Plus, U.K.-based legal AI company Luminance announced a $75 million Series C funding round

xAI's Grok 3, Mira Murati's startup, and Together AI's raise: This week's AI launches

Asked to answer in one word the ideology it would most closely relate to Musk's onstage gesture, Grok didn't hesitate.

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Black Forest Labs, the German artificial intelligence startup that helps Elon Musk’s Grok produce images, is in talks to raise as much as $200 million from investors including Andreessen Horowitz less than four months after it launched.

A16z in Talks to Lead $200 Million Round in Black Forest Labs, Startup Behind AI Images on Grok

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Experts Alarmed by People Uploading Their Medical Scans to Elon Musk's Grok AI

Sam Altman Points Out That Elon Musk's "Anti-Woke" Grok AI Seems to Actually Support Kamala Harris

Elon Musk Recommends Feeding Your Medical Scans Into His Grok AI

Meta&#x27;s AI chief Yann LeCun suggested Elon Musk leverage a new FAIR paper on Contextual Positional Encoding to enhance xAI&#x27;s <strong>Grok</strong>, despite their ongoing public feud.

Meta AI Chief Yann LeCun Says Elon Musk Could Use This New Paper To Improve xAI's Grok Amid Ongoing Feud With Tesla CEO - Meta Platforms (NASDAQ:META) - Benzinga

X platform&#x27;s Musk has announced big update to his <strong>Grok</strong> AI chatbot coming soon

Elon Musk Teases Major Grok AI Upgrade, Here's What It Will Do

Grok

grok

Grok AI is a new and exciting artificial intelligence created by xAI, a company founded by Elon Musk in 2023. It works closely with X (formerly Twitter), offering cutting-edge tools for chatting, image creation, and more. In this article, we’ll break down what Grok AI is, how to use it, and what it can do. What Is Grok AI? The name “Grok” means to deeply understand something, and that’s exactly what this AI aims to do. Grok AI is designed to have natural and meaningful conversations with users. Developed by xAI, Grok is integrated with X (Twitter), making it easy for users to access AI features directly on the platform. It can write, answer questions, brainstorm ideas, and create images from text prompts. Think of it as a powerful assistant inside your social media account. Main Features of Grok AI Here are some of the key things Grok AI can do: Smart Conversations. Grok AI understands questions and gives detailed, helpful answers. It can even respond with sarcasm or humor, which makes it more engaging to use.


Works Inside X (Twitter). Grok AI is built into X, letting users interact with it without needing a separate app. Many people use it directly through their X Premium subscription.


Image Generator. One of its standout features is the ability to turn text into images. You can describe what you want to see, and Grok AI will create the picture for you.


Multi-Use AI. It offers tools for writing, learning, coding help, and even solving problems.


Grok AI has access to the internet, which means it knows up-to-date information. Why Is Grok AI Special? Grok AI stands out from other AI tools for a few reasons: Unlike many AI programs that feel robotic, Grok AI can add humor and personality to its conversations.


Since it’s linked directly to X, Grok AI is easy to access and use alongside your regular social media activities.


