Thursday, September 19, 2024

39 AI Code Tools - The Ultimate Guide in 2024

To See All Articles About Technology: Index of Lessons in Technology

What are the best AI code tools in 2024?

TL;DR - As of September 2024, most programmers achieve the best results by using Cursor with Anthropic Sonnet 3.5 or OpenAI o1.

AI coding tools are becoming standard practice for many developers. And today, you’ll learn which code generators  and tools are the best ones out there for creating high-quality code with the help of artificial intelligence.

Want to learn more? Read on!

Is it possible to code with AI tools?

Yes, it is possible to code with AI tools.  In fact, leveraging AI tools for coding is not only possible, but it can also significantly enhance productivity and accuracy.

AI code is code written by artificial intelligence (AI), often times utilizing large language models (LLMs). These AI programs can write their own programs or translate from one programming language to another. They also perform tasks like offering assistance in auto-generating documentation and finding code snippets faster.

One of the most popular tools is Open AI’s Codex, an AI system that translates natural language to code. Codex powers GitHub Copilot, another popular AI code tool.

OpenAI Codex is capable of interpreting simple commands in natural language and carrying them out for the programmer. This makes it possible to build on top of the existing application with a natural language interface.

As a general-purpose programming model, OpenAI Codex can be applied to almost any programming task. That said, the tool is in beta and so results will vary.

AlphaCode by DeepMind is another tool that is shaking up the industry. Interestingly, this tool outperforms human coders in certain situations. You see, AlphaCode outperformed 45% of programmers in coding competitions with at least 5,000 participants.

However, there are problems with code generators, too. That's why AI coding tools are used to help developers become more productive and efficient, rather than to replace them entirely.

For example, a Stanford-affiliated research team found that engineers who use AI tools are more likely to cause security vulnerabilities in their apps. Plus, questions around copyright are not entirely resolved.

In other words, AI code tools are not yet completely safe to use. That said, the popularity of these tools means that they can’t be overlooked.

What is AI code written in?

AI code is written in languages supported by the AI code generator. For example, OpenAI Codex is most fluent in Python but is also quite capable in several languages, including JavaScript, Ruby, and TypeScript.

Now, let’s take a look at the best code generators out there.

The best AI code generators and AI development tools

What are some effective AI code generators? The most popular ones include OpenAI Codex, Copilot by Github,  ChatGPT by OpenAI as well as open-source models such as Llama 3.

But there are plenty of other tools out there. I’ve listed them here below, including their features, capabilities, and which companies are behind them. Let’s dive in!

Here are the best AI code generators of 2024.

1. OpenAI (ChatGPT, GPT-4, o1)

GPT-4, OpenAI's latest AI model, is a multimodal tool that excels in programming tasks. It understands and explains code, writes new code, and outperforms existing models on Python coding tasks. Despite its ability to handle complex tasks, it has limitations like reasoning errors and potential security vulnerabilities in the code it produces.  

ChatGPT is primarily a user-friendly interface developed by OpenAI that allows you to interact conversationally with advanced language models like GPT-4 and o1-mini. While it's often referred to as a model, ChatGPT is essentially the platform that enables you to generate or debug code and perform other text-based tasks by communicating with these underlying AI models.

Update May 14th: OpenAI just releaded GPT-4o - their new flagship model that’s as smart as GPT-4 Turbo and much more efficient. With 50% reduced pricing and 2x faster latency, it achieves impressive results.

Update September 16th:  o1 is a new series of AI models designed to enhance reasoning by spending more time thinking through problems before responding, excelling in complex tasks in science, coding, and math. OpenAI o1-mini is a faster, more cost-effective model particularly effective at coding, offering an affordable solution for applications that require reasoning but not extensive world knowledge. Both models are now available in ChatGPT and via the API for users to tackle complex problems efficiently.

Price: Free or $20 for GPT Plus

2. Copilot

Copilot uses publicly available code from GitHub repositories so that users can access large datasets and quickly develop accurate code. The tool detects errors in code and recommends changes to it. You can start using GitHub Copilot by installing one of the extensions in your preferred environment.

Price: $10-$19 - GitHub Copilot is free to use for verified students, teachers, and maintainers of popular open source projects.

3. AWS Bedrock

AWS Bedrock is Amazon Web Services' fully managed service that provides developers with access to a variety of powerful foundation models for building and scaling generative AI applications. For programmers, it offers APIs to interact with models like Amazon's Titan and others from leading AI startups, enabling tasks such as code generation, debugging, and text synthesis. While AWS Bedrock simplifies integrating AI into applications, it may have limitations like model accuracy and potential security vulnerabilities in generated code, so developers should exercise caution and perform thorough testing.

Pricing information can be found here

4. AlphaCode

Another AI-based code generator is Google-backed DeepMind’s AlphaCode, which gives developers access to source code from various language libraries. With AlphaCode, developers can leverage thousands of pre-made libraries, helping them connect and use third-party APIs quickly and easily. AlphaCode is not yet available to the public.

Price: No information available

5. Tabnine

Tabnine is an AI code completion tool that utilizes deep learning algorithms to provide the user with intelligent code completion capabilities. Tabnine supports several programming languages such as Java, Python, C++, and more. This tool is open-source and is used by leading tech companies like Facebook and Google.

Price: Paid plans start from $12/month per seat

6. CodeT5

CodeT5 is an open AI code generator that helps developers to create reliable and bug-free code quickly and easily. It is also open-source and provides support for various programming languages such as Java, Python, and JavaScript. CodeT5 also has an online version as well as an offline version for data security.

Price: Free

7. Polycoder

Polycoder is an open-source alternative to OpenAI Codex. It is trained on a 249 GB codebase written in 12 programming languages. With Polycoder, users can generate code for web applications, machine learning, natural language processing and more. It is well-regarded amongst programmers because of its capability of generating code quickly.

