# Software Engineering Published articles for Software Engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Minimizing the length of regular expressions, in practice DevFeed: [Minimizing the length of regular expressions, in practice]() Original publisher: [Read original article]() Author: Khan Academy Published: 2016-05-23T22:00:00Z Content type: tutorial Language: en Sources: [Khan Academy]() Topics: [Algorithms, Complexity](), [Regular expression](), [Programming](), [cloud-infrastructure]() Tags: [algorithms](), [cdn](), [configuration](), [engineering](), [google](), [hosting](), [infrastructure](), [news](), [re](), [repository](), [software-engineering]() ### AI overview Craig Silverstein explains how Khan Academy needed a compact regular expression to distinguish static URLs from dynamic URLs for CDN routing. The article describes why simple patterns were unsuitable, reports that a naive generated expression reached 402,025 characters, and introduces a second approach that reduced the length to 2,400 characters. ### Source excerpt By Craig Silverstein The problem Software engineering interviews tend to be full of "algorithms" questions, because they're easy ... Read more ## How end-to-end SLO monitoring detected a livestream failure that component dashboards missed DevFeed: [How end-to-end SLO monitoring detected a livestream failure that component dashboards missed]() Original publisher: [Read original article]() Author: Whatnot Engineering Published: 2026-09-15T16:31:01Z Content type: article Language: en Sources: [Whatnot Engineering]() Topics: [dashboards](), [observability](), [incident](), [Monitoring](), [Web](), [client](), [Network]() Tags: [dashboards](), [distributed-systems](), [incident](), [monitoring](), [observability](), [security](), [server](), [site-reliability-engineer](), [software-engineering](), [web]() ### AI overview The article examines a June 8, 2026 incident in which a third-party client-side security script fetched from a provider CDN added URL validation that the video provider did not pass, causing black screens for newly loaded web clients. Most component-level dashboards remained green, while end-to-end service-level objective monitoring detected the broken livestream experience and paged the owning teams within five minutes. ### Source excerpt Karol Gil | Reliability Platform (Poland) On June 8, 2026, newly loaded web clients began showing black screens instead of livestream video. For our platform, that's a serious problem: it's pretty hard to sell Pokรฉmon cards that no one can see. It turned out that a third-party script we use for client-side security monitoring wasn't bundled with our release, but was rather fetched live from the provider's CDN. When the provider updated the script all new web clients fetched it, and it included an additional URL validation which our video provider didn't pass. The result? Black screens for users of the affected web clients, with most internal dashboards staying green. 3,000 users were impacted in the first 30 minutes of the incident. One system did catch it. Our end-to-end service-level objective (E2E SLO) monitoring was already in production and paged the owning teams within five minutes. Here's what it saw. The real problem Most of our dashboards stayed green because they monitor component-level health: a server, an endpoint, a specific function. These are all useful, but can all be healthy while the actual user experience is completely broken. This problem gets worse the more external dependencies there are, or the more sophisticated an experience you want to deliver. In complex, integrated product experiences like ours, a "small" problem can have an outsize impact on the user experience. Measuring this requires a different approach to observability, namely, to model the user journey across multiple surfaces that must be true for a customer to have a good experience. So how do we measure this in a complex distributed application? Joining a livestream is not one thing Joining a livestream sounds like one action, but the user expects at least three things: Video to be playing Auction details to be shown Chat to be visible and up to da Each of those can succeed or fail completely independently of the other two. Our video depends on third-party providers and CDN netwo ## Port vs. Jellyfish: Engineering Intelligence Compared DevFeed: [Port vs. Jellyfish: Engineering Intelligence Compared]() Original publisher: [Read original article]() Author: Tomasz Skora Published: 2026-09-15T12:02:54Z Content type: comparison Language: en Sources: [Developer Experience & Platform Engineering Blog | Port]() Topics: [Software Engineering](), [Developer experience](), [dora metrics](), [AI Strategy](), [code productivity]() Tags: [ai-adoption](), [compare](), [devex](), [dora-metrics](), [engineering](), [software-engineering](), [workflows]() ### AI overview A comparison of Port and Jellyfish as Engineering Intelligence platforms. Jellyfish focuses on analytics and decision