CodeGem is people-first engineering management software. Employee engagement and satisfaction are supported through a healthy culture; wellness is prioritized by top managers, and development analytics can help leaders focus on what matters. With CodeGem, you can expect a performance boost from your engineering team. Drive continuous improvement by connecting industry-leading KPIs, actionable alerts, career development, and wellness analytics into a 1-on-1 framework that unifies it all.

CodeGem

How to quickly get past ubiquitous «echo bots» tutorials and build an assistant that actually does something. Just clone, tweak and launch. Can it become a «bot-end» framework? *a reference to Kraftwerk’s single “The Robot” (1978) It started with a screw up I have to admit: as a former journalist, I still write faster than I think. Turns out, it applies to code too. in my series. Even though saw it and some readers used it to create their own projects, no one seemed to have noticed the gaping design flaw. I misused gem in a way that when several users try talking to the tutorial bot at the same time, their messages get mixed up. When one user is in the middle of the interaction, bot is hung up on the task in progress and any other message from any other user would be treated as the next step in the conversation thread. and I desperately wanted to make up for it. I had to rethink the architecture to handle requests from different users and remember their place in conversation. A simple refactoring led to more refactoring, and more refactoring, and more… As a result, I created a simple tool that solves a number of design problems with bots, so you don’t have to solve them yourself**.** I royally screwed up “Build your first Messenget bot in Ruby” more than 6000 people facebook-messenger That sucked M — your starting point to mess with Ruby chatbots for Messenger, the largest messaging platform out there. At this early stage it’s more a and I haven’t packaged it as a separate gem yet, so when you download the boilerplate, you can see how its internals are implemented inside the folder. It also contains some example code that you’re free to tweak and replace. eet Rubotnik Facebook proof of concept /rubotnik What it does You can bind keywords (“help”, for instance, as in or to your bot’s functions (we will call them “commands” from now on) with a straightforward syntax: . Forget about with a dozen of ’s to parse your input. “help!” “Would you help me, please” bind ‘help’, to: :give_help case when You write all your bot’s logic as separate methods inside boilerplate’s module and you can chain them together to create threads of dialogue with Commands next_command :method_name You can generate UI elements with the help of convenience classes: (it also makes those Facebook’s Generic Templates a bit easier to generate) UI::ImageAttachment.new(‘http://your.image/url’).send(@user) You can set up a nested persistent menu and a greeting screen for your bot. Alter and to create your own. rubotnik/persistent_menu.rb rubotnik/bot_profile.rb Its will walk you through all the steps necessary to hook up your bot on Facebook and bring it to life. README It’s a standalone service that runs on Rack (it uses Puma server with default config). Think of it as framework for your web app, if you have one. It has enabled by default so you can create your own webhooks and handle incoming API connections. “bot-end” Sinatra It’s primed for easy deployment with Heroku. Just do and once you’re done testing on localhost. heroku create heroku push origin master What it doesn’t do It has no persistence, but can be extended to work with any database. By default, Rubotnik has simple in-memory storage that keeps track of connected users and their state (see and to see how it’s implemented). rubotnik/user.rb rubotnik/user_store.rb It doesn’t work inside of a Rails project (this is one of the features that can be thought about in future). On the other hand, it can talk to your existing Rails app through REST API. How it works So how do you build your bot with Rubotnik? First, make sure you have your and installed — we will need this for development, as well as for moving your bot to the cloud. Then follow this steps: Heroku CLI ngrok git clone :progapandist/rubotnik-boilerplate.git git@github.com mv rubotnik-boilerplate NAME_OF_YOUR_PROJECT cd NAME_OF_YOUR_PROJECT rm -rf .git  # remove boilerplate’s git history git init     # track your own project Open the project in your favorite editor and take a look at the file. There’s a bunch of stuff already written, that constitutes logic for a that you can play with . bot.rb demo bot here The that demoes boilerplate features bot It implements an example of a simple API call to Geocoding to determine details about your location, a sample questionnaire to demonstrate gathering data from user message by message, and it also renders some of Facebook’s UI elements to get a taste what your own bot can offer in terms of user experience. Google If you don’t change anything at this point and just follow in the boilerplate’s readme, you’ll get the exact copy of the demo bot. It lets you understand what does what inside the boilerplate, but you probably want to build your own thing, right? Then just take a quick glance at the sample code and replace it with your own. instructions for bot setup Note that all the heavy lifting (sending data to Facebook Messenger Platform and handling incoming requests to your bot’s webhook) is done by the gem ( from Norway did a terrific job, and a community around his project is growing steadily). Rubotnik merely piggybacks on its API, like so: facebook-messenger that guy Same logic applies to postbacks, which are events triggered when the user interacts with buttons in your bot’s UI. Let’s take a closer look, two things are happening in this snippet: is an example of Rubotnik’s . bind ‘string' DSL is a helper function that allows you to send a text message to connected user with a simplicity of say say "Hello!" Take a look at to see how commands can be chained together to form threads. /commands/questionnaire.rb Check the and read through comments in boilerplate’s code to learn more about DSL, helper functions and where to apply them. README How it’s done Rubotnik’s main classes are called and , they can be found inside of folder. Here’s a snippet that shows how message dispatching is implemented: MessageDispatch PostbackDispatch /rubotnik A instance variable is accessible everywhere through common Rubotnik namespace and represents any given incoming message. It’s an instance of facebook-messenger’s class and has all its properties (refer to facebook-messenger’s ) @message Facebook::Messenger::Incoming::Message README @message.id          # => 'mid.1457764197618:41d102a3e1ae206a38'@message.sender      # => { 'id' => '1008372609250235' }@message.seq         # => 73@message.sent_at     # => 2016-04-22 21:30:36 +0200@message.text        # => 'Hello, bot!'@message.attachments # => [ { 'type' => 'image', 'payload' => { 'url' => 'https://www.example.com/1.jpg' } } ] Same goes for that’s an instance of @postback Facebook::Messenger::Incoming::Postback references the connected user and is an instance of class defined in . The main point of this class is to hold onto user’s facebook ID and keep track of commands bound for execution. You can add your custom containers for state (as in the case with property, used to track user’s answers in ). @user User user.rb answers questionnaire.rb That’s pretty much all the not-so-secret sauce that ensures all users are served independent of each other and the conversation with them continues exactly at the point it stopped, even after a long interruption. How to go live Once you’re done building and testing your bot, just create a heroku app from your console and run . Then update your webhook in Facebook developer console and that’s it! git push heroku master How to say if you ❤️ it or 👺 it For now Rubotnik is just a bunch of code organised in some folders. It’s raw. It’s a proof of concept. It needs polishing. Some of the code has to do with actual “framework” logic, while other bits just demo example bot’s features. It has statements sprinkled everywhere. There’s a bunch of debug statements that are designed to help with understanding how bot performs. But they can’t be allowed to stay there for long. require_relative Some of my practices may be frowned upon by seasoned programmers. That’s understandable, I only discovered Ruby magic . last year That’s exactly why I invite everyone to collaborate. Feel free to fork the boilerplate and make PR’s or just and I’ll add you as a collaborator for the main repo. email me I’m also looking for ways to test Rubotnik with some real world examples, so if you are planning to integrate your service with Messenger, contact me and I’ll help you out. What next? Obviously, Rubotnik should be turned into a gem. But as it’s tightly coupled with , I don’t know where to start yet. It should also probably have -style generators to create directory structure for new projects. facebook-messenger rails new I’m also looking into and tools to enable easy interactions with NLU engines that would make your bot a better conversationalist. wit.ai api.ai Recommend 💚 this article on Medium and stay tuned! 🤖 BOT POWER! 🤖

