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This post is part of the Hacker Noon Shareholder Series , where we interview some of the super-investors who made the site you're on right now possible. George Georgallides is the co-founder of Darwin: an AI-enabled platform that uses machine learning models and aggregates corresponding expert evaluations to assess the overall risk and attractiveness of a startups for both investors and founders. We sat down with Georgallides to learn more about this fascinating application of AI and machine learning. Q1 How exactly does Darwin use A.I. and the wisdom of experts to make financing an innovation less risky and more transparent for both entrepreneurs and investors? Our team spent two years researching the process of making investment decisions at the earliest stage, initially examining groups of experts making those decisions - the status quo - and later various machine learning approaches. What emerged was the combination of experts and AI in a collaborative process on what each does best. Machine learning is great at processing vast amounts of complex data. Experts among other things are great at making intuitive decisions or interpreting data creatively. We call that Hybrid Intelligence. Darwin uses machine learning models and aggregates corresponding expert evaluations to assess the overall risk and attractiveness of a startup. Darwin also actively learns from the evaluations and creates digital twins of each expert to scale the decision-making process further. "For investors, Hybrid Intelligence provides both a de-risking and scalability partner." When using Darwin, founders receive feedback on the overall attractiveness of their idea before going to an investor. The process is dynamic - they can test how changes impact their score as much as they want. In addition, they receive feedback on how to improve and, ultimately, the means to accelerate their funding. For investors, Hybrid Intelligence provides both a de-risking and scalability partner. Darwin enhances investors’ due diligence stack and allows them to evaluate more deal flow in a timely and knowledgeable manner. Q2 Much has been written about just how far AI can go in terms of performing complex decision-making tasks. Darwin is advocating for Hybrid Intelligence as the new frontier for collaboration between humans and AI - can you tell us more about Darwin's application/s of that? The applications for Hybrid Intelligence can certainly go far beyond just the early stage innovation environment and really apply to any complex decision-making tasks where uncertainty is high. Medical imaging, sports predictions and movie production are just some examples. Beyond better decision-making though, we also believe that Hybrid Intelligence is a piece of the puzzle in shaping the future of work. One where A.I is not replacing humans but is integrated with humans. For tasks that human intuition is critical, we can now scale it and digitize it. More importantly, for the first time, through the use of our digital twin technology, humans can monetize their intelligence in a way in which they don't have to actively do the work. They can ‘outsource’ their intelligence and monetize it passively. Overall, we believe that Hybrid Intelligence has the potential to help align AI and human goals. Q3 How is building an AI investment platform different from building other VC companies? There are some similarities in that, VC funds source deals and conduct due diligence in order to find profitable investment opportunities. But the similarities end there. First of all, Darwin doesn’t invest in startups itself. Rather, Darwin is an intelligence partner representing the interests of both founders and investors. Building Darwin involved years of research and data gathering from founders, accelerators, investors and other stakeholders followed by technical execution and ongoing development to build a scalable solution for identifying successful investment opportunities. Second, unlike VCs, Darwin places a big emphasis on helping founders develop their ideas and make them investable whereas VCs mostly work with startups ready to receive funding. Third, Darwin can scale its core competency whereas a VC fund is inherently limited by how many people work directly or indirectly at the fund. Darwin can help remove that bottleneck for VCs. There are certainly many other ways that Darwin, being a platform, differs from a VC, which is a service company. But the important thing to note, as I mentioned, is that Darwin is a complement to the VC process. Q4 has developed a Digital Disruption Design (D3) model that helps corporations, investors and entrepreneurs with concrete strategic decision-making and data-driven decisions - walk us through that framework, and how it’s integrated into Darwin’s process? Darwin We developed the Digital Disruption Design (D3) model with the support of business incubators, accelerators, VCs and corporate innovation arms. We built on the idea of the Lean Startup for iterative development, testing, and learning in order to provide a process that helps entrepreneurs make decisions. The model has been tested in collaboration with the University of St. Gallen with over 200 participating digital ventures. D3 guides innovators through the use of data-driven methods for validating their assumptions and build their ideas. It’s a stage gate process with decision points at the end of each stage. Each stage is an iterative process of making decisions, testing hypothesis, validating assumptions, and learning. It also provides a set of decision possibilities for each dimension and metrics for making the progress measurable. This framework is integrated into the Darwin platform. The D3 model can be applied for a variety of stakeholders, including startups developing their ideas or corporate venturers trying to define innovation pathways. In addition, we believe it can help incubators, accelerators, and investors monitor their portfolio’s progress. Q5 The VC market is still fairly non-inclusive, in terms of who can actively participate. How might Darwin aid in democratizing opportunities on both sides of the fence - for both investors and entrepreneurs? We’ve met with hundreds of investors and entrepreneurs across the world in the past two years. What we’ve learned is that there’s an incredible opportunity to make cross-border and cross-cultural connections for all parties involved. There are many opportunities to leverage hybrid intelligence to integrate otherwise timid early stage investors by equipping them with more confidence on what represents a good investment opportunity by connecting the dots. In addition, there’s a big opportunity to present highly qualified deals from under-represented areas of the US and the world to investors who otherwise would not be exposed to those deals - thereby making the access to “smart money” more widespread. Q5 For entrepreneurs interested in joining Darwin’s innovation platform, what can they expect in terms of investment opportunities, users/customers, mentorship? We want to work with founders as early as idea-stage and help them cultivate their startup all the way through fundraising. First and foremost, we want to help them get feedback on whether their product has product-market fit. We’ve seen too many founders spend months fundraising with no success before they’ve validated if they provide enough value in their market. On Darwin, they can get feedback from investors and other industry experts on their business model, team-solution fit, traction and much more. Darwin also wants to help startups improve their chances of fundraising by providing opportunities to create traction. One way we are doing that is by matching startups with corporations who are willing to be a startup’s first paying customer. In addition, we’re focused on providing value to the startups through a growing network of partner offers. Something really exciting that we are working on bringing to founders very soon, is the ability to get feedback from the digital twin of the investor they want to pitch and adapt the product to make a killer pitch before the real meeting. Overall, Darwin wants to make fundraising and more transparent for founders and help them go to market the right way. Q6 What are the key actions early-stage startup founders should focus on to reduce their risk of failure? Product-market fit and execution top the list in a fairly wide spectrum of variables that can impact early stage success or failure. The