Zerobox – Sandbox any command with file, network, credential controls
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
- Addresses a critical and growing security problem, especially relevant for AI agents.
- Clear differentiation by offering local, cross-platform, single-binary sandboxing without VMs/Docker.
- Solves specific user frustrations like complexity, overhead, and true isolation.
- Strong signals for willingness to pay from a high-value, identifiable audience.
- Excellent market timing with the rise of AI agents.
Risks
- High technical complexity for a solo builder, especially for cross-platform native sandboxing and MITM proxy maintenance.
- Sustaining ongoing maintenance for different OS sandboxing mechanisms will be a significant challenge.
- Risk of incumbents (Google, AWS, Cloudflare) expanding their lightweight sandboxing efforts into this specific niche.
- Requires careful validation to confirm developers prioritize this specific local, non-VM/Docker solution over existing alternatives.
Next steps
- Conduct in-depth interviews with AI agent developers to validate the specific pain points addressed by Zerobox and confirm their willingness to pay for a simplified, dedicated solution.
- Launch a more polished MVP to a small group of beta users for early feedback on usability, performance, and feature prioritization.
- Explore partnerships or integrations with popular AI agent frameworks or platforms to accelerate adoption and distribution.
- Prioritize one operating system (e.g., Linux due to `bwrap`) for the initial MVP to reduce initial build complexity and achieve speed to market.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 23
- Willingness to pay
- 23
- Buildability
- 10
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
- Conduct in-depth interviews with AI agent developers to validate the specific pain points addressed by Zerobox and confirm their willingness to pay for a simplified, dedicated solution.
- Launch a more polished MVP to a small group of beta users for early feedback on usability, performance, and feature prioritization.
- Explore partnerships or integrations with popular AI agent frameworks or platforms to accelerate adoption and distribution.
- Prioritize one operating system (e.g., Linux due to `bwrap`) for the initial MVP to reduce initial build complexity and achieve speed to market.
Who is already there, emerging market
- FirejailVisit
A SUID security sandbox program that restricts the running environment of untrusted applications using Linux namespaces and seccomp-bpf.
Pricing: Free and Open Source
- Sandboxie PlusVisit
Creates an isolated operating environment where applications can run without permanently modifying the local system.
Pricing: Free and Open Source
A user-space kernel developed by Google that intercepts system calls, providing strong isolation for containers without full virtual machines.
Pricing: Free and Open Source
An orchestration framework that integrates multiple Virtual Machine Monitors (VMMs) with Kubernetes, providing hardware-level isolation through lightweight VMs.
Pricing: Free and Open Source
A lightweight Virtual Machine Monitor (VMM) built by AWS in Rust, designed for serverless and container workloads.
Pricing: Free and Open Source
Allows instantiating new Workers at runtime in isolated V8 isolates for safe execution of AI-generated code.
Pricing: Free tier available; paid plans for increased usage and features. Specific pricing for Dynamic Workers not explicitly detailed beyond being available to 'paid Workers users'.
A daemonless container engine that provides a secure and user-friendly alternative to Docker for managing containers and pods.
Pricing: Free and Open Source
- LXC (Linux Containers)Visit
Manages system containers on Linux, providing advanced Linux virtualization capabilities.
Pricing: Free and Open Source
- Cuckoo SandboxVisit
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
- Agent SandboxVisit
Provides AI agents with sandboxed compute, cloud storage, and artifact retrieval to run Python/Bash, install packages, and return deliverables without local risk.
Pricing: Not explicitly stated on Product Hunt, but implies a SaaS model with API usage.
What they charge
Recent news
- Cloudflare Launches Dynamic Workers Open Beta: Isolate-Based Sandboxing for AI Agent Code Execution
InfoQ, April 01 2026
- Show HN: Zerobox – Sandbox any command with file, network, credential controls
Hacker News, April 01 2026
- Zerobox: Sandbox any command with file, network, and credential controls
Reddit, April 01 2026
- Sandboxing AI agents, 100x faster - The Cloudflare Blog
The Cloudflare Blog, March 24 2026
- Docker Sandboxes: Run Agents in YOLO Mode, Safely
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
- Complexity of configuration and writing custom profiles for tools like Firejail.
- Resource limits (memory, execution time) and runtime restrictions in serverless sandboxing environments like Cloudflare Workers.
- Slow cold start times and infrastructure overhead associated with VM or Docker-level sandboxing solutions.
- The perception that Docker is not a true sandbox for untrusted code, despite its common use for isolation.
- Lack of full control over runtime environments or too much infrastructure to manage with existing sandbox tools.
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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