Alter (YC S25) – Access control for multi-user agents
Hi HN! We’re Srikar and Kevan, founders of Alter – https://alterauth.com/. We’re building access control for agents that can take actions on behalf of multiple users.If you’ve built an internal agent people use through Slack, you may have experienced this scenario: everyone signs in with their own account, but the agent connects to the CRM or billing system with one shared API key, so the agent can do anything that the key can do. User-level authentication in Slack does nothing to control what the agent can do in an external system like the CRM or billing system.Here’s what Alter does:-It acts as a proxy and manages downstream credentials for every API request that an agent takes;-It takes identity into account for every action the agent does. Automatically take into account the end user identity of the person that the agent is taking actions on behalf of.-It enforces fine grained runtime policies and records compliance grade audit logs.Here’s a demo video: https://www.youtube.com/watch?v=9ZqPU14ipGEWe started this because we saw first hand how hard it is to build agents in regulated industries. To unlock the true benefits of agents we need to allow agents to take actions, sometimes actions on behalf of end users. I (Srikar) was working at Goldmans and I saw how prohibitively expensive it was for me as a developer to write my own custom auth to enable multi-tenancy in agents and to allow agents to have strict policy controls as they act on behalf of end users. My co-founder, Kevan, is a second time founder and was previously working on a database startup. He has experience building and selling deep tech into highly regulated environments. So this problem seemed like a natural fit for the both of us.Building and maintaining custom auth for multi tenant agents was challenging. But so was ensuring that the solution we developed was secure, scalable and keeping track of security grade audit logs. As a result we realized there needs to be a
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Alter provides secure, fine-grained access control for multi-user agents acting on behalf of users in regulated industries.
The pain
The gap
Build angle
Strengths
- Clear and painful problem in regulated industries
- Strong founder domain expertise
- Compliance and auditability built-in
- Niche market with few direct competitors
Risks
- Long sales cycles and trust-building in regulated sectors
- Complexity of integrating with diverse external systems
- Potentially limited market size outside regulated industries
- Need for continuous updates to comply with evolving regulations
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 90
- Solution gap
- 85
- Willingness to pay
- 75
- Buildability
- 78
The problem of secure multi-user agent access in regulated industries is clear, the solution addresses a real gap, customers are likely to pay for compliance and security, and the founders have relevant experience to build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a painful and costly problem with a clear value proposition, but customer acquisition may require deep industry trust and long sales cycles.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The market is niche but growing, the product fits well with compliance needs, and the founders have domain expertise, though scaling beyond initial customers may be challenging.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The solution is technically feasible and addresses a compliance-critical issue, but the complexity of integrations and regulatory variance could slow adoption.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The founders have strong domain knowledge and a clear problem, the product solves a real pain in regulated environments, and the market is underserved with few direct competitors.
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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