Diddy&#x27;s former assistant Suzi Siegel, who worked for the mogul from 2008 to 2009, told CNN that she &#x27;observed nothing that would lead me to believe&#x27; he was abusive, but that she is not &quot;surprised&quot; by the new video of him assaulting Cassie.

Diddy's Former Assistant Says 'Not One Cell in My Body' Was 'Surprised' by New Video

Google Home and Google Assistant were Google’s answer to growing interest in voice search and a real threat from Amazon’s Alexa and Echo smart speakers, but Google is seemingly leaving them both behind for generative AI with Gemini.

8 Years Ago, Google Beat Alexa. Then It Just Let the Assistant Waste Away

“He just didn’t see your humanity when he looked at you,“ she told CNN.

Diddy’s Former Assistant Suzi Siegi Says Her ‘Intuition’ Told Her He Was Violent

&#x27;There was not one cell in my body that was surprised,&#x27; Suzi Siegel admitted after the emergence of the harrowing video depicting the 54-year-old rapper attacking his ex-girlfriend.

Diddy's former assistant admits she was NOT 'surprised' by disturbing footage of him violently attacking his ex Cassie Ventura: 'I knew that it was something that he could be capable of' | Daily Mail Online

Sean “Diddy” Combs’ former assistant said she was not in surprised when video surfaced this week showing the rapper violently beating his ex-girlfriend Cassie Ventura in a hotel.

Sean 'Diddy' Combs's former assistant says she was not surprised by video showing rapper beating ex Cassie Ventura in hotel hallway

Google and OpenAI unveiled new tools to bring ‘intelligent systems’ a step closer. Will this be a milestone for generative AI?

The race for an AI-powered personal assistant

GREENEVILLE, Tenn. (WJHL) — A lawsuit against the Town of Greeneville filed by a former assistant police chief was dismissed on Wednesday. Michael Crum, a former assistant police chief at the Greeneville Police Department (GPD), filed the lawsuit after he was terminated in December 2021 following ...

Former assistant police chief’s lawsuit against Greeneville dismissed

It’s been a week of A.I. vertigo — the feeling that we’ve been dragged five years into the future and aren’t sure what to do about it.

OpenAI’s Flirty New Assistant, Google Guts the Web and We Play HatGPT - The New York Times

Keante Harris, a 45-year-old assistant principal, is among four men arrested in connection with a triple murder that occurred in January 2013.

Alabama middle-school assistant principal arrested in 2013 cold-case triple murder in Georgia | Fox News

An assistant principal at John Adams High School in Ozone Park was arrested Wednesday and charged with endangering the welfare of a child and third-degree sexual abuse, according to police.

Ozone Park assistant principal charged with sex abuse | | qchron.com

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Mistral’s New AI Assistant Sends Shockwaves With 10x the Speed of Chatgpt

Besides a regular webapp and a Mac app, which uses the same endpoints as the webapp, Home Assistant also provides mobile apps. In this post, I want to describe its advantages over the former. I'll use iOS to do this: Samsung's French partner to recycle used mobiles tricked me once, and I moved away from Samsung. I decided to buy Apple for better integration since I've had Mac computers for the last few years. Installing and Configuring The companion app is readily available at the Apple Store. Note that the app is both free as a beer and free as a bird. It's provided under the Apache v2 License. If interested, the source code is on GitHub. Once installed, you must configure at least one Home Assistant server. At this point, you should get the same UI as the web app. The app's power lies in its widgets, though. Widgets Some iOS applications allow you to add widgets to the UI. I'm not a massive user of widgets. I've Google Calendar, the Weather, and a VPN on my phone. Widgets serve a dual purpose: visualization and possibly action; if a widget doesn't offer an action, pressing it will open the associated app. That's the case with the Google Calendar and Weather apps. My VPN widget displays a button allowing you to connect and pause. The Companion App Widget The app offers two distinct kinds of widgets, each serving a specific purpose. The first kind allows opening the app on a specific "page", e.g., Overview or Developer tools. IMHO, it's not very useful. The second kind allows calling  Home Assistant commands. However, it requires significantly more effort. First, we must create action objects. Here's one to switch the light on: actions:
  - name: OfficeLightsOn                 #1
    label:
      text: "Turn ON Office Lights"      #2
    icon:
      icon: lightbulb-on-outline         #3
      color: "#ffff00"                   #3
    show_in_carplay: false
    show_in_watch: true Name, but you should probably think about it as a String ID.


The label is shown in the companion app.


Graphic details used by the companion app After creating the action, you must restart Home Assistant for devices to detect the new action. Once restarted, you can add a Home Assistant widget that is bound to that action. However, pressing the widget does nothing but a slight haptic feedback. We need to bind the action to an automation object. I explained automations when we migrated away from the Hue bridge in the previous post. At the time, we created an automation via the UI; we can also create one in the /homeassistant/configuration.yaml. We configure the action as the automation's trigger. - alias: Turn On Office Lights On        #1
  trigger:
    - platform: event                    #2
      event_type: ios.action_fired       #2
      event_data:
        actionName: OfficeLightsOn       #3
  action:
    - service: light.turn_on             #4
      entity_id: light.all_office        #5 Friendly alias for easier maintenance.