support for engineering performance, investment, developer experience, and AI impact, while Port connects findings to software context, owners, standards, and governed workflows so teams can act and measure outcomes. ### Source excerpt Compare Port and Jellyfish across Engineering Intelligence, AI impact, DevEx, software context, governed workflows, and measurable outcomes. ## Why Coding Agents Still Need Human Code Reviews DevFeed: [Why Coding Agents Still Need Human Code Reviews]() Original publisher: [Read original article]() Author: Anton Zaides Published: 2026-09-15T06:01:00Z Content type: opinion Language: en Sources: [Manager.dev]() Topics: [AI-assisted coding](), [Code review](), [engineering-culture](), [Claude](), [Software Engineering]() Tags: [claude](), [code-review](), [code-reviews](), [coding-agents](), [software-engineering]() ### AI overview The article argues that coding-agent workflows still require human code reviews. It describes how making reviews optional caused them to drop sharply, allowing passable implementations to become templates that spread bugs, performance problems, race conditions, and confusion among agents. ### Source excerpt When my most experienced engineer got tired of our shitty code ## Blog posts that influenced Simon Willison's thinking about software engineering DevFeed: [Blog posts that influenced Simon Willison's thinking about software engineering]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-14T20:21:48Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Software Engineering](), [Tech Debt](), [Database]() Tags: [charity-majors](), [charity-majors-10](), [individual-contributor](), [joel-spolsky](), [joel-spolsky-10](), [management](), [software-engineering](), [software-engineering-64](), [tech-debt](), [will-larson](), [will-larson-17]() ### AI overview Simon Willison describes three blog posts that influenced his thinking: Joel Spolsky's "The Law of Leaky Abstractions," Will Larson's "Migrations: the sole scalable fix to tech debt," and Charity Majors' "The Engineer/Manager Pendulum." He says they shaped his approach to understanding software layers, treating migrations as an important engineering skill, and moving between engineering management and individual-contributor work. ### Source excerpt My comment on What blog posts influenced your thinking the most? -- Lobste.rs. An early Joel Spolsky one for me was The Law of Leaky Abstractions. I read that near the start of my career and it's encouraged me to always be looking for improved understanding of the layers under where I'm working, just in case one of those abstractions leaks. A more recent one, from 2018, is Migrations: the sole scalable fix to tech debt by Will Larson. I absolutely love his idea that migrations (e.g. replacing one service with a new one, or switching database engines, or whatever) are part and parcel of software engineering and are a skill that you should invest in and get good at, not avoid or treat as special one-offs. The Engineer/Manager Pendulum by Charity Majors was hugely influential for me. I was stuck in engineering management and worried that if I switched back to being an "Individual Contributor" (ugh I hate that term) I'd damage my career. Charity gave me permission to make the switch by pointing out that many of the most successful software developers pendulum from one track to the other multiple times over their career, and doing so makes you better at both sides. Tags: joel-spolsky, software-engineering, will-larson, charity-majors ## What a Technical Program Manager actually does DevFeed: [What a Technical Program Manager actually does]() Original publisher: [Read original article]() Author: Dean Hume Published: 2026-09-14T15:54:31Z Content type: opinion Language: en Sources: [Dean Hume]() Topics: [Software Engineering]() Tags: [delivery](), [dependency](), [engineering](), [execution](), [meetings](), [ownership](), [project](), [software-engineering](), [teams](), [technical](), [technical-program-manager]() ### AI overview This article explains the role of a Technical Program Manager, focusing on the gaps between engineering teams, explicit ownership, cross-team dependencies, and proactive identification of release-blocking problems. ### Source excerpt What does a Technical Program Manager actually do? A look at the gaps they fill, the decisions they chase, and the reactive vs proactive split. ## Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman DevFeed: [Podcast: How Will We Train Developers If AI Does the Routine Work: A Conversation with Scott Hanselman]() Original publisher: [Read original article]() Author: Scott Hanselman Published: 2026-09-14T11:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [Artificial Intelligence](), [AI Development](), [Architecture & Design](), [coding]() Tags: [agentic-ai](), [agents](), [ai](), [ai-agents](), [ai-architecture](), [ai-ml-data-engineering](), [architecture](), [architecture-design](), [artificial-intelligence](), [code](), [developers](), [infoq](), [junior-developers](), [ml-data-engineering](), [podcast](), [software-engineering](), [the-infoq-podcast](), [train-developers-ai-routine-work]() ### AI