Articles for category: AI Tools

March 28, 2025

ikayaniaamirshahzad@gmail.com

Amazon Bedrock Guardrails image content filters provide industry-leading safeguards, helping customer block up to 88% of harmful multimodal content: Generally available today

Amazon Bedrock Guardrails announces the general availability of image content filters, enabling you to moderate both image and text content in your generative AI applications. Previously limited to text-only filtering, this enhancement now provides comprehensive content moderation across both modalities. This new capability removes the heavy lifting required to build your own image safeguards or spend cycles on manual content moderation that can be error-prone and tedious. Tero Hottinen, VP, Head of Strategic Partnerships at KONE, envisions the following use case: “In its ongoing evaluation, KONE recognizes the potential of Amazon Bedrock Guardrails as a key component in protecting generative

March 28, 2025

ikayaniaamirshahzad@gmail.com

How to build a strong code review culture

Code review isn’t just about catching bugs. It’s not just about keeping code quality high. A good code review culture is about how a team learns together. How it grows, improves collective quality, and avoids knowledge being stuck with just a few people. If you’re leading an engineering team, you need to see this not as an isolated practice, but as a cultural system. In this post, we’ll talk about how to build a code review culture that actually works: one that doesn’t slow down deliveries, doesn’t hurt relationships, and helps the whole team. Start with the “why” Before changing

March 28, 2025

ikayaniaamirshahzad@gmail.com

lightweight, robust word vectors · Explosion

floret is an extended version of fastText that uses Bloom embeddings to create compact vector tables with both word and subword information. floret brings fastText’s subwords into spaCy pipelines with vectors that are up to 10× smaller than traditional word vectors. In this blog post, we’re going to dive deeper into these vectors. We’ll explain how they work and show when they’re helpful. If you’re already familiar with how floret works, jump ahead to the comparison of fastText vs. floret. For many vector tables, including the default vectors in spaCy, the vector table contains entries for a fixed list of

March 28, 2025

ikayaniaamirshahzad@gmail.com

Bringing serverless GPU inference to Hugging Face users

Update (November 2024): The integration is no longer available. Please switch to the Hugging Face Inference API, Inference Endpoints, or other deployment options for your AI model needs. Today, we are thrilled to announce the launch of Deploy on Cloudflare Workers AI, a new integration on the Hugging Face Hub. Deploy on Cloudflare Workers AI makes using open models as a serverless API easy, powered by state-of-the-art GPUs deployed in Cloudflare edge data centers. Starting today, we are integrating some of the most popular open models on Hugging Face into Cloudflare Workers AI, powered by our production solutions, like Text

March 28, 2025

ikayaniaamirshahzad@gmail.com

Parallelize work using parwork – DEV Community

In order to process a lot of work we have to parallelize work across all cores, and especially if it’s CPU bound.Go has goroutines, which can be used to parallelize the work, but there is the cost of context switching for a lot of goroutines.Minimizing this context switching can be achieved by using a fork-join model when processing work. Parwork solves this problem by using goroutines, channels and waitgroups. It creates workers (goroutines) that pullwork of a queue (channel), process the work and report the work back to a queue (channel).This is done in a abstracted way so the user

March 28, 2025

ikayaniaamirshahzad@gmail.com

library patterns & design concepts explained · Explosion

Developer productivity has been central to our design of spaCy, both in smaller decisions and some of the bigger architectural questions. We believe in embracing the complexities of machine learning, not hiding it away under leaky abstractions, while also maintaining the developer experience. Read on to learn some of the design patterns within the library, how we’ve implemented them, and most importantly, why. Within spaCy, we work on prioritizing how to: balance ease-of-use vs. customizability help prevent bugs, and debug them when they happen improve readability provide tooling for complex and adaptable software projects In this blog post, we’ll be

March 28, 2025

ikayaniaamirshahzad@gmail.com

Blazing Fast SetFit Inference with 🤗 Optimum Intel on Xeon

SetFit is a promising solution for a common modeling problem: how to deal with lack of labeled data for training. Developed with Hugging Face’s research partners at Intel Labs and the UKP Lab, SetFit is an efficient framework for few-shot fine-tuning of Sentence Transformers models. SetFit achieves high accuracy with little labeled data – for example, SetFit outperforms GPT-3.5 in 3-shot prompting and with 5 shot it also outperforms 3-shot GPT-4 on the Banking 77 financial intent dataset. Compared to LLM based methods, SetFit has two unique advantages: 🗣 No prompts or verbalisers: few-shot in-context learning with LLMs requires handcrafted

March 28, 2025

ikayaniaamirshahzad@gmail.com

Release notes for Deephaven Core version 0.38

from deephaven import new_tablefrom deephaven.column import string_col, int_colfrom deephaven.constants import NULL_INTfrom deephaven.table import NaturalJoinTypesource_left = new_table( [ string_col("LastName", ["Rafferty", "Jones", "Steiner", "Robins", "Smith"]), int_col("DeptID", [31, 33, 33, 34, 34]), string_col( "Telephone", [ "(303) 555-0162", "(303) 555-0149", "(303) 555-0184", "(303) 555-0125", "", ], ), ])source_right = new_table( [ int_col("DeptID", [31, 33, 34, 35]), string_col("DeptName", ["Sales", "Engineering", "Clerical", "Marketing"]), string_col( "DeptTelephone", ["(303) 555-0136", "(303) 555-0162", "(303) 555-0175", "(303) 555-0171"], ), ])result_error_on_duplicates = source_left.natural_join(table=source_right, on=["DeptID"], type=NaturalJoinType.ERROR_ON_DUPLICATE)result_first_match = source_left.natural_join(table=source_right, on=["DeptID"], type=NaturalJoinType.FIRST_MATCH)result_last_match = source_left.natural_join(table=source_right, on=["DeptID"], type=NaturalJoinType.LAST_MATCH)result_exact_match = source_left.natural_join(table=source_right, on=["DeptID"], type=NaturalJoinType.EXACTLY_ONE_MATCH) Source link

March 28, 2025

ikayaniaamirshahzad@gmail.com

AWS Security Tools for Your Environment.

Amazon Web Services enables organizations to build and scale applications quickly and securely. However, continuously adding new tools and services introduces new security challenges. According to reports, 70 percent of enterprise IT leaders are concerned about how secure they are in the cloud and 61 percent of small- to medium-sized businesses (SMBs) believe their cloud data is at risk. AWS provides security tools designed to improve both account security and application and service security. Top 6 AWS Account Security Tools1. AWS Identity and Access Management (IAM):AWS IAM is essential for controlling access to your AWS resources. It enables you to

March 28, 2025

ikayaniaamirshahzad@gmail.com

New coreference components · explosion/spacy-experimental · GitHub

new coreference components (#17) This release includes an experimental English coref pipeline. You can install the pipeline by downloading it from the assets in this release page, or install it directly with the following command: Update 2022-11-07: Some issues in the coref implementation have been fixed in the v0.6.1 release of this package. While the pipeline below can still be installed and will be left up for posterity, note that it should only be used with 0.6.0, but by default will pull in the newer version. If you want to use the below package (which is not recommended), be sure