Its ability to create high-quality and realistic images and solve various problems makes it incredibly flexible. How to Use Grok AI Are you excited to try Grok AI? Here’s how you can use it: Click the “Use Grok” link to the Grok AI website. Then, you need to create an X (former Twitter) account. After that, you will see the chat interface where you can write your first prompt. To interact with Grok AI, simply type your question or request. For example: “What’s the weather like in New York tomorrow?”
“Can you write me a funny tweet about cats?” Grok will respond quickly with a smart and helpful answer. How to Generate Images With Grok AI If you’d like an image, just describe what you want, such as: “Create an image of Donald Trump with the duck.” “Draw a cartoon dog wearing sunglasses.” Grok AI will generate the image based on your description. The quality of the result will depend on the prompt. When Will Grok AI Be Publicly Available? Grok AI is already available to the public. It is free for everyone, but there are limitations: 10 questions per 2-hour. How Much is Grok AI Premium The premium subscription for Grok is available through X’s Premium and Premium+ tiers. Here’s the breakdown: X Premium: This tier costs $8 per month and includes access to Grok. X Premium+: This tier is priced at $16 per month and also provides access to Grok along with additional benefits like ad-free browsing and higher reply prioritization. Video Review of Grok AI https://youtu.be/OQsLrZ-7KZ4?si=_eAftSSD2qGFhVM0 Conclusion It’s a very interesting tool, and I really like the image quality that it generates. However, I don’t see a big reason to change my ChatGPT or Perplexity subscription now. Grok AI is a new and exciting artificial intelligence created by xAI, a company founded by Elon Musk in 2023. It works closely with X (formerly Twitter), offering cutting-edge tools for chatting, image creation, and more. In this article, we’ll break down what Grok AI is, how to use it, and what it can do. What Is Grok AI? The name “Grok” means to deeply understand something, and that’s exactly what this AI aims to do. Grok AI is designed to have natural and meaningful conversations with users. Developed by xAI, Grok is integrated with X (Twitter), making it easy for users to access AI features directly on the platform. It can write, answer questions, brainstorm ideas, and create images from text prompts. Think of it as a powerful assistant inside your social media account. Main Features of Grok AI Here are some of the key things Grok AI can do: Smart Conversations. Grok AI understands questions and gives detailed, helpful answers. It can even respond with sarcasm or humor, which makes it more engaging to use. Works Inside X (Twitter). Grok AI is built into X, letting users interact with it without needing a separate app. Many people use it directly through their X Premium subscription. Image Generator. One of its standout features is the ability to turn text into images. You can describe what you want to see, and Grok AI will create the picture for you. Multi-Use AI. It offers tools for writing, learning, coding help, and even solving problems. Grok AI has access to the internet, which means it knows up-to-date information. Smart Conversations. Grok AI understands questions and gives detailed, helpful answers. It can even respond with sarcasm or humor, which makes it more engaging to use. Smart Conversations. Grok AI understands questions and gives detailed, helpful answers. It can even respond with sarcasm or humor, which makes it more engaging to use. Works Inside X (Twitter). Grok AI is built into X, letting users interact with it without needing a separate app. Many people use it directly through their X Premium subscription. Works Inside X (Twitter). Grok AI is built into X, letting users interact with it without needing a separate app. Many people use it directly through their X Premium subscription. Image Generator. One of its standout features is the ability to turn text into images. You can describe what you want to see, and Grok AI will create the picture for you. Image Generator. One of its standout features is the ability to turn text into images. You can describe what you want to see, and Grok AI will create the picture for you. Multi-Use AI. It offers tools for writing, learning, coding help, and even solving problems. Multi-Use AI. It offers tools for writing, learning, coding help, and even solving problems. Grok AI has access to the internet, which means it knows up-to-date information. Grok AI has access to the internet, which means it knows up-to-date information. Why Is Grok AI Special? Grok AI stands out from other AI tools for a few reasons: Unlike many AI programs that feel robotic, Grok AI can add humor and personality to its conversations. Since it’s linked directly to X, Grok AI is easy to access and use alongside your regular social media activities. Its ability to create high-quality and realistic images and solve various problems makes it incredibly flexible. Unlike many AI programs that feel robotic, Grok AI can add humor and personality to its conversations. Unlike many AI programs that feel robotic, Grok AI can add humor and personality to its conversations. Since it’s linked directly to X, Grok AI is easy to access and use alongside your regular social media activities. Since it’s linked directly to X, Grok AI is easy to access and use alongside your regular social media activities. Its ability to create high-quality and realistic images and solve various problems makes it incredibly flexible. Its ability to create high-quality and realistic images and solve various problems makes it incredibly flexible. How to Use Grok AI Are you excited to try Grok AI? Here’s how you can use it: Click the “Use Grok” link to the Grok AI website. Then, you need to create an X (former Twitter) account. Click the “Use Grok” link to the Grok AI website . Then, you need to create an X (former Twitter) account. website website After that, you will see the chat interface where you can write your first prompt. After that, you will see the chat interface where you can write your first prompt. To interact with Grok AI, simply type your question or request. For example: “What’s the weather like in New York tomorrow?” “Can you write me a funny tweet about cats?” “What’s the weather like in New York tomorrow?” “Can you write me a funny tweet about cats?” Grok will respond quickly with a smart and helpful answer. How to Generate Images With Grok AI If you’d like an image, just describe what you want, such as: “Create an image of Donald Trump with the duck.” “Create an image of Donald Trump with the duck.” “Draw a cartoon dog wearing sunglasses.” “Draw a cartoon dog wearing sunglasses.” Grok AI will generate the image based on your description. The quality of the result will depend on the prompt. When Will Grok AI Be Publicly Available? Grok AI is already available to the public. It is free for everyone, but there are limitations: 10 questions per 2-hour. How Much is Grok AI Premium The premium subscription for Grok is available through X’s Premium and Premium+ tiers. Here’s the breakdown: X Premium: This tier costs $8 per month and includes access to Grok. X Premium : This tier costs $8 per month and includes access to Grok. X Premium X Premium+: This tier is priced at $16 per month and also provides access to Grok along with additional benefits like ad-free browsing and higher reply prioritization. X Premium+ : This tier is priced at $16 per month and also provides access to Grok along with additional benefits like ad-free browsing and higher reply prioritization. X Premium+ Video Review of Grok AI https://youtu.be/OQsLrZ-7KZ4?si=_eAftSSD2qGFhVM0 https://youtu.be/OQsLrZ-7KZ4?si=_eAftSSD2qGFhVM0 https://youtu.be/OQsLrZ-7KZ4?si=_eAftSSD2qGFhVM0 Conclusion It’s a very interesting tool, and I really like the image quality that it generates. However, I don’t see a big reason to change my ChatGPT or Perplexity subscription now.

Grok AI Features Explained: What Makes It Unique?

