RYO Coin is a Japanese cryptocurrency project aiming to be more than just a token. It's building a full-scale digital ecosystem integrating real-w...

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SEOUL, South Korea, March 1, 2025 /PRNewswire/ -- Milk Partners (CEO Jayden Jo), which operates the blockchain-based loyalty integration platform MiL.k, announced on the 27th that it plans to migrate to Arbitrum, the world's largest Ethereum Layer 2, to expand its global Web3 ecosystem.

MiL.k migrates to Arbitrum for the full-scale expansion of global web3 business | The Manila Times

As Anjney Midha, a partner at a16z generator, noted on Yahoo Finance’s Opening Bid podcast, these infrastructure build-outs are just beginning. It is worth noting that the scale of these investments is massive.

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Moca Network, a flagship project ... communications technology platforms, today announced the launch of OKI Club, a premium membership program that integrates Web3 technology into SK Planet’s OK Cashbag app, which is South Korea’s largest integrated rewards service....

Moca Network and SK Planet launch OKI Club, marking first large-scale enterprise use of AIR Kit for Web3 onboarding | Currency News | Financial and Business News | Markets Insider

The White House says federal agencies have until March 13 to present plans for large scale layoffs. Among the targets is the Cybersecurity and Infrastructure Security Agency, which defends Americans from cyberattacks from U.S. adversaries. Nicole Sganga has more.

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Core portfolio growth, geographical diversification will drive scale

Repco Home Finance: Focused on affordable housing loans, undervalued

Accenture (NYSE: ACN) is launching new capabilities, accelerators and training to help organizations build and scale generative AI solutions and agents that enhance the impact of the integrations of Google Cloud’s Gemini models and Salesforce’s Agentforce, announced today.

Accenture Helps Organizations Supercharge Growth and Customer Experiences with AI from Google Cloud and Salesforce | Morningstar

scale

The Hidden Cost of Hypergrowth: Scale Without Sacrificing Stability

If you’re a marketing department and you want to reach the widest audience possible, text messaging is the best channel to do that with. Most people check their texts more often than email, and text messages have a 90%+ open rate within three minutes of reception. To send SMS and MMS messages at scale you need a cloud communications platform — a.k.a. a communications platform as a service (CPaaS). Peer-to-peer review site G2 lists more than 100 of them, with Plivo ranked number one for customer satisfaction. Throughout this article we’ll use Plivo and Twilio, the CPaaS market share leader, as examples. What’s Your Number? Once you’ve settled on a CPaaS, you have another decision to make: What number type should you use to send your messages? g follows different rules from person-to-person (P2P) texting. Businesses have three options: Commercial application-to-person (A2P) messagin 10-digit long codes (10DLC), toll-free numbers, and short codes. 10DLC Numbers Long codes are normal 10-digit phone numbers — the kind that individuals use every day to engage in P2P conversational messaging. is a service offered by major US carriers to explicitly allow commercial A2P SMS traffic over long code phone numbers. 10DLC is available only in the US. 10DLC You can turn long codes into 10DLC numbers by registering them with a carrier, which you can do through your CPaaS provider. Provisioning a 10DLC number takes less than a week. A vetting process determines the maximum throughput a carrier will allow for a 10DLC number. Throughput is the number of messages they can send in a given time period — messages per second, for example. Toll-Free are used by businesses that want their customers to reach them at no cost. In North America, 800, 888, 877, 866, 855, 844, and 833 denote toll-free numbers. Toll-free numbers Businesses provide toll-free numbers so that incoming calls are free of charge for the calling party, but they can also be enabled for SMS messaging through your carrier or CPaaS, generally in about one business day. Short Codes are five- or six-digit numbers used to send and receive SMS messages. Short codes Unlike long code numbers, shortcodes are not tied to an area code, so they can be used across a country to provide a nationwide brand identity under one easy-to-remember number. Short codes take the longest to acquire — up to 16 weeks. Throughput and Cost Two major differences between number types are their maximum throughput and cost. Throughput Limits Carriers impose varying maximum throughput on different number types. Generally speaking, short codes support the highest throughput and toll-free numbers the lowest. 10DLC numbers sit in the middle. Carrier-imposed throughput for 10DLC numbers depends on vetting scores assigned by an independent organization called The Carrier Registry. Cost Considerations There are three kinds of costs involved in messaging: one-time setup costs, number rental costs, and cost per message. All pricing numbers that we quote below were taken from the two representative vendors’ pricing pages. Short codes incur the highest setup costs: $1,500 for Plivo and $500 for Twilio. 10DLC numbers incur $44 charges for brand and campaign registration and vetting. There are no one-time charges for toll-free numbers. Number rental costs for long codes and toll-free numbers are negligible compared to usage costs for organizations that send a lot of messages. Short code rental costs, however, are significant. Cost per message depends on several factors: whether you’re sending or receiving messages, whether you’re using SMS or MMS, and which carrier you use. Your CPaaS vendor’s pricing page can break it down for you. For comparison purposes, we put together a table showing pricing for sending SMS messages, which for many businesses is the most common use case, and arbitrarily chose T-Mobile as the carrier for which to show surcharges in this table; the “plus” numbers are the T-Mobile carrier surcharges. Run the Numbers All of the different options make it difficult to figure out which number type makes the most economic sense. The best way to decide is to create a spreadsheet that includes the number of SMS and MMS messages you plan to send and receive per month, and factor in your carrier’s surcharges. Regardless of which number type you use, message senders must obtain opt-in consent before texting consumers — and be aware of other considerations. Plivo’s SMS best practices guide lays out those considerations for businesses that want to take advantage of the best channel for business communications — text messaging.

How to Send SMS and MMS Messages at Scale: What is Your Number?

Cómo enviar mensajes SMS y MMS a escala: ¿Cuál es su número?

Comment envoyer des messages SMS et MMS à grande échelle : quel est votre numéro ?

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SMS および MMS メッセージを大規模に送信する方法: あなたの番号は何ですか?

Como enviar mensagens SMS e MMS em escala: Qual é o seu número?

Cách gửi tin nhắn SMS và MMS ở quy mô lớn: Số của bạn là gì?