biggest challenge we’ve heard, from first-time founders especially, is not knowing how to go about validating their idea and what to focus on with regards to early execution in order to make their venture more attractive to investors and potential users/customers. That’s why we think Darwin fills such a big piece of that puzzle in providing an on-demand tool to get support on both those key areas. Q7 What’s a ‘digital twin’, and how does Darwin use this to help startups succeed in pitching? A digital twin is a digital copy of a user on Darwin. Our machine learning models actively learn from the input of Darwin experts. That means we can employ the knowledge of experts even without their physical input to increase the confidence in the evaluation of a particular idea. Our digital twin feature is still in development as we gather data from those users but when it’s ready to launch, will include VCs, successful entrepreneurs, and other specialists. Q8 For VCs looking for investable startups, what’s to be gained from joining Darwin as an investor? Almost every VC that identifies herself as such will have more deal flow than they can evaluate at present. What we offer is a scalable way to vet not only that deal flow, but also additional opportunities originating from our partners around the world with less time spent on due diligence. This way, VCs are less likely to miss out on an investment opportunity because they “didn’t have enough time”. Furthermore, due to the fact that opportunities on Darwin are evaluated by a diverse brain trust of industry experts and then further evaluated by our AI, VCs can use Darwin to de-risk their own investment decisions. This ensures that they don’t miss out on deals because of cognitive biases or limited knowledge on an opportunity’s addressable market, and at the same time not make bad investments due to overconfidence. "The early stage investment space is such a foundational driver in shaping the future of our world... Darwin is trying to organize the vast number of incredible innovation opportunities that exist, help shape them through the platform and present them to investors who can help place them in the market." Using Darwin’s digital twin technology, VCs can also optimize what opportunities they see based on their investment preferences and only see deals that are pre-qualified for them to invest in. This allows for faster and more confident investment decisions. Lastly, Darwin offers several upgrades to the VC tech stack to help VCs better manage their data internally, better manage their relationship with startups and their industry reputation in general. Q8 What’s your take on impact investing - is this a trend Darwin subscribes to when making decisions about who to partner with? The early stage investment space is such a foundational driver in shaping the future of our world. Companies who were born less than a decade ago are transforming transportation, housing, work, energy and so much more. Darwin is trying to organize the vast number of incredible innovation opportunities that exist, help shape them through the platform and present them to investors who can help place them in the market. In that spirit, we invite founders, investors, accelerators and other key stakeholders from the impact investing community to partner with Darwin in making capital allocation in impactful innovations more efficient. Not only investors and founders can join Darwin. Accelerators, experts and startup scouts are all encouraged to apply on their site . Learn more at askdarwin.com ; and check out more of the Hacker Noon Shareholder Series .

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

AI chatbots have truly found their place in today's business technology scene, reshaping how companies engage with customers and becoming an integral part of daily operations. Gone are the days when they were just a futuristic concept; now, these AI-powered chatbots engage in remarkably human conversations, providing round-the-clock support that's reshaping customer service and leaving its mark on various business areas. What's exciting is how they keep getting better at handling all sorts of user queries, constantly improving the user experience. focusing on innovative technology like AI chatbots to create more effective and engaging user interactions, marking a new era where technology and business seamlessly come together. Businesses are embracing this shift, Understanding the Challenges with Current AI Chatbots Even with all the advancements in AI chatbot technology, there are still some kinks to work out that can make the experience a bit frustrating at times. The main issue that pops up is that sometimes, chatbots just don't get what you're trying to say. This usually happens because, let's face it, chatbots aren't perfect at picking up on the little subtleties of language – think idioms, complex sentences, or the way we naturally chat with each other. This can lead to those annoying moments where you have to repeat yourself or reword your question, which can be pretty irritating. Another thing where AI chatbots can drop the ball is in figuring out if you're happy with the answers they're giving. For example, a chatbot might give you a technically correct answer, but it might miss the real worry or feeling behind your question. It's especially noticeable when you're feeling frustrated or confused, and all you get in return are standard, kind of robotic responses. It shows that these chatbots need a bit more emotional smarts. It's not just about understanding our words but also getting the emotions behind them. All this highlights the journey we're still on with AI – the aim isn't just to make chatbots that can talk back but to create ones that get us and connect on a more human level. Techniques for Refining AI Understanding of User Intent and Satisfaction AI development has come a long way, and now we're seeing some cool stuff being used to make AI chatbots better at figuring out what we want and how we feel. It's all about making these chatbots more effective and, well, more human-like. One big thing they're working on is Contextual Understanding. It's like teaching the AI to read between the lines, not just taking our words at face value but getting the whole picture. Say you're chatting with a bot and you keep talking about the same thing - the bot can now follow along and keep up with the conversation. It's smart enough to know if you're still on the same topic, bringing up something from before, or moving on to something new. This makes the chatbot's responses way more on point. Then there's Sentiment Analysis, which is super interesting. It's all about the chatbot picking up on your feelings based on your writing – like the words you choose, how you put together your sentences, or even the punctuation you use. This way, the AI can tell if you're happy, annoyed, or confused and can change how it replies to match your mood. It's not just about giving right answers; it's about giving answers that feel right. Personalization is another cool area. It's like the chatbot gets to know you personally. It remembers your past chats or uses info you've shared to make its responses more about you. So, it might suggest things you like, call you by your name, etc. This makes chatting with a bot feel like a real conversation with a human. All these new tricks mean that AI chatbots are getting better at answering questions and doing it in a way that feels more natural, more understanding, and more tailored to you. It's a big step towards making our interactions with AI more like talking to another person. How Microsoft Copilot is Innovating AI Chatbots Microsoft Copilot is an advancement in AI chatbots, extending beyond the conventional scope of such technologies. It is integrated across Microsoft's range of products, functioning as an AI tool that can handle various tasks. The key feature of Copilot is its ability to adapt to different user requirements, which marks a notable development in its capabilities. Initially launched as Bing Chat, Microsoft Copilot has been expanded to integrate into numerous Microsoft applications. It aids users in various activities, including coding, writing, and image generation, by adapting to their specific needs and understanding the context of their tasks. This represents a significant step in AI technology, particularly contextual understanding and application versatility. In the realm of Microsoft 365, Copilot is utilized to enhance the functionality of productivity