Ready to outsmart the hackers? Dive into this week’s must-know updates.

THN Recap: Top Cybersecurity Threats, Tools, and Practices (Nov 11 - Nov 17)

There’s a future where enterprises can use a local—and more accurate—cybersecurity model built on top of the global model.

Council Post: Cybersecurity Is Ready For Local Models

Oracle, Broadcom, and Arm are still great long-term AI investments.

3 Top Artificial Intelligence (AI) Stocks Ready for a Bull Run

It would be the first ever for one of these companies.

Stock-Split Watch: 2 Artificial Intelligence (AI) Companies That Look Ready to Split

Benefiting from the convergence of cloud computing and generative AI will hinge upon putting both technologies to work on their data.

Cloud Computing is Ready for AI, Is Your Data? - RTInsights

Ready

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Building a Global Production Ready Marketplace With Medusa JS

Introduction Building generative AI requires working with a variety of developing machine learning and AI technologies. It also requires buckets of patience. These two requirements are not unrelated. Through working alongside extremely talented machine learning experts from different firms building generative AI applications, it’s clear that there are a number of ‘yikes’ aspects to developing fully realized generative AI tools. In this article, I’ll briefly summarise five pitfalls to avoid when developing a generative AI application. Don’t Assume the Outputs of LLMs Will Be Accurate This may seem obvious, but I was surprised at how unreliable the output of commercial LLMs could be. When testing major LLMs (in particular, ChatGPT, Anthropic’s Claude AI, and Amazon Bedrock) on complex financial datasets, I noted that they can generate errors or hallucinations at a rate of one per page. LLMs are guilty of often generating information that sounds plausible but is false. For example, when testing financial data, I found that LLMs produced financial ratios like ‘Non-operating income and expense’. These sound legitimate but don’t exist in the document and are completely hollow financial terms. Though effective prompt engineering can mitigate some of these hallucinations, many of them occur for initially unexplainable reasons (hence why every LLM has a recommendation for manually reviewing its output somewhere in its user interface and documentation). OCR (Optical Character Recognition)—the technology that converts images to machine-readable text—also generates a high rate of errors. It is challenging to develop a mechanism to combat OCR infidelity for each model. AI-based adaptive learning for user-flagged errors can be a helpful strategy but requires huge amounts of manual intervention. Overall, the lack of a calibrated confidence score in commercial LLMs is a huge limitation. Application developers must develop their own methods for calculating confidence and validating errors. 2. Don’t Be Complacent With Your Production Credits Any generative AI dependent on LLM production credits (such as Google’s Tensor Processing Units (TPUs)) needed to be technologically regulated to avoid spending more than accounted for - potentially bankrupting the company or developer. Bankruptcy through overenthusiastic spending of production credits might seem like an urban myth - certainly, there are no noted cases of production credits running amok - but better safe than sorry, right? There are several ways to stretch out your production credits to their fullest potential: Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. 3. Don’t Forget to Test User Experience (UE) Again, it may seem obvious, but when stuck looking at a profusion of code, it’s easy to forget that a real human being will be using the product. In a production environment, consider the pathways the user will take, even in complex and code-heavy environments. Don’t be like Google Bard, whose generative AI model couldn’t answer simple user questions about space upon release or, later, what to put on pizza (hint: it’s not glue). Many traditional UX testing tools can be adapted for generative AI products - such as a card sorting tool. Another effective way to test the technology is with a human-in-the-loop system, like beta testers or reviewers. At the moment, the company I’m with is using human beta testers to test their new generative AI technology. The testers use the tool for free - and they can harvest the usage patterns. 4. Don’t Forget the Importance of Training Data Clean, normalize, and potentially enrich your data to improve the training process. Techniques like tokenization and feature engineering might be helpful. Generally, the more data, the better (the AI algorithms I used were from a document store of 25 million documents, for example), but too much data may result in overfitting or computational bottlenecks. Slightly off-topic, but in the future, it’s possible that wrangling with training data may not be an issue. Promising advancements like AutoML automatically cleanse training data and assess the model’s performance using a number of techniques like meta-learning, Bayesian inference, and neural architecture search. For SMEs with limited coding resources, AutoML might be a promising innovation. 5. Don’t Choose the Wrong Scale Modeling Strategy There are many ways to scale a model. For example, you might split the model across multiple machines (model parallelism) or replicate the model across multiple machines (data parallelism). Complex models will likely benefit from the model’s parallelism’s device distribution; larger datasets or models with smaller architectures may benefit from increased throughput. Another consideration is whether to upgrade compute resources on a single machine, such as by increasing GPU memory (vertical scaling) or adding more resources (e.g. GPUs) - also known as horizontal scaling. Don’t forget to containerize your generative AI application to ensure consistent behaviors across different environments. Of course, rigorous testing and validation after model scaling is a must. To ensure it can handle the increased load, consider trying a variety of by-the-book testing plus load and stress testing. Summary It’s unrealistic to expect building a generative AI product to work without encountering at least one of these challenges. Ultimately, each of these challenges presents a fork in decision-making. At each juncture, the choice you make gets you one step closer to building a mature, production-ready model. Working with the latest advances in academic and industrial machine learning will help counter some of the typical frustrations, as machine learning’s competitive landscape is constantly pushing out new innovations. I organize the London Machine Learning Meetup (the largest community of AI experts in Europe), which is a free community that hosts events that unpack the latest technical advances in machine learning. Above all, accuracy and cost-effectiveness don’t have to be mutually exclusive. With controlled and strategic experimentation, building a generative AI product can be more rewarding and less frustrating than you might think. Good luck! Introduction Introduction Building generative AI requires working with a variety of developing machine learning and AI technologies. It also requires buckets of patience. These