[logseq-plugin-git:commit] 2026-01-24T21:55:59.311Z

pages/KaraKeep-Highlights.md

@@ -1,44 +1,29 @@
 icon:: ✏️
-generation_time:: [[2026-01-24]] T20:00:02Z
+generation_time:: [[2026-01-24]] T21:54:54Z
 
 - # **TODOs and IDEAs**
 	- {{query (and [[TODO]] [[KaraKeep-Highlights]] (or [[Ideas]] [[KaraKeep-Highlights]]))}}
 	  query-table:: true
 	  query-properties:: [:block]
+- ## **How Agent Handoffs Work in Multi-Agent Systems | Towards Data Science**
+  source:: https://search.app/TLKfg
+  url:: https://karakeep.diruscio.org/dashboard/preview/biijh0k3p2kzivskr2b16t1c
+  tags:: [[Artificial Intelligence]] [[Multi-Agent Systems]] [[agent handoffs]] [[large language models]] [[workflow orchestration]]
+	- Conditional edges
+	  background-color:: blue
+
+- ## **GitHub - muratcankoylan/Agent-Skills-for-Context-Engineering: A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.**
+  source:: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering
+  url:: https://karakeep.diruscio.org/dashboard/preview/mujjzfwwjb2xbqiugz3i92nv
+  tags:: [[AI System Optimization]] [[Agent Architectures]] [[Artificial Intelligence]] [[Context Engineering]] [[Multi-Agent Systems]] [[agent skills]]
+	- Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.
+	  background-color:: blue
+		- > **Note:** #card
+
 - ## **“Vibe Coding”: The Future of Development or a Generational Divide? | CodeGPT**
   source:: https://www.codegpt.co/blog/vibe-coding-future-or-hype
   url:: https://karakeep.diruscio.org/dashboard/preview/osk8ozdguev9vbkw6ufwgesi
   tags:: [[AI-Assisted-Coding]] [[Artificial Intelligence]] [[Programming-Productivity]] [[Software Development]] [[Tech-Industry-Trends]]
-	- The AI is treated as a "typing assistant" or "pair programmer."
-	  background-color:: green
-	- The human acts as: The "guide, tester, and refiner"—maintaining full control and responsibility.
-	  background-color:: blue
-	- The proponents of "vibe coding" (Definition 2) argue for a fundamental shift in the developer's role, from implementer to designer and strategist.
-	  background-color:: blue
-	- A healthcare professional with no coding background can now prototype a patient tracking system.
-	  background-color:: green
-	- "weekend toy projects" with tools like Cursor and Claude
-	  background-color:: green
-	- "programming by intention". The AI handles the "syntactic details" and "technical implementation", liberating the human to focus on "higher-level design" and "creative problem-solving".
-	  background-color:: green
-	- The promise of vibe coding is cognitive liberation, reframing the bottleneck in software from technical skill (writing syntax) to product vision (knowing what to build).
-	  background-color:: blue
-	- Security Vulnerabilities
-	  background-color:: blue
-	- Technical Debt and Maintainability
-	  background-color:: blue
-	- This creates a "debugging nightmare". As one developer put it: "Debugging AI-generated code can be harder than writing it manually" because when an AI writes the code, the developer has no "mental map" of its logic.
-	  background-color:: green
-		- > **Note:** #P1
-	- The hype of "10x productivity gains" is directly challenged by empirical data. A Randomized Controlled Trial (RCT) studying experienced developers working on real open-source repositories found that using AI tools made them 19% SLOWER.
-	  background-color:: green
-	- The Skillset Developers "manage AI rather than just write code".
-	  background-color:: green
-	- it's intelligent orchestration. The developers who will thrive in this new landscape are not those who abdicate responsibility to AI, but those who learn to conduct it.
-	  background-color:: blue
-	- it's about humans with AI, working in a careful, intentional partnership.
-	  background-color:: green
-		- > **Note:** [[P1]]
 	- From Meme to Mainstream: Defining the Phenomenon
 	  background-color:: green
 		- > **Note:** This is related to [[PAPERS/J-Collaborative-Development]]
@@ -47,13 +32,12 @@ generation_time:: [[2026-01-24]] T20:00:02Z
   source:: https://www.quotidianosanita.it/uncategorized/leclissi-del-pensiero-critico-intelligenza-artificiale-debito-cognitivo-e-responsabilita/
   url:: https://karakeep.diruscio.org/dashboard/preview/q145b7y1pzntp7p8rcnkuwvy
   tags:: [[Artificial Intelligence]] [[Cognitive-Science]] [[Critical-Thinking]] [[Human-Computer-Interaction]] [[Medical-Education]]
-	- DEFT-AI che può offrire un approccio strutturato e di buon senso per promuovere il pensiero critico nelle interazioni con i sistemi di IA.
+	- Il modello cyborg, invece, prevede un’integrazione molto più stretta, con una co-produzione continua tra umano e IA, particolarmente efficiente per attività ripetitive e a basso rischio ma esposta al rischio di deskilling e dipendenza.
-	  background-color:: yellow
-	- saper riconoscere quando poter fare affidamento (modalità cyborg) e quando invece è necessario confermare criticamente gli output (modalità centauro).
-	  background-color:: yellow
-	- sostituire la logica del “trust and go” (fidarsi e andare avanti) con la metodologia del “verify and trust” (verificare e fidarsi) in cui l’output dell’IA viene prima valutato secondo il principio di esplicabilità e poi utilizzato.
-	  background-color:: yellow
-	- nuove competenze o si potenziano quelle esistenti per adattarsi a tecnologie e pratiche emergenti (up-skilling).
-	  background-color:: yellow
-	- In definitiva con l’up-skilling non solo si incrementa efficienza e sicurezza ma ci si protegge anche dai rischi di deskilling, garantendo che l’apprendimento e la pratica professionale evolvano insieme alla tecnologia.
 	  background-color:: yellow
+
+- ## **Machine Learning Model Development and Model Operations: Principles and Practices - KDnuggets**
+  source:: https://www.kdnuggets.com/2021/10/machine-learning-model-development-operations-principles-practice.html
+  url:: https://karakeep.diruscio.org/dashboard/preview/q984a0m36auekgemwpga0ybi
+  tags:: [[MLOps]] [[Machine Learning]] [[data science]] [[model development]] [[model operations]]
+	- In some cases, a set of dummy variables or derived variables are created, especially in handling ‘date’ data types.
+	  background-color:: green

