The book “Algorithmic Trading with Python” discusses modern quant trading methods in Python with a heavy focus on PANDAS, Numpy, and scikit-learn. Get it at: https://
amzn.to/3uvYlKk +
Get free code from the book here: https://
github.com/chrisconlan/al
gorithmic-trading-with-python
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MACHINE LEARNING
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Algorithmic Trading with Python: Modern Quant Methods Guide
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Python Data Cleaning Snippets and Cookbook for Messy Data
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Simple Practical Data Cleaning Python Code Snippets for messy data: https://
bit.ly/3hpthSD
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Data Cleaning Cookbook to prepare your data for analysis — PANDAS, NumPy, Matplotlib, scikit-learn,…: https://
amzn.to/4c6DAWf v/ @PacktDataML —————
#DataScience #DataScientist -

Data Engineering Best Practices for Cloud Architecture and Cost Optimization
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Data Engineering Best Practices — Architect robust and cost-effective data solutions in the cloud era: http://
amzn.to/3BkZ6df v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Architect and engineer optimized data solutions in the cloud with best practices for performance and -

Python Machine Learning By Example: 518-Page Guide to ML Best Practices
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Python Machine Learning By Example: https://
amzn.to/3MczBwk v/ @PacktDataML +
GitHub: https://
github.com/PacktPublishin
g/Python-Machine-Learning-By-Example-Third-Edition
… 518-pages! What you will learn: Machine learning best practices throughout data preparation and model development Build and improve image classifiers using -

DRIFT Framework: Dual-Model Approach for Long Document Processing
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Tired of LLMs losing context on super long documents? Researchers from Shanghai AI Lab, Fudan, & Tongji Universities unveil DRIFT! Their new framework uses a clever dual-model approach: one AI finds and compresses relevant facts into "implicit fact tokens" — query-specific
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Agent 4 Content Challenge Winner: Dari.fyi for Central Asia Diaspora
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Week 1 winner of the Agent 4 Content Challenge:
— Replit ⠕ (@Replit) 27 mars 2026
Rahim Siddiq 🎉
Built: https://t.co/S8aozLfvQh Duolingo for the Central Asia diaspora https://t.co/eemfRd7r0Z pic.twitter.com/QodIKVNI11Week 1 winner of the Agent 4 Content Challenge: Rahim Siddiq Built: http://
Dari.fyi Duolingo for the Central Asia diaspora -
AI advancement: new models on same hardware produce impressive results
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One way to see the advancement of AI is to see how much further you can get with new models on the same hardware
— Ethan Mollick (@emollick) 27 mars 2026
Here is "an otter using a laptop on an airplane" generated on my home computer using the open weights Wan 2.1, first try. We have come pretty far in 18 months. https://t.co/loxNM2PRyb pic.twitter.com/c4iz9UcmTBOne way to see the advancement of AI is to see how much further you can get with new models on the same hardware Here is "an otter using a laptop on an airplane" generated on my home computer using the open weights Wan 2.1, first try. We have come pretty far in 18 months.
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Why I Shill Droid 24/7: Eight Major Accomplishments Today
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Here's why I shill Droid 24/7 ———- Today Droid single-handedly: 1. Published a REAP of GLM-5 in FP8, there's a reason no one else has done it DSA is still very new: huggingface.co/0xSero/GLM-5-… 2. Found and Fixed an upstream issue with VLLM + DSA + Hopper where GLM-5's kv-cache would need to recompute and spend 20x the time needed, fixed. 3. Created multiple working quantisations on it's own, it tried exl3 and autoround but both failed so resorted to GGUF (autoround 3 bits doesn't work on ampere) huggingface.co/0xSero/GLM-5-… 4. Implemented github.com/0xSero/turboquant within 24 hours of the research paper coming out, tested it across 5090s, 3090s, H100s, and B200s 5. Has been distilling larger models into LoRA to help me test arxiv.org/abs/2505.21835 and it got an 80% prune to be semi-coherent again. 6. Helped my find research papers, clean up slop with the human-writing skill. 7. Got BYOK working with Anthropic, ZAI, Kimi, MiniMax, OpenAI working in Cursor github.com/0xSero/factory-cu… 8. Helped me Implement blog.comfy.org/p/dynamic-vra… 's dynamic loading, only works on a tiny model, but still. ——- I only have to check in on it every 30-45 minutes (I am talking all 8 of my sessions) the thing will run for 16 hours with like 0 prep All this while I am mostly focused on my actual job and tweeting 24/7 Keep in mind each one of these experiments is running on a different server, with different constraints, like I don't understand how I can get such good results here. ——— I love novelty. Which is why I jump around and talking about all these different tools. I have used all of these harnesses and messed around with every feature. I keep coming back to this, and I keep shilling it because I sincerely wish others get to experience this.
→ View original post on X — @nathanlands, 2026-03-27 19:36 UTC
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AVO: Agentic Variation Operators for Autonomous Evolutionary Search
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AVO Agentic Variation Operators for Autonomous Evolutionary Search paper: https://
huggingface.co/papers/2603.24
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SlopCodeBench: New Evaluation for Iterative Code Generation Quality
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Congrats on the release 🚀 Proud to support research like this that moves the needle on evals and real-world agent performance. Gabe Orlanski (@GOrlanski) We found that agents generate progressively worse code with each iteration. Real developers do not. SlopCodeBench is the only eval that faithfully measures quality degradation on iterative, long-horizon coding tasks. arxiv.org/abs/2603.24755 scbench.ai 🧵 — https://nitter.net/GOrlanski/status/2037560777356238881#m
→ View original post on X — @snorkelai, 2026-03-27 18:57 UTC