Fixed payload sent to the device.


Configured payload sent to the device. If the payload matches, it will trigger the action.


Service to call.


Call the service on the device ID. Now, pressing the widget turns on the light as expected. Summary Integrating Home Assistant on your device is straightforward. The pressing on the widget is the trigger for an automation - an action. You can create the automation or reuse an existing one. To go further: Home Assistant Companion Docs Originally published at A Java Geek on January 12th, 2025 Besides a regular webapp and a Mac app, which uses the same endpoints as the webapp, Home Assistant also provides mobile apps. In this post, I want to describe its advantages over the former. I'll use iOS to do this: Samsung's French partner to recycle used mobiles tricked me once, and I moved away from Samsung. I decided to buy Apple for better integration since I've had Mac computers for the last few years. Installing and Configuring The companion app is readily available at the Apple Store . Note that the app is both free as a beer and free as a bird . It's provided under the Apache v2 License. If interested, the source code is on GitHub . Apple Store free as a beer free as a bird GitHub Once installed, you must configure at least one Home Assistant server. At this point, you should get the same UI as the web app. The app's power lies in its widgets, though. Widgets Some iOS applications allow you to add widgets to the UI . I'm not a massive user of widgets. I've Google Calendar, the Weather, and a VPN on my phone. to add widgets to the UI Widgets serve a dual purpose: visualization and possibly action; if a widget doesn't offer an action, pressing it will open the associated app. That's the case with the Google Calendar and Weather apps. My VPN widget displays a button allowing you to connect and pause. The Companion App Widget The app offers two distinct kinds of widgets, each serving a specific purpose. The first kind allows opening the app on a specific "page", e.g. , Overview or Developer tools. IMHO, it's not very useful. e.g. The second kind allows calling  Home Assistant commands. However, it requires significantly more effort. First, we must create action objects. Here's one to switch the light on: action actions:
  - name: OfficeLightsOn                 #1
    label:
      text: "Turn ON Office Lights"      #2
    icon:
      icon: lightbulb-on-outline         #3
      color: "#ffff00"                   #3
    show_in_carplay: false
    show_in_watch: true actions:
  - name: OfficeLightsOn                 #1
    label:
      text: "Turn ON Office Lights"      #2
    icon:
      icon: lightbulb-on-outline         #3
      color: "#ffff00"                   #3
    show_in_carplay: false
    show_in_watch: true Name, but you should probably think about it as a String ID. The label is shown in the companion app. Graphic details used by the companion app Name, but you should probably think about it as a String ID. Name, but you should probably think about it as a String ID. The label is shown in the companion app. The label is shown in the companion app. Graphic details used by the companion app Graphic details used by the companion app After creating the action, you must restart Home Assistant for devices to detect the new action. Once restarted, you can add a Home Assistant widget that is bound to that action. must However, pressing the widget does nothing but a slight haptic feedback. We need to bind the action to an automation object. I explained automations when we migrated away from the Hue bridge in the previous post . At the time, we created an automation via the UI; we can also create one in the /homeassistant/configuration.yaml . We configure the action as the automation's trigger . automation previous post /homeassistant/configuration.yaml trigger - alias: Turn On Office Lights On        #1
  trigger:
    - platform: event                    #2
      event_type: ios.action_fired       #2
      event_data:
        actionName: OfficeLightsOn       #3
  action:
    - service: light.turn_on             #4
      entity_id: light.all_office        #5 - alias: Turn On Office Lights On        #1
  trigger:
    - platform: event                    #2
      event_type: ios.action_fired       #2
      event_data:
        actionName: OfficeLightsOn       #3
  action:
    - service: light.turn_on             #4
      entity_id: light.all_office        #5 Friendly alias for easier maintenance. Fixed payload sent to the device. Configured payload sent to the device. If the payload matches, it will trigger the action. Service to call. Call the service on the device ID. Friendly alias for easier maintenance. Friendly alias for easier maintenance. Fixed payload sent to the device. Fixed payload sent to the device. Configured payload sent to the device. If the payload matches, it will trigger the action. Configured payload sent to the device. If the payload matches, it will trigger the action. Service to call. Service to call. Call the service on the device ID. Call the service on the device ID. Now, pressing the widget turns on the light as expected. Summary Integrating Home Assistant on your device is straightforward. The pressing on the widget is the trigger for an automation - an action . You can create the automation or reuse an existing one. action automation To go further: To go further: Home Assistant Companion Docs Home Assistant Companion Docs Home Assistant Companion Docs Originally published at A Java Geek on January 12th, 2025 Originally published at A Java Geek on January 12th, 2025 A Java Geek