overview The podcast discusses how to train software engineers when AI agents perform much of the routine work traditionally assigned to junior developers. Scott Hanselman advocates a preceptorship model, experienced engineers overseeing AI-generated work, and long-term investment in mentorship and human connection. ### Source excerpt In this podcast, Michael Stiefel spoke to Scott Hanselman about developing new software engineers when artificial intelligence agents are doing most of the work on which junior developers were trained. Hanselman suggests the software industry should adopt a preceptorship model similar to the nursing profession. By Scott Hanselman ## Why Go is an Ideal Language for AI-Assisted Software Engineering DevFeed: [Why Go is an Ideal Language for AI-Assisted Software Engineering]() Original publisher: [Read original article]() Author: Cameron Balahan; Richard Seroter Published: 2026-09-12T11:04:33.891311Z Content type: opinion Language: en Sources: [Google Developers Blog]() Topics: [Go Language](), [Software Engineering](), [AI Development](), [Architecture & Design](), [Tooling]() Tags: [ai](), [ai-coding](), [compatibility](), [developer-tools](), [engineering](), [go](), [software-development](), [software-engineering]() ### AI overview The article argues that AI-assisted software engineering shifts developers' work from writing boilerplate toward reviewing, verifying, maintaining, and architecting systems. It presents Go as well suited to this model because its simplicity, readability, tooling, compatibility guarantees, and team-oriented design provide consistency and guardrails for AI-generated code. ### Source excerpt As AI coding assistants shift the developer's primary role from writing boilerplate to reviewing and maintaining systems, language choice becomes critical for long-term architectural integrity. Go directly addresses this new paradigm by utilizing its strict compiler, integrated toolchain, and uncompromising readability to provide deterministic guardrails that help AI models self-correct and generate highly standardized code. By enforcing ecosystem-wide consistency and strict backward compatibility, the Go platform empowers engineering teams to efficiently verify, optimize, and maintain high-velocity, AI-generated output in production environments. ## What Is an Agent Harness? The Architecture Behind Claude Code, DeepSeek Harness, and Hermes Agent DevFeed: [What Is an Agent Harness? The Architecture Behind Claude Code, DeepSeek Harness, and Hermes Agent]() Original publisher: [Read original article]() Author: Rudrendu Paul Published: 2026-09-11T15:07:18Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [AI Bots](), [AI-assisted coding]() Tags: [agent](), [ai-agents](), [architecture](), [artificial-intelligence](), [claude-code](), [deepseek](), [developer-tools](), [llm](), [machine-learning](), [mcp](), [memory](), [python](), [sandbox](), [software-engineering](), [tools]() ### AI overview An explainer and hands-on guide to agent harnesses: the runtime infrastructure around an LLM that manages model calls, tool execution, memory, and filesystem sandboxing. It compares popular harnesses and introduces a small Python implementation. ### Source excerpt On August 13, 2026, DeepSeek published a GitHub repository called deepseek-harness. Within two days, it had passed 95,386 stars and 8,826 forks (a vanity metric on its own, but a spike this fast signa ## Policy as Code in 2026: OPA, Kyverno, Cedar and What's Next DevFeed: [Policy as Code in 2026: OPA, Kyverno, Cedar and What's Next]() Original publisher: [Read original article]() Author: Abhijit Pujare Eric Minick Published: 2026-09-11T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [policy-as-code](), [Open Policy Agent](), [rego](), [Kyverno](), [Software Engineering](), [Kubernetes](), [JSON](), [YAML](), [AI Agent]() Tags: [ai-agent](), [compliance](), [json](), [kyverno](), [opa](), [open-policy-agent](), [policies](), [policy-as-code](), [rego](), [security](), [software-engineering](), [yaml]() ### AI overview This article surveys the 2026 Policy as Code ecosystem, comparing general-purpose Open Policy Agent and Rego with specialized approaches such as Kyverno, Cedar, and agent-oriented governance. It discusses the shift toward automated, machine-readable governance, the separation of policy from business logic, and the challenges of authoring and maintaining Rego as schemas evolve. ### Source excerpt | Blog ## Big Tech Laid Him Off. He Realized He Didn't Need a Job. -- Asian Dad Energy DevFeed: [Big Tech Laid Him Off. He Realized He Didn't Need a Job. -- Asian Dad Energy]() Original publisher: [Read original article]() Author: Daniel Beach Published: 2026-09-09T14:07:55Z Content type: opinion Language: en Sources: [Data Engineering Central]() Topics: [Job](), [future of software](), [Software Engineering](), [Artificial Intelligence](), [cobol](), [mainframes](), [ibm]() Tags: [ai](), [career](), [cobol](), [future-of-software](), [ibm](), [job](), [layoff](), [mainframes](), [software-engineering](), [tech]() ### AI overview A Data Engineering Central Podcast episode features AsianDadEnergy discussing his career from BASIC, IBM 386 systems, and COBOL mainframes to consulting and a chief architect role in Big Tech, followed by a layoff. The conversation covers financial independence, AI's effects on software engineering, communication skills, identity beyond work, and content creation. ### Source excerpt Data Engineering Central Podcast ## Honoring #IconsOfQuality: Richard Bradshaw DevFeed: [Honoring #IconsOfQuality: Richard Bradshaw]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-09T11:44:44Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Software Engineering](), [Artificial Intelligence](), [Automation]() Tags: [ai](), [ai-agents](), [automation](), [engineering](), [evals](), [icons-of-quality](), [qa](), [quality-engineering](), [software-engineering](), [software-testing](), [telemetry](), [testing]() ### AI overview BrowserStack profiles Richard Bradshaw, a software testing and quality engineering leader, and discusses his views on AI agents, human-centric automation, and evaluating probabilistic AI systems. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## GPT-6 Astra: The next generation in intelligence for work DevFeed: [GPT-6 Astra: The next generation in intelligence for work]() Original publisher: [Read original article]() Published: 2026-09-09T11:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [computer-use](), [coding](), [Software Engineering](), [Latency]() Tags: [api](), [chatgpt](), [codex](), [coding](), [computer-use](), [developer](), [engineering](), [gpt](), [latency](), [model](), [openai](), [product](), [software-engineering](), [work](), [workflows]() ### AI overview GPT-6 Astra is presented as a model for business work, with computer-use capabilities, coding support, and use within existing applications and workflows. ### Source excerpt Meet GPT-6 Astra, OpenAI's most capable model for business, with advanced reasoning, computer use, and stronger writing and design judgment. ## ๐Ÿ”๐Ÿง  How OpenAI Built GPT-Live for Low-Latency Voice AI DevFeed: [๐Ÿ”๐Ÿง  How OpenAI Built GPT-Live for Low-Latency Voice AI]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-09-07T15:31:33Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Conversational AI](), [Low Latency](), [voice ai](), [OpenAI](), [real-time](), [Streaming](), [Remote Procedure Call (RPC)](), [Go]() Tags: [go](), [low-latency](), [openai](), [real-time](), [rpc](), [software-engineering](), [streaming](), [voice-ai]() ### AI overview The article explains how OpenAI built GPT-Live as a low-latency, full-duplex voice system. It describes a fast audio path that listens and speaks simultaneously, asynchronous delegation of deeper reasoning and backend work, stateful handoffs, optimized protocol boundaries, and a Go-based media frontend. ### Source excerpt PLUS: Zero-knowledge proofs โšก, Design.md agent automation ๐Ÿ‘จ๐Ÿ’ป, System Design Docs 101 ๐Ÿ“š ## How Anthropic Uses AI in Software Engineering DevFeed: [How Anthropic Uses AI in Software Engineering]() Original publisher: [Read original article]() Author: Ryan Peterman Published: 2026-09-07T13:17:19Z Content type: opinion Language: en Sources: [The Developing Dev]() Topics: [anthropic](), [Claude Code](), [Claude](), [Software Engineering](), [Artificial Intelligence]() Tags: [ai](), [anthropic](), [claude-code](), [software-engineering](), [talk]() ### AI overview A conversation with Anthropic Claude Code engineer Thariq Shihipar about how the company uses AI models in software engineering, including onboarding, code maintenance, preventing AI-written breakages, and autonomous work. ### Source excerpt When I talk with my friends who work at Anthropic it always surprises me how far ahead they are in adopting AI within their processes and workflows. ## GPT 6 Astra's performance in a software-engineering workflow DevFeed: [GPT 6 Astra's performance in a software-engineering workflow]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-07T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [AI Engineering](), [Software Engineering](), [computer-use](), [ChatGPT](), [Python](), [Concurrency]() Tags: [agent](), [ai](), [ai-engineering](), [chatgpt](), [computer-use](), [python](), [software-engineering](), [thoughts]() ### AI overview The author argues that AI engineering can intensify effort without improving productivity and examines GPT 6 Astra's usefulness for software engineering. A self-managed software factory using Astra produced substantial code and prompts over 35 hours but, according to the author, delivered nothing of value and provided no clear lessons for improving the workflow. ### Source excerpt I'm more and more convinced that