# durability Published articles for durability. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## The thread is the Workflow: Durable AI agents without changing Agent code DevFeed: [The thread is the Workflow: Durable AI agents without changing Agent code]() Original publisher: [Read original article]() Author: Greg Haskins Published: 2026-09-03T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Langgraph](), [Code](), [Framework]() Tags: [agent](), [ai-agents](), [community](), [crashes](), [durability](), [langgraph](), [state](), [workflow]() ### AI overview The article explains how Temporal Workflows add durable execution to LangGraph-based AI agents without requiring changes to the agent code. It describes mapping Agent Protocol Threads to Temporal Workflows, Runs to Activities, and checkpoints to Workflow state so executions can recover after worker crashes. ### Source excerpt Learn how Temporal Workflows can make LangGraph-based AI agents durable, preserving threads and checkpoints across crashes without changing agent code. ## 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. ## Durable Digest: August highlights DevFeed: [Durable Digest: August highlights]() Original publisher: [Read original article]() Author: Temporal Technologies Published: 2026-08-27T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Cloud](), [AI Development](), [AWS Lambda](), [Workers](), [autoscaling](), [Amazon Web Services](), [AI Agent](), [AWS IAM](), [SDKs]() Tags: [ai](), [ai-agent](), [announcements](), [autoscaling](), [aws](), [aws-lambda](), [cloud](), [distributed](), [durability](), [genai](), [google](), [iam](), [infrastructure](), [workers]() ### AI overview Temporal's August 2026 Durable Digest highlights new and preview features for running Workers on AWS Lambda, organizing Temporal Cloud resources, invoking Nexus Operations, and building durable AI applications. ### Source excerpt Highlights this month include new ways to run Workers without managing infrastructure, organize Temporal Cloud resources and build more durable AI applications. ## Where Kafka Fits in Modern Systems DevFeed: [Where Kafka Fits in Modern Systems]() Original publisher: [Read original article]() Author: Nikki Siapno Published: 2026-08-25T12:02:09Z Content type: tutorial Language: en Sources: [Level Up Coding System Design Newsletter]() Topics: [Kafka](), [Event-Streaming](), [systems](), [distributed-systems](), [data]() Tags: [architecture](), [consumer](), [distributed-systems](), [durability](), [event-streaming](), [kafka](), [parallelism](), [partition](), [pipelines](), [real-time](), [systems]() ### AI overview This article explains when Apache Kafka is appropriate for distributed systems. It presents Kafka as a durable event log whose replayability, consumer fan-out, and partition-based parallelism support real-time data pipelines and shared event-driven architectures, while warning that Kafka can add unnecessary operational complexity for simpler asynchronous workflows. ### Source excerpt When should you actually use Kafka? And when is it just unnecessary complexity? ## How fsync affects data durability, latency, and group commit DevFeed: [How fsync affects data durability, latency, and group commit]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-08-14T00:00:00Z Content type: article Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [data](), [Latency](), [NVMe](), [Replication]() Tags: [databases](), [durability](), [flush](), [fsync](), [postgresql](), [replication](), [ssd](), [stack](), [storage]() ### AI overview This article explains that a successful write() may leave data only in the kernel page cache, so a power loss can destroy it. It describes fsync as the operation that pushes data to the device, discusses its latency cost, and explains how group commit amortizes the synchronization barrier. It also distinguishes disk durability from replication across failure domains. ### Source excerpt A successful write() only copies your data into the kernel page cache, where a power loss destroys it. Only fsync pushes it to the device, and because fsync is a barrier rather than a write, a system that syncs once per commit is capped at roughly one divided by the fsync latency, no matter how fast the rest of the stack is. Group commit exists to amortise that barrier, and replication solves a different failure domain than fsync does, which is why acks=all does not mean the data is on any disk. ## The fuel of the future is already here: Why TRISO matters DevFeed: [The fuel of the future is already here: Why TRISO matters]() Original publisher: [Read original article]() Author: Katy Huff Published: 2026-06-24T19:57:09Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [AI Infrastructure](), [amazon](), [cloud-computing]() Tags: [advanced-nuclear-reactors](), [advanced-reactor-fuel-fabrication](), [ai-infrastructure](), [amazon](), [amazon-nuclear-energy](), [clean-energy-data-centers](), [cloud-computing](), [decarbonization](), [durability](), [energy](), [haleu](), [integrity](), [modular-nuclear-reactors](), [nuclear-energy-for-ai](), [physical-science](), [renewable-energy](), [resilience](), [safety](), [science](), [sustainability](), [triso-fuel](), [triso-particles](), [x-energy-xe-100]() ### AI overview Amazon explains how TRISO nuclear fuel particles use layered carbon and ceramic coatings to contain radioactive byproducts and withstand extreme temperatures. The article connects the technology to rising energy demands from AI infrastructure and cloud computing, citing testing that found no detectable failures at 1600°C for 300 hours. ### Source excerpt Millimeter-scale particles of nuclear-reactor fuel are encased in four layers of different materials that act as a "miniature containment system". ## Agentic AI Architecture: How CockroachDB Supports Memory, Context, and Control DevFeed: [Agentic AI Architecture: How CockroachDB Supports Memory, Context, and Control]() Original publisher: [Read original article]() Author: Alejandro Infanzon Published: 2026-06-11T00:00:00Z Content type: article Language: en Sources: [Cockroach Labs]() Topics: [Agentic AI Architecture](), [CockroachDB](), [Artificial Intelligence](), [AI Architecture](), [context](), [Databases](), [data](), [Resilience](), [audit](), [SQL](), [Back end](), [Cockroach Labs]() Tags: [agentic](), [agentic-ai](), [agentic-ai-architecture](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [architecture](), [audit](), [backend](), [cockroachdb](), [context](), [data](), [database](), [databases](), [durability](), [infrastructure](), [latency](), [low-latency](), [memory]() ### AI overview This article explains how CockroachDB can support enterprise agentic AI architectures by storing durable agent state, long-term memory, retrieval metadata, schema context, permissions, execution metadata, cost information, latency telemetry, and SQL audit trails. It presents the database as an operational layer for observing, governing, and improving autonomous agent behavior. ### Source excerpt What happens when you connect a fleet of autonomous AI agents to your enterprise data stack? You quickly discover... ## How Bitovi Built a Durable Customer Support AI Agent with Temporal and AWS Bedrock DevFeed: [How Bitovi Built a Durable Customer Support AI Agent with Temporal and AWS Bedrock]() Original publisher: [Read original article]() Author: Mark Repka Published: 2026-05-19T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Amazon Bedrock](), [Amazon Web Services](), [Platform Engineering](), [Large Language Model](), [Inference]() Tags: [ai-agent](), [architecture](), [aws](), [bedrock](), [community](), [customer](), [durability](), [observability](), [retries](), [sessions]() ### AI overview Bitovi built a customer support AI agent that remembers users across sessions and handles issues without losing conversation context. Its architecture uses a ReAct agent loop in a Temporal Workflow, with workflow state for conversation context, AWS Bedrock Java SDK calls in Activities, and Activity-backed tool execution for retries and observability. ### Source excerpt How Bitovi's team built a customer service AI agent on Temporal and AWS Bedrock, and why durability was never optional. ## Temporal Sandbox Orchestration Harness: The missing layer for running agents DevFeed: [Temporal Sandbox Orchestration Harness: The missing layer for running agents]() Original publisher: [Read original article]() Author: Stefan Richter Published: 2026-05-07T00:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [agentic workflows](), [Persistence](), [Provisioning]() Tags: [agentic-workflows](), [agents](), [ai-agents](), [cleanup](), [durability](), [execution](), [files](), [harness](), [isolation](), [network](), [orchestration](), [persistence](), [process](), [product-news](), [provisioning](), [sandbox](), [temporal]() ### AI overview Temporal introduces reference materials and Code Exchange samples for orchestrating secure, isolated, temporary sandbox compute environments within Temporal Workflows used by AI agents. The materials address interfaces, provisioning, persistence, and cleanup. ### Source excerpt Standardize how AI agents in Temporal Workflows orchestrate sandbox compute. New Code Exchange samples cover provisioning, persistence, and cleanup. ## The hero's journey to AI durability with Temporal DevFeed: [The hero's journey to AI durability with Temporal]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2026-01-14T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [Large Language Model](), [reliability](), [Development](), [retry](), [Networks](), [Databases](), [Filesystems]() Tags: [ai](), [apis](), [databases](), [durability](), [how-to](), [networks](), [reliability](), [retry](), [temporal](), [workflows]() ### AI overview The article explains how Temporal Workflows and Activities can make multi-step AI features durable when workers restart or networks, APIs, databases, file systems, and user actions fail. It focuses on recovering without duplicate work, lost state, wasted resources, or reduced user trust. ### Source excerpt Ship AI features that survive real-world failures. Learn how Temporal Workflows and Activities make multi-step LLM and tool chains durable--so restarts, timeouts, and rate limits don't cause duplicate work or lost state. ## How Temporal Workflows and Activities support dynamic, reliable AI agents DevFeed: [How Temporal Workflows and Activities support dynamic, reliable AI agents]() Original publisher: [Read original article]() Author: Steve Androulakis Published: 2025-11-12T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [AI Agent](), [Orchestration]() Tags: [ai-agents](), [durability](), [long-running](), [production](), [temporal](), [temporal-voices]() ### AI overview This guide explains how Temporal can support dynamic AI agents despite its requirement for deterministic Workflow code. It distinguishes deterministic orchestration from non-deterministic Activities, where agents can call LLMs, tools, and APIs. The article argues that this separation helps agents withstand crashes, outages, and other failures, especially in long-running workloads. ### Source excerpt Temporal can absolutely handle your AI agents. This guide shows you how Temporal Workflows and Activities make the perfect foundation for reliable agents. ## Major compute price reduction on Neon DevFeed: [Major compute price reduction on Neon]() Original publisher: [Read original article]() Author: Nikita Shamgunov Published: 2025-11-03T19:23:07Z Content type: release Language: en Sources: [Blog -- Neon Docs]() Topics: [Databases](), [autoscaling](), [databricks](), [cloud-infrastructure](), [Supabase]() Tags: [autoscaling](), [company](), [compute](), [cost](), [cost-savings](), [databases](), [databricks](), [durability](), [infrastructure](), [load](), [serverless](), [snapshots]() ### AI overview Neon announces compute price reductions of up to 25% across its plans, including lower rates for the Launch and Scale Plans. The article also reviews earlier reductions to storage, minimum spend, enterprise features, and Free Plan compute, then compares costs with Aurora Serverless v2 and Supabase in selected usage scenarios. ### Source excerpt Databases are often one of the biggest infrastructure expenses for any company. From day one, Neon's mission has been to make databases radically more efficient through separation of storage and compute, allowing instant autoscaling and better unit economics. Now, with Neon runni... ## Building long-running interactive MCP tools with Temporal DevFeed: [Building long-running interactive MCP tools with Temporal]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2025-08-21T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Model Context Protocol](), [Orchestration](), [Building AI Systems](), [Artificial Intelligence]() Tags: [agentic-ai](), [durability](), [how-to](), [interactive](), [long-running](), [mcp](), [model-context-protocol](), [workflow]() ### AI overview This tutorial explains how to build long-running, interactive MCP tools with Temporal Workflows. It describes using MCP to trigger, monitor, and interact with durable workflows, including workflows that wait for human approval, while Temporal handles orchestration, memory, timeouts, and retries. ### Source excerpt Learn how to interact with a long-running tool workflow using MCP from one of our Solutions Architects, Josh Smith. ## Build resilient Agentic AI with Temporal DevFeed: [Build resilient Agentic AI with Temporal]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2025-02-25T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Artificial Intelligence](), [Orchestration](), [reliability](), [Scalability](), [retry](), [real-time](), [Processes](), [SDK](), [context]() Tags: [agentic-ai](), [agents](), [ai-agents](), [continuous](), [durability](), [metrics](), [orchestration](), [state-management](), [temporal-concepts]() ### AI overview This tutorial explains how Temporal supports durable, scalable Agentic AI workflows that can preserve state, involve humans, schedule execution, provide observability, and recover from failures through retries. ### Source excerpt Learn how Temporal powers durable, scalable Agentic AI workflows with reliable state management, human-in-the-loop, and seamless orchestration. ## Announcing new Temporal training courses for .NET developers DevFeed: [Announcing new Temporal