From the team

Thinking out loud.

Product notes, practical guides, and honest lessons from building a proactive AI companion.

LatestPerspectives14 min readBy Zoey

When AI Is Everywhere, What Kind of Future Are We Building?

We do not need a world that is merely more intelligent. We need a world that is better at turning intelligence into long-term public value.

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A green riverside city with renewable energy and public transport, beside a person and an AI companion, illustrating a sustainable future with AI.

Archive

More from AirJelly

02Product philosophyBy Ritchie

People Are Vessels of Context and Agents of Action. So Is AirJelly.

On shared context, continuity, and an intelligence that remembers our past and helps us act—while leaving our judgment and our context in our own hands.

6 min read →
Diagram linking shared context to understanding, judgment, and action, with continuity and user control at the center.
03PerspectivesBy Mercer

You know that, I am a hardware engineer

Music, games, and the everyday context beyond a chat window: a hardware engineer’s thoughts on building AI that understands us while leaving room to be ourselves.

16 min read →
Diagram connecting music, games, and daily life to context beyond chat and AI help that fits the user’s day.
04Low-Entropy Operations Notes 01By Shan

Does the Company Really Own What AI Creates?

How companies can build a defensible chain of rights when people and AI create together.

24 min read →
Diagram of an AI creation’s chain of rights, linking provenance, permissions, and intended use to evidence and review.
05Product philosophyBy Rui

What If Socrates Had Used AirJelly?

A thought experiment on self-knowledge, unfinished business, and how personal memory gives experience a chance to become wisdom.

8 min read →
Diagram linking experience to retrievable memory, fresh reflection, and action while preserving original context.
06DesignBy Hua

Designing the Jellyfish: Shape, Pixels, and Light

How AirJelly draws its pixel jellyfish at runtime from bell curves, a low-resolution renderer, and a small dithered light map.

12 min read →
Diagram of AirJelly’s runtime pixel jellyfish, connecting body shape, low-resolution pixels, and a dithered light map.
07PerspectivesBy Jerry

Exploring Proactive Agents at AirJelly: Working the Way You Do

An architectural perspective on proactive agents, user context, ongoing collaboration, and plugins that both trigger agents and provide tools.

6 min read →
Diagram connecting triggers and user context to a main agent, tools, sub-agents, and a results-and-feedback loop.
08Founder playbooksBy Bolt

The Medieval Art of Promotion for AI Founders

What Renaissance Florence can teach AI founders about winning capital, legitimacy, users, and public trust.

10 min read →
Diagram comparing an AI founder’s relationships with capital, legitimacy, users, and public trust through four medieval social groups.
09PerspectivesBy Error

When AI Can Decide What to Do Next, What Is Software For?

Models generate possibilities; software turns possibility into order — eight principles from building a complex AI system.

13 min read →
Diagram showing model possibilities becoming software order, illustrating the shift from instructions to institutions.
10PerspectivesBy ziwen

DeepSeek Harness: A Coding Agent and a Meta Harness

DeepSeek Harness ships two things at once — an out-of-the-box Coding Agent and a pluggable Meta Harness framework. Why the second one matters more.

6 min read →
Diagram linking a coding agent to a meta harness, illustrating how the surrounding execution framework can also be delivered.
11DesignBy yyy

How to Extend Your Own IP Fast with Claude

Why generating pixel GIFs directly kept losing the character — and how a Pixelorama MCP plus a reusable Skill turned it into a repeatable workflow.

9 min read →
Diagram contrasting drifting generated frames with consistent locked frames produced through Pixelorama MCP and a reusable Skill.
12Product philosophyBy KUKOO

From Context Accumulation to Context Consumption

Why AirJelly is moving from sensing and storing context to helping people actively use it.

8 min read →
Diagram connecting captured context to immediate use, illustrating the shift from accumulating information to using it at the right moment.
13EngineeringBy wanghelan

The Granularity Problem in GenUI: Where Should LLM Generation Stop?

Why more generation freedom made our UI worse — and why we stop the LLM at the product-semantics layer.

9 min read →
Diagram placing generative UI at the level of user intent and product semantics rather than component code.
14Team & orgBy Alex

Two Flywheels: A Management Model Built for AI Startups

One big office holds down the present, one small office runs toward the future — and three roles emerge on their own.

6 min read →
Diagram combining today’s bets and a future timeline into three roles with deliberately different perspectives.