如何大规模发送 SMS 和 MMS 消息：您的电话号码是多少？

Building a food delivery fleet of hundreds of robots is like teaching math to third graders. Their ability to learn and interact with their surroundings emotionally is a work in progress, but the sky is the limit. If carried out well, growth and success are very likely. Since starting in 2017, we’ve been scaling robotic technology to create smart cities while demonstrating to the general public and investors the extraordinary potential of the latest technological advances. Not only was and automation important, but setting boundaries and acknowledging the difficulties of the competitive tech space are key to success. company culture Most importantly, some of the most progressive companies deliberately take on employees with track records reflecting both as failing is an essential part of growth. failure and success So if your fintech launch doesn’t go to plan or SaaS doesn’t gain traction, however painful it may be to let go and move on, you’re not alone. Almost of startups pivot by changing their business plan. 60% With plans to grow tenfold next year and expand our team, this is what I’ve learned about scaling operations. Assess Your Company’s Capability to Take on Large Projects There are key performance indicators (KPIs) and quantitative metrics to measure a company´s success and help calculate the mid to long-term predictions to achieve profitability. I call them complex or compounded metrics – they are formed by input variables and governed by cost and time. For example, at Kiwibot, we look at how much it costs to operate our robots per kilometer. Any journey in a pilot program that costs over $2 per km is not considered a progression from regular delivery fees. The project would need far more testing to be able to scale up. What robotic delivery fleets are doing is similar to what Amazon did when eCommerce was less prevalent a few years ago: Exploring uncharted territory and working their way into the spotlight. There were plenty of attempts to set up eCommerce sites and sidewalk robotic delivery services but only those that leverage existing technological capabilities, and apply them in new ways, can succeed. Amazon didn’t invent anything new, they just leveraged the infrastructure and technology that already existed to take shipping speed to the next level. is a vital aspect of your company’s capability to scale too – and is an indicator of this. Employees must show flexibility, persistence, resilience, and be prepared for challenges. Human capital culture Startups are the kings of ambiguity, and the people you want by your side when times get tough are those that have equal levels of passion and persistence, otherwise known as . grit Search for individuals who have and have irreplaceable knowledge from overcoming hardships. Most importantly, fail as fast as possible. survived failure According to Jeff Bezos, the defining attribute of Amazon is its : “Most large organizations embrace the idea of invention, but are not willing to suffer the string of failed experiments necessary to get there.” appreciation of failure And as soon as you see something that doesn't work, say goodbye. It doesn’t matter to an idea or innovation, there’s just no point going over old territory. how attached you felt Start Small to Control the Risk of Growing Too Fast Startups must be optimistic, but they often tend to overshoot with overly hopeful objections. Always make sure to underpromise and overdeliver. Take calculated risks as you assess opportunities, start with small operations, and set up shop in one major city, allowing you to sprawl. This is what would be referred to as a “cluster” – where concentrations of interconnected companies and institutions from a specific field are situated – a generic concept used by many years ago that still serves today. Michael Porter As a company scales, it must cluster around points of interest and use cases that deliver traction. It’s vital to partner with organizations in a chosen region and improve relationships with city authorities. Once you have one client signed or a city in the bag, there will be proof of your product gaining customers, and the company will grow swiftly. For example, university campuses or cities like Los Angeles and Miami are significant markets for food delivery, so that’s where Kiwibot has active operations. We have the capacity as a company to expand to Scotland and Wales, for example, but would that be good for business? Probably not. My advice would be: Always choose those “second cities,” know your limits, and set boundaries. Pushbacks: Why It’s Harder to Scale Tech Companies Scaling a tech company is notoriously difficult because of ongoing public antitrust sentiment and the sheer cost. Andy Grove, one of Intel’s founders, once warned that it can be difficult for smaller US tech companies to expand while , and this can mean missed opportunities. competing with giant rivals Delivery robots are part of the green revolution and have a set speed governed by laws in cities across the US. Indeed, technology needs regulation, but with that comes added taxes or licenses which can impact tech companies’ budgets. However, these taxes could help generate revenue for city authorities and cover the costs associated with robotic delivery service operations. Tax implications are what transport technology companies should watch out for – it’s a repetition of the Uber and Lyft stories all over again. Now, consider this example. Buildings have not been designed for robots, but there is a high demand for delivery robots to have self-autonomy indoors. Imagine getting your food order delivered to your desk or a dress from Bloomingdales brought to your apartment before a date. In theory, it’s an incredible invention. Still, it involves APIs to facilitate communication between robots and elevators, the merging of technologies, and a lift company to actually have the appropriate software. It poses a costly venture which can be a blocker to innovation. Malls have the potential to drastically innovate with robots – like Westfield or Halfords – as they search for a leading edge. But will they take that leap? This challenge is similar to what Google struggled with twenty years ago – the development of the cloud, servers, mapping data, as well as physical infrastructure. All the foundations had to be laid before the infrastructure could be built. Over time, robotic technology will become cheaper as demand skyrockets. Think about Yelp’s algorithm, for example, which allows you to find a restaurant that has a view or allows pets – something that is now viewed as indispensable. That opportunity came from mapping technology, and billions were spent on that by Google who now own about 80 percent of the market. Learning to Manage a Lack of Resources US Cities are now busy planning to revive the economy and help local businesses grow post-pandemic. One initiative that was introduced was food ordering apps in restaurants. But when 80% of deliveries are made by cars powered by gas, cities are searching for green opportunities for local businesses. Diverging demand to delivery robots could be the answer, but there aren’t sufficient resources. Covid-19 exposed the lack of tech infrastructure at a local level. From a technology standpoint, there are several major obstacles for robotic fleets to work with local businesses: The cost, legal issues, logistical aspects, and the fact that each has a different app. The solution is to integrate with their apps and offer an alternative tool for delivery. It is vital that technology providers work together to offer free IT support and advice to those local businesses who need it, in an effort to protect jobs and the wider economy. When more and more tasks are becoming digitized or automated, but workers do not possess the skills that the technology requires, the outcome will be greater inequality. Overall, it’s vital for tech companies wanting to scale to do market research, spend time creating demand for their product, assess their qualitative and quantitative metrics, and strive for continuous growth.  The best way to move forward is by searching for innovative solutions to stop a lack of resources or high costs from being a barrier to innovation.

