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Learn how to run parallel agents in the GitHub Copilot app, and experience the moment it stops feeling scary and starts feeling powerful. The post GitHub Copilot app for Beginners: Run several agents at once appeared first on The GitHub Blog .
Why shorter outputs can cost more, and how GitHub Copilot reduces wasted work across the complete coding task. The post How we make AI coding more cost efficient without sacrificing task quality appeared first on The GitHub Blog .
Some fortnights the complaints about AI come from people who barely use it. This one they came from the people who use it most. Scroll Hacker News over the past week — the forum where developers argue about their tools in unusual detail — and the grievances about AI coding assistants weren’t existential. Nobody was worried about the robots waking up. They were worried about their bill, their UI, and the effort of reading what the model just wrote. Quotes sourced from: Hacker News. Every quote below was located at its comment permalink and reproduced verbatim; each is listed with its username, the platform, and the date in the Sources section. As always, we quote experiences, not verdicts — a forum comment is one practitioner’s account, often mid-argument, and we’ve framed them as exactly that. What makes this batch worth reading isn’t volume; it’s specificity. These are checkable complaints. “Enshittified at a surprising clip”: the dark-pattern gripe The sharpest thread of the fortnight was about Cursor, the AI code editor, and it wasn’t about the quality of its completions. It was about the way the product behaves around you. A user posting as jmuguy , on 20 August, laid out a bill of particulars that will sound familiar to anyone who’s watched a beloved tool curdle: “Cursor isn’t covering itself in glory regardless. The flagship app is getting enshittified at a surprising clip. It constantly pops up and interrupts your work pushing new features, changes your model to whatever the latest Grok is without prompting, has this mystery meat UI that is constantly changing, pushes cloud agents in ways that are definitely designed to trick you. We’re actively looking at alternatives, I wouldn’t touch anything this company produces from here on out.” Set aside the verdict at the end — that’s one person’s conclusion, not ours — and look at the specifics, because they’re the kind you can check: interruptions pushing new features, a model silently swapped to Grok, a UI that ke
Managing library updates can be tedious at times. Learn how the GitHub Copilot app can handle this type of repetitive task. The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog .
If you’re juggling multiple Copilot sessions, use the My work pane to track what's in flight, what's done, and what's next. The post GitHub Copilot app for Beginners: Managing your work appeared first on The GitHub Blog .
Secret parameter allowed hackers to steal passwords when a target clicked on a link.
Chat is great for intent, but agent work gets lost in the scroll. Here is how I use canvases with my agentic workflows—and why your workflow also deserves a canvas. The post How canvases make agentic workflows visible, steerable, and cost-efficient appeared first on The GitHub Blog .
See how four GitHub Agent Apps can help you scope, secure, roll out, and ship a feature across the SDLC–all without leaving GitHub. The post How to bring your software delivery workflow into GitHub with agent apps appeared first on The GitHub Blog .
Microsoft is simplifying Copilot by combining its consumer and business apps, and dropping AI-generated podcasts, Group Chats, Deep Research, and its Mico character.
Learn how to write your first prompt in the GitHub Copilot app, choose the right context and model, and start your first task with confidence. The post Write your first prompt with the GitHub Copilot app appeared first on The GitHub Blog .
Enterprise Java developers have a new superpower—drive GitHub Copilot from idiomatic Java code with annotations, virtual threads, and more. The post Using the GitHub Copilot SDK for Java appeared first on The GitHub Blog .
