Embed Workflowis in the Business Process Management Software Industry

Embed Workflow

In the same league as the United States with of respondents using artificial intelligence (AI) in at least one of their business functions, progress in the Middle East and North Africa (MENA) can be seen — but there’s still significant untapped potential. Already AI is poised to deliver over 9% of the Middle East’s Gulf Cooperation Council (GCC) countries’ combined GDP, and the swift progress of generative AI implies that the figure will be quickly exceeded. 62% While many energy and materials companies, operating and competing internationally, are keen to raise production, distribution, and maintenance efficiency with AI, retail sectors are racing ahead; having long-mined consumer data to inform pricing and promotions. However, for industries like construction, getting started is a little more tricky since they either cannot yet collect the data required to train AI models or need additional technical expertise. Going deeper into the business functions, marketing and sales is the most common application of AI across MENA industries. What needs to be done to tap into other functions, such as supply chain management, risk, and corporate finance? The answer lies in the intersection of cultural, regulatory, and infrastructure changes — which are individually complex, if not thought out carefully. Bridging the Cultural Divide for AI in MENA While McKinsey’s highlights the need for internal transformation across the GCC, the UAE stands out as a leader in fostering the right culture and workflows to embed AI tools seamlessly. report As part of the region’s strategy to ensure students and employees have access to the right training and knowledge to build digital-native enterprises, the UAE established . This university educates and develops skills in computer vision, machine learning, natural language processing, and beyond. MBZUAI When it comes to making cultural shifts, it’s critical to ensure senior leadership is aware and on board with the scope of AI potential in each business function to justify budgets and needs holistically. Leaders must get the right training and unite board members to develop a business-led use case roadmap. The ideal blueprint comes with clear communication of the benefits of workflow changes, and what they mean for individual roles. As part of the change management process, appointed leaders that open multi-channel two-way communication can formally collect feedback and informally gather worker perceptions, praising or addressing any ideas and concerns. Involving team members in the process this way helps to boost acceptance and engagement with the strategy. Working with MBZUAI students will help UAE businesses build an AI-focused culture from the beginning. Businesses in the region may choose to invest in upskilling, collaborating with graduated tech specialists, or consider offshore services to bridge any talent gaps — although, this is where the regulatory challenges come in. Clearing the Red Tape: Streamlining Regulations for MENA’s AI Stringent data storage regulations in many MENA countries restrict companies from leveraging public cloud options abroad. This, coupled with a lack of globally established AI risk frameworks, creates roadblocks for organizations seeking to deploy AI, despite its perceived value. For this reason, predicts a significant rise in security and risk management (SRM) spending across the MENA region. Their forecast expects a 12.1% increase in 2024, reaching a total of US$3.3 billion. Investment in AI-powered SRM helps regional leaders to reduce the threat landscape, proactively identifying potential vulnerabilities or malicious behavior across their digital ecosystems. Gartner Organizations must establish policies that govern application usage and also implement training on cybersecurity incidents associated with employee behavior in their change management strategy. This will help minimize the risks of privacy and copyright infringement and align with the for handling personal data of identifiable individuals — which require MENA organizations to maintain a higher standard of data privacy and cyber security measures. GCC’s latest data protection laws With increased security, training, and governance, MENA companies reduce the risk of collaborating overseas, enabling them to maximize the global market. Building the Infrastructure for High-Speed AI It takes agile companies and infrastructure to scale globally. Yet, only of companies in the MENA region feel they have the technology infrastructure and architecture to support AI. UAE paves the way with the recently creation of MGX, a company focused on investing in: 35% announced data centers and connectivity AI infrastructure: logic and memory chip design and manufacturing Semiconductors: AI models, software, data, life sciences and robotics AI core technologies and applications: Data needs to flow through data pipelines like items on a production line. You can add parts and modify them to different customers or business use cases, but the core product is the same. This type of data structure is beneficial in the short term as it makes data transfer seamless. It also provides longer-term value by making it easier to integrate new technologies later down the line. This is where the UAE’s advanced training programs set the region as an AI frontrunner, ensuring regional data users understand how to navigate data pipelines efficiently. This is a two-pronged effort of flexible technological architecture and user-friendly analytics. With the right training and API-based middleware, employees can maximize a combination of intuitive applications across IT and business teams. These modern-built apps are rich in automation, helping teams alleviate manual, technical tasks and improve the employee experience. AI adoption in MENA is in full swing, with industries across retail, energy, and financial services looking at ways to increase efficiency. A solid change management plan rooted in the company culture makes a successful rollout possible. Strong regional performers will have a well-informed leadership team, clear AI objectives communicated, and investment into agile and scalable infrastructure. This article was originally published by on Maryam A Hassani 150sec.