two requirements are not unrelated. Through working alongside extremely talented machine learning experts from different firms building generative AI applications, it’s clear that there are a number of ‘yikes’ aspects to developing fully realized generative AI tools. In this article, I’ll briefly summarise five pitfalls to avoid when developing a generative AI application. Don’t Assume the Outputs of LLMs Will Be Accurate This may seem obvious, but I was surprised at how unreliable the output of commercial LLMs could be. When testing major LLMs (in particular, ChatGPT, Anthropic’s Claude AI, and Amazon Bedrock) on complex financial datasets, I noted that they can generate errors or hallucinations at a rate of one per page. LLMs are guilty of often generating information that sounds plausible but is false. sounds For example, when testing financial data , I found that LLMs produced financial ratios like ‘Non-operating income and expense’. These sound legitimate but don’t exist in the document and are completely hollow financial terms. testing financial data testing financial data Though effective prompt engineering can mitigate some of these hallucinations, many of them occur for initially unexplainable reasons (hence why every LLM has a recommendation for manually reviewing its output somewhere in its user interface and documentation). effective prompt engineering effective prompt engineering OCR (Optical Character Recognition)—the technology that converts images to machine-readable text—also generates a high rate of errors. It is challenging to develop a mechanism to combat OCR infidelity for each model. AI-based adaptive learning for user-flagged errors can be a helpful strategy but requires huge amounts of manual intervention. OCR OCR Overall, the lack of a calibrated confidence score in commercial LLMs is a huge limitation. Application developers must develop their own methods for calculating confidence and validating errors. 2. Don’t Be Complacent With Your Production Credits 2. Don’t Be Complacent With Your Production Credits Any generative AI dependent on LLM production credits (such as Google’s Tensor Processing Units (TPUs)) needed to be technologically regulated to avoid spending more than accounted for - potentially bankrupting the company or developer. Bankruptcy through overenthusiastic spending of production credits might seem like an urban myth - certainly, there are no noted cases of production credits running amok - but better safe than sorry, right? There are several ways to stretch out your production credits to their fullest potential: Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Take advantage of short-lived, discounted, preemptible VMs from your cloud application provider (but only if your application is designed to handle interruptions and then resume from checkpoints). Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Leverage spot instances for non-critical workloads to significantly reduce costs while automatically shutting down idle instances. Cloud provider tools can be very helpful for this. Leverage spot instances Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. Where appropriate, you could experiment with open-source frameworks that don’t require production credits. I’m very pro the democratic approach of open source models and their potential for collaborative innovation. And, of course, open-source models can be accessed for free. open-source frameworks 3. Don’t Forget to Test User Experience (UE) Again, it may seem obvious, but when stuck looking at a profusion of code, it’s easy to forget that a real human being will be using the product. In a production environment, consider the pathways the user will take, even in complex and code-heavy environments. Don’t be like Google Bard, whose generative AI model couldn’t answer simple user questions about space upon release or, later, what to put on pizza (hint: it’s not glue ). simple user questions about space simple user questions about space it’s not glue it’s not glue Many traditional UX testing tools can be adapted for generative AI products - such as a card sorting tool. Another effective way to test the technology is with a human-in-the-loop system, like beta testers or reviewers. At the moment, the company I’m with is using human beta testers to test their new generative AI technology. The testers use the tool for free - and they can harvest the usage patterns. card sorting tool. card sorting tool. 4. Don’t Forget the Importance of Training Data Clean, normalize, and potentially enrich your data to improve the training process. Techniques like tokenization and feature engineering might be helpful. tokenization tokenization Generally, the more data, the better (the AI algorithms I used were from a document store of 25 million documents, for example), but too much data may result in overfitting or computational bottlenecks. Slightly off-topic, but in the future, it’s possible that wrangling with training data may not be an issue. Promising advancements like AutoML automatically cleanse training data and assess the model’s performance using a number of techniques like meta-learning, Bayesian inference, and neural architecture search. For SMEs with limited coding resources, AutoML might be a promising innovation. AutoML AutoML 5. Don’t Choose the Wrong Scale Modeling Strategy There are many ways to scale a model. For example, you might split the model across multiple machines (model parallelism) or replicate the model across multiple machines (data parallelism). Complex models will likely benefit from the model’s parallelism’s device distribution; larger datasets or models with smaller architectures may benefit from increased throughput. Another consideration is whether to upgrade compute resources on a single machine, such as by increasing GPU memory ( vertical scaling ) or adding more resources (e.g. GPUs) - also known as horizontal scaling. Don’t forget to containerize your generative AI application to ensure consistent behaviors across different environments. vertical scaling vertical scaling Of course, rigorous testing and validation after model scaling is a must. To ensure it can handle the increased load, consider trying a variety of by-the-book testing plus load and stress testing. Summary It’s unrealistic to expect building a generative AI product to work without encountering at least one of these challenges. Ultimately, each of these challenges presents a fork in decision-making. At each juncture, the choice you make gets you one step closer to building a mature, production-ready model. Working with the latest advances in academic and industrial machine learning will help counter some of the typical frustrations, as machine learning’s competitive landscape is constantly pushing out new innovations. I organize the London Machine Learning Meetup (the largest community of AI experts in Europe), which is a free community that hosts events that unpack the latest technical advances in machine learning. London Machine Learning Meetup London Machine Learning Meetup Above all, accuracy and cost-effectiveness don’t have to be mutually exclusive. With controlled and strategic experimentation, building a generative AI product can be more rewarding and less frustrating than you might think. Good luck!