Ya tvoy Rubotnik*. Beginner-friendly Ruby boilerplate to build your own Messenger bot

For the past 6 months, I’ve been working on GPT Pilot ( ) to understand how much we can really automate coding with AI, so I wanted to share our learnings so far and how far it’s able to go. https://github.com/Pythagora-io/gpt-pilot When I started building GPT Pilot, I wrote on how it is envisioned. Now, I want to share my revised thinking and go into what worked and what didn’t work. this blog post Finally, you’ll see examples of apps that were created with GPT Pilot and how you can create an app with a AI pair programmer. real What Is the Idea Behind GPT Pilot? GPT Pilot is envisioned as a r – . Rather, it is a developer who creates a plan for how your app or feature should be built and starts coding. It wants to do most of the coding by itself, but when it gets stuck, it needs clarification about the given requirements, or requires a code review, it asks you for help. eal AI developer not an autocomplete or a chatbot Is AI Like a Junior Developer? Or… I often see CodeGen GPT-4-based tools that say they are building an AI junior developer. Somehow, I’ve always had a problem with that because when I use ChatGPT for coding, it gives me answers and ideas that only a super-senior person could give – something that absolutely no junior dev would even be able to grasp. Still, no LLM can build an app nearly as well as a senior developer can, but the knowledge GPT-4 has about coding is way beyond any junior developer. I would say that GPT-4 has so much knowledge about every part of software development like it’s the most senior developer in the world but with the memory of a goldfish. I picture it as a superhuman robot that just stands in the middle of a room and can only do a single small action at a time, but it cannot combine many actions and work repetitively. You must tell it exactly what it should do next. This is what we’re after with GPT Pilot – we want to create a framework of thinking for the LLM that gets that superhuman robot to continuously work by revising its previous actions, have a feedback loop, and determine what should it do next in order to finish the end goal, which is to build a production-ready application. , I outlined the main pillars on which GPT Pilot was built. But these have changed a bit based on our team’s learnings, so here are the revised pillars: In the blog post I mentioned above not only because AI is not good enough but also because you might want to change how something works or looks after it’s implemented. It’s common for a developer or product manager, once they see what an implementation looks like, to decide to change it. A human is needed to supervise the AI Or, you realize there are more edge cases than you initially anticipated and think it’s easier to refactor your current implementation than to fix every issue. The problem is when you finish the entire app and then try to refactor it – this is when it becomes much harder because every change will impact all the other features. On the other hand, if you do the refactor before you commit your changes, you’ll be able to proceed with the next features on top of well-written code. This is why . it’s crucial for an AI developer to have a human in the loop whenever a task is implemented This way, the human can review the implementation of each task (just like a code review before merging a PR) before GPT Pilot continues on to the next task. If a human tells GPT Pilot what is wrong, it will be much easier to fix the issues within the task itself. At the same time, the LLM has the context of what needs to be done in the task and what has been done so far. I have a feeling that many people judge ChatGPT’s ability to write code by how well it delivers the first time you ask it to code something. If it doesn’t produce working code, many will think it’s not impressive. In reality, . Instead, you write code, run it, see the errors, and iterate. This is exactly what GPT Pilot enables GPT-4 to do – after it writes code, GPT Pilot can run the code, take the output, and ask the LLM if the output is correct, if something should be fixed, and if so, how. AI can iterate over its own mistakes. humans almost never write working code on the first try . There are many repetitive routines that all developers go through when building an app. One of the routines can be – write code, run it, read the errors, change code, rerun it, etc. Another higher-level one can be – take a task, implement it, test the implementation (repeat until all tests pass), send it for review, fix the issues (repeat until the reviewer approves), and deploy. Many of these routines can be orchestrated if we have an intelligent decision-maker in the loop (like an LLM). Software development can be orchestrated . When we created the first version of GPT Pilot, we thought it would need to iterate over tasks, implement code, fix it, and move on. In reality, you don’t continuously progress when coding an app – you rewrite your code all the time. The coding process is not a straight line Sometimes, you refactor the codebase because, after the initial implementation, you realize there is a better way to implement something. Other times, you do it because of a change in requirements. Like I mentioned in #1, after you see that a solution isn’t working, you sometimes need to roll back a bunch of changes, think about an alternative solution to the problem, and try solving it that way. To make GPT Pilot, or any other AI developer, work at scale, it needs to have a mechanism that will enable it to go back, choose an alternative path, and reimplement a task. What Did We Learn? LLMs, in general, are a new technology that everyone is trying to understand – how it works, what can be done better, how to do proper prompt engineering, etc. . Our approach is to focus on building the application layer instead of working on getting LLMs to output better results The reasoning is that LLMs will get better, and if we spend weeks optimizing a prompt, it might be completely solved with the new version of GPT. Instead, we’re focusing on what the user experience should look like and which mechanisms are needed to control the LLM to enable it to continuously work, getting closer and closer to the final solution. So, here are our learnings so far: . Our original thinking was that, with the human’s input, GPT Pilot would be able to navigate in the right direction and get closer and closer to the working solution, even if the initial description was vague. The initial description of the app is much more important than we thought However, GPT Pilot’s thinking branches out throughout prompts, beginning with the initial description. And with that, if something is misleading in the initial prompt, all the other info that GPT Pilot has will lead in the wrong direction. So, when you correct it down the line, it will be so deep into this incorrect way that it will be almost impossible to get it onto the right path. Now, as I’m writing this, it seems so obvious, but that is something we needed to learn – to focus much more on the initial description. So, we built a new agent called “Spec Writer,” which works with you to break down the project requirements before it starts coding. . As I mentioned above in the pillars section, refactoring happens all the time, and GPT Pilot must do so as well. We haven’t implemented a solution for this yet, but it will likely work by adding the ability for GPT Pilot to create markers around its decision tree so that whenever something isn’t working, it can review markers and think about where it could have made a wrong turn. Coding is not a straight line . My thinking was that if an agent reviews what the other agent did, it would be redundant because it’s the same LLM reprocessing the same information. But it turns out that when an agent reviews the work of another agent, it works amazingly well. We have 2 different “Reviewer” agents that review how the code was implemented. Agents can review themselves One does it on a high level, such as how the entire task was implemented, and another one reviews each change before they are made to a file (like doing a git add -p). compared to multiple problems in a single prompt. For example, if you tell GPT Pilot to make 2 different changes in a single description, it will have difficulty focusing on both. So, we split each human input