In this passage, we will discuss further about the use of medical image segmentation, the existing models that are used in the industry, as well as the AI research that Meticuly has applied to our ...

Transforming Healthcare: The Power of AI in Medical Image Segmentation and Bone Implant Design

The major global manufacturers of Finger Joint Prostheses include DePuy Synthes(Johnson and Johnson), Stryker, Smith and Nephew, Teijin Nakashima Medical Co, Meticuly Co, 3S Ortho, Keri Medical ...

Finger Joint Prostheses Market Insights Report 2023-2030 | 93 Pages Report

Jun (The Expresswire) -- "Finger Joint Prostheses Market" Insights 2023 By Types (Distal Interphalangeal (DIP), Metacarpophalangeal (MCP),

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Finger Joint Prostheses Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done ...

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Finger Joint Prostheses Market Growth and Forecast till 2031

The Global Finger Joint Prostheses market is anticipated to rise at a considerable rate during the forecast period, between 2022 and 2030. In 2021, the market is growing at a steady rate and with the rising adoption of strategies by key players,

Finger Joint Prostheses Market Research | 2023-2030

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Finger Joint Prostheses Market Size By 2030

Feb (The Expresswire) -- "Finger Joint Prostheses Market" Report provides a Detailed analysis of global market size, regional and

Finger Joint Prostheses Market by Top Manufacturers, Growing Demand and Supply 2023-2028

The case was brought into discussion with Meticuly’s team. With a group of experts in virtual surgical planning, the team has no doubt that it can be done. Within ten days, Meticuly supplied Dr ...

Discovering a New Smile : A Patient's Tale of Transformation with Oral and Maxillofacial Surgery

Global main Finger Joint Prostheses players cover DePuy Synthes(Johnson and Johnson), Stryker, Smith and Nephew, Teijin Nakashima Medical Co, Meticuly Co, 3S Ortho, Keri Medical Hand Specialist ...

Finger Joint Prostheses Market 2023 : Company Profile Analysis, Industry Segmentation, Opportunity Assessment and Forecast by 2029

Meticuly

meticuly

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! 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It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

DOE v. GITHUB Court Filing, retrieved on January 26, 2023 is part of . You can jump to any part in this filing . This part is 18 of 21. HackerNoon’s Legal PDF Series here MEMORANDUM OF POINTS AND AUTHORITIES IV. ARGUMENT C. Plaintiffs’ Claims Fail for Reasons Specific to Each Claim. Plaintiffs Fail to State a Claim for Negligence. Plaintiffs’ claim for negligent handling of personal data suffers from the same defects as their CCPA claim and must also be dismissed. To prevail on a negligence claim, a plaintiff must establish: “(1) defendant’s obligation to conform to a certain standard of conduct for the protection of others against unreasonable risks (duty); (2) failure to conform to the standard (breach of the duty); (3) a reasonably close connection between the defendant’s conduct and resulting injuries (proximate cause); and (4) actual loss (damages).” Aguilar v. Hartford Accident & Indem. Co., No. CV 18-8123-R, 2019 WL 2912861, at *2 (C.D. Cal. Mar. 13, 2019) (citing Corales v. Bennett, 567 F.3d 554, 572 (9th Cir. 2009)). The negligence pleading standard in the context of a data breach is “particularly demanding.” See, e.g., In re Sony Gaming Networks and Customer Data Sec. Breach Litig., 996 F. Supp. 2d 942, 971-72 (S.D. Cal. 2014), order corrected, No. 11MD2258 AJB (MDD), 2014 WL 12603117 (S.D. Cal. Feb. 10, 2014) (“[T]he Court will not allow expensive, potentially burdensome class action discovery to ensue in the absence of a viable” negligence claim). Here, Plaintiffs have not pled any of the required elements. , the complaint fails to allege any duty of care the OpenAI Entities owed to Plaintiffs. Plaintiffs merely allege that the OpenAI Entities negligently “collect[ed], maintain[ed], and control[ed] their customers’ sensitive personal information.” (Compl. ¶ 237.) But Plaintiffs have not alleged that they are customers of any OpenAI Entity or had any relationship with an OpenAI Entity that would have created a duty. First Nor have they alleged that the OpenAI Entities collected or maintained any of Plaintiffs’ “sensitive personal information” or “personal data.” (Id. ¶¶ 19- 20, 23-28.) In fact, Plaintiffs allege that the data at issue is available in public Github repositories. (Id. ¶¶ 10, 46, 82-83.) Plaintiffs have failed to plead the existence of a duty owed by any OpenAI Entity. Schmitt v. SN Servicing Corp., No. 21-CV-03355-WHO, 2021 WL 3493754, at *4 (N.D. Cal. Aug. 9, 2021) (finding duty inadequately pled where plaintiffs fail to address the relevant factors, including the “foreseeability of harm to plaintiff, the degree of certainty that plaintiff suffered injury, the closeness of the connection between the defendant’s conduct and the injury suffered, … the extent of the burden to defendant and the consequences to the community of imposing a duty with resulting liability for breach”) (cleaned up). , Plaintiffs also have not shown a breach of any duty. Despite generic allegations that the OpenAI Entities learned of “multiple instances of release of [PII],” Plaintiffs offer no facts regarding these purported disclosures. (Id. ¶ 238.) On these threadbare allegations, Plaintiffs have not sufficiently alleged duty or breach. Second See, e.g., Schmitt, 2021 WL 3493754, at *5 (dismissing negligence claim where plaintiffs failed to provide “factual allegations from which [the Court] can draw a reasonable inference that PII. . . was among the information compromised during the data breach); Anderson, 2019 WL 3753308, at *5 (dismissing claim where “plaintiffs fail to allege any facts in support of their conclusory assertion” that defendant “fail[ed] to implement and maintain reasonable security procedures and practices”). Plaintiffs’ citations to statutory standards—Cal. Civ. Code sections 1798.82, et seq, and 1798.100, et seq.—and to the right to privacy under California Constitution—also fail to support a breach. Even if Plaintiffs can rely on these provisions to define the standard of care, Plaintiffs have not pled any facts demonstrating how OpenAI purportedly violated them. See, e.g., Schmitt, 2021 WL 3493754, at *5; Anderson, 2019 WL 3753308, at *5. , Plaintiffs cannot establish proximate cause as they cannot make “the requisite connection between the alleged breach and damages.” See Schmitt, 2021 WL 3493754, at *6 (dismissing negligence claim because plaintiffs failed to show causation when “plaintiffs have not plausibly pleaded that PII or identifiable information was disclosed (information that [defendant] had a duty to protect)”). Third , Plaintiffs fail to adequately plead any redressable injuries. None of Plaintiff’s alleged injuries satisfy the requisite damages standard for negligence. (See Compl. ¶ 239.) Fourth is insufficient. Aguilar, 2019 WL 2912861 at *2 (“alleged loss of control [over] medical information and personal financial information” does not establish damages). • Alleged loss of control over identity is “too speculative to constitute cognizable injury.” Corona v. Sony Pictures Ent., Inc., No. 14-CV-09600 RGK (Ex), 2015 WL 3916744, at *4 (C.D. Cal. June 15, 2015). Plaintiffs fail to specify what steps they took “to cure [the] harm to their privacy.” (See Compl. ¶ 239.) In any event, “the cost of lost time” is “not recoverable under the economic loss doctrine.” See Gardiner v. Walmart Inc., No. 20-CV-04618-JSW, 2021 WL 2520103, at *8 (N.D. Cal. Mar. 5, 2021) (time spent not recoverable in tort). • Alleged lost time “is insufficient to constitute actual loss.” Corona, 2015 WL 3916744, at *3; see Huynh v. Quora, Inc., No. 18-CV-07597-BLF, 2020 WL 7408230, at *6 (N.D. Cal. June 1, 2020) (dismissing negligence claim because plaintiffs alleged the “mere danger of future harm,” without “specific factual statements that Plaintiffs’ [PII] has been misused”). • Alleged threat of future harm are conclusory and inadequately pled. In any event, “an alleged loss of property in the form of personal information is insufficient to support a claim for negligence.” Aguilar, 2019 WL 2912861 at *2. • Alleged privacy injuries is unsupported by any facts demonstrating economic loss associated with the alleged disclosure of their PII. To the extent plaintiffs are attempting to allege economic losses, the economic loss rule bars recovery for any economic losses in tort unless there is a “special relationship” between the parties. Corona, 2015 WL 3916744, at *5. Plaintiffs have not alleged that a special relationship exists here. • Alleged economic loss Continue Reading . here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case 4:22-cv-06823-JST retrieved on September 8, 2023, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. DocumentCloud.org