Building Production-Ready Generative AI: Five Things Not to Do

The Journey of Using AI to Help Me Code, But Is It Production-Ready? Using AI for coding has quickly become more than just a prototype. It is now part of the developer toolkit. I still remember when I first came across AI-generated code. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. Something that began in jest, as an absurd test, became the realization that with proper use of AI, it genuinely could change the way we coded. Like most developers, I had a pretty standard process when dealing with a new problem. It was to gather the requirements, outline a plan, get down to writing the code, and then debug when necessary. Introducing AI into this workflow felt odd at first. It wasn’t just faster writing of code; it was how AI reshaped how I thought about coding. It wasn’t fill-in-the-blank; it was suggesting code structures that I may not have thought of pointing out possible inefficiencies, helping see different approaches to the same problem. For example, I was working on a backend project using. NET. The task was to optimize a database query, which was pulling in a lot of data. Normally, I would dive into the query, break it up, and then refactor it until it was as efficient as possible. And so this all changed the day I started writing the query out. GitHub Copilot gave me options & much more efficient ways to do pagination and filtering that I hadn’t even thought of. And here’s the crazy thing: these weren’t just suggesting something completely random; they were really good improvements. AI saved me time but taught me optimization techniques in real-time. The few first attempts at working on AI tools proved that I still needed to understand the logic of codes that were being suggested to me. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. AI can help you code faster but isn’t a substitute for knowing why you’re writing that code. And that is where developers can get it wrong — too heavy on AI, not enough actual due diligence. A perfect case in point is that I was trying to work out how to integrate Kafka with a minimal API. I knew that this kind of asynchronous consumption of messages could be optimized. Of course, it’s still not for you to put it into the correct architecture. This time, I feel I need to share it with everyone & AI can make your life more efficient. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. The beauty of coding with AI is that it does not focus solely on the small things — those rote, boilerplate tasks. Over time, I realized how AI was guiding me through other deep design decisions, like How do I structure an application?
When should I scale? It's almost like having a junior developer who’s good at the basics, but instead of just taking the AI’s output at face value, you could guide it, mentor it even, and adapt its suggestions to your needs. That’s where AI shines, the tool for exploration and learning. It’s not like “Here’s how you write a for loop,” but more “Have you considered this alternative approach?” Whether it’s optimizing that PostgreSQL query, implementing bulk copy functionality with NPGSQL, or tweaking connection pooling in EF Core, AI gives you options you might not have otherwise thought of on your own. It tests your assumptions and pushes you to think outside of the box. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It reminded me that AI tools are only as good as the data that trains them. This is still more of a human intuition and experience play in many complex scenarios. This fusion of the strengths of both AI and human talents will define the future of coding. Applying AI the right way involves gaining the limitations of its usage and playing to its strengths. I now see AI as a partner in coding and not as a person writing code for me but as one that helps me code smarter. It can help me get ideas, clean up the code structure, and carry out repetitive tasks, but I still make the final calls. To developers who haven’t yet embraced AI, I understand that. It is a natural fear of the tools that seem to take over part of our job. Nevertheless, I always advise beginners to start small. Let AI help with unit tests for instance, or even refactoring suggestions. As you grow more comfortable, start using them in larger contexts — architecture decisions, system optimizations, or debugging. It is very important to treat AI as an evolving tool. Like how we learn new frameworks or languages, AI systems such as Codex and GitHub Copilot improve every day. The more you use them, the better they get at understanding your coding style and preferences, along with how you think about problems. The magic of such a system is in how it evolves with you by giving progressively more valuable insights the more you work with it. What is unmagical about this is that it doesn’t do the work for you. It’s like learning to code all over again, but you have an assistant. You wouldn’t let someone else write all your codebase, but you would listen if they offered some brilliant new way to approach a problem. That’s how I use AI now as a crutch, but as a way to broaden my horizons and improve my craft. Now, with maturing AI, I expect the future of coding to lie not in how machines are going to replace us but in helping machines become some kind of tool that makes us better developers. If you do AI right, it is not only about making you more productive but also about making you a better coder, opening up new possibilities, pushing you to think deeper, solving problems faster, and delivering more robust solutions. And, in the end, that is what code is all about. Reference GitHub Copilot Link
OpenAI Codex Link
PostgreSQL Npgsql Link
