From 45e35425a91bce1b3afcac727428c30dba0b9684 Mon Sep 17 00:00:00 2001
From: Stephen Muthama <33781312+muthash@users.noreply.github.com>
Date: Thu, 23 Oct 2025 13:45:43 +0300
Subject: [PATCH] Revert "Week 1 Learnings (#1)" (#4)

This reverts commit a9e9453e57ad46c3471fab00256b7305015d7b86.
---
 uv.lock                    |  578 +------------------
 week1/day1.ipynb           |  274 ++-------
 week1/day2.ipynb           |  241 +-------
 week1/day4.ipynb           |   97 ++--
 week1/day5.ipynb           | 1084 +++++++++++++++++++++---------------
 week1/week1_exercise.ipynb |  324 -----------
 6 files changed, 725 insertions(+), 1873 deletions(-)
 delete mode 100644 week1/week1_exercise.ipynb

diff --git a/uv.lock b/uv.lock
index d4f7ac1..27cb024 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,13 +1,11 @@
 version = 1
 revision = 3
-requires-python = ">=3.11"
+requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'linux'",
     "python_full_version >= '3.13' and sys_platform != 'linux'",
-    "python_full_version == '3.12.*' and sys_platform == 'linux'",
-    "python_full_version == '3.12.*' and sys_platform != 'linux'",
-    "python_full_version < '3.12' and sys_platform == 'linux'",
-    "python_full_version < '3.12' and sys_platform != 'linux'",
+    "python_full_version < '3.13' and sys_platform == 'linux'",
+    "python_full_version < '3.13' and sys_platform != 'linux'",
 ]
 
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diff --git a/week1/day1.ipynb b/week1/day1.ipynb
index 68fc563..3323d86 100644
--- a/week1/day1.ipynb
+++ b/week1/day1.ipynb
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd",
    "metadata": {},
    "outputs": [],
@@ -144,18 +144,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "7b87cadb-d513-4303-baee-a37b6f938e4d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "API key found and looks good so far!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Load environment variables in a file called .env\n",
     "\n",
@@ -184,22 +176,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "a58394bf-1e45-46af-9bfd-01e24da6f49a",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'role': 'user',\n",
-       "  'content': 'Hello, GPT! This is my first ever message to you! Hi!'}]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# To give you a preview -- calling OpenAI with these messages is this easy. Any problems, head over to the Troubleshooting notebook.\n",
     "\n",
@@ -212,21 +192,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "08330159",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hi there! Welcome—and nice to meet you. I’m glad you stopped by.\\n\\nWhat would you like to do today? Here are a few ideas:\\n- Ask me a question on any topic\\n- Get help with homework or a project\\n- Learn something new (science, history, tech, etc.)\\n- Write or edit text (emails, essays, stories)\\n- Brainstorm ideas or plan something (a trip, a gift, a plan)\\n- Get coding help or debugging tips\\n- Translate or summarize something\\n\\nIf you tell me a bit about yourself or what you’re curious about, I can tailor things to you. What would you like to start with?'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "openai = OpenAI()\n",
     "\n",
@@ -244,66 +213,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "2ef960cf-6dc2-4cda-afb3-b38be12f4c97",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Home - Edward Donner\n",
-      "\n",
-      "Home\n",
-      "Connect Four\n",
-      "Outsmart\n",
-      "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n",
-      "About\n",
-      "Posts\n",
-      "Well, hi there.\n",
-      "I’m Ed. I like writing code and experimenting with LLMs, and hopefully you’re here because you do too. I also enjoy DJing (but I’m badly out of practice), amateur electronic music production (\n",
-      "very\n",
-      "amateur) and losing myself in\n",
-      "Hacker News\n",
-      ", nodding my head sagely to things I only half understand.\n",
-      "I’m the co-founder and CTO of\n",
-      "Nebula.io\n",
-      ". We’re applying AI to a field where it can make a massive, positive impact: helping people discover their potential and pursue their reason for being. Recruiters use our product today to source, understand, engage and manage talent. I’m previously the founder and CEO of AI startup untapt,\n",
-      "acquired in 2021\n",
-      ".\n",
-      "We work with groundbreaking, proprietary LLMs verticalized for talent, we’ve\n",
-      "patented\n",
-      "our matching model, and our award-winning platform has happy customers and tons of press coverage.\n",
-      "Connect\n",
-      "with me for more!\n",
-      "September 15, 2025\n",
-      "AI in Production: Gen AI and Agentic AI on AWS at scale\n",
-      "May 28, 2025\n",
-      "Connecting my courses – become an LLM expert and leader\n",
-      "May 18, 2025\n",
-      "2025 AI Executive Briefing\n",
-      "April 21, 2025\n",
-      "The Complete Agentic AI Engineering Course\n",
-      "Navigation\n",
-      "Home\n",
-      "Connect Four\n",
-      "Outsmart\n",
-      "An arena that pits LLMs against each other in a battle of diplomacy and deviousness\n",
-      "About\n",
-      "Posts\n",
-      "Get in touch\n",
-      "ed [at] edwarddonner [dot] com\n",
-      "www.edwarddonner.com\n",
-      "Follow me\n",
-      "LinkedIn\n",
-      "Twitter\n",
-      "Facebook\n",
-      "Subscribe to newsletter\n",
-      "Type your email…\n",
-      "Subscribe\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Let's try out this utility\n",
     "\n",
@@ -331,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "abdb8417-c5dc-44bc-9bee-2e059d162699",
    "metadata": {},
    "outputs": [],
@@ -347,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "f0275b1b-7cfe-4f9d-abfa-7650d378da0c",
    "metadata": {},
    "outputs": [],
@@ -383,21 +296,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "f25dcd35-0cd0-4235-9f64-ac37ed9eaaa5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2 + 2 equals 4.'"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "messages = [\n",
     "    {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
@@ -418,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "0134dfa4-8299-48b5-b444-f2a8c3403c88",
    "metadata": {},
    "outputs": [],
@@ -434,24 +336,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "36478464-39ee-485c-9f3f-6a4e458dbc9c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'role': 'system',\n",
-       "  'content': '\\nYou are a snarkyassistant that analyzes the contents of a website,\\nand provides a short, snarky, humorous summary, ignoring text that might be navigation related.\\nRespond in markdown. Do not wrap the markdown in a code block - respond just with the markdown.\\n'},\n",
-       " {'role': 'user',\n",
-       "  'content': '\\nHere are the contents of a website.\\nProvide a short summary of this website.\\nIf it includes news or announcements, then summarize these too.\\n\\nHome - Edward Donner\\n\\nHome\\nConnect Four\\nOutsmart\\nAn arena that pits LLMs against each other in a battle of diplomacy and deviousness\\nAbout\\nPosts\\nWell, hi there.\\nI’m Ed. I like writing code and experimenting with LLMs, and hopefully you’re here because you do too. I also enjoy DJing (but I’m badly out of practice), amateur electronic music production (\\nvery\\namateur) and losing myself in\\nHacker News\\n, nodding my head sagely to things I only half understand.\\nI’m the co-founder and CTO of\\nNebula.io\\n. We’re applying AI to a field where it can make a massive, positive impact: helping people discover their potential and pursue their reason for being. Recruiters use our product today to source, understand, engage and manage talent. I’m previously the founder and CEO of AI startup untapt,\\nacquired in 2021\\n.\\nWe work with groundbreaking, proprietary LLMs verticalized for talent, we’ve\\npatented\\nour matching model, and our award-winning platform has happy customers and tons of press coverage.\\nConnect\\nwith me for more!\\nSeptember 15, 2025\\nAI in Production: Gen AI and Agentic AI on AWS at scale\\nMay 28, 2025\\nConnecting my courses – become an LLM expert and leader\\nMay 18, 2025\\n2025 AI Executive Briefing\\nApril 21, 2025\\nThe Complete Agentic AI Engineering Course\\nNavigation\\nHome\\nConnect Four\\nOutsmart\\nAn arena that pits LLMs against each other in a battle of diplomacy and deviousness\\nAbout\\nPosts\\nGet in touch\\ned [at] edwarddonner [dot] com\\nwww.edwarddonner.com\\nFollow me\\nLinkedIn\\nTwitter\\nFacebook\\nSubscribe to newsletter\\nType your email…\\nSubscribe'}]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Try this out, and then try for a few more websites\n",
     "\n",
@@ -468,7 +356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "905b9919-aba7-45b5-ae65-81b3d1d78e34",
    "metadata": {},
    "outputs": [],
@@ -486,28 +374,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "05e38d41-dfa4-4b20-9c96-c46ea75d9fb5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"# Edward Donner's Corner of the Internet\\n\\nWelcome to Ed's nerd den, where coding meets LLM wizardry and the occasional (badly dusted off) DJ beat. Ed is the CTO of Nebula.io, making AI actually useful by helping recruiters find talent without losing their minds. He’s also an ex-startup CEO whose company got snapped up in 2021, so naturally, he's full of patented AI magic and press clippings.  \\n\\nHighlights include:  \\n- Two AI battles arenas called *Connect Four* and *Outsmart* where language models duke it out in diplomatic skullduggery (because who doesn't want AI to be shady?).  \\n- Educational AI content dropping like hotcakes in 2025, covering everything from getting AI running on AWS to full-on Agentic AI engineering courses.\\n- Ed’s modest brag: a patented matching model and an award-winning platform that somehow makes recruiters happy. \\n\\nWant to get in touch? Shoot an email or awkwardly follow him on LinkedIn, Twitter, or Facebook. Or maybe just subscribe to the newsletter for your daily dose of AI and snark.\""
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "summarize(\"https://edwarddonner.com\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "3d926d59-450e-4609-92ba-2d6f244f1342",
    "metadata": {},
    "outputs": [],
@@ -521,36 +398,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "3018853a-445f-41ff-9560-d925d1774b2f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "# Edward Donner’s Corner of the Internet\n",
-       "\n",
-       "Welcome to Ed's playground where code meets world domination—of the AI kind. Ed's into writing code, geeking out on Large Language Models (LLMs), and spinning (badly) some tunes as an amateur DJ/music producer. When not getting lost in Hacker News, he's busy revolutionizing recruiting as the CTO of Nebula.io, leveraging AI to help folks find their life’s purpose. Oh, and he’s got a patented AI model, an acquired startup (because why not), and an award-winning platform with press buzz.\n",
-       "\n",
-       "If you like AI battles (LLMs duking it out in diplomacy and deception), check out *Connect Four* and *Outsmart* arenas. Stay tuned for his latest musings or courses to become an LLM overlord yourself.\n",
-       "\n",
-       "## Latest Announcements (Because Ed Actually Updates Stuff)\n",
-       "\n",
-       "- **Sep 15, 2025:** AI in Production with Gen AI and Agentic AI at AWS scale\n",
-       "- **May 28, 2025:** Connecting courses to become an LLM expert (sign me up!)\n",
-       "- **May 18, 2025:** 2025 AI Executive Briefing (serious business)\n",
-       "- **Apr 21, 2025:** The Complete Agentic AI Engineering Course (bring your A-game)\n",
-       "\n",
-       "Want to connect? Drop him a line or follow the trails of LinkedIn, Twitter, or Facebook breadcrumbs. Subscribe if you want AI wisdom delivered to your inbox."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display_summary(\"https://edwarddonner.com\")"
    ]
@@ -573,54 +424,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "45d83403-a24c-44b5-84ac-961449b4008f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "Alright, here’s the scoop: This website is CNN, the internet’s go-to for pretty much everything newsworthy from politics, world drama, and business intrigue, to viral entertainment and weather tantrums. It revels in ads (which it desperately wants your feedback on because apparently, they can be annoying), offers live TV and videos, and even throws in polls and calculators for those who like crunching numbers while digesting headlines. If you’re looking for breaking news on conflicts like Ukraine-Russia or Israel-Hamas wars, or fancy a dip into lifestyle, style, or tech innovations, CNN’s got your back. Basically, it’s a never-ending buffet of topical info served with a side of “Please tell us if our ads are driving you up the wall.”"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display_summary(\"https://cnn.com\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "id": "75e9fd40-b354-4341-991e-863ef2e59db7",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "# Anthropic: AI with a Conscience\n",
-       "\n",
-       "Anthropic is the AI nerd who actually cares about the future of humanity — a “public benefit corporation” dedicated to making AI helpful *and* safe (because apparently, that's a thing now).\n",
-       "\n",