The Home Assistant Companion App: How to Integrate it to Your Device

Table of Links Abstract and 1 Introduction 2. Related Work 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 4. Experiments 5. Conclusion, Limitation, and Future Works and References Abstract In this paper, we introduce LLaVA-ϕ (LLaVA-Phi), an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2, to facilitate multi-modal dialogues. LLaVA-Phi marks a notable advancement in the realm of compact multi-modal models. It demonstrates that even smaller language models, with as few as 2.7B parameters, can effectively engage in intricate dialogues that integrate both textual and visual elements, provided they are trained with high-quality corpora. Our model delivers commendable performance on publicly available benchmarks that encompass visual comprehension, reasoning, and knowledge-based perception. Beyond its remarkable performance in multi-modal dialogue tasks, our model opens new avenues for applications in timesensitive environments and systems that require real-time interaction, such as embodied agents. It highlights the potential of smaller language models to achieve sophisticated levels of understanding and interaction, while maintaining greater resource efficiency. The project is available at https://github.com/zhuyiche/llava-phi. 1. Introduction Large vision language models, including Flamingo [1], GPT-4V [30], and Gemini [33], have exhibited remarkable proficiency in executing instructions, engaging in multi-turn dialogues, and handling image-based question-answering tasks. The progression of open-source vision language models has been significantly propelled by the rapid advancement of open-source Large Language Models like LLaMA [34] and Vicuna [5]. These developments primarily focus on leveraging language models with a minimum of 7B parameters, integrated with a vision encoder to enhance visual comprehension. However, this approach often results in increased test time and reduced inference speed, which are less than ideal for time-sensitive or real-time interactive applications, such as autonomous driving and robotics. This leads to an important inquiry: How effectively can small vision-language assistants perform in comparison? Gemini [33] has blazed a trail for multi-modal models in mobile technology. Its streamlined variant, Gemini-Nano, boasts 1.8/3.25 billion parameters and is deployable on mobile devices. However, details like the model architecture, training data, and training methodologies remain proprietary and inaccessible to the public. In the realm of small language models, there have been notable advancements: TinyGSM [23], with 2.6 billion parameters, achieves over 80% accuracy on the GSM8k [7] benchmark. Additionally, models such as Phi [13] have demonstrated capabilities in language understanding, commonsense reasoning, and code generation, rivaling larger language models like LLaMA2-7B. This progress underscores the significant strides being made in the efficiency and effectiveness of smaller-scale language models. In this paper, we introduce LLaVA-Phi, a compact vision-language assistant powered by a small language model. Our work combines the powerful opensourced multi-modal model, LLaVA-1.5 [24], with the best-performing open-sourced small language models, Phi2 [21]. We follow a two-stage training pipeline and leverage high-quality visual instruction tuning data from LLaVA. LLaVA-Phi was evaluated across eight diverse benchmarks. Despite possessing only 3 billion parameters, it achieves performance comparable to, or even surpassing, some larger multi-modal models that are three times larger. Notably, LLaVA-Phi-3B demonstrates exceptional proficiency in ScienceQA [28], outperforming existing large multimodal models. Additionally, we qualitatively demonstrate LLaVA-Phi’s strong generalization ability in handling challenging questions, generating code based on instructions, and solving mathematical problems. This paper is available on arxiv under CC BY 4.0 DEED license. (1) Yichen Zhu, Midea Group;
(2) Minjie Zhu, Midea Group and East China Normal University;
(3) Ning Liu, Midea Group;
(4) Zhicai Ou, Midea Group;
(5) Xiaofeng Mou, Midea Group. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. LLaVA-Phi and 3.1. Training 3. LLaVA-Phi and 3.1. Training 3.2. Qualitative Results 3.2. Qualitative Results 4. Experiments 4. Experiments 5. Conclusion, Limitation, and Future Works and References 5. Conclusion, Limitation, and Future Works and References Abstract In this paper, we introduce LLaVA-ϕ (LLaVA-Phi), an efficient multi-modal assistant that harnesses the power of the recently advanced small language model, Phi-2, to facilitate multi-modal dialogues. LLaVA-Phi marks a notable advancement in the realm of compact multi-modal models. It demonstrates that even smaller language models, with as few as 2.7B parameters, can effectively engage in intricate dialogues that integrate both textual and visual elements, provided they are trained with high-quality corpora. Our model delivers commendable performance on publicly available benchmarks that encompass visual comprehension, reasoning, and knowledge-based perception. Beyond its remarkable performance in multi-modal dialogue tasks, our model opens new avenues for applications in timesensitive environments and systems that require real-time interaction, such as embodied agents. It highlights the potential of smaller language models to achieve sophisticated levels of understanding and interaction, while maintaining greater resource efficiency. The project is available at https://github.com/zhuyiche/llava-phi. 1. Introduction Large vision language models, including Flamingo [1], GPT-4V [30], and Gemini [33], have exhibited remarkable proficiency in executing instructions, engaging in multi-turn dialogues, and handling image-based question-answering tasks. The progression of open-source vision language models has been significantly propelled by the rapid advancement of open-source Large Language Models like LLaMA [34] and Vicuna [5]. These developments primarily focus on leveraging language models with a minimum of 7B parameters, integrated with a vision encoder to enhance visual comprehension. However, this approach often results in increased test time and reduced inference speed, which are less than ideal for time-sensitive or real-time interactive applications, such as autonomous driving and robotics. This leads to an important inquiry: How effectively can small vision-language assistants perform in comparison? Gemini [33] has blazed a trail for multi-modal models in mobile technology. Its streamlined variant, Gemini-Nano, boasts 1.8/3.25 billion parameters and is deployable on mobile devices. However, details like the model architecture, training data, and training methodologies remain proprietary and inaccessible to the public. In the realm of small language models, there have been notable advancements: TinyGSM [23], with 2.6 billion parameters, achieves over 80% accuracy on the GSM8k [7] benchmark. Additionally, models such as Phi [13] have demonstrated capabilities in language understanding, commonsense reasoning, and code generation, rivaling larger language models like LLaMA2-7B. This progress underscores the significant strides being made in the efficiency and effectiveness of smaller-scale language models. In this paper, we introduce LLaVA-Phi, a compact vision-language assistant powered by a small language model. Our work combines the powerful opensourced multi-modal model, LLaVA-1.5 [24], with the best-performing open-sourced small language models, Phi2 [21]. We follow a two-stage training pipeline and leverage high-quality visual instruction tuning data from LLaVA. LLaVA-Phi was evaluated across eight diverse benchmarks. Despite possessing only 3 billion parameters, it achieves performance comparable to, or even surpassing, some larger multi-modal models that are three times larger. Notably, LLaVA-Phi-3B demonstrates exceptional proficiency in ScienceQA [28], outperforming existing large multimodal models. Additionally, we qualitatively demonstrate LLaVA-Phi’s strong generalization ability in handling challenging questions, generating code based on instructions, and solving mathematical problems. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv (1) Yichen Zhu, Midea Group; (2) Minjie Zhu, Midea Group and East China Normal University; (3) Ning Liu, Midea Group; (4) Zhicai Ou, Midea Group; (5) Xiaofeng Mou, Midea Group. (1) Yichen Zhu, Midea Group; (2) Minjie Zhu, Midea Group and East China Normal University; (3) Ning Liu, Midea Group; (4) Zhicai Ou, Midea Group; (5) Xiaofeng Mou, Midea Group.

