# parallel Parallel computing is the simultaneous use of multiple compute resources to solve computational problems, while parallel programming writes software that executes multiple computations simultaneously. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Working Effectively with Claude Code DevFeed: [Working Effectively with Claude Code]() Original publisher: [Read original article]() Author: Amy Laws Published: 2026-06-18T09:09:00Z Content type: article Language: en Sources: [Scott Logic]() Topics: [Claude Code](), [parallel](), [Terminal](), [Code](), [Orchestration](), [Git]() Tags: [agent](), [agents](), [artificial-intelligence](), [claude-code](), [code](), [copilot](), [github-copilot](), [orchestration](), [parallel](), [round-robin](), [terminal](), [vs-code]() ### AI overview This article shares practical lessons from switching from GitHub Copilot in VS Code to Claude Code. It focuses on parallel development with multiple agents, using terminal sessions and Git worktrees, and managing concurrent work through Claude Code's agent view. ### Source excerpt After months working with GitHub Copilot in VS Code and recently switching to Claude Code, the transition turned out to be more involved than expected. Claude Code operates differently and in ways that take time to adjust to. In this post, I share my experiences and tips drawn from that experience. ## How to build deep research agents using Temporal and Braintrust DevFeed: [How to build deep research agents using Temporal and Braintrust]() Original publisher: [Read original article]() Author: Martin Bergman Published: 2026-06-03T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Multi Agent Systems](), [observability](), [Pydantic](), [parallel](), [retry](), [Large Language Model]() Tags: [agents](), [build](), [how-to](), [multi-agent](), [observability](), [parallel](), [research](), [retry](), [tracing]() ### AI overview This tutorial explains a multi-agent deep research pipeline built with Temporal and Braintrust. It covers planning, query generation, parallel web search, report synthesis, and the use of Durable Execution, evals, and observability to handle timeouts, partial failures, and difficult debugging. ### Source excerpt Deep research agents are fragile in production. Here's how Temporal and Braintrust make them resilient with Durable Execution, evals, and tracing. ## Per-worker, and global, IO bandwidth in explain plans DevFeed: [Per-worker, and global, IO bandwidth in explain plans]() Original publisher: [Read original article]() Author: depesz Published: 2026-02-21T13:24:57Z Content type: article Language: en Sources: [select \* from depesz;]() Topics: [PostgreSQL](), [IO](), [parallel]() Tags: [analyze](), [execution](), [explain](), [explain-depesz-com](), [io](), [parallel](), [plan](), [postgresql](), [speed](), [timing](), [uncategorized](), [worker]() ### AI overview This article explains how PostgreSQL explain plans can report per-worker and global I/O bandwidth during parallel execution. It describes how summed worker timings differ from wall-clock time and introduces an exclusive-time bandwidth figure for the disks' total workload. ### Source excerpt Jeremy Schneider suggested a change to how plans are displayed - adding another bit of information in case we have timing information for IO for explain node. Took me a while to research, but it finally made it's way... Let's consider this simple plan. In it's Parallel Seq Scan node we see: -> Parallel Seq ... Continue reading "Per-worker, and global, IO bandwidth in explain plans" ## From VHS to Interactive Streams: Customization at Scale with the NFL Sunday Ticket on YouTube DevFeed: [From VHS to Interactive Streams: Customization at Scale with the NFL Sunday Ticket on YouTube]() Original publisher: [Read original article]() Author: Shawn DeVries Published: 2026-01-23T23:05:00Z Content type: article Language: en Sources: [8th Light Insights]() Topics: [Streaming](), [personalization](), [Sports](), [real-time](), [Playback](), [ui](), [parallel](), [Mobile]() Tags: [customization](), [interactive](), [mobile](), [parallel](), [personalization](), [platform-innovation](), [playback](), [real-time](), [scale](), [sports](), [streams](), [ui]() ### AI overview This article examines how NFL Sunday Ticket on YouTube moved live sports viewing from a fixed broadcast toward personalized, interactive streams. It describes custom Multiview layouts, fantasy integrations, spoiler controls, playback options, and interfaces that work across TVs, mobile devices, and tablets. ### Source excerpt It's the mid-90s, it's Sunday morning, and you're about to set your VCRs to record on your NFL game of choice You'd capture everything, commercials, dead time, halftime filler, and hope the tape didn't cut out in overtime. It was static. One-size-fits-all. Then TiVo arrived. That pause button changed the game. Suddenly, fans could freeze a field goal, run to grab game day food, and unpause just in time for the kick. Control entered the living room. Fast forward to NFL Sunday Ticket on YouTube. Fans are now building their own broadcast in real time. The cultural shift is clear: live sports have moved from passive consumption to