The context-engineering log.
Posts from grāmatr on classification, retrieval, governance, and what changes when AI runs through a disciplined five-stage Loop instead of a model-plus-memory stack.
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More from the blog
Shape Matters as Much as Ship: Why AI Outputs Need Audit Trails
Speed is half the AI win. The other half is the audit trail your General Counsel will sign off on. Why quality criteria set before the work begins — with every output's PASS or FAIL recorded as evidence — is the difference between an AI vendor that closes a procurement review and one that bounces back.
Memory Is Commodity. Just-in-Time Context Engineering Is the Moat.
Every AI memory product is racing to commoditize the same retrieval primitive. The value isn't in remembering things — it's in delivering exactly the right context, at exactly the right moment, on every single request. That's the moat.
The Trailing Twelve: What the Floor Looks Like Eight Weeks In
Eight weeks ago a productivity floor moved. It hasn't moved back. The trailing twelve months of public GitHub data show three distinct eras, one dated inflection, and a sustained new baseline roughly fourteen times a typical team's output — held steady through eight straight weeks.
The Fully Local AI Stack Is Here
On-device models, local inference, and MCP-native context engineering. The zero-cloud AI workflow is no longer theoretical.
AI Memory in 2026: Five Approaches Compared
Mem0, Zep, Letta, LangMem, and grāmatr take fundamentally different approaches to AI context. Here's an honest comparison of what each does and doesn't solve.
MCP Just Became the TCP/IP of Agentic AI
Anthropic donated MCP to the Linux Foundation. OpenAI, Google, Microsoft, and AWS are co-governing it. What the Agentic AI Foundation means for your stack.
66% of Developers Say AI Gets Close But Misses the Mark. Here's Why.
Stack Overflow's 2025 survey: trust dropped to 29%, 66% say AI is 'almost right.' The problem isn't capability — it's context delivery.
NVIDIA Thinks AI Agents Need Guardrails. They Also Need Context.
NemoClaw launched at GTC with Adobe, Salesforce, and SAP. It solves security. It doesn't solve context. Here's what's missing.
$2.5 Trillion in AI Spending. 95% Zero ROI. The Problem Isn't the Models.
Gartner says $2.52 trillion in AI spending this year. MIT says 95% of organizations saw zero ROI. The gap isn't model quality — it's context delivery.
Context Engineering: What Anthropic, Karpathy, and Shopify's CEO Agree On
Karpathy, Shopify's CEO, and Anthropic converge on context engineering. Then MCP became an industry standard under the Linux Foundation. Here's what that means.
The 12-Month Velocity Story: How Two Breakthroughs Raised the Floor
Two architectural breakthroughs. Each one permanently raised the productivity baseline. 52 weeks of GitHub contribution data tells the story of compounding intelligence.
How grāmatr Mirrors the Human Brain
Six architectural parallels between grāmatr's intelligence pipeline and how the human brain processes information — convergent solutions to the same problem.
From Digital Agency to AI Brain: The 19-Year Journey of grāmatr
Founded in 2007 as a digital agency. Same domain, same thread: data in silos, context lost. Through patents and platforms — to context engineering.
Store and Retrieve Is Not Intelligence
Every AI context tool on the market stores and retrieves. That's a filing cabinet. The unsolved problem is learning — getting smarter over time, not just remembering more.
Why I Killed My 40,000-Token System Prompt
My CLAUDE.md file grew to 40,000 tokens. My AI got worse, not better. Here's how context engineering replaced brute-force instructions — and why it matters.
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