A GitHub product launch is being held back by the CI/CD platform underneath it. On August 6 GitHub filed a Changelog entry announcing that Kimi K3, an open-weight model, is now generally available in GitHub Copilot, then added an editor's note the same day: the rollout is temporarily paused while GitHub mitigates an incident with GitHub Actions. What the entry says, and what it does not Per the note, GitHub will resume the rollout as soon as possible and update the docs with Kimi K3 pricing: $3 per 1M input tokens, $15 per 1M output tokens, and $0.30 per 1M cached input tokens. That is the extent of the disclosure. The Changelog does not describe the Actions incident, does not put a scale on its blast radius, and does not commit to a resume time. It also does not explain how a Copilot model rollout ends up gated on Actions in the first place; a reader can infer that some provisioning or feature-flag step rides the same platform, but the entry does not say so. Availability is qualified in a way worth flagging. Kimi K3 is GA on paper, but the switch that actually turns it on for end users is paused. The operational read There is a coupling here worth naming plainly. GitHub sells Actions as CI/CD for everyone else, and it also uses Actions to ship its own products. When Actions has a bad day, GitHub's launch calendar has a bad day too, in public. That is not a scandal; it is what dogfooding looks like when the changelog is a live document. It is also a data point for any team running a rollout on top of a hosted CI platform: your feature-flag flip is downstream of somebody else's incident queue, and you inherit that queue's MTTR whether or not it is on your status page. Two follow-ups are worth watching. First, whether the resumed rollout entry names the incident and its cause, or whether it stays silent. Second, whether Kimi K3's published pricing survives the pause unchanged. Until then, the GA label is doing work the runtime cannot back up.
Go beyond chat in the GitHub Copilot app with these slash commands. They'll help you plan, collaborate, automate, and customize your dev workflow. The post A guide to slash commands in the GitHub Copilot app appeared first on The GitHub Blog .
Learn how to build tools to simplify how you work—without writing a single line of code. The post How the GitHub legal team used Copilot CLI to streamline their workflows appeared first on The GitHub Blog .
One thing Microsoft is not good at is naming things, and sadly it's happened again. But let's go back to the beginning: what are Skills? Skills are targeted prompts/context that are modular, so they are not always included in the LLM session. They are Markdown files with selected metadata in YAML, all in a file normally named skill.md (the parent folder and YAML metadata identify it). They were created by Anthropic (Claude) and were designed for both the user to add in a prompt ( /Skill ), or for the LLM to decide. Similar to Skills are Plug-ins. These can (and often do) include skill.md files, but can also have scripts, MCP servers, and other tools. So back to Microsoft naming things badly. Copilot Studio (Azure Bot Framework version) had skills, but they were not skills. The new Copilot Studio has Skills, but they are not Skills, they are actually Plug-ins. Plug-ins include Skills, so why does it matter? Well, it doesn't really, but I like to moan, and it means sometimes cool functionality can be left on the table because we presume Microsoft names things accurately. Anyway I digress (I like to do that), now we understand what Skills/Plug-ins are I wanted to dive into them within Copilot Studio and cover: Why Are They Cool Building Powerful Skills Adding Scripts/Templates Using Skills 1. Why Are They Cool I often go on about skills being cool, but why? There are a few reasons. Context Management Before skills, the standard approach was to give the LLM everything and let it figure out what it needed. The problem with this is twofold. First, more context equals more tokens, which equals more cost. Second—and more importantly—too much unrelated context can have a detrimental impact on the LLM response. LLMs work by using input tokens to predict the next token, so polluted input tokens can make the LLM predict the wrong next token (this is a huge simplification, but you get what I mean). Transferable As skills are simple Markdown files, they can easily be transferred
Learn how I modernized an old codebase of mine using stacked sessions and pull requests in the GitHub Copilot app. The post Stacked sessions and pull requests in the GitHub Copilot app appeared first on The GitHub Blog .
A practical GitHub Copilot workflow for prototyping, planning, implementing, and reviewing software without chasing every new AI tool. The post The harness is all you need (mostly) appeared first on The GitHub Blog .
New to the GitHub Copilot app? Learn how to start projects, work with AI agents, explore canvases, and streamline your development workflow. The post GitHub Copilot app for Beginners: Getting started appeared first on The GitHub Blog .
Copilot now bills usage at listed API rates. Compare direct model access with the coding workflow, policy, and harness work around it. The post Copilot vs. raw API access: What are you actually paying for? appeared first on The GitHub Blog .
Canvases turn AI into interactive workspaces where you can visualize information, explore workflows, and take action across complex tasks. The post How to build interactive experiences with canvases appeared first on The GitHub Blog .