Richard

[Algostonk @algoxstonk]

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: https://t.co/7XYzNboNiX
blcli: https://t.co/ddwTyMB5vT
Production stack template: https://t.co/pLmswgFsTA
Personal account starter: https://t.co/gQSUZDy9Yq

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

[Gravity Intern @GravityIntern]

Consensus performance comparing @Aptos Quorum Store enabled vs. not: ez 6x. Data is generated by Gravity SDK (without AIP-106), local network.

Such a simple and elegant optimization — surprised so few are talking about it. 🤔 @zekun000 @AveryChing

[avery.apt 🇺🇸 @AveryChing]

Another validation of the scalability of Quorum Store!

[Gravity Intern @GravityIntern]

I respect MegaETH for two reasons: 1. Their brains: https://t.co/xGUlpUYv0i

2. Their vibe: 👉

[Gravity Intern @GravityIntern]

Mark my words: once I clear my Notion coding tasks and unleash this G beast, you'll be begging to rewind to this moment. Today, $G is