Gilisoft Releases AI Toolkit 10.8 For Workflows - Let's Data Science
<a href="https://news.google.com/rss/articles/CBMikgFBVV95cUxNakpKOE1EMDdnV1M2eUxqU0J5UTcxTUxTQVVjS084Mkc1TGdNdE1ydmIxQjJ1ZlNIZjJsRFdXNnV0b1hTTDJiTVBnQWNXQkNpbXlmOW9NUldTWUtaTlZtU1l0bGFTWjVKOU1SYTA2NlNPOEwyeWNRVzU3Q0xKeTJlRjV4S3Y0TmJsMFBuc2RLYUtfUQ?oc=5" target="_blank">Gilisoft Releases AI Toolkit 10.8 For Workflows</a> <font color="#6f6f6f">Let's Data Science</font>
Could not retrieve the full article text.
Read on GNews AI multimodal →Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
releaseHugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows
Hugging Face has officially released TRL (Transformer Reinforcement Learning) v1.0, marking a pivotal transition for the library from a research-oriented repository to a stable, production-ready framework. For AI professionals and developers, this release codifies the Post-Training pipeline—the essential sequence of Supervised Fine-Tuning (SFT), Reward Modeling, and Alignment—into a unified, standardized API. In the early stages […] The post Hugging Face Releases TRL v1.0: A Unified Post-Training Stack for SFT, Reward Modeling, DPO, and GRPO Workflows appeared first on MarkTechPost .
Antropic's Claude Code leaked and Axios NPM Inflitration
<p><a href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fb1ka41vwv76ehjjesu4d.png" class="article-body-image-wrapper"><img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fb1ka41vwv76ehjjesu4d.png" alt=" " width="784" height="478"></a></p> <h2> THE CODE LEAK THAT SHOCKED THE TECH WORLD </h2> <p>This week, Anthropic accidentally opened the floodgates to a wealth of secret information by leaking the full source code of Claude Code via an npm source map. With internal architecture, unreleased features, and multi-agent workflows thrust into the
5 Rust patterns that replaced my Python scripts
<p>I used to reach for Python every time I needed a quick script.<br> File renaming, log parsing, API polling, directory cleanup --<br> Python was the default because it was fast to write and good enough to run.</p> <p>That changed gradually.<br> Not because I decided to rewrite everything in Rust,<br> but because I kept running into the same friction points:<br> shipping the script to another machine, handling errors properly,<br> or running it somewhere Python wasn't available.</p> <p>Here are five patterns where Rust has genuinely replaced Python for me.</p> <h2> 1. Error handling that forces you to think </h2> <p>In Python, the path of least resistance is letting exceptions propagate and hoping for the best.<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight pytho
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases
Q2, Day 1: When Concepts Have to Become Code
<p>Q1 is over. Yesterday I closed it with a retrospective — 20+ build-log entries, four bots running in production, one AI agent writing half of them. The numbers were real, the gaps were real, the promises for Q2 were real.</p> <p>Today is April 1st. Q2, Day 1.</p> <p>The temptation is to write an April Fools post. "I shipped Aether Dynamo overnight." "The bots tripled." "MiCA compliance is a solved problem."</p> <p>None of that is true. The build-log exists to make those gaps visible. So here they are, visible.</p> <h2> The gap between concept and code </h2> <p>Three things were declared for Q2 at the end of yesterday's retrospective:</p> <ol> <li> <strong>AI Compliance Stack</strong> — a MiCA regulatory feed monitor. Not a platform. A working Python script that polls ESMA/EBA feeds and
Handling Extreme Class Imbalance in Fraud Detection
<p><em>Originally published at <a href="https://riskernel.com/blog/extreme-class-imbalance-fraud-detection.html" rel="noopener noreferrer">Riskernel</a>.</em></p> <p>Fraud is one of the easiest machine learning problems to misunderstand because the target is so rare.</p> <p>In many portfolios, fraud is well below one percent of total events. That means a model can look excellent in offline evaluation while still creating a terrible operational outcome once it meets production traffic.</p> <p>If you are evaluating a fraud vendor or building your own stack, the first thing to understand is that this is not a standard classification problem. It is a rare-event decisioning problem with operational consequences.</p> <h2> Why the base rate changes everything </h2> <p>When fraud is extremely rare
5 Rust patterns that replaced my Python scripts
<p>I used to reach for Python every time I needed a quick script.<br> File renaming, log parsing, API polling, directory cleanup --<br> Python was the default because it was fast to write and good enough to run.</p> <p>That changed gradually.<br> Not because I decided to rewrite everything in Rust,<br> but because I kept running into the same friction points:<br> shipping the script to another machine, handling errors properly,<br> or running it somewhere Python wasn't available.</p> <p>Here are five patterns where Rust has genuinely replaced Python for me.</p> <h2> 1. Error handling that forces you to think </h2> <p>In Python, the path of least resistance is letting exceptions propagate and hoping for the best.<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight pytho
Available Careers with a Master’s Degree in Business in AI - Boston University
<a href="https://news.google.com/rss/articles/CBMiigFBVV95cUxQUGtldVVjYnFlMlZ6cGFMaXhEVHhHTGFiQWI4RjNVS2ZDWlBjM3ZTejdnS3VyOFlWbFFkY1dlSmxjQ21MczVGcG40YTJoT29NTU1MaXJxa2s5MThQN2xrcDh5TVY1MDViSzdxM1d4VDhQUnJlWThTRFhhY1p4ZmNIUFBNeHlCbDNWRnc?oc=5" target="_blank">Available Careers with a Master’s Degree in Business in AI</a> <font color="#6f6f6f">Boston University</font>
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!