#GPT3

@llms.activitypub.awakari.com.ap.brid.gy

2 months ago

🚀 From Software & DevOps Engineer to Generative AI Engineer — A 4-Month Hands-On Journey 🚀 The Generative AI space is moving incredibly fast. Every week there’s a new model, a new framework,...

🚀 From Software & DevOps Engineer to Generative AI Engineer — A 4-Month Hands-On Journey 🚀 The Generative AI space is moving incredibly fast. Every week there’s a new model, a new fra...

#ai #llm #gpt3 #startup

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fetchfeeds.com

@fetchfeeds.bsky.social

3 months ago

Explore the power of AI with GPT-3, a cutting-edge language model that can generate human-like text. Experience limitless creativity and endless possibilities! #GPT3 https://fefd.link/5qv7E

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LLMs

@llms.activitypub.awakari.com.ap.brid.gy

3 months ago

The Evolution of GPT: How AI Became Your Everyday Assistant The Beginning It started in 2015 when Open AI was founded. Its key products include ChatGPT, DALL-E...

The Evolution of GPT: How AI Became Your Everyday Assistant The Beginning It started in 2015 when Open AI was founded. Its key products include ChatGPT, DALL-E and Whisper tools that helped spark t...

#ai #openai #chatgpt #gpt3

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LLMs

@llms.activitypub.awakari.com.ap.brid.gy

4 months ago

PromptCurator Your AI Prompts, Perfected and Reusable Tired of copying, pasting, and tweaking the same AI prompts...

PromptCurator Your AI Prompts, Perfected and Reusable Tired of copying, pasting, and tweaking the same AI prompts dozens of times a day? PromptCurator transforms your best prompts into reusable tem...

#ai #gpt3

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MaltaHoy

@maltahoy.bsky.social

5 months ago

"L-Introduzzjoni għall-GPT-3 Data tat-tnedija: 28 ta' Mejju, 2020 Eżatt wara sena oħra tat-tnedija Last reply by Admin on Thu, 09 Oct 2025 19:48:41 +0000

ICYMI: "L-Introduzzjoni għall-GPT-3 Data tat-tnedija: 28 ta' Mejju, 2020 Eżatt wara sena oħra tat-tnedija: Last reply by Admin on Thu, 09 Oct 2025 19:48:41 +0000 #GPT3 #OpenAI #AIGħallLingwa #Teknoloġija #MudelliAvvanzati

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MaltaHoy

@maltahoy.bsky.social

5 months ago

"L-Introduzzjoni għall-GPT-3 Data tat-tnedija: 28 ta' Mejju, 2020 Eżatt wara sena oħra tat-tnedija Last reply by Admin on Thu, 09 Oct 2025 19:48:41 +0000

"L-Introduzzjoni għall-GPT-3 Data tat-tnedija: 28 ta' Mejju, 2020 Eżatt wara sena oħra tat-tnedija: Last reply by Admin on Thu, 09 Oct 2025 19:48:41 +0000 #OpenAI #GPT3 #IntelliġenzaArtifiċjali #Tnedija #MudelliAvvanzati

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Stacey Carroll

@staceycarroll.bsky.social

5 months ago

Remember Clippy? He’s Back — and Now He’s an AI Assistant That Might Write Your Novel or Destroy Your Office Microsoft’s mischievous paperclip, Clippy, returns as an AI-powered ghostwriter. Discover what happens when nostalgia meets GPT.

Remember Clippy? Now He’s an AI Assistant That Might Write Your Novel or Destroy Your Office This time powered by AI — drafting romance novels, causing corporate chaos, and generally being mischievously helpful. #Clippy #AI #GPT3 #TechHumor #Nostalgia #OfficeChaos staceycarroll.org/sc-thoughts/...

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GetNews.me

@getnews-me.bsky.social

5 months ago

Adversarial Fine-Tuning for Prompt Injection Defense in GPT-3

In Sep 2025 researchers revisited a 2022 study and found fine‑tuning cut prompt‑injection success from ~31% to near zero on GPT‑3 Ada, Babbage and Curie, while Davinci stayed vulnerable. getnews.me/adversarial-fine-tuning-... #gpt3 #adversarialfinetuning

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LLMs

@llms.activitypub.awakari.com.ap.brid.gy

6 months ago

Retrieval Augmented Generation (RAG) for Dummies Retrieval-Augmented Generation (RAG) is a technique that optimises an AI model’s performance by...