Price: Free

8. Deepcode

DeepCode is a cloud-based AI code analysis tool that automatically scans the codebase of a project and identifies potential bugs and vulnerabilities. It offers support for multiple languages such as Java, Python, and JavaScript. DeepCode is well-regarded for its accurate bug detection.

Price: No information available

9. WPCode

WPCode is an AI-driven WordPress code generator created by Isotropic. It supports both developers and non-technical WordPress creators, allowing them to quickly generate high-quality code snippets. CodeWP supports not only HTML and CSS but languages such as Java and Python. It even includes AI assistants to suggest improvements to code snippets.

Price: Starting at $49

10. AskCodi

AskCodi is a code generator that offers a full suite of development tools to help developers build and ship projects faster. With its AI-based code generation, it helps developers write better code and shorter code blocks, with fewer mistakes. AskCodi can be used to develop both web and mobile applications.

Price: Paid plans start from $7.99/month per seat

11. Codiga

Codiga is a static analysis tool that ensures code is secure and efficient. It supports popular languages like JavaScript, Python, Ruby, Kotlin, and more. With Codiga, you can test your code for vulnerabilities and security issues in real time. It also includes an auto-fixer to quickly address any issues in the code.

Price: Paid plans start from $14/month per seat

12. Visual Studio IntelliCode

Visual Studio IntelliCode is an extension of the Visual Studio Code editor created by Microsoft that provides AI-assisted development experiences to improve developer productivity. It offers smarter IntelliSense completions and helps reduce the amount of time developers spend navigating and debugging code.

Price: Starting from $45/month

13. PyCharm

PyCharm is an AI code completion tool from JetBrains which provides developers with intelligent code completion capabilities. This tool supports various programming languages such as Java, Python, and JavaScript. PyCharm is well regarded for its accuracy and can help developers reduce the amount of time spent on coding tasks.

Price: Starting from $24.90/month per seat

14. AIXcoder

AIXcoder is an AI-powered programming pair designed to aid development teams in writing code. It supports languages such as Java, Python, and JavaScript. This tool also offers a range of features such as automated routine tasks, AI-powered code completion, real-time code analysis and error checks while typing.

Price: No information available

15. Ponicode

Ponicode is an AI-powered code assistant designed to help developers optimize their coding workflow. It uses natural language processing and machine learning to generate code from user-defined descriptions. The tool is maintained by CircleCI.

Price: No information available

16. Jedi

Jedi is an open-source option for code completion in AI. It mostly functions as a plugin for editors and IDEs that use Python static analysis tools.

Price: Free

17. Wing Python IDE Pro

Created by Wingware, Wing IDE is a Python-specific software setup that combines the code editing, code navigation, and debugging mechanisms required to Code and Test Software applications. It offers various features such as an intelligent auto-completing Editor, Refactoring, Multi-Selection, and Code Snippets, which make coding much easier and more efficient.

Price: Annual licenses starting at $179/month

18. Smol Developer

Smol is an open-source artificial intelligence agent designed to function as a personal junior developer, capable of generating an entire codebase from your specific product specifications. Unlike traditional, rigid starter templates, Smol can create any kind of application based on your unique requirements. Boasting a codebase that is simple, safe, and small, it offers the perfect blend of ease-of-understanding, customization, and a helpful, harmless, and honest approach to AI development.

Price: Smol is open-source with a MIT License.

19. Cody (Sourcegraph)

Cody (not to be confused with AskCodi), Sourcegraph's AI tool, is a comprehensive coding assistant. It understands your entire codebase, answers queries, and writes code. Beyond guidance, Cody provides detailed code explanations, locates specific components, and identifies potential issues with suggested fixes. Cody works directly in VS code with an extension.

Price: Cody is free for personal use, Sourcegraph starts at $5k/year

20. CodeWhisperer (Amazon)

CodeWhisperer is a tool developed by Amazon. It offers real-time, AI-driven code suggestions and identifies potential open-source code matches for easier review. It even scans for security vulnerabilities, suggesting immediate patches. An added bonus is its commitment to code safety, always aligning with best security practices such as OWASP guidelines.

Price: Free for personal use, $19/month professional use

21. Bard (Google)

Bard can help with programming and software development tasks, including code generation, debugging and code explanation. These capabilities are supported in more than 20 programming languages including C++, Go, Java, Javascript, Python and Typescript. And you can easily export Python code to Google Colab — no copy and paste required. Bard can also assist with writing functions for Google Sheets.

Price: Google Bard is Free

22. Code Llama (Meta)

Code Llama is a set of large language models specialized for coding, built on the Llama 2 platform. It includes different models for various needs: the general-purpose Code Llama, Code Llama - Python for Python-specific tasks, and Code Llama - Instruct for instruction-based coding. These models vary in size (7B, 13B, and 34B parameters) and can handle up to 16k token inputs, with some improvements on up to 100k tokens. The 7B and 13B models also offer content-based infilling.

Code Llama’s training recipes are available on their Github repository - Model weights are also available.

23. Claude 2 & 3, 3.5 (Anthropic)

Claude 3.5 Sonnet is the latest natural language AI model introduced by Anthropic, a firm established by Dario Amodei, formerly of OpenAI. This new iteration is engineered for enhanced input and output lengths and boasts superior performance relative to its earlier version. In an internal agentic coding evaluation, Claude 3.5 Sonnet solved 64% of problems, outperforming Claude 3 Opus which solved 38%. Users can input up to 100K tokens in each prompt, which means that Claude can work over hundreds of pages of technical documentation. The earlier version, Claude 2 scored a 71.2% up from 56.0% on the Codex HumanEval, a Python coding test.

Their evaluation tests the model’s ability to fix a bug or add functionality to an open source codebase, given a natural language description of the desired improvement. When instructed and provided with the relevant tools, Claude 3.5 Sonnet can independently write, edit, and execute code with sophisticated reasoning and troubleshooting capabilities. It handles code translations with ease, making it particularly effective for updating legacy applications and migrating codebases.

