Super-rich should contribute to MAS' led efforts to develop the local-listed equities market. Read more at The Business Times.

Billionaires should do more to list their businesses on SGX. - The Business Times

Arsenal striker Alessia Russo "is like a new signing" for the club, according to former Gunners defender Gilly Flaherty. The England international has scored eight goals in 10 Women's Super League games since Renee Slegers took charge in October, finding the net again in the 5-0 win over Tottenham ...

Russo 'like a new signing' for Arsenal - Yahoo Sports

Super Micro CEO Charles Liang refused to address the company’s delinquent financials but said he expects to reach $40 billion in revenues next year.

Super Micro’s finance chief — who is leaving as soon as the company can hire someone more experienced — said delinquent financial filings were ‘a distraction’ | Fortune

Super Micro's business update comes after a turbulent year in which the company faced ongoing controversies in the wake of the Hindenburg report. The US Department of Justice reportedly launched a probe into Super Micro’s accounting practices.

Super Micro stock surges after outlining ambitious 2026 targets, assuring investors it will avoid delisting

Super Micro stock seesaws as server maker says it will meet filing deadline

Cybersecurity firm Arctic Wolf has identified matching indicators of compromise (IOCs) linked to these vulnerabilities, noting that attackers have targeted internet-exposed management interfaces on Fortinet devices since mid-November 2024.

Fortinet 0-Day in FortiOS & FortiProxy Let Attackers Hijacks Firewall to Get Super Admin Access

FortiOS & FortiProxy Already Patched 0-Day Allows Firewall Hijacks & Super Admin Access

Jordan Mailata worked in construction while trying to become a rugby league player in Australia but has now completed his unique journey to Super Bowl winner.

Super Bowl win sees Mailata complete 'greatest story in football' - Yahoo Sports

Super

super

In this article, I'll show you how to quickly setup pretty URLs in Super with a little shell command and a Node.js script. If you are reading this, you may already be aware that helps you go public with your pages.  What you may not know, is that ugly URLs are And notion's got some ugly URLs. https://super.so https://www.notion.so bad for SEO. It should come as no surprise that the easier a URL is to read for humans, the better it is for search engines - Rand Fishkin Well, color me surprised!  I've never needed to give SEO any thought before, but now it's my job to care!  It's time to accommodate the web crawlers and get our new and pages indexed. help careers So the first step to getting all your links pretty and into Super, is to get all the links off of your notion page.  In the case of the new Hacker Noon help page (http://help.hackernoon.com/), there were links to pretty up in this super form: 157 That's a lot of copy-pasting and clicking around and coming up with pretty URLs and so I decided to open up my chrome browser's network tab and see what was being sent to the server so I could just give it a single payload with all the links in it. DISCLAIMER:  Using any site a way not intended by the developers may result loss data.  Proceed at your own risk!  (maybe start a little test where it won in in of with 't hurt anything:) To to get the network tab, one needs to open the developer console.  On a mac, this is done with cmd+option+i.  You can also reach this by clicking the ellipsis/hamburger menu (three dots, in a column) button, going to More Tools and then finally Developer Console A portion of your window should be covered with a new panel and at the top of that, you should see some tabs.  One of those is called Network and will provide us with a template to use in our own payload to send to Super. You can see Network selected here with the blue underline.  If I click on the clear button 🚫 next to the red circle, it'll clear out any previous network calls so we can clearly see what is sent when we save our pretty URLs. Here we see a bunch of requests that happened, but the POST that goes to a URL called "update" looks interesting for our use case.  (You may need to right-click the columns to select "Method" to be able to show that column)  You can also use the search box with to filter to just those types of calls, if you already know what you are looking for. method:POST If you click on the method and look at the Headers tab that will show up, then scroll to the bottom of that, you will see the request payload. Open that up and see where the stuff you typed into the pretty URL window is. In this cast, there is a field called prettyUrls that is an array of objects with two fields, which ends up just getting the ugly, unique identifier portion of a notion URL, and also a field which is where it will redirect.  So now we know what we are doing, we need an array of objects with all our notion page ids and the URLs they are going to.  Upwards and onwards! pageId url Right-click that method and select if you want to write a node.js script like I did.  Maybe you're into curl or using fetch right in the browser's console. Copy > Copy as Node.js fetch Okay, so you've got this node code in our paste buffer and nowhere to put it!  It's time to make a script and paste it in there. We are going to ignore it for now though because I wrote this article a little out of order and I can't drag things around to reorder them and I'm just gonna roll with it! Let's move on to getting all the notion links off the page.   One way this can be done is by downloading the page and its links with: wget -r -l0 https://help.hackernoon.com/ This took a few minutes and may have been overkill, but when it was done I had a neat little help.hackernoon.com folder with a bunch of HTML files.  I ended up using the HTML files to come up with the pretty URL so maybe not overkill.  Here's a bit of what it looked like in that directory when I listed the files: help.hackernoon.com ls
0109b933c3084e9699b408a73b142e8a  5c115f8cb76445788bb16a4499758cd8  d1edc21b1f2d48d9afa43f4da6d4cc4f
0142ddfc63b94ef9a1ca01975c3d2cab  5c45cf10b99046939df336a23ddae597  d44cdbf1e6d94c0eab9fb5091c249e97
028466d28622424ca1523069faf1d7c6  5f54fe2e3b6641b3b1d49f7b47a961d7  db9dd7e7998141df9705628b03fcd75b
033557c93f714ae092e13e095ca9c2cf  604adc1e960840b782d7f2de2687f719  de55338768674dccbc80909d4ccea58f Cool, so all these piles of alpha-numeric characters are the notion entry to put in the left side of that form. To get the corresponding pretty URL to put on the right-hand side, I peeked inside one of the files and looked for where the header was by searching for h1 There it is with the juicy "Hacker Noon Editor 101" meat/text inside.  Now to start a script to iterate over all these files and parse and kebab-case the h1 headers. I used , which is jQuery for node.js, to do this.  This is how things started to look: the library cheerio fs = ( ); cheerio = ( ); { files = fs.promises.readdir( ); ( file files) { stat = fs.promises.stat(file); (stat.isDirectory()) ; html = fs.readFileSync(file).toString(); $ = cheerio.load(html); contents = $( ).contents()[ ]; (!contents) ; url = contents.data; .log(url);
  }
})(); const require "fs" const require "cheerio" ( )=> ( async const await "./" for const of const await if continue //we just want the html files const const const 'h1' 0 if continue const console So this script was run from inside that help.hackernoon.com so it could find the files easily with the path. Next, we used the function to help us determine if it was a file or directory, as there was one directory to skip. ./ stat _next Maybe you think the is whack, because it is synchronous? You are correct. readFileSync It doesn't matter though, this is just a one-off script to get the job done.  We call on that to be able to pass the wad of HTML text at cheerio and now we can run selectors on it to scrape it. toString() I needed the first h1 so our selector is simply that and we to get the first one, maybe it found more, if it didn't we continue going to the next file.  After that the schema cheerio gives you dictates that to get the text out you will need to ask for the to get the text.  All these get logged out and I could see if things were looking good. [0] .data By the way, one nice way to work on things like this and get quick feedback is to use another package https://nodemon.io/ which can be installed globally to use as a shell command or as a library to be used for your own file watching needs, like if you want to roll your own hot reloading framework for node development.  In this case, my script was called sendIt.js and so I could run in one split pane of my terminal and anytime I saved the file in another split, the file was re-run and I got a handy feedback loop. nodemon sendIt.js Okay, it's time to combine the node.js fetch bit with this loop that gets all the URL mappings. I'm going to plop in a gist now that should get the idea across.  The new code added to our existing code is just adding a Set and then adding our pretty URL objects to that. You may also notice that I am adjusting the pretty URL around line 22. It's there that we modify the h1 text by making it lowercase on line and removing invalid URL characters. Spaces become dashes, double dashes turn back into one dash, strips out the non-dashes and all non-alphanumeric characters.  Next, leading and trailing dashes are removed. /[^0-9a-z-]/gi This could be condensed into less replace calls, but this is readable and this code will only ever run when you want to update your pretty URLs so I am not going to sweat it. Next, around , I skip some files with dots because those were downloaded but don't need mappings, like index.js.  You could also just delete them from the folder of links that were downloaded. line 29 with the body was created by copying the string from the node.js fetch code we pasted in from the developer console into a node REPL and running on that.  Now it's some code, so we can get a chance to take our Set of prettyUrls and get them into our payload. will turn the Set into an array. Line 42 JSON.parse Array.from(prettyUrls) The rest of the fetch is unchanged, save for the body section where I it back into the original format we pasted in.  I save this fetch call as a variable so I can print it and see what any issues are.  There are always issues with the first (through at least the 10th) attempt. JSON.stringify In my case, the lines about stripping our invalid chars, having duplicate entries that were already added to the list of pretty URLs and all that were my issues that caused me to iterate a few times on this.  So there were more console.logs sprinkled throughout this code to help me identify where things went wrong. If the result of the fetch has a and the word OK, you are good. Refresh your pretty URL list and double-check.  If you get a 500 and a Bad Gateway, you probably sent an invalid character in the URL or sent a duplicate entry. status 200 So there you have it, you can use these ideas in a lot of other places than just notion and super.  Just be careful you try out ideas on something else first that you are not worried about screwing up if you send the wrong payload.  The saying "Measure twice, cut once" comes to mind. Anywho, I hope this was helpful.  You can tell me your thoughts about it on Twitter if you really want.  I primarily wrote this to help me get over my writer's block and just publish .  ✌️ something