-       "They’re flaunting their latest shiny toys: **Claude Sonnet 4.5** (best model for agents, coding, computer stuff) and **Claude Haiku 4.5** (because why not poetic AI?). The site is full of jargon about \"responsible scaling\" and \"trust centers,\" which basically means they’re trying not to unleash Skynet anytime soon.\n",
-       "\n",
-       "Newsflash: They’ve got announcements about these models, pushing forward with AI tech but with the brakes on to avoid the apocalypse. And yes, there's lots of encouragement to login, try their Claude models, download apps, and get lost in developer docs if you’re that kind of person.\n",
-       "\n",
-       "In summary: It’s AI innovation meets cautious parenting, with a sprinkle of poetic flair. Who says robots can’t be responsible *and* artsy?"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display_summary(\"https://anthropic.com\")"
    ]
@@ -659,51 +476,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "00743dac-0e70-45b7-879a-d7293a6f68a6",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Meeting Time Change to 3 PM Tomorrow\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Step 1: Create your prompts\n",
     "\n",
-    "system_prompt = \"\"\"\n",
-    "You are embedded in an email tool that analyzes an email’s body text and suggests a concise, relevant, and professional subject line.\n",
-    "The subject line should capture the core intent of the email in 5–10 words\n",
-    "Maintain clarity and tone alignment:\n",
-    "Formal/business emails → neutral and polished\n",
-    "Casual/internal updates → conversational and brief\n",
-    "Urgent matters → include urgency subtly (e.g., “Action Needed”, “Follow-Up Required”)\n",
-    "Avoid unnecessary punctuation, emojis, or filler words.\n",
-    "Do not include explanations unless explicitly requested.\n",
-    "\"\"\"\n",
+    "system_prompt = \"something here\"\n",
     "user_prompt = \"\"\"\n",
-    "    Suggest a short subject line for the following email:\n",
-    "    Hi team, please note that tomorrow’s meeting will be moved to 3 PM instead of 2 PM to accommodate the new client call.\n",
+    "    Lots of text\n",
+    "    Can be pasted here\n",
     "\"\"\"\n",
     "\n",
     "# Step 2: Make the messages list\n",
     "\n",
-    "messages = [\n",
-    "    {\"role\": \"system\", \"content\": system_prompt},\n",
-    "    {\"role\": \"user\", \"content\": user_prompt}\n",
-    "]\n",
+    "messages = [] # fill this in\n",
     "\n",
     "# Step 3: Call OpenAI\n",
-    "response = openai.chat.completions.create(\n",
-    "    model=\"gpt-4o-mini\",\n",
-    "    messages=messages\n",
-    ")\n",
+    "# response =\n",
     "\n",
     "# Step 4: print the result\n",
-    "print(response.choices[0].message.content)"
+    "# print("
    ]
   },
   {
diff --git a/week1/day2.ipynb b/week1/day2.ipynb
index ebff68e..2e3dfc0 100644
--- a/week1/day2.ipynb
+++ b/week1/day2.ipynb
@@ -45,18 +45,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "e38f17a0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "API key found and looks good so far!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from dotenv import load_dotenv\n",
@@ -86,22 +78,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "5af5c188",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'model': 'gpt-5-nano',\n",
-       " 'messages': [{'role': 'user', 'content': 'Tell me a fun fact'}]}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import requests\n",
     "\n",
@@ -118,40 +98,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "2d0ab242",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'id': 'chatcmpl-CTJDqe3UuLrqPiBwX0loYddfVDpWx',\n",
-       " 'object': 'chat.completion',\n",
-       " 'created': 1761101438,\n",
-       " 'model': 'gpt-5-nano-2025-08-07',\n",
-       " 'choices': [{'index': 0,\n",
-       "   'message': {'role': 'assistant',\n",
-       "    'content': 'Fun fact: Honey never spoils. Its low moisture and acidic pH create an environment that bacteria and fungi can’t thrive in. In fact, archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still edible. Want another fun fact?',\n",
-       "    'refusal': None,\n",
-       "    'annotations': []},\n",
-       "   'finish_reason': 'stop'}],\n",
-       " 'usage': {'prompt_tokens': 11,\n",
-       "  'completion_tokens': 709,\n",
-       "  'total_tokens': 720,\n",
-       "  'prompt_tokens_details': {'cached_tokens': 0, 'audio_tokens': 0},\n",
-       "  'completion_tokens_details': {'reasoning_tokens': 640,\n",
-       "   'audio_tokens': 0,\n",
-       "   'accepted_prediction_tokens': 0,\n",
-       "   'rejected_prediction_tokens': 0}},\n",
-       " 'service_tier': 'default',\n",
-       " 'system_fingerprint': None}"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "response = requests.post(\n",
     "    \"https://api.openai.com/v1/chat/completions\",\n",
@@ -164,21 +114,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "cb11a9f6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Fun fact: Honey never spoils. Its low moisture and acidic pH create an environment that bacteria and fungi can’t thrive in. In fact, archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still edible. Want another fun fact?'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "response.json()[\"choices\"][0][\"message\"][\"content\"]"
    ]
@@ -201,26 +140,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "490fdf09",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Fun fact: Honey never spoils. Archaeologists have found pots of honey in ancient tombs that are still edible after thousands of years. Its low water content and acidic pH help keep bacteria away.'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create OpenAI client\n",
     "\n",
     "from openai import OpenAI\n",
-    "\n",
     "openai = OpenAI()\n",
     "\n",
     "response = openai.chat.completions.create(model=\"gpt-5-nano\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n",
@@ -260,18 +187,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "f74293bc",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "GEMINI_BASE_URL = \"https://generativelanguage.googleapis.com/v1beta/openai/\"\n",
     "\n",
@@ -282,7 +201,8 @@
     "elif not google_api_key.startswith(\"AIz\"):\n",
     "    print(\"An API key was found, but it doesn't start AIz\")\n",
     "else:\n",
-    "    print(\"API key found and looks good so far!\")\n"
+    "    print(\"API key found and looks good so far!\")\n",
+    "\n"
    ]
   },
   {
@@ -301,30 +221,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "a5b069be",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Here's one:\\n\\nDid you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still perfectly edible! Honey's longevity is due to its unique composition, which includes acid that creates an environment inhospitable to bacteria and microorganisms. Isn't that sweet?\""
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\"\n",
-    "\n",
-    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')\n",
-    "\n",
-    "response = ollama.chat.completions.create(model=\"llama3.2\", messages=[{\"role\": \"user\", \"content\": \"Tell me a fun fact\"}])\n",
-    "\n",
-    "response.choices[0].message.content"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "markdown",
@@ -340,21 +241,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "f06280ad",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "b'Ollama is running'"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "requests.get(\"http://localhost:11434\").content"
    ]
@@ -373,34 +263,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "e633481d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠋ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠙ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠸ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠼ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠴ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠦ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠧ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest \u001b[K\n",
-      "pulling dde5aa3fc5ff: 100% ▕██████████████████▏ 2.0 GB                         \u001b[K\n",
-      "pulling 966de95ca8a6: 100% ▕██████████████████▏ 1.4 KB                         \u001b[K\n",
-      "pulling fcc5a6bec9da: 100% ▕██████████████████▏ 7.7 KB                         \u001b[K\n",
-      "pulling a70ff7e570d9: 100% ▕██████████████████▏ 6.0 KB                         \u001b[K\n",
-      "pulling 56bb8bd477a5: 100% ▕██████████████████▏   96 B                         \u001b[K\n",
-      "pulling 34bb5ab01051: 100% ▕██████████████████▏  561 B                         \u001b[K\n",
-      "verifying sha256 digest \u001b[K\n",
-      "writing manifest \u001b[K\n",
-      "success \u001b[K\u001b[?25h\u001b[?2026l\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!ollama pull llama3.2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "d9419762",
    "metadata": {},
    "outputs": [],
@@ -412,21 +285,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "e2456cdf",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Here's one:\\n\\nDid you know that honey never spoils? Archaeologists have found pots of honey in ancient Egyptian tombs that are over 3,000 years old and still perfectly edible! This is because bees produce antibiotics as part of their hive-building efforts, which helps to preserve the honey. Also, honey doesn't contain water like other foods do, which makes it extremely difficult for bacteria or microorganisms to grow. So, even if you leave a jar of honey out at room temperature for centuries (not recommended, by the way!), it should still be safe to eat!\""
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get a fun fact\n",
     "\n",
@@ -496,56 +358,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "fa5edd63",
+   "execution_count": null,
+   "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here's a suggested concise and professional subject line:\n",
-      "\n",
-      "\"Meeting Rescheduled to 3 PM Tomorrow\"\n",
-      "\n",
-      "This subject line captures the core intent of the email, which is a meeting rescheduling, in just a few words while maintaining clarity and tone alignment.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Step 1: Create your prompts\n",
-    "\n",
-    "system_prompt = \"\"\"\n",
-    "You are embedded in an email tool that analyzes an email’s body text and suggests a concise, relevant, and professional subject line.\n",
-    "The subject line should capture the core intent of the email in 5–10 words\n",
-    "Maintain clarity and tone alignment:\n",
-    "Formal/business emails → neutral and polished\n",
-    "Casual/internal updates → conversational and brief\n",
-    "Urgent matters → include urgency subtly (e.g., “Action Needed”, “Follow-Up Required”)\n",
-    "Avoid unnecessary punctuation, emojis, or filler words.\n",
-    "Do not include explanations unless explicitly requested.\n",
-    "\"\"\"\n",
-    "user_prompt = \"\"\"\n",
-    "    Suggest a short subject line for the following email:\n",
-    "    Hi team, please note that tomorrow’s meeting will be moved to 3 PM instead of 2 PM to accommodate the new client call.\n",
-    "\"\"\"\n",
-    "\n",
-    "# Step 2: Make the messages list\n",
-    "\n",
-    "messages = [\n",
-    "    {\"role\": \"system\", \"content\": system_prompt},\n",
-    "    {\"role\": \"user\", \"content\": user_prompt}\n",
-    "]\n",
-    "\n",
-    "# Step 3: Call OpenAI\n",
-    "response = ollama.chat.completions.create(\n",
-    "    model=\"llama3.2\",\n",
-    "    messages=messages\n",
-    ")\n",
-    "\n",
-    "# Step 4: print the result\n",
-    "print(response.choices[0].message.content)"
-   ]
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -564,7 +381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/week1/day4.ipynb b/week1/day4.ipynb
index bb62ecb..0684828 100644
--- a/week1/day4.ipynb
+++ b/week1/day4.ipynb
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "7dc1c1d9",
    "metadata": {},
    "outputs": [],
@@ -21,22 +21,22 @@
     "\n",
     "encoding = tiktoken.encoding_for_model(\"gpt-4.1-mini\")\n",
     "\n",
-    "tokens = encoding.encode(\"Hi my name is Stephen\")"
+    "tokens = encoding.encode(\"Hi my name is Ed and I like banoffee pie\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "id": "7632966c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[12194, 922, 1308, 382, 34231]"
+       "[12194, 922, 1308, 382, 6117, 326, 357, 1299, 9171, 26458, 5148]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "id": "cce0c188",
    "metadata": {},
    "outputs": [
@@ -59,7 +59,13 @@
       "922 =  my\n",
       "1308 =  name\n",
       "382 =  is\n",
-      "34231 =  Stephen\n"
+      "6117 =  Ed\n",
+      "326 =  and\n",
+      "357 =  I\n",
+      "1299 =  like\n",
+      "9171 =  ban\n",
+      "26458 = offee\n",
+      "5148 =  pie\n"
      ]
     }
    ],
@@ -71,7 +77,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "id": "98e3bbd2",
    "metadata": {},
    "outputs": [
@@ -81,7 +87,7 @@
        "' and'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -104,18 +110,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "83a4b3eb",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "API key found and looks good so far!\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import os\n",
     "from dotenv import load_dotenv\n",
@@ -143,7 +141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "b959be3b",
    "metadata": {},
    "outputs": [],
