# Software Engineering Software engineering is the systematic application of engineering principles to the design, development, testing, maintenance, and retirement of software systems. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How AI Is Changing Job Applications and Hiring DevFeed: [How AI Is Changing Job Applications and Hiring]() Original publisher: [Read original article]() Author: David Pereira Published: 2026-09-17T12:29:36Z Content type: opinion Language: en Sources: [Untrapping Product Teams]() Topics: [Job](), [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [cabling](), [computer-science](), [hiring](), [java](), [job](), [network](), [software-engineering]() ### AI overview The article argues that AI is changing the job market by helping candidates apply to more positions while helping companies filter applicants. It presents this as an AI-versus-AI dynamic and suggests that candidates need a different strategy to stand out. The author also recounts an indirect path into software engineering. ### Source excerpt I have a confession for you. ## AI Skills with Matt Pocock DevFeed: [AI Skills with Matt Pocock]() Original publisher: [Read original article]() Author: Gergely Orosz Published: 2026-09-17T11:29:00Z Content type: article Language: en Sources: [The Pragmatic Engineer]() Topics: [Artificial Intelligence](), [Software Engineering](), [coding](), [ai-coding](), [Test-driven development](), [Claude Code](), [JavaScript](), [cursor](), [Web app](), [codex](), [Linear](), [Vercel]() Tags: [agents](), [ai](), [ai-coding](), [coding](), [developer](), [engineering](), [software]() ### AI overview Matt Pocock discusses using AI coding skills and agents to plan, delegate, and build software while emphasizing software engineering fundamentals. The conversation covers strategic coding, managing agent context, local versus cloud workflows, TDD, and how engineers learn in an AI-assisted environment. ### Source excerpt Matt Pocock explains how he uses AI coding skills and agents to plan and build software, and why engineering fundamentals matter more than ever. ## It's Okay to Break Things: Reflections on Khan Academy's Healthy Hackathon DevFeed: [It's Okay to Break Things: Reflections on Khan Academy's Healthy Hackathon]() Original publisher: [Read original article]() Author: Khan Academy Published: 2017-03-06T23:00:00Z Content type: article Language: en Sources: [Khan Academy]() Topics: [Hackathon](), [Software Engineering](), [API](), [Web]() Tags: [api](), [collaboration](), [engineering](), [equity](), [hackathon](), [mozilla](), [news](), [reflections](), [software-engineer](), [web-frontend]() ### AI overview Kimerie Green reflects on participating in Khan Academy's internal Healthy Hackathon while working as a Software Engineering Fellow. She describes building Read-to-Me, an experimental project using Mozilla's Web Speech API to read Perseus widget content aloud for early learners. The project was not shipped because it needed further code review and design work, but it provided lessons about curiosity, collaboration, and software engineering. ### Source excerpt By Kimerie Green For the past few months, I have been working as a Software Engineering Fellow at ... Read more ## Honoring #IconsOfQuality: Huib Schoots DevFeed: [Honoring #IconsOfQuality: Huib Schoots]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-16T08:21:48Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Testing](), [Software Testing](), [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [icons-of-quality](), [quality](), [software-testing](), [thought-leadership]() ### AI overview An interview with Huib Schoots explores software testing as a thinking activity centered on questioning assumptions, exploring risks, and helping teams make better decisions. Schoots also discusses using AI to support testing while preserving human judgment. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## Building the new GitHub Copilot Inline Suggestions Model: Part One DevFeed: [Building the new GitHub Copilot Inline Suggestions Model: Part One]() Original publisher: [Read original article]() Author: Julia Gong, Ben Liggett, Ulugbek Abdullaev Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Visual Studio Code - Code Editing. Redefined.]() Topics: [GitHub Copilot](), [AI-assisted coding](), [coding](), [Development](), [GitHub](), [Software Engineering]() Tags: [agent](), [ai-assisted-coding](), [blog](), [coding](), [github](), [github-copilot](), [quality](), [vs-code](), [workflows]() ### AI overview GitHub Copilot unified completion-style ghost text, nearby next edit suggestions, and long-distance edits into a single "3-in-1" model. The article explains that training, evaluation, and editor design evolved together, and that the unified model can improve suggestion selection and cache additional edits for faster subsequent interactions. ### Source excerpt Explore how GitHub Copilot unified completion, next edit, and long-distance suggestions into one model for a faster, more cohesive coding experience. Read the full article ## 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 ## The Dodo Digest: The Report Is the Product DevFeed: [The Dodo Digest: The Report Is the Product]() Original publisher: [Read original article]() Author: Rishabh Goel Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [anthropic](), [Threat Research](), [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [anthropic](), [newsletter](), [product](), [report](), [threat-intelligence](), [v1]() ### AI overview This newsletter discusses Anthropic's report on eight months of AI misuse, including a campaign in which one person built an AI-assisted platform for mass attacks. It argues that startups can use reports to document learning, demonstrate expertise, and build trust, and mentions Dodo Payments v1.113.28 product improvements. ### Source excerpt Anthropic turned eight months of threat intelligence into a public report. Why every startup should document what it learns, plus v1.113.28: Reports, grace