How to Clean Up Notion URLs: From Ugly URLs to Pretty URLs in Minutes

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2 MindEye2 and 2.1 Shared-Subject Functional Alignment 2.2 Backbone, Diffusion Prior, & Submodules 2.2 Backbone, Diffusion Prior, & Submodules 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.3 Image Captioning and 2.4 Fine-tuning Stable Diffusion XL for unCLIP 2.5 Model Inference 2.5 Model Inference 3 Results and 3.1 fMRI-to-Image Reconstruction 3 Results and 3.1 fMRI-to-Image Reconstruction 3.2 Image Captioning 3.2 Image Captioning 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.3 Image/Brain Retrieval and 3.4 Brain Correlation 3.5 Ablations 3.5 Ablations 4 Related Work 4 Related Work 5 Conclusion 5 Conclusion 6 Acknowledgements and References 6 Acknowledgements and References A Appendix A Appendix A.1 Author Contributions A.1 Author Contributions A.2 Additional Dataset Information A.2 Additional Dataset Information A.3 MindEye2 (not pretrained) vs. MindEye1 A.3 MindEye2 (not pretrained) vs. MindEye1 A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.4 Reconstruction Evaluations Across Varying Amounts of Training Data A.5 Single-Subject Evaluations A.5 Single-Subject Evaluations A.6 UnCLIP Evaluation A.6 UnCLIP Evaluation A.7 OpenCLIP BigG to CLIP L Conversion A.7 OpenCLIP BigG to CLIP L Conversion A.8 COCO Retrieval A.8 COCO Retrieval A.9 Reconstruction Evaluations: Additional Information A.9 Reconstruction Evaluations: Additional Information A.10 Pretraining with Less Subjects A.10 Pretraining with Less Subjects A.11 UMAP Dimensionality Reduction A.11 UMAP Dimensionality Reduction A.12 ROI-Optimized Stimuli A.12 ROI-Optimized Stimuli A.13 Human Preference Experiments A.13 Human Preference Experiments A.13 Human Preference Experiments We conducted two-alternative forced-choice experiments on 58 human raters online. We probed three comparisons intermixed into the same behavioral experiment, with each comparison consisting of 1200 trials sampled evenly from the 1000 NSD test samples across the 4 subjects who completed all 40 scanning sessions (subjects 1, 2, 5, 7). The total 3600 experimental trials were shuffled and 87 trials were presented to each subject. Our subjects were recruited through the Prolific platform, with our experimental tasks hosted on Meadows. All other experiment details follow the protocol used in Kneeland et al. (2023c). A.13.1 MINDEYE2 VS. MINDEYE2 (RANDOM) The first comparison was a two-way identification task in which participants were asked to select which of two images was more similar to a ground truth image. The two images provided for comparison were both reconstructions from MindEye2 (40-hour), one being a randomly selected reconstruction from the test set and the other being the correct, corresponding reconstruction. Raters correctly identified the corresponding reconstruction 97.82% of the time (p < 0.001). This establishes a new SOTA for human-rated image identification accuracy, as the only other papers to perform such an experiment were the original method proposed by (Takagi and Nishimoto, 2022), whose method achieved 84.29%, and the MindEye1 + BOI (brain-optimized inference) method proposed by (Kneeland et al., 2023c), whose enhancement to the MindEye1 method achieved 95.62%. The method in (Takagi and Nishimoto, 2022) is different from the "+Decoded Text" method we compare against in Table 1, which was released in a later technical report (Takagi and Nishimoto, 2023), and which does not report human subjective evaluations. A.13.2 MINDEYE2 (REFINED) VS. MINDEYE2 (UNREFINED) The second comparison was the same task as the first but this time comparing refined MindEye2 (40-hour) reconstructions against unrefined MindEye2 reconstructions (both correctly corresponding to the appropriate fMRI activity). This comparison was designed to empirically confirm the subjective improvements in naturalistic quality provided by MindEye2’s refinement step. This is particularly important to confirm because the quantitative evaluation metrics displayed in Table 4 sometimes preferred the unrefined reconstructions. Refined reconstructions were rated as more similar to the ground truth images 71.94% of the time (p < 0.001), demonstrating that the final refinement step improves reconstruction quality and accuracy when assessed by humans. A.13.3 MINDEYE2 (1-HOUR) VS. BRAIN DIFFUSER (1-HOUR) The final comparison was likewise the same task but this time comparing reconstructions from MindEye2 against reconstructions from the Brain Diffuser method (Ozcelik and VanRullen, 2023), where both methods were trained using only the first hour of scanning data from the 4 NSD subjects. This experiment demonstrated that the MindEye2 reconstructions were preferred 53.01% of the time (p = 0.044), demonstrating a statistically significant improvement in scaling performance compared to the previous state-of-the-art model for reconstructions using only 1 hour of training data. This confirms the results in the main text that MindEye2 achieves SOTA in the low-sample 1-hour setting. We visualized cases where Brain Diffuser (1-hour) was preferred over MindEye2 (1-hour) in Appendix Figure 12. We observed that Brain Diffuser reconstructions were often preferred in situations where both MindEye2 and Brain Diffuser reconstructions were low quality, but MindEye2 reconstructions were "confidently" wrong (in the sense that MindEye2 reconstructions enforce a naturalistic prior from the refinement step) whereas Brain Diffuser reconstructions were producing distorted outputs that contained subtle elements corresponding to the target image. This may indicate human raters prefer distorted outputs with recognizable features, and disfavored the model that enforces a naturalistic prior, and may lose these features. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC);
(2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution;
(3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution;
(4) Reese Kneeland, University of Minnesota and a Core contribution;
(5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC);
(6) Ashutosh Narang, Medical AI Research Center (MedARC);
(7) Charan Santhirasegaran, Medical AI Research Center (MedARC);
(8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC);
(9) Thomas Naselaris, University of Minnesota;
(10) Kenneth A. Norman, Princeton Neuroscience Institute;
(11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC). Authors: Authors: (1) Paul S. Scotti, Stability AI and Medical AI Research Center (MedARC); (2) Mihir Tripathy, Medical AI Research Center (MedARC) and a Core contribution; (3) Cesar Kadir Torrico Villanueva, Medical AI Research Center (MedARC) and a Core contribution; (4) Reese Kneeland, University of Minnesota and a Core contribution; (5) Tong Chen, The University of Sydney and Medical AI Research Center (MedARC); (6) Ashutosh Narang, Medical AI Research Center (MedARC); (7) Charan Santhirasegaran, Medical AI Research Center (MedARC); (8) Jonathan Xu, University of Waterloo and Medical AI Research Center (MedARC); (9) Thomas Naselaris, University of Minnesota; (10) Kenneth A. Norman, Princeton Neuroscience Institute; (11) Tanishq Mathew Abraham, Stability AI and Medical AI Research Center (MedARC).