training courses for .NET developers]() Original publisher: [Read original article]() Author: Angela Zhou Published: 2025-02-19T00:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [.NET](), [SDK](), [Error Handling](), [Tooling]() Tags: [announcements](), [business-logic](), [consistency](), [debug](), [deploy](), [developers](), [durability](), [error-handling](), [free](), [how-to](), [net](), [training]() ### AI overview Temporal announces that its Temporal 101, Temporal 102, and Crafting an Error Handling Strategy courses are now available for .NET developers. The free, self-paced courses cover durable execution, core concepts, architecture, production practices, debugging, deployment, durability, and error-handling strategies. ### Source excerpt Our Temporal 101, 102, and Crafting an Error Handling Strategy are now available in .NET. Take one of our free, self-paced, hand-on courses to learn everything you need to know about Temporal. ## Reduce latency and speed up your Temporal Workflows DevFeed: [Reduce latency and speed up your Temporal Workflows]() Original publisher: [Read original article]() Author: Joshua Smith Published: 2025-02-12T00:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Latency](), [reliability](), [systems](), [Cloud](), [Network](), [Server](), [Transactions]() Tags: [cloud](), [durability](), [e-commerce](), [latency](), [measuring](), [optimizing](), [payment](), [problems](), [reduce](), [speed](), [system](), [techniques](), [temporal-concepts](), [workflows]() ### AI overview This tutorial explains how to reduce latency in Temporal workflows used by interactive applications while preserving durability and reliability. It identifies workflow startup, task scheduling, network calls, and activity execution as contributors to response time. ### Source excerpt Latency can be a challenge when responsiveness matters. The good news is, there are ways to make your workflows snappier without sacrificing durability. ## Get $1,000 in free credits and build better Workflows with Temporal Cloud DevFeed: [Get $1,000 in free credits and build better Workflows with Temporal Cloud]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2024-11-06T08:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Cloud](), [Development](), [Scalability](), [Availability](), [Open Source](), [Self-hosted]() Tags: [announcements](), [capabilities](), [cloud](), [development](), [durability](), [efficiency](), [free](), [high-availability](), [introduction](), [workflows]() ### AI overview Temporal Cloud announces self-service sign-up and $1,000 in free credits for new users. The article explains that the managed platform handles infrastructure tasks such as task distribution, error handling, and retries for developers building scalable workflows. ### Source excerpt Start your free trial with Temporal Cloud and get $1,000 in free credits to build better workflows. Experience powerful cloud solutions today! ## SQL vs NoSQL Explained DevFeed: [SQL vs NoSQL Explained]() Original publisher: [Read original article]() Author: Nelson Djalo Published: 2024-07-02T16:00:57Z Content type: comparison Language: en Sources: [Amigoscode Newsletter]() Topics: [NoSQL](), [SQL](), [Databases](), [clickhouse]() Tags: [acid](), [business-intelligence](), [consistency](), [data-analysis](), [database](), [database-scalability](), [durability](), [microsoft](), [mysql](), [nosql](), [olap](), [oracle](), [postgresql](), [rdbms](), [relational-database-management-systems-rdbms](), [relational-databases](), [schema](), [sql]() ### AI overview This comparison explains the differences between SQL and NoSQL databases, including their characteristics, scalability models, and common use cases. It covers SQL database features such as structured schemas and ACID compliance, along with relational and OLAP database categories. ### Source excerpt Choosing the Right Database for Your Needs ## Kafka for System Design Interviews DevFeed: [Kafka for System Design Interviews]() Original publisher: [Read original article]() Author: Evan King Published: 2024-07-01T03:18:09Z Content type: tutorial Language: en Sources: [Evan King]() Topics: [Kafka](), [Apache-Kafka](), [Event-Streaming](), [stream-processing](), [Scalability](), [data]() Tags: [apache-kafka](), [distributed](), [durability](), [google](), [high-performance](), [kafka](), [meta](), [open-source](), [real-time](), [scalability](), [stream-processing](), [system-design-interview]() ### AI overview A tutorial on Apache Kafka for system design interviews. It explains Kafka as an open-source distributed event streaming platform that can function as a message queue or stream processing system, then uses a real-time sports statistics example to discuss producers, consumers, scaling, and event ordering. ### Source excerpt By the