* Markov chains are mathematical systems that hop from one "state" to another.
* A state space is a list of all possible states in a system.
* A Markov chain tells you the probability of hopping, or "transitioning," from one state to any other state.
* Real modelers use a "transition matrix" to tally transition probabilities instead of drawing diagrams.
* The number of cells in a transition matrix grows quadratically as you add states to your Markov chain.
* One use of Markov chains is to include real-world phenomena in computer simulations.
* A two-state Markov chain can mimic the "stickyness" of real-world data, such as weather patterns.
* Markov chains are used by meteorologists, ecologists, computer scientists, and financial engineers to model big phenomena.
* Examples of Markov chains include PageRank algorithm used by Google and customizable Markov chains in a playground.
* You can access more examples and explanations at setosa.io/markov or visit the Explained Visually project homepage.
* Define main function: `get_chain(ticker, expiration_list)`
* Queries market data for options contracts
* Iterates over expiration list and contract details
* Processes data into pandas DataFrame
* Define additional helper function:
+ `get_individual(ticker, exp, strike, kind)`: gets individual option snapshot
* Use IB connection to execute requests
* Handle potential errors with `ib.sleep(0.025)`
* Set display options for pandas DataFrame
* This is a collection of apple-native tools for the MCP protocol.
* To install Apple MCP for Claude Desktop automatically via Smithery, use:
npx -y @smithery/cli@latest install @Dhravya/apple-mcp --client claude
* Or for cursor, use:
npx -y @smithery/cli@latest install @Dhravya/apple-mcp --client cursor
* Messages can be sent using the Apple Messages app or read out.
* Notes can be listed, searched and read in the Apple Notes app.
* Contacts can be searched for sending messages.
* Emails can be sent with multiple recipients, scheduled and searched.
* Reminders can be listed, searched and created with optional due dates and notes.
* TODO: Search and open calendar events in Apple Calendar app, photos in Apple Photos app, and music in Apple Music app.
* Commands can be daisy-chained to create workflows.
- Browser-Based Development: Access VS Code directly from your browser
- Goose AI Agent: Pre-installed and configured Goose AI Coding agent
- Shared Terminal Session: The same Goose session is visible in all browser windows
- Goose Terminal API: REST API for sending commands to the terminal and retrieving session logs
- Streaming Conversations: Real-time streaming of Goose AI conversations using Server-Sent Events (SSE)
- Material Design: Dark theme with Material icons for a beautiful coding experience
- Secure Environment: Password-protected VS Code Server instance
- Git Integration: Git pre-installed and ready for repository operations
- Persistent Configuration: Environment variables and configuration preserved between sessions (Unless workspace is deleted)
* This plugin provides a canvas for organizing bookmarks in IntelliJ IDEs such as IntelliJ IDEA, PyCharm, Android Studio, and WebStorm.
* The goal is to make it easy to create a visual representation both of bookmarks and of the relationships between them.
* The canvas should make it easy to quickly jump to a bookmarked location.
- 🚀 FastAPI + PostgreSQL Backend: The perfect match for your Next.js Frontend!
* 🔥 FastAPI - Lightning-fast API with automatic docs (seriously, it's FAST!)
* 🐘 PostgreSQL - Reliable database that just works
* 🐳 Docker - One command to rule them all
* 🔄 CORS - Already configured for your frontend needs
* 📝 Todo API - Ready-to-use example endpoints
* In payments engineering, it's essential to have an architecture that facilitates easy and accurate money movement functions with domain awareness for long-term scalability and regulatory compliance in mind.
* Best practices include:
+ Log every event (request - response calls) and use a state-machine on top of this database to access the latest state.
+ Build a double entry ledger (reconciliation daily EOD).
+ Always store money in integer format (in cents), not float ($ in decimal).
+ Ensure idempotency for consistent responses.
* Audited financials require immutable and double entry ledgers.
* A Payments PM should map out all events triggering a payment capture() call to ensure consistency.
* Understanding your codebase and state machine is crucial for architecting a good payments engineering platform.
* Best practices include:
+ Using microservices architecture to avoid spaghetti code.
+ Having a consistent approach when displaying pricing on product SKUs or in the cart.
+ Optimistic locking (tx_id, event#) on database logs.
+ Maintaining an immutable ledger and a transaction state machine for auditing.
* Data engineering and Data Science use the state machine's latest state for analysis.
