### Is your feature request related to a problem? No, only a request ### Describe the solution Add support to run inside iOS and Android mobile applications ### Alternative methods The current alternative is use sqlite ### SurrealDB version any ### Contact Details paulocoutinhox@gmail.com ### Is there an existing issue for this? - [X] I have searched the existing issues ### Code of Conduct - [X] I agree to follow this project's Code of Conduct
FL score
out of 100
Verdict
high confidence
Competition
7
competitors found, emerging market, funded players
Trend
No signal yet
A complex idea to bring SurrealDB to mobile, facing high build challenges and strong incumbents, requiring significant validation for a specific, unserved niche.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Support for iOS and Android”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
The problem of mobile data management is real, but the provided competitor analysis is misaligned with the idea of a mobile database. The gap for a new mobile database is moderate, facing strong incumbents. Buildability is high for a solo builder given the complexity of a database port.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea faces high build complexity and moderate differentiation in a market with strong free/low-cost incumbents, making profitability challenging.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The problem is clear for developers, but the idea lacks creator fit and simplicity for a solo builder, with a broad target and challenging monetization in a competitive landscape.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The idea has a clear but somewhat niche target audience with a plausible value proposition, but significant technical and market risks make validation challenging and the business model uncertain.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
There's clear demand for better mobile data management, but the challenge lies in creating a compelling, narrow wedge against existing solutions and the high build complexity for a solo founder.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
A platform designed to bring advanced machine learning capabilities directly to mobile applications, supporting both iOS and Android with a focus on on-device ML.
Pricing: Hobby: Free; Starter: $69/month; Pro: $149/month; Enterprise: $299/month.
An open-source machine learning framework developed by Google, optimized for mobile and edge devices, enabling on-device ML inference with low latency and a small binary size.
Pricing: Free (open-source).
A PyTorch tool that enables models to be deployed on iOS and Android, offering flexibility and ease of debugging due to dynamic computation graphs.
Pricing: Free (open-source).
A Google SDK for mobile developers that brings Google's machine learning expertise to mobile apps on iOS and Android, offering ready-to-use APIs and the ability to use custom models.
Pricing: Free tier available; may require upgrading Firebase plan to a paid one depending on usage.
Apple's on-device machine learning framework for iOS, macOS, and tvOS, allowing developers to integrate trained ML models into their apps, with a focus on privacy and performance.
Pricing: Free (part of Apple's ecosystem).
An SDK that allows developers to use state-of-the-art multimodal AI models fully on-device on iOS & Android apps with NPU acceleration, supporting text, vision, and audio.
Pricing: Not explicitly stated on Product Hunt, but emphasizes 'no cloud API cost'.
An SDK + control plane for on-device LLMs that intelligently routes model requests locally or to the cloud based on policy, offering native runtime for iOS and Android.
Pricing: Not explicitly stated; demo available upon request.
What they charge
Recent news
The Impact of AI Integration into Mobile Apps for an Enhanced User Experience
UXmatters, February 16 2026
On-Device Intelligence: Core ML vs ML Kit for Future Apps
Goodfirms, January 22 2026
Edge AI: TensorFlow Lite vs. ONNX Runtime vs. PyTorch Mobile
DZone, June 03 2025
AI Integration with Mobile Apps: Comprehensive Guide
Techies, April 24 2025
AI in Mobile App Development: Key Trends, Benefits & Challenges
Techstack Ltd, November 07 2024
Market signals
The market for integrating AI into mobile applications is large and growing, driven by the demand for personalized user experiences, automation, and real-time decision-making. Key trends include the increasing importance of on-device AI for privacy and low-latency, and advancements in AI-based chip innovation. Recent funding rounds indicate continued investment in mobile AI development platforms.
What frustrates people
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
Hi HN!I recently switched from a Fedora/GNOME laptop to a MacBook Air. My old setup served me well as a portable workstation, but I’ve started traveling more while working remotely and needed something with similar performance but better battery life. The main thing I missed was a simple taskbar that shows the windows in the current workspace instead of a Dock that mixes everything together.I built boringBar so I would not have to use the Dock. It shows only the windows in the current Space, lets you switch Spaces by scrolling on the bar, and adds a desktop switcher so you can jump directly to any Space. You can also hide the system Dock, pin apps, preview windows with thumbnails, and launch apps from a searchable menu (I keep Spotlight disabled because for some reason it uses a lot of system resources on my machine).I’ve been dogfooding it for a few months now, and it finally felt polished enough to share.It’s for people who like macOS but want window management to feel a bit more like GNOME, Windows, or a traditional taskbar. It’s also for people like me who wanted an easier transition to macOS, especially now that Windows feels increasingly user-hostile.I’d love feedback on the UX, bugs, and whether this solves the same Dock/Spaces pain for anyone else.P.S. It might also appeal to people who feel nostalgic for the GNOME 2 desktop of yore. I started my Linux journey with it, and boringBar brings back some of that feeling for me.
AI
### Describe the project you are working on Godot C# bindings ### Describe the problem or limitation you are having in your project For the past weeks, I've been discussing with several Unity users intending to move to Godot C# regarding dealing with the C# garbage collector. The most common complaint I hear from users is that, in Unity, allocations can trigger unexpected GC spikes into the game. In Godot, we target to make all of the high performance APIs (those that intended to be called every frame) not allocate any memory, so theoretically the GC should not be a problem. Additionally, Godot starting from 4.0, uses the Microsoft CoreCLR version of .net, which also supposedly has a better garbage collector than Unity. But in all, after several discussions with Unity users, neither is enough reassurance for them, and they would really feel safer if Godot exposed a zero allocation API. ### Describe the feature / enhancement and how it helps to overcome the problem or limitation The idea of this proposal is that Godot exposes zero allocation versions of many functions in the C# API, that users can use if they desire. Technically, this could be done from the binding generator itself, without breaking compatibility, and without doing any modification to Godot itself. ### Describe how your proposal will work, with code, pseudo-code, mock-ups, and/or diagrams **WARNING** I am not familiar with C#, so take this as pseudocode. Imagine you have two functions exposed as to C#: ```C# void MyClass.SetArray( Vector2[] array); Vector2[] MyClass.GetArray(); ``` This works and is pretty and intuitive. However, it has two problems: * GC is allocated on return * Memory is copied to Godot native formats every time there is a call. The idea is to add NoAlloc versions, which can be generated directly by the binder automatically when required: ```C# void MyClass.SetArrayNoAlloc( Godot.Collections.PackedVector2Array array); void MyCl
AI