Opportunities › Conflict detection for parallel coding agents › Proposed app
Lanemark
Catch clashes between parallel AI coding agents before they write code
- Score
- 58
- confidence 72
- Evidence
- 21
- items on the page
- Rivals
- 3
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2025
- New market · AI estimate
ScreensConcept
Desktop app: Engineering teams work with the coordination dashboard and resolve agent conflicts during focused development sessions at their desks. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Active lanes
1 open conflict5 agents
About this appAI proposal
Lanemark is a coordination layer for teams that run several AI coding agents on one repository. Before writing code, each agent declares what it plans to change: which symbols, and whether it will add, modify, replace or remove them. Lanemark compares those declarations and flags incompatible plans while there is still nothing to merge.
Git compares lines. It cannot see that one agent is replacing a class while another is extending it. Lanemark checks intent against the symbols and the real git state, so these clashes show up before PR review instead of during it. There are no locks, so no agent waits on another.
A web dashboard (installable as an app) shows every active agent, its declared scope and open conflicts across your repositories. Agents connect through an MCP server, so Claude, Codex, Cursor and others can use the same layer.
- Tagline
- Agents say what they'll change first. You hear about clashes before code exists.
- Voice
- PreciseCalmDirect
- Why this name
- Lanemark: each agent works in its own lane, and marks what it intends to touch. Two short plain words, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Declare intent before code
Agents publish scope and operation type (add, modify, replace, remove) for named symbols before they edit anything.
No member yet
Destructive vs additive conflicts
Replacing a class while another agent extends it is flagged as destructive. Two additive changes are allowed through.
No member yet
No locks, no deadlocks
Agents are warned, not blocked. Each can proceed, re-plan or ask for a human decision.
No member yet
Checked against real git state
Declared intents are compared with the actual branches and worktrees, so stale or finished plans drop out.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F5C5ADeep Teal · primary#F6F3ECPaper · surface#1E2A2BInk · text#E0742BSignal Orange · accent, conflict marker#3C6EA8Slate Blue · data#B3392FBrick · destructive conflict
Typography
Aa
Lanemark
PricingAI proposal
Material gives GitHub's page as 2 paid plans from $4 to $21 by usage, plus a free plan. These tiers sit in that range as a starting point to test; no conflict-detection competitor price is given.
Solo
Free
- 1 repository
- Up to 3 agents
- Local intent store
- Community support
Team
$12 a month
- Per seat, 10 repositories
- Up to 20 agents
- Shared dashboard
- Push alerts
Scale
$21 a month
- Per seat, unlimited repositories
- Unlimited agents
- Custom conflict rules
- Decision history
- Priority support
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
- Users invite others. One engineer sets up Lanemark on a repo, then invites teammates to the shared dashboard so their agents declare intents into the same store. Conflict links can be pasted into PRs and chats.
- Do they become customers?
- Invitees are engineers on the same team, so they can become paying seats or push the team to upgrade. Test whether a second teammate joins within a week and whether the team moves off Free.
- First customers
- Engineering leads at 5 to 30 person software teams already running three or more AI coding agents in parallel on one mid-to-large repository.
- Where to reach them
- Hacker News and dev forums: Show a demo of two agents colliding on one class and Lanemark catching it before any code is written.
- Agent tool communities: Post MCP setup guides in Claude Code, Cursor and Codex user communities and Discords.
- Direct outreach: Message leads who publicly write about running parallel agents and offer a free pilot on one repo.
- First milestone
- Ten teams run it on a real repo for two weeks, and at least three pay for a Team plan.
Why people already pay for this
- VerifiedHacker News post received 45 points and 20 comments
- Public claimTeams waste time reviewing and fixing conflicts at PR time when multiple coding agents work on the same repository in parallel.
- Public claimGit's line-based conflict detection misses architectural conflicts where one agent replaces a component while another extends it.
- Public claimManual code review often fails to catch architectural incompatibilities between agents' changes.
- 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 · Agent connectorMCP server and small CLIAgents declare and query intents through MCP; the CLI installs it and reads local git state.
- This app · Code parsingTree-sitter symbol extractionMaps declared and changed code to named symbols across several languages.
RisksAI inference
- TechnicalSymbol-level detection needs language-aware parsing. Covering many languages well is hard, and weak coverage gives false alarms or missed conflicts.
- PlatformAgents must choose to declare intents. If Claude, Codex or Cursor change their tool protocols, or agents skip the call, coverage drops.
- CommercialA free open-source tool with a similar idea exists in the material, with setup in 30 seconds. Teams may not pay for a hosted dashboard.
- DataTeams may not want symbol names and plans from private repositories stored in a hosted service, which pushes toward local-first sync.
- CommercialClaims of 98 agents with zero conflicts are unverified. Real demand at that scale may be small, since most teams run only a few agents.
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.