[logseq-plugin-git:commit] 2025-12-29T21:25:48.526Z
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icon:: ✏️
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generation_time:: [[2025-12-27]] T17:00:54Z
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- # **TODOs and IDEAs**
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- {{query (and [[TODO]] [[KaraKeep-Highlights]] (or [[Ideas]] [[KaraKeep-Highlights]]))}}
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query-table:: true
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query-properties:: [:block]
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- ## **Open - Le regole del gioco**
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source:: https://www.open.online/le-regole-del-gioco/
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url:: https://karakeep.diruscio.org/dashboard/preview/au4h7zjntjdakspb0gcl51bd
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tags:: [[behavioral addiction]] [[digital addiction]] [[gaming disorder]] [[mental health]] [[youth risk]]
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- Le cosiddette New Addiction sono capaci di diventare l’obiettivo primario della nostra mente finché l’opzione di smettere non rappresenta più una scelta libera.
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background-color:: green
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- in Italia tra il 10 e il 15% della popolazione presenti comportamenti che rientrano, in forma più o meno manifesta, nei criteri delle nuove dipendenze
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background-color:: green
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- Durante il primo lockdown uno studio pubblicato su Frontiers in Psychiatry, condotto dall’Istituto di Neuroscienze di Firenze in collaborazione con il Dipartimento di Psichiatria e Scienze del Comportamento della Albert Einstein College of Medicine di New York, ha rilevato che il 23,6% dei soggetti coinvolti mostrava sintomi compatibili con una forma di gioco d’azzardo patologico.
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background-color:: green
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- Nello studio condotto su studenti delle scuole superiori pubblicato su CNS Spectrums, i ricercatori hanno registrato il 5,4% degli studenti nella categoria di “internet addicted”; percentuali preoccupanti sono emerse anche per altre dipendenze: il 16% degli studenti ha ottenuto per esempio punteggi talmente alti nella scala dedicata al gioco d’azzardo da essere classificato nella fascia clinica definita come “problema estremo”
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background-color:: green
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- id:: 69500f28-4528-4606-9cdb-acc40518e3a4
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> **Note:** #card
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- ## **Top AI Agentic Workflow Patterns**
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source:: https://blog.bytebytego.com/p/top-ai-agentic-workflow-patterns
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url:: https://karakeep.diruscio.org/dashboard/preview/iz529wt1xybxstag5cmcrl1l
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tags:: [[AI automation]] [[Agentic Workflows]] [[Artificial Intelligence]] [[Reflective Systems]] [[Tool Integration]]
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- An agentic workflow doesn’t just respond to a single instruction. Instead, it operates with a degree of autonomy, making decisions about how to approach a task, what steps to take, and how to adapt based on what it discovers along the way. This represents a fundamental shift in how we think about using AI systems.
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- > **Note:** #IMPORTANT
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- An agentic system, however, might first search the web for current information on the topic, then organize the findings into themes, draft sections of the report, review each section for accuracy and coherence, revise weak areas, and finally compile everything into a polished document
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background-color:: green
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- Instead of generating output in a single pass, agentic workflows involve cycles where the agent takes an action, observes the result, and uses that observation to inform the next action
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background-color:: green
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- Agentic workflows bring this same adaptive, iterative quality to AI systems.
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- five essential agentic workflow patterns
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- The reflection pattern works best for tasks where quality matters more than speed and where there are subjective aspects that benefit from review.
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background-color:: blue
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- > **Note:** Reflection Pattern
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- In the tool use pattern, agents are equipped with a set of capabilities they can invoke when needed. These might include web search engines for finding current information, APIs for accessing services like weather data or stock prices, code interpreters for running programs and performing calculations, database query tools for retrieving specific records, file system access for reading and writing documents, and countless other specialized functions. The critical distinction from traditional software is that the agent itself decides when and how to use these tools based on the task at hand.
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background-color:: blue
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- > **Note:** Tool use pattern
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- ## **Contratto “Istruzione e Ricerca” 2022/2024, FLC CGIL: retribuzioni insufficienti, non firmiamo**
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source:: https://m.flcgil.it/comunicati-stampa/flc/contratto-istruzione-e-ricerca-2022-2024-flc-cgil-retribuzioni-insufficienti-non-firmiamo.flc
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url:: https://karakeep.diruscio.org/dashboard/preview/khv9wkq94ax8wi79swli9hnz
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tags:: [[collective bargaining]] [[education]] [[labor union]] [[public sector]] [[research]]
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- La nostra non firma di oggi vuole essere un messaggio chiaro nei confronti di un Governo che ha scientemente programmato il taglio delle retribuzioni di oltre 1,3 milioni di lavoratrici e lavoratori, già con gli stipendi più bassi di tutto il settore pubblico. Ora il Governo si deve far carico di colmare il divario retributivo con il resto dei dipendenti della pubblica amministrazione e garantire il completo recupero dell’inflazione
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background-color:: green
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- ## **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.**
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source:: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering
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url:: https://karakeep.diruscio.org/dashboard/preview/mujjzfwwjb2xbqiugz3i92nv
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tags:: [[AI System Optimization]] [[Agent Architectures]] [[Artificial Intelligence]] [[Context Engineering]] [[Multi-Agent Systems]] [[agent skills]]
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- The fundamental challenge is that context windows are constrained not by raw token capacity but by attention mechanics
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- "lost-in-the-middle" phenomenon, U-shaped attention curves, and attention scarcity.
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background-color:: yellow
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- Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes.
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background-color:: blue
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- > **Note:** #card
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- ## **7 Tiny AI Models for Raspberry Pi - KDnuggets**
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source:: https://www.kdnuggets.com/7-tiny-ai-models-for-raspberry-pi
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url:: https://karakeep.diruscio.org/dashboard/preview/wrwx75un37buyqnusi33ygm7
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tags:: [[Artificial Intelligence]] [[Local AI]] [[Machine Learning]] [[READ]] [[Raspberry Pi]] [[Tiny AI Models]]
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- aggressive quantization
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- llama.cpp
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- > **Note:** What's llama.cpp? #TODO To be checked.
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- tool calling, vision understanding, and structured outputs
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- vision‑language model
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- > **Note:** Vision Language Models (VLMs) are powerful AI systems that merge computer vision and natural language processing, allowing them to understand, interpret, and generate content from both images/videos and text inputs, enabling tasks like describing photos (captioning), answering questions about visuals (VQA), generating images from text, and understanding complex documents. #card
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- Tiny models have reached a point where size is no longer a limitation to capability. The Qwen 3 series stands out in this list, delivering performance that rivals much larger language models and even challenges some proprietary systems. If you are building applications for a Raspberry Pi or other low-power devices, Qwen 3 is an excellent starting point and well worth integrating into your setup.
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background-color:: green
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- > **Note:** #IMPORTANT
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