Qlip is a digital platform that provides after sales services in real estate market.

Qlip - Crunchbase Company Profile & Funding

The Milk Competence Center, better known as Z-net, is a part of Qlip. Z-net is the innovative data platform for effective and reliable information exchange between dairy companies and dairy farmers to promote quality, transparency and sustainability.

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Find company research, competitor information, contact details &amp; financial data for Qlip B.V. of Zutphen, GELDERLAND. Get the latest business insights from Dun &amp; Bradstreet.

Qlip B.V. Company Profile | Zutphen, GELDERLAND, Netherlands | ...

Information on valuation, funding, cap tables, investors, and executives for Qlip (Business/Productivity Software). Use the PitchBook Platform to explore the full profile.

Qlip (Business/Productivity Software) Company Profile: Valuation ...

Qlip supports companies in the agro-food industry, in particular companies in the dairy chain in the area of food safety, quality and sustainability. Qlip performs assessments, audits and certifications in the dairy-, dairy cattle- and beef cattle industries.

Qlip - World Food Innovations

QLIP - Online NFT marketplace platform. Raised a total funding of $4.14M over 1 round from 1 investor.

QLIP - Company Profile - Tracxn

Innovative and advanced data platform for effective and reliable information exchange between dairy companies and dairy farmers to promote quality, transparency and sustainability. We are proud to announce that Qlip has been accredited for various research parameters in dairy alternatives.

The expert in analysis, certification and calibration in dairy | Qlip

1 billion members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

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Qlip B.V. - Quality Assurance in Dairy | 3,941 followers on LinkedIn. Experts in Testing, Inspection and Certification in Dairy | From milk to cheese, and milk powder to milk formula for infants. As a consumer, you naturally expect that these products are safe and of good quality.

Qlip B.V. - Quality Assurance in Dairy | LinkedIn

Qlip is a brand new company that recently secured a patent for its product – a stylish accessory engineered to securely hold the user’s glasses. Qlip came to Lumina with the goal of establishing both an online profile and legitimate company branding materials as part of a complete product ...

Qlip - Website Design Company in NYC | Lumina

Qlip (company). Qlip is a software developer of a platform for real estate agencies digitizing the after-sales proces in which they can register and control changes and registration of all services and supplies that the client requires.