Kafka Integration Link The Journey of Using AI to Help Me Code, But Is It Production-Ready? Using AI for coding has quickly become more than just a prototype. It is now part of the developer toolkit. I still remember when I first came across AI-generated code. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. It was GitHub Copilot, and I thought to myself, “Could this help me write better code?” Initially, I was skeptical. Something that began in jest, as an absurd test, became the realization that with proper use of AI, it genuinely could change the way we coded. Like most developers, I had a pretty standard process when dealing with a new problem. It was to gather the requirements, outline a plan, get down to writing the code, and then debug when necessary. Introducing AI into this workflow felt odd at first. It wasn’t just faster writing of code; it was how AI reshaped how I thought about coding. It wasn’t fill-in-the-blank; it was suggesting code structures that I may not have thought of pointing out possible inefficiencies, helping see different approaches to the same problem. For example, I was working on a backend project using. NET. The task was to optimize a database query, which was pulling in a lot of data. Normally, I would dive into the query, break it up, and then refactor it until it was as efficient as possible. And so this all changed the day I started writing the query out. GitHub Copilot gave me options & much more efficient ways to do pagination and filtering that I hadn’t even thought of. And here’s the crazy thing: these weren’t just suggesting something completely random; they were really good improvements. AI saved me time but taught me optimization techniques in real-time. AI saved me time but taught me optimization techniques in real-time. AI saved me time but taught me optimization techniques in real-time. The few first attempts at working on AI tools proved that I still needed to understand the logic of codes that were being suggested to me. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. There is a real temptation to just accept the output that AI spits out, especially when you are running behind a deadline, but that’s a slippery slope. AI can help you code faster but isn’t a substitute for knowing why you’re writing that code. And that is where developers can get it wrong — too heavy on AI, not enough actual due diligence. A perfect case in point is that I was trying to work out how to integrate Kafka with a minimal API. I knew that this kind of asynchronous consumption of messages could be optimized. Of course, it’s still not for you to put it into the correct architecture. This time, I feel I need to share it with everyone & AI can make your life more efficient. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. You must learn when to accept the AI’s decision and when to adjust its suggestions in the light of your project’s context. The beauty of coding with AI is that it does not focus solely on the small things — those rote, boilerplate tasks. Over time, I realized how AI was guiding me through other deep design decisions, like How do I structure an application? When should I scale? How do I structure an application? When should I scale? It's almost like having a junior developer who’s good at the basics, but instead of just taking the AI’s output at face value, you could guide it, mentor it even, and adapt its suggestions to your needs. That’s where AI shines, the tool for exploration and learning. It’s not like “Here’s how you write a for loop,” but more “Have you considered this alternative approach?” Whether it’s optimizing that PostgreSQL query, implementing bulk copy functionality with NPGSQL, or tweaking connection pooling in EF Core, AI gives you options you might not have otherwise thought of on your own. It tests your assumptions and pushes you to think outside of the box. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. AI still has a very long way to go before it can get the subtleties of every development environment. I once asked ChatGPT to help me solve the issue of race conditions in a multi-threaded application. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It gave me an answer but failed to account for edge cases that I knew would be a certain trouble in production. It reminded me that AI tools are only as good as the data that trains them. This is still more of a human intuition and experience play in many complex scenarios. This fusion of the strengths of both AI and human talents will define the future of coding. Applying AI the right way involves gaining the limitations of its usage and playing to its strengths. I now see AI as a partner in coding and not as a person writing code for me but as one that helps me code smarter. It can help me get ideas, clean up the code structure, and carry out repetitive tasks, but I still make the final calls. To developers who haven’t yet embraced AI, I understand that. It is a natural fear of the tools that seem to take over part of our job. Nevertheless, I always advise beginners to start small. Let AI help with unit tests for instance, or even refactoring suggestions. As you grow more comfortable, start using them in larger contexts — architecture decisions, system optimizations, or debugging. It is very important to treat AI as an evolving tool. Like how we learn new frameworks or languages, AI systems such as Codex and GitHub Copilot improve every day. The more you use them, the better they get at understanding your coding style and preferences, along with how you think about problems. Codex GitHub Copilot The magic of such a system is in how it evolves with you by giving progressively more valuable insights the more you work with it. What is unmagical about this is that it doesn’t do the work for you. It’s like learning to code all over again, but you have an assistant. You wouldn’t let someone else write all your codebase, but you would listen if they offered some brilliant new way to approach a problem. That’s how I use AI now as a crutch, but as a way to broaden my horizons and improve my craft. Now, with maturing AI, I expect the future of coding to lie not in how machines are going to replace us but in helping machines become some kind of tool that makes us better developers. If you do AI right, it is not only about making you more productive but also about making you a better coder, opening up new possibilities, pushing you to think deeper, solving problems faster, and delivering more robust solutions. And, in the end, that is what code is all about. Reference GitHub Copilot Link OpenAI Codex Link PostgreSQL Npgsql Link Kafka Integration Link GitHub Copilot Link Link OpenAI Codex Link Link PostgreSQL Npgsql Link Link Kafka Integration Link Link

