Open Source

Building in
Public.

The Zambo Stack is built in public by Brennan Zambo - solo, no VC, no team. The MCP protocol is open. The discovery files (llms.txt, agent.json, entities.json) are open. The build log is public. This is what open AI infrastructure looks like.

Open Discovery

llms.txtLLM context file - structured description of the full Zambo Stack for AI crawlersView
llms-full.txtExtended LLM context - complete tool documentation and API specsView
agent.jsonAgent discovery manifest - machine-readable tool catalog and capability mapView
entities.jsonEntity graph - products, people, and relationships in structured JSONView
mcp.jsonMCP server discovery - standard MCP protocol discovery fileView
openapi.jsonOpenAPI 3.0 spec - complete REST API documentationView
robots.txtCrawler policy - all bots welcome, all tools discoverableView

Build Log

The full build log documents every major decision in the Zambo Stack - architecture choices, tradeoffs, mistakes, and lessons from building 17 products solo in 6 months.

MCP Protocol

The Zambo MCP server implements the open Model Context Protocol. Any MCP client can connect - Claude, Cursor, Windsurf, Cline, or custom clients. The server returns its full tool list at /mcp via standard MCP initialize + tools/list calls.

Community

Join the conversation on X (@zambodotdev) and Telegram (@zambodotdev). Build log updates, new tool announcements, and the real story of shipping in public.

Read the Build Log Follow @zambodotdev

Open source evidence in practice

An open implementation is easier to review when its schema, fixtures, verifier behavior, and change history are visible. The useful unit is not a confident sentence from an assistant. It is a bounded claim that names what happened, when it happened, and which evidence another person can retrieve. That framing makes the page useful to an operator who is reviewing a real run rather than reading a product slogan.

Build the evidence boundary first

Write down the question before collecting records. Is the question whether a call was attempted, whether a service accepted it, whether bytes were returned, or whether an outside system now shows the desired state? Those are different questions. A receipt should answer the execution question it was designed to answer, while a source system, approval record, or human check answers the questions beyond that boundary.

Keep the record narrow enough to share safely. A stable identifier, timestamp, tool name and scope, provenance class, exact committed bytes, output hash, and verification status are often more useful than a complete prompt transcript. When private inputs matter, retain a reference or commitment and keep the underlying material under the access rules of the system that owns it.

Use a repeatable review loop

  1. State the expected action and the acceptance condition in plain language.
  2. Locate the receipt created by the execution layer and match its identity, time, tool, scope, and status.
  3. Check the exact bytes or output commitment using the published verification procedure.
  4. Read back the important result from the system that owns the state, without treating a receipt as that read-back.
  5. Record supported, pending, contradicted, and unobserved claims separately.

Pin the revision, run the fixtures, and link the exact artifact used. This makes a short review reproducible: a second reviewer can see which artifact supported each conclusion and which uncertainty remains open. It also gives an agent a safe stopping rule. If a required record is missing, the agent should report that boundary instead of filling it with an invented success.

Failure handling and retention

Preserve failed calls, retries, rejected permissions, timeouts, and partial writes. They explain why a later state differs from the requested state and prevent a clean-looking final summary from erasing the path that led there. Attach the capture time and source URL to any external read-back, because a changing system can make yesterday's confirmation impossible to reproduce.

Public source does not guarantee that a particular deployment ran the code or preserved the expected bytes. For high-impact work, add a human approval or a second-system confirmation before sending a message, changing durable data, spending funds, or publishing a result. For low-risk reads, a receipt plus a spot check may be enough. The standard should follow the cost of being wrong, not the agent's confidence.

Finally, describe the result with precise language. Say that a call was recorded, that bytes matched, or that a source system displayed a state at a given time. Do not silently upgrade those observations into a claim that a model was correct, a transaction settled, a customer received a message, or a legal requirement was satisfied. Honest limits are part of a useful verification record.

For a live demonstration, make one low-risk tool call through the Zambo demo, then inspect its receipt through the verification page. Keep the example separate from private production data, and use the same evidence vocabulary when you document the workflow.

What a good review record contains

A useful review note is short but specific. Record the question being answered, the receipt identifier, the capture time, the verifier result, the system queried for confirmation, and the reviewer decision. If the evidence is incomplete, name the missing artifact instead of adding a confidence score that has no defined source. This format lets a later reviewer repeat the check without reading an entire conversation.

For example, a reviewer might write that a tool call was recorded with the expected scope, that its committed bytes matched the receipt hash, and that the destination system did not yet show the requested change at the time of read-back. That is a useful finding. It is more precise than saying the agent failed, because the available evidence distinguishes execution from the external outcome.

When this method is not enough

Receipts are one layer in a control process. They do not replace access control, approval policy, backups, monitoring, incident response, or domain review. They also cannot recover data that was never observed or prove that a third-party response was truthful. If the workflow involves regulated records, money, safety, or an irreversible publication, add the controls required by the organization that owns that risk.

The best implementation makes uncertainty visible at the same place it reports success. Keep pending and rejected states queryable, make corrections append-only when possible, and preserve the original bytes used for verification. That discipline gives operators a durable explanation for what happened without asking them to trust a generated narrative.

Before closing the review, ask whether another person could reach the same conclusion from the retained evidence. If not, identify the missing link, assign its owner, and leave the result pending. A transparent incomplete record is safer and more useful than a complete-looking record that quietly joins assumptions to facts.

For developers and agents

Zambo gives AI work a receipt you can check. For people: add Zambo to your AI app and ask for a job. These technical links are for developers and AI agents. One hosted MCP at https://zambo.dev/mcp. Check the proof for recorded tool calls (verify a receipt). Agents: 24-hour Day Pass, $0.99 USDC on Base via x402, not per call. People: Zambo Pass $29/mo or $290/yr; holder pass $19/mo for verified $ZAMBO holders.

Start here: For AI agents · Add Zambo · Check a receipt · Answers · Guides · What a receipt is · Open receipt format (draft) · All tools · Pricing · Day Pass: $0.99 · Agent Day Pass · Switch AIs without losing your work · Connect your AI · Founder · Source code
Brennan on X · Zambo on X