Navigating the Future of AI in MENA Countries

Making sense of real-world data: ROC curves, and when to use them

The opinions on this page are for general information purposes only and do not constitute legal advice on which you should rely Entrepreneurs need to focus on growing their business whilst staying compliant. Not investing in compliance can jeopardize the business even when everything else is going well. Investing in compliance means meeting regulatory requirements and proving to regulators or compliance experts that the business is compliant. Whilst a lot of investment will go into complying with the regulations, businesses tend to struggle with proving their compliance in a continuous way. Compliance as a service (CaaS) software is a great way to comply continuously by moving compliance monitoring to the cloud. CaaS providers tend to automate compliance for such as ISO. This article explains how contract management can help businesses automate their legal compliance. technical audits ‍ Data protection Businesses need to prove that they process and manage sensitive data according to data protection laws. In particular, employers will need to prove that their employees have read and understood the company's privacy notice, GDPR privacy standard, and various other policies they may provide during the employee onboarding process. Legal software like Legislate helps employers issue lawyer-approved data protection documents to employees seamlessly whilst proving their legal compliance. Employees are invited to review and sign the data protection documentation within Legislate so that the employer can satisfy their compliance requirements. Moreover, by using Legislate, employers maintain compliance by centralizing all contract data and by being able to issue new documents if and when legal or regulatory updates occur. ‍ Risk management Contracts help establish legal relationships between parties and manage the risk a party is willing to take in the legal relationship should something not go according to plan. If a business does not have clear visibility of its contract terms, it will be difficult for the business to conduct a legal risk assessment. Companies that track contract terms with traditional approaches such as in a spreadsheet are creating a potential risk for their business as contracts are evergreen. This means that contracts can be amended, broken, or renewed which can render the data in spreadsheets out of date. A better solution is to use contract management software which acts as a central repository for all of a business's contracts. A company that uses contract management software, that allows them to query and track contract terms, is in a much better position to make complete business decisions. ‍ Software compliance services The compliance landscape includes both compliance as a service (CaaS) and legal software solutions. Businesses that use cloud-based services can automate their compliance more easily as they can be easily tracked by a CaaS provider. Compliance as a service tool will, for example, monitor a business's IT information security, the location and data transfers of their cloud provider, or its compliance with specific regulations such as data protection. Legal software on the other hand helps businesses embed compliance into their contracting workflows so that they can prove contracts were signed. Moreover, legal software helps businesses track the data in their contracts so that they can manage legal risks and make better-informed decisions. Using a cloud-based platform like Legislate helps businesses address compliance concerns cost-effectively. Businesses can create lawyer-approved contracts in the cloud and prove they were read and signed by the counterparty. Businesses can then track the data in their contracts and prove for example that they are complying with gender pay gap requirements or other strategic objectives. The compliance function of the business can also access this data within Legislate or via integrations. ‍ About Legislate Legislate is a legal technology startup that allows landlords, letting agents, and small businesses to easily create, sign and manage contracts that are prudent and fair. Legislate’s platform is built on its which streamlines the contracting process and aggregates contract statistics to quickly unlock valuable insights. Legislate’s team marries technical and legal expertise to create a painless, smart contracting experience for its users. Legislate is backed by , , and angel investors. patented knowledge graph Parkwalk Advisors Perivoli Innovations ‍ Also published here.