Introducing LLaVA-Phi: A Compact Vision-Language Assistant Powered By a Small Language Model

New AI Model Outshines Popular Smart Assistants by Navigating, Interacting with Smartphone Screens

Apple’s Siri has long been the iPhone’s trusted voice assistant, helping users with weather updates, reminders, and directions. But in the AI arms race, Siri has struggled to keep pace with conversational powerhouses like OpenAI’s ChatGPT and Google’s Gemini. A significant change might be on the horizon, as Apple is reportedly working on a new version of Siri powered by large language models (LLMs). Internally referred to as "LLM Siri," this upgrade could transform the way users interact with their devices. What We Know So Far According to a recent Bloomberg report, Apple’s advanced Siri version is scheduled for release in 2026, with an official announcement expected as early as 2025. While the full rollout might take years, some features could arrive sooner. iOS users may see incremental updates that bridge the gap between today’s Siri and its future LLM-powered counterpart. The revamped Siri is expected to handle complex, multi-step tasks, including navigating third-party apps and delivering more conversational responses. This contrasts sharply with Siri’s current limitations, which often leave users reaching for rival apps to complete advanced queries or tasks. Why It Matters Voice assistants have become a central aspect of the mobile ecosystem, but the rise of advanced AI tools like ChatGPT has shifted expectations. ChatGPT, which landed on iOS in May 2023, already integrates cutting-edge capabilities such as history synchronization and voice input powered by Whisper, OpenAI’s speech recognition technology (OpenAI Blog). With Gemini rumored to join the platform soon, Apple faces mounting pressure to maintain Siri's relevance. Integrating LLMs into Siri could open the door to more personalized and proactive interactions. Imagine Siri scheduling your day, booking travel, or managing third-party apps—all without the need for specific commands. However, this move also raises questions about Apple’s relationships with companies like Alphabet (the parent company of Google) and OpenAI, especially as these platforms gain more traction on iOS. The Broader AI Landscape Apple isn’t alone in its pursuit of smarter AI. Google recently launched Gemini, its next-generation conversational AI, and Microsoft has integrated OpenAI’s models into Bing. While Apple has been relatively quiet about its AI advancements, the company is undoubtedly investing heavily in this area. Earlier this year, reports suggested Apple spent millions daily on training its AI models (The Verge). This competition isn’t just about who can build the smartest assistant; it’s also about shaping the future of AI monetization. Enhanced AI tools could generate billions in new revenue streams through app integrations, subscriptions, and services, as highlighted in Barron’s. What’s Next for Apple and Siri? While "LLM Siri" promises a major leap forward, Apple’s traditional approach to product secrecy leaves much unknown. Will Siri’s enhancements be tied exclusively to iPhones, or will we see broader integrations across Apple’s ecosystem, from Macs to wearables? And more importantly, can it match—or surpass—the user experience offered by ChatGPT and Gemini? For now, Apple is playing the long game. The arrival of ChatGPT and potentially Gemini on iOS devices will challenge Siri to evolve rapidly. Whether "LLM Siri" delivers on its promise will ultimately depend on how Apple balances innovation with user trust and privacy—two cornerstones of its brand ethos. What are your thoughts on Apple’s plans for Siri? Is "LLM Siri" the upgrade it needs to stay competitive, or has the AI train already left the station? Let us know in the comments! References: Apple Prepares Siri for AI Overhaul (Bloomberg)
Introducing ChatGPT for iOS (OpenAI)
Apple’s AI Efforts Intensify (The Verge)
