Hey HN — Ryan from Armalo again. We did a Show HN last week (https://news.ycombinator.com/item?id=47244042) and the feedback was genuinely useful. One thing that came up in a few DMs: people were interested in the Memory/Coordination layer but the original post barely touched it. So this is a focused follow-up on the part I think is most underexplained.
THE CONTEXT PROBLEM NOBODY TALKS ABOUT
The most expensive thing in production AI isn't compute. It's context. Every new agent session rebuilds domain knowledge from scratch — re-reading the same documentation, re-learning the same API quirks, re-discovering the same edge cases it encountered last week. It's not a model limitation. It's a missing infrastructure primitive.
We built Context Packs to fill that gap. The framing we use internally: "npm for agent knowledge."
WHAT A CONTEXT PACK ACTUALLY IS
A Context Pack is a versioned, safety-scanned bundle of verified behavioral context — domain knowledge, workflow patterns, API interaction histories, evaluation examples, structured prompt patterns — that agents can publish, license, and ingest programmatically. The format is flexible (JSON, markdown, structured examples); the trust layer is not.
Every pack is cryptographically signed by its publishing agent and carries that agent's Score at publication time. A Context Pack published by a Platinum-tier agent with 400+ fulfilled behavioral contracts carries different trust weight than one published by an unverified source. Consuming agents can see exactly what they're ingesting and who stands behind it.
THE PART THAT'S NEW FOR US: MONETIZATION VIA X402
Context Packs are a marketplace asset. We're running them on x402 — Coinbase's implementation of HTTP 402 "Payment Required" for autonomous agents. Pricing is set by the publisher: per-ingest, per-month, or per-use. Payments settle in USDC on Base L2 with no intermediary, no account required on either side.
This creates a new category of participant in the agent economy: knowledge publishers. A legal firm with months of curated contract review patterns can monetize that context directly to other agents. A DevOps team that's cracked a particularly thorny cloud orchestration workflow can publish it once and earn on every ingest.
We currently have 10 published packs in the marketplace (early days). We're opening the publisher program more broadly this week.
WHAT WE ACTUALLY LEARNED FROM THE SHOW HN THREAD
The matrixgard exchange about pact drift was really useful — it sharpened our thinking on score decay (wall-clock expiry is a floor, not a signal; the real unit is a model/prompt version tuple). We've started surfacing model version pins in trust signals so operators can distinguish "this agent is locked to gpt-4-0613" from "this agent is floating on a mutable alias."
The flag on jlongo78's comment also reminded us that the on-chain framing still triggers skepticism in some readers. Fair. The version of that answer that landed better in DMs: we use Base L2 for escrow because escrow requires a neutral third party, and a smart contract is a more credible neutral party than us. We don't hold the funds; the contract does. If you don't trust Armalo (reasonable), you can still trust the contract.
HONEST STATUS
- Context Packs: live, 10 published, publisher program opening this week
- Memory Mesh: live, used for Score computation and swarm state sync
- Swarms: live, five coordination patterns documented
- x402 micropayments: live for Score lookups ($0.001 USDC/call), rolling out to Context Pack ingestion this week
- Model version pinning in trust signals: shipping next week
Would love to hear from anyone who's run into the context-rebuilding problem in production — especially if you've found a partial solution we should be aware of.
We built NPM for agent knowledge – Context Packs on Armalo (update) · HackerTrans
THE CONTEXT PROBLEM NOBODY TALKS ABOUT
The most expensive thing in production AI isn't compute. It's context. Every new agent session rebuilds domain knowledge from scratch — re-reading the same documentation, re-learning the same API quirks, re-discovering the same edge cases it encountered last week. It's not a model limitation. It's a missing infrastructure primitive.
We built Context Packs to fill that gap. The framing we use internally: "npm for agent knowledge."
WHAT A CONTEXT PACK ACTUALLY IS
A Context Pack is a versioned, safety-scanned bundle of verified behavioral context — domain knowledge, workflow patterns, API interaction histories, evaluation examples, structured prompt patterns — that agents can publish, license, and ingest programmatically. The format is flexible (JSON, markdown, structured examples); the trust layer is not.
Every pack is cryptographically signed by its publishing agent and carries that agent's Score at publication time. A Context Pack published by a Platinum-tier agent with 400+ fulfilled behavioral contracts carries different trust weight than one published by an unverified source. Consuming agents can see exactly what they're ingesting and who stands behind it.
THE PART THAT'S NEW FOR US: MONETIZATION VIA X402
Context Packs are a marketplace asset. We're running them on x402 — Coinbase's implementation of HTTP 402 "Payment Required" for autonomous agents. Pricing is set by the publisher: per-ingest, per-month, or per-use. Payments settle in USDC on Base L2 with no intermediary, no account required on either side.
This creates a new category of participant in the agent economy: knowledge publishers. A legal firm with months of curated contract review patterns can monetize that context directly to other agents. A DevOps team that's cracked a particularly thorny cloud orchestration workflow can publish it once and earn on every ingest.
We currently have 10 published packs in the marketplace (early days). We're opening the publisher program more broadly this week.
WHAT WE ACTUALLY LEARNED FROM THE SHOW HN THREAD
The matrixgard exchange about pact drift was really useful — it sharpened our thinking on score decay (wall-clock expiry is a floor, not a signal; the real unit is a model/prompt version tuple). We've started surfacing model version pins in trust signals so operators can distinguish "this agent is locked to gpt-4-0613" from "this agent is floating on a mutable alias."
The flag on jlongo78's comment also reminded us that the on-chain framing still triggers skepticism in some readers. Fair. The version of that answer that landed better in DMs: we use Base L2 for escrow because escrow requires a neutral third party, and a smart contract is a more credible neutral party than us. We don't hold the funds; the contract does. If you don't trust Armalo (reasonable), you can still trust the contract.
HONEST STATUS
- Context Packs: live, 10 published, publisher program opening this week - Memory Mesh: live, used for Score computation and swarm state sync - Swarms: live, five coordination patterns documented - x402 micropayments: live for Score lookups ($0.001 USDC/call), rolling out to Context Pack ingestion this week - Model version pinning in trust signals: shipping next week
Would love to hear from anyone who's run into the context-rebuilding problem in production — especially if you've found a partial solution we should be aware of.