* Always store money in cents for calculations to avoid inconsistencies.
* Use AWS instead of internal datastores for industry-guaranteed reliability.
* Immutable ledgers are essential for audited financials.
* Consider mapping out events triggering a payment capture() call for consistency.
* Terminal-based AI coding tool that uses any model supporting OpenAI-style API
* Fixes spaghetti code
* Explains function purpose
* Runs tests, shell commands and more
* Manually set models in /config if not available on list
* Requires openai-like endpoint to work
* Install with npm install -g anon-kode
* Run with pnpm i, pnpm run dev, pnpm run build
* Get verbose debug logs with NODE_ENV=development pnpm run dev --verbose --debug
* Submit bugs via /bug, creates GitHub issue template
* Use at own risk, no telemetry or backend servers other than AI providers
This document outlines a set of principles and guidelines for developing and maintaining the GitLab platform. Here's a summary of the key points:
**General Principles**
1. **Customer First**: Prioritize customer needs and provide support for all features in paid tiers, regardless of their license level.
2. **Developer-First**: Focus on solving problems for developers first, then adapt to other personas (e.g., operations, security) while maintaining a developer-centric approach.
3. **Cloud-Native First**: Build features for cloud-native first and then support the rest, focusing on modern development flows and architectures.
**Feature Naming**
1. Use prepositions when referring to third-party products and services in names (e.g., "GitLab.com for Jira Cloud").
2. Prioritize complete maturity for developers building cloud-native applications before moving to other development methodologies and personas.
3. Use a "modern first" approach, solving problems for modern development teams before addressing legacy teams.
**Prioritization**
1. Focus on next-generation development flows, personas, and use cases, even if they're not currently adopted by your initial users.
2. Optimize GitLab to support the larger number of current and future adopters of next-generation principles.
3. Clearly communicate with users what the preferred path is and ensure that legacy methods are deprecated.
**Core Values**
1. **Stewardship**: Ensure that no feature is removed from Core into paid tiers, but can build additional features around existing ones for paying customers only.
2. **Preparation**: Plan ahead and invest in modern workflows and architectures to support the future needs of your users.
3. **Adaptability**: Be willing to adapt and evolve based on user feedback and changing market conditions.
**Guidelines**
1. Use prepositions when naming features that integrate with third-party products or services (e.g., "GitLab for Slack").
2. Avoid using internal terminology or acronyms when referring to external products or services.
3. Document all changes, deprecations, and additions to features in a clear and concise manner.
Overall, this document outlines a set of guidelines and principles that aim to ensure the long-term success and growth of the GitLab platform. By prioritizing customer needs, developer-centricity, cloud-native first development, and continuous improvement, GitLab can maintain its position as a leader in the DevOps and software development space.
* tl;dr: We explored various approaches to code navigation for AI SWEs and share our findings on what works and what doesn't.
* We're building the best platform to run AI SWEs with a purpose-built code navigation system.
* Current research approaches include:
- SWE-Agent
- CodeMonkey
- Moatless
* Our approach is similar to OpenHands, exposing tools for finding all references and going to definition.
* Our vision for code navigation includes:
- Scalability
- Incremental indexing
- Flexibility
- Permissively licensed
* We explored different systems, including:
- lsproxy (purpose-built LSP library)
- Stack Graphs (advanced data structure)
- Pros: incremental, theoretically language agnostic, fast queries
- Cons: limited support for languages, complex files required
- Glean (production code indexing system at Meta)
- Pros: incremental, scalable, flexible, can navigate arbitrary commits
- Cons: proprietary Thrift protocol, requires custom parser for languages
- multilspy (Python library providing a convenient wrapper around LSP servers)
* Our solution is to add convenience wrapping around multilspy.
* Looking forward to further explorations and comparisons with the AI SWE community.
* Further reading:
- Nuanced.dev
- CodeMonkeys
* Aider uses `pip install aider-install && aider-install` to install in an isolated virtual environment.
* The `aider-install` package depends on the `uv` library.
* When running `aider-install`, it executes Python code that:
+ Finds the location of the `uv` binary
+ Installs Aider using `uv tool install`
+ Updates the shell to include the Aider directory in the PATH
* The installation process creates a new standalone copy of Python 3.12 and places it in `uv's managed directory structure.
* The `--force` flag is used to overwrite any previous attempts at installing Aider.
* Running `uv tool update-shell` ensures that the bin directory is on the user's PATH.