software. It assists in activities such as composing emails in Outlook and creating data visualizations in Excel. This demonstrates the potential of AI to simplify and improve the efficiency of common tasks. Microsoft has also developed specialized versions of Copilot. For example, the Security Copilot focuses on cybersecurity, analyzing data to identify and counteract threats. This application of AI in security is indicative of its broader potential. Additionally, Copilot for Service and Sales are tailored for business operations, providing AI-driven customer relations and sales support. Overall, the diverse applications of Microsoft Copilot across different digital interactions and workflows reflect Microsoft's aim to offer AI assistance in various settings. By doing so, Microsoft contributes to the evolution of AI chatbots towards more personalized, context-sensitive, and multifaceted tools. This strategy aims to improve user efficiency and align with the growing expectations for AI technology in various sectors. Align AI's Approach to Analyzing and Monitoring Conversational Data Coxwave's Align AI platform functions in conversational AI by providing tools to businesses for monitoring and analyzing data from AI chatbot interactions. Align AI addresses the challenge of ensuring efficient and user-aligned interactions in the evolving AI technology landscape, particularly with systems like ChatGPT. The platform is designed to manage the occasional discrepancies between the outputs of AI models and their training data, which can affect user satisfaction. Align AI features real-time data ingestion, facilitated through a pre-built software development kit (SDK) and integration with Large Language Model (LLM) toolkits such as Langchain and LlamaIndex. The integration process, which can be completed in under 10 minutes, is advantageous for businesses aiming to swiftly implement and manage AI chat solutions. This capability allows for the prompt collection and analysis of conversational data, to enhance AI chatbot performance and responsiveness. The platform also includes a natural language universal search feature, functioning as a contextual semantic search engine. This enables product teams to use plain English for searches, like locating sessions where user dissatisfaction is evident. The system is designed to efficiently identify relevant conversational logs, quickly resolving specific interaction issues based on user feedback. Additionally, Align AI integrates a data analytics assistant named Copilot, providing a conversational interface for in-depth analysis of conversational data. Copilot's role is to generate insights, recommendations, and metrics to simplify the data analysis process. This allows product teams to process conversational data into actionable insights, potentially improving AI chatbot interactions and user experiences. Google Bard and Conversational AI An artificial intelligence chatbot created by Google, Google Bard is defined by its ability to incorporate data from the internet into its conversational framework. This approach differs from traditional chatbots that typically rely on predefined data. Bard's capability to access current information from the web provides a distinct feature compared to other AI chatbots like ChatGPT and Bing Chat. The core of Google Bard is built on Initially, Bard utilized LaMDA based on Google's Transformer neural network architecture. This architecture is also used in other AI models, such as GPT-3, which is the foundation for ChatGPT. Google's transition to PaLM 2, as announced at Google I/O 2023, reflects its focus on improving Bard's efficiency and performance. Google's Language Model for Dialogue Applications (LaMDA) and its more recent version, PaLM 2. A notable feature of Google Bard is its multimodal search capability, facilitated by integrating Google Lens. This feature lets users incorporate images into their queries, expanding the chatbot's utility for information gathering and learning. Adding image-based responses in late May enhances Bard's ability to provide more comprehensive answers, particularly for queries where visual context is beneficial. Google Bard was initially available to a select group but was later made accessible to the general public, as announced at Google I/O. This move expands the reach of advanced AI technologies to a broader user base. Best Practices for Iterative Testing and User Feedback Incorporation The whole deal with keeping AI chatbots sharp and useful boils down to constant testing and listening to what users say. It's like a never-ending cycle of improvement to make sure these chatbots stay relevant and do a good job of understanding what people need. Creating a solid system for getting feedback is super important. Imagine a chatbot as a student and every piece of feedback as a lesson. Users might rate how well the chatbot did or give specific comments about their chat. This is gold for figuring out how real people use the AI and what they think about it. But it's not just about collecting feedback; you've got to dig into it, really get what it means, and then use it to improve the chatbot. It's a team effort, with AI developers, the folks who design how the chatbot feels, and data whizzes all working together to polish up the chatbot. Then there's this thing called iterative testing. It's not just testing once and calling it a day; it's doing it repeatedly, tweaking things. It's like trying different recipes to see which cake tastes the best. They might do A/B testing, where they try out different versions of the chatbot to see which one hits the mark, or they might set up specific tests with users to pinpoint exactly what needs fixing. It's all about making the chatbot smarter and more in tune with users' wants. There are some cool real-life stories about this stuff working wonders. Like, this big online store used user feedback to make their chatbot better at suggesting products. Boom, happier customers and more sales. Or a telecom company's customer service chatbot that got so much better at understanding what people were asking that it solved more problems, faster. In short, the secret sauce to keeping AI chatbots on their A-game is this cycle of testing, getting feedback, and using that to make constant improvements. It's what makes sure these chatbots are high-tech and get what users need and want.

Building Smarter Chatbots: Enhancing AI Responsiveness to User Needs

I. Introduction Web applications keep getting more advanced and multifaceted, providing users with tons of features. But this also makes them more vulnerable to performance problems, especially when handling lots of requests simultaneously. That can lead to slow response times, crashes, errors, and even downtime - all of which hurt the user experience and company reputation. To make sure web apps deliver high-quality service and meet user expectations, it's crucial to do load testing. Load testing simulates real user scenarios and measures how the app handles different load levels. It can identify and fix bottlenecks, optimize resources, improve scalability, and ensure reliability and availability. Mastering load testing for web apps takes understanding the basics, the process, tools, best practices, common pitfalls, and future trends. So, let's dive in and take a look. II. Load Testing Basics Before we get into the process and tools for load testing, let's first define what load testing is and the key metrics used. Definition Load testing simulates realistic user traffic to see how a web application handles varying load levels. It should be done on all the steps of the software lifecycle like development, integration, and deployment. The main goals are to: Verify that the app can handle expected and peak traffic without hurting performance. Find and fix bottlenecks that cause slow responses, crashes, errors, or downtime. Optimize resource use like CPU, memory, disk, and network by tuning configs. Improve scalability by determining max capacity and throughput, and how to increase them. Ensure reliability and availability by testing recovery from failures and consistent performance. Key Metrics To evaluate performance under load, we measure key metrics like: - how long requests take to process and respond. Affects user satisfaction so should be low. Response time - requests handled per unit of time. Indicates efficiency and capacity so should be high. Throughput - the percentage of failed requests. Indicates reliability so should be low. Error rate - resources