# Batching Published articles for Batching. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Optimizing CPU-side Rendering Code DevFeed: [Optimizing CPU-side Rendering Code]() Original publisher: [Read original article]() Author: Clay John Published: 2026-09-15T17:00:00Z Content type: tutorial Language: en Sources: [Godot Engine Official]() Topics: [Godot](), [Optimization](), [cpu](), [GPU](), [shaders]() Tags: [batching](), [bug](), [cpu](), [gpu](), [optimization](), [performance](), [performance-optimization](), [progress-report](), [shaders]() ### AI overview This article explains how Godot optimizes CPU-side rendering code. It describes balancing CPU and GPU workloads, identifying performance bottlenecks, investigating solutions, measuring results, and repeating the process. ### Source excerpt Optimizing CPU code is a lot of fun. Here's how we do it ## JDBC batching can still issue one round trip per row unless driver statement rewriting is enabled DevFeed: [JDBC batching can still issue one round trip per row unless driver statement rewriting is enabled]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [Databases](), [Latency](), [client](), [Code]() Tags: [batching](), [bulk-load](), [client](), [cost](), [database](), [database-performance](), [durability](), [jdbc](), [latency](), [mysql](), [postgresql](), [server](), [sql]() ### AI overview For small database writes, network round trips and transaction overhead can dominate the actual insert work. The article explains that JDBC batching may accept rows into a client-side batch while still sending separate statements unless driver-side statement rewriting is enabled, and that autocommit adds a transaction and durability barrier per row. ### Source excerpt For small writes the per-statement overhead dominates the actual work, so throughput is set by round trips rather than by the database. JDBC batching is the usual fix and it silently does nothing on the wire unless the driver is told to rewrite the statements, so addBatch can look correct while still issuing one round trip per row. Autocommit compounds it by turning every row into its own transaction and its own durability barrier, which is the difference between one fsync and a hundred thousand. ## jetc.dev Newsletter Issue #328 DevFeed: [jetc.dev Newsletter Issue #328]() Original publisher: [Read original article]() Author: CommonsWare Published: 2026-08-25T14:00:00Z Content type: article Language: en Sources: [jetc.dev | Issues]() Topics: [Jetpack Compose](), [modules](), [API]() Tags: [api](), [batching](), [exceptions](), [images](), [jetpack-compose](), [modules](), [newsletter](), [published](), [videos]() ### AI overview Newsletter Issue #328 covers Jetpack Compose development topics, including API modules and composables, lazy-container lifecycle exceptions, recomposition and state-change batching, and selecting and copying text from images and videos. ### Source excerpt API modules and composables! LazyColumn() exceptions! 'Select and copy text' for images and videos! ## LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation DevFeed: [LLM inference batching strategies: static, dynamic, continuous, chunked prefill, and disaggregation]() Original publisher: [Read original article]() Author: Dr. Ashish Bamania Published: 2026-08-22T11:44:27Z Content type: tutorial Language: en Sources: [Into AI]() Topics: [Large Language Model](), [AI Inference](), [Benchmark](), [GPU](), [Latency]() Tags: [ai-engineer](), [batching](), [gpu](), [inference](), [latency](), [llm]() ### AI overview A developer guide explains how static, dynamic, and continuous batching affect LLM inference throughput, latency, and GPU utilization. It also identifies chunked prefill and prefill-decode disaggregation as additional serving strategies. ### Source excerpt Static, Dynamic, and Continuous batching, Chunked prefill, and Prefill-Decode disaggregation, simply explained. ## Bootstrap Confidence Intervals for LLM Evaluation DevFeed: [Bootstrap Confidence Intervals for LLM Evaluation]() Original publisher: [Read original article]() Author: James Fiedler Published: 2026-07-08T14:30:02Z Content type: article Language: en Sources: [Indeed]() Topics: [LLM evaluation / benchmarking](), [Large Language Model]() Tags: [analytics](), [batching](), [confidence-interval](), [data-science](), [developers](), [evaluation](), [llm](), [llm-evaluation](), [machine-learning](), [metric](), [performance](), [research](), [variance](), [vs]() ### AI overview This article explains how to construct confidence intervals for non-deterministic LLM evaluations using bootstrap resampling. It recommends resampling inputs while carrying along all model runs for each selected input, then using the 2.5th and 97.5th percentiles for a 95% interval. It also discusses the