What Tech Companies Should Watch Out for When Scaling Operations 

This will be the first article on a series dedicated to the micro-frontends topic. This one will focus on and an suitable for an enterprise application at scale while keeping the advantages that a Single Page Application ( provides to our end users. analysing designing architecture SPA) By the end, you’ll have a working platform with a scalable micro-frontends approach. Why Micro-Frontends? First of all, you might be wondering why do you even need this, right? Well, there are several answers to this question, and I’m definitely not an advocate of using this pattern/architecture “just because”. I’ll provide a concrete example where this makes sense: You’re in a big enterprise project, it’s a big monolithic application encompassing several different “products” inside one single project/product. The company most likely chose this initial model to start fast and get to market soon. Now, they are established and new engineers come to the team. The team grows a lot, and dedicated teams for each sub-product starts to feel like a good and realistic possibility. Maybe each sub-product can have its own product manager, to extract the most out of each product and to enable the teams to focus on smaller parts of the platform. Up until now, the teams have been working horizontally, meaning that they are working focused on the technology itself (front-end, back-end), instead of focused on the Product. The company decides that vertical teams will be more productive on the long term and also more scalable as new engineers get into the company. These vertical teams will hold all the knowledge inside a specific product and will be composed by the 3 related horizontal layers (front-end, back-end, infrastructure), which will also allow growing each product individually. With this approach, the company can also get more independent teams and deployments, by not being dependent on a single gigantic release to get a new version out to the clients. Given these changes, how will the front-end reflect them? Solutions After a lot of investigation, I concluded that micro-frontends would be the solution to the scenario presented above. But micro-frontends has many faces, many technologies possible, many opinion articles (just like this one) and it’s not easy to know where and what to bet on. I’ll be presenting an that is but that still leverages the and performance that we love to rely on to deliver a great experience to our customers. architecture suitable for scale SPA feel The imaginary business that we are going to use for these articles is a store of clothing items (dresses, shirts, pants, etc). This business is a case of the scenario presented above, working in a horizontal structure on a monolithic application. Now, to increase team independence, they want to create 3 vertical teams for the different sub-products of their platform: , and the . Items Checkout, Blog Architecture To achieve the objective of the clothing company presented in the previous section, we need to design an architecture that enables all the teams to be independent while connecting all these individual products into a coherent platform. The following technologies will allow us to achieve what we need: ReactWebpack 5 with Module Federation Yarn Workspaces with lerna The following architectural diagram represents an overview of the final solution. This is what we’ll achieve at the end of this series of articles on ( ). micro-frontends MFEs All of the components presented in the architecture are , and as such, each is , and but all of them tie together to deliver a consistent experience to the customer. MFEs independently developed tested deployed, App Shell This MFE will be the into the platform. single entry point It will be responsible for rendering the first page to our user and also for invoking the respective micro-frontends as the user navigates through our platform. Using , we will be able to asynchronously load the necessary micro-frontends , keeping the solution memory and performance efficient. client-side navigation at run-time It’s pretty common to find this App Shell approach in micro-frontend projects, but some of the approaches are not exactly like this. They follow more of a where each MFE can be the entry point for the user, and then that MFE will import the “setup” code from the App Shell (e.g. Base Layout with the Header and Footer). Although this approach is perfectly valid, it will most definitely not feel like an SPA to the end user while switching between different entry points for the platform, since it will force a refresh each time the user navigates to a domain from a different MFE. micro-services approach Shared Inevitably, there are components that will be shared between our different MFEs, and for that reason the Shared MFE will be their home. Regarding this MFE, in my opinion, there is not a team specifically responsible for this, instead, there should be a “caretaker” of the MFE that would review any pull request created regarding shared components. This person would be responsible for keeping order in Shared and also for keeping a critical thinking on the following question: “Will this change affect other MFEs?”. Items / Checkout / Blog These MFEs are the actual products with dedicated teams working on them. They’re all in development and deployment. None of them is directly dependent on the other. Naturally, since the platform works as a whole, we need to cover the possibility of some MFE being broken and handle that gracefully. However, this approach will not let any MFE break the whole platform, as long as we handle the remote imports properly. independent Repository Structure We’re going to use a for this solution. monorepo The option to use a is arguable, and for all the cons it may have, the I’m after by using it are: monorepo pros Easily shareable configurations (linters, tasks, setup jobs, code styles)Better / easier overview of the whole systemEasier to make a gradual migration from an existing monolith Architecture (in detail) Let’s see in more detail how these components interact with each other and how exactly we enable the features mentioned in the previous section. The key to making this whole architecture work is . Module Federation Webpack 5 Module Federation aims to solve the sharing of modules in a distributed system, by shipping those critical shared pieces as macro or as micro as you would like. It does this by pulling them out of the build pipeline and out of your apps. The excerpt is from the official a new feature shipped officially on . If you want to learn more you can read the information on the website and also check out their section. There is also a whole with examples using this new technology. Module Federation page , Webpack 5 videos GitHub project Very briefly, Module Federation provides us the chance to code (components, functions, variables) at , while efficiently managing dependencies between different remotes. remotely import javascript run-time Basically, any application can be classified as a (in case it’s the entry point) or a (in case it’s imported by a host). An application can be both a host and a remote at the same time, in which case it’s called . host remote bi-directional Next, let’s analyse some common micro-frontends pitfalls and see how we can get around them using the presented architecture. Common pitfalls Dependency Duplication Using a micro-frontend approach may lead to importing dependencies multiple times. When the components are remotely imported from another MFE, they need certain dependencies to work. Chances are that the MFE importing that piece of code already has some (or all) of those dependencies. Without highly customised caching for dependencies, the imported components will most likely bring along their own dependencies, causing the host to have multiple instances of the same libraries (e.g. having different versions of React in scope may lead to unexpected problems). With the presented architecture we’ll take advantage of to manage these dependencies. When a remote module is imported at run-time, Webpack will actually check if it already has the dependencies required in the . If they are there, then the only thing imported from the remote is the actual code. Module Federation host Dependency Mismatches In micro-frontends, sometimes different versions of the same library are loaded in the same scope. To avoid this, we’ll be using a combination of to manage our monorepo and to manage the dependencies of our different MFEs. lerna yarn workspaces By using yarn workspaces we’ll make sure that the versions of critical libraries are locked for all the micro-frontends, preventing this whole class of problems. This approach reduces independence, but it allows a more robust overall solution. UI / UX In a micro-frontends solution, it often happens that the and diverge between different MFEs. It’s a natural process when different independent teams are each developing their products. At the same time, we need to keep in mind that the different products form the platform as a whole and that platform should provide a consistent experience to the user. UI UX This should happen both in the UI components and in the flows / user experience provided. In the solution presented, by using a Shared MFE, we are keeping these same visual components and reusable behaviours in the same package. This will guarantee that the UI experience is unique and consistent. At the same time, we’re giving this Shared MFE the independence it needs to release frequently. This will help fix small problems in the common components without needing to redeploy any other product MFE (like Items and Blog). Local Development Local development can be taxing in a micro-frontends solution, sometimes needing to run the whole solution in order to test what you are developing in a small part of that platform. The way we’re going to solve this is by providing the chance for all the different MFEs to be run in mode. This will be done by providing 2 separate entry points to our MFE, one for the integration with the whole MFE solution and another for the stand-alone entry point. stand-alone Conclusion The architecture presented in this article was achieved after a lot of investigation on the topic and you can check a lot of material in the section below. References In the next articles, we’ll be deep-diving on how to implement this architecture by following along a github repository with the example project for our imaginary business use case. We’ll also talk about common challenges that occur when using and what can we do to solve them. Module Federation Stay tuned 😉 If you find this article interesting, please share it, because you know — Sharing is caring! If you enjoy working at a large scale in projects with global impact and if you like a real challenge, feel free to reach out to us at xgeeks ! We are growing our team and you might be the next one to join this group of talented people 😉 Check out our social media channels if you want to get a sneak peek of life at xgeeks! See you soon! References https://martinfowler.com/articles/micro-frontends.html https://dev.to/marais/webpack-5-and-module-federation-4j1i https://blog.nrwl.io/monorepos-and-react-microfrontends-a-perfect-match-d49dca64489a https://dev.to/brandonvilla21/micro-frontends-module-federation-with-webpack-5-426 https://www.nicolasdelfino.com/blog/micro-frontends-module-federation-webpack https://indepth.dev/webpack-5-module-federation-a-game-changer-in-javascript-architecture https://blog.bitsrc.io/revolutionizing-micro-frontends-with-webpack-5-module-federation-and-bit-99ff81ceb0 https://medium.com/dazn-tech/orchestrating-micro-frontends-a5d2674cbf33 https://medium.com/hacking-talent/two-years-of-micro-frontends-a-retrospective-522526f76df4 https://single-spa.js.org/docs/recommended-setup/ https://medium.com/swlh/webpack-5-module-federation-a-game-changer-to-javascript-architecture-bcdd30e02669 https://module-federation.github.io/videos Also read behind a paywall at https://medium.com/xgeeks/micro-frontends-at-scale-part-1-a8ab67bfb773

