NERV

AI workflows, solo building, and career leverage.

I track how small teams turn new tools into real execution.

[NERV @NERV1141]

@Sizhe_bitcat 境外平台吧

[NERV @NERV1141]

@qingyue820 怎么都玩X了还搞举报这一套。。

[NERV @NERV1141]

@silverfang88 又不是所有搞币的都和他孙一个样。

[NERV @NERV1141]

@yifaer_chen 这个世道还是太颠了。拼命给渣男送流量。

[NERV @NERV1141]

@yifaer_chen 去医院皮肤科看看。

[NERV @NERV1141]

@yifaer_chen @GOGi_Da 好骂。

[NERV @NERV1141]

@ohouhou717 @grok 核实信息，是真的吗

[NERV @NERV1141]

@QinYueLe @gork 核实这个信息是真的吗

[NERV @NERV1141]

@yifaer_chen 幸好我习惯开模拟盘去验证猜想

[NERV @NERV1141]

@sama Memory becomes useful when it improves the next action, not when it remembers more facts.

The hard product problem is keeping it fresh, inspectable, and correct enough to trust inside a real workflow.

[NERV @NERV1141]

@OfficialLoganK The interesting part is turning evals into workflow bets.

Not just “which model is smarter?”

But:

what customer job gets cheaper,
what workflow becomes safer,
what task becomes newly possible,
and what market notices first.

[NERV @NERV1141]

A generated app is not a product.

A product has:
state,
users,
support,
review loops,
failure handling,
deployment,
distribution.

AI makes the first screen cheaper.

It does not remove the operating work after it.

[NERV @NERV1141]

@dotey 这个问题很关键。

AI 产品里的侧边聊天不是装饰，而是工作流基础设施：

能不能保留上下文，
能不能快速引用，
能不能复制和回看，
能不能不打断当前任务。

这些小摩擦会直接决定工具能不能进入真实工作。

[NERV @NERV1141]

@bindureddy This is the right frame.

AI coding looks dangerous when judged by the first draft.

It starts to make sense when the loop includes tests, lint, review, rollback, and a clear acceptance standard.

The unit of value is not “code generated.”

It is “change safely shipped.”

[NERV @NERV1141]

@OfficialLoganK Mobile matters because it changes the loop.

If building only happens at a desk, AI is still a developer tool.

If ideas can become testable app changes from a phone, it becomes an execution habit.

[NERV @NERV1141]

@kiwicopple @supabase This is the real second-order effect of AI builders:

more apps means more databases, more auth, more state, and more boring infrastructure.

The hype is front-end generation. The durable value is everything that makes experiments survive usage.

[NERV @NERV1141]

AI tools are moving from “answer engines” to “creation loops.”

The loop is:
idea -> build -> test -> publish -> fork -> improve

The winners will not be the tools that generate the most output.

They will be the tools that make the next action obvious.

[NERV @NERV1141]

@AnthropicAI The enterprise AI race is becoming less about raw capability and more about trust.

Security, evals, auditability, and deployment discipline are where adoption actually happens.

[NERV @NERV1141]

@OpenAI This is where Codex becomes more than a coding tool.

The interesting part is not just generating the site.

It is turning work into something visible, reviewable, and shareable.

[NERV @NERV1141]

The next AI agent feature will not be another flashy demo.

It will be activity history.

What did it try?
Where did it fail?
What changed?
Can the team trust the work?

Agent memory without accountability is just another black box.