* This installation method is recommended for non-expert Python users due to its minimal risk of breaking the system.
* The method has significantly reduced GitHub issues related to conflicted or broken Python environments.
* An alternative installation mechanism may be preferred by experienced users.
* Tailscale is a useful tool for creating a virtual private network, allowing access to devices anywhere with domain shorthands.
* It was recommended after experiencing issues with DDNS and CGNAT2, which made port forwarding impossible.
* The client software needs to be installed on devices, logging in is an easy process.
* Additional benefits include:
* Exposing a port from laptop to phone for web application testing.
* Taildrop: file transfer between devices without close proximity.
* Exit nodes: appointing a machine as an exit node for VPN-like functionality.
* Mullvad integration: two-tier VPN with privacy features.
* Note that Tailscale is open-source, but some wrappers are not.
* Headscale is an open-source server implementation compatible with Tailscale's client software.
* Some links to external resources mentioned in the article:
* Dynamic DNS
* Carrier-grade NAT
* Home theater PC
* Snapdrop and Pairdrop comparison
* PySheets is a spreadsheet UI for Python, implemented in Python, running logic and saving data in the browser, using PyScript and IndexedDB.
* To run PySheets without locally installing it, simply visit pysheets.app
* To install and run PySheets on your local machine, run: pip install pysheets-app pysheets
* Tired of seeing articles from certain sources on Hacker News? HN Blacklist is for you.
* HN Blacklist is a userscript which can be run with tools like Greasemonkey and Tampermonkey right in your browser.
* Find it on Greasy Fork, or copy and paste it right from hn-blacklist.js in this repository.
* HN Blacklist provides a UI at the bottom of HN. You can add entries and see helpful output from HN Blacklist there.
* Prefix blacklist entries with source: to filter out articles.
This text describes a new networking system called "Uncloud" that allows multiple machines to work together without a centralized control plane or master nodes. Here's a summary of how it works:
**Key Components**
1. **WireGuard**: Uncloud uses WireGuard for secure, encrypted communication between machines.
2. **Caddy**: Caddy is used as a reverse proxy and watches the cluster state for new services.
3. **uncloudd**: uncloudd is the CLI tool that communicates with uncloudd on target machines to launch containers.
**How it Works**
1. Multiple machines are provisioned, each with its own subnet (e.g., `10.210.X.2-254`).
2. Machines establish a WireGuard connection with each other, creating an overlay network.
3. When a service is run using the CLI (`uc run`), uncloudd on a target machine launches a Docker container in the bridge network.
4. The container gets a cluster-unique IP address from the bridge network and becomes accessible from other machines in the cluster.
5. Caddy watches the cluster state for new services and updates its configuration to route traffic to the new container.
**Benefits**
1. **Decentralized**: No need for control plane or master nodes.
2. **Fault-tolerant**: If one machine goes offline, others can still serve cluster operations.
3. **Easy maintenance**: Any machine has access to the complete cluster state and can make changes.
* Abstract: Generating high-quality question-answer pairs for specialized technical domains remains challenging, with existing approaches facing a tradeoff between leveraging expert examples and achieving topical diversity.
* We present ExpertGenQA, a protocol that combines few-shot learning with structured topic and style categorization to generate comprehensive domain-specific QA pairs.
* Using U.S. Federal Railroad Administration documents as a test bed, we demonstrate that ExpertGenQA achieves twice the efficiency of baseline few-shot approaches while maintaining 94.4% topic coverage.
* Through systematic evaluation, we show that current LLM-based judges and reward models exhibit strong bias toward superficial writing styles rather than content quality.
* Our analysis using Bloom's Taxonomy reveals that ExpertGenQA better preserves the cognitive complexity distribution of expert-written questions compared to template-based approaches.
* When used to train retrieval models, our generated queries improve top-1 accuracy by 13.02% over baseline performance, demonstrating their effectiveness for downstream applications in technical domains.
* # AI-powered Local Automation Tool
* ## Description
* "Automate the tedious, reclaim your time for life"
*
* * autoMate is a revolutionary AI+RPA automation tool built on OmniParser that can:
* - Understand your needs and automatically plan tasks
* - Intelligently comprehend screen content, simulating human vision and operations
* - Make autonomous decisions, judging and taking actions based on task requirements
* - Support local deployment to protect your data security and privacy
* - No-Code Automation
* - Full Interface Control
* - Simplified Installation
* SQLite-on-the-Server Is Misunderstood: Better At Hyper-Scale Than Micro-Scale
* We're Rivet, a new open-source, self-hostable serverless platform.