consumed during testing like CPU, memory, etc. Should be within acceptable limits. Resource utilization III. The Load Testing Process The load testing process consists of four main steps: identifying workload and performance requirements, test scenarios and environment setup, test execution, and result analysis. Identifying Workload and Performance Requirements The first step in load testing is defining the expected performance and goals under different loads. This includes: - The minimum response time, throughput, etc. that should be met. Performance requirements - How users interact with the app. This covers user types, actions, locations, concurrency, arrival rate, think time, and more. Workload - The normal, peak, and stress loads that will be tested. Load levels These points should be realistic, based on business needs, user feedback, historical data, industry benchmarks, and other sources. Test Scenarios and Environment Setup The next step is designing a test plan and setting up the test environment to execute the load tests. The test cases should cover the most critical and frequently used functions, as well as the riskiest scenarios under load. They should align with the workload model and load levels. The test environment should mirror production as closely as possible in terms of hardware, software, network, configs, data, etc. It should include: - Generates and sends requests per the test cases and workload model. Load generator - Distributes requests across multiple app instances or servers. Load balancer - The app being tested. Application under test - Collects and displays performance metrics of the app and components during testing. Monitoring system Test Execution and Result Analysis The third step in load testing is to execute the test cases and analyze the results. The test execution should be done according to a predefined test plan that specifies the order, duration, frequency, and intensity of each test case. The test execution should also be monitored and controlled by adjusting the load levels, stopping, or restarting the tests, etc. The result analysis should be done by comparing the actual performance metrics with the performance requirements and identifying any deviations or anomalies. The result analysis should also include identifying and diagnosing the root causes of any performance issues, such as code defects, configuration errors, resource bottlenecks, etc. Test Reporting and Improvement The final step is reporting the findings and recommendations and implementing improvements based on the analysis. The test report should summarize the objectives, methodology, environment, execution, results, analysis, findings, recommendations, and conclusions. It should include supporting data like graphs, charts, tables, screenshots, logs, etc. Improvements may involve applying fixes or optimizations to the code, configs, architecture, design, etc. Changes should be verified by re-running load tests to validate their effectiveness. IV. Load Testing Tools A diverse array of tools exists for load-testing web applications, each with unique capabilities. , an open-source Java tool, enables load generation and performance measurement across various protocols like HTTP, WebSocket, and FTP. It provides recording, scripting, dynamic data simulation, result analysis, and more. Extensibility through plugins is a key benefit. JMeter by Micro Focus excels at load testing beyond just the web, with support for mobile, IoT, and media. Scripting uses languages like C and JavaScript. Visual analysis is facilitated through dashboards. LoadRunner , written in Scala, separates test logic from execution for scalability. Recording HTTP sessions and converting them to code accelerates test creation. Interactive charts empower insightful results analysis. Gatling leverages Python for load test scripting. Its master-slave architecture readily scales simulated users across multiple machines. An intuitive web interface displays live test metrics. Locust These tools and many others each have unique strengths. Careful selection based on the technology, test environment, metrics, integrations, and workflows to be assessed is advised. Thorough evaluation guides the way to the optimal choice. V. Best Practices and Common Pitfalls Mastering the art of load testing requires adhering to key best practices while avoiding frequent missteps. To conduct effective load testing: Set clear, realistic performance targets based on business needs, user feedback, historical data, and industry benchmarks. Don't define criteria that are unattainably high or low; Design comprehensive test cases that cover critical functions and probable high-risk scenarios under load. Align cases to workload models and load levels; Pay special attention to possible peak load scenarios: new marketing campaign planned and a lot of new users expected? Make sure to test the registration and login parts. Black Friday coming? Check scenarios for huge simultaneous checkouts; Build a test environment that closely resembles production in terms of hardware, software, network, configs, and data. Isolate it from anything that could skew results; Use capable, reliable load-testing tools that can simulate workflows and measure metrics accurately per the technology stack. Avoid inappropriate or unstable tools; Make sure test servers and bandwidth can generate the load required for testing. Using one request-generating server to test 100 servers might not generate the required load that would result in incorrect test results; Execute load tests per a systematic plan defining order, duration, frequency, and intensity. Monitor and adjust load levels carefully during execution; Analyse results by comparing metrics to criteria and diagnosing issues through data visualization and statistics. Avoid raw data or unclear graphs; Implement optimizations, then re-test to validate improvements. Don't deploy changes without verifying effectiveness. Common pitfalls to avoid: Inaccurate performance targets leading to false positives or negatives; Incomplete test cases missing critical functions and scenarios; Unrealistic test environments that skew metrics due to differences from production; Poorly matched tools that can't simulate workflows, generate required load, or measure metrics properly; Lack of execution plan causing disorganized, inefficient test runs; Weak analysis lacking visualization and statistics to surface meaningful insights; Failure to re-test and validate the impact of optimizations before deployment. VI. Future Trends in Load Testing Load testing continues to evolve as web development advances. Key emerging trends include: leverages scalable cloud computing services like and for benefits such as flexibility, cost savings, and geographic reach. The cloud enables large-scale distributed testing. Cloud-based load testing BlazeMeter Grafana Cloud k6 are transforming load testing through intelligent automation. AI can generate realistic workloads, predict metrics, diagnose issues, and recommend solutions. Tools like , , and incorporate these technologies. Artificial intelligence and machine learning Dynatrace Neoload Apica helps identify and prevent vulnerabilities under load. , and are examples of WAST tools that can detect risks like denial-of-service attacks and cross-site scripting. Integrating web application security testing (WAST) OWASP ZAP , Nmap Burp Suite Mastering these emerging practices will be key for load testing excellence as the web evolves. Forward-looking organizations are already putting them into practice. VII. Conclusion Mastering load testing is critical for releasing high-performing web applications. It requires blending technical and analytical expertise to model realistic user behaviors, select appropriate tools, develop comprehensive test cases, conduct controlled experiments, and extract meaningful performance insights. As technology evolves, agile adaptation alongside diligent mastery of load testing fundamentals will remain key. By building robust in-house competencies or working with experienced partners, organizations can gain a significant competitive advantage through expertise in this vital discipline. References Top 15 Open-Source Load Testing Software 2023 | TestGuild 9 Load Testing Best Practices (Don't Make These Mistakes) (testguild.com) Load testing best practices  |  Cloud Run Documentation  |  Google Cloud What is Load Testing: Process, Tools, & Best Practices | BrowserStack Load Testing Best Practices (multiple.dev)