An Introduction to Micro-Frontends in Enterprise Applications (part 1)

I’ve come to talk with you again Photo by on Shubham Dhage Unsplash I’ve written a whole bunch of blogs about running Cloud Foundry on Kubernetes. They all work, to varying degrees of success and have the support of a smattering of communities (read: companies with large budgets + engineers + open source ideals). Sometimes you just want BOSH. That sweet, sweet, familiar BOSH. Enter . bosh-lite What is ? bosh-lite works by spinning up a VM and placing BOSH on it. From there, you can use it to spin , , or anything else that has a BOSH manifest. bosh-lite cf-deployment zookeeper The BOSH director is configured with runtime configs, cloud configs, stemcells, releases, and all the other BOSH-like functionality you’ve come to expect. Installation The tool has been around forever, with a very good tutorial on installing and configuring located at . The rest of this blog assumes that you have followed these instructions up to step #7. bosh-lite https://bosh.io/docs/bosh-lite/ I had an issue with a newer version of VirtualBox (6.1.28), what worked for me is version 6.1.26 which is available . here It ends with an example deployment of which I guess is cool, but I’m guessing most BOSH directors are associated with a Cloud Foundry deployment. Read on for tips that maybe aren’t obvious from the documentation! zookeeper Deploying Cloud Foundry In -land there is documentation for using BBL to deploy and CF to . I point these instructions out in case you have an unlimited IAAS budget, however, I’ll show you how to deploy this to your Mac. cf-deployment bosh-lite GCP or AWS There is also a Readme within . I’ll be using this as the basis for the scripting below. cf-deployment For Operators Deploying CF to local bosh-lite Clone cf-deployment For these examples, I’ve assumed you will clone the CF repo to your home folder: cd ~

git clone https://github.com/cloudfoundry/cf-deployment.git Setup BOSH Environment variables This is spread across a few places in the documentation, I’ve gathered them all together: export CREDHUB_SERVER=https://192.168.56.6:8844
export CREDHUB_CLIENT=credhub-admin
export CREDHUB_SECRET=$(bosh interpolate ~/deployments/vbox/creds.yml --path=/credhub_admin_client_secret)
export CREDHUB_CA_CERT="$(bosh interpolate ~/deployments/vbox/creds.yml --path=/credhub_tls/ca )"$'\n'"$( bosh interpolate ~/deployments/vbox/creds.yml --path=/uaa_ssl/ca)"

export BOSH_CLIENT=admin
export BOSH_CLIENT_SECRET="$(bosh int ~/deployments/vbox/creds.yml --path /admin_password)"
export BOSH_CA_CERT="$(bosh interpolate ~/deployments/vbox/creds.yml --path /director_ssl/ca)"
export BOSH_ENVIRONMENT=vbox

bosh alias-env vbox -e 192.168.56.6 --ca-cert <(bosh int ~/deployments/vbox/creds.yml --path /director_ssl/ca) You might consider adding this to your bash/zsh profile. Upload Stemcell, Cloud Config and Runtime Config bosh upload-stemcell --sha1 f399044d2ebe3351f0f1b0b3f97ef11464d283b4 "https://bosh.io/d/stemcells/bosh-warden-boshlite-ubuntu-xenial-go_agent?v=621.125"
bosh update-runtime-config ~/workspace/bosh-deployment/runtime-configs/dns.yml --name dns
bosh update-cloud-config ~/cf-deployment/iaas-support/bosh-lite/cloud-config.yml As time goes by, CF may complain of wanting a newer stemcell version, update the command with the requested version. The version and are listed , note that uses stemcells. bosh upload-stemcell sha1 here bosh-lite warden Go Get a Drink This part will take around 45 minutes to complete, unless you’ve changed the IP address of the BOSH director, the following command will deploy CF. cd ~/cf-deployment
bosh -e 192.168.56.6 -d cf deploy \
  cf-deployment.yml \
  -o operations/bosh-lite.yml \
  -v system_domain=bosh-lite.com Log Into CF This is done in two steps, targeting the API and then logging in, pulling the admin password out of credhub without printing it to the screen: cf api https://api.bosh-lite.com --skip-ssl-validation
cf login -u admin -p $(credhub get -n $(credhub find -n admin | grep cf_admin | cut -d: -f2) | grep value | cut -d: -f2) -o system -s test Tada! Create a space and start pushing apps. (Optional) Setup SSH to BOSH Director Why? I like to be able to SSH to my BOSH Director to poke around. I’m weird. This is not required for the CF deployment, but nice to have: bosh int ~/deployments/vbox/creds.yml --path /jumpbox_ssh/private_key > ~/deployments/vbox/jumpbox.key
chmod 600 ~/deployments/vbox/jumpbox.key
ssh jumpbox@192.168.56.6 -i ~/deployments/vbox/jumpbox.key Beware of Dragons Issue #1 – Reboot Rebooting your Mac causes to misbehave, badly, unless you follow a couple of easy steps. bosh-lite Before shutdown: In Oracle VM VirtualBox Manager select the VM, right click and navigate to > Close Save State After reboot: In Oracle VM VirtualBox Manager select the VM, right click and navigate to > Start Headless Start Even performing these steps I can’t always bring the BOSH Director back online. If you have accidentally rebooted the host you can recreate the BOSH director using the state file and use to recover the broken CF deployment: bosh cck cd ~/deployments/vbox