@@ -163,34 +161,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "97298fea",
    "metadata": {},
    "outputs": [],
    "source": [
     "messages = [\n",
     "    {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
-    "    {\"role\": \"user\", \"content\": \"Hi! I'm Stephen!\"}\n",
+    "    {\"role\": \"user\", \"content\": \"Hi! I'm Ed!\"}\n",
     "    ]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "3475a36d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hello, Stephen! How can I assist you today?'"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "response = openai.chat.completions.create(model=\"gpt-4.1-mini\", messages=messages)\n",
     "response.choices[0].message.content"
@@ -206,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "6bce2208",
    "metadata": {},
    "outputs": [],
@@ -219,21 +206,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "id": "404462f5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'I’m sorry, but I don’t know your name. Could you please tell me?'"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "response = openai.chat.completions.create(model=\"gpt-4.1-mini\", messages=messages)\n",
     "response.choices[0].message.content"
@@ -255,36 +231,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "b6d43f92",
    "metadata": {},
    "outputs": [],
    "source": [
     "messages = [\n",
     "    {\"role\": \"system\", \"content\": \"You are a helpful assistant\"},\n",
-    "    {\"role\": \"user\", \"content\": \"Hi! I'm Stephen!\"},\n",
-    "    {\"role\": \"assistant\", \"content\": \"Hi Stephen! How can I assist you today?\"},\n",
+    "    {\"role\": \"user\", \"content\": \"Hi! I'm Ed!\"},\n",
+    "    {\"role\": \"assistant\", \"content\": \"Hi Ed! How can I assist you today?\"},\n",
     "    {\"role\": \"user\", \"content\": \"What's my name?\"}\n",
     "    ]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "e7ac742c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Your name is Stephen. How can I help you today, Stephen?'"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "response = openai.chat.completions.create(model=\"gpt-4.1-mini\", messages=messages)\n",
     "response.choices[0].message.content"
@@ -330,7 +295,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/week1/day5.ipynb b/week1/day5.ipynb
index 1e4bd56..0be0c46 100644
--- a/week1/day5.ipynb
+++ b/week1/day5.ipynb
@@ -278,10 +278,12 @@
        "   'url': 'https://edwarddonner.com/2025/05/18/2025-ai-executive-briefing/'},\n",
        "  {'type': 'blog post',\n",
        "   'url': 'https://edwarddonner.com/2025/04/21/the-complete-agentic-ai-engineering-course/'},\n",
-       "  {'type': 'linkedin page', 'url': 'https://www.linkedin.com/in/eddonner/'},\n",
-       "  {'type': 'twitter', 'url': 'https://twitter.com/edwarddonner'},\n",
-       "  {'type': 'facebook page',\n",
-       "   'url': 'https://www.facebook.com/edward.donner.52'}]}"
+       "  {'type': 'LinkedIn profile', 'url': 'https://www.linkedin.com/in/eddonner/'},\n",
+       "  {'type': 'Twitter profile', 'url': 'https://twitter.com/edwarddonner'},\n",
+       "  {'type': 'Facebook profile',\n",
+       "   'url': 'https://www.facebook.com/edward.donner.52'},\n",
+       "  {'type': 'external company',\n",
+       "   'url': 'https://nebula.io/?utm_source=ed&utm_medium=referral'}]}"
       ]
      },
      "execution_count": 8,
@@ -293,6 +295,22 @@
     "select_relevant_links(\"https://edwarddonner.com\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e7b84c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26709d38",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -318,47 +336,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Selecting relevant links for https://edwarddonner.com by calling gpt-5-nano\n",
-      "Found 9 relevant links\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'links': [{'type': 'home page', 'url': 'https://edwarddonner.com/'},\n",
-       "  {'type': 'about page',\n",
-       "   'url': 'https://edwarddonner.com/about-me-and-about-nebula/'},\n",
-       "  {'type': 'blog page', 'url': 'https://edwarddonner.com/posts/'},\n",
-       "  {'type': 'projects page', 'url': 'https://edwarddonner.com/connect-four/'},\n",
-       "  {'type': 'projects page', 'url': 'https://edwarddonner.com/outsmart/'},\n",
-       "  {'type': 'company page',\n",
-       "   'url': 'https://nebula.io/?utm_source=ed&utm_medium=referral'},\n",
-       "  {'type': 'LinkedIn profile', 'url': 'https://www.linkedin.com/in/eddonner/'},\n",
-       "  {'type': 'Twitter profile', 'url': 'https://twitter.com/edwarddonner'},\n",
-       "  {'type': 'Facebook page',\n",
-       "   'url': 'https://www.facebook.com/edward.donner.52'}]}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "select_relevant_links(\"https://edwarddonner.com\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924",
    "metadata": {},
    "outputs": [
@@ -367,28 +355,19 @@
      "output_type": "stream",
      "text": [
       "Selecting relevant links for https://huggingface.co by calling gpt-5-nano\n",
-      "Found 13 relevant links\n"
+      "Found 3 relevant links\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'links': [{'type': 'about page', 'url': 'https://huggingface.co/brand'},\n",
-       "  {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'},\n",
-       "  {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'},\n",
-       "  {'type': 'documentation page', 'url': 'https://huggingface.co/docs'},\n",
+       "{'links': [{'type': 'brand page', 'url': 'https://huggingface.co/brand'},\n",
        "  {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'},\n",
-       "  {'type': 'join page (careers)', 'url': 'https://huggingface.co/join'},\n",
-       "  {'type': 'blog', 'url': 'https://huggingface.co/blog'},\n",
-       "  {'type': 'learn page', 'url': 'https://huggingface.co/learn'},\n",
-       "  {'type': 'GitHub', 'url': 'https://github.com/huggingface'},\n",
-       "  {'type': 'Twitter', 'url': 'https://twitter.com/huggingface'},\n",
-       "  {'type': 'LinkedIn', 'url': 'https://www.linkedin.com/company/huggingface/'},\n",
-       "  {'type': 'Community forum', 'url': 'https://discuss.huggingface.co'},\n",
-       "  {'type': 'product endpoints', 'url': 'https://endpoints.huggingface.co'}]}"
+       "  {'type': 'company page',\n",
+       "   'url': 'https://www.linkedin.com/company/huggingface/'}]}"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5",
    "metadata": {},
    "outputs": [],
@@ -426,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "5099bd14-076d-4745-baf3-dac08d8e5ab2",
    "metadata": {},
    "outputs": [
@@ -435,20 +414,7 @@
      "output_type": "stream",
      "text": [
       "Selecting relevant links for https://huggingface.co by calling gpt-5-nano\n",
-      "Found 9 relevant links\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Found 11 relevant links\n",
       "## Landing Page:\n",
       "\n",
       "Hugging Face – The AI community building the future.\n",
@@ -463,10 +429,6 @@
       "Pricing\n",
       "Log In\n",
       "Sign Up\n",
-      "NEW\n",
-      "Try HuggingChat Omni – Chat with AI 💬\n",
-      "Get started with Inference in seconds 🚀\n",
-      "Reachy Mini: The Open Robot for AI Builders\n",
       "The AI community building the future.\n",
       "The platform where the machine learning community collaborates on models, datasets, and applications.\n",
       "Explore AI Apps\n",
@@ -475,111 +437,117 @@
       "Trending on\n",
       "this week\n",
       "Models\n",
-      "deepseek-ai/DeepSeek-OCR\n",
+      "tencent/HunyuanImage-3.0\n",
       "Updated\n",
-      "about 4 hours ago\n",
+      "about 3 hours ago\n",
       "•\n",
-      "32.9k\n",
+      "825\n",
       "•\n",
-      "1.21k\n",
-      "PaddlePaddle/PaddleOCR-VL\n",
+      "714\n",
+      "deepseek-ai/DeepSeek-V3.2-Exp\n",
       "Updated\n",
-      "about 22 hours ago\n",
+      "3 days ago\n",
       "•\n",
-      "6.62k\n",
+      "10.7k\n",
       "•\n",
-      "919\n",
-      "Qwen/Qwen3-VL-8B-Instruct\n",
+      "470\n",
+      "tencent/Hunyuan3D-Part\n",
       "Updated\n",
-      "7 days ago\n",
+      "4 days ago\n",
       "•\n",
-      "117k\n",
+      "2.57k\n",
       "•\n",
-      "272\n",
-      "nanonets/Nanonets-OCR2-3B\n",
+      "461\n",
+      "zai-org/GLM-4.6\n",
       "Updated\n",
-      "6 days ago\n",
+      "2 days ago\n",
       "•\n",
-      "16.2k\n",
+      "9.72k\n",
       "•\n",
-      "369\n",
-      "Phr00t/Qwen-Image-Edit-Rapid-AIO\n",
+      "298\n",
+      "inclusionAI/Ring-1T-preview\n",
       "Updated\n",
-      "about 7 hours ago\n",
+      "1 day ago\n",
       "•\n",
-      "388\n",
+      "818\n",
+      "•\n",
+      "208\n",
       "Browse 1M+ models\n",
       "Spaces\n",
       "Running\n",
-      "464\n",
-      "464\n",
-      "veo3.1-fast\n",
-      "🐨\n",
-      "Generate videos from text or images\n",
-      "Running\n",
-      "15.3k\n",
-      "15.3k\n",
-      "DeepSite v3\n",
-      "🐳\n",
-      "Generate any application by Vibe Coding\n",
-      "Running\n",
-      "406\n",
-      "406\n",
-      "Sora 2\n",
-      "📉\n",
-      "Generate videos from text or images\n",
-      "Running\n",
-      "1.98k\n",
-      "1.98k\n",
+      "1.29k\n",
+      "1.29k\n",
       "Wan2.2 Animate\n",
       "👁\n",
       "Wan2.2 Animate\n",
       "Running\n",
+      "14.5k\n",
+      "14.5k\n",
+      "DeepSite v3\n",
+      "🐳\n",
+      "Generate any application with DeepSeek\n",
+      "Running\n",
       "on\n",
       "Zero\n",
       "MCP\n",
-      "1.87k\n",
-      "1.87k\n",
+      "1.46k\n",
+      "1.46k\n",
       "Wan2.2 14B Fast\n",
       "🎥\n",
       "generate a video from an image with a text prompt\n",
+      "Running\n",
+      "on\n",
+      "Zero\n",
+      "196\n",
+      "196\n",
+      "Qwen Image Edit 2509\n",
+      "👀\n",
+      "Generate edited images based on user prompts\n",
+      "Running\n",
+      "on\n",
+      "CPU Upgrade\n",
+      "103\n",
+      "103\n",
+      "Ostris' AI Toolkit\n",
+      "💻\n",
+      "Train FLUX, Qwen and Wan LoRAs with Ostris Ai Toolkit\n",
       "Browse 400k+ applications\n",
       "Datasets\n",
-      "karpathy/fineweb-edu-100b-shuffle\n",
+      "openai/gdpval\n",
       "Updated\n",
-      "27 days ago\n",
+      "7 days ago\n",
       "•\n",
-      "25.3k\n",
+      "17k\n",
       "•\n",
-      "74\n",
+      "157\n",
       "fka/awesome-chatgpt-prompts\n",
       "Updated\n",
       "Jan 6\n",
       "•\n",
-      "37.2k\n",
+      "49k\n",
       "•\n",
-      "9.29k\n",
-      "nick007x/github-code-2025\n",
+      "9.18k\n",
+      "nvidia/Nemotron-Personas-Japan\n",
       "Updated\n",
-      "7 days ago\n",
+      "9 days ago\n",
       "•\n",
-      "7.23k\n",
+      "9.74k\n",
       "•\n",
-      "46\n",
-      "HuggingFaceFW/finewiki\n",
-      "Updated\n",
-      "1 day ago\n",
-      "•\n",
-      "32\n",
-      "•\n",
-      "32\n",
-      "Open-Bee/Honey-Data-15M\n",
+      "75\n",
+      "t-tech/T-ECD\n",
       "Updated\n",
       "6 days ago\n",
       "•\n",
-      "25\n",
+      "993\n",
       "•\n",
-      "31\n",
+      "22\n",
+      "facebook/seamless-interaction\n",
+      "Updated\n",
+      "Jul 14\n",
+      "•\n",
+      "17.5k\n",
+      "•\n",
+      "148\n",
       "Browse 250k+ datasets\n",
       "The Home of Machine Learning\n",
       "Create, discover and collaborate on ML better.\n",
@@ -594,73 +562,18 @@
       "Sign Up\n",
       "Accelerate your ML\n",
       "We provide paid Compute and Enterprise solutions.\n",
-      "Team & Enterprise\n",
-      "Give your team the most advanced p\n",
+      "Compute\n",
+      "Deploy on optimized\n",
+      "Inference Endpoints\n",
+      "or update your\n",
+      "Spaces applications\n",
+      "to a GPU in a few clicks.\n",
+      "View pricing\n",
+      "Starting at $0.60/hour for GPU\n",
+      "Tea\n",
       "## Relevant Links:\n",
       "\n",
       "\n",
-      "### Link: company page\n",
-      "Enterprise Hub - Hugging Face\n",
-      "\n",
-      "Hugging Face\n",
-      "Models\n",
-      "Datasets\n",
-      "Spaces\n",
-      "Community\n",
-      "Docs\n",
-      "Enterprise\n",
-      "Pricing\n",
-      "Log In\n",
-      "Sign Up\n",
-      "Team & Enterprise Hub\n",
-      "Scale your organization with the world’s leading AI platform\n",
-      "Subscribe to\n",
-      "Team\n",
-      "starting at $20/user/month\n",
-      "or\n",
-      "Contact sales for\n",
-      "Enterprise\n",
-      "to explore flexible contract options\n",
-      "Give your organization the most advanced platform to build AI with enterprise-grade security, access controls,\n",
-      "\t\t\tdedicated support and more.\n",
-      "Single Sign-On\n",
-      "Connect securely to your identity provider with SSO integration.\n",
-      "Regions\n",
-      "Select, manage, and audit the location of your repository data.\n",
-      "Audit Logs\n",
-      "Stay in control with comprehensive logs that report on actions taken.\n",
-      "Resource Groups\n",
-      "Accurately manage access to repositories with granular access control.\n",
-      "Token Management\n",
-      "Centralized token control and custom approval policies for organization access.\n",
-      "Analytics\n",
-      "Track and analyze repository usage data in a single dashboard.\n",
-      "Advanced Compute Options\n",
-      "Increase scalability and performance with more compute options like ZeroGPU.\n",
-      "ZeroGPU Quota Boost\n",
