Notes & Meditations

Hive frames agent development as outcome-driven and iterative: define goals, generate a graph, run it, capture failures, and evolve the agent based on what breaks. I like that it’s trying to be production-oriented (human-in-the-loop nodes, credentials, monitoring, cost controls) instead of yet another “chain some tools” demo framework. The “self-improving” angle matters most if the failure data is actually actionable and you can keep the evolution process auditable. It’s also notable they explicitly target coding-agent workflows (Claude Code/Cursor/OpenCode) as the interface for building and debugging. This feels like an attempt to turn agent-building into a maintainable system, not a pile of prompts.

GSD is a bluntly pragmatic layer on top of Claude Code/OpenCode/Gemini aimed at one thing: stopping “context rot” from killing long projects. The pitch is spec-driven development without the enterprise cosplay—lightweight commands that still enforce structure (requirements, roadmap, phased plans, verification). The workflow is basically: extract intent, generate plans small enough to run in fresh context windows, execute with tight state management, and keep the git history clean. I’m sympathetic to the premise: reliability comes more from process + context engineering than from hoping the model “stays smart” 80k tokens in. Even if you don’t adopt the tool, the patterns are a useful blueprint.

Simon’s writeup is a great tour of the “dark factory” end of the spectrum, with the real question front-and-center: if agents write both the code and the tests, what does “proof it works” even mean? The StrongDM approach—scenario holdout sets + LLM-as-judge + a “digital twin universe” for dependencies—reads like the first serious attempt at an answer. The clever bit is treating scenarios like evaluation data: useful for validating, dangerous to leak into the training loop (or the agent’s context). The takeaway isn’t “never review code”; it’s that verification needs to move up a level to behavior under realistic conditions. It’s an uncomfortable idea, which is why it’s interesting.

StrongDM’s “software factory” pitch is basically: push humans out of the code path and move correctness into specs + scenarios + harnesses. The part that sticks is the insistence that humans shouldn’t even review the code—which forces you to build a validation loop you actually trust. I like their reframing from “tests” to “scenarios” as an external holdout set, plus “satisfaction” as a probabilistic measure rather than a green checkmark. The Digital Twin Universe idea (cloning SaaS dependencies to test at huge volume) is a pragmatic answer to reward hacking and rate limits. Even if you don’t buy the extremism, the constraints feel like a useful direction-of-travel: validate behavior, not diffs.

“Memory” sounds like a UX feature until you try to build it. Then it turns into: retrieval quality, write policies, privacy boundaries, and failure modes.

A good reminder: persistent memory isn’t magic. It’s a system.

If your 1:1s feel “fine” but you never learn anything new, you might be running a status meeting with better branding.

I like tactics that make it safer for people to tell you the uncomfortable truth.

MCP is one of those things that looks like plumbing… because it is. But it’s the kind of plumbing that determines whether your “agent” can actually do work.

Worth tracking if you’re building tool-enabled systems.

I like the outcome-driven framing for agent work: stop evaluating “did it write code,” start evaluating “did it move an outcome forward.”

Most agent setups fail for the same reason delegation fails: unclear intent + mismatched authority.

The best hiring writing is really about systems design for humans:

• what you reward
• what you tolerate
• what you make easy

“Cracked” is just a label for unmanaged variance.

Hiring advice usually fails because it ignores constraints.

I like writing that admits the real trade: speed vs quality vs cohesion — and then tells you what to do anyway.

A great framing for agent-assisted development: treat code as an artifact, but do your real thinking at the level of specs, tests, and constraints.

Feels like the “staff engineer” version of using LLMs: you win by shaping the work, not by typing faster.

Good personal sites feel less like a resume and more like a product:

• clear entry points
• consistent voice
• obvious navigation
• useful defaults

Which is comforting, because at least we know how to build products.

A useful mental model: agents are not “one prompt.” They’re a loop:

• observe
• plan
• act
• verify
• recover

If your loop can’t verify, you don’t have an agent. You have autocomplete with delusions.