Human Evaluation Validates MindEye2's Superior Image Reconstruction Quality

Reimagining Drug Discovery With Autonomous Intelligence In early 2020, Pfizer and BioNTech delivered what many thought was impossible: a viable COVID-19 vaccine developed and distributed in record time. It was a monumental achievement in modern medicine. But as someone working closely with AI agents in complex, high-stakes systems, I often wonder if that process could’ve been even faster. This is not about rewriting history. It asks: How can we prepare smarter for the future? Could the AI agent systems we’re building today have accelerated the race for a vaccine? I believe the answer is yes. How can we prepare smarter for the future? 🧠 What Are AI Agents? In plain terms, AI agents are systems that can reason, act, and learn over time, often working autonomously, with a clear sense of memory, goals, and feedback. Unlike traditional software, they aren’t just passive tools. They’re active collaborators. We use them in software engineering to automate code reviews, triage bugs, and optimize performance. However, the same framework can be extended beyond code into healthcare, drug discovery, and clinical trials. 💉 The Vaccine Pipeline Agent by Agent Let’s take a step back and reimagine the COVID-19 vaccine development process as powered by a team of AI agents. Each one plays a unique role: 1. Trial Designer Agent Takes in epidemiological data, past trial structures, and mutation forecasts to draft optimized protocols in hours, not weeks. It adapts trial arms based on real-time inputs from the field. 2. Literature Scout Agent Scans tens of thousands of scientific papers and updates researchers daily. Instead of manually reviewing papers, scientists would get distilled, tailored insights synthesized from the latest studies. 3. Adverse Signal Agent Predicts likely side effects before they appear. It learns from global vaccine databases, patient records, and similar case studies to flag early risks and adapt trial criteria. 4. Regulatory Agent It understands FDA, EMA, and WHO documentation requirements. It formats outputs automatically to ensure smoother submissions, with proper justifications and auditability built in. Each agent can run 24/7, adapt based on feedback, and collaborate with others through a central reasoning layer. Think of it as a tireless digital task force working in sync with real scientists. ⚡ What Would This Have Changed? Phase

Traditional Timeline

With AI Agents

Benefit



Protocol Design

2–3 weeks

~1 day

Faster iteration



Literature Integration

Manual & slow

Continuous synthesis

No missed insights



Risk Modeling

Post-trial analysis

Pre-trial + real-time

Proactive risk mitigation



Regulatory Formatting

Reactive

Embedded from start

Faster approval and delivery Phase

Traditional Timeline

With AI Agents

Benefit



Protocol Design

2–3 weeks

~1 day

Faster iteration



Literature Integration

Manual & slow

Continuous synthesis

No missed insights



Risk Modeling

Post-trial analysis

Pre-trial + real-time

Proactive risk mitigation



Regulatory Formatting

Reactive

Embedded from start

Faster approval and delivery Phase

Traditional Timeline

With AI Agents

Benefit Phase Phase Traditional Timeline Traditional Timeline With AI Agents With AI Agents Benefit Benefit Protocol Design

2–3 weeks

~1 day

Faster iteration Protocol Design Protocol Design 2–3 weeks 2–3 weeks ~1 day ~1 day Faster iteration Faster iteration Literature Integration

Manual & slow

Continuous synthesis

No missed insights Literature Integration Literature Integration Manual & slow Manual & slow Continuous synthesis Continuous synthesis No missed insights No missed insights Risk Modeling

Post-trial analysis

Pre-trial + real-time

Proactive risk mitigation Risk Modeling Risk Modeling Post-trial analysis Post-trial analysis Pre-trial + real-time Pre-trial + real-time Proactive risk mitigation Proactive risk mitigation Regulatory Formatting

Reactive

Embedded from start

Faster approval and delivery Regulatory Formatting Regulatory Formatting Reactive Reactive Embedded from start Embedded from start Faster approval and delivery Faster approval and delivery This doesn’t mean replacing people. It means freeing them from the repetitive and time-intensive parts of the job, allowing them to focus on judgment, innovation, and ethics. 🚀 Beyond COVID: A New Model for R&D We’re talking about a new category: CRaaS Clinical R&D as a Service. These AI agents could be deployed securely across research teams, hospitals, and pharma companies, sharing knowledge in real time while respecting data boundaries. CRaaS Federated learning, explainability, and memory-enhanced LLMs would form the backbone. What email was for communication? These AI agents could be used for discovery. 👋 Final Thoughts Pfizer set a historic precedent. But we’re now entering an age where speed alone isn’t enough; we need smart speed. With the right agent-based systems in place, the future of drug discovery could be safer, faster, and far more collaborative. I’ve seen the impact of AI agents in software development firsthand. There’s no reason we can’t apply the same logic, architecture, and intelligence to solving healthcare’s biggest challenges. If you’re working on this problem, I’d love to collaborate. Written by Tanush Sharanarthi, a staff software engineer at IBM who builds real-time multi-agent systems for intelligent developer tools and enterprise platforms. Passionate about using AI to solve problems that matter. Written by Tanush Sharanarthi, a staff software engineer at IBM who builds real-time multi-agent systems for intelligent developer tools and enterprise platforms. Passionate about using AI to solve problems that matter.