Mastering the Art of Load Testing for Web Applications

Artificial intelligence is a powerful tool that can boost business operations in many ways. As AI adoption grows, more organizations want to integrate these technologies effectively. However, the process does present some common challenges. This article explores the five most frequent mistakes made during the integration of into your company’s non-technical teams. AI Lack of Clear Objectives Many companies rush into AI projects without taking the necessary time upfront to define clear goals. Establishing objectives is a critical first step. Carefully consider what problems they want AI to solve and what benefits it will deliver to the business. Without a well-defined purpose, it’s easy for initiatives to lose focus or produce underwhelming results. It’s important to examine your teams’ workflows and pinpoint areas where AI could create a real impact. Look for repetitive, data-driven processes or inefficiencies where automation may help streamline operations and boost productivity. Conduct interviews with employees across departments to fully understand pain points from their perspectives. Process mapping can also reveal friction points or inconsistencies in existing systems. With insights from stakeholder research, teams should then set objectives around how . Goals may involve automating specific repetitive tasks to free up employee bandwidth, improving customer experiences like reducing wait times or increasing productivity through more efficient processes. It’s also critical to establish measurable metrics for evaluation. Examples include cost savings, time savings per task, increased sales or leads, and improved customer satisfaction scores. With clear targets, teams can effectively gauge an initiative’s success and return on investment over time. AI can resolve issues Seeing AI Projects in Isolation When bringing AI into your organization and into teams, it’s important not to view projects as standalone initiatives. Some teams make the mistake of adopting AI solutions without considering how they can integrate into existing workflows. However, the real power of AI comes from augmenting core operations rather than operating separately. By taking inventory of your current processes, you can identify opportunities for AI to provide the most value. Whether automating repetitive data entry, streamlining approvals, or enabling personalized customer experiences – finding the right connections between new technology and daily tasks is key. Don’t just focus on standalone pilots. Ask how AI can boost productivity across your entire operation. Approaching projects with an integration mindset from the beginning helps ensure successful adoption. Employees are more likely to embrace changes that build on familiar workflows rather than introducing unfamiliar systems. Connecting directly to core operations allows you to maximize benefits such as time savings or increased work quality in a more seamless way. AI solutions Insufficient Data Preparation Data is the fuel that powers AI. But garbage in means garbage out. So, it’s critical to ensure your data is clean, organized, and properly structured before developing models. Many teams underestimate how much effort is required at this stage. To get the most value from AI, you need high-quality input. Take time to audit your existing data sources. Look for inconsistencies, missing fields, duplicate records, and out-of-date information. This data clean-up process is crucial but often overlooked. Establish governance guidelines around data collection, storage, and access as well. Clearly defining roles and standards helps facilitate model training down the road. It’s also important to map your data to identify relationships between fields. This will help when integrating AI systems into workflows later on. Outlining a data cleaning plan with specific steps, timelines, and responsibilities holds teams accountable. Don’t wait until models are being built to address data issues either – it’s better to front-load this work. Inadequate Change Management Change management is crucial when introducing new technologies like AI. Employees may feel uncertain or threatened by automation, putting their roles at risk. Adopting the technology without addressing these concerns often backfires. It’s important to communicate proactively about anticipated impacts on jobs. While some tasks may be automated, AI also creates new opportunities that may require re-skilling or adjusting performance metrics. Developing a change plan with inputs from stakeholder teams helps facilitate buy-in. Consider hosting information sessions to explain how the AI system works and what it means for day-to-day work. Discuss potential job changes openly and honestly. Where roles are expected to evolve, provide training, or partner employees with managers to discuss career paths. Having transparent conversations early on can relieve anxiety about the unknowns of technology adoption. Don’t underestimate the power of change management, either. Pilot projects may go smoothly, but rolling out AI solutions company-wide requires change leadership. Designating change agents and giving managers tools to address concerns locally helps the transition go more smoothly. With an inclusive process, you can leverage your employees’ expertise to fully realize the benefits of new technologies. Limited Cross-Functional Collaboration Every new technology integration project involves expertise and data from multiple departments like IT, operations, and finance. AI integration is not an exception to this. However, bringing together stakeholders can be challenging without the right coordination. It’s important to establish an with dedicated representation from relevant teams. This group can then facilitate communication and ensure all perspectives are considered throughout the project. Regular meetings allow members to provide updates on data collection efforts, development progress, and plans for rollout or ongoing maintenance. AI task force Don’t overlook the value employees outside the task force can offer, too. Empower cross-team “champions” to solicit input from their departments and bring feedback to governing body discussions. Continual collaboration helps align AI applications more closely with real-world operations right from inception. It also builds organizational support – and troubleshooting partnerships – to help ensure long-term success.

Looking to Adopt AI for Your Company's Non-Technical Teams? Avoid These 5 Pitfalls.

After a year of layoffs and outages, leaders are under intense pressure to increase efficiency and reliability despite having fewer resources. , 70% of software teams are adopting AI. According to this survey That means that capitalizing on is becoming an industry standard. AI Of the teams who have adopted AI, on average, they’ve reported a 250% increase in software development speed. To put that another way, they’re able to achieve in 5 hours what they’d otherwise achieve in 8. Those same teams expect their efficiency to increase to 350% within a year. Successfully adopting AI can, and regularly does, lead to engineering teams unlocking a substantial competitive advantage. On the flip side, teams not actively adopting (or actively avoiding) AI are signing up for an enormous opportunity cost that leaves vast amounts of resources and money on the table. AI is capable of drastically improving efficiency in several areas. s and engineering leaders commonly evaluate AI for multiple purposes, which include: CTO AI for operations: streamlining internal processes, automating tasks, improving decision-making AI for data analytics: advanced data processing, analytics, and insight generation AI for product development: enhancing product features, UX, and scalability AI for customer service: chatbots, automated support, customer insights Different AI adoption types have unique requirements but need a robust foundational infrastructure. Step 1: Robust, scalable data and tech infrastructure Your AI adoption's success hinges on a robust data and tech infrastructure. This applies across multiple AI adoption scenarios – whether it's for enhancing products, optimizing operations, refining data analytics, elevating customer engagement, or something else. Data availability, quality, and integrity Low-quality data disrupts AI adoption (not to mention your operations). Set data integrity standards early. Protocols vary: For customer engagement, data should be current and relevant, while operational AI