Introduction

Cloud-based software solutions have revolutionized the way businesses operate and leverage technology. With the power of the cloud, organizations can access and manage their software applications, data, and resources over the internet, without the need for on-premises infrastructure. This blog will provide an introduction to cloud-based software solutions, highlighting their benefits, impact on businesses, and future trends.

Cost savings and scalability benefits

One of the significant advantages of cloud-based software solutions is the potential for cost savings and scalability. Instead of investing in expensive hardware infrastructure, businesses can leverage the cloud to access computing resources on-demand. Cloud providers offer flexible pricing models, allowing organizations to pay for what they use and scale their resources up or down as needed. This eliminates the need for upfront investments and provides cost savings in terms of hardware procurement, maintenance, and upgrades.

Increased accessibility and collaboration

Cloud-based software solutions provide increased accessibility and collaboration capabilities. Users can access their applications and data from anywhere, at any time, using internet-connected devices. This enables remote work, facilitates collaboration among geographically dispersed teams, and promotes real-time communication and document sharing. Cloud-based collaboration tools enhance productivity, streamline workflows, and foster innovation through seamless collaboration.

Improved data security and disaster recovery

Data security is a critical concern for businesses, and cloud-based software solutions offer robust security measures. Cloud providers invest heavily in security infrastructure, including encryption, access controls, and threat detection systems. They implement stringent security protocols to protect sensitive data from unauthorized access and ensure compliance with industry regulations. Additionally, cloud-based solutions offer reliable disaster recovery mechanisms, ensuring data backups and business continuity in the event of disruptions.

Flexibility and customization options

Cloud-based software solutions offer flexibility and customization options to meet the unique needs of businesses. Cloud platforms provide a range of services and resources that can be tailored to specific requirements. Organizations can choose from a variety of software applications, storage capacities, computing power, and other resources to align with their business goals. This flexibility allows businesses to scale their operations, adapt to changing demands, and explore innovative solutions.

Integration with existing systems and applications

Cloud-based software solutions enable seamless integration with existing systems and applications. Cloud platforms offer APIs (Application Programming Interfaces) and integration tools that allow businesses to connect their cloud-based software with other software systems, databases, or third-party applications. This integration capability facilitates data exchange, process automation, and streamlines workflows across different software solutions.

Enhanced performance and reliability

Cloud-based software solutions provide enhanced performance and reliability compared to on-premises solutions. Cloud providers operate in high-availability data centers with robust infrastructure and redundant systems. This ensures minimal downtime and offers reliable access to applications and services. Additionally, cloud platforms leverage advanced technologies, such as load balancing and auto-scaling, to optimize performance and handle increased workloads effectively.

Streamlined maintenance and updates

With cloud-based software solutions, businesses can offload the burden of software maintenance and updates to the cloud provider. Cloud platforms handle routine maintenance tasks, including software updates, bug fixes, and security patches, ensuring that businesses always have access to the latest features and improvements. This frees up internal resources, allowing organizations to focus on strategic initiatives rather than routine maintenance.
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Case studies of successful cloud adoption