Is AI Actually Writing Production-Ready Code?

Next week will be extremely important for financial markets. On September 18, the Fed will announce its interest rate decision and start the new rate cut cycle. Markets expect that the most probable scenario is a 25 bps rate cut. It should be noted that traders do not rule out the possibility of a 50 bpd cut. From a big-picture point of view, the Fed is expected to cut rates by 75 - 100 bpd by the end of this year to provide support to the labor market, which has already started to show signs of weakness. Rate cuts improve the liquidity situation in the markets and make money cheaper, which usually serves as support for riskier assets. However, markets do not look too bullish right now. Crypto markets have settled well below highs that were seen back in March and remain under some pressure. The tech-heavy NASDAQ index has pulled back from this year’s highs by roughly 10%. Why are markets not rallying ahead of the start of the new rate-cut cycle? Markets are worried about the potential slowdown of the global economy. The situation in China’s economy is a key problem for investors. The country’s economy has not fully recovered after the coronavirus pandemic, and the government’s attempts to provide stimulus have not yielded tangible results. China’s real estate sector, which was one of the key drivers of the country’s growth, shows no signs of improvement. The recent data indicates that China’s imports increased by just 0.5% year-over-year in August. Put simply, China’s demand remains under material pressure. Meanwhile, it looks like the U.S. labor market has started to cool down, which signals that the U.S. economy may be slowing down. Worries about weak China and the potential slowdown of the American economy have impacted investors’ risk appetite. Both cryptocurrencies and tech stocks are sensitive to fluctuations in market sentiment. When investors are ready to increase their risks in search of profits, they buy cryptocurrencies and tech stocks. In the opposite scenario, which implies rising demand for safe-haven assets, crypto and tech find themselves under pressure. The flight to quality has been going on in crypto markets for quite some time as investors increased their exposure to safe-haven assets. As a result, Bitcoin's dominance has been growing steadily since the start of the year and has already surpassed the 55% level. Jerome Powell’s comments may change the market sentiment and boost investors’ appetite for risk. Traditionally, markets pay close attention to the Fed Chair’s comments, so they will have a major impact on market dynamics. In case Powell signals that the Fed is ready to start cutting rates aggressively to provide additional support to the economy, investors will rush to buy riskier assets, which will be bullish for cryptocurrencies. Most likely, Bitcoin will be the biggest beneficiary of dovish Powell. In this scenario, investors’ demand for altcoins should also increase as they will search for bargains among coins that have suffered serious pullbacks in recent months. Next week will be extremely important for financial markets. On September 18, the Fed will announce its interest rate decision and start the new rate cut cycle. Markets expect that the most probable scenario is a 25 bps rate cut. It should be noted that traders do not rule out the possibility of a 50 bpd cut. From a big-picture point of view, the Fed is expected to cut rates by 75 - 100 bpd by the end of this year to provide support to the labor market, which has already started to show signs of weakness. expect expect Rate cuts improve the liquidity situation in the markets and make money cheaper, which usually serves as support for riskier assets. However, markets do not look too bullish right now. Crypto markets have settled well below highs that were seen back in March and remain under some pressure. The tech-heavy NASDAQ index has pulled back from this year’s highs by roughly 10%. Why are markets not rallying ahead of the start of the new rate-cut cycle? Markets are worried about the potential slowdown of the global economy. The situation in China’s economy is a key problem for investors. The country’s economy has not fully recovered after the coronavirus pandemic, and the government’s attempts to provide stimulus have not yielded tangible results. China’s real estate sector, which was one of the key drivers of the country’s growth, shows no signs of improvement. The recent data indicates that China’s imports increased by just 0.5% year-over-year in August. Put simply, China’s demand remains under material pressure. Meanwhile, it looks like the U.S. labor market has started to cool down, which signals that the U.S. economy may be slowing down. Worries about weak China and the potential slowdown of the American economy have impacted investors’ risk appetite. Both cryptocurrencies and tech stocks are sensitive to fluctuations in market sentiment. When investors are ready to increase their risks in search of profits, they buy cryptocurrencies and tech stocks. In the opposite scenario, which implies rising demand for safe-haven assets, crypto and tech find themselves under pressure. The flight to quality has been going on in crypto markets for quite some time as investors increased their exposure to safe-haven assets. As a result, Bitcoin's dominance has been growing steadily since the start of the year and has already surpassed the 55% level. Jerome Powell’s comments may change the market sentiment and boost investors’ appetite for risk. Traditionally, markets pay close attention to the Fed Chair’s comments, so they will have a major impact on market dynamics. In case Powell signals that the Fed is ready to start cutting rates aggressively to provide additional support to the economy, investors will rush to buy riskier assets, which will be bullish for cryptocurrencies. Most likely, Bitcoin will be the biggest beneficiary of dovish Powell. In this scenario, investors’ demand for altcoins should also increase as they will search for bargains among coins that have suffered serious pullbacks in recent months.