Apple AI Revenue Potential (Barron’s) Apple’s Siri has long been the iPhone’s trusted voice assistant, helping users with weather updates, reminders, and directions. But in the AI arms race, Siri has struggled to keep pace with conversational powerhouses like OpenAI’s ChatGPT and Google’s Gemini. A significant change might be on the horizon, as Apple is reportedly working on a new version of Siri powered by large language models (LLMs). Internally referred to as "LLM Siri," this upgrade could transform the way users interact with their devices. What We Know So Far What We Know So Far According to a recent Bloomberg report , Apple’s advanced Siri version is scheduled for release in 2026, with an official announcement expected as early as 2025. While the full rollout might take years, some features could arrive sooner. iOS users may see incremental updates that bridge the gap between today’s Siri and its future LLM-powered counterpart. Bloomberg report The revamped Siri is expected to handle complex, multi-step tasks, including navigating third-party apps and delivering more conversational responses. This contrasts sharply with Siri’s current limitations, which often leave users reaching for rival apps to complete advanced queries or tasks. Why It Matters Why It Matters Voice assistants have become a central aspect of the mobile ecosystem, but the rise of advanced AI tools like ChatGPT has shifted expectations. ChatGPT, which landed on iOS in May 2023, already integrates cutting-edge capabilities such as history synchronization and voice input powered by Whisper, OpenAI’s speech recognition technology ( OpenAI Blog ). OpenAI Blog With Gemini rumored to join the platform soon, Apple faces mounting pressure to maintain Siri's relevance. Integrating LLMs into Siri could open the door to more personalized and proactive interactions. Imagine Siri scheduling your day, booking travel, or managing third-party apps—all without the need for specific commands. However, this move also raises questions about Apple’s relationships with companies like Alphabet (the parent company of Google) and OpenAI, especially as these platforms gain more traction on iOS. The Broader AI Landscape The Broader AI Landscape Apple isn’t alone in its pursuit of smarter AI. Google recently launched Gemini, its next-generation conversational AI, and Microsoft has integrated OpenAI’s models into Bing. While Apple has been relatively quiet about its AI advancements, the company is undoubtedly investing heavily in this area. Earlier this year, reports suggested Apple spent millions daily on training its AI models ( The Verge ). The Verge This competition isn’t just about who can build the smartest assistant; it’s also about shaping the future of AI monetization. Enhanced AI tools could generate billions in new revenue streams through app integrations, subscriptions, and services, as highlighted in Barron’s . Barron’s What’s Next for Apple and Siri? What’s Next for Apple and Siri? While "LLM Siri" promises a major leap forward, Apple’s traditional approach to product secrecy leaves much unknown. Will Siri’s enhancements be tied exclusively to iPhones, or will we see broader integrations across Apple’s ecosystem, from Macs to wearables? And more importantly, can it match—or surpass—the user experience offered by ChatGPT and Gemini? For now, Apple is playing the long game. The arrival of ChatGPT and potentially Gemini on iOS devices will challenge Siri to evolve rapidly. Whether "LLM Siri" delivers on its promise will ultimately depend on how Apple balances innovation with user trust and privacy—two cornerstones of its brand ethos. What are your thoughts on Apple’s plans for Siri? Is "LLM Siri" the upgrade it needs to stay competitive, or has the AI train already left the station? Let us know in the comments! References: References: Apple Prepares Siri for AI Overhaul (Bloomberg) Introducing ChatGPT for iOS (OpenAI) Apple’s AI Efforts Intensify (The Verge) Apple AI Revenue Potential (Barron’s) Apple Prepares Siri for AI Overhaul (Bloomberg) Apple Prepares Siri for AI Overhaul (Bloomberg) Introducing ChatGPT for iOS (OpenAI) Introducing ChatGPT for iOS (OpenAI) Apple’s AI Efforts Intensify (The Verge) Apple’s AI Efforts Intensify (The Verge) Apple AI Revenue Potential (Barron’s) Apple AI Revenue Potential (Barron’s)

Apple’s LLM Siri: The Voice Assistant Gets Smarter, But Can It Rival ChatGPT and Gemini?

