Smartboard input output tables
Unsure how to proceed on a simple code
2024.05.14 18:32 zmoss1 Unsure how to proceed on a simple code
I recently started a college course that teaches beginner java. Wow I am hooked now! I have finished all of the assignments due and decided I wanted to experiment and write my own code outside of the class just for fun and growth. So far I have been able to apply what I have learned to make the first aspect of my code successful but I'm getting pretty hung up on the second part.
I'm writing a program that will ideally calculate the number of fish needed for someone to reach level 99 fishing in RuneScape. So far within the console I've been able to prompt the user to enter their current xp as in integer and the program will then output the xp remaining to reach level 99. After that the console then prompts the user to enter the type of fish.
This is where I'm stuck. Ideally I want the user to be able to simply type the name of the fish and have the program calculate the number needed (each type of fish has a specific xp value) to reach level 99. My question is: How do I accept user input in the form of a word that will have a numerical value assigned to it?
runescapeFishing
submitted by zmoss1 to javahelp [link] [comments]
I'm writing a program that will ideally calculate the number of fish needed for someone to reach level 99 fishing in RuneScape. So far within the console I've been able to prompt the user to enter their current xp as in integer and the program will then output the xp remaining to reach level 99. After that the console then prompts the user to enter the type of fish.
This is where I'm stuck. Ideally I want the user to be able to simply type the name of the fish and have the program calculate the number needed (each type of fish has a specific xp value) to reach level 99. My question is: How do I accept user input in the form of a word that will have a numerical value assigned to it?
runescapeFishing
2024.05.14 18:26 archiegillis Seeking Feedback on Real-Time Multilingual Communication System
Hey Reddit community,
I'm currently doing some customer discovery for an new communication system that I’ve been working on, and I’d love to get your feedback!
The Innovation: My invention utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio using the user's vocal profile. The core feature of this system is that it enables real-time, in-person multilingual communication with no delay in conversations. The silent input does not interfere with the audio output, ensuring seamless and immediate translation.
Patent Pending: I have patent pending status for this technology and am in the process of determining if it truly addresses a significant pain point for potential users.
The Question: I’m particularly interested in understanding whether or not delays in current translation and communication systems are a major issue for you. Do you find it bothersome when there's a lag between spoken input and translated output? How much of a difference would a system with no delay make in your daily communications, whether it be in customer service, education, healthcare, or business meetings?
Use Cases: Here are a few scenarios where this technology could be particularly beneficial:
Thanks in advance for your help!
Abstract: The invention focuses on a novel communication system that utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio that uses the user's vocal profile for real-time communications in different languages. While it leverages Large Language Models (LLMs) and text conversion, the core feature is enabling real-time, in-person multilingual communication. (Think Google Glasses).
Key Application: Real-Time Multilingual Communication: Converts visual data into expressive audio in real-time using the user's vocal profile, facilitating in-person conversations in different languages.
Novel Elements
submitted by archiegillis to startups [link] [comments]
I'm currently doing some customer discovery for an new communication system that I’ve been working on, and I’d love to get your feedback!
The Innovation: My invention utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio using the user's vocal profile. The core feature of this system is that it enables real-time, in-person multilingual communication with no delay in conversations. The silent input does not interfere with the audio output, ensuring seamless and immediate translation.
Patent Pending: I have patent pending status for this technology and am in the process of determining if it truly addresses a significant pain point for potential users.
The Question: I’m particularly interested in understanding whether or not delays in current translation and communication systems are a major issue for you. Do you find it bothersome when there's a lag between spoken input and translated output? How much of a difference would a system with no delay make in your daily communications, whether it be in customer service, education, healthcare, or business meetings?
Use Cases: Here are a few scenarios where this technology could be particularly beneficial:
- Multilingual Customer Service: Seamless communication with international clients in real time.
- Educational Settings: Real-time translation for multilingual classrooms.
- Healthcare: Bridging language gaps between healthcare providers and patients.
- International Business Meetings: Facilitating real-time, multilingual discussions.
- Are delays in current communication systems a significant issue for you?
- In which scenarios do you think this technology would be most beneficial?
- Would you be likely to use or recommend such a system if it were available?
Thanks in advance for your help!
Abstract: The invention focuses on a novel communication system that utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio that uses the user's vocal profile for real-time communications in different languages. While it leverages Large Language Models (LLMs) and text conversion, the core feature is enabling real-time, in-person multilingual communication. (Think Google Glasses).
Key Application: Real-Time Multilingual Communication: Converts visual data into expressive audio in real-time using the user's vocal profile, facilitating in-person conversations in different languages.
Novel Elements
- Visual to Expressive Audio Conversion: Utilizes lip-reading algorithms to transform silent lip movements into expressive audio.
- Real-Time Multilingual Translation: Converts visual data into different languages in real time, using the user's vocal profile.
- Personalized Voice Synthesis: Synthesizes the translated audio with personalized voice profiles, retaining emotional nuances from visual input.
- Input Does Not Interfere with Output: A listener can hear the communication as it is being said and with much less delay making the conversation flow more seamlessly.
- Multilingual Customer Service:
- Scenario: A customer service representative communicates with international clients in person.
- Process: The representative’s silent lip movements are captured and converted into expressive audio in the client's language using the representative's vocal profile.
- Benefit: Facilitates seamless in-person communication across different languages without the need for audio input from the speaker.
- Educational Settings:
- Scenario: A multilingual classroom where the teacher instructs students from various linguistic backgrounds.
- Process: The teacher’s lip movements are captured and converted into expressive audio in the students’ preferred languages, using the teacher's vocal profile.
- Benefit: Enhances understanding and participation in a diverse classroom, enabling real-time multilingual communication.
- Healthcare Communication:
- Scenario: A doctor communicates with a patient who speaks a different language.
- Process: The doctor’s silent lip movements are captured and converted into expressive audio in the patient's language using the doctor’s vocal profile.
- Benefit: Ensures clear and real-time communication without relying on audio input, bridging language gaps between healthcare providers and patients.
- International Business Meetings:
- Scenario: Executives from different countries participate in an in-person meeting.
- Process: Each participant’s silent lip movements are captured and converted into expressive audio in the other participants' languages using their vocal profiles.
- Benefit: Facilitates real-time, multilingual discussions, improving collaboration and decision-making across different languages.
2024.05.14 18:21 archiegillis Seeking Feedback on Real-Time Multilingual Communication System
Hey Reddit community
I'm currently doing some customer discovery for an new communication system that I’ve been working on, and I’d love to get your feedback!
The Innovation: My invention utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio using the user's vocal profile. The core feature of this system is that it enables real-time, in-person multilingual communication with no delay in conversations. The silent input does not interfere with the audio output, ensuring seamless and immediate translation.
Patent Pending: I have patent pending status for this technology and am in the process of determining if it truly addresses a significant pain point for potential users.
The Question: I’m particularly interested in understanding whether or not delays in current translation and communication systems are a major issue for you. Do you find it bothersome when there's a lag between spoken input and translated output? How much of a difference would a system with no delay make in your daily communications, whether it be in customer service, education, healthcare, or business meetings?
Use Cases: Here are a few scenarios where this technology could be particularly beneficial:
Thanks in advance for your help!
Abstract: The invention focuses on a novel communication system that utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio that uses the user's vocal profile for real-time communications in different languages. While it leverages Large Language Models (LLMs) and text conversion, the core feature is enabling real-time, in-person multilingual communication. (Think Google Glasses).
Key Application: Real-Time Multilingual Communication: Converts visual data into expressive audio in real-time using the user's vocal profile, facilitating in-person conversations in different languages.
Novel Elements
submitted by archiegillis to Entrepreneur [link] [comments]
I'm currently doing some customer discovery for an new communication system that I’ve been working on, and I’d love to get your feedback!
The Innovation: My invention utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio using the user's vocal profile. The core feature of this system is that it enables real-time, in-person multilingual communication with no delay in conversations. The silent input does not interfere with the audio output, ensuring seamless and immediate translation.
Patent Pending: I have patent pending status for this technology and am in the process of determining if it truly addresses a significant pain point for potential users.
The Question: I’m particularly interested in understanding whether or not delays in current translation and communication systems are a major issue for you. Do you find it bothersome when there's a lag between spoken input and translated output? How much of a difference would a system with no delay make in your daily communications, whether it be in customer service, education, healthcare, or business meetings?
Use Cases: Here are a few scenarios where this technology could be particularly beneficial:
- Multilingual Customer Service: Seamless communication with international clients in real time.
- Educational Settings: Real-time translation for multilingual classrooms.
- Healthcare: Bridging language gaps between healthcare providers and patients.
- International Business Meetings: Facilitating real-time, multilingual discussions.
- Are delays in current communication systems a significant issue for you?
- In which scenarios do you think this technology would be most beneficial?
- Would you be likely to use or recommend such a system if it were available?
Thanks in advance for your help!