An Overview of Legal Compliance as a Service

DevSecOps offers great potentials, but it is also a challenge in practice for many CISOs and DevOps teams. Today, companies typically leverage several automated tools. However, they are to a large extent separate silos that identify separate lists — most often long lists — of risks and vulnerabilities. This creates complexities, inefficiencies, costs, and risks, and do often slows down DevOps organizations. A key capability has been missing in the toolset. A capability that empowers both DevOps Teams and CISO by continuously: there are plenty of different risks. But what is the holistic risk exposure of my high-value assets? Are we good, or do we need to take action? Contextualizing Risks; there are plenty of ways that we can reduce risks. But what possible actions shall I prioritize? What shall I not prioritize? It is simply not feasible to do everything everywhere. Pin-Pointing Actions; we should do these analyses continuously but it is simply not possible to do it manually. It would be great to integrate automated holistic analyses in our CI/CD. Conducting Automated Attack Simulations; This article includes: DevSecOps Challenges in Practice Current State of DevSecOps Workflows and Tooling The Missing Piece Needed How Leading CISOs and DevOps Teams are Leveraging New Capabilities 1. DevSecOps Challenges in Practice DevSecOps is a major evolution of the cybersecurity practice. So, before going into practical details, let’s take a starting point from the overall business perspective. In the overall requirements, challenges, and opportunities that DevOps and DevSecOps bring to organizations, CISOs, and DevOps teams in practice. An organization — DevOpsCo — has a DevOps way of working. It could have two, ten, fifty, or even hundreds of DevOps teams. Each team is Developing and Operating their part of the overall company system environment. It is not uncommon that one team push several releases per day. And each release naturally has an impact on the security posture of the environment. And not only on the team’s environment but also on other teams’ environments and the total infrastructure. Let us explore a typical illustrative example: This small illustration does in itself illustrate both how important it is to embed security into the DevOps workflows to make DevSecOps practically viable and the magnitude of the challenge. But this dynamic is just one part of the challenge. A longer list of key challenges includes the following: , and each release impact both their own team’s, other teams’, and the total organization’s cybersecurity risk posture. * Multiple DevOps teams all make frequent releases * .  Just one example of this is how easy it is to make mistakes in terms of setting up IAM policies. In both cloud environments and on-prem environments, it can very easily happen that IAM is set up in a way that grants too much/ wrong access that creates weak security postures and high business risks. It can very easily happen that teams make security mistakes * and put them into context to what needs to be addressed to continuously manage risk over time. In addition, there is the challenge to keep up to date with new vulnerabilities, security updates, etc, * Even if we think that it would be great if they were, this will never be the case. It is actually not even desirable as it would require too many security resources. DevOps teams are not the security experts. * that both empower the DevOps teams with automated actionable insights and provides the CISO function with a continuous overview, tracking, and management of the cybersecurity risk posture. CISO-function crucially needs mechanisms * Identifying long lists of risks and best practices is a good starting point. But we truly need to identify what risks are key, what actions have the best effect, and when is our security good enough. This prioritization needs to be done from a holistic perspective. And it is not practically viable to do it manually, it is just too complex and time-consuming. We all live in a world of scarce resources. 2. Current State of DevSecOps Workflows and Tooling “Sec” in DevSecOps is not a discrete step or phase, but an integrated part of the activities required to deliver software or service in a secure fashion, as illustrated in the typical DevSecOps loop. Today different activities and tools of the AppSec program will typically attach to different phases of the DevOps loop. Security training of developers, design reviews based on , design and code reviews as well as SAST tools like SonarQube for source code inspection are all part of the Plan and Code phases. In the Build and Packaging phases, you typically find security scanning of vulnerabilities in the supply chain dependencies through solutions from companies like Debricked or Snyk. threat modeling Moving into the Test phase, security testing is done and often automated by DAST tools, and from the Release phase and onwards a set of more traditional cyber operational tools are employed, including vulnerability scanners for the infrastructure, WAFs, and various types of log monitoring and correlation tools including SIEMs. Interestingly, a perfect secure development process will still not be a guarantee against breaches of the application after it has transitioned into a live, deployed state. The application context, such as e.g. the Identity and Access Management (IAM) configuration will often be different from a test environment and is bound to change over time. Time will also cause changes to interdependent services and will cause new vulnerabilities to be discovered, both inside the application as well as in the infrastructure on which the application depends. Time is clearly not on the defender’s side. Furthermore, continuous deployment into public Cloud environments such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform creates an even more challenging situation as both the control plane (asset management operations) and data plane (the application and related service assets) are available over the Internet and often delegated directly to DevOps team. And, maybe most importantly, the different tools are to a large extent separate silos. Silos that identify separate lists — most often long lists — of risks and vulnerabilities. This creates complexities, inefficiencies, costs, and risks, and do often slows down DevOps organizations. 3. The Missing Piece Needed As described in the earlier sections; DevSecOps is a natural way forward for DevOps organizations. But it also imposes several quite significant challenges. To make DevSecOps practically viable, automated tooling has a key role. Today, companies typically leverage a number of automated tools for DevSecOps. However, they are to a large extent separate silos that identify separate lists, most often long lists, of risks and vulnerabilities. A key capability has been missing in the toolset. A tool that continuously: What is the holistic risk exposure of my high-value assets? Are we good, or do we need to take action? Contextualizes Risks. There are plenty of ways that we can reduce risks. But what of all possible actions shall I prioritize? And what shall I not prioritize? It is simply not feasible to do everything everywhere. Pin-Points Actions. We should do these analyses continuously but it is simply not possible to do it manually. It would be great to integrate automated holistic analyses in our CI/CD. Conducts Automated Attack Simulations. These capabilities empower DevOps teams to get continuous insights on key questions as “Are we secure enough?”, “What are the weakest links?”, and “What of all things possible should we do to improve our security posture?”. It enables the CISO function to get an overview and tracking of the security risk posture and get pin-pointed insights when and where needed. 4. How Leading CISOs and DevOps Teams are Leveraging New Capabilities So how can we address the challenges and get the capabilities needed? Historically, the answer has most often been to implement more generic guidelines on patching, authentication, etc — which means that you will typically overspend in lower-risk areas and underspend in high-risk areas — and/or to try to conduct these analyses manually — which then often turns into not doing them at all or doing them at a too high level only. Now, new technology enables organizations to get these central capabilities needed through automated tooling. By leveraging AI-based, automated attack simulations, organizations are able to cut through complexity, gain key insights, and take proactive actions where it really matters. One leading company that is leveraging fully automated attack simulations is Klarna. Klarna is a payments company that is one of Europes’ largest banks and one of the world’s highest-valued and fastest-growing fintech. to continuously manage its security risk posture in highly dynamic cloud environments. Klarna leverages automated attack simulations The concepts that Klarna leverages consist of three steps. In the first step, the tool generates digital twin models of the systems in scope. The second step is to simulate thousands of attacks towards the digital twins, capturing all possible ways attackers can potentially reach your high-value assets. The third step is to provide the user with key insights from the simulations; risk levels, key risks, and effective risk mitigation actions. By leveraging automated attack simulations: get automated and continuous insights on the cybersecurity risk posture of their environment. When the risk exposure is too high/ posture is too weak, they get automated insights on what are the weak spots in the architecture and suggested mitigation actions. The analyses can be fully integrated into the CI/CD pipelines. * DevOps Teams * gets automated and continuous insights on the posture of the total environment. How does my overall risk posture improve over time? How do the different teams perform? How do the different teams’ environments interact, when/how does one team create risk for another team and provide those insights automated to the teams? CISO Function * securiCAD provides fully automated in-depth analyses. It can easily be integrated in the CI/CD pipelines. And it is not interfering with your live environment as all the simulations are conducted on digital twins of your environments. Automated and Continuous, throughout the DevOps Cycle. While simulation is probably one of the few ways to risk assess a large-scale environment in continuous change, the key is to build the model continuously based on the real environment. Automation is an important leap to moving away from human subjective assessment and the rigidity of formal strict security frameworks, and to keeping consistent security. Through automated simulations, companies can take on the challenge of looking at the whole while having clear control of all the details and moving pieces. The simulation capability increases both the capability to see how changes in one team’s environment can affect others and to make assessments more consistent. In the end, it increases security where it really counts.