Co-founder of www.hellointerview.comIntro There is a good chance you've heard of Kafka. It's popular. In fact, according to their website, it's used by 80% of the Fortune 100. If it's good enough to help scale the largest companies in the world, it's probably good enough for your next system design interview. In fact, it's one of the top 5 technologies we see used in design interviews. Apache Kafka is an open-source distributed event streaming platform that can be used either as a message queue or as a stream processing system. Kafka excels in delivering high performance, scalability, and durability. It's engineered to handle vast volumes of data in real-time, ensuring that no message is ever lost and that each piece of data is processed as swiftly as possible. In this deep dive, we're going to take a top down approach. Starting with a zoomed out view of Kafka and progressing into more and more detail. If you know the basics, feel free to skip ahead to the more advanced sections. A Motivating Example It's the World Cup (my personal favorite competition). And we run a website that provides real-time statistics on the matches. Each time a goal is scored, a player is booked, or a substitution is made, we want to update our website with the latest information. Events are placed on a queue when they occur. We call the server or process responsible for putting these events on the queue the producer. Downstream, we have a server that reads events off the queue and updates the website. We call this the consumer. Now, imagine the World Cup expanded from just the top 48 teams to a hypothetical 1,000-team tournament, and all the games are now played at the same time. The number of events has increased significantly, and our single server hosting the queue is struggling to keep up. Similarly, our consumer feels like it has its mouth under a firehose and is crashing under the load. We need to scale the system by adding more servers to distribute our queue. But how do we e ## Maximizing availability with Multi-region Namespaces DevFeed: [Maximizing availability with Multi-region Namespaces]() Original publisher: [Read original article]() Author: Nikitha Suryadevara Published: 2024-06-03T06:00:00Z Content type: release Language: en Sources: [Temporal Blog]() Topics: [Availability](), [Cloud](), [Replication](), [reliability](), [migration](), [Deployment]() Tags: [announcements](), [availability](), [cloud](), [deployment](), [dogfooding](), [durability](), [failover](), [incident](), [preview](), [public](), [region](), [reliability]() ### AI overview Temporal introduces Multi-region Namespaces, formerly called Global Namespaces, in Public Preview. The feature uses active-standby replication across user-defined regions and automated failover to support 99.99% availability for critical workloads. ### Source excerpt Now in Public Preview: Multi-region Namespaces (formerly Global Namespaces) enable seamless failover and 99.99% availability--no manual config required. ## Some opinionated thoughts on SQL databases DevFeed: [Some opinionated thoughts on SQL databases]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2021-03-30T17:32:31Z Content type: opinion Language: en Sources: [Nelson Elhage]() Topics: [Databases](), [SQL](), [MySQL](), [PostgreSQL](), [SQLite](), [backends](), [Web APIs](), [Latency]() Tags: [acid](), [backends](), [consistency](), [databases](), [durability](), [high-availability](), [low-latency](), [mysql](), [open-source](), [postgresql](), [sql](), [sqlite](), [web-apis]() ### AI overview An opinionated discussion of SQL databases, focused mainly on MySQL and PostgreSQL, with some coverage of SQLite. Drawing on experience using databases as backends for web applications and APIs, the article argues that open-source SQL databases provide excellent storage engines, throughput, durability, and transactional guarantees when properly tuned. ### Source excerpt People who work with me tend to realize that I have Opinions about databases, and SQL databases in particular. Last week, I wrote about a Postgres debugging story and tweeted about AWS' policy ban on internal use of SQL databases, and had occasion to discuss and debate some of those feelings on Twitter; this article is an attempt to write up more of them into a single place I can refer to. ## Virtual consensus in Delos DevFeed: [Virtual consensus in Delos]() Original publisher: [Read original article]() Author: adriancolyer Published: 2020-11-09T13:34:00Z Content type: article Language: en Sources: [Adrian Colyer]() Topics: [distributed-systems](), [consistency](), [Protocol (disambiguation)](), [Availability](), [upgrade](), [maintenance]() Tags: [availability](), [consensus](), [consistency](), [distributed-systems](), [durability](), [implementation](), [maintenance](), [protocol](), [uncategorized](), [upgrade]() ### AI