needs historical and real-time data. When using operational tools like Jira or Slack, teams should adopt communication best practices to ensure data availability, ensuring meaningful discussions are in public channels, not DMs. Strategic technology selection and scalability can be impediments or even liabilities, depending on the type of AI adoption you're considering. For product-focused AI, sluggish data access due to legacy systems can be a bottleneck, whereas, for operational AI, an outdated system or tools will impede real-time decision-making. Legacy systems Don't forget to plan for data backup and recovery strategies. These measures are necessary not just for operational continuity but also to ensure the long-term viability of your AI systems. Step 2: Building an AI-ready team and culture Our endeavor of injecting AI into your operations is as much about people as it is about algorithms. AI adoption isn't just the task of a lone AI specialist or an isolated task force – it's an organizational shift. Here's your playbook: Getting the right skills Effective AI adoption often involves a designated team or individual. Depending on company size, some roles might be part-time. I often see these kinds of people on AI task forces: Data security leaders (e.g. CISO) Product and project management leaders Engineering leaders Data and machine learning leaders UX professionals Domain experts, depending on your industry (e.g. healthcare, finance) Stakeholder representatives from different departments (like HR, sales, marketing) Ethics officers and legal counsel, if appropriate Your task force needs to develop familiarity with AI tools, how AI works, and AI ethics if they don’t already have this. If they don’t, form partnerships or upskilling programs to fill the gaps. Your task force needs to understand the language of engineering operations, and the language of AI. AI-positive culture AI adoption is a company-wide undertaking that needs the backing of the C-Suite. If you’re the CTO, or another top-ranked engineering leader, you’ll know that the active support and continuous engagement with projects directly correlate with successful outcomes. Take an active leadership role in your AI adoption project. If that isn’t you, you’ll want to make the case for this endorsement and activity from your decision-makers. Transparency and good operational hygiene are prerequisites for the operational adoption of AI. Getting this right may look like: Universal high standards for project management software use. Project management software (like Jira or Linear) is kept up-to-date consistently. Tickets are described completely and accurately. Process-driven, verbose version control and CI/CD. Pull requests are appropriately described, commit messages are uniform and follow best practices. Teams communicate openly and transparently. Conversations happen in public channels unless there is a specific reason (such as data sensitivity or operational and strategic irrelevance). This is particularly important for distributed or remote teams. Your task force needs more than just a passing familiarity with AI. They need operational know-how, executive backing, and a transparent, accountable culture. Step 3: Strategic planning and project prioritization Here's how to ensure your AI adoption isn't just robust, relevant, and value-generating. Be agile and experiment When we get it right, innovation is a competitive advantage. The most successful AI-adopting businesses move fast and flexibly with their AI strategy. They conduct experiments to assess the effectiveness of operational AI solutions and make budget available to make them happen. Place decision-making power for experiments with your AI task force. It’s a good idea to select pilot projects. Always define what success will look like and be prepared to learn lessons from failed pilots. Align AI Initiatives with Business Strategy Define clear objectives for AI adoption, ensuring AI efforts align with desired business outcomes. AI can offer quick wins, but it also promises long-term transformation. Start small but focus on projects that deliver immediate ROI. Use these quick wins as proof of concept for larger, more complex projects. Assess every AI project's potential value and feasibility. Avoid just following AI trends; emphasize projects that significantly improve operations. Strategic planning and prioritization are foundational for a fruitful AI strategy. Agile approaches to AI AI adoption is most successful when AI taskforces have open minds about the kinds of solutions that can unlock efficiency gains. After all, AI is one of the fastest-evolving technologies in our space. Be open to reassessing, pivoting and/or expanding your AI strategy to adapt to market evolutions, shifts and emerging opportunities. Continually reassess and pivot your AI strategy. This isn't a luxury; it's a necessity for adapting to market shifts and emerging opportunities. Stakeholder Alignment Don't make AI decisions in a vacuum. Involve key stakeholders in the process. Ensure everyone is aligned and invested in the AI journey and the tools you’re adopting. Step 4: AI ethics, governance and compliance Businesses are often held back from AI adoption because of a lack of grasp of the nuances around AI ethics. Responsibility and accountability Don't treat AI ethics as an afterthought. Make it central to your AI adoption strategy. A dedicated AI ethics committee – or AI ethics lead at smaller businesses – is a great way to establish awareness, education, and accountability around AI ethics. Your AI ethics function should have knowledge and authority on a range of topics, including: Fairness and bias Transparency and explainability Data security and privacy Accountability and responsibility Ethical data sourcing Social and environmental impact Regulatory compliance Your AI ethics function isn’t there to roll out the red tape, but rather to help others feel confident in their AI adoption approach and position your organization for sustainable, responsible AI-driven growth. Assessing high and low-risk projects Some AI implementations are inherently riskier than others. For example, you might be using AI to help your team understand the code in your projects. This doesn’t carry the same risk as, say, using AI to segment user data. Develop thresholds of risk to help fast-track low-risk projects and reduce risk for high-risk projects. For example, giving an AI model read-only access to infrastructure code that does not involve user data might constitute a low risk. Conversely, an AI project involving user-facing actions based on AI analysis of user data would carry numerous risks, from data security and privacy risks to bias and discrimination risks. Data privacy and regulatory awareness Understanding AI regulations is vital to avoid legal risks. Embed data privacy from the project's start to respect user data from the start. High-risk projects may benefit from – or require – third-party audits to help identify vulnerabilities and blind spots. Ethical AI practices are both the right thing to do, and crucial for building and maintaining consumer trust. Lose that trust and you risk all kinds of things from bad PR to legal issues. Chasing operational efficiency in your product engineering team? You’re probably already sitting on a goldmine of data – your product engineering team’s operational data. We’re talking about the flow of tickets through Jira or Linear, the commits and pull request activity in GitHub, and even your team’s conversations in Slack. I’m building a tool that makes your operational data work for you, with my team at Stepsize AI. We’re working hard on our tool and I’d absolutely love your feedback on its features and what alignment or collaboration problems you’re trying to solve on your product engineering team. Our goal is to build a tool that’s easy to set up, security-first and enables you to get automatic updates and insights that drive actions right when needed. The Stepsize AI Operational Intelligence Engine understands everything happening in your product engineering org. It uses this to create powerful, reliable updates, including automated standups, sprint and Kanban reviews, executive summaries, and more. With Stepsize AI, you can Effortlessly achieve alignment Cut meetings and boost productivity Get instant answers with zero context-switching Again, if you’ve got a collaboration or alignment problem you’re trying to solve, I’d love to chat and would hugely appreciate your feedback. Also published here.