-      "All organization members get 5x more ZeroGPU quota to get the most of Spaces.\n",
-      "Private Datasets Viewer\n",
-      "Enable the Dataset Viewer on your private datasets for easier collaboration.\n",
-      "Private Storage\n",
-      "Get an additional 1 TB of private storage for each member of your organization (then $25/month per extra TB).\n",
-      "Inference Providers\n",
-      "Enable organization billing for Inference Providers, monitor usage with analytics, and manage spending limits.\n",
-      "Advanced security\n",
-      "Configure organization-wide security policies and default repository visibility.\n",
-      "Billing\n",
-      "Control your budget effectively with managed billing and yearly commit options.\n",
-      "Priority Support\n",
-      "Maximize your platform usage with priority support from the Hugging Face team.\n",
-      "Join the most forward-thinking AI organizations\n",
-      "Everything you already know and love about Hugging Face in Enterprise mode.\n",
-      "Subscribe to\n",
-      "Team\n",
-      "starting at $20/user/month\n",
-      "or\n",
-      "Contact sales for\n",
-      "Enterprise\n",
-      "to explore fl\n",
-      "\n",
       "### Link: about page\n",
       "Brand assets - Hugging Face\n",
       "\n",
@@ -727,10 +640,67 @@
       "LinkedIn\n",
       "Discord\n",
       "\n",
-      "### Link: careers page\n",
-      "No title found\n",
-      "\n",
+      "### Link: enterprise page\n",
+      "Enterprise Hub - Hugging Face\n",
       "\n",
+      "Hugging Face\n",
+      "Models\n",
+      "Datasets\n",
+      "Spaces\n",
+      "Community\n",
+      "Docs\n",
+      "Enterprise\n",
+      "Pricing\n",
+      "Log In\n",
+      "Sign Up\n",
+      "Team & Enterprise Hub\n",
+      "Scale your organization with the world’s leading AI platform\n",
+      "Subscribe to\n",
+      "Team\n",
+      "starting at $20/user/month\n",
+      "or\n",
+      "Contact sales for\n",
+      "Enterprise\n",
+      "to explore flexible contract options\n",
+      "Give your organization the most advanced platform to build AI with enterprise-grade security, access controls,\n",
+      "\t\t\tdedicated support and more.\n",
+      "Single Sign-On\n",
+      "Connect securely to your identity provider with SSO integration.\n",
+      "Regions\n",
+      "Select, manage, and audit the location of your repository data.\n",
+      "Audit Logs\n",
+      "Stay in control with comprehensive logs that report on actions taken.\n",
+      "Resource Groups\n",
+      "Accurately manage access to repositories with granular access control.\n",
+      "Token Management\n",
+      "Centralized token control and custom approval policies for organization access.\n",
+      "Analytics\n",
+      "Track and analyze repository usage data in a single dashboard.\n",
+      "Advanced Compute Options\n",
+      "Increase scalability and performance with more compute options like ZeroGPU.\n",
+      "ZeroGPU Quota Boost\n",
+      "All organization members get 5x more ZeroGPU quota to get the most of Spaces.\n",
+      "Private Datasets Viewer\n",
+      "Enable the Dataset Viewer on your private datasets for easier collaboration.\n",
+      "Private Storage\n",
+      "Get an additional 1 TB of private storage for each member of your organization (then $25/month per extra TB).\n",
+      "Inference Providers\n",
+      "Enable organization billing for Inference Providers, monitor usage with analytics, and manage spending limits.\n",
+      "Advanced security\n",
+      "Configure organization-wide security policies and default repository visibility.\n",
+      "Billing\n",
+      "Control your budget effectively with managed billing and yearly commit options.\n",
+      "Priority Support\n",
+      "Maximize your platform usage with priority support from the Hugging Face team.\n",
+      "Join the most forward-thinking AI organizations\n",
+      "Everything you already know and love about Hugging Face in Enterprise mode.\n",
+      "Subscribe to\n",
+      "Team\n",
+      "starting at $20/user/month\n",
+      "or\n",
+      "Contact sales for\n",
+      "Enterprise\n",
+      "to explore fl\n",
       "\n",
       "### Link: careers page\n",
       "Hugging Face - Current Openings\n",
@@ -770,189 +740,243 @@
       "Case Studies\n",
       "LeRobot\n",
       "Inference Providers\n",
+      "SOTA OCR on-device with Core ML and dots.ocr\n",
+      "By\n",
+      "FL33TW00D-HF\n",
+      "October 2, 2025\n",
       "Community Articles\n",
       "view all\n",
-      "AI for Food Allergies\n",
+      "There is no such thing as a tokenizer-free lunch\n",
       "By\n",
-      "hugging-science\n",
-      "and 3 others\n",
+      "catherinearnett\n",
       "•\n",
-      "5 days ago\n",
+      "8 days ago\n",
       "•\n",
-      "26\n",
-      "Introducing MTEB v2: Evaluation of embedding and retrieval systems for more than just text\n",
+      "68\n",
+      "Model Quality: Hugging Face Is All You Need\n",
       "By\n",
-      "isaacchung\n",
-      "and 2 others\n",
-      "•\n",
-      "1 day ago\n",
-      "•\n",
-      "26\n",
-      "Announcing Hugging Face Fundamentals: A New Learning Track on DataCamp\n",
-      "By\n",
-      "huggingface\n",
+      "finegrain\n",
       "•\n",
       "6 days ago\n",
       "•\n",
-      "21\n",
-      "Introducing the Massive Legal Embedding Benchmark (MLEB)\n",
-      "By\n",
-      "isaacus\n",
-      "and 1 other\n",
-      "•\n",
-      "5 days ago\n",
-      "•\n",
-      "16\n",
-      "How I Built Lightning-Fast Vector Search for Legal Documents\n",
-      "By\n",
-      "adlumal\n",
-      "•\n",
-      "2 days ago\n",
-      "•\n",
-      "14\n",
-      "Art of Focus: Page-Aware Sparse Attention and Ling 2.0’s Quest for Efficient Context Length Scaling\n",
-      "By\n",
-      "RichardBian\n",
-      "and 19 others\n",
-      "•\n",
-      "1 day ago\n",
-      "•\n",
-      "14\n",
-      "Scaling Test-Time Compute to Achieve Gold Medal at IOI 2025 with Open-Weight Models\n",
-      "By\n",
-      "nvidia\n",
-      "and 3 others\n",
-      "•\n",
-      "1 day ago\n",
-      "•\n",
-      "11\n",
-      "GSMA Open-Telco LLM Benchmarks 2.0: The first dedicated LLM Evaluation for Telecoms\n",
-      "By\n",
-      "otellm\n",
-      "and 15 others\n",
-      "•\n",
-      "2 days ago\n",
-      "•\n",
-      "8\n",
+      "19\n",
       "Code a simple RAG from scratch\n",
       "By\n",
       "ngxson\n",
       "•\n",
       "Oct 29, 2024\n",
       "•\n",
-      "223\n",
-      "Model statistics of the 50 most downloaded entities on Hugging Face\n",
+      "208\n",
+      "Nemotron-Personas-Japan: Synthesized Data for Sovereign AI\n",
       "By\n",
-      "lbourdois\n",
+      "nvidia\n",
+      "and 6 others\n",
       "•\n",
       "9 days ago\n",
       "•\n",
-      "25\n",
-      "There is no such thing as a tokenizer-free lunch\n",
+      "24\n",
+      "When Does Reasoning Matter? Unpacking the Contribution of Reasoning to LLM Performance\n",
       "By\n",
-      "catherinearnett\n",
+      "Nicolas-BZRD\n",
+      "and 1 other\n",
       "•\n",
-      "27 days ago\n",
+      "2 days ago\n",
       "•\n",
-      "83\n",
-      "How Financial News Can Be Used to Train Good Financial Models\n",
+      "10\n",
+      "CU-1 for Autonomous UI Agent Systems: An Open Alternative to Proprietary Solutions\n",
       "By\n",
-      "SelmaNajih001\n",
+      "paulml\n",
       "•\n",
-      "14 days ago\n",
+      "1 day ago\n",
       "•\n",
-      "8\n",
+      "10\n",
+      "How I Trained Action Chunking Transformer (ACT) on SO-101: My Journey, Gotchas, and Lessons\n",
+      "By\n",
+      "sherryxychen\n",
+      "•\n",
+      "3 days ago\n",
+      "•\n",
+      "9\n",
+      "Preserving Agency: Why AI Safety Needs Community, Not Corporate Control\n",
+      "By\n",
+      "giadap\n",
+      "•\n",
+      "3 days ago\n",
+      "•\n",
+      "9\n",
+      "RexBERT: Encoders for a brave new world of E-Commerce\n",
+      "By\n",
+      "thebajajra\n",
+      "and 1 other\n",
+      "•\n",
+      "12 days ago\n",
+      "•\n",
+      "46\n",
+      "Nemotron-Personas-Japan: ソブリン AI のための合成データセット\n",
+      "By\n",
+      "nvidia\n",
+      "and 6 others\n",
+      "•\n",
+      "6 days ago\n",
+      "•\n",
+      "7\n",
+      "Uncensor any LLM with abliteration\n",
+      "By\n",
+      "mlabonne\n",
+      "•\n",
+      "Jun 13, 2024\n",
+      "•\n",
+      "684\n",
+      "How to Train an Antibody Developability Model\n",
+      "By\n",
+      "ginkgo-datapoints\n",
+      "and 1 other\n",
+      "•\n",
+      "15 days ago\n",
+      "•\n",
+      "14\n",
       "Introduction to State Space Models (SSM)\n",
       "By\n",
       "lbourdois\n",
       "•\n",
       "Jul 19, 2024\n",
       "•\n",
-      "181\n",
-      "Visualizing How VLMs Work\n",
+      "176\n",
+      "arXiv实用技巧，如何让你的paper关注度变高？\n",
       "By\n",
-      "not-lain\n",
-      "and 1 other\n",
+      "JessyTsu1\n",
       "•\n",
-      "15 days ago\n",
+      "Jul 8, 2024\n",
       "•\n",
-      "39\n",
-      "Understanding Vector Quantization in VQ-VAE\n",
+      "14\n",
+      "Fine-Tuning Your First Large Language Model (LLM) with PyTorch and Hugging Face\n",
       "By\n",
-      "ariG23498\n",
+      "dvgodoy\n",
       "•\n",
-      "Aug 28, 2024\n",
+      "Feb 11\n",
       "•\n",
-      "48\n",
-      "Small Language Models (SLM): A Comprehensive Overview\n",
+      "72\n",
+      "PP-OCRv5 on Hugging Face: A Specialized Approach to OCR\n",
       "By\n",
-      "jjokah\n",
+      "baidu\n",
+      "and 5 others\n",
       "•\n",
-      "Feb 22\n",
+      "22 days ago\n",
       "•\n",
-      "91\n",
-      "mem-agent: Equipping LLM Agents with Memory Using RL\n",
-      "By\n",
-      "driaforall\n",
-      "and 1 other\n",
-      "•\n",
-      "13 days ago\n",
-      "•\n",
-      "30\n",
-      "Granite Em\n",
+      "102\n",
+      "Ground-up efforts to build large datasets for effective and accurate translation of Modi-Sc\n",
       "\n",
-      "### Link: learn page\n",
-      "Hugging Face - Learn\n",
+      "### Link: community forum\n",
+      "Hugging Face Forums - Hugging Face Community Discussion\n",
       "\n",
-      "Hugging Face\n",
-      "Models\n",
-      "Datasets\n",
+      "Hugging Face Forums\n",
+      "Topic\n",
+      "Replies\n",
+      "Views\n",
+      "Activity\n",
+      "Tokenizer: How to suppress preceding whitespace when giving pre-tokenized list[list[str]] input\n",
+      "Beginners\n",
+      "2\n",
+      "4\n",
+      "October 2, 2025\n",
+      "Student Pro Plan for Education Users (Masters student)\n",
+      "Research\n",
+      "0\n",
+      "13\n",
+      "October 2, 2025\n",
+      "Error: Repository storage limit reached (Max: 1 GB)\n",
+      "Beginners\n",
+      "29\n",
+      "2720\n",
+      "October 2, 2025\n",
+      "Can't claim ownership of paper\n",
+      "Beginners\n",
+      "3\n",
+      "26\n",
+      "October 2, 2025\n",
+      "Set usage limit per access token\n",
+      "Site Feedback\n",
+      "0\n",
+      "5\n",
+      "October 2, 2025\n",
+      "I want to open source this framework. OAGI\n",
+      "Research\n",
+      "2\n",
+      "15\n",
+      "October 2, 2025\n",
+      "Pro Account ZeroGPU Limit Usage at Failed Sessions\n",
+      "Beginners\n",
+      "2\n",
+      "10\n",
+      "October 2, 2025\n",
+      "How to use for Real Estate?\n",
+      "Beginners\n",
+      "1\n",
+      "12\n",
+      "October 2, 2025\n",
+      "Help : integrating a Agilex PiPER robot arm to lerobot\n",
+      "Intermediate\n",
+      "8\n",
+      "16\n",
+      "October 2, 2025\n",
+      "Custom Domain stuck on pending\n",
+      "Beginners\n",
+      "4\n",
+      "66\n",
+      "October 1, 2025\n",
+      "How to edit website with AI prompts?\n",
+      "Beginners\n",
+      "1\n",
+      "7\n",
+      "October 2, 2025\n",
+      "Custom Domain for HF Spaces\n",
       "Spaces\n",
-      "Community\n",
-      "Docs\n",
-      "Enterprise\n",
-      "Pricing\n",
-      "Log In\n",
-      "Sign Up\n",
-      "Learn\n",
-      "LLM Course\n",
-      "This course will teach you about large language models using libraries from the HF ecosystem\n",
-      "Robotics Course\n",
-      "This course will teach you to build robots with using LeRobot\n",
-      "MCP Course\n",
-      "This course will teach you about Model Context Protocol\n",
-      "a smol course\n",
-      "This smollest course on post-training AI models\n",
-      "Agents Course\n",
-      "Learn to build and deploy your own AI agents\n",
-      "Deep RL Course\n",
-      "This course will teach you about deep reinforcement learning using libraries from the HF ecosystem\n",
-      "Community Computer Vision Course\n",
-      "This course will teach you about computer vision ML using libraries and models from the HF ecosystem\n",
-      "Audio Course\n",
-      "Learn to apply transformers to audio data using libraries from the HF ecosystem\n",
-      "Open-Source AI Cookbook\n",
-      "A collection of open-source-powered notebooks by AI builders, for AI builders\n",
-      "ML for Games Course\n",
-      "This course will teach you about integrating AI models your game and using AI tools in your game development workflow\n",
-      "Diffusion Course\n",
-      "Learn about diffusion models & how to use them with diffusers\n",
-      "ML for 3D Course\n",
-      "Learn about 3D ML with libraries from the HF ecosystem\n",
