https://www.youtube.com/watch?v=ETvz1qhQDXE

"I was a backend lead at Manus before the Meta acquisition. I've spent the last 2 years building AI agents — first at Manus, then on my own open-source agent runtime (Pinix) and agent (agent-clip). Along the way I came to a conclusion that surprised me: A single run(command="...") tool with Unix-style commands outperforms a catalog of typed function calls. Here's what I learned." https://www.reddit.com/r/LocalLLaMA/comments/1rrisqn/i_was_backend_lead_at_manus_after_building_agents NOT MY CONTENT

Hello everyone I do present to the CEO a Ai platform design. Mind you i do not have any experience with AI so I have been do crazy amount of research and learning Python. My question to all of you is. What am i missing? Do you recommend other tools? This is to complicated? Tools to be used: Task Engine = LangGrapgh Tool Registry= developed internally Prompt builder = Jinja template

Thought this may be insightful to those running analytical tasks: "The car wash problem asks a simple question: “I want to wash my car. The car wash is 100 meters away. Should I walk or drive?” Every major LLM tested—Claude, GPT-4, Gemini— recommended walking. The correct answer is to drive, because the car itself must be at the car wash. We ran a variable isolation study to determine which prompt architectural layer resolves this failure. Six conditions were tested, 20 trials each, on Claude Sonnet 4.5. A bare prompt with no system instructions scored 0%. Adding a role definition alone also scored 0%. A STAR reasoning framework (Situation, Task, Action, Result) reached 85%. User profile injection with physical context—car model, location, parking status—reached only 30%. STAR combined with profile injection reached 95%. The full stack combining all layers scored 100%. The central finding is that structured reasoning outperformed direct context injection by a factor of 2.83×(Fisher’s exact test, p = 0.001). STAR forces the model to articulate the task goal before generating a conclusion, which surfaces the implicit physical constraint that context injection leaves buried. The addition of a sixth condition resolved a confound in the original five-condition design by isolating per-layer contributions: STAR accounts for +85pp, profile adds +10pp, and RAG provides the final +5pp to reach perfect reliability." Read the whole paper below...

Tell us three things: 1. What you do (job, industry, student, career-changer, whatever) 2. What brought you to Clief Notes 3. One thing you're trying to figure out right now related to computing or AI I'll respond to every single one. And read each other's intros too because the person who's stuck on the same problem as you might already be in this thread. I'll go first I am Jake, I have been working in tech for 15 Years, building with Generative AI for 3 Years straight now! Excited to teach and learn! That's it. Simple, scannable, gives you data on who's joining and what they need, and keeps the feed clear for content that retains people past week one.