The "No API" Excuse is Dead 🚫 "Sorry, that system is too old. It doesn't have an API." I hear this in meetings all the time. It usually means a project is dead in the water. But here is the truth: If it has a user interface, it has an API. It's just an API designed for humans, not computers. The Inputs: Text boxes and buttons. The Outputs: Screen text and tables. With Python and Playwright, we can turn that "Legacy User Interface" into a structured, programmable API. We don't need to wait for a digital transformation project. We can build the bridge today. #LegacySystems #API #Python #Playwright #DigitalTransformation Truly yours Bot.
Legacy UIs Have APIs: Unlock with Python and Playwright
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For agentic skills, I find using 3 files better instead of single .md file. 1. Code.py (Async run method to run any python code) 2. Conf.json (with skill description, model description, input & output) 3. prompt.txt or .md(if llm used). These gives us more control and also, skill's code can be reused + we choose specific model per skill for token efficiency. These can be generated using chat system itself and obviously, we can connect these skills(neuros) like deep graphs to create complex skills.
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For agentic skills, I find using 3 files better instead of single .md file. 1. Code.py (Async run method to run any python code) 2. Conf.json (with skill description, model description, input & output) 3. prompt.txt or .md(if llm used). These gives us more control and also, skill's code can be reused + we choose specific model per skill for token efficiency. These can be generated using chat system itself and obviously, we can connect these skills(neuros) like deep graphs to create complex skills.
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PyFuncAI Launches LLM-Generated Python Functions at Runtime 📌 PyFuncAI lets LLMs dynamically generate and run Python functions at runtime-no static toolsets needed. Developers can now build flexible AI agents that solve novel problems on the fly, reducing maintenance overhead while keeping code adaptable. Perfect for agentic systems craving real-time, adaptive logic. 🔗 Read more: https://lnkd.in/d_W49MNx #Pyfuncai #Llmgenerated #Pythonruntime #Naturallanguage #Functionsynthesis
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Free Voice Cloning Software!! I found this gem the other day when trying to figure out cheaper ways to make a TTS model for one of my apps https://lnkd.in/gyNiyrsZ Free and easily integrated in Python, but need a strong infrastructure to run at a good speed! Hope this helps anyone building a voice agent. #AI
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🚀 Exciting News! 🚀 We are thrilled to announce that our latest web automation tool, ai_tools_box , is now available on PyPI! Install : pip3 install ai_tools_box ai-tools-box is designed to streamline your web automation workflows, making development faster and more efficient. Visit our website for more details: https://lnkd.in/eJCcw3fQ #WebAutomation #Python #PyPI #SoftwareDevelopment #TechInnovation #AI #AutomationTools
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Those cool times when we could have endless conversations about which language is better in a given area are finished. Now we just have benchmarks, so we don't have to argue. We can focus on the meaning of life or the existence of god. Because if it comes to discussing which languages are best for generating code using Claude in terms of cost, for now, we are settled.
Types don't help AI, they actually hinder it. Proof: look at Ruby with Steep - a Ruby type checker - and without! Or Python vs Python with mypy. Token efficiency and expressiveness is way more important for AI generated code. That's why Ruby was the best language in this test! https://lnkd.in/dyP6fQmC
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We might see a spike in the Ruby and Rails communities as people discover how well-suited they are for AI. Excited to see what the future holds.
Types don't help AI, they actually hinder it. Proof: look at Ruby with Steep - a Ruby type checker - and without! Or Python vs Python with mypy. Token efficiency and expressiveness is way more important for AI generated code. That's why Ruby was the best language in this test! https://lnkd.in/dyP6fQmC
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Hot take, possibly spicy. We're in such early days of using these tools that comparisons like this are best viewed as transient artifacts that help us think about the problem. "Types don't help AI, they actually hinder it" -- for now, in this situation, with the current state of both tooling and training codebases, .....
Types don't help AI, they actually hinder it. Proof: look at Ruby with Steep - a Ruby type checker - and without! Or Python vs Python with mypy. Token efficiency and expressiveness is way more important for AI generated code. That's why Ruby was the best language in this test! https://lnkd.in/dyP6fQmC
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One thing keeps coming up when I build agents with LLMs. Tool interfaces matter more than people admit. If the tool description is vague or the inputs/outputs shift unexpectedly, the agent starts guessing and loops or picks the wrong path. I used to stuff long explanations into the tool schema hoping it would help reasoning. It mostly added noise. Now I keep descriptions short, explicit about formats, and add a quick example return if the output can be tricky. The model follows the contract better and wastes fewer tokens. It isn't magic. Clear interfaces cut hallucinations and retries more than tweaking prompts ever did. Anyone else notice this when switching between simple scripts and real agent loops? #aiagents #llms #python #toolcalling
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🚀 I Just built a Python script powered by AI to automate ACL updates in my free ADBs! 🔧 The AI helped generate the boilerplate logic, spot edge‑case patterns, and speed up development. 👥 But the human factor remains essential – we still review every change, validate permissions, and ensure compliance before anything goes live.
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