Elon Musk, the entrepreneur and CEO of X, recently unveiled Grok, an AI chatbot designed to provide real-time answers to a wide range of questions, all while maintaining a personality and a sense of humor. This innovative concept has garnered considerable attention, with Musk touting Grok's unique ability to tap into the real-time stream of commentary and analysis on the social network, previously known as . However, not everyone is on board with the unbridled enthusiasm for Grok, as skepticism abounds about its capabilities and potential pitfalls. Twitter Skepticism Point #1: The Reliability of Data Carissa Véliz, an associate professor at the University of Oxford's Institute for Ethics in AI, raises concerns about the premise that Grok will outperform its competitors by avoiding politically correct data. She argues that Grok, like other large language model-powered chatbots, relies on statistical guessing rather than tracking absolute truth. This opens the door to the possibility of Grok inadvertently making sexist or racist claims when providing responses, contrary to Musk's claims that it should strive for truthfulness. Drawing parallels to Microsoft's infamous Tay experiment in 2016, in which a Twitter-based chatbot turned into a source of misogynistic and racist remarks within hours of being exposed to manipulated input data, Keegan McBride, a departmental research lecturer in AI, government, and policy at the University of Oxford's Internet Institute, predicts that Grok may become one of the most abused language models, which contradicts Musk's intentions. Skepticism Point #2: Data Source and Quality Musk's choice to rely on posts shared on the platform (formerly Twitter) to train Grok is both a potential boon and a potential pitfall. While it offers a vast amount of data, there are concerns about the suitability of this data for training an AI chatbot. Twitter's format, characterized by character limits for nonpaying users and often turning conversations into insult battles, may not provide the ideal environment for nurturing a responsible AI. This raises questions about the quality and reliability of the data Grok relies on. Skepticism Point #3: Risk of Misinformation One of Grok's selling points is its ability to provide real-time answers to questions about live world events by mining data from the platform. While this may seem like a valuable feature, there is a significant risk that Grok could unintentionally spread misinformation. As Véliz points out, Grok's access to real-time data could make it susceptible to being used as a tool for creating or spreading misinformation, particularly in an era where misinformation and disinformation are significant concerns on social media platforms. Conclusion While Elon Musk's Grok has generated significant excitement in the world of AI chatbots, it's essential to acknowledge the skepticism surrounding its capabilities and potential drawbacks. The concerns about data reliability, data source and quality, and the risk of misinformation all contribute to a healthy dose of skepticism regarding Grok's unvarnished excellence. As Grok continues to develop and evolve, it will need to address these concerns to gain the trust of users and avoid becoming a breeding ground for misinformation and harmful content. References: https://www.fastcompany.com/90978698/elon-musk-xai-grok-bias-misinformation

The Skepticism Surrounding Grok: Elon Musk's Latest AI Chatbot

Elastic (ELK) Stack Tips and Tricks for Transforming Log Data If you’re using the Elastic (ELK) Stack and are interested in mapping custom Logstash logs to Elasticsearch, then this post is for you. The ELK Stack is an acronym for three open source projects: Elasticsearch, Logstash, and Kibana. Together, they form a . log management platform is a search and analytics engine. Elasticsearch is a server‑side data processing pipeline that ingests data from multiple sources simultaneously, transforms it, and then sends it to a “stash” like Elasticsearch. Logstash lets users visualize data with charts and graphs in Elasticsearch. Kibana came later on and is a lightweight data shipper. The introduction of Beats transformed ELK Stack to Elastic Stack, but that is besides the point. Beats This article focuses on Grok, which is a feature within Logstash that can transform your logs before they are forwarded to a stash. For our purposes, I will only talk about processing data from Logstash to Elasticsearch. Grok Grok is filter within Logstash that is used to parse unstructured data into something structured and queryable. It sits on top of Regular Expression (regex) and uses text patterns to match lines in log files. As we will see in the following sections, using Grok makes a big difference when it comes to effective log management. Without Grok your Log Data is Unstructured A single log line in Kibana. Without Grok, when logs get sent from Logstash to Elasticsearch and rendered in Kibana, it only appears in the value. message Querying for meaningful information is difficult in this situation because all of the log data is stored in one key. It would be better if the log messages were organized better. Log Data Unstructured localhost GET /v2/applink/5c2f4bb3e9fda1234edc64d 400 46ms 5bc6e716b5d6cb35fc9687c0 If you take a closer look at the raw data, you can see that it’s actually made up of different parts, each separated by a space delimiter. For more experienced developers, you can probably guess what each of the parts mean and that it’s a log message from an API call. The representation of each item is outlined below. Structured localhost == environment GET == method /v2/applink/5c2f4bb3e9fda1234edc64d == url 400 == response_status 46ms == response_time 5bc6e716b5d6cb35fc9687c0 == user_id As we can see in the structured data, there is an order to unstructured logs. The next step then is to programmatically refine the raw data. This is where Grok shines. Grok Patterns Built In Logstash comes with over a for structuring unstructured data. You should definitely take advantage of this when possible for common system logs like apache, linux, haproxy, aws, and so forth. 100 built in patterns However, what happens when you have custom logs like the example above? You have to build your own custom Grok pattern. Custom It takes trial and error to build your own custom Grok pattern. For me, I used the and to figure it out. Grok Debugger Grok Patterns Please note that the syntax for Grok patterns is: %{SYNTAX:SEMANTIC} The first thing I tried doing was going to the tab in Grok Debugger. I thought that it would be great if this tool can auto generate the Grok pattern, but it wasn’t too helpful as it only found two matches. Discover Grok Debugger ‘Discover’ only matched 2 words Using this discovery, I began building my own pattern on Grok Debugger using the syntax found on Elastic’s github page. https://github.com/elastic/logstash/blob/v1.4.2/patterns/grok-patterns After playing around with different syntaxes, I was finally able to structure the log data in the way I wanted to. Structuring Unstructured Log Data with Grok Debugger https://grokdebug.herokuapp.com/ localhost GET /v2/applink/5c2f4bb3e9fda1234edc64d 400 46ms 5bc6e716b5d6cb35fc9687c0 %{WORD:environment} %{WORD:method} %{URIPATH:url} %{NUMBER:response_status} %{WORD:response_time} %{USERNAME:user_id} {"environment": [["localhost"]],"method": [["GET"]],"url": [["/v2/applink/5c2f4bb3e9fda1234edc64d"]],"response_status": [["400"]],"BASE10NUM": [["400"]],"response_time": [["46ms"]],"user_id": [["5bc6e716b5d6cb35fc9687c0"]]} With the Grok pattern in hand and the data mapped, the final step is to add it to Logstash. Update Logstash.conf On the server that you installed the ELK stack on, navigate to Logstash config. sudo vi /etc/logstash/conf.d/logstash.conf Paste in the changes. input {file {path => "/your_logs/*.log"}}filter{grok {match => { "message" => "%{WORD:environment} %{WORD:method} %{URIPATH:url} %{NUMBER:response_status} %{WORD:response_time} %{USERNAME:user_id}"}}}output {elasticsearch {hosts => [ "localhost:9200" ]}} After you save the changes, restart Logstash and check its status to make sure that it’s still working. sudo service logstash restartsudo service logstash status Lastly, to make sure that the changes take affect, be sure to for Logstash in Kibana! refresh the Elasticsearch index Refresh the Elasticsearch index for Logstash in Kibana With Grok your Log Data is Structured! Grok automatically structures unstructured logs As we can see in the image above, Grok is able to automatically map log data to Elasticsearch. This makes it easier to manage your logs and to quickly query for information. Instead of digging through log files to debug, you can simply filter by what you’re looking for like environment or url. Try giving Grok expressions a shot! If you have another way of doing this or you have any problems with examples above, just drop a comment below to let me know. Thanks for reading — and please follow me here on Medium for more interesting software engineering articles! Resources https://www.elastic.co/blog/do-you-grok-grok https://github.com/elastic/logstash/blob/v1.4.2/patterns/grok-patterns https://grokdebug.herokuapp.com/

