Coding agents always had one target function to loop to: code_compiles == true
Most other fields need to find ways to bake domain expertise into their 'target function' first to loop anywhere.
I made the case for domain expertise in datasets bootstrapping your agent performance with the clearest of target functions: a classification task.
Check out my @aiDotEngineer talk!
https://t.co/nNJBbgO85C
Catching user signals from production traces with LLM-as-a-judge is a very good pointer where you should dig deeper into your agent behavior.
Find users cursing, disagreeing or following up to know where to look and evaluate more!
🧠 How @openclaw is Winning by Thinking fast and slow: The Kahneman Architecture 🦞
OpenClaw crossed 200,000 GitHub stars. It’s not just another "wrapper" - it’s the first agentic system to almost fully map to Daniel Kahneman’s "Thinking, Fast and Slow."
Most AI feels "incremental." OpenClaw feels evolutionary.
Here’s the cognitive stack enabling this:
1. 🧠System 1 (The LLM): Fast, fluent, but suffers from WYSIATI (What You See Is All There Is). It’s brilliant but stateless. iI has no memory and no awareness of its own gaps.
2. 🧠System 2 (The Reasoning Layer): Models like o3/Claude add deliberate, "slow" thinking. But reasoning alone is just a genius locked in a room.
3. The Breakthrough is the Expertise Loop. In humans, System 2 trains System 1. Effortful thought eventually "compiles" into intuition.
OpenClaw implements this via its file-based system:
- https://t.co/W9sJdfnhv5 (The Remembering Self): Creates continuity. The agent accumulates a narrative of who you are across sessions.
- https://t.co/0qFtSQfvNN (Compiled Intuition): When it solves a complex task, it writes a "Skill" file. Next time, it doesn't "think" - it just executes. If it hits an edge case, System 2 triggers, resolves it, and updates the skill.
4. Intelligence Needs "Hands" OpenClaw’s massive reach (WhatsApp, iMessage, Slack, Local Shell) isn't just convenience - it’s the feedback loop.
Act → Observe Outcome → Update Skill.
Once that loop closes, improvement is no longer a feature - it’s a property of the system itself.
The "Age of the Lobster" is here.
👇 Read my full breakdown on the "Lobster Shell" architecture:
https://t.co/P93OiHGolg
I dug out my first LLM pipeline from 2023.
4 services, multiple clouds, and a prompt that bribed the model with a $200 tip.
A thread on how far LLM engineering has come - and how unhinged the early days actually were. 🧵
This is bigger than Cursor.
What Tal and Aman built isn't just a great tutorial - it's a preview of how all education can work.
Why read a blog post when you can turn every content into an interactive learning experience with AI.
@MarcKlingen Really appreciated your input on my demo. I really believe there is a lot of value in dynamic forms instead of free text input for certain situations. 🚀
We've added Workflows to @langdock_hq. 🎉
Writing emails, summarising/improving text, and performing bulk actions are the first ones we added. More coming soon!