Decision Tree Regression Implementation in JavaScript

A developer just wrote about trying to get Claude to write Lisp. They were piping `tmux capture-pane | tail -n 1` into the model to simulate a REPL. It spun its wheels. They finally gave up and switched to Python because the AI tooling just worked. We used to pick languages for their elegant human abstractions. Now we pick them for agent legibility. The friction has moved. It's no longer about whether a language is expressive to a developer, but whether it takes $20 in wasted tokens for an agent to interact with its toolchain. We are bootstrapping into a new paradigm, and the path of least resistance is whatever the model already knows how to read. #AIAgents #SoftwareEngineering #LLM

We benchmarked LLMs on their ability to analyze code files across 25 files in TypeScript, Python, Rust, Go, Markdown, and HTML on parameters like Search, Graph, Semantics, Integration, Security Mapping, Business Context, and JSON handling. Models can top leaderboards like SWE-Bench Pro while failing at basic creative and practical tasks. Optimizing for the test is not the same as optimizing for the work. Results: #claude-sonnet-4.6 ranked #1 with a weighted score of 121.2 at $1.66, the only Pareto Optimal pick among premium models. #claude-opus-4.6 scored 117.0 but at $8.00, sonnet-4.6 beats it cheaper. #glm-5v-turbo emerged as the best budget Pareto Optimal pick at just $0.28 with a score of 113.0. #gpt-5.4-nano is the most cost-efficient Pareto Optimal option at $0.04 with a score of 103.3. Notably, glm-5.1 despite ranking #1 on SWE-Bench Pro scored only 106.7 here at $0.60, beaten by cheaper alternatives. Models like Gemini 3.1 Pro Preview and GPT-4o-mini ranked near the bottom despite their popularity. The leaderboard confirms that benchmark performance rarely translates to real-world task quality. If you would like to reduce your AI agents cost including your AI copilots, please get in touch with us.

If you’re a developer starting with AI agents in 2026, here’s your stack: 🐍 Language → Python 🤖 Framework → CrewAI (start here) 🔌 Tool connectivity → MCP 🔍 Web search → Tavily 🧠 LLM → Claude or GPT-4 🗄️ Vector DB → ChromaDB 🚀 Deploy → Railway or Render That’s it. You don’t need more to ship your first agent. Save this for later 🔖 #AIAgents #Python #CrewAI #Developers #AIEngineering

📣 Every LLM framework eventually adds async support. SynapseKit started there. There's a difference between async-retrofitted and async-native. Most frameworks started synchronous, bolted async on later, and shipped the seams - hidden event loop management, sync wrappers that infect the core, bugs that only surface under concurrent load. SynapseKit was designed async-first from the first commit. Every public API is async/await. No exceptions. No hidden sync layers underneath. If you understand Python and async, you understand SynapseKit. What that means in practice: → Stream tokens from any of 33 providers identically- not a special mode, the default → Run parallel graph nodes via real asyncio.gather - not simulated concurrency → No event loop surprises under load → Sync wrappers exist for scripts and notebooks - they call into the async layer, they don't replace it And the dependency story: 2 hard dependencies. numpy and rank-bm25. That's it. Everything else - LLM providers, vector stores, document loaders, tools - is behind an optional install extra. You pay only for what you use. No transitive conflicts. No 267-package installs. No surprise breakage when a framework you didn't know you depended on ships a breaking change. pip install synapsekit[openai] # 2 deps + openai pip install synapsekit[all] # everything Async-native. Minimal. Transparent. #Python #AsyncPython #LLM #RAG #OpenSource #AI #MLEngineering #SynapseKit

⭐Built AI-Based Text Summarizer Web App using T5 Transformer & FastAPI Recently worked on a Text Summarization app powered by a Transformer-based model (T5), where users can input raw text and get concise summaries instantly. 🛠️ Tech Stack: -> FastAPI -> Hugging Face Transformers (T5) -> Python -> HTML, CSS, JavaScript 💡 Key takeaways: -> Integrating deep learning models into real-world applications -> Building and exposing APIs using FastAPI -> Handling end-to-end flow from input → processing → output -> Debugging environment and dependency issues This project gave me hands-on experience in applying AI/Deep Learning concepts to solve practical problems. Open to feedback and suggestions! #AI #DeepLearning #MachineLearning #FastAPI #Transformers #Python #BuildInPublic

