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Amazon

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Process millions of observability events with Apache Flink and write directly to Prometheus

AWS introduced a new Apache Flink connector for Prometheus, allowing data to be directly sent to Amazon Managed Service for Prometheus.
Amazon Managed Service for Prometheus enables real-time observability without managing infrastructure, utilizing Prometheus's data model and query language.
Observability extends beyond IT assets, including IoT devices, and metrics play a crucial role in capturing measurements from these distributed devices.
Prometheus serves as a specialized time series database for real-time dashboards and alerting, optimized for various distributed assets, not just compute resources.
Handling observability data at scale poses challenges like high cardinality, frequency, irregular events arrival, lack of contextual information, and deriving metrics from events.
Preprocessing raw observability events before storing can optimize real-time dashboards and alerting by reducing frequency and cardinality.
Apache Flink is a suitable engine for preprocessing observability events, allowing efficient enrichment, aggregation, and derivation of metrics at scale.
The new Flink Prometheus connector seamlessly writes preprocessed data to Prometheus, eliminating the need for intermediate components and scaling horizontally.
Example use case scenarios demonstrate how preprocessing with Flink enhances visualization and analysis of metrics for improved observability.
The Flink Prometheus connector optimizes write throughput by parallelizing and batching writes, adhering to Prometheus Remote-Write specifications.

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Hackernoon

278

Image Credit: Hackernoon

Master DolphinScheduler Time Parameter With These Dynamic Configuration Secrets

DolphinScheduler allows flexible configuration of time parameters for task scheduling needs.
Time parameters can be defined as global parameters and used throughout workflows or tasks.
Time window parameters like ${startTime} and ${endTime} are useful for data processing tasks.
Cron expressions enable precise task scheduling, e.g., running tasks daily or weekly.
Complement tasks in DolphinScheduler involve specifying time ranges for re-execution.
Time formatting functions allow for date addition and subtraction in time parameters.
Built-in time parameters like ${system.biz.date} are available for immediate use in scripts.
Time-dependent task chains can be configured using time parameters across workflows.
Functions like add_months() allow for time manipulation, e.g., adding/subtracting months or days.
Overall, time parameters in DolphinScheduler support various scheduling and data processing needs efficiently.

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Siliconangle

332

Image Credit: Siliconangle

CodeSecure and FOSSA partner to enhance visibility into open source and binary code

CodeSecure and FOSSA have partnered to enhance visibility into open source and binary code.
CodeSecure's CodeSentry Binary Composition Analysis capabilities will be integrated into FOSSA's platform.
The integrated solution provides continuous visibility for detecting and mitigating software security vulnerabilities and compliance violations.
The partnership aims to facilitate comprehensive management of software supply chains from development through deployment.

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Medium

103

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⚡ Most Tools Track Hours. EmpMonitor Open-Source Tracks Impact.

EmpMonitor Open-Source is an open-source workforce management tool designed to track user activity, time tracking, productivity, behavior analytics, and insider threat detection.
The platform offers real-time user activity monitoring to track work patterns and engagement without intrusion, automated time tracking, and accurate attendance logs.
It also provides productivity and behavior analytics to gain insights into how teams spend their time and identifies areas for performance improvement.
EmpMonitor Open-Source is part of the broader EmpCloud suite and empowers companies to self-host the tool, customize workflows, and contribute to its development on GitHub.

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Medium

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Github Official MCP Server is Here

GitHub has introduced the MCP Server to simplify building custom tools on top of GitHub's API.
The MCP Server brings structure and simplifies the process of building complex, customized tools.
It is ready to use out of the box, with no complicated installation or dependencies.
The MCP Server provides more flexibility and capabilities compared to GitHub Actions.