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The Challenge of Developing Truly Rational Systems "AI is racist." This provocative headline grabbed the world's attention a few years ago when reports surfaced about racial and gender bias in artificial intelligence systems. Facial recognition algorithms struggle to identify people of color. Hiring tools favor male applicants over females with identical qualifications. The machines we built to be rational and objective—unlike biased human minds—have prejudice coded into their decision-making. How did this happen? Why does as people, even though it lacks human cognitive limitations? The root causes trace back to AI's data sources and optimization objectives. AI systems absorb implicit biases from datasets that reflect existing societal inequities. And algorithms programmed to maximize accuracy propagate these biases rather than challenge them. AI exhibit many of the same biases This begs the question—can we ever develop truly rational AI free of unfair bias? To achieve this ideal, we need to rethink how we design datasets, choose performance metrics beyond accuracy, and implement ethics reviews during development. With diligence and a cross-disciplinary approach, the same technology that absorbed our biases could someday help counteract them. AI has immense potential, but first, it needs an upgrade to move beyond mimicking our flaws. The path won't be easy, but the promise of AI unencumbered by irrational prejudice makes it a worthy goal. TLDR; Can AI Overcome Built-In Human Biases? AI systems today exhibit biases along race, gender, and other factors that reflect societal prejudices and imbalanced training data. The main causes are a lack of diversity in data and teams, and a focus on pure accuracy over fairness. Mitigation tactics like adversarial debiasing, augmented data, and ethics reviews can help reduce bias. Fundamentally unbiased AI requires rethinking how we build datasets, set objectives, and make ethical design central. Future challenges include pursuing general AI safely while removing bias, and cross-disciplinary collaboration. AI has the potential as a rational foil to counteract irrational human biases and promote fairness if developed responsibly. Choices made now in how AI is created and applied will determine whether it reduces or amplifies discrimination in the long run. Examples of Biased AI Systems One prominent example of racial bias in AI is . Studies have found that leading facial recognition algorithms have error rates upwards of 10 to 100 times higher when identifying people with darker skin tones compared to lighter skin tones. These systems were trained mostly on datasets of white faces, causing them to struggle with accurately recognizing other races. facial recognition systems Beyond issues identifying people of color, some facial recognition systems have also exhibited prejudice in how they categorize people based on race and gender. Systems trained on imbalanced data have associated images of Black people with negative labels like “criminal” at higher rates. Gender bias has also been observed in hiring algorithms meant to screen job candidates. previously used an AI recruiting tool that was found to penalize resumes containing the word “women’s” and downrank graduates of two all-women's colleges. The system learned these biases from patterns in the male-dominated resume data it was trained on. In another case, a recruiting algorithm favored male applicants over equally qualified females for . These examples illustrate how unchecked AI can amplify gender inequality in hiring rather than reduce it. Amazon technical jobs Many other instances demonstrate AI absorbing implicit human biases around race, gender, age, ability status, and more. For example, natural language processing algorithms have exhibited bias in how they complete partial sentences about different demographic groups. Algorithms meant to support clinical decision-making have also demonstrated racial bias in estimated risk scores and treatment recommendations when given the same symptoms. Causes of Bias in AI One major source of bias in AI systems is that the data used to train them often reflects societal biases and lack of representation. Models only learn from the information they are exposed to. If that data over-represents certain groups or exhibits prejudiced associations, those biases will be propagated through the algorithms. For example, facial recognition systems trained mostly on light-skinned faces will inevitably struggle with dark-skinned faces. The same goes for natural language systems trained on text containing stereotypes and hate speech. Additionally, because AI models are frequently optimized for accuracy and performance metrics above all else, they will latch onto any "signal" in training data that helps them achieve those goals - even if that signal represents an unfair bias. Unless told otherwise, models have no mechanism for distinguishing between factual statistical differences vs inappropriate prejudices or distortions. So algorithms tasked with predicting employment success will pick up on and rely upon imbalanced gender representation and discriminatory practices in the hiring data. Finally, the lack of diverse perspectives among AI researchers and developers also contributes to bias being baked into systems. The field suffers from gender gaps and underrepresentation of minority groups. With AI teams and workplace culture dominated by one demographic, many blindspots will go unchecked. Having diverse voices involved in asking the right questions about possible sources of unfair bias could help identify and mitigate many issues early on. Mitigating Bias in Current AI Systems While biased AI reflects problematic data and design choices, techniques do exist to help mitigate these issues in current systems. One approach is adversarial debiasing, which uses an adversarial network to penalize models that exhibit bias and force them to correct it. By explicitly trying to expose bias, the adversarial network helps shift the model away from relying on prejudiced patterns or labels. However, this technique is still limited by the capabilities of the underlying training data. Expanding the diversity of training data to better represent populations and perspectives is another mitigation strategy. Ensuring datasets include reasonable distributions of gender, race, age groups, etc. reduces the chance of imbalance-driven bias. Synthetic data generation can also help augment underrepresented groups. However, diversity alone does not guarantee fairness, as models may still learn latent biases in new data. Incorporating ethics reviews and bias testing into standard AI development pipelines is important for catching issues early. Checking for statistical bias across groups in a validation set, surveying minority populations for perceived harms, and having a diverse team perform ethical reviews of models and data can surface problems missed by homogeneous teams and metrics. Making ethical AI an integral part of the process improves outcomes. Building Unbiased AI from the Ground Up To work towards fundamentally unbiased AI, we must rethink how we build datasets themselves. Rather than passively absorbing collected data with all its biases, active efforts to balance representation and exclude prejudice are needed. This requires extra work soliciting data from underrepresented groups and consciously auditing datasets for imbalance or harmful associations. Building datasets as diverse and ethically sound training environments from the start establishes a stronger foundation. We should also prioritize goals like group fairness and avoidance of harm above pure accuracy. Setting objectives that minimize gaps in performance across populations guides models away from biases even if accuracy takes a slight hit. Models should align with ethics first, using accuracy as a secondary metric. Architecting AI to maximize fairness rather than unconstrained optimization prevents maximizing performance at the cost of discrimination. Finally, transparency and ethical design principles should be core to development from day one, not an afterthought. Explaining model behaviors builds trust. Extensive testing for disparate impacts across populations ensures even subtle biases are caught. And human oversight for high-stakes decisions reduces harm from inevitable errors. Grounding AI in ethical design and responsible development practices is key to preventing irrational prejudices of the past from tainting the rational systems of the future. Outlook for the Future The path toward developing AI systems free of human-like biases brings both challenges and opportunities. A major challenge lies in continuing to pursue more expansive, general artificial intelligence while also proactively removing biases. Self-driving cars, personalized medicine, and other applications require increasingly capable AI, but sophisticated models are also more prone to complex behavior reflecting subconscious prejudice. Maintaining momentum in AI safety and ethics as capabilities advance will require vigilance. Truly overcoming bias will necessitate cross-disciplinary collaboration. Ethicists, social scientists, legislators, activists, and other stakeholders must work alongside computer scientists to guide AI progress. Diverse teams asking difficult ethical questions will strengthen solutions. Establishing norms and regulations around transparency and testing for different groups will also help enact positive change. While AI has absorbed our biases, its rational capacities also give it the potential to help counteract human irrationality in the long run. Rather than acting as a passive mirror of humanity’s flaws, advanced AI systems could someday model and promote fairness in society. AI could be leveraged to audit biased decisions, identify instances of discrimination, and suggest ways to equitably improve systems. We must remain cautious about anthropomorphizing AI given its lack of human context and lived experience. But consciously building AI as a rational foil to humanity's irrational biases may foster gradual progress towards greater justice. The road to ethical, unbiased AI has obstacles but is full of promise. With diligence, creativity, and collaboration, the same technology plagued by our prejudices could be transformed into a tool for exposing and reducing them. The choices we make today in how AI is built, audited, and applied will determine what role it plays in either perpetuating or breaking down unfair barriers. Progress will not be automatic, but by treating AI as a mirror to guide our self-improvement rather than just a tool for optimization, we can work towards fairer, more rational systems benefiting all groups equally. The Bottom Line: Can AI Overcome Built-In Human Biases? AI stands at a crossroads today. While current systems propagate unfair biases, they also have immense potential as tools for exposing and reducing such biases in society. But this future is not guaranteed - the path toward ethical, unbiased AI requires diligence and collaborative effort across disciplines. How we choose to build, audit, and apply AI now will determine what role it plays in either perpetuating or breaking down discrimination in years to come. Though progress will be gradual and require overcoming many challenges, the goal of developing AI as a truly rational foil to counteract irrational human biases is a worthy pursuit. With creative solutions, responsible regulation, and diverse teams asking tough questions, we can transform these promising technologies into forces for greater fairness. AI is only as flawed as the data and directives we imbue it with. These remarkable tools are only as biased as their human creators. But if STEM fields make diversity, transparency, and ethics core priorities of AI development today, we can set these systems on a trajectory towards reducing, rather than amplifying, the unfair biases that have too long plagued society. AI offers immense power; it is up to all of us to wield that power responsibly and shape AI into a driving force for equality.