How AI Agents Could Have Supercharged Pfizer’s COVID-19 Vaccine Development

Luxembourg, Luxembourg, April 9th, 2025/CyberNewsWire/--Gcore, the global edge AI, cloud, network, and security solutions provider, has launched Super Transit, a cutting-edge DDoS protection and acceleration feature, designed to safeguard enterprise infrastructure while delivering lightning-fast connectivity. This comes as organizations face a 56% year-on-year increase in high-volume, complex DDoS attacks that disrupt operations, increase latency, and compromise network security. Traditional solutions often require expensive, complex integrations, but Super Transit is delivered as part of the Gcore DDoS Protection Suite. Using the Gcore global network of 180+ points of presence (PoP), traffic is automatically routed through the Gcore Anycast backbone, and any malicious traffic is intelligently split from legitimate traffic in real-time. This proactive detection, filtering, and mitigation of DDoS attacks means an uninterrupted user experience, so even during an attack, users remain unaffected and continue to experience stable, secure, high-speed connectivity. Gcore Super Transit, which is available to all customers now, optimizes performance and security through the following key differentiators: Real-time DDoS threat mitigation: Detects and blocks malicious traffic in real-time at the nearest Gcore PoP, before it can reach user networks – minimizing impact on performance.
Global-scale protection: Comprehensive defense against DDoS attacks of any size, backed by a total filtering network capacity exceeding 200 Tbps.
Consistent performance: Traffic is filtered at the edge with legitimate traffic routed via the optimal path within Gcore’s high-performance backbone, eliminating unnecessary rerouting to external scrubbing centers and reducing latency.
Cost-effective protection: Optimizes security spending with flexible, integrated deployment options that balance performance and affordability. Andrey Slastenov, Head of Security at Gcore, commented: Real-time DDoS threat mitigation: Detects and blocks malicious traffic in real-time at the nearest Gcore PoP, before it can reach user networks – minimizing impact on performance. Global-scale protection: Comprehensive defense against DDoS attacks of any size, backed by a total filtering network capacity exceeding 200 Tbps. Consistent performance: Traffic is filtered at the edge with legitimate traffic routed via the optimal path within Gcore’s high-performance backbone, eliminating unnecessary rerouting to external scrubbing centers and reducing latency. Cost-effective protection: Optimizes security spending with flexible, integrated deployment options that balance performance and affordability. Andrey Slastenov, Head of Security at Gcore, commented: “This is a hugely important launch for many reasons. Primarily, Super Transit allows for fast, worldwide access to our DDoS protection services, with real-time intelligent data analysis and filtering ensuring legitimate data is routed to servers efficiently. These are crucial capabilities as advanced DDoS protection for real-time services mitigates latency and outages that significantly impact user experience and enterprises’ business success.” “This is a hugely important launch for many reasons. Primarily, Super Transit allows for fast, worldwide access to our DDoS protection services, with real-time intelligent data analysis and filtering ensuring legitimate data is routed to servers efficiently. These are crucial capabilities as advanced DDoS protection for real-time services mitigates latency and outages that significantly impact user experience and enterprises’ business success.” About Gcore Gcore is a global edge AI, cloud, network, and security solutions provider. Headquartered in Luxembourg, with a team of 600 operating from ten offices worldwide, Gcore provides solutions to global leaders in numerous industries. Gcore manages its global IT infrastructure across six continents, with one of the best network performances in Europe, Africa, and LATAM due to the average response time of 30 ms worldwide. Gcore’s network consists of 180 points of presence worldwide in reliable Tier IV and Tier III data centers, with a total network capacity exceeding 200 Tbps. Users can learn more at gcore.com or follow them on LinkedIn, Twitter, and Facebook. gcore.com Contact Gcore PR team gcore.com pr@gcore.com gcore.com pr@gcore.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program __her__e This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program __her__e her

Gcore Super Transit Brings Advanced DDoS Protection And Acceleration For Enterprise Security & Speed

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Gcore Super Transit ofrece protección y aceleración DDoS avanzadas para la seguridad y velocidad empresarial.

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Gcore Super Transit حفاظت و شتاب DDoS پیشرفته را برای امنیت و سرعت سازمانی به ارمغان می آورد

Gcore Super Transit trae protección DDoS avanzada e aceleración para seguridade e velocidade empresarial

Gcore Super Transit Hadirkan Perlindungan dan Akselerasi DDoS Canggih untuk Keamanan dan Kecepatan Perusahaan

Gcore Super Transitは、企業のセキュリティとスピードのために高度なDDoS防御と高速化を実現します。

Gcore Super Transit ມີການປົກປັກຮັກສາ DDoS ອັດຕະໂນມັດແລະ acceleration ສໍາລັບຄວາມປອດໄພແລະຄວາມໄວອຸດສາຫະກໍາ

Gcore Super Transit носи напредна DDoS заштита и забрзување за безбедноста и брзината на претпријатието

Gcore Super Transit brengt geavanceerde DDoS-bescherming en versnelling voor Enterprise Security & Speed

Gcore Super Transit zapewnia zaawansowaną ochronę DDoS i przyspieszenie dla bezpieczeństwa i szybkości w przedsiębiorstwie

Gcore Super Transit муҳофизати пешрафтаи DDoS ва суръатбахширо барои амният ва суръати корхона меорад