Home Assistant is a massive beast. It can be overwhelming for a newcomer; it was for me. In this post, I want to describe the underlying model of Home Assistant, which is a good entry point for your home automation journey. The biggest issue in describing the Home Assistant is the number of conflicting sources for this model: The helpers package of the GitHub repository. The database; disclaimer: I didn't find the schema generation in the code, and I wasn't bold enough to check the deployed SQLite database. The documentation. The web interface. As far as I understand it, here's the conceptual model. Integrations Integrations are pieces of software that allow Home Assistant to connect to other software and platforms. For example, a product by Philips called Hue would use the Philips Hue integration and allow Home Assistant to talk to the hardware controller Hue Bridge. Any Home Assistant compatible devices connected to the Hue Bridge would appear in Home Assistant as devices.
-- Home Assistant documentation When you switch on Home Assistant for the first time, it's a blank slate. You must make it communicate with your different devices. That's the role of Integration; thus, I think it's a critical entity. You can add an Integration on the integration dashboard: there's an ADD INTEGRATION in the lower left corner. Alternatively, you can add it from its documentation page, via a ADD INTEGRATION TO MY HOME ASSISTANT button. In this case, clicking on the latter opens a new page. You can change it; the URL is stored on the browser. Some integrations require additional configuration, e.g., authentication. Devices Devices are a logical grouping for one or more entities. A device may represent a physical device, which can have one or more sensors. The sensors appear as entities associated with the device. For example, a motion sensor is represented as a device. It may provide motion detection, temperature, and light levels as entities. Entities have states such as detected when motion is detected and clear when there is no motion.
-- Home Assistant documentation Once you've added the integration, you can add Device: Some categorize as Service when they need a Cloud connection, e.g., Google Cloud SDK or GitHub.



Some as Device when they need one, e.g., Philips Hue. Entities Entities are the basic building blocks to hold data in Home Assistant. An entity represents a sensor, actor, or function in Home Assistant. Entities are used to monitor physical properties or to control other entities. An entity is usually part of a device or a service. Entities have states.
-- Home Assistant documentation For example, the Philips Hue motion sensor device has three different available sensors: Illuminance in Lux
Motion as a boolean
Temperature in Celsius Unexpectedly, a motion sensor provides a temperature sensor. Check your devices for such sensors; you might be in for a good surprise. Other Objects Areas:
An Area represents a location. You can put a physical  Device in an Area.

Automations:
An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition.For example, let's imagine a Welcome Home Automation:
The trigger is when the user enters a geofenced perimeter around one's home.
The action is to switch on the lights
Yet, switching on the lights in daylight doesn't make sense.

The condition would be "only during the night."

Scripts:
As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY.

Add-ons:
While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate.

Scenes:
A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Conclusion In this post, I described the model objects available in Home Assistant. In the next post, I'll migrate from the proprietary Philips Hue automation to Home Assistant. To go further: Home Assistant
Concepts and terminology Originally published at A Java Geek on December 2nd, 2024 Home Assistant is a massive beast. It can be overwhelming for a newcomer; it was for me. In this post, I want to describe the underlying model of Home Assistant, which is a good entry point for your home automation journey. The biggest issue in describing the Home Assistant is the number of conflicting sources for this model: The helpers package of the GitHub repository. The helpers package of the GitHub repository . helpers GitHub repository The database; disclaimer: I didn't find the schema generation in the code, and I wasn't bold enough to check the deployed SQLite database. The database; disclaimer: I didn't find the schema generation in the code, and I wasn't bold enough to check the deployed SQLite database. The documentation. The documentation . documentation The web interface. The web interface. As far as I understand it, here's the conceptual model. Integrations Integrations are pieces of software that allow Home Assistant to connect to other software and platforms. For example, a product by Philips called Hue would use the Philips Hue integration and allow Home Assistant to talk to the hardware controller Hue Bridge. Any Home Assistant compatible devices connected to the Hue Bridge would appear in Home Assistant as devices. -- Home Assistant documentation Integrations are pieces of software that allow Home Assistant to connect to other software and platforms. For example, a product by Philips called Hue would use the Philips Hue integration and allow Home Assistant to talk to the hardware controller Hue Bridge. Any Home Assistant compatible devices connected to the Hue Bridge would appear in Home Assistant as devices. -- Home Assistant documentation Home Assistant documentation When you switch on Home Assistant for the first time, it's a blank slate. You must make it communicate with your different devices. That's the role of Integration ; thus, I think it's a critical entity. Integration You can add an Integration on the integration dashboard : there's an ADD INTEGRATION in the lower left corner. Alternatively, you can add it from its documentation page, via a ADD INTEGRATION TO MY HOME ASSISTANT button. In this case, clicking on the latter opens a new page. Integration integration dashboard ADD INTEGRATION ADD INTEGRATION TO MY HOME ASSISTANT You can change it; the URL is stored on the browser. Some integrations require additional configuration, e.g. , authentication. e.g. Devices Devices are a logical grouping for one or more entities. A device may represent a physical device, which can have one or more sensors. The sensors appear as entities associated with the device. For example, a motion sensor is represented as a device. It may provide motion detection, temperature, and light levels as entities. Entities have states such as detected when motion is detected and clear when there is no motion. -- Home Assistant documentation Devices are a logical grouping for one or more entities. A device may represent a physical device, which can have one or more sensors. The sensors appear as entities associated with the device. For example, a motion sensor is represented as a device. It may provide motion detection, temperature, and light levels as entities. Entities have states such as detected when motion is detected and clear when there is no motion. -- Home Assistant documentation Home Assistant documentation Once you've added the integration, you can add Device : Device Some categorize as Service when they need a Cloud connection, e.g., Google Cloud SDK or GitHub. Some as Device when they need one, e.g., Philips Hue. Some categorize as Service when they need a Cloud connection, e.g., Google Cloud SDK or GitHub. Some categorize as Service when they need a Cloud connection, e.g. , Google Cloud SDK or GitHub . Service e.g. Google Cloud SDK GitHub Some as Device when they need one, e.g., Philips Hue. Some as Device when they need one, e.g. , Philips Hue . Device e.g. Philips Hue Entities Entities are the basic building blocks to hold data in Home Assistant. An entity represents a sensor, actor, or function in Home Assistant. Entities are used to monitor physical properties or to control other entities. An entity is usually part of a device or a service. Entities have states. -- Home Assistant documentation Entities are the basic building blocks to hold data in Home Assistant. An entity represents a sensor, actor, or function in Home Assistant. Entities are used to monitor physical properties or to control other entities. An entity is usually part of a device or a service. Entities have states. -- Home Assistant documentation Home Assistant documentation For example, the Philips Hue motion sensor device has three different available sensors: Illuminance in Lux Motion as a boolean Temperature in Celsius Illuminance in Lux Motion as a boolean Temperature in Celsius Unexpectedly, a motion sensor provides a temperature sensor. Check your devices for such sensors; you might be in for a good surprise. Other Objects Areas:
An Area represents a location. You can put a physical  Device in an Area. Automations:
An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition.For example, let's imagine a Welcome Home Automation:
The trigger is when the user enters a geofenced perimeter around one's home.
The action is to switch on the lights
Yet, switching on the lights in daylight doesn't make sense.

