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Startups Of The Year 2023: Asia Winners

The wait is over! - it’s time to celebrate the champion startups of the world. Shout out to all of the participants in our ! You did a wonderful job showcasing your incredible startups and powering through another year. 2023 Startups of the Year is a community-driven voting platform brought to you by HackerNoon that recognizes and celebrates tech startups changing technology and the world for the better. The award started on May 8th, 2023, where the internet nominated and voted for 30k startups around the world. The voting period remained open until December 31, 2023, and now we are here with the winner announcement after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 To see the top 3 startups in each respective city, visit and pick a city. hackernoon.com/startups You’ll get a little surprise 😉 Here are the Startups of the Year Regional Announcements: North America Europe Asia Africa South America Oceania Misc/Remote From all of us at HackerNoon, thank YOU for your excellent campaign perseverance 🥺 This year has been awesome! Startups of the Year 2023 is concluded with 623k+ total votes, up from 215k+ votes in the inaugural startup competition. We look forward to seeing you again in the next campaign (stay tuned 😉)! Without further ado, let us kick off the announcement by celebrating the cities with top votes per region for 2023 🎉 North America (11,697 votes): Winner: Bloomfilter Cleveland, Ohio (8,835 votes) - Winner: SerpApi Austin, Texas , California (5,564 votes) - Winner: Finch San Francisco (4,258 votes) - Winner: RELAYTO SoMa, California - (3339 votes) Winner: Windscribe Toronto, Canada, North America (3304 votes) - Winner: Vouched Seattle (2716 votes) - Winner: Seccuri Palo Alto, CA, North America (2671 votes) - Winner: Salsa Valley Miami, FL, North America (2,613 votes) - Winner: Majhi Group Manhattan, New York (2423 votes) - Winner: Flieber, Inc. New York City, NY, North America See the complete announcement for the . Startups of the Year, North America, here Europe (45,612 votes) - Winner: Algomo London, UK (2,344votes) - Winner: Data Lake Warsaw, Poland (1,955 votes) - Winner: Hoken Tech Bari, Italy (1716 votes) - Winner: Not8 Amsterdam, Netherlands (1657 votes) - Winner: KYVE Frankfurt Am Main, Germany (1566 votes) - Winner: BazaIT Kyiv, Ukraine (1496 votes) - Winner:  Unity Network Tallinn, Estonia (1,478 votes) - Winner: Volcaino Ghent, Belgium (1,288 votes) - Winner: Owl Me Dublin, Ireland (1198 votes) - Winner: Groover Paris, France See the complete announcement for the . Startups of the Year, Europe, here Oceania (3,350 votes) - Winner: Sharepass Melbourne (2,107 votes) - Winner: The Catfisher Project Perth, Australia (220 votes) - Winner: Gemini AI Solutions Auckland, New Zealand, Oceania (213 votes) - Winner: Nexustorage Wellington, New Zealand, Oceania (138 votes) - Winner: Coviu Sydney (124 votes) - Winner: Bopple Brisbane, Australia, Oceania (124 votes) - Winner: Regional CFO New South Wales, Australia, Oceania (106 votes) - Winner: Edit on the Spot Gold Coast, Australia, Oceania (93 votes) - Winner: Knowledge Satnav Canbera, Australia, Oceania , Australia, Oceania (48 votes) - Winner: Peptalk360 North Melbourne, Melbourne See the complete announcement for the . Startups of the Year, Oceania, here Asia (16,345 votes) - Winner: Serenity Shield Singapore (13,588 votes) - Winners: QUIXY Telangana, India (10,168 votes) - Winner: HealthySure Mumbai, India (3,865 votes) - Winner: InterviewBuddy Visakhapatnam, India (3,733 votes) - Winner: Omniflo Bengaluru, India (4589 votes) - Winner: MeNow Tel Aviv (3877 votes) - Winner: ForestVPN Dubai (2478 votes) - Winner: TechGraph Powai, Mumbai, India (1004 votes) - Winner: Reelo Ahmedabad, GJ, India (875 votes) - Winner: Debt Hunter Hong Kong, China See the complete announcement for the . Startups of the Year, Asia, here Africa (8,179 votes) - Winner: GO PLATFORM Algiers, Algeria (767 votes) - Winner: Tudu Accra, Ghana (415 votes) - Winner: Treford Lagos, Nigeria (265 votes) - Winner: Phalconwise Ogbomosho, Nigeria (157 votes) - Winner: AbuErdan Cairo (151 votes) - Winner: Eazipay Lekki (144 votes) - Winner: KaziConnect Dar es Salaam, Tanzania (101 votes) - Winner: faceATT Gauteng, South Africa (79 votes) - Winner: Dimagi Cape Town (71 votes) - Winner: Sabi Addis Ababa See the complete announcement for the . Startups of the Year, Africa, here South America (376 votes ) - Winner: UpGirl Santiago, Chile (217 votes) - Winner: Klever Sao Paulo, Brazil (191 votes) - Winner: Tributo Simple Buenos Aires, Argentina (127 votes) - Winner: Light-it Montevideo, Uruguay (87 votes) -Winner: Thousand Antioquia, Colombia (108 votes) - Winner: GetLavado Lima (101 votes) - Winner: Cogniflow Medellin (73 votes) - Winner: TPAGA Bogota (73 votes) - Winner: Reliv Quito (47 votes) - WInner: Quantex Cordoba See the complete announcement for the . Startups of the Year, South America, here Misc/Remote: Featured Startups (1500 votes) - Winner: CIC Chain Remote, Earth (1193 votes) - Winner: Reship Colony US Misc (146 Votes) - Winner: Partful UK, Misc (116 votes) - Winner: GoMining British Virgin Islands Misc (283 Votes) - Winner: tomorrowSpace Germany Misc (225 votes) - Winner: Dtravel Cayman Islands See the complete announcement for Misc/Remote startups . here Once again, HUGE CONGRATULATIONS and BIG PROPS to all of the top Startups of each region this year! The whole team at HackerNoon would like to thank you for your perseverance in the campaign, and we look forward to seeing you in the next campaign. About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Stand Up for the Champion Startups of the Year! 