The Fed is Ready to Cut Rates: Where’s the Crypto Market Growth?

Transitioning from Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. Many organizations are transitioning to leverage MinIO's modern, scalable, and high-performance object storage optimized for AI infrastructure. This tutorial provides a comprehensive step-by-step guide to ensure a smooth and efficient transition to MinIO. Why Transition to MinIO? Customers often migrate from HCP to MinIO for several compelling reasons, particularly when optimizing for AI-driven applications: Modern Application Requirements: MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads.


Performance at Scale: MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations.


Flexibility: Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. HCP-to-MinIO Migration Process During migration, it's recommended to write exclusively to MinIO and not to HCP. This approach ensures you start as you plan to go on: using MinIO as your primary object store. Here’s the process: Initial Read Operations: While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed.


Preserving Metadata: The tool retains the modified timestamps of objects during the migration. Step 1: Create a Temporary Directory After downloading the HCP-to-MinIO tool the first step is to create a temporary directory for the results of the list command in Step 2: $ mkdir /tmp/data Next, set the following environment variables: $ export MINIO_ACCESS_KEY=<Your-MinIO-Access-Key>
$ export MINIO_SECRET_KEY=<Your-MinIO-Secret-Key>
$ export MINIO_ENDPOINT=https://<Your-MinIO-IP-Address>:9000
$ export MINIO_BUCKET=newbucket  # optional, if unspecified HCP bucket name is used Step 2: List Objects in HCP The list command generates a list of objects stored in HCP and saves it to a local file. This file can be split into smaller lists and processed in parallel by multiple client machines to speed up the migration. Here is the command: hcp-to-minio list --auth-token --namespace-url --host-header --namespace-url --dir And an example of what it might look like: $ hcp-to-minio list --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "HOST:s3testbucket.tenant.hcp.example.com" --namespace-url "https://hcp-vip.example.com" --dir "/tmp/data" Step 3: Migrate Objects to MinIO The migrate command uses the list generated in the first step to transfer objects from HCP to MinIO. The command: hcp-to-minio migrate --namespace-url --auth-token --host-header --data-dir --bucket --input-file The example: $ hcp-to-minio migrate --namespace-url https://finance.europe.hcp.example.com --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "s3testbucket.sandbox.hcp.example.com" --data-dir /mnt/data --bucket s3testbucket --input-file /tmp/data/to-migrate.txt Get Started The HCP-to-MinIO tool is designed to help you move to a modern, scalable object storage solution. Download the tool from GitHub and start your migration today. For any questions or additional support, contact us at hello@min.io. Transitioning from Hitachi Content Platform (HCP) to MinIO has never been easier, thanks to our HCP-to-MinIO tool. Developed to support our customers' evolving storage needs, this tool is freely available on GitHub and greatly simplifies the migration process. Many organizations are transitioning to leverage MinIO's modern, scalable, and high-performance object storage optimized for AI infrastructure. This tutorial provides a comprehensive step-by-step guide to ensure a smooth and efficient transition to MinIO. GitHub GitHub Why Transition to MinIO? Customers often migrate from HCP to MinIO for several compelling reasons, particularly when optimizing for AI-driven applications: Modern Application Requirements: MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads. Performance at Scale: MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations. Flexibility: Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. Modern Application Requirements: MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads. Modern Application Requirements : MinIO is Kubernetes-native and built for the cloud operating model, which means that it inherently supports most modern application stacks for analytics, statistical analysis and AI/ML workloads. Modern Application Requirements Performance at Scale: MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations. Performance at Scale : MinIO efficiently handles mixed object sizes, from very small to very large, ensuring optimal performance for data-intensive AI operations. Performance at Scale Flexibility: Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. Flexibility : Software-defined, S3-compatible object storage offers greater flexibility and cloud neutrality compared to dedicated storage appliances. Flexibility HCP-to-MinIO Migration Process During migration, it's recommended to write exclusively to MinIO and not to HCP. This approach ensures you start as you plan to go on: using MinIO as your primary object store. Here’s the process: Initial Read Operations: While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed. Preserving Metadata: The tool retains the modified timestamps of objects during the migration. Initial Read Operations: While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed. Initial Read Operations : While migrating, getobject calls are directed to MinIO. If an object isn't found, it's read from HCP. Once all objects are copied, this fallback can be removed. Initial Read Operations getobject Preserving Metadata: The tool retains the modified timestamps of objects during the migration. Preserving Metadata : The tool retains the modified timestamps of objects during the migration. Preserving Metadata Step 1: Create a Temporary Directory After downloading the HCP-to-MinIO tool the first step is to create a temporary directory for the results of the list command in Step 2: $ mkdir /tmp/data $ mkdir /tmp/data Next, set the following environment variables: $ export MINIO_ACCESS_KEY=<Your-MinIO-Access-Key>
$ export MINIO_SECRET_KEY=<Your-MinIO-Secret-Key>
$ export MINIO_ENDPOINT=https://<Your-MinIO-IP-Address>:9000
$ export MINIO_BUCKET=newbucket  # optional, if unspecified HCP bucket name is used $ export MINIO_ACCESS_KEY=<Your-MinIO-Access-Key>
$ export MINIO_SECRET_KEY=<Your-MinIO-Secret-Key>
$ export MINIO_ENDPOINT=https://<Your-MinIO-IP-Address>:9000
$ export MINIO_BUCKET=newbucket  # optional, if unspecified HCP bucket name is used Step 2: List Objects in HCP The list command generates a list of objects stored in HCP and saves it to a local file. This file can be split into smaller lists and processed in parallel by multiple client machines to speed up the migration. list Here is the command: hcp-to-minio list --auth-token --namespace-url --host-header --namespace-url --dir hcp-to-minio list --auth-token --namespace-url --host-header --namespace-url --dir And an example of what it might look like: $ hcp-to-minio list --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "HOST:s3testbucket.tenant.hcp.example.com" --namespace-url "https://hcp-vip.example.com" --dir "/tmp/data" $ hcp-to-minio list --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "HOST:s3testbucket.tenant.hcp.example.com" --namespace-url "https://hcp-vip.example.com" --dir "/tmp/data" Step 3: Migrate Objects to MinIO The migrate command uses the list generated in the first step to transfer objects from HCP to MinIO. migrate The command: hcp-to-minio migrate --namespace-url --auth-token --host-header --data-dir --bucket --input-file hcp-to-minio migrate --namespace-url --auth-token --host-header --data-dir --bucket --input-file The example: $ hcp-to-minio migrate --namespace-url https://finance.europe.hcp.example.com --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "s3testbucket.sandbox.hcp.example.com" --data-dir /mnt/data --bucket s3testbucket --input-file /tmp/data/to-migrate.txt $ hcp-to-minio migrate --namespace-url https://finance.europe.hcp.example.com --auth-token "HCP bXl1c2Vy:3f3c6784e97531774380db177774ac8d" --host-header "s3testbucket.sandbox.hcp.example.com" --data-dir /mnt/data --bucket s3testbucket --input-file /tmp/data/to-migrate.txt Get Started The HCP-to-MinIO tool is designed to help you move to a modern, scalable object storage solution. Download the tool from GitHub and start your migration today. For any questions or additional support, contact us at hello@min.io. GitHub GitHub

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Is Bitcoin Ready for the Mainstream? How Rootstock is Paving the Way