A Stability AI Membership is required for commerical application

24. Stable Code 3B

Stability AI's Stable Code 3B, a new 3 billion parameter Large Language Model specialized in code completion, which is 60% smaller yet performs similarly to the larger CodeLLaMA 7b. This model, trained on diverse programming languages and software engineering-specific data, can run in real-time on modern laptops without a GPU. Stable Code 3B is part of Stability AI's Membership program and offers advanced features like Fill in the Middle capabilities and expanded context size, demonstrating state-of-the-art performance in multi-language coding tasks.

A Stability AI Membership (Starting at $20/mo) is required for commercial applications. Free for non-commercial.

25. Replit AI

Replit AI is an innovative code completion tool designed to streamline your coding experience by offering tailored suggestions that align with the context of your current file. As you delve into coding, the tool intuitively presents inline suggestions, enhancing your efficiency and accuracy. Additionally, Replit AI offers advanced features such as the ability to refine suggestions through code comments, the application of prompt engineering for more relevant results, and the flexibility to toggle the code completion feature on or off within the editor settings, ensuring a customized coding environment tailored to your preferences.

Replit AI is available in Replit's Free tier (Limited) and in their Core tier (Advanced Model).  

26. Plandex

Plandex employs persistent agents that tackle extensive tasks spanning numerous files and involving multiple steps. It segments sizable tasks into manageable subtasks, executing each in sequence until the entire task is accomplished. This tool aids in clearing your backlog, navigating new technologies, overcoming obstacles, and reducing the time spent on mundane activities.

Plandex is open-source on Github

27. Meta AI (Meta Lama 3)

Meta has launched Meta AI, powered by the Llama 3 model with 70 billion parameters.  The model positions itself as a powerful asset for improving application functionalities, but it does not match the customization and transparency of more advanced models like GPT-4 Turbo and Claude Opus. The benefits of Meta's approach to open-source AI are multifaceted, including attracting top talent, leveraging community contributions, fostering standardization and lower costs, building goodwill, and aligning with business models that do not rely solely on AI products.  While it is described as "open weight," providing access to the model's weights, it does not include the full toolkit necessary for reproduction. They also co-developed Llama 3 with torchtune, the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs.

Moreover, Meta is also currently pretraining a 405B parameter model, signaling an ambitious expansion of its AI capabilities. This larger model, set to be released later, promises even more powerful functionalities and potential industry leadership if it surpasses current leaders like GPT-4 and Claude Opus. Such a development could reshape industry standards and perceptions, especially against competitors who guard their models under the guise of safety concerns. This bold move by Meta not only showcases their commitment to advancing AI technology but also challenges the industry's more cautious narratives around the sharing and utilization of AI models, setting new benchmarks for what’s achievable in AI development.

28. MetaGPT

Not to be confused with Meta AI, MetaGPT is a tool that automates the generation of software development outputs such as user stories, competitive analysis, requirements, data structures, APIs, and documents from a single line of input. It integrates roles typically found in a software company—product managers, architects, project managers, and engineers—into its workflow. These roles are executed by large language models (LLMs) following detailed Standard Operating Procedures (SOPs). The core philosophy behind MetaGPT is "Code = SOP(Team)," emphasizing the application of SOPs to organize and direct the work of its LLM teams. This structure aims to mimic the entire process of a software company, simplifying and automating complex tasks.

MetaGPT is MIT licensed and open-source

29. AutoRegex

AutoRegex is my favorite tool to translate natural language to regex. If you're like me, you wiped all traces of regex syntax from your memory the moment ChatGPT released - this helps!

30. llama.cpp

Llama.cpp is designed to facilitate LLM inference with optimal performance and minimal initial setup across various hardware, both locally and in the cloud. It is implemented in plain C/C++ without dependencies and features extensive support for Apple silicon through ARM NEON, Accelerate, and Metal frameworks. It also supports AVX, AVX2, and AVX512 for x86 architectures and offers integer quantization from 1.5 to 8 bits to enhance inference speed and reduce memory consumption. For NVIDIA GPUs, llama.cpp includes custom CUDA kernels, with AMD GPU support through HIP. Additionally, it supports Vulkan, SYCL, and partial OpenCL backends and can perform hybrid CPU+GPU inference to manage models that exceed VRAM capacity.

31. Aider

Aider is a  command line tool  allowing you to pair program with LLMs directly in your terminal. It seamlessly integrates with your local git repository, editing code directly in your source files and crafting smart commit messages for each change.

Aider is open-source on Github

32. Codestral (Mistral)

A model fluent in 80+ programming languages, Codestral, is Mistrral's first-ever code model. Codestral is an open-weight generative AI model explicitly designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code and English, it can be used to design advanced AI applications for software developers.

Codestral is a 22B open-weight model licensed under the new Mistral AI Non-Production License, which means that you can use it for research and testing purposes. Codestral can be downloaded on HuggingFace

Update July 16th: Codestral Mamba release:  For easy testing, they made Codestral Mamba available on la Plateforme (codestral-mamba-2407), alongside its big sister, Codestral 22B. While Codestral Mamba is available under the Apache 2.0 license, Codestral 22B is available under a commercial license for self-deployment or a community license for testing purposes.

33. Cursor

Cursor is an AI-enhanced code editor designed to boost productivity by enabling developers to interact with their codebase through conversational AI and natural language commands. It includes features like Copilot++, which predicts your next code edit, and Cmd-K, which allows code modifications through simple prompts.

You can try Cursor for free

34. Warp

Warp is a modern, Rust-based terminal with AI built in. Type ‘#’ on your command line and start describing the command you want to run using natural language. Warp will load AI Command Suggestions as you type.

Warp AI is free to use up to 40 requests per user per month. You can create a Team and upgrade to a Team plan to unlock higher Warp AI request limits. Visit the pricing page to learn more.