personalized control over the gameday experience. Raising the Stakes For nearly three decades, NFL Sunday Ticket lived only on DirecTV. It was a premium cable-era product; expensive, exclusive, and tied to satellite hardware. Fans outside their home market had little choice but to buy into the bundle if they wanted every game. That era is over. When YouTube secured the Sunday Ticket package, it leapt from the satellite dish into the streaming stack. And with that leap came a redefinition of expectations: reliability is still required, but now the baseline includes interactivity and personalization at a massive scale. Inside Sunday Ticket's interactive layer: Custom Multiview Fans can build their own layout with two, three, or four games running in parallel. Local broadcasts integrate seamlessly for YouTube TV base subscribers. Multiview works across TVs, mobile, and tablets, keeping the experience consistent. Fantasy View Integration Users can link NFL Fantasy and Yahoo Fantasy accounts. This allows fantasy stats and team updates surface directly in the stream. Spoiler Mode & Playback Features Hide scores until you're ready to see them. Advanced playback controls such as broadcast delay toggle All the features create more ways to personalize the live feed. Improved UI & Side-by-Side Experience Dual-column layouts show live action next to stats ## Trusting AI agents: A reinsurance case study DevFeed: [Trusting AI agents: A reinsurance case study]() Original publisher: [Read original article]() Author: Sophia Barnes Published: 2026-01-22T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [data-processing](), [datasets](), [decision-making](), [parallel](), [file]() Tags: [agents](), [ai](), [ai-agents](), [automate](), [case-study](), [data-processing](), [excel](), [how-to](), [insurance](), [multi-agent](), [pricing](), [risk](), [workflows]() ### AI overview This case study explains how a multi-agent AI system with human-in-the-loop safeguards automates reinsurance data workflows. The system parses unstandardized Excel submission packs, matches catastrophe events with historical records, creates cedant loss records, and flags changes to existing data. ### Source excerpt Learn how to build a reliable multi-agent AI system with human-in-the-loop safeguards using Temporal. A detailed case study on automating complex reinsurance data workflows. ## Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption DevFeed: [Packing Matrix-Vector Multiplication in Fully Homomorphic Encryption]() Original publisher: [Read original article]() Published: 2024-09-07T04:18:09Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [homomorphic encryption](), [FHE](), [Encryption](), [layout](), [parallel](), [Python]() Tags: [arithmetic](), [code](), [cryptography](), [data](), [encryption](), [fhe](), [github-repository](), [homomorphic-encryption](), [layout](), [linear-algebra](), [lwe](), [mathematics](), [packing](), [parallel](), [programming](), [python](), [rlwe](), [simd](), [strategies]() ### AI overview This article explains packing for SIMD-style fully homomorphic encryption. It describes how to arrange plaintext data in RLWE ciphertexts so matrix-vector multiplication requires fewer alignment multiplications and rotations, then introduces two basic packing techniques and a computational model. ### Source excerpt In my recent overview of homomorphic encryption, I underemphasized the importance of data layout when working with arithmetic (SIMD-style) homomorphic encryption schemes. In the FHE world, the name given to data layout strategies is called "packing," because it revolves around putting multiple plaintext data into RLWE ciphertexts in carefully-chosen ways that mesh well with the operations you'd like to perform. By "mesh well" I mean it reduces the number of extra multiplications and rotations required merely to align data elements properly, rather than doing the actual computation you care about. ## Hello Deep Learning: Hyperparameters, inspection, parallelism, ADAM DevFeed: [Hello Deep Learning: Hyperparameters, inspection, parallelism, ADAM]() Original publisher: [Read original article]() Published: 2023-03-30T10:00:06Z Content type: tutorial Language: en Sources: [Bert Hubert's writings]() Topics: [Deep learning](), [Neural Network](), [parallel](), [GPU](), [cpu](), [data]() Tags: [cpu](), [deep-learning](), [faster](), [gpu](), [matrices](), [neural-network](), [parallelism](), [tensors]() ### AI overview A Hello Deep Learning tutorial explains how neural networks can train faster through parallel computation, including batched tensor operations, GPU capacity, CPU SIMD instructions, multiple CPU cores, and distributed processing. It also begins discussing network parameter inspection and matrix multiplication. ### Source excerpt This page is part of the Hello Deep Learning series of blog posts. You are very welcome to improve this page via GitHub! In the previous chapter we successfully trained a network to recognize handwritten letters, but it took an awfully long time. This is not just inconvenient: networks that take too long to train mean we can experiment less. Some things really are out of reach if each iteration takes 24 hours, instead of 