Alvin

@shrey_sancheti @algoxstonk AI infrastructure is quickly shifting from "can agents do it?" to "can we prove what agents actually did?" Signed execution logs feel like a foundational building block for that future.

@algoxstonk Following up on my own reply above: this is the project I'm building on exactly that problem, signed logs of what an agent actually did on the network, not just a post-hoc diff. clawdlinux.org

We are open-sourcing blcli: an Agentic Infra Stack, battle-tested at 30M+ user scale. It allows coding agents like Codex or Claude Code to help manage your whole cloud infrastructure through code, PRs, dry-runs, and deterministic apply workflows. A solid & serious infra that can support to millions of users. This is a collaboration across multiple teams, the same stack that powers @AlvaApp, @Galxe, @GravityChain, and @ReahPlatform. Check it out here: Docs: blbl.gitbook.io/blcli-docs/ blcli: github.com/ggsrc/blcli Production stack template: github.com/ggsrc/bl-templ… Personal account starter: github.com/ggsrc/bl-templ… A common take today is: AI agents are useful for toy apps and prototypes, but not for serious infrastructure. The conclusion is wrong, because the issue is not that agents cannot work on real systems. The issue is that real infrastructure requires a large amount of expert context to get it correct in the first place, and even more context to guide agents through the next 18 months of iteration. Production infrastructure is not just a few Terraform files or Kubernetes YAMLs. It includes: cloud projects IAM boundaries networking VPC / subnet / firewall design Terraform state and backend management Kubernetes clusters cluster add-ons secrets management Git-based deployment workflows observability and telemetry (logs, metrics, traces. All integrated together and ready for your Agents to debug live on your prod env) databases, often self-hosted for cost efficiency and control environment separation: stg / beta / prd operational runbooks rollback paths production failure patterns Most of this knowledge usually lives in senior engineers’ heads, internal docs, shell scripts, Slack threads, old runbooks, and lessons learned from real incidents. If an agent does not have that context, of course it will build toy infrastructure. So the real question is: How do we package production infrastructure expertise into a form that AI agents can read, reason about, modify, and operate safely? That is what blcli does. At its core, blcli is a CLI tool plus a whole package of best practices of Infrastructure as Code. The key design principle is simple: Agents are already very good at reading and modifying code. So we make infrastructure code-first. The generated repo is intentionally self-explanatory. An agent can open the repo and understand what happened, and what's next. Who blcli is for? We built blcli for two types of users. 1. Product teams that need to scale beyond prototypes The first group is teams building real products that need infrastructure capable of growing beyond the prototype stage. These teams want the speed of AI-assisted development, but they cannot afford toy infrastructure. 2. Frontier labs and agent teams building self-improving systems The second group is frontier labs, data companies, and agent teams that need infrastructure not just to run applications, but to train, evaluate, and improve agents. If you are building coding agents, infra agents, or long-horizon autonomous systems, blcli stack is a good agent harness/env. Authors: @SiriJhui @p0pUBhv35I8308 @alvinFu1 @ryan4yin @algoxstonk

Strong project！

@algoxstonk strong project！