Case Study 1: Netflix
Netflix is a prime example of successful cloud adoption. As an online streaming platform, Netflix relies heavily on cloud computing to deliver its content to millions of users worldwide. By migrating its infrastructure to the cloud, Netflix gained the flexibility and scalability needed to handle massive amounts of data and traffic. The company leverages Amazon Web Services (AWS) to store and process its vast library of movies and TV shows, as well as to deliver a seamless streaming experience to users on various devices. The cloud infrastructure allows Netflix to dynamically scale its resources based on demand, ensuring high availability and performance for its users.
Case Study 2: Airbnb
Airbnb, the popular accommodation rental platform, turned to cloud technology to support its rapid growth and global presence. By utilizing cloud computing services, specifically AWS, Airbnb was able to scale its infrastructure to accommodate a growing user base and handle a significant volume of transactions. The cloud-based architecture enabled Airbnb to quickly expand its services to new markets and ensure high availability of its platform around the clock. Furthermore, Airbnb leverages various AWS services, such as Amazon S3 for storage and Amazon EC2 for compute resources, to deliver a reliable and seamless experience for hosts and guests worldwide.
Case Study 3: Spotify
Spotify, the leading music streaming service, has embraced cloud technology to deliver personalized music experiences to millions of users. By leveraging the cloud infrastructure of Google Cloud Platform (GCP), Spotify can store and process vast amounts of music data and deliver it to users in real time. The cloud-based architecture allows Spotify to analyze user preferences and behaviors to provide personalized recommendations and playlists. Additionally, the scalability of the cloud enables Spotify to handle traffic spikes during peak usage periods, such as when new albums are released or popular events occur.
Case Study 4: NASA
NASA has adopted cloud computing to support its extensive scientific research and data analysis. By utilizing the cloud, NASA can efficiently store and process vast amounts of space-related data collected from satellites, telescopes, and other instruments. The cloud infrastructure enables scientists and researchers to collaborate and analyze data from different locations, enhancing their ability to make discoveries and advancements. NASA leverages cloud services from multiple providers, including AWS and Microsoft Azure, to meet its diverse needs and ensure the security and accessibility of its valuable data.
Case Study 5: Coca-Cola
Coca-Cola, one of the world's largest beverage companies, has embraced cloud technology to streamline its operations and enhance efficiency. By migrating its IT infrastructure to the cloud, Coca-Cola was able to consolidate its global systems and improve collaboration among its employees. The cloud-based solution enables seamless access to data and applications from any location, facilitating better decision-making and faster response times. Furthermore, the scalability and flexibility of the cloud allow Coca-Cola to adjust its resources based on demand, ensuring optimal performance and cost efficiency.
These case studies demonstrate the successful adoption of cloud computing by organizations from different industries. By leveraging the power of the cloud, these companies have gained scalability, flexibility, and cost efficiency, enabling them to focus on their core business objectives and deliver exceptional services to their customers. The cloud has proven to be a transformative technology, revolutionizing how businesses operate and providing a competitive edge in the digital age.

Future trends in cloud technology

Cloud technology continues to evolve rapidly, and several future trends are shaping the landscape of cloud-based software solutions. Some key trends to watch out for include:

1. Edge computing: Edge computing brings computing power closer to the data source, enabling real-time processing and reducing latency. This trend allows for faster and more efficient data processing, especially in applications requiring immediate response times.
2. Serverless computing: Serverless computing abstracts away the infrastructure management, allowing developers to focus solely on writing code. This trend simplifies application development, enhances scalability, and reduces costs by charging based on actual resource usage.
3. Multi-cloud and hybrid cloud: Organizations are increasingly adopting multi-cloud and hybrid cloud strategies to leverage the best features of different cloud providers and maintain flexibility in their infrastructure. This trend allows businesses to distribute workloads across multiple cloud platforms or combine public and private cloud environments.
4. Artificial Intelligence (AI) and Machine Learning (ML): Cloud platforms are incorporating AI and ML capabilities, enabling businesses to leverage intelligent algorithms, data analytics, and automation. AI-powered cloud services provide advanced functionalities, such as natural language processing, image recognition, and predictive analytics.
5. Serverless databases: Serverless databases eliminate the need for managing database infrastructure, enabling developers to focus on application logic. These databases automatically scale based on usage, providing cost efficiency and eliminating operational complexities.

Conclusion

Cloud-based software solutions offer numerous benefits, including cost savings, scalability, increased accessibility and collaboration, improved data security, flexibility, and streamlined maintenance. Organizations that embrace cloud technology gain a competitive edge by harnessing the power of advanced computing resources, seamless integration, and reliable infrastructure. As cloud technology continues to evolve, businesses should stay abreast of emerging trends and leverage cloud-based solutions to boost efficiency, drive innovation, and unlock new opportunities in the digital landscape.
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This week's newsletter covers the impact of AI on education and language models. Chegg's AI learning companion, ChatGPT's success over Chegg, and OpenAI's plans for containing AI risks are discussed. Additionally, we cover the latest advancements in LLM compression and deployment, as well as the Falcon family's arrival in the Hugging Face ecosystem. Lastly, we share a unique tool that uses GPT models to suggest and consolidate Linux shell commands.

