Peter Pang @intuitiveml
Co-Founder and CTO of CREAO | Previously Meta creao.ai Cupertino Joined January 2020-
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@st3v3li That’s a great question. I’m actually preparing an article specifically on this topic, so stay tuned. In short, we use the platform layer to handle that logic.
@itselhakamy Nailed it, state + security are the two that quietly eat all your time. Thanks for reading
@DayuBuilds Exactly, multi-user, multi-session from day one changes every design decision.
@RatrektLabs Agreed, persistence + recovery is the dividing line between a demo and a real system.
@ar_io_network Reproducibility is underrated until it bites you. Would be curious to see the snapshot-proof approach
We rebuilt our entire company development and operation around AI. AI builds the tools, runs them, and improves them. Now we're building a platform so any team can build the same self-improvement loop. I did an interview talking through some of the details • “AI-first” means redesigning the workflow, not adding AI to the old one. • Engineers are moving from writing code to designing the systems that govern how agents write code. • Self-healing software is closer than most people think. • The real product is not the agent. It is the harness around the agent. here's the link: unite.ai/peter-pang-co-…
Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.
@yyyiiillluuu Claude Code remembering failures is useful. Learning reusable execution patterns from them is where compounding productivity starts. Love the focus on local-first + open source too.
This is the missing layer for AI coding workflows: not just memory, but adaptation.
Claude Code can now self-improve with this plugin. Introducing claude-smart — an open-source plugin that helps Claude Code learn from every session. Memory helps Claude Code remember what happened. claude-smart helps Claude Code improve what it does next. Example: Claude Code
small fix: had the colorbar legend reversed on the rubric chart, green now correctly maps to low poor%, red to high. same data, just the scale reading the right way around.
@CreaoAI @aritdeveloper Good job, looks very nice
Yeah, you are right about human in the middle, we always have engineer to look through the ticket from triage system. Currently, only a small fraction of ticket is solved with no human in the loop. Majority of them still need engineer guidance. But it already saved majority of time to identify and triage the issue
You’re absolutely right, and I should clarify further. Our grader does have full visibility into the entire tool-use history. However, we have a separate agent dedicated to optimizing that aspect, I’ll share more details on that in a separate post. This system, on the other hand, is primarily designed to detect system issues and bugs, which is where the healing process is focused.
A note on intent. We care a lot about accuracy and fairness, but we’re not building a leaderboard or ranking models against each other. The Grader exists to surface issues in our agent system: bad prompts, broken tool contracts, drifted integrations, infra flakes, regressions from our own deployments. Per-model scores are just a debugging signal, not a benchmark. If two judges score a response "poor" on the same messageId, we don’t learn that one model is better than another. We learn that something in our pipeline produced a bad answer, and we need to fix it.
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