Qlip (company) | Nordic 9

qlipis in the Video Creation Tools Industry

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Automation in finances, particularly within the banking sector, traditionally commenced with high-frequency processes and transactions. In banking, the most frequent activities involve transaction processing and retail lending. These areas have been the primary focus of automation efforts, encompassing aspects such as application processing, risk assessment, and the transition from manual review of forms and documents to automated checks and model-based evaluations. Mezzanine financing stands in stark contrast to these high-frequency processes. Characterized by its unique, tailor-made nature, mezzanine financing occupies a specific niche space within the financial landscape: it represents a financing solution with heightened risk levels, strategically positioned between standard corporate loans and equity investments. Mezzanine financing either involves providing funding at the shareholder level (which is structural subordination) or through equity acquisition coupled with return instruments like put options (contractual subordination). Given the one-off and highly individualized nature of mezzanine deals, automating processes in this area presents a significant challenge. The natural question would be the following: how can banks, especially major ones, approach the task of enhancing the profitability of their mezzanine business through automation and digital transformation? As an expert with substantial experience in the areas of Private Equity, Risk Management, and Finance, I aim to provide an introductory exploration into the digital transformation in mezzanine lending which seeks to unravel the complexities and provide success stories in applying technological advancements to an area of banking traditionally reliant on bespoke, individualized deal-making. The Traditional Landscape of Mezzanine Lending Mezzanine financing is a distinctive and nuanced sector within the broader financial landscape. As was stated earlier, it occupies a middle ground between conventional corporate loans and equity investments and is characterized by an elevated risk level. Uniquely, each mezzanine deal is tailor-made, and crafted to suit the specific needs and circumstances of each client, much like a custom-made suit. Challenges in Traditional Mezzanine Lending The nature of mezzanine financing inherently brings about significant challenges. Traditional methods rely mainly on manual processes and individualized deal-making. This approach demands a deep understanding of the unique aspects of each deal, a skill set that is often . scarce and expensive In large banks with established corporate lending divisions, client and credit managers are well-versed in standard credit products. However, their encounter with mezzanine transactions is infrequent, which in turn limits their expertise in effectively selling or attracting such deals. Establishing a specialized team of client managers exclusively for mezzanine products is not just expensive, but it also escalates the costs in comparison to the more standard lending procedures. While it is possible to combine complex investment products into groups to reduce costs, digitalization in this regard emerges as a more economical and efficient alternative. Digitalization as a Solution The digital transformation in mezzanine lending primarily focuses on deal identification and attraction. Most credit and client specialists in banks use systems that record negotiations, deal ideas, and initial deal parameters. Integrating mezzanine criteria into these existing systems could automate the identification of potential mezzanine deals. When a transaction meets these criteria, it could automatically be forwarded to the mezzanine division for further processing. Further advancements might include AI models trained to differentiate between standard corporate loans and mezzanine deals based on a multitude of incoming deal parameters. The scale of this transformation is significant: while a major bank might conduct thousands of corporate lending transactions annually, mezzanine financing deals are much rarer, often numbered in single digits. Implementing a system for identifying mezzanine deals could potentially lead to a tenfold increase in their volume. Streamlining the Process: Automation and Standardization Digital transformation can streamline execution by automating and standardizing the process. Instead of diving straight into full-scale automation and platform development, you could initially focus on simpler technologies like RPA, which automate data collection, verification, calculation, and reporting. This reduces manual work and errors and improves accounting and compliance. Standardized mezzanine instruments and documents like term sheets and loan agreements can also be quickly customized for each deal without loss of quality. This speeds up processing and documentation, which further improves transparency and consistency. Digital platforms and tools like databases and dashboards help track and manage increased deal volume and variety by organizing information. This enables monitoring deal status and progress, identifying and resolving issues. Overall, automation and standardization simplify execution, reducing friction and costs. By streamlining the end-to-end process, digital transformation makes mezzanine lending more efficient, scalable, and profitable. The Role of Reporting Tools Mezzanine portfolio reporting is problematic without standardized data. These heterogeneous