periods, and blocklists. ## AI Security Governance: A Practical Framework for 2026 DevFeed: [AI Security Governance: A Practical Framework for 2026]() Original publisher: [Read original article]() Author: Christine Ferrusi Ross Published: 2026-09-15T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [Security](), [Securing AI](), [ai-governance](), [Software Engineering]() Tags: [2026](), [ai](), [ai-security](), [governance](), [practical](), [security]() ### AI overview This article presents AI security governance as a structured program built on four pillars: policy, risk assessment, monitoring and audit, and accountability. It explains how these controls address untracked AI systems, unclear ownership, and missing records of automated actions, and discusses regulatory and organizational pressures driving adoption. ### Source excerpt Build an AI security governance framework covering policy, risk assessment, monitoring, and accountability, plus a risk register template to copy today. | Blog ## 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 ## Learning at Scale: FreeBSD Foundation Intern Nimish Jain on Exploring the FreeBSD Codebase DevFeed: [Learning at Scale: FreeBSD Foundation Intern Nimish Jain on Exploring the FreeBSD Codebase]() Original publisher: [Read original article]() Author: Florine Kamdem Published: 2026-09-14T18:42:26Z Content type: article Language: en Sources: [FreeBSD Foundation]() Topics: [freebsd](), [Operating system](), [Software Engineering](), [Code review]() Tags: [blog](), [code-review](), [engineering](), [freebsd](), [intern]() ### AI overview A profile of FreeBSD Foundation intern Nimish Jain's summer work on the FreeBSD operating system. His projects included virtualization-driver upgrades, network filesystem connectivity work, and porting a Raspberry Pi installer; the article also describes code review and navigating a large codebase as learning experiences. ### Source excerpt For Nimish Jain, working on FreeBSD this summer meant getting an up-close look at something he had long been curious about: how large, complex pieces of software work together. A Software Engineering student at the University of Waterloo, Nimish is preparing to enter his second year. He was already a longtime FreeBSD user when he [...] The post Learning at Scale: FreeBSD Foundation Intern Nimish Jain on Exploring the FreeBSD Codebase first appeared on FreeBSD Foundation. ## From AI demos to real work: How Engineering and Operations learn side by side DevFeed: [From AI demos to real work: How Engineering and Operations learn side by side]() Original publisher: [Read original article]() Author: Nubank Editorial Published: 2026-09-14T16:28:23Z Content type: article Language: en Sources: [Nubank]() Topics: [Artificial Intelligence](), [Generative AI](), [AI Development](), [Software Engineering](), [Development]() Tags: [agents](), [ai](), [ai-research](), [applied-ai](), [data-science-machine-learning](), [development](), [engineering](), [mcps]() ### AI overview This article describes Nubank's Ops AI Acceleration Program and an applied AI workshop with PJ Operations. The workshop combined foundational technical concepts about generative AI, agents, models, tools, context, skills, and MCPs with a real operational challenge to connect engineering knowledge with operational expertise. ### Source excerpt Building shared technical understanding so operational expertise can turn AI into practical improvements The post From AI demos to real work: How Engineering and Operations learn side by side appeared first on Building Nubank. ## 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. ## Honoring #IconsOfQuality: Jesper Ottosen DevFeed: [Honoring #IconsOfQuality: Jesper Ottosen]() Original publisher: [Read original article]() Author: Rajrupa Roychowdhury Published: 2026-09-14T12:02:58Z Content type: article Language: en Sources: [BrowserStack Blog]() Topics: [Software Testing](), [Software Engineering](), [developer tooling]() Tags: [blog](), [engineering](), [eu](), [icons-of-quality](), [software-testing]() ### AI overview BrowserStack profiles Jesper Ottosen as an Icon of Quality and discusses his work in test strategy, software quality, GRC engineering, regulatory compliance, and security. The article highlights his views on beginning testing strategies with clear purpose, aligning testing with delivery and stakeholder concerns, and developing future quality professionals. ### Source excerpt To celebrate the relentless passion and invaluable contributions of leaders in software quality, BrowserStack is proud to honour Icons of Quality. ## 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. ## Software Engineers Adapting to AI Coding Agents DevFeed: [Software Engineers Adapting to AI Coding Agents]() Original publisher: [Read original article]() Author: Simon Willison Published: 2026-09-11T17:28:37Z Content type: opinion Language: en Sources: [Simon Willison's Weblog]() Topics: [Artificial Intelligence](), [Software Engineering]() Tags: [ai](), [ai-2-236](), [deep-blue](), [deep-blue-12](), [generative-ai](), [generative-ai-1-982](), [llms](), [llms-1-948]() ### AI overview An opinion piece arguing that software engineers can adapt to AI coding agents by applying their experience to broader problems beyond translating a specification into code. ### Source excerpt My comment on Feeling sad about AI -- Hacker News. I'm not sure how useful it is to say this, but I think a lot of people (myself included, a few years ago now) have been through this moment of existential crisis and come out the other side. The initial reaction, when some coding agent does a piece of work that would have taken you a week in an hour and does it well, is to be very disheartened by it. Once you come to terms with the idea that translating an exact specification into decent code isn't a unique skill any more, you can start looking at the larger set of problems that you face as a software engineer and realize that there is so much left, and your existing skill and experience