SINGAPORE, Singapore, April 3rd, 2024/Chainwire/-- Bitlayer Labs, the first Bitcoin Layer 2 solution based on BitVM, announced a seed round of $5M led by Framework Ventures and ABCDE Capital, participation by StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital and many more. The announcement comes as Bitlayer prepares the launch of its open-incentive program Ready Player One, inviting developers worldwide to augment, enhance, and build on the protocol. Bitlayer Labs, the developer of Bitcoin Layer 2 protocol , announced today the close of a $5 million seed funding round at an $80 million valuation, driving a vision to become the computation layer of Bitcoin. Bitlayer Investors & Ecosystem Partners Blockchain-based venture firms Framework Ventures and ABCDE Capital led the round, with participation from StarkWare, OKX Ventures, Alliance DAO, UTXO Management, Asymmetric Capital, Kenetic Capital, Kestrel, Global Coin Research, Pivot Global, and Web3Port. Many other incredible investors added value to the seed round. Prominent angel investors including Messari CEO Ryan Selkis, Messari co-founder Dan McArdle, Asymmetric Capital founder Dan Held, Hacken CEO Dyma, Sky Mavis CEO Trung and CTO Andy, and Kyber Network founder Loi Luu, among others, joined in capitalizing the round. Bitlayer’s seed round was delivered soaring value-add support from ecosystem partners including Hacken, AWS Cloud, Ankr, Polyhedra, Babylon, Particle Network, Meson, Nubit, BitSmiley, TokenPocket, Xverse, Flash Protocol, Umoja.xyz, RunesTerminal, and more. The Bitlayer Network Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing completeness. The protocol aims to build a more scalable and interconnected Bitcoin ecosystem. The securing of new capital pushes Bitlayer's lead in paving the way for more Bitcoin Layer 2 use cases, a race that will propel Bitcoin to prevail over other blockchains in size, scalability, and security. The fund will also enable Bitlayer to grow its team by hiring across business development and engineering faculties to support global expansion efforts. Investment firms are increasingly optimistic about the Bitcoin ecosystem as Bitlayer is rapidly gaining prominence, capturing the industry's attention. Investor Remarks Roy Learner, partner at Framework Ventures, spoke on the augmentation of Bitlayer with BitVM: “We are excited to back Bitlayer as they launch the first Bitcoin Layer 2 network based on BitVM. Looking at the trade-off space, BitVM struck us as the most practical approach to bring scalability and expressiveness into the BTC ecosystem without sacrificing security.With over $1T of global wealth looking to productively utilize their BTC, Bitlayer opens up the design space to introduce novel DeFi use cases and applications. Led by a team of OGs, we've been impressed by Kevin and Charlie's blend of deep technical background and rich crypto experience and are excited to partner with them as they deliver on their vision of making Bitcoin a more productive asset.” Qiao Wang, co-founder of Alliance DAO, emphasized the future of Bitlayer and Bitcoin L2s in the emerging era: “I spent the vast majority of my intellectual, social, and financial capital in the Ethereum ecosystem in the last decade, but deep down I've always been a Bitcoiner. The possibility to enhance Bitcoin's expressiveness and scalability via L2s such as Bitlayer is the most exciting moment in the history of Bitcoin. That is because there's over $1T of wealth stored on Bitcoin, and the holders would want to do something productive with it. Greater expressiveness and scalability enable this. Bitlayer is one of the most experienced, passionate, and technically capable teams to tackle this problem.” In the midst of building Bitlayer, co-founder Charlie Hu shared his thoughts on the protocol’s milestone: "This support signifies investors’ and builders’ trust in Bitlayer and the team, and demonstrates a robust backing for the Bitcoin ecosystem and the future of Bitcoin DeFi at large." Bitlayer ventures into the future of Bitcoin with newly supplied capital, anticipating a rush of development activity and investor interest in the Bitcoin Layer 2 space. "Bitlayer is dedicated to bringing super scalability into the Bitcoin ecosystem, with hundreds of millions of potential users as an addressable market," said Kevin He, co-founder of Bitlayer. "We’ve created the best, easiest-to-use scalability product in order to give users better fee rates and faster actions, all based on Bitcoin’s security foundation." With the approach of the Bitcoin halving event, the need for a robust Bitcoin ecosystem becomes more important than ever. Bitlayer’s exacting design and rigorous security auditing in preparation for public mainnet launch primes the protocol for a flourishing builder ecosystem. Mainnet launches on April 5th, with more milestones arriving soon on the project’s development pipeline. Ready Player One– Ecosystem Incentive Program. To accelerate ecosystem development and incentivize projects to deploy on the Bitlayer mainnet, Bitlayer is launching a series of ecosystem incentive programs– starting with the first event, Ready Player One. The campaign is an open incentive program designed to distribute token rewards valued at over $50M to protocols and teams that deploy to the Bitlayer mainnet and demonstrate exceptional performance. Bitlayer’s growth is accelerating, urging strong projects to join its ecosystem to grow the protocol in exponential ways. Bitlayer commits to providing comprehensive ecosystem support for all projects, offering the following resources to builders: Potential investment from the foundation and institutions Initial liquidity support Comprehensive product development resources Mentorship and pitch opportunities from top-tier incubators Support from the Bitcoin community and OGs Ecosystem collaboration and co-creation Users can find the full article for more information. here About Bitlayer is the first Bitcoin Layer 2 network based on BitVM, offering Bitcoin-equivalent security and Turing-completeness. Bitlayer is committed to becoming the computation layer for Bitcoin. It introduces ultra-scalability and inherits Bitcoin’s L1 security, providing users with high throughput and a low-cost transaction experience. Bitlayer Github Contact Digital Marketing Manager Sky Harris Bitlayer skyler@bitlayer.org This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

Bitlayer, a Leading Bitcoin L2, Nets $5M In Funding, Unveils $50M 'Ready Player One' Program

