Loading…
Latest reads
The engineering internet, summarised so you can actually read it.
SyGra: The One-Stop Framework for Building Data for LLMs and SLMs
Training and aligning large language models and small language models requires datasets tailored for complex reasoning, preference tuning, multi-turn questioning, and format conversion. To eliminate the need for bespoke data preparation scripts, SyGra provides a low-code and no-code Python framework for automated dataset creation, transformation, and alignment. The library integrates with diverse inference backends, including vLLM, Hugging Face TGI, Triton, and Ollama, allowing teams to focus on prompt engineering rather than pipeline infrastructure. It supports data workflows ranging from converting PDFs and knowledge bases into question-answering pairs to filtering low-quality samples and generating Direct Preference Optimization datasets. These plug-and-play workflows reduce manual curation effort while accelerating model fine-tuning and retrieval-augmented generation pipelines.
Bidyapati Pradhan, Vipul Mittal, Amit Kumar Saha, Surajit DasguptaGaia2 and ARE: Empowering the community to study agents
Existing AI agent evaluation environments are often tightly coupled to specific tasks and fail to model real-world challenges such as API failures, spontaneous events, and asynchronous conditions. To address this limitation, the Gaia2 benchmark and the Meta Agents Research Environments (ARE) framework introduce interactive read-and-write evaluation. Gaia2 incorporates 1,000 human-created scenarios spanning multi-step execution, cross-source search, ambiguity handling, adaptability, temporal reasoning, agent collaboration, and noise tolerance. Using a simulated smartphone interface equipped with 101 tools, evaluations showed that GPT-5 with high reasoning scored highest overall, while Kimi K2 was the leading open-source model. The results demonstrated that instruction following and search do not reliably predict performance on closer-to-real-world tasks.
Clémentine Fourrier, Grégoire Mialon, Maxime Lecanu, Pierre Andrews, Adrien Carreira, frere thibaud, Avijit Ghosh, Romain Froger, Dheeraj Mekala, Caroline Pascal, Ulyana PiterbargMongoDB ·
MongoDB Community Edition to Atlas: A Migration Masterclass With BharatPE
BharatPE, an Indian fintech processing over ₹12,000 crore monthly across 450 cities, operated a 45-terabyte self-hosted MongoDB Community Edition deployment. Managing three sharded three-node clusters resulted in uneven data distribution, high maintenance costs, scaling friction, and disaster recovery gaps under strict regulatory requirements. To modernize infrastructure, the database operations team partnered with MongoDB to execute a structured five-stage migration to MongoDB Atlas. The initiative utilized mongosync for secure data transition alongside environment mirroring and automated integrity validation scripts. Transitioning to MongoDB Atlas yielded a 40 percent improvement in query response times and provided automated failover backed by a 99.995 percent uptime SLA.
Nick BellScaleway on Hugging Face Inference Providers 🔥
Scaleway is integrated as a supported serverless Inference Provider on the Hugging Face Hub, expanding model deployment options across Hub model pages and official client SDKs for JavaScript and Python. Operating out of European data centers located in Paris, France, Scaleway Generative APIs host open-weight models including gpt-oss, Qwen3, DeepSeek R1, and Gemma 3 with structured outputs, function calling, multimodal processing, and sub-200ms first-token response times. Developers can route inference requests using their Hugging Face tokens or supply direct Scaleway API keys. Billing for routed requests charges standard provider rates starting at €0.20 per million tokens without markups, while direct requests bill to Scaleway accounts. Hugging Face PRO subscribers receive two dollars of monthly inference credits applicable across providers.
Guillaume Noale, Franck Pagny, Fred Bardolle, Guillaume Calmettes, Constance Morales, Célina Hanouti, Julien Chaumond, Simon Brandeis, Lucain PougetMongoDB ·
Modernizing Core Insurance Systems: Breaking the Batch Bottleneck
Migrating core insurance platforms from legacy relational databases with PL/SQL to Java and MongoDB Atlas frequently degrades batch processing performance. Like-for-like migrations can cause batch jobs to run 25 to 30 times slower due to high network round-trips, inefficient per-record operations, and under-utilized bulk capabilities. To resolve these bottlenecks, an optimization framework was developed that leverages MongoDB native bulkWrite operations, intelligent reference data prefetching, and parallel execution. An architecture combining a Spring Boot controller and a dedicated executor framework partitions workloads across threads or event processors. This framework recovered lost performance, completing previously failing batch jobs within service-level agreements and achieving 10 to 15 times better execution speeds than legacy systems in certain workloads.