A Peek at the Week: Top News

🤖 Chegg Embraced AI. ChatGPT Ate Its Lunch Anyway (link) Chegg's AI learning companion, CheggMate, powered by GPT-4, Chegg's own algorithms, and its repository of 100 million study questions built over previous years, was no match for ChatGPT's ability to play teacher and tutor. Chegg lost half of its market value overnight and revealed stunted growth to investors.
🔍 Sail 7B: New Fine Tuned LLM Outperforms ChatGPT and Vicuna with Search (link) Researchers at MIT CSAIL and MIT Linguistics and CUHK & CPII have fine-tuned the LLaMA-7b model with a search-augmented instruction training set, outperforming ChatGPT and Vicuna with search. The model was trained on 4 NVIDIA RTX A6000 GPUs and the training takes ~24 hours.
🚀 Real-Time ML Models with Serverless AWS (link) The article explains how to deploy a real-time machine learning model using AWS Lambda and API Gateway. It covers building a Scikit-learn text classifier, creating and invoking a Lambda function, creating an API Gateway REST endpoint, and testing the endpoint with POST requests.
🧮 Activation-Aware Weight Quantization for LLM Compression Outperforms GPTQ (link) The authors propose Activation-aware Weight Quantization (AWQ), a hardware-friendly approach for LLM low-bit weight-only quantization. AWQ outperforms existing work on various language modeling, common sense QA, and domain-specific benchmarks. The method provides a turn-key solution to compress LLMs to 3/4 bits for efficient deployment.
🌍 OpenAI boss 'heartened' by talks with world leaders over will to contain AI (link) OpenAI CEO Sam Altman is encouraged by world leaders' desire to contain any risks posed by AI technology. Altman suggested the formation of a global organization that could have a framework to license models, audit their safety, and propose required tests to be passed.

Tech Treasures: Tools of the Week

📝 storly.ai - AI interview algorithm for writing your life's story (link)
🤖 Michael AI - The world's first AI investment analyst (link)
🎨 Hologram - AI-generated art platform with upcoming NFT integration (link)
📈 ClientZen - AI-powered customer feedback analysis (link)
🎥 Klap - AI tool for generating TikToks from YouTube videos (link)
Keep making sense of the world with us at Nuse. Every day, we're working hard to filter through the noise and bring you the stories that matter most in AI and Tech. If you have any questions, suggestions, or feedback, we'd love to hear from you.
Stay curious and keep learning
Nuse

Hi, I happened to see my USG-3P kept being adopted but never succeed, so I thought I could just reset it, and re-adopt, what could go wrong... Now I'm here asking for help.
My USG-3P ``` [email protected]:~$ info
Model: UniFi-Gateway-3 Version: 4.4.55.5377096 MAC Address: IP Address: Hostname: ubnt Uptime: 2294 seconds
Status: Server Reject (http://:8080/inform) ```
And my controller: Network 7.4.156
The controller log indicates an issue, but I don't know how to fix: ERROR [InformServlet] - Servlet.service() for servlet [InformServlet] in context with path [] threw exception java.lang.UnsupportedOperationException: isValidCompressedBuffer is not supported in pure-java mode at org.xerial.snappy.pure.PureJavaSnappy.isValidCompressedBuffer(PureJavaSnappy.java:259) at org.xerial.snappy.Snappy.isValidCompressedBuffer(Snappy.java:331) at org.xerial.snappy.Snappy.isValidCompressedBuffer(Snappy.java:343) at com.ubnt.net.InformServlet.Ô00000(Unknown Source) at com.ubnt.net.InformServlet.Õ00000(Unknown Source) at com.ubnt.net.InformServlet.o00000(Unknown Source) at com.ubnt.net.InformServlet.ô00000(Unknown Source) at com.ubnt.net.InformServlet.service(Unknown Source) at javax.servlet.http.HttpServlet.service(HttpServlet.java:764) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:227) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162) at org.apache.tomcat.websocket.server.WsFilter.doFilter(WsFilter.java:53) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162) at com.ubnt.ace.view.UbiosHttpsFilter.doFilter(Unknown Source) at org.apache.catalina.core.ApplicationFilterChain.internalDoFilter(ApplicationFilterChain.java:189) at org.apache.catalina.core.ApplicationFilterChain.doFilter(ApplicationFilterChain.java:162) at org.apache.catalina.core.StandardWrapperValve.invoke(StandardWrapperValve.java:197) at org.apache.catalina.core.StandardContextValve.invoke(StandardContextValve.java:97) at org.apache.catalina.authenticator.AuthenticatorBase.invoke(AuthenticatorBase.java:541) at org.apache.catalina.core.StandardHostValve.invoke(StandardHostValve.java:135) at org.apache.catalina.valves.ErrorReportValve.invoke(ErrorReportValve.java:92) at org.apache.catalina.core.StandardEngineValve.invoke(StandardEngineValve.java:78) at org.apache.catalina.connector.CoyoteAdapter.service(CoyoteAdapter.java:360) at org.apache.coyote.http11.Http11Processor.service(Http11Processor.java:399) at org.apache.coyote.AbstractProcessorLight.process(AbstractProcessorLight.java:65) at org.apache.coyote.AbstractProtocol$ConnectionHandler.process(AbstractProtocol.java:890) at org.apache.tomcat.util.net.NioEndpoint$SocketProcessor.doRun(NioEndpoint.java:1789) at org.apache.tomcat.util.net.SocketProcessorBase.run(SocketProcessorBase.java:49) at org.apache.tomcat.util.threads.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1191) at org.apache.tomcat.util.threads.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:659) at org.apache.tomcat.util.threads.TaskThread$WrappingRunnable.run(TaskThread.java:61) at java.base/java.lang.Thread.run(Thread.java:829)
Any help would be appreciated.

