Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
I know the Spanish case. The hotel lobby does not only push for airbnb restrictions but also for banning new hotels (so that they can push prices up). For example famously there are no new airbnb or hotel licenses since more than 10 years and obviously the problem has only worsened. Regardless, the problem is in general still a demand that grows roughly at 2.5x the supply growth rate.
No, that problem was not generated by Airbnb. There’s growing demand and, because of regulation, not enough is built every year. For example, according to INE, 250k new families are formed in Spain (more than 500k people) and only 100k new houses/flats are built and the yearly deficit has been accumulating for 12 years. That is the real issue and blaming corporations is just the politicians’ easy path to deflect blame, which unfortunately too many citizens eagerly buy into.
Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
Thanks! I have other ideas, following Jeff Hawkins's Thousand Brains Project, but in this one I'm trying to get to cortical columns from the other side, from "standard" deep neural networks.
The short version: each layer trains itself independently using Hinton's Forward-Forward algorithm. Instead of propagating error gradients backward through the whole network, each layer has its own local objective: "real data should produce high activation norms, corrupted data should produce low ones." Gradients never cross layer boundaries. The human brain is massively parallel and part of that is not using backprop, so I'm trying to use that as inspiration.
You're right that the brain has backward-projecting circuits. But those are mostly thought to carry contextual/modulatory signals, not error gradients in the backprop sense. I'm handling cross-layer communication through attention residuals (each layer dynamically selects which prior layers to attend to) and Hopfield memory banks (per-layer associative memory written via Hebbian outer products, no gradients needed).
The part I'm most excited about is "sleep". During chat, user feedback drives reward-modulated Hebbian writes to the memory banks (instant, no gradients, like hippocampal episodic memory). Then a /sleep command consolidates those into weights by generating "dreams" from the bank-colored model and training on them with FF + distillation. No stored text needed, only the Hopfield state. The model literally dreams its memories into its weights.
Still early, training a 100M param model on TinyStories right now, loss is coming down but I don't have eval numbers yet.
Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
"According to Scott, Mafia-style protection rackets compelled people to produce grain."
James Scott—an anarchist—lays the point bare: the state functions much like an aged, institutionalized mafia. A stationary bandit, not fundamentally different from the predatory groups it claims to suppress.
Sequent builds cryptographically secure online voting infrastructure used in 200+ real elections across multiple countries. We're a fully remote team working on an open-source platform combining Rust, TypeScript, and modern DevOps. We handle End-to-end encrypted voting, cryptographic mixnets, and tamper-evident logging.
Tech Stack: Rust, TypeScript/React, WebAssembly, GraphQL, PostgreSQL, Keycloak
Open Roles:
Senior Fullstack Engineer (Rust + TypeScript/React)
Reach out: [email protected]