Drive any macOS app in the background without stealing the cursor
Hi HN, Francesco from Cua here. I hacked this project together last weekend, inspired by the Codex Computer-Use release and lessons learned from deploying GUI-operating agents for our customers.The main problem: when a UI automation process controls a desktop app today, it usually takes over the human’s session. Your cursor moves, keyboard focus gets stolen, windows jump to the front, and you have to stop working until the agent is done. That is why we have historically avoided encouraging users to run these processes directly on their host machine, instead relying on VMs or GUI containers for concurrency and background execution.But computer-use - the tools we give agents to operate computers like humans - does not scale cleanly that way. As models get smarter, agents need to share hosts safely, run in the background, and avoid collisions with the human or other agents using the same machine.We realized macOS has no first-class API for "drive this app without touching the cursor". CGEventPost routes through the hardware input stream, so it moves your cursor. CGEvent.postToPid avoids the cursor warp, but Chromium treats those events as untrusted and silently drops clicks at the renderer boundary. Activating the target app first raises the window and pulls focus, defeating the point of background execution.Cua Driver is our attempt at a real fix: a background computer-use driver for macOS that lets an agent click, type, scroll, and read native apps while your cursor, frontmost app, and Space stay where they are. The default interface is a CLI, so it is easy to script or call from any coding agent shell.Try it on macOS 14+:/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/trycua/cua/main/libs/cua-d...)"The first internal use case was delegated demo recording. We ask Claude Code to drive an app while 'cua-driver recording start' captures the trajectory, screenshots, actions, and click
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A macOS background automation driver that lets AI agents control apps without hijacking the user's cursor, keyboard, or window focus.
The pain
The gap
Build angle
Strengths
- Solves a real, specific technical problem that existing APIs don't address
- Timing is excellent, AI agents are moving from labs to production, and this removes a major deployment friction point
- Low barrier to adoption: CLI interface, easy to integrate into agent shells and scripts
- Founder has domain expertise (deployed GUI agents at scale, understands the pain firsthand)
- macOS-first positioning is smart, smaller market but higher willingness to pay and fewer competitors
- Defensible: requires deep OS-level knowledge and likely needs to stay close to Apple's API changes
Risks
- Apple could block or restrict the underlying APIs at any OS update, no guarantee of long-term stability
- Market size is narrow: only relevant to teams running AI agents on macOS. Windows TAM is much larger but requires a rewrite
- Monetization path unclear: is this a $10/month tool, a $50k/year enterprise product, or a free layer under a larger platform?
- Competitive response: if this gains traction, Anthropic or OpenAI could build equivalent functionality into their own agent products
- Security and liability: background automation without user visibility could be misused; regulatory or trust issues could emerge
- Adoption depends on agent frameworks integrating it, requires partnership work and doesn't have organic pull
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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