Hello fellow Apes,
I am supposed to be playing D4 with my kids but instead I am writing this post for you. It's ok. I'm having fun because I'm actually into talking about this shit. Anyway, I was inspired to write an explanation regarding other companies used of AI based on u/Sandro316 's reply from this post I made earlier https://www.reddit.com/CLOV/comments/144na72/the_reasons_why_big_names_are_linked_with_this/. I'm posting it here to we can highlight this topic, and the information doesn't get lost in the comments section.
The purpose of this post is to explain in laymen terms what these companies are doing in AI from the perspective of a healthcare administrator. A little bit about my background. I started out in healthcare research and did my fellowship at NIH. I have 13+ years of experience working at director and c level positions in healthcare and social services. I'm also a big computer geek, and I current have my staff working on an AI system so I can train staff faster and equip them with the skills to help clients/consumers/patients navigate the complex healthcare and social service systems that we have in California. With that said, let get started.
As a side note, I will make another post regarding the patents CLOV has which pertain to how its AI's is and will be functioning in the future.
Humana for example is mainly focused on improving the customer experience and streamlining administrative tasks
https://www.forbes.com/sites/neiledwards/2020/03/02/why-doesnt-your-healthcare-insurer-use-ai-like-humana-does/?sh=f6faed5608ea
This article is using a lot of big words to explain to you that they trying to build a system that mimic Kaiser and a automated call bot that will hopefully answer all of your questions. Here is a same quote
" Call Center Experience
Humana members call for many reasons: understanding a claim status, inquiring about benefits eligibility, placing orders for new or re-filled prescriptions or appealing a medical coverage decision.
The answers to these questions are stored in different information systems. The company uses artificial intelligence and intelligent automation to help bridge gaps between systems. The aim is a better call experience. "
This is just a small reduction of the current problem regarding claim processing. They are not giving providers a better tool to process claims faster and more accurately. They are just improving their call bot.
The rest of the stuff that was wrote in the article are just Humana trying to copy Kaiser's current system. IF you have Kaiser and appreciate how fast and seamless they make some process such as drug refill, you should get a rough idea of what Humana's AI is trying to achieve.
"United is a little closer to Clover with their optum subsidiary but it is mainly focused on improving patient outcomes by getting them care faster and with the right physician for their case."
https://emerj.com/ai-sector-overviews/artificial-intelligence-at-united-health/

• An AI-enhanced virtual assistant platform to collect patient data and offer customized solutions: UnitedHealth Group’s virtual assistance platform uses conversational AI and enhanced agents that reroute calls to sufficient internal resources, collect and classify patient information, and offer services that bring down costs and improve patient service experiences.

This is exactly what you think it is. They are creating an AI to hopefully understand patients and providers communication better so that they can route them to the appropriate people to process the claim. The reason why United is doing this way go back to the comments I made in my previous posts. They are a very old healthcare company that has a lot of money. However, because their system is so old and deeply rooted in their money making process, it is hard for them to build something like CA which can process treatment, make recommendation, and process the claim--"giving providers more time to treat patients instead of doing paperwork." Want to know what happen when a provider fuck up on their claim? They delay your treatment until the claim is approved by the insurance. This is what United System is trying to reduce with AI.
​

• Developing a centralized data platform to improve patient outcomes: UnitedHealth Group’s resident tech-focused subsidiary, Optum, develops a platform that uses data analytics, NLP, machine and deep learning models to improve patient outcomes by predicting conditions and decreasing the cost of care.