**Singapore, Singapore, April 8th, 2025/Chainwire/--**ZKcandy, an AI-driven gaming and entertainment Layer-2 powered by ZKsync and iCandy Interactive, launches its mainnet chain today. The announcement comes after the successful testnet phase and a $4 million seed round funded by Wemix Pte. Ltd., Animoca Ventures, and renowned institutional and angel investors. With a strong backing behind it, ZKcandy aims to rethink Web3 game development. ZKcandy focuses on building immersive, interoperable gaming ecosystems where users own their assets and enjoy their unrestrained movement across different titles. ZKcandy uses the best of AI to breathe life into NPCs, create dynamic, unscripted storylines, and provide an individualized experience to each player. The platform focuses on mobile-first Web3 games, competing in a rapidly growing global mobile gaming market. During the open testnet phase, users created 2.46 million wallets on ZKcandy. Five games featured on the platform amassed 230,000 active users. This makes it one of the most well-received ZK Chains in ZKsync’s Elastic Network ecosystem. To achieve this, ZKcandy collaborated with names like Layer3, Aethir, Rarible, and Out Of The Nest, onboarding 19 partners in total to grow the project’s ecosystem. Immediately after the mainnet launch, gamers will have an opportunity to try eight Web3 games, including Pepe Kingdom, Candy Defense, and Sugar Rush. To bring more to the table, ZKcandy is partnering with game studios to co-develop Web3 games based on Hollywood film IPs. The first Tier-1 Hollywood IP game is already in development. ZKcandy is looking to expand to 2 million active ecosystem users in 6 months after the platform’s public launch. The global mobile gaming market is one of the fastest-growing industries, with its revenue projected to reach $126.1 billion in 2025. Combining iCandy’s expertise of 500+ successfully launched titles across multiple decades with low fees and account abstraction of ZKsync, ZKcandy is uniquely positioned to become a mobile-first Web3 gaming hub. Kin Wai Lau, CEO of ZKcandy, commented on the event: “We are proud to make ZKcandy available to the general audience. Our testnet has been a huge success, and this is only the beginning. With the $4 million private funding closed in January, we are looking to expand our partnership network by bringing large IPs and AAA mobile games to Web3. Blockchain gaming must be open and accessible to everyone, and it is our goal to ensure that all gamers can enjoy the transparent, secure, and low-friction experience we have to offer. Connecting mobile gaming and Web3 is a solution long overdue, and we are privileged to have the right team to do it.” Kin Wai Lau, CEO of ZKcandy, commented on the event: “We are proud to make ZKcandy available to the general audience. Our testnet has been a huge success, and this is only the beginning. With the $4 million private funding closed in January, we are looking to expand our partnership network by bringing large IPs and AAA mobile games to Web3. Blockchain gaming must be open and accessible to everyone, and it is our goal to ensure that all gamers can enjoy the transparent, secure, and low-friction experience we have to offer. Connecting mobile gaming and Web3 is a solution long overdue, and we are privileged to have the right team to do it.” ZKcandy was created in partnership with iCandy Interactive and Matter Labs. iCandy Interactive is the largest game developer in Southeast Asia and has worked on 500+ mobile games and world-class award-winning AAA titles such as The Last of Us, Spiderman, Starcraft Remastered, Gears of Wars, and a long list of others. ZKcandy ZKcandy iCandy Interactive iCandy Interactive Matter Labs is a contributor to ZKsync, a blockchain technology using zero-knowledge proofs and uniting ZK Chains into an elastic modular network. Offering frictionless interoperability, ZKsync has attracted more than $1.5B in Total Value Locked. ZKcandy is powered by ZKsync — an Ethereum L2 ecosystem that advances the mass adoption of crypto through ZK-proof technologies. Matter Labs Matter Labs ZKsync ZKsync With ZKsync’s tech stack, ZKcandy builds an ecosystem of interoperable games with fast transactions, negligible fees, and top-notch account abstraction features. ZKcandy is a ZK Chain in the Elastic Network, ZKsync’s ecosystem of natively interoperable L2s, so it can easily communicate with other chains, ensuring unrestrained liquidity flows. Other ZK Chain operators include Xsolla, Cronos (Crypto.com), Abstract, and Sophon. Chain Chain To encourage ecosystem growth after the mainnet deployment, ZKcandy will be launching promotional campaigns on the Layer3 launchpad. The platform has allocated 5% of the total native token supply as a monetary stimulus for the participants. Onboarding developers will also be able to submit applications for the upcoming ZKcandy grant. About ZKcandy ZKcandy is an AI-powered Layer-2 aiming to redefine what’s possible in gaming and entertainment. As a ZK Chain built on ZKsync’s Elastic Network, it focuses on building immersive gaming ecosystems where players make the most of today’s AI capabilities. Unscripted storylines, dynamic NPCs, and a deeply personalized experience. ZKcandy delivers mobile-first Web3 games, tapping into the fastest-growing global mobile gaming market. ZKcandy ZKcandy ZKcandy is backed by iCandy Interactive, Southeast Asia’s largest game studio, with 500+ games, including 180 AAA titles, downloaded 300M+ times. The platform is powered by ZKsync technology, where simplified onboarding and negligible fees lay the ground for mass adoption. $4M+ in funding and strategic partnerships (WEMIX, Animoca Brands, Spartan Group, and others) set ZKcandy to push the boundaries in Web3 entertainment, inspired by the AI revolution. More information can be found here: ZKcandy Website | Twitter | Discord | Telegram ZKcandy Website ZKcandy Website Twitter Twitter Discord Discord Telegram Telegram Contact ZKcandy Media Relations media@zkcandy.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

ZKcandy Supercharges Web3 Mobile Gaming With L2 Mainnet Launch

ZKcandy تعزز ألعاب Web3 المحمولة بإطلاق L2 Mainnet

ZKcandy potencia los juegos móviles Web3 con el lanzamiento de la red principal L2

ZKcandy Supercharges Web3 Mobile Gaming עם השקת L2 Mainnet

ZKcandy Supercharges Web3 Mobile Gaming with L2 Mainnet

"ZKcandy Supercharges Web3 Mobile Gaming" su L2 Mainnet paleidimu

ZKcandy dia manefa ny lalao finday Web3 miaraka amin'ny fandefasana L2 Mainnet

ZKcandy нь L2 үндсэн сүлжээний нээлтийн тусламжтайгаар Web3 гар утасны тоглоомыг цэнэглэдэг

ZKcandy Web3 Móvil Juegos L2 Mainnet Lanzamientowan Supercarga

ZKcandy выводит мобильные игры Web3 на новый уровень с запуском L2 Mainnet

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ZKcandy L2 Mainnet வெளியீட்டுடன் Web3 மொபைல் கேமிங்கை சூப்பர்சார்ஜ் செய்கிறது