* There's been a lot of discussion recently about the pros and cons of SQLite on the server.
* After reading many of these conversations, I realized that my perspective on the power of SQLite-on-the-server is lopsided from popular opinion:
+ SQLite's strengths really shine at scale, instead of with small hobbyist deployments.
* Background
* Why Developers Love SQLite for Micro-Scale Deployments
- Low infrastructure costs: No need for separate database servers—just a single file.
- Seamless development and testing: The same database file can be used across client and server.
- Minimal management overhead: No complex configurations or database daemons.
- Proven reliability: It's been around forever. It's the world's most widely deployed database and built to withstand battleships getting blown to bits.
* Tools for SQLite-on-the-server
* Enhance SQLite with replication and high availability tools like LiteFS, Litestream, rqlite, Dqlite, and Bedrock:
- Specifically focusing on Cloudflare Durable Objects and Turso
* Databases at Hyper-Scale Today: Sharded Databases & Partitioning Keys
+ High-scale systems struggle with scaling databases like Postgres or MySQL.
+ Companies often turn to sharded databases like Cassandra, ScyllaDB, DynamoDB, Vitess (sharded MySQL), and Citus (sharded Postgres).
- These systems use partitioning keys to co-locate related & similarly structured data.
* Benefits of Sharded Databases
+ Efficient batch reads with data grouped in the same partition.
+ Horizontal scalability by partitioning data across nodes.
+ Optimized writes for high-ingestion workloads
* Challenges with Current Partitioning Solutions
+ Rigid schemas limiting flexibility.
+ Complex schema changes requiring significant operational overhead.
+ Complex cross-partition operations enforcing ACID properties is difficult.
+ Data inconsistency without strong constraints between tables & partitions.
* Enter SQLite at Scale: Cloudflare Durable Objects & Turso
* Provide dynamic scaling, infinite cheap databases, global distribution, and built-in replication and durability:
- Leverage to replace partitioning keys with SQLite database per partition
* Benefits of SQLite-Per-Partition
+ Local ACID transactions without cross-partition complexities.
+ Efficient I/O within partitions for high performance.
+ Leverage existing SQLite extensions and SQL migrations tools
+ Lazy schema migrations allowing changes on demand.
* Who are the Turso founders?
* Founded by people who used to work at ScyllaDB and saw the challenges of large-scale partitioned databases firsthand.
* Where SQLite Still Falls Short
+ Lack of an open-source, self-hosted solution.
+ Limited database tooling like SQL browsers, ETL pipelines, monitoring, and backups.
+ Non-standard protocols for communicating with SQLite-on-the-server.
+ No case studies at hyper-scale using SQLite with this architecture.
LiteWebAgent is an open-source library developed by PathOnAI that provides a framework for building web-agent applications using large language models (LLMs). Here's an overview of LiteWebAgent:
**Key Features:**
1. **LLM-based web-agents**: LiteWebAgent allows developers to build web-agents using popular LLMs such as transformer-based models.
2. **Auto-login and session management**: The library provides a simple way to auto-login to websites and manage sessions, allowing the agent to interact with the website seamlessly.
3. **Task induction**: LiteWebAgent enables users to induce workflows from mind2web datasets, which are pre-defined tasks that can be executed on various websites.
4. **Workflow memory**: The library includes an "Agent Workflow Memory" module that allows developers to store and retrieve induced workflows for later use.
**Agents:**
LiteWebAgent provides several pre-built agents that can be used as a starting point for building web-agents:
1. **PromptAgent**: A simple agent that uses prompt engineering to interact with websites.
2. **ContextAwarePlanningAgent**: An agent that uses context-aware planning to navigate through websites.
3. **LLMWebAgent**: A basic LLM-based web-agent that can be customized for specific tasks.
**Integration:**
LiteWebAgent can be integrated into various frameworks and tools, including:
1. **Chrome extensions**: Developers can build Chrome extensions using LiteWebAgent as the AI backend server.
2. **Python APIs**: The library provides a Python API that allows developers to interact with websites and execute tasks programmatically.
3. **Other frameworks**: LiteWebAgent can be integrated into other frameworks and tools, such as TensorFlow or PyTorch.