tradeoff between the number of inputs and repeated runs, recommending k = 3 or 5 when feasible. ### Source excerpt Introduction As Large Language Models (LLMs) move from research prototypes to production systems, the developers of these systems need rigorous performance evaluation. In particular, we need confidence intervals around estimates of system accuracy. However, LLMs introduce a challenge that is unusual for ML systems: they are (operationally) non-deterministic. Even with the temperature set to zero, [...] ## Native-speed vLLM transformers modeling backend DevFeed: [Native-speed vLLM transformers modeling backend]() Original publisher: [Read original article]() Author: Harry Mellor; Lysandre Published: 2026-07-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vllm](), [Inference Performance](), [Inference](), [LLMs](), [Machine learning](), [Open Source]() Tags: [architecture](), [batching](), [gpu](), [inference](), [inference-performance](), [llama](), [llama-cpp](), [llms](), [mixture-of-experts](), [mlx](), [open-source](), [optimization](), [sglang](), [transformers](), [vllm]() ### AI overview This article describes the transformers modeling backend for vLLM and its role in serving Hugging Face models without requiring model authors to port their implementations. It explains how transformers supplies modeling code while vLLM contributes optimized inference techniques, and outlines comparisons with vLLM's native implementations across several Qwen3 model configurations. It also discusses inference-performance factors including GPU parallelization, compilation, fused kernels, and custom attention kernels. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Adaptive write request scheduling in Redpanda's Cloud Topics DevFeed: [Adaptive write request scheduling in Redpanda's Cloud Topics]() Original publisher: [Read original article]() Author: Evgeny Lazin Published: 2026-06-18T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Kafka](), [Amazon S3](), [Latency](), [Cloud](), [data]() Tags: [batching](), [cloud](), [concurrent](), [cost](), [efficiency](), [engineering](), [kafka](), [latency](), [object-storage](), [s3]() ### AI overview This article explains Redpanda's adaptive write-request scheduler for Cloud Topics. Because each produce batch is uploaded to Amazon S3 before acknowledgment, the scheduler dynamically adjusts upload parallelism across CPU cores to balance batching efficiency, producer latency, throughput, and storage cost. It also describes how centralized batching avoids the latency and request-cost problems of running one batcher per shard. ### Source excerpt How we turned to the buddy allocator algorithm for Redpanda's Cloud Topics to balance batching efficiency against latency and cost. ## Billing API Gateway Calls: Architecture for API as a Service DevFeed: [Billing API Gateway Calls: Architecture for API as a Service]() Original publisher: [Read original article]() Author: Ayush Agarwal Published: 2026-05-18T00:00:00Z Content type: tutorial Language: en Sources: [Dodo Payments Blog]() Topics: [API](), [Amazon API Gateway](), [Software as a service](), [telemetry](), [data]() Tags: [api](), [api-gateway](), [architecture](), [batching](), [build](), [guide](), [infrastructure](), [instrumentation](), [pipeline](), [pitfalls](), [saas](), [usage-based-billing]() ### AI overview A guide to designing an API call metering pipeline for API-as-a-service products. It covers gateway instrumentation, asynchronous batching, aggregation, customer attribution, failure handling, and reconciliation against gateway logs. ### Source excerpt Build a metering pipeline that bills API calls accurately at high volume. Architecture, batching, and pitfalls for API as a service products. ## Protocol Priorities Update for 2026 DevFeed: [Protocol Priorities Update for 2026]() Original publisher: [Read original article]() Author: Protocol track leads Published: 2026-02-18T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Protocol (disambiguation)](), [Ethereum](), [Network](), [interoperability]() Tags: [2026](), [batching](), [ethereum](), [interoperability](), [network](), [protocol](), [research-development](), [smart-contract](), [update]() ### AI overview Ethereum's Protocol update reviews progress made in 2025, including the Pectra and Fusaka upgrades, increased blob capacity, a higher gas limit, history expiry, and UX and interoperability work. It also describes a revised track structure for 2026 focused on scaling, UX, and hardening the L1. ### Source excerpt We introduced Protocol last June which organized our work around three strategic initiatives: Scale L1, Scale Blobs, and Improve UX. A lot has happened since then! In this post, we want to share what we accomplished last year, how our thinking has evolved, and where