বছরের চ্যাম্পিয়ন স্টার্টআপের জন্য দাঁড়ান!

Setzen Sie sich für die Champion-Startups des Jahres ein!

¡Defiende a las startups campeonas del año!

Défendez les startups championnes de l’année !

वर्ष के चैंपियन स्टार्टअप के लिए खड़े रहें!

今年のチャンピオンスタートアップのために立ち上がろう!

올해의 챔피언 스타트업을 위해 일어서세요!

Defenda as startups campeãs do ano!

Встаньте на защиту стартапов-чемпионов года!

Yılın Şampiyon Startup'ları için Ayağa Kalkın!

Hãy ủng hộ các công ty khởi nghiệp vô địch của năm!

为年度冠军初创企业挺身而出！

With all the buzz about large language models, vectors, and vector search, it's important to take a step back and understand how these artificial intelligence technology advancements translate into results for an organization and, ultimately, its customers. In an , I told the story of a hypothetical contractor who was hired to help implement AI/ML solutions at a big-box retailer. Here, we continue that journey as our distributed systems and AI specialist uses to drive results with customer promotions at a big-box retailer. earlier article vector search The problem Today, we met with the Promotions team. They’re looking for our help in making some smarter decisions about customer advertisements, offers, and coupons. Right now, promotions are mainly based on geographic markets. So, promotions sent out to customers in one city will differ from those offered to customers in another city. Thus, these campaigns have to be planned out ahead of time. The issue is that the marketing department is putting pressure on them to provide more strategic methods of targeting customers, geographic regions notwithstanding. The thought is that specific customers might be more likely to take advantage of certain offers based on their purchase history. If we can find a way to offer them in real-time, like a 10-percent discount on a related product (per cart), we might be able to drive some additional sales. First, I decided to have a look at some of our anonymized order data in their Apache Cassandra cluster. It‘s clear that there are definitely some patterns in the data, with some customers purchasing the same items (mostly grocery products) with some predictable regularity. Maybe there’s some way we can leverage this data? Improving the experience One thing that we have going for us: Our customers tend to engage with us via multiple channels. Some folks use the website, some use the mobile app, and some folks will still walk into any one of our 1,000 or so brick-and-mortar stores. And more than half of those customers in the store use the mobile app concurrently. Another interesting point: If we aggregate the item sale data by household address instead of just by customer ID, we see even more rigid shopping patterns. After pulling data together from a few different sources, we can start to paint a picture of just what this data looks like. Example: A couple has a dog. Usually, one spouse buys the dog food. But sometimes, the other spouse does. Those events at the individual customer level don’t make much of a pattern. But when aggregated together at the household level, they do. In fact, they consistently buy more than one 6-pound roll of “HealthyFresh - Chicken raw dog food” each week. Judging by the recommended serving sizes, they also either have a half-dozen little dogs or one big dog, probably the latter. A visualization of this data can be seen in Figure 1. The stores that each customer frequents can also come into play here. For example, our customer Marie might be traveling for work and might visit one of our stores that she has never been to. If the store is a significant distance from her list of frequent stores, we can assume that Marie is likely shopping for specific items related to her travel. In this case, her normal shopping habits would not apply (she probably will not buy dog food), and we do not need to present her with a promotion to encourage her to spend. However, if Marie is in one of her frequent stores, it might make sense to trigger a promotion on her mobile device. If we encourage our customers to scan items with their phones, we will know which items are in their physical shopping cart. Then, we can present similar items to complement the products already in the cart. Computing vectors to find similar products Finding similar products means