**Use Cases:**
LiteWebAgent has various use cases, including:
1. **Automating web tasks**: Developers can use LiteWebAgent to automate repetitive web tasks, such as filling out forms or clicking buttons.
2. **Web scraping**: The library can be used for web scraping tasks, where the agent extracts data from websites.
3. **E-commerce automation**: LiteWebAgent can be used to automate tasks on e-commerce websites, such as booking flights or hotels.
This text appears to be a blog post or article about a machine learning experiment using the GRPO (Generative Reward Policy Optimization) algorithm on a coding model. The author describes the setup of the experiment, including the dataset, reward functions, and training process.
Here are some key points from the article:
1. **GRPO Algorithm**: The author explains that GRPO is a type of reinforcement learning algorithm that uses generative models to optimize rewards for a policy.
2. **Dataset**: The experiment uses a small dataset of coding tasks with unit tests, including writing functions and fixing errors from the compiler.
3. **Reward Functions**: The author defines multiple reward functions, including:
* `build`: passing the build step
* `test`: passing the unit tests
* `error`: fixing errors from the compiler
* `fill_in_middle`: filling in missing code
* `diff_given_prompt`: creating a patch/diff given a prompt
4. **Training Process**: The author describes the training process, including:
* Training the model with the defined reward functions for a single epoch (24 hours)
* Logging the results to Oxen.ai, which allows for visualization and monitoring of performance over time
5. **Results**: The article reports impressive results, including:
* A 20% increase in build pass rate from 61% to 80%
* A 15% increase in test pass rate from 22% to 37%
6. **Cost and Time**: The experiment was run on a Lambda Labs instance for under $100 over 24 hours.
7. **Future Work**: The author mentions plans to extend the dataset with additional task categories, such as creating a patch/diff given a prompt or next edit (tab) prediction.
Overall, this article presents an exciting example of how GRPO can be applied to coding tasks and achieve impressive results in a relatively short period of time.
- creates semantically meaningful diffs that ignore formatting differences like spacing
- uses tree-sitter project parsers to parse source code, supporting 11 languages
- supports the following languages: Bash, C#, C++, CSS, Go, Java, OCaml, PHP, Python, Ruby, Rust, Typescript/TSX, HCL
- generates diffs with line numbers and formatting options via config file
- has logging and terminal aware formatting features
- can exclude or include specific tree-sitter node types in diffs using config file
- uses Github actions to build and publish binaries for each tagged release
- can be installed from source using cargo, brew, apk, or Docker
- supports customizing config file associations and formatting options
- provides completion files and other assets with the diffsitter_completions binary
* 🚀 I managed to obtain FULL official v0, Manus, Cursor, Same.dev & Lovable system prompts and internal tools.
* Omni CLI is a command-line interface for interacting with different AI models within the same session. It's currently in early alpha stages and may have bugs or incomplete functionality.
* The project follows a clean architecture with separation of concerns:
+ Contexts: Manage global state and provide access to it through hooks
+ Hooks: Encapsulate reusable logic and side effects
+ Components: UI elements that consume contexts and hooks
+ Utils: Pure utility functions
* Multiple AI Model Support: Integrates with OpenAI, Anthropic, Mistral, and Google AI models.
* Persistent Conversation Context: Maintains conversation history throughout the session.
* Pocket Flow: 100-line minimalist LLM framework
* Deploy projects directly from local computer to production server with Airo.
* Focus on building product, not managing infrastructure.
* Build and push Docker images directly from local machine to container registry.
* Deploy instantly with a single command from computer.
* Easily update configurations and containers securely using SSH.
* Set up HTTPS and reverse proxy automatically using Caddy.
* Define services in compose.yml file.
* Configure deployment details in env.yml file.
* Prepare Dockerfile for each service.
* Set up Caddyfile for automatic HTTPS and reverse proxy setup.
* Run command `airo deploy` to deploy new updates.
* Create a new project directory and navigate to it.
* Configure env.yaml file with server IP, user credentials, and services.
* Add Dockerfile to project for each service.
* Configure compose.yml file with services and their details.
* Deploy project using command `airo deploy`.
* Use `airo compose` to build and push Docker image without deploying.
* Use `airo caddy` to update Caddyfile.