Abstract: The invention focuses on a novel communication system that utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio that uses the user's vocal profile for real-time communications in different languages. While it leverages Large Language Models (LLMs) and text conversion, the core feature is enabling real-time, in-person multilingual communication. (Think Google Glasses).
Key Application: Real-Time Multilingual Communication: Converts visual data into expressive audio in real-time using the user's vocal profile, facilitating in-person conversations in different languages.
Novel Elements
- Visual to Expressive Audio Conversion: Utilizes lip-reading algorithms to transform silent lip movements into expressive audio.
- Real-Time Multilingual Translation: Converts visual data into different languages in real time, using the user's vocal profile.
- Personalized Voice Synthesis: Synthesizes the translated audio with personalized voice profiles, retaining emotional nuances from visual input.
- Input Does Not Interfere with Output: A listener can hear the communication as it is being said and with much less delay making the conversation flow more seamlessly.
- Multilingual Customer Service:
- Scenario: A customer service representative communicates with international clients in person.
- Process: The representative’s silent lip movements are captured and converted into expressive audio in the client's language using the representative's vocal profile.
- Benefit: Facilitates seamless in-person communication across different languages without the need for audio input from the speaker.
- Educational Settings:
- Scenario: A multilingual classroom where the teacher instructs students from various linguistic backgrounds.
- Process: The teacher’s lip movements are captured and converted into expressive audio in the students’ preferred languages, using the teacher's vocal profile.
- Benefit: Enhances understanding and participation in a diverse classroom, enabling real-time multilingual communication.
- Healthcare Communication:
- Scenario: A doctor communicates with a patient who speaks a different language.
- Process: The doctor’s silent lip movements are captured and converted into expressive audio in the patient's language using the doctor’s vocal profile.
- Benefit: Ensures clear and real-time communication without relying on audio input, bridging language gaps between healthcare providers and patients.
- International Business Meetings:
- Scenario: Executives from different countries participate in an in-person meeting.
- Process: Each participant’s silent lip movements are captured and converted into expressive audio in the other participants' languages using their vocal profiles.
- Benefit: Facilitates real-time, multilingual discussions, improving collaboration and decision-making across different languages.
2024.05.14 18:14 archiegillis Seeking Feedback on Real-Time Multilingual Communication System
Hey Reddit community,
I'm currently doing some customer discovery for an new communication system that I’ve been working on, and I’d love to get your feedback!
The Innovation: My invention utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio using the user's vocal profile. The core feature of this system is that it enables real-time, in-person multilingual communication with no delay in conversations. The silent input does not interfere with the audio output, ensuring seamless and immediate translation.
Patent Pending: I have patent pending status for this technology and am in the process of determining if it truly addresses a significant pain point for potential users.
The Question: I’m particularly interested in understanding whether or not delays in current translation and communication systems are a major issue for you. Do you find it bothersome when there's a lag between spoken input and translated output? How much of a difference would a system with no delay make in your daily communications, whether it be in customer service, education, healthcare, or business meetings?
Use Cases: Here are a few scenarios where this technology could be particularly beneficial:
Thanks in advance for your help!
....a bit more info...
Abstract: The invention focuses on a novel communication system that utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio that uses the user's vocal profile for real-time communications in different languages. While it leverages Large Language Models (LLMs) and text conversion, the core feature is enabling real-time, in-person multilingual communication. (Think Google Glasses).
Key Application: Real-Time Multilingual Communication: Converts visual data into expressive audio in real-time using the user's vocal profile, facilitating in-person conversations in different languages.
Novel Elements
submitted by archiegillis to TranslationStudies [link] [comments]
I'm currently doing some customer discovery for an new communication system that I’ve been working on, and I’d love to get your feedback!
The Innovation: My invention utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio using the user's vocal profile. The core feature of this system is that it enables real-time, in-person multilingual communication with no delay in conversations. The silent input does not interfere with the audio output, ensuring seamless and immediate translation.
Patent Pending: I have patent pending status for this technology and am in the process of determining if it truly addresses a significant pain point for potential users.
The Question: I’m particularly interested in understanding whether or not delays in current translation and communication systems are a major issue for you. Do you find it bothersome when there's a lag between spoken input and translated output? How much of a difference would a system with no delay make in your daily communications, whether it be in customer service, education, healthcare, or business meetings?
Use Cases: Here are a few scenarios where this technology could be particularly beneficial:
- Multilingual Customer Service: Seamless communication with international clients in real time.
- Educational Settings: Real-time translation for multilingual classrooms.
- Healthcare: Bridging language gaps between healthcare providers and patients.
- International Business Meetings: Facilitating real-time, multilingual discussions.
- Are delays in current communication systems a significant issue for you?
- In which scenarios do you think this technology would be most beneficial?
- Would you be likely to use or recommend such a system if it were available?
Thanks in advance for your help!
....a bit more info...
Abstract: The invention focuses on a novel communication system that utilizes lip-reading technology to convert silent lip movements and facial expressions into expressive audio that uses the user's vocal profile for real-time communications in different languages. While it leverages Large Language Models (LLMs) and text conversion, the core feature is enabling real-time, in-person multilingual communication. (Think Google Glasses).
Key Application: Real-Time Multilingual Communication: Converts visual data into expressive audio in real-time using the user's vocal profile, facilitating in-person conversations in different languages.
Novel Elements
- Visual to Expressive Audio Conversion: Utilizes lip-reading algorithms to transform silent lip movements into expressive audio.
- Real-Time Multilingual Translation: Converts visual data into different languages in real time, using the user's vocal profile.
- Personalized Voice Synthesis: Synthesizes the translated audio with personalized voice profiles, retaining emotional nuances from visual input.
- Input Does Not Interfere with Output: A listener can hear the communication as it is being said and with much less delay making the conversation flow more seamlessly.
- Multilingual Customer Service:
- Scenario: A customer service representative communicates with international clients in person.
- Process: The representative’s silent lip movements are captured and converted into expressive audio in the client's language using the representative's vocal profile.
- Benefit: Facilitates seamless in-person communication across different languages without the need for audio input from the speaker.
- Educational Settings:
- Scenario: A multilingual classroom where the teacher instructs students from various linguistic backgrounds.
- Process: The teacher’s lip movements are captured and converted into expressive audio in the students’ preferred languages, using the teacher's vocal profile.
- Benefit: Enhances understanding and participation in a diverse classroom, enabling real-time multilingual communication.
- Healthcare Communication:
- Scenario: A doctor communicates with a patient who speaks a different language.
- Process: The doctor’s silent lip movements are captured and converted into expressive audio in the patient's language using the doctor’s vocal profile.
- Benefit: Ensures clear and real-time communication without relying on audio input, bridging language gaps between healthcare providers and patients.
- International Business Meetings:
- Scenario: Executives from different countries participate in an in-person meeting.
- Process: Each participant’s silent lip movements are captured and converted into expressive audio in the other participants' languages using their vocal profiles.
- Benefit: Facilitates real-time, multilingual discussions, improving collaboration and decision-making across different languages.
2024.05.14 18:13 cryptokaykay What are your current challenges with evaluations
What are your current challenges with evaluations?
What challenges are you facing and what tools are you using? I am thinking about building out a developer friendly open source evaluations tool kit. Thinking of starting with a simple interface where you pass the context, input, output and expected output and run it through some basic tests - both LLM based and non LLM based and also allow the ability to write custom assertions.
But, am wondering if you all have any insights into what other capabilities might be useful.
submitted by cryptokaykay to PromptEngineering [link] [comments]
What challenges are you facing and what tools are you using? I am thinking about building out a developer friendly open source evaluations tool kit. Thinking of starting with a simple interface where you pass the context, input, output and expected output and run it through some basic tests - both LLM based and non LLM based and also allow the ability to write custom assertions.
But, am wondering if you all have any insights into what other capabilities might be useful.
2024.05.14 18:13 cryptokaykay What are you current challenges with evaluations?
What are your current challenges with evaluations?
What challenges are you facing and what tools are you using? I am thinking about building out a developer friendly open source evaluations tool kit. Thinking of starting with a simple interface where you pass the context, input, output and expected output and run it through some basic tests - both LLM based and non LLM based and also allow the ability to write custom assertions.
But, am wondering if you all have any insights into what other capabilities might be useful.
submitted by cryptokaykay to LocalLLaMA [link] [comments]
What challenges are you facing and what tools are you using? I am thinking about building out a developer friendly open source evaluations tool kit. Thinking of starting with a simple interface where you pass the context, input, output and expected output and run it through some basic tests - both LLM based and non LLM based and also allow the ability to write custom assertions.
But, am wondering if you all have any insights into what other capabilities might be useful.
2024.05.14 18:12 AliveNet5570 Offering: English Seeking: French
Hello Language Exchange,
I'm an 18 year old English speaker currently learning French (at B1/B2 I think?), and I want to start doing output - I'm decently good at input so far, to the point where I've read books in French, but I still have no confidence in my output, so I want to help build that up via text.