35. CodiumAI

CodiumAI is a trending tool that developers can use to enhance their coding experience with the power of AI. Key features: When compared to the other tools, CodiumAI provides a set of unique features: Precise code suggestions: CodiumAI thoroughly analyzes your code, providing tailored suggestions. These include adding docstrings, refining exception handling, and implementing best practices, directly improving your code’s quality. Code explanation: This tool offers detailed descriptions of your source code or snippets, breaking down each component and offering insights and sample usage scenarios to enhance code comprehension. Automated test generation: Testing is essential in large codebases. CodiumAI simplifies this by swiftly generating accurate and reliable unit tests without manual intervention, saving significant time and effort and ensuring thorough testing of your codebase. Code behavior coverage: Comprehensive testing means covering all possible code behaviors. CodiumAI’s “Behavior Coverage” feature generates test cases covering various code behaviors and seamlessly applies related changes to your source code. Streamlined collaboration: CodiumAI facilitates teamwork by enabling seamless collaboration among developers. Its Git platform integration allows for sharing and reviewing code suggestions and test cases within your development team, promoting efficient workflows and code quality. Seamless implementation: With CodiumAI’s intelligent auto-completion agent, implementation becomes effortless. It seamlessly integrates with your task plans, ensuring smooth execution from concept to completion of your code. Multiple language and IDE support: CodiumAI supports popular programming languages such as Python, JavaScript, and TypeScript while seamlessly integrating with leading IDEs, including VSCode, WebStorm, IntelliJ IDEA, CLion, PyCharm, and JetBrains. Pricing The pricing of CodiumAI offers free code integrity for developers at $0/user per month, while teams can access optimized collaboration for $19/user per month.

36. MutableAI

MutableAI is a tool that revolutionizes the coding experience with features such as AI autocomplete, one-click production code enhancements, prompt-driven development, test generation, and extensive language and IDE integration, empowering developers to write code more efficiently and effectively. Key features Here are the key features of MutableAI: AI Autocomplete: Minimize time spent on boilerplate code and searching for solutions on Stack Overflow with specialized neural networks providing intelligent code suggestions. Production Quality Code: Refactor, document, and add types to your code effortlessly, ensuring high-quality code output. Prompt-driven Development: Interact directly with the AI by giving instructions to modify your code, enabling a more intuitive and interactive coding experience. Test Generation: Automatically generate unit tests using AI and metaprogramming techniques, ensuring comprehensive test coverage for your code. Language and IDE Integration: Supports popular languages like Python, Go, JavaScript, TypeScript, Rust, Solidity, and more, as well as integration with IDEs like JetBrains and Visual Studio (VS) Code. Pricing MutableAI’s basic plan offers $2 per repo per month, while its premium plan offers $15 per repo per month.

37. Figstack

Figstack is an innovative AI tool that provides developers with various features to improve code understanding, translation, documentation, and optimization. Figstack caters to developers at all levels, from beginners looking to understand complex code to experienced professionals aiming to automate tedious tasks like writing documentation or measuring code efficiency. Key features Code explanation in natural language: This feature helps users easily understand the code written in any language by translating it into clear, natural language descriptions. Cross-Language code translation: Developers can easily convert code from one programming language to another. This simplifies the process of porting applications across different technology stacks. Automated function documentation: Figstack automatically generates detailed docstrings that describe the function’s purpose, parameters, and return values, ensuring that your code is always readable, maintainable, and well-documented. Time complexity analysis: The tool helps developers assess the efficiency of their code in Big O notation, pinpoint bottlenecks, and optimize their code for better performance by identifying the time complexity of a program. Pricing Figstack is free to use and includes most of the essential features.

38. CodeGeeX

CodeGeeX is an AI-powered code generation tool designed to assist developers in writing, completing, and optimizing code more efficiently. It leverages deep learning models trained on a wide variety of programming languages and codebases, where it can provide context-aware code suggestions, complete code snippets, and even generate entire functions or modules. Key features Code generation and completion: CodeGeeX offers accurate code generation capabilities based on natural language descriptions. Also, it can complete the current line or multiple lines ahead, making the development process faster. Code translation: Developers can effortlessly convert their code from one programming language to another. Automated comment generation: The tool saves time by automatically generating line-level comments, which helps improve code readability and maintainability. AI chatbot: The AI chatbot in CodeGeeX provides quick answers to technical questions directly within the development environment instead of having developers find solutions on the internet. Wide IDE and language support: CodeGeeX supports various popular IDEs, including Visual Studio Code, JetBrains IDEs and multiple programming languages, such as Python, C++, JavaScript, and Go. Pricing CodeGeeX offers their plugin completely free for individual users. If there are more advanced requirements, they provide an enterprise plan.

39. Codeium

One I personally use. Millions of engineers, including our own, use these features every single day. Autocomplete Autocomplete faster than thought. Codeium's generative code can save you time and help you ship products faster. Command Give instructions in your editor to perform inline refactors, whether it is generating code, adding comments, or something even more complex. Chat Generate boilerplate, refactor code, add documentation, explain code, suggest bug fixes, and so much more. Powered by the largest models, optimized for coding workflows and Codeium's industry-leading reasoning engine. Context All of Codeium's features are powered by an industry-leading context awareness and reasoning engine. With full repository and multi repository codebase awareness, Codeium provides 35% more value purely from providing more grounded results.