Leading CISOs and DevOps Teams Are Leveraging DevSecOps

Since the advent of software programming, the hunt for a platform that could reduce the software development life cycle (SDLC) and deploy applications rapidly has been pretty intense. Finally, this eternal search seems to have come to fruition with the rise of low-code platforms. What is a Low-Code CPaaS Platform? A low-code development requires minimal manual coding, enabling enterprises to build and deploy applications and solutions quickly. As a result, it has a faster turnaround time and a much shorter go-to-market cycle than traditional agile and waterfall-based software development models. With the advent of low code CPaaS platforms on the horizon, developing and integrating robust, scalable and secure communications like video meetings is now possible with just a few lines of code. No wonder enterprises are excited to be a part of the low code revolution. According to a , the low-code platform market is projected to surpass $13.8 billion by 2021. Gartner estimate Critical Factors for Selecting Right CPaaS Low Code Platform With its unmatched ability to build and integrate communications APIs into the business workflow, a low code (aka ‘Embed’) CPaaS solution can empower organisations. Considering the following four aspects can help you make an informed decision: Ease of Use As the name implies, a low code platform should be simple, intuitive and easy to deploy. Therefore, your objective should be to select the platform that needs minimum server-side and no client-side coding. For example, some low code platforms allow embedding video meetings quickly into your application or browser with few lines of code, thus eliminating manual programming. Value For Money Every low code CPaaS platform comes with its pros and cons. It is recommended to go with the platform offering maximum value for money. Asking the following questions can help: - Does it provide pre-built UI templates? -  Do they provide commonly used features, such as screen sharing,   whiteboard, group chat, live streaming, noise-cancellation and room lock? -  Does this support all major browsers and platforms? The point to note here is that several basic CPaaS features may come with riders, whereas some advanced features may not be necessarily available with low code offerings. Scalability A low code platform should be able to scale up fast as a business grows. For example, it should be able to provide 1-to-1 and large-scale video conferencing with equal ease. Security & Compliance Following standard security protocols and regulatory compliance applicable in the region, such as HIPPA or GDPR, is mandatory for low code providers involved with sensitive industries, such as healthcare and finance. Along with this, ensure that all users’ data should be end-to-end encrypted. 4 Popular Low Code CPaaS Platforms Taking the World of Communications by Storm 1.    Whereby Embedded Whereby Embedded allows to build and deploy video meetings on any platform with a simple iframe code snippet, along with a host of configurable options suitable for conducting both small and large-scale video conferencing. And yes, it also has a free plan (with reasonable benefits) for users. : Available Free Trial : It comes with two plans: Pricing Pro (Suitable for small teams): $69.90 per annum. Business (suitable for large teams): $99.90 per annum. Pros Simple and easy to use. 9-Pax UI view. Comes with different platform languages besides English. Affordable price with no hidden cost. Cons Lacks comprehensive features, such as live streaming, private chat, webinar, room lock, annotation. The Business plan doesn’t offer compelling benefits over Pro plan. 2. Vonage Video Chat Embed Counted among leading CPaaS players globally, Vonage offers a range of CPaaS solutions to meet different communications requirements. With Vonage Chat Embeds, setting up a video meeting application on browsers is easy, and you can do it in just four steps. : Available Free Trial : Vonage offers a usage-based pricing model with a multi-tiered pricing structure: Pricing The basic plan starts from $9.99 per month for 2000 subscribed minutes. For additional calls, it charges $0.00475/subscribed minute (up to 10,000 minutes), $0.00450/subscribed minute (up to 20,000 minutes). : Available Free Plan Pros Easy to set up and deploy. Able to create private rooms. Can be integrated with WordPress, Squarespace and Zoho platform. Cons Mobile app integration not available currently. Significantly expensive compared to providers that offer on a Participant Minutes scheme. Extremely limited 2-pax UI view. Lacks many commonly used features, such as recording & saving, group chat, screen sharing, private chat, live streaming. 3. 8x8 Jitsi as a Service 8x8 Jitsi as a Service is another popular enterprise-grade video meeting solution, which enables you to build and deploy secure and reliable video meetings directly into your platform with ease. Loaded with power-packed features, such as raise your hand, push-to-talk mode, screen sharing and remote desktop control, Jitsi as a Service pushes the customer experience a few notches above. In addition, being both HIPPA and GDPR compliant, it’s a name to reckon with regarding security and regulatory compliances. : Available Free Trial :  Available in four-tiered plans: Pricing Basic: $99/month Standard: $499/month Business: $999/month Enterprise: Available on Request *A Monthly Active User: It’s defined as a unique user who participated in at least one video meeting along with another user for 30 days. Pros Smart UI layout Robust security A broad array of CPaaS features Vibrant developer community Cons Expensive pricing Only up to 30 participants can participants per session Lacks some essential features, such as annotation and whiteboard Not suitable for small companies and developers 4. EnableX Low Code Video Embed EnableX has emerged as a significant CPaaS player recently. With its Low Code Video Embed, you can build and deploy a high-quality video meeting solution right into your application workflow in just three steps using 1-line of code. Having a superior capability, whether scalability, flexibility, security and pricing, EnableX Video Embed offers value for money. Loved by both developers and CXOs, it offers fully customisable features, with greater UI control than similar platforms available in the market. : Available Free Trial : It has a simple billing formula based on the pay-as-you-use pricing model. The basic plan starts at $0.0040/Participant/Min. The pricing plan is affordable, with no hidden or additional charges for value-added features like video resolution and screen sharing. Pricing Pros Multiple pre-built UI templates. Option to choose from Permanent, Scheduled and Ad hoc meeting room. Comprehensive features comprising whiteboard, live streaming, room lock etc. Robust security protocols, such as HIPPA and GDPR compliant. Cons Some advance level features are missing that includes raising your hand, no breakout room and annotation. Yet to have a webinar solution. Conclusion In the era of changing business needs and fast-evolving market dynamics, the need for rapid development and deployment of applications is a necessity for enterprises. The low code platforms, therefore, are here to stay for long. Sensing the vast potential, many CPaaS providers have jumped on to join the low code bandwagon; the right choice, however, depends on the correct assessment of business needs and budget allocated for the purpose.

Comparing 4 Low Code CPaaS Platforms

As a freelance web developer, app creator, and open-source maintainer, I have to constantly switch back and forth between multiple workflows, such as editing code, designing mockups, managing to-do lists, searching for icons and docs, executing command lines, checking emails with different accounts and so on. Each of these switches requires a constant stream of repetitive actions: Launch a code or image editor, and open the files of a specific project in it. Open a web browser, and navigate to the project in a task manager. Navigate to an icon website, set image filters, and perform a search. Open a webmail app, and switch accounts. Launch Terminal, and enter commands into it. etc… Everything is scattered in different places, and it turns the whole process into a real mess. And when working on multiple projects, things get even worse. I thought I could greatly improve my productivity by collecting everything I needed to get my work done in one place and organizing it into projects and workflows so that they had the right context. Projects and workflows will have clear boundaries, and switching between them will no longer be a nightmare. So, I came up with Freeter, an organizer app that does just that. And recently released it as a forever free and open-source project. In this short post, I'll show you how I’ve increased my productivity with Freeter, using three workflows as examples. I hope this gives you some ideas on how you too can improve your productivity. Workflows First, I analyzed my workflows and everything I often do when I’m looking for something I need while working on a project: When I’m developing an app or website, I often need to be able to access the task manager, open project files in code and image editors, search for icons and docs on specific websites, jot down quick ideas, and open the project repository in a web browser.


When I check email and Twitter DMs, I need access to the webmail and the Twitter DM page. I have multiple accounts and need to be logged in with project-specific ones.


