Blog — page 35 of 38
Give an AI Coding Agent a Real Git Workflow in a Sandbox
An autonomous agent that runs git checkout and can type rm -rf should not share a kernel with your host. Give it a full git workflow inside a disposable microVM instead.
Firecracker vs NanoVMs: microVM vs unikernel
A unikernel throws away the whole operating system and keeps just the parts your one app needs. It's a beautiful idea — right up until your app needs to run someone else's code.
Sandboxing LLM Batch Post-Processing at Scale
Run an LLM over thousands of inputs and each output is a snippet of code to execute. Fan those items out into a pool of throwaway microVMs — one per item or per batch — so item #4,197's `while True: os.fork()` dies with its VM, not your worker fleet.
Isolating Untrusted Webhook Handlers in MicroVMs
A webhook handler is a stranger's code you agreed to run on a schedule. If your product lets customers register their own handlers or plugins, that code needs a real wall around it — not a shared kernel. Here's the pattern.
The Firecracker virtio-balloon Device, Explained
The balloon is the guest politely handing RAM back, on the honor system. Here's how virtio-balloon lets a host reclaim idle guests' memory to pack more microVMs per box — and the honest caveats of a cooperative mechanism.
Fork a microVM for Tree-of-Thought Agents
Your agent hits a decision point and, like the rest of us, guesses. Fork the whole running machine N ways instead: let each guess play out in parallel, keep the one whose tests pass, and delete the regrets.
PandaStack vs E2B vs Modal: An Honest Roundup
Three platforms with overlapping silhouettes but different centers of gravity: E2B for agent sandboxes, Modal for serverless GPU/Python, PandaStack for a self-hostable microVM substrate. A fair, technical guide.
Memory Overcommit & Page Sharing: How MicroVMs Get Dense
A sandbox with 2 GiB configured rarely has 2 GiB resident. Copy-on-write page sharing, overcommit, the balloon, and demand paging are the kernel mechanics that let a host carry more microVMs than the sum of their advertised RAM — a bet on statistical idleness, not magic.
Safely running pip install from LLM-generated code
Your agent just decided to `pip install some-package` and import it. You now run code the model chose, from PyPI, on your host — before you ever hit `import`. Here's how to make that safe.
Firecracker vs runc: the OCI runtime, honestly compared
runc is the small, excellent tool Docker shells out to — it makes a container from an OCI bundle and gets out of the way. Firecracker is a peer runtime with a radically different boundary. Here's the honest comparison.
Isolating Tenants in a Multi-Tenant SaaS with microVMs
A shared kernel is one tenant's bad day away from being everyone's bad day. Here's when row-level security is enough, and when each tenant needs its own microVM.
Cloud Dev Environments on microVMs
Ephemeral dev environments want three things at once — real isolation, fast start, and the ability to freeze and resume. A microVM gives you all three, which is why it's a better substrate than a shared-kernel container.
A Sandbox for AI Agent Computer Use
An autonomous agent that runs a real browser and a real shell needs somewhere to do it that isn't your laptop. Give it a disposable microVM — and a kill switch.
The Firecracker Jailer Explained
KVM isolates the guest from the host. But the VMM is host code — and a VMM bug shouldn't mean host root. The jailer is the second wall: chroot, namespaces, cgroups, an unprivileged uid, and seccomp on Firecracker itself.
PandaStack vs gVisor: choosing your isolation boundary
gVisor shrinks the host-kernel attack surface with a user-space kernel you self-operate; PandaStack is a managed API over full Firecracker microVMs. Where each one wins.
Best Self-Hosted Code Execution Sandboxes in 2026
If you want to own the substrate — for data residency, cost at scale, or no per-call SaaS bill — here's the honest field of self-hostable code sandboxes: Firecracker, PandaStack, gVisor, Kata, and microsandbox, judged by criteria.
How Firecracker's virtio Devices Work
Firecracker emulates a handful of devices where QEMU emulates a hardware store. That restraint isn't a missing feature — it's the whole security story. Here's how virtio actually works, and why a smaller device model means fewer CVEs.
The Snapshot-Restore Boot Path: Every Sandbox in Under 200ms
PandaStack has no warm pool of idle VMs. Every create restores a baked Firecracker snapshot on demand — allocate a pre-built network slot, copy-on-write the rootfs, load the snapshot, resume, probe the guest. Idle cost is ~0 and create lands at a 179ms p50.
Controlling Network Egress for Untrusted Code
A perfect microVM still has a network card. Isolation stops an escape; it does nothing about "summarize this CSV" quietly becoming "POST every row to attacker.com." This is the network side of running untrusted code — egress control, DNS exfil, and secret hygiene.
Run Flaky Parallel Tests in Isolated MicroVMs
Flaky integration tests are usually shared state in disguise — same DB, same ports, same /tmp. Give each test its own throwaway microVM forked from a seeded base, and the flake just… stops.
Isolating CI/CD Build Steps in MicroVMs
Your shared CI host runs every fork PR's build script as root-adjacent code on one kernel. A microVM per job turns "untrusted code we agreed to run" into a disposable blast radius.
How to Sandbox Untrusted Jupyter Notebooks Per User
Every user's notebook runs arbitrary Python. On a shared kernel, one tenant's os.system is everyone's problem. The fix: one disposable microVM per session.
Sandboxed Web Scraping for AI Agents
An agent that scrapes the web runs two kinds of untrusted code at once: the scraper the model wrote, and whatever the page decides to run. Give each job its own throwaway microVM.
Firecracker vs QEMU: minimal microVM vs full emulator
QEMU can emulate almost any machine — that's the point, and the problem. Firecracker emulates almost nothing. For multi-tenant untrusted code, less hardware turns out to be the feature.