OpenAI Is Meticulously Destroying All the Allegations Against It

In the " " story, I describe the approach enhancing your debugging possibilities. On the one hand, I decided that adding description would add misleading details to the narrative. On the other hand, real-life applying might be an excellent opportunity to demonstrate method flexibility. 3 Sharpest Instruments for Debugging a meticulous coding This contradiction led to emerging of this auxiliary story. What Is Going on? On GitHub, there is a project called . I discovered it by searching through the trending repositories. I narrowed down my search by filtering to only those repositories that use the Clojure language I like. My specialization is mostly front-end, so I've chosen that might not be of considerable interest to other developers. I acted so as not to discover that someone passed over me with fixing it. After some investigation described in the primary article about fixing, I found out that the bug exists in the component. Metabase UI issue #14782 AccordionList Diving Into the Details Here's how the initial checklist would look: 1. Understand the actions required to implement task #14782
2. Write the commit messages for task #14782
3. Create a branch for task #14782
4.
5. Verify the correspondence of the implemented solution to task #14782
6. Create task #14782 PR
7. Pass the review of task #14782
8. Merge task #14782 into `master` In , there were 11 steps in the checklist. We do not need three of the administrative tasks, so I removed them. Also, I rewrote the last one to reflect the aspects of the situation. the original article about meticulous coding Let's act on the first task here and understand what do we need to do: 2. ...
3. Create a branch for task #14782
4. Prevent `AccordionList` from jumping on inner search value change task #14782
5. Verify the correspondence of the implemented solution to task #14782
6. ... The second task asks to write the commit messages. I didn't find any recommendations about the formatting of commit messages, so here is mine: Prevent Saved Questions search input from jumping The third task is about creating the branch. I also didn't find any recommendations on branch naming, so here we go: fix/14782-prevent-saved-questions-search-input-from-jumping Now it's time to solve the problem. The full description of discovery and solution is in the dedicated "Three Sharpest Instruments on Your Debugging Service" story. While trying to prepare the necessary state of the component, I discovered that the problem might no longer exist. I need to verify this. AccordionList 3. ...
4. Ask if task #14782 is still relevant after the "Add saved questions picker" update
5. Wait for someone's reply to my question about the relevance of task #14782
6. Prevent `AccordionList` from jumping on inner search value change task #14782
7. Verify the correspondence of the implemented solution to task #14782
8. ... I've shifted all the undone tasks by adding two on the fourth position. The fifth might sound unusual. I learned such a type of task by reading " " by David Allen. You set something reminding you about liabilities others have taken. By doing so, you clearly state the next step of the process and know the action required to unblock it. Or at least to try doing so. Getting Things Done It turned out that, indeed removed the problem I was tackling. As I mentioned before, the meticulous coding method is flexible. We delete all the tasks from the sixth, add another verificatory one and end up with the following completed checklist: the PR #16808 ☑ Understand the actions required to implement task #14782
☑ Write the commit messages for task #14782
☑ Create a branch for task #14782
☑ Ask if task #14782 is still relevant after the "Add saved questions picker" update
☑ Wait for someone's reply to my question about the relevance of task #14782
☑ Ensure the closing of task #14782 Thank you for reading! Please reach out if you want to discuss the ideas I describe. on Twitter