Installing an AI agent runtime shouldn't require reading 3 docs pages. So we made it one command. curl -fsSL https://lnkd.in/dZSQ5Mqc | bash That's it. pydantic-deepagents — the modular agent runtime for Python — installed. Here's what the install experience looked like before: 1. Install Python (which version? 3.10? 3.12?) 2. Create a virtual environment 3. pip install pydantic-deep[cli] 4. Figure out why `pydantic-deep` isn't in your PATH 5. Realize textual wasn't included in the base install 6. Install again with the right extras 7. Try again Seven steps. Fifteen minutes minimum. That's before you've even seen the tool. What install.sh actually does: 1. Detects whether `uv` is already installed 2. If not: installs `uv` automatically from astral.sh 3. Runs `uv tool install "pydantic-deep[cli]"` — no virtualenv management needed 4. Verifies the binary is accessible 5. Prints PATH instructions if needed (and explains exactly how to fix it) After the first install, updating is one command too: pydantic-deep update That's brew upgrade for AI agents. It uses `uv tool upgrade` if uv is available, falls back to pip. No version juggling. We also added startup update notifications. Every time pydantic-deep starts, it quietly checks PyPI for a newer version (2-second timeout, never blocks startup). If one exists, you see: Checks are cached for 24 hours so it doesn't hit PyPI on every single invocation. The features we built this week — context window awareness, smarter history search, agent loop detection — none of it matters if the install experience drives people away at step 1. Tomorrow: same agent, different environment. Docker sandbox. What's the most painful install experience you've had with an AI or ML tool? (I'll go first: a certain GPU-accelerated library that required matching CUDA versions, driver versions, and Python versions to a compatibility matrix that was 2 versions out of date.) Pydantic | Vstorm #Python #DeveloperExperience #OpenSource

🚀 Excited to share my latest Machine Learning project: Fake Certification Detector In an era where digital credentials matter more than ever, this project aims to identify and flag potentially fraudulent certificates using intelligent ML techniques. 🔧 Tech Stack: • Frontend: HTML • Backend: Python (Flask) • Deployment: Streamlit This project combines practical web development with machine learning to solve a real-world problem—enhancing trust in digital verification systems. Looking forward to feedback and discussions! 💡 #MachineLearning #Python #Flask #Streamlit #WebDevelopment #AI #Projects

🚀 Excited to share my latest Machine Learning project: Fake Certification Detector In an era where digital credentials matter more than ever, this project aims to identify and flag potentially fraudulent certificates using intelligent ML techniques. 🔧 Tech Stack: • Frontend: HTML • Backend: Python (Flask) • Deployment: Streamlit This project combines practical web development with machine learning to solve a real-world problem—enhancing trust in digital verification systems. Looking forward to feedback and discussions! 💡 #MachineLearning #Python #Flask #Streamlit #WebDevelopment #AI #Projects

Today's topic is a tool combo breakdown focusing on three exciting combinations that can revolutionize your workflow and save you time. Whether it’s integrating Claude Code with Obsidian for a seamless knowledge management system or harnessing n8n combined with the Claude API to automate complex tasks, these tools offer specific benefits. Let's dive into one of our options: using Python along with the Claude API. This combo allows developers to leverage AI capabilities directly within their existing workflows. Here’s how you can set it up: 1. **Setup**: First, ensure you have Python installed on your machine. You'll also need n8n and Claude Code plugins for n8n. 2. **Write Your Script**: Start by writing a simple Python script that uses the Claude API to process text inputs. For example: ```python import n8n from claude import ClaudeAPI # Initialize Claude API cl = ClaudeAPI() # Function to get AI generated response def get_response(prompt): response = cl.get_completion(prompt) return response # Example usage of the function result = get_response("What's the weather like in New York today?") print(result) 3. **Integrate with Obsidian**: Next, you can integrate this script with Obsidian using n8n to automate tasks. This setup can save significant time and effort, reducing manual processing and allowing for more efficient workflows. Would you be interested in exploring further AI integration opportunities like this one? Let us know your thoughts or challenges in the comments below. #ClaudeCode #AIAutomation #AITools #BuildWithAI #loopfeedai

I built an AI agent from scratch. No LangChain. No LangGraph. No CrewAI. Just Python, Gemini 2.5 Flash, and raw tool calling. Here's what I learned that no framework tutorial will teach you: 1. The agentic loop is embarrassingly simple Build messages → call LLM → if tool_call → execute → feed result back → repeat. That's it. Every framework is just a wrapper around this. Once you see it raw, you can never unsee it. 2. Frameworks hide your bugs from you When something breaks in LangChain, you're debugging the framework. When something breaks in raw Python, you're debugging your logic. Big difference. One makes you smarter. One makes you dependent. 3. Tool schema design is where agents actually fail The LLM doesn't call the wrong tool because it's dumb. It calls the wrong tool because your schema description was ambiguous. Write your tool descriptions like you're explaining them to a junior dev on their first day. Precise. No assumptions. 4. 50 lines of Python is enough to go to production My personal concierge agent — the one that lives on my portfolio, captures leads, and pings my phone instantly — is ~50 lines. No overhead. No magic. Just code I fully understand and can debug at 2am. 5. You should build one without a framework at least once Not because frameworks are bad. LangGraph is excellent. I'm using it next. But if you've never written the raw loop yourself, you're flying blind. You're trusting abstractions you don't understand. Build it raw first. Then use the framework. You'll use it 10x better. --- Full source code in the comments — ~50 lines, no magic, just the loop. Follow along if you're into agentic AI and building real things, not just demos. #AgenticAI #Python #BuildingInPublic #LLM #SoftwareEngineering