15 minutes. ## Postgres 11 - A First Look DevFeed: [Postgres 11 - A First Look]() Original publisher: [Read original article]() Author: Map Published: 2018-09-20T20:55:56Z Content type: article Language: en Sources: [Craig Kerstiens]() Topics: [PostgreSQL](), [releases](), [Usability](), [parallel]() Tags: [performance](), [postgres](), [postgresql](), [release-notes](), [usability]() ### AI overview A first look at PostgreSQL 11 highlights usability improvements, safer addition of NOT NULL columns with default values without a full table rewrite, and maturing parallelism support. ### Source excerpt Postgres 11 is almost here, in fact the latest beta shipped today, and it features a lot of exciting improvements. If you want to get the full list of features it is definitely worth checking out the release notes, but for those who don't read the release notes I put together a run down of some what I consider the highlight features. Quitting Postgres This is a small usability feature, but so long over due. Now you can quit Postgres by simply typing quit or exit. Previously you had to use Ctrl + D or \q. As a begginer it's one thing to jump into a psql terminal, but once in if you can't figure out how to quit it's a frustrating experience. Small usability features, such as this and watch in an earlier release, are often lost in the highlighted features which talk about performance or new data types. Improvements like this really go a long way for making Postgres a better database for everyone. Fear column addition no more Brandur had a great in depth write-up on this feature already, but it falls somewhere into the category of the above as well as a performance improvement. Previously when you added a new column that was NOT NULL with a default value Postgres would have to take a lock and re-write the entire table. In a production environment on any sizable table for all practical purposes the result was an outage. The work around was to break your migrations apart to be a several step process. With Postgres 11 you can add a new column to a table that is not null with a default value. The new row will get materialized on your database without requiring a full re-write. Here is to having to think less about your migrations. Of course performance is a highlight No Postgres release would be complete without some performance improvements. This release there are really two areas that feature key improvements around performance. Parallelism continuing to mature We first saw parallelism support back in PostgreSQL 9.6. At the time it was primarily for sequential scans, whic ## Parallel commits with gitwalk DevFeed: [Parallel commits with gitwalk]() Original publisher: [Read original article]() Published: 2015-11-18T00:00:00Z Content type: tutorial Language: en Sources: [Radek Pazdera]() Topics: [Git](), [Repositories](), [parallel](), [Script](), [Bash]() Tags: [automated](), [bash](), [git](), [merge](), [parallel](), [repositories](), [script]() ### AI overview This tutorial explains how to use gitwalk to select and process multiple Git repositories in parallel. It demonstrates selecting repositories with expressions and using a Bash script to create, commit, and push a file across matching repositories. ### Source excerpt A tool to manipulate multiple git repositories at the same time ## Bitsliced SipHash DevFeed: [Bitsliced SipHash]() Original publisher: [Read original article]() Author: Marek Published: 2013-01-29T23:00:00Z Content type: tutorial Language: en Sources: [Marek Majkowski]() Topics: [Algorithm](), [parallel](), [implementation](), [C](), [Python]() Tags: [algorithm](), [c](), [cpu](), [cycles](), [implementation](), [matrix](), [parallel](), [simd](), [speed](), [sse]() ### AI overview This article explains bitslicing through a SipHash implementation. It describes rewriting an algorithm as simple bit-level logical operations applied to wide SIMD vectors, which can reduce repeated parallel computations. It also discusses the substantial cost of transposing input and output bit matrices. ### Source excerpt Bitsliced SipHash Few days ago I presented a Python and a C implementation of SipHash. This time for no reason whatsoever I implemented a bitsliced version of it. Bitslicing a crypto algorithm is usually done to speed it up when doing massively parallel operations. For example when trying to find a collision with brute force. Bitsliced implementation is only useful if you have a large number of exactly the same mathematical operations to be computed in parallel. ## pgloader 3: A planned Common Lisp rewrite with parallel loading and additional data sources DevFeed: [pgloader 3: A planned Common Lisp rewrite with parallel loading and additional data sources]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2013-01-28T09:48:00Z Content type: opinion Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [Lisp](), [MySQL](), [migration](), [parallel](), [Python]() Tags: [lisp](), [migration](), [mysql](), [parallel](), [postgresql](), [programming](), [python]() ### AI overview The article discusses plans for pgloader 3, including a Common Lisp implementation intended to improve loading performance, support real parallel processing, and fetch