No offense to anyone, but if you don't know how to use a Python script then I can't help you. This will remove the 1-250hz bass tones from the music, so it won't play in the vest. The rest of the game(and cutscenes and any other sound effect) will still retain bass. You don't need to use this script, you should just do what I did and start by telling Chatgpt to make you a script to do a high pass filter to remove the 1-250hz from the game sound files. This is one specifically was made for Ghosts Of Tsushima.
First install Python libraries to handle audio files and FFMPEG and ffprobe binaries.
https://www.gyan.dev/ffmpeg/builds/

pip install pydub scipy 

I put my ffmpeg binaries in a folder then added them to PATH. If you don't want to do this, you can just put the FFMPEG binaries in a folder and then tell chatgpt to modify the script to point to those specific files so you can use them. If you have an error you can just copy the whole error into chatgpt and it will fix it.
I put this script inside of woojer.py in the root folder where the folder is called soundtrack. You can just change it to whatever folder contains your audio files if the game is different. The script will run silently without errors then finish without any message. The script at the end is a batch script to toggle the filtered game files on and off.

import os from pydub import AudioSegment from scipy.signal import butter, lfilter import numpy as np # High-pass filter function def butter_highpass(cutoff, fs, order=5): nyq = 0.5 * fs normal_cutoff = cutoff / nyq b, a = butter(order, normal_cutoff, btype='high', analog=False) return b, a def highpass_filter(data, cutoff, fs, order=5): b, a = butter_highpass(cutoff, fs, order=order) y = lfilter(b, a, data) return y # Function to process a file def process_file(input_path, output_path, cutoff=250, sample_rate=48000): # Load the audio file audio = AudioSegment.from_file(input_path, format="mp3") # Convert to numpy array samples = np.array(audio.get_array_of_samples()) # Apply high-pass filter filtered_samples = highpass_filter(samples, cutoff, sample_rate) # Create new audio segment filtered_audio = audio._spawn(filtered_samples.astype(np.int16).tobytes()) # Export the filtered audio filtered_audio.export(output_path, format="mp3") # Function to process all files in a directory def process_directory(input_dir, output_dir, cutoff=250, sample_rate=48000): if not os.path.exists(output_dir): os.makedirs(output_dir) for filename in os.listdir(input_dir): if filename.endswith(".mp3"): input_path = os.path.join(input_dir, filename) output_path = os.path.join(output_dir, filename) process_file(input_path, output_path, cutoff, sample_rate) # Example usage root_directory = os.path.dirname(os.path.abspath(__file__)) input_directory = os.path.join(root_directory, "_Soundtrack") output_directory = os.path.join(root_directory, "_Soundtrack_Filtered") process_directory(input_directory, output_directory) 

a

@echo off setlocal REM Directories set "ROOT_DIR=%~dp0" set "ORIGINAL_DIR=%ROOT_DIR%_Soundtrack" set "FILTERED_DIR=%ROOT_DIR%_Soundtrack_Filtered" set "CURRENT_DIR=%ROOT_DIR%_Soundtrack_Current" REM Check current state if exist "%CURRENT_DIR%\filtered" ( REM Switch to original echo Switching to original music... xcopy "%ORIGINAL_DIR%\*" "%CURRENT_DIR%\" /Y del "%CURRENT_DIR%\filtered" ) else ( REM Switch to filtered echo Switching to filtered music... xcopy "%FILTERED_DIR%\*" "%CURRENT_DIR%\" /Y echo.>"%CURRENT_DIR%\filtered" ) echo Done. endlocal pause 

vec2 vertex_uvs = VERTEX.xz / mesh_dimensions.xz + vec2(0.5); // 0 <-> 1. Vertex local coordinates were given in relation to the centre pivot of the mesh, therefore we add 0.5 to normalise them 0 <-> 1 vec2 texture_position = vertex_uvs + vec2(0.0, 0.1); VERTEX.y += texture(heightmap, texture_position).r * height; 

ALBEDO = texture(heightmap, UV.xy).rrr; 

I have no idea what I have messed up here; when I play, the flashlight will not start. Thank you.
https://preview.redd.it/atu2h43cqh1d1.png?width=1516&format=png&auto=webp&s=611207c90fd8e7ee481c27ca6bf5ee4423ae9920

I am trying to create a sieve filter that will filter emails from linkedin and other social media accounts(dont really use much) into a folder and put other emails that contain list-unsubscribe into another folder. This is the command I am using.
require ["include", "environment", "variables", "relational", "comparator-i;ascii-numeric", "spamtest"];
require ["fileinto", "imap4flags"];
# Generated: Do not run this script on spam messages
if allof (environment :matches "vnd.proton.spam-threshold" "*",
spamtest :value "ge" :comparator "i;ascii-numeric" "${1}")
{
return;
}
/**
* u/type or
* u/comparator contains
*/
# Filter all lists into the same folder
if anyof(exists "x-facebook", exists "x-linkedin-id") {
fileinto "linkedin emails";
addflag "\\linkedin";
}
elsif exists "list-unsubscribe"{
fileinto "Junk";
addflag "\\advertisements";
}
However, linkedin emails are being placed into the junk folder. What am I doing wrong?

Can anyone recommend some learning resources for writing and compiling actual executable software? I have some c++ and programming experience but it is limited to scripting and programming Arduino. In college I took intro to c++ and a computational fluid dynamics course in which I wrote c++ scripts to solve and model differential equations. I am a mechanical engineer so I have also used matlab extensively for work, programmed robots in a language similar to python, lua for writing touchOSC scripts, and VBA for automating excel. It’s quite a hodgepodge of programming skill sets and again all individual scripts of varying complexity.
I am now working on a project developing software for a multitrack looper for making music. I am currently using an open source software looping program but would like to write my own.
This would be a “real” program, something with a rudimentary UI and multiple functions that can be compiled and run as an executable. I am having trouble figuring out how to make the jump from simple scripts to actual software development. Can you point me in the direction of some learning resources to move from this sort of “beginner” level programming to something more intermediate level?

Unlocking Your Inbox's Potential

In today's digital era, managing your email efficiently is more crucial than ever, especially when your inbox is inundated with messages. The Gmail API offers a powerful tool for developers to interact with their Gmail account programmatically, enabling tasks that would otherwise be tedious and time-consuming. One common task is retrieving the most recent emails that have not been marked as read. This capability is particularly useful for automating email processing, ensuring that you never miss important communications amidst the ever-growing pile of unread messages.
Python, with its simplicity and vast array of libraries, stands out as the perfect language to harness the capabilities of the Gmail API for this task. By leveraging Python, developers can write scripts that interact with their Gmail accounts, fetching emails based on specific criteria such as the absence of the "read" label. This process not only streamlines your workflow but also opens up a multitude of possibilities for automating email management, whether for personal productivity or for integrating into larger systems that require email processing capabilities.

Command/Function	Description
build()	Constructs a Resource object for interacting with an API.
users().messages().list()	Lists all messages in the user's mailbox.
users().messages().get()	Gets a specific message.
labelIds	Specifies the labels to filter the messages by.

Deep Dive into Email Automation with Python

Email automation through the Gmail API using Python represents a significant leap towards efficient inbox management and process automation. By leveraging the API, users can automate various tasks such as sorting emails, managing labels, and even sending responses. This not only saves a substantial amount of time but also enhances productivity by allowing individuals and businesses to focus on more critical tasks. The process of fetching unread emails without the "read" label, as illustrated in our example, is just the tip of the iceberg. Beyond this, the Gmail API provides functionalities for creating, sending, and modifying emails, managing email threads, and applying labels to emails programmatically.
The practical implications of these capabilities are vast. For instance, customer support systems can be automated to provide instant responses to common queries, marketing emails can be organized more efficiently, and important notifications can be flagged automatically. Moreover, integrating these email operations within broader applications or workflows opens up endless possibilities for customization and automation tailored to specific needs. Understanding and implementing Gmail API with Python not only equips developers with the tools to enhance email-related operations but also provides a foundation for exploring more advanced features and applications of the API in streamlining communication and workflow automation.

Fetching the Latest Unread Email

Python & Gmail API

from googleapiclient.discovery import build from oauth2client.service_account import ServiceAccountCredentials SCOPES = ['https://www.googleapis.com/auth/gmail.readonly'] credentials = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', SCOPES) service = build('gmail', 'v1', credentials=credentials) results = service.users().messages().list(userId='me', labelIds=['UNREAD'], maxResults=1).execute() messages = results.get('messages', []) if not messages: print('No unread messages.') else: for message in messages: msg = service.users().messages().get(userId='me', id=message['id']).execute() print('Message Snippet: ', msg['snippet']) 

Enhancing Email Management with Python and Gmail API

Integrating Python with the Gmail API to manage emails programmatically opens up a plethora of opportunities for enhancing productivity and email management strategies. This powerful combination allows for the automation of routine email tasks, such as sorting through incoming messages, identifying and categorizing important emails, and even responding to them without manual intervention. The ability to fetch the most recent unread emails without the "read" label is a fundamental step towards achieving an organized inbox, ensuring that no critical communication is overlooked amidst the clutter of less important emails.
The application of such automation extends beyond individual productivity; it plays a crucial role in business operations, customer service, and marketing efforts. Automating email processes can significantly reduce the workload on customer service teams, enable timely and personalized responses to customer inquiries, and streamline the distribution of marketing content. Moreover, by leveraging the Gmail API, developers can create custom filters, automate email categorization, and even integrate email functionality into broader software solutions, thereby creating a more connected and efficient digital ecosystem.

FAQs on Email Automation with Python and Gmail API

1. Question: Can I use the Gmail API to send emails programmatically?
2. Answer: Yes, the Gmail API allows you to send emails programmatically by creating and sending messages directly from your application.
3. Question: Do I need special permissions to access my Gmail account via the API?
4. Answer: Yes, you need to authorize your application with the necessary OAuth 2.0 credentials to access and manage your Gmail account through the API.
5. Question: Can the Gmail API manage attachments in emails?
6. Answer: Yes, the Gmail API supports managing email attachments, allowing you to add, retrieve, and delete attachments in your emails.
7. Question: Is it possible to filter emails by date using the Gmail API?
8. Answer: Yes, you can use the Gmail API to filter emails by various criteria, including date, by specifying the appropriate query parameters in your API requests.
9. Question: Can I automate email responses for specific types of emails?
10. Answer: Yes, by using the Gmail API with Python, you can analyze incoming emails and automate responses based on the content or type of the emails.
11. Question: How do I handle rate limits when using the Gmail API?
12. Answer: You should implement exponential backoff in your application to handle API request retries gracefully in case of rate limit errors.
13. Question: Can I use the Gmail API to read emails from a specific sender?
14. Answer: Yes, the Gmail API allows you to search and read emails from specific senders by using the appropriate search queries.
15. Question: Is there a way to categorize emails into custom labels using the Gmail API?
16. Answer: Yes, the Gmail API enables you to create custom labels and apply them to your emails for better organization.
17. Question: How secure is it to use the Gmail API for email automation?
18. Answer: The Gmail API is secure, using OAuth 2.0 for authentication and providing fine-grained control over which parts of your account can be accessed by the application.

Wrapping Up the Inbox Automation Journey

As we've navigated through the intricacies of automating email management using the Gmail API with Python, it's clear that this technology offers a significant advantage in managing digital communications efficiently. The ability to programmatically control one's inbox, from fetching unread messages to categorizing and responding to emails, not only saves valuable time but also opens new avenues for optimizing workflows and enhancing responsiveness. This exploration into email automation underscores the power of combining Python's versatility with Gmail's comprehensive API, offering a robust solution for individuals and organizations alike to stay on top of their email communication. Embracing these technologies can transform the way we interact with our inboxes, turning a potential source of stress into a well-organized component of our digital lives.
https://www.tempmail.us.com/en/gmail/fetching-unread-emails-using-the-gmail-api-in-python

What's the difference between these two filters?

! Network - offline service worker youtube.com/sw.js$script,domain=youtube.com www.youtube.com##+js(set, yt.config_.EXPERIMENT_FLAGS.service_worker_enabled, false) 

To my understanding, they accomplish the same thing.

• Is one faster than the other?
• Is one less prone to deprecation in the future?

So, I just re-installed Windows 10 LTSC. This time the ISO was from the official microsoft website and not from a forum.
I want to keep as safe as possible and do things right. Will only download from trusted open source repositories, and only torrent mkv films/anime/etc. For pirated software (matlab e.g) I'm thinking of using virtualbox or smth to emulate another Windows and use it from there, is it ok? Or there's another sandbox where I can use it in a safe and fast way?.
And yeah, this post is asking u guys if you know some tips to be safe and what u guys have learnt from piracy over these years. Also, is Microsoft Activation Scripts ok? Ive heard it disables some stuff related to privacy (not being a virus but maybe it makes u vulnerable)
Thank u guys

Idk why but I always think of drag race as a two chapters , pre season 7 where we got 100% genuine drag personalities with the most unhinged editing, and post 7 where the queens filter every word and think a million time of what they say which seems disingenuous and scripted. I feel like the most genuine season was 14.

``` obs debug: Found portal inhibitor debug: Attempted path: share/obs/obs-studio/locale/en-US.ini debug: Attempted path: /usshare/obs/obs-studio/locale/en-US.ini debug: Attempted path: share/obs/obs-studio/locale.ini debug: Attempted path: /usshare/obs/obs-studio/locale.ini debug: Attempted path: share/obs/obs-studio/locale/en-GB.ini debug: Attempted path: /usshare/obs/obs-studio/locale/en-GB.ini info: Using preferred locale 'en-GB' debug: Attempted path: share/obs/obs-studio/themes debug: Attempted path: /usshare/obs/obs-studio/themes debug: Attempted path: share/obs/obs-studio/themes/Yami.qss debug: Attempted path: /usshare/obs/obs-studio/themes/Yami.qss info: Platform: Wayland info: CPU Name: AMD Ryzen 5 5500U with Radeon Graphics info: CPU Speed: 1397.311MHz info: Physical Cores: 6, Logical Cores: 12 info: Physical Memory: 15317MB Total, 1947MB Free info: Kernel Version: Linux 6.7.12-amd64 info: Distribution: "Debian GNU/Linux" Unknown info: Desktop Environment: KDE (KDE) info: Session Type: wayland info: Qt Version: 6.4.2 (runtime), 6.4.2 (compiled) info: Portable mode: false info: OBS 30.1.2.1-1 (linux) info: --------------------------------- info: --------------------------------- info: audio settings reset: samples per sec: 48000 speakers: 2 max buffering: 960 milliseconds buffering type: dynamically increasing info: --------------------------------- info: Initializing OpenGL... info: Using EGL/Wayland info: Initialized EGL 1.5 info: Loading up OpenGL on adapter AMD AMD Radeon Graphics (radeonsi, renoir, LLVM 17.0.6, DRM 3.57, 6.7.12-amd64) info: OpenGL loaded successfully, version 4.6 (Core Profile) Mesa 24.0.6-1+b1, shading language 4.60 info: --------------------------------- info: video settings reset: base resolution: 1920x1080 output resolution: 1280x720 downscale filter: Bicubic fps: 30/1 format: NV12 YUV mode: Rec. 709/Partial info: NV12 texture support not available info: P010 texture support not available info: Audio monitoring device: name: Default id: default info: --------------------------------- warning: Failed to load 'en-US' text for module: 'decklink-captions.so' warning: Failed to load 'en-US' text for module: 'decklink-output-ui.so' libDeckLinkAPI.so: cannot open shared object file: No such file or directory warning: A DeckLink iterator could not be created. The DeckLink drivers may not be installed warning: Failed to initialize module 'decklink.so' info: [pipewire] Available captures: info: [pipewire] - Desktop capture info: [pipewire] - Window capture warning: v4l2loopback not installed, virtual camera disabled warning: LIBVA_DRIVER_NAME variable is set, this could prevent FFmpeg VAAPI from working correctly info: VAAPI: API version 1.20 info: FFmpeg VAAPI H264 encoding supported info: FFmpeg VAAPI AV1 encoding not supported info: FFmpeg VAAPI HEVC encoding supported info: [obs-websocket] [obs_module_load] you can haz websockets (Version: 5.4.2 RPC Version: 1) info: [obs-websocket] [obs_module_load] Qt version (compile-time): 6.4.2 Qt version (run-time): 6.4.2 info: [obs-websocket] [obs_module_load] Linked ASIO Version: 102801 info: [obs-websocket] [obs_module_load] Module loaded. info: [vlc-video]: VLC 3.0.20 Vetinari found, VLC video source enabled info: --------------------------------- info: Loaded Modules: info: vlc-video.so info: text-freetype2.so info: rtmp-services.so info: obs-x264.so info: obs-websocket.so info: obs-transitions.so info: obs-outputs.so info: obs-filters.so info: obs-ffmpeg.so info: linux-v4l2.so info: linux-pulseaudio.so info: linux-pipewire.so info: linux-jack.so info: linux-capture.so info: linux-alsa.so info: image-source.so info: frontend-tools.so info: decklink-output-ui.so info: decklink-captions.so info: --------------------------------- info: ==== Startup complete =============================================== info: All scene data cleared info: ------------------------------------------------ info: pulse-input: Server name: 'PulseAudio (on PipeWire 1.0.5) 15.0.0' info: pulse-input: Audio format: s32le, 48000 Hz, 2 channels info: pulse-input: Started recording from 'alsa_output.pci-0000_04_00.6.HiFi__hw_Generic_1__sink.monitor' (default) info: [Loaded global audio device]: 'Desktop Audio' info: pulse-input: Server name: 'PulseAudio (on PipeWire 1.0.5) 15.0.0' info: pulse-input: Audio format: s32le, 48000 Hz, 2 channels info: pulse-input: Started recording from 'alsa_input.pci-0000_04_00.6.HiFi__hw_acp__source' (default) info: [Loaded global audio device]: 'Mic/Aux' info: PipeWire initialized info: Switched to scene 'Scene' info: ------------------------------------------------ info: Loaded scenes: info: - scene 'Scene': info: - source: 'Screen Capture (PipeWire)' (pipewire-desktop-capture-source) info: ------------------------------------------------ info: [pipewire] Screencast session created info: [pipewire] Asking for desktop warning: [pipewire] Failed to start screencast, denied or cancelled by user info: adding 21 milliseconds of audio buffering, total audio buffering is now 21 milliseconds (source: Desktop Audi o) warning: [rtmp-services plugin] Remote update of URL "https://obsproject.com/obs2_update/rtmp-services/v5/package.j son" failed: info: PipeWire initialized info: [pipewire] Screencast session created info: [pipewire] Asking for desktop warning: [pipewire] Failed to start screencast, denied or cancelled by user info: PipeWire initialized info: [pipewire] Screencast session created info: [pipewire] Asking for desktop warning: [pipewire] Failed to start screencast, denied or cancelled by user info: ==== Shutting down ================================================== info: pulse-input: Stopped recording from 'alsa_output.pci-0000_04_00.6.HiFi__hw_Generic_1__sink.monitor' info: pulse-input: Got 1221 packets with 1465200 frames info: pulse-input: Stopped recording from 'alsa_input.pci-0000_04_00.6.HiFi__hw_acp__source' info: pulse-input: Got 1220 packets with 1464000 frames info: All scene data cleared info: ------------------------------------------------ info: [obs-websocket] [obs_module_unload] Shutting down... error: Tried to call obs_frontend_remove_event_callback with no callbacks! info: [obs-websocket] [obs_module_unload] Finished shutting down. info: [Scripting] Total detached callbacks: 0 info: Freeing OBS context data info: == Profiler Results ============================= info: run_program_init: 688.679 ms info: ┣OBSApp::AppInit: 4.944 ms info: ┃ ┗OBSApp::InitLocale: 0.901 ms info: ┗OBSApp::OBSInit: 650.323 ms info: ┣obs_startup: 2.632 ms info: ┗OBSBasic::OBSInit: 612.048 ms info: ┣OBSBasic::InitBasicConfig: 0.159 ms info: ┣OBSBasic::ResetAudio: 0.364 ms info: ┣OBSBasic::ResetVideo: 106.805 ms info: ┃ ┗obs_init_graphics: 103.059 ms info: ┃ ┗shader compilation: 31.469 ms info: ┣OBSBasic::InitOBSCallbacks: 0.008 ms info: ┣OBSBasic::InitHotkeys: 0.039 ms info: ┣obs_load_all_modules2: 411.631 ms info: ┃ ┣obs_init_module(decklink-captions.so): 0 ms info: ┃ ┣obs_init_module(decklink-output-ui.so): 0 ms info: ┃ ┣obs_init_module(decklink.so): 0.192 ms info: ┃ ┣obs_init_module(frontend-tools.so): 19.278 ms info: ┃ ┣obs_init_module(image-source.so): 0.012 ms info: ┃ ┣obs_init_module(linux-alsa.so): 0.003 ms info: ┃ ┣obs_init_module(linux-capture.so): 0.001 ms info: ┃ ┣obs_init_module(linux-jack.so): 0.005 ms info: ┃ ┣obs_init_module(linux-pipewire.so): 19.888 ms info: ┃ ┣obs_init_module(linux-pulseaudio.so): 0.006 ms info: ┃ ┣obs_init_module(linux-v4l2.so): 3.116 ms info: ┃ ┣obs_init_module(obs-ffmpeg.so): 29.419 ms info: ┃ ┃ ┗nvenc_check: 0.978 ms info: ┃ ┣obs_init_module(obs-filters.so): 0.028 ms info: ┃ ┣obs_init_module(obs-outputs.so): 0.005 ms info: ┃ ┣obs_init_module(obs-transitions.so): 0.019 ms info: ┃ ┣obs_init_module(obs-websocket.so): 4.634 ms info: ┃ ┣obs_init_module(obs-x264.so): 0.005 ms info: ┃ ┣obs_init_module(rtmp-services.so): 0.641 ms info: ┃ ┣obs_init_module(text-freetype2.so): 0.018 ms info: ┃ ┗obs_init_module(vlc-video.so): 2.247 ms info: ┣OBSBasic::InitService: 1.334 ms info: ┣OBSBasic::ResetOutputs: 0.232 ms info: ┣OBSBasic::CreateHotkeys: 0.029 ms info: ┣OBSBasic::InitPrimitives: 0.09 ms info: ┗OBSBasic::Load: 47.236 ms info: obs_hotkey_thread(25 ms): min=0 ms, median=0.001 ms, max=0.015 ms, 99th percentile=0.002 ms, 100% below 25 ms info: audio_thread(Audio): min=0.013 ms, median=0.047 ms, max=3.06 ms, 99th percentile=0.146 ms info: obs_graphics_thread(33.3333 ms): min=0.275 ms, median=0.982 ms, max=18.942 ms, 99th percentile=2.125 ms, 100% below 33.333 ms info: ┣tick_sources: min=0 ms, median=0.011 ms, max=0.336 ms, 99th percentile=0.036 ms info: ┣output_frame: min=0.141 ms, median=0.352 ms, max=4.685 ms, 99th percentile=0.896 ms info: ┃ ┗gs_context(video->graphics): min=0.141 ms, median=0.351 ms, max=4.679 ms, 99th percentile=0.894 ms info: ┃ ┣render_video: min=0.034 ms, median=0.059 ms, max=0.354 ms, 99th percentile=0.105 ms info: ┃ ┃ ┗render_main_texture: min=0.025 ms, median=0.051 ms, max=0.325 ms, 99th percentile=0.093 ms info: ┃ ┗gs_flush: min=0.003 ms, median=0.007 ms, max=0.084 ms, 99th percentile=0.026 ms info: ┗render_displays: min=0.005 ms, median=0.57 ms, max=18.484 ms, 99th percentile=1.528 ms info: ================================================= info: == Profiler Time Between Calls ================== info: obs_hotkey_thread(25 ms): min=25.045 ms, median=25.102 ms, max=28.376 ms, 99.0354% within ±2% of 25 ms (0% lo wer, 0.96463% higher) info: obs_graphics_thread(33.3333 ms): min=32.449 ms, median=33.333 ms, max=34.169 ms, 99.5717% within ±2% of 33.33 3 ms (0.321199% lower, 0.107066% higher) info: ================================================= info: Number of memory leaks: 0 ``` 

Screenshot of OBS
Neofetch Output

Important news

This is an incremental update over the 1.12.0-preview and includes several breaking changes.
View the Migration Guide for details on how to prepare for the update.
Also, several new features require having a modern web browser that supports CSS Nesting. If you experience visual glitches - update your browser and/or operating system to the latest available version.
External media in chats is now blocked by default. Enable it manually if required.

Improvements

• Migrated to a new format for user data storage and added support for user accounts. See the documentation: Users.
• Implemented the Data Bank: a method for adding external sources of LLM knowledge. See the documentation: Data Bank.
• Added vLLM as a Text Completion source and Groq as a Chat Completion source
• Added new models from OpenAI (GPT-4o), PerplexityAI (Llama 3 Sonar), and Google (Gemini Flash 1.5).
• Added an ability to select OpenRouter providers in both API modes.
• TabbyAPI: added min_tokens and banned_strings parameters.
• LlamaCpp: allowed JSON schema usage (conflicts with grammar).
• Added the AI assistant contest winner character card (Sakana) to the "Download Extensions & Assets" menu and first run onboarding dialog. There you can also view and download past contest winners.
• World Info improvements: added ability to activate entries using the Vector Storage extension, Example Messages insertion position, weighted and scored inclusion groups, prioritized entries in inclusion groups, support for multiple inclusion groups per entry, ability to delay the entry evaluation until the first recursion step, regular expression type for the entry key, entry duplication, and many more. See the documentation: World Info.
• Settings are now backed up automatically every 10 minutes instead of on startup. Increased the limit of snapshots to 50 per user.
• Added Llama 3 model tokenizer. Claude tokenizer is now initialized lazily.
• Added instruct/context templates for the Phi model, and variants of Llama 3 and ChatML with message names inclusion.
• The "Advanced search" option now sorts the search results by relevance.
• Tags & Folders: added the ability to show only or hide folders. Added a toggle for alphabetical tag sorting.
• Added the ability to hide avatar images in the chat. Reorganized User Settings menu.
• Added the ability to import from AICharacterCards.com and generic URLs using the external import dialog.
• Added a quality setting for image inlining in Chat Completion.
• Forwarded IP whitelisting: can now be disabled with a configuration option.
• Added a console script for server plugin management.
• Various localization improvements.

STscript

• A brand new parser with support for closures, auto-complete, and syntax highlighting. See the documentation: STscript.
• Added a "negative" argument to the /imagine slash command.
• Added "role" and "hidden" arguments to the /messages slash command.
• Added optional "name" argument to the /send slash command.
• Added the /renamechat slash command.
• /summarize can now summarize any text, not just the chat messages.
• /sendas old syntax is now deprecated. Use the new syntax: /sendas name= .

Extensions

• Image Generation: added new NovelAI model, prompt prefixes now support macros.
• Vector Storage: added settings for WI activation and Data Bank inclusion.
• Chat Translation: added an option to translate user input text.
• TTS: MsEdgeTTS is now supported via the server-side plugin. Added a universal playback speed setting.
• Regex: added an ability to run Regex on the WI entries.
• Macros: added {{char_version}}, {{timeDiff}} macros.
• Added more downloadable extensions to the "Download Extensions & Assets" menu. Check them out!

Bug fixes

• Fixed InfermaticAI non-streamed response parsing.
• Fixed start.sh script to allow starting the server from any directory on Mac.
• Fixed a vertical alignment of zoomed avatars in visual novel mode on phones.
• Fixed the main prompt being purged from the prompt manager when disabled.
• Fixed the "Replace/Update card" function with UTF-8 characters in the filter name.
• Fixed text copy not working on messages hidden from the prompt.
• Fixed whole-word WI search to include punctuation and other non-alphanumeric characters.
• Fixed Trigger% being displayed as 0% when imported from external lorebooks.
• Fixed the "Narrate translated" option not working with TTS.
• Fixed first message regeneration on character modification if the message was edited.
• Fixed boolean values matching when searching for WI entries with /findentry.
• Fixed custom ordering of WI entries being able to sort past the header element.
• Fixed auto-selection of settings presets by name match (now requires an exact match instead of partial).
• Fixed multiswipe interaction with response streaming.

https://github.com/SillyTavern/SillyTavern/releases/tag/1.12.0
How to update: https://docs.sillytavern.app/usage/update/