Hey HN, I’m excited to share Antfly: a distributed document database and search engine written in Go that combines full-text, vector, and graph search. Use it for distributed multimodal search and memory, or for local dev and small deployments.I built this to give developers a single-binary deployment with native ML inference (via a built-in service called Termite), meaning you don't need external API calls for vector search unless you want to use them.Some things that might interest this crowd:Capabilities: Multimodal indexing (images, audio, video), MongoDB-style in-place updates, and streaming RAG.Distributed Systems: Multi-Raft setup built on etcd's library, backed by Pebble (CockroachDB's storage engine). Metadata and data shards get their own Raft groups.Single Binary: antfly swarm gives you a single-process deployment with everything running. Good for local dev and small deployments. Scale out by adding nodes when you need to.Ecosystem: Ships with a Kubernetes operator and an MCP server for LLM tool use.Native ML inference: Antfly ships with Termite. Think of it like a built-in Ollama for non-generative models too (embeddings, reranking, chunking, text generation). No external API calls needed, but also supports them (OpenAI, Ollama, Bedrock, Gemini, etc.)License: I went with Elastic License v2, not an OSI-approved license. I know that's a topic with strong feelings here. The practical upshot: you can use it, modify it, self-host it, build products on top of it, you just can't offer Antfly itself as a managed service. Felt like the right tradeoff for sustainability while still making the source available.Happy to answer questions about the architecture, the Raft implementation, or anything else. Feedback welcome!
FL score
out of 100
Verdict
high confidence
Competition
13
competitors found, growing market, big tech present, funded players
Trend
1 community mentions
A highly ambitious, technically advanced distributed multimodal search and graph database with native AI inference, targeting developer frustration with existing vector DB costs and complexity.
The pain
The gap
Build angle
Strengths
Questions about this idea?
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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 idea addresses a real and severe pain around scaling, cost, and complexity of multimodal AI search/memory, with specific complaints about incumbents. However, the market is crowded with strong, funded competitors, and the build complexity for a solo builder is exceptionally high. The proposed angle is compelling but might not be sufficient to carve out a significant gap against highly resourced players.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
High market pain and growth potential, but high build complexity and uncertain differentiation/pricing power against strong incumbents are major challenges.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
While the problem is clear, the project's massive complexity makes it an unsuitable solo endeavor, despite strong leverage potential.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear target audience and value proposition, but significant challenges in distribution, business model execution for a solo, and high assumption risk.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Clear demand and specific user, but the 'narrowest wedge' and competitive landscape suggest a challenging path for long-term growth and defensibility for a solo project.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, growing market
Open-source vector database with hybrid full-text and vector search, multimodal support, graph-like capabilities via modules, cloud and self-hosted distributed options.<grok:render type="render_inline_citation"><argument name="citation_id">39</argument></grok:render><grok:render type="render_inline_citation"><argument name="citation_id">14</argument></grok:render>
Pricing: Free open-source self-host, cloud free tier to $25+/mo scaling up
Managed serverless vector database with hybrid search support, focused on scalable similarity search for RAG.<grok:render type="render_inline_citation"><argument name="citation_id">22</argument></grok:render>
Pricing: Pay-per-use, expensive at scale ($500+/mo for production)
Lightweight open-source embedding database for local prototyping and vector search.<grok:render type="render_inline_citation"><argument name="citation_id">22</argument></grok:render>
Pricing: Free open-source
High-performance Rust-based vector search engine, supports hybrid search, self-hosted or cloud.<grok:render type="render_inline_citation"><argument name="citation_id">67</argument></grok:render>
Pricing: Free open-source, cloud paid tiers
Distributed scalable database combining SQL, vector search, graph (GQL), and full-text search.<grok:render type="render_inline_citation"><argument name="citation_id">30</argument></grok:render>
Pricing: Enterprise ~$0.90/node-hour
Distributed search engine with full-text, vector search, and some graph features via plugins.<grok:render type="render_inline_citation"><argument name="citation_id">47</argument></grok:render>
Pricing: Free OSS, cloud/enterprise paid
Open-source graph database optimized for AI/GraphRAG with vector search integration.<grok:render type="render_inline_citation"><argument name="citation_id">28</argument></grok:render>
Pricing: Free open-source
A distributed graph database written in Go that combines GraphQL with a graph backend, offering high throughput and low latency for deep joins and complex traversals. It supports full-text search.
Pricing: Free (open source), Shared Tier ($39.99/backend/month), Dedicated Tier ($199/month starting price). Free trial available. No cost for data storage or per query, but data transfer fees apply.
A versatile, document-oriented, distributed database designed for modern application developers and the cloud landscape. It offers flexible document data models and a single query interface, with capabilities for scalability and high performance. It has been winning work from AI-native companies.
Pricing: Free, Shared (USD 39.99/month), Dedicated (USD 199.00/month). MongoDB Atlas (managed service) pricing is based on usage across AWS, Google Cloud, and Azure.
A transactional (ACID) NoSQL document database that is distributed and offers high availability and high performance. It's designed as an all-in-one database to minimize the need for third-party add-ons. It recently launched an AI Agent Creator.
Pricing: Offers a Database as a Service solution. Specific pricing details not immediately available but offers a free tier/trial.
An open-source multimodal AI database specifically designed for complex data types like vectors, images, videos, and audio. It is built using the open-source Lance columnar format and optimizes for high-performance, scalable, and cloud-native AI data management and retrieval.
Pricing: Open-source; specific managed service pricing not available in snippets.
An AI startup offering an enterprise-grade multimodal search engine for social video that analyzes video content beyond text. It claims a 1,000-fold reduction in video ingestion costs.
Pricing: Starts at $25 per month.
Gaps they leave open
What people say, 1 mentions
I vibe-coded a functional SaaS in 48 hours, got G2 verified, but hit 0 upvotes on launch day. Lessons learned on the 'Build vs. Market' trap.
r/SaaS
Recent news
Antfly unifies text, vector, and graph search in one Go binary
The Agentic Digest, March 18 2026
Show HN: Antfly: Distributed, Multimodal Search and Memory and Graphs in Go
Hacker News, March 17 2026
Antfly: Distributed, Multimodal Search and Memory and Graphs in Go! - DEV Community
DEV Community, March 17 2026
NVIDIA, T-Mobile and Partners Integrate Physical AI Applications on AI-RAN-Ready Infrastructure
Stock Titan, March 16 2026
Build a Multimodal AI Agent with Graph RAG, ADK & Memory Bank - Google Codelabs
Google Codelabs, March 16 2026
Market signals
The market for distributed multimodal search, memory, and graph solutions is growing rapidly, driven by the increasing demand for AI-powered applications, real-time data analytics, and the need to unify diverse data types within scalable and performant systems.
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