Opportunities › AI-powered coding assistance for developers › Proposed app
Rungs
Start cheap, climb only when needed. AI debugging with each answer's cost shown.
- Score
- 38
- confidence 40
- Evidence
- 4
- items on the page
- Rivals
- 3
- named in sources
- First version
- 30 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2021
- Emerging market · AI estimate
ScreensConcept
Desktop app: Developers work through multi-step debugging flows with code review, cost analysis, and detailed verification that require a full screen and keyboard at a desk. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New case
Ready to run- InputTrace + 3 files
About this appAI proposal
Rungs is a debugging and code-review workspace. It tries a problem on a low-cost model first and moves to a stronger, pricier one only when the cheaper answer fails a check. You paste an error, a diff or a set of files, and Rungs shows each attempt, its cost and whether it held up.
It is for developers who already mix models by price: a light one for quick lookups, a strong one for hard bugs. Rungs does the switching and keeps a running cost ledger, so regular use does not mean a surprise bill.
Every claim in an answer is tied to a line of your code. Rungs flags claims it cannot tie to one, so you spend review time where it matters instead of reading everything.
- Tagline
- Pay for the hard rung only when the easy ones fail.
- Voice
- PlainFrugalPrecise
- Why this name
- A rung is one step on a ladder. The product climbs from cheap to capable models one step at a time, and the name is short and easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Escalation ladder
Each task starts on a cheap model and moves up a rung only if checks fail or confidence is low. You set the ceiling per task.
No member yet
Claims tied to code
Each statement in an answer links to the file and line it relies on. Unlinked claims are marked so you review them first.
No member yet
Cost ledger
See spend per task, repo and month, next to what the same task would have cost on the top rung.
No member yet
Case files for hard bugs
Bundle logs, a stack trace, a diff and notes into one case. Every attempt is kept so a long hunt can resume later.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F3D36Pine · primary#F6F1E7Oat · surface#1B2420Ink · text#E8A33DAmber · accent#6F8F5EMoss · data
Typography
Aa
Rungs
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Pay for the hard rung only when the easy ones fail.
PricingAI proposal
The material gives no competitor prices, only that one model is described as 1/20 the price of another. These tiers are a starting point to test, not a market-based price.
Free
Free
- 20 tasks a month
- Bring your own keys
- Cost ledger
- Two rungs
Solo
$12 a month
- Unlimited tasks on your own keys
- All rungs and ceilings
- Case files and history
- Claims tied to code
Team
$24 a month
- Per seat, 3 seat minimum
- Shared cases and review queue
- Team budgets and limits
- Repo-level cost reports
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. A solved case exports as a read-only page with the claims, linked lines and cost. Developers paste it into pull requests, issues or chat, where teammates see the evidence and the Rungs name.
- Do they become customers?
- Teammates who open a case page are developers and could sign up. Test how many of those who open a shared page start their own case, and how many then pay.
- First customers
- Independent developers and small teams of 2 to 10 engineers who already pay for more than one AI model and debug backend services, such as queues and caches.
- Where to reach them
- Hacker News and dev forums: Post worked case pages showing a hard bug solved on a cheap rung, with costs, in threads on model prices.
- Open source maintainers: Offer free Team seats to maintainers in return for public case pages on real bugs in their projects.
- First milestone
- Ten developers each run 20 or more cases in a month, and five of them pay for Solo or Team.
Why people already pay for this
- Public claimDevelopers are price-sensitive when choosing between AI models for coding tasks and will use cheaper alternatives even if less capable.
- Public claimHigh-capability models can solve very difficult debugging problems that most models cannot, such as finding race conditions in large codebases.
- Public claimCurrent AI coding assistants require significant manual review and validation before code can be used.
- AI inferenceDevelopers stratify their use of AI models by cost and capability based on task type.
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 · Model gatewayMulti-provider LLM routing layerRungs must call light and strong models from several providers, track token cost and fall back when one fails.
- This app · Background jobsDurable queue for long runsHard debugging cases can run for many minutes and must survive closed tabs and retries.
RisksAI inference
- AIEscalation checks may pass wrong answers. Linking claims to lines shows where an answer points but not that it is right, so users may over-trust it.
- CommercialDevelopers may already switch models by hand, or use editor tools with built-in routing. Willingness to pay for the ladder is unproven.
- PlatformModel providers can change prices, limits or terms. Margins on hosted plans depend on them, and bring-your-own-keys depends on provider access.
- DataTeams may refuse to upload proprietary code to a new service. Retention limits and clear data handling are needed before sales to companies.
- TechnicalLarge codebases do not fit in a model's context. Choosing which files to send decides both cost and quality, and is hard in a browser app.
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.