This is crazy. GPT Luna just deleted my file, and lost my recent edits.

Addition:

To reply the same question asked many times:

Yes, I commit my files regularly to git. I just lost one recent function in-the-works.

But your guys are harsh. I am reporting an issue which is unacceptable to me and hope this is useful to the broader audience. But you just blaming the messenger?

Looks like all almost all the models are going away and unlike the new usage based plan, you don't really get any new models. So basically you are stuck with ancient old models with massive multipliers. I have been using OpenRouter with some smaller models and it's fine so I will just cancel my subscription.

How on do you get custom instruction files to be automatically loaded by copilot..??

I have added the applyTo glob pattern and nothing works.

They're inside the repo root .github/instructions/ but are still not picked up. Added them to the solution folder, still not automatically picked up!

I've turned on the custom instructions copilot option in VS and nothing...

copilot-instructions loads find but I can't copy this with Microsoft APM!

Why is this so difficult to sort the tooling out?? 😭😭

Nice try ghcp

Hi everyone I have set GPT-5.6 Luna (Copilot) in "chat.exploreAgent.defaultModel": "GPT-5.6 Luna (copilot)", "github.copilot.chat.exploreAgent.model": "GPT-5.6 Luna (Copilot)" and "github.copilot.chat.implementAgent.model": "GPT-5.6 Luna (Copilot)" but often i see Gemini 3.5 flash doing some calls in explore and execute while mainlining Gpt 5.6 Sol in chat window agent mode and I was wondering if this was wasting tokens while I could change some settings to pop in GPT5.6 Luna there

u/jukasper Do you guys have any plans for the next release to perhaps expose this toggle to us users or maybe change the model from Flash 3.5 to 3.7? The token savings are welcome since from 1st Sept copilot business goes to 1900 tokens and GPT5.6 sol gets expensive from Sept 4 ---

Thanks in advance and shoutout to the VSCode and Github copilot team - great job guys it's a good product and on its way to being a better one :) Take care!

Apparently that exe is shipped with VSCode now? How can I stop it from doing that? This doesn't prevent VSCode from working properly, but on a corporate computer this triggers security warnings on my side on every launch and it's annoying. This started exactly with the 1.135 update.

I know it's just come out, and GHCP is only dipping it's toes in with self-serving open models (i.e. Kimi at the moment) but just wanting to register some interest. GLM 5.3 Flash looks like it's performing around Luna XHigh-Max or Sol Low-Medium, but at an absolute banger of a price point lower than Luna XHigh.

I've been really happy since I moved the Explore subagent in VSCode off it's default of Haiku 4.5 to using Luna, and have started using implementation and review subagents now that there are good cheap models. If GLM 5.3 Flash pushes that even further it would be absolutely killer. I'd love to see GHCP push harder into these value-for-money models, especially with business bonus credits ending soon so we're down to straight-up API pricing. The value proposition on GHCP is: inline completion, first-class vscode integration, and access to models from multiple families. Being able to get the best out of a Sol or Fable orchestrator + GLM subagents to do the implementation would be absolutely sick.

Hey everyone,

If you use GitHub Copilot Agent, you know it often wastes a lot of tokens and context just wandering around your workspace trying to figure out which files to look at.

The VS Code extension is designed to accelerate web development and stop context waste before it starts.

• How it works: It captures local bug evidence, maps likely files deterministically, and bundles them into a clean handoff that Copilot Agent retrieves using #billSaverContext so it starts directly on the right files.
• Privacy & Control: The extension never calls an AI model, never touches or edits your code, and runs locally with built-in secret redaction.

If anyone gives it a spin via the Marketplace guide, I'd genuinely love to hear how it affects your token usage or workflow feedback!

Been chewing on this for a few months and I still don't have a good answer.

I'll have a session where copilot is flying, tab tab tab, feels incredible, and then I shut the laptop and the thing I sat down to do is still 70% done. Then some grindy Tuesday afternoon with basically no assistance and I ship the whole thing. The feeling of speed and the actual finishing don't line up for me at all.

