Fireship - Claude 3.7 goes hard for programmers…
Anthropic has launched Claude 3.7, a large language model that has shown significant improvements in programming capabilities. It introduces a new 'thinking mode' inspired by Deep Seek R1 and includes a CLI tool named Claude Code. This tool allows users to build, test, and execute code within any project, potentially creating an infinite feedback loop that could replace programmers. Despite its high cost, Claude 3.7 has outperformed other models in solving GitHub issues, achieving a 70.3% success rate according to benchmarks. The CLI tool can be installed via npm and provides full context of existing code in projects, although it is expensive at $15 per million output tokens. The model has demonstrated proficiency in generating front-end UIs, although it has some limitations, such as not using specified technologies like TypeScript or Tailwind in certain scenarios. Additionally, it struggles with complex tasks like building encrypted apps, indicating room for improvement.
Key Points:
- Claude 3.7 introduces a CLI tool, Claude Code, for building and testing code, potentially replacing programmers.
- The model excels in solving GitHub issues, with a 70.3% success rate, outperforming other models.
- Installation of the CLI tool is via npm, but it is costly at $15 per million output tokens.
- Claude 3.7 can generate front-end UIs but may not always use specified technologies correctly.
- The model struggles with complex tasks like building encrypted apps, showing areas for improvement.
Details:
1. 📢 Exciting Release Announcement
- Anthropic has launched a new product designed to significantly enhance AI capabilities, signaling a major advancement in the industry.
- This release is anticipated for its innovative features, which could redefine AI applications.
- While specific metrics and impacts are pending, the industry is expecting substantial improvements in efficiency and functionality.
- The product is expected to cater to various sectors, potentially increasing AI integration in business processes.
2. 🎉 Claude 3.7 Sona: First Impressions
- Claude 3.7 Sona is highly anticipated in the tech community, reflecting its expected potential impact and advancements.
- The model is both loved and feared by programmers, indicating its powerful capabilities and the significant changes it might bring.
- The announcement video generated significant excitement and engagement, as shown by the top comment about people eagerly waiting for the video release, highlighting community buzz.
- The speaker feels honored by the community's trust in their AI reviews, which underscores their influence and credibility in assessing AI models.
- Despite the excitement, there is an underlying tension about the transformative changes Claude 3.7 Sona might introduce, suggesting a need for adaptation.
- The community's reaction is a mix of enthusiasm for new features and apprehension about the learning curve associated with the model's capabilities.
3. 🚀 Enhanced Programming Capabilities
- Claud 3.7 has undergone extensive testing, burning through millions of tokens to ensure performance.
- The new model, Claud 3.7, demonstrates significantly improved performance, often described as 'highkey goated,' indicating top-tier capabilities.
- The base model has surpassed its previous iteration, becoming even better at executing programming tasks.
4. 🛠️ Introducing Claude Code CLI
- Claude Code CLI is an innovative tool designed to enhance programming workflows by building, testing, and executing code across various projects.
- The tool fosters an infinite feedback loop, streamlining the development process significantly.
- Inspired by the success of deep seek R1 in open AO models, Claude Code CLI aims to replicate and extend these successes in programming environments.
- The CLI tool's architecture leverages insights from previous advanced models, suggesting a potential for transformative impacts on software development and code management.
5. 👨💻 AI's Role in the Workforce
- Influencers have raised concerns about AI potentially replacing programmers, reflecting a growing anxiety in the tech community.
- Anthropic's recent paper explores AI's influence on labor, suggesting that AI could significantly alter workforce dynamics, particularly in programming roles.
- The paper provides detailed analysis and metrics on AI's capabilities in automating coding tasks, which could lead to a shift in how programming jobs are structured and executed.
6. 🏆 Benchmarking Against Competitors
6.1. AI Models and Workforce Impact
6.2. Sector-specific AI Impact
7. 💸 Installing and Using CLA Code CLI
- The CLA code CLI claims to solve 70.3% of GitHub issues based on their Benchmark.
- The CLI is in research preview and can be installed using npm, though it utilizes the anthropic API, which is costly.
- CLA is over ten times more expensive than models like Gemini Flash and deepsea, with a cost of $15 per million output tokens.
- Upon installation, the CLA command provides full context of existing code in a project.
- Text decoration in the CLI closely resembles that of SST, an open-source tool.
- Installation steps: Use npm to install the CLI, ensuring that you have access to the anthropic API for full functionality.
- Usage example: After installation, run the CLA command in your project directory to analyze and provide context for existing code.
8. 🔍 Testing Code Generation Features
- The 'in' command efficiently scans projects to create a markdown file, setting initial context and instructions for development.
- Tracking expenses with the 'cost' command is precise, showing that creating an AIT file incurs a cost of approximately 8 cents, promoting cost-effectiveness.
- The task of creating a random name generator in Dino serves as a straightforward example of the system's capabilities.
- User control is prioritized with a confirmation step before any file generation, ensuring intentional actions.
- Testing involves creating a dedicated file to validate code using a strongly typed language and test-driven development principles, ensuring thorough verification.
- AI actively corrects code based on failing tests, using feedback to refine and improve reliability, demonstrating an adaptive and iterative development process.
9. 🎨 Building a Front-End UI
- The project involved creating a visual front-end UI using Svelte instead of React, focusing on accessing a microphone and visualizing the waveform.
- The tech stack included TypeScript and Tailwind, but issues arose when Claude's code did not utilize these technologies, impacting integration.
- Development required iterating through 20 different elements to refine business logic, achieving a 'perfect code' status, indicating high interaction and detail.
- The project was more time-consuming compared to traditional web UI development due to the complexity and new component additions.
- The final application featured interactive waveform frequency and a circular graphic visualizing voice sound, demonstrating comprehensive UI functionality.
- Comparative testing with AAI 03 mini High initially led to errors but was corrected, although results were not as favorable, highlighting integration challenges.
10. ⚠️ Challenges and Limitations
- The session using the new spell 5 Rune syntax cost about 65 cents, which was considered inefficient spending.
- Apple discontinued end-to-end encryption in the UK due to government demands for a backdoor, which they refused to build, leading to privacy concerns for users.
- Building a custom end-to-end encrypted app is a potential solution for those affected by Apple's decision, but it faces practical challenges.
- Large language models tested for building encrypted apps in JavaScript consistently failed, highlighting limitations in current AI capabilities for specific technical tasks.
- Despite modifications, AI solutions like Claud code and Chat GPT failed to resolve coding issues, indicating limits in their problem-solving abilities.
- There's a significant dependency on AI, leading to difficulties in addressing technical errors independently, which underscores the need for skilled human intervention in technical development.
11. 📈 Exploring Backend Solutions with Convex
- Convex is an open-source reactive database that enhances backend development with features such as typesafe queries, scheduled jobs, server functions, and real-time data synchronization, offering a comprehensive solution akin to Firebase.
- Developers can write database queries in pure TypeScript with Convex, which enhances productivity by providing IDE autocomplete and reducing coding errors.
- The integration with AI models like Claude improves coding efficiency, making Convex a powerful tool for autonomous development.
- Convex is particularly beneficial for developers transitioning from front-end to back-end development, as it allows for rapid application building through its familiar and simplified environment.
- By providing a free project initiation link, Convex encourages developers to explore its capabilities and discover its potential for simplifying complex backend tasks.
- Unlike traditional backend solutions, Convex offers a complete stack experience that integrates seamlessly with existing front-end workflows, enhancing developer efficiency and project turnaround times.