When I release a new version of the app, I need to run the release command in the Terminal app, open the releases page in the git repository, open the task manager, and open the "planned feature" post editor in the Freeter community. Now, it’s time to turn them into Freeter workflows. App/Website Development To have quick access to the things I need to develop the app/website, I set up a workflow screen using the following widgets: Tasks: Webpage widget, to embed the project’s task manager right into the workflow screen.
Edit Code: File Opener widget, to open the project folder in the code editing program.
Edit Mockup: File Opener widget, to open the mockup file in the image editing program.
MDN: Web Query widget, to search MDN Web Docs website.
Node.js Docs: Web Query widget, to search Node.js Docs website.
Outline Icons: Web Query widget, to search a website with icons, filtered by outline icons.
Fill Icons: Web Query widget, to search a website with icons, filtered by fill icons.
Notes: Note widget, to jot down quick ideas while developing a feature.
Open Repo: Link Opener widget, to open the project repository in a web browser.
Bug Reports: Link Opener widget, to open the bug reports page in a web browser.
Feature Requests: Link Opener widget, to open the feature requests page in a web browser. This workflow allows me to immediately switch to the development context, launch everything I need to start the development process with a simple click, quickly search docs & icons, and quickly access the task list. Messages To check emails and Twitter DMs, I set up a workflow using two Webpage widgets: To embed the Google Mail inbox page. To embed the Twitter DM page. I also set Session Scope to Project in the widget settings so that I can be logged in under different accounts in other projects. This workflow allows me to quickly get simultaneous access to Google Mail and Twitter DMs for project-specific accounts. New Release To release a new version of the app, I set up a workflow with these five widgets: Release: Commander widget, to execute a command line in Terminal that asks for a new version number and starts a draft build of the new version. Open Releases: Link Opener widget, to open the releases page in the web browser. Tasks: A copy of Tasks from the App Dev workflow. I will need it to see all the Finished tasks in the current release. Planned Features: Webpage widget, to embed the Freeter community's "planned features" page into the workflow screen. With its help, I update the planned features and post about implemented features in the new release. Release steps: Note widget, to not forget to do something during the release. Thanks to this workflow, I can easily release a new version and post updates about new features. Switch Between Workflows Like a Superhero Now, when I switch between projects and workflows, I simply press Ctrl+Shift+F to bring Freeter to the front, open the workflow tab I need at the time, and get right to work. I hope this inspires you to organize your workflows too. To get started, visit the Freeter homepage. As a freelance web developer, app creator, and open-source maintainer, I have to constantly switch back and forth between multiple workflows, such as editing code, designing mockups, managing to-do lists, searching for icons and docs, executing command lines, checking emails with different accounts and so on. Each of these switches requires a constant stream of repetitive actions: Launch a code or image editor, and open the files of a specific project in it. Launch a code or image editor, and open the files of a specific project in it. Open a web browser, and navigate to the project in a task manager. Open a web browser, and navigate to the project in a task manager. Navigate to an icon website, set image filters, and perform a search. Navigate to an icon website, set image filters, and perform a search. Open a webmail app, and switch accounts. Open a webmail app, and switch accounts. Launch Terminal, and enter commands into it. Launch Terminal, and enter commands into it. etc… etc… Everything is scattered in different places, and it turns the whole process into a real mess. And when working on multiple projects, things get even worse. I thought I could greatly improve my productivity by collecting everything I needed to get my work done in one place and organizing it into projects and workflows so that they had the right context. Projects and workflows will have clear boundaries, and switching between them will no longer be a nightmare. So, I came up with Freeter , an organizer app that does just that. And recently released it as a forever free and open-source project . Freeter Freeter open-source project In this short post, I'll show you how I’ve increased my productivity with Freeter, using three workflows as examples. I hope this gives you some ideas on how you too can improve your productivity. Workflows First, I analyzed my workflows and everything I often do when I’m looking for something I need while working on a project: When I’m developing an app or website, I often need to be able to access the task manager, open project files in code and image editors, search for icons and docs on specific websites, jot down quick ideas, and open the project repository in a web browser. When I check email and Twitter DMs, I need access to the webmail and the Twitter DM page. I have multiple accounts and need to be logged in with project-specific ones. When I release a new version of the app, I need to run the release command in the Terminal app, open the releases page in the git repository, open the task manager, and open the "planned feature" post editor in the Freeter community. When I’m developing an app or website, I often need to be able to access the task manager, open project files in code and image editors, search for icons and docs on specific websites, jot down quick ideas, and open the project repository in a web browser. When I’m developing an app or website, I often need to be able to access the task manager, open project files in code and image editors, search for icons and docs on specific websites, jot down quick ideas, and open the project repository in a web browser. When I check email and Twitter DMs, I need access to the webmail and the Twitter DM page. I have multiple accounts and need to be logged in with project-specific ones. When I check email and Twitter DMs, I need access to the webmail and the Twitter DM page. I have multiple accounts and need to be logged in with project-specific ones. When I release a new version of the app, I need to run the release command in the Terminal app, open the releases page in the git repository, open the task manager, and open the "planned feature" post editor in the Freeter community. When I release a new version of the app, I need to run the release command in the Terminal app, open the releases page in the git repository, open the task manager, and open the "planned feature" post editor in the Freeter community. Now, it’s time to turn them into Freeter workflows. App/Website Development To have quick access to the things I need to develop the app/website, I set up a workflow screen using the following widgets: Tasks: Webpage widget, to embed the project’s task manager right into the workflow screen. Edit Code: File Opener widget, to open the project folder in the code editing program. Edit Mockup: File Opener widget, to open the mockup file in the image editing program. MDN: Web Query widget, to search MDN Web Docs website. Node.js Docs: Web Query widget, to search Node.js Docs website. Outline Icons: Web Query widget, to search a website with icons, filtered by outline icons. Fill Icons: Web Query widget, to search a website with icons, filtered by fill icons. Notes: Note widget, to jot down quick ideas while developing a feature. Open Repo: Link Opener widget, to open the project repository in a web browser. Bug Reports: Link Opener widget, to open the bug reports page in a web browser. Feature Requests: Link Opener widget, to open the feature requests page in a web browser. Tasks: Webpage widget, to embed the project’s task manager right into the workflow screen. Edit Code: File Opener widget, to open the project folder in the code editing program. Edit Mockup: File Opener widget, to open the mockup file in the image editing program. MDN: Web Query widget, to search MDN Web Docs website. Node.js Docs: Web Query widget, to search Node.js Docs website. Outline Icons: Web Query widget, to search a website with icons, filtered by outline icons. Fill Icons: Web Query widget, to search a website with icons, filtered by fill icons. Notes: Note widget, to jot down quick ideas while developing a feature. Open Repo: Link Opener widget, to open the project repository in a web browser. Bug Reports: Link Opener widget, to open the bug reports page in a web browser. Feature Requests: Link Opener widget, to open the feature requests page in a web browser. This workflow allows me to immediately switch to the development context, launch everything I need to start the development process with a simple click, quickly search docs & icons, and quickly access the task list. Messages To check emails and Twitter DMs, I set up a workflow using two Webpage widgets: To embed the Google Mail inbox page. To embed the Google Mail inbox page. To embed the Twitter DM page. To embed the Twitter DM page. I also set Session Scope to Project in the widget settings so that I can be logged in under different accounts in other projects. This workflow allows me to quickly get simultaneous access to Google Mail and Twitter DMs for project-specific accounts. New Release To release a new version of the app, I set up a workflow with these five widgets: Release: Commander widget, to execute a command line in Terminal that asks for a new version number and starts a draft build of the new version. Release: Commander widget, to execute a command line in Terminal that asks for a new version number and starts a draft build of the new version. Open Releases: Link Opener widget, to open the releases page in the web browser. Open Releases: Link Opener widget, to open the releases page in the web browser. Tasks: A copy of Tasks from the App Dev workflow. I will need it to see all the Finished tasks in the current release. Tasks: A copy of Tasks from the App Dev workflow. I will need it to see all the Finished tasks in the current release. Planned Features: Webpage widget, to embed the Freeter community's "planned features" page into the workflow screen. With its help, I update the planned features and post about implemented features in the new release. Planned Features: Webpage widget, to embed the Freeter community's "planned features" page into the workflow screen. With its help, I update the planned features and post about implemented features in the new release. Release steps: Note widget, to not forget to do something during the release. Release steps: Note widget, to not forget to do something during the release. Thanks to this workflow, I can easily release a new version and post updates about new features. Switch Between Workflows Like a Superhero Now, when I switch between projects and workflows, I simply press Ctrl+Shift+F to bring Freeter to the front, open the workflow tab I need at the time, and get right to work. Ctrl+Shift+F I hope this inspires you to organize your workflows too. To get started, visit the Freeter homepage . Freeter homepage Freeter homepage

