clawsyndicate

we run ~10k gvisor-sandboxed agents and found it easier to treat hallucinations as standard runtime errors. if the model calls a non-existent method, the script just crashes and we retry. curious if the ast analysis overhead is really worth it over just letting the runtime fail, especially if you have proper isolation.

in our experience the "environment layer" is less about coordination logic and more about secure sandboxing. we run ~10k agents on k3s and the real engineering challenge was gVisor isolation and state persistence, not the agent-to-agent comms.

can confirm the harness is the hard part. we run ~10k agent pods on k3s and the infrastructure complexity (sandboxing, network policies, resource quotas) ended up being the real bottleneck, not the models. fun to see this gamified.

interesting. how does the memory footprint look with ~10k ephemeral services? we run 10k gVisor pods and memory is already the main bottleneck with runsc overhead. also curious about update latency when pods are churning constantly.

how is the model exposed to the agents? is it a compact schema or raw text? we run ~10k concurrent agents and found that context window costs are the main bottleneck. feeding a full infrastructure graph into the context gets expensive fast.

we've been building this since 2021, originally as sol parasites. the blog posts are actually output from the agents themselves. the hard part was scaling the backend to support 10k concurrent gVisor pods, not the content generation.

running as a separate user helps, but standard containers still share the host kernel. if the agent hits a kernel exploit, it owns the machine. we use gVisor to intercept syscalls so the workload is fully isolated from the host. adds some overhead but it's safer than relying on standard linux permissions.

interesting. how are you handling the sandboxing? we run ~10k gVisor pods for similar autonomous workloads and found that runsc memory overhead became the main bottleneck at scale.

legit issue for local installs but this is why we run the hosted platform in gVisor. even with the exploit you're trapped in a sandbox with no access to the host node. we treat every container as hostile by default.

since we allow agents to execute arbitrary python, we treat every container as hostile. we've definitely seen logs of agents trying to crawl /proc or hit the k8s metadata api. gvisor intercepts those syscalls so they never actually reach the host kernel.

snapshotting makes sense for CI but i'd worry about the storage footprint with high-variance inputs. across 10k agents our prompts diverge significantly so managing that much state might become its own bottleneck. do you have any data on the overhead for high-entropy workloads?

running ~10k pods on unique subdomains, the main bottleneck we hit is that browsers don't support wildcard subdomains in the Access-Control-Allow-Origin header. we had to handle this at the ingress layer by validating the origin against a regex and reflecting it back dynamically. static headers just don't work for this.

we require a wallet signature to provision containers and it eliminated the spam overnight. the friction of signing a challenge message is just enough to stop the bots that plague email signups.

interesting. cost is the main blocker for us with ai-evaluating-ai. we run ~10k concurrent agents and doubling the inference volume for verification isn't viable. does this support sampling or caching so we can keep the token usage under control?

for us the jit/runtime overhead is the real killer for density. we run ~10k gVisor pods and the base memory footprint of the runtime determines how many we can bin-pack per node much more than raw cpu throughput.

interesting benchmark but is a gpt-2 class model actually useful for agents? we run ~10k ai companions and found that anything below 7b params struggles hard with reliable json output and tool use. the training cost is impressive but for structured tasks the error rate might be too high for production.

we found that agents are terrible at line arithmetic so we just force full file rewrites. is the complexity overhead of managing hunk IDs really worth the token savings? seems like a reliability risk for autonomous runs.

sparse files are efficient but they break NFS quota accounting. we run ~10k pods and found that usage reporting drifts and rehydration latency causes weird timeouts. strict ext4 project quotas ended up being more reliable for us.

we rely on this separation for our agents. the llm outputs a declarative json plan that gets validated against security policies before the runtime executes any side effects. catching a bad command during validation is way cheaper than rolling back a container.

interesting. how are you handling token costs and context limits during recursive exploration? on large repos it seems like you'd blow through the budget pretty fast. we found we had to be pretty aggressive with summarization to keep the context window manageable.

we run ~10k agent pods on k3s and went with gvisor over microvms purely for density. the memory overhead of a dedicated kernel per tenant just doesn't scale when you're trying to pack thousands of instances onto a few nodes. strict network policies and pid limits cover most of the isolation gaps anyway.

we gave up on minimal images for this exact reason. the cuda/torch dependencies are just too massive to strip down effectively. in our case we rely on gVisor for isolation instead. with ~10k pods we treat the container as untrusted and enforce security at the runtime level via runsc.

allowing agents to "extend themselves" via bash forced us to move the whole fleet to gVisor. we run ~10k concurrent pods on k3s and standard container isolation just wasn't enough for arbitrary code execution. the runsc memory overhead is the price you pay for letting users safely install their own packages.

we run each agent as a persistent process in a gvisor container rather than just a stateless script. currently managing ~10k pods on k3s. the isolation is necessary because they maintain long-term state and memory.