into multiple pieces in case the input contains several different requests. LLMs work best when they can focus on one problem . This is very obvious now, but initially, we didn’t tell GPT-4 to add any logs around the code. Now, it creates code with verbose logging, so that when you run the app and encounter an error, GPT-4 will have a much easier time debugging when it sees which logs have been written and where those logs are in the code. Verbose logs help . This is also an obvious conclusion, but we had to learn it. It’s much easier for GPT-4 to implement features and fix bugs if the code is split into many files instead of a few large ones. Just like we, humans, think – it’s much easier to process smaller chunks of code rather than big ones. We do that by creating levels of abstraction – functions, classes, etc. Splitting the codebase into smaller files helps a lot One of the easiest ways to get the LLM to create a more manageable abstraction is just to tell it to create more modular code and split it into more files. It works amazingly well, and the end result is also better for us, developers. . GPT Pilot’s goal is to do 90% of all coding tasks and leave the other 10% to a human. This 10% usually comprises fixes or small changes that the LLM struggles to notice, but for the human, it might be a simple change. For a human to be able to fix the code, they need to be clearly shown what was written in the task and the idea behind it However, the problem is that it’s not easy to tell the human what is not working and what code they should look at. If GPT Pilot writes 3,000 lines of code, the human developer, if they want to help GPT Pilot, needs to understand the entire codebase before diving into the actual code. In future versions of GPT Pilot, the human developer will have a detailed explanation of what code has been added to the current task and the reasoning behind it. This way, you will be able to help GPT Pilot. . LLMs are better off asking humans questions rather than letting humans think about all the possible errors. Again, it’s very obvious looking back, but one of the things we noticed was that people were much more willing to answer questions that GPT Pilot asked them instead of having an open-ended input field where they could write unconstrained feedback. Humans are lazy . This was one of the biggest learnings for me. I thought you could prompt GPT-4 by giving it a couple of solutions it had already used to fix an issue and tell it to think of another solution. However, this is not as remotely easy as it sounds. It’s hard to get an LLM to think outside the box What we ended up doing was asking the LLM to list all the possible solutions it could think of and save them into memory. When we needed to try something else, we pulled the alternative solutions and told it to try a different, but specific, solution. Apps Created With GPT Pilot Currently, you can create simple but non-trivial apps with GPT Pilot. We’ve also seen people create apps with GPT Pilot that are very impressive, such as an app that can fine-tune a ResNet model to count palm trees and then, when you upload an image, count the trees in it. Here are a couple of apps we created, along with the code, stats, and demos: Prompt Lab ( ) DEMO https://www.youtube.com/watch?v=UXhC4nx0pvs&embedable=true Think of this as OpenAI Playground on steroids – it enables you to load a conversation from a JSON array or enter it manually, run the conversation with the LLM X number of times, save the conversation to the database, and load it back in. We actually use this app when we engineer a prompt and want to see how it behaves. The Playground is not good enough because it’s time-consuming to repetitively rerun a single prompt. With Prompt Lab, you can choose how many times to run it and let the app run the prompt repeatedly. Once it’s finished, you can analyze the results. This might be useful for people who are building an AI app and need to optimize their prompts. ⏳ Time spent: ~2 days 💾 Github repo ( ) SQLite Database Analyzer Tool DEMO https://www.youtube.com/watch?v=gKgswTm5tFo&embedable=true This is also an app we use internally to analyze a local SQLite database. It pulls the data from the database in a format that’s very specific to the GPT Pilot database structure, but it can easily be modified to fit other structures. It reads and uploads your SQLite database, splits the rows by a specific field, unpacks the rows into values, loads the LLM conversation data into a form, and enables you to simply change one of the messages and submit the LLM request to GPT-4 to see how the results will look. This way, you can analyze the conversations GPT Pilot’s agents have with the LLM and easily explore what would happen if the prompts were different. ⏳ Time spent: ~2 days 💾 Github repo Code Whisperer ( ) DEMO https://www.youtube.com/watch?v=8qzq6F5DtyE&embedable=true Code Whisper is a fun project we created as an example to showcase. The idea is that you can use it to ask the LLM questions about your codebase. You paste in the link to a public Github repo. Then, it clones the repository, sends the relevant files to the LLM for analysis, which creates a description for each file about what the code does, and saves those descriptions into the database. After that, you can ask the app questions about the codebase, and the codebase shows you the response. In this demo, we use GPT-3.5. ⏳ Time spent: 7 hours 💾 Github repo Star History ( ) DEMO https://www.youtube.com/watch?v=mjc4vnlLz3M&embedable=true I’ve been releasing open-source projects for years now, and I’ve always wanted to see how fast my Github repo is growing compared to other successful repositories on . The problem is that on Star History, I’m unable to zoom into the graph, so a new repo that has 1,000 stars cannot be compared with a big repo that has 50,000 because you can’t see how the bigger repo does in its beginning. https://star-history.com/ So, I asked GPT Pilot to build this functionality. It scrapes Github repos for stargazers, saves them into the database, plots them on a graph, and enables the graph to be zoomed in and out. ⏳ Time spent: 6 hours 💾 Github repo Conclusion I hope you gained some insight into the current state, problems, and findings that we deal with at GPT Pilot. So, to recap: We think that a real AI developer tool should be based on the following pillars: A human is needed to supervise the AI. We should enable the AI to iterate over its own mistakes. , which should be implemented on a layer on top of LLMs. Software development can be orchestrated because, in reality, the coding process is not a straight line. The AI developer should be able to refactor the codebase So far, we’ve learned that: The initial app description is much more important than we thought Coding is not a straight line, agents can review themselves LLMs work best when they focus on one problem compared to multiple problems in a single prompt Verbose logs do miracles Splitting the codebase into smaller files helps a lot For a human to be able to fix the code They must be clearly shown what has been written and the idea behind it Humans are lazy It’s hard to get the LLM to think outside the box What do you think? Have you noticed any of these behaviors in your interactions with LLMs, or do you have a different opinion on any of these? I would be super happy to hear from you, so leave a comment below or shoot me an email at . zvonimir@gpt-pilot.ai You can try creating an app with AI with GPT Pilot here: GPT Pilot open source repo GPT Pilot VS Code extension If you liked this post, it would mean a lot if you star the , and if you try it out, let us know what you think. Your feedback is really important to the entire GPT Pilot Team. GPT Pilot Github repo Also published here

After 6 Months of Working on a CodeGen Dev Tool (GPT Pilot), This Is What I Learned

কোডজেন ডেভ টুলে (GPT পাইলট) 6 মাস কাজ করার পর, আমি এটাই শিখেছি

Nach 6 Monaten Arbeit an einem CodeGen-Entwicklungstool (GPT-Pilot) habe ich Folgendes gelernt

Después de 6 meses de trabajar en una herramienta de desarrollo CodeGen (piloto GPT), esto es lo que aprendí

Après 6 mois de travail sur un outil de développement CodeGen (pilote GPT), voici ce que j'ai appris

कोडजेन डेव टूल (जीपीटी पायलट) पर 6 महीने काम करने के बाद, मैंने यही सीखा

CodeGen 開発ツール (GPT パイロット) を 6 か月間使って学んだこと

6개월간 CodeGen 개발 도구(GPT 파일럿) 작업을 하면서 배운 점은 다음과 같습니다.