bosh create-env ~/workspace/bosh-deployment/bosh.yml \
  --state ./state.json \
  -o ~/workspace/bosh-deployment/virtualbox/cpi.yml \
  -o ~/workspace/bosh-deployment/virtualbox/outbound-network.yml \
  -o ~/workspace/bosh-deployment/bosh-lite.yml \
  -o ~/workspace/bosh-deployment/bosh-lite-runc.yml \
  -o ~/workspace/bosh-deployment/uaa.yml \
  -o ~/workspace/bosh-deployment/credhub.yml \
  -o ~/workspace/bosh-deployment/jumpbox-user.yml \
  --vars-store ./creds.yml \
  -v director_name=bosh-lite \
  -v internal_ip=192.168.56.6 \
  -v internal_gw=192.168.56.1 \
  -v internal_cidr=192.168.50.0/24 \
  -v outbound_network_name=NatNetwork --recreate

bosh cck If that doesn’t work, you can completely wipe and recreate the BOSH and CF deployment by: Logging into Oracle VM VirtualBox Manager, select the VM, right click and select > Remove Delete all files Delete the state file at ~/deployments/vbox/state.json Rerun the command then redeploy CF. bosh create-env Issue 2 – Networking issues If you get: Deploying:
  Creating instance 'bosh/0':
    Waiting until instance is ready:
      Post "https://mbus:<redacted>@192.168.56.6:6868/agent": dial tcp 192.168.56.6:6868: connect: connection refused

Exit code 1 You probably skipped the step to add the host routing: sudo route add -net 10.244.0.0/16     192.168.56.6 Final Thought We all deserve nice things. is one of those nice things for folks who enjoy BOSH and want to use it on their own computer. bosh-lite Enjoy! Also published . here

Spin Up a BOSH VM Faster Using BoshLite

When I started working on a start-up nine months ago, this statement didn’t satisfy me or my co-founders: “There is no way to build a Slack bot more reliable than a Slack itself.” — common sense. Our product is an incident management tool called . It’s a Slack app and yes, it should be super reliable. We didn’t leave our SE jobs in Silicon Valley and London to give up when faced with our first technical challenge. Amixr.IO What’s a reliable web service? And what’s not? In terms of reliability, Slack is pretty good. They do a lot of good things. For example they keep users posted about problems through and . status page twitter Unfortunately, Slack has also experienced serious periods of downtime, and users remember that. That’s why users don’t expect an uptime-critical service, such as the our tool, to rely on it. We’ve found a way to achieve that goal. Over the last few months, our service has proved much more reliable than Slack, and we’ve even unveiled an SLA because we’ve built something that customer can rely on. Six simple principles described below helped us here. 1. Draw a red line Any Slack app, as well as any other bot for Facebook or Telegram, uses a web server. Slack is just an interface that allows users to interact with a system. That means we can draw a red line across all of our applications. Parts that are connected to Slack are in danger; other parts depend only on us. Imagine you’re creating a small Slack app. Its purpose is to post a message about new cards on your Trello board. The bot could be a simple web server that will wait for a from Trello in order to post a new message to Slack. It could be as simple as: webhook slackclient SlackClient rest_framework.views APIViewё sc = SlackClient(<bot_access_token>)
        sc.api_call( ,
           channel=<channel_id>,
           text= ,
        ) from import from import : class TrelloForwarderAPIView (APIView) : def get (self, request) "chat.postMessage" "New Cards in Trello detected" Let’s imagine that sc.api_call raises an Exception because the Slack API doesn’t work. I’m sure it won’t last long, so there’s no need to panic. However, you won’t be able to publish a message about this particular Trello card. It will be missed, forever, and your business could be damaged. Now, let’s draw a line between the Trello part and the Slack part and sandbox everything where Slack could cause data loss. To do that, we’ll receive a message from Trello, write it to a queue, and then only remove it from the queue when the message has been successfully posted. celery shared_task slackclient SlackClient rest_framework.views APIView sc = SlackClient(<bot_access_token>)
    sc.api_call( ,
        channel=<channel_id>,
        text= ,
    ) notify_slack.apply_async() from import from import from import @shared_task(autoretry_for=(Exception,), retry_backoff=True, max_retries=None) : def notify_slack () "chat.postMessage" "New Cards in Trello detected" : class TrelloForwarderAPIView (APIView) : def get (self, request) We use Celery, RabitMQ, and Django. Celery’s apply_async method publishes a task to RabbitMQ. In our case, it’s a highly available cluster. Celery workers look out for new tasks and try to execute them until they’re executed, without exception. Now, we can be sure that we won’t lose data if the Slack API causes exceptions on our side. 2. “Broken” doesn’t always mean that “everything is broken” Slack is huge. It has millions of users online and uses multiple servers in regions across the world. If something goes wrong in one place, it doesn’t mean that everything is broken at another location. Even if 99% of servers aren’t able to proceed with a request, there’s another 1% that can. That’s exactly what to Slack in June when its servers weren’t able to process a small fraction of our requests. However, our server didn’t give up, and sent everything successfully. Just nine messages got stuck at one moment, but we’ve sent them successfully since then. happened 3. Be notified early We love it when Slack works, but we need to know when it doesn’t. That’s why we monitor Slack ourselves. We check message delivery, verify API responses, and so forth. We proactively monitor Slack and sometimes detect issues, such as the one detailed in this message regarding the , before anyone else. blocks API If you’re building your service on top of another service, then write scripts and automated tests that proactively monitor the third party. That’s the only way to be the first to learn about a problem. 4. Prepare a Plan B OK, so you’ve built an awesome Slack app that works even when Slack doesn’t. How can you be sure that everything is ready? . It’s just a small example of a fascinating subject called chaos engineering ( ). Simulate a problem, even in production, then check your monitoring and your backup systems. Try simulate Slack downtime yourself https://principlesofchaos.org/ A huge part of this trick is known as “graceful degradation.” It’s the ability to deactivate non-critical parts if they don’t work as expected. In the case of total Slack downtime, our app will switch to a back-up delivery channels, such as e-mail, phone, or another messaging service. It reduces functionality and delivers business value anyway. 5. People should be close We don’t let a critical situation evolve without qualified people watching the process carefully. Even if a case-critical situation is 100% what we expected and recently tested for, there’s still a huge chance of the unexpected occurring. Sometimes, engineers consider how to fix problems temporarily while causing as little damage as possible. For instance, during the issue with the blocks API, we were able to find a workaround pretty quickly. We dealt with the issue before Slack fixed the problem on its side. 6. Talk with clients When discussing outages, we’ve chosen a proactive strategy. We actively inform our users about outages before they notice anything suspicious. If that happens, they won’t consider our service unreliable — they’ll be confident that we know about the problem and are doing our best to fix it. Fun fact: At the very beginning ,we did not have reserve notification channels about a downtime. For the first time, I had to look for users on social networks, write on Facebook, introduce ourselves, and make sure that we didn’t let anyone down. It was really creepy, but, in the end, people reacted positively and became regular users. The six principles described above are universal. They can help to build a reliable service on top of any other third party, and they’ve already helped us with Slack. Each product has different requirements so it's up to you which practices to implement.