This second part of their AI is exactly what CA is currently doing. While Optum and United are in development of a system that they still have to figure out how to integrate into their legacy system without causing catastrophic errors--CA is being used and has proven to improve healthcare outcomes and reduce cost (MCR). As a side note, if you think integrating, migrating, or switching to a new system is easy, just take a look at the shit show that is redetermination for Medi-caid that is currently going on. So many people are being dropped from Medi-caid and they don't even know it.
As for the rest of the article and the fancy words they used, it's fucking Kaiser. I'm sorry that I get annoyed, but whenever I read an article that used fancy words to trick laymen people, I get mad. I advocate for the people with 9th grade literacy, and this kind of shit are just describing Kaiser's system which is the current king of primary care if you haven't heard about it. Their system is used at the role model at so many meetings, it's not even funny. They have amazing coordinated care because they are the insurance and provider.
Anyway, next article..
"Oscar is again trying to improve outcomes by routing members to the correct place. Then also improve efficiency."
https://www.hioscar.com/deepdive/oscar-strategy
The AI being deploy here is to reduce the amount of fraudulent claims basically medical misuses. I'm actually very knowledgeable about Oscar, and they not investing in AI as much as they should. They are just improving their current system to detect bad claims and speed up the processing time.
In summary, CLOV's current competitors are trying to build AI on top of their current medical billing systems/platforms. Consequently, due to the limitation of those systems/platforms, they are not able to innovate as much as CA. By the end of the day, their system is still heavily reliant on a human being on insurance side to process the claim and another human being on the provider side to file the claim correctly.
This is very different than providing providers with the tools to
1) process claims
2) make recommendation on what claims (treatment) should be used based on the data from previous patients.
The big problem with health insurance companies now a day is they are so out of touch with the amount of time a provider need to invest into their staff to understand each company's process to file claims, reconciliation, out of network referral, and etc so they keep finding ways to make it easier for their staff while introducing a bunch of new rules and procedures that are not helping providers. Providers cannot provide services without authorization. Authorization should not require a fucking manual for every god damn insurance company. Ever wonder why you have to check whether your provider accept your insurance? What you are really checking is whether or not they are vendorized or are on the panel to bill the insurance company. Low payout and complicated procedures for processing claim and vendorization are barriers to care.
The only company that even come close to CLOV is United and they are still in development. CLOV's AI is live with multiple patents. https://www.reddit.com/CLOV/comments/144pi0m/clover_health_54_patents_from_2018_to_2023/
The best analogy I can give here to compare Tesla (pre-big-ass company) to GM during the EV1 era. GM made the first electric car and was much bigger than Tesla. Tesla was testing out it's battery powered roadster. However, Tesla had years to test out building their car, battery, and production line.
While CLOV is working on testing and proving the profitability of its AI, other companies are just in the process of developing them. Some of them aren't working on it at all.
Before I forgot, all of the healthcare company mentioned above are basically trying to reduce their turnaround time for when a claim is submitted for both new and prior authorization.

This comes from Lex Fridman's recent podcast with Mark Zuckerberg.
Summary:

• No news to announce yet but they're developing it and leaning toward open source.
• Crowdsourcing finetuning with the community has been considered, like a Wikipedia style way for the community to drive finetuning.
• WhatsApp will be integrated with future LLaMA versions. Metaverse and all future products will be underlied by AI language infrastructure.
• Mark sees a future of smaller LLMs covering each category instead of one major LLM like OpenAI. Smaller LLaMA models could be good for this.

On LLaMA v1:

Mark: LLaMA is the language model that our research team made, and we did a limited open source release for it. For the first one, there were a bunch of questions around whether we should be releasing this commercially, so we kind of punched it on that for v1 of LLaMA and just it released it for research. We're working on the follow-up models for this and thinking through how exactly this should work for follow-on now that we've had time to work on a lot more of the safety and the pieces around that.
I just kind of think that it would good if there were a lot of different folks who had the ability to build state of the art technology here and not just a small number of big companies. I just think that like there's all this innovation out there that people can create and I just think that we'll also learn a lot by seeing what the whole community of students, hackers, startups and different folks build with this, and that's kind of been how we've approached this.
Lex: So for people who don't know, you did the limited release I think in February of this year of LLaMA, and it got quote unquote leaked, meaning like it's escaped the limited release aspect, but it was, you know, that was something you probably anticipated given that it's just released to researchers.
Mark: We shared it with researchers, so yeah.
Lex: It's just trying to make sure that there's like a slow release.
Mark: Yeah.