overview This article discusses Delos, a system designed for Facebook's control-plane services. It describes an API-based abstraction over consensus using a shared log, with a runtime that supports hot-swapping consensus implementations so the system can be upgraded without interrupting operation. ### Source excerpt Virtual consensus in Delos, Balakrishnan et al. (Facebook, Inc.), OSDI'2020 Before we dive into this paper, if you click on the link above and then download and open up the paper pdf you might notice the familiar red/orange splash of USENIX, and appreciate the fully open access. USENIX is a nonprofit organisation committed to making content and ... Continue reading Virtual consensus in Delos ## Modules Part 06: Vendoring DevFeed: [Modules Part 06: Vendoring]() Original publisher: [Read original article]() Published: 2020-04-13T00:00:00Z Content type: tutorial Language: en Sources: [William Kennedy]() Topics: [modules](), [Go Language](), [Tutorial](), [Development]() Tags: [ardan-labs](), [blog](), [compatibility](), [dependencies](), [durability](), [external](), [go](), [go-programming](), [golang](), [modules](), [network](), [programming](), [project](), [source](), [tooling](), [tutorial]() ### AI overview This article explains Go vendoring, including its benefits for reproducible and durable application builds that do not depend on module mirrors or network access. It reviews Go's vendoring support and changing defaults, demonstrates running Go 1.13 alongside Go 1.14, and introduces a practical vendoring workflow. ### Source excerpt Series Index Why and What Projects, Dependencies and Gopls Minimal Version Selection Mirrors, Checksums and Athens Gopls Improvements Vendoring Introduction It's no secret that I am a fan of vendoring when it's reasonable and practical to use it for your application projects. I believe vendoring gives your application projects the most durability since the project owns every line of source code it needs to build the applications. If you want a reproducible build without needing to rely on external services (like module mirrors) and being connected to the network, vendoring is the solution. ## Broken Hardware, Fixes and Hacks over 8 Years DevFeed: [Broken Hardware, Fixes and Hacks over 8 Years]() Original publisher: [Read original article]() Published: 2016-06-20T22:00:00Z Content type: article Language: en Sources: [Dennis Felsing]() Topics: [Hardware](), [Image]() Tags: [article](), [durability](), [electronics](), [experience](), [hardware](), [personal]() ### AI overview A personal overview of eight years of using, repairing, and working around failures in computing hardware. The article emphasizes frugal reuse and discusses common problems such as failed capacitors in consumer electronics. ### Source excerpt After reading the feedback of my recent article about running DDNet, I noticed that people found it interesting how I'm trying to minimize money and resources. I also noticed that I had been doing something similar with my personal computing hardware setup for an even longer time. I've mostly been using the same hardware for personal computation purposes over the last 8 years. In this article I want to talk about some of the hardware I've been using, how it broke and how I fixed the problems or worked around them. My goal was to be frugal about hardware, to keep using the same hardware for a long time and repair it when possible instead of simply buying new hardware. The reason for this post is that I'm moving and abandoning my old hardware setup. There may be some interesting tales in here. Depending on how you value your time it is probably cheaper to simply buy new stuff instead of repairing old ones, but I consider it a fun activity and a much more rewarding experience. Whether you call it planned obsolescence or just cheap manufacturing, it's a fact that electronic hardware tends to break rather quickly. Most reviews don't take any note of this and so it is difficult to find out which hardware to buy if you value durability. The best I can do is tell you what you should and should not have bought 8 years ago. For each of these topics there are probably much more detailed reports and guides online, so I will keep it short and just give an overview. If you're interested in fixing your own hardware, a search engine of your choice is your friend. Tools used The tools I use are absolutely simple: Mostly I just used a Weller WECP-20 for soldering, regular screwdrivers and some other, older hardware to steal replacement parts from. Nothing fancy and I don't claim to do anything special with it. Liquid-Crystal Displays Let's start with what in my experience was the most common problem in consumer electronics, resulting in their failure: broken capacitors Or at least it [Next page]()