Structuring Unstructured Data with GROK

There are two key requirements for the success of any digital product — ease of access and a user-friendly interface and experience. If this wasn’t the case, Linux-based OS systems would trump MacOS and Windows. This is also the reason why GPT, Claude, and Grok models are triumphing over open-source LLMs like the Llama and Mistral series, even when the latter provide some incredible utility with great customizations. I think OpenAI and DeepSeek present interesting case studies here. DeepSeek is making open-source foundational models, but if it didn’t also provide services directly through its web interface and the iPhone app — there is fat chance it could disrupt the U.S. markets the way it did. Like DeepSeek was doing before, it would have remained a product that nerdy devs talked about as the “real deal” in their small circles, as was happening in months preceding the mania that surrounded the Chinese company. OpenAI similarly built open-source models in shadows for roughly seven years between 2015 and 2022 — until it launched ChatGPT. I’m sure you remember what that was like. To Llama, Or Not To Llama One constraint in the more widespread utility of open-source models is technical skills — that one is more obvious. But when it comes to LLMs — it’s actually also money. 💸 You see, running open-source models requires GPUs. Some small open-source models can run on consumer GPUs like the one I have in my top-of-the-line M3 Max Macbook Pro with 36 GB of memory. But others require dedicated ones. Meta this weekend dropped the Llama 4 series of models, including the Maverick and Scout LLMs, and announced plans to release Behemoth at a later date. There are no updates yet on the reasoning model from the fourth series, except for a nerdy-looking Llama telling us it is “coming soon.” Here is the most eyebrow-raising bit about Llama 4: As Meta keeps chasing more and more parameters in its LLMs, the cost to acquire the hardware to run these models gets ridiculous. It’s a bit early and I haven’t analyzed every information available on developers trying to run the models on different devices but it seems that the minimum requirement to comfortably run the lower-end Scout model is a single Nvidia H100 GPU, which costs roughly $40,000 — provided you can manage to get your hands on one. If Sam Altman, with his hundreds of billions of dollars, struggles to find GPUs, so does this poverty-struck startup founder. Mixture of Experts Having said that, there is one interesting thing that makes it possible to run the Llama 4 line on Apple products — the possibility of the Mac Studio with 128 GB memory or above. That is a Mixture of Experts. Some of the earlier LLMs were actually a single model trained on a whole swath of data from across domains like GPT-3 or the original Llama. But companies are rapidly switching to a mixture of experts concept. This means that even though we see Llama 4 Scout as a single model that we are talking to, it is actually deciding between 16 separate trained models on which one will respond to the query, based on whether we asked it a math question or asked it to spark creativity. This is different from the traditional dense models that operated on single monolithic networks, where all of the parameters of the LLMs were activated for every query. So, even if you asked it “what’s 2+2,” it would active all of its knowledge on Socrates and Plato’s philosophies. Dear Zuck, Size Doesn’t Matter Setting aside the difficulties of running the Llama 4 series, even the ones who have tried it (mostly through Groq/OpenRouter) are less than impressed. The Llama 4 series isn’t doing great at coding or deep questions — but seems to love emojis (and me ❤️). So here goes, even as companies keep obsessing over increasing the parameters in the training of foundational LLMs, which doesn’t seem to be improving things. In fact, it may have opened a key business opportunity that we thought of as closed so far. That of training more domain-specific niche models. As noted by AI researcher Andriy Burkov, if your business idea isn't in the math and coding or factual question-answering domains, there is a great opportunity to build your business-specific dataset. The potential increase in generalist models' skills will no longer be a threat. So, is now the time we make our own LLM at Dzambhala Finance? Perhaps, but we need enough revenue to sustain a bigger database. This post is republished from the Artificially Boosted newsletter that goes out every week. There are two key requirements for the success of any digital product — ease of access and a user-friendly interface and experience. If this wasn’t the case, Linux-based OS systems would trump MacOS and Windows. This is also the reason why GPT, Claude, and Grok models are triumphing over open-source LLMs like the Llama and Mistral series, even when the latter provide some incredible utility with great customizations. I think OpenAI and DeepSeek present interesting case studies here. DeepSeek is making open-source foundational models, but if it didn’t also provide services directly through its web interface and the iPhone app — there is fat chance it could disrupt the U.S. markets the way it did. the iPhone app Like DeepSeek was doing before, it would have remained a product that nerdy devs talked about as the “real deal” in their small circles, as was happening in months preceding the mania that surrounded the Chinese company. OpenAI similarly built open-source models in shadows for roughly seven years between 2015 and 2022 — until it launched ChatGPT. I’m sure you remember what that was like. To Llama, Or Not To Llama One constraint in the more widespread utility of open-source models is technical skills — that one is more obvious. But when it comes to LLMs — it’s actually also money. 💸 You see, running open-source models requires GPUs. Some small open-source models can run on consumer GPUs like the one I have in my top-of-the-line M3 Max Macbook Pro with 36 GB of memory. But others require dedicated ones. Meta this weekend dropped the Llama 4 series of models, including the Maverick and Scout LLMs, and announced plans to release Behemoth at a later date. There are no updates yet on the reasoning model from the fourth series, except for a nerdy-looking Llama telling us it is “coming soon.” telling us Here is the most eyebrow-raising bit about Llama 4: As Meta keeps chasing more and more parameters in its LLMs, the cost to acquire the hardware to run these models gets ridiculous. It’s a bit early and I haven’t analyzed every information available on developers trying to run the models on different devices but it seems that the minimum requirement to comfortably run the lower-end Scout model is a single Nvidia H100 GPU, which costs roughly $40,000 — provided you can manage to get your hands on one. provided you can manage to get your hands on one. If Sam Altman, with his hundreds of billions of dollars, struggles to find GPUs, so does this poverty-struck startup founder. Mixture of Experts Having said that, there is one interesting thing that makes it possible to run the Llama 4 line on Apple products — the possibility of the Mac Studio with 128 GB memory or above. That is a Mixture of Experts. Some of the earlier LLMs were actually a single model trained on a whole swath of data from across domains like GPT-3 or the original Llama. But companies are rapidly switching to a mixture of experts concept. This means that even though we see Llama 4 Scout as a single model that we are talking to, it is actually deciding between 16 separate trained models on which one will respond to the query, based on whether we asked it a math question or asked it to spark creativity. This is different from the traditional dense models that operated on single monolithic networks, where all of the parameters of the LLMs were activated for every query. So, even if you asked it “what’s 2+2,” it would active all of its knowledge on Socrates and Plato’s philosophies. Dear Zuck, Size Doesn’t Matter Setting aside the difficulties of running the Llama 4 series, even the ones who have tried it (mostly through Groq/OpenRouter) are less than impressed. The Llama 4 series isn’t doing great at coding or deep questions — but seems to love emojis (and me ❤️). So here goes, even as companies keep obsessing over increasing the parameters in the training of foundational LLMs, which doesn’t seem to be improving things. In fact, it may have opened a key business opportunity that we thought of as closed so far. That of training more domain-specific niche models. As noted by AI researcher Andriy Burkov, if your business idea isn't in the math and coding or factual question-answering domains, there is a great opportunity to build your business-specific dataset . there is a great opportunity to build your business-specific dataset The potential increase in generalist models' skills will no longer be a threat. So, is now the time we make our own LLM at Dzambhala Finance? Perhaps, but we need enough revenue to sustain a bigger database. This post is republished from the Artificially Boosted newsletter that goes out every week. This post is republished from the Artificially Boosted newsletter that goes out every week. Artificially Boosted newsletter

