# Loops Published articles for Loops. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Session Traces and Cost Controls Help Diagnose AI Agent Failures DevFeed: [Session Traces and Cost Controls Help Diagnose AI Agent Failures]() Original publisher: [Read original article]() Author: Mark Silvester Published: 2026-09-11T08:14:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [ai observability](), [AI Bots](), [debugging]() Tags: [agents](), [ai](), [ai-agent](), [ai-ml-data-engineering](), [artificial-intelligence](), [cost](), [debugging](), [devops](), [llm](), [loops](), [ml-data-engineering](), [monitoring](), [news](), [observability](), [observability-ai-agents](), [opentelemetry](), [traces]() ### AI overview The article describes using nested session traces, execution metrics, and cost limits to investigate and contain AI agent failures such as repeated tool calls and runaway spending. ### Source excerpt Session traces and cost controls are emerging as key observability techniques for diagnosing AI agent failures, helping teams spot tool-call loops and runaway spend while preserving enough execution context for post-incident debugging. By Mark Silvester ## Loop engineering: stop prompting, start looping DevFeed: [Loop engineering: stop prompting, start looping]() Original publisher: [Read original article]() Author: Anthony Viard Published: 2026-09-08T16:00:00Z Content type: article Language: en Sources: [Postman Blog]() Topics: [Loop Engineering](), [AI Agent](), [AI-assisted coding](), [API]() Tags: [2026](), [agent](), [agentic](), [agents](), [ai](), [ai-agents](), [ai-coding](), [api](), [api-testing](), [blog](), [code](), [coding-agents](), [developer](), [engineering](), [general](), [guide](), [idea](), [loops](), [model](), [post](), [postman-cli](), [tool](), [verify](), [work]() ### AI overview This article explains loop engineering as a way to improve AI coding agent reliability. Instead of relying on a single prompt, the system repeatedly generates code, runs it against a real API or other source of truth, verifies the result, and decides whether to stop or continue. The article distinguishes this outer verification loop from the inner ReAct tool-use cycle and presents a reproducible setup for allowing an agent to correct its own mistakes. ### Source excerpt Stop prompting your AI agent, start looping. A guide to loop engineering: wire a real API call in as the oracle so generated code stops guessing. The post Loop engineering: stop prompting, start looping appeared first on Postman Blog. ## Scaling your money safely with AI DevFeed: [Scaling your money safely with AI]() Original publisher: [Read original article]() Published: 2026-09-08T07:40:00Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [AI-assisted coding](), [sdlc](), [Code]() Tags: [agentic-ai](), [ai](), [ai-coding](), [autonomous](), [loops](), [podcast](), [sdlc](), [se-stackoverflow](), [se-tech](), [security]() ### AI overview A podcast conversation about validating AI-generated code for security, building autonomous SDLC harnesses with feedback loops, and creating a headless checkout experience. ### Source excerpt Ryan chats with Srini Venkatesan, CTO at PayPal, about validating AI-generated deterministic code for security, developing autonomous SDLC harnesses with iterative feedback loops, and creating a seamless headless checkout experience. ## How to Build Effective Product Loops in Claude Code, with Chief AI and Product Officer at JobNimbus, Tyler Folkman DevFeed: [How to Build Effective Product Loops in Claude Code, with Chief AI and Product Officer at JobNimbus, Tyler Folkman]() Original publisher: [Read original article]() Author: Aakash Gupta Published: 2026-09-04T23:01:00Z Content type: tutorial Language: en Sources: [Product Growth]() Topics: [Loop Engineering](), [Claude Code](), [Artificial Intelligence](), [Prompt Engineering]() Tags: [advice](), [agentic](), [ai](), [claude-code](), [loops]() ### AI overview A practical guide for PMs and product builders on creating product loops in Claude Code. It defines a loop as a process that fetches its own inputs, performs work, passes a gate, writes a named artifact, and repeats. The article also recommends writing a first loop by hand before using AI. ### Source excerpt People keep saying loops are the new prompts. Here's a practical walkthrough of what that actually means for PMs and product builders ## Loony Laps Is a Toy Car Racing Game Coming to Quest 3 in Q1 2027 DevFeed: [Loony Laps