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Marktechpost

395

Huawei Noah’s Ark Lab Released Dream 7B: A Powerful Open Diffusion Reasoning Model with Advanced Planning and Flexible Inference Capabilities

Researchers from the University of Hong Kong and Huawei Noah’s Ark Lab have released Dream 7B, a powerful open diffusion reasoning model.
The model overcomes limitations of autoregressive models by providing bidirectional contextual modeling and flexible controllable generation.
Dream 7B outperforms larger autoregressive models on structured tasks and displays exceptional zero-shot planning capabilities and inference flexibility.
This breakthrough in language model development showcases the effectiveness and viability of diffusion models.

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Siliconangle

440

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Deep Cogito releases open-source language models that outperform Llama

Startup Deep Cogito Inc. launched with language models that claim to outperform open-source alternatives.
The Cogito v1 series models are available in sizes ranging from 3 billion to 70 billion parameters.
Deep Cogito uses a hybrid architecture combining large language models and reasoning models.
The company's IDA training method improves the models' performance by distilling knowledge from the generated answers.

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Mjtsai

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Swiftly 1.0

Swiftly 1.0 is the first stable release of Swift version manager.
Swiftly makes it easier to install, manage, and update Swift toolchain.
It supports different platforms like Linux and macOS.
Swiftly is now officially supported as part of the core Swift toolchain.

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Medium

220

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I Found This Buried on Page 10 of Google — and It’s a Game-Changer

Google is known for tracking and storing user data, leading to personalized ads and curated search results.
Searx is a privacy-focused meta-search engine that aggregates results from various search engines, offering a diverse range of sources.
Users can experience a mix of search results, including those from Bing, Qwant, Reddit, and more, creating a unique and unfiltered browsing experience.
Searx prioritizes user privacy by not logging IP addresses or search history, enhancing anonymity while browsing.
SearxNG, a successor to the original Searx project, offers faster performance, enhanced features, and support for a wide range of search services.
The meta-search capability of Searx allows users to access a variety of content from different sources simultaneously, promoting a more comprehensive search experience.
Unlike Google, Searx enables users to customize their search preferences, select preferred search engines, and maintain full control over their search queries.
While Searx may have slower load times and occasional duplicate results, its emphasis on privacy and diversity in search results makes it a valuable alternative to traditional search engines.
By offering complete privacy, diverse result sources, and extensive customization options, Searx challenges the dominance of mainstream search engines like Google.
Searx's open-source nature, lack of tracking, and active developer community contribute to its appeal as a privacy-conscious and user-centric search tool.

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Hackernoon

201

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The HackerNoon Newsletter: Testing LLMs on Solving Leetcode Problems in 2025 (4/8/2025)

Testing LLMs on Solving Leetcode Problems in 2025 - Large-scale LLMs were tested on solving Leetcode algorithmic problems.
Returns are Rising Fast — and Not Enough Tools are Focused on Stopping Them at the Source - In 2024, nearly one in five online purchases ended up being returned.
Escape Prompt Hell With These 8 Must-have Open-source Tools - Discover 8 powerful tools transforming prompt engineering from trial-and-error into scalable systems.
Can GPT Outsmart Social Media Regulations? Inside an AI Language Evolution Experiment - Learn how Large Language Models creatively adapt language strategies under supervision.

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Sdtimes

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Report: Keeping up with patches is the number one challenge when using open source software

The most challenging aspect of utilizing open source projects is keeping up with updates and patches, according to a new report.
Over half of the respondents ranked keeping software updated, meeting security and compliance requirements, and maintaining end-of-life (EOL) versions as the top challenges.
CentOS 7 reaching EOL highlighted the difficulty organizations face in staying on the latest versions and accessing security updates.
The report also revealed that cloud and container technologies, databases and data technologies, and programming languages and frameworks are the top areas where open source software is being used.

