Show Notes
When you ask Fable or GPT-5.6 Sol to complete a task, it feels like you’re talking to one agent. But behind that single conversation, the harness may be spawning multiple sub-agents, giving each one a fresh context window, running them in parallel, and passing summaries between them. You gave one prompt. It built a graph. In this episode of Superlinear, Christine Yip and Brandon Kase unpack how modern agent harnesses grow from simple agent loops into hidden graphs of sub-...
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An agent that handles new GitHub issues needs to keep working through crashes, restarts, and changes in who's using it. Pi Durable gives you a foundation for building that kind of persistent workflow. The key is separating agent state from the process running it. Cloudflare Durable Objects can give that state a shared cloud home, with execution triggered by requests and configured wakeups. In this episode, we explain how the two fit together, use an interrupted refund to examine recovery, a...
Your coding agent can spend more time waiting for tests and builds than writing code. As inference gets faster, tool calls can become a larger share of the wait. Speeding up that feedback loop means examining which checks run, how they run, and where they run. Crabbox, from Peter Steinberger and the OpenClaw team, keeps agents local while expensive work runs on other machines. In this episode, we share our experience offloading Electron tests with Crabbox, discuss the tradeoffs of selective...
How do you build an agent like Dots, Muse, Instinct, or Grok Bot? Start high up the stack, and move down only when you need more control. YC F26-startup Agent 37 founder, Vishnu Krishnaprasad, explains when to use Hermes, OpenClaw, or Pi, why each user needs a separate sandbox, and how to turn a proven manual workflow into skills. He also demos a Dots-like agent built on Hermes. Superlinear is a podcast about emerging practices for building with AI. Hosted by Brandon Kase and Christine Yip....
Bend2 puts code and proofs in the same language, so coding agents can write both. Tests check examples. A checked proof can establish that a specific rule holds for every allowed input. This is formal verification, an additional verification layer alongside types and tests. You still need to review whether the rule captures the behavior you actually want. In this episode, we show how to choose rules worth proving, get the agent to finish the proofs, and decide where the extra effort pays of...
Your coding agent does not need a frontier LLM call for every judgment. Deciding which tests to run, which skill to load, or whether a prompt needs more context can be a bounded question. Jev and other decision models return typed choices, scores, or yes/no probabilities quickly enough to sit inside repeated workflows. A valid answer can still be wrong, so placement and fallback rules matter. We show a live prompt checker and a small, six-commit test-selection experiment, then explore agent...