Sub-millisecond VM sandboxes using CoW memory forking
I wanted to see how fast an isolated code sandbox could start if I never had to boot a fresh VM.So instead of launching a new microVM per execution, I boot Firecracker once with Python and numpy already loaded, then snapshot the full VM state. Every execution after that creates a new KVM VM backed by a `MAP_PRIVATE` mapping of the snapshot memory, so Linux gives me copy-on-write pages automatically.That means each sandbox starts from an already-running Python process inside a real VM, runs the code, and exits.These are real KVM VMs, not containers: separate guest kernel, separate guest memory, separate page tables. When a VM writes to memory, it gets a private copy of that page.The hard part was not CoW itself. The hard part was resuming the snapshotted VM correctly.Rust, Apache 2.0.
FL score
out of 100
Verdict
high confidence
Competition
10
competitors found, nascent market, big tech present, funded players
Trend
5 community mentions
A platform offering sub-millisecond KVM sandboxes for AI agents, leveraging CoW memory forking to solve latency and cost at scale.
The pain
The gap
Build angle
Strengths
- Extremely clear and painful problem for a growing market (AI agents).
- Specific, measurable value proposition (sub-millisecond latency).
- Strong signals for willingness to pay (expensive competitors, high commitments).
- Technological proof-of-concept (Zeroboot) exists for the core innovation.
Risks
- Very high technical complexity for a solo builder to productize beyond a demo.
- Crowded market with well-funded incumbents (Vercel, Modal, E2B, Daytona).
- Need to deliver a full-featured, production-ready platform, not just raw speed.
- Risk of over-engineering if the 'sub-millisecond' requirement is only critical for a tiny niche.
Next steps
- Deeply interview 10-20 AI agent developers/MLOps engineers: What specific problems does 'tens of milliseconds' cause? How much would 'sub-millisecond' save them in real costs or enable new use cases?
- Validate the demand for a full-featured *production* platform vs. a bare-bones API for just code execution.
- Explore existing Zeroboot users: What features do they wish it had for production use? How much would they pay for those additions?
- Identify the 'narrowest wedge' of features for a paid MVP beyond raw speed, e.g., 'sub-millisecond Python sandboxes with persistent scratch space for AI agents'.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 24
- Solution gap
- 14
- Willingness to pay
- 19
- Buildability
- 12
A highly technical idea with clear demand from AI agent developers for ultra-low latency sandboxes, but faces strong competition and significant build complexity for a solo founder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea targets a growing, high-value problem in the AI space but requires significant technical execution and faces established competition.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A technically challenging but clearly needed solution for a specific developer audience, assuming the builder has deep expertise.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Strong value prop for a clear audience, but faces high technical and market validation risks for a micro-SaaS.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a critical, growing need for ultra-fast, secure compute in AI, with a clear narrow entry point, but needs strong execution.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Deeply interview 10-20 AI agent developers/MLOps engineers: What specific problems does 'tens of milliseconds' cause? How much would 'sub-millisecond' save them in real costs or enable new use cases?
- Validate the demand for a full-featured *production* platform vs. a bare-bones API for just code execution.
- Explore existing Zeroboot users: What features do they wish it had for production use? How much would they pay for those additions?
- Identify the 'narrowest wedge' of features for a paid MVP beyond raw speed, e.g., 'sub-millisecond Python sandboxes with persistent scratch space for AI agents'.
Who is already there, nascent market
- Zeroboot
Sub-millisecond (0.8ms p50) VM sandboxes via CoW memory forking of Firecracker snapshots into KVM VMs for isolated code execution, preloaded with Python/Numpy.
Pricing: free (open source)
- Vercel SandboxFunded
API for instant microVM sandboxes to run AI-generated code securely, supports git sources, commands, ports, runtimes like Node.
Pricing: active CPU pricing (pay per use)
- Deno SandboxFunded
Instant Linux microVMs with TypeScript/Python SDKs + REST API, secure against prompt injection for code execution.
Pricing: unknown (likely pay per use)
- Microsandbox
Self-hosted platform for running untrusted/AI-generated code with microVM speed + container UX, full hardware isolation.
Pricing: free (open source)
- DaytonaFunded
Persistent sandboxes (<60ms spin-up) for AI agents, isolated minicomputers for multi-step workflows, state saving, API control.
Pricing: pay per use ($500 spend commitment for full internet)
- ModalFunded
Serverless platform with ridiculously fast sandboxes/containers for AI/ML, supports forking VMs, cheap for many parallel runs.
Pricing: pay per use (e.g. $0.10 for 30 sandboxes)
- E2BFunded
Secure cloud sandboxes/infrastructure for AI code interpreters/agents, fast spin-up, millions/month, supports code/terminal/packages/files.
Pricing: pay per use (free credits for startups)
Offers cloud development environments with fast VM cloning (within 2 seconds) by using Copy-on-Write for filesystem and then userfaultfd for in-memory CoW. This allows forking of running VMs for immediate development environment duplication.
Pricing: Not explicitly detailed for CoW forking, but offers various plans for development environments.
- Kata ContainersVisit
An open-source project that runs container workloads inside lightweight VMs, providing stronger workload isolation using hardware virtualization. Each container gets its own kernel and memory space, with minimal overhead (around 20-30 MB per VM) and sub-second boot times. It supports various hypervisors including Firecracker and uses CoW for security concerns in containers.
Pricing: Open-source, no direct pricing.
- gVisorVisit
Google's sandboxing technology for containers, reimplementing the Linux kernel in userspace to intercept syscalls. It aims to provide strong isolation. While it uses memory management, explicit CoW memory forking for sub-millisecond VM sandboxes as described in the problem is not its primary feature, and it has shown issues with drastic memory increases when multiprocessing with Python due to CoW interactions.
Pricing: Open-source, no direct pricing.
Gaps they leave open
- full persistence/state across runs
- networking/SSH
- robust filesystems/multi-tools
- long-running without timeouts
- on-prem flexibility
- cheaper long-run/high-volume
- better uptime/reliability
What people say, 5 mentions
eLearning platform
r/Entrepreneur
I wanted an Airbyte alternative that doesn't eat RAM, so I built one in Rust (SQL-based)
r/SaaS
2026 AI Coding Agent Dev Tool Market Map
r/SaaS
A dev tool isn’t a dev tool until it has a 4-panel editor (even if my design is "Engineer-forward")
r/SaaS
Save Hours Debugging with This 10-Second Commenting Habit, What’s Your Worst Bug Story?
r/SaaS
Recent news
Show HN: Sub-millisecond VM sandboxes using CoW memory forking
Hacker News, March 18 2026
Show HN: Zeroboot – sub-millisecond VM sandboxes using CoW memory forking
Hacker News, March 17 2026
ZeroBoot: Sub‑millisecond VM Sandbox with Copy‑on‑Write Forking
UBOS.tech, March 18 2026
[New Project Friday] ZeroBoot – sub-millisecond VM sandboxes using Firecracker + KVM copy-on-write forking (self-hostable)
Reddit (r/selfhosted), March 20 2026
Sub-millisecond VM sandboxes for AI agents via copy-on-write forking
daily.dev, March 18 2026
Market signals
There is a strong and recent surge of interest in sub-millisecond VM sandboxing using CoW memory forking, particularly driven by the demand for fast and secure execution environments for AI agents and serverless functions, with new open-source projects and related discussions appearing on platforms like Hacker News and Reddit. Existing solutions like CodeSandbox, Daytona, E2B, and Modal address similar needs but may not achieve the same sub-millisecond VM fork times, indicating a potential market gap for ultra-low-latency cold starts.
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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