deals lack transparency, which makes it difficult to consistently evaluate key return and risk metrics. Digital analytics tools for data visualization and Business Intelligence can help by integrating fragmented data for consolidated visibility: it enables interactive dashboards that visualize portfolio performance sliced by the borrower, industry, geography, and other dimensions — this empowers holistic monitoring. also enables advanced analytics like predictive modeling, scenario analysis, and stress testing. Lenders can simulate future portfolio performance under various assumptions and conditions — these data-driven insights help optimize strategy and risk mitigation. Qlik and data analytics in this regard are the best to provide the transparency and insights needed to actively manage mezzanine portfolios. By enabling comprehensive reporting and predictive analytics on these complex deals, digital transformation gives lenders the visibility required to maximize returns and minimize risk. It's a powerful lever for optimizing results in mezzanine lending. Qlik Sense Case Studies In this section, we'll explore instances of credit process automation. Notably, there are limited prominent cases of optimizing mezzanine transactions beyond our work at Sberbank. However, I'll present some examples that demonstrate how similar approaches can be applied to mezzanine business, akin to what we achieved at Sberbank between 2018 and 2021, and what I’ll unveil later in the text. Case Study 1: Major European Bank Implements End-to-End Digital Lending Platform : A large European bank sought to reposition its SME lending business amid competition from agile fintech rivals. It aimed to create a digital ecosystem with seamless customer journeys. The first step was reinventing its commercial lending with streamlined applications, mobile access, and real-time approvals. Background : The bank partnered with Deloitte to define customer and technology requirements for a new cloud-based Digital Lending System. Deloitte's OpenDATA platform on AWS enabled flexible, scalable, modular development. This allowed the adoption of Agile methodologies, delivering the first version in just 13 weeks. Approach The system uses advanced analytics including AI and ML to integrate and analyze data from internal systems, external databases, social media, and other sources to create comprehensive, up-to-date borrower profiles. It applies predetermined rules to filter and rank suitability for mezzanine financing. RPA, blockchain, and smart contracts automate manual tasks like documentation, calculations, and reporting. Qlik Sense enables interactive data visualization, predictive modeling, scenarios, and stress testing to optimize mezzanine strategies and risk management. As a result, the loan application time was reduced from 20 days to 15 minutes, the approval rate increased from 50% to 90%, and processing costs decreased by 70%. Lead flow, quality, and conversion also improved, whereas transparency and consistency of mezzanine offerings were enhanced. Analytics and simulations optimized strategies and decisions. Case Study 2: Private Lending Company Leverages GoDocs to Modernize Lending : theLender is a private lending company that specializes in providing bridge loans to real estate investors. theLender sought to differentiate itself from other lenders by offering a faster, simpler, and more transparent lending process. Background : The company partnered with GoDocs, a leading provider of commercial loan document generation software, to implement a digital lending platform that automates the entire loan origination and closing process. Approach The platform leverages cloud computing, artificial intelligence, and blockchain technology to streamline workflows, reduce errors, and enhance security. Further, theLender’s digital lending platform enabled it to reduce the time to generate loan documents from hours to minutes, eliminate manual data entry and human errors, provide real-time visibility and collaboration among all parties involved in the loan transaction, securely store and share loan documents on a distributed ledger, and finally integrate with third-party services such as credit bureaus, title companies, and escrow agents. As a result, the company’s digital lending platform helped it increase its loan volume and revenue by 300% in one year, improve its customer satisfaction and retention rates, reduce its operational costs and risks, as well as gain a competitive edge in the private lending market. Conclusion Digital transformation presents a compelling opportunity for mezzanine lenders to innovate and create value. Advanced technologies including automation, AI, and data analytics can address current pain points around deal sourcing, process efficiency, and portfolio management. The potential benefits are multifaceted — improved customer experience, employee productivity, risk management, and strategic agility. The main benefit is an increase in the number of transactions, and therefore in income. Drawing on my experience working at Sberbank from 2018 to 2021, it's clear that implementing such changes in the mezzanine business can significantly boost both the volume of business and the number of deals. Initially, Sberbank managed around 10 mezzanine deals per year. However, by 2022, following the adoption of effective automation strategies, the bank's capacity surged to over 100 deals annually. This remarkable growth highlights the substantial influence of digital innovations on enhancing both the scale and efficiency of operations in mezzanine lending.

