Our years of background in crypto data informed the design of Trails
Bottom up, from contract primitives (functions, events, constants, etc) to our Explorer (search, understand) to Trails (app layer)
So bullish on @andrewhong5297 and @herd_eco.
Starting from an LLM, without building the primitives / structures to understand where the LLM is failing, is futile.
@herd_eco trails solve for this gap as do @glider__'s building blocks
Ahead of the curve. There's a reason we're both building this way.
Software is having its Industrial Revolution and it’s changing what matters in software frameworks. Today’s frameworks were designed for the crafty carpenter, tomorrow’s are designed for mass manufacturing processes where rigor > conciseness.
Take foldkit vs react/solid, for example. You can argue that foldkit is more verbose or complicated for some use cases compared to the traditional alternatives, but everything doable in react/solid is also doable in foldkit. Everything doable in foldkit is not (or at least not easily or consistently) doable in react/solid because they lack the rigor of command+event state machines with story & scene testing. Getting that back isn’t easy and is a substantial loss for agentic coding.
Before AI, there was the valid argument of picking what humans like best. Humans kinda suck at programming because we are constrained by our skills, taste and (lack of) discipline, so frameworks that worked in this era catered to the lowest common denominator of mass market human developers. While I agree that react/solid are powerful in the hands of humans, that simply doesn’t matter anymore.
What’s best for humans does not necessarily translate to what’s best for agents. Some tasks may indeed be “a bit harder” with foldkit in human terms, but AI is capable of solving all of them in a few minutes, so who cares. Instead of bike-shedding philosophy and taste, just choose the more rigorous (and scalable) option. It costs you nothing and gains you everything.
Effect is another example of this. With AI, now everyone of all skill levels can use advanced programming techniques that are objectively safer and more scalable, but were out of reach for many human programmers. Or didn’t appeal to the stubborn tastes of a particular person.
With humans now out of the way of technology adoption - I think it’s obvious that the following techniques are going to become way more commonplace:
1. State Machines
2. Functional programming
3. Declarative infrastructure
4. Test Driven Development
5. Domain Driven Design
6. Actor Systems
7. Rigorous API schemas
8. Type Safety and Formal Verification
9. Disciplined Operations (monitoring, on-call)
Alchemy is designed to bring these techniques together into a seamless experience for building and deploying applications of massive scale. It is the “rails” and “parts” of the ai software assembly line.
every product i use should just be a headless mcp, that feeds into an agent, where i never have to think about anything underneath.
except for our product, of course.
in our product, customers love the ui and love us very much.
we're special that way.