Opportunities › Production debugging agents for distributed systems › Proposed app
Tracepin
Let your coding agent read live variables in production, no redeploy needed
- Score
- 31
- confidence 62
- Evidence
- 17
- items on the page
- Rivals
- 3
- named in sources
- First version
- 60 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: On-call engineers use agents to diagnose incidents by placing probes and reading production state—incident response work at a desk. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New probe
Read-only- Servicecheckout-api
About this appAI proposal
Tracepin gives coding agents safe, read-only probes into running Node, Python and Java services. When an incident hits, the agent asks for the exact variable values at the line where it fails. Tracepin captures them without pausing the service and without a redeploy.
It is built for on-call engineers who use agents such as Cursor or Claude to diagnose incidents. Instead of reasoning backward from logs that may lack the needed data, or adding log lines and waiting for the bug to recur, the agent places a probe and reads real state.
Probes are limited by design: read-only, rate-capped, auto-expiring, and with redaction rules for sensitive fields. Agents connect over MCP, and every probe is recorded in an audit trail your team can review.
- Tagline
- Ask the running code, not the logs.
- Voice
- CalmPreciseDirect
- Why this name
- A trace is the record of what happened; a pin marks one exact spot. Tracepin means fixing a probe on one line of live code. Two plain syllables.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
On-demand probes
Agents set a probe on a file and line, with an optional condition, and get local variables and call stack at the moment it fires.
No member yet
No pause, no redeploy
Snapshots are captured at runtime without stopping the service, ending the add-logging-and-redeploy loop.
No member yet
Read-only by design
Probes cannot change state or run side effects. Each has a hit cap, a time limit and a CPU budget.
No member yet
MCP for your agent
Connect Cursor, Claude or any MCP client. The agent drops probes and reads results in the same session it is fixing code in.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14232BDeep Slate · primary#F6F3ECPaper · surface#1B2A30Ink · text#E8A317Signal Amber · accent#1F8A84Probe Teal · data
Typography
Aa
Tracepin
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Ask the running code, not the logs.
PricingAI proposal
The material gives no price for probing tools. The tiers echo Cursor's verified pricing page (free, $20 to $40 a month, larger customers on request), since buyers already pay that for agents. A starting point to test.
Free
Free
- 1 service
- 20 snapshots a month
- Node, Python or Java
- MCP connection
Pro
$20 a month
- 5 services
- 1,000 snapshots a month
- Redaction rules
- 30-day audit trail
Team
$40 a month
- Per seat, unlimited services
- 10,000 snapshots a month
- Shared probe policies
- Audit export
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Partners and integrations. Tracepin is listed where agents find tools: MCP directories and agent setup guides. Teams add it to their agent config, and the agent then calls it during incidents.
- Do they become customers?
- Engineers who install it are the paying buyers, so exposure and acquisition coincide. To test: whether individuals on a free plan convert to Team once a second engineer joins the on-call rotation.
- First customers
- On-call teams of 5 to 30 engineers running Node or Python services in production, who already use Cursor or Claude daily for coding and incident work.
- Where to reach them
- Hacker News and dev forums: Post a demo of an agent finding a real bug through one probe, with no redeploy, and invite readers to try the free plan.
- MCP directories: List the MCP server with a two-minute setup for Cursor and Claude.
- Founder outreach: Offer a free incident-response pilot to 20 small teams found through engineering communities.
- First milestone
- Five teams use probes during real incidents, and three pay for Pro or Team within 60 days.
Why people already pay for this
- VerifiedHacker News post received 69 points and 51 comments
- AI inferenceEngineers waste time digging through logs and traces or redeploying with console logs to find root causes
- Public claimAI agents lack sufficient telemetry from their own written code to debug effectively
- Public claimThe log-and-redeploy cycle is slow and painful, making engineers hate on-call
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Runtime agentsIn-process probe libraries for Node, Python, JVMCapturing locals at a line without pausing needs runtime hooks per language.
- This app · Agent interfaceMCP serverCursor and Claude reach probes through MCP tool calls.
RisksAI inference
- TechnicalCapturing variables at runtime across Node, Python and the JVM without measurable overhead or pauses is hard; each runtime needs its own safe mechanism and heavy testing.
- CommercialTeams may favor fixing prod over adopting new tools; one source claim says earlier testing tools struggled for this reason. Value must show in the first incident.
- DataSnapshots can contain secrets or personal data. A redaction miss would break trust, so redaction must run on the host and default to strict.
- PlatformAgent vendors could add their own production inspection, or change how MCP tools work, reducing the need for a separate product.
- RegulatorySecurity reviews at larger companies may block production access by agents without audit, approvals and self-hosting options.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.