mean you can master these new tools, provide value, and execute at a level far greater than anyone who is just getting started building software using agents without any of your depth. If you don't want your profession to change at all then you're going to have a tough time with this - but that's surely been true for the history of software engineering? Has there ever been any stability to the tools and language we use beyond about a five year time horizon? These changes are happening a bit faster, but if you chose software development as a passion you've opted into pretty frequent radical change from the start. Tags: ai, generative-ai, llms, deep-blue ## How to Build a Self-Evaluating AI System: Automated Testing and Evaluation Pipelines for LLM Applications DevFeed: [How to Build a Self-Evaluating AI System: Automated Testing and Evaluation Pipelines for LLM Applications]() Original publisher: [Read original article]() Author: Jude Otine Published: 2026-09-11T15:24:04Z Content type: tutorial Language: en Sources: [freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More]() Topics: [LLM evaluation / benchmarking](), [Large Language Model](), [Software Engineering]() Tags: [ai](), [ai-agents](), [artificial-intelligence](), [evaluation](), [how-to](), [llm](), [machine-learning](), [python](), [software-testing](), [testing]() ### AI overview A tutorial on building automated evaluation pipelines for LLM applications, focusing on why conventional deterministic tests are inadequate for variable model outputs. ### Source excerpt So you shipped your AI feature and it works in demos. Your team is impressed. Then a user asks a question slightly outside your test cases and the model confidently returns something completely wrong. ## 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 ## Beyond Autonomous Teams in Software Product Development DevFeed: [Beyond Autonomous Teams in Software Product Development]() Original publisher: [Read original article]() Author: Ben Linders Published: 2026-09-10T10:29:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [Self-organizing Team](), [Software Engineering]() Tags: [agile-conferences](), [autonomous](), [autonomous-software-teams](), [business-value](), [complex-systems](), [complexity](), [conference](), [culture-methods](), [delivering-value](), [management](), [networking](), [news](), [product-development](), [scrum](), [self-organizing-team](), [teamwork]() ### AI overview A conference talk argues for moving from product-focused autonomous teams to value centers, with organizational design shaped by value and its dependencies. It frames agency and coherence as a trade-off and uses the Viable Systems Model to suggest five questions for teams. ### Source excerpt Autonomous teams are an article of faith in modern software development. The shape of our value determines what we can do; it determines our trade-offs between agency and coherence. We all have a purpose, and we all have agency to do something, hence the suggestion is moving from product focus to value center thinking. By Ben Linders ## Article: When Spec-Driven Development Pays Off DevFeed: [Article: When Spec-Driven Development Pays Off]() Original publisher: [Read original article]() Author: Nitin Garg Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [InfoQ]() Topics: [AI-assisted coding](), [AI Development](), [Software Engineering](), [code productivity]() Tags: [ai](), [ai-assisted-coding](), [ai-coding](), [ai-development](), [ai-ml-data-engineering](), [architecture](), [article](), [artificial-intelligence](), [code-generation](), [generative-ai](), [ml-data-engineering](), [productivity](), [reasoning](), [review](), [security](), [software-development](), [spec-driven-development](), [when-spec-driven-development-pays-off]() ### AI overview The article argues that AI-assisted coding shifts the main constraint from writing code to verifying it. It presents specification-first development as a governance approach for hard, multi-constraint work, while noting its time and cost and warning that apparent gains may instead come from reasoning. ### Source excerpt AI coding assistants have become a core part of software development. AI-generated code has shown productivity gains, but it's also contributing to security weaknesses and familiar bug patterns. In this article, author Nitin Garg highlights the bottleneck has moved from code generation to code verification, and how to detect & mitigate it when the AI-generated behavior diverges from the intent. By Nitin Garg ## 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 ## Introducing Consort: Test-driven development on a branching database DevFeed: [Introducing Consort: Test-driven development on a branching database]() Original publisher: [Read original article]() Author: Kevin Hartman Published: 2026-09-09T13:41:25Z Content type: article Language: en Sources: [Databricks]() Topics: [Test-driven development](), [Database](), [Integration testing](), [Software Engineering](), [Refactoring](), [Continuous Delivery (CD)](), [Pull Request](), [Reverse Engineering]() Tags: [code](), [continuous-delivery](), [database](), [development](), [integration](), [partners](), [platform](), [postgres](), [pull-request](), [refactoring](), [reverse-engineering](), [software](), [software-development](), [testing]() ### AI overview The article introduces Consort, a workflow for applying test-driven development to a branching database. It argues that copy-on-write branches of a real database can replace mocks, bring integration testing into the inner development loop, and let engineers run destructive tests in isolation. Versioned schema migrations can move with the dependent code, enabling what the article calls Data CD and helping catch production database problems during pull requests and merges. ### Source excerpt For 25 years I built software on the practices I grew up with: Kent Beck's TDD, Martin... ## 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. 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