How I Boosted My Productivity While Working on Multiple Projects

একাধিক প্রকল্পে কাজ করার সময় আমি কীভাবে আমার উত্পাদনশীলতা বাড়িয়েছি

Wie ich meine Produktivität bei der Arbeit an mehreren Projekten steigerte

Cómo impulsé mi productividad mientras trabajaba en varios proyectos

Comment j'ai augmenté ma productivité tout en travaillant sur plusieurs projets

मैंने कई परियोजनाओं पर काम करते हुए अपनी उत्पादकता कैसे बढ़ाई

複数のプロジェクトに取り組みながら生産性を高める方法

여러 프로젝트를 진행하면서 생산성을 높이는 방법

Como aumentei minha produtividade ao trabalhar em vários projetos

Как я повысил свою продуктивность, работая над несколькими проектами

Birden Fazla Projede Çalışırken Verimliliğimi Nasıl Artırdım

Cách tôi tăng năng suất khi làm việc trên nhiều dự án

我如何在处理多个项目时提高工作效率

As creatives in the tech industry, we rely on our digital portfolios to showcase our skills and expertise. A picture can speak a thousand words, but it can also take up valuable space on our websites or Notion pages. As a solution, many of us turn to cloud storage services like Google Photos to save and organize our images. Although Google offers no option for you to embed the uploaded pictures onto your website, did you know that there's a way to easily embed single photos from your Google Photos album directly onto your website or Notion page? In this post, I'll share with you two simple ways to generate an embed link that you can use to display your photos without having to sacrifice precious storage space or worry about Notion's 5MB picture size limit. 🥇__ __ Labnol's embed link generator tool This is by far the easiest and my favorite way to get embed links and code for my web design and Notion projects. Labnol is a free web-based tool that easily generates embed links for any photograph hosted in your Google Photos album. With Labnol, you can quickly embed photos in your websites, email messages, Notion pages, or blogs in just three simple steps. Steps Open your preferred photo in Google Photos and click on "Share" then "Create Link" to get a shareable link for that image. Copy the link and paste it into the "Google Photos link" box on the Labnol homepage. Confirm that you are human and click "Generate Embed Link," and your link will be ready. Why I love Labnol 🏆 ✅ Aside from being extremely easy to use and fast, this tool also generates links tailored to your specific needs. Labnol allows you to copy the generated link as a direct link (great for Notion page cover and email messages) or as an embed code for your HTML page (complete with the < and < tags. Prefered for Notion image embed). a> img> ✅ Lanbol also has other tools and guide videos that are very useful for automating your workflow and maximizing your Google tools. It's worth noting that Labnol's embed link generator only works for embedding single photos from your Google album. For embedding albums, you'll need a different tool entirely. The Downside 💔 ❌ The site can be unreliable at times as there may be times when the website is down or inaccessible. This can be frustrating when you are overflowing with inspiration and the tool is unavailable. This specific experience is what led me to discover the alternative method I will be talking about next. 🥈 The Manual Route This next step is one I learned on stack exchange sometime back. Although it's hardly as fast or straightforward as using Labnol, it does the job efficiently. With the link to your photo and a few open tabs on your browser, you can generate your embed link whenever you want. Here's how! Steps Open your preferred photo in Google Photos and click on "Share," then "Create Link" to get a shareable link for that image. Copy the link and open a new browser tab. Paste the link address into the new browser window. This will bring you to a new shared album with just that photo in it. Click the image to zoom in and right-click on the image. Select "Copy image address" from the menu. You now have your embed link, which can be used on your Notion page (both for your covers and image embed), email messages, and HTML website. The links generated here don’t have the added < and < tags you get from using Labnol. You have to add that in by yourself. a> img> Why I love the Manual Route 🏆 ✅ It's always available! You never have to worry about service outages. The Downside 💔 ❌ The manual route can be time-consuming and tedious, especially if you have to embed several photos. ❌ You have to manually add the < and < tags to the embed link, which can be challenging for users unfamiliar with HTML coding. a> img> ❌ Since the manual route involves copying and pasting links, there is a higher risk of errors or broken links if you accidentally copy the wrong link or make a mistake in the process. Conclusion These are my tried and tested methods using Google Photos to embed photos on a website, Notion page, email message, or blog. I hope that you have found the tips and tools that I have shared to be helpful. If you have any other methods or tools that you have found to be useful, please share them in the comments below. I look forward to reading them 😃 Photo by on Hal Gatewood Unsplash