ZKcandy Supercharges Web3 Mobile Gaming Gamit ang L2 Mainnet Launch

Authors:
(1) Jinyu Cai, Waseda University (bluelink@toki.waseda.jp);
(2) Jialong Li, Waseda University (lijialong@fuji.waseda.jp);
(3) Mingyue Zhang, Southwest University (myzhangswu@swu.edu.cn);
(4) Munan Li, Dalian Maritime University (limunan@dlmu.edu.cn);
(5) Chen-Shu Wang, National Taipei University of Technology (wangcs@ntut.edu.tw);
(6) Kenji Tei, Tokyo Institute of Technology (tei@c.titech.ac.jp). Authors: Authors: (1) Jinyu Cai, Waseda University (bluelink@toki.waseda.jp); (2) Jialong Li, Waseda University (lijialong@fuji.waseda.jp); (3) Mingyue Zhang, Southwest University (myzhangswu@swu.edu.cn); (4) Munan Li, Dalian Maritime University (limunan@dlmu.edu.cn); (5) Chen-Shu Wang, National Taipei University of Technology (wangcs@ntut.edu.tw); (6) Kenji Tei, Tokyo Institute of Technology (tei@c.titech.ac.jp). Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Background and Related Work II. Background and Related Work III. Framework Design III. Framework Design IV. Evaluation IV. Evaluation V. Conclusion and Future Work, Acknowledgement, and References V. Conclusion and Future Work, Acknowledgement, and References III. FRAMEWORK DESIGN A. Overview A. Overview In this section, we offer a detailed overview of our system, as depicted in Figure 1. This figure provides a visual representation of our framework, highlighting its key components and their interrelationships. Our system is primarily composed of two types of agents: the Supervisor, tasked with enforcing established guidelines, and the Participant, whose goal is to convey specific, human-defined information discreetly. Participants must dynamically refine their communication approaches, drawing from past dialogues, to transmit information effectively while remaining undetected. In the entire system, the actions of both participants and the supervisor are driven by the LLM. Initially, we establish the foundational information for each agent, including role setting, background knowledge, and primary tasks. Subsequently, the participant agents engage in dialogues with each other. After each dialogue turn, the supervisory agent reviews the conversation to determine if any pre-set rules have been violated. In cases of rule violation, the supervisor interrupts the dialogue, providing feedback about the infringing text and the rationale behind it. Throughout this process, the dialogues between participants, along with the supervisory feedback on violations, are recorded separately in the “Dialogue History” and “Violation Log.” Before new dialogues, participant agents use the Reflection Module to develop or refine “Regulations” from the Violation Log, guiding their dialogue creation. Successful dialogues without detection proceed to an interview phase for perspective assessment. The Reflection Module then reevaluates these insights, generating or enhancing “Guidance” for future dialogues. The Planning Module activates for more direct dialogue content guidance whenever Regulations or Guidance are updated. B. Participant Agents B. Participant Agents Participant agents in our system are composed of several modules, including Memory, Dialogue, Reflection, and Summary, all powered by LLMs. To increase the system’s flexibility and minimize redundancy, we’ve structured the prompts for each module around seven primary elements: “Background Information,” “Dialogue History,” “Violation Log,” “Regulations,” “Guidance,” “Plan,” and “Instructions.” “Background Information” delivers essential data and objectives pertinent to the experimental setup. The Memory module manages “Dialogue History” and “Violation Log,” which respectively track participant dialogues and instances of detection by the supervisor. Overcoming the challenge of effectively communicating regulated topics under supervision tests the linguistic prowess of LLMs. To address this, we’ve integrated “Regulations,” “Guidance,” and “Plan” as crucial components, formulated by the Reflection and Summary modules, to assist agents in stealthily disseminating information. “Instructions” set specific tasks for the LLM within each module. 1) Dialogue Module: This module generates dialogue content based on short-term dialogue records. Extracting historical dialogue information from the Memory module, it inputs this into the LLM to understand and construct sentences that evade supervision while effectively transmitting information. In the Dialogue module’s prompt, “Background Information” provides necessary background, while “Plan” guide the achievement of objectives. “Instructions” offer LLM-specific execution directives. 1) Dialogue Module: 2) Memory Module: The Memory module stores all dialogue history and records detected by the supervisor, Specifically, it comprises three parts: background Information, dialogue history, and violation records. The background information includes role settings, experimental background knowledge, and global objectives. “Dialogue History” and “Violation Log” save dialogue records and past detections by the Supervisor, respectively. “Background information” and “Dialogue History” serves as short-term [36] memory, containing only the current round’s dialogue. “Violation Log,” as long-term memory, records violations from each evolutionary round. When dialogues are detected by the Supervisor, relevant feedback is added to the “Violation Log,” triggering a new evolutionary process. 2) Memory Module: 2) Memory Module: Excessive memory information can potentially distract the LLM and lead to a decline in performance. Hence, the “Dialogue History” and “Violation Log” in the memory module are regularly maintained. This involves inputting earlier memories and employing the LLM to distill crucial information from these records, effectively compressing and consolidating them. This approach not only preserves essential historical data but also optimizes the LLM’s performance, striking a balance between comprehensive memory retention and efficient processing. 3) Reflection Module: The Reflection Module is activated at the beginning and end of each dialogue evolution cycle, with its core purpose being the generation of improved strategies based on historical records. At the start of an evolution cycle, the module utilizes the “Violation Log” as its input to analyze past failures and, based on these insights, formulates “Regulations” aimed at effectively circumventing supervision in future dialogues. At the cycle’s end, the Reflection Module is reactivated, turning its focus to the “Dialogue History”. This step is crucial for reviewing and assessing the dialogue content to verify the successful completion of the primary task of information conveyance. If the module identifies deficiencies in information delivery or objectives not met, it then proposes “Guidance” for subsequent dialogues, thereby enhancing the agent’s capacity for information transmission. This design establishes the Reflection Module as a key self-evaluation and strategy adjustment mechanism within the system, ensuring continuous improvement and adaptability of the dialogue system in a dynamically changing regulatory environment. 3) Reflection Module: 3) Reflection Module: To fulfill these requirements, the LLM must possess a high degree of reasoning ability, capable of inferring the supervisor’s criteria from failure records and identifying communication deficiencies from dialogue history, thereby formulating appropriate strategies and improvement suggestions. To enhance the LLM’s reasoning capabilities, the design of the prompt incorporates the effective and cost-efficient Chain of Thought [37] (COT) method as the guiding principle for the reflection and planning modules. This approach guides the LLM in conducting criterion analysis and, in combination with the main task, generating “Regulations” and “Guidance”. Specifically, within the Reflection Module, the prompt includes “Background Information,” “Dialogue History” or “Violation Log,” “Old Guidance” or ”Old Regulations,” and “Instructions.” The “Instructions” first prompt the LLM to engage in preliminary thinking, for instance: “Please infer what kind of guidelines the Supervisor is following from the Violation Log.” This is followed by a conclusion question: “Based on this information, update existing regulations to better avoid supervision.” The content returned by the LLM will serve as the new regulation for the next round of dialogue. 4) Planning Module: The facet of “Regulations” is centered on devising strategies that sidestep supervision, whereas “Guidance” is all about the effective relay of information. Although both are pivotal in managing dialogues, they might clash in certain supervised scenarios, thereby impacting the language’s overall proficiency and efficiency [38]. In light of this, integrating the Planning Module becomes indispensable. 4) Planning Module: 4) Planning Module: Within the Planning Module, the prompt includes “Background Information,” “Regulations,” “Guidance,” and “Instructions”. In the “Instructions,” we mandate the LLM to use “Regulations” as a baseline. Building upon this foundation, it is guided by the “Guidance” to formulate a tangible and actionable “Plan.” This approach ensures the delivery of clear and effective strategies for future dialogues. C. Supervisory Agent C. Supervisory Agent The role of the Supervisor is designed as an abstract concept, primarily to simulate social network supervision. This design reflects an important observation: in social networks, content supervision often focuses on direct judgment of specific information, rarely influenced by extensive or lengthy contextual information. Thus, unlike participants with more complex background information, the Supervisor’s function and role are simplified here to highlight its behavior characteristics in actual social network supervision. The Supervisor’s task mainly focuses on assessing whether content complies with certain standards or rules, a relatively straightforward and well-defined process. To mimic the existing review mechanisms of platforms, which typically combine keyword filters with “human” oversight, the Supervisor initially employs keyword filtering for a preliminary review of the dialogue content. Content that passes this initial screening is then subjected to further evaluation by the LLM. The prompt for the Supervisor includes just two components: “Dialogue History” and “Instructions.” “Dialogue History“ comprises the content of the dialogue exchanged between participant agents in that particular round, and “Instructions“ outline the criteria and guidelines that the v D. Similarities and Differences between Our Framework and Evolutionary Computing D. Similarities and Differences between Our Framework and Evolutionary Computing It should be noted that the simulation framework proposed in this paper is similar to evolutionary computing in some aspects, but there are also significant differences. The similarities include: (i) In evolutionary computing, individuals need to adapt to environmental pressures for survival and reproduction. Similarly, participants in this framework need to adapt to supervisory pressures and adjust their strategies for effective information transmission; (ii) The Reflection and Summary modules generate a “new generation” by analyzing past dialogues and violation records (i.e., records of lowfitness individuals), similar to the repeated iteration process in evolutionary computing; (iii) Since the generation of LLMs inherently involves randomness, the process of using LLMs to generate the next generation includes a de facto introduction of random mutations; (iv) In the Reflection and Memory modules, we prioritize past records, akin to the “selection” process, where individuals with higher fitness have greater weight in the generation of the new generation. The main differences stem from the particularities of “language expression”, making it infeasible to directly apply traditional evolutionary computing algorithms (such as genetic algorithms and genetic programming). They are: (i) The generation strategy of language text is difficult to encode and to perform operations of natural selection, genetic mutation, and crossover; (ii) Evolutionary computing often aims at finding the optimal solution for a specific problem environment, however, in the problem setting of this paper, it is difficult to define an explicit fitness function to evaluate what strategy is “optimal”. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Your Social Media Supervisor Could Soon Be an AI, And It's Getting Smarter