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Hackernoon

153

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Escape Prompt Hell With These 8 Must-have Open-source Tools

Prompt engineering has evolved from creativity to a more systematic approach resembling software development, requiring tools to optimize prompts systematically.
AdalFlow is a PyTorch-inspired framework that declaratively builds and optimizes LLM workflows, focusing on latency, performance, and cost optimizations.
Ape, by Weavel, helps test, debug, and improve LLM applications by providing structured feedback on agent behavior, removing the need for manual prompt tuning.
AutoRAG assists in evaluating and optimizing RAG pipelines automatically using plug-and-play modules and pipeline search functionalities.
DSPy, from Stanford NLP, treats LLM components as programmable modules, facilitating structured prompt engineering workflow with auto-tuning and reproducible pipelines.
Zenbase Core focuses on turning research ideas into production tools, emphasizing automatic prompt optimization and reliability for software engineering workflows.
AutoPrompt automates improving prompt performance based on real data, making prompt writing a measurable and scalable process.
EvoPrompt, backed by Microsoft, uses evolutionary algorithms to optimize prompts, reframing prompt crafting as a population-based search problem.
Promptimizer is an experimental Python library for optimizing prompts using feedback loops, ensuring systematic prompt quality improvement.
These tools transform prompt engineering into a disciplined practice with benefits like cost control, speed, accuracy improvements, and enhanced governance.
The future of LLM applications lies in scalable infrastructure, moving from intuition-based methods to reliable engineering practices for better prompts and systems.

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Medium

306

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AI Society: How Networks of Specialized Models Could Shape the Future of Artificial Intelligence

As Large Language Models (LLMs) continue to scale, challenges of extreme model scaling are emerging, leading to questions about sustainability.
An alternative approach, known as 'AI Society,' suggests using networks of specialized AI models working together like human societies for more efficient AI advancement.
Specialized AI models under 100B parameters each offer advantages over larger monolithic models, including better performance in specific domains and more efficient resource utilization.
The 'AI Society' approach involves a coordinating system that routes queries to specialized models, exemplified by an implementation called 'Frida,' a home assistant system.
Frida consists of orchestrated specialized systems with clear communication protocols, enabling efficient task delegation and reliable outputs.
The modular design of Frida allows for extensions with new capabilities without disrupting existing functionality, in contrast to monolithic models requiring expensive retraining.
The AI Society model aligns with theories in cognitive science and offers potential implications for achieving more general artificial intelligence through coordination of specialized intelligences.
By mimicking the modular architecture of human cognition, AI societies may address safety concerns and be more aligned with human values.
This approach democratizes advanced AI capabilities by using smaller open-source models that can run on consumer hardware, making AI more accessible and affordable.
Building better societies of AI systems with improved communication and coordination between specialized components is seen as the next frontier in AI development, with potential benefits for all contributors.
Shifting the focus from scaling up models to designing efficient networks of specialized AI components may lead to more impactful advancements in artificial intelligence.

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Hackernoon

409

Image Credit: Hackernoon

To Hit $1T TVL, Ethereum Must Play the Ace

Protecting intellectual property is crucial for business survival, as seen in the hardware and algorithm design industries.
Openly sharing development plans in the blockchain space allows competitors to replicate strategies, leading to fragmentation.
Fragmentation in the blockchain market poses challenges for established giants like Bitcoin and Ethereum.
Newer projects with larger initial funding can front-run leaders by leveraging open-source development.
The transparency of blockchain protocols threatens the market share and growth potential of established chains.
Large financial institutions benefit from open-source research without significant contribution back to the community.
Implementing more competitive and incentivized models for accessing research data could benefit the ecosystem.
Creating knowledge marketplaces based on contributions could enhance collaboration and development in open-source projects.
Prioritizing community involvement and trust is crucial for the growth and sustainability of blockchain ecosystems like Ethereum.
Balancing openness with strategic data sharing and fostering a committed community can lead to the success of blockchain projects.

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Siliconangle

441

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PyannoteAI raises $9M for its speech processing AI

French startup pyannoteAI raises $9 million in funding to enhance its speech processing AI.
The company's open-source AI toolkit is downloaded over 45 million times per month and has an installed base of over 100,000 developers.
pyannoteAI's commercial offering is twice as fast as the open-source edition and provides a 20% accuracy increase in distinguishing speakers in audio recordings.
The company plans to invest the funding in product development initiatives, including features to split an audio file into multiple files featuring only a single speaker and enabling AI models to run on a broader range of devices.

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