Digital Transformation Success Stories in Mezzanine Lending

Will you love me, no matter what? Will you hold me, even when nobody else will? Will you see me, The way I see you? -Day 345. The year of the plague. My name is Kevwe. I am a runner. That’s what the regulators called me. One of the few who survived the holocaust, I was a living legend. They said so many things about me, not all of them good. But I didn’t mind, I was a runner. An elite. A force of nature. My feet landed on the rough surface of a debilitated house, and I took off into the air again. The wind felt alive somehow, caressing my face with gentle touches that’d make a hard guy cry. My legs were my weapons, my sole reason for existence. I jumped again. The sole of my foot lightly grazed a serrated edge, and I stifled a wince as the pain lanced straight up my body. It was a spark; it was burning me from within. I gritted my teeth and kept running, leaping from one ruin to another, making the wasteland into my playground. A runner like me was necessary in the efforts of the Resistance. We were spread thin, and the commanders feared a retaliation from the unknown forces. Nigeria. A wasteland of ruins and darkness. I ran, my breath coming in ragged gasps. My eyes stayed ahead, not daring to look at the large gash on my toes. The pain gave me laser focus as I leapt from one edge to another.  Brown, the world was brown. Drained of colour, like rust. Like the days I wished to die. The sky was tinged with a coppery colour. It began as soon as the plague ravaged through the world like quickfire. It's more like an inferno. The death rates were enough to cause alarm and halt regular working hours. School too. Everything came to a standstill. That was the end of the future I designed from an overactive imagination. I didn’t want to be a runner at first, but a runner could outrun the plague. The humanoid viruses mutated at an alarming rate. The death bugs. Or so they were called. That was the first thing they mutated into; Bugs. Flying bugs. As disgusting as that would seem. Their intelligence seemed to increase the higher the form they took. Pale, translucent beings. Beings that stole faces and futures. Beings of unknown origin. That’s what they’d become. I didn’t know if that was the last stage of their evolution, but knowing how low humans were on the food chain? It was probably not. That’s why I run. To outrun my death. I was told that the beings would become stronger and probably outrun a runner but, I didn’t bank on that. Their intelligence was limited. They remained in the base form of whatever organism they consumed; Humans included. Which means that they were blissfully average. Good news? Definitely not. Imagine humans that never tire out, humans that have no heart to pump blood or a brain to signify when they need to stop. They were unstoppable demons with no known way to die. How do you kill an unstoppable creature? The answer is, you don’t. You run and pray to all the gods that they don’t catch you. You run until your heart fails you and you don’t stop even after that. You run, even if it kills you. Funnily enough, there was a reason for running. The soul tablets. The infected are those the virus creatures couldn’t completely consume. But their minds are gone, worse than patients with dementia. They don’t live, their hearts still beat rhythmically in their chests but they remain living husks; caricatures of what they once were. Death sometimes seemed merciful. But, killing them would mean killing half of the population. It would mean the bastard creatures won. The soul tablets can only be found in those ruins and more often than not, the vile creatures ingest those tablets to understand humans better. They don’t need minds, the soul tablets sort of dances in their vision and they see what they have to do. Hence, the soul tablets are important. For us runners, because we get to see our loved ones come alive again, even when they never say a word. It was worth heaven and earth. To see the smile that’d light up their faces. That’s why I run. But, I was already compromised. The cut from the roof, the trail of blood that followed in my wake with every leap. The creatures could smell blood. I didn’t know how or why but a lot of them have consumed those I used to run with. Kemi, Shola, Etus, Akachi, etc. I once called them friends. But, they were taken. By the Bloodless. The only virus with intelligence. They could run even better than me, but they couldn’t outrun their deaths. Fucking hell. I almost missed a step when I saw the dim light of a soul tablet underneath a jumbled mass of buildings that had seen better days. I stopped abruptly, glad that I didn’t miss it. I took a look around the ruins, some of the walls unable to survive the rust and falling apart from a light touch. The sky still shone as copper, a sickly colour. On most evenings, it was tinged with red. The walls of most of the houses in the ruins had paint peeling off the few that still remained standing. Sometimes, I wondered how the world went to hell in such a short time. “You got here first. And… you’re bleeding.” I heard a voice from behind me and turned to stare at the girl that just landed, as lithe as a cat. She had a small grin on her face, as though smiling was beneath her. Her full eyebrows raised a millimeter, her coffee brown irises staring straight through me and into my soul. She was dressed like I was, an all black getup. But hers made her seem… hot. Yes, I am stupid. The wall is falling apart and I’m thinking about a girl’s body. How original. “Don’t gawk at me. You’re not Keno. I can swear her eyes follow me wherever I go.” The new arrival, Zenith, shuddered as though in remembrance of something awful. I didn’t blame her, Keno could be overbearing sometimes. “Slow poke.” I quipped, earning an elbow for my troubles. Zenith could be violent. Very. “Just because you got here first doesn’t make you faster. Just stupid. Belligerent. Insufferable. Obnoxious. You never listen. We were meant to form ranks..” Ah yes, one of Zenith’s famous lectures. Form ranks, go in pairs or groups. Nobody left alone. Or some other balderdash. How infuriating. “You know, I can’t tell which words are real and which are fake. You know, with you throwing words You’re literally a walking dictionary.” She bristled, and I knew that I hit a nerve. Zenith was always so particular about grammar. What a waste. The world had fallen apart. Who would she have read Shakespeare to? The ? upandan. Bloodless “Don’t. Luis and Raheem are up ahead. Scouting and trying to erase the blood with those mighty knapsack of theirs. You might just get whooped this time. Now, let me see that cut.” She ordered, as matronly as ever. That was our Zenith, the mother of the group. A pity Etus couldn’t see her anymore, they were a thing for the longest time. She cleaned the cut and applied antiseptic, not risking to use anything over the top while we were out in the open. She could seal something as small as a cut but it took time and time was what we didn’t have. I stared at the sky as it darkened and Raheem more or