Retrieval Augmented Generation (RAG) for Dummies Retrieval-Augmented Generation (RAG) is a technique that optimises an AI model’s performance by connecting it with external knowledge bases. RAG t...

#nlp #ai #rag #gpt3

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Ismael - Dev

@ismael1361.bsky.social

7 months ago

#chatgpt #gpt #chatgpt4 #gpt3 #chatgpt3 #gpt4o #gptraining #gptrainee #chatgptai #chatgptprompt #openai #artificialintelligence #ia #machinelearning #inteligenciaartificial #datascience #technews #transformaçãodigital

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Real Code Ltd

@realcode.co.uk

7 months ago

Myth: AI can replace human creativity in essay writing.
Fact: AI tools like GPT-3 enhance brainstorming and structure, but your unique insights still matter! ✍️✨ #AcademicWriting #GPT3 #AIAssistance brainpod.ai/mastering-gpt-3-essays-a...

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AWS Community Builder Blog Posts

@awscmblogposts.bsky.social

8 months ago

🧠 Unlocking Real-World AI with MCP: The USB-C of LLMs⚡ 👋 Hey there, tech enthusiasts! I'm Sarvar, a Cloud Architect with a passion for transforming...

✍️ New blog post by Sarvar Nadaf

🧠 Unlocking Real-World AI with MCP: The USB-C of LLMs⚡

#aws #ai #beginners #gpt3

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Sharp Coder Blog

@sharpcoderblog.com

9 months ago

Intro to Procedural Animation in Unity Procedural animation is a technique in computer graphics used to generate motion algorithmically rather than using pre-defined keyframes. This method allows for more dynamic ...

Intro to Procedural Animation in Unity #Ai #Chatbot #Gpt #Openai #Transformer #Nlp #Deeplearning #Gpt3 #Gpt2 #Conversational #Languagemodel #Neuralnetwork #Pretraining #Finetuning

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Pure Tech

@puretech.news

9 months ago

GPT Prompting Performance: Explanatory Feedback for Tutor Praise Table of Links Abstract and 1 Introduction 2. Background 2.1 Effective Tutoring Practice 2.2 Feedback for Tutor Training 2.3 Sequence Labeling for Feedback Generation 2.4 Large Language Models in Education...

GPT Prompting Performance: Explanatory Feedback for Tutor Praise #Technology #Other #GPT3 #AI #Education

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Naoya

@naoyacreates.bsky.social

9 months ago

GPT-3 vs ChatGPT: Interview Analysis | GameFi News GPT-3 interviewed pre-ChatGPT! See what it predicted about crypto, and where it went wrong.

Blockchainbulletin News!
Remember GPT-3? We interviewed it in 2022. See its wild predictions about crypto before ChatGPT changed everything! #GPT3 #ChatGPT #AIpredictions

Click here↓↓↓
blockchainbulletin.net/2025/05/28/g...

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閒聊

@heatalk.com

9 months ago

人工智能(AI)的飛躍 | 閒聊 人工智能（AI）技術在過去十年的發展速度，已遠遠超越人類最初的想像。從早期僅能執行簡單分類任務的機器學習模型，到今天能夠理解自然語言、生成逼真圖像甚至輔助科學發現的巨型神經網絡，AI的進步不僅重塑科技產業的格局，更深入影響人類社會的運作方式。

人工智能(AI)的飛躍

[閱讀全文: is.gd/9Wyo5R ]

人工智能（AI）技術在過去十年的發展速度，已遠遠超越人類最初的想像。從 [...]

#閒聊, #HeaTalk, #AI, #AI聊天機器人, #EmbodiedAI, #GNOME, #GPT3, #NLP, #人工智能, #微軟, #無人機, #經濟, #美元, #自動駕駛, #門檻

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GroupifyAI

@groupifyai.bsky.social

9 months ago

Generate high-quality, human-readable content with GPT-3—plus keyword research tools to boost your visibility 📈📝.

Try it now! 🖇️ groupify.ai/ai-tool/cont...