How to build a Slack App more reliable then Slack

This is part of an ongoing series: see first and second posts. first second Principle III: Modularize The Prompts A hideous monstrosity. Every experienced engineer has seen one: code that is so vast, high risk, and difficult to understand that no one dares to touch it. There are no unit tests, every change is cause for a minor heart attack. The only ones who venture near it are the old timers - those who were around when the monster was built and they only come close when there is no alternative. It's stale, unmodularized and the dependencies are out of date. The component is too dangerous to seriously alter. I remember the first monstrosity I encountered. A 5,000-line function that was central to the operations of a business worth hundreds of millions of dollars; barely anybody had confidence to touch it. When it broke, whole teams were woken up in the middle of the night. All development in the company was slowed down because of a dependency on this key component. Millions of dollars were spent trying to manage the monster. What does all of this have to do with LLM prompts? They can become monstrosities too! So scary to change, that no one touches them. Or conversely, teams try fixing them and cause an avalanche of incidents. What Customers Need Customers don’t want to pay for software that works correctly only on Tuesdays and Thursdays; they demand constant reliability and a stream of new features. When building long term high-reliability systems it's essential to enable the application to evolve, while constantly keeping the lights on. This applies to Gen AI powered applications as much as traditional software. So how do you get a healthy AI powered application and not a monstrosity? There are over a dozen approaches all covered in this series. They all start with one principle: instead of one ginormous prompt, you want multiple smaller focused prompts that each aim to solve a single problem. What is Modularization Modularization is the practice of breaking down a complex system into smaller, self-contained, and reusable components. In traditional software engineering, this means writing functions, classes, and services that each handle a specific task. In the context of prompt engineering for LLMs, modularization means splitting a large, monolithic prompt into smaller, focused prompts—each designed to perform a single, well-defined job. Benefits of Modularization Modularization allows you to safely introduce changes to your system over time. Its importance grows when: The length of time the application will be maintained increases.
The number and complexity of features expected to be added increases.
The reliability requirements on the system get stricter. The length of time the application will be maintained increases. The number and complexity of features expected to be added increases. The reliability requirements on the system get stricter. All of these dimensions need to be understood when planning out the system. But how specifically does modularization help maintain the system? The main benefits are described below. Risk Reduction LLM prompt performance is inherently unstable. Their nature is such that any change can affect output in unpredictable ways. You can manage this risk by breaking big prompts into components, where a change can only affect the performance of a part of the system. Even if one prompt is broken, the rest of the system will operate as before the change. But what if prompts operate as a chain? Wouldn’t breaking one component still break the chain? Yes, it would, but the damage is still reduced in this scenario. An erroneous output in a prompt chain can supply the downstream prompts with faulty inputs, but each component would still operate as before the change on the set of valid inputs. Contrast this with altering a giant prompt - the change can (and will!) affect every bit of logic encoded in that prompt. You didn’t break one aspect of the system - you potentially broke every part of it. (Operating chains of prompts safely is a future chapter in the series. You need to plan for various types of failures and have contingency plans. But this is beyond the scope here) Improved Testability Anyone who has written unit tests knows that a simple function that does a single thing is WAY easier to test than a complex function that tries to do many different things. The same applies to prompts - a small, focused prompt can be tested much more thoroughly both manually and in a fully automated manner. Better Performance A wide body of evidence shows that shorter prompts tend to outperform longer ones: 1, 2, 3. 1 2 3 Research on the effects of multitasking on prompt performance is more mixed: 4, 5. A perfectly optimized prompt can, under the right circumstances multitask. In practice though, it is much easier to optimize focused prompts, where you can track performance along a single main dimension. You should aim for more focused prompts wherever possible. 4 5 Ease of Knowledge Share Explaining the intricacies of a super prompt with 3 thousand words to a new team member is a journey. And no matter how much you explain, the only ones who have a feel for this beast will be contributing authors. A system of prompts, with each part being relatively simple can be onboarded to much faster; engineers will start being productive sooner. Cost Optimization By using different models in different parts of the system, you can achieve significant cost and latency savings without affecting response quality. For example, a prompt that determines input language doesn’t have to be particularly smart - it doesn’t require your latest and most expensive model. On the other hand, the prompt that generates the reply based on documentation could benefit from a built-in chain of thought reasoning embedded in high-end models. chain of thought reasoning When to NOT Modularize Most software-powered applications require safely adding features over extended periods of time. There is, however, an exception. Prototype applications are not intended to be maintained for long; they won’t get new features, and are not meant for high reliability. So don’t waste time with modularization when building prototypes. In fact, most of the patterns in this series do not apply to prototype applications. When building a prototype - go quick, verify the critical unknowns and then throw the code away. Another consideration is knowing when to stop modularizing. There is overhead to managing extra prompts and if benefits of further modularization are low - you should stop breaking the system up further. Infrastructure for Modularization If modularizing prompts was trivial - everybody would be doing it. To manage many prompts in a system, you need to invest in infrastructure - without it you will get chaos. Here are the minimal requirements for the LLM prompt infrastructure: Ability to add prompts quickly and pain-free in a standardized way. Particularly important when prompts are loaded from outside the codebase. See Principle II: Load Prompts Safely (If You Really Have to).


Ability to deploy prompts in an automated way.


Ability to log and monitor inputs/outputs of individual prompts.


Ability to add automated tests that cover prompts.


A way to easily track token/$ spend on various prompts. Ability to add prompts quickly and pain-free in a standardized way. Particularly important when prompts are loaded from outside the codebase. See Principle II: Load Prompts Safely (If You Really Have to). Ability to add prompts quickly and pain-free in a standardized way. Particularly important when prompts are loaded from outside the codebase. See Principle II: Load Prompts Safely (If You Really Have to). Principle II: Load Prompts Safely (If You Really Have to) Ability to deploy prompts in an automated way. Ability to deploy prompts in an automated way. Ability to log and monitor inputs/outputs of individual prompts. Ability to log and monitor inputs/outputs of individual prompts. Ability to add automated tests that cover prompts. Ability to add automated tests that cover prompts. A way to easily track token/$ spend on various prompts. A way to easily track token/$ spend on various prompts. Case Study Let’s see how building a Gen AI powered system plays out in practice with and without modularization. No Modularization You are building a tech support app and are determined to implement it with a single prompt. In the simplest version, you can imagine a monolith prompt that generates responses while loading relevant documentation through RAG. RAG Looks nice and easy, right? But as you add features - problems with this architecture emerge: You want to respond to messages in a fixed list of languages, but not handle others. To achieve this you add prompt instructions to only respond in certain languages and get the LLM to return the “language” field for reporting purposes.