less flew out from over a building. He scaled the building right in front of my eyes. One seconds he wasn’t there and the next, he was. “Such petty tricks. I do it better.” Luis grumbled as he came into view about two seconds after Raheem did. Raheem was the narcissist, his looks didn’t help either. He had the hots for Zenith but she acted blind to his advances. It was usually tension filled running with them. I preferred to do it alone. “Grab all the soul tablets you can. We meet in the food canteen after all of this. Kevwe, can you run?” Zenith turned to me. I nodded in the affirmative. Thankfully, Luis and Raheem ignored me. They always did. I didn’t exist to them, not in the ways that mattered. I was the insubordinate brat. Or so they put it. I took off in a run after carrying as much soul tablets as I could, placing them in the backpack I carried. They weighed me down a little but not by much. Zenith was close behind. I could heard an otherworldly sound as the creatures rose from whatever they were doing. They set sights on us, hoarding the soul tablets. “Fuck you, Kevwe! Fuck you! You caused this with your need to prove a point!” Raheem cursed out loud but I didn’t say a word in retaliation. We weren’t going to make it. When the creatures were fully awake, they had no limits. They ran like normal humans but they didn’t lose stamina. They didn’t die. Will she forgive me, if I go this way? I stopped thinking and focused on running, hoping that maybe I could do something about the hoard coming for us. “Any ideas?” Zenith asked Luis. Thankfully, she didn’t mention Raheem words even if she knew that I doomed all of us. “We survive? Get to the gate and use a soul tablet? I don’t know.” Luis replied honestly, trying to not show fear in his voice. But I knew that tone. Etus had it too. Dami did too. That reckless bravado. “Let’s do this, boys!” Zenith said, her voice carrying over to the other two. Raheem nodded and quickened his pace. We vaulted over buildings like they were made of straw sacks, our shoes never really touching the ground before we are moving again. “I’ll buy you time. Run.” I said, grinding to a halt. I caused it, I had to handle the consequences. “Don’t fucking do it. You’re not a saint! You’re a selfish bastard who only thinks of himself and nothing else. Run!” Raheem screamed at me, stopping abruptly too. Zenith and Luis were long gone. “I won’t. I’m sorry. It’s my turn now.” I said, totally ready to sacrifice myself. “Like hell I’ll let you.” He said and then, cracked a soul tablet, flinging the essence on me. “Now run, you bastard. And don’t stop, or I’ll haunt you.” He turned to face the hoard already almost upon us, the soul tablet doing its job by clearing my head and doing everything to remind me of the fear of death. So, I ran. I turned to look back at him, wondering why he did what he did. Hate still burned in his eyes. But this time, I could understand. I activated my shoes as I got close to the gate, phasing in as my body became as translucent as the creatures who stole from us. I was in. It was a modest place with walls high enough for me to crane my neck to get a good view. A fortress of stone. I made my way up to stairs to report to the leader of my platoon; Zenith. “Where’s Raheem?” She asked as soon as her eyes landed on me. Another one. Dead. “He didn’t make it. He sacrificed himself to save me.” I responded as truthfully as I could as I heard her sharp intake of breath. The world stopped. “You may leave.” She said, a certain steeliness in her voice. I walked out, unable to say the words I wanted to. The tales of Raheems’s bravery at the last moments. I made my way to the infirmary, a soul tablet in hand. My mind fractured. I never expected to feel pain for someone like Raheem. But the pain threatened to tear my heart asunder. People made way for me as I headed to the bed at the furthest corner of the room. A sickly form of a woman laid on a cot that’d seen better days. I placed the soul tablet gingerly on her hand, watching her for any signs of recognition. Nothing. And like that, one soul tablet was absorbed without any noticeable change. I walked back out dejected, my back hunched. Zenith was walking towards me, tears dotting her eyes. They were red from tears. She grabbed me by the front of my clothes, slamming my back on the nearest wall. “Bring. Raheem. Back.” She spoke through gritted teeth, her pain was so acute, I could taste it on my tongue. “I can’t.” I said, the words being choked out of my mouth. “A vegetable lays on that damn bed you haven’t left in over 300 days. You’ve been responsible for the deaths of so many of us because of your hypocrisy. You watched him die!” She screamed, her voice accusing. No trace of the leader she had always been. “He did. He died to protect me.” I replied, sincerely hoping she did me the favour and killed me right there. “You..” She managed to let me go, grief wracking her body. She screamed, her voice breaking. I just watched. It was a festival of pain. That night, she apologized to me for the statements she made. I was grateful she forgave. She was found dead the next day. Suicide. How many more will you kill to resurrect the dead? Zenith didn’t deserve to die. Raheem didn’t. Etus didn’t. I was the problem, the anomaly. They hated me, I hated me. I went back to the infirmary, taking hold of her hand. Her skin was hollow and almost translucent. I recovered her from the jaws of death. But maybe, I should have died instead. “I hate me. I love you. I hate what I have to put you through, but if you ever wake, I apologize. They died, to save me. To save my childish fantasy. I never wanted to lose so much. But then, if it is going to save you, I don’t mind. Even if the world itself turns against me, I’ll stand by you. Even when all I want to do is die. I hope you remember the days I spent, talking to you about nothing at all. I hope you don’t cry when you wake. I hope… you find something. Someone. You’re the one I’ve always loved and if I could sacrifice the world to save you… goodbye, Seta.” I felt her hand squeeze mine. I stared again but it seemed to be a trick of my mind. I placed a kiss on her forehead and walked out. The last sacrifice. * We will rise, Even from ashes. -Day 7321. The years after the plague. I stand and watch his tombstone. I barely remember his voice, I barely remember everything. But I stand by his tombstone, as an act of penance. By all of their tombstones. The runners. Humanity’s last bastion of defense. The viruses mutated once more and migrated to space. They lost interest in humans. When I awoke, I could only remember a voice. His voice. They’ll always be remembered, even though they died needlessly. Luis still stands. The last runner standing. Although he doesn’t have to run anymore, but I’ve never seen him smile. Not once. He stares into the distance, looking over the horizon. The sky is blue. Mankind is building again. We’re a few millions in Nigeria now. Immigrants, hidden bunkers. The sky no longer looks dead. Can you see this, runners? I do hope you can. You deserve to. Heroes. Of earth. -Unknown.

