MeowDisplay Is iPhone Mirroring, but in Reverse
Hey HN and Cats, thank you for checking this out!Github: https://github.com/raiseCatError/meowdisplayEDIT 2: I should've mentioned that MeowDisplay started as a fork of OpenDisplay: https://github.com/peetzweg/opendisplay which provided the foundational screen capture, virtual display, encoding and iPhone/iPad receivers. I've since taken it in a more phone-first direction, with work on the interaction, streaming and security layers. I've credited OpenDisplay and its developer Philip in the README under "Built on OpenDisplay".MeowDisplay is my take on the control your Mac from your phone scene. Available on iPhones, iPads and Macs, with Android rn in closed testing.The website has interactive illustrative demos if you'd like to see the app demonstrated.I think MeowDisplay does a few things differently, some of which I prefer and you might too.Such as Window view: allowing multiple tabs in split-screen type panes. Traditional Mirror/Extend views exist as alternatives. Floating windows: coming in the 2.1 release.Touch input approaches: Direct Touch(Classic or Natural), experimental Smart Touch, and Trackpad mode alongside Apple Pencil support. Doesn't sacrifice display space for a trackpad, use all of the phone's screen with your preferred mode.Stage Peek: shows a scrollable and searchable list of your Mac's windows on your phone along with an app drawer when you hold and pull on the left edge of your screen.It's free and open source so you can try it and see if it's for you without having to commit to it or anything.EDIT: I've used MeowDisplay during development whilst I was travelling between cities, left my acbook at home clamshell, powered, external display attached, and used it for small tasks remotely, it was pretty reliable even on a 5G network on a moving bus.Meow, Jai
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
MeowDisplay lets users control their Mac from their phone with advanced interaction modes and window management.
The pain
The gap
Build angle
Strengths
- Open source and free to try, lowering adoption barriers
- Innovative interaction modes like Smart Touch and Stage Peek
- Cross-device support including iPhone, iPad, Mac, and Android testing
- Technical feasibility demonstrated with real-world usage
Risks
- Unclear willingness to pay given free alternatives
- Niche market with limited user base outside power users
- Competition from established remote desktop and mirroring apps
- Challenges in scaling user acquisition and monetization
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 75
- Solution gap
- 65
- Willingness to pay
- 60
- Buildability
- 80
The problem of controlling a Mac remotely via phone is clear and real for some users, but the market gap is moderate and willingness to pay is uncertain given free alternatives; the project is technically feasible for a solo developer.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The value proposition is niche and moderately compelling, with some differentiation in interaction modes, but the target market size and monetization strategy are unclear, limiting overall potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea addresses a specific user need with a functional prototype and open source model, but lacks strong evidence of market demand or clear competitive advantage.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The product shows technical innovation and user-centric features, but faces challenges in user acquisition and monetization in a crowded remote control and screen sharing space.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The startup idea has a focused technical solution and early user validation, but needs clearer customer segmentation and a scalable business model to attract investment.
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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