Is a Toy Car Racing Game Coming to Quest 3 in Q1 2027]() Original publisher: [Read original article]() Author: James Tocchio Published: 2026-09-03T23:35:19Z Content type: news Language: en Sources: [UploadVR]() Topics: [arcade](), [racing-game]() Tags: [loops](), [play](), [release](), [toys](), [vr-gaming]() ### AI overview Loony Laps is a solo-developed arcade toy racing game for Quest 3 and Quest 3S, planned for release in Q1 2027. It features handcrafted tracks, customizable die-cast-style cars, and VR and mixed-reality play. ### Source excerpt The solo-developed Loony Laps is an arcade toy racer for VR and mixed reality coming to Quest 3 headsets next year. ## Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! DevFeed: [Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my!]() Original publisher: [Read original article]() Author: Cassidy Williams Published: 2026-09-02T21:00:00Z Content type: article Language: en Sources: [GitHub Engineering]() Topics: [AI Development](), [Software Engineering](), [Open Source Models & Datasets]() Tags: [agent](), [agentic-ai](), [agents](), [ai](), [ai-ml](), [engineering](), [github-podcast](), [loops](), [models](), [observability](), [open-source](), [routing](), [skills](), [validation](), [workflow]() ### AI overview A guide to emerging AI-development terminology, explaining loop engineering and Ralph loops, multi-agent squads and fleets, and related concepts such as open weights and open source models. ### Source excerpt From loop engineering to harnesses, squads, and open weights, the GitHub Podcast breaks down the AI terms showing up in developer conversations. The post Decoding the new AI lingo: Loops, harnesses, squads, hill climbing... oh my! appeared first on The GitHub Blog. ## Practical Loop Engineering DevFeed: [Practical Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-14T14:30:53Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [AI Agent](), [Claude Code](), [codex](), [Code review](), [Scripting, bash]() Tags: [agent](), [agents](), [bash](), [claude-code](), [code-review](), [codex](), [engineering](), [loops](), [workflows]() ### AI overview An opinion article about practical loop engineering for AI-assisted software development. It describes running multiple agents in parallel, delegating bounded tasks with explicit stopping conditions, and using goal- and timer-based loops in Claude Code and Codex. It emphasizes careful supervision and well-defined goals and constraints, especially for complex or high-risk codebases. ### Source excerpt Goals, loops, and the discipline of not delegating your judgment ## Practical Loop Engineering DevFeed: [Practical Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-08-14T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Loop Engineering](), [AI Agent](), [Claude Code](), [codex](), [Bash]() Tags: [agent](), [agents](), [ai](), [ai-agent](), [bash](), [claude-code](), [code-review](), [codex](), [complexity](), [engineering](), [loops](), [workflows]() ### AI overview The article explains loop engineering as an autonomous, self-correcting feedback cycle in which an AI agent acts, tests results, and adjusts its approach toward a defined goal. It discusses Claude Code and Codex primitives, earlier Bash-based loops, and the need to define goals and constraints carefully before delegating work, especially in complex or high-risk codebases. ### Source excerpt Goals, loops, and the discipline of not delegating your judgment ## ๐Ÿ”๐Ÿง  Software Factories: Harnessing Loops At Scale DevFeed: [๐Ÿ”๐Ÿง  Software Factories: Harnessing Loops At Scale]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-07-27T15:30:10Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Loop Engineering](), [Orchestration](), [Software Engineering](), [Artificial Intelligence]() Tags: [agent](), [ai](), [architecture](), [automated](), [code](), [loops](), [parallel](), [review](), [sandbox](), [sandboxes](), [scale](), [software](), [software-engineering](), [test](), [verification]() ### AI overview The article explains how software factories use repeated agent loops, safety harnesses, parallel orchestration, work queues, and review gates to automate code production. It argues that verification, rather than code generation, is the main bottleneck, and warns that fully automated "dark factories" can accumulate comprehension debt when humans stop reading the code. ### Source excerpt PLUS: In-process load balancing ๐Ÿ’จ, On-disk ANN indexes ๐Ÿ’พ, Rust integ tests ๐Ÿงช ## Software Factories, Light and Dark DevFeed: [Software Factories, Light and Dark]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-22T03:44:07Z Content type: opinion Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [Software](), [Artificial Intelligence]() Tags: [agentic](), [agents](), [harness](), [loops](), [software]() ### AI overview The article revisits the idea of software factories through loops, harnesses, and factories. It distinguishes light factories, where humans remain involved, from dark factories, where agents scope, build, and ship code with limited human review. It argues that autonomy requires carefully designed checks, boundaries, tools, memory, and completion gates. ### Source excerpt A software factory is harnessing loops at scale. ## Software Factories, Light and Dark DevFeed: [Software Factories, Light and Dark]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-07-20T00:00:00Z Content type: article Language: en Sources: [Addy Osmani]() Topics: [Loop Engineering](), [Software](), [Artificial Intelligence]() Tags: [agentic](), [agents](), [ai](), [build](), [code](), [loops](), [software]() ### AI overview The article revisits the idea of software factories as repeatable, instrumentable production systems and describes light and dark factories based on how much human oversight they retain. It presents loop engineering as the design of agentic systems that repeatedly gather context, act, check results, and continue until a condition is met, with harnesses providing tools, memory, sandboxes, and completion gates. ### Source excerpt A software factory is harnessing loops at scale - the system that builds your software instead of you. You can run the loop with humans in it (light factory) or you can ignore the humans (dark factory) and let those agents scope, build and ship code, without anyone really reading the details. But if people stop reading, they'll stop understanding your software. Your hardest job now is knowing which checks to build and how much autonomy to delegate. ## How to use loops in Claude Code DevFeed: [How to use loops in Claude Code]() Original publisher: [Read original article]() Author: Jeff Morhous Published: 2026-07-13T11:10:13Z Content type: tutorial Language: en Sources: [The AI-Augmented Engineer]() Topics: [Claude Code]() Tags: [claude-code](), [how-to](), [loops]() ### AI overview A tutorial about using loops in Claude Code, with the description indicating that it explains why the /loop feature has attracted attention. ### Source excerpt The internet won't shut up about /loop in Claude Code. I'll show you why ## Irreducible loops DevFeed: [Irreducible loops]() Original publisher: [Read original article]() Published: 2026-07-12T07:00:00Z Content type: tutorial Language: en Sources: [MaskRay]() Topics: [Graphs](), [Algorithms](), [Code](), [LLVM]() Tags: [algorithm](), [analysis](), [code](), [entries](), [flow](), [graph](), [graphs](), [llvm](), [loops](), [static](), [structure]() ### AI overview This technical post explains why dominator-based natural-loop detection fails for irreducible control-flow graphs, which can have multiple entries. It describes reducibility, the irreducible three-node pattern, and a DFS-based loop-nesting forest using Havlak's convention. ### Source excerpt The dominator tree lets us identify natural loops: a back edge T->H whose head H dominates its tail T defines a loop with the single entry H. This works only for reducible control flow graphs. Optimized machine code and decompiler output routinely contain irreducible loops, which have more than one entry and thus no dominating header, so the dominator-based method cannot see them. This post builds a loop-nesting forest for an arbitrary CFG with the single-pass depth-first search of ้Ÿฆ้Ÿฌใ€ๆฏ›ๅ‰‘ใ€้‚น็ปดใ€้™ˆๅฎ‡(Tao Wei, Jian Mao, Wei Zou & Yu Chen) A New Algorithm for Identifying Loops in Decompilation, SAS 2007 (The 14th International Static Analysis Symposium). ## Why Agent Loops Fail in Production (and the Database Patterns That Fix Them) DevFeed: [Why Agent Loops Fail in Production (and the Database Patterns That Fix Them)]() Original publisher: [Read original article]() Author: Quentin Packard Published: 2026-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Cockroach Labs]() Topics: [Loop Engineering](), [Databases](), [AI Agent](), [CockroachDB](), [PostgreSQL]() Tags: [agent](), [ai-agent](), [ai-agents](), [article](), [cockroachdb](), [database](), [loops](), [postgresql](), [production]() ### AI