Protocol is headed in 2026.... ## Fusaka Update - Transaction Gas Limit Cap arrives with EIP-7825 DevFeed: [Fusaka Update - Transaction Gas Limit Cap arrives with EIP-7825]() Original publisher: [Read original article]() Author: Toni Wahrstätter Published: 2025-10-21T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Transactions](), [Deployment](), [systems]() Tags: [batch](), [batching](), [deployment](), [ethereum](), [monitoring](), [parallel](), [refactor](), [research-development](), [transactions]() ### AI overview The Fusaka hard fork will introduce EIP-7825, capping gas per transaction at 2²⁴, approximately 16.78 million gas. The cap is already active on Holesky and Sepolia and will apply to Ethereum mainnet with Fusaka. It may affect very large transactions, batch operations, deployment scripts, transaction builders, and related infrastructure, while leaving the overall block gas limit and eth_call limits unchanged. ### Source excerpt Starting with the upcoming Fusaka hard fork, EIP-7825 introduces a per-transaction gas limit cap of 2²⁴ (~= 16.78 million gas). This change is already live on Holesky and Sepolia, and will activate on mainnet with Fusaka. Developers and users who rely on very large transactions should verify that... ## Migrating to Nvidia Triton: High-Throughput, Low-Cost Inference at Scale DevFeed: [Migrating to Nvidia Triton: High-Throughput, Low-Cost Inference at Scale]() Original publisher: [Read original article]() Author: Eric Luu Published: 2025-09-19T01:05:13Z Content type: article Language: en Sources: [GumGum]() Topics: [Inference](), [model-serving](), [Nvidia](), [Machine learning](), [Natural language processing](), [databricks](), [Docker](), [Kafka](), [Python]() Tags: [batching](), [databricks](), [docker](), [gpu](), [inference](), [kafka](), [latency](), [machine-learning](), [nvidia](), [optimization](), [performance](), [triton-inference-server]() ### AI overview This article describes GumGum's migration of NLP inference from Databricks-based jobs using Docker images, Kafka streaming, and UDFs toward NVIDIA Triton. It introduces Triton's model-serving features, including batching, ensembles, dynamic model loading, request scheduling, and reduced GPU-CPU data transfers, while outlining limitations of the previous approach such as latency, scalability, GPU-metrics, and PySpark performance issues. ### Source excerpt Photo by Igor Omilaev on Unsplash There are tons of guides on what Nvidia Triton is and how to set it up, so I'll only go over those very briefly, instead focusing on the migration we've had and the small quirks that come from working with Python Backends. Nvidia Triton is an open source inference serving software, capable of serving virtually any machine learning model. Real time processing, batching, dynamic batching, ensembles, specialized backends, all possible with enough knobs and dials to maximize hardware use and minimize cost. Core features include File based model repository to store your models. If wanted, can be dynamically loaded onto the server Ensemble capabilities, can define whole inference pipelines, even ones that branch, on triton Ability to define each step in the inference pipeline as its own model (e.g., tokenizer -> model -> decoder), which can get hit as its own endpoint Dynamic batching between models, even in ensembles Automatic scheduling of requests between models and ensembles Minimal data transfers between GPU & CPU Our Previous Solution At GumGum we've previously deployed the bulk of our NLP inference on Databricks. These would run 24 hour jobs loading custom Docker images, streaming inputs from a Kafka topic and relying on UDF's to contain our inference logic. This was a move to mitigate issues we had on ECS related to starvation, scalability, and parallelism. Workers being paired on consumers lead to starvation. As a result, scalability of the service suffered and maintaining threading code became a pain. We've also experienced other issues as well: GPU metrics were not easily available, as we need to use an ML runtime, which was not compatible with docker containers (Although modern runtime do support this more). Latency was high as with Kafka structured streaming, microbatches must wait for all workers to finish. So if one worker hangs, the entire batch hangs. Performance on PySpark was always tough. Serializing between Java to Pyth ## Exporting data from Redpanda to S3 in batched JSON DevFeed: [Exporting data from Redpanda to S3 in batched JSON]() Original publisher: [Read original article]() Author: Chandler Mayo Published: 2025-08-07T00:00:00Z Content type: tutorial Language: en Sources: [Redpanda]() Topics: [Amazon S3](), [JSON](), [Streaming](), [Serverless](), [parquet](), [Compression](), [schema-evolution]() Tags: [batch-json-arrays-to-s3](), [batching](), [batching-messages-in-s3](), [building-event-driven-pipelines-with-sqs-and-s3](), [data](), [exporting-data-from-redpanda-to-s3](), [exporting-parquet-files-to-s3](), [how-to](), [json](), [optimize-redpanda-s3-pipeline](), [performance](), [pipelines](), [redpanda-connect](), [redpanda-s3-integration](), [redpanda-to-s3-json-export](), [s3](), [serverless](), [setting-up-redpanda-observability-in-datadog](), [streaming](), [streaming-data-to-s3-with-redpanda](), [streaming-optimized-data-to-s3-for-analytics](), [tutorial]() ### AI overview A tutorial showing how to configure a Redpanda Connect pipeline to batch multiple Redpanda messages into JSON files stored in Amazon S3. It explains the performance benefits of batching, deployment considerations for Redpanda Serverless, and production tuning options, then points to Parquet for larger analytical workloads. ### Source excerpt Learn how to write messages from Redpanda to its own JSON file in S3. ## Efficient MultiModal Data Pipeline DevFeed: [Efficient MultiModal Data Pipeline]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Luis; Andres Marafioti; Sergio Paniego; Pedro Cuenca Published: 2025-07-08T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [multimodal](), [data](), [datasets](), [GPU](), [Mathematics](), [Open Source]() Tags: [artificial-intelligence](), [batching](), [community](), [data](), [data-pipeline](), [gpu](), [hardware](), [multimodal](), [nanovlm](), [open](), [open-source](), [pipeline](), [research](), [vlm]() ### AI overview This article explains how to build an efficient multimodal data pipeline for nanoVLM training. It examines waste caused by idle GPUs and excessive padding, then introduces progressively improved data preparation and batching strategies, including a knapsack-based approach to fit more useful data into each batch. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Handling the N+1 Problem Declaratively with Apollo Connectors DevFeed: [Handling the N+1 Problem Declaratively with Apollo Connectors]() Original publisher: [Read original article]() Author: Lenny Burdette Published: 2025-06-24T08:53:29Z Content type: tutorial Language: en Sources: [Apollo Blog]() Topics: [Apollo Connectors](), [GraphQL](), [dataloaders](), [API]() Tags: [apollo-connectors](), [batching](), [connectors](), [dataloaders](), [graphql](), [performance]() ### AI overview This tutorial explains how Apollo Connectors can address the N+1 query problem declaratively with Apollo Router 2.3 and Federation 2.11. It introduces the problem, compares the approach with DataLoaders, and describes how batching, scheduling, deduplication, and entity association work in Connectors. ### Source excerpt With the release of Apollo Router 2.3 and Federation 2.11, Apollo Connectors can now avoid the dreaded N+1 query problem! You can make your API faster and more efficient by changing your Connectors to use the variable instead of the variable. If you want to learn more about the N+1 problem and how Connectors batching works internally, please read on! The N+1 query problem is a classic performance killer that sneaks up on developers. ## How Long Prompts Block Other Requests - Optimizing LLM Performance DevFeed: [How Long Prompts Block Other Requests - Optimizing LLM Performance]() Original publisher: [Read original article]() Author: Benjamin Merkel Published: 2025-06-12T08:00:13Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [vllm](), [Large Language Model](), [Latency](), [GPU]() Tags: [article](), [batching](), [blog](), [blog-post](), [gpu](), [hugging-face](), [latency](), [vllm]() ### AI overview The article explains how long prompts can block other requests in vLLM. Because prefill processes prompt tokens in parallel and can saturate GPU utilization, chunked-prefill normally processes chunks from different requests sequentially. A long prompt can therefore delay subsequent requests, increasing latency. The article describes an improved vLLM strategy that permits parallel prefills while limiting how many concurrently processed requests may contain very long prompts. ### Source excerpt A Blog post by TNG Technology Consulting GmbH on Hugging Face ## Systems Fun at HotOS DevFeed: [Systems Fun at HotOS]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2025-06-02T00:00:00Z Content type: article Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [systems](), [Concurrency](), [Serverless](), [Latency](), [Cloud](), [Cache]() Tags: [batching](), [cache](), [cloud](), [concurrency](), [latency](), [performance](), [serverless](), [systems]() ### AI overview The article reflects on attending HotOS for the first time and highlights several papers and talks. It discusses integrating NIC scheduling with OS state, the relevance of this approach to cloud and Serverless