The Meticulous Coding Strategy Applied to Metabase Issue #14782

The Meticulous Approach to Coding

International art-tech company ArtCollecting, specializing in art authentication technologies, has dedicated its resources to analyzing around 100 works from Nina Moleva’s collection. The study was conducted using a custom-developed algorithm. The machine learning process was overseen by cultural heritage expert and ArtCollecting director Marina Nadeeva, in collaboration with developers who previously worked on blockchain integration for the company. ArtCollecting ArtCollecting Marina Nadeeva ![Manet? (c) ArtCollecting ](https://cdn.hackernoon.com/images/2Tt2A52YmFPgzIYQUhOJEwFt4D23-2025-07-10T04:44:30.959Z-zwu7alwf2x6dquu8jc9e3qii) Nina Moleva’s collection is of particular interest because the owner herself valued it at $2 billion and named the Russian president as the heir in her will. But more importantly, this is the first time experts have encountered a collection with such an enigmatic provenance. According to Moleva, the wife of nonconformist artist Ely Belyutin, their holdings include works by Leonardo da Vinci, Michelangelo, Titian, Van Dyck, Monet, and even Natalia Goncharova. If true, this would be the largest private collection of world-class masterpieces. Authenticating even a portion of these works could have significant repercussions for the art world. valued it at $2 billion and named the Russian president as the heir in her will works by Leonardo da Vinci, Michelangelo, Titian, Van Dyck, Monet, and even Natalia Goncharova Now, researchers have a chance to unravel the mystery. Marina Nadeeva, a cultural heritage expert, examined part of the collection using ArtCollecting’s innovative AI alongside traditional art analysis methods, including comparative analysis, stylistic assessment, and technical examination. The study by Marina Nadeeva focused on works by Ely Belyutin, 16th–19th-century icons, and paintings and graphic works attributed to Claude Monet, Édouard Manet, and Natalia Goncharova. The algorithm analyzed not only visual features but also paint microstructure, signs of aging, and even hidden layers—details that might escape the human eye. Marina Nadeeva Marina Nadeeva works by Ely Belyutin, 16th–19th-century icons, and paintings and graphic works attributed to Claude Monet, Édouard Manet, and Natalia Goncharova "This is an unprecedented case," says Marina Nadeeva. "Technology has never before encountered so many potential masterpieces at once. If even one work is verified, it could reshape our understanding of 20th-century private collections." "This is an unprecedented case," Marina Nadeeva "Technology has never before encountered so many potential masterpieces at once. If even one work is verified, it could reshape our understanding of 20th-century private collections." Research examples ArtCollecting’s experts have achieved significant breakthroughs in the study of provincial Russian iconography through their AI-powered examination of “The Miracle of Christ in Latome“, an icon from art historian Nina Moleva’s private collection. ArtCollecting’s experts have achieved significant breakthroughs in the study of provincial Russian iconography through their AI-powered examination of “The Miracle of Christ in Latome“, an icon from art historian Nina Moleva’s private collection. ArtCollecting’s experts have achieved significant breakthroughs in the study of provincial Russian iconography through their AI-powered examination of “The Miracle of Christ in Latome“, an icon from art historian Nina Moleva’s private collection. Cultural heritage expert Marina Nadeeva has completed a comprehensive analysis of the icon using AI technology developed by ArtCollecting. This project marks a new chapter in the study of provincial Russian icon painting, a field that has long been under-researched. Marina Nadeeva ArtCollecting How AI Unlocked the Icon’s Secrets How AI Unlocked the Icon’s Secrets A custom algorithm was created specifically for this study, capable of processing and cross-referencing vast datasets. The system analyzed information from over 100,000 icons (15th–19th centuries) produced in provincial workshops, drawing from museum catalogs, antique dealer archives, historical records, and obscure private collections. The AI not only reconstructed the icon’s likely original appearance but also identified works with similar stylistic and technical features—an especially valuable contribution, given that provincial iconography remains a "blank spot" in art history. Unlike major icon-painting centers, provincial workshops rarely documented their output, and their surviving works are scattered across small regional museums and private collections. Why This Is a Major Breakthrough Why This Is a Major Breakthrough Traditionally, such research required years of work by art historians, restorers, and chemical analysts. Now, AI accelerates the process dramatically, analyzing enormous datasets in days. Crucially, the technology doesn’t replace experts—it empowers them with advanced analytical tools. "We’re not just scanning images; we’re teaching the system to recognize stylistic traits, technical methods, even restoration marks," explains Marina Nadeeva. "For instance, our algorithm can pinpoint characteristics of a specific workshop or even an individual icon painter by comparing minute details—the brushwork of highlights, the rendering of fabric folds, or the use of particular pigments." Marina Nadeeva Discoveries About “The Miracle of Christ in Latome“ Discoveries About “The Miracle of Christ in Latome“ Beyond stylistic analysis, a full technical examination was conducted. Researchers determined the icon’s approximate creation period, identified pigments and binding materials, and clarified its artistic tradition. The most striking achievement, however, was the AI-assisted reconstruction of damaged elements. Since iconography follows strict compositional rules, recurring motifs allowed the algorithm to compare the damaged areas with thousands of analogues—proposing the most probable original composition. Why This Matters for Art History Why This Matters for Art History Russian iconography isn’t limited to famed schools like Moscow, Novgorod, or Palekh. Hundreds of unnamed provincial masters created works of equal significance—yet without systematic study, many such icons are dismissed as "ordinary" or even mislabeled as forgeries. This research helps reclaim their place in art history. Authenticating a Work Attributed to Édouard Manet Authenticating a Work Attributed to Édouard Manet Experts have begun verifying the authenticity of a painting attributed to Édouard Manet using artificial intelligence. A specialized algorithm analyzes the artist’s brushwork by comparing it to verified masterpieces. The neural network has already been trained on open-access databases from the Musée d'Orsay, the Musée Marmottan Monet in Paris, and the National Gallery of Art in Washington. Researchers are now refining the analysis by studying Manet’s drawings and watercolors to cross-reference details. Simultaneously, the algorithm is being adjusted to identify other potential authors should the attribution to Manet prove incorrect. Musée d'Orsay, the Musée Marmottan Monet in Paris, and the National Gallery of Art in Washington Microscopic technical analysis has helped narrow down the likely timeframe of the painting’s creation. Additionally, archival research in Russian libraries—including French publications that entered Imperial Russia and later the USSR—has been completed. Investigators examined records dating back to 1912 for further clues on provenance. The study is ongoing, with new findings expected soon. AI Helps Date the “Transfiguration” Icon AI Helps Date the “Transfiguration” Icon A custom algorithm developed by ArtCollecting’s team enabled a comprehensive analysis of the artwork. The system processed over 100,000 samples of 15th–19th-century iconography from Russian provincial workshops, as well as Greek, Bulgarian, and Turkish collections. This large-scale study was made possible by a unique database aggregating museum catalogs, antique collections, and archival materials. A key focus was the analysis of inscriptions on the icon. The algorithm was specifically trained to recognize stylistic evolutions in lettering across five centuries (15th to early 20th century), incorporating all known alphabets, typefaces, and their usage in iconography. This allowed precise dating based on palaeographic features. Parallel technical examinations—including pigment and binding medium analysis—corroborated the AI’s findings through microscopy, creating a multilayered understanding of the icon’s origins. About the Technology in Development About the Technology in Development ArtCollecting is pioneering the world's first AI system specifically trained for artwork attribution and authentication. Unlike traditional methods requiring months of meticulous analysis, its algorithm evaluates style, technique, and materials within hours by cross-referencing data against 10 million verified reference samples. While currently tested on only one-third of the collection, even preliminary results could revolutionize art market technologies. If the AI confirms authentic masterpieces, it would prove that lost treasures had been hidden for decades in an ordinary Moscow apartment. Conversely, should the collection be debunked as a hoax, the revelation would be equally sensational. One thing is already clear: ArtCollecting's technology provides the first viable tool for solving such art historical mysteries. And who knows—perhaps the next "Moleva List" is already awaiting its investigator. Note: "Moleva List" is retained as a proper noun referencing the collection's controversial nature, similar to terms like "Gurlitt Trove." For broader audiences, consider adding a brief contextual phrase like "the so-called 'Moleva List' of disputed artworks".