overview This article explains why AI agent loops can fail in production even when the underlying model reasons correctly. It focuses on database state, recovery, consistency, and auditability, with patterns and working code for PostgreSQL and CockroachDB. ### Source excerpt Agent loops fail in production for reasons that have little to do with the model, and everything to do with what happens to their state between iterations. ## Responsible Loop Engineering DevFeed: [Responsible Loop Engineering]() Original publisher: [Read original article]() Author: Kaushik Gopal Published: 2026-06-22T20:39:22Z Content type: opinion Language: en Sources: [Kaushik Gopal's Site]() Topics: [Loop Engineering](), [Artificial Intelligence](), [Software Engineering](), [future of software](), [Agent Skills]() Tags: [agent](), [agents](), [ai](), [engineering](), [future-of-software](), [loops](), [software-engineering]() ### AI overview The author argues for responsible loop engineering: designing and operating agent systems that can run continuously while controlling costs. The article distinguishes capped one-shot loops from autonomous loops that select tasks, use subagents, research, test, and return results for review. It argues that autonomous loops require bespoke engineering, integrations, execution strategies, and queueing. ### Source excerpt Loop engineering convinced me. Not because it's clever -- because done right, it doesn't bankrupt you. This post captures where I've landed: a responsible way to run loops at scale without burning a hole in your pocket. Naysayer -> Believer ## I've been a vocal naysayer. Not because the approach doesn't work -- it works. The costs never justified it. No surprise -- the people singing its praises usually aren't the ones paying the API bills. But when Peter & Boris tell you something, you look closer. Same thing happened with agent skills -- Simon W saw something early, and that became the biggest hammer in our AI toolbox. Types of loops ## The public discourse mixes loops with loop "engineering," so let's disambiguate. One-shot loops ### Today, an agent can execute a task, have an independent judge review the result1, apply the feedback, and repeat. You put a cap on the number of loops. Or you let it run until it's "satisfied" -- a bad idea. These are easy to set up. Many people are already demonstrating them. I call these one-shot loops. They're easy enough that I'll focus on the other kind. Autonomous loops ### But when Peter Steinberger and Boris Cherny talk about loops, I think they mean autonomous loops. You set up agents to run continuously. They decide when to act, pick up the right tasks, spin off subagents, research, test theories, and send results back for review. Or ship, if confidence is high enough. An entire system running on its own -- you shovel tasks at the speed of thought or voice. These loops are self-sustaining and take real engineering to get right. I'll go out on a limb: Future of Software Engineering is loop engineering Most of the software engineers of tomorrow are going to be spending their time setting up and engineering loops. Because it's hard and it's going to require skill -- there's no one loop we can template for all solutions. From my experimenting so far, this feels bespoke in the way good software is bespoke. You can't just use an agent sk ## The feedback loops behind Kubernetes DevFeed: [The feedback loops behind Kubernetes]() Original publisher: [Read original article]() Author: Fatih Arslan Published: 2026-06-16T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Kubernetes](), [Database]() Tags: [containers](), [database](), [engineering](), [kubernetes](), [linux](), [loops](), [storage]() ### AI overview An explanation of Kubernetes operators as feedback controllers that reconcile desired and observed state, illustrated through running a production database in containers. ### Source excerpt Kubernetes is a framework for feedback controllers: write down what you want, observe what exists, make the next change, and repeat. ## Agentic Development Loops Shift the Unit of Work and Increase the Need for Verification DevFeed: [Agentic Development Loops Shift the Unit of Work and Increase the Need for Verification]() Original publisher: [Read original article]() Author: Arjun Iyer Published: 2026-06-13T16:00:00Z Content type: opinion Language: en Sources: [Kubernetes Overview, News and Trends | The New Stack]() Topics: [ai-coding](), [Development](), [coding](), [Kubernetes](), [Cloud]() Tags: [agentic-development](), [agents](), [ai](), [ai-coding](), [ai-engineering](), [cloud-native](), [coding](), [development](), [engineering](), [kubernetes](), [loops](), [platform-engineering](), [post-contributed](), [signadot](), [sponsor-signadot](), [sponsored-post-contributed](), [verification]() ### AI overview The article argues that agentic development is moving from prompt-driven and specification-driven workflows toward loops that generate, evaluate, and retry work. For cloud-native teams, this shift increases the importance of verification and the surrounding infrastructure needed to help agent-driven work converge on correct results. ### Source excerpt Something shifted in the AI coding discourse this month. The argument is no longer about whether agents can write production The post Loops are replacing prompts. Verification is about to be your biggest problem. appeared first on The New Stack. ## How I'm building autonomous agent loops DevFeed: [How I'm building autonomous agent loops]() Original publisher: [Read original article]() Author: Owain Lewis Published: 2026-06-13T14:02:17Z Content type: tutorial Language: en Sources: [The AI Engineer]() Topics: [Loop Engineering](), [Artificial Intelligence](), [GitHub Issues](), [Claude Code](), [codex](), [GitHub]() Tags: [agent](), [ai](), [autonomous](), [claude-code](), [codex](), [github-issues](), [loops](), [workflow]() ### AI overview This guide explains how to build autonomous agent loops in which agents read project state, perform work, update that state, and repeat on a schedule or in response to events. It presents GitHub Issues as a control plane and describes manager and worker loops built with Claude Code and Codex, including risk classification, ticket management, and safeguards against destructive actions. ### Source excerpt A guide on loop engineering and autonomous AI loops ## Gamification 2.0. Beyond Points and Badges: Designing for Players, Not Metrics. Chapter 5: Implementation DevFeed: [Gamification 2.0. Beyond Points and Badges: Designing for Players, Not Metrics. Chapter 5: Implementation]() Original publisher: [Read original article]() Author: Montgomery Singman Published: 2026-06-09T03:26:13Z Content type: tutorial Language: en Sources: [UX Magazine]() Topics: [implementation](), [User experience (UX)](), [App]() Tags: [app](), [building](), [complex-systems](), [core](), [creative](), [design](), [expression](), [games](), [implementation](), [interface](), [leaderboard](), [loops](), [management](), [multiplayer](), [puzzle](), [rpg](), [sandbox](), [simulation](), [strategy](), [ux]() ### AI overview This chapter presents a practical framework for implementing Gamification 2.0. It recommends choosing a dominant game genre that matches an app's core activities, aligning that genre with user psychology, and designing satisfying intrinsic interaction loops before adding points, badges, or other extrinsic rewards. ### Source excerpt Part 5 of the "Gamification Series." A framework for developers: from theory to practice Everything I've outlined so far is meaningless if you can't apply it. So let me give you a practical framework for actually implementing Gamification 2.0. Step 1: Stop copying mechanics; choose a genre Your first question isn't "What gamification mechanics should The post Gamification 2.0. Beyond Points and Badges: Designing for Players, Not Metrics. Chapter 5: Implementation appeared first on UX Magazine. ## Loop Engineering DevFeed: [Loop Engineering]() Original publisher: [Read original article]() Author: Addy Osmani Published: 2026-06-08T14:31:21Z Content type: article Language: en Sources: [Elevate]() Topics: [Loop Engineering](), [AI-assisted coding](), [Claude](), [Claude Code](), [Agent Harness](), [codex]() Tags: [agent](), [agents](), [ai](), [claude-code](), [codex](), [coding-agents](), [engineering](), [harness](), [loops]() ### AI overview The article introduces loop engineering: designing systems that repeatedly prompt, check, record, and direct coding agents instead of manually prompting them turn by turn. It describes the approach as early and potentially costly in token usage, and compares its building blocks with features in Codex and Claude Code. ### Source excerpt Loop engineering is replacing yourself as the person who prompts the agent. ## Ralph Loops Use Fresh Context and External Verification to Improve AI Agent Workflows DevFeed: [Ralph Loops Use Fresh Context and External Verification