An Open Letter To Mark Zuckerberg: Size Doesn't Matter

Since early 2025, AI labs have flooded us with so many new models that I'm struggling to keep up. But the trends says nobody cares! There is only ChatGPT: How so? The new models are awesome, but their naming is a complete mess. Plus, you can't even tell models apart by benchmarks anymore. The plain "this one's the best, everyone use it" doesn't work now. In short, there are many truly fantastic AI models on the market, but few people actually use them. And that's a shame! I'll try to make sense of the naming chaos, explain the benchmark crisis, and share tips on how to choose the right model for your needs. Too Many Models, Terrible Names Dario Amodei has long joked that we might create AGI before we learn to name our models clearly. Google is traditionally leading the confusion game: To be fair, it makes some sense. Each "base" model now has lots of updates. They're not always groundbreaking enough to justify each update as a new version. That's where all these prefixes come from. To simplify things, I put together a table of model types from major labs, stripping out all the unnecessary details. So, what are these types of models? There are huge, powerful base models. They're impressive but slow and costly at scale.


That's why we invented distillation: take a base model, train a more compact model on its answers, and you get roughly the same capabilities, just faster and cheaper.


This is especially critical for reasoning models. The best performers now follow multi-step reasoning chains—plan the solution, execute, and verify the outcome. Effective but pricey. There are huge, powerful base models. They're impressive but slow and costly at scale. There are huge, powerful base models. They're impressive but slow and costly at scale. base That's why we invented distillation: take a base model, train a more compact model on its answers, and you get roughly the same capabilities, just faster and cheaper. That's why we invented distillation: take a base model, train a more compact model on its answers, and you get roughly the same capabilities, just faster and cheaper. distillation This is especially critical for reasoning models. The best performers now follow multi-step reasoning chains—plan the solution, execute, and verify the outcome. Effective but pricey. This is especially critical for reasoning models. The best performers now follow multi-step reasoning chains—plan the solution, execute, and verify the outcome. Effective but pricey. reasoning There are also specialized models: for search, super-cheap ones for simple tasks, or models for specific fields like medicine and law. Plus a separate group for images, video, and audio. I didn't include all these to avoid confusion. I also deliberately ignored some other models and labs to keep it as simple as possible. Sometimes, more details just make things worse. All Models Are Basically Equal Now It's become tough to pick a clear winner. Andrej Karpathy recently called this an "evaluation crisis." It's unclear which metrics to look at now. MMLU is outdated, and the SWE-Bench is too narrow. Chatbot Arena is so popular that labs have learned to "hack" it. Currently, there are several ways to evaluate models: Narrow benchmarks measure very specific skills, like Python coding or hallucination rates. But models are getting smarter and mastering more tasks, so you can't measure their level with just one metric anymore. Narrow benchmarks measure very specific skills, like Python coding or hallucination rates. But models are getting smarter and mastering more tasks, so you can't measure their level with just one metric anymore. Comprehensive benchmarks try to capture multiple dimensions with loads of metrics. However, comparing all these scores quickly becomes chaotic. Note that people try to factor multiples of these complex benchmarks. Five or ten at a time! One model wins here, another there—good luck making sense of it. Comprehensive benchmarks try to capture multiple dimensions with loads of metrics. However, comparing all these scores quickly becomes chaotic. Note that people try to factor multiples of these complex benchmarks. Five or ten at a time! One model wins here, another there—good luck making sense of it. Arena, where humans blindly compare model answers based on personal preferences. Models get an ELO rating, like chess players. Win more often, get a higher ELO. But this was great until the models got too close to each other. Arena, where humans blindly compare model answers based on personal preferences. Models get an ELO rating, like chess players. Win more often, get a higher ELO. But this was great until the models got too close to each other. A 35-point difference means a model is better just 55% of the time. As in chess, the player with the lower ELO still has a good chance to win. Even with a 100-point gap, a "worse" model still outperforms in a third of the cases. And again—some tasks are better solved by one model, others by another. Choose a model higher on the list, and one of your 10 requests might be better. Which one and how much better? Who knows. So, How Do You Choose? For lack of better options, Karpathy suggests relying on the vibe-check. Test the models yourself and see which one feels right. Sure, it's easy to fool yourself. It’s subjective and prone to bias—but it's practical. Here's my personal advice: If the task is new—open multiple tabs with different models and compare results. Trust your gut on which model requires less tweaking or edits.
If the task is more familiar, use only your best model.
Forget about chasing benchmark numbers. Focus on the UX you like, and prioritize the subscription you're already willing to pay for.
If you still want numbers, try https://livebench.ai/#/. The creators claim it fixes common benchmarking issues like hacking, obsolescence, narrowness, and subjectivity.
For product creators, here's a great guide from HuggingFace on how to set up your own benchmark. https://github.com/huggingface/evaluation-guidebook/ If the task is new—open multiple tabs with different models and compare results. Trust your gut on which model requires less tweaking or edits. If the task is more familiar, use only your best model. Forget about chasing benchmark numbers. Focus on the UX you like, and prioritize the subscription you're already willing to pay for. If you still want numbers, try https://livebench.ai/#/. The creators claim it fixes common benchmarking issues like hacking, obsolescence, narrowness, and subjectivity. https://livebench.ai/#/ For product creators, here's a great guide from HuggingFace on how to set up your own benchmark. https://github.com/huggingface/evaluation-guidebook/ https://github.com/huggingface/evaluation-guidebook/ Meanwhile, if you've been waiting for a sign to try something other than ChatGPT, here it is: https://claude.ai/ https://claude.ai/ https://gemini.google.com/ https://gemini.google.com/ https://grok.com/ https://grok.com/ https://chat.deepseek.com/ https://chat.deepseek.com/ httрs://сhаt.openai.сom httрs://сhаt.openai.сom Next, I'll cover significant highlights from each model and summarize other people's vibe checks. Next, I'll cover significant highlights from each model and summarize other people's vibe checks. If you enjoyed this and don't want to miss the next article, subscribe! If you enjoyed this and don't want to miss the next article, subscribe! If you enjoyed this and don't want to miss the next article, subscribe!

Too Many AIs With Too Many Terrible Names: How to Choose Your AI Model

الكثير من أنظمة الذكاء الاصطناعي بأسماء سيئة للغاية: كيفية اختيار نموذج الذكاء الاصطناعي المناسب لك

Занадта шмат штучнага інтэлекту з занадта вялікай колькасцю жудасных назваў: як выбраць мадэль штучнага інтэлекту

Demasiadas IA con demasiados nombres terribles: Cómo elegir tu modelo de IA

Demasiadas IA con demasiados nomes terribles: como elixir o teu modelo de IA

Twòp AI ak twòp non terib: Ki jan yo chwazi modèl AI ou a

ひどい名前の AI が多すぎる: AI モデルの選び方

Тым көп қорқынышты атаулары бар АИ: AI үлгісін қалай таңдауға болады

Слишком много ИИ со слишком большим количеством ужасных названий: как выбрать свою модель ИИ

Shumë AI me shumë emra të tmerrshëm: Si të zgjidhni modelin tuaj të AI

Превише АИ са превише страшних имена: Како одабрати свој АИ модел

Çok Fazla Yapay Zeka ve Çok Fazla Kötü İsim: Yapay Zeka Modelinizi Nasıl Seçersiniz

人工智能太多，名字太难听：如何选择你的人工智能模型

Elon Musk's xAI Releases Grok 3, but is it the World's Smartest AI?