AI for Art Examining: Claude Monet, Édouard Manet, and Natalia Goncharova

AI za ispitivanje umetnosti: Claude Monet, Édouard Manet i Natalia Goncharova

KI für Kunstprüfung: Claude Monet, Édouard Manet und Natalia Goncharova

AI para el examen de arte: Claude Monet, Édouard Manet y Natalia Goncharova

AI para o exame de arte: Claude Monet, Édouard Manet e Natalia Goncharova

AI művészeti vizsga: Claude Monet, Édouard Manet és Natalia Goncharova

AI for Art Examining: Claude Monet、Édouard Manet、Natalia Goncharova

AI meno egzaminui: Claude Monet, Édouard Manet ir Natalia Goncharova

कला परीक्षणको लागि AI: Claude Monet, Édouard Manet, र Natalia Goncharova

AI dla badań artystycznych: Claude Monet, Édouard Manet i Natalia Goncharova

AI pentru examinarea artei: Claude Monet, Édouard Manet și Natalia Goncharova

ИИ для изучения искусства: Клод Моне, Эдуард Мане и Наталья Гончарова

艺术审查的AI:克劳德·莫奈,埃杜华·马内特和纳塔利亚·冈查罗娃

Pixels, Paperbacks, and Power

Pixels, Paperbacks y Power

Pixels, Paperbacks ja Power

Pixels, Paperbacks եւ Power

Pixel, Paperbacks e potenza

ピクセル、パッケージバック、パワー

Pixels, Paperbacks និងថាមពល

Пиксели, Paperbacks и моќ

Pixels, Paperbacks болон Power

Pixely, Paperbacks a Power

Pixels, Paperbacks dhe pushtet

Пиксели, Папербацкс и Повер

Pixel, Paperbacks ve Güç

Road Town, Tortola, June 25th, 2025/Chainwire/--Neo Pepe Coin ($NEOP) has crossed past the $2 million milestone in record-breaking speed, propelling it toward Stage Four of its anticipated presale. Launching less than a week ago, the project’s early growth reflects strong interest, with investors rallying behind its revolutionary approach to decentralization, governance, and liquidity. Neo Pepe Coin Neo Pepe Coin Over $2,000,000 Raised Within days, Neo Pepe has hit a notable $2 million raised and strong participation in the project’s presale. This achievement not only underscores investor confidence but highlights the project's compelling narrative—a serious, thematic rebellion against traditional financial centralization, aptly branded as the Memetrix. Neo Pepe Coin recently achieved a 71.96 score on its Certik Audit, validating its credibility as a legitimate and secure project. Certik Audit Certik Audit Neo Pepe’s presale is meticulously structured across 16 dynamic stages, progressively increasing the token price to reward early supporters. Now, as it rapidly approaches Stage Four, the window to secure tokens at advantageous pricing is narrowing swiftly. Community Governance Treasury DAO Further differentiating Neo Pepe Coin is its innovative 2.5% auto-liquidity mechanism. Each transaction enhances liquidity pools, with LP tokens permanently burned, creating sustained price stability and growth potential. Complementing this powerful feature is a fully decentralized governance model, empowering token holders with real decision-making power on strategic listings and treasury allocations. Auto Liquidity Mechanism Neo Pepe Coin’s early performance has been marked by steady presale participation and a structured rollout strategy. With its ongoing stage-based pricing model, thematic framing, and auto-liquidity mechanics, $NEOP continues to progress through its planned presale phases. Real Meme Community With Stage Four approaching, Neo Pepe Coin continues through its presale schedule, supported by consistent participation and structured pricing mechanics. The project’s distinctive theme and token model remain central to its current phase of growth. Users can secure a spot now and discover why Neo Pepe Coin is setting new standards in crypto innovation. For more information, users can join the Neo Pepe community on socials or visit the official website today. About Neo Pepe Coin Neo Pepe Coin ($NEOP) is a decentralized cryptocurrency designed to challenge centralization, regulatory overreach, and market manipulation. Leveraging the thematic narrative of the Memetrix, Neo Pepe Coin symbolizes a bold movement towards financial democratization and innovation. Neo Pepe Coin Neo Pepe Coin The project features a structured 16-stage presale, robust community-driven governance, and an auto-liquidity mechanism ensuring sustainable growth and stability. To Get Started with $NEOP Website: https://neopepe.ai
Telegram: https://t.me/NeoPepeProtocol
Twitter/X: https://x.com/NeoPepeProtocol Website: https://neopepe.ai https://neopepe.ai https://neopepe.ai Telegram: https://t.me/NeoPepeProtocol https://t.me/NeoPepeProtocol https://t.me/NeoPepeProtocol Twitter/X: https://x.com/NeoPepeProtocol https://x.com/NeoPepeProtocol https://x.com/NeoPepeProtocol Contact CMO Logan Roy CrypTechnologies Ltd. press@neopepe.ai This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. This story was published as a press release by Chainwire under HackerNoon’s Business Blogging Program. Program Program

Neo Pepe $NEOP Presale Passes $2M Raised With Stellar CertiK Audit

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‘नेपाल आइडल’ बनाउन उनका समर्थकले विषेश साथ दिइरहेका छन् ।

