API functionality
### Discussed in https://github.com/louislam/uptime-kuma/discussions/96 Originally posted by **TheGuyDanish** July 12, 2021 I'd love to see the ability to retrieve, update, remove or add monitors via API calls. Maybe using an API key that's tied to the user. In a way that when spinning up a new docker container or doing an automated workflow like Terraform, the new device could be added to the software automatically.
FL score
out of 100
Verdict
low confidence
Competition
No competitor data yet
Trend
No signal yet
Adding API functionality to Uptime Kuma would significantly empower dev/ops teams with automation, but requires validating a monetization strategy for a solo builder.
The pain
The gap
Build angle
Strengths
- Explicit user demand from the target audience.
- Addresses a specific and recurring pain point (automation) for a technical user base.
- Leverages an existing, popular open-source project (Uptime Kuma).
- High value proposition for time-saving and efficiency.
- The market for infrastructure automation and monitoring is growing.
Risks
- Monetization strategy for an open-source project feature is unclear and needs validation.
- Building a secure, robust, and well-integrated API can be complex for a solo builder.
- Reliance on the Uptime Kuma project's architecture and future development.
- Potential for the core Uptime Kuma project to add its own API, potentially devaluing a separate product.
Next steps
- Conduct surveys or interviews with Uptime Kuma users (especially those involved in dev/ops, SRE, or IaC) to gauge specific API needs and willingness to pay for automation features.
- Research existing paid APIs/integrations in the self-hosted monitoring space for pricing models.
- Explore potential partnerships or contributions to the Uptime Kuma project to become the official API provider, or develop a clear premium offering that complements the open-source version.
- Define the minimum viable feature set for an API that provides immediate value for automation, focusing on CRUD operations for monitors.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 25
- Solution gap
- 19
- Willingness to pay
- 21
- Buildability
- 10
High demand for an API within an existing open-source tool for automation, but monetizing this specific feature as a solo builder requires a creative approach.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Strong market demand for automation in a growing niche, but requires a clear commercial model for an open-source project feature.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A clear problem in a specific niche with high leverage for users, but solo monetization and complexity are key considerations.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong target audience and value proposition, but clear monetization and validation are needed for a micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A highly demanded feature for a specific audience that makes an open-source tool more future-proof and valuable.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct surveys or interviews with Uptime Kuma users (especially those involved in dev/ops, SRE, or IaC) to gauge specific API needs and willingness to pay for automation features.
- Research existing paid APIs/integrations in the self-hosted monitoring space for pricing models.
- Explore potential partnerships or contributions to the Uptime Kuma project to become the official API provider, or develop a clear premium offering that complements the open-source version.
- Define the minimum viable feature set for an API that provides immediate value for automation, focusing on CRUD operations for monitors.
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
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