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diff --git a/Biz/idea/duree-pitch.org b/Biz/idea/duree-pitch.org
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-#+TITLE: Duree: automated universal database
-#+SUBTITLE: seeking pre-seed funding
-#+AUTHOR: Ben Sima <ben@bsima.me>
-#+EMAIL: ben@bsima.me
-#+OPTIONS: H:1 num:nil toc:nil
-#+LATEX_CLASS: article
-#+LATEX_CLASS_OPTIONS:
-#+LATEX_HEADER:
-#+LATEX_HEADER_EXTRA:
-#+LATEX_COMPILER: pdflatex
-#+DATE: \today
-#+startup: beamer
-#+LaTeX_CLASS: beamer
-#+LaTeX_CLASS_OPTIONS: [presentation,smaller]
-Start with this:
-  - https://news.ycombinator.com/item?id=14605
-  - https://news.ycombinator.com/item?id=14754
-Then build AI layers on top.
-* Problem
-Developers spend too much time managing database schemas. Every database
-migration is a risk to the business because of the high possibility of data
-corruption. If the data is modeled incorrectly at the beginning, it requires a
-lot of work (months of developer time) to gut the system and re-architect it.
-* Solution
-- Using machine learning and AI, we automatically detect the schema of your data.
-- Data can be dumped into a noSQL database withouth the developer thinking much
-  about structure, then we infer the structure automatically.
-- We can also generate a library of queries and provide an auto-generated client
-  in the choosen language of our users.
-* Existing solutions
-- Libraries like alembic and migra (Python) make data migrations easier, but
-  don't help you make queries or properly model data.
-- ORMs help with queries but don't give you much insight into the deep structure
-  of your data (you still have to do manual joins) and don't help you properly
-  model data.
-- Graph QL is the closest competitor, but requires manually writing types and
-  knowing about the deep structure of your data. We automate both.
-
-* Unsolved problems
-- Unsure whether to build this on top of existing noSQL databases, or to develop
-  our own data store. Could re-use an existing [[https://en.wikipedia.org/wiki/Category:Database_engines][database engine]] to provide an
-  end-to-end database solution.
-* Key metrics
-- How much time do developers spend dealing with database migrations? What does
-  this cost the business? We can decrease this, decreasing costs.
-- How costly are failed data migrations and backups? We reduce this risk.
-* Unique value proposition
-We can automate the backend data mangling for 90% of software applications.
-* Unfair advantage
-- I have domain expertise, having worked on similar schemaless database problems
-  before.
-- First-mover advantage in this space. Everyone else is focused on making
-  database migrations easier, we want to make them obsolete.
-* Channels
-- Cold calling mongoDB et al users.
-* Customer segments
-- *Early adopters:* users of mongoDB and graphQL who want to spend time writing
-  application code, not managing database schemas. The MVP would be to generate
-  the Graph QL code from their Mongo database automatically.
-- Will expand support to other databases one by one. The tech could be used on
-  any database... or we expand by offering our own data store.
-* Cost structure
-** Fixed costs
-  - Initial development will take about 3 months (~$30k)
-  - Each new database support will take a month or two of development.
-** Variable costs
-  - Initial analysis will be compute-heavy.
-  - Following analyses can be computationally cheap by buildiing off of the
-    existing model.
-  - Customer acquisition could be expensive, will likely hire a small sales
-    team.
-* Revenue streams
-- $100 per month per database analyzed
-  - our hosted service connects to their database directly
-  - includes client libraries via graphQL
-  - may increase this if it turns out we save companies a lot more than $100/mo,
-    which is likely
-- enterprise licenses available for on-prem
-  - allows them to have complete control over their database access
-  - necessary for HIPAA/PCI compliance
diff --git a/Biz/idea/flash.org b/Biz/idea/flash.org
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-#+title: Flash
-#+description: a system for quickly testing business ideas
-
-- Each marketing iteration for a product requires some gear. A "gear" pack is just a yaml
-  file with all data for a single flash test. It will include ad content,
-  pricing info, links to necessary images, and so on.
-  - even better: store these in a database? Depends on how often we need to edit them...
-- Data gets marshalled into a bunch of templates, one for each sales pipeline in
-  the /Traction/ book by Gabriel Weinberg (7 pipelines total)
-- Each sales pipeline will have a number of integrations, we'll need at least
-  one for each pipeline before going to production. E.g.:
-  - google adwords
-  - facebook ads
-  - email lists (sendgrid)
-  - simple marketing website
-  - producthunt
-  - etc
-- Pipelines will need to capture metrics on a pre-set schedule.
-  - Above integrations must also pull performance numbers from Adwords etc APIs.
-  - Will need some kind of scheduled job queue or robot background worker to handle this.
-  - A simple dashboard might also be useful, not sure.
-- Metrics determine the performance of a pipeline. After the defined trial
-  duration, some pipelines will be dropped. The high-performing pipelines we
-  double-down on.
-- Metrics to watch:
-  - conversion rate
-  - usage time - minutes spent on site/app
-  - money spent per customer
-  - see baremetrics for more ideas
-- This can eventually be integrated to a larger product design platform (what Sam
-  Altman calls a "product improvement engine" in his playbook - PIE?).
-  - metric improvement can be plotted on a relative scale
-  - "If you improve your product 5% every week, it will really compound." - Sam
-  - PIE will differ from Flash in that Flash is only for the early stages of a
-    product - sell it before you build it. PIE will operate on existing products
-    to make them better.