we’ll need to compute similarity vectors for our products. There are a few ways that we can do this. In the interest of putting a minimum viable product together, we can focus only on the product names and build a natural language processing (NLP) model based on a “bag of words” approach. In this approach, we take every word from all of the product names and give each unique word an entry. This is our vocabulary. The similarity vectors that we create and store with each product become an array of ones and zeros, indicating whether the current product name possesses that word, as shown below in Table 1. We can use a platform like to build and train our machine learning (ML) model. TensorFlow Table 1 - A bag of words NLP vocabulary for product names under the Pet Supplies category, showing how each vector is assembled. One problem with the “bag of words” approach is that the vectors can end up with many more zeros than ones. This can lead to longer model training times and longer prediction times. To cut down on those problems, we’ll build a unique vocabulary for each major product category.  Vectors will not be usable across different categories, but that’s OK because we can filter by category at query time. We can then create a table in our Apache Cassandra cluster to support vector searching for each specific category.  As our vocabulary contains 14 words, the size of our vector will also need to be 14: CREATE TABLE pet_supply_vectors (
    product_id TEXT PRIMARY KEY,
    product_name TEXT,
    product_vector vector<float, 14>); For the vector search to function appropriately, we will need to create a storage-attached secondary index (SASI) on the table: CREATE CUSTOM INDEX ON pet_supply_vectors(product_vector) USING 'StorageAttachedIndex'; We can then load the output from our ML model into Cassandra.  With the data present, the next step is to add a new service, which performs the vector search using the following query: SELECT * FROM pet_supply_vectors
    ORDER BY product_vector
    ANN OF [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] LIMIT 2; This runs an approximate nearest neighbor (ANN) algorithm using the vector stored with the current product. That will return both the current product and the next-closest (nearest neighbor) by vector search, thanks to the clause. LIMIT 2 In the above query, we are using the vector from the “HealthyFresh - Chicken raw dog food” product, assuming that a customer has either just added it to their cart online or scanned it with their phone.  We process this event and compose the following message: customer_id: a3f5c9a3
device_id: e6f40454
product_id: pf1843
product_name: “HealthyFresh - Chicken raw dog food”
product_vector: [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0] We then send this message to an topic. We consume from the topic and use the above data to call the endpoint on our Promotions microservice. This runs the query indicated above, returning both of the “HealthyFresh” flavors. We disregard the data for the product_vector that matches at 100% (the product we already have) and trigger a promotion for the “HealthyFresh - Beef” flavor on their device: Apache Pulsar getPromotionProduct Conclusions and next steps After running with this logic in place for a couple of weeks, our Promotions team reached out and reported that our methods are triggering an additional sale roughly 25% of the time.  While it is tempting to call that a “win,” there are definitely some additional things we could look at improving. We went with a “bag of words” NLP approach just to get an initial (software) product out the door. I’ve read good things about different NLP algorithms like “Word2Vec,” which may be a better approach in the long run. Our model was also only concerned with building a vocabulary with the words that made up the product names.  Perhaps expanding our model inputs to include other product details (e.g., size, color, brand) might help to fine-tune things a bit?) In an upcoming article, we’ll show how we used vector search to help our transportation services team improve their delivery route efficiency. Check out the GitHub repository for an example . getPromotionProduct method, which performs a vector search in DataStax Astra DB By Aaron Ploetz, DataStax Also published . here