all of AI engineering is Neijuan (ๅ†…ๅท, meaning curl inwards). In China it describes a system that demands ever more effort and competition without improving output. The way in which it sometimes shows up in the West is the 996 nonsense. The English term for Neijuan is "Involution" from the book Agricultural Involution. Agricultural involution describes the intensification of farming that raises productivity per square meter while leaving productivity per head unchanged. That's how I feel about AI right now. Which brings me to GPT 6 Astra. Astra is by all accounts an incredibly impressive model. There is really not much I can say against this. It's amazing at computer use, understands images and complex topics, and it's relentless in its pursuit of completion. It is absolutely impressive; these types of models are going to change the world in one form or another. But at least for the moment I don't know how to work with it for actual software engineering. Since that got quite a bit of attention on Twitter, I figured I might summarize my thoughts and just share what kind of code comes out of this thing. My Slop Factory "Armin, you should run a software factory!" I've heard that a few times now, so I figured I might celebrate the release of it by running a little software factory over the weekend. If everybody builds slop 3D games, then I should do something useful with it. My software factory was intentionally set up to let the model decide the how of the workflow entirely. It was free to manage its own context and could maintain its own records in an agent-notes folder. Then it spun off subagents to work on stuff. The goal? What if we had a Python with virtual threads and lexical scoping. And well, I burned a full reset's worth of ChatGPT tokens on this which appears to be around 4 billion tokens. 35 hours later, the factory has delivered absolutely nothing of value and also not taught me anything about how to operate a better one. But ## How Coding Agents Are Shifting the Focus of Software Engineering DevFeed: [How Coding Agents Are Shifting the Focus of Software Engineering]() Original publisher: [Read original article]() Author: Dr Milan Milanoviฤ‡ Published: 2026-09-03T15:00:51Z Content type: opinion Language: en Sources: [Tech World With Milan Newsletter]() Topics: [Software Engineering](), [AI-assisted coding](), [future of software](), [AI Development]() Tags: [agent](), [architecture](), [coding-agents](), [future-of-software](), [guardrails](), [junior-developer](), [software-engineering]() ### AI overview This commentary argues that coding agents are making code generation, testing, refactoring, and bug investigation cheaper and faster. As a result, software engineering increasingly emphasizes judgment, specification, verification, architecture, ownership, team scope, and managing technical debt. ### Source excerpt Since the beginning of writing software, it has been a hard thing to do. ## GPT-6 Astra: A new generation of intelligence DevFeed: [GPT-6 Astra: A new generation of intelligence]() Original publisher: [Read original article]() Published: 2026-09-03T11:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [computer-use](), [coding](), [Software Engineering](), [Cybersecurity](), [Amazon Bedrock](), [hugging face]() Tags: [api](), [aws](), [azure](), [bedrock](), [browser](), [chatgpt](), [coding](), [computer-use](), [cybersecurity](), [evaluation](), [gpt](), [latency](), [model](), [openai](), [reinforcement-learning](), [research](), [safety](), [software-engineering]() ### AI overview OpenAI introduces GPT-6 Astra, a model positioned for computer use, coding, cybersecurity, science, and professional work. The article highlights alignment evaluations, benchmark results, availability through ChatGPT and cloud/API channels, and simulated computer-use performance versus GPT-5.6 Sol. ### Source excerpt Introducing GPT-6 Astra, our most intelligent and aligned model yet, with state-of-the-art capabilities across computer use, coding, cybersecurity, and science. ## Introducing Gemini 3.8 Flash and 3.8 Flash Cyber DevFeed: [Introducing Gemini 3.8 Flash and 3.8 Flash Cyber]() Original publisher: [Read original article]() Author: Tulsee Doshi Published: 2026-09-02T16:18:31Z Content type: release Language: en Sources: [Google DeepMind News]() Topics: [releases](), [AI Chat](), [AI Bots]() Tags: [agentic](), [benchmark](), [coding](), [cybersecurity](), [flash](), [gemini](), [none](), [reasoning](), [release](), [software-engineering](), [vulnerability]() ### AI overview Google introduces Gemini 3.8 Flash and Gemini 3.8 Flash Cyber, highlighting coding, multi-step reasoning, autonomous-agent use, cybersecurity capabilities, pricing, and benchmark claims. ### Source excerpt Gemini 3.8 Flash and 3.8 Flash Cyber deliver next-generation intelligence for agentic workflows and cybersecurity. ## The Pragmatic Engineer: Five years DevFeed: [The Pragmatic Engineer: Five years]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-01T17:14:34Z