Vinod Bagal, Jagpreet SinghMongoDB ·
MongoDB.local NYC 2025: Defining the Ideal Database for the AI Era
At MongoDB.local NYC, MongoDB introduced new database capabilities, AI integrations, and modernization tools designed for agentic and generative AI workloads. The release of MongoDB 8.2 arrives alongside public previews of Search and Vector Search for both MongoDB Community Edition and Enterprise Server. MongoDB also highlighted Voyage AI embedding models and rerankers to improve retrieval accuracy across raw data, metadata, and embeddings. To address the costs and constraints of legacy infrastructure, the new MongoDB Application Modernization Platform combines AI-assisted tooling and specialized expertise to migrate legacy systems. According to MongoDB, early migrations using this platform run two to three times faster, while code rewriting tasks accelerate by an order of magnitude.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
MongoDB.local NYC 2025: Definiendo la base de datos ideal para la era de la IA
At MongoDB.local NYC 2025, MongoDB introduced new capabilities and product updates tailored for artificial intelligence workloads. MongoDB 8.2 was unveiled alongside public previews of full-text and vector search for both MongoDB Community Edition and Enterprise Server. The company also detailed its integration with Voyage AI embedding and reranking models to deliver precision retrieval across raw data, metadata, and vectors. To address legacy infrastructure migration hurdles, MongoDB launched the Application Modernization Platform, which combines AI-assisted tooling and specialized workflows to accelerate legacy application refactoring two to three times faster. These architectural updates aim to position the JSON document model as the persistent memory and context store required by autonomous, agentic AI workflows.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
MongoDB.local NYC 2025 : définir la base de données idéale à l'ère de l'IA
At the MongoDB.local NYC 2025 event, MongoDB unveiled several platform updates aimed at modernizing enterprise database architectures and supporting emerging agentic artificial intelligence workloads. The newly released MongoDB 8.2 version provides enhanced performance alongside expanded developer features across the broader document database ecosystem. The company also integrated Voyage AI embedding and reranking models to improve context retrieval precision and efficiency when building production-grade AI systems. Furthermore, dedicated Search and Vector Search capabilities have reached general availability across both MongoDB Community Edition and Enterprise Server environments. To streamline infrastructure transitions, the newly introduced Application Modernization Platform leverages AI-powered tooling and specialized techniques to migrate legacy database systems two to three times faster.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
MongoDB.local NYC 2025: Definindo o Banco de Dados Ideal para a Era da IA
At MongoDB.local NYC, new product capabilities were unveiled to support modern enterprise workloads and artificial intelligence applications. The release of MongoDB 8.2 delivers improved performance alongside embedding and reranking models from Voyage AI for AI application development. Additionally, search and vector search capabilities have entered public preview for both MongoDB Community Edition and Enterprise Server across customer runtime environments. To address the high operational costs of legacy database systems, MongoDB introduced the Application Modernization Platform to accelerate migration and automated code rewriting using AI-driven tooling. These capabilities position the JSON-based document database as a foundational data layer providing persistent state, contextual retrieval, and memory for agentic AI systems.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
MongoDB.local NYC 2025: AI 시대를 위한 이상적인 데이터베이스 정의
MongoDB presented multiple updates at MongoDB.local NYC 2025 aimed at supporting modern AI workloads and enterprise modernization. The event detailed MongoDB 8.2 alongside Voyage AI embedding models and rerankers designed to enhance accuracy and efficiency in AI applications. Search and vector search capabilities have expanded into public preview for MongoDB Community Edition and Enterprise Server. Furthermore, the company launched the MongoDB Application Modernization Platform, which combines AI-driven tools and expert workflows to migrate legacy systems to MongoDB two to three times faster. MongoDB positions its JSON document model as a foundation providing memory, facts, and state continuity for agentic AI applications.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
MongoDB.local NYC 2025: Definition der idealen Datenbank für das KI-Zeitalter