to Improve AI Agent Workflows]() Original publisher: [Read original article]() Author: Paul Iusztin Published: 2026-04-23T11:02:54Z Content type: article Language: en Sources: [Decoding ML]() Topics: [Artificial Intelligence](), [AI Engineering]() Tags: [agent-orchestration](), [agents](), [ai](), [ai-agents](), [ai-engineering](), [loops]() ### AI overview The article argues that replacing complex multi-agent systems with a writer-and-reviewer loop can simplify AI workflows. Ralph loops reset the conversation on each iteration, reload the specification, use filesystem and git for memory, and rely on objective signals such as tests or linters for verification. ### Source excerpt How one simple loop beats multi-agent orchestration and context rot in production. ## Branch Prediction: Why an if Inside a Hot Loop Costs Milliseconds DevFeed: [Branch Prediction: Why an if Inside a Hot Loop Costs Milliseconds]() Original publisher: [Read original article]() Author: hello@ankit-rana.com Published: 2026-03-21T00:00:00Z Content type: tutorial Language: en Sources: [Ankit Rana | Mechanical Sympathy]() Topics: [cpu](), [Hardware]() Tags: [branch](), [branch-prediction](), [cpu](), [cycles](), [loops](), [optimization](), [performance](), [pipeline](), [pipelining](), [sorting](), [systems](), [wall-clock-time]() ### AI overview This tutorial explains how CPU pipelining and branch prediction affect performance in hot loops. It describes the cost of mispredictions and suggests sorting data to create predictable branch patterns or using branchless techniques and loop unrolling, while recommending measurement on the target workload. ### Source excerpt CPUs pipeline instructions and speculate on branch outcomes. A misprediction discards the speculative work and flushes the pipeline at roughly 10 to 20 cycles. In a million-iteration loop with random branch outcomes, those flushes dominate runtime. Sorting the data so the branch resolves the same way for long runs, or removing the branch entirely with a mask or 0/1 multiplier, is what recovers the time. ## IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST DevFeed: [IBM and UC Berkeley Diagnose Why Enterprise Agents Fail Using IT-Bench and MAST]() Original publisher: [Read original article]() Author: Ayhan Sebin; Rohan Arora; Saurabh Jha Published: 2026-02-18T16:15:45Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Benchmark](), [ibm](), [Large Language Model](), [site-reliability-engineering](), [incident](), [Kubernetes](), [finops](), [Security]() Tags: [agentic](), [agents](), [analysis](), [automation](), [benchmarks](), [enterprise](), [evaluation](), [gpt-oss](), [hallucinations](), [ibm](), [incident](), [kubernetes](), [llm](), [logs](), [loops](), [metrics](), [performance](), [research](), [systems](), [tool]() ### AI overview IBM Research and UC Berkeley analyze why agentic LLM systems fail in enterprise IT automation using ITBench traces and the MAST failure taxonomy. Their analysis of 310 SRE traces compares Gemini-3-Flash, Kimi-K2, and GPT-OSS-120B, identifying verification errors, cascading failures, premature termination, looping, and hallucinations as major reliability problems. ### Source excerpt IBM Research and UC Berkeley collaborated to study how agentic LLM systems break in real-world IT automation, for tasks involving incident triage, logs/metrics queries, and Kubernetes actions in long-horizon tool loops. Benchmarks typically reduce performance to a single number, telling you whether an agent failed but never why. To solve this black-box problem, we applied MAST (Multi-Agent System Failure Taxonomy), an emerging practice for diagnosing agentic reliability ). ## Using go fix to modernize Go code DevFeed: [Using go fix to modernize Go code]() Original publisher: [Read original article]() Author: Alan Donovan Published: 2026-02-17T00:00:00Z Content type: article Language: en Sources: [The Go Blog]() Topics: [Go Language](), [toolchain]() Tags: [algorithms](), [analysis](), [best-practices](), [code](), [git](), [go](), [how-to](), [infrastructure](), [loops](), [maintainers](), [release](), [review](), [toolchain](), [tools](), [update]() ### AI overview The article explains how the rewritten Go 1.26 `go fix` command modernizes code by applying analyzers and automated fixes, and outlines its supporting infrastructure and self-service analysis goals. ### Source excerpt Go 1.26 includes a new implementation of go fix that can help you use more modern features of Go. [Next page]()