I'm excited to introduce Zerobox, a cross-platform, single binary process sandboxing CLI written in Rust. It uses the sandboxing crates from the OpenAI Codex repo and adds additional functionalities like secret injection, SDK, etc.Watch the demo: https://www.youtube.com/watch?v=wZiPm9BOPCgZerobox follows the same sandboxing policy as Deno which is deny by default. The only operation that the command can run is reading files, all writes and network I/O are blocked by default. No VMs, no Docker, no remote servers.Want to block reads to /etc? zerobox --deny-read=/etc -- cat /etc/passwd cat: /etc/passwd: Operation not permitted How it works:Zerobox wraps any commands/programs, runs an MITM proxy and uses the native sandboxing solutions on each operating system (e.g BubbleWrap on Linux) to run the given process in a sandbox. The MITM proxy has two jobs: blocking network calls and injecting credentials at the network level.Think of it this way, I want to inject "Bearer OPENAI_API_KEY" but I don't want my sandboxed command to know about it, Zerobox does that by replacing "OPENAI_API_KEY" with a placeholder, then replaces it when the actual outbound network call is made, see this example: zerobox --secret OPENAI_API_KEY=$OPENAI_API_KEY --secret-host OPENAI_API_KEY=api.openai.com -- bun agent.ts Zerobox is different than other sandboxing solutions in the sense that it would allow you to easily sandbox any commands locally and it works the same on all platforms. I've been exploring different sandboxing solutions, including Firecracker VMs locally, and this is the closest I was able to get when it comes to sandboxing commands locally.The next thing I'm exploring is `zerobox claude` or `zerobox openclaw` which would wrap the entire agent and preload the correct policy profiles.I'd love to hear your feedback, especially if you are running AI Agents (e.g. OpenClaw), MCPs, AI
FL score
out of 100
Verdict
high confidence
Competition
10
competitors found, emerging market, funded players
Trend
No signal yet
A Rust CLI for cross-platform, local sandboxing of untrusted commands, especially AI agents, with integrated secret injection, eliminating VM/Docker overhead.
The pain
The gap
Build angle
Strengths
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Zerobox – Sandbox any command with file, network, credential controls”.
Risks
Next steps
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Strong problem with a clear gap in a growing market, but the build complexity for a solo builder is high.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Excellent market and value proposition, but significant build complexity for a solo founder.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A complex, niche technical problem well-suited for a capable founder but with high ongoing maintenance.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong micro-SaaS potential with clear value and audience, but needs more direct validation.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a critical, growing pain point for a specific user with a promising narrow wedge.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
Who is already there, emerging market
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Allows instantiating new Workers at runtime in isolated V8 isolates for safe execution of AI-generated code.
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A daemonless container engine that provides a secure and user-friendly alternative to Docker for managing containers and pods.
Pricing: Free and Open Source
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Pricing: Free and Open Source
A modular, automated malware analysis system that uses python and virtualization to create an isolated Windows guest environment to safely analyze files.
Pricing: Free and Open Source
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Pricing: Not explicitly stated on Product Hunt, but implies a SaaS model with API usage.
What they charge
Recent news
InfoQ, April 01 2026
Hacker News, April 01 2026
Reddit, April 01 2026
The Cloudflare Blog, March 24 2026
Docker Blog, March 31 2026
Market signals
The sandboxing market is large and experiencing rapid growth, projected to reach $18.35 billion by 2030 with a CAGR of 12.2%. Cloud-based sandboxing solutions are a significant growth driver, along with the increasing sophistication of cyber threats and the rising adoption of AI-driven threat analysis. The container security market, a related segment, is also experiencing substantial growth, expected to reach between $9.42 billion and $26.6 billion by 2031-2034 with CAGRs ranging from 19.30% to 26.51%. Recent funding rounds indicate strong investment in cybersecurity, including solutions that incorporate AI for threat detection.
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