The End of Everything 

দ্য এন্ড অফ এভরিথিং

Das Ende von allem

El fin de todo

La fin de tout

हर चीज़ का अंत

すべての終わり

모든 것의 끝

O fim de tudo

Конец всего

Herşeyin Sonu

Sự kết thúc của mọi thứ

一切的终结

The process of building a platform to help users practice programming challenges guided by AI: A summary from the first few user tests I built a platform helping people practice programming challenges guided by AI to help improve interviewing outcomes. My rationale behind this stemmed from an anecdotal experience: I practice programming challenges and found myself regularly asking GPT or Bard to help me break down solutions I didn't fully understand. Breaking problems and solutions down as simply as possible, helps me build a better understanding as I problem solve, and to be honest, it most importantly helps me stay more consistent. My platform leverages AI to facilitate this process. How? It enables you to receive a random programming challenge based on the difficulty level you preselect. I use AI to send you pseudo-code phrased as questions. Hypothesis One Hypothesis: Breaking the pseudo-code for building an optimal solution for a programming challenge down into questions can help you think through the process of constructing an optimal solution for a programming challenge. This will facilitate the learning process as this retains the element of 'thinking for yourself' required to retain information, while simultaneously reducing the friction involved in practicing these problems. In this scenario, friction refers to the optimal balance between struggling through a problem and realizing you need help and leveraging that help to move quicker. Video Review: https://youtu.be/sbtGBlQ-zeM?si=QhIPW9Ej_cSqUv7c&embedable=true Reviewing Feedback From analyzing feedback from the first 4 tests, I sense that there is some veracity to the above hypothesis. The operative word in the phrase, 'pseudo code broken down into useful questions' is 'useful'. The questions were only useful in one test. More work needs to be carried out to ensure consistency in question quality. This means, 'reducing question redundancy' and 'increasing the relevance of the questions' This is a top priority as it is core to validating this hypothesis. I categorize this as a high-priority hypothesis underlying a core assumption of this entire platform. Hypothesis: Having an AI assistant to review your code and help you understand where you might be going wrong and generate questions that get incrementally descriptive, would help accentuate the core value of this platform. Isolating things I am working on, helps me zone into the core pillar of this platform. Since this hypothesis accentuates the core value, it is necessary to first test the core value before worrying about an ancillary value. Long Wait Time It takes a very long time to receive a response from LLM models, particularly with longer prompts. This is certainly a problem that affects the entire usability of the platform. I plan to run this platform through at least 20 - 50 user tests, incrementally exposing more and more of the platform. I believe this process will expose the platform to enough eyes to ensure the solution is based on tested assumptions with real-world validation. Additionally, this process would expose and enable me to fix a fair amount of bugs affecting overall functionality without falling into the trap of perfectionism. Most importantly, it is insanity not to talk to your first users. This process will help me learn to ask better questions about my assumptions, understand the problem I am solving from a more holistic perspective backed by tangible data, and understand my target audience substantially more. The intuition to know what to build and how will come from this experience. Potential Solutions Potential solutions at this point would be creating a function retrieving each programming challenge from my database, using AI to generate pseudo code questions for each challenge, and storing the result in a cache with some references for the level of difficulty of the problem (for the user selection), other useful tags and a unique identifier. With these results cached, I would speed up the time it would take for the initial start time. Current WaitTime Video https://vimeo.com/883456606/f301f02e2b?embedable=true With the AI assistant - A few offline conversations with engineers along with a few users make me lean towards streaming responses based on a timer. This means experimenting with keeping track of the length of time it takes for a user to solve a problem and sending clues through the AI Assistant in intervals, with a 'Help me' button showing up every 5 minutes to trigger the AI Assistant to return this response. This could involve keeping track of the time and sending a request with whatever code has been written on the editor every 4:10 minutes (assuming the user has stopped typing for say about 10-15 seconds). This would be used to generate the AI Assistance tips based on the state of their code a few seconds prior. Methodologies employed to generate valuable user feedback In testing this platform, I will go through a few phases of tests which will serve different purposes: This will be the primary mode of acquiring feedback at the 200-500 user tests threshold. Most of these tests will be followed up with short calls to get user feedback through conversational questions. This is reserved for when my platform is a lot more robust, and I have solved issues related to wait times for responses. Give users a link to the platform, and ask for feedback after they have played around with it. This is good to get deeper feedback regarding usability and the quality of the questions. This is one of the formats I currently use. Give users access access to the platform, and ask them to play around with it in real time asking questions as they test it out. . I intend to shift to this phase between user tests 15-50. This is necessary to observe how users are interacting with the platform at a phase where I would be a little more comfortable with the overall usability of the platform (fixing the major issues to fully test the core assumptions of the platform). Schedule a Zoom call, and ask them to a programming challenge and then ask questions solve I believe a greater value for this platform comes when you enable users to pair program using the core functionality already built into the platform. Anecdotally, this has made practicing programming challenges more exciting for me, encouraging more consistency while helping you practice explaining your logic as you would in an interview. Asking users to a programming challenge and asking questions after the session. pair program Simply put, this will facilitate more consistency by allowing friends to pair programs in preparation for interviews. Having spoken to graduates from App Academy in particular, I know this is a concept they already incorporate into their curriculum. However, this is done purely via VSCode and a Slack huddle/Zoom call. Next Steps Moving Forward Improve the quality of questions generated. Fixing core internal bugs. There are 2-3 at this moment that are top priority, specifically when I first receive a programming challenge, the code editor shows the pseudo-code questions from the previous question, assuming I had been using the platform before. I need to either make changes as to how I am persisting with programming challenges and the solutions generated or restructure my code to clear up the previous question when a user solves a challenge, closes the browser, and reopens the platform again. This would probably solve other issues I have regarding programming challenges not always being in sync with the challenges shown and having to click the reset button to force this consistency. I have a hunch that I will need to disable the ability to run your code against randomly generated test cases. I don't think this functionality is necessary now; it adds more complications to things that need to be tested, especially since I am leveraging AI to generate these test cases. The more layers of complexity I include so soon, the more complex the platform becomes without deeply validating its core assumption Here's a breakdown of past issues discovered and resolved: Comments in code upon code execution generates an error response Building an auto-grader to evaluate code solutions and improving user interface Adding a dumbed-down explanation to each programming challenge based on user feedback Fixing app crashes and making improvements to assistance generated by AI An introduction to InterviewSageAI Also published here

