Opportunities › Cloud deployment platform for AI coding agents › Proposed app
Fieldhand
Run many coding agents in the cloud and review what they built, not the code
- Score
- 46
- confidence 62
- Evidence
- 8
- items on the page
- Rivals
- 2
- named in sources
- First version
- 60 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: Developers port local agent setups, review outputs and diffs, send feedback for retries—extended code review and QA work at a workstation. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Import setup
Ready to deploy3 of 3
About this appAI proposal
Fieldhand moves your local coding agent setup (sessions, memories and MCP server settings) to the cloud in one step, then runs as many agents side by side as you need. Each agent works in its own sandbox and hands back a result you can look at.
Instead of reading every diff, you open a review page for each run: screenshots and recordings of new user flows, API call results, and command output from the platforms you target. Approve, comment or send the agent back to try again.
It is made for developers and small teams building or using coding agents who want to check outputs across environments without babysitting terminals. Bring your own model subscription or pay for execution through Fieldhand.
- Tagline
- Send your agents out. Review what they bring back.
- Voice
- PracticalCalmDirect
- Why this name
- A fieldhand does the work in the field while the owner checks the harvest. It mirrors agents doing the work and you reviewing results.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
One-step local to cloud port
Import sessions, memories and MCP server configs from your machine. Secrets are asked for once and stored encrypted, so cloud runs start like local ones.
No member yet
Many agents at once
Start dozens of runs from one task list. Each gets an isolated sandbox, a queue position and a live status so nothing collides.
No member yet
Output review page
Every run ends in a page with screenshots, recordings and a short summary of what changed. Judge the result first, open the code only if needed.
No member yet
API behavior checks
Define expected requests and responses once. Fieldhand replays them against each agent's build and flags differences.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F4D3AField Green · primary#F6F3EAPaper · surface#22201BSoil · text#E3A21AMarigold · accent#B8432FClay Red · data: failed checks#3B8EA5Sky Teal · data: running
Typography
Aa
Fieldhand
PricingAI proposal
The material gives no competitor prices. It only suggests a free tier, credits and inference-based pricing. These tiers are a starting point to test, with execution charged by usage beyond credits.
Free
Free
- Starter credits
- 2 concurrent agents
- Local to cloud import
- 7-day run history
Builder
$29 a month
- Monthly credits included
- 10 concurrent agents
- Output review and API checks
- Bring your own model
Team
$99 a month
- 50 concurrent agents
- Cross-platform check runs
- Shared review and audit trail
- Pooled credits for 5 seats
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. Each run's review page can be shared by link, so teammates, clients or open source maintainers see screenshots, recordings and check results, with a small Fieldhand mark on the page.
- Do they become customers?
- Reviewers who open a link are often developers and could sign up, but this is untested. Test whether link viewers click through and create a free account.
- First customers
- Founders and small teams of two to ten engineers already running coding agents locally on web or API products, who review agent work daily.
- Where to reach them
- Hacker News: Share a write-up and demo of porting a local setup and reviewing outputs, since the topic drew 81 points and 70 comments.
- Agent and MCP communities: Post in developer Discords and forums with a free import tool and a sample review page.
- Direct outreach: Message founders who publicly describe running several agents and offer a setup call.
- First milestone
- Ten teams run agents weekly on the free tier and five convert to paid within 60 days.
Why people already pay for this
- VerifiedHacker News post received 81 points and 70 comments
- Public claimDevelopers need to review product output from agents rather than code as models improve
- Public claimEvaluating agent outputs requires multi-faceted testing across platforms and APIs at scale
- AI inferenceExisting agent solutions lack cloud optimization and performance
- 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 · Sandboxed computeContainer or microVM sandbox serviceAgents run untrusted code and need isolated, fast-starting environments in parallel.
- This app · Job queue and orchestrationDurable workflow queueLong agent runs need retries, cancellation and fair scheduling across hundreds of tasks.
RisksAI inference
- TechnicalPorting sessions, memories and MCP configs faithfully from several local agent tools is hard, and formats may change without notice.
- CommercialModel providers ship their own cloud agents, and the founders in the material chose to build in-house; buyers may do the same.
- TechnicalRunning many sandboxes with browsers and multiple operating systems can make compute cost exceed what the usage pricing recovers.
- DataCustomers' source code, secrets and model keys sit in the platform, so a breach or weak isolation would end trust.
- PlatformDepending on third-party model subscriptions may be restricted by their terms for cloud or shared use.
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.