You want all conversations classified. Add a field “label” to the prompt output.


When the user is unhappy - escalate the case to human support. Add “escalate_to_human” output variable along with instructions in the prompt.


Need a translation of all messages sent for internal audit. Return the “translated” field with a message in English.


Need protection to make sure that the app never asks users about their location and who they voted for in the last election. Add prompt instructions and test it out manually.


Need a summary for every conversation? Add “summary” field to every output. You want to respond to messages in a fixed list of languages, but not handle others. To achieve this you add prompt instructions to only respond in certain languages and get the LLM to return the “language” field for reporting purposes. You want to respond to messages in a fixed list of languages, but not handle others. To achieve this you add prompt instructions to only respond in certain languages and get the LLM to return the “language” field for reporting purposes. You want all conversations classified. Add a field “label” to the prompt output. You want all conversations classified. Add a field “label” to the prompt output. When the user is unhappy - escalate the case to human support. Add “escalate_to_human” output variable along with instructions in the prompt. When the user is unhappy - escalate the case to human support. Add “escalate_to_human” output variable along with instructions in the prompt. Need a translation of all messages sent for internal audit. Return the “translated” field with a message in English. Need a translation of all messages sent for internal audit. Return the “translated” field with a message in English. Need protection to make sure that the app never asks users about their location and who they voted for in the last election. Add prompt instructions and test it out manually. Need protection to make sure that the app never asks users about their location and who they voted for in the last election. Add prompt instructions and test it out manually. Need a summary for every conversation? Add “summary” field to every output. Need a summary for every conversation? Add “summary” field to every output. Perhaps you are beginning to see the problem - this prompt now has six outputs. Testing it will be a nightmare. You add support for another language, and suddenly your app begins to return the summary in Spanish instead of English. Why? Who knows, LLM outputs are unstable, so changing the prompt has unpredictable results. Congratulations - you’ve created a monster! Over time it will grow and cause even more pain. With Modularization Both Prompt Chain and an entirely separated classification prompt is used. The original large prompt is modularized as much as practical. Prompt Chain Prompt Chain One prompt detects the language, one provides translation, one determines if the user is upset and escalates to humans, response prompt generates the response, guardrail verifies compliance of response. Outputs of one prompt are chained to be inputs of the next; traditional code can operate between these prompts to, for example, check language eligibility, without involving LLMs. A change can still break a given prompt, but risks are greatly reduced because: A change to one part doesn’t risk breaking every part of the application logic.
Testing is much easier and the odds of catching failure early are high.
Each prompt is relatively simple, so it's easier to understand and you are less likely to do damage with a change.
Changes are easier to review. A change to one part doesn’t risk breaking every part of the application logic. Testing is much easier and the odds of catching failure early are high. Each prompt is relatively simple, so it's easier to understand and you are less likely to do damage with a change. Changes are easier to review. You get all the benefits of Gen AI, but the risks are greatly reduced. Plus, you can use cheaper models for some components to save money. Conclusion Modularization allows you to isolate errors, improve maintainability, and build a more reliable system. Even moderately sized applications will have dozens, if not hundreds, of component prompts. Break up prompts until they each perform a single task, and until the benefits of further modularization are outweighed by added operational complexity. Modularizing your prompts is a necessity if your AI-driven applications are to remain reliable, and continue to add features over the long run. There are plenty of “monster” systems around already - take care not to create new ones! If you’ve enjoyed this series - subscribe for more posts.

This One Practice Makes LLMs Easier to Build, Test, and Scale

Esta práctica hace que los LLM sean más fáciles de construir, probar y escalar

این یک تمرین ساخت، آزمایش و مقیاس LLM را آسان‌تر می‌کند

Cette pratique unique facilite la création, le test et la mise à l'échelle des LLM

תרגול אחד זה מקל על בניית, בדיקה וקנה מידה של לימודי LLM

この 1 つのプラクティスにより、LLM の構築、テスト、拡張が容易になります

Бұл бір тәжірибе LLM құруды, сынауды және масштабтауды жеңілдетеді

Энэ нэг практик нь LLM-ийг бий болгох, туршиж, түлхүүрлэхэд хялбар болгодог

Ta jedna praktyka ułatwia budowanie, testowanie i skalowanie LLM

Esta prática torna os LLMs mais fáceis de construir, testar e dimensionar

Эта одна практика делает LLM легче строить, тестировать и масштабировать

Kjo praktikë e vetme i bën LLM-të më të lehta për t'u ndërtuar, testuar dhe shkallëzuar

Ця єдина практика полегшує створення, тестування та масштабування LLM

ChatGPT Goes Ghibli, Google Gets Smarter, and Microsoft Embeds Knowledge at Scale

