Early Fusion Memory — not an open-source stack.

早融合记忆架构，有别于任何开源记忆工程。

No Setup / No App / Zero Skill — create unique loops for you.

零预装、自进化的智能体和硬件体验，一切能力都为一个人生成。

Nearly done: 1st hardware — ultra-low power, proactive, instant memory.

接近完成第一款超低能耗、完全主动、即时记忆的硬件定义。

Open Oura or Whoop. It shows the metrics — does anyone know how to improve?

打开 Oura /Whoop App，它展示指标之后，用户知道可以怎么改善吗？

Add Plaud or Looki plugin into ChatGPT Health or Claude. Will you set up loops?

+Plaud/Looki 接入 ChatGPT Health/Claude Loops，你会仔细设置循环任务吗？

Coach, dietitian, doctor, mindful shows — can Apple Watch make it your protocol?

教练、营养师、医生、心灵播客说的，Apple Watch能不能让你在日常实践反思？

AI has never remembered or absorbed your taste or state. It only reads input.

AI 从未觉察和习得你的品味和状态，都是读取你的输入。

Body, brain, ambient, context, wearable — a recursive loop has to be built.

身体、大脑、环境、上下文、可穿戴，需要建立这样的递归循环。

Data volume sets tunable parameters. Battery has to hold always-on.

数据量决定了可调节参数量：续航一定要能Always-on。

Personalization sets reward quality. So it has to be a Zero Skill start.

个性化决定了Reward质量：一定是无预装的Zero Skill Start。

Health, focus, creation — if AI's gain is first-order, you become the overseer.

无论健康、效率、创作，如果AI只是做一阶加速，人就会沦为监工。

Opus 4.6 wrote wisely, 5.0 only codes. Mourning GPT-4o still trends in the US.

Opus 4.6 写作绝佳，5.0只能代码。至今怀念GPT4o是美区热门话题。

Dense models come close on domain skills, at a fraction of the cost.

Dense model在特定能力上接近，人均后训练成本显著降低。

MCP collections like OpenConnector rewire internet context.

类似OpenConnector等MCP集合重新拉通互联网上下文。

No other memory project carries multi-modal, real-time human alignment.

Mem0等开源记忆项目，都无法承载多模态、实时的人类数据对齐。

Hazy Cartridges, Transformer² — far from any consumer product feasibility.

Hazy Cartridge、Transformer²，远没有消费产品工程可行性。

SOTA model steers, follows expert recipes, patrols loops.

最强模型发现和创造Skill，遵循专家意见和巡视问题。

Dense models move from a cohort to a person.

Dense Model 会从人群、领域，走向个人化。

Reward comes from real context trajectories, and gamified preference.

reward 来自真实的上下文轨迹，也来自游戏化互动中的偏好。

A month of fused memory may hold 100x the tunable parameters of open work.

1个月融合记忆形成的可调节参数，可能是现有学术和开源项目的100x。

Privacy and a model of your own are one. More profitable, scalable revenue.

隐私诉求和想要专属模型是一体两面，激活更高利润更持续的收入流。