TL;DR: Generative AI models like DALL·E are reshaping digital art - enabling instant image generation from text. This article explores how it works and what it means for the future of creativity. TL;DR: Generative AI models like DALL·E are reshaping digital art - enabling instant image generation from text. This article explores how it works and what it means for the future of creativity. Just a few years ago, creating digital art required a mastery of complex tools, expensive equipment, and weeks - if not months - of practice. Today, with a simple sentence and a few seconds, artificial intelligence can conjure a painting that once took an artist days to create. But what have we truly gained, and what might we have left behind? And perhaps most critically - does it render the artist obsolete? From Brushstrokes to Pixels: The Traditional Digital Artist’s Journey From Brushstrokes to Pixels: The Traditional Digital Artist’s Journey Becoming a digital artist demands quite a lot - far more than meets the eye. As an artist, the transition to digital creation is neither seamless, nor cheap. It begins with choosing the right hardware, a decision complicated by countless variables: operating systems, device comfort, screen size, pen sensitivity, price, and more. Next comes the platform. Whether you’re on a tablet or a computer, the number of applications available can feel overwhelming. From Photoshop to Procreate, Clip Studio to Corel Painter, the choices are vast - and each comes with a steep learning curve as the variety of options and abilities in each is immense. Mastering the software is only half the battle. One must also learn to translate traditional skills into the digital realm. Colour theory, composition, and brushwork remain foundational, but the tactile experience of graphite or watercolour doesn’t intuitively carry over to an Apple Pencil or stylus. Even seasoned artists face a period of unlearning and retraining. And for newcomers, the journey is even steeper. The digital world doesn’t bypass the need for artistic fundamentals - it simply reshapes the way they’re learned. Understanding form, light, depth, and style remains just as essential, making the learning process longer and more layered for those starting from scratch. Whether you’re an experienced painter or a complete beginner, the transition into digital art is anything but quick. For professionals, it means adapting years of muscle memory to new tools and workflows. For newcomers, it involves building foundational art skills from the ground up. In both cases, the path is long - filled with trial and error, endless hours of practice, and a deep well of patience and dedication. The journey can take months - just to render something as deceptively simple as an apple, a cat, or a chair. Then Came AI: A Paradigm Shift in Creative Process Then Came AI: A Paradigm Shift in Creative Process Fortunately - or perhaps inevitably - this landscape has changed. With the rise of artificial intelligence, the time-consuming initiation into digital art is no longer a necessity. Today, AI models like OpenAI’s DALL·E can generate art with just a prompt. Type a sentence, and a digital masterpiece materialises in seconds. DALL·E The art world, like nearly every other domain, has been shaken by the capabilities of AI. From GPT models answering questions and fixing code, to DALL·E generating illustrations, the creative process has been transformed. But how does it actually work? And is it as magical as it seems? Learning Like a Human: The Foundation of AI Art Learning Like a Human: The Foundation of AI Art To understand how AI can generate images, we must first understand how it learns. The concept is surprisingly human. Imagine a toddler - let’s call him Oliver - learning to identify animals. His mother points to a black cat and says, “That’s a cat.” Later, she points to a ginger cat and repeats the word. Oliver, applying what he’s learned, then sees a white cat and proclaims, “Cat!” Despite the new colour, Oliver recognises shared features: four legs, whiskers, pointed ears, a tail, and a meow. This is clustering: the brain’s ability to identify patterns from limited data. But mistakes happen. One day, Oliver sees a Shih-Tzu dog and calls it a cat. It’s his best guess, based on the information he has. His mother corrects him: “No, that’s a dog.” From that point forward, Oliver refines how he distinguishes cats from dogs in his mind. This is the essence of how artificial neural networks learn. Neural Networks: Digital Brains Built on Data Neural Networks: Digital Brains Built on Data A neural network is a type of computer model inspired by how the human brain works. It’s built to recognise patterns and learn from experience by analysing large amounts of data, just like baby Oliver. Neurons in human brain. Illustration created by the author using DALL-E 3. These networks are made up of layers of tiny processing units (called “nodes” or “synapses”) that are connected to each other - similar to how neurons connect in the brain. Each connection has a value that controls how strongly one node influences another. These values are called “weights”. During training, the network adjusts these weights over time based on how well or poorly it performs - just like Oliver gradually learns to tell the difference between a cat and a dog by correcting his mistakes. This process helps the network get better at making accurate predictions. The training process requires a dataset - a structured collection of information used to teach the model. For a language model, this typically means millions of sentences, each constructed from words, which in turn are built from letters of the alphabet. For a visual model, it means millions of images - each composed of objects, which themselves are formed from individual pixels. The larger and more diverse the dataset, the more accurate the model becomes. Inside the Neural Network: How Data Flows and Decisions Form Inside the Neural Network: How Data Flows and Decisions Form Let’s imagine a neural network as it is often visualised in modern diagrams: a vast graph made up of interconnected nodes. These nodes are organised into distinct layers - stretching from top to bottom like the tiers of a layered circuit. Each node, or artificial neuron, plays a role in processing information. This overall structure is known as the topology of the network. Topology defines how many layers the network contains, how many nodes exist in each layer, and how data flows between them. It’s the architectural blueprint of the model. topology Training begins by feeding input data - such as images or sentences - into the first layer of the network. This data then moves forward through the network, layer by layer. At each stage, nodes apply mathematical operations to the data, such as matrix multiplications, activation functions like ReLU (Rectified Linear Unit), or other transformations. As this process unfolds, a computation graph is constructed in the background. This graph meticulously records each operation and the flow of data: which node performed what transformation. Think of it as a detailed recipe or map of the model’s thought process. computation graph This computation graph is essential - not just for making predictions, but also for learning from mistakes. When the model produces an incorrect result (prediction), the graph allows the system to trace back through each step and adjust all weights. This backward path is what sets the stage for backpropagation - the core mechanism through which neural networks improve over time. backpropagation - Backpropagation: Learning by Error Backpropagation: Learning by Error Let’s go back to Oliver. When he mislabels a dog as a cat, his mom corrects him and Oliver is able to readjust his understanding. After a network makes a prediction, the result is compared to the true value using a loss function. The loss function measures how far from the truth the prediction was from the actual result. loss function In the process of backpropagation, the layers of the graph are traversed backwards in order to calculate how much each weight in the network contributed to the error. By using the chain rule from calculus, gradients are being computed. Each gradient describes the loss with respect to each weight. These calculated gradients allow us to indicate the way to fix the mistake by telling the direction and magnitude of change required to reduce the error. Using these gradients, the weights are updated accordingly to become more precise. chain rule gradients This feedback loop - forward pass, error calculation, backward pass - is repeated over and over. The result? A trained model capable of identifying patterns and making increasingly accurate predictions. Teaching Oliver to Draw: The Power of Generative Models Teaching Oliver to Draw: The Power of Generative Models Now that we’ve explored how predictions are made using predictive models, we can begin to understand the more intricate workings of generative models. As their names suggest, predictive models are designed to analyse existing data and make informed estimates about likely outcomes - much like young Oliver, who, after studying animals, can confidently predict the type of a new one he encounters. But imagine asking Oliver not to identify a cat, but to draw one. This task requires more than recognition - it calls for creation. We assume he doesn’t have a cat in front of him, nor can he recall every precise detail of the cats he’s seen. Yet, he is now asked to conjure an entirely new image of a cat based on memory, imagination, and learned patterns. This act of constructing something new - rather than selecting from what already exists - is the essence of generative models Generative models, such as DALL·E, go beyond pattern recognition. They are trained to produce original content that resembles what they’ve encountered during training. Rather than merely answering, “What is this?”, they respond to the question, “What might this look like if it existed?” These models don’t just understand data - they create with it, generating entirely new images, text, audio, or video that align with the structures and styles they’ve learned. “What is this?” “What might this look like if it existed?” When Language Takes Shape When Language Takes Shape Generative models like DALL·E are trained on vast datasets of image-text pairs, learning to associate visual elements with language. During training, the model sees an image alongside its caption and gradually learns which words correspond to which shapes, textures, colours, and concepts. It builds an internal map of meaning, understanding that “a red apple” implies roundness, a specific hue, a stem, and so on. Later, when given a new text prompt, the model converts the words into a structured representation and uses that as a guide to generate an image - starting from random noise and refining it step by step until a coherent visual emerges that matches the text. This process allows the model to create entirely new images it has never seen before, while still staying true to the patterns it learned during training. A Thousand Apples a Second: What AI Sees That Artists Can’t A Thousand Apples a Second: What AI Sees That Artists Can’t So how does this apply to art? Let’s say you want to create a digital painting of an apple using DALL·E. You simply type your request - and within seconds, you receive an image. For the prompt “generate a digital painting of an apple” DALL-E 3 responded with the following image: That apple is the product of millions of images the model has seen during training. In contrast, a human artist would begin by seeking inspiration - studying references by browsing specialised platforms like Pinterest, sketching rough shapes, refining detail, experimenting with colours, and applying texture. This process can take any time between hours to days. This process of browsing, collecting references, and closely observing objects - like apples - is an essential part of any artist’s workflow. It’s how they build a visual library in their mind: examining form, texture, lighting, colour variations, and stylistic choices. In many ways, this mirrors how an AI model is trained. Before it can generate images, the model must also be exposed to thousands - often millions - of examples. These examples, compiled into a dataset, serve the same purpose: to teach the model what an apple looks like from various angles, in different styles, and under varying lighting conditions. dataset But there are some crucial differences such as: scale and speed. scale and speed While a human artist relies solely on their own memory, experience, and ability to process inspiration over time, AI models are trained using vast computational resources. Large-scale models like DALL·E are trained in powerful data-centers equipped with thousands of interconnected GPUs, TPUs or in the case of my company - Accelerators specialised for training (such as Gaudi3). These machines work in parallel, processing and analysing massive volumes of images at incredible speeds. High bendwith network connections between machines, high-throughput storage systems, and specialised AI hardware enable these models to be trained on enormous datasets in days or weeks - what could take a human years to absorb, if ever. Gaudi3 In contrast, the artist’s brain is the only “hardware” available. No high-speed clusters or petabytes of image data - just intuition, memory, and practice. It’s this human limitation that AI bypasses, enabling it to “see” more examples, more variations, and more styles than a single person ever could in a lifetime. Matching Human Style: Mimicking Mediums Matching Human Style: Mimicking Mediums Digital artists today aren’t limited to one visual style. With tools like Procreate, they can simulate oil, watercolour, pencil, and ink - complete with paper textures and brush dynamics. Want to mimic the blotchy softness of watercolour on rough paper? There’s a brush for that. Prefer the dense richness of oil on canvas? That too. AI can mimic these styles too - if prompted correctly. For example: Each of these prompts instructs the model to mimic not only the subject but also the medium, colour tone, and artistic texture. The Artistic Process The Artistic Process To create those same effects without AI, an artist must gather reference material, build sketches layer by layer, experiment with brush settings, apply base colours, add highlights and shadows, and adjust textures manually. It’s a time-intensive but emotionally rich experience. With AI, the process becomes more immediate - but also more detached. Let’s retrace the steps of the creative journey I embarked on, one stage at a time. The Ghost in the Gallery: Why AI Still Can’t Replace You The Ghost in the Gallery: Why AI Still Can’t Replace You AI appears more than capable of replicating the artistic process - only faster and at scale. This raises a critical question: why would anyone choose traditional digital media anymore? Is there still room for authentic artistic expression and creativity, or has that pursuit become obsolete? And if human artists still have a place, can they surpass AI - and in what contexts might that be possible? Let’s explore what happens when we try generating a prompt with more precise and demanding instructions: In response to the prompt “Generate an apple with two leaves in watercolour style”, the model produces the following result: This is where the curtain lifts - and the cracks are revealed: when presented with a more refined and specific prompt - “Generate an apple with two leaves that face the same direction in watercolour style” - the result takes an intriguing and unforeseen turn. Does it look the same to you? I would have to agree. But does it fulfil the requirement - a clear and straightforward one, at that? Absolutely not. Could it simply not comprehend? Was the fault mine - was my prompt too vague? To remove any ambiguity, I refined the request further: “Generate an apple with two leaves where both leaves face to the left side in watercolour style” As seen above, the result diverged even further from the intended outcome. This instance of miscommunication with the AI model is far from isolated. In fact, it feels like the more precisely one tries to guide it, the more elusive the desired outcome becomes - often resulting in frustration and wasted time. What’s striking is that a request so simple a child could grasp it proved incomprehensible to the model. Imagine a client offering a straightforward instruction to a human artist - only to be met with blank incomprehension, as if the sentence were spoken in a foreign tongue. In such a case, the client would undoubtedly take their business elsewhere. And in our scenario, it is the model that loses the commission. Final Thoughts: The Art We Make Together Final Thoughts: The Art We Make Together In the end, this is not a war between brush and code, but a dialogue. The machine offers speed, precision, and infinite variation; the artist brings emotion, intuition, and soul. One is not here to replace the other, but to expand what is possible. We are fortunate to live in a time where imagination is no longer bound by the limits of our hands alone. The future of creation lies not in rivalry, but in harmony - where human spirit and artificial intelligence create side by side, each lending its own kind of magic. About me About me About me I am Maria Piterberg - an AI expert leading the Runtime software team at Habana Labs (Intel) and a semi-professional artist working across traditional and digital mediums. I specialise in large-scale AI training systems, including communication libraries (HCCL) and runtime optimisation. Bachelor of computer science.