Can AI Ever Overcome Built-In Human Biases? 

Let's learn about via these 27 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Pitching /Learn Repo 1. What's your growth rate? Guys! During the YC Startup School I gradually started to implement different ideas to track primary metrics and I have involved my team to set OKR (Objectives and Key Results) and track their important metrics too. Eventually it has been structured on a dashboard using just google spreadsheets and formulas and charts. 2. How to Ace Public Relations with Itai Elizur People do not buy goods and services. They buy relations, stories and magic - Seth Godin 3. Stop Blindly Targeting, Start Creating Smarter Content for the Funnel Businesses want to make sales. 4. Hiring Tips: How to Pitch to a Potential Startup Founding Team Member Hiring for your startup's founding team, but struggling to recruit the best candidates? This guide will help you perfect your pitch and attract top talent! 5. 10 Hacks to Pitch Your Idea and Have Stakeholders Love It These simple steps will help you to avoid common mistakes during a presentation, catch stakeholder's attention and receive the green light for your project. 6. In Conversation with Stewart Kohl on What He Looks For in an Investment Stewart Kohl is Co-Chief Executive Officer of The Riverside Company, a global private equity firm based in Cleveland, Ohio with offices across North America, Europe and the Asia-Pacific region. The Riverside Company has more than $7 billion in assets under management and oversees a global portfolio of more than 80 companies. Prior to joining Riverside in 1993, Kohl served as vice president of Citicorp Venture Capital and COO of the National Cooperative Business Association in Washington, D.C 7. How to Get Into a Startup Accelerator and Secure Series A Funding If a company doesn’t have solid capital from the start, it can get funding from other resources such as venture capitalists, business angels, and accelerators. 8. Using Artificial Intelligence and Big Data To Deliver Your Pitch A good pitch tells the story of your idea. From its inception to its present form and everything in between. Utilising multimedia, graphs and visuals is a good way to keep your audience engaged and up to speed. Most fundamentally, using data is important for both your audience and your idea. 9. Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs This post is part of the Hacker Noon Shareholder Series, where we interview some of the super-investors who made the site you're on right now possible. 10. Six Tips to Create a Powerful Elevator Pitch Looking for tips to develop a robust and incredible elevator pitch? A compelling elevator pitch is pretty essential for achieving your business goals and objectives. Whether you want to raise money from venture capitalists or angel investors, or just willing to hire a perfect team for your business, elevator pitch is the most important task. 11. The Role of Change in a Pitch Deck Answering the "Why Now?" Question A successful pitch starts with a well-thought-out pitch deck. And the "Why now" section is arguably one of the most important aspects of that deck. 12. Pitching VCs? Use This Reverse Psychology Trick To Get Funded You’re planning a big adventure — skydiving or swimming with the sharks, for example — you’ve probably got the details mapped out. 13. 'Smart people with bad ideas can always pivot.' —LA MP Mark Tung This post is part of the Hacker Noon Shareholder Series, where we interview some of the super-investors who made the site you're on right now possible. 14. Seven Finalists, including Hacker Noon, Announced for Rural Colorado Virtual Pitch Event A distributed workforce, long prophesied by the tech community, has been accelerated by a decade in the last six months. 15. 5 Storytelling Tips That Improve Pitching for Startups A very concise article with 5 tips for storytelling when fundraising for a startup or pitching at a competition. 16. 3 Main Reasons Why TechCrunch Won't Feature Your Startup - Part 2 How should I pitch my company to journalists? Good question! Journalists love two things: respect for their personality and exclusive information. What else? 17. How to Pitch Like a Pro: Grab a Journalist's Attention In the past two posts, you learned about what piques a reporter's interest and how to make it newsworthy. It's time to take the knowledge and create a pitch for journalists. 18. Cold E-mailing: The Wonders It Could Do for You Aleksandr Volodarsky goes over the benefits of cold e-mailing. 19. Pitch the World's Most Misbegotten Podcast A listicle of unfeasible, unpleasant, and unproduceable podcast concepts, as nominated by the Hacker Noon top writers and editors Slogging community. 20. Funding: No Longer Twice as Likely to Come Through if Pitched by a Man 🤞 The entrepreneurial road to business success is never easy. But one way to make the path to entrepreneurial success even more challenging is simply to be born female. Female entrepreneurs face challenges that their male counterparts have never even considered. 21. My First Pitch to Investor: From Outsourcing to Product Hi community! My name is Sergii, I'm a CEO at WellSpeak from Kyiv Ukraine. We build a mobile app for practicing English speaking skills. 22. The Funding Ask Slide: How to Make it Better? Asking for money isn't always as straightforward as it seems. Ask for too much, and you can end up looking for funds for months or years on end. Fail to show ho 23. How To Pitch Your Story and Get Published Tons of websites offer guest post opportunities, and probably you would love to get published at least in one of them. Working on both sides - publisher at Abstract Stylist and an independent blogger, I have a rare opportunity to share my insights and most common mistakes people make while pitching their guest posts. Let's take a look at 5 mistakes I see people make daily. 24. The 5 Steps to Becoming the God of Pitch Decks A step-by-step plan on becoming the God of Pitches for investors and start-up contests. 25. 6 Tips to Create a Powerful Elevator Pitch Looking for tips to develop a robust and incredible elevator pitch? A compelling elevator pitch is pretty essential for achieving your business goals and objectives. Whether you want to raise money from venture capitalists or angel investors, or just willing to hire a perfect team for your business, defining your elevator pitch is the most important task. 26. Master the Art of Public Speaking with these 8 Simple Tips It's been a week of preparation and you’ve rehearsed in front everything and everyone, including, but notwithstanding mirrors, husbands and even cats. You’re geared up and ready to go. The stage is set, you start talking and within a few minutes, half of your audience, instead of glazing at you, locks their eyes into their gadgets. How to keep people entertained and win back those whose attention you’ve lost? I’ve uncovered the eight secrets of successful stage performance, which will help you keep even the most capricious audience. 27. Inside PG&E: What I Learned Pitching When a culture of risk aversion creates risk of harm Thank you for checking out the 27 most read stories about Pitching on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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