Opportunities › CLI output filtering for LLM cost reduction › Proposed app
Trimline
Shared CLI output filters that cut tokens before commands reach your LLM agents
- Score
- 47
- confidence 51
- 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
- 1973
- Mature market · AI estimate
ScreensConcept
Desktop app: DevOps teams configure and test CLI filters at their workstations, tuning settings and reviewing savings data during infrastructure work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Filter preview
Safe to publish-92%
About this appAI proposal
Trimline sits between a command and the language model reading its output. It strips the noise from kubectl, docker, git, terraform and your own internal tools, and passes on only what an agent needs to act. Filters run locally, so raw output never leaves the machine.
Platform teams write or pick a filter per command, set how aggressive it is, and share the set with everyone. The web app is the team's filter library: preview a filter against a pasted sample, see the tokens it saves, and publish a version to the team's agents.
Unlike grep or jq, filters know the command and are built around token cost, not one-off pipes. Each has a safe level that keeps errors, status and identifiers, and a stricter level for tight budgets.
- Tagline
- Send the model what matters. Trim the rest.
- Voice
- PlainPrecisePractical
- Why this name
- Trim is what the product does to output, and a line is what a terminal prints. Two plain words joined, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Per-command filters
Ready-made filters for common infrastructure commands, each tuned to keep errors, status and names while dropping decoration and repeats.
No member yet
Aggressiveness dial
Choose light, balanced or strict per command. Preview exactly what is removed before an agent ever sees the result.
No member yet
Plugins for internal CLIs
Add filters for proprietary tools from a sample of their output, using simple rules you can read and review.
No member yet
Sample playground
Paste real output, run a filter, and compare before and after with the token count for each.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14352EDeep Pine · primary#F6F4EEPaper · surface#1B1F1DInk · text#E8A33DSignal Amber · accent#5E8C6AMoss · data
Typography
Aa
Trimline
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Send the model what matters. Trim the rest.
PricingAI proposal
The material gives no competitor prices. Tiers are a starting point to test, following the freemium and per-seat ideas in the opportunity, not a claim about market rates.
Free
Free
- Base filters for common CLIs
- Local runner
- One user
- Playground
Team
$29 a month
- Shared filter library
- Custom plugins
- Version history and review
- Savings dashboard
- Up to 15 seats
Business
Contact us
- Unlimited seats
- SSO
- Audit log
- Private filter packs
- 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
- Shared outputs. Savings reports and filter packs can be shared by link or repository file. Teammates and other teams who open them see the filter library and a before-and-after example.
- Do they become customers?
- Readers are often engineers who could start a free account, but the buyer is a platform lead. Test whether shared packs lead to sign-ups and then to team plans.
- First customers
- Platform teams of 5 to 30 engineers running LLM agents against Kubernetes and Terraform on a metered service such as AWS Bedrock.
- Where to reach them
- Hacker News and DevOps forums: Share a free filter pack with measured before and after token counts, and follow up with people who comment.
- Open-source filter packs: Publish base filters on GitHub with a link to the team library for sharing and review.
- Direct outreach to platform leads: Offer a one-week savings measurement on a team's own agent logs.
- First milestone
- Five platform teams each pay for a Team plan within 60 days, with measured token savings on their own commands.
Why people already pay for this
- VerifiedHacker News post received 156 points and 80 comments
- Public claimThe tool reduced token consumption by 91.8% over 2 months of personal use across multiple CLI commands
- Public claimThe tool is currently helping the creator avoid reaching token limits on their company's AWS Bedrock subscription
- AI inferenceSpecific commands show dramatic token savings: kubectl get reduced 93.9%, grep reduced 96.2%, docker reduced 96.1%
- AI inference
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 · Local runnerSmall cross-platform CLI wrapperFilters must run on developer machines and CI so raw output stays local.
- This app · Token countingOffline tokenizer libraryShows before and after token counts without sending output to a service.
RisksAI inference
- TechnicalOutput formats change between tool versions, so filters can break or drop needed lines. Needs version-aware filters and a test suite of sample outputs.
- CommercialOpen-source filter tools exist and developers may write their own scripts. Paying demand may sit only in team sharing and reporting, which must be tested.
- AIOver-aggressive filtering can remove context an agent needs and cause wrong actions. The safety fallback and previews must be reliable.
- DataSample outputs pasted into the web app can contain secrets. Needs redaction before upload and clear guidance, or local-only previews.
- CommercialThe 91.8% saving is a public claim from one user's own use. Savings for other teams may be lower and need measuring.
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.