Agent Vault – Open-source credential proxy and vault for agents
Hey HN! Today we're launching Agent Vault - an open source HTTP credential proxy and vault for AI agents. Repo is at https://github.com/Infisical/agent-vault, and there's an in-depth description at https://infisical.com/blog/agent-vault-the-open-source-crede....We built Agent Vault in response to a question that been plaguing the industry: How do we give agents secure access to services without them reading any secrets?Most teams building agents have run into this exact problem: They build an agent or agentic system and come to realize at some point that it needs credentials in order to access any services. The issue is that agents, unlike traditional workloads, are non-deterministic, highly-prone to prompt injection, and thus can easily be manipulated to leaking the credentials that they need to operate. This is the problem of credential exfiltration (not to be confused with data exfiltration).In response to this, some teams we've seen have implemented basic guardrails and security controls to mitigate this risk in their agentic environments including using short-lived access tokens. The more advanced teams have started to converge toward a pattern: credential brokering, the idea being to separate agents from their credentials through some form of egress proxy. In this model, the agent makes a request to a proxy that attaches a credential onto it and brokers it through to the target service. This proxy approach is actually used in Anthropic's Managed Agents architecture blog with it being that "the harness is never made aware of the credentials." We've seen similar credential brokering schemes come out from Vercel and in Cloudflare's latest Outbound Workers.Seeing all this made us think: What if we could create a portable credential brokering service plugged seamlessly into agents' existing workflows in an interface agnostic way, meaning that agents could continue to work with APIs
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Open-source proxy that lets AI agents access services without ever seeing credentials, solving credential exfiltration in agentic systems.
The pain
The gap
Build angle
Strengths
- Solves a real, specific problem that major AI companies (Anthropic, Vercel, Cloudflare) have independently identified and built solutions for
- Team has existing distribution and credibility through Infisical's secrets management product
- Open-source approach can become the standard before proprietary solutions lock in the market
- Clear technical architecture (HTTP proxy pattern) that's proven to work at scale
- Timing is good, agent adoption is accelerating and security concerns are top-of-mind
Risks
- Market may fragment: major cloud providers (AWS, GCP, Azure) could build this into their agent platforms, making open-source version less relevant
- Adoption requires agent framework integration work; won't be automatic, teams need to actively adopt it
- Security tool category has high bar for trust; one vulnerability or breach could kill adoption
- Monetization path unclear; open-source security tools struggle to convert to revenue
- Problem may be solved at the framework level (e.g., LangChain adds this natively) before this becomes standard
- Enterprise sales cycle for security tools is long; open-source adoption doesn't automatically convert to revenue
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Five lenses, one composite. How scoring works
The angle
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!
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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