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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/com/simatime/idea/flash.org b/com/simatime/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.