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Feed me that dick🍆💦 Am mobile doing meeting✅ Dm with your zip code and be payment ready🤤😋 Dm me now⬇️⬇️👇 Telegram: iMessage:[email protected] #Contentcreator #dtxfreaks #facial #deepthroat #ebonypyt #dicksucking #Thot #Freakymood #fortworthtx
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Dm me with your zip code I’m mobile Daddy, HMU for menu I offer the best content, FaceTime and Dropbox💕💦🔥 Telegram:
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Many months back I met my soon to be wife, same day @Petersookoposi prudent gabriel got married under Bolaji Idowu comment section, I took a leap of faith before giving up on love and marriage and this wonderful woman sent a dm (she says when I saw your comment, I said this is my husband) and the rest has been history, our first date meant to happen in fine dinning ended up on a street in her sterling gown and my tuxedo, dark in the night holding her heels while she held my hand tight , of course a 30 mins dinner would not be enough to know eachother, we walked round town, spoke about our expectations, desires, values and places we matter most that was the toughest dates as it was questions and straight answers 😂, we realised we couldn’t have met sooner as there were much to learn and unlearn in other to make “what are you bringing to the table” worthy , my wife to be is filled with knowledge and fulfilled, she crowns my imperfection, common, I was swept off how natural everything occurred, we were not at the interest of impressing each other, just two adults that were genuinely seeking love for the last time 😂. @@ is the perfect narration of woman that makes me whole, her readiness, kindness and commitment, intentionality, she would bant me and yet praise me, for someone who scolds me, remains calm and accepting, girl, you got me drooling plenty time, your strength, and many times when I say “woman na good night prayer not vigil”. I am selective to eat from women but Puff puff and Peppersoup was what made man fall finally 😂😂😂 I munched like I was hungry, dear Friends, family and well wishers, plenty weeks later, after doing my findings and asking God personally, I went on my kneels in front our families and asked @@ to be my wife forever. I know her face isn’t showing, yes, I have promised my marriage will not be a part of social media and my len will only share views I want the world to see. Let’s get ready in Summer, it’s “The Wedding Concert”, invites will roll out soon, and our union will be a significant one to say yes, marriage works, Amen. Husband to be :ME 💃💃💃💃 Venue @oisoisheffield Videographer @wale_sweet Decoration @shonahcreations
OBA OF SHEFFIELD 🇬🇧
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![✨ I spent the last 48 hours making GPT-4 read the entire Solana validator codebase and write documentation, so doesn't have to. Introducing — an AI-powered chatbot trained on nothing but code that can answer deep technical questions. How it works 👇 But first... A huge shoutout to , Zahid Khawaja, and Sean. Their hard work made prototyping this thing a breeze. Without further ado... Devs like to write code, not documentation. Tribal knowledge is lost when devs move on to other projects, leaving future devs to sort through mountains of code and figure out not just how it works, but why it works that way. This is all about to change. GPT-4's ability to write code is stunning. It seems to understand something fundamental about writing software that previous models just didn't. This comprehension of the principles that drive the design behind a complex system carries over into its ability to document existing codebases in a truly impressive way. With the enlarged context window(s), it's now feasible to feed GPT-4 entire files of code and ask it to write documentation about how the code works. Taking this as a starting point, the process looks something like this: 1. Download repo. 2. Depth-first traversal of repo contents, ignoring things like package-lock and binary files. 3. For each file, feed to GPT-4 and ask it to write documentation in markdown. 4. Save the output in a separate location as [outputRoot]/[inputFilepath][inputFilename].md 5. For each folder, we ask GPT-4 to write a summary of the folder, taking the newly generated documentation for all files in the folder and the summaries from each of its subfolders as context. Write this to the filesystem as markdown. Now we have a filesystem that matches the structure of the input repo, but all files in the tree are markdown documentation of the corresponding code file. From here, we: 1. Load markdown documents into LangChain. 2. Embed all documents via OpenAI embeddings. 3. Store embeddings in Pinecone. When a user sends a query: 1. Embed query. 2. Find k-nearest markdown files. 3. Feed to GPT-4 with a prompt asking to answer the query based on k-nearest markdown documents provided. The craziest part of all this? GPT-4 actually wrote ~30% of the code. The results are pretty good for 2 days of work. There is certainly room for improvement. Some items that are top of mind: 1. TolyGPT will occasionally hallucinate answers. It is especially bad with links to external sources, like GitHub. The base model seems to know a bit about Solana already, and sometimes this creeps in. Fine-tuning the prompt can solve some of this. 2. Context selection is difficult in a codebase this large. For example, sometimes it will pull in details about the Solana SDK when asked about transaction processing. The SDK files can seem relevant depending on the phrasing of the question. It may be worth breaking the documentation into subsystems to limit this. 3. Not all files fit into the 32k token window. As of now, there are 23 (out of ~1,100) files that cannot be documented in their entirety. Some of these files are very important to how Solana works. Final thoughts: 1. GPT-4 is super powerful, and we're going to see a ton of tools that supercharge the entire software development lifecycle. This is not 12 months away. For the people that can afford it, these tools are here now. And they're only getting better. Act accordingly. 2. The price of inference has to come down for this to go mainstream. I spent about $300 prototyping this project, and the final crawl cost about the same. The high cost of GPT-4 will push developers to other, cheaper alternatives with similar performance. This is coming very soon. If you have a large software project and you're interested in something like this for your codebase, fill out this form and we'll be in touch this week. Or just DM me :)](https://image.24vids.com/tw-1637521141236658176/amplify_video_thumb/1637520929566908417/img/_ACBce6WhDBxVegD.jpg)
![I got rejected by 144 investors before raising $150M for my $200M+ rev/year startup. After 144 rejections, I started questioning our approach. Were we solving the right problem? What were we doing wrong? Why weren’t investors seeing what we were seeing? Were we the right team to build this? We tried everything: different pitch angles, new deck structures, and reframing the problem. Then came the 145th meeting, where we closed our first growth round. That yes made everything worth it. But getting there took years of mistakes and hard work. We went through a lot of trial and error just to figure out what resonates with investors. We tried dozens of approaches to figure out what made investors engage. Some landed, most didn't. But each iteration taught us something about what builds conviction versus what just sounds good on paper. And once we cracked that code, our Series C closed faster than expected. And today, I see so many founders in the exact same position I was in 10 years ago: grinding through rejections, questioning everything, and trying to figure out what works. So today I want to give you the resource I wish I had back then: Something that shows you exactly how to structure these conversations and navigate the entire process (because the fundraising cycle can be a big distraction and take a toll on you as a founder). So I've partnered with Notion's Startups Team to create the essential fundraising resource that helps you avoid the mistakes that cost me years. Here's what you are getting: • The actual decks I used to raise $150M for Super[.]com (Series B, C) • 50 real examples from funded startups like Eleven Labs and Artisan AI • A searchable database of 10,000+ investors - angels, VCs, and accelerators you can reach out to immediately (this alone would take months to build manually) • An AI-powered fundraising agent built into Notion with step-by-step prompts (no separate ChatGPT needed) Want access? • Like and share this post • Comment "FUNDRAISE" • Follow me so I can DM you the link I'll send it over ASAP. P.S.: If you are serious about fundraising (now or in the future), you should grab it right away.](https://image.24vids.com/tw-1983558192006103464/amplify_video_thumb/1983462016657297408/img/fRxtToaWFP2a2i5v.jpg)