Após 6 meses trabalhando em uma ferramenta CodeGen Dev (GPT Pilot), foi isso que aprendi

Вот что я узнал после 6 месяцев работы над инструментом разработки CodeGen (пилотная версия GPT)

CodeGen Geliştirme Aracı (GPT Pilot) Üzerinde 6 Ay Çalıştıktan Sonra Öğrendiğim Şey Bu

Sau 6 tháng làm việc trên CodeGen Dev Tool (GPT Pilot), đây là điều tôi học được

使用 CodeGen 开发工具（GPT Pilot）6 个月后，这是我学到的东西

The world of artificial intelligence (AI) is undergoing a seismic shift, largely driven by the emergence of Large Language Models (LLMs). These open-source LLMs are pushing the boundaries of what AI can achieve, and in this blog post, we'll delve into some of the most remarkable models that are shaping the future of technology and communication. Each LLM offers unique strengths and capabilities, making them indispensable tools for developers, researchers, and organizations. Let's embark on a journey to discover the potential of these cutting-edge models. : Empowering AI Interactions LLaMA 2 LLaMA 2, developed collaboratively by Meta AI and Microsoft, is a groundbreaking AI language model with three available sizes, ranging from 7 to a staggering 70 billion parameters. It's not just an upgrade but a monumental leap in AI capabilities. LLaMA 2 possesses the ability to comprehend both text and images, making it ideal for multimodal tasks. Supported on platforms like Azure and Windows, this model democratizes AI access. Safety is at its core, with extensive training to minimize harmful outputs. Key Advantages: Multimodal capabilities for text and image understanding, suitable for diverse tasks. Availability in three sizes, catering to a wide range of use cases. Seamless integration with platforms like Azure and Windows. Rigorous safety measures to ensure responsible AI use. Open availability for fine-tuning on multiple platforms. Diverse training data for comprehensive understanding, reducing biases. : Elevating AI Performance Claude 2 Claude 2, developed by Anthropic, is a model designed to elevate AI performance to new heights. This model achieved an impressive score in the Bar exam, surpassing its predecessor. In GRE reading and writing exams, Claude 2 performed above the 90th percentile, showcasing its proficiency in comprehending and generating intricate content. It excels in processing extensive documents and demonstrates enhanced coding capabilities. Safety is paramount, ensuring responsible AI use. Key Advantages: Remarkable performance in academic evaluations, highlighting its competency. Ability to process inputs of up to 100K tokens, enabling in-depth analysis. Enhanced coding proficiency for complex programming tasks. Focus on reducing harmful content generation, prioritizing ethical AI. Plans for global availability in the near future, expanding its reach. : A Versatile Text-To-Text Model T5 T5, or Text-To-Text Transfer Transformer, is a versatile pre-trained language model developed by researchers at Google AI. It’s based on the Transformer architecture and designed to handle a wide range of natural language processing tasks through a unified “text-to-text” framework. With 11 different sizes, T5’s models vary from small to extra-large, with the largest having 11 billion parameters. Key features of T5: Encoder-decoder architecture: T5 employs an encoder-decoder architecture, treating almost all NLP tasks as a text-to-text problem. This results in enhanced consistency in model design. Pre-training for diverse tasks: In T5’s pre-training process, the model generates target text from the source text, which includes various tasks like translation, summarization, classification, and more. This approach results in a versatile and unified model. Flexible input-output paradigm: It operates in a “text as input, text as output” paradigm. Framing tasks in this manner reduces complexity and allows fine-tuning for specific objectives. Adapter-based architecture: T5 uses a modular architecture that adapts to new tasks through several additional parameters. Contextual consistency: T5 maintains coherence in lengthy interactions and produces natural-flowing conversations. : The Open-Source Powerhouse GPT-NeoX-20B GPT-NeoX-20B, developed by EleutherAI, is a formidable open-source AI model with 20 billion parameters. It builds upon the architecture of GPT-3 while introducing innovations like synchronous data parallelism and gradient checkpointing. GPT-NeoX-20B is known for its ability to produce coherent and contextually relevant content, efficient multi-GPU training, and fine-tunability for various applications. Key Advantages: Innovative features like synchronous data parallelism enhance training efficiency. Efficient multi-GPU training for faster model development. Coherent and contextually relevant content generation, ensuring high-quality outputs. Fine-tunable for various applications, making it adaptable to specific needs. : Scaling Down Without Sacrificing Quality GPT-J GPT-J is a model with 6 billion parameters, making it more accessible compared to larger models. Trained on the Pile dataset, it shares its roots with the GPT-2 architecture. GPT-J employs parallel decoders for efficient text processing, excelling in powerful text generation capabilities. With a user-friendly API, it's a cost-effective alternative to larger models. Key Advantages: Accessibility with 6 billion parameters, balancing cost and performance. Efficient text processing with parallel decoders, speeding up text generation tasks. Powerful text generation capabilities for natural language processing tasks. User-friendly API, simplifying integration into various applications. Cost-effective alternative to larger models, reducing computational expenses. : Efficiency and Scale Unleashed OPT-175B OPT-175B boasts a colossal size of 175 billion parameters and is primarily trained on unlabeled text data containing English sentences. It utilizes gradient checkpointing for memory efficiency, excels at few-shot learning, supports mixed precision training, and is committed to reducing its carbon footprint. Key Advantages: Massive size with 175 billion parameters, enabling comprehensive learning. Memory-efficient gradient checkpointing, reducing memory consumption during training. Excellence in few-shot learning, allowing the model to adapt quickly to new tasks. Support for mixed precision training, optimizing training speed and efficiency. Focus on reducing carbon footprint, promoting environmental responsibility. : Fostering Scientific Collaboration BLOOM Description: BLOOM, developed by BigScience, is a monumental achievement with 176 billion parameters. It's designed to foster scientific collaboration and breakthroughs. BLOOM relies on 46 natural world languages and 13 programming languages, ensuring inclusivity. With advanced contextual comprehension and ethical communication, it prioritizes responsible AI use and cultural sensitivity. Key Advantages: Large-scale with 176 billion parameters, enabling in-depth scientific research. Multilingual competence for global collaboration, breaking language barriers. Advanced contextual comprehension, providing nuanced responses. Ethical communication and cultural sensitivity, promoting responsible AI. Inclusive language for a diverse user base, reducing biases. : China's AI Contender Baichuan-13B Baichuan-13B, introduced by China's Baichuan Inc., is a formidable open-source LLM designed to compete on the global stage. With 13 billion parameters and a pre-training dataset of 1.3 trillion tokens, it excels in both English and Chinese AI language processing. It empowers applications spanning sentiment analysis to Mandarin content creation, aligning with Baichuan's mission to democratize generative AI. Key Advantages: Proficiency in understanding and generating Chinese content. Simplified data interaction for research and trend analysis. A vast capacity of 13 billion parameters for nuanced communication. Industry-grade performance across various applications. : Bidirectional Context Understanding BERT BERT (Bidirectional Encoder Representations from Transformers) was created by researchers at Google AI. With a model size of up to 340 million parameters, BERT has been trained on a diverse dataset comprising 3.3 billion words, including BookCorpus and Wikipedia. Key features of BERT: Bidirectional context: BERT comprehends context from both directions in a sentence, enhancing its grasp of nuanced relationships and improving understanding. Attention mechanism: It employs attention mechanisms focusing on relevant words, capturing intricate dependencies, and enabling the model to give context-aware responses. Masked language model: During training, BERT masks certain words and predicts them using surrounding context, enhancing its ability to infer relationships and meaning. Next sentence prediction: BERT also learns to predict whether a sentence follows another in a given text. It enhances BERT’s understanding of sentence relationships, which is beneficial for tasks like question answering and summarization. Task agnostic: BERT’s pretraining and fine-tuning approach enables easy adaptation to different tasks. It can achieve remarkable results even with limited task-specific data by fine-tuning the pre-trained model on specific tasks. : Streamlining Software Development CodeGen CodeGen, a creation by Salesforce AI Research, is inspired by the GPT-3.5 architecture and offers a range of sizes, including 350 million, 2 billion, 6 billion, and an impressive 16 billion parameters. It has been trained on a diverse set of programming languages and frameworks, making it a valuable tool for generating accurate and reliable code solutions. Key Advantages: Accurate and reliable code generation using a vast training dataset. Flexibility in understanding and generating code in multiple programming languages. Error identification and handling, improving code quality. Potential to streamline software development processes and enhance developer productivity. *** These open-source LLMs are transforming the landscape of AI, from enhancing language understanding to promoting ethical AI use. As they continue to evolve, they hold the potential to redefine the possibilities of technology and communication. Explore these models and embark on your journey into the future of AI. Here at Linguix we’ve experimented with lots of those LLMs and our team is happy to consult you or help you implement them. Just us here to get started! email