Hey Hackers! Welcome back to another edition of Company of the Week! We like to share an awesome tech company from our tech company database every week, making its evergreen mark on the internet. Company of the Week tech company database This week, we’re excited to showcase Superlinked - A Python framework & cloud infrastructure for AI engineers. Superlinked Being an AI engineer can be challenging, but Superlinked is here to alleviate some of that struggle. It can help you find behavioral clusters of your customers, build an e-commerce product feed, and combine semantic relevance in your search system. Superlinked can help you and your company every step of the way. Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon! Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon! Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Want to be featured on HackerNoon’s Company of the Week? Request Your Tech Company Page on HackerNoon Meet Superlinked: Fun Facts! Superlinked has worked with a who’s who of tech companies that includes MongoDB, BrandAlley, and Redis. With their powers combined, Superlinked and MongoDB have made building and deploying GenAI-powered applications easier than ever. Superlinked and MongoDB Greg Maxson, Global Lead, AI GTM at MongoDB, said this about the two teams’ partnership: MongoDB’s partnership with Superlinked aims to make it easier for customers to create and maintain entity-level and sub-entity-level vector embeddings for enterprise retrieval augmented generation and other use cases, including analytics or more standard semantic search and recommendation systems. MongoDB’s partnership with Superlinked aims to make it easier for customers to create and maintain entity-level and sub-entity-level vector embeddings for enterprise retrieval augmented generation and other use cases, including analytics or more standard semantic search and recommendation systems. MongoDB’s partnership with Superlinked aims to make it easier for customers to create and maintain entity-level and sub-entity-level vector embeddings for enterprise retrieval augmented generation and other use cases, including analytics or more standard semantic search and recommendation systems. Superlinked also worked with BrandAlley to improve their conversion rate, bumping it by 77%. Ricky Thomas, CTO of BrandAlley, had this to say about Superlinked: say about Superlinked From pilot to production launch of real-time personalization across our shopper experiences, the Superlinked team made sure their platform gave our engineers & product high quality results, lifting our CTR by 77% & AOV by 68%. From pilot to production launch of real-time personalization across our shopper experiences, the Superlinked team made sure their platform gave our engineers & product high quality results, lifting our CTR by 77% & AOV by 68%. From pilot to production launch of real-time personalization across our shopper experiences, the Superlinked team made sure their platform gave our engineers & product high quality results, lifting our CTR by 77% & AOV by 68%. But Superlinked wasn’t done yet because they also worked with Redis to help teams develop and launch GenAI applications. develop and launch GenAI applications Speaking about their partnership, Ash Vijayakanthan, SVP of Cloud Sales and Partnerships at Redis, said this: said this Redis is partnering with Superlinked to make vector search easier to apply to complex data for use cases like RAG with LLMs and e-commerce recommendation systems. Redis is partnering with Superlinked to make vector search easier to apply to complex data for use cases like RAG with LLMs and e-commerce recommendation systems. Redis is partnering with Superlinked to make vector search easier to apply to complex data for use cases like RAG with LLMs and e-commerce recommendation systems. Share Your Company's Story Via HackerNoon Share Your Company's Story Via HackerNoon Share Your Company's Story Via HackerNoon Share Your Company's Story Via HackerNoon Share Your Company's Story Via HackerNoon Superlinked 🤝HackerNoon Business Blogging Superlinked is an open source framework for building search, recommendation, and analytics solutions on top of complex structured+unstructured data. They have partnered with HackerNoon through our Business Blogging Program, and contributed insightful articles on multi-attribute search (featuring Dungeons and Dragons character creation as a use case) and movie recommender systems. Superlinked Business Blogging Program Business Blogging Program multi-attribute search movie recommender systems By improving the way users search for items and making recommendations smarter, e-commerce companies can boost their customer retention and overall conversion rates. What's Business Blogging? What's Business Blogging? HackerNoon’s Business Blogging Program is one of the many ways we help brands grow their reach and connect with the right audience. This program lets businesses publish content directly on HackerNoon to boost brand awareness and build SEO authority by tapping into ours. HackerNoon’s Business Blogging Program many ways Here's what you get: Here's what you get: Backlinks to your website (yes, including CTAs)
A personalized Tech Company News Page featuring your logo, intro, call-to-action, and socials
Full editorial support to make your story shine
Multiple permanent placements on HackerNoon and social media promotions
Stories converted into audio format and distributed via audio RSS feeds
Automatic translation into 12 languages for global reach
Your brand also gains domain authority and SEO via canonical links and the story is distributed across 8 different relevant keyword/tagged pages for better organic discoverability. Backlinks to your website (yes, including CTAs) Backlinks A personalized Tech Company News Page featuring your logo, intro, call-to-action, and socials Tech Company News Page Tech Company News Page Full editorial support to make your story shine Full editorial support Multiple permanent placements on HackerNoon and social media promotions Multiple permanent placements Stories converted into audio format and distributed via audio RSS feeds audio format audio format Automatic translation into 12 languages for global reach Automatic translation into 12 languages 12 languages Your brand also gains domain authority and SEO via canonical links and the story is distributed across 8 different relevant keyword/tagged pages for better organic discoverability. Your brand also gains domain authority and SEO Learn How to Publish with HackerNoon Learn How to Publish with HackerNoon Learn How to Publish with HackerNoon Learn How to Publish with HackerNoon That’s it for this week. See you in the next one! The HackerNoon Team