I Traded My Sketchpad for a Prompt Box—And Art Will Never Be the Same

እኔ የእኔ Sketchpad ለ Prompt Box - እና የአትክልት ሁልጊዜ ተመሳሳይ ይሆናል

Trgovao sam Sketchpad za promptnu kutiju - i umetnost nikada neće biti ista

Traduje mi plancha para una caja de prompt —y el arte nunca será el mismo

Vaihdoin piirustustani prompt-laatikkoon - ja taide ei koskaan ole sama

Cseréltem a rajzlapomat egy prompt dobozért - és a művészet soha nem lesz ugyanaz

I Tradeed My Sketchpad for a Prompt Box - And Art Will Never Be the Same (私はプロンプトボックスのために私のスケッチパッドを取引しました。

ຂ້າມ Sketchpad ຂອງຂ້າພະເຈົ້າສໍາລັບ Box Prompt - ແລະ Art Will Never Be The Same

Ik verhandelde mijn sketchpad voor een promptbox - en kunst zal nooit hetzelfde zijn

زه زما Sketchpad د پروپټ باکس لپاره سوداګریز کړ - او هنر به هیڅکله ورته نه وي

Obchodoval som s mojím náčrtníkom za prompt box - a umenie nikdy nebude rovnaké

Я торгував моїм скетчпадом за посібник - і мистецтво ніколи не буде однаковим

میں نے ایک پروپٹ باکس کے لئے میرا اسٹیکپڈ ٹریڈ کیا - اور فن کبھی نہیں ہو گا

Another week, another AI trend lighting up the timeline. This time, it’s ReAct (nope, not the JavaScript one you already know and love). We're talking about the Reasoning + Acting pattern that’s making serious noise in the world of AI agents. Reasoning + Acting Originally introduced back in 2022 (which is practically ancient in AI years), the ReAct pattern is suddenly everywhere—and for good reason… Follow along as we unpack what it is, how it works, and how to implement it in your own agentic workflow. Scared of the AI wave? 🌊 Nah. It’s time to Re-Act! What’s the ReAct Design Pattern? You might be thinking, ”Ugh… another React article in 2025? Haven’t we talked about this for like… a decade? Is this React… but for AI now?” or maybe "Sure, I know React design patterns!” Ugh… another React article in 2025? Haven’t we talked about this for like… a decade? Is this React… but for AI now? Sure, I know React design patterns! ✋ Hold up! ✋ We're talking about a different kind of ReAct here! We're talking about a different kind of ReAct here! In the world of AI, ReAct—which comes from “Reasoning” + “Acting”—is a design pattern where LLMs combine reasoning and acting to solve complex tasks more effectively or produce more adaptable and accurate results. ReAct 👇 Let’s break it down with a tasty analogy! 👇 Say you’re building an AI robot chef 🤖 👨‍🍳 🤖. If you just say “make a sandwich,” a basic AI system might ask an LLM for instructions and return a static recipe. 📝 But a ReAct-powered agent? Totally different game! First, it reasons: “Wait—what kind of sandwich? Do I have the ingredients? Where’s the bread?” Then it acts: opens the fridge, grabs what it needs, slices, stacks, and voilà—BLT complete! 🥪 ReAct-powered agent reasons acts Thus, ReAct doesn’t just reply. It thinks, plans, and executes. Step. By. Step. 👣 👣 👣 thinks, plans, executes. That pattern was first introduced in the 2022 paper “ReAct: Synergizing Reasoning and Acting in Language Models” and it’s blowing up in 2025 as the backbone of modern agentic AI and Agentic RAG-based agents. 🤯 ReAct: Synergizing Reasoning and Acting in Language Models ReAct: Synergizing Reasoning and Acting in Language Models Now, how's that possible, and how does this design pattern actually work? Let’s find out! 🔍 ReAct Origins: How a 2022 Paper Sparked an AI Workflow Revolution Back in late 2022, the ReAct: Synergizing Reasoning and Acting in Language Models paper built on this idea: ReAct: Synergizing Reasoning and Acting in Language Models “[LLMs'] abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. [Here, we] explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner…” “[LLMs'] abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. [Here, we] explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner…” In other words: 🧠 + 💪 = 💥. At that time, LLMs were mostly brainy assistants—generating text, answering questions, writing code. But then came the shift. By late 2022 (yep, right when ChatGPT launched on Nov 30), devs started wiring LLMs into real software workflows. Things got real. the shift Fast-forward to today: welcome to the age of AI agents 🤖 🕵 🤖—autonomous systems that reason, take action, self-correct, and get stuff done. age of AI agents In this new AI “agentic” era, the ReAct pattern—once just a neat academic idea—is now one of the most common architectures for building goal-oriented, decision-making AI agents. Even IBM mentions ReAct as a core building block for agentic RAG workflows: AI “agentic” era most common architectures Alright, so ReAct comes from the past… but it’s shaping the future. 🔮 Now hop in the DeLorean (88 MPH, baby! ⚡)—we’re heading back to the future to see how this pattern works in practice, and how to implement it. React Applied to Modern Agentic AI Workflows Think of ReAct as the MacGyver of AI. 🔧 🪛 🧰 MacGyver of AI Instead of just spitting out an answer like your typical LLM, ReAct systems think, act, and then think again. It’s not magic ✨—it’s when chain-of-thought reasoning meets real-world action. think act think again Specifically, a ReAct agent is based on a Think 🤔 → Act 🛠️ → Observe 🔍 → Repeat 🔁 loop: Think 🤔 → Act 🛠️ → Observe 🔍 → Repeat 🔁 Reasoning (Think 🤔): Start with a prompt like “Plan a weekend trip to NYC.” The agent generates thoughts: “I need flights, a hotel, and a list of attractions.”
Action selection (Act 🛠️): Based on its reasoning, the agent picks a tool (for example, via an MCP integration)—say, an API to search for flights—and executes it.
Observation (Observe 🔍): The tool returns data (e.g., flight options). This is fed back to the agent, which incorporates it into the next reasoning step. Reasoning (Think 🤔): Start with a prompt like “Plan a weekend trip to NYC.” The agent generates thoughts: “I need flights, a hotel, and a list of attractions.” Reasoning (Think 🤔) Action selection (Act 🛠️): Based on its reasoning, the agent picks a tool (for example, via an MCP integration)—say, an API to search for flights—and executes it. Action selection (Act 🛠️) via an MCP integration Observation (Observe 🔍): The tool returns data (e.g., flight options). This is fed back to the agent, which incorporates it into the next reasoning step. Observation (Observe 🔍) Loop (Repeat 🔁): The cycle continues. The agent uses new thoughts to select another tool (e.g., hotel search), gets more data, updates its reasoning—all inside a top-level loop. Loop (Repeat 🔁) You can picture that thinking of a “while not done” loop. At each iteration, the agent: Generates a new reasoning step.
Selects the best tool for the task.
Executes the action.
Parses the result.
Checks if the goal is met. Generates a new reasoning step. Selects the best tool for the task. Executes the action. Parses the result. Checks if the goal is met. This loop continues until a final answer or goal state is reached. How to Implement ReAct So, you want to put ReAct into action with real-world agents? Here's a common setup! The show kicks off with an Orchestrator Agent (think CrewAI or a similar framework) driving the main ReAct loop. This top-level agent, powered by your LLM of choice, delegates the initial request to a dedicated Reasoning Agent. Orchestrator Agent think CrewAI or a similar framework Reasoning Agent The Reasoning Agent, instead of rushing, breaks down the original prompt into a precise list of actionable steps or sub-tasks. It's the brain, meticulously planning the strategy. Reasoning Agent breaks down Next, these tasks are handed off to an Acting Agent. This is where the rubber meets the road! This agent is your tool-wielder, integrated directly with an MCP server (for accessing external data or tools like web scrapers or databases) or communicating with other specialized agents via A2A protocols. It's tasked with actually performing the required actions. Acting Agent other specialized agents via A2A protocols performing The results of these actions aren't ignored. They're fed to an Observing Agent. This agent scrutinizes the outcome, deciding if the task is complete and satisfactory, or if more steps are needed. If further action is required, the loop restarts, sending the agents back to refine the process. Observing Agent This continuous Reasoning -> Acting -> Observing cycle runs until the Observing Agent declares the result "ready," sending that final output back up to the Orchestrator Agent, which then delivers it to the inquirer. Reasoning -> Acting -> Observing Observing Agent Orchestrator Agent As you can see, the easiest way to bring ReAct to life is with a multi-agent setup! Still, you can pull it off with a single, simple, mini agent, too. Just check out the example in the video below: can https://www.youtube.com/watch?v=pEMhPBQMNjg&embedable=true https://www.youtube.com/watch?v=pEMhPBQMNjg&embedable=true ReAct vs “Regular” AI Workflows Aspect

