers are, but there is a chance the unknowns could slip through. We might do another A/B test simply adding "brands" as one option since that's a customer persona, but very generic.
I like how Foursquare appeal to their customer personas, "merchants", "brands", "developers", without making it too obvious but still very findable.
Exactly! We're monitoring which customer persona gets the most clicks (with good old Google Analytics). Down the line we might even try 8-10 different customer types with A/B testing and see which ones attract the most clicks.
That's totally true. I guess the lesson is that it matters who acquires you. If you can't go for the public, cross your fingers and hope your parent company won't do anything evil.
I've also had fun piping the results into other command-line tools, such as Simon Willison's llm