Geiger – See every AI agent on your machine and what it can touch: VALIDATE, 72/100 | Ideas Lab

A visibility tool that shows users which AI agents are running on their machine and what system resources or data each one can access.

The pain

Users install AI tools, browser extensions, and applications without understanding what background processes are actually running or what files, APIs, and system permissions each agent has claimed. A single compromised or malicious AI agent can silently access emails, files, credentials, or execute commands. Users have no dashboard to audit this.

The gap

Operating systems show running processes, but they don't contextualize AI agents specifically or explain their permission scope in human terms. There's no layer between the OS and user that says 'Claude Desktop can read your Documents folder' or 'this agent is making 50 API calls per hour to external services.' Security tools focus on malware, not on the growing surface area of AI agent permissions.

Build angle

Start as a lightweight system monitor that identifies AI agent processes (by signature, config files, or registry entries), maps their declared permissions, and surfaces what they can actually touch—files, network endpoints, environment variables, clipboard. Build a simple dashboard showing agent name, vendor, permissions granted, and last activity. Charge per machine or per organization. Position as 'AI agent firewall' or 'AI supply chain visibility.'

Strengths

Timing is strong—AI agents are proliferating (Claude Desktop, ChatGPT plugins, local LLMs, automation tools) and permission creep is real and under-discussed.
Clear, specific problem with measurable scope—you can enumerate what an agent can touch.
Low barrier to initial MVP—parse process lists, config files, and permission manifests; no deep OS kernel work required.
Defensible moat if you build accurate agent fingerprinting and permission mapping before competitors.
B2B2C path exists: sell to enterprises for employee machine auditing, or to security teams.

Risks

Market size unclear—how many users actually care about AI agent visibility today? Adoption depends on security incidents or regulatory pressure.
Technical fragmentation—agents run differently on Windows, Mac, Linux; permissions model varies wildly. Maintenance burden is high.
Competitive pressure from OS vendors (Apple, Microsoft) or security incumbents (CrowdStrike, Kandji) who could add this feature.
False sense of security—showing permissions doesn't stop a malicious agent; users still need to make trust decisions.
Monetization is hard—this is a utility, not a revenue driver for most users. Enterprise adoption requires sales and compliance integration.
If positioned as 'security,' you inherit liability and need to be bulletproof; if positioned as 'visibility,' adoption is slower.

Fly Labs Method

Is the pain real, is there a gap, is it the right time, can one person build it.

72

Value Equation

Dream outcome and how likely it feels, against the time and effort it costs.

58

One-Person Business

Curiosity pull, identity fit, and a path from free value to paid for a solo creator.

75

Viral Frameworks

Hook strength, shareability, and how cheaply it can be tested.

68

Builder Lens

Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.

71

Five lenses, one composite. How scoring works

The angle

Start as a lightweight system monitor that identifies AI agent processes (by signature, config files, or registry entries), maps their declared permissions, and surfaces what they can actually touch—files, network endpoints, environment variables, clipboard. Build a simple dashboard showing agent name, vendor, permissions granted, and last activity. Charge per machine or per organization. Position as 'AI agent firewall' or 'AI supply chain visibility.'

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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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

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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

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