From LLaMA 2 to CodeGen: Navigating the World of Open-Source LLMs

Our company faced enormous challenges when the full-scale Russian invasion of Ukraine started. But instead of cutting costs, we decided to continue actively expanding. India became one of our top-priority markets, so after research and planning, we went for it. The team is still under a lot of stress: most employees stay in Ukraine and sometimes have to work from bomb shelters, and others are spread across the globe. But expansion is going well, and I’m ready to share some insights and lessons learned. Choosing the market: the paradise for Java developers We help people learn Java because we know this profession can be fulfilling and well-paid simultaneously. Over the last ten years, we've gained tremendous experience in working with international students (over 30,000 people from 40 countries have already become our graduates) and entered many markets (the USA, China, and European countries). So, we weren't new to this business. Nevertheless, India seemed like a big challenge. To name a few reasons, the huge cultural gap between Ukraine and India, and the circumstances in which our employees have to work. After the war started, some people fled the country, and some moved to other regions of Ukraine. It got harder to communicate daily and hold meetings. But we knew the potential of this market and were ready to put extra effort into this idea. What made India so attractive to us? This country's economy is growing fast, and the share of the young population is pretty high ( ). There are over 658 million Internet users in India, which explains the high demand for online education and the booming growth of IT companies. people between 15 and 29 years old constituted 27,2% in 2021 India's the world's leader in IT outsourcing, and the demand for programmers here continues to increase. For example, in 2021, India witnessed the launch of 14,000 new startups. With every year, we can expect this number to get bigger, so the competition for skilled employees will get tougher. One of the top hiring websites in India, , offers over 100,000 job openings for IT specialists. And the salaries are appealing: Java developers here can earn up to 2 million rupees per year (the average annual salary in India is 240,000 rupees, according to ). Naukri.com AmbitionBox.com It's only logical that young people want to acquire a profession of a Java developer. But unfortunately, universities can't meet this demand. They aren't flexible enough to change the curriculum as fast as the market requires, and it takes too much time (several years!) to get a degree. On the other hand, online courses can offer up-to-date programs, a lot of practice, and sometimes even help with a job search. That's why EdTech has been flourishing in India in the last few years. This market is expected to reach , with 37 million paid users. Seven among 36 in the world are Indian. Moreover, the biggest EdTech unicorn is also Indian – it’s ByJu’s with a market value of $22 billion. $10.4 billion by 2025 EdTech unicorns Some segments of the EdTech market in India are more competitive than others. For example, K-12 is quite filled compared to additional professional education. It's not a blue ocean for us, but also not a red ocean. In our segment, we see big and small companies, but the "market pie" isn't sliced yet, and the leaderboard isn't final. And what's more important, no Java learning platforms offer such a high-quality product as we do. With these considerations in mind, we decided to start our journey. Research and preparation: better thorough than sorry Having an idea is one thing, and transforming it into a business plan is the other. That’s why we decided to enter the Indian market only after detailed research. How did we conduct it? First, we examined every study we could find on the Indian EdTech market, its potential, and our soon-to-be competitors. We analyzed the information, shared our conclusions with team members, and discussed our findings. Second, we had multiple meetings with market experts. We reached out to some representatives of Indian EdTech companies and asked them questions to get a better perspective on the local market. Third, we couldn’t proceed without talking to potential customers. So, we interviewed Indian clients of our main product – a self-paced online Java course. Also, we surveyed them. We already had several hundred users, and it was beneficial to ask for their feedback. Moreover, we talked to Indian employers and browsed local job search websites. We wanted to understand if the requirements for Java developers differ in India compared to other countries. Our team made everything possible – and impossible – to gather as much information as we needed. And we did it under air raid sirens which are pretty common in Ukraine, and from bomb shelters. Our employees adjusted even to these circumstances. Some of them had to catch the Internet connection to work. But their motivation remained strong despite the high level of stress and discomfort. Eventually, this stage gave us tons of valuable insights. For example, we discovered that the main thing our potential Indian students desired was employment. It’s not enough for them to gain Java knowledge or improve their coding skills. They want to get a job offer after they complete the course. And it’s the main reason Indian customers aren’t satisfied with existing courses. Most of them last just a few months, and finding a job is impossible after such a brief study. Also, we came to another conclusion: we really can deliver what the target audience needs. As our surveys say, 95% of our graduates find their first job within three months after completing the course. We were sure that in 12 months, we could train Java developers who were able to get job offers immediately. The first hypothesis, first results… Our research helped us create the first hypothesis, i.e., if we offer potential students a job guarantee, they will gladly choose us and be willing to pay for the first month or the entire course at once. To test it, we did several steps: Talked to employers to identify their needs and reach preliminary agreements to hire our graduates. Surprisingly, some companies wanted to start hiring our graduates immediately! It proved our point that the demand for Java developers in India is huge, and our reputation is substantial and works for us. Tailored the existing product (self-paced course) for the Indian market. CodeGym Java University is a 12-month course with mentors and a job offer guarantee at the end. It combines all the benefits of our online course (gamification, lots of practice, automatic verification of tasks completed, etc.) with a guided approach (live lectures and mentors’ help). Created a landing page for CodeGym Java University and started holding free online events about IT in general, Java, and our offer. We invited local speakers to gain more trust from the audience. Over 1,500 people visited our online events, and about 100 filled out applications for our course. As a result, we had our first group of students in a month. They are studying now, and so far, they are satisfied with what they get. So, our first hypothesis worked, and we moved to the next challenge – growing and scaling. … and first complications Not everything was going as smoothly as we had hoped. The first problem became evident when the sales started. We had a sales team, but it wasn't local. Our employees live in different countries and mostly use Western approaches to communicate with potential clients. For example, go straight to business, discuss the offer, and close the deal quickly. In India, on the other hand, people prefer an entirely different approach. To close the deal here, you must establish personal communication first and talk about family, relatives, life, etc. Only then can you start discussing your offer. Also, we discovered that the trust issue was crucial. If a potential client hears an unfamiliar accent, they immediately understand that there's a foreigner on the other end and start asking questions. The USA or European experience isn't relevant here. Therefore, potential customers may doubt your expertise. And, of course, we didn't know much about the cultural differences between Indian states. For instance, there are many local holidays, different for different states. And selling something on these dates is pointless. All these things created barriers to communication. As soon as we understood that, we decided to hire a local team. Now, we already have four sales managers in India, several career consultants, and Java mentors. They help us understand the cultural features, comprehend the needs of our target audience, and process applications more efficiently. Our mentors are professional Java developers who work for companies like SalesForce, Walmart Tech, and Microsoft. We also trained them to use our teaching methods, and now they combine understanding the local culture with CodeGym's DNA. Typically, we realize the need for a particular employee first and then start looking for them. But sometimes, we find an interesting person for a position we don't have yet. If this person can make us stronger, and we can afford them, we go for it. For example, that's how Shubham Dumbre joined our team. He's an IT entrepreneur, an author, and a speaker with immense expertise in EdTech companies. He helps us to hold our open events and communicate with potential students. Another complication was out of our control. Russians started bombing the civil infrastructure with missiles and drones, and electricity became a luxury. Blackouts can last for several hours, even days (and we can’t complain because, in some regions, people live without electricity for months). Again, we had to adjust. We’ve bought equipment (power generators, power banks, WiFi routers that can work without electricity, etc.) and learned to keep all the devices charged. And we continue working. Getting closer to success, iteration by iteration As I've said before, scaling is our next big challenge. To keep the process going and fix all the problems as soon as they appear, we conduct special meetings. Every week (usually on Friday), we get together with the "Indian" team and discuss the previous week's results. We've divided the entire project into funnel stages to discuss each part's success separately. For example: Awareness and advertising. What experiments did we conduct last week? What else did we do? What results did we get? Our free webinars. How did the chatbots, emails, and SMS messages work? What was the attendance of the webinar? What questions did people ask there? What do the statistics about each webinar say? During these meetings, we analyze how the audience's interest changes and what data show. The sales process. What insights did we get from our leads and sales managers? What are the sales metrics? Who among the sales managers was the most efficient? The learning process. How are students of current groups learning? What's their performance? How many tasks per week did they solve on our platform? What feedback did students give to mentors? And what feedback did mentors give to students? In wartime, not every employee can attend a meeting, and it's not their fault. To keep everyone on the same page, we take notes and share them with people who couldn't participate in a discussion. Our internal communications have become somewhat asynchronous, but it doesn't affect our results. The last months were challenging for us, but also, they taught us many valuable lessons. For example, we should always be prepared for anything, both on personal and company levels. We are much stronger than we thought. People really are our main asset (that's why we've kept the whole team and are proud of this decision). Sure, there will inevitably be other challenges as we continue to pursue our "Indian dream.” But we stay together and stay true to our mission – teach people Java and give them an in-demand profession. In this uncertain world, all we can count on is ourselves. By Alex Yelenevych, Co-Founder & Chief Marketing Officer at , a platform for learning Java CodeGym.cc Also Published Here