Content type: opinion Language: en Sources: [The Pragmatic Engineer]() Topics: [Software Engineering](), [Software]() Tags: [engineering](), [events](), [publication](), [software-engineering]() ### AI overview The Pragmatic Engineer reflects on reaching its fifth anniversary, describing the publication's growth from its 2021 launch to more than 1.1 million readers, tens of thousands of paid subscribers, a podcast, and more than 500,000 YouTube subscribers. The article also announces upcoming events and a temporary annual subscription offer. ### Source excerpt As the newsletter reaches its fifth birthday, we reflect on how the publication has changed, and what to expect. Also: the launch of the 'How Software Engineering is Changing' essay contest ## How software engineering is changing: an essay challenge DevFeed: [How software engineering is changing: an essay challenge]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-01T16:06:50Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [future of software](), [AI Development](), [Large Language Model](), [Software Engineering]() Tags: [ai](), [competition](), [essay](), [llms](), [software-engineering](), [tooling]() ### AI overview The Pragmatic Engineer announces an essay competition about how software engineering is changing as companies adopt LLMs, AI tooling, and AI infrastructure. It invites software engineers to submit 3,000-10,000-word essays about changes in engineering practices, culture, and workflows, with selected entries to be published and prizes of up to $10,000. ### Source excerpt The pace of change in software engineering is only accelerating, especially since January of this year. This is all to do with the industry-wide adoption of LLMs, AI tooling, and AI infrastructure. At The Pragmatic Engineer, we always want to capture as much of what's going on ## AI-assisted customization may reshape how software is built DevFeed: [AI-assisted customization may reshape how software is built]() Original publisher: [Read original article]() Author: Mike Yerou Published: 2026-09-01T12:42:38Z Content type: opinion Language: en Sources: [Rock and Null]() Topics: [Software](), [Vibe coding](), [AI-assisted coding](), [Agent Harness](), [Cloudflare]() Tags: [agent-harness](), [ai](), [ai-coding-agents](), [cloudflare](), [software](), [software-engineering](), [thoughts](), [vibe-coding]() ### AI overview The article argues that AI coding agents and vibe coding are lowering the cost of custom software, but suggests the future may involve customizing capable software platforms rather than building and maintaining applications entirely from scratch. ### Source excerpt AI is changing the economics of custom software. The future may not be everyone building from scratch, but rather customizing a strong software base to fit their specific needs. ## QE Platform Strategy: 3 Paths and Where Each Hides Its Cost DevFeed: [QE Platform Strategy: 3 Paths and Where Each Hides Its Cost]() Original publisher: [Read original article]() Author: Rick Crawford Published: 2026-08-31T17:37:05Z Content type: opinion Language: en Sources: [Postman Blog]() Topics: [Testing](), [Software Engineering](), [Development](), [Artificial Intelligence](), [Open Source]() Tags: [ai](), [api-testing](), [blog](), [development](), [general](), [open-source](), [qe-program](), [quality-engineering](), [sdlc](), [software-engineering](), [strategy](), [testing]() ### AI overview This opinion article compares three QE platform strategies: continuing with the current approach, building an internal platform, or integrating specialized tools. It explains the hidden costs of each path, including test maintenance, delayed releases, escaped defects, long-term platform maintenance, dependency updates, security patches, and the need to modernize for AI-assisted testing. ### Source excerpt In Part 1 I named the six structural problems compounding inside QE programs as AI accelerates development. In Part 2 I walked... The post QE Platform Strategy: 3 Paths and Where Each Hides Its Cost appeared first on Postman Blog. ## ๐Ÿ”๐Ÿง  How Cloudflare Freed 100TB RAM With 5 Cache Changes DevFeed: [๐Ÿ”๐Ÿง  How Cloudflare Freed 100TB RAM With 5 Cache Changes]() Original publisher: [Read original article]() Author: Alexandre Zajac Published: 2026-08-31T15:30:54Z Content type: article Language: en Sources: [Hungry Minds]() Topics: [Cloudflare](), [Caching](), [Cache](), [CPU Cache](), [Software Engineering]() Tags: [cache](), [cloudflare](), [cpu-cache](), [software](), [software-engineering]() ### AI overview Cloudflare optimized the storage of DNS cache entries in its Big Pineapple platform through five changes, reducing per-entry memory usage by more than 50% and freeing roughly 100 terabytes across its fleet. The reported changes also increased insert throughput by 43% and reduced lookup latency by 19%. ### Source excerpt PLUS: Anthropic hardware standard ๐Ÿค–, Rust state machines ๐Ÿฆ€, Agent context compression ๐Ÿ’พ [Next page]()