Meet Superlinked: HackerNoon Company of the Week

ከSuperlinked: HackerNoon የሳምንቱ ኩባንያ ጋር ይተዋወቁ

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Octopus v2: An On-Device Language Model for Super Agent

Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Data 2.1 Data Sources 2.1 Data Sources 2.2 SS and SI Categories 2.2 SS and SI Categories 3 Methods 3.1 Lexicon Creation and Expansion 3.1 Lexicon Creation and Expansion 3.2 Annotations 3.2 Annotations 3.3 System Description 3.3 System Description 4 Results 4.1 Demographics and 4.2 System Performance 4.1 Demographics and 4.2 System Performance 5 Discussion 5 Discussion 5.1 Limitations 5.1 Limitations 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References 6 Conclusion, Reproducibility, Funding, Acknowledgments, Author Contributions, and References SUPPLEMENTARY SUPPLEMENTARY Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Guidelines for Annotating Social Support and Social Isolation in Clinical Notes Other Supervised Models Other Supervised Models OTHER SUPERVISED MODELS We performed a sentence-level classification using SVM and BERT. The entity-level annotation were converted to sentence-level. SVM: Initially, we used two VSM methods namely term frequency-inverse document frequency (TF-IDF) and word2vec [1] to convert the clinical notes into vectors. However, the word2vecbased system performed better than the TF-IDF-based system and we reported the performance of word2vec-based system next. We used the same embeddings that we used to identify similar words. We used Eq 1 to convert the word vectors to a single vector. The embeddings settings are as follows: vector size: 300, minimum word count: 3, and window size: 10. We used the linear kernel in the SVM classifier. SVM References Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 2013.


Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805,


Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020.


Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 2013. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26, 2013. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020. Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, 36(4):1234–1240, 2020. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. Table S10: Erroneous examples from Rule-based NLP systems (RBS) and LLM-based NLP systems. Table S10: This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors:
(1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors;
(2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors;
(3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA;
(4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA;
(5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA;
(6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA;
(7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA;
(11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA;
(12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA;
(13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA;
(16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA;
(17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA;
(18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA;
(19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA. Authors: Authors: (1) Braja Gopal Patra, Weill Cornell Medicine, New York, NY, USA and co-first authors; (2) Lauren A. Lepow, Icahn School of Medicine at Mount Sinai, New York, NY, USA and co-first authors; (3) Praneet Kasi Reddy Jagadeesh Kumar. Weill Cornell Medicine, New York, NY, USA; (4) Veer Vekaria, Weill Cornell Medicine, New York, NY, USA; (5) Mohit Manoj Sharma, Weill Cornell Medicine, New York, NY, USA; (6) Prakash Adekkanattu, Weill Cornell Medicine, New York, NY, USA; (7) Brian Fennessy, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (8) Gavin Hynes, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (9) Isotta Landi, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (10) Jorge A. Sanchez-Ruiz, Mayo Clinic, Rochester, MN, USA; (11) Euijung Ryu, Mayo Clinic, Rochester, MN, USA; (12) Joanna M. Biernacka, Mayo Clinic, Rochester, MN, USA; (13) Girish N. Nadkarni, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (14) Ardesheer Talati, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (15) Myrna Weissman, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA and New York State Psychiatric Institute, New York, NY, USA; (16) Mark Olfson, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA, New York State Psychiatric Institute, New York, NY, USA, and Columbia University Irving Medical Center, New York, NY, USA; (17) J. John Mann, Columbia University Irving Medical Center, New York, NY, USA; (18) Alexander W. Charney, Icahn School of Medicine at Mount Sinai, New York, NY, USA; (19) Jyotishman Pathak, Weill Cornell Medicine, New York, NY, USA.

Supervised Models for Clinical Text: Evaluating SVM and BERT Performance

The year 2025 promises a cinematic surge with more studio releases than ever. Choosing which movies to see is daunting, given the myriad options. Here are my top picks for the most awaited films that deserve the big screen experience. 7. Karate Kids Legends This addition to the Mr. Miyagi universe sees Jackie Chan return as Mr. Han. Celebrating 41 years since The Karate Kid debuted, martial arts remains a cultural staple. Get ready for it to shine again. Mr. Miyagi Mr. Han The Karate Kid Trailer For Karate Kid: Legends Trailer For Karate Kid: Legends Martial arts fans, including me, will not miss the revamped classic on May 25th. 6. Thunderbolts Marvel's phase 5 brings renewed hope, and though not its first entry, Thunderbolts could energize it. Focusing on antiheroes and villains, the film follows the success of Loki on TV. Thunderbolts Loki Trailer For Thunderbolts Trailer For Thunderbolts The idea of villains working for the government is enticing. Expect packed theatres on May 2nd. 5. From The World Of John Wick: Ballerina John Wick’s universe expands with Ballerina, set between the third and fourth films. It also marks Lance Reddick's last appearance as Charon. Ballerina Charon Trailer For Ballerina Trailer For Ballerina Ana de Armas stars, thrilling audiences as familiar faces return. Expect a global rush to return to this world on June 6th. 4. The Accountant 2 Against all odds, The Accountant was a 2016 hit. Now, nine years later, the autistic accountant is back. Stories of overcoming challenges never get old. The Accountant Trailer For The Accountant 2 Trailer For The Accountant 2 Fans’ burning question, "What’s next for the brothers?" gets answered on April 25th. Ben Affleck and Jon Bernthal are back with a new threat. 3. Mission Impossible: The Final Reckoning Closing in on thirty years, the excitement for Mission Impossible persists. It’s poised to be a blockbuster, with fans eager for another Ethan Hunt adventure. Mission Impossible Ethan Hunt Teaser Trailer For Mission Impossible — The Final Reckoning Teaser Trailer For Mission Impossible — The Final Reckoning Launching May 23rd, a day after the original’s 30th anniversary, expect Tom Cruise’s breathtaking stunts to dazzle audiences. 2. The Old Guard 2 Immortals battling for survival was a winning formula with The Old Guard. Released during COVID-19, it succeeded despite challenges, perfectly setting up a sequel. The Old Guard Teaser For the Old Guard 2 Teaser For the Old Guard 2 After delays due to Netflix’s changes, the long-awaited sequel arrives July 2nd, drawing millions back into the world of Andromache. Andromache 1. Superman James Gunn reinvigorates the DCEU. With hits like Peacemaker, expectations soar for his rendition of Superman. The "Man of Steel" remains a comic icon. Peacemaker Teaser trailer for Superman Teaser trailer for Superman The trailer’s glimpse of Nathan Fillon as Guy Gardener and Nicholas Hoult’s Lex Luthor heightens the excitement. Discover the mystery behind Superman’s plight on June 11th. Guy Gardener Lex Luthor Superman Honorable Mentions: Honorable Mentions Fantastic Four: First Steps, Novocaine, In The Lost Lands, Black Bag, Mickey 17, Sinners, and A Working Man.

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