And of course, similarly offering to any English learners an offer to help them learn English like that - I've got a (fairly rare?) dialect of British English so may be useful to learn some of the more non-standard parts of English common to native speakers if you're interested in that as well 👀
submitted by AliveNet5570 to language_exchange [link] [comments]
I'm an 18 year old English speaker currently learning French (at B1/B2 I think?), and I want to start doing output - I'm decently good at input so far, to the point where I've read books in French, but I still have no confidence in my output, so I want to help build that up via text.
And of course, similarly offering to any English learners an offer to help them learn English like that - I've got a (fairly rare?) dialect of British English so may be useful to learn some of the more non-standard parts of English common to native speakers if you're interested in that as well 👀
2024.05.14 18:12 Jpwolfe99 PyMuPdf doesn't recognize every fillable element in a PDF form
I am trying to use Python to read in a PDF form so that I can fill all the elements and then create a new filled in PDF. I found code from this repo and everything works correctly for the most part, but some elements aren't being recognized. Below is what the form looks like when I am editing the elements: https://i.sstatic.net/oTn5wiGA.png
However, when I run my code most but not all of the elements get filled in. In this example I am filling each box with "STRING". https://i.sstatic.net/AQn06u8J.png
In my code, when I list all of the element names ("other, route_to_1, route_to_2, etc) all the names are correct and have been checked over and over. When I debug my code and look at the variable that stores all the form elements, it's simply misreading some of the elements. I am not sure what is causing this. Whether Acrobat made the form incorrectly, or if there's a problem with the code. Any help is appreciated. Here's the code I have:
create_pdf.py
submitted by Jpwolfe99 to learnpython [link] [comments]
However, when I run my code most but not all of the elements get filled in. In this example I am filling each box with "STRING". https://i.sstatic.net/AQn06u8J.png
In my code, when I list all of the element names ("other, route_to_1, route_to_2, etc) all the names are correct and have been checked over and over. When I debug my code and look at the variable that stores all the form elements, it's simply misreading some of the elements. I am not sure what is causing this. Whether Acrobat made the form incorrectly, or if there's a problem with the code. Any help is appreciated. Here's the code I have:
create_pdf.py
from pdf_processing import ProcessPdf DATA_OBJECT = { "other": "string", "route_to_1": "string", "route_to_2": "string", "route_to_3": "string", "route_to_4": "string", "route_to_5": "string", "route_to_6": "string", "route_to_7": "string", "route_to_8": "string", "route_to_9": "string", "route_to_10": "string", "route_to_11": "string", "route_to_alt_1": "string", "route_to_alt_2": "string", "route_to_alt_3": "string", "route_to_alt_4": "string", "route_to_alt_5": "string", "dep_aerodrome": "string", "dep_elev": "string", "dep_atis_id": "string", "dep_atis_freq": "string", "dest_aerodrome": "string", "dest_elev": "string", "alt_dest": "string", "alt_elev": "string", "chan_id_1": "string", "chan_freq_1": "string", "chan_id_2": "string", "chan_freq_2": "string", "chan_id_3": "string", "chan_freq_3": "string", "chan_id_4": "string", "chan_freq_4": "string", "chan_id_5": "string", "chan_freq_5": "string", "chan_id_6": "string", "chan_freq_6": "string", "chan_id_7": "string", "chan_freq_7": "string", "chan_id_8": "string", "chan_freq_8": "string", "chan_id_9": "string", "chan_freq_9": "string", "chan_id_10": "string", "chan_freq_10": "string", "chan_id_11": "string", "chan_freq_11": "string", "chan_id_alt_1": "string", "chan_freq_alt_1": "string", "chan_id_alt_2": "string", "chan_freq_alt_2": "string", "chan_id_alt_3": "string", "chan_freq_alt_3": "string", "chan_id_alt_4": "string", "chan_freq_alt_4": "string", "chan_id_alt_5": "string", "chan_freq_alt_5": "string", "course_1": "string", "course_2": "string", "course_3": "string", "course_4": "string", "course_5": "string", "course_6": "string", "course_7": "string", "course_8": "string", "course_9": "string", "course_10": "string", "course_11": "string", "course_alt_1": "string", "course_alt_2": "string", "course_alt_3": "string", "course_alt_4": "string", "course_alt_5": "string", "dep_clearance_id": "string", "dep_clearance_freq": "string", "time_off": "string", "dep_app_cont_id": "string", "dep_app_cont_freq": "string", "dist_1": "string", "dist_2": "string", "dist_3": "string", "dist_4": "string", "dist_5": "string", "dist_6": "string", "dist_7": "string", "dist_8": "string", "dist_9": "string", "dist_10": "string", "dist_11": "string", "dist_total": "string", "alt_route": "string", "alt_app_cont_id": "string", "alt_app_cont_freq": "string", "dist_alt_1": "string", "dist_alt_2": "string", "dist_alt_3": "string", "dist_alt_4": "string", "dist_alt_5": "string", "ete_1": "string", "ete_2": "string", "ete_3": "string", "ete_4": "string", "ete_5": "string", "ete_6": "string", "ete_7": "string", "ete_8": "string", "ete_9": "string", "ete_10": "string", "ete_11": "string", "ete_total": "string", "ete_alt_1": "string", "ete_alt_2": "string", "ete_alt_3": "string", "ete_alt_4": "string", "ete_alt_5": "string", "eta_1": "string", "ata_1": "string", "eta_2": "string", "ata_2": "string", "eta_3": "string", "ata_3": "string", "eta_4": "string", "ata_4": "string", "eta_5": "string", "ata_5": "string", "eta_6": "string", "ata_6": "string", "eta_7": "string", "ata_7": "string", "eta_8": "string", "ata_8": "string", "eta_9": "string", "ata_9": "string", "eta_10": "string", "ata_10": "string", "eta_11": "string", "ata_11": "string", "eta_total": "string", "ata_total": "string", "eta_alt_1": "string", "ata_alt_1": "string", "eta_alt_2": "string", "ata_alt_2": "string", "eta_alt_3": "string", "ata_alt_3": "string", "eta_alt_4": "string", "ata_alt_4": "string", "eta_alt_5": "string", "ata_alt_5": "string", "dep_gnd_cont_id": "string", "dep_gnd_cont_freq": "string", "tas": "string", "mach": "string", "dest_tower_id": "string", "dest_tower_freq": "string", "leg_fuel_1": "string", "leg_fuel_2": "string", "leg_fuel_3": "string", "leg_fuel_4": "string", "leg_fuel_5": "string", "leg_fuel_6": "string", "leg_fuel_7": "string", "leg_fuel_8": "string", "leg_fuel_9": "string", "leg_fuel_10": "string", "leg_fuel_11": "string", "leg_fuel_total": "string", "alt_altitude": "string", "alt_tower_id": "string", "alt_tower_freq": "string", "leg_fuel_alt_1": "string", "leg_fuel_alt_2": "string", "leg_fuel_alt_3": "string", "leg_fuel_alt_4": "string", "leg_fuel_alt_5": "string", "efr_1": "string", "afr_1": "string", "efr_2": "string", "afr_2": "string", "efr_3": "string", "afr_3": "string", "efr_4": "string", "afr_4": "string", "efr_5": "string", "afr_5": "string", "efr_6": "string", "afr_6": "string", "efr_7": "string", "afr_7": "string", "efr_8": "string", "afr_8": "string", "efr_9": "string", "afr_9": "string", "efr_10": "string", "afr_10": "string", "efr_11": "string", "afr_11": "string", "efr_total": "string", "afr_total": "string", "efr_alt_1": "string", "afr_alt_1": "string", "efr_alt_2": "string", "afr_alt_2": "string", "efr_alt_3": "string", "afr_alt_3": "string", "efr_alt_4": "string", "afr_alt_4": "string", "efr_alt_5": "string", "afr_alt_5": "string", "cont_fuel": "string", "cont_fuel_1": "string", "cont_fuel_2": "string", "cont_fuel_3": "string", "cont_fuel_4": "string", "cont_fuel_5": "string", "cont_fuel_6": "string", "cont_fuel_7": "string", "cont_fuel_8": "string", "cont_fuel_9": "string", "cont_fuel_10": "string", "cont_fuel_11": "string", "alt_fuel": "string", "cont_fuel_alt_1": "string", "cont_fuel_alt_2": "string", "cont_fuel_alt_3": "string", "cont_fuel_alt_4": "string", "cont_fuel_alt_5": "string", "dep_tower_id": "string", "dep_tower_freq": "string", "lbs_ph": "string", "lbs_pm": "string", "dest_gnd_cont_id": "string", "dest_gnd_cont_freq": "string", "notes_1": "string", "notes_2": "string", "notes_3": "string", "notes_4": "string", "notes_5": "string", "notes_6": "string", "notes_7": "string", "notes_8": "string", "notes_9": "string", "notes_10": "string", "notes_11": "string", "notes_12": "string", "alt_gnd_cont_id": "string", "alt_gnd_cont_freq": "string", "notes_alt_1": "string", "notes_alt_2": "string", "notes_alt_3": "string", "notes_alt_4": "string", "notes_alt_5": "string", "alt_time": "string", "route_dest_iaf_fuel": "string", "route_alt_iaf_fuel": "string", "approaches_fuel": "string", "in_air_used_fuel": "string", "reserve_fuel": "string", "rwy_length_dest": "string", "lighting_dest": "string", "fuel_dest": "string", "ils_dest": "string", "loc_dest": "string", "asr_dest": "string", "par_mins_dest": "string", "tac_mins_dest": "string", "arr_gear_dest": "string", "pubs_dest": "string", "notams_dest": "string", "fuel_packet_dest_1": "string", "fuel_packet_dest_2": "string", "fuel_packet_dest_3": "string", "fuel_packet_dest_4": "string", "etc_dest": "string", "last_cruise_req_fuel": "string", "map_to_iaf_req_fuel": "string", "bingo_req_fuel": "string", "last_cruise_appr_fuel": "string", "map_to_iaf_appr_fuel": "string", "rwy_length_alt": "string", "lighting_alt": "string", "fuel_alt": "string", "ils_alt": "string", "loc_alt": "string", "asr_alt": "string", "par_mins_alt": "string", "tac_mins_alt": "string", "arr_gear_alt": "string", "pubs_alt": "string", "notams_alt": "string", "fuel_packet_alt_1": "string", "fuel_packet_alt_2": "string", "fuel_packet_alt_3": "string", "fuel_packet_alt_4": "string", "etc_alt": "string", "last_cruise_res_fuel": "string", "map_to_iaf_fuel": "string", "add_res_fuel": "string", "stto_fuel": "string", "total_req_fuel": "string", "total_aboard_fuel": "string", "spare_fuel": "string", "last_cruise_total_fuel": "string", "map_to_iaf_total_fuel": "string", "bingo_total": "string", "waypoint_1": "string", "waypoint_2": "string", "waypoint_3": "string", "waypoint_4": "string", "waypoint_5": "string", "waypoint_6": "string", "waypoint_7": "string", "waypoint_8": "string", "waypoint_9": "string", "waypoint_10": "string", "waypoint_11": "string", "waypoint_12": "string", "waypoint_13": "string", "waypoint_14": "string", "waypoint_15": "string", "waypoint_16": "string", "clearance_cleared_to": "string", "clearance_altitude": "string", "clearance_freq": "string", "clearance_transp": "string", "clearance_route": "string" } data = DATA_OBJECT output_file = 'final_pdf.pdf' temp_files = [] pdf = ProcessPdf('pdf_temp/', output_file) ''' PDF_TEMPLATE_PATH = path/to/your.pdf ''' data_pdf = pdf.add_data_to_pdf("Blank Jet Log Fillable.pdf", data) temp_files.append(data_pdf) pdf_processing.pyimport os import re import fitz # requires fitz, PyMuPDF import pdfrw import subprocess import os.path import sys from PIL import Image ''' replace all the constants (the one in caps) with your own lists ''' ''' FORM_KEYS is a dictionary (key-value pair) that contains 1. keys - which are all the key names in the PDF form 2. values - which are the type for all the keys in the PDF form. (string, checkbox, etc.) Eg. PDF form contains 1. First Name 2. Last Name 3. Sex (Male or Female) 4. Mobile Number FORM_KEYS = { "fname": "string", "lname": "string", "sex": "checkbox", "mobile": "number" } This FORM_KEYS(key) returns the type of value for that key. I'm passing this as 2nd argument to encode_pdf_string() function. ''' FORM_KEYS = { "other": "string", "route_to_1": "string", "route_to_2": "string", "route_to_3": "string", "route_to_4": "string", "route_to_5": "string", "route_to_6": "string", "route_to_7": "string", "route_to_8": "string", "route_to_9": "string", "route_to_10": "string", "route_to_11": "string", "route_to_alt_1": "string", "route_to_alt_2": "string", "route_to_alt_3": "string", "route_to_alt_4": "string", "route_to_alt_5": "string", "dep_aerodrome": "string", "dep_elev": "string", "dep_atis_id": "string", "dep_atis_freq": "string", "dest_aerodrome": "string", "dest_elev": "string", "alt_dest": "string", "alt_elev": "string", "chan_id_1": "string", "chan_freq_1": "string", "chan_id_2": "string", "chan_freq_2": "string", "chan_id_3": "string", "chan_freq_3": "string", "chan_id_4": "string", "chan_freq_4": "string", "chan_id_5": "string", "chan_freq_5": "string", "chan_id_6": "string", "chan_freq_6": "string", "chan_id_7": "string", "chan_freq_7": "string", "chan_id_8": "string", "chan_freq_8": "string", "chan_id_9": "string", "chan_freq_9": "string", "chan_id_10": "string", "chan_freq_10": "string", "chan_id_11": "string", "chan_freq_11": "string", "chan_id_alt_1": "string", "chan_freq_alt_1": "string", "chan_id_alt_2": "string", "chan_freq_alt_2": "string", "chan_id_alt_3": "string", "chan_freq_alt_3": "string", "chan_id_alt_4": "string", "chan_freq_alt_4": "string", "chan_id_alt_5": "string", "chan_freq_alt_5": "string", "course_1": "string", "course_2": "string", "course_3": "string", "course_4": "string", "course_5": "string", "course_6": "string", "course_7": "string", "course_8": "string", "course_9": "string", "course_10": "string", "course_11": "string", "course_alt_1": "string", "course_alt_2": "string", "course_alt_3": "string", "course_alt_4": "string", "course_alt_5": "string", "dep_clearance_id": "string", "dep_clearance_freq": "string", "time_off": "string", "dep_app_cont_id": "string", "dep_app_cont_freq": "string", "dist_1": "string", "dist_2": "string", "dist_3": "string", "dist_4": "string", "dist_5": "string", "dist_6": "string", "dist_7": "string", "dist_8": "string", "dist_9": "string", "dist_10": "string", "dist_11": "string", "dist_total": "string", "alt_route": "string", "alt_app_cont_id": "string", "alt_app_cont_freq": "string", "dist_alt_1": "string", "dist_alt_2": "string", "dist_alt_3": "string", "dist_alt_4": "string", "dist_alt_5": "string", "ete_1": "string", "ete_2": "string", "ete_3": "string", "ete_4": "string", "ete_5": "string", "ete_6": "string", "ete_7": "string", "ete_8": "string", "ete_9": "string", "ete_10": "string", "ete_11": "string", "ete_total": "string", "ete_alt_1": "string", "ete_alt_2": "string", "ete_alt_3": "string", "ete_alt_4": "string", "ete_alt_5": "string", "eta_1": "string", "ata_1": "string", "eta_2": "string", "ata_2": "string", "eta_3": "string", "ata_3": "string", "eta_4": "string", "ata_4": "string", "eta_5": "string", "ata_5": "string", "eta_6": "string", "ata_6": "string", "eta_7": "string", "ata_7": "string", "eta_8": "string", "ata_8": "string", "eta_9": "string", "ata_9": "string", "eta_10": "string", "ata_10": "string", "eta_11": "string", "ata_11": "string", "eta_total": "string", "ata_total": "string", "eta_alt_1": "string", "ata_alt_1": "string", "eta_alt_2": "string", "ata_alt_2": "string", "eta_alt_3": "string", "ata_alt_3": "string", "eta_alt_4": "string", "ata_alt_4": "string", "eta_alt_5": "string", "ata_alt_5": "string", "dep_gnd_cont_id": "string", "dep_gnd_cont_freq": "string", "tas": "string", "mach": "string", "dest_tower_id": "string", "dest_tower_freq": "string", "leg_fuel_1": "string", "leg_fuel_2": "string", "leg_fuel_3": "string", "leg_fuel_4": "string", "leg_fuel_5": "string", "leg_fuel_6": "string", "leg_fuel_7": "string", "leg_fuel_8": "string", "leg_fuel_9": "string", "leg_fuel_10": "string", "leg_fuel_11": "string", "leg_fuel_total": "string", "alt_altitude": "string", "alt_tower_id": "string", "alt_tower_freq": "string", "leg_fuel_alt_1": "string", "leg_fuel_alt_2": "string", "leg_fuel_alt_3": "string", "leg_fuel_alt_4": "string", "leg_fuel_alt_5": "string", "efr_1": "string", "afr_1": "string", "efr_2": "string", "afr_2": "string", "efr_3": "string", "afr_3": "string", "efr_4": "string", "afr_4": "string", "efr_5": "string", "afr_5": "string", "efr_6": "string", "afr_6": "string", "efr_7": "string", "afr_7": "string", "efr_8": "string", "afr_8": "string", "efr_9": "string", "afr_9": "string", "efr_10": "string", "afr_10": "string", "efr_11": "string", "afr_11": "string", "efr_total": "string", "afr_total": "