Scalability in Microservices: Creating Systems That Can Scale Effortlessly

**Austin, TX, United States, March 19th, 2025/CyberNewsWire/--**SpyCloud, the leading identity threat protection company, today released its 2025 SpyCloud Annual Identity Exposure Report, highlighting the rise of darknet-exposed identity data as the primary cyber risk facing enterprises today. 2025 SpyCloud Annual Identity Exposure Report 2025 SpyCloud Annual Identity Exposure Report As cybercriminals move beyond single data points and leverage stolen data from a number of sources – breaches, malware and phishes – they are embracing a more sophisticated approach to identity exploitation, and organizations must shift their focus to a comprehensive and holistic defense strategy that accounts for the interconnected nature of digital identities. Holistic Identity: The New Cyber Battleground Organizations have traditionally focused on securing individual account credentials, but SpyCloud’s research indicates that cybercriminals have expanded their tactics beyond conventional account takeover. Attackers now have access to extensive identity data from multiple sources—including data breaches, infostealer malware infections, phishing campaigns, and combolists—posing a challenge for organizations whose security measures have not yet adapted to address the full scope of interconnected identity exposures holistically. SpyCloud’s collection of recaptured darknet data grew 22% in the past year, now encompassing more than 53.3 billion distinct identity records and over 750+ billion total stolen assets that are now circulating in the criminal underground, fueling identity-based cybercrime. These assets are a vast array of personal and professional credentials, session cookies, personally identifiable information (PII), financial data, IP addresses, national IDs and more that criminals are weaponizing in attacks against individuals and businesses. "The cybersecurity industry has spent years defending against traditional credential-based threats, but the reality is that attackers have advanced as the data they have access to has exploded in volume,” said Damon Fleury, Chief Product Officer, SpyCloud. “Identity is the ultimate frontier of cyber risk, with users’ exposure across past and present, personal and professional identities the new attack surface. It requires organizations to rethink the risks posed by employees, consumers, partners and suppliers.” "The cybersecurity industry has spent years defending against traditional credential-based threats, but the reality is that attackers have advanced as the data they have access to has exploded in volume,” said Damon Fleury, Chief Product Officer, SpyCloud. “Identity is the ultimate frontier of cyber risk, with users’ exposure across past and present, personal and professional identities the new attack surface. It requires organizations to rethink the risks posed by employees, consumers, partners and suppliers.” Fleury continues, “At SpyCloud, we’ve created holistic identity analytics built on the industry’s largest collection of recaptured darknet data, enabling our customers to correlate disparate data points that encompass an individual’s digital footprint—providing a truly holistic view of identity risk.” Fleury continues, “At SpyCloud, we’ve created holistic identity analytics built on the industry’s largest collection of recaptured darknet data, enabling our customers to correlate disparate data points that encompass an individual’s digital footprint—providing a truly holistic view of identity risk.” New Definition for Identity Risk Emerges With the explosion of available identity data, attackers can now piece together historical and present-day records to bypass security barriers. Traditionally, cybersecurity teams were only able to see a fraction of an individual’s darknet exposures – primarily only the exposed assets tied to a corporate identity – which were not comprehensive nor in correlation with other exposures. SpyCloud’s report shows that an individual’s identity exposure is more expansive than traditional cyber risk tools would indicate; in fact, it’s a sprawling web of interrelated assets that provide cybercriminals with a roadmap to exploit vulnerabilities and the keys to unlock valuable access. Of particular concern for businesses, a single corporate user now has an average of 146 stolen records linked to their identity – across 13 unique emails and 141 credential pairs (a username or email and its associated password) per corporate user, which highlights how attackers correlate historical data to uncover active enterprise access points.
In the consumer realm, the numbers are even higher with 229 records per consumer, frequently including exposed PII such as full names, dates of birth, and phone numbers, as well as Social Security/ID numbers, addresses, and credit card or bank information. Consumer exposure averages 27 unique emails and 227 credential pairs per user. Of particular concern for businesses, a single corporate user now has an average of 146 stolen records linked to their identity – across 13 unique emails and 141 credential pairs (a username or email and its associated password) per corporate user, which highlights how attackers correlate historical data to uncover active enterprise access points. In the consumer realm, the numbers are even higher with 229 records per consumer, frequently including exposed PII such as full names, dates of birth, and phone numbers, as well as Social Security/ID numbers, addresses, and credit card or bank information. Consumer exposure averages 27 unique emails and 227 credential pairs per user. "The record-breaking breaches of 2024, including the Mother of All Breaches (MOAB) and the National Public Data Breach, along with the growing use of infostealing malware and crafty phishing campaigns illustrate just how vast the pool of exposed identity data has become,” said Trevor Hilligoss, Senior Vice President of Security Research, SpyCloud Labs at SpyCloud. "The record-breaking breaches of 2024, including the Mother of All Breaches (MOAB) and the National Public Data Breach, along with the growing use of infostealing malware and crafty phishing campaigns illustrate just how vast the pool of exposed identity data has become,” said Trevor Hilligoss, Senior Vice President of Security Research, SpyCloud Labs at SpyCloud. “By understanding how cybercriminals aggregate stolen data and the new tactics and trends they are leveraging to assume even more valuable information and access, organizations can take proactive steps to mitigate identity-based threats from these large underground sources before they escalate." “By understanding how cybercriminals aggregate stolen data and the new tactics and trends they are leveraging to assume even more valuable information and access, organizations can take proactive steps to mitigate identity-based threats from these large underground sources before they escalate." Additional Report Findings: 17.3 billion cookies were recaptured from malware-infected devices, enabling attackers to bypass MFA and hijack active user sessions.
548 million credentials were exfiltrated via infostealer malware, highlighting the growing role of stealthy, targeted data theft in enterprise attacks.
3.1 billion passwords were recaptured in 2024, marking a 125% increase from the previous year.
70% of users whose credentials were exposed in breaches last year reused previously compromised passwords, significantly increasing their risk of account takeover attacks – a 9+ jump from 2023.
44.8 billion PII assets – a 39% increase from 2023 are opening the door for new fraudulent activities.
97% of recaptured phished data logs in 2024, from popular phishing-as-a-service (PHaaS) platforms like ONNX, included an email address and 64% had an associated IP address, giving criminals direct opportunities to perpetrate as the user and make lateral movements within an organization.
In the public sector, SpyCloud recaptured 127K .gov credentials and observed a 67% all-time password reuse rate – an increase of 13% over the previous year – highlighting persistent security risks for our federal agencies and national security. 17.3 billion cookies were recaptured from malware-infected devices, enabling attackers to bypass MFA and hijack active user sessions. 548 million credentials were exfiltrated via infostealer malware, highlighting the growing role of stealthy, targeted data theft in enterprise attacks. 3.1 billion passwords were recaptured in 2024, marking a 125% increase from the previous year. 70% of users whose credentials were exposed in breaches last year reused previously compromised passwords, significantly increasing their risk of account takeover attacks – a 9+ jump from 2023. 44.8 billion PII assets – a 39% increase from 2023 are opening the door for new fraudulent activities. 97% of recaptured phished data logs in 2024, from popular phishing-as-a-service (PHaaS) platforms like ONNX, included an email address and 64% had an associated IP address, giving criminals direct opportunities to perpetrate as the user and make lateral movements within an organization. In the public sector, SpyCloud recaptured 127K .gov credentials and observed a 67% all-time password reuse rate – an increase of 13% over the previous year – highlighting persistent security risks for our federal agencies and national security. Evolving Cybersecurity Strategies The findings highlight that cybercriminals are moving well-beyond their own legacy tactics and businesses must recognize that traditional defenses are no longer enough. SpyCloud’s approach leverages holistic identity analytics, powered by the industry’s largest collection of recaptured darknet data, to help organizations correlate disparate identity elements and shore up identity threat protection measures, while mitigating risk more effectively. For further insights, the full 2025 SpyCloud Identity Exposure Report is available here. here here About SpyCloud SpyCloud transforms recaptured darknet data to disrupt cybercrime. Its automated holistic identity threat protection solutions leverage advanced analytics to proactively prevent ransomware and account takeover, safeguard employee and consumer accounts, and accelerate cybercrime investigations. SpyCloud’s data from breaches, malware-infected devices, and successful phishes also powers many popular dark web monitoring and identity theft protection offerings. SpyCloud SpyCloud Customers include seven of the Fortune 10, along with hundreds of global enterprises, mid-sized companies, and government agencies worldwide. Headquartered in Austin, TX, SpyCloud is home to more than 200 cybersecurity experts whose mission is to protect businesses and consumers from the stolen identity data criminals are using to target them now. To learn more and see insights, users can visit spycloud.com. spycloud.com spycloud.com Contact Emily Brown REQ on behalf of SpyCloud spycloud@req.co This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

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