References

Tags: Technology,Artificial Intelligence,Generative AI,Large Language Models,Python,JavaScript,

Wednesday, September 18, 2024

Cisco’s second layoff of 2024 affects thousands of employees

To See All Articles About Layoffs / Management: Index of Layoff Reports
U.S. tech giant Cisco has let go of thousands of employees following its second layoff of 2024. The technology and networking company announced in August that it would reduce its headcount by 7%, or around 5,600 employees, following an earlier layoff in February, in which the company let go of about 4,000 employees. As TechCrunch previously reported, Cisco employees said that the company refused to say who was affected by the layoffs until September 16. Cisco did not give a reason for the month-long delay in notifying affected staff. One employee told TechCrunch at the time that Cisco’s workplace had become the “most toxic environment” they had worked in. TechCrunch has learned that the layoffs also affect Talos Security, the company’s threat intelligence and security research unit. Cisco said in its August statement that its second layoff of the year would allow the company to “invest in key growth opportunities and drive more efficiencies.” On the same day, Cisco published its most recent full-year earnings report, in which the company said 2024 was its “second strongest year on record,” citing close to $54 billion in annual revenue. Cisco chief executive Chuck Robbins made close to $32 million in total executive compensation during 2023, according to the company’s filings. When reached by email, Cisco spokesperson Lindsay Ciulla did not provide comment, or say if Cisco’s executive leadership team planned to reduce their compensation packages following the layoffs. Are you affected by the Cisco layoffs? Get in touch. You can contact this reporter on Signal and WhatsApp at +1 646-755-8849, or by email. You can send files and documents via SecureDrop. A look at Cisco’s response to the current economic climate and transition trajectory leading to significant layoffs: Cisco’s focus on subscription-based services Cisco's $28 billion acquisition of Splunk in March signals a strategic shift towards subscription-based services. This move marked a significant shift for Cisco, traditionally known for networking equipment, as it entered the competitive cybersecurity market alongside players like Palo Alto Networks, Check Point, CrowdStrike, and Microsoft, as ET followed this development. Cisco’s funding to AI startups Since 2018, Cisco has been actively involved in the AI space, acquiring Accompany and CloudCherry to expand its presence in this rapidly growing technology. In 2019, the company launched the Silicon One ASIC chip, offering speeds of 25.6 Tbit/s, directly competing with Intel and Nvidia. Cisco has allocated $1 billion to fund AI startups. Earlier in February, Cisco partnered with Nvidia. The latter agreed to use Cisco's ethernet with its own technology that is widely used in data centers and AI applications. In June, Cisco invested in AI startups like Cohere, Mistral AI, and Scale AI. The company announced that it had made 20 acquisitions and investments related to AI in recent years. Focus on emerging technologies Cisco offers data center technologies like the Unified Computing System (UCS) and Nexus switches, designed to support modern data center and cloud environments. Additionally, their collaboration tools, such as WebEx and Cisco Jabber, enhance communication and productivity. Shifting focus on cybersecurity Since 2013, with the acquisition of Sourcefire, a network security and threat detection provider Cisco strengthened its security portfolio. Open DNS acquired in 2015, provides cloud based threat detection and prevention. CloudLock, a cloud security solutions provider for $293 million protects users and data in cloud environments. Duo Security, for $2.35 billion, provides cloud based authentication and access control.
References Tags: Technology,Layoffs,Management,Artificial Intelligence,

Tuesday, September 17, 2024

How to use AI for coding the right way

To See All Articles About Technology: Index of Lessons in Technology

Devs: “Yeah Cursor/ChatGPT/AI is great and all, but you still need to know what you want, or know how to check for hallucinations. A complete beginner won’t be able to code working apps with it.”

Not really true anymore…

I’ve been coding in an unfamiliar language (Ruby) for a freelance gig, and PHP for personal projects, so I’m often unsure how correct looks like.

What I do to make sure it’s correct:

  • Overall approach: Using AI for coding is like having a super knowledgeable programming intern who’s knows everything but not so good at applying said knowledge to the right context, and we just have to help nudge it along. Put another way, Claude/Cursor are like outsourced devs, and my work mostly is managing them, pointing them to the right direction. More creative direction than actual coding. I think 80% of my code written by AI now, but that doesn’t mean I can fall asleep at the wheel. I got to stay alert to errors, follow conventions, check their work all the time.

  • Before I start, I chat with Claude 3.5 Sonnet on Cursor on the broad steps to take, the overall architecture. Progressive prompting. I can reference the whole codebase with Cursor for context. Only use Sonnet. Not Opus. Not Haiku.

  • I also add system prompts or “rules” for Cursor to give it a better context frame from which to answer. Adapted the prompt from the Cursor forum. It goes something like "You are an expert AI programming assistant in VSCode that primarily focuses on producing clear, readable Python code. You are thoughtful, give nuanced answers… "

  • In Cursor setting, you can also upload documentation of the framework, language or gems/packages you’re using, so that it can refer to it for best practices and conventions.

  • AI can be not just coder but also code reviewer. Get it to review its own code, using prompts like “Any mistakes in this code?”, “Does this follow best practices for Rails/PHP?” Sometimes I ask “Does it follow convention in this codebase?” and @ the entire codebase and @ the documentation of the language.

  • Sometimes I use a different LLM to as a checker. I open a separate window, and get Llama 3.1 or GPT-4o to double check the code for bugs. It’s like getting a second opinion from a doctor.

  • Share error messages, highlight the code, cmd-L and link the right files to give it enough context. I can’t emphasize this enough but with Cursor, using the @ to link the right files/components, or even a docs on the internet, is killer. It’s tempting to @ the entire codebase every time but from personal experience/observation, giving too much context might hinder too, make it ‘confused’ and it starts hallucinating or giving weird suggestions. There seems to be a sweet spot in terms of amount of context given - more art than science.

  • Or use cmd-K to edit the line directly. Otherwise I ask it to explain line by line how it works, and ask it questions, reason with it. I learn from the process. Knowledge and skill goes up. This is an important step, because people are right that AI can make you lazy, waste away your coding muscles, but I think it’s 100% how you use it. I try not to use AI in a way that makes me lazy or atrophy, by asking questions, reasoning with it, learning something each time. Mental disuse would be simply copypasting without thinking/learning. It’s a daily practice to stay disciplined about it. Kind of like eating your veges or going to the gym. Simple but ain’t easy.

