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AutoMorningPaper

Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!

Installation

1. Download the repo and install the requirements:

git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot

Create a bot on Telegram using BotFather and get the token. Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates

3. Download Llama 2

Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.

4. Create the configuration file

Create a file called config.yaml in the cloned repository, structured as follows:

lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer

See it in action!

Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0

Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0

TBI

  • Compatibility with other messaging platforms:
    • Discord

About

An ArXiv summarizer

Resources

Readme
GPL-3.0 license
Activity

Stars

8 stars

Watchers

1 watching

Forks

0 forks
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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Repository files navigation
AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
About
An ArXiv summarizer
Resources
Readme
GPL-3.0 license
Activity
Stars
8 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
About
An ArXiv summarizer
Resources
Readme
GPL-3.0 license
Activity
Stars
8 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
About
An ArXiv summarizer
Resources
Readme
GPL-3.0 license
Activity
Stars
8 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
About
An ArXiv summarizer
Resources
Readme
GPL-3.0 license
Activity
Stars
8 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
About
An ArXiv summarizer
Resources
Readme
GPL-3.0 license
Activity
Stars
8 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
About
An ArXiv summarizer
Resources
Readme
GPL-3.0 license
Activity
Stars
8 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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AutoMorningPaper
Do you fancy your own zero-effort "The morning paper"? Fear not! With the power of LLMs and LangChain you can run your own ArXiv paper summarizer that automatically scans for new papers and sends them to you over Telegram!
Installation
1. Download the repo and install the requirements:
git clone https://github.com/leocus/AutoMorningPaper
pip install -r ./requirements.txt

2. Set up the bot
Create a bot on Telegram using BotFather and get the token.
Then, send a message to the bot and retrieve the chat id from https://api.telegram.org/bot<TOKEN>/getUpdates
3. Download Llama 2
Choose one of the quantized models from https://huggingface.co/TheBloke/Llama-2-7B-GGML/tree/main.
4. Create the configuration file
Create a file called config.yaml in the cloned repository, structured as follows:
lists: # Add lists of interest from arxiv, e.g.,
- "cs.LG"
- "cs.AI"
- "cs.CV"
- "cs.GL"
- "cs.NE"bot:
class_name: # Currently supports SlackBot and TelegramBotparameters:
token: <your token here>channel: <your chat id here> # Only for SlackBotchat_id: <your chat id here> # Only for TelegramBotcriteria: # Add some keywords to detect topics of interest, e.g.,
- interpretability
- xai
- explainabilitymodel_path: "/path/to/llama-2-7b-chat.ggmlv3.q8_0.bin"# Choose as summarizer one of the classes defined in `summarizers.py`summarizer: BulletListSumarizer
See it in action!
Explainable and interpretable AI: https://t.me/+ENLgtQWBzHk2OWU0
Federated Learning and Tiny ML: https://t.me/+onXvUQpsJpUwZTY0
TBI
  • Compatibility with other messaging platforms:

    •  Discord
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An ArXiv summarizer
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