I Built a Platform to Help Users Practice Programming Challenges Guided by AI

Introduction Imagine standing on the edge of a vast ocean, the waves whispering secrets of the deep—secrets of power, potential, and transformation. This is not the ocean but the realm of and Machine Learning (ML), an expanse that's just as deep and mysterious. In this article, we embark on a voyage to demystify these technologies that are reshaping our world. Artificial Intelligence (AI) From the basic building blocks to the towering achievements of AI, this guide promises to illuminate the path for the overwhelmed, the curious, and the aspiring. Here, we not only uncover the essence of machine learning and its implications for the future but also empower you with the knowledge to navigate its waves and harness its power for innovation and growth. Welcome to your first step towards AI mastery. Topics that will be covered: The Foundations of AI and Machine Learning : Simplifying jargon for a clearer understanding. Demystifying Terminology : Tracing the journey from its inception to the current state. The Evolution of AI The Mechanics of Machine Learning : The fuel of AI. Understanding Data : How machines learn from data. Algorithms at Play : Diving into supervised, unsupervised, and reinforcement learning. The Spectrum of Learning Practical Applications and Real-World Impact : From smart assistants to personalized medicine. AI in Everyday Life : Success stories across industries—healthcare, finance, and more. Case Studies : Navigating the moral landscape. Ethics and AI The Foundations of AI and Machine Learning Demystifying Terminology: Simplifying Jargon for a Clearer Understanding The world of Artificial Intelligence (AI) is brimming with technical lingo that can seem daunting at first glance. At its core, AI is the science of making machines mimic human intelligence—learning, reasoning, and problem-solving. Machine Learning (ML), a subset of AI, focuses on the idea that machines can learn from data, identify patterns, and make decisions with minimal human intervention. By breaking down terms like 'neural networks' (inspired by the human brain to process information) and 'algorithm' (a set of rules for solving problems), we start demystifying this field, making it more approachable and understandable. The Evolution of AI: Tracing the Journey from Its Inception to the Current State The concept of AI dates back to the mid-20th century when the dream of creating intelligent machines first took shape. From the early days of simple algorithms to today’s advanced neural networks capable of deep learning, AI has undergone a remarkable evolution. This journey has been marked by milestones such as the development of the Turing Test, the chess match between IBM's Deep Blue and Garry Kasparov, and the advent of self-driving cars, showcasing the growing capabilities and potential of AI technologies. The Mechanics of Machine Learning Understanding Data: The Fuel of AI Data acts as the cornerstone of all AI and ML endeavors. It's the raw material that feeds algorithms, allowing them to learn and improve. Whether it's images, text, or numbers, the quality and quantity of data directly influence the performance of AI models. Data preprocessing, including cleaning and formatting, is a critical step to ensure that models learn from the best possible information. Algorithms at Play: How Machines Learn from Data Algorithms are the heart of machine learning, giving machines the rules and methods to process data and learn from it. These range from simple linear regression algorithms used for predicting numerical values to complex deep learning models that power voice recognition systems. The choice of algorithm depends on the type of data and the specific task at hand. The Spectrum of Learning: Diving into Supervised, Unsupervised, and Reinforcement Learning Machine learning is categorized into three primary types based on how models are taught to make decisions: : Models are trained using labeled data, learning to predict outcomes for new data. Supervised Learning : Models infer patterns from unlabeled data without specific instructions on what to learn. Unsupervised Learning : Models learn to make decisions by performing actions and receiving feedback in the form of rewards or penalties. Reinforcement Learning Practical Applications and Real-World Impact AI in Everyday Life: From Smart Assistants to Personalized Medicine AI has seamlessly integrated into daily life, powering smart assistants like Siri and Alexa, enabling personalized recommendations on streaming services, and advancing precision medicine by tailoring treatments to individual genetic profiles. These applications are just the tip of the iceberg, showcasing AI's potential to enhance and simplify human life. Case Studies: Success Stories Across Industries—Healthcare, Finance, and More In healthcare, AI is revolutionizing diagnostics and patient care through tools that can predict disease progression and personalize treatment plans. In finance, AI algorithms detect fraudulent transactions and automate trading strategies. Each case study highlights AI's transformative power across sectors, driving efficiency, innovation, and improved outcomes. Ethics and AI: Navigating the Moral Landscape As AI becomes more ingrained in society, ethical considerations come to the forefront. Issues such as privacy, security, and the potential for bias in AI models raise important questions about the responsible development and deployment of AI technologies. Navigating this moral landscape requires ongoing dialogue among technologists, ethicists, policymakers, and the public to ensure AI benefits all of humanity while minimizing harm. This exploration into the foundations, mechanics, and practical applications of AI and ML illustrates the profound impact these technologies have on our world. By understanding the principles that underpin AI, we can appreciate its potential and address the challenges it poses, guiding its development toward positive and ethical outcomes. *—the grand tour through the labyrinth of AI and Machine Learning, a journey I daresay has been more enlightening than bewildering. We've untangled the spaghetti of jargon, traipsed down the memory lane of AI's illustrious past, and peeked under the hood at the cogs and wheels that make machines almost as smart as a well-trained Labrador—almost. Ah, there we have it We've meandered through the garden of AI's real-world blossoms, from chatty assistants who still can't quite grasp sarcasm to the wizardry behind popping just the right ad at just the right time (creepy, yet impressive). And who could forget our foray into the ethical bramble patch? A place where philosophers and programmers meet for tea and tussles over the moral fiber of our silicon companions. Now, as we stand at the precipice of the future, pondering, "What does tomorrow hold for work in an AI-infused wonderland?" one might feel a tidbit of good old fashioned angst. Fear not. While the crystal ball remains stubbornly cloudy, it's a safe bet that AI will not only take over the tedious tasks but also become the muse for our creativity, pushing us to invent new ways to procrastinate effectively. So, dear reader, as you ponder whether robots will steal your job or simply make it easier to pretend you're working while binge-watching your favorite series, remember this: the future of AI is as much in our hands as it is in the circuits of the next breakthrough algorithm. The question then is not if AI will reshape our world but how we choose to dance with it. Will we lead with grace, ensuring that as we sprint toward tomorrow, we don't trample over the values we hold dear? In wrapping up this delightful saunter through the realms of AI, let's not bid adieu but rather a cheeky "see you later." For the story of AI and human collaboration is only just beginning. It's a narrative brimming with potential plot twists, unexpected heroes, and perhaps a blooper reel worth of missteps. As we march arm-in-arm into the future, let's do so with a twinkle in our eye and a steadfast resolve to make it a journey worth tweeting about. And remember, as we forge ahead into this brave new world of algorithms and automation, keep your humor close, your ethics closer, and your curiosity unbounded. After all, in the grand tapestry of the universe, we're simply trying to teach old silicon new tricks. How hard can it be? Ta-ta! Jin Park Computer Science Student and Redbull-fueled nerd. If my digital doodles tickle your fancy and you fancy fueling my caffeine crusade, consider tipping the jar at . Every sip powers another quip! https://ko-fi.com/aloneinseoul