How a Ukrainian Startup  CodeGym Managed to Enter a New Market During the War

Over the last few years in the developer world, November has been taken over by a creative coding challenge called Codevember. I’m attempting Codevember myself this year, and hopefully, after reading this you will challenge yourself too! What is Codevember? Codevember is a challenge by and which invites developers to post a new creative piece of code for every day of November. Arthur Rob Valentin Daguenet The has a list of hints for each day of the month, e.g. , and . Using the hint, participants come up with a creative bit of code inspired by the word. For example, for the hint , you could create a usable piano with JavaScript. Codevember website 1. Infinity 2. Time 3. Carrot Music Why should I do Codevember? Here are a few reasons why you should partake: Gives you a chance to experiment with new technologies that you might not always have a chance to Helps expand your knowledge of HTML, CSS and JavaScript whilst having fun After 30 days you will have a collection of code creations that you can be proud of How can I do Codevember? The Codevember team aim to make the challenge as accessible as possible and allow you to submit your creations using any platform/tool you like. If you want to get your work featured on the Codevember website, you can do so in a few ways. To attempt , post a solution each day with the hashtag #codevember and the day of the month e.g. . CodePen users can get their pen’s featured with the same title format. Alternatively, you can directly to the Codevember site. Codevember on Codier #codevember — 12 — My awesome work submit your work And don’t worry if you have missed a day or two, you can join in whenever!

Challenge yourself this #codevember!