How to Drive Personalized Retail Offers with Vector Search

The scene is very common: a sleepless night, whether due to studying, working or even insomnia – no matter the reason – is followed by a miserably tiring day. You go to school, or you go to work, trying to focus on the work that needs to be done, but the lack of sleep just won't allow it. And this ends up happening over and over again. What is the long-term impact of lack of sleep for students and workers? We will discuss this topic in this article, focusing on what happens to our ability to study, learn and retain information in memory because of sleep deprivation. The Importance of Sleeping Well Numerous studies suggest that sleep plays a very important role in memory formation. There is general agreement that a good night's sleep is necessary for the consolidation of learning and memory. Lack of adequate sleep negatively affects mood, motivation, judgment, and even our perception of events taking place around us. Both sleep and learning and memory are processes that we still don't fully understand. Still, several studies suggest that the duration and quality of sleep have a very big impact on the process of learning and memory. How does this occur? According to research, getting enough sleep helps with memory consolidation in two main ways: It increases the level of attention (focus), as a person who sleeps inadequately cannot focus in order to learn efficiently. Furthermore, sleep itself plays a role in memory consolidation – recording information in the brain – which is evidently essential for learning new studied content. Learning and memory are generally described as three functions: Acquisition: Introduction of new information into the brain Consolidation: Stabilize memory by recording new information. Recall: Access information that is stored in the brain. The processes of acquisition and recall only occur while we are awake, but research suggests that the consolidation process occurs during sleep, consisting of the strengthening of neuronal connections that form our memories. Impact of Sleep Deprivation on Learning When we don't get enough sleep, our focus, attention and vigilance are widely scattered, which clearly makes it difficult for us to assimilate new information. Without adequate sleep and rest, our neurons are overloaded and can no longer coordinate information in a satisfactory way, and so we end up losing the ability to access information that was previously learned. Our mood is affected, which also has negative consequences for learning. The problem can become even more serious if lack of sleep leads to chronic fatigue and tiredness, as we lose the ability to make conscious decisions, and lapses in focus and attention can even lead to accidents, often with harmful consequences. How To Get a Good Night’s Sleep To Improve Your Learning Some basic tips: Turn off electronic devices at least 30 minutes before bed. Limit exposure to bright light at night. Blue and violet light from TV sets, smartphones, laptops or PCs stimulates the production of the neurotransmitter serotonin, which keeps you awake. On the other hand, orange or red light stimulates the production of the hormone melatonin, released by the pineal gland, which facilitates and induces sleep. If you have to use a computer, tablet or smartphone before bed, install an app to manage screen colors and brightness. Keep a consistent sleep schedule. Try to go to bed at the same time, and get up at the same time every day, even on weekends or during holidays and vacation periods. Set a bedtime that allows you to get at least 7 hours of sleep. Ideally, between 7.5 and 8 hours of sleep. Don't go to bed unless you're starting to feel sleepy. By keeping a strict bedtime and waking time, sleep should naturally come at the expected time. However, if after lying down you don't fall asleep within about 20 minutes, get out of bed and wait a little longer before returning and trying to sleep again. Establish a relaxing bedtime routine. Put on soft music, read a fiction book, do meditation or something along those lines. Important: Use your bed only for sleeping (and having sex). No working or Netflix watching, for example. Make your room peaceful and relaxing. Keep the room at a comfortable, cool temperature. The ideal temperature is around 20° C / 68° F for a good sleeping night. Don't overeat before bed, and do not eat a heavy meal. If you feel hungry at night, eat a light, healthy snack. Reduce your fluid intake before bed, even if it's just water. Exercise regularly and try to maintain a healthy diet. Avoid drinking caffeinated drinks (coffee, tea, energy drinks) in the late afternoon or evening. And avoid drinking alcohol close to bedtime. References "Sleep, Learning, and Memory", http://healthysleep.med.harvard.edu/healthy/matters/benefits-of-sleep/learning-memory "Healthy Sleep Habits", http://www.sleepeducation.org/essentials-in-sleep/healthy-sleep-habits

Sleep, Learning and Memory

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