MongoDB unveiled several core database updates aimed at supporting artificial intelligence workloads during MongoDB.local NYC 2025. The release of MongoDB 8.2 introduces performance enhancements alongside Voyage AI embedding models and rerankers for AI applications. Furthermore, Search and Vector Search are now available in public preview for both MongoDB Community Edition and MongoDB Enterprise Server. To address the costs and operational friction of legacy systems, MongoDB launched the Application Modernization Platform (AMP), which combines AI-powered tools with specialized talent to accelerate database migrations. These capabilities position the JSON-based document model to supply the persistent state, memory, and retrieval mechanisms required by emerging agentic AI architectures.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
MongoDB.local NYC 2025: Definire il database ideale per l'era dell'AI
MongoDB announced several product releases and platform updates aimed at supporting artificial intelligence and agentic workflows at MongoDB.local NYC. The release of MongoDB 8.2 arrives alongside integrations with Voyage AI embedding and reranker models designed to enhance data retrieval precision. In addition, MongoDB launched Search and Vector Search in public preview for both Community Edition and Enterprise Server deployments, bringing vector capabilities to self-managed environments. To assist organizations transitioning away from rigid legacy database systems, MongoDB also introduced the Application Modernization Platform, which combines AI-driven tooling and specialized migration expertise. Early benchmarks from the modernization platform demonstrate legacy migrations running two to three times faster while accelerating code rewriting tasks by an order of magnitude.
Dev Ittycheria, President and CEO, MongoDBMongoDB ·
Celebrating Excellence: MongoDB Global Partner Awards 2025
MongoDB announced the recipients of its 2025 Global Partner Awards, recognizing cloud providers, systems integrators, and technology vendors for driving enterprise modernization and artificial intelligence adoption. Microsoft earned Global Cloud Partner of the Year for joint integrations linking MongoDB Atlas on Azure with native Microsoft services across healthcare, telecommunications, and financial services. Amazon Web Services received the Global AI Cloud Partner award, highlighted by a joint deployment with Novo Nordisk that reduced a key workflow from twelve weeks to ten minutes using Amazon Bedrock and Atlas. Confluent was named Global Tech Partner of the Year with over 550 joint customer deployments focused on event-driven streaming and multi-agent systems alongside LangChain. Additional honorees included Google Cloud, Accenture, BigID, Pureinsights, gravity9, IBM, and Alibaba Cloud for their contributions across public sector solutions, database-as-a-service offerings, and generative AI frameworks.
Olivier ZielenieckiMongoDB ·
庆祝卓越：MongoDB 全球合作伙伴奖 2025
MongoDB announced its 2025 Global Partner Award recipients to recognize organizations driving AI adoption, legacy system modernization, and collaborative market expansion. Microsoft earned Global Cloud Partner for Azure integrations, while Amazon Web Services took Global AI Cloud Partner after assisting Novo Nordisk in cutting a key workflow from twelve weeks to ten minutes with Amazon Bedrock. Google Cloud received the Global Cloud GTM Partner award, and Confluent was named Global Technology Partner with over 550 joint deployments. LangChain and Pureinsights received honors for their integrations supporting retrieval-augmented generation and search solutions. Additional awards celebrated Accenture, BigID, gravity9, IBM, and Alibaba Cloud for their enterprise and public-sector impact.
Olivier ZielenieckiMongoDB ·
우수성을 기념하기: 2025년 MongoDB 글로벌 파트너 어워드
MongoDB announced the recipients of its 2025 Global Partner Awards to recognize partner contributions across cloud modernization, artificial intelligence, and enterprise integration. Microsoft received the Global Cloud Partner award for Azure integrations, while Amazon Web Services secured the Global AI Cloud Partner award following joint generative AI implementations such as reducing workflow durations for Novo Nordisk. Google Cloud earned the Global Cloud GTM Partner distinction through shared sales development programs, and Accenture took Global SI Partner honors after establishing a dedicated engineering Center of Excellence. Confluent was named Global Tech Partner with more than 550 joint streaming deployments, and LangChain was recognized as Global AI Tech Partner for vector search and agentic frameworks. The awards demonstrate partner-led technical solutions spanning distributed databases, data streaming pipelines, and scalable enterprise cloud architectures.