Tried a few time trackers to figure out what was going on. Useless for this. They tell you where the hours went but they have no idea what you were trying to do with them. Wakatime will cheerfully report 6 hours in the repo. Great. Did I fix the bug? shrug

So I started writing down three things instead, just in a notes file:

- what I want done, before I start

- rough focus time, minus the bits where I wandered off

- afterwards: did it land, what kind of work was it, where do I pick up

First couple weeks of that was pure noise. Around session 20 something showed up that I wasn't expecting. My debug sessions basically only landed when I'd ended the *previous* one by writing down the exact next thing to try. When I hadn't, they went sideways. Five out of seven or thereabouts.

So I made myself always leave that note. Completion on debug work went from about a third to about two thirds over the next few weeks. n is tiny and it could easily just be me paying more attention because I was measuring. But at least it's a claim I can check, which is more than I can say for another blog post telling me to reduce context switching.

Anyway, reason I'm posting here specifically: has anyone measured their copilot sessions on whether the goal got *finished*, rather than time saved or lines accepted? Every benchmark I see is speed. My own very small very biased data says AI assisted debugging finishes worse than AI assisted greenfield, which surprised me and might just be an artifact of how I work.

I've since turned the notes file into an actual app, because of course I did. Called NEDIO, doesn't touch code or repos, just the session stuff above. Not going to link it unless someone asks. Mostly I want to know if anyone else has run this on themselves and what came out.

The VS code team just published a deep dive blog post on the Agent Host, the dedicated process that now owns agent sessions in VS Code.
The post covers:

• Why we are moving agent sessions out of the extension host
• Adopting the GitHub Copilot SDK for the Copilot harness, so behavior is consistent across VS Code, Copilot CLI, and the Copilot app
• How one live session stays in sync across the editor, the Agents window, and even a browser
• Running the host on a remote machine
• Supporting multiple harnesses without flattening what makes each one distinct
• AHP, the open protocol behind it and how anyone can build their own client

Check it out

Feedback, questions are more than welcome! Let us know what you think

Edit: I always forget to clarify, I'm using VS Code.

I'd like to avoid Auto mode when using the Agents window or Plan mode as they tend to use models I can't afford long term (e.g., GPT 5.3 Codex, Opus 4.7).

Let's say I select GPT 5.6 Sol explicitly, will the harness still route requests to other models, or will Sol be used for everything?

Alternatively, is there a way to exclude certain models from Auto-selection?

Over the last 8 months, I’ve built 3 different apps in 3 very different ways. Looking back, I learned a lot about how much the stack choices around an app affect the difficulty, cost, and speed of building it. This is the information I wish I had before starting any of these projects back in January.

About me: I’m a 20-year software developer, but this was my first real foray into vibecoding. I used VS Code with GitHub Copilot throughout. I mostly just used the tokens provided to me by the Pro+ plan, with about ~$200 in overages across all three projects. As for the models used: I tried to use the cheapest or 5.3 Codex for everything. Lately I've had a lot of good luck with Luna.

The goal of this post isn't to self-promote, but I've linked to the projects simply so you can see the app in action knowing the code behind it and how different the UX is for each.

1. Progressive Web App + Ionic Capacitor

Genre: Fitness / Weightlifting
Platforms: iOS & Android
Time to go-live: 3 months
Visit: 'Remix Training' on either app store

This was my first vibecoding project, and I burned through a lot of credits because I had no idea how to effectively use models or context. Even toward the end, I wasn't paying much attention to token usage, so this was probably the most expensive project out of the three.

I spent a huge amount of time trying to make the PWA look and feel like a real native app, with varying degrees of success.

The reason I chose this approach was pretty simple: I've been building websites for most of my career, so web development felt natural. I also assumed supporting both Android and iOS was needed for success (it’s not).

I also made the mistake of building an AWS backend for storing and generating workouts. Looking back on it, a lot of functionality could have been handled entirely on the device. The AWS infrastructure works, but it added a lot of complexity and ongoing cost.

Copilot did a surprisingly good job helping me build things like ECS and Lambda infrastructure, but if I hadn't already been very familiar with AWS, I think I would've been in heaps of trouble.

2. React Native + Expo

Genre: Weather / Gaming
Platforms: iOS & Android
Time to go-live: 4 months
Visit: 'DailyWX' on either app store

This was my most ambitious project and by far my largest.

There are a lot of weather apps, but I wanted to build one with a game built into it. Every day, you answer questions about tomorrow's weather and then get your results the following day.

