Most people whot want to do AI jump straight to code.
I almost did the same. My hostil mentor stopped me cold: "Start with LADR4e — §1.A of Axler."
Here's what I learned — and why it changes everything about training a neural network.
#LearningInPublic#Mathematics#AI
MCP is USB for LLMs. Before: every tool integration was a custom cable.
After: one port, anything plugs in. The boring infrastructure decisions are usually the ones that end up mattering most.
Things that sound boring until you need them at 3am:
• Go's explicit error handling
• MCP's standardized tool access
• A repro path for your bug report
• Tests you wrote 6 months ago
Boring is a feature.
Spent 3 days thinking pointers were about memory addresses.
They're about commitment.
A value says "here's a copy, do whatever."
A pointer says "this is the real one, changes are permanent."
Go forces you to be explicit about which promise you're making.
You can now proactively verify your identity (with a passport or government ID) in case it’s needed for future frontier model access in Amp.
We think it probably will be, and we want Amp to keep giving you access to the best models available to you.
We can’t guarantee access criteria or timelines. Those depend on (highly uncertain) government and model lab policy. We don’t plan to impose any additional restrictions beyond what is required by law and the model labs.
We are covering the cost for identity verification for all users, and we’re using Stripe for identity verification, so Amp stores nothing and sees only the outcome.
https://t.co/PSBSPXTAdK
@FourEyedWiz@araseb_ Not at all, I just want to show you that AIs can innovate, even if we're not at that stage today. It's a possibility, and it has actually already started with recent models.
Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946.
For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids.
An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better.
This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.