It also said it interpreted my prompt to be 'dangerous' so it wouldn't use Sonnet 4.5. I was asking for help with a JavaScript error and fed it the logs from my console, a tail command, and a curl command. The yooj. (2/2)

2nd time using #Claude today. Earlier I had 3 fact checks for my fun facts. This time I asked it a question and it started writing a ridiculous amount of code I didn't need. When I stopped it, I got a useless rate exceeded page and am now blocked. (1/2)

Tip for my fellow Python developers: Use docstrings to make your code more explainable and don't let AI tools—which all share a tendency toward lazy coding—remove them. https://t.co/khJFCoReF5 #Python #Coding #Developer

"What story is this data really trying to tell?" I've put together real examples you can filch for your own projects. (3/3) https://t.co/y4KvY0HwHa #Data #MachineLearning #DataModeling #DataModels #DataVisualization #AI

That simple transformation of taking raw timestamps and grouping them into seasons became the strongest predictor in the entire model. Whether you're running predictive analytics or not, sometimes the best insights come from asking... (2/3)

Here's something that surprised me: I was building a predictive model with 21 different features, and the most powerful one wasn't in my original dataset at all. I created it by converting customer call dates into seasons. (1/3)

This prompt turns boring spreadsheets into executive-ready stories: "Turn this data/spreadsheet into a narrative story that a non-technical executive would understand, with the key insight as the headline." Learn more: https://t.co/2RJmkesj4I #ai #aiprompting #data #datascience

Result: Complex multi-agent workflows that actually work. No spaghetti code. No state management headaches. Just configure your agents and let #AutoGen handle the orchestration. (3/3) https://t.co/PCPrmtR7Hg #AI #AIAgent #DataScience

Built a utility analysis system: Technical Analyst (GPT-4) → Compliance Reviewer (Mistral) → Strategic Planner (Claude). Each agent isolated, no context bleeding. The UserProxy pattern handles all coordination automatically. (2/3)

Ever tried coordinating multiple AI models? Without a framework, you're managing API calls, conversation state, message routing... it's a mess. Here's how AutoGen changed everything for me. (1/3)

My AI Timeline hit 1,500 entries yesterday. Almost half (748) of those are announcements from sundry model creators and AI tools (select 'announcement' from the tag filter to follow announcements). https://t.co/GRt2FeJ9l8 #AI #Data #DataScience

I built a dashboard that demonstrates making AI metrics accessible to non-technical stakeholders. No more "ROC AUC = 0.85" 🤷‍♀️ Tooltips, plain English. Because if they can't understand it, they won't trust it. Demo app: https://t.co/VNwAnMLZTL Post: https://t.co/oe2lD2XqjP #AI

Here's a #Python tip for visualizing your codebase for a project. Code in the post. https://t.co/zwjOA2EY7a

I took the pup for a walk, came back to return to my project, and all of the responses from ChatGPT in the thread I've been working in since last night are gone. 🫠 #ChatGPT #ChatGPT5 #ai #hellooooo #isthisthingon

ChatGPT 5 is not ready for primetime. I had to keep uploading the same data, even data it sent me in its previous response. And today messages from yesterday went missing or unanswered. #ChatGPT #ux

I've experimented with Python libraries that generate data, but they have all lacked this kind of diversity. (6/6) #Data #DataScience #AI #ArtificialIntelligence #MachineLearning

I could have customized those values further before generating an updated dataset, if I had wanted to. One thing I appreciate about its generated names is the diversity. (5/6)

Screws fall out all the time. The world is an imperfect place. 🤷‍♀️ At the time of writing, they have 211 fields to choose from. But you also have the option to generate a custom datatype using AI. I tested this with ‘US regions’. (4/6)

One thing I love is that there are custom options that vary with the field you choose, and you can inject blanks, which is a great if you’re learning how to deal with null values in Python, SQL, or your automation tool. (3/6)

To use it, just select the fields you want your dataset to have, define the column labels, and click the GENERATE DATA button. You can also preview your data first by clicking the PREVIEW button. (2/6)