data from sources such as MySQL, HTTP, and S3. It also notes that existing Python reformatting modules would need to be ported. ### Source excerpt pgloader is a tool to help loading data into PostgreSQL, adding some error management to the COPY command. COPY is the fast way of loading data into PostgreSQL and is transaction safe. That means that if a single error appears within your bulk of data, you will have loaded none of it. pgloader will submit the data again in smaller chunks until it's able to isolate the bad from the good, and then the good is loaded in. ## Reformatting Input Columns with pgloader DevFeed: [Reformatting Input Columns with pgloader]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2011-08-05T09:30:00Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [data type](), [Python](), [parallel](), [Scripting]() Tags: [article](), [how-to](), [parallel](), [postgresql](), [python](), [scripting](), [transformation]() ### AI overview This tutorial explains how pgloader reformats input columns before PostgreSQL receives them. It describes configuring reformat modules and functions, and implementing the transformation in Python, including an example that converts certain MySQL timestamp representations into a PostgreSQL-readable form. ### Source excerpt Back to our series about pgloader. The previous articles detailed How To Use PgLoader then How to Setup pgloader, then what to expect from a parallel pgloader setup. This article will detail how to reformat input columns so that what PostgreSQL sees is not what's in the data file, but the result of a transformation from this data into something acceptable as an input for the target data type. ## pgloader en parallèle DevFeed: [pgloader en parallèle]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2011-08-01T10:15:00Z Content type: tutorial Language: fr Sources: [Dimitri Fontaine]() Topics: [parallel](), [configuration]() Tags: [article](), [configuration](), [parallel]() ### AI overview This article discusses configuring parallel data loading in pgloader and refers readers to the English reference article "Parallel pgloader." ### Source excerpt Dans la série des articles sur pgloader, nous détaillons cette fois la configuration du paraléllisme de chargement des données. Une fois de plus, je vous réfère à l'article de référence en anglais : Parallel pgloader. ## Parallel pgloader DevFeed: [Parallel pgloader]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2011-08-01T10:15:00Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [parallel](), [Concurrent Programming](), [Processes](), [Python](), [round robin](), [Database]() Tags: [article](), [benefit](), [code](), [how-to](), [parallel](), [parallelism](), [process](), [python](), [queues](), [round-robin](), [threading]() ### AI overview This tutorial explains how pgloader loads data in parallel. It describes loading multiple files concurrently, splitting large files among workers, and using a reader thread with worker threads and queues. It also cautions that Python's Global Interpreter Lock limits runtime parallelism and recommends testing thread configurations. ### Source excerpt This article continues the series that began with How To Use PgLoader then detailed How to Setup pgloader. We have some more fine points to talk about here, today's article is about loading your data in parallel with pgloader. ## pg\_basebackup Adds Parallel File Fetching for PostgreSQL Base Backups DevFeed: [pg\_basebackup Adds Parallel File Fetching for PostgreSQL Base Backups]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2010-11-07T12:45:00Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [Script](), [parallel](), [Concurrent Programming]() Tags: [jobs](), [parallelism](), [postgresql](), [processes](), [script]() ### AI overview The article describes an update to pg_basebackup that repeatedly fetches changed xlog files during a base backup. It adds a --jobs option to launch multiple helper processes, each using its own PostgreSQL connection to fetch backup files in compressed chunks. ### Source excerpt Hannu just gave me a good idea in this email on -hackers, proposing that pg_basebackup should get the xlog files again and again in a loop for the whole duration of the base backup. That's now done in the aforementioned tool, whose options got a little more useful now: Usage: pg_basebackup.py [-v] [-f] [-j jobs] "dsn" dest Options: -h, --help show this help message and exit --version show version and quit -x, --pg_xlog backup the pg_xlog files -v, --verbose be verbose and about processing progress -d, --debug show debug information, including SQL queries -f, --force remove destination directory if it exists -j JOBS, --jobs=JOBS how many helper jobs to launch -D DELAY, --delay=DELAY pg_xlog subprocess loop delay, see -x -S, --slave auxilliary process --stdin get list of files to backup from stdin Yeah, as implementing the xlog idea required having some kind of parallelism, I built on it and the script now has a --jobs option for you to setup how many processes to launch in parallel, all fetching some base backup files in its own standard ( libpq) PostgreSQL connection, in compressed chunks of 8 MB (so that's not 8 MB chunks sent over).