AI, startup hacks, and engineering miracles from your friends at Faraday

PRESS RELEASE - State of Vermont chooses Faraday to optimize targeted marketing campaigns

Perry McDermott on

Local Artificial Intelligence Company To Work With State Of Vermont To Help Grow Workforce

BURLINGTON, Vt., Feb. 13, 2019 /PRNewswire/ -- Faraday is pleased to announce a strategic contract with the State of Vermont to use their platform to drive more interest and engagement in people looking to relocate to Vermont. This technology will be used in conjunction with the Vermont Department of Economic Development's ThinkVermont initiative and website, which engage a wide-ranging audience around opportunities to live and work in Vermont.

Read more here



The DTC movement, AI, and snowshoes

Macallan Atkins on

Last week the Faraday sales team took some time off from the usual day-to-day grind to reflect on the last year, talk about emerging trends in the consumer landscape, and get some much-needed exercise in the Green Mountains of Vermont—I'll admit they're a little greener in the summer.

faraday-sales-retreat-2019

We briefly talked goals and tactics (would it really be a sales retreat without some numbers?), but the bigger discussions revolved around Faraday's why — specifically, why data-driven companies will eventually dominate every consumer market. I'm not going to pitch you here, but I will share my biggest takeaways from the day...

  1. We've all noticed the direct-to-consumer (DTC) movement that's rendering traditional marketing channels obsolete. Bypassing distribution channels is better for the bottom line and gives brand-manufacturers greater control over end-customer data—that is to say, meaningful data—which smart brands are using to optimize everything about their business. Here's the big takeaway: the DTC movement is expanding beyond retail and goods to literally every single consumer market (i.e., consumer finance, real estate, transportation, home improvement—the list goes on). My fellow colleague, Riley, just published a great article about how the DTC movement is transforming financial services. Definitely worth checking it out for a deeper dive into this DTC shift.
  2. To echo Riley, companies who embrace this movement will thrive, while others slowly fade away. Intelligent use of data will distinguish the thrivers and faders, making it clear that AI/ML adoption is no longer a luxury—it's an absolute necessity.
  3. Here's my third and final takeaway: if your team ever plans a snowshoe expedition (which is probably just a Vermont thing) make sure to wear snowshoes that actually fit, or you'll end up tripping yourself constantly, as I did (Dad's gigantic snowshoes).

Regardless of your company's industry, we can all agree on one emergent truth: the future of consumer marketing requires AI/ML to stay relevant.




Buy or build: things we built

Seamus Abshere on

Here are Faraday's contributions to open source that we use every day in production. No experiments here; this is the stuff that we looked for on the shelf, found the options wanting, and built ourselves.

A new standard for secrets: Secretfile

secret_garden (Ruby), vault-env (JS), and credentials-to-env (Rust) all implement a standard we call Secretfile(s):

# /app/Secretfile
DATABASE_URL secrets/database/$VAULT_ENV:url
REDIS_URL secrets/redis/$VAULT_ENV:url

Then you use it like this SecretGarden.fetch('DATABASE_URL').
Clients implementing this standard are meant to first check the environment for DATABASE_URL, then failing that look up the secret in Hashicorp Vault (interpolating $VAULT_ENV into production, staging, etc. first). It's very useful for development where your DATABASE_URL is just postgres://seamus@127.0.0.1:5432/myapp - you can save this in a local .env file and only mess with Vault in production/staging.

Lightning fast CSV processing: catcsv and scrubcsv

catcsv is a very fast CSV concatenation tool that gracefully handles headers and compression. It also supports Google's Snappy compression. We store everything on S3 and GCS szip'ed using burntsushi's szip.

$ cat a.csv
city,state
burlington,vt

$ cat b.csv
city,state
madison,wi

$ szip a.csv

$ ls
a.csv.sz
b.csv

$ catcsv a.csv.sz b.csv
city,state
burlington,vt
madison,wi

Of course, before you cat files, sometimes you need to clean them up with scrubcsv:

$ scrubcsv giant.csv > scrubbed.csv
3000001 rows (1 bad) in 51.58 seconds, 72.23 MiB/sec

Lightning-fast fixed-width to CSV: fixed2csv

fixed2csv converts fixed-width files to CSV very fast. You start with this:

first     last      middle
John      Smith     Q
Sally     Jones

You should be able to run:

$ fixed2csv -v 10 10 6 < input.txt
first,last,middle
John,Smith,Q
Sally,Jones,

World's fastest geocoder: node_smartystreets

node_smartystreets is the world's fastest geocoder client. We shell out to its binary rather than using it as a library. It will do 10k records/second against the smartystreets geocoding API. If you don't have an Unlimited plan, use it with extreme caution.

Better caching: lock_and_cache

lock_and_cache (Ruby) and lock_and_cache_js (JS) go beyond normal caching libraries: they lock the calculation while it's being performed. Most caching libraries don't do locking, meaning that >1 process can be calculating a cached value at the same time. Since you presumably cache things because they cost CPU, database reads, or money, doesn't it make sense to lock while caching?

def expensive_thing
  @expensive_thing ||= LockAndCache.lock_and_cache("expensive_thing/#{id}", expires: 30) do
    # do expensive calculation
  end
end

It uses Redis for distributed caching and locking, so this is not only cross-process but also cross-machine.

Better state machine: status_workflow

status_workflow handles state transitions with distributed locking using Redis. Most state machine libraries either don't do locking or use Postgres advisory locks.

class Document < ActiveRecord::Base
  include StatusWorkflow
  status_workflow(
    archive_requested: [:archiving],
    archiving: [:archived],
  )
end

Then you can do

document.enter_archive_requested!