How To Embed Single Photos From Google Photos on Your Website and Notion Page

Finding the right AI model to build a workflow around is hard. With so many models available across different platforms, it’s impossible to know where to start or how to find the one that best fits your specific needs. That’s the problem I set out to solve with AIModels.fyi, the only search engine for discovering and comparing AI models from across the web. The site has a simple premise: describe your problem in natural language, and get back a list of AI models that can potentially solve it. For example, you might search for “I need a model that can help upscale my images without losing clarity” or “make my cat look like it’s singing”. The goal is to , not just ML engineers, to find useful AI tools for their projects. make it easy for anyone Under the hood, the search works by taking the user’s query and comparing it to a database of over 240,000 AI models from 7,500+ creators across major platforms like Hugging Face, Replicate, Cerebrium, and DeepInfra. When a user types in a search, the query gets embedded into a vector using OpenAI’s text-embedding-ada-002 model. This query vector is then compared to pre-computed vector representations of the model names, descriptions, and use cases in the database. The most similar models by cosine distance are returned as the search results. Here’s a simplified version of the core search logic I’ve been using for about six months: const fetchData = async (query) => {
  // Embed the search query
  const embeddingResponse = await openAi.createEmbedding({
    model: "text-embedding-ada-002",
    input: query,
  });
  
  const embedding = embeddingResponse.data.data[0].embedding;

// Find the most similar models in the vector database 
  const { data: modelsData, error } = await supabase.rpc("search_models", {
    query_embedding: embedding,
    similarity_threshold: 0.75,
    match_count: 10,
  });
  return modelsData;
}; This vector search approach works reasonably well for surfacing relevant models when there’s a direct overlap between the query terms and the model descriptions. However, it struggles with more abstract, complex, or niche queries where the user’s language doesn’t exactly match the technical terminology in the database. For example, I recently wanted to find a model that could animate a portrait image using an audio voice clip to create a talking head effect. I knew there were some good models out there for this, but my searches for things like “talking portrait from image and audio” weren’t turning up the results I expected. The problem was that my query terms didn’t have enough literal matches with the specific words used in the model descriptions, even though the semantic intent was similar. This got me thinking about ways to improve the search experience to better handle those kinds of abstract or complex queries. How could I make the system smarter at understanding the user’s true intent and mapping it to relevant models in the database? That’s when I came across the idea of Hypothetical Document Embeddings (HyDE) from after reading about it in . this research paper this Medium post Meet Mr. HyDE — The query expander! The key insight behind HyDE is to use a large language model (LLM) to expand the user’s short query into a more detailed hypothetical “ideal” document before embedding it for search. So, instead of directly matching the query embedding to the database embeddings, it first translates the query into a richer representation in the same semantic space as the real documents. Here’s how the process works: The user enters a natural language query expressing their needs, like “I want to convert an image into a 3D model.” This generated pseudo-document is then embedded into a dense vector using a model like OpenAI’s text-embedding-ada-002. The resulting embedding is used to query the vector database and find the most similar real model documents. The query is fed into an instruction-tuned LLM like GPT-3 to generate a hypothetical document. The prompt asks the model to imagine an ideal document for answering the query. For example: Query: I want to convert an image into a 3D model Hypothetical Document: There are several AI models available that can convert a 2D image into a 3D model. The process is known as single-view reconstruction or 3D estimation. It typically involves using deep learning to predict the depth map and 3D shape from the 2D input image. Some popular models for this task include Pix2Vox, Pixel2Mesh, AtlasNet, and OccNet. These models are trained on large datasets of 3D objects and can handle complex shapes. To use them, you provide an input image and the model will output a 3D mesh or voxel representation that you can render or edit further in 3D modeling tools… The LLM step helps bridge the gap between the user’s simple query and the more technical language used in the model descriptions. It expands on the initial query to infer additional relevant terms, concepts, and context that can help retrieve better matches. At the same time, using real document embeddings as the final search index keeps the results grounded and prevents hallucination. I implemented a basic version of HyDE on top of the existing AIModels search, and the initial results are promising. In some sample queries I tested, HyDE surfaced relevant models that the original keyword-matching approach missed. For the talking head portrait query, it was able to generate a hypothetical document with key terms like “lip sync”, “facial animation”, and “audio-driven animation”, which helped retrieve some strong model matches that the plain search had overlooked. two to three times more How to implement HyDE in your own search system Let me show you how HyDE can be added in on top of an existing semantic search system to dramatically improve its quality. Let’s start with a simplified code sketch based on my current search flow. Here’s the full file — you can glance through it in totality before we look at the individual parts: const fetchData = async (query) => {
  // Generate a hypothetical document embedding for the query 
  const hypotheticalDocument = await generateHypotheticalDocument(query);
  
  const embeddingResponse = await openAi.createEmbedding({
    model: "text-embedding-ada-002",
    input: hypotheticalDocument,
  });
  
  const embedding = embeddingResponse.data.data[0].embedding;

