For AI builders · transparent by default

ZAMBO INTEL

A live readout of what is running across the Zambo Stack: aggregate tool activity, current products, and the exact place to start if you want your AI to use Zambo.

PRODUCTION READOUT · REFRESHES EVERY 30S
Recorded tool events
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Aggregate telemetry, not unique users or revenue
Activity Today
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Rolling 24-hour window
ZAMBRO Scans
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Recorded production calls
ZAMBOT Sparks
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Recorded production calls
LeadSignal Searches
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Recorded searches, not a promise of fit
CreditHunt Matches
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Matches returned by the live index
ProvibeCode Audits
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Audits requested through the product
Agent Pings
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Recorded registry and health events

Live products - follow the receipt

ZAMBRO
21-branch opportunity scanner · free
OPEN →
LeadSignal
AI leads for contractors
OPEN →
ZAMBOT
Strategy + code fix engine
OPEN →
CreditHunt
Verified startup credit programs
OPEN →
Signal
Verified trust passport
OPEN →
ProvibeCode
AI code audit
OPEN →
Zambo Pass
Connected access to the current stack
VIEW →
Live activity
Verified MCP tool calls in real time
OPEN →
X711
MCP execution and x402 payment rail
OPEN →
Entangler
Cross-agent messaging fabric
OPEN →
Criptic
Wallet intelligence on Monad and Base
OPEN →
BountyLayer
Autonomous value discovery
OPEN →
Nad.City
3D Monad explorer
OPEN →
BaseHawk
Base wallet intelligence
API →
Lioncage Protocol
Gamified DeFi · Monad
STACK →
Elysium Lab
Vertical AI for funeral directors
OPEN →
SCAMRA
Free scammer intelligence
OPEN →
Govri
AI governance policy generator
OPEN →
VeriAI
Cryptographic trust for AI output
OPEN →

This page shows the broader live stack; product bundles and eligibility can change. See the current pricing page for what is included today.

Intel API - Free, No Auth

curl https://zambo.dev/api/forge/traction # Returns: zambro_scans, zambot_sparks, leadsignal_searches, # credithunt_matches, provibecode_audits, today, this_week, total

ZAMBARTICLE - A SHORT DAILY NOTE FOR AI BUILDERS

One practical insight from operating Zambo, one free tool you can use today, and one pattern worth watching. Delivered at 9 AM UTC. Not generic AI news.

Free. One-click unsubscribe in every email. Generated from current Zambo Stack data and clearly labeled when a number is an estimate.

Research intelligence review in practice

Use execution receipts to distinguish a source lookup, an observed response, and an analyst's interpretation. 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.

Preserve source time, query boundary, raw observation, transformation, and human conclusion separately. 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.

A traceable lookup still may return stale, incomplete, or adversarial information. 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
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