"Regular" AI Workflow

ReAct-Powered AI Workflow



Core Process

Direct generation; single inference pass

Iterative "Reasoning + Acting" loop; step-by-step thinking and execution



External interaction

May be limited to no external tool use

Actively leverages tools



Adaptability

Less adaptable; relies on training data.

Highly adaptable; refines strategy based on real-time feedback.



Problem solving

Best for straightforward, single-turn tasks.

Excels at complex, multi-step problems requiring external info and dynamic solutions



Feedback Loop

Generally no explicit feedback for self-correction

Explicit real-time feedback loop to refine reasoning and adjust actions



Transparency

Often a black box; hard to trace logic.

High visibility; explicit Chain-of-Thought and sequential actions show reasoning and output at each step



Use case fit

Simple Q&A, content generation

Complex tasks: trip planning, research, multi-tool workflows



Implementation

Simple; requires AI chat integrations

Complex; requires loop logic, tool integration, and might involve a multi-agent architecture Aspect

"Regular" AI Workflow

ReAct-Powered AI Workflow



Core Process

Direct generation; single inference pass

Iterative "Reasoning + Acting" loop; step-by-step thinking and execution



External interaction

May be limited to no external tool use

Actively leverages tools



Adaptability

Less adaptable; relies on training data.

Highly adaptable; refines strategy based on real-time feedback.



Problem solving

Best for straightforward, single-turn tasks.

Excels at complex, multi-step problems requiring external info and dynamic solutions



Feedback Loop

Generally no explicit feedback for self-correction

Explicit real-time feedback loop to refine reasoning and adjust actions



Transparency

Often a black box; hard to trace logic.

High visibility; explicit Chain-of-Thought and sequential actions show reasoning and output at each step



Use case fit

Simple Q&A, content generation

Complex tasks: trip planning, research, multi-tool workflows



Implementation

Simple; requires AI chat integrations

Complex; requires loop logic, tool integration, and might involve a multi-agent architecture Aspect

"Regular" AI Workflow

ReAct-Powered AI Workflow Aspect Aspect Aspect "Regular" AI Workflow "Regular" AI Workflow "Regular" AI Workflow ReAct-Powered AI Workflow ReAct-Powered AI Workflow ReAct-Powered AI Workflow Core Process

Direct generation; single inference pass

Iterative "Reasoning + Acting" loop; step-by-step thinking and execution Core Process Core Process Core Process Direct generation; single inference pass Direct generation; single inference pass Iterative "Reasoning + Acting" loop; step-by-step thinking and execution Iterative "Reasoning + Acting" loop; step-by-step thinking and execution External interaction

May be limited to no external tool use

Actively leverages tools External interaction External interaction External interaction May be limited to no external tool use May be limited to no external tool use Actively leverages tools Actively leverages tools Adaptability

Less adaptable; relies on training data.

Highly adaptable; refines strategy based on real-time feedback. Adaptability Adaptability Adaptability Less adaptable; relies on training data. Less adaptable; relies on training data. Highly adaptable; refines strategy based on real-time feedback. Highly adaptable; refines strategy based on real-time feedback. Problem solving

Best for straightforward, single-turn tasks.

Excels at complex, multi-step problems requiring external info and dynamic solutions Problem solving Problem solving Problem solving Best for straightforward, single-turn tasks. Best for straightforward, single-turn tasks. Excels at complex, multi-step problems requiring external info and dynamic solutions Excels at complex, multi-step problems requiring external info and dynamic solutions Feedback Loop

Generally no explicit feedback for self-correction

Explicit real-time feedback loop to refine reasoning and adjust actions Feedback Loop Feedback Loop Feedback Loop Generally no explicit feedback for self-correction Generally no explicit feedback for self-correction Explicit real-time feedback loop to refine reasoning and adjust actions Explicit real-time feedback loop to refine reasoning and adjust actions Transparency

Often a black box; hard to trace logic.