On the open source community:

Mark: I think it's been really neat to see. I mean there's been folks who are getting it to run on local devices, right, so if you're an individual who just wants to experiment with this at home, you probably don't have a large budget to get access to like a large amount of cloud compute, so getting it to run on your local laptop is pretty good, pretty relevant.
I think that for the scale of what we're seeing now with AI, I think we're more likely to get to good alignment and good understanding of kind of what needs to do to work well by having it be open source, and that's something that I think is quite good to have out there and happening.
I think that at least for the stage that we're at now, the equities balance strongly in my view towards doing this more openly. I think if you got something that was closer to superintelligence, then I think you'd have to discuss that more and think through that a lot more.

On LLaMA v2:

Lex: You mentioned the next of LLaMA. What can you say about the next version of LLaMA?
Mark: A lot of what we're doing is taking the first version which was primarily this research version and trying to now build a version that has all of the latest state of the art safety precautions built in. We're using some more data to train it from across our services, but a lot of the work that we're doing internally is really just focused on making sure that this is, you know, as aligned and responsible as possible.
You'll have an assistant that you can talk to in WhatsApp. I think in the future, every creator will have kind of an AI agent that can kind of act on their behalf that their fans can talk to. I want to go get to the point where every small business basically has an AI agent that people can talk to to do commerce and customer support and things like that.
So they're going to be all these different things, and LLaMA or the language model underlying this is basically going to be the engine that powers that. The reason to open source is that, as we did with the first version, is that it basically unlocks a lot of innovation in the ecosystem. We'll make our products better as well and it also gives us a lot of valuable feedback on security and safety which is important for making this good. I mean, the work that we're doing to advance the infrastructure, it's basically at this point taking it beyond a research project into something which is ready to be kind of core infrastructure not only for our own products but hopefully for a lot of other things out there too.
Lex: Do you think that LLaMA or the language model underlying that version two will be open sourced?
Mark: I think the question is how to do it. I think we did the research license for v1 and I think the big thing that we're thinking about is basically like what's the right way.
People have tried a lot of different things, and we've tried a bunch of stuff internally, so we're making progress here but also we're able to learn from some of the best ideas in the community and I think we want to just continue pushing that forward.

On OpenAI's large model:

Mark: We sort of believe in this more community oriented, more democratic approach to building out the products and technology around this. We don't think that's going to be the one true thing. We think that there should kind of be a lot of development.

​

I mean there's been folks who are getting it to run on local devices

What's the chances Mark has browsed this sub, or someone else at Meta? I think everyone should show their support for an open source LLaMA v2. Talk about it online, mention how it's helped you in real use cases, link this community, advocate for open research, and anything else to show support for a next generation open LLaMA.

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I'm mostly mellow, chill, but can be wildly affectionate in ways with my lady, indoors and out, and appreciate nature, culture, arts and architecture around Los Angeles and SoCal. I love to share pleasurable and mentally stimulating, creative experiences, making out, to making love for hours, deeply satisfying your mind and body. I can take charge, teaching, being patient, but I can also switch roles, learning, letting you be in control. I like regular exercise, and partner stretching, massages with workouts. I mainly like naturally straight to wavy-curly hair types, of any color. I go for a petite but curvy body, up to a healthy, fuller figure, pinup model type and other features I can explain. I like natural facial beauty, not into cake-face, highly filtered Insta, lip injected LA looks, or with elective surgery, enhanced or fake body parts. I'm not anti-makeup, winged eyeliner can look hot. I'm white, attractive, in nice shape, grey blue eyes, wavy brownish hair, never married, no children, kind of edgy artist skater musician looks that cleans up well.

Hello, I recently graduated this past month with a BS in Information Science and am an aspiring data analyst. Unfortunately I didn't have any internships and my portfolio is currently empty. But I'm actively working on projects to feature in it.
I would greatly appreciate any criticism towards my resume! Thank you so much.
https://preview.redd.it/u6924im55x4b1.jpg?width=815&format=pjpg&auto=webp&s=9ea768ee689c3ff77c90a12d66f7748997053086

