@NainsiDwiv50980: Everyone is talking about AI agents. Very few people are building the thing that actually makes them powerful: Context.…
Summary
A Twitter thread argues that the key to powerful AI agents is not better prompts but accumulated personal context and memory systems, highlighting Obsidian as a tool for compounding knowledge. The author predicts a widening gap between those using AI alone and those combining it with personal context.
View Cached Full Text
Cached at: 07/16/26, 02:20 PM
Everyone is talking about AI agents.
Very few people are building the thing that actually makes them powerful:
Context.
The people who win with AI over the next few years won’t necessarily have better prompts.
They’ll have better memory systems.
Because every time you don’t save an insight, connect an idea, or capture a thought…
you’re forcing yourself to start from zero again.
Meanwhile, a small group is quietly building something different:
→ years of notes → connected ideas → reading highlights → project histories → personal patterns → accumulated context
Then they plug AI into it.
That’s when AI stops being a chatbot.
And starts becoming a thinking partner.
This is why I’m so bullish on Obsidian.
Not because it’s a note-taking app.
Because it’s an engine for compounding knowledge.
Every note can become: • a future insight • a content idea • a business opportunity • a connection you would’ve otherwise missed
The gap between people using AI and people using AI + personal context is going to get ridiculously large.
One group will ask better questions.
The other group will build systems that think with them.
Five years from now, your most valuable asset may not be your prompts.
It may be the context you’ve been compounding in private.
I made this infographic to show the framework I use to turn Obsidian from a storage app into a second brain that actually creates leverage.
Bookmark it.
Your future self might thank you.
@NainsiDwiv50980 @nainsidwiv50980 totally agree. context is king. saw a project fail just cuz it couldn’t remember past interactions. brutal lesson.
Similar Articles
After using AI agents for a few months, these are my biggest observations
A personal reflection on the transformative potential of AI agents with persistent memory, arguing that context and workflow organization will become more important than the models themselves.
A developer shares insights on how to maximize AI agent capabilities, arguing that simpler setups and understanding core principles are more effective than complex harnesses and libraries.
A developer shares insights on how to maximize AI agent capabilities, arguing that simpler setups and understanding core principles are more effective than complex harnesses and libraries.
Effective context engineering for AI agents
Anthropic publishes a guide defining context engineering as the evolution of prompt engineering, focusing on curating optimal context tokens for AI agents to maintain performance and focus during multi-turn inference.
The Real Truth About AI Agents
An experienced practitioner shares hard-won lessons from deploying 25+ AI agents to production, arguing that memory, orchestration, and auditability matter far more than model choice. The article details common failure modes like context loss and silent cost loops, and recommends a stack including Claude Sonnet 4, Pydantic AI, and dedicated memory layers like Octopodas.
@SuJinyan6: https://x.com/SuJinyan6/status/2073955240349770069
This blog post by SuJinyan6 examines the evolution of AI agents from simple LLM+tool use to context engineering and long-running harnesses, citing Anthropic's recent work and discussing how agent capability is now a system-level property involving multiple components.