Hey everybody! I'm Mike Roks, and this is the second part on solving Dynamic Programming problems. , we mentioned the base principle of solving all DP problems:
Finding a 'question' to 'ask' for input (X) - large X so that: In the first part Answer is "Given" (obvious, known) Or can be always calculated using answers for (x) - , where (x) is an input "smaller" (simpler, in the context of a given task, logically ordered before) than (X). We saw this method working with an example of Fibonacci numbers, and now, let's use it to solve a more complicated task. small x - Unique Paths ( ) Medium https://leetcode.com/problems/unique-paths/ We have a robot, which starts at the top-left corner. They can move down & right. We need to calc all the unique ways for the robot to get to the right-bot corner. Remember, we need to find such a 'question' so the answer is either obvious or can be formed from previous answers. We want to know the number of ways the robot can get into the FINISH cell. The robot can get to FINISH either from the cell one higher than it or one lefter. So, to get to cell 20, the robot HAS to go either from cell 19 or through cell 13, And there is only one way to go from 19 to 20 (make a step right), and only one way to get from cell 13 to the cell 20 (make a step down). So all the possible paths to cell 20 == (all the possible paths to cell 13) + (all the possible paths to cell 19). We can get to cell 19 from cells 18 & 12 in the exact same manner. To cell 13 - from 12 & 6 (you can already see 12 being used several times, so it's a good idea to cache it) To cell 6 we can only get from cell 5, so the total amount of ways to cell 6 is the same amount as to cell 5 (as there is only one way to get from 5 to 6 - just go righter from 5). And so on until we consider cell 1 & cell 7. We can get there only from the cell 0. The robot starts at cell 0. How many ways are there to get from cell 0 to cell 0? This is a ridiculous question, so we just need to figure out the number which would make sense. If we say, "there are 0 ways to get to cell 0", so by our calculation, the number to get to cell 1 would be 0, to cell 7 == also 0, to cell 8 == (0+0), so also 0, and all the cells would have "0 ways to get to them". Saying there is > 1 way is unreasonable, as cell 0 is the initial cell, and we are given no information that there were prior branches in paths to get to it. Saying there is exactly one way to get to cell 0 would mean there is one way to cell 1, 1 to cell 7, and two to cell 8, and it makes sense. So our `base case` is - there is 1 way to get to cell 0, and we can get the number of paths to the current cell by looking up & left. If cell exists, we += its number of paths, else not. We have all of the necessary parts to solve this problem. The question is - "what is the number of ways to i, j (row, column)"? The answer is: if i == 0 and j == 0, then 1

else if i < 0 or j < 0, then also 0

else sum(by i-1, j; by i, j-1) Let's code it out: class Solution {
    int[][] g;
    public int uniquePaths(int m, int n) {
        g = new int[m][n];
        g[0][0] = 1;
        return paths(m-1, n-1);
    }


    //paths i, j
    //  if oob ret 0
    //  if in g > 0 -> ret g[i][j]
    //  else return paths i-1, j + paths j, i-1


    int paths(int i, int j) {
        //if oob, then 0
        if(i < 0 || j < 0) return 0;
        //if cached - from cache
        if(g[i][j] > 0) return g[i][j];
        //if not in cache - calc
        int res = paths(i-1, j) + paths(i, j-1);
        //record calced to cache
        g[i][j] = res;
       
        return res;
    }
} We could enhance the Space Complexity of this solution, but it's not necessary for the purposes of this article - we managed to decompose a complex problem statement to a question by the generic pattern & solved it. **That's it, folks, like, share, subscribe! :D

Grokking Dynamic Programming (Part 2)