In this article, I want to share my experience of creating a terraform provider from scratch, show how to add it to the Terraform and OpenTofu registries, and share some useful links and tips. I wrote the provider for Dodo pizza, and I was inspired by . Instead of Dominos, Dodo pizza doesn't allow you to create an order via API - only retrieving some data, but for learning purposes, this is more than enough. Terraform Dominos Pizza provider Which SDK to use? First of all, you need to decide which HashiCorp SDK you will use. For today HashiCorp offers 2 Go programming language Software Development Kits (SDKs) for building Terraform providers: and . For newly created providers they recommend using the framework, and for existing providers to migrate from SDKv2 to the framework. The Terraform Plugin Framework  “offers significant advantages as compared to the SDKv2”, as HashiCorp says. I decided to use the old, good SDKv2 for two reasons: almost all examples and existing providers that I know are using SDKv2, and it will be interesting for me to understand how to migrate from SDKv2 to the new framework. Terraform Plugin Framework SDKv2 How to interact with API? All articles that I found provide examples of limited sample APIs or include API calls inside the provider itself. But all “large” providers use Go libraries to interact with existing API. For example, Cloudflare provider uses their own , and GitHub provider uses library maintained by Google. And I decided to do the same - create my own library and use it in the provider. cloudflare-go go-github uses swagger, which is such a widely used tool, that I believe it should have some library generator for it. I started to google it and found about swagger-codegen - the standard way to generate client SDK for swagger. Dodo Global API an article In my case, I used only one command to generate an almost fully functional client SDK for Dodo API: swagger-codegen generate -i https://globalapi.dodopizza.com/api/swagger/v2/swagger.json -l go -o . You can find dodo-go here: https://github.com/Nmishin/dodo-go How to start writing the provider? I found several articles about terraform provider creation, from HashiCorp and independent writers: article from Mark McDonnell was the most useful for me; “Beginner's guide to creating a Terraform Provider” a good example from HashiCorp; “Writing Custom Terraform Providers” article contain not only info about terraform provider, but also a lot of useful links; “Terraform Custom Provider with Data Source” I realized that some terraform providers use API libraries very similar to my generated dodo-go library, and it was super helpful to compare my provider with existing provider, for example: . terraform-provider-authentik also can be a good example, since it’s well maintained and not as complicated as, for example, . Cloudflare terraform provider terraform-provider-aws What to do next after creating the provider? When your provider is ready, or you are happy with existing functionalities, you need to publish it to the Terraform registry and OpenTofu registry if you plan to use it with opentofu. All the needed steps are well described in HashiCorp’s documentation: https://developer.hashicorp.com/terraform/registry/providers/publishing#terraform-registry-manifest-file And this article from Kevin Wang can be useful as well: https://thekevinwang.com/2023/10/05/build-publish-terraform-provider After publishing provider to the Terraform registry, publishing to OpenTofu will be much easier, because you have already done all the necessary presteps. You need to create two issues: the first one is the provider itself, and second one is about the provider’s GPG Key. Example of adding provider: https://github.com/opentofu/registry/issues/228 Example of adding GPG Key: https://github.com/opentofu/registry/issues/230 And that's it! After your provider is added to all registries, you can add it to the config as any regular provider. Let’s check how it works for my terraform-provider-dodo. In my test configuration, I have three files and I want to retrieve data about available brands: cat provider.tf 

terraform {
  required_providers {
    dodo = {
      source = "Nmishin/dodo"
      version = "0.0.1"
    }
  }
}

provider "dodo" {} cat main.tf 

data "dodo_brand" "test" {} cat outputs.tf 

output "brands" {
  value = data.dodo_brand.test.names
} tofu plan

data.dodo_brand.test: Reading...
data.dodo_brand.test: Read complete after 1s [id=bf4b2096fab07279e4a6a9db5fb704b5]

Changes to Outputs:
  + brands = [
      + "dodopizza",
      + "doner42",
      + "drinkit",
    ]

You can apply this plan to save these new output values to the OpenTofu state, without changing any real infrastructure. Conclusion Right now everyone with basic programming skills and terraform/opentofu experience can create its own provider. There is so much information about how to start to write code and publish provider, but in this article I wanted to provide high level instruction with good useful links. And I hope my story will help you not to be afraid to start creating a provider from scratch and I am absolutely sure that you will be successful with it!

My Path for Terraform Provider Creation

How are you, hacker?
🪐 has got you covered with fresh content from our trending stories of the day! Set email preference . Want to know what's trending right now?: The Techbeat by HackerNoon here To Focus or Not Focus: How to Find the Right Product-Market Fit By [ 3 Min read ]
To all the product managers out there searching for that elusive product-market fit, remember to keep your eyes on the prize and focus on what matters most! @densmr Read More. Everything You Need to Know to Implement Generative AI for Your Enterprise By [ 8 Min read ]
The easiest way to conceptualize what is possible with Generative AI is to imagine a customized Large Language Model running inside your firewall. @minio Read More. Elevating Product Management with Marketing Insights By [ 9 Min read ]
Learn how to having marketing skills makes you a better product manager and how they help build great products. @dashadobrego Read More. Breaking the CPO Bubble: My Strategy for Staying Close to Our Products By [ 3 Min read ]
Explore a CPO's journey in "Breaking the CPO Bubble," detailing strategies for connecting with products and teams and enhancing customer alignment. @densmr Read More. How to Manage a Product Backlog in a Fast-Growing B2B Startup By [ 7 Min read ]
In this article, I’d like to share my tips for working as a product manager in fast-growing startups. I’ll also explain how these tips could be used in B2B prod @dashadobrego Read More. How to Teach People to Make the Right Decisions and Delegate Important Business Tasks With no Stress By [ 6 Min read ]
Discover effective strategies for decision-making and delegation in business, fostering a culture of responsibility and continuous improvement. @densmr Read More. Is AGI Getting Closer? Anthropic's Claude 3 Opus Model Shows Glimmers of Metacognitive Reasoning By [ 5 Min read ]
Anthropic's new LLM told prompters it knew they were testing it @mikeyoung44 Read More. Mixing the Cross-Chain magic with Tokenized Real Worlds Assets By [ 4 Min read ]
The future of finance is with tokenized RWAs and cross-chain DeFi; dig into real world assets on Avalanche and ICON. @helloicon Read More. Sam Mangel to Sam Bankman-Fried: Acceptance is the Path Forward By [ 4 Min read ]
The approach SBF’s legal team are taking toward sentence mitigation makes one thing clear: the former FTX CEO has not yet accepted what he did was wrong. @drewchapin Read More. 7 Monetization Options to Consider for Your Blog By [ 5 Min read ]
Looking for ways to generate stable revenue through your blog? The article showcases a few noteworthy options for you to consider. @griffinjt Read More. How To Create a Static, Personal Website for Less Than $1 a Month By [ 12 Min read ]
The goal of this post is to show you how you can create your own static personal website for under $1 a month. @afrocloud Read More. GitHub Copilot Review: Does it Really Give a 55% Speed Boost to Development? By [ 7 Min read ]
This article presents a GitHub Copilot review and assesses its impact on enhancing development speed in real-life scenarios. @elekssoftware Read More. Chrome Password Manager Betrayed My Trust 13 Years Ago. I Never Forgot By [ 10 Min read ]
Chrome's broken trust led to Neulock: a password manager built on user control, not encryption & blind faith. Learn the 3 security principles behind it. @lucasneves Read More. Tokenizing Real-World Assets (RWA) Part 1: A Deep Dive into ERC-3643 By [ 6 Min read ]
Discover the concept of  Real-World Assets (RWA) tokenization and its benefits, along with an examination of the ERC-3643 standard tailored for this purpose. @leonidcryptonstudio Read More. After 6 Months of Working on a CodeGen Dev Tool (GPT Pilot), This Is What I Learned By [ 13 Min read ]
What I learned in 6 months of working on the most powerful CodeGen dev tool GPT Pilot @zvone187 Read More. From Leadership to Lines of Code: A Team Leader's Guide By [ 8 Min read ]
To balance leading and coding, block time for tech tasks, prioritize, and delegate. Embrace continuous learning and personal projects to stay sharp. @lookingforere Read More. 100 Days of AI, Day 13: How Instruction Finetuning Improves a Pre-trained LLM By [ 6 Min read ]
Instruction finetuning is a finetuning technique used to convert base models like GPT3 into a chat-gpt like product. @sindamnataraj Read More. How to Use RunLoop in IOS Applications By [ 5 Min read ]
Run Loop is a very important tool in iOS development. Let's explore how to use it. @alekseimarinin Read More. Top Crypto To Buy This Month: BlockDAG's Presale Hits $3M, Challenging Kaspa Amid Cosmos' ATOM Rise By [ 3 Min read ]
Discover BlockDAG, hailed as the 'Kaspa Killer,' breaks all presale records, reaching $3 million and grabbing the attention of Cosmos investors. @btcwire Read More. Testing in Godot: How I Personally Approach It By [ 4 Min read ]
Some time ago, I participated in a game jam and made this game – Of Mice and Bad Choices, I think this is a good opportunity how automated testing can be done 🧑‍💻 What happened in your world this week? It's been said that , , . Feeling stuck? We got you covered ⬇️⬇️⬇️ We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.
See you on Planet Internet! With love,
The HackerNoon Team ✌️ @dlowl Read More. writing can help consolidate technical knowledge establish credibility and contribute to emerging community standards ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME

The TechBeat: To Focus or Not Focus: How to Find the Right Product-Market Fit (3/10/2024)

How are you, hacker?
🪐 has got you covered with fresh content from our trending stories of the day! Set email preference . Want to know what's trending right now?: The Techbeat by HackerNoon here To Focus or Not Focus: How to Find the Right Product-Market Fit By [ 3 Min read ]
To all the product managers out there searching for that elusive product-market fit, remember to keep your eyes on the prize and focus on what matters most! @densmr Read More. Elevating Product Management with Marketing Insights By [ 9 Min read ]
Learn how to having marketing skills makes you a better product manager and how they help build great products. @dashadobrego Read More. Breaking the CPO Bubble: My Strategy for Staying Close to Our Products By [ 3 Min read ]
Explore a CPO's journey in "Breaking the CPO Bubble," detailing strategies for connecting with products and teams and enhancing customer alignment. @densmr Read More. How to Manage a Product Backlog in a Fast-Growing B2B Startup By [ 7 Min read ]
In this article, I’d like to share my tips for working as a product manager in fast-growing startups. I’ll also explain how these tips could be used in B2B prod @dashadobrego Read More. Everything You Need to Know to Implement Generative AI for Your Enterprise By [ 8 Min read ]
The easiest way to conceptualize what is possible with Generative AI is to imagine a customized Large Language Model running inside your firewall. @minio Read More. How to Teach People to Make the Right Decisions and Delegate Important Business Tasks With no Stress By [ 6 Min read ]
Discover effective strategies for decision-making and delegation in business, fostering a culture of responsibility and continuous improvement. @densmr Read More. Is AGI Getting Closer? Anthropic's Claude 3 Opus Model Shows Glimmers of Metacognitive Reasoning By [ 5 Min read ]
Anthropic's new LLM told prompters it knew they were testing it @mikeyoung44 Read More. Nintendo Breaks Its Silence On Palworld By [ 4 Min read ]
Nintendo finally broke its silence after Palworld’s launch raised controversy among the Pokemon community. @playerauctions Read More. Sam Mangel to Sam Bankman-Fried: Acceptance is the Path Forward By [ 4 Min read ]
The approach SBF’s legal team are taking toward sentence mitigation makes one thing clear: the former FTX CEO has not yet accepted what he did was wrong. @drewchapin Read More. Data Lake Mysteries Revealed: Nessie, Dremio, and MinIO Make Waves By [ 6 Min read ]
Let's see how Nessie, Dremio and MinIO work together to enhance data quality and collaboration in your data engineering workflows. @minio Read More. 7 Monetization Options to Consider for Your Blog By [ 5 Min read ]
Looking for ways to generate stable revenue through your blog? The article showcases a few noteworthy options for you to consider. @griffinjt Read More. 5 Low-Cost Cybersecurity Measures SMBs Can’t Afford to Not Implement By [ 4 Min read ]
A successful cyber attack can be devastating; many cybersecurity measures that can help SMBs can be implemented at surprisingly little cost. @jonstojanmedia Read More. GitHub Copilot Review: Does it Really Give a 55% Speed Boost to Development? By [ 7 Min read ]
This article presents a GitHub Copilot review and assesses its impact on enhancing development speed in real-life scenarios. @elekssoftware Read More. After 6 Months of Working on a CodeGen Dev Tool (GPT Pilot), This Is What I Learned By [ 13 Min read ]
What I learned in 6 months of working on the most powerful CodeGen dev tool GPT Pilot @zvone187 Read More. What is the Language Processing Unit (LPU)? Is It GPU's Rival? By [ 7 Min read ]
So: What is LPU, how does it work, and where is Groq (such an unfortunate name, given Musk’s Grok is all over the media) coming from? @kseniase Read More. How to Analyze Anything - Master Data Analysis With ChatGPT (Beginner's Tutorial) By [ 3 Min read ]
Today, we’re diving into an exciting feature within ChatGPT that has the potential to enhance your productivity by 10, 20, 30, or even 40%. @proflead Read More. Chrome Password Manager Betrayed My Trust 13 Years Ago. I Never Forgot By [ 10 Min read ]
Chrome's broken trust led to Neulock: a password manager built on user control, not encryption & blind faith. Learn the 3 security principles behind it. @lucasneves Read More. Mixing the Cross-Chain magic with Tokenized Real Worlds Assets By [ 4 Min read ]
The future of finance is with tokenized RWAs and cross-chain DeFi; dig into real world assets on Avalanche and ICON. @helloicon Read More. Tokenizing Real-World Assets (RWA) Part 1: A Deep Dive into ERC-3643 By [ 6 Min read ]
Discover the concept of  Real-World Assets (RWA) tokenization and its benefits, along with an examination of the ERC-3643 standard tailored for this purpose. @leonidcryptonstudio Read More. Bitcoin's Surge Past $60,000: Rally or Short-Lived Surge? By [ 4 Min read ]
Bitcoin has crossed the $60,000 mark, and it looks like this is not the limit yet. How sustainable is this growth and what to expect further? 🧑‍💻 What happened in your world this week? It's been said that , , . Feeling stuck? We got you covered ⬇️⬇️⬇️ We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.
See you on Planet Internet! With love,
The HackerNoon Team ✌️ @dshishov Read More. writing can help consolidate technical knowledge establish credibility and contribute to emerging community standards ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME

CODEGEM

codegem.app

1-10 employees

Since 2019

COMPANY RANKING

CODEGEM

EVERGREEN INDEX #3532

Representing this company? Click here to claim and customize this page!

CodeGem WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

CODEGEM

codegem.app

1-10 employees

Since 2019

COMPANY RANKING

CODEGEM

EVERGREEN INDEX #3532

RELATED COMPANIESRANK

STORIES ABOUT CODEGEM

Representing this company? Click here to claim and customize this page!

CodeGem WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!