High visibility; explicit Chain-of-Thought and sequential actions show reasoning and output at each step Transparency Transparency Transparency Often a black box; hard to trace logic. Often a black box; hard to trace logic. High visibility; explicit Chain-of-Thought and sequential actions show reasoning and output at each step High visibility; explicit Chain-of-Thought and sequential actions show reasoning and output at each step Use case fit

Simple Q&A, content generation

Complex tasks: trip planning, research, multi-tool workflows Use case fit Use case fit Use case fit Simple Q&A, content generation Simple Q&A, content generation Complex tasks: trip planning, research, multi-tool workflows Complex tasks: trip planning, research, multi-tool workflows Implementation

Simple; requires AI chat integrations

Complex; requires loop logic, tool integration, and might involve a multi-agent architecture Implementation Implementation Implementation Simple; requires AI chat integrations Simple; requires AI chat integrations Complex; requires loop logic, tool integration, and might involve a multi-agent architecture Complex; requires loop logic, tool integration, and might involve a multi-agent architecture Pros and Cons 👍 Super accurate and adaptable: Thinks, acts, learns, and course-corrects on the fly. 👍 Handles gnarly problems: Excels at complex, multi-step tasks requiring external info 👍 External tool power: Integrates with useful tools and external data sources. 👍 Transparent and debuggable: See every thought and action, making debugging a breeze. 👍 Super accurate and adaptable 👍 Handles gnarly problems 👍 External tool power 👍 Transparent and debuggable 👎 Increased complexity: More moving parts means more to design and manage. 👎 Higher latency and calls: Iterative loops, external calls, and orchestration overhead can make the overall fees higher and responses slower (that's the cost to pay for more power and accuracy). 👎 Increased complexity 👎 Higher latency and calls What You Need To Master ReAct Let’s be real—without the right tools, a ReAct agent isn’t much more powerful than any other run-of-the-mill AI workflow. Tools are what turn reasoning into action. Without them, agents are just... thinking really hard. At Bright Data, we’ve seen the pain of connecting AI agents to meaningful tools. So, we’ve built an entire infrastructure to fix that. No matter how you design your agents, we’ve got them covered: Data packs: Curated, real-time, AI-ready datasets perfect for RAG workflows. 📦
MCP servers: AI-ready servers loaded with tools for data parsing, browser control, format conversion, and more. ⚙️
SERP APIs: Search APIs your LLMs can tap into for fresh, accurate web results — built for RAG pipelines. 🔎
Agent browsers: AI-controllable browsers that can scrape the web, dodge IP bans, solve CAPTCHAs, and keep going. 🕸️ Data packs: Curated, real-time, AI-ready datasets perfect for RAG workflows. 📦 Data packs MCP servers: AI-ready servers loaded with tools for data parsing, browser control, format conversion, and more. ⚙️ MCP servers MCP servers SERP APIs: Search APIs your LLMs can tap into for fresh, accurate web results — built for RAG pipelines. 🔎 SERP APIs Agent browsers: AI-controllable browsers that can scrape the web, dodge IP bans, solve CAPTCHAs, and keep going. 🕸️ Agent browsers ...And this toolstack is constantly expanding. 📈 ➡️ Take a look at what Bright Data’s AI & BI infrastructure can unlock for your next-gen agents. ➡️ Take a look at what Bright Data’s AI & BI infrastructure can unlock for your next-gen agents. Take a look at what Bright Data’s AI & BI infrastructure can unlock for your next-gen agents. Bright Data’s AI & BI infrastructure [Extra] The ReAct Cheat Sheet Before wrapping up, take a moment to clear the air. There’s a lot of buzz (and confusion) around the term "ReAct"—especially since multiple teams are using it in different contexts. So, here’s a no-fluff glossary to help you keep it all straight: “ReAct design pattern”: An AI pattern that merges reasoning and acting. An agent first thinks (like chain-of-thought reasoning), then acts (like doing a web search), and finally gives a refined answer.
“ReAct prompting”: A prompt-engineering technique that nudges LLMs to show their reasoning process step-by-step and take actions mid-thought. It’s designed to make responses more accurate, transparent, and less hallucination-prone. Learn more about ReAct prompting.
“ReAct agentic pattern”: Just another name for saying “ReAct design pattern.”
“ReAct agent”: Any AI agent that follows the ReAct loop. It reasons about the task, performs actions based on that reasoning (like calling a tool), and returns the answer.
“ReAct agent framework”: The architecture (or library) you should use to build ReAct-style agents. It helps you implement the whole "reason-act-answer" logic in your custom AI systems. “ReAct design pattern”: An AI pattern that merges reasoning and acting. An agent first thinks (like chain-of-thought reasoning), then acts (like doing a web search), and finally gives a refined answer. ReAct design pattern” thinks acts “ReAct prompting”: A prompt-engineering technique that nudges LLMs to show their reasoning process step-by-step and take actions mid-thought. It’s designed to make responses more accurate, transparent, and less hallucination-prone. Learn more about ReAct prompting. ReAct prompting” and ReAct prompting “ReAct agentic pattern”: Just another name for saying “ReAct design pattern.” ReAct agentic pattern” ReAct design pattern “ReAct agent”: Any AI agent that follows the ReAct loop. It reasons about the task, performs actions based on that reasoning (like calling a tool), and returns the answer. ReAct agent” “ReAct agent framework”: The architecture (or library) you should use to build ReAct-style agents. It helps you implement the whole "reason-act-answer" logic in your custom AI systems. ReAct agent framework Final Thoughts Now you’ve got the gist of what ReAct means in the realm of AI—especially when it comes to AI agents. You’ve seen where this design pattern came from, what it brings to the table, and how to actually implement it to power up your agentic workflows. As we explored, bringing these next-gen workflows to life becomes easier when you have the right AI infrastructure and toolchain to back your agents up. At Bright Data, our mission is simple: make AI more usable, more powerful, and more accessible to everyone, everywhere. Until next time—stay curious, stay bold, and keep building the future of AI. 🏄

Inside the ReAct Design Pattern: How Modern AI Thinks and Acts

“React” dizayn şablonunun içində: Modern AI necə düşünür və işləyir

Вътре в дизайна на ReAct: Как съвременният AI мисли и действа

Inden for ReAct Design Pattern: Hvordan moderne AI tænker og handler

Unutar ReAct dizajna: kako moderna AI razmišlja i djeluje

Nan modèl konsepsyon ReAct: Ki jan modèn AI panse ak fè

ReActデザインパターンの内部:現代のAIがどのように考え、行動するか

នៅក្នុងគំរូរចនាសម្ព័ន្ធ ReAct: តើធ្វើដូចម្តេចដែលអេឡិចត្រូនិអេឡិចត្រូនិអេឡិចត្រូនិអេឡិចត្រូនិអេឡិចត្រូនិអេឡិចត្រូនិធ្វើការនិងធ្វើការ

În interiorul modelului de proiectare ReAct: Cum gândește și acționează AI modernă

Ndani ya Mfano wa Mipango wa ReAct: Jinsi AI ya kisasa inavyofikiri na kutenda

Внутрішній шаблон дизайну ReAct: як сучасний AI думає і діє

Ngezinye I-ReAct Design Pattern: Indlela I-AI Modern Thinks and Acts

Agentic AI and Agentic RAG: Hyped Buzzwords or Game-Changers?

Agentiese AI en Agentiese RAG: Hyped Buzzwords of Game-veranderers?

Agentic AI y Agentic RAG: ¿Hiped Buzzwords o Game-Changers?

Agentički AI i Agentički RAG: Hyped Buzzwords ili Game-Changer?

Agentic AIと Agentic RAG: Hyped Buzzwords or Game-Changer?

អាជីវកម្មអាជីវកម្មអាជីវកម្មនិងអាជីវកម្មអាជីវកម្ម RAG: Buzzwords hyped ឬការផ្លាស់ប្តូរការប្រកួត?

에이전트 AI와 에이전트 RAG : Hyped Buzzwords 또는 Game-Changer?

Agentic AI un Agentic RAG: Hyped Buzzwords vai spēļu mainītāji?

Agentay AI iyo Agentay RAG: buugga buugga ama game-changers?

ஆங்கிலத்தில் இதை Single Orgasm, Multiple Orgasm என்றும் கூறுகிறார்கள்.

Agentic AI และ Agentic RAG: Hyped Buzzwords หรือ Game-Changer?

Agentic AI at Agentic RAG: Hyped Buzzwords o Game-changers?

Агентський AI і Агентський RAG: хибні бузи або ігрові змінювачі?

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