zopf's comments

Shameless plug, I suppose: https://www.wellthapp.com/careers

Most of our engineers are WFH 3-5 days per week.

Come build systems (TypeScript, Postgres, ElasticSearch, Redis, AWS, Redshift, etc) that improve the lives of the neediest patients in the U.S. healthcare system through the power of behavioral economics.

I wonder to what degree an LLM could now produce frames/slots/values in the knowledge graph. With so much structure already existing in the Cyc knowledge graph, could those frames act as the crystal seed upon which an LLM could crystallize its latent knowledge about the world from the trillions of tokens it was trained upon?

Wellth | Mobile Engineer | Remote (US), Los Angeles HQ | Full-time | https://wellthapp.com/

Technologies: React Native, TypeScript, GraphQL

Our smartphone app uses behavioral economics to help sick patients get better - so that health plans can save millions of dollars per year and patients can get paid. We need you to help us add awesome new features to our mobile app!

We raised our Series B a few months ago and are gradually growing our team as we ~double revenue each year. We hire for the long-term; many of our employees have been with us for more than four years. Come work with us and do well by doing good!

Two roles: Senior Mobile Engineer: https://boards.greenhouse.io/wellth/jobs/4059627006 Mobile Engineer: https://boards.greenhouse.io/wellth/jobs/4237999006

Wellth | Playa Vista, CA | Director of Engineering

50% of chronic disease patients don't take their medications as prescribed. We motivate that 50% with a tech-enabled rewards program, delivered through a mobile app and image recognition back-end, by paying each person up to $30/month. That gets them back on track, healthy, and keeps them out of the hospital.

We just raised a $7mm round and are positioning ourselves for the next two years of growth. We need you to come lead our engineering team to success.

Stack: TypeScript, React, React Native, Node.js, Apollo GraphQL, Postgres, RabbitMQ, ElasticSearch

Job link: https://wellthapp.freshteam.com/jobs/JbwX7-VF37oS/director-o...

Send any questions to our CTO, Alec at [email protected]

The reality is that you need to be able to trust the founders and board members you work with. A corporation's board typically has the authority to issue new shares (thus diluting existing shareholders), so even having unrestricted common stock or even preferred stock does not guarantee that you will always own a certain % of a company's outstanding shares.

Between all the preferred share terms, warrants, options, contingent earn-outs, etc, there's a million ways to engineer the divvying-up of proceeds in a sale.

Ultimately, it comes down to whether you trust your leaders to hold to the spirit of their agreement with you.

All that said... it would be really interesting to see a corporation with bylaws and governance/voting structured in such a way that employee ownership and payout %s could be maintained within certain guaranteed bands. It would severely limit that corporation's flexibility, and might lead it to have a higher risk of failure in bad funding market environments... but it could become a big draw for high-value employees who strongly prefer equity certainty over cash comp and company autonomy.

Wellth | Senior Software Engineer | Playa Vista, CA | ONSITE | https://wellthapp.com

Wellth is a 4.5yo healthcare tech startup that builds mobile applications to remind and motivate chronic disease patients to take their medications on time and use their medical devices, which keeps them out of the hospital and avoids billions of dollars of unnecessary spending. Our customers are health insurers, risk-bearing hospital systems, and life insurers. We've raised $7.1mm to date from a mix of long-term-view healthcare and life insurance investors (most recently $5mm in Sep '18), and have had a product live in the market for over two years.

We build mobile apps in React Native, front-ends in React, use Apollo and GraphQL, a Postgres DB, and Docker images for deployment onto AWS. We're an agile product/engineering team of 7, have a dedicated head of product, run two-week sprints, and have CI/CD across the stack.

We're looking for a senior engineer with strong back-end experience and at least moderate front-end experience. Bonus points for experience in healthcare or other regulated industries, and for interest in mentoring other engineers and improving engineering processes. 2019 is the year where we prepare for significant scale; help us build kick-ass systems that will live for a long time.

Apply here:

https://angel.co/wellth/jobs/368903-senior-full-stack-engine... or email [alec at wellthapp dot com] (the CTO)

Wellth (https://wellthapp.com) | Los Angeles, CA | Lead Engineer | Full-time | Onsite

I'm the co-founder and CTO of Wellth, a fast-growing, venture-backed healthcare technology startup that is solving the multibillion dollar problem of medication non-adherence. We help patients stick to their care plan and have better health outcomes, saving their hospitals and insurers millions of dollars each year - and ultimately saving lives as a direct result of our work.

We're a team of ten, half of that product engineers and designers, and we're looking for an experienced Lead Engineer to own our technical architecture, build awesome new features, and mentor our talented development team.

Our tech stack is cutting-edge JS: React Native, React, Node.js, GraphQL, Postgres, with some TypeScript and Flow for good measure.

Come join us in the quest to save lives and make patients healthier: https://angel.co/wellth/jobs/368903-lead-engineer

After some investigation, you are correct! Knowing that some of TrueNorth's creators previously worked on mixed-mode systems, I made the assumption that this one was too.

It seems the TrueNorth is indeed fully digital, but takes advantage of the event-driven architecture and peer-to-peer communication between many tiny cores to keep things low-power.

( http://paulmerolla.com/merolla_main_som.pdf for some details )

Thank you for the correction!

I still think the defining moment for ML inference (and maybe even training!) on embedded devices will come when there are viable special-purpose, low-power ML chips.

As much as I hate to do this, I'm going to make a comparison to Bitcoin mining.