The condition would be "only during the night." Scripts:
As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY. Add-ons:
While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate. Scenes:
A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Areas: An Area represents a location. You can put a physical  Device in an Area. Areas: An Area represents a location. You can put a physical Device in an Area . Area physical Device Area Automations: An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition. For example, let's imagine a Welcome Home Automation: The trigger is when the user enters a geofenced perimeter around one's home.
The action is to switch on the lights
Yet, switching on the lights in daylight doesn't make sense. The condition would be "only during the night." Automations: An Automation is pretty self-explanatory. Each consists of two main components: a trigger and an action. Note that you can add an optional condition. Automation For example, let's imagine a Welcome Home Automation : Automation The trigger is when the user enters a geofenced perimeter around one's home. The action is to switch on the lights Yet, switching on the lights in daylight doesn't make sense. The trigger is when the user enters a geofenced perimeter around one's home. The action is to switch on the lights Yet, switching on the lights in daylight doesn't make sense. The condition would be "only during the night." Scripts: As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY. Scripts: As per the documentation, a Script is akin to an Automation but with no trigger; it's a named action. It lets you define the action once and reuse it across several Automation objects. Think DRY. Script Automation Automation Add-ons: While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate. Add-ons: While an Integration allows interfacing with an external device or service, an Add-On provides a service. In other words, an Add-On extends the platform itself. For example, I installed the Let's Encrypt add-on to my Home Assistant installation to automatically manage a TLS certificate. Integration Add-On Add-On Let's Encrypt Scenes: A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Scenes: A Scene for a Device is akin to a profile for your phone. It's a named set of configurations that you save and reuse. I must admit that I don't use profiles on my phone; I don't have any Scene objects at the moment, and I have to understand their usage beyond lighting. Scene Device profile Scene Conclusion In this post, I described the model objects available in Home Assistant. In the next post, I'll migrate from the proprietary Philips Hue automation to Home Assistant. To go further: To go further: Home Assistant Concepts and terminology Home Assistant Home Assistant Concepts and terminology Concepts and terminology Originally published at A Java Geek on December 2nd, 2024 Originally published at A Java Geek on December 2nd, 2024 A Java Geek