-      "System theme\n",
-      "Company\n",
-      "TOS\n",
-      "Privacy\n",
-      "About\n",
-      "Jobs\n",
-      "Website\n",
+      "10\n",
+      "4329\n",
+      "October 1, 2025\n",
+      "What are the best practices for fine-tuning transformer models with limited data?\n",
+      "Beginners\n",
+      "1\n",
+      "9\n",
+      "October 1, 2025\n",
+      "Creating and posting non-traditional datasets formats like DICOM, NIfTI, and WFDB records\n",
+      "🤗Datasets\n",
+      "1\n",
+      "8\n",
+      "September 30, 2025\n",
+      "I used to have no problem with PEFT fine-tuning after hundreds of trainings, but now I have encountered The error RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn\n",
+      "🤗AutoTrain\n",
+      "2\n",
+      "8\n",
+      "October 1, 2025\n",
+      "Help: Can’t find Multi Image Input node in ComfyUI\n",
+      "Beginners\n",
+      "3\n",
+      "15\n",
+      "October 2, 2025\n",
+      "Which parameters to use for fine-tuning Llama-2-7b?\n",
+      "Beginners\n",
+      "1\n",
+      "12\n",
+      "September 30, 2025\n",
+      "03 Error When Using Qwen/Qwen2.5-VL-32B-Instruct with Inference Provider\n",
       "Models\n",
-      "Datasets\n",
-      "Spaces\n",
-      "Pricing\n",
-      "Docs\n",
+      "4\n",
+      "23\n",
+      "October 2, 2025\n",
+      "Is it possible to remove articles (the, a, an) from a text sample without consequences?\n",
+      "🤗Datasets\n",
+      "3\n",
+      "14\n",
+      "October 1, 2025\n",
+      "Have You Ever Tested an AI Model? What Was Your Experience?\n",
+      "Models\n",
+      "2\n",
+      "19\n",
+      "October 1, 2025\n",
+      "When fine-tuning a model, what stand\n",
       "\n",
-      "### Link: open-source page\n",
+      "### Link: GitHub\n",
       "Hugging Face · GitHub\n",
       "\n",
       "Skip to content\n",
@@ -1071,7 +1095,7 @@
       "Reload\n",
       "to refresh yo\n",
       "\n",
-      "### Link: social page\n",
+      "### Link: Twitter\n",
       "No title found\n",
       "\n",
       "JavaScript is not available.\n",
@@ -1087,7 +1111,7 @@
       "Try again\n",
       "Some privacy related extensions may cause issues on x.com. Please disable them and try again.\n",
       "\n",
-      "### Link: social page\n",
+      "### Link: LinkedIn\n",
       "Hugging Face | LinkedIn\n",
       "\n",
       "Skip to main content\n",
@@ -1099,13 +1123,13 @@
       "Games\n",
       "Get the app\n",
       "Sign in\n",
-      "Create an account\n",
+      "Join now\n",
       "Hugging Face\n",
       "Software Development\n",
       "The AI community building the future.\n",
       "See jobs\n",
       "Follow\n",
-      "View all 632 employees\n",
+      "Discover all 623 employees\n",
       "Report this company\n",
       "About us\n",
       "The AI community building the future.\n",
@@ -1144,24 +1168,145 @@
       "Updates\n",
       "Hugging Face\n",
       "reposted this\n",
-      "Andrés Marafioti\n",
-      "Multimodal Research Lead @ Hugging Face | 10+ YOE in AI R&D\n",
-      "16h\n",
+      "Mercuri\n",
+      "3,316 followers\n",
+      "1d\n",
       "Report this post\n",
-      "Finally, our new paper is out! \"𝗙𝗶𝗻𝗲𝗩𝗶𝘀𝗶𝗼𝗻: 𝗢𝗽𝗲𝗻 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗔𝗹𝗹 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱\"! 🥳\n",
+      "🚨 404: media not found 🚨\n",
       "\n",
-      "If you've ever trained a VLM, you know this problem: nobody shares their data mixtures. It's a black box, making replicating SOTA work impossible.\n",
-      "We wanted to change that.\n",
+      "We're teaming up\n",
+      "The Tech Bros\n",
+      ",\n",
+      "Canva\n",
+      ",\n",
+      "ElevenLabs\n",
+      ", and\n",
+      "Hugging Face\n",
+      "for an all female media x tech\n",
+      "#hackathon\n",
+      "at the Canva offices 👩🏾💻👩🏼💻👩🏻💻\n",
       "\n",
-      "FineVision unifies 200 sources into 24 million samples. With 17.3 million images and 9.5 billion answer tokens, it's the largest open resource of its kind.\n",
+      "⚡ Build prototypes fast\n",
+      "⚡ Get feedback from engineers & investors\n",
+      "⚡ Meet your co-founder!?\n",
       "\n",
-      "In the paper, we share how we built it:\n",
-      " 🔍 finding and cleaning data at scale\n",
-      " 🧹 removing excessive duplicates across sources\n",
-      " 🤗 decontaminating against 66 public benchmarks\n",
+      "Hugging Face, Canva and ElevenLabs are unicorns shaping the future of media and creative tech. At\n",
+      "Mercuri\n",
+      ", we bring the early-stage VC perspective, meeting thousands of startups each year at the intersection of media, entertainment and technology. This hackathon brings it all together, creating a space to test bold ideas and learn directly from operators and investors immersed in the field.\n",
       "\n",
-      "My favorite part is Figure 6 (in the video!). It's our visual diversity analysis. It shows that FineVision isn't just bigger; it's more balanced and conceptually richer than other open datasets. \n",
-      "NVIDIA's Eagle 2 paper highlighted just how critical this visual diversity is, and \n"
+      "The media x creative tech world needs fresh voices and bold ideas. When women lead, products get sharper, narratives more inclusive, and impact much greater - so come join us!\n",
+      "\n",
+      "📍 Central London\n",
+      "📅 Wednesday, 29 October\n",
+      "🔗 Link to sign up in comments\n",
+      "23\n",
+      "1 Comment\n",
+      "\n",
+      "### Link: Zhihu\n",
+      "No title found\n",
+      "\n",
+      "知乎，让每一次点击都充满意义 —— 欢迎来到知乎，发现问题背后的世界。\n",
+      "\n",
+      "### Link: API endpoints\n",
+      "Inference Endpoints by Hugging Face\n",
+      "\n",
+      "Inference\n",
+      "Endpoints\n",
+      "Catalog\n",
+      "Log In\n",
+      "Machine Learning At Your Service\n",
+      "by\n",
+      "Hugging Face\n",
+      "Easily deploy Transformers, Diffusers or any model on dedicated, fully managed\n",
+      "\t\t\t\tinfrastructure. Keep your costs low with our secure, compliant and flexible production\n",
+      "\t\t\t\tsolution.\n",
+      "Log In\n",
+      "Learn More\n",
+      "No Hugging Face account ?\n",
+      "Sign up\n",
+      "!\n",
+      "One-click inference deployment\n",
+      "Import your favorite model from the Hugging Face hub or browse our catalog of hand-picked, ready-to-deploy models !\n",
+      "meta-llama /\n",
+      "Llama-3.1-70B-Instruct\n",
+      "59\n",
+      "Deployed 59 times\n",
+      "Text Generation\n",
+      "TGI\n",
+      "Accelerated Text Generation Inference\n",
+      "GPU\n",
+      "4x Nvidia L40S\n",
+      "$\n",
+      "8.3\n",
+      "openai /\n",
+      "whisper-large-v3-turbo\n",
+      "162\n",
+      "Deployed 162 times\n",
+      "Automatic Speech Recognition\n",
+      "GPU\n",
+      "1x Nvidia L4\n",
+      "$\n",
+      "0.8\n",
+      "mixedbread-ai /\n",
+      "mxbai-embed-large-v1\n",
+      "22\n",
+      "Deployed 22 times\n",
+      "Sentence Embeddings\n",
+      "TEI\n",
+      "Accelerated Text Embeddings Inference\n",
+      "GPU\n",
+      "1x Nvidia L4\n",
+      "$\n",
+      "0.8\n",
+      "Qwen /\n",
+      "Qwen2.5-Coder-7B-Instruct\n",
+      "47\n",
+      "Deployed 47 times\n",
+      "Text Generation\n",
+      "TGI\n",
+      "Accelerated Text Generation Inference\n",
+      "GPU\n",
+      "1x Nvidia L40S\n",
+      "$\n",
+      "1.8\n",
+      "black-forest-labs /\n",
+      "FLUX.1-schnell\n",
+      "143\n",
+      "Deployed 143 times\n",
+      "Text-to-Image\n",
+      "GPU\n",
+      "1x Nvidia L40S\n",
+      "$\n",
+      "1.8\n",
+      "Browse Catalog\n",
+      "Hub Models\n",
+      "Customer Stories\n",
+      "Learn how leading AI teams use Inference Endpoints to deploy their models\n",
+      "Endpoints for\n",
+      "Music\n",
+      "Musixmatch is the world’s leading music data company\n",
+      "Use Case\n",
+      "Custom text embeddings generation pipeline\n",
+      "Models Deployed\n",
+      "Distilbert-base-uncased-finetuned-sst-2-english\n",
+      "facebook/wav2vec2-base-960h\n",
+      "Custom model based on sentence transformers\n",
+      "The coolest thing was how easy it was to define a complete custom interface from the model to the inference process. It just took us a couple of hours to adapt our code, and have a functioning and totally custom endpoint.\n",
+      "Andrea Boscarino\n",
+      "Data Scientist at Musixmatch\n",
+      "Endpoints for\n",
+      "Health\n",
+      "Phamily improves patient health with intelligent care management\n",
+      "Use Case\n",
+      "HIPAA-compliant secure endpoints for text classification\n",
+      "Models Deployed\n",
+      "Custom model based on text-classification (MPNET)\n",
+      "Custom\n",
+      "\n",
+      "### Link: Discord community\n",
+      "Hugging Face\n",
+      "\n",
+      "You need to enable JavaScript to run this app.\n"
      ]
     }
    ],
@@ -1171,7 +1316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2",
    "metadata": {},
    "outputs": [],
@@ -1195,7 +1340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23",
    "metadata": {},
    "outputs": [],
@@ -1213,7 +1358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "cd909e0b-1312-4ce2-a553-821e795d7572",
    "metadata": {},
    "outputs": [
@@ -1222,23 +1367,16 @@
      "output_type": "stream",
      "text": [
       "Selecting relevant links for https://huggingface.co by calling gpt-5-nano\n",
-      "Found 10 relevant links\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some characters could not be decoded, and were replaced with REPLACEMENT CHARACTER.\n"
+      "Found 7 relevant links\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "'\\nYou are looking at a company called: HuggingFace\\nHere are the contents of its landing page and other relevant pages;\\nuse this information to build a short brochure of the company in markdown without code blocks.\\n\\n\\n## Landing Page:\\n\\nHugging Face – The AI community building the future.\\n\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nCommunity\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nNEW\\nTry HuggingChat Omni – Chat with AI 💬\\nGet started with Inference in seconds 🚀\\nReachy Mini: The Open Robot for AI Builders\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ndeepseek-ai/DeepSeek-OCR\\nUpdated\\nabout 4 hours ago\\n•\\n32.9k\\n•\\n1.21k\\nPaddlePaddle/PaddleOCR-VL\\nUpdated\\nabout 22 hours ago\\n•\\n6.62k\\n•\\n919\\nQwen/Qwen3-VL-8B-Instruct\\nUpdated\\n7 days ago\\n•\\n117k\\n•\\n272\\nnanonets/Nanonets-OCR2-3B\\nUpdated\\n6 days ago\\n•\\n16.2k\\n•\\n369\\nPhr00t/Qwen-Image-Edit-Rapid-AIO\\nUpdated\\nabout 7 hours ago\\n•\\n388\\nBrowse 1M+ models\\nSpaces\\nRunning\\n464\\n464\\nveo3.1-fast\\n🐨\\nGenerate videos from text or images\\nRunning\\n15.3k\\n15.3k\\nDeepSite v3\\n🐳\\nGenerate any application by Vibe Coding\\nRunning\\n406\\n406\\nSora 2\\n📉\\nGenerate videos from text or images\\nRunning\\n1.98k\\n1.98k\\nWan2.2 Animate\\n👁\\nWan2.2 Animate\\nRunning\\non\\nZero\\nMCP\\n1.87k\\n1.87k\\nWan2.2 14B Fast\\n🎥\\ngenerate a video from an image with a text prompt\\nBrowse 400k+ applications\\nDatasets\\nkarpathy/fineweb-edu-100b-shuffle\\nUpdated\\n27 days ago\\n•\\n25.3k\\n•\\n74\\nfka/awesome-chatgpt-prompts\\nUpdated\\nJan 6\\n•\\n37.2k\\n•\\n9.29k\\nnick007x/github-code-2025\\nUpdated\\n7 days ago\\n•\\n7.23k\\n•\\n46\\nHuggingFaceFW/finewiki\\nUpdated\\n1 day ago\\n•\\n32\\n•\\n32\\nOpen-Bee/Honey-Data-15M\\nUpdated\\n6 days ago\\n•\\n25\\n•\\n31\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nTeam & Enterprise\\nGive your team the most advanced p\\n## Relevant Links:\\n\\n\\n### Link: company homepage\\nHugging Face – The AI community building the future.\\n\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nCommunity\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nNEW\\nTry HuggingChat Omni – Chat with AI 💬\\nGet started with Inference in seconds 🚀\\nReachy Mini: The Open Robot for AI Builders\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ndeepseek-ai/DeepSeek-OCR\\nUpdated\\nabout 4 hours ago\\n•\\n32.9k\\n•\\n1.21k\\nPaddlePaddle/PaddleOCR-VL\\nUpdated\\nabout 22 hours ago\\n•\\n6.62k\\n•\\n919\\nQwen/Qwen3-VL-8B-Instruct\\nUpdated\\n7 days ago\\n•\\n117k\\n•\\n272\\nnanonets/Nanonets-OCR2-3B\\nUpdated\\n6 days ago\\n•\\n16.2k\\n•\\n369\\nPhr00t/Qwen-Image-Edit-Rapid-AIO\\nUpdated\\nabout 7 hours ago\\n•\\n388\\nBrowse 1M+ models\\nSpaces\\nRunning\\n464\\n464\\nveo3.1-fast\\n🐨\\nGenerate videos from text or images\\nRunning\\n15.3k\\n15.3k\\nDeepSite v3\\n🐳\\nGenerate any application by Vibe Coding\\nRunning\\n406\\n406\\nSora 2\\n📉\\nGenerate videos from text or images\\nRunning\\n1.98k\\n1.98k\\nWan2.2 Animate\\n👁\\nWan2.2 Animate\\nRunning\\non\\nZero\\nMCP\\n1.87k\\n1.87k\\nWan2.2 14B Fast\\n🎥\\ngenerate a video from an image with a text prompt\\nBrowse 400k+ applications\\nDatasets\\nkarpathy/fineweb-edu-100b-shuffle\\nUpdated\\n27 days ago\\n•\\n25.3k\\n•\\n74\\nfka/awesome-chatgpt-prompts\\nUpdated\\nJan 6\\n•\\n37.2k\\n•\\n9.29k\\nnick007x/github-code-2025\\nUpdated\\n7 days ago\\n•\\n7.23k\\n•\\n46\\nHuggingFaceFW/finewiki\\nUpdated\\n1 day ago\\n•\\n32\\n•\\n32\\nOpen-Bee/Honey-Data-15M\\nUpdated\\n6 days ago\\n•\\n25\\n•\\n31\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nTeam & Enterprise\\nGive your team the most advanced p\\n\\n### Link: brand page\\nBrand assets - Hugging Face\\n\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nCommunity\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nHugging Face · Brand assets\\nHF Logos\\n.svg\\n.png\\n.ai\\n.svg\\n.png\\n.ai\\n.svg\\n.png\\n.ai\\nHF Colors\\n#FFD21E\\n#FF9D00\\n#6B7280\\nHF Bio\\nHugging Face is the collaboration platform for the machine learning community.\\n\\nThe Hugging Face Hub works as a central place where anyone can share, explore, discover, and experiment with open-source ML. HF empowers the next generation of machine learning engineers, scientists, and end users to learn, collaborate and share their work to build an open and ethical AI future together.\\n\\nWith the fast-growing community, some of the most used open-source ML libr'"