Olivier ZielenieckiMongoDB ·
Celebrando la Excelencia: Premios Globales de Emparejar de MongoDB 2025
MongoDB presented its 2025 Global Partner Awards to honor partner organizations across cloud infrastructure, systems integration, generative AI, and enterprise data management. Microsoft received the Global Cloud Partner award for joint go-to-market solutions integrating MongoDB Atlas with native Azure services across healthcare, telecommunications, and financial services. Amazon Web Services earned the Global AI Cloud Partner award after delivering an Amazon Bedrock and MongoDB Atlas workflow implementation for Novo Nordisk that reduced processing time from twelve weeks to ten minutes. Confluent was recognized as Global Technology Partner for exceeding 550 joint deployments leveraging Apache Kafka for real-time data streaming and event-driven AI architectures. Other honored partners include Google Cloud for joint sales development programs, Accenture for software engineering centers of excellence, LangChain for retrieval-augmented generation tooling, and IBM for Watsonx.ai integrations.
Olivier ZielenieckiMongoDB ·
Hommage à l’excellence : MongoDB Global Partner Awards 2025
MongoDB announced the recipients of its 2025 Global Partner Awards, recognizing enterprise collaboration across cloud infrastructure, artificial intelligence, and systems modernization. Microsoft received the Global Cloud Partner award for joint integrations with Azure, while Amazon Web Services earned the Global AI Cloud Partner award following deployments combining Amazon Bedrock and MongoDB Atlas. Google Cloud was recognized for joint go-to-market initiatives, and Accenture received honours for establishing a dedicated MongoDB Center of Excellence within its software engineering division. Additional recognitions included Confluent for event-driven streaming deployments exceeding 550 customer implementations, BigID for data governance, gravity9 for modernization services, and Alibaba Cloud for managed database services. IBM and Accenture Federal Services were also acknowledged for strategic enterprise impact across mainframe architectures and public sector programs.
Olivier ZielenieckiDemocratizing AI Safety with RiskRubric.ai
Cloud Security Alliance and Noma Security introduced RiskRubric.ai to provide standardized, transparent risk assessments across the open AI model ecosystem. The framework evaluates AI models across six pillars—transparency, reliability, security, privacy, safety, and reputation—using over 1,000 reliability tests, 200 adversarial security probes, automated code scanning, and harmful content evaluations. Each model receives 0–100 scores and A–F letter grades, supplemented by specific vulnerability findings and recommended mitigation strategies to assist deployment filtering. Initial benchmark results across models showed composite scores ranging from 47 to 94 with a median of 81, revealing polarized safety distributions and indicating that security hardening directly correlates with reduced safety risks.
Gal MoyalPublic AI on Hugging Face Inference Providers 🔥
Hugging Face has integrated Public AI as a supported Inference Provider on the Hugging Face Hub. Public AI operates as a nonprofit, open-source project providing access to sovereign and public models from institutions such as the Swiss AI Initiative and AI Singapore. Its distributed infrastructure combines a vLLM-powered backend serving OpenAI-compatible APIs across partner-donated clusters with a global load-balancing routing layer. Users can access these models through the Hugging Face web UI, Python client SDK, and JavaScript SDK using either direct provider API keys or routed Hugging Face tokens. At the time of announcement, inference through the Public AI provider is free of charge, supported by donated GPU time and advertising subsidies.
Joseph Low, Joshua Tan, Célina Hanouti, Julien Chaumond, Simon Brandeis, Lucain PougetGrab ·
Taming the monorepo beast: Our journey to a leaner, faster GitLab repo
Grab's decade-old Go monorepo grew to 12.7 million commits and 250GB of Git data, causing Gitaly replication delays of up to four minutes that routed all read traffic exclusively to the primary node and slowed developer operations. After staging tests proved that shallow history reduced replication lag from hundreds of seconds to under three seconds, standard rewriting tools like git filter-repo and git rebase failed due to complex merge histories and repository scale. To overcome runner memory limits and lengthy git garbage collection cycles, the engineering team implemented a custom two-phase migration script. The script selectively migrated 2,000+ critical dependency tags and one month of recent history, flattening merge commits, embedding legacy hashes for traceability, and reducing total commit volume by 99.9%.
Nagendra Gangwar