You've raised an excellent point about natural language flexibility! Indeed, a strict pass/fail system would struggle with the grammatical variations found in everyday language.
Textbook examples are ideal for this approach precisely because they demonstrate grammatical structures with intentional precision. Rather than binary pass/fail results, we could implement degrees of type conformance with specific error messages tied to common mistakes. This makes the approach particularly valuable in educational contexts, where a textbook-aligned type system could provide scaffolded feedback as learners progress.
In essence, textbook examples with their corresponding type annotations could serve as a "test262" suite for natural language learning - a reference collection against which learners can validate their understanding within a controlled, educational environment.
My grandpa is an agriculture scientist. He graduated from university before the Culture Revolution disaster, and still served the country selflessly. In the 1980s he won Chinese national science first prize.
He raised me up and I am proud of him. All the difficulties and chaos that happened in this country doesn't affect his perseverance doing researches and making my family better off. He is still optimistic and healthy today. With your stories, I found that as human being, we are really seeking for and sharing similar happiness, which can go beyond ideology.
I successfully build a React renderer that displays text and pixels on a 0.96-inch OLED screen. The renderer is built on top of a C driver for I2C protocol, which is used as a C module for the embeddable QuickJS engine. The whole codebase, including JS engine, C modules and React Reconciler, are compiled into binary within 780KB on a Raspberry Pi. This encourages me to try out React for developing Linux GUI in the future, hope you like it!
Textbook examples are ideal for this approach precisely because they demonstrate grammatical structures with intentional precision. Rather than binary pass/fail results, we could implement degrees of type conformance with specific error messages tied to common mistakes. This makes the approach particularly valuable in educational contexts, where a textbook-aligned type system could provide scaffolded feedback as learners progress.
In essence, textbook examples with their corresponding type annotations could serve as a "test262" suite for natural language learning - a reference collection against which learners can validate their understanding within a controlled, educational environment.