* httpdbg: a tool for Python developers to debug HTTP(S) client and server requests in a Python program
* To use it, execute your program using `pyhttpdbg` command instead of `python`
* Open a browser to `http://localhost:4909` to view the requests
* Full documentation => https://httpdbg.readthedocs.io/
* pip install httpdbg
* By default, both client and server requests are recorded
* To record only client requests, use the `--only-client` command line argument
* Gem-Assist is a Python-based personal assistant that leverages Google's Gemini models (and other) to help with various tasks, designed to be versatile and extensible.
* Powered by LLM: Utilizes LLMs for natural language understanding and generation.
* Tool-based architecture: Equipped with a variety of tools for tasks like web searching, file system operations, system information retrieval, Reddit interaction, running shell commands, and more.
* Customizable: Easily configure the assistant's behavior and extend its capabilities with new tools.
* Simple Chat Interface: Interact with the assistant through a straightforward command-line chat interface.
* Memory: Can save notes between conversation and remember them.
* Saving Conversation: Save and load previous conversations.
* Commands: Supports creating/executing code, use /commands for more information.
* Extension: For now you are required to write some code to extend its capabilities like adding commands to CommandExecutor or making new tools.
* A simple, scalable, and powerful architecture for building production ready React applications.
* React has a diverse ecosystem with hundreds of great libraries but can be overwhelming due to the many choices.
* The repo presents a way of creating React applications using some of the best tools in the ecosystem with a good project structure that scales well.
* The goal is to serve as a collection of resources and best practices for developing React applications, showcasing practical solutions to real-world problems.
* This repo provides a solid foundation for building applications based on principles such as ease of use, maintainability, using the right tools, clean boundaries, security, performance, scalability, and early issue detection.
* The guide is opinionated but not a template or framework, allowing developers to decide what works best for their team and stay consistent with their style.
* The tools and libraries used are just suggestions, and developers can replace them with something that fits their needs better.
* Key concepts include application overview, project standards, project structure, components and styling, API layer, state management, testing, error handling, security, performance, deployment, and additional resources.
* Template React Project for Fast SPA Prototyping
* A lite version of the React Proto - React TypeScript Boilerplate
* Contains only the essentials for Single Page Application (SPA) projects
* Fleur is a desktop application that serves as an app marketplace for MCPs, allowing discovery, installation, and management without using a command line.
* It's made for non-technical users but is open-source and extensible for developers.
The text describes an open-source framework called Evolving Agents Framework (EAF), which is designed to simplify the development of intelligent agents using a combination of machine learning, natural language processing, and software engineering principles.
**Key Components**
1. **SystemAgent**: A ReAct agent that orchestrates operations by leveraging specialized tools.
2. **ArchitectZero**: Handles solution design and multi-framework support through Providers and Adapters.
3. **SmartLibrary**: Powers semantic search capabilities for component persistence and discovery via ChromaDB.
4. **SmartAgentBus**: Enables capability-based routing for agent interactions.
5. **CreateComponentTool**: Invokes the AgentFactory to create agents from different frameworks.
**Features**
1. **Multi-framework support**: Supports multiple machine learning frameworks, including BeeAI and OpenAI Agents SDK.
2. **Dependency injection**: Simplifies component wiring and initialization.
3. **Modular design**: Core components are decoupled, facilitating extension and testing.
4. **Clear logging**: Provides insights into agent thinking and component interactions.
5. **LLM caching**: Reduces API costs during development by caching completions and embeddings.
**Usage**
1. Run the Comprehensive Demo: `python examples/invoice_processing/architect_zero_comprehensive_demo_refactored.py`
2. Explore Output files, including:
* `architect_design_output.json`: Solution blueprint from ArchitectZero
* `workflow_execution_output.json`: Executable workflow generated by SystemAgent
* `smart_library_demo.json`: State of the component library after the run
3. Use the toolkit to design and process conversational forms (forms/), create and evolve agents (agent_evolution/), or explore autocomplete systems (autocomplete/).
- A minimalist framework for building AI agents with full user control and zero abstraction layers
- Complete transparency: No hidden prompts or "magic" under the hood
- Full control: You define exactly how your agent behaves
- Minimal infrastructure: Only the essentials needed to run capable AI agents
- Simplicity first: Ability to build complex behaviors from simple, understandable components
- User-defined state management is completely up to you
- Flexible implementation options for state management
- Direct manipulation of state between tools creates a simple and transparent flow of information
- NoteChat is a desktop application that enables you to interact with your Apple Notes through a chat interface, built with Electron and React.
- The application uses Vite as the development server and build tool for the React part of the application.
