Opportunities › AI model efficiency for long-running development tasks › Proposed app
Longhaul Ledger
Track token spend, stuck agents and project rules across multi-week AI coding
- Score
- 23
- confidence 55
- Evidence
- 6
- items on the page
- Rivals
- 2
- named in sources
- First version
- 25 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: Developers monitor token spend, track stuck agents, and maintain project rules across multi-week sessions—sustained desk-based AI development work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
This week
Retries are 21% of spend4.2M tokens
About this appAI proposal
Longhaul Ledger is a companion dashboard for developers who run AI coding sessions for days or weeks. It shows where tokens go, flags helper agents that never return an answer, and keeps a short, editable list of project rules and learnings you can paste into any session.
It sits beside your model of choice and does not replace it. You import usage exports or log sessions by hand, and it points to loops, repeated tool calls and drift from your rules, so you step in once instead of watching all day.
It is built to cut your overhead: one page, a few alerts, and a rules file you control. Because it works across providers, you can compare how different models spend tokens on the same project.
- Tagline
- Fewer tokens wasted. Fewer sessions babysat.
- Voice
- CalmPlainPrecise
- Why this name
- A long haul is a multi-week session; a ledger is a plain record of what it cost and what was learned.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Spend by task, not just by day
Break token use down by task, tool and session so you see which parts of a long project cost the most.
No member yet
Stuck-agent alerts
Flags helper agents or tool calls that run past a set time or repeat without a result, so you can stop them early.
No member yet
Living rules file
Keep project rules and learnings in one short list. Edit, retire or pin them, then export as a prompt block.
No member yet
Context drift check
Compare recent session output to your pinned rules and get a note when it starts to break them.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12403CDeep Teal · primary#F6F1E7Paper · surface#1D2423Ink · text#E0902BAmber · accent#6E9A6BMoss · data
Typography
Aa
Longhaul Ledger
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Fewer tokens wasted. Fewer sessions babysat.
PricingAI proposal
The material gives no competitor tool prices, only that premium AI plans cost about $200/month (public claim). These tiers are a starting point to test, set well below that.
Solo
Free
- One project
- Manual session logging
- Rules file with export
Pro
$15 a month
- Unlimited projects
- Usage file import
- Stuck-agent alerts
- Budget guardrails
- Model comparison
Team
$12 a month
- Per seat, 3 seat minimum
- Shared rules and projects
- Team spend view
Would you pay Team · $12 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
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
- Shared outputs. Users export a project rules block or a spend report as a link or file and share it with teammates or post it in repos and forums, showing the product name.
- Do they become customers?
- Teammates who open a report could sign up, but a one-time viewer may not. Test whether report viewers who also run long AI sessions start their own free project.
- First customers
- Solo developers and small teams of two to ten who run multi-week AI-assisted coding projects and pay for premium AI plans.
- Where to reach them
- Hacker News: Post a Show HN with a real spend breakdown from a long session and a free rules-file tool.
- Developer communities: Share in subreddits and Discords for AI coding, offering free setup help to early users.
- First milestone
- Ten active free users logging at least two weeks of sessions, and five converting to paid Pro within 60 days.
Why people already pay for this
- Public claimUsers are spending substantial amounts on AI model subscriptions for extended coding work
- Public claimPremium tier subscriptions cost around $200/month
- Public claimClaude Opus 4.7 is inefficient with tool usage, spawning multiple subagents that get stuck without returning
- Public claimToken management and monitoring tooling adds friction rather than reducing it
- Public claimDevelopers run extended multi-week AI sessions for project work
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 · Background jobsScheduled worker for alert checksStuck-run and budget alerts must be checked while the app is closed.
- This app · Push notificationsWeb Push serviceAlerts must reach developers on phone or desktop quickly.
RisksAI inference
- DataUsage exports differ by provider and may change or be missing; without live data, logging by hand may feel like more overhead, the problem this fixes.
- PlatformModel providers may ship their own monitoring and memory features, making a separate tool less needed.
- CommercialThe evidence is only public claims from one forum; how many developers would pay for this is untested.
- AIDrift checks against rules may give false alarms or miss real problems, which would erode trust.
- TechnicalDetecting stuck agents from imported logs needs formats that expose tool and helper calls; many may not.
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.