pages/todoist___2026-01-31.md

@@ -41,4 +41,20 @@
   todoist-due:: 2026-01-31
   todoist-desc:: [Link to Logseq](logseq://graph/Logseq?block-id=69304744-f3d6-45cb-9a31-079a9a397f74)
   todoist-labels:: #source_logseq
+  todoist-status:: ◼️
+- [[2026-01-31]] Provare ad installare email server trasformare topohost [diruscio.org](https://diruscio.org) come dns solo
+  todoist-id:: [6fmMRwHC35PGgP5X](https://todoist.com/showTask?id=6fmMRwHC35PGgP5X)
+  todoist-project:: #🧓🏻 DIY
+  todoist-due:: 2026-01-31
+  todoist-status:: ◼️
+- [[2026-01-31]] [Stalwart Mail Server | Stalwart Labs](https://stalw.art/mail-server/)
+  todoist-id:: [6fqFM3gpHGP3mWjg](https://todoist.com/showTask?id=6fqFM3gpHGP3mWjg)
+  todoist-project:: #🧓🏻 DIY
+  todoist-due:: 2026-01-31
+  todoist-status:: ◼️
+- [[2026-01-31]] Sito MODES: primo draft
+  todoist-id:: [6fCrg62G5wXpxXqc](https://todoist.com/showTask?id=6fCrg62G5wXpxXqc)
+  todoist-project:: #📧EmailsToAnswer
+  todoist-due:: 2026-01-31
+  todoist-desc:: From: juri.dirocco@univaq.it Received: 2025-11-05T16:34:03Z ✉: [Web Link](https://outlook.office365.com/owa/?ItemID=AAMkADM1NGNiNjk0LTY0ZGUtNDgzOC04MDM5LWNhODNkYWNjNjU4YwBGAAAAAACT6qp78kRgRKuUMBdWEga%2FBwCOhWlC8F7PRKzlljZYZYQmAAAAAAEMAACOhWlC8F7PRKzlljZYZYQmAAhreqlnAAA%3D&exvsurl=1&viewmodel=ReadMessageItem) --- Ciao a tutti, a tempo perso ho cominciato a buttar giù il sito del laboratorio modes [MODES Lab](https://modes-laboratory.github.io/) E’ parametrizzato in base alle collezioni di dati, quindi possiamo lavorarci sui contenuti senza modificare il sito. Se Alf fa una
   todoist-status:: ◼️