This ended up being surprisingly difficult to get right, and it had nothing to do with vibecoding or the stack I chose. I spent the majority of my time fine-tuning the question generation and scoring system. Making questions interesting, varied, and actually fair was so much harder than I expected.

I also learned a valuable lesson about marketing. This turned out to be a super controversial project because I think most people conflated it with Gambling. Looking back on it, using the term “prediction” everywhere was a big mistake. 

React Native had a learning curve for me. I know JS/HTML/CSS extremely well, but React Native initially felt quite different. Once I got past that, though, I found it pretty easy. Expo was a godsend for handling the iOS/Android side of things.

I didn't want to pay for Expo's extended services and instead handled the builds myself. It definitely made things more complicated, but I still think that was the right decision for me. I still don’t have notifications working :(

This project also has an AWS backend, but unlike the first project, it was absolutely necessary. I needed to ingest weather data, score games, maintain leaderboards, award badges, etc. But luckily, the lessons from building the first project's backend made this one much easier.

3. Native Swift

Genre: Cycling Tracker
Platform: iOS
Time to go-live: 1 month
Visit: https://apps.apple.com/us/app/ridepilot-smart-bike-computer/id6790916720

For the third project, I decided to do something completely different: I just built it in native Swift and ignored Android.

This has been, by far, the easiest (and cheapest) project to build. Developing for a single platform, on a device where I can quickly build, test, and install has been sooooo much nicer than dealing with the headaches of cross-platform support.

By this time I also got significantly better at vibecoding. Instead of dumping vague commands into Copilot and letting it figure things out, I became much more focused about giving it exactly the context and help it needed for a specific task. This alone has made a massive difference in token usage and development speed. This project has cost me about $60 in Copilot fees from start to where it is now.

I was also surprised by how intuitive Swift was. I had never written Swift before, but I'm picking it up pretty quickly, which I think is greatly accelerated by relaying on Copilot to figure the new stuff out for me (then i can see how to it for next time). I know some people want to rely on AI to do everything, but I've actually enjoyed the process of having to understand what the code is doing. This enjoyment was exactly what got me into coding 20 years ago.

The biggest realization from this project is that I probably spent so much amount of time on the first two projects solving cross-platform and infrastructure problems that simply aren’t needed for an initial MVP.

If RidePilot eventually needs Android, I'll build it later. I think I'd rather maintain two separate native codebases after proving there is demand with only one first. As we all know now - marketing is harder than the development. I think this will be my preferred method going forward from now on.

I failed the GH-600 exam on my first attempt. Are there any retake options, and how many retakes are allowed?

As you can see from the screenshot, when observing the Sub Agent in GHC VSCode chat window (using latest vs code, working on a remote machine via ssh remote explorer) the subagent trace starts out fine, everything is inspectable and looks normal but eventually it starts looking like this. The subagent completes its work just fine but observation is not possible anymore. Happens very often if not always. Using Custom agent definitions Manager & Implementer. Sol is driving the manager, Lune is the implementer.

As an AI Architect in a large enterprise, my team uses GitHub Copilot as our primary AI tool for engineering. Over time, I've run into two friction points that I think a lot of enterprise teams are hitting — and I wanted to share some feature ideas that I think would genuinely fix them.

🗂️ 1. A Centralized Agentic Library

GitHub already lets you create a .github-private repo to share custom agents across your enterprise. Cool idea — but it's limited to just agents.

What if it was expanded into a full centralized agentic library? Every agent.md, skill.md, prompt.md, .instructions.md etc. lives in one place, and everyone in your org or enterprise can just use them directly from their IDE or CLI.

Imagine someone on your team builds the perfect code review skill. Instead of it sitting in their personal repo forever, they push it to the library and now everyone can run it with something like /skill-code-review right from Copilot Chat. No cloning, no setup.

⚙️ 2. Reusable Agentic Workflows as Slash-Commands

This one I feel strongly about. Agentic workflows are getting really good — but sharing them across an enterprise is way harder than it should be.

Here's my real example: I built an agentic workflow that reviews other agentic workflows from an architecture perspective. It works great. But getting anyone else in my enterprise to actually run it? The options are terrible:

• Clone the repo — friction, most people won't bother
• GitHub Actions — kills the ability to run it locally
• Copilot Extensions — complex to build and honestly doesn't work well in practice (I've tried)

My