Ask HN: What problem are you close to solving and how can we help?

This is super interesting stuff.

First of all, I think the caching layer (which we currently don’t have) is going to be a necessity in the coming weeks as we scale for an additional project (that will be relying on this architecture)

Second of all, it is just PK lookups. We don’t actually have a single fk (contractor did not set up any relations), which makes me think moving all of this replicated JSON data from fields to tables may help.

The queries that are currently causing issues are not filtering out any data but returning entire records. In ORM terms, it is Video.objects.all(), and from a URL param in our GET to the api, limiting the amount of entries returned. What’s interesting is this latency scales linearly, and at the point we ask for ~50 records we hit the maximum raw memory alloc for PG (1GB) causing the entire app to crash.

The solution you propose for s3 blob store is enormously fascinating. The one thing I’d mention is these JSON fields on the Video table have a defined schema that is replicated for each Video record (this is video/sensor metadata, including stuff like gps coords, temperature, and a lot more).

So retrieving a Video record will retrieve those JSON fields, but not just the values: the entire nested BLOB. And does so for each and every record if we are fetching >1

Would defining this schema with something like Marshmallow/JSON-Schema be a good idea when you mention JSON schemas we control? As well as explicitly migrating those JSON fields to their own tables, replaced with an FK on the Video table?

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I have posted this here before- hexafarms.com. I am trying to use ML to discover optimal phenotype for growing plants in vertical indoor farms to a. have the higest quality produce b. to lower the cost of producing leafy green/med plants, etc. within cities itself.

Basically, every leafy green (and herbs, and even mushrooms), can grow in a range of climatic condition (phenotype, roughly) ie temperature, humidity, water, CO2 level, pH, light (spectrum, duration and intensity) etc. As you might have seen around the world there is a rise in indoor vertical farms, but the truth is that 50% of those are not even profitable. My startup wants to discover the optimal parameters for each plant grown in our indoor vertical farm and eventually I would let our AI system control everything (something like alphaGo, but for growing plant X (lettuce, kale, chard, ). Think of it as reinforcement learning with live plants! I am betting on the fact that our startup will discover the 'plant recipes' and figure out the optimal parameters for the produce that we would grow. Then, the goal is that cities can grow food cheaper in more secure and sustainable way than our 'outsourced' approach in country side or far away lands.

So now I have secured some funding to be able to start working on optimizations, but I realized that *hardware* startups are such a different kind of beast (I am a good software product dev though, I think). Honestly, if anyone with experience in hardware related startups (or experience in the kind of venture I am in) would just want to meet me and advise me, I would take it any day. Being the star of the show, it's hard for me to handle market segmentation, tech dev, team, next round of funding, European tech landscape, etc. I am foreseeing so many ways that our decisions can kill my startup, all I need is advise from someone qualified/experienced enough. My email: david[at]hexafarms.com

Disclaimer : I know nothing about anything.

Hawking radiation ?

Laser tunnel ?

Magnetic canon ?

Centrifugal launcher ?

Vacuum diffusion ?

Electrical beam lensing ?