It's safe to use in a horizontally sharded environment because it uses distributed locking - the second process that tries to do this will get a InvalidTransition error even if it's the same microsecond.

Rust build tools: rust-musl-builder and heroku-buildpack-rust

rust-musl-builder is how we build Rust apps on top of Alpine. It also drives heroku-buildpack-rust, the preeminent way of running Rust on Heroku.

Minimal postgres for node: simple-postgres

simple-postgres (JS) is just the essentials to talk to Postgres from Node. We particularly love its use of template literals for apparently magical escaping:

let account = await db.row`
  SELECT *
  FROM accounts
  WHERE id = ${id}
`

Yes, that's safe!

Minimal HTTP server: srvr

srvr (JS) is a small HTTP server that speaks for itself:

  • everything express does
  • better
  • less code
  • no dependencies
  • websockets

Proper Docker API support for Rust: boondock

boondock is a rewrite of rust-docker to be more correct.

Coordinate docker-compose: cage

cage boots multiple docker-compose.ymls, each as a pod. It's sortof like a local k8s. You configure it with a bunch of docker-compose files:

pods/
├── admin.yml (a pod containing adminweb and horse)
├── common.env (common env vars)
├── donkey.yml (a pod containing donkey)
├── placeholders.yml (development-only pod with redis, db, etc.)
[...]

Local development looks like this:

$ cage pull
==== Fetching secrets from vault into config/secrets.yml
==== Logging into ECR
Fetching temporary AWS 'administrator' credentials from vault
Pulling citus        ... done
Pulling citusworker1 ... done
Pulling citusworker2 ... done
Pulling queue        ... done
Pulling redis        ... done
Pulling s3           ... done
Pulling smtp         ... done
Pulling vault        ... done
Pulling horse        ... done
Pulling adminweb     ... done
[...]
$ cage up
Starting fdy_citusworker2_1 ... done
Starting fdy_smtp_1         ... done
Starting fdy_citus_1        ... done
Starting fdy_vault_1        ... done
Starting fdy_citusworker1_1 ... done
Starting fdy_queue_1        ... done
Starting fdy_s3_1           ... done
Starting fdy_redis_1        ... done
Starting fdy_horse_1 ... done
Starting fdy_adminweb_1 ... done
[...]
$ cage stop
Stopping fdy_citusworker2_1 ... done
Stopping fdy_vault_1        ... done
Stopping fdy_citus_1        ... done
Stopping fdy_s3_1           ... done
[...]

Fixed up rust crates: rust-amqp

rust-amqp@tokio (Rust) is our rewrite of the internals of the rust-amqp crate in proper tokio. It is much more reliable and needs to be merged upstream.

(beta release) 3rd gen batch processing on k8s: falconeri

falconeri is a distributed batch job runner for kubernetes (k8s). It is compatible with Pachyderm pipeline definitions, but is simpler and handles autoscaling, etc. properly.

(alpha release) Seamless transfer between Postgres/Citus and BigQuery: dbcrossbar

dbcrossbar handles all the details of transferring tables and data to and from Postgres and Google BigQuery. Additionally, it knows about citus, the leading Postgres horizontal sharding solution - so it can do highly efficient transfers between Citus clusters and BigQuery.

Conclusion

That's it. I only mentioned tools that we use every day.

PRESS RELEASE - NYSERDA chooses Faraday AI Platform to reduce customer acquisition costs

Perry McDermott on

NYSERDA Chooses Faraday AI Platform To Reduce Customer Acquisition Costs For Clean Energy Technologies

BURLINGTON, Vt., Oct. 10, 2018 /PRNewswire/ -- Faraday, Inc. has been chosen to provide its multichannel data-driven customer targeting tools and complementary consulting services to contractors participating in New York State Energy Research and Development Authority (NYSERDA) programs base on their unique artificial intelligence (AI) platform and experience in clean energy.

Read more here



A big takeaway from the Symitar Fintech Panel on Practical AI Use Cases

Rob Trail on

I recently participated in the Fintech Panel on Practical AI Use Cases at the 2018 Symitar Educational Conference (SEC). It was great to see that there's a lot of excitement about AI and how it's being used by credit unions and other consumer finance organizations.

On top of that, Jack Henry did an amazing job of organizing the panel, providing valuable insights to attendees in varying stages of AI adoption by bringing together participants from companies operating in very different areas of the field.

  • Infosys consults on large-scale AI projects, helping credit unions and banks develop complex, in-house capabilities to be applied across numerous business functions.
  • Clinc specializes in advanced conversational AI, helping banks develop, train, and deploy superior conversational AI solutions.
  • Faraday, yours truly, specializes in AI-powered customer lifecycle optimization, helping credit unions, banks, and fintech platforms better understand their consumers, personalize experiences, and improve member interactions from acquisition to retention.

The big takeaway

We covered a lot, but the biggest concern in the room was how credit unions with little to no experience with AI can operationalize the right solutions quickly.

There are seemingly endless ways AI is being used by innovative consumer finance organizations. Every day, we're hearing about new, creative ways organizations are applying data science and machine learning to extract more value from their data. Frankly, it can become overwhelming for credit unions that are just getting started.

You're not going to transform every process with AI overnight (or even over the next year). Start with one or two solutions that are relatively easy to implement and will yield significant results in the short term.

If you're considering using AI to draw deeper member insights, personalize experiences, and optimize outreach, Faraday can get you up and running in 6-8 weeks. Here are a few ways to get started:


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