Arghh, why so hard... tested 2021:
- SendGrid: this one is the first one I got working on free tier!
- Mailgun: the Heroku add-on creates a free plan. This is smaller than the flex plan and does not allow custom domains, and is not available when signing up on mailgun.com directly: help.mailgun.com/hc/en-us/articles/203068914-What-Are-the-Differences-Between-the-Free-and-Flex-Plans- And without custom domains you cannot send emails to anyone, only to people in the 5 manually whitelisted list, thus making this worthless. Also, gmail is not able to verify the DNS of the sandbox emails, and they go to spam.Mailgun does feel good otherwise if you are willing to pay. Their Heroku integration feels great, exposes everything you need on environment variables straight away.
- CloudMailin: does not feel as well developed as Mailgun. More focus on receiving. Tried adding TXT xxx._domainkey.ourbigbook.com and CNAME mta.ourbigbook.com entires with custom domain to see if it works, took forever to find that page... www.cloudmailin.com/outbound/domains/xxx Domain verification requires a bit of human contact via email.
Have a look at some interesting examples under nodejs/sequelize/raw/many_to_many.js.
4 K. Enough for to make "low temperature superconductors" like regular metals superconducting, e.g. the superconducting temperature of aluminum if 1.2 K.
Contrast with liquid nitrogen, which is much cheaper but only goes to 77K.
Like Google custom silicon, Amazon server operations are so large that with the slowdown of Moore's law, it started being worth it for them to develop custom in-house silicon to serve as a competitive advantage, not to be sold for external companies. Can you imagine the scale required to justify silicon development investment that is not sold externally!
Ciro's 2D reinforcement learning games by
Ciro Santilli 35 Updated 2025-04-24 +Created 1970-01-01
Prototype: github.com/cirosantilli/Urho3D-cheat
Top Down 2D Continuous Game with Urho3D C++ SDL and Box2D for Reinforcement learning by Ciro Santilli (2018)
Source. Source code at: github.com/cirosantilli/Urho3D-cheat.Screenshot of the basketball stage of Ciro's 2D continuous game
. Source code at: github.com/cirosantilli/rl-game-2d-grid. Big kudos to game-icons.net for the sprites.Less good discrete prototype: github.com/cirosantilli/rl-game-2d-grid YouTube demo: Video 1. "Top Down 2D Continuous Game with Urho3D C++ SDL and Box2D for Reinforcement learning by Ciro Santilli (2018)".
Top Down 2D Discrete Tile Based Game with C++ SDL and Boost R-Tree for Reinforcement Learning by Ciro Santilli (2017)
Source. The goal of this project is to reach artificial general intelligence.
A few initiatives have created reasonable sets of robotics-like games for the purposes of AI development, most notably: OpenAI and DeepMind.
However, all projects so far have only created sets of unrelated games, or worse: focused on closed games designed for humans!
What is really needed is to create a single cohesive game world, designed specifically for this purpose, and with a very large number of game mechanics.
Notably, by "game mechanic" is meant "a magic aspect of the game world, which cannot be explained by object's location and inertia alone" in order to test the the missing link between continuous and discrete AI.
The question then becomes: do we have enough computational power to simulation a game worlds that is analogous enough to the real world, so that our AI algorithms will also apply to the real world?
To reduce computation requirements, it is better to focus on a 2D world at first. Such world with the right mechanics can break any AI, while still being faster to simulate than a 3D world.
The initial prototype uses the Urho3D open source game engine, and that is a reasonable project, but a raw Simple DirectMedia Layer + Box2D + OpenGL solution from scratch would be faster to develop for this use case, since Urho3D has a lot of human-gaming features that are not needed, and because 2019 Urho3D lead developers disagree with the China censored keyword attack.
Simulations such as these can be viewed as a form of synthetic data generation procedure, where the goal is to use computer worlds to reduce the costs of experiments and to improve reproducibility.
Ciro has always had a feeling that AI research in the 2020's is too unambitious. How many teams are actually aiming for AGI? When he then read Superintelligence by Nick Bostrom (2014) it said the same. AGI research has become a taboo in the early 21st century.
Related projects:
- github.com/deepmind/lab2d: 2D gridworld games, C++ with Lua bindings
Related ideas:
- www.youtube.com/watch?v=MHFrhIAj0ME?t=4183 Can't get you out of my head by Adam Curtis (2021) Part 1: Bloodshed on Wolf Mountain :)
- www.youtube.com/watch?v=EUjc1WuyPT8 AI alignment: Why It's Hard, and Where to Start by Eliezer Yudkowsky (2016)
Bibliograpy:
- agents.inf.ed.ac.uk/blog/multiagent-learning-environments/ Multi-Agent Learning Environments (2021) by Lukas Schäfer from the Autonomous agents research group of the University of Edinburgh. One of their games actually uses apples as visual represntation of rewards, exactly like Ciro's game. So funny. They also have a 2d continuous game: agents.inf.ed.ac.uk/blog/multiagent-learning-environments/#mpe
- humanoid robot simulation
- Section "AI training game"
- Section "Software-based artificial life"
Much bigger simulation, AIs learn Phalanx by Pezzza's Work (2022)
Source. 2d agents with vision. Simple prey/predator scenario. Pinned article: ourbigbook/introduction-to-the-ourbigbook-project
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