workloads, batching's context-dependent effects on latency and throughput, and a proposed dynamic replacement for fork using posix_spawn. The supplied text ends mid-discussion of the final paper. ### Source excerpt Systems Fun at HotOS One day somebody will tell me what systems means. Last week I attended HotOS1 for the first time. It was super fun. Just the kind of conference I like: single-track, a mix of academic and industry, a mix of normal practical ideas and less-normal less-practical big thinking. I went partially because a colleague twisted my arm, and partially because of this line in the CFP: The program committee will explicitly favor papers likely to stimulate reflection and discussion. That sounds like a good time. Some Papers or Talks I Enjoyed I thought I'd give a shout-out to some of the papers I enjoyed the most. These aren't the best papers, just ones I particularly liked for my own arbitrary reasons2. The NIC should be part of the OS by Xu and Roscoe exposes a lot of what was traditionally the OS kernel's state (like the run queue for each core) to the NIC, allowing the NIC to use that state to decide which packets to deliver and when. Their goal is to do better than existing kernel bypass methods. After all, many modern cloud applications are short bursts of compute between waiting for packets. They do this with CXL 3.0's cache-coherent peripheral interconnect, but I don't think peripheral cache coherence is actually critical to the big picture here. This approach of pushing scheduling work down to the NIC seems especially interesting for straight packet processing workloads, and for Serverless workloads, both of which tend to be very run and wait heavy with high concurrency. Just super fun deep systems work3. Batching with End-to-End Performance Estimation by Borisov et al. This paper starts by questioning the common wisdom that batching is good for throughput and bad for latency, by presenting a set of scenarios that show that it can be good or bad for both latency and throughput depending on small timing differences. I love questioning the common wisdom, so this is my jam. They then point out that this situation can be improved by making batching logic ## Pectra Mainnet Announcement DevFeed: [Pectra Mainnet Announcement]() Original publisher: [Read original article]() Author: EF Protocol Support Published: 2025-04-23T00:00:00Z Content type: release Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [releases](), [Transactions](), [Authentication](), [Passkeys](), [Security]() Tags: [2025](), [abstraction](), [authentication](), [batching](), [ethereum](), [protocol-announcements](), [releases](), [security](), [upgrade]() ### AI overview The Pectra network upgrade is scheduled to activate on Ethereum mainnet on May 7, 2025. The article explains how EIP-7702 adds smart contract capabilities to externally owned accounts and outlines related safety mechanisms. ### Source excerpt The Pectra network upgrade is scheduled to activate on the Ethereum mainnet on May 07, 2025 at epoch 364032 (10:05:11 UTC)! Mainnet client releases are listed below.... ## 🚀 Accelerating LLM Inference with TGI on Intel Gaudi DevFeed: [🚀 Accelerating LLM Inference with TGI on Intel Gaudi]() Original publisher: [Read original article]() Author: Baptiste Colle; Régis Pierrard; Ilyas Moutawwakil; Ella Charlaix; Ke Ding Published: 2025-03-28T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [tgi](), [Inference](), [intel](), [model-deployment](), [Large Language Model](), [Open Source]() Tags: [aws](), [batching](), [dell](), [docker](), [gaudi](), [gemma](), [hardware](), [ibm](), [inference](), [intel](), [llm](), [mixtral](), [partnerships](), [quantization](), [tgi]() ### AI overview This article announces the integration of Intel Gaudi support into the main TGI codebase through TGI's multi-backend architecture. It describes deployment options across Gaudi hardware generations, production features such as dynamic batching and streamed responses, support for multiple LLMs, multi-card inference, vision-language models, FP8 precision, and an official Docker image. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Replacing AWS Step Functions with SQS FIFO queues and cutting the cost in half DevFeed: [Replacing AWS Step Functions with SQS FIFO queues and cutting the cost in half]() Original publisher: [Read original article]() Author: Alexei Liulin Published: 2025-03-14T14:47:27Z Content type: article Language: en Sources: [Homegate Engineering Blog - Medium]() Topics: [Amazon Simple Queue Service (SQS)](), [AWS Step Functions](), [Amazon Web Services]() Tags: [aws](), [aws-step-functions](), [batching](), [cloud-computing](), [cost](), [cost-optimization](), [email](), [fifo](), [lambda](), [notifications](), [push-notifications](), [queue](), [sqs](), [sqs-queue](), [workflow]() ### AI overview The article describes SMG Real Estate's Search Alerts system, which batches property-match notifications for delivery by email or mobile push notification. It explains the original AWS Step Functions implementation and its transition-cost problem, motivating a more cost-efficient approach using SQS FIFO queues. ### Source excerpt One of the core services for SMG Real Estate is Search Alerts -- a service that allows users to be notified about new properties published on ImmoScout24 and Homegate. For example, if a user is looking for a new apartment having N rooms and costing less than X, and not finding any matches now, they can create a search alert with those search criteria. When a new property matching those criteria is published, the user will be notified either via email or a mobile push notification. About the Search Alerts system Users can choose the frequency of notifications -- either every 5 minutes or every 4 hours (assuming there are any to be delivered). The vast majority of search alerts are configured with the 5-minute frequency. Just to get an idea of the scale, we have: Millions of published properties match saved search alerts every day More than a million emails and push notifications sent daily Check out our "Homegate's fast and modern search experience helps users find their dream home" blog post for an overview of how Search Alerts work. The Original Implementation In the original implementation we used an AWS Step Function to achieve the 5- minute batching of notifications: When a new property matching a search alert arrived, the StartSendNotificationProcess lambda would start a Step Function execution specific to that search alert. All it did was waiting for 5 minutes while the matches accumulated in the matches-{searchAlertId} SQS queue, which was programmatically created for that specific search alert If more matching listings arrived during the 5-minute waiting period, the StartSendNotificationProcess lambda attempted to start the step function execution with the same name. When it failed with the ExecutionAlreadyExists error, we knew there was already a SF for waiting. That way the deduplication of notification processes was guaranteed. After 5 minutes of waiting time, the Step Function execution proceeded with triggering the LoadMatches lambda that received the acc ## ClickHouse Input format matchup: Which is fastest & most efficient DevFeed: [ClickHouse Input format matchup: Which is fastest & most efficient]() Original publisher: [Read original article]() Author: Tom Schreiber Published: 2025-02-26T07:41:17Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Benchmark](), [client](), [Sorting](), [data]() Tags: [batching](), [benchmark](), [clickhouse](), [data](), [insights](), [performance](), [sorting](), [speed]() ### AI overview This article benchmarks ClickHouse input formats to identify the fastest and most hardware-efficient options for server-side data ingestion. It also examines batching, pre-sorting, and client-side insert optimizations. ### Source excerpt Discover the fastest and most efficient ClickHouse input formats with our benchmark-driven insights--plus how to optimize inserts for maximum performance. ## Hugging Face and FriendliAI partner to supercharge model deployment on the Hub DevFeed: [Hugging Face and FriendliAI partner to supercharge model deployment on the Hub]() Original publisher: [Read original article]() Author: Ahnjae Shin; Soomin Chun; Byung-Gon Chun; Julien Chaumond Published: 2025-01-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [friendliai](), [model-deployment](), [AI Inference](), [hugging face](), [Inference Performance](), [autoscaling](), [GPU](), [Open Source Models & Datasets](), [quantization]() Tags: [ai-inference](), [autoscaling](), [batching](), [deployment](), [friendliai](), [gpu](), [hub](), [hugging-face](), [latency](), [model](), [model-deployment](), [open-source](), [partnerships](), [performance](), [quantization]() ### AI overview Hugging Face and FriendliAI announce an integration that adds FriendliAI Endpoints as a deployment option in the Hugging Face Hub. Developers can deploy open-source or custom generative AI models through FriendliAI's inference infrastructure, including NVIDIA H100 GPUs, continuous batching, quantization, and autoscaling. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Blog: Falcosidekick 2.30.0 DevFeed: [Blog: Falcosidekick 2.30.0]() Original publisher: [Read original article]() Published: 2024-12-04T00:00:00Z Content type: release Language: en Sources: [Falco - Falco](), [Falco - The Falco blog]() Topics: [Falco](), [Cloud Native Ecosystem](), [elasticsearch](), [Prometheus]() Tags: [api](), [batching](), [cloud-native](), [compression](), [elasticsearch](), [falco](), [falcosidekick](), [http](), [prometheus](), [release]() ### AI overview Falcosidekick 2.30.0 adds Webex, OTLP Metrics, and Datadog Logs integrations. The release also improves throughput and Elasticsearch integration, updates Prometheus metric naming, supports multiple AlertManager hosts, and fixes several bugs. ### Source excerpt A few days after a new release of Falco Talon, our response engine, it's time for our favorite proxy forwarder to do the same. New outputs A new release means new integrations. Thanks to our contributors for their helps. Webex Notify your team on Webex with the integration developed by @k0rventen. OTLP Metrics The adoption of Open Telemetry is bigger and bigger in the Cloud Native ecosystem, @ekoops introduced the OTLP Metrics in Falcosidekick. Datalog Logs The Falco alerts can be forwarded to Datadog as events for a while in Falcosidekick, you can now use their Logs service thanks to @yohboy. New features Here's a non exhaustive list of the great features and enhancements which come with this new release: x3 throughput @alekmaus spotted a bottleneck with the http client used to forward the events to the outputs. His fix increases up to 300% the throughput!!! Better integration with Elasticsearch @alekmaus worked hard to improve the integration with Elasticsearch. In addition improvments for the clients, new settings have been introduced, like the possibility to specify an ingest pipeline or an api key, to enable batching and compression. See the docs to know them all. Better consistency for the Prometheus metrics Falco recently integrated a direct endpoint to expose metrics in the Prometheus format. After a lot of discussions between the maintainers and the community, a convention has been chosen for the names of the metrics. This release adapts the metrics exposed by Falcosidekick to follow this convention and have a consistency accross the different components of the ecosystem. Breaking changes: The renaming of the metrics might impact the queries for your alerts and dashboards. Multi hosts for AlertManager You can now specify a list of servers for the AlertManager output, which is a requirement when it's deployed in HA mode. Fixes The contributors fixed several bugs, here's a non exhaustive list of the more important ones: Fix PolicyReports created in the same n ## Promise queues and batching concurrent tasks in Deno DevFeed: [Promise queues and batching concurrent tasks in Deno]() Original publisher: [Read original article]() Author: Liran Tal Published: 2024-09-25T04:00:00Z Content type: tutorial Language: en Sources: [Blog RSS Feed | Snyk]() Topics: [Deno](), [Promise](), [Concurrency](), [JavaScript](), [Programming](), [Security]() Tags: [ai](), [application-security](), [batching](), [blog](), [code-security](), [concurrent](), [developer](), [java](), [javascript](), [memory](), [performance](), [queue](), [queuing](), [security](), [snyk-apprisk](), [snyk-code]() ### AI overview This tutorial explains how Promise queues and task batching can manage concurrent asynchronous tasks in Deno more efficiently. It covers JavaScript's single-threaded, event-driven model, the limitations of running many promises together, and how queuing can reduce memory pressure and prevent one failed task from causing complete failure. ### Source excerpt Learn how to create secure applications using Deno. Explore the default security measures provided by Deno, understand potential vulnerabilities such as Server-Side Request Forgery (SSRF), and uncover the best practices for minimizing risks. Enhance your application's security by leveraging Deno's advanced features. ## GraphOS Summer 2024 Launch Recap DevFeed: [GraphOS Summer 2024 Launch Recap]() Original publisher: [Read original article]() Author: Andrew I. Carlson Published: 2024-06-17T09:00:00Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphOS](), [GraphQL](), [API Platform](), [Platform Engineering]() Tags: [announcement](), [batching](), [graphos](), [graphql](), [graphql-federation](), [launch](), [operational-excellence](), [performance](), [platform-engineering](), [recap](), [telemetry]() ### AI overview Apollo's GraphOS Summer 2024 launch introduced capabilities aimed at improving the performance and operational excellence of GraphQL platforms. The article highlights GraphQL federation, supergraph operations, query batching, and telemetry and insights. ### Source excerpt On, June 12, 2024, we hosted our GraphOS summer launch event, where we announced new features designed to improve the performance and operational excellence of GraphQL platforms. These new capabilities improve how API platform teams implement and optimize GraphQL federation, providing faster baseline performance and greater control over API request execution in a supergraph. [Next page]()