  • Following these steps, I’m able to solves bugs 99% of time. The 1% is when there’s some special configuration or a key part of the context is hidden or not part of codebase. That’s when I tend to need help from the senior devs, or from code reviews or tests to pick up on. The usual way. The processes are there to mitigate any potential drawbacks of AI generated code.

Cursor + Claude Sonnet are like code superpowers.

References
Tags: Artificial Intelligence,Technology,Generative AI,Large Language Models,

Sunday, September 15, 2024

AI is here, and so are job losses and inequality

To See All Articles About Layoffs / Management: Index of Management Lessons

Meet my new secretary, ChatGPT. Over the last couple of weeks, tied up by several unending writing projects, I’ve done what I once deemed unthinkable. I’ve found myself going to ChatGPT — OpenAI’s artificial intelligence bot — for everything from proof-reading and copy-editing to research and review.

I most certainly realise that I’m quite late to the chase. A lot of my friends have been employing ChatGPT for ages now to write and draft all sorts of documents. But I’m a bit of a purist writer, to be honest. I’ve always believed that words are deeply personal. If you’re writing a letter, email or essay, every word ought to come from your heart -- not from digital algorithms operating mysteriously. Admittedly, therefore, I still don’t use ChatGPT to do any of my actual writing (I assure you this column has not been written by ChatGPT).

But as I began using ChatGPT, I realised why I had previously been afraid of it. This thing is addictive and eerily efficient. It understands more about the world than I was led to believe. It reads and writes rapidly. And I hate to say this, but it can do a lot of the work that so many of us get paid to do -- for free.

To be sure, none of this makes AI all that different from the world’s previous tech revolutions. Every time a new machine has been invented, fear has followed.

In 1830, Britain was about to flag off the world’s first passenger train to run between Liverpool and Manchester. Among the railroad project’s most ardent supporters was a local Member of Parliament, William Huskisson. In the run-up to the railway’s grand opening, Huskisson had just undergone surgery and was advised by his doctor to cancel all upcoming appointments. Huskisson refused. The train’s debut was far too important an occasion, he argued.

It was a fateful decision. On the big day, as the train’s demo got underway, Huskisson walked across the tracks to shake hands with Prime Minister Arthur Wellesley. Then, disaster struck. Before he knew it, Huskisson saw the train barreling down towards him as he watched in horror. His feet got stuck in the tracks and the MP was knocked out clean.

In the aftermath of the accident, much British press coverage of the event naturally dwelt on Huskisson’s tragic death. Writers shuddered at the thought of speedy steam engines mowing down people all over England.

But something else also happened: the train cut down the usual travel time between Liverpool and Manchester to less than half. Soon, the railway became the cornerstone of Britain’s Industrial Revolution and powered the most extensive and influential empire the world has ever seen.

AI has the potential for such pathbreaking efficiencies, too, but it could also change the nature of work in unprecedented ways.

Previous tech revolutions had replaced relatively lower income and lower skilled jobs. In return, several more jobs were created further up the ladder. Trains, for instance, rendered horse carriages obsolete. But over time, the sons of carriage-drivers learnt to operate steam engines, and the economic pie expanded on the whole.

What sets AI apart is that it is also upending higher income, higher skilled jobs. That means that while economic activity might expand, the jobs that AI is likely to create will be far more skill-heavy and potentially fewer in number. Those at the top will benefit disproportionately. The masses below will have few opportunities.

To make the most of this new beast, governments will have to find ways to preempt that inequality. Otherwise, millions could risk getting their feet caught in its tracks.

References Tags: Layoffs,Technology,Management,

Thursday, September 12, 2024

Mass layoffs hit tech industry: Over 27,000 jobs cut as Intel, Cisco, IBM, and Apple slash workforce

