Top Tweets for #ServerLESS
1 week to go! London on 15 July. Guilherme Dalla Rosa on secure multi-tenant #SaaS on AWS #Serverless, plus Matt Johnson on the engineering behind MongoDB Atlas on AWS. Save your spot: https://t.co/o12d5waTOF #AWS #AWSCommunity #Atlas
SQS dead letter queues catch every message that exhausts retries.
No trace, no retry, no clue - that's the problem DLQs solve.
Every failed message needs a home. Set up DLQs today.
#AWS #Serverless
S3 Object Lambda transforms data on the fly before it reaches the client.
Redact PII. Convert formats. Filter fields.
Same S3 object - different view per consumer. No duplicate storage.
#AWS #Serverless
Python ecosystem for Data Science & AI #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode #SQL
Postman cheat sheet every developer needs #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode #SQL
Paul Graham says a startup should be a cockroach. So cheap to run nothing can kill it. I moved my stack to serverless. Bill went from $100 to $30 a month. My $1000 in AWS credits now last 2 years, not 10 months. Cut your burn before you chase revenue.
#BuildInPublic #Serverless
1847 Gaussian Machine Learning. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode
https://t.co/2cLy2VxY8X
The Future of #DataScience and #ParallelComputing! #BigData #Analytics #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/HDuCHROHlX
Get Your Book at Your Local Library 2020!
@marquiswhoswho
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://t.co/emawVQsVjX
Charlotte! 143,000 #Books Reserved at North Carolina Library. #BigData #Analytics #DataScience #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode
https://t.co/lntb7P9ss0
#MachineLearning Puts New Lens on #IoT Computing! by @gp_pulipaka! #BigData #Analytics #DataScience #AI #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/Vp9xfgfBwk
History of AI! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/2icYYoPIyX
GPT with LLM, and Generative AI! @AverConferences
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysOfCode
https://t.co/LL5phLRkMP
120 GUI Projects in Python-220 Engineering Principles in Python! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysOfCode
https://t.co/pewyR6Mvni
Top Expert in Machine Learning.@DataToBiz #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/aJIsLEUsyN
Gigatron for Linear Algebra in Machine Learning! @gp_pulipaka! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Coding #100DaysofCode
https://t.co/speD9uDmwm
Strategy Management! #BigData #Analytics #DataScience #AI #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
https://t.co/YURmp1mafc
RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HMsLfgYXIAA3JN5.jpg)
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HMsLfYsXEAAeRGi.jpg)
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HMsLfQVXUAEtZhb.jpg)
![gp_pulipaka's tweet photo. RAG with LLM: Creating an AI-Powered File Reader!
#BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode
This is the type of application I've already built earlier at the beginning of this year for a conference demonstration!
GPT Paper-Reader, to show how you can leverage the summarization capabilities of large language models to retrieve research papers from databases, summarize their content, and engage in Q&A interactions with people. The future is here. In addition to the regular theoretical presentation, I'll be presenting a practical GPT. This application utilizes GPT, to effectively read and analyze complete academic papers: Basically the app chunks the PDF paper into manageable sections for detailed reading and generates concise summaries for each segment. By maintaining context from previous sections within the token limit, it enhances comprehension. Before diving into the paper, you can outline specific questions in the prompt. This approach allows GPT to extract the most important information during its reading and summarizing process, leading to superior outcomes. After summarizing all parts, you will receive comprehensive answers to your inquiries based on the consolidated summaries. By default, the initialized prompt will target essential points such as: These inquiries are tailored for research articles within the computer science domain. Upon completion of the paper review, feel free to engage with the question() interface to ask further questions.
References
Khan, A. A., Hasan, M. T., Kemell, K. K., Rasku, J., & Abrahamsson, P. (2025). Developing retrieval augmented generation (RAG) based LLM systems from PDFs: An experience report [Preprint]. arXiv. Retrieved April 3, 2025, from https://t.co/YCGfBCNY4v
Santos, G. (2025, March 3). LLM + RAG: Creating an AI-powered file reader assistant. Towards Data Science. Retrieved April 3, 2025, from https://t.co/YubIqDgyP8](https://pbs.twimg.com/media/HMsLfIKWUAAuLgN.jpg)
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