New stories

laphilosophia 9 minutes ago

A (humble) new proposal for the FE ecosystem

For many years, I focused quietly on my work, but now I feel compelled to point out a problem that is becoming increasingly apparent.

--

1. Correct Model ≠ Adopted Model

Historical fact: In the frontend ecosystem, the winners aren't those who create the most accurate abstraction; they're those who provide the “feel of working” with the least friction.

The result: correct thought model → low adoption and incorrect but easy model → explosion.

This is no coincidence.

2. Why Didn't Intent-Based / Deterministic Models Succeed?

There are several obvious reasons.

Cognitive Tax:

Intent + FSM + timeline requires:

“I know what I'm doing,” “I'm designing the lifecycle,” “I'm consciously producing the state.”

But for today's FE crowd, this isn't a feature, it's a barrier. Because the ecosystem rewarded: quick demo, quick job, quick CV line

For someone with this profile:

FSM = fear, determinism = unnecessary, explicit lifecycle = “overengineering”

Failure Tolerance is Very High in UI:

In the backend: wrong abstraction → system crashes, money is lost, data is corrupted.

In the UI: the spinner spins a moment too long, the state glitches, the user refreshes.

In other words: Frontend errors can be tolerated for a long time. This has allowed bad abstractions to survive.

React's Side Effect: The “Hide, Save” Culture:

React did this: hid the lifecycle, hid concurrency, hid reconciliation.

Result: a “it works even if you don't understand it” culture.

This culture: grew ritual memorization, not engineering.

Write a hook, if it works, fine. But why does it work, how does it work? No one asks.

The Vibe-Coder Explosion is Not a Cause, but a Consequence

Because this situation didn't start with ChatGPT, Gemini, Cluade or Copilot. These accelerated the existing decay. The groundwork was already laid.

There was already a crowd that didn't know abstraction, didn't know what state was, didn't know what concurrency was, but had memorized the framework.

AI just did this: it formalized the feeling of “I don't need to think.”

4. Not a Framework, but an Infrastructure Layer

The biggest mistake was: “Let's build a new UI framework.” This dies, and it did die.

-> “https://dayssincelastjsframework.com/”

However, if a structure is created that positions itself as a runtime layer, intent engine, state coordinator, commit resolver, it becomes an invisible layer above giants like React, Vue, and Svelte.

Adoption comes like this: “Use it if you want” or “Don't see it at all if you don't want to.”

5. The Most Important Lesson (Perhaps All of Them)

I'm writing this sentence clearly: “The frontend world is not producing engineering right now; it's producing conveyor belt-like behavior.”

That's why the right abstraction doesn't win immediately, but it is inevitable.

Because: UIs are becoming more stateful, AI interaction is increasing, concurrency is inevitable.

At this point: the “spinner + hook” model will collapse, and people will have to ask “why?” again.

And yes: “It's pointless to shout for standards in this mess.”

But when approached from the right layer, with the right problem, and the right pain point, this model inevitably creates value.

This isn't about hype; it's about patience.

---

After all these words, I realized there's complaint, but where's the solution?

I opened a GitHub repo and added an “AI-supported” RFC-Proposal. Participation is open to anyone who wants to join.

Thank you.

https://github.com/laphilosophia/temporal-intent-resolution

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Built in 3 days with Node.js, TypeScript, React, Cloudflare Workers (edge deployment), D1, and Redis. Just added prompt normalization today to boost cache hit rates even further.

It's drop-in - literally just change your baseURL and keep using your existing OpenAI/Claude SDKs. No code changes needed.

Currently in beta with a generous free tier (50K requests/month). Would love feedback from anyone building LLM apps - especially on the semantic similarity threshold and normalization strategies.

Live demo on the site shows real-time cache hits and savings.

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You can add it as an MCP tool to anything.

It works because it's just focused on doing research for code. You can check out the prompts https://github.com/803/sensei/blob/main/sensei/prompts.py. I combined this with a couple specialized tools for this use-case:

  - Kura - Query cache. Works even better because the prompt instructs the agent to decompose queries into sub-queries, and usually the sub-queries are more generic. Each gets cached as a reusable building block.
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  - Tome - llms.txt ingestion. Super simple. Does it on-demand for any domain and then stores the results in postgres for search.
I'm not charging for this right now and it's completely open-source and you can run it yourselves with your own API keys. If things go nuts I'll have to change course.

I made it to scratch my itch, maybe it'll scratch yours as well.

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