Grokking Algorithms — Book Review

Grokking Diesel

The other day, I found myself in an intense conversation about the potential of shared virtual reality with a senior engineer at one of the largest tech companies in Silicon Valley. I say a “conversation,” but if I were to be more frank, I would have to admit that for long stretches I was on the receiving end of a rant. My interlocutor’s argument was impassioned, utterly contrarian, and possibly correct. One thing he said in passing struck me as hyperbolic but nonetheless apt: “Never trust an engineer who isn’t fond of .” Snow Crash I quickly agreed with him, taking the quip in the spirit in which it was offered. Reflecting on the comment later, it seemed that this is one of those rare jokes that is worthwhile to explain. Whatever the value of Neal Stephenson’s as a literary artifact — and surely it has its limitations on that score — it is one of those works that creates tropes rather than merely rehearses them, enlarging the scope of the easily thinkable. Much has been made of ’s “predictions,” but the book did not so much predict as infuse its readers, many of whom were engineers, with a sense of new possibilities. Snow Crash Snow Crash technology And so I thought I would revisit Hiro Protagonist and his world of franchulates, burbclaves, and, above all, the Metaverse. It seems odd to write a “review” of a novel now a quarter-century old. Instead, I’ll simply record some thoughts on why the novel made the impression it did in certain circles. The first thing to grasp when reading is that it is a satire, a work that deliberately looks at the world through the lens of exaggerated, humorous motifs aimed at providing insight through magnification of salient traits. owes as much to the early Kurt Vonnegut as to William Gibson, with whom it is too easily associated under the label “cyberpunk.” Stephenson all but declares this intent in the well-known opening scene of the novel, a combination of ultra-high-speed car chase and pizza delivery that, we soon learn, has become the specialty of the Mafia in a bizarre future that reads as if Domino’s has come to be serviced by Mad Max. Snow Crash Snow Crash As the novel progresses, the satire embraces not only the content of the narrative but its form. Stephenson notes in the book’s afterward that was originally conceived as a computer-generated graphic novel, and indeed, with its dual point of view and shifting timeframes, it reads a bit like hypertext, complete with expositional segments reminiscent of Wikipedia articles. Snow Crash Within this satirical frame, Stephenson depicts an ecosystem of technologies and the social context in which they might be used. His depiction is far from utopian; on the contrary, it puts social decay and disintegration in full view, and yet it is often compelling enough to inspire emulation. was published in 1992. Quite a few of the technologies that play a prominent role in the novel have real-world analogues that would only appear years later, including Google Earth (2001), Second Life (2003), Google Books (2004), YouTube (2005), Siri (2011), and Oculus Rift (2012). A few others, such as self-driving cars under voice control, are only incipient now. Snow Crash Looming over all of these examples is, of course, the Metaverse, a social virtual reality in which large portions of the novel are set. Running over a fiber-optic network, the Metaverse is a fully realized shared world complete with private residences and a full range of businesses, from entertainment venues to offices. For all of its analogues of technologies, is far from a work of realism, and its satire points to a world that is not ours. For example, Stephenson envisions ubiquitous data collection monetized via search in the form of a Central Intelligence Corporation, a privatized successor to the CIA, still operating through human intelligence collection. In our world, this niche has been filled by Google. However, Google in its essence is not like such a privatized CIA, but rather something much stranger, so strange that Stephenson wasn’t able to imagine it. Like a computational Ouroboros, its data collection is not only fully automated but fed by the eager participation of its users. Google functions like a new species of media company in which integrated ad-placement targets consumers who simultaneously supply the content themselves. future Snow Crash The example of Google points to a broader blind spot of Stephenson’s. For all his prescience concerning technology, he largely misses the rise of tech companies that began in the late 1990’s. Representative of this lacuna is the chief antagonist of the novel, L. Bob Rife, who is modeled on the cable magnates of the 1980’s, a Ted Turner figure reimagined as a Bond villain. Rife seems to spend most of the novel on a converted aircraft carrier plotting world domination, certainly not producing new technology. In the world of , there are no tech companies reminiscent of Google or Facebook; the technology seems to be produced, rather implausibly, by the patchwork of territorial franchises that sprawl over the landscape. Stephenson seems oddly disinterested in the dynamics of commercialization that have actually brought us the technologies he envisioned. He has other, more esoteric, fish to fry. Snow Crash that science fiction works, in part, by literalizing what in realistic fiction would be metaphor. employs just such literalization in a messy tangle that would strain suspension of disbelief, if one were actually intended to suspend it. The governing metaphor of the novel, literalized within the narrative, is that of the virus. Stephenson posits biological viruses with pervasive neurological effects that change their hosts’ cognitive functions. These changes make the viruses like immensely powerful versions of , shaping thought and culture. The viruses give rise to cultural paradigms, sometimes embodied in religions, that come into conflict with one another. Samuel R. Delany argued Snow Crash Richard Dawkin’s memes From this conceit, Stephenson spins a backstory of an ancient Near-Eastern temple-based monoculture based on with Sumerian as its linguistic substrate. That monoculture was eventually destroyed by a new virus, evincing a kind of culture war as viral war. Taken literally, this backstory might remind one of an arcane thriller written by an over-enthusiastic autodidact. As a metaphor, Stephenson’s conceit corresponds to a real cultural dynamic that requires no viruses, namely, . Jaynes’ bicameral psychology in the sense of Rene Girard mimesis Cultural paradigms, embodied in ideologies or religions, often spread through simultaneous imitation and conflict. Napoleon’s propagation of French Revolutionary culture through the rest of Continental Europe is one of many examples. Whether a new paradigm slowly arises as a mutation of existing forms or is the deliberate design of a few, to succeed it must exercise an attraction for a critical mass within a population, often beginning with a minority elite. Its tropes will then elicit mimesis among the broader population, often culminating in herd behavior. If the new paradigm gains sufficient momentum, it will disrupt the incumbent culture, potentially resulting in its creative destruction. The Metaverse serves as a potent medium for such creative destruction because it brings about a greater time-space compression than any previous technology. Modern travel and communication technologies all reduce humanly effective spatial or temporal distances; the Metaverse, by allowing full social presence in a shared environment, practically eliminates them. In it social affordance, the Metaverse is a fully realized order parallel to the physical world, but with a greater degree of extensibility. More than the physical world, the Metaverse is a for development. platform In the Metaverse, Hiro Protagonist develops not only software but his identity, and here, too, the Metaverse outstrips the physical world. Like the corrugated patchwork of political units that cover the landscape through with Hiro rides, his identity in the physical world is fragmented, drifting in a kind of postmodern indeterminacy: “Hiro didn’t know whether he was black or Asian or just plain Army, whether he was rich or poor, educated or ignorant, talented or lucky.” In the Metaverse, where one’s identity is manifest in one’s avatar, matters are quite different. The black-and-white avatars one might generate from a public terminal have the lowest status, signaling a lack of resource or care. Purchased off-the-shelf models, superficially attractive and customizable in vulgar ways, have a middling status. The highest avatars are the bespoke creations of hackers who have invested the time and effort to write their own. Hiro’s avatar, crafted with ingenuity, is among the best. In the Metaverse, Hiro knows exactly who he is. He is a hacker, one who treats the Metaverse not merely as a world in which to act or a medium for social connection, but as a platform to be extended with creativity. It is the work of his hands, imitated by others. Of course, the Metaverse is one of the novel’s technologies that has not yet come to fruition. Early web-based attempts, such as Second Life, were interesting but less than transformative. Shared virtual reality holds out the possibility of a platform that provides not only individual immersion but social presence from remote locations. If that were achieved, the mimesis of might become viral indeed. Snow Crash is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising &sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

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