Looking for my misses to devote my kisses, and some of the best parts of my mind and body, with caring, sharing, giving mutual deep loving mind-body bliss. Early on with chatting, we should verify if there is attraction to pursue IRL romantic fun. Casual stuff is not fun enough, plus it's risky and leaves one feeling empty. It also seems disrespectful, like saying someone is not worthy of more than temporary lust. If you feel someone is special, it's not a big ask to be patient and faithful, whether dating for shorter or longer term. I'm confident to make my lady love to be faithful by giving her endless hours of fun and loving bliss. My passions to persuade with pleasures relates to my attraction to her mind, personality, physical features while learning what she likes and helping her to enjoy herself.
I'm mostly mellow, chill, but can be wildly affectionate in ways with my lady, indoors and out, and appreciate nature, culture, arts and architecture around Los Angeles and SoCal. I love to share pleasurable and mentally stimulating, creative experiences, making out, to making love for hours, deeply satisfying your mind and body. I can take charge, teaching, being patient, but I can also switch roles, learning, letting you be in control. I like regular exercise, and partner stretching, massages with workouts. I mainly like naturally straight to wavy-curly hair types, of any color. I go for a petite but curvy body, up to a healthy, fuller figure, pinup model type and other features I can explain. I like natural facial beauty, not into cake-face, highly filtered Insta, lip injected LA looks, or with elective surgery, enhanced or fake body parts. I'm not anti-makeup, winged eyeliner can look hot. I'm white, attractive, in nice shape, grey blue eyes, wavy brownish hair, never married, no children, kind of edgy artist skater musician looks that cleans up well.

Looking for my misses to devote my kisses, and some of the best parts of my mind and body, with caring, sharing, giving mutual deep loving mind-body bliss. Early on with chatting, we should verify if there is attraction to pursue IRL romantic fun. Casual stuff is not fun enough, plus it's risky and leaves one feeling empty. It also seems disrespectful, like saying someone is not worthy of more than temporary lust. If you feel someone is special, it's not a big ask to be patient and faithful, whether dating for shorter or longer term. I'm confident to make my lady love to be faithful by giving her endless hours of fun and loving bliss. My passions to persuade with pleasures relates to my attraction to her mind, personality, physical features while learning what she likes and helping her to enjoy herself.
I'm mostly mellow, chill, but can be wildly affectionate in ways with my lady, indoors and out, and appreciate nature, culture, arts and architecture around Los Angeles and SoCal. I love to share pleasurable and mentally stimulating, creative experiences, making out, to making love for hours, deeply satisfying your mind and body. I can take charge, teaching, being patient, but I can also switch roles, learning, letting you be in control. I like regular exercise, and partner stretching, massages with workouts. I mainly like naturally straight to wavy-curly hair types, of any color. I go for a petite but curvy body, up to a healthy, fuller figure, pinup model type and other features I can explain. I like natural facial beauty, not into cake-face, highly filtered Insta, lip injected LA looks, or with elective surgery, enhanced or fake body parts. I'm not anti-makeup, winged eyeliner can look hot. I'm white, attractive, in nice shape, grey blue eyes, wavy brownish hair, never married, no children, kind of edgy artist skater musician looks that cleans up well.

Looking for my misses to devote my kisses, and some of the best parts of my mind and body, with caring, sharing, giving mutual deep loving mind-body bliss. Early on with chatting, we should verify if there is attraction to pursue IRL romantic fun. Casual stuff is not fun enough, plus it's risky and leaves one feeling empty. It also seems disrespectful, like saying someone is not worthy of more than temporary lust. If you feel someone is special, it's not a big ask to be patient and faithful, whether dating for shorter or longer term. I'm confident to make my lady love to be faithful by giving her endless hours of fun and loving bliss. My passions to persuade with pleasures relates to my attraction to her mind, personality, physical features while learning what she likes and helping her to enjoy herself.
I'm mostly mellow, chill, but can be wildly affectionate in ways with my lady, indoors and out, and appreciate nature, culture, arts and architecture around Los Angeles and SoCal. I love to share pleasurable and mentally stimulating, creative experiences, making out, to making love for hours, deeply satisfying your mind and body. I can take charge, teaching, being patient, but I can also switch roles, learning, letting you be in control. I like regular exercise, and partner stretching, massages with workouts. I mainly like naturally straight to wavy-curly hair types, of any color. I go for a petite but curvy body, up to a healthy, fuller figure, pinup model type and other features I can explain. I like natural facial beauty, not into cake-face, highly filtered Insta, lip injected LA looks, or with elective surgery, enhanced or fake body parts. I'm not anti-makeup, winged eyeliner can look hot. I'm white, attractive, in nice shape, grey blue eyes, wavy brownish hair, never married, no children, kind of edgy artist skater musician looks that cleans up well.