Since , I was dying to try the and see all its potential for my usual tasks. Apple showcased how this technology could revolutionize our workspaces and redefine our approach to tasks and creativity. I was particularly intrigued by its implications for IT management and systems design. WWDC2023 Apple Vision Pro Currently, we rely on a variety of tools to create, design, and present our ideas. But what if we could perform these tasks in a 3D space on a daily basis? I believe Apple might have the answer. Recently, I finally had the chance to try out available apps for presentations, mind mapping, drawing, and designing. I'm here to share my impressions and thoughts. Presentations: Key Note and Microsoft PowerPoint Already, you can find two of the most popular solutions adapted for Vision Pro: Key Note and Microsoft PowerPoint. It´s hard for me to choose between them, and I don´t think I have to. Both products are terrific and suggest new opportunities. I guess everyone can stick to their favorite or familiar product without worrying about quality and responsiveness. Interface For both KeyNote and Microsoft PowerPoint, the interface hasn't undergone significant changes; rather, it has been refined to accommodate eye control. While some actions took me time to figure out, overall, the interface feels intuitive and user-friendly. I enjoyed it both with and without the environment, but the former is preferable for me as it allows me to concentrate on my work without distractions. Killer Features For KeyNote, definitely, rehearsal environments is a killer feature! It works amazing, and you can feel yourself in a conference room or in a theater. You can see your slides for practice and choose the view: with or without notes, show the next slide or not, and so on. Unfortunately, there are no people simulations. I suppose there might be Apple personas in the future. For PowerPoint, there is information from the : Microsoft blog Use PowerPoint to make a lasting impression with powerful and customizable slides and presentations that make you stand out. The Immersive Environment of Apple Vision Pro allows you to practice your presentation like you are presenting to an audience. Truly you can work with any app in the Vision Pro environment (not just PowerPoint), those are forest, moon, sky, and so on. However, rehearsal environments with a conference room or a theater are particularly a KeyNote feature, and you cannot use it in PowerPoint, at least for now. For sure, you can practice your presentation in any standard environment, but that´s not the same experience. Drawbacks The main drawback I've encountered while working with presentations via Vision Pro is the virtual keyboard. As someone who frequently creates detailed presentations involving numbers and text, composing these elements directly on Vision Pro can be challenging. The responsiveness of the virtual keyboard is lacking, resulting in slower typing and more errors. For now, I would choose either to connect a physical keyboard or to use my laptop as the foundation for presentations - Apple Vision Pro allows me to do it. Moreover, I would say that it was difficult for me to work with text and numerical blocks for a long time in Vision Pro. It's too cumbersome for me, and I can't relax like when watching a movie. I have to sit, type, and think while wearing a heavy thing on my head. That isn't as comfortable for me, but this is a very specific problem. The Verdict Keynote and PowerPoint on Vision Pro are perfect for making final adjustments and practicing your presentation. You can immerse yourself in the environment and focus on your work. You can enlarge the screen significantly, allowing you to easily view all your notes and slides. Moving slides and blocks, changing pictures, and leaving notes are all effortless. This experience is much better than working with just a laptop. Unfortunately, even though the virtual keyboard is among the best on the market, it's not yet perfect. Therefore, it might be challenging to create an entire presentation from scratch using only Vision Pro. However, connecting with a laptop works seamlessly, making it still possible. I will use Vision Pro for final preparations and to practice my presentations, and I would definitely recommend giving it a try. Drawing and Designing: FreeForm, Figma, Miro This section, to be honest, left me somewhat disappointed. While FreeForm performs adequately, its functionality is quite limited. Miro and Figma offer a broader range of features, but, unfortunately, none of them are adapted for Vision Pro (Miro is available as an iPad version, and Figma is available in Safari and in a wrapper application), so the responsiveness is low. Killer Features FreeForm is a standard Apple app available on iPhone, MacBook, and iPad. As an Apple application, it works smoothly. Gestures are instantly recognized. I particularly enjoyed the ability to bring it close and use it in a manner reminiscent of an iPad where you can draw with a finger instead of gestures. The initial experience of feeling the paper with my brain was genuinely perplexing. There is a library with different elements, so I see it as a specific tool for designing and not just drawing, but for now, it's more like a vision of the future. Figma and Miro have all their powerful features available but only as unadapted apps. Drawbacks Regrettably, FreeForm offers only a limited set of functions for now. Whether additional features will be introduced remains uncertain. Presently, it seems suitable only for basic drawing tasks. Moreover, even though the gestures are recognized well, it's still difficult to design something quickly. It took too much time and patience to draw this simple scheme: Figma and Miro, among other apps, encounter challenges in adapting to the Vision Pro interface. A common issue is the difficulty in processing gestures, which hampers smooth usage. For example, selecting an object may unintentionally move the entire canvas, leading to inefficiencies in work processes. Or you can struggle with object capture. These challenges pose significant obstacles to productivity and make those apps uncomfortable to use on Vision Pro. The Verdict I recognize the potential of this technology. Combining the strengths of the apps could result in amazing tools. I believe that utilizing Vision Pro for drawing and designing has the capacity to revolutionize the entire process. The flexibility to design while standing, sitting, or using the screen as an iPad is truly remarkable! However, we must exercise patience and allow the apps the time needed to refine and expand their functionalities further. I eagerly await the opportunity to see and experience these advances firsthand. Mind Mapping: Miro and Mind Note As I mentioned earlier, many well-known apps like Miro and others are not optimized for Vision Pro. While you can access them through a web browser or their iPad versions, it's not as convenient. When discussing Vision Pro-specific apps, one notable mention is Mind Note. The interface caught my eye with its aesthetic appeal, although not everything functions flawlessly. Killer Features One standout feature in Mind Note is the fluid movement of nodes, which makes working with mind maps exceptionally convenient. While Miro also offers this feature, Mind Note on Vision Pro expands it with its smooth performance and adaptivity. Vision Pro seems like the most efficient way to work with a mind map, making you feel like a hero creating something in a movie. Manipulating nodes, switching between them, grouping and ungrouping, and adding new ones are all effortlessly executed using gestures, much quicker than on a laptop or iPad. Furthermore, expanding the display to view the entire map without physically moving it just by turning your head facilitates a comprehensive view, fulfilling the primary purpose of mind mapping. This approach to creation resonated well with me. Even printing is a seamless process here. Printing extensive text blocks within the mind map is unnecessary, so the virtual keyboard is more than sufficient. Drawbacks However, there are some basic functionalities that have yet to be fully adapted or completed. A notable drawback, from my perspective, is the absence of pinch-to-zoom functionality (both in Miro and Mind Note). This feature seems essential, and its absence is somewhat disappointing; I hope the creators address this in the near future. Additionally, there is a cost associated with Mind Note, and without it, the functionality is significantly restricted, rendering the app more of a visual treat than a practical tool. The Verdict In my opinion, Vision Pro stands out as an ideal tool for mind mapping. It provides focus, a holistic view, and effortless node manipulation, offering optimal conditions for creative and effective mind mapping. Using Vision Pro exceeds the experience of using a traditional paper or browser version. Despite existing drawbacks, Vision Pro is a promising choice for mind-mapping enthusiasts. Conclusion Vision Pro is a powerful tool for creating, designing, and presenting ideas. It allows us to extend our current tools, giving us more space and possibilities. Unfortunately, since Vision Pro has just launched, there isn't a huge selection of apps in the App Store, and some apps are limited in their functionality. However, the speed at which new apps appear and existing ones improve is incredible. So, I'm sure we'll see plenty of updates in the near future. Let me know what you think in the comments!

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