“Bitcoin was the first distributed systems paper I read with an economic layer built into it — and that changed everything.” @kenzi_mori catches up with @averyching, Co-Founder & CTO of @Aptos, to trace his journey from high-performance computing and supercomputers, to scaling data infrastructure at Meta, to discovering Bitcoin and realizing that crypto was distributed systems with incentives natively embedded — the insight that ultimately led him to co-found Aptos Labs.

Building AI is a huge distributed systems challenge. To post-train NVIDIA Nemotron, we bring data generation, inference, reinforcement learning, and evaluation into one loop across CPUs and GPUs. I’ll be speaking about how we do this using Ray during the Ray Summit. August 26 at 9:50 AM PT. 👉

Pierre Computer Company is hiring for a Distributed Systems Engineer and Infrastructure Engineer. Come work with us on Code Storage, open source primitives, and other dope shit.

From scaling supercomputers to scaling Facebook to scaling Aptos. One thread runs through them: making distributed systems work at massive scale—the backbone of today's AI systems. @AptosLabs' @AveryChing with @theindexshow's @afkehaya

Sometimes the things I do / say sound so ambitious and far-fetched enough that it's hard to believe we can even do it. And then we do it. again and again. In the AI and information age, truly anything can be done. You can just go out and learn distributed systems, post training, inference engineering, databases, find the people, have a mission and build whatever you want to build. It's all about operational excellence now, which is a skill I'm trying to learn i dont know why most companies stop at B2B AI SaaS when there's so much to build.

Great article from @googledevs on Stateless MCP. Sessions were a big bottleneck, which is way x402 has chosen the stateless route. Maintaining session state across distributed systems introduces coordination overhead that scales linearly with concurrency. Payment authorization is per-request state. A protocol that needs a session has bound settlement to connection lifetime. TLDR, the best way to do a 402 payment protocol is not with sessions.

Looking to build the data infrastructure powering onchain finance? We're hiring a Senior Platform Engineer to strengthen the cloud and production systems behind Chronicle's data infrastructure for tokenized assets and onchain markets. If you have deep experience with AWS, Kubernetes, distributed systems, and building reliable production environments, we'd love to talk. 🚀 Apply here:

While LLMs are generally deficient at creating production-grade algorithmic trading systems, they are adept at a number of discrete tasks and components that go into building automated trading strategies or larger distributed systems in finance. Here are the top few: Order entry protocols: All trading operations that connect directly to exchanges involve a time-consuming, developer-intensive task of adapting each exchange’s proprietary order protocol to their algorithmic trading system. Most venues provide detailed specification documents for FIX, binary, and JSON-based messaging that can be read by LLMs, saving tens of hours of protocol normalization work. If the exchange provides a sandbox environment, an LLM can test its own adaptation in a REPL-style loop. Market data: Similar to order entry, LLMs are great at normalizing L1, L2, and L3 feeds for consumption by strategies and modeling processes. LLMs are also able to build the rote connectivity and authentication stack required to retrieve the data streams. LLMs lack the level of taste required to develop the right abstraction for feed normalization that preserves venue-specific details while also enabling developers to write venue-agnostic strategies. However, once such abstractions are established, there is little need to manually adapt market data protocols. Research database management: It’s common practice to record every event that passes through a trading system in databases: book updates, orders, cancels, trades, fair value updates, error messages, etc. General-purpose time-series databases are the common solution, and form the foundation of research, monitoring, and debugging stacks. Assuming that the trading system uses a well-known database technology such as Postgres or ClickHouse, LLMs can handle schema generation, table compaction, version migrations, backups, and automated reports. User interfaces: Whether an algorithmic trading system is black-box (minimal human input during the order lifecycle) or gray-box (automated order placement with manual human intervention on parametrization), all traders leverage some form of user interface for trade, risk, and P&L monitoring. Trading engineers tend not to be the most skilled GUI developers, but internal interfaces do not need to be pretty to be highly functional. Out of all areas of software development, frontier coding models have reached expert level in building terminal- and web-based applications. In building our derivatives exchanges, we’ve designed our APIs and external-facing components with both institutional traders and agentic developers in mind. This should be the financial industry norm. Most of our institutional customers have made use of our Claude skill for protocol integration, and we expect other brokerages and exchanges to support these emerging methods of trading system development in addition to agentic trading.

we're scaling the performance team in prime intellect, if you want to make large scale RL run fast, you know your way around inference, gpus and large scale distributed systems send me a dm (preferably a lot of them bc twitter dms suck)