#GroupifyAI #ContentEdge #AIContent #GPT3

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DEV Community

@dev.to.web.brid.gy

10 months ago

DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering ## 選定理由と所感 Tianjin Universityの研究 paper: https://arxiv.org/abs/2504.18243 code: N/A Blog: https://zenn.dev/knowledgesense/arti./cles/10b2b5f772b810 ## 概z要【社会課題】っっmっk・＿b¥r 【技術課題】知識の動的変化への対応：推論の進行に伴う新たな情報ニーズに柔軟に対応できない。情報の組織化と活用：取得した情報のノイズや断片化により、推論の一貫性が損なわれる。【提案】 DualRAGは、以下の2つのプロセスを密接に連携させることで、複雑な質問に対する高精度な回答を実現します： Reasoning-augmented Querying (RaQ)：推論の進行に応じて、動的に情報検索クエリを生成します。 progressive Knowledge Aggregation (pKA)：取得した情報を体系的に統合し、推論の一貫性を保ちます。これにより、知識の強化と推論の洗練が相互に促進される好循環が生まれます。【効果】 HotpotQAでEM 65.0・F1 78.3を達成し、従来手法より最大+2.7 EM、+3.7 F1向上。小型モデルでもF1が+5.4改善し、オラクル知識なしでも高精度を維持 ## DualRAG ### 3.2 Fine-Tuning for Compact Models ハイエンドなLLMは利用コストも高いため、ロウエンドモデルを Finetuneもしくは知識蒸留して用いる。 ## 実験

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fetchfeeds.com

@fetchfeeds.bsky.social

10 months ago

Explore the world of AI with OpenAI's GPT-3, a powerful language model capable of generating human-like text. Discover endless possibilities and join the conversation using ! #GPT3 https://fefd.link/0hyY5

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DEV Community

@dev.to.web.brid.gy

10 months ago

Understanding MCP Architecture: The Control Plane for Responsible AI at Scale Understanding MCP Architecture: The Control Plane for Responsible AI at Scale As large-scale AI systems mature, enterprises are moving beyond just training and deploying models — they're looking for governance, reliability, and visibility across every part of the model lifecycle. That’s where the Model Control Plane (MCP) comes in. MCP is an emerging architectural pattern that centralizes policy enforcement, observability, and access control across all AI components — including training, serving, monitoring, and feedback pipelines. In this post, I’ll break down how MCP fits into a modern LLMOps stack and why it's crucial for enterprises building responsible AI systems. 🧱 What Is MCP? A Model Control Plane is the centralized orchestration and governance layer for model operations. Inspired by cloud-native control planes (like Kubernetes), MCP serves to: * Route model access * Enforce usage policies * Monitor model behavior * Track metadata, versions, and access logs 🗂️ Core Components of MCP Architecture 🧭 1. Model Registry & Metadata Store Stores version info, ownership, training context, and lineage for all deployed models. 🔐 2. Policy Engine Controls who can access which model, with what permissions — integrates with RBAC/ABAC. 📊 3. Observability Layer Centralized dashboard for model usage, token consumption, latency, and quality metrics. 🧪 4. Shadow & Canary Testing Supports gradual rollouts and side-by-side evaluation of model versions in production. 🔁 5. Feedback Loop Integration Hooks into user feedback, logs, or labeling systems to feed insights into future training. 🧠 Why MCP Matters for LLMOps * 🔒 Security: Prevents misuse of powerful foundation models. * 📈 Scalability: Enables standardized deployment of multiple models across teams. * 📄 Compliance: Provides traceability and audit trails for regulated industries. * 🚨 Reliability: Routes traffic intelligently, handles failovers, and tracks SLAs. 🌐 Final Thoughts As AI systems scale across teams and industries, the Model Control Plane is becoming as critical as the models themselves. By decoupling control from execution, MCP enables faster innovation without sacrificing governance or trust 💬 Are you designing or using a Model Control Plane in your AI stack? Share your learnings or questions below!