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Synopsis Tech companies cut over 27,000 jobs in August 2024, with major firms like Intel, IBM, and Cisco among those announcing layoffs. Intel plans to reduce its workforce by 15%, while Cisco is shifting focus to AI and cybersecurity. IBM is discontinuing R&D in China. Other companies like Infineon, GoPro, Apple, Dell Technologies, ReshaMandi, Brave, and ShareChat also announced significant job cuts. Tech companies continued to cut jobs at a rapid pace in August 2024. More than 27,000 workers in the industry lost their jobs as over 40 companies, including big names like Intel, IBM, and Cisco, as well as numerous smaller startups, announced layoffs. To date, more than 136,000 tech workers have been laid off by 422 companies in 2024, indicating significant upheaval in the sector. Intel Intel is undergoing one of the most challenging periods in its history, announcing 15,000 job cuts, which represents over 15% of its workforce. These layoffs are part of a $10 billion spending reduction plan for 2025, spurred by a disappointing second-quarter earnings report and outlook. Annual revenues for the company fell by $24 billion between 2020 and 2023, despite a 10% increase in its workforce during the same time frame. CEO Pat Gelsinger stated, "Intel’s revenue growth shortfall is attributed to high costs and low margins, despite our leadership in the CPU chip revolution 25 years ago." Cisco Systems Cisco Systems has also announced it is laying off around 6,000 employees, or about 7% of its global workforce, as it shifts its focus to high-growth areas such as AI and cybersecurity. This is the company's second major round of job cuts this year. CEO Chuck Robbins remains hopeful about the future, noting efforts to pivot toward emerging technologies. "Cisco is optimistic about rebounding demand for our networking equipment," he said. The company is restructuring to capitalize on these technologies and has committed $1 billion to investing in AI startups. Additionally, Cisco recently acquired cybersecurity firm Splunk for $28 billion. As part of the restructuring, Cisco plans to consolidate its networking, security, and collaboration departments into a single organization. IBM IBM has decided to discontinue its research and development operations in China, leading to over 1,000 layoffs. Chinese media outlet Yicai reported on the situation, which stems from a decline in demand for IT hardware and difficulties in expanding within the Chinese market. IBM pledged that despite these changes, customer support in China will remain unaffected. "IBM will now prioritize serving private enterprises and select multinationals within the Chinese market," the company affirmed. Infineon Infineon, a German chipmaker, is also making significant cuts, with plans to reduce 1,400 jobs and relocate another 1,400 to countries with lower labor costs. CEO Jochen Hanebeck explained these measures were necessary due to third-quarter revenue falling short of expectations. "The slow recovery in target markets is due to prolonged weak economic momentum and excess inventory levels," he said, leading to a downgraded forecast for the third time in recent months. GoPro GoPro, the action camera manufacturer, will cut about 15% of its staff, totaling around 140 employees, as part of a restructuring plan. These layoffs aim to reduce operating expenses by $50 million from projected fiscal 2024 expenses. Apple Apple has laid off around 100 employees primarily from its services group, which includes the Apple Books app and Apple Bookstore teams, with some engineering roles also affected. The company is redirecting resources toward AI programs, seeing Apple Books as a lower priority now. However, Apple News remains a focal point. This is not Apple's first round of layoffs this year; previously, it cut 600 employees from its Special Projects Group and shuttered a 121-person AI team in San Diego in January. As of the last report, Apple had 161,000 full-time equivalent employees. Apple declined to comment on the latest layoffs. Dell Technologies Dell Technologies is reportedly reorganizing its sales teams, including establishing a new AI-focused group. Sales executives Bill Scannell and John Byrne mentioned in a memo that Dell aims to become leaner by streamlining management and reprioritizing investments. Rumors suggest that the company may have laid off about 12,500 employees, or 10% of its global workforce, but this has not been officially confirmed. ReshaMandhi ReshaMandi, a fabric startup based in Bengaluru, has laid off its entire workforce, according to sources cited by Entrackr. The company's website has been inactive for a week, coinciding with the resignation of its auditor. "It’s all over for ReshaMandi," a source said. "The company is struggling to pay liabilities and bear operational costs, including salaries, for the past several months." Brave Brave, a web browser and search startup, has laid off 27 employees across various departments, as confirmed by TechCrunch. This represents a 14% reduction from its estimated 191 employees. Brave previously cut 9% of its workforce in October 2023 due to cost management challenges in a difficult economic environment. ShareChat ShareChat, a social media company also based in Bengaluru, has cut around 30-40 jobs, or roughly 5% of its workforce, following a bi-annual performance review in August 2024. [ Ref ]
Tags: Layoffs,Management,

Engineering admissions decline: More than 30% seats lying vacant, student enrolment declines first time in at least 5 years

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The highest vacancy rates are observed in regions such as Chhatrapati Sambhaji Nagar and Mumbai, with 42.2% and 36.64% of seats remaining vacant, respectively. Nearly one in three engineering seats in Maharashtra remains unfilled this year, revealing a significant supply-demand imbalance. The state’s Common Entrance Test (CET) Cell reported a total of 164,336 seats available across engineering colleges for the current admission cycle. However, only 112,981 students have confirmed their admissions, resulting in a vacancy rate of 31%, up from last year’s 25.82%. The issue of high vacancy rates in engineering courses has persisted over the past decade. Although there were improvements in recent years—vacancy rates dropped from over 44% in 2020-21 to 26% in 2022-23—the situation has worsened again in the past two years. Despite a modest increase in total intake capacity by 3.6%, from 158,000 seats in 2022-23 to 164,336 seats in 2023-24, the number of students enrolling has declined by 4%, dropping from 117,000 in 2022-23 to 112,981 this year. The highest vacancy rates are observed in regions such as Chhatrapati Sambhaji Nagar and Mumbai, with 42.2% and 36.64% of seats remaining vacant, respectively. Engineering admissions past five years in Maharashtra
While there is an increase in vacant seats in engineering, computer science engineering and allied new-age technology courses such as Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML) see lesser vacancies. The engineering admission data with branch-wise break-up shows that computer science engineering continues to remain the most popular branch of engineering in Maharashtra with great preference for allied new-age technology courses such as AI, IoT, ML and cyber security among others. Out of a total of 25,065 seats available in computer science engineering, including those offering AI, ML and IoT; 19,544 admissions have been confirmed; leaving 5,521 seats vacant. In super-specialised courses offering AI-ML and AI-Data Science, out of 13,286 seats admissions are confirmed on 12,678 seats; leaving a vacancy of only 608 seats. Region-wise engineering admissions for this year
Whereas in conventional engineering branches such as mechanical engineering, out of 20,960 seats available, 12,882 admissions have been confirmed leaving 8,078 seats vacant. And in civil engineering branches, out of 14,994 total seats admission have been confirmed on 8,722 seats leaving a vacancy of 6,272 seats. Stating that this has been a trend in the past few years, an official from the CET Cell said, “Even at the time of option-form filling, among the preferred engineering branches computer engineering and other new-age branches were seen in great demand. Whereas conventional branches like mechanical and civil continue to see less demand. The same trends are seen in confirmation of admissions. Students prefer to change their higher education plans if they do not get a seat in a desired branch or good engineering college.” [ Ref ]
Tags: Management,Technology,

CEOs from Mark Zuckerberg to Sundar Pichai explain why companies are making cuts this year

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# Tech industry layoffs are ongoing and widespread, impacting companies like Google, Tesla, and Apple. # CEOs at big tech companies blame the cuts on overhiring and a shift towards a smaller workforce. Layoffs have been plaguing the tech industry since the start of 2023 — and for many companies, the cuts have continued into 2024 and aren't over. A number of Big Tech companies have laid off staff this year, including Google, Tesla, Apple, and dozens more. Ironically, companies haven't been slowing down on innovation, with many releasing a constant stream of AI updates and product launches. Mark Zuckerberg shared his theory on the first round of industry-wide layoffs in an interview with "Morning Brew Daily" in February. He said companies overhired during the pandemic due to e-commerce sales skyrocketing and had to cut back once sales returned to normal. That seems to ring true for a lot of CEOs. Discord CEO Jason Citron also said in an employee memo in January that the company had increased its workforce by fivefold since 2020. Google CEO Sundar Pichai said in 2023 that the company experienced "dramatic growth" over the past two years, which led to hiring "for a different economic reality" than the present. Salesforce CEO and cofounder Mark Benioff also relayed the same sentiment in a letter to employees announcing layoffs in 2023. He said as revenue increased during the pandemic, the company hired "too many people leading into this economic downturn." But why are industry-wide layoffs still so widespread and ongoing? We took a look at what CEOs have said about staff cuts to help us understand why it's still going on.