Machine Learning Made Simple: A Beginner's Guide to AI

A journey from the origins of computing and data analytics to what we now call the "Modern Data Stack". What comes next? The Origins of Computing and Data Analytics The origins of computing and began in the mid-1950s and started taking shape with the introduction of SQL in 1970: data analytics 1954: - “ ”, machine translation of Russian to English. Natural Language Processing (NLP) Georgetown-IBM experiment 1960: Punch Cards 1970: Structured Query Language (SQL) 1970s: - Create a language to “allow executives to build models without intermediaries” Interactive Financial Planning Systems 1972: - One of the earliest applications of modern computing, a natural language information retrieval system, helped geologists access, compare and evaluate chemical-analysis data on moon rock and soil composition C, LUNAR 1975: - The first Online Analytical Processing (OLAP) system, intended to analyse business data from different points of view Express 1979: - The first spreadsheet computer program VisiCalc 1980s: - “Computerized Collaborative Work System” Group Decision Support Systems The “Modern Data Stack” The "Modern Data Stack" is a set of technologies and tools used to collect, store, process, analyze, and visualize data in a well-integrated cloud-based platform. Although QlikView was pre-cloud, it is the earliest example of what most would recognize as an analytics dashboard used by modern platforms like Tableau and PowerBI: 1994: - “Dashboard-driven Analytics” QlikView 2003: Tableau 2009: - “Computational Search Engine” Wolfram Alpha 2015: PowerBI 2017: - “Search-driven Analytics” ThoughtSpot Paper, Query Languages, Spreadsheets, Dashboards, Search, what next? Some of the most innovative analytics applications, at least in terms of user experience, convert human language to some computational output: A tale as old as time, LUNAR was first developed in the 70s to help geologists access, compare, and evaluate chemical analysis data using natural language. Salesforce WikiSQL introduced the first extensive compendium of data built for the text-to-SQL use case but only contained simple SQL queries. The Yale Spider dataset introduced a benchmark for more complex queries, and most recently, BIRD introduced real-world “dirty” queries and efficiency scores to create a proper benchmark for text-to-SQL applications. Text-to-SQL: Wolfram Alpha, ThoughtSpot Text-to-Computational-Language: ChatGPT Advanced Data Analysis Text-to-Code: Is "Conversation-Driven Data Analytics" a natural evolution? UX of modern analytics interfaces like , becoming more intuitive, enabled by NLP and LLMs search and chat are evolving Analytics interfaces have origins in but decision-makers are still largely enabling decision-makers, reliant on data analysts Many decision-maker , best suited to “throwaway analytics” queries are ad-hoc Insight generation is a where many insights are gained in conversations about data, possibly with peers creative process The data analytics , from the imagination of analysis to the presentation of results workflow is disjointed Acknowledgments Dates for the section "The Origins of Computing and Data Analytics" thanks to and . https://web.paristech.com/hs-fs/file-2487731396.pdf http://dssresources.com/history/dsshistoryv28.html Also published here.

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