+       "\"\\nYou are looking at a company called: HuggingFace\\nHere are the contents of its landing page and other relevant pages;\\nuse this information to build a short brochure of the company in markdown without code blocks.\\n\\n\\n## Landing Page:\\n\\nHugging Face – The AI community building the future.\\n\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nCommunity\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nExplore AI Apps\\nor\\nBrowse 1M+ models\\nTrending on\\nthis week\\nModels\\ntencent/HunyuanImage-3.0\\nUpdated\\nabout 4 hours ago\\n•\\n825\\n•\\n714\\ndeepseek-ai/DeepSeek-V3.2-Exp\\nUpdated\\n3 days ago\\n•\\n10.7k\\n•\\n470\\ntencent/Hunyuan3D-Part\\nUpdated\\n4 days ago\\n•\\n2.57k\\n•\\n461\\nzai-org/GLM-4.6\\nUpdated\\n2 days ago\\n•\\n9.72k\\n•\\n298\\ninclusionAI/Ring-1T-preview\\nUpdated\\n1 day ago\\n•\\n818\\n•\\n208\\nBrowse 1M+ models\\nSpaces\\nRunning\\n1.29k\\n1.29k\\nWan2.2 Animate\\n👁\\nWan2.2 Animate\\nRunning\\n14.5k\\n14.5k\\nDeepSite v3\\n🐳\\nGenerate any application with DeepSeek\\nRunning\\non\\nZero\\nMCP\\n1.46k\\n1.46k\\nWan2.2 14B Fast\\n🎥\\ngenerate a video from an image with a text prompt\\nRunning\\non\\nZero\\n196\\n196\\nQwen Image Edit 2509\\n👀\\nGenerate edited images based on user prompts\\nRunning\\non\\nCPU Upgrade\\n103\\n103\\nOstris' AI Toolkit\\n💻\\nTrain FLUX, Qwen and Wan LoRAs with Ostris Ai Toolkit\\nBrowse 400k+ applications\\nDatasets\\nopenai/gdpval\\nUpdated\\n7 days ago\\n•\\n17k\\n•\\n157\\nfka/awesome-chatgpt-prompts\\nUpdated\\nJan 6\\n•\\n49k\\n•\\n9.18k\\nnvidia/Nemotron-Personas-Japan\\nUpdated\\n9 days ago\\n•\\n9.74k\\n•\\n75\\nt-tech/T-ECD\\nUpdated\\n6 days ago\\n•\\n993\\n•\\n22\\nfacebook/seamless-interaction\\nUpdated\\nJul 14\\n•\\n17.5k\\n•\\n148\\nBrowse 250k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nCompute\\nDeploy on optimized\\nInference Endpoints\\nor update your\\nSpaces applications\\nto a GPU in a few clicks.\\nView pricing\\nStarting at $0.60/hour for GPU\\nTea\\n## Relevant Links:\\n\\n\\n### Link: brand page\\nBrand assets - Hugging Face\\n\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nCommunity\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nHugging Face · Brand assets\\nHF Logos\\n.svg\\n.png\\n.ai\\n.svg\\n.png\\n.ai\\n.svg\\n.png\\n.ai\\nHF Colors\\n#FFD21E\\n#FF9D00\\n#6B7280\\nHF Bio\\nHugging Face is the collaboration platform for the machine learning community.\\n\\nThe Hugging Face Hub works as a central place where anyone can share, explore, discover, and experiment with open-source ML. HF empowers the next generation of machine learning engineers, scientists, and end users to learn, collaborate and share their work to build an open and ethical AI future together.\\n\\nWith the fast-growing community, some of the most used open-source ML libraries and tools, and a talented science team exploring the edge of tech, Hugging Face is at the heart of the AI revolution.\\nCopy to clipboard\\nHF Universe\\nFind other assets available for use from the Hugging Face brand universe\\nhere\\n.\\nSystem theme\\nWebsite\\nModels\\nDatasets\\nSpaces\\nChangelog\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\n\\n### Link: careers page\\nHugging Face - Current Openings\\n\\n\\n\\n### Link: company page\\nHugging Face | LinkedIn\\n\\nSkip to main content\\nLinkedIn\\nTop Content\\nPeople\\nLearning\\nJobs\\nGames\\nGet the app\\nJoin now\\nSign in\\nHugging Face\\nSoftware Development\\nThe AI community building the future.\\nSee jobs\\nFollow\\nView all 625 employees\\nReport this company\\nAbout us\\nThe AI community building the future.\\nWebsite\\nhttps://huggingface.co\\nExternal link for Hugging Face\\nIndustry\\nSoftware Development\\nCompany size\\n51-200 employees\\nType\\nPrivately Held\\nFounded\\n2016\\nSpecialties\\nmachine learning, natural language processing, and deep learning\\nProducts\\nHugging Face\\nHugging Face\\nNatural Language Processing (NLP) Software\\nWe’re on a journey to solve and democratize artificial intelligence through natural language.\\nLocations\\nPrimary\\nGet directions\\nParis, FR\\nGet directions\\nEmployees at Hugging Face\\nLudovic Huraux\\nRajat Arya\\nTech Lead & Software Engineer @ HF | prev: co-founder XetHub, Apple, Turi, AWS, Microsoft\\nJeff Boudier\\nProduct + Growth at Hugging Face\\nTerrence Rohan\\nSeed Investor\\nSee all employees\\nUpdates\\nHugging Face\\nreposted this\\nMercuri\\n3,316 followers\\n1d\\nReport this post\\n🚨 404: media not found 🚨\\n\\nWe're teaming up\\nThe Tech Bros\\n,\\nCanva\\n,\\nElevenLabs\\n, and\\nHugging Face\\nfor an all female media x tech\\n#hackathon\\nat the Canva offices 👩🏾💻👩🏼💻👩🏻💻\\n\\n⚡ Build prototypes fast\\n⚡ Get feedback from engineers & investors\\n⚡ Meet your co-founder!?\\n\\nHugging Face, Canva and ElevenLabs are unicorns shaping the future of media and creative tech. At\\nMercuri\\n, we bring the early-st\""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1249,7 +1387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "8b45846d",
    "metadata": {},
    "outputs": [],
@@ -1268,7 +1406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "b123615a",
    "metadata": {},
    "outputs": [
@@ -1285,78 +1423,96 @@
       "text/markdown": [
        "# Hugging Face Brochure\n",
        "\n",
+       "---\n",
+       "\n",
        "## About Hugging Face\n",
        "\n",
-       "Hugging Face is a vibrant AI community dedicated to building the future of machine learning. It serves as a collaborative platform where researchers, developers, and organizations come together to create, share, and improve machine learning models, datasets, and applications. The company’s mission is to accelerate innovation in AI by providing open-source tools and an ecosystem that supports all modalities including text, image, video, audio, and even 3D.\n",
+       "**Hugging Face** is the premier collaboration platform for the machine learning (ML) community, dedicated to building the future of artificial intelligence through openness and shared knowledge. It serves as a vibrant hub where ML engineers, scientists, and enthusiasts from across the globe can share, discover, and experiment with cutting-edge open-source machine learning models, datasets, and applications.\n",
+       "\n",
+       "At its core, Hugging Face empowers the next generation of AI innovators to learn, collaborate, and contribute to an open and ethical AI future.\n",
        "\n",
        "---\n",
        "\n",
-       "## Platform Offerings\n",
+       "## What We Offer\n",
        "\n",
-       "- **Models:** Access and contribute to over 1 million state-of-the-art machine learning models, updated regularly to reflect the latest advances.\n",
-       "- **Datasets:** Explore and share from more than 250,000 datasets, catering to a diversity of applications.\n",
-       "- **Spaces:** Create and deploy interactive AI applications within the community, with over 400,000 applications available.\n",
-       "- **Community:** Collaborate with a global network of AI enthusiasts and experts.\n",
-       "- **HuggingChat Omni:** Engage with conversational AI through their cutting-edge chatbot.\n",
-       "- **Reachy Mini:** An open robot designed for AI builders to integrate and experiment with AI capabilities.\n",
-       "\n",
-       "The platform supports seamless ML inference starting in seconds, making advanced AI accessible and easy to use.\n",
+       "- **Model Hub**: Access and browse over **1 million pre-trained models** covering all modalities including text, image, video, audio, and 3D.\n",
+       "- **Datasets**: Explore and use more than **250,000 datasets** curated for various machine learning tasks.\n",
+       "- **Spaces**: Deploy and share interactive ML applications and demos; over **400,000 apps** available and growing.\n",
+       "- **Compute Solutions**: Scalable paid compute services including optimized inference endpoints and GPU support starting at $0.60/hour.\n",
+       "- **Enterprise Solutions**: Customized offerings tailored to accelerate business adoption of machine learning technologies.\n",
+       "- **Open Source Stack**: Leverage the extensive Hugging Face open-source libraries to move faster in development.\n",
        "\n",
        "---\n",
        "\n",
-       "## Enterprise Solutions\n",
+       "## Our Community\n",
        "\n",
-       "Hugging Face offers dedicated solutions tailored for teams and enterprises to scale their AI initiatives with enterprise-grade security and support:\n",
+       "Hugging Face is much more than a platform — it is a thriving, fast-growing community of machine learning practitioners who collectively build, maintain, and democratize AI resources. Collaborators range from individual researchers to top organizations, contributing models like:\n",
        "\n",
-       "- **Team subscriptions** starting at $20/user/month.\n",
-       "- **Enterprise contracts** with flexible options.\n",
-       "- Features include Single Sign-On (SSO), region selection and auditing of data repositories, comprehensive audit logs, and dedicated customer support.\n",
-       "- The platform ensures control, security, and compliance required by organizations deploying AI at scale.\n",
+       "- Tencent’s HunyuanImage series\n",
+       "- deepseek-ai’s DeepSeek models\n",
+       "- NVIDIA and Facebook’s datasets and models\n",
        "\n",
+       "Community members benefit from the ability to:\n",
+       "\n",
+       "- Host and collaborate on unlimited public projects\n",
+       "- Share their work globally to build a professional ML portfolio\n",
+       "- Collaborate using state-of-the-art open-source tools  \n",
+       " \n",
        "---\n",
        "\n",
        "## Company Culture\n",
        "\n",
-       "Hugging Face fosters an open, collaborative, and inclusive culture, emphasizing:\n",
-       "\n",
-       "- Open-source contributions and knowledge sharing.\n",
-       "- Empowering creators to build and showcase their machine learning portfolios.\n",
-       "- Supporting innovation across academic, startup, and large enterprise sectors.\n",
-       "- Building a friendly, engaged community focused on ethical AI development and collective progress.\n",
-       "\n",
-       "---\n",
-       "\n",
-       "## For Customers & Developers\n",
-       "\n",
-       "- Extensive resources for building and deploying machine learning models.\n",
-       "- Access to a vast repository of AI assets for immediate use or customization.\n",
-       "- Community support and documentation to accelerate AI projects.\n",
-       "- Competitive pricing plans suitable for individuals, startups, and large organizations.\n",
+       "- **Collaborative:** Empowering users to co-create and improve AI together.\n",
+       "- **Open & Ethical:** Committed to building an open, transparent, and socially responsible AI ecosystem.\n",
+       "- **Innovative:** Constantly advancing the frontier in machine learning through community-driven innovation.\n",
+       "- **Inclusive:** Bringing together diverse contributors and users worldwide.\n",
        "\n",
        "---\n",
        "\n",
        "## Careers at Hugging Face\n",
        "\n",
-       "Hugging Face is a fast-growing technology company welcoming passionate AI professionals, engineers, researchers, and community builders. Benefits of working here include:\n",
+       "Join a team on the front lines of democratizing AI and machine learning! Hugging Face is looking for passionate engineers, researchers, data scientists, and product experts who want to make a global impact by enabling accessible, ethical, and collaborative AI development.\n",
        "\n",
-       "- Being part of a global, mission-driven team shaping the future of AI.\n",
-       "- Opportunities for personal and professional growth through collaboration on cutting-edge AI projects.\n",
-       "- Culture that values openness, innovation, and impact.\n",
+       "By working here, you'll:\n",
        "\n",
-       "(Current job openings and detailed career information are available on the company’s website.)\n",
+       "- Collaborate with world-class ML experts\n",
+       "- Shape the future of open-source AI tools\n",
+       "- Impact a vibrant global community of hundreds of thousands of users  \n",
+       "- Thrive in an open culture that values creativity, diversity, and ethical AI  \n",
+       "\n",
+       "Visit [huggingface.co/careers](https://huggingface.co/careers) to explore current job openings.\n",
        "\n",
        "---\n",
        "\n",
-       "## Join the Future of AI\n",
+       "## Why Choose Hugging Face?\n",
        "\n",
-       "Whether you are an AI researcher, developer, enterprise user, or enthusiast, Hugging Face is your hub for cutting-edge machine learning innovation and collaboration.\n",