- Electron handles the desktop application wrapper and native system interactions.
* A command line tool for rebalancing a portfolio of stocks and ETFs via Interactive Brokers, installed with `pipx install git+https://github.com/riazarbi/iblncr.git`.
* The Interactive Brokers API requires a locally installed Trader Workstation or IB Gateway application to be used.
* To start the IB Gateway Docker container, use either:
* Manual command: `docker run -it --rm --name broker -p 4003:4003 ghcr.io/riazarbi/ib-headless:10.30.1t`
* Built-in launch command: `iblncr launch`
* To start the application, use: `iblncr rebalance --account <account_number> --model <model_file> --port <port_number>`
This document provides an overview of Bytebot, a platform for building intelligent computer use agents. The agent uses AI models to understand visual contexts, interact with desktop applications, and automate tasks.
**Key Features:**
1. **AI Model Integration:** Bytebot integrates with various AI models, including Anthropic Claude, OpenAI GPT-4V, Google Gemini, Mistral Large, DeepSeek, OmniParser, CLIP/ViT, Segment Anything Model (SAM), and others.
2. **Customization:** Developers can integrate their preferred programming languages or frameworks to build agents using Bytebot's REST API.
3. **REST API:** The platform provides a REST API for easy integration with other applications.
4. **Desktop Automation:** Bytebot allows users to automate tasks on desktop computers, including UI interactions and task automation.
**Integrating AI Models:**
1. **API-Based Integration:** Use the model provider's API to send screenshots and receive instructions.
2. **Local Model Deployment:** Run smaller models locally alongside Bytebot.
3. **Hybrid Approaches:** Combine local processing with cloud-based intelligence.
**Building Agents:**
1. **Python Integration:** Ideal for data science and ML integration using libraries like requests, Pillow, and PyTorch.
2. **JavaScript/TypeScript Integration:** Great for web-based agents using Node.js or browser environments.
3. **Java/Kotlin Integration:** Robust options for enterprise applications.
4. **Go Integration:** Excellent for high-performance, concurrent agents.
5. **Rust Integration:** For memory-safe, high-performance implementations.
6. **C#/.NET Integration:** Strong integration with Windows environments and enterprise systems.
**Best Practices:**
1. **Automated Testing:** Run end-to-end tests in a consistent environment.
2. **Web Scraping:** Automate web browsing and data collection.
3. **UI Automation:** Create agents that interact with desktop applications.
4. **AI Training:** Generate training data for computer vision and UI interaction models.
* Chat with a local LLM that can respond with information from your files, folders and websites on your Mac without installing any other software.
* All conversations happen offline, and your data stays secure.
* Sidekick is a local first application –– with a built in inference engine for local models, while accomodating OpenAI compatible APIs for additional model options.
* Access files, folders, and websites from your experts, which can be individually configured to contain resources related to specific areas of interest.
* Sidekick accesses files, folders, and websites from your experts, which can be individually configured to contain resources related to specific areas of interest.
This is an excellent article on improving code readability. Here's a summary of the key points:
**The importance of readability**
Readability is crucial for maintainable and efficient software development. It allows developers to quickly understand the code, fix issues, and make changes without getting bogged down in complexity.
**8 patterns for improving code readability**
The article identifies 8 patterns that can help improve code readability:
1. **Line/Operator/Operand count**: Break up long functions with fewer variables and operators.
2. **Novelty**: Avoid novelty in function shapes, operators, or syntactic sugars; reuse common patterns instead.
3. **Grouping**: Split series of long function chains, iterators, or comprehensions into logical groupings via helper functions or intermediate variables.
4. **Conditional simplicity**: Keep conditional tests as short as possible and prefer sequences of the same logical operator over mixing operators within a conditional.
5. **Gotos**: Use gotos only when necessary (e.g., to avoid alternatives that are worse).
6. **Nesting**: Minimize nested logic (avoid large variations in indentation) and isolate deep nesting in separate functions.
7. **Variable distinction**: Always use descriptive and visually distinct variable names; avoid variable shadowing.
8. **Variable liveness**: Prefer shorter liveness durations for variables, especially with regard to function boundaries.
**Additional tips**
* Reuse familiar code patterns and follow the Principle of Least Surprise when writing code.
* Use templated or generic functions to reduce repetition and make code more predictable.
* Avoid premature optimization; computers are fast enough.
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# Install codemcp to make Claude Desktop a pair programming assistant