The Home Assistant Model: A Good Entry Point

Last June, I spoke at Berlin Buzzwords. In all honesty, I rarely attend others' talks for a variety of reasons: lack of time, lack of energy, no interest in the proposed subjects, etc. When I do, I go either for subjects I know and want to deepen my understanding of or for subjects I know nothing about to get a foot in the door. This time, I attended Monitoring your home, with DevOps observability tools. I thought it would be about OpenTelemetry for your home. After the speaker mentioned Home Assistant, however, I didn't pay much attention to the rest. Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server. I have many different "smart" devices at home and no centralized system to manage them all. Plus, I'm concerned about privacy and am an ardent Open Source proponent. I immediately decided to invest in Home Assistant. In the first post of this series, I describe my current context and goals. The context includes a collection of 'smart' devices at home, each managed separately without a centralized system. My goals are to enhance privacy and control by adopting an open-source, local-first approach to home automation. My Existing Infrastructure My smart home didn't start as a conscious thought but as a speaker's gift of DevFest Vienna. In 2018, Google wanted to promote Google Home Mini, which was part of the package. I put it in my living room, but it delivered little value besides answering a few questions. Soon afterward, a then-colleague gave me a regular Google Home. I moved the Mini to my office and put the regular one in its place. At this point, I had a Deezer account (think Spotify, but French), so I could listen to music by asking Google. With these two assistant devices, I decided to acquire smart lamps, which I could switch on and off with a voice command. I don't remember why, but I set my eyes on a Philips Hue pack. With the solution, you need a dedicated Hub that needs to be physically connected to the network with a cable—no Wi-Fi is possible. I installed lights in a couple of rooms; their setup is relatively easy via the dedicated Hue mobile app. Icing on the cake, the lights were easily imported into Google Home. Through the app, I discovered that the Hub could manage sensors. I configured one to switch on the toilets' light. If the sensor detected no activity, it would switch off after a set period. I was happy with the setup, with one exception: if anybody pressed the button to switch on the light, it would achieve its goal, but I wouldn't be able to control it vocally later. In rooms with people coming in regularly, it defeats the purpose. Worse, people naturally switched off the light when they left the toilets, so the sensor didn't switch on the light back again. The next logical step was to acquire smart switches to fix this issue, which I installed in the relevant rooms. My Goals Everything works as expected. Still, some improvements are in order. I rely on two different providers, Philips and Hue. They do whatever they want with my data, although I unchecked every possible checkbox I could. I can live with it, but I still dislike the lack of privacy. I'd like a local but centralized entry point to monitor and command my devices. These limitations often leave me feeling frustrated and restricted in my choices. They must be compatible with either one or the other. I have other devices which I want to manage, but I can't. This was an introductory post. In the following posts of this series, I'll delve deeper into a specific subject. Stay tuned! To go further: Home Assistant Originally published on A Java Geek on November 25th, 2024 Last June, I spoke at Berlin Buzzwords . In all honesty, I rarely attend others' talks for a variety of reasons: lack of time, lack of energy, no interest in the proposed subjects, etc. When I do, I go either for subjects I know and want to deepen my understanding of or for subjects I know nothing about to get a foot in the door. Berlin Buzzwords or This time, I attended Monitoring your home, with DevOps observability tools . I thought it would be about OpenTelemetry for your home. After the speaker mentioned Home Assistant , however, I didn't pay much attention to the rest. Monitoring your home, with DevOps observability tools Home Assistant Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server. Open source home automation that puts local control and privacy first. Powered by a worldwide community of tinkerers and DIY enthusiasts. Perfect to run on a Raspberry Pi or a local server. I have many different "smart" devices at home and no centralized system to manage them all. Plus, I'm concerned about privacy and am an ardent Open Source proponent. I immediately decided to invest in Home Assistant. In the first post of this series, I describe my current context and goals. The context includes a collection of 'smart' devices at home, each managed separately without a centralized system. My goals are to enhance privacy and control by adopting an open-source, local-first approach to home automation. My Existing Infrastructure My smart home didn't start as a conscious thought but as a speaker's gift of DevFest Vienna . In 2018, Google wanted to promote Google Home Mini, which was part of the package. I put it in my living room, but it delivered little value besides answering a few questions. Soon afterward, a then-colleague gave me a regular Google Home. DevFest Vienna I moved the Mini to my office and put the regular one in its place. At this point, I had a Deezer account (think Spotify, but French), so I could listen to music by asking Google. With these two assistant devices, I decided to acquire smart lamps, which I could switch on and off with a voice command. I don't remember why, but I set my eyes on a Philips Hue pack. With the solution, you need a dedicated Hub that needs to be physically connected to the network with a cable—no Wi-Fi is possible. I installed lights in a couple of rooms; their setup is relatively easy via the dedicated Hue mobile app. Icing on the cake, the lights were easily imported into Google Home. Through the app, I discovered that the Hub could manage sensors. I configured one to switch on the toilets' light. If the sensor detected no activity, it would switch off after a set period. I was happy with the setup, with one exception: if anybody pressed the button to switch on the light, it would achieve its goal, but I wouldn't be able to control it vocally later. In rooms with people coming in regularly, it defeats the purpose. Worse, people naturally switched off the light when they left the toilets, so the sensor didn't switch on the light back again. The next logical step was to acquire smart switches to fix this issue, which I installed in the relevant rooms. My Goals Everything works as expected. Still, some improvements are in order. I rely on two different providers, Philips and Hue. They do whatever they want with my data, although I unchecked every possible checkbox I could. I can live with it, but I still dislike the lack of privacy. I'd like a local but centralized entry point to monitor and command my devices. local centralized These limitations often leave me feeling frustrated and restricted in my choices. They must be compatible with either one or the other. I have other devices which I want to manage, but I can't. This was an introductory post. In the following posts of this series, I'll delve deeper into a specific subject. Stay tuned! To go further: To go further: Home Assistant Home Assistant Originally published on A Java Geek on November 25th, 2024 Originally published on A Java Geek on November 25th, 2024 A Java Geek

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