The less, the better

Zuckerberg said in the "Morning Brew Daily" interview that companies realized the benefits of being leaner, which led to more layoffs. Meta's an example of that — after thousands were cut in Zuckerberg's "year of efficiency," in 2023, the company appeared to make a comeback. "It was obviously really tough. We parted with a lot of talented people we cared about," Zuckerberg said in the interview. "But in some ways, actually becoming leaner kind of makes the company more effective." Google seems to be enacting a similar strategy this year. CEO Sundar Pichai told Bloomberg reporter Emily Chang in May that the company is removing some teams completely to "improve velocity." The tech giant conducted multiple rounds of layoffs this year, with the most recent being in its Cloud unit at the end of May. Wayfair's cofounders also seem to think the company operates better with fewer people. The company has conducted multiple rounds off layoffs since 2022 and most recently laid off 13% of its workforce in January. CEO Niraj Shah and cofounder Steve Conine wrote in a letter to shareholders in February that several rounds of layoffs helped the company get more done at a faster rate and lower cost.

Jobs are being restructured for AI

Google's CEO also said in the Bloomberg interview in May that the company is "reallocating people" to its "highest priorities." Some of those priorities include AI projects, like the creation of an ARM-based central processing unit, the advancement of Gemini, an AI-powered Search, and various updates to Google Workspace. Google isn't the only one to restructure its workforce to make room for AI. Microsoft CEO Satya Nadella explained similar reasoning in a memo last year and said the company would continue to hire in "key strategic areas." Last May, IBM CEO Arvind Krishna said he could easily see 30% of HR and non-consumer-facing roles "replaced by AI and automation" in the next five years. The company conducted its latest round of cuts in March. Dropbox CEO Drew Houston similarly said in a 2023 layoff announcement that its next stage of growth required a different set of skills, "particularly in AI and early-stage product development." It's unclear how long the restructuring will last. But for the moment, tech companies don't seem to be slowing down on AI advancement. [ Ref ] Dated: Jun 2024
Tags: Layoffs

Wednesday, September 11, 2024

Books on Building Financial IQ (Sep 2024)

To See All The Other Book Lists: Index of Book Lists And Downloads
Download Books
1. 
The Intelligent Investor, The Definitive Book on Value Investing (2006)
Benjamin Graham and Jason Zweig

2.
The Little Book of Common Sense Investing
Bogle, John C 
Wiley (2017)

3.
The Essays of Warren Buffett. Lessons for Corporate America.
Lawrence A. Cunningham 
3rd Edition (2013)

4.
Rich Dad Poor Dad
What the Rich Teach Their Kids About Money That the Poor and Middle Class Do Not
Robert T. Kiyosaki
2017

Teaser: Kiyosaki's seminal work is a game-changer in personal finance literature. Through contrasting tales of his "two dads", he highlights the mindset that distinguishes the wealthy from the rest. Central to his philosophy is the emphasis on financial literacy, the power of assets, and the potential of entrepreneurial ventures.

5. 
The Psychology of Money 
Morgan Housel

Teaser: This isn't your traditional finance book. Housel focuses on the emotional and psychological aspects of money, shedding light on how our perceptions shape our financial decisions. By understanding and mastering our emotional triggers, we can make better-informed decisions that lead to wealth.

6.
Multibagger Stocks
How to Multiply Wealth In The Share Market 
By: Prasenjit K Paul

7.
Get a Financial Life 
Beth Kobliner (Fireside Books, 1996) 

8.
Your Money or Your Life 
Joe Dominguez and Vicki Robin (Penguin, 1992).

5 Must-Read Books for a Millionaire Retirement

1. "Learn To Earn" by Peter Lynch and John Rothchild A comprehensive beginner's guide to investing. Lynch, one of the investment world's luminaries, and Rothchild simplify the maze of the stock market. Their approach underlines the importance of thorough research, understanding businesses at a granular level, and maintaining a long-term perspective in investments. 2. "The Most Important Thing" by Howard Marks Marks, an investment titan, shares wisdom from his illustrious career. He delves into understanding market rhythms, the nuances of risk, and the investor's psyche. Advocating a contrarian viewpoint, he stresses the virtues of patience and discernment in successful investing. 3. "Total Money Makeover" by Dave Ramsey A financial reboot manual. Ramsey meticulously outlines a plan designed to clear debt, build a safety net, and initiate investments. His methodology, rooted in personal responsibility and stringent discipline, offers a clear roadmap to financial rejuvenation. 4. "The Millionaire Fastlane" by MJ DeMarco Challenging mainstream notions of wealth-building, DeMarco proposes a radical approach. He underscores that the quickest path to affluence isn't a traditional job but through entrepreneurial ventures that can scale. The book is a clarion call to value time and harness business systems for wealth and autonomy. 5. "The Rules of Wealth" by Richard Templar A holistic guide to amassing wealth. Templar delineates a set of rules, covering a spectrum from foundational money beliefs to intricate investment strategies. He accentuates the pillars of consistency, unwavering discipline, and the quest for knowledge in one's wealth-building journey.
Tags: Finance,List of Books,Non-fiction,Investment,