// Search the real model embeddings using the hypothetical document embedding
  const { data: modelsData, error } = await supabase.rpc("search_models", {
    query_embedding: embedding,
    similarity_threshold: 0.75,
    match_count: 10,
  });
  return modelsData;
};
const generateHypotheticalDocument = async (query) => {
  // Use GPT-3 to expand the query into a hypothetical ideal document
  const prompt = `The user entered the following search query: 
  "${query}"
  
  Please generate a detailed hypothetical document that would be highly relevant to answering this query. The document should use more technical language and expand on the key aspects of the query.
  Hypothetical Document:`;
  const response = await openAi.createCompletion({
    model: "text-davinci-002",
    prompt: prompt,
    max_tokens: 200,
    n: 1,
    stop: null,
    temperature: 0.5,
  });
  const hypotheticalDocument = response.data.choices[0].text.trim();
  return hypotheticalDocument;
} Let’s see how it works. We’ll start with the function, which is the main entry point for the search process. First up: fetchData const hypotheticalDocument = await generateHypotheticalDocument(query); This line calls the function (which we'll explain in more detail later) to generate a hypothetical ideal document based on the user's search query. generateHypotheticalDocument The hypothetical document is a key part of the HyDE approach, as it aims to capture the user’s search intent in a more detailed and technical way than the original query. const embeddingResponse = await openAi.createEmbedding({
  model: "text-embedding-ada-002",
  input: hypotheticalDocument,
});
const embedding = embeddingResponse.data.data[0].embedding; These lines use OpenAI’s model to generate an embedding vector for the hypothetical document. I think it's the best embedding option out there so far. text-embedding-ada-002 Embeddings are a way to represent text as a dense vector of numbers, where similar texts have similar vectors. This allows us to perform semantic similarity searches. By embedding the hypothetical document instead of the original query, we aim to get a vector representation that better captures the expanded search intent. Next up: const { data: modelsData, error } = await supabase.rpc("search_models", {
  query_embedding: embedding,
  similarity_threshold: 0.75,
  match_count: 10,
}); This part performs the actual similarity search in the model database. It is called a remote procedure ( ) on the Supabase database, passing in the embedding of the hypothetical document as the . search_models query_embedding The and parameters control how closely the results need to match and how many to return. similarity_threshold match_count The search looks for model embeddings that are most similar to the query embedding based on cosine similarity. Now let’s look at the function: generateHypotheticalDocument const prompt = `The user entered the following search query: 
"${query}"

Please generate a detailed hypothetical document that would be highly relevant to answering this query. The document should use more technical language and expand on the key aspects of the query.
Hypothetical Document:`; This part constructs the prompt that will be sent to the language model to generate the hypothetical document. The prompt includes the user’s original query and instructions for the model to generate a detailed, technical document that expands on the query’s key aspects. Crafting an effective prompt is crucial for guiding the language model to generate useful hypothetical documents. Now for the next part: const response = await openAi.createCompletion({
  model: "text-davinci-002",
  prompt: prompt,
  max_tokens: 200,
  n: 1,
  stop: null,
  temperature: 0.5,
}); These lines use OpenAI’s model to generate the hypothetical document based on the prompt. text-davinci-002 The parameter limits the length of the generated document while controls the randomness (higher values make the output more diverse but potentially less focused). Adjusting these parameters can help tune the quality and variety of the generated documents. max_tokens temperature Next, we have: const hypotheticalDocument = response.data.choices[0].text.trim(); This line extracts the generated hypothetical document text from the GPT-3 API response. The generated text is then returned to be used in the embedding step. Then we can just retreive the data as before, this time using the hypothetical document as the query, but getting a lot more relevant results due to the expanded search term and all it’s extra, juicy context! Recap — what did we just build? I hope the code and my explanation demonstrate how the HyDE approach can be implemented on top of an existing semantic search system. It’s not that hard! The key idea is this: By inserting the hypothetical document generation and embedding steps into the search pipeline, we aim to improve the search results by better capturing the user’s intent. To recap, the key parts were: Using a language model to expand the user’s query into a more detailed, technical hypothetical document Embedding the hypothetical document to get a vector representation that aligns with the model database Performing a similarity search between the hypothetical document embedding and the model embeddings to find the most relevant results Of course, this is just a simplified implementation (simplementation?), and there are many details and optimizations to consider in a real implementation. But it illustrates the core flow and components of the HyDE approach. Taking it further Since this is just an initial prototype there’s still a lot of room for experimentation and refinement. Some of the key open questions I’m thinking about: What’s the optimal prompt structure and output length for generating useful hypothetical documents? I’d like to try different prompt templates and output sizes to see how they impact result quality. How can the hypothetical documents be made more interpretable and transparent to users? It could be confusing if users don’t understand why certain terms are being added. I’m considering showing parts of the generated text as a “query interpretation” to provide more insight into the process. To what extent does the result quality depend on the specific LLM and embedding models used? I’m curious to benchmark different model combinations (e.g. GPT-4 vs GPT-3, vs OpenAI embeddings, etc.) to find the best recipe. Contriever How can this approach be efficiently scaled to work over millions of database entries? Inference on large LLMs like GPT-3 is expensive and slow. I’ll need to look into caching, optimizing, or distilling the models to make this viable at scale. I’m excited about the potential for language models to help make search interfaces like AIModels more intuitive and powerful for a wider audience. Bridging the semantic gap between how users naturally express their needs and how AI models are technically described feels like an important piece of the puzzle. Techniques like HyDE point a way toward search experiences that deeply understand the intent and meet users where they are. My long-term goal is to keep refining this interface until anyone can easily find relevant AI models for their needs, no matter their level of technical expertise. I’ll be sharing more details as I continue experimenting with HyDE and other approaches. If you’re curious to learn more about the technical side, I recommend checking out the original paper. And if you have ideas or feedback on the AIModels search experience, I’d love to hear from you! Let me know what you think in the comments. Also published . here

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