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Diffstat (limited to 'plan')
| -rw-r--r-- | plan/duree-pitch.org | 76 | ||||
| -rw-r--r-- | plan/flash.org | 36 |
2 files changed, 0 insertions, 112 deletions
diff --git a/plan/duree-pitch.org b/plan/duree-pitch.org deleted file mode 100644 index 485dd39..0000000 --- a/plan/duree-pitch.org +++ /dev/null @@ -1,76 +0,0 @@ -#+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] -* 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/plan/flash.org b/plan/flash.org deleted file mode 100644 index 1c392f0..0000000 --- a/plan/flash.org +++ /dev/null @@ -1,36 +0,0 @@ -#+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. |