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Forem

@forem.com.web.brid.gy

10 months ago

Understanding MCP Architecture: The Control Plane for Responsible AI at Scale Understanding MCP Architecture: The Control Plane for Responsible AI at Scale As large-scale AI systems mature, enterprises are moving beyond just training and deploying models — they're looking for governance, reliability, and visibility across every part of the model lifecycle. That’s where the Model Control Plane (MCP) comes in. MCP is an emerging architectural pattern that centralizes policy enforcement, observability, and access control across all AI components — including training, serving, monitoring, and feedback pipelines. In this post, I’ll break down how MCP fits into a modern LLMOps stack and why it's crucial for enterprises building responsible AI systems. 🧱 What Is MCP? A Model Control Plane is the centralized orchestration and governance layer for model operations. Inspired by cloud-native control planes (like Kubernetes), MCP serves to: * Route model access * Enforce usage policies * Monitor model behavior * Track metadata, versions, and access logs 🗂️ Core Components of MCP Architecture 🧭 1. Model Registry & Metadata Store Stores version info, ownership, training context, and lineage for all deployed models. 🔐 2. Policy Engine Controls who can access which model, with what permissions — integrates with RBAC/ABAC. 📊 3. Observability Layer Centralized dashboard for model usage, token consumption, latency, and quality metrics. 🧪 4. Shadow & Canary Testing Supports gradual rollouts and side-by-side evaluation of model versions in production. 🔁 5. Feedback Loop Integration Hooks into user feedback, logs, or labeling systems to feed insights into future training. 🧠 Why MCP Matters for LLMOps * 🔒 Security: Prevents misuse of powerful foundation models. * 📈 Scalability: Enables standardized deployment of multiple models across teams. * 📄 Compliance: Provides traceability and audit trails for regulated industries. * 🚨 Reliability: Routes traffic intelligently, handles failovers, and tracks SLAs. 🌐 Final Thoughts As AI systems scale across teams and industries, the Model Control Plane is becoming as critical as the models themselves. By decoupling control from execution, MCP enables faster innovation without sacrificing governance or trust 💬 Are you designing or using a Model Control Plane in your AI stack? Share your learnings or questions below!

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Kubernetes

@kubernetes.activitypub.awakari.com.ap.brid.gy

10 months ago

Understanding MCP Architecture: The Control Plane for Responsible AI at Scale Understanding MCP Architecture: The Control Plane for Responsible AI at Scale As large-scale AI systems mature, enterprises are moving beyond just training and deploying models — they're looking for governance, reliability, and visibility across every part of the model lifecycle. That’s where the Model Control Plane (MCP) comes in. MCP is an emerging architectural pattern that centralizes policy enforcement, observability, and access control across all AI components — including training, serving, monitoring, and feedback pipelines. In this post, I’ll break down how MCP fits into a modern LLMOps stack and why it's crucial for enterprises building responsible AI systems. 🧱 What Is MCP? A Model Control Plane is the centralized orchestration and governance layer for model operations. Inspired by cloud-native control planes (like Kubernetes), MCP serves to: * Route model access * Enforce usage policies * Monitor model behavior * Track metadata, versions, and access logs 🗂️ Core Components of MCP Architecture 🧭 1. Model Registry & Metadata Store Stores version info, ownership, training context, and lineage for all deployed models. 🔐 2. Policy Engine Controls who can access which model, with what permissions — integrates with RBAC/ABAC. 📊 3. Observability Layer Centralized dashboard for model usage, token consumption, latency, and quality metrics. 🧪 4. Shadow & Canary Testing Supports gradual rollouts and side-by-side evaluation of model versions in production. 🔁 5. Feedback Loop Integration Hooks into user feedback, logs, or labeling systems to feed insights into future training. 🧠 Why MCP Matters for LLMOps * 🔒 Security: Prevents misuse of powerful foundation models. * 📈 Scalability: Enables standardized deployment of multiple models across teams. * 📄 Compliance: Provides traceability and audit trails for regulated industries. * 🚨 Reliability: Routes traffic intelligently, handles failovers, and tracks SLAs. 🌐 Final Thoughts As AI systems scale across teams and industries, the Model Control Plane is becoming as critical as the models themselves. By decoupling control from execution, MCP enables faster innovation without sacrificing governance or trust 💬 Are you designing or using a Model Control Plane in your AI stack? Share your learnings or questions below!

Understanding MCP Architecture: The Control Plane for Responsible AI at Scale Understanding MCP A...

dev.to/rajarshi_tarafdar/unders...

#mcp #ai #llm #gpt3

Result Details

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Micha the DevOp

@michabbb.bsky.social

1 year ago

#OLMo2 32B: First fully #opensource model to outperform #GPT3.5 and #GPT4o mini 🔥

🧵👇 #MachineLearning #AI #llm

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Biblioteca UPM

@biblioupm.bsky.social

1 year ago

a man with a badge that says ' smith ' on it stands in front of a fire ALT: a man with a badge that says ' smith ' on it stands in front of a fire

#Writefull [short.upm.es/cqwq3] ha lanzado también un comprobador de plagio adaptado a #ChatGPT y #GPT3 que comprueba si un texto ha sido autogenerado utilizando los modelos #GPT + info [http://short.upm.es/jwxbr] + [http://short.upm.es/hhr3c] - acceso [http://short.upm.es/stx98]