Mining is all about optimizing hashes/joule to get the best ROI. We watched it go from CPU -> GPU -> FPGA -> ASIC in the quest for efficiency.

In ways, we're seeing the same thing in ML model training and inference. CPU -> GPU -> TPU. We're even seeing some special-purpose coprocessors deployed, as in the iPhone X. (https://www.wired.com/story/apples-neural-engine-infuses-the...)

But I think the final leap will come by going from digital execution to application-specific analog computing. If you don't need high precision, you can compute extremely quickly and efficiently using properly-configured analog circuits.

IBM is working on this kind of system with their TrueNorth line (https://techcrunch.com/2017/06/23/truenorth/)

It hasn't been proven yet, but I think there is huge potential.

Wellth | Lead Engineer | Venice, CA | Onsite / Remote | Full-time

Wellth (https://wellthapp.com/) helps patients stay healthy after they leave the hospital by rewarding them for taking their medications and taking good care of themselves. We're funded by large insurers and healthcare VCs, and our mobile apps have shown great results in clinical populations with diabetes, heart failure, or heart attack discharges. We love hearing from our users how the app has changed their lives for the better!

We're based in Brooklyn currently, but moving part of the team to the Los Angeles area (probably Venice or Santa Monica) in a few months. We're looking for a great Lead Engineer who can contribute to all areas of our stack, mentor junior developers, and help whip our engineering processes into even better shape in tandem with our CTO. Looking to hire anytime in the next few months, with the possibility of starting remote until we move to LA.

We're React Native for our mobile apps (iOS and Android), React for our admin dashboard, with Apollo and GraphQL for both, connected to a Node.js backend with Postgres / Postgraphile, deployed to a HIPAA-compliant PaaS. The current product team is two mobile engineers, one web engineer, a designer, and the CTO who does back-end and AI work.

Come help us build awesome, engaging applications that help patients stay healthy and lower the healthcare cost burden in the US!

Apply at https://angel.co/wellth/jobs/368903-lead-engineer, or email [email protected]

10/10, would invest

HyperTalk was my first programming language!

I helped a friend build a choose-your-own-adventure murder mystery game called Blood Hotel, and found myself obsessed with the feeling of inventive power that programming enabled.

I ended up building an animated space invaders game, and even tried my hand at writing a "virus" in HyperTalk that would infect other stacks with its code.

Ah, the good old days. Lovely to see this at the top of HN!

I made a thing that turns my Instagram posts into ambient soundscapes by understanding the content of the images, searching for relevant sounds, and mixing them into looped audio scenes.

It was made for the Monthly Music Hackathon in NYC held at Spotify, but it ended up being not terribly musical and more about just fun with audio and convnets :)

https://zopf.github.io/ambiance

Oh yeah! And for another instance of the same meetup, I teamed up with a guy who was great with audio synthesis, and I hooked up an Arduino and a gyroscope and microphone to my drumstick, and we made a wireless throat-singing, spatially-aware percussion instrument:

https://www.youtube.com/watch?v=G-g1GBaTevk

First off: isn't this from June of 2016?

Second: I don't get it. The primary example they use to illustrate the need has almost nothing to do with model building or selection, and everything to do with selecting and painstakingly cleaning data. This mirrors my experience with data science so far.

"A recent exercise conducted by researchers from New York University illustrated the problem. The goal was to model traffic flows as a function of time, weather and location for each block in downtown Manhattan, and then use that model to conduct “what-if” simulations of various ride-sharing scenarios and project the likely effects of those ride-sharing variants on congestion. The team managed to make the model, but it required about 30 person-months of NYU data scientists’ time and more than 60 person-months of preparatory effort to explore, clean and regularize several urban data sets, including statistics about local crime, schools, subway systems, parks, noise, taxis, and restaurants."

So - the meta part isn't such a big deal. But if DARPA has found a way to properly automate the painstaking process of selecting, cleaning, validating, and normalizing data, well THEN we'll really have something to be impressed about.

Unsure if it's CNN-based, but check out http://www.wowtune.net/

Here's their patents page... https://www.knuedge.com/about-us/patents-and-ip/

Where'd you get your images? Food-101 plus something else?

We're working on a similar component for our commercial behavioral-economics-driven app suite, targeted toward patients with chronic diseases.

Do you use location as a way to filter the set of possible foods, as in Google's im2calories project/paper last year?

They do some pretty awesome depth calculation stuff too: https://www.google.com/?ion=1&espv=2#q=im2calories+type:pdf

Edit: oops, should have read further. I see that you pulled appropriate terms from WordNet and used ImageNet to gather training images from Flickr. Cool!

Only 4 years behind Bitcasa...

These guys are using similar technology to the Boahen lab's:

http://web.stanford.edu/group/brainsinsilicon/index.html

In fact, I think some of the alumni now work at IBM, on this very project.

I've spoken to friends who've played with billion+ row Oracle RDBMS installs, and we (at Next Big Sound) have an offline snapshot MySQL instance with tables of up to about a hundred million rows (with over a hundred columns).

That said, I agree that distributed columnar stores end up being much more useful for large-scale analytics, and the power of high computation parallelism seals the deal. We've mostly moved on from those snapshot MySQL databases to Impala running on top of our Hadoop cluster, so you're preaching to the choir :)

That said, a hell of a lot of analytics can be done in a properly-structured SQL database, and schema changes aren't a big deal as long you don't need to do them online in a production system.

More info: http://stackoverflow.com/questions/14733462/can-mysql-handle...