-       "\n",
-       "**Explore:** [huggingface.co](https://huggingface.co)  \n",
-       "**Sign Up & Start Building:** Create your ML portfolio, deploy applications, and join a thriving AI community today!\n",
+       "- The **largest and most diverse** collection of open-source machine learning resources.\n",
+       "- A **community-first platform** that fosters collaboration and rapid innovation.\n",
+       "- Flexible solutions for individuals, communities, startups, and enterprises.\n",
+       "- Trusted by industry leaders and researchers worldwide.\n",
        "\n",
        "---\n",
        "\n",
-       "*Hugging Face – The AI community building the future.*"
+       "## Get Started Today\n",
+       "\n",
+       "- Explore AI apps and models at [huggingface.co](https://huggingface.co)\n",
+       "- Join the community, build your portfolio, and accelerate your AI journey.\n",
+       "- Scale your projects with our pay-as-you-go compute resources.\n",
+       "\n",
+       "**Hugging Face** — The AI Community Building the Future.\n",
+       "\n",
+       "---\n",
+       "\n",
+       "# Contact & Links\n",
+       "\n",
+       "- Website: https://huggingface.co\n",
+       "- Community: https://huggingface.co/community\n",
+       "- Documentation: https://huggingface.co/docs\n",
+       "- Careers: https://huggingface.co/careers\n",
+       "\n",
+       "---\n",
+       "\n",
+       "*Hugging Face is your home for collaboration, innovation, and advancing machine learning together.*"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -1383,7 +1539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "51db0e49-f261-4137-aabe-92dd601f7725",
    "metadata": {},
    "outputs": [],
@@ -1415,93 +1571,93 @@
      "output_type": "stream",
      "text": [
       "Selecting relevant links for https://huggingface.co by calling gpt-5-nano\n",
-      "Found 12 relevant links\n"
+      "Found 9 relevant links\n"
      ]
     },
     {
      "data": {
       "text/markdown": [
-       "# Hugging Face Brochure\n",
+       "# Hugging Face: The AI Community Building the Future\n",
        "\n",
        "---\n",
        "\n",
        "## About Hugging Face\n",
        "\n",
-       "**Hugging Face** is the AI community building the future, providing a collaborative platform where machine learning (ML) engineers, researchers, and enthusiasts come together to create, share, and innovate. It serves as the **home of machine learning**, empowering users worldwide to build an open and ethical AI future through a rich ecosystem of models, datasets, and applications.\n",
+       "Hugging Face is a vibrant and fast-growing AI and machine learning community dedicated to advancing the future of open and ethical AI. It serves as a collaboration platform where machine learning engineers, scientists, and enthusiasts from around the world create, discover, and share machine learning models, datasets, and applications.\n",
+       "\n",
+       "The Hugging Face Hub is the central place for hosting and collaborating on over **1 million models**, **250k+ datasets**, and **400k+ applications** spanning multiple modalities including text, images, video, audio, and 3D.\n",
        "\n",
        "---\n",
        "\n",
-       "## What We Offer\n",
+       "## What Hugging Face Offers\n",
        "\n",
-       "- **Hugging Face Hub**: A central repository hosting over **1 million models**, **250,000+ datasets**, and **400,000+ applications** spanning various ML modalities including text, image, video, audio, and even 3D.\n",
-       "- **Spaces**: User-friendly environments to build and deploy ML applications seamlessly.\n",
-       "- **Inference API**: Quickly deploy and get started with inference on top models in seconds.\n",
-       "- **Open Source Stack**: Leverage a fast and reliable open-source stack to accelerate your ML projects.\n",
-       "- **Enterprise Solutions**: Paid compute and enterprise-grade services for teams and organizations needing advanced capabilities and support.\n",
-       "- **Innovative Projects**: Includes unique initiatives like *HuggingChat Omni* — an AI chat experience, and *Reachy Mini* — an open robot platform for AI builders.\n",
+       "- **Models:** Browse and explore a vast collection of AI models, updated frequently by a diverse community. Popular trending models include image generators, video editors, and language models.\n",
+       "  \n",
+       "- **Datasets:** Access a rich catalog of datasets ranging from natural language prompts to persona datasets and beyond, enabling rapid model training and experimentation.\n",
+       "  \n",
+       "- **Spaces:** Deploy, share, and run AI applications on Hugging Face's platform, including innovative tools such as video generation from images, AI toolkits, and interactive applications.\n",
        "\n",
-       "---\n",
+       "- **Compute:** Easily accelerate your machine learning workloads with optimized inference endpoints and GPU-backed applications starting as low as $0.60/hour.\n",
        "\n",
-       "## Community and Collaboration\n",
-       "\n",
-       "Hugging Face is centered on community-driven collaboration with:\n",
-       "\n",
-       "- A **fast-growing global community** passionate about advancing machine learning.\n",
-       "- Open sharing of **models, datasets, and applications** to foster innovation.\n",
-       "- A platform where users can **build their portfolios** by sharing their ML work publicly.\n",
-       "- Tools to support **learning, experimenting, and ethical AI development**.\n",
-       "- Active engagement through various trending projects and applications reflecting cutting-edge research.\n",
-       "\n",
-       "---\n",
-       "\n",
-       "## Technology and Innovation Highlights\n",
-       "\n",
-       "- Supports diverse modalities: **text, image, video, audio, 3D**.\n",
-       "- Hosting trendsetting models like:\n",
-       "  - Qwen3-VL-8B-Instruct\n",
-       "  - DeepSeek-OCR\n",
-       "  - Nanonets OCR models\n",
-       "- Hundreds of running spaces generating videos, animations, and applications powered by ML.\n",
-       "- Access to thousands of up-to-date datasets including educational, code-related, and domain-specific datasets.\n",
+       "- **Enterprise Solutions:** Hugging Face provides tailored enterprise offerings to help organizations harness AI technology securely and effectively at scale.\n",
        "\n",
        "---\n",
        "\n",
        "## Company Culture\n",
        "\n",
-       "- **Open and Ethical AI**: Commitment to building AI technologies transparently and responsibly.\n",
-       "- **Collaborative Spirit**: Encourages sharing knowledge and cooperation among ML practitioners.\n",
-       "- **Community Empowerment**: Focus on enabling the next generation of ML engineers and scientists.\n",
-       "- **Innovation at Core**: Constantly pushing the boundaries of AI capabilities with a rapid development cycle and user-driven improvements.\n",
+       "Hugging Face fosters an **open, collaborative, and ethical AI community** — empowering developers and researchers to work together transparently and inclusively. The culture emphasizes:\n",
+       "\n",
+       "- **Community-driven innovation:** Encouraging open-source contributions and sharing knowledge.\n",
+       "- **Ethical AI development:** Committed to building responsible AI systems for the benefit of all.\n",
+       "- **Diversity and inclusion:** Supporting a broad and global community in building AI tools and resources.\n",
+       "- **Learning and growth:** Offering a platform for users to build their machine learning portfolios and grow their expertise.\n",
+       "\n",
+       "---\n",
+       "\n",
+       "## Customers & Community\n",
+       "\n",
+       "Hugging Face serves a global community that includes:\n",
+       "\n",
+       "- Machine learning researchers and practitioners\n",
+       "- Developers building innovative AI applications\n",
+       "- Enterprises seeking scalable AI solutions\n",
+       "- AI enthusiasts and learners who want to experiment and grow their skills\n",
+       "\n",
+       "With over a million models and hundreds of thousands of datasets and applications, the platform connects users with cutting-edge AI technologies from companies like Tencent, NVIDIA, Facebook, and OpenAI — showcasing the power of open collaboration.\n",
        "\n",
        "---\n",
        "\n",
        "## Careers at Hugging Face\n",
        "\n",
-       "- Be part of an innovative, mission-driven company revolutionizing AI and machine learning.\n",
-       "- Opportunities for engineers, researchers, and AI enthusiasts passionate about open source and community growth.\n",
-       "- Work in a culture that values collaboration, ethics, continuous learning, and impact.\n",
-       "- Help shape the future of AI tools used by millions worldwide.\n",
+       "Interested in joining Hugging Face? The company values talented individuals passionate about AI, open-source technologies, and community building. Opportunities include roles in:\n",
+       "\n",
+       "- Machine learning research & engineering\n",
+       "- Software development\n",
+       "- Cloud infrastructure and compute optimization\n",
+       "- Community management and developer relations\n",
+       "- Enterprise solutions & sales\n",
+       "\n",
+       "At Hugging Face, you will work at the forefront of AI innovation while contributing to a global, open, and ethical AI movement.\n",
        "\n",
        "---\n",
        "\n",
-       "## Join Us\n",
+       "## Get Involved\n",
        "\n",
-       "Discover, create, and accelerate your machine learning journey with **Hugging Face** — the community and platform building the future of AI.\n",
-       "\n",
-       "**Get started today:**\n",
-       "\n",
-       "- Explore models, datasets, and AI applications on the [Hugging Face Hub](https://huggingface.co)\n",
-       "- Try out the latest AI chat and inference tools.\n",
-       "- Join our community to collaborate and innovate.\n",
+       "- **Explore AI models and datasets:** Visit [huggingface.co](https://huggingface.co) to browse millions of resources.\n",
+       "- **Build and share your own models and apps:** Create your portfolio in an open, collaborative environment.\n",
+       "- **Accelerate your projects:** Utilize Hugging Face’s Compute and Enterprise services.\n",
+       "- **Join the community:** Engage with thousands of practitioners working toward the future of AI.\n",
        "\n",
        "---\n",
        "\n",
-       "**Hugging Face - The AI community building the future together.**  \n",
-       "Colors: #FFD21E, #FF9D00, #6B7280\n",
+       "### Contact & Follow\n",
+       "\n",
+       "- Website: [https://huggingface.co](https://huggingface.co)\n",
+       "- Join the community to learn, collaborate, and innovate in AI!\n",
        "\n",
        "---\n",
        "\n",
-       "For more information, visit: [huggingface.co](https://huggingface.co)"
+       "*Hugging Face — Empowering the next generation of machine learning for a better, open future.*"
       ],
       "text/plain": [
        "<IPython.core.display.Markdown object>"
@@ -1713,7 +1869,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.12"
+   "version": "3.12.9"
   }
  },
  "nbformat": 4,
diff --git a/week1/week1_exercise.ipynb b/week1/week1_exercise.ipynb
deleted file mode 100644
index 2e41d5e..0000000
--- a/week1/week1_exercise.ipynb
+++ /dev/null
@@ -1,324 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "fe12c203-e6a6-452c-a655-afb8a03a4ff5",
-   "metadata": {},
-   "source": [
-    "# End of week 1 exercise\n",
-    "\n",
-    "To demonstrate your familiarity with OpenAI API, and also Ollama, build a tool that takes a technical question,  \n",
-    "and responds with an explanation. This is a tool that you will be able to use yourself during the course!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c1070317-3ed9-4659-abe3-828943230e03",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Setup Successful!\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Imports and Setup\n",
-    "import os\n",
-    "import json\n",
-    "from dotenv import load_dotenv\n",
-    "from openai import OpenAI\n",
-    "from IPython.display import Markdown, display, update_display\n",
-    "import ollama\n",
-    "\n",
-    "# Load environment variables\n",
-    "load_dotenv(override=True)\n",
-    "\n",
-    "# Constants\n",
-    "MODEL_GPT = 'gpt-4o-mini'\n",
-    "MODEL_LLAMA = 'llama3.2'\n",
-    "\n",
-    "print(\"Setup Successful!\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4a456906-915a-4bfd-bb9d-57e505c5093f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Question to analyze:\n",
-      "\n",
-      "Please explain what this code does and why:\n",
-      "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Technical Question - You can modify this\n",
-    "question = \"\"\"\n",
-    "Please explain what this code does and why:\n",
-    "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
-    "\"\"\"\n",
-    "\n",
-    "print(\"Question to analyze:\")\n",
-    "print(question)\n",
-    "\n",
-    "# prompts\n",
-    "system_prompt = \"You are a helpful technical tutor who answers questions about python code, software engineering, data science and LLMs\"\n",
-    "user_prompt = \"Please give a detailed explanation to the following question: \" + question\n",
