Keeper – embedded secret store for Go (help me break it)
Keeper is an embeddable secret store (Argon2id, XChaCha20-Poly1305 by default). Four security levels, audit chains, crash-safe rotation. Vault is overkill for most use cases. This is for when you ge paranoid about env and need encrypted local storage that doesn't suck. No security through obscurity, hence, It's still early, so now's the best time to find weird edge cases, race conditions, memory leaks, crypto misuse, anything that breaks. The README has a full security model breakdown if you want to get adversarial.
FL score
out of 100
Verdict
medium confidence
Competition
6
competitors found, emerging market, funded players
Trend
No signal yet
An embedded, crash-safe secret store for Go developers, simplifying secure credential management where Vault is overkill, but needs to solidify its unique angle against an existing niche competitor.
The pain
The gap
Build angle
Strengths
- Addresses a real and severe pain point (secrets management) in a growing market.
- Specific target audience (Go developers) with clear frustrations (Vault's complexity).
- Strong technical foundation and expertise demonstrated by the creator.
- Potential for a 'just right' solution between insecure env vars and overkill enterprise vaults.
- Good distribution channels through Go developer communities.
Risks
- Direct competition from existing open-source Go embedded secret stores (e.g., 'untold').
- The inherent complexity and high stakes of building and maintaining a robust cryptographic security library as a solo builder.
- Market size for this specific 'embedded for Go' niche might be smaller than anticipated.
- Monetization strategy needs to be clearly defined beyond open-source to ensure long-term viability.
- Convincing users of its security robustness against established players and potential 'not invented here' syndrome.
Next steps
- Conduct in-depth user interviews with Go developers to validate the specific need for Keeper's features (audit chains, crash-safe rotation) over existing embedded Go libraries like 'untold'.
- Refine the unique selling proposition to clearly differentiate from 'untold' and other simple secrets management solutions.
- Explore potential commercialization paths (e.g., enterprise features, managed service, security audit services) and test willingness-to-pay for these advanced offerings.
- Continue engaging with the Go community to gather feedback, identify edge cases, and build trust in the security model.
- Consider getting a third-party security audit of the core library once it matures to build user confidence.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 22
- Solution gap
- 14
- Willingness to pay
- 19
- Buildability
- 13
A promising niche for Go developers needing an embedded, secure secret store, addressing Vault's complexity, but facing competition in the specific 'embedded Go' space.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Keeper has strong market tailwinds and a clear value proposition but needs a clearer differentiation strategy against direct niche competitors and faces high build complexity for security.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A strong fit for a technically adept solo builder targeting Go developers, but needs to solidify its unique position against existing embedded Go solutions and establish a clear path to monetization.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A well-targeted micro-SaaS candidate with a clear value proposition and solid distribution, but needs to mitigate assumption risks about market size and differentiate clearly from existing niche competitors.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A strong idea with clear demand from a specific user, but needs to articulate its narrowest wedge and ongoing usage insights against direct embedded Go competitors.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct in-depth user interviews with Go developers to validate the specific need for Keeper's features (audit chains, crash-safe rotation) over existing embedded Go libraries like 'untold'.
- Refine the unique selling proposition to clearly differentiate from 'untold' and other simple secrets management solutions.
- Explore potential commercialization paths (e.g., enterprise features, managed service, security audit services) and test willingness-to-pay for these advanced offerings.
- Continue engaging with the Go community to gather feedback, identify edge cases, and build trust in the security model.
- Consider getting a third-party security audit of the core library once it matures to build user confidence.
Who is already there, emerging market
A secrets management tool that secures, stores, and tightly controls access to tokens, passwords, certificates, encryption keys, and other sensitive data.
Pricing: Vault Community Edition is free (self-hosted) but lacks enterprise features. HCP Vault Dedicated Essentials (small cluster) starts around $1,152/month plus $72.92/month per client. Enterprise pricing is custom and often in the low six figures annually.
A universal secrets management platform that helps developers manage environment variables and secrets across projects and environments.
Pricing: Free for up to 5 users. Developer plan starts at $7/user/month (billed annually, $12/user/month billed annually for Team, $14/month when billed monthly), Professional at $24/user/month (billed annually), Enterprise with custom pricing.
- PassboltVisit
An open-source, collaborative password manager designed for teams to centralize secure storage, sharing, and management of digital credentials.
Pricing: Community (free), Business: $49/month, Enterprise: Custom Pricing. Passbolt Pro is €4.5/user/month.
A SaaS-native secrets management platform that aims to simplify security and cut costs by replacing complex vault infrastructure.
Pricing: Not publicly available; focuses on custom pricing based on needs. They emphasize being a cost-effective alternative to Vault.
An AI-powered coding platform with a multi-agent LLM that helps developers write, debug, test, and ship code faster.
Pricing: Free plan available. Pro plan at $10/month (or $2/month for the first month), Pro Plus at $20/month, Pro Max at $40/month. Enterprise plans offer custom pricing.
- untold (GitHub)Visit
An embedded secrets library for Go web services that allows embedding encrypted secrets directly into a Go application.
Pricing: Free (open-source).
What they charge
Recent news
- Show HN: Keeper – embedded secret store for Go (help me break it)
Hacker News, April 10 2026
- Keeper - embedded secret store for Go : r/golang
Reddit (r/golang), April 10 2026
Market signals
The secrets management market is experiencing significant growth, projected to reach USD 8.05 billion by 2030, advancing at a 13.8% CAGR. The secrets detection market alone is expected to grow from USD 1.24 billion in 2024 to USD 14.87 billion by 2033 at a CAGR of 32.1%. Key drivers include the ongoing migration towards DevSecOps, the rapid growth in machine identities (45:1 machine-to-human identity ratio), expanding multi-cloud footprints, increasing cyber threats, and stringent regulatory requirements. There is a shift from reactive credential vaults to proactive secrets governance, with a growing preference for consolidated platforms over point tools. Large enterprises dominated the market in 2024 with a 71.3% share, but SMEs are projected for the fastest growth (15.5% CAGR) due to SaaS vaults making enterprise-grade credential hygiene more accessible. The encryption key management market, which includes secrets management, is also growing rapidly, projected to reach $11.2 billion by 2034 at a CAGR of 12.8%.
What frustrates people
- HashiCorp Vault is complex to set up and maintain, requiring significant IT overhead for availability and disaster recovery.
- HashiCorp Vault requires considerable command line work both upfront and during usage.
- HashiCorp Vault's free Community Edition lacks essential enterprise features like namespaces and disaster recovery.
- The change in HashiCorp Vault's license to BSL 1.1 has limited some usage rights compared to the previous open-source license.
- Passbolt's initial setup can be a bit complex.
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!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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