string", "efr_alt_1": "string", "afr_alt_1": "string", "efr_alt_2": "string", "afr_alt_2": "string", "efr_alt_3": "string", "afr_alt_3": "string", "efr_alt_4": "string", "afr_alt_4": "string", "efr_alt_5": "string", "afr_alt_5": "string", "cont_fuel": "string", "cont_fuel_1": "string", "cont_fuel_2": "string", "cont_fuel_3": "string", "cont_fuel_4": "string", "cont_fuel_5": "string", "cont_fuel_6": "string", "cont_fuel_7": "string", "cont_fuel_8": "string", "cont_fuel_9": "string", "cont_fuel_10": "string", "cont_fuel_11": "string", "alt_fuel": "string", "cont_fuel_alt_1": "string", "cont_fuel_alt_2": "string", "cont_fuel_alt_3": "string", "cont_fuel_alt_4": "string", "cont_fuel_alt_5": "string", "dep_tower_id": "string", "dep_tower_freq": "string", "lbs_ph": "string", "lbs_pm": "string", "dest_gnd_cont_id": "string", "dest_gnd_cont_freq": "string", "notes_1": "string", "notes_2": "string", "notes_3": "string", "notes_4": "string", "notes_5": "string", "notes_6": "string", "notes_7": "string", "notes_8": "string", "notes_9": "string", "notes_10": "string", "notes_11": "string", "notes_12": "string", "alt_gnd_cont_id": "string", "alt_gnd_cont_freq": "string", "notes_alt_1": "string", "notes_alt_2": "string", "notes_alt_3": "string", "notes_alt_4": "string", "notes_alt_5": "string", "alt_time": "string", "route_dest_iaf_fuel": "string", "route_alt_iaf_fuel": "string", "approaches_fuel": "string", "in_air_used_fuel": "string", "reserve_fuel": "string", "rwy_length_dest": "string", "lighting_dest": "string", "fuel_dest": "string", "ils_dest": "string", "loc_dest": "string", "asr_dest": "string", "par_mins_dest": "string", "tac_mins_dest": "string", "arr_gear_dest": "string", "pubs_dest": "string", "notams_dest": "string", "fuel_packet_dest_1": "string", "fuel_packet_dest_2": "string", "fuel_packet_dest_3": "string", "fuel_packet_dest_4": "string", "etc_dest": "string", "last_cruise_req_fuel": "string", "map_to_iaf_req_fuel": "string", "bingo_req_fuel": "string", "last_cruise_appr_fuel": "string", "map_to_iaf_appr_fuel": "string", "rwy_length_alt": "string", "lighting_alt": "string", "fuel_alt": "string", "ils_alt": "string", "loc_alt": "string", "asr_alt": "string", "par_mins_alt": "string", "tac_mins_alt": "string", "arr_gear_alt": "string", "pubs_alt": "string", "notams_alt": "string", "fuel_packet_alt_1": "string", "fuel_packet_alt_2": "string", "fuel_packet_alt_3": "string", "fuel_packet_alt_4": "string", "etc_alt": "string", "last_cruise_res_fuel": "string", "map_to_iaf_fuel": "string", "add_res_fuel": "string", "stto_fuel": "string", "total_req_fuel": "string", "total_aboard_fuel": "string", "spare_fuel": "string", "last_cruise_total_fuel": "string", "map_to_iaf_total_fuel": "string", "bingo_total": "string", "waypoint_1": "string", "waypoint_2": "string", "waypoint_3": "string", "waypoint_4": "string", "waypoint_5": "string", "waypoint_6": "string", "waypoint_7": "string", "waypoint_8": "string", "waypoint_9": "string", "waypoint_10": "string", "waypoint_11": "string", "waypoint_12": "string", "waypoint_13": "string", "waypoint_14": "string", "waypoint_15": "string", "waypoint_16": "string", "clearance_cleared_to": "string", "clearance_altitude": "string", "clearance_freq": "string", "clearance_transp": "string", "clearance_route": "string" } def encode_pdf_string(value, type): if type == 'string': if value: return pdfrw.objects.pdfstring.PdfString.encode(value.upper()) else: return pdfrw.objects.pdfstring.PdfString.encode('') elif type == 'checkbox': if value == 'True' or value == True: return pdfrw.objects.pdfname.BasePdfName('/Yes') # return pdfrw.objects.pdfstring.PdfString.encode('Y') else: return pdfrw.objects.pdfname.BasePdfName('/No') # return pdfrw.objects.pdfstring.PdfString.encode('') return '' class ProcessPdf: def __init__(self, temp_directory, output_file): print('\n########## Initiating Pdf Creation Process #########\n') print('\nDirectory for storing all temporary files is: ', temp_directory) self.temp_directory = temp_directory print("Final Pdf name will be: ", output_file) self.output_file = output_file def add_data_to_pdf(self, template_path, data): print('\nAdding data to pdf...') template = pdfrw.PdfReader(template_path) for page in template.pages: annotations = page['/Annots'] if annotations is None: continue for annotation in annotations: if annotation['/Subtype'] == '/Widget': if annotation['/T']: key = annotation['/T'][1:-1] if re.search(r'.-[0-9]+', key): key = key[:-2] if key in data: annotation.update( pdfrw.PdfDict(V=encode_pdf_string(data[key], FORM_KEYS[key])) ) annotation.update(pdfrw.PdfDict(Ff=1)) template.Root.AcroForm.update(pdfrw.PdfDict(NeedAppearances=pdfrw.PdfObject('true'))) pdfrw.PdfWriter().write(self.temp_directory + "data.pdf", template) print('Pdf saved') return self.temp_directory + "data.pdf" def convert_image_to_pdf(self, image_path, image_pdf_name): print('\nConverting image to pdf...') image = Image.open(image_path) image_rgb = image.convert('RGB') image_rgb.save(self.temp_directory + image_pdf_name) return self.temp_directory + image_pdf_name def add_image_to_pdf(self, pdf_path, images, positions): print('\nAdding images to Pdf...') file_handle = fitz.open(pdf_path) for position in positions: page = file_handle[int(position['page']) - 1] if not position['image'] in images: continue image = images[position['image']] page.insertImage( fitz.Rect(position['x0'], position['y0'], position['x1'], position['y1']), filename=image ) file_handle.save(self.temp_directory + "data_image.pdf") print('images added') return self.temp_directory + "data_image.pdf" def delete_temp_files(self, pdf_list): print('\nDeleting Temporary Files...') for path in pdf_list: try: os.remove(path) except: pass def compress_pdf(self, input_file_path, power=3): """Function to compress PDF via Ghostscript command line interface""" quality = { 0: '/default', 1: '/prepress', 2: '/printer', 3: '/ebook', 4: '/screen' } output_file_path = self.temp_directory + 'compressed.pdf' if not os.path.isfile(input_file_path): print("\nError: invalid path for input PDF file") sys.exit(1) if input_file_path.split('.')[-1].lower() != 'pdf': print("\nError: input file is not a PDF") sys.exit(1) print("\nCompressing PDF...") initial_size = os.path.getsize(input_file_path) subprocess.call(['gs', '-sDEVICE=pdfwrite', '-dCompatibilityLevel=1.4', '-dPDFSETTINGS={}'.format(quality[power]), '-dNOPAUSE', '-dQUIET', '-dBATCH', '-sOutputFile={}'.format(output_file_path), input_file_path] ) final_size = os.path.getsize(output_file_path) ratio = 1 - (final_size / initial_size) print("\nCompression by {0:.0%}.".format(ratio)) print("Final file size is {0:.1f}MB".format(final_size / 1000000)) return output_file_path 2024.05.14 18:12 cryptokaykay What are your current challenges with evaluations?
What challenges are you facing and what tools are you using? I am thinking about building out a developer friendly open source evaluations tool kit. Thinking of starting with a simple interface where you pass the context, input, output and expected output and run it through some basic tests - both LLM based and non LLM based and also allow the ability to write custom assertions.
But, am wondering if you all have any insights into what other capabilities might be useful.
submitted by cryptokaykay to LangChain [link] [comments]
But, am wondering if you all have any insights into what other capabilities might be useful.
2024.05.14 17:56 kfspai Spice v0.12.2-alpha (May 13, 2024) is now available!
The v0.12.2-alpha release introduces data streaming and key-pair authentication for the Snowflake data connector, enables general append mode data refreshes for time-series data, improves connectivity error messages, adds nested folders support for the S3 data connector, and exposes nodeSelector and affinity keys in the Helm chart for better Kubernetes management.
submitted by kfspai to spiceai [link] [comments]
Highlights
- Improved Connectivity Error Messages: Error messages provide clearer, actionable guidance for misconfigured settings or unreachable data connectors.
- Snowflake Data Connector Improvements: Enables data streaming by default and adds support for key-pair authentication in addition to passwords.
- API for Refresh SQL Updates: Update dataset Refresh SQL via API.
- Append Data Refresh: Append mode data refreshes for time-series data are now supported for all data connectors. Specify a dataset time_column with refresh_mode: append to only fetch data more recent than the latest local data.
- Docker Image Update: The spiceai/spiceai:latest Docker image now includes the ODBC data connector. For a smaller footprint, use spiceai/spiceai:latest-slim.
- Helm Chart Improvements: nodeSelector and affinity keys are now supported in the Helm chart for improved Kubernetes deployment management.
Breaking Changes
- API to trigger accelerated dataset refreshes has changed from POST /v1/datasets/:name/refresh to POST /v1/datasets/:name/acceleration/refresh to be consistent with the Spicepod.yaml structure.
Contributors
- @mach-kernel
- @y-f-u
- @sgrebnov
- @ewgenius
- @Jeadie
- @Sevenannn
- @digadeesh
- @phillipleblanc
- @lukekim
What's Changed
- Fix list type support in spark connect by @y-f-u in spiceai/spiceai#1341
- Add nested folder support in S3 Parquet connector by @y-f-u in spiceai/spiceai#1342
- Improves S3 connector using DataFusion ListingTable table provider by @y-f-u in spiceai/spiceai#1326
- Update ROADMAP May 6, 2024 by @lukekim in spiceai/spiceai#1315
- List flightsql and snowflake as supported connectors in README.md by @sgrebnov in spiceai/spiceai#1317
- Helm chart for v0.12.1-alpha by @ewgenius in spiceai/spiceai#1323
- Read sqlite_file param and use it as path by @Sevenannn in spiceai/spiceai#1309
- Compile spiced with release feature in docker image by @ewgenius in spiceai/spiceai#1324
- Add support for Snowflake key-pair authentication by @sgrebnov in spiceai/spiceai#1314
- Wrap postgres errors in common DataConnectorError by @ewgenius in spiceai/spiceai#1327
- Fix TPCH tests runner by @sgrebnov in spiceai/spiceai#1330
- Spice CLI support for Snowflake key-pair auth by @sgrebnov in spiceai/spiceai#1325
- sql_provider_datafusion: Support TimestampMicrosecond, Date32, Date64 by @mach-kernel in spiceai/spiceai#1329
- Resolve dangling reference for SQLite by @Sevenannn in spiceai/spiceai#1312
- Select columns from Spark Dataframe according to projected_schema by @Sevenannn in spiceai/spiceai#1336
- Expose nodeselector and affinity keys in Helm chart by @mach-kernel in spiceai/spiceai#1338
- Use streaming for Snowflake queries by @sgrebnov in spiceai/spiceai#1337
- Publish ODBC images by @mach-kernel in spiceai/spiceai#1271
- Include Postgres acceleration engine to types support tests by @sgrebnov in spiceai/spiceai#1343
- Refactor dataconnector providers getters to return common DataConnectorResult and DataConnectorError by @ewgenius in spiceai/spiceai#1339
- s3 csv support to validate the listing table extensibility by @y-f-u in spiceai/spiceai#1344
- Move model code into separate, feature-flagged crate by @Jeadie in spiceai/spiceai#1335
- Initial setup for federated queries by @phillipleblanc in spiceai/spiceai#1350
- Refactor dbconnection errors, and catch invalid postgres table name case by @ewgenius in spiceai/spiceai#1353
- Rename default datafusion catalog to "spice", add internal "spice.runtime" schema by @ewgenius in spiceai/spiceai#1359
- Add API to set Refresh SQL for accelerated table by @sgrebnov in spiceai/spiceai#1356
- Set next version to v0.12.2 by @phillipleblanc in spiceai/spiceai#1367
- Upgrade to DataFusion 38 by @phillipleblanc in spiceai/spiceai#1368
- Incremental append based on time column by @y-f-u in spiceai/spiceai#1360
- Update README.md to include correct output when running show tables from quickstart by @digadeesh in spiceai/spiceai#1371
2024.05.14 17:56 FAGADEEE Need help with charger
The default charger I received with my laptop had a wattage of 135w and 19v and 1.9 A input I bought a replacement with an input current of 2.5 A with both the chargers having same output specifications will this do any harm to my laptop so far its only having heat issues my model is acer nitro 5 AN515-54
submitted by FAGADEEE to AcerNitro [link] [comments]
2024.05.14 17:46 lazyhorsee Found a good ui library that goes well with htmx (mdui)
By coincidence I found a clone of material design 3 ui library that are html based, with some javascript that I could integrate nicely with my htmx frontend.
You could use the cdn and be done with it, but it's also possible to use their npm package and only pick whatever you need in your application and then use a bundler like esbuild to package the components you used into one file, like this:
pnpm exec esbuild ./app/static_dev/input.js --bundle --outfile=./app/static/js/output.js
Of course you should first initialize a npm project, then install mdui & esbuild.
The docs are nice, I don't see anyone mention this library on this subreddit but it's a good find imo.
Hopefully this helps somebody who wants a material design ui but can't find one that integrates with raw html.
submitted by lazyhorsee to htmx [link] [comments]
You could use the cdn and be done with it, but it's also possible to use their npm package and only pick whatever you need in your application and then use a bundler like esbuild to package the components you used into one file, like this:
pnpm exec esbuild ./app/static_dev/input.js --bundle --outfile=./app/static/js/output.js
Of course you should first initialize a npm project, then install mdui & esbuild.
The docs are nice, I don't see anyone mention this library on this subreddit but it's a good find imo.
Hopefully this helps somebody who wants a material design ui but can't find one that integrates with raw html.
2024.05.14 17:42 AnkiHubOfficial 👑 AnKing Step Deck Update #5
 | Check out the update here: https://community.ankihub.net/t/anking-step-deck-update-5/222542 submitted by AnkiHubOfficial to medicalschoolanki [link] [comments] Make sure to participate in the poll as well: https://community.ankihub.net/t/anking-step-deck-update-5/222542#poll-of-the-month-16 👑 AnKing Step Deck Update #5 (April 13th - May 14th)Hi everyone! 👋Hope you are all having an amazing month! Let’s catch you up on what’s been going on every time you click the sync button 🎉 27,535 note updates!🫶 3,527 new subscribers!✅ Deck Updates❓Question Banks★ NBME: New tags added for OBGYN CMS form 5 (thanks to @taylordugan). Find it under this tag:#AK_Step2_v12::#Resources_by_rotation::ObGyn::nbme::form_5★ AMBOSS: New step 2 self-assessment tag added! (thanks to @taylordugan) ★ UWorld Self Assessment: Step 1 UWSA #3 has been tagged! (thanks to @herstein.jacob) ★ Step 3 UWorld Tags: New Step 3 UWorld tags added for various QIDs (thanks to @dollajas)! 🎇 Sketchy & Pixorize★ SketchyPathology: New tags added for missing cards (thanks to @joshuamb)★ SketchyPhysiology: Tons of new images + tags + hyperlinks added for various videos (thanks to @epcase) ★ Sketchy: 100s of pre-existing screenshots updated with higher quality screenshots (thanks to @musamalik) ★ Pixorize: 100+ images and hyperlinks added, thanks to the official Pixorize team! 🎥 Video Resources★ BNB Step 1: New tags added for missing cards in antihypertensive video (thanks to @lawsonspence)★ BNB Step 2: New tags added for many gastroenterology videos (thanks to @a11exa) ★ Bootcamp: 100+ tags and hyperlinks added to various cards (thanks to the official Bootcamp team! 😋 Other★ PANCE: 1000+ new tags added! (thanks to @camicardona)★ New Addon: A brand new AnKing table addon for formatting is out! Use this addon to format existing AnKing tables (thanks to @shmuelsash for creating the addon!)
The list above does not include the 1000s of spelling, grammar, formatting, image, GIF additions and changes the community (you all) have submitted! 📈 Project Progress🎉 OnlineMedEd (OME) Project21,000+ updated hyperlinks have been added. Tags will also roll out in the future!🚨 Don’t miss out on this exclusive 25% discount on a multi-month membership to OME: ANKING25 🧠 Algorithm Card ProjectA new algorithm card covering the workup for blunt abdominal trauma was pushed out (thanks to @Sameem!)Also check out the accompanying management flow chart made by @beejumm! https://preview.redd.it/fncfjdh7we0d1.jpg?width=2070&format=pjpg&auto=webp&s=94b0d678b8a3786cf1f11f801c984adb888a3a28 https://preview.redd.it/sg6425h7we0d1.jpg?width=2912&format=pjpg&auto=webp&s=5a59b1caec4dbcb55d348c3e05feef3573da100e 🎨 Illustration Projects@beejumm and @ianthebfg created some gorgeous illustrations to aid in your learning! Check them out:https://preview.redd.it/qtjgowdewe0d1.jpg?width=2489&format=pjpg&auto=webp&s=834d207d35db9a38fbc08d9de25383993fbe36e7 https://preview.redd.it/48q7asdewe0d1.jpg?width=2475&format=pjpg&auto=webp&s=f7d482c33c96bbec30df8d05a2797c69a0d2a341 https://preview.redd.it/dzkg5vdewe0d1.jpg?width=1782&format=pjpg&auto=webp&s=e541bdc4fce48f3d4ac7871d0778fcad9fdd28f1 https://preview.redd.it/p0si9rdewe0d1.jpg?width=10240&format=pjpg&auto=webp&s=734b593a22c527d1651fb8d571f6b48ba092dd2f 🫶🏼 Community ShoutoutsA few community members were outstanding with their suggestions this month and we want to highlight their dedication!Top 5 community members with the most suggestions accepted in the last 30 days:
👨🔧 New MaintainerWe’re happy to announce this month’s new maintainer! This user has dedicated a ton of time submitting helpful suggestions for content changes/tag additions and general deck improvements. Please give a warm welcome to:📣 We Need Your Input!We are looking for current or soon-to-be medical students to conduct a 45 minute virtual interview for research purposes. If you are interested, please sign up here ($25 Amazon gift card for those who complete the interview):❗️Sign up if interested: AnkiHub User Study We are also looking for more information regarding what type of curriculum your school hosts (systems-based, traditional histology/anatomy approach for M1 years vs PBL). This quick survey will help us improve AnkiHub in the coming months. It’s a 2-3 minute survey! ❗️Survey link: https://forms.gle/gDM9Dq1TG8cjq2GG6 ❓Poll of the MonthRecently, we have started adding video hyperlinks to the extra section of certain cards, typically under a minute long, illustrating certain various physical exam findings. Some of these include:
https://preview.redd.it/vh6s3henwe0d1.jpg?width=2862&format=pjpg&auto=webp&s=fdfa04d3888ca856edf98cd0d0f0dc99a853b11f We want to know more from you below (poll is anonymous)! Vote here: https://community.ankihub.net/t/anking-step-deck-update-5/222542#poll-of-the-month-16 👋 Wrapping upWe hope you all enjoyed this month’s update!Take care everyone ❤️ Regards, The AnKing Step Deck Maintainers ❤️ 🔗 Useful LinksWant to make a suggestion? Follow the guidelines → AnKing Step Deck Submission GuidelinesWant to volunteer to tag/add images for Sketchy/Pixorize/Boards & Beyond Step 2 or volunteer to make illustrations for the AnKing deck? Send an email to → [anking.ahmedd@gmail.com](mailto:anking.ahmedd@gmail.com) Get support from our team → https://community.ankihub.net Frequently asked questions → FAQs - AnkiHub Community 20 Check out the AnKing Step Deck wiki → [Wiki] AnKing Overhaul for Step 1 & 2 by AnKingMed Follow us on Instagram → The AnKing (@ankingmed) • Instagram photos and videos |
2024.05.14 17:35 Ooooooooosh Specific mini amp features
This is a new world to me so please excuse my ignorance. I've just binned an old stereo system and kept the speakers (passive), which I want to use to hopefully give my LG C2 TV a bit more boom. I've also got a smallish passive subwoofer. From what I can work out all I need is an amp, and going on the various reviews on YouTube I reckon one of the small Chinese mini amps will be good enough for me. I'm happy to spend about a hundred pounds to make it all work.
So what I think I need is an amp with an optical input and outputs for passive speakers and a passive subwoofer. This is the closest I can find, its just got RCA inputs instead of the digital one.
https://imgur.com/86XgqZp
The questions I put to you are; 1. Just to check that with an optical to RCA converter this would work? 2. I've googled and amazoned as much as my patience has allowed but can't find one with an optical in and a passive speakers and sub out... surely this must exist?! 3. If it doesn't exist then any suggestions on the best set up to get it all working? Better to go with the optical to RCA converter or get a separate amp for the subwoofer or something else entirely? 3. Honestly - is it worth it? I've got these speakers kicking about and just looking to do something with them.
Finally some more details about what I have; - LG C2 48" TV, it's got an optical output or a 3.5mm output. - Passive speakers from a Kenwood rxd980md, max input power 100w, impedance 6ohm. - Passive subwoofer from a broken Orbitsound spatial sound bar, model M10LX, minimum 4ohm, speaker wire connection.
And more about the amp I've seen on Amazon; Power Adapter: 24V/4.5A DC Input Range: 12-24V THD: ≤ 0.04%, SNR: ≥ 98dB Frequency Range: 20Hz - 20kHz (±1 dB) Input Sensitivity: ≤ 280mV; Terminating Impedance: 2 - 8 Ohm MAX Power Output: 50W x 2+100W Input Mode: Bluetooth and RCA Bluetooth Transmission Distance: Up to 50 Ft Package Include: BT3D Amp, Power Supply, User Manual
submitted by Ooooooooosh to hometheater [link] [comments]
So what I think I need is an amp with an optical input and outputs for passive speakers and a passive subwoofer. This is the closest I can find, its just got RCA inputs instead of the digital one.
https://imgur.com/86XgqZp
The questions I put to you are; 1. Just to check that with an optical to RCA converter this would work? 2. I've googled and amazoned as much as my patience has allowed but can't find one with an optical in and a passive speakers and sub out... surely this must exist?! 3. If it doesn't exist then any suggestions on the best set up to get it all working? Better to go with the optical to RCA converter or get a separate amp for the subwoofer or something else entirely? 3. Honestly - is it worth it? I've got these speakers kicking about and just looking to do something with them.
Finally some more details about what I have; - LG C2 48" TV, it's got an optical output or a 3.5mm output. - Passive speakers from a Kenwood rxd980md, max input power 100w, impedance 6ohm. - Passive subwoofer from a broken Orbitsound spatial sound bar, model M10LX, minimum 4ohm, speaker wire connection.
And more about the amp I've seen on Amazon; Power Adapter: 24V/4.5A DC Input Range: 12-24V THD: ≤ 0.04%, SNR: ≥ 98dB Frequency Range: 20Hz - 20kHz (±1 dB) Input Sensitivity: ≤ 280mV; Terminating Impedance: 2 - 8 Ohm MAX Power Output: 50W x 2+100W Input Mode: Bluetooth and RCA Bluetooth Transmission Distance: Up to 50 Ft Package Include: BT3D Amp, Power Supply, User Manual
2024.05.14 17:31 RoyalReverie New OpenAi e-mail
I have just received the following e-mail with a few more details from OpenAi:
Hi there,
We launched GPT-4o in the API—our new flagship model that’s as smart as GPT-4 Turbo and much more efficient. We’re passing on the benefits of the model’s efficiencies to developers, including:
We recommend that developers using GPT-4 or GPT-4 Turbo consider switching to GPT-4o. You can access GPT-4o in the Chat Completions API and Assistants API, or in the Batch API where you get a 50% discount on batch jobs completed asynchronously within 24 hours.
To get started, test the model in Playground, which now supports vision capabilities, and check out our API documentation. To learn how to use vision to input video content with GPT-4o today, check out the Introduction to GPT-4o cookbook. If you have questions, please reach out in the OpenAI developer forum.
—The OpenAI team
submitted by RoyalReverie to singularity [link] [comments]
Hi there,
We launched GPT-4o in the API—our new flagship model that’s as smart as GPT-4 Turbo and much more efficient. We’re passing on the benefits of the model’s efficiencies to developers, including:
- 50% lower pricing. GPT-4o is 50% cheaper than GPT-4 Turbo, across both input tokens ($5 per 1 million tokens) and output tokens ($15 per 1 million tokens).
- 2x faster latency. GPT-4o is 2x faster than GPT-4 Turbo.
- 5x higher rate limits. Over the coming weeks, GPT-4o will ramp to 5x those of GPT-4 Turbo—up to 10 million tokens per minute for developers with high usage.
We recommend that developers using GPT-4 or GPT-4 Turbo consider switching to GPT-4o. You can access GPT-4o in the Chat Completions API and Assistants API, or in the Batch API where you get a 50% discount on batch jobs completed asynchronously within 24 hours.
To get started, test the model in Playground, which now supports vision capabilities, and check out our API documentation. To learn how to use vision to input video content with GPT-4o today, check out the Introduction to GPT-4o cookbook. If you have questions, please reach out in the OpenAI developer forum.
—The OpenAI team
2024.05.14 17:29 gob_magic Which smart glasses are easier to program for? MetaRayBan/TeamOSG/Brilliant Labs
I am building a solution to a problem I am facing, and after speaking to a few folks in sales/journalism I can see they may use it too. Funny to see this is already being worked on by Cayden; u/hackalackolot. Love their concept. Mine is much simpler, and specific use case without the eye overlay.
This fun demo is by Cayden: https://www.youtube.com/watch?v=3n6DzuYQ_v8&t=12s
However, for my use case I would love to know which hardware has an external audio input, audio output and vision input (speaker + mic + camera). No need for the AR eye display.
For prototyping I am planning on using one of these.
Mata Ray Ban Glasses: Cannot find much information on creating your own software over the hardware. Excellent mic and camera hardware. Pre-order for USD 450.
TeamOpenSmartGlasses (Vuzix Z100): Love their concept! Much similar to what I am working on. I will message them and see if there's a way to collaborate. The device they use is Vuzix Z100. USD 800
Brilliant Labs: Similar to the rest, has an AR display. USD 350
submitted by gob_magic to hardware [link] [comments]
This fun demo is by Cayden: https://www.youtube.com/watch?v=3n6DzuYQ_v8&t=12s
However, for my use case I would love to know which hardware has an external audio input, audio output and vision input (speaker + mic + camera). No need for the AR eye display.
For prototyping I am planning on using one of these.
Mata Ray Ban Glasses: Cannot find much information on creating your own software over the hardware. Excellent mic and camera hardware. Pre-order for USD 450.
TeamOpenSmartGlasses (Vuzix Z100): Love their concept! Much similar to what I am working on. I will message them and see if there's a way to collaborate. The device they use is Vuzix Z100. USD 800
Brilliant Labs: Similar to the rest, has an AR display. USD 350
2024.05.14 17:23 rapttured How do you export organic google search clicks by day?
this is the data I am looking for but I can't figure out how to convert it to table format
Under our "Google organic search traffic: Landing page + query string" report I see the chart pictured above. GA4 is obviously collecting data on organic search clicks by day, but when I create a CSV export, the data portrayed here isn't included.
For context, my boss is wanting to do paid search. She wants to know how many organic clicks we get by day on average. This chart is fine, but it doesn't indicate what is the best day of the week for organic search clicks on average. I want to export said data (CSV) so I can create a heat map, that way my boss can easily see what the best day of the week for organic clicks is by the shading.
I've checked similar reports and tried to create my own reports in acquisitions and user behavior, but I can't find the data I need except in the above chart. If push comes to shove I can manually input each data point into excel, but if someone knows how to export it instead, please share.
submitted by rapttured to GoogleAnalytics [link] [comments]
Under our "Google organic search traffic: Landing page + query string" report I see the chart pictured above. GA4 is obviously collecting data on organic search clicks by day, but when I create a CSV export, the data portrayed here isn't included.
For context, my boss is wanting to do paid search. She wants to know how many organic clicks we get by day on average. This chart is fine, but it doesn't indicate what is the best day of the week for organic search clicks on average. I want to export said data (CSV) so I can create a heat map, that way my boss can easily see what the best day of the week for organic clicks is by the shading.
I've checked similar reports and tried to create my own reports in acquisitions and user behavior, but I can't find the data I need except in the above chart. If push comes to shove I can manually input each data point into excel, but if someone knows how to export it instead, please share.
2024.05.14 17:15 WinbuzzerMaria How to Enable the Ultimate Performance Power Plan in Windows 11 and Windows 10
 | https://preview.redd.it/9dnj4r61se0d1.png?width=768&format=png&auto=webp&s=3589f6a61841e58359af689712a2872c7713e08a submitted by WinbuzzerMaria to winbuzzer [link] [comments] Table of Contents:
What is the Ultimate Performance Power Plan?The Ultimate Performance Power Plan enhances system performance by fully utilizing hardware capabilities, albeit at the cost of increased power consumption and potential impacts on hardware longevity. This guide provides a concise overview of enabling this power plan on Windows systems, offering step-by-step instructions tailored for users aiming to leverage their PC's full potential.
|
2024.05.14 17:14 Ok-Breakfast-990 What do you look for in a looper?
I’m in the midst of a development project for a new multitrack loop station. My buddy and I were starting a new project and found that the current looper options on the market didn’t really satisfy our needs so we set out to make our own. All the ones we looked at were some combination of too expensive, too confusing or with not enough tracks/inputs.
Now it has come far enough that our little DIY project might become a full blown product. It will feature 8 tracks, some where from 4-8 inputs and outputs, and individual controls for each track. Our focus is primarily on live performance but the looper will also feature recording.
Since our music primarily features synths, and we are developing with that in mind, I thought I’d ask this sub what your experiences with loop stations are, what they were missing and what features you’d like to have?
Feel free to ask any questions about the project and I will do my best to answer them. I know this post is lacking in specific detail but that is because we are early enough in the development cycle to plan and implement large changes, I will be sure to share more information here in the future.
submitted by Ok-Breakfast-990 to synthesizers [link] [comments]
Now it has come far enough that our little DIY project might become a full blown product. It will feature 8 tracks, some where from 4-8 inputs and outputs, and individual controls for each track. Our focus is primarily on live performance but the looper will also feature recording.
Since our music primarily features synths, and we are developing with that in mind, I thought I’d ask this sub what your experiences with loop stations are, what they were missing and what features you’d like to have?
Feel free to ask any questions about the project and I will do my best to answer them. I know this post is lacking in specific detail but that is because we are early enough in the development cycle to plan and implement large changes, I will be sure to share more information here in the future.
2024.05.14 17:11 Johnmayer000 Formula marking overdue tasks that are already done
So in a Notion table, I have a:
prop("Status") == "Done",
"On time",
if(
prop("Deadline") < now(),
"Overdue",
"On time"
)
)
The problem is that when I add old tasks that have already been done in a date previous from today (since I'm just now making this Notion data base) it marks them overdue. I need a formula that only checks if the task hasn't been marked as "Done" after the date on the "deadline" column, to give me the overdue output; otherwise I need to get the on time output.
Please help!
submitted by Johnmayer000 to Notion [link] [comments]
- "status" column with three options: in progress, done, not started.
- "deadline" column in date format.
- "Timing" column which is the following formula:
prop("Status") == "Done",
"On time",
if(
prop("Deadline") < now(),
"Overdue",
"On time"
)
)
The problem is that when I add old tasks that have already been done in a date previous from today (since I'm just now making this Notion data base) it marks them overdue. I need a formula that only checks if the task hasn't been marked as "Done" after the date on the "deadline" column, to give me the overdue output; otherwise I need to get the on time output.
Please help!
2024.05.14 17:00 FreegheistOfficial GPT-4o is an Encoder-Decoder from the original Attention paper. Change my mind..
As we know, LLMs only represent the decoder part from the encoder-decoder transformer models in the original 'Attention is All You Need' paper.
Now we see a real-time version that can input/output audio, text, and images seamlessly, using a single model. This isn't possible in a pure-decoder LLM, but if we add the Encoder back in, it probably is:
submitted by FreegheistOfficial to LocalLLaMA [link] [comments]
Now we see a real-time version that can input/output audio, text, and images seamlessly, using a single model. This isn't possible in a pure-decoder LLM, but if we add the Encoder back in, it probably is:
- Encoders can integrate a single context across modalities into a unified semantic representation (live audio, intermittent images, text encoded within a single latent space).
- Encoders are bidirectional, meaning they can look at the entire input context simultaneously, making them better at picking up nuances in the data, like acoustic features and temporal patterns, and integrating these features between modalities.
- Encoders and decoders working together could do things like the image updating. The request for the image is within the encoder context, and when you instruct it to make changes, the context takes the original image and your new instructions and updates everything. The decoder then regenerates the image with those changes.
- Tight synchronization between the encoder and decoder can explain the coherence and contextual relevance across modes, like the real-time feedback and adjustments in the demos
- When the model is used in a Chatbot context (like 4o is right now on Chat GPT site), prompts are decoded as text completions autoregressively like a traditonal LLM (except it can also do continuous-image generation via the input being fed from the encoder).
- For the voice app, the decoder continuously processes the updated modalities from the encoder in real-time (or at a certain frame rate), outputting speech synthesis that's contextually and temporally aware (so it can speed up or slow down speech etc)
2024.05.14 16:58 PinguKuah Discord voice input not working
I used to be able to utilize my speakers for audio output and connect my headphones to my mac mini in order to talk in voice apps like discord. Never had to really workout anything, now suddenly discord can't find my voice, even though I've tried connecting my headphones directly into my scarlet 18i20.
So - is this due to a new update from discord or is there a specific way for me to go on Focusrite app and change the settings?
To clarify, the goal would be:
Audio output - speakers that are connected to my scarlett 18i20;
Voice input - headphones that are connected to my mac mini's audio port;
Scarlet 18i20 is connected to mac mini.
submitted by PinguKuah to Focusrite [link] [comments]
So - is this due to a new update from discord or is there a specific way for me to go on Focusrite app and change the settings?
To clarify, the goal would be:
Audio output - speakers that are connected to my scarlett 18i20;
Voice input - headphones that are connected to my mac mini's audio port;
Scarlet 18i20 is connected to mac mini.