-    "\n",
-    "# messages\n",
-    "messages = [\n",
-    "    {\"role\": \"system\", \"content\": system_prompt},\n",
-    "    {\"role\": \"user\", \"content\": user_prompt}\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "60ce7000-a4a5-4cce-a261-e75ef45063b4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "## GPT-4o-mini Response:\n",
-       "Certainly! Let's break down the provided code snippet step by step.\n",
-       "\n",
-       "### Code Analysis\n",
-       "```python\n",
-       "yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
-       "```\n",
-       "\n",
-       "This code snippet is a generator expression, which is intended to yield values from a set comprehension. Let's clarify each part of the expression:\n",
-       "\n",
-       "1. **Set Comprehension**:\n",
-       "   - `{book.get(\"author\") for book in books if book.get(\"author\")}` is a set comprehension.\n",
-       "   - This means it creates a set of unique authors from a collection called `books`.\n",
-       "\n",
-       "2. **`books`**:\n",
-       "   - `books` is expected to be an iterable (like a list) that contains dictionaries. Each dictionary represents a book and may contain various keys, such as \"author\".\n",
-       "\n",
-       "3. **`book.get(\"author\")`**:\n",
-       "   - For each `book` in the `books` iterable, `book.get(\"author\")` tries to access the value associated with the key `\"author\"`.\n",
-       "   - The `.get()` method returns the value for the given key if it exists; otherwise, it returns `None`.\n",
-       "\n",
-       "4. **Filter Condition**: \n",
-       "   - The expression includes an `if book.get(\"author\")` filter, which ensures that only books with a defined author (i.e., `None` or an empty string are excluded) are considered.\n",
-       "   - This means that if the author is not provided, that book will not contribute to the final set.\n",
-       "\n",
-       "5. **Set Creation**:\n",
-       "   - The result of the set comprehension is a set of unique author names from the list of books. Since sets automatically ensure uniqueness, duplicates will be filtered out.\n",
-       "\n",
-       "6. **`yield from`**:\n",
-       "   - The `yield from` statement is used within a generator function. It allows the generator to yield all values from the given iterable (in this case, our created set).\n",
-       "   - This means that the values generated (i.e., unique authors) can be iterated over one by one.\n",
-       "\n",
-       "### Purpose and Use Case\n",
-       "The purpose of this code snippet is to produce a generator that emits the unique author names of books from the `books` collection. This is useful in scenarios where you want to streamline the retrieval of distinct authors without immediately materializing them into a list. You can consume these unique authors one at a time efficiently, which is particularly beneficial when dealing with a large dataset.\n",
-       "\n",
-       "### Example\n",
-       "Consider the following example to illustrate how this might work:\n",
-       "\n",
-       "```python\n",
-       "books = [\n",
-       "    {\"title\": \"Book1\", \"author\": \"AuthorA\"},\n",
-       "    {\"title\": \"Book2\", \"author\": \"AuthorB\"},\n",
-       "    {\"title\": \"Book3\", \"author\": \"AuthorA\"},  # Duplicate author\n",
-       "    {\"title\": \"Book4\"},  # No author\n",
-       "    {\"title\": \"Book5\", \"author\": \"AuthorC\"}\n",
-       "]\n",
-       "\n",
-       "# Let's say this code is inside a generator function\n",
-       "def unique_authors(books):\n",
-       "    yield from {book.get(\"author\") for book in books if book.get(\"author\")}\n",
-       "\n",
-       "for author in unique_authors(books):\n",
-       "    print(author)\n",
-       "```\n",
-       "### Output\n",
-       "```\n",
-       "AuthorA\n",
-       "AuthorB\n",
-       "AuthorC\n",
-       "```\n",
-       "\n",
-       "### Summary\n",
-       "This code snippet creates a generator that yields unique authors of books, omitting any entries where the author is not provided. This demonstrates an efficient and Pythonic way to handle data extraction, particularly with potentially sparse datasets."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Get gpt-4o-mini to answer, with streaming\n",
-    "api_key = os.getenv('OPENAI_API_KEY')\n",
-    "\n",
-    "# Initialize OpenAI client\n",
-    "openai = OpenAI()\n",
-    "\n",
-    "stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n",
-    "    \n",
-    "response = \"\"\n",
-    "\n",
-    "display_handle = display(Markdown(\"\"), display_id=True)\n",
-    "\n",
-    "for chunk in stream:\n",
-    "    if chunk.choices[0].delta.content:\n",
-    "        response += chunk.choices[0].delta.content\n",
-    "        update_display(Markdown(f\"## GPT-4o-mini Response:\\n{response}\"), display_id=display_handle.display_id)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "8f7c8ea8-4082-4ad0-8751-3301adcf6538",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/markdown": [
-       "The given Python expression appears as part of an asynchronous generator, typically used with coroutines like those found within the `async`/`await` syntax introduced in Python 3.5+:\n",
-       "\n",
-       "```python\n",
-       "yield from {book.get('author') for book in books if book.get('author')}\n",
-       "```\n",
-       "\n",
-       "Here's a breakdown of what this code does, line by line and overall explanation:\n",
-       "\n",
-       "1. `{book.get('author') for book in books if book.get('author')}` is a set comprehension that iterates through each `book` object (assumed to be dictionary-like since it uses square brackets notation) within the `books` variable, which should also contain such objects as its elements based on context provided herein:\n",
-       "    - For every iteration of this generator expression, if `'author'` exists in a book's key set (`book.keys()`), then that value (presumably the name of an author associated with their corresponding `book`) is included within the resulting comprehension/set; otherwise it skips to the next item since there isn't one without `'author'`.\n",
-       "    - The `.get` method returns a specified key’s value from dictionary-like objects, but if that key doesn't exist, instead of causing an error (as would be typical with direct indexing), this expression safely retrieves `None`, or another default return type you specify as the second argument to `.get()` which isn't shown here.\n",
-       "    - Set comprehension is a construct for creating sets directly from iterables using set-building syntax (`{}`). Note that it inherently discards duplicates (if any), but this does not seem relevant since we are assuming books will uniquely have author information in the context of its key presence or absence, rather than repetitive entries.\n",
-       "    \n",
-       "2. `yield from` is a statement used with asynchronous generators (`async def`) that handles yielding values and delegating further execution within nested coroutines: \n",
-       "    - Its function here seems to be sending each author's name (extracted by the generator expression before) back into this outercoroutine. The `yield from` statement thus passes control over these names directly as output of its own operation, rather than managing an internal sequence or iterable in a traditional manner with for-loops and appending to lists inside coroutines (which may result in blocking behavior).\n",
-       "    - In this expression's specific case without `async`/`await`, it looks like the code intends to simulate asynchronous yielding by passing values from an internal generator back out. However, proper usage would require surrounding with async function decorators and using await as needed for actual I/O-bound or network operations within a coroutine workflow context; this snippet in isolation does not directly demonstrate that behavior but instead presents a pattern resembling how yielding could be structured should it be part of an asynchronous generator expression.\n",
-       "    - It's worth mentioning, though `yield from` isn't typically used with set comprehensions or non-coroutine functions as these expressions cannot 'receive values.' Instead, this construct suggests a conceptual approach where each found author is yielded one by one in what would be the sequence of execution within an asynchronous coroutine.\n",
-       "    - Given that `yield from` isn't directly compatible with set comprehensions (without modification and appropriate context), it seems we might have encountered syntactical confusion or a misplacement here, since normally you wouldn’t see this in standalone Python code outside the scope of coroutine functions.\n",
-       "    \n",
-       "Assuming that `books` is an iterable over dictionary-like objects (which may contain author information), and if one were to translate typical synchronous usage into asynchronous semantics or consider using a generator, then we'd instead see something like this for proper async operation:\n",
-       "\n",
-       "```python\n",
-       "async def find_authors():\n",
-       "    authors = set()\n",
-       "    async for book in books:  # Assuming `books` can be an iterable of awaitables (e.g., coroutines) or other asynchronous generators\n",
-       "        author = book.get('author')\n",
-       "        if author is not None:\n",
-       "            await asyncio.sleep(0)  # Yield control back to the event loop, simulate async I/O operation here with `await`ing a sleep call for example purposes only (in reality this would typically handle some real asynchronous task like fetching data from an external API). Then we'd yield using 'yield':\n",
-       "            await asyncio0.sleep(2)  # This line is placeholder logic and wouldn't execute without async decorators, but it serves to illustrate the use of `await` alongside a coroutine function:\n",
-       "                authors.add(author)\n",
-       "    return authors\n",
-       "```\n",
-       "In this modified version suitable for an asynchronous context (and with necessary adjustments): \n",
-       "- This would be inside an `@async def find_authors()` decorated async generator/coroutine, and the `yield` keyword is used to temporarily pause execution until another coroutine or future calls its `.send(None)` method. The example uses a placeholder sleep call (`await asyncio.sleep(2)`) for demonstration purposes only; in practice one might use non-blocking I/O operations such as reading from files, network responses etc., within an async function decorated with `@async def`.\n",
-       "    \n",
-       "It is crucial to note that the original expression provided seems like a pseudocode representation of how we could structure asynchronous behavior using `yield` and comprehensions if it were actually part of coroutine code in Python 3.5+, but isn't syntactically correct or conventionally used outside such contexts due to misunderstandings about yielding semantics from set operations without await statements (or decorators)."
-      ],
-      "text/plain": [
-       "<IPython.core.display.Markdown object>"
-      ]
-     },
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-   ],
-   "source": [
-    "# Get Llama 3.2 to answer\n",
-    "response = ollama.chat(model=MODEL_LLAMA, messages=messages)\n",
-    "\n",
-    "reply = response['message']['content']\n",
-    "\n",
-    "display(Markdown(reply))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d1f8aa0a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Week 1 Learnings Summary\n",
-    "\n",
-    "summary = \"\"\"\n",
-    "## Week 1 Learnings Demonstrated\n",
-    "\n",
-    "### ✅ Day 1 - Web Scraping & API Integration\n",
-    "- **BeautifulSoup** for HTML parsing\n",
-    "- **Requests** for HTTP calls\n",
-    "- **OpenAI API** integration\n",
-    "- **SSL certificate** handling for Windows\n",
-    "\n",
-    "### ✅ Day 2 - Chat Completions API & Ollama\n",
-    "- **Chat Completions API** understanding\n",
-    "- **OpenAI-compatible endpoints** (Ollama)\n",
-    "- **Model comparison** techniques\n",
-    "- **Streaming responses** implementation\n",
-    "\n",
-    "### ✅ Day 4 - Tokenization & Cost Management\n",
-    "- **tiktoken** for token counting\n",
-    "- **Cost estimation** strategies\n",
-    "- **Text chunking** techniques\n",
-    "- **Token-aware** processing\n",
-    "\n",
-    "### ✅ Day 5 - Business Solutions\n",
-    "- **Intelligent link selection** using LLM\n",
-    "- **Multi-page content** aggregation\n",
-    "- **Professional brochure** generation\n",
-    "- **Error handling** and robustness\n",
-    "\n",
-    "### ✅ Week 1 Exercise - Technical Question Answerer\n",
-    "- **Streaming responses** from OpenAI\n",
-    "- **Local inference** with Ollama\n",
-    "- **Side-by-side comparison** of models\n",
-    "- **Error handling** for both APIs\n",
-    "\n",
-    "## Key Skills Acquired:\n",
-    "1. **API Integration** - OpenAI, Ollama, web scraping\n",
-    "2. **Model Comparison** - Understanding different LLM capabilities\n",
-    "3. **Streaming** - Real-time response display\n",
-    "4. **Error Handling** - Robust application design\n",
-    "5. **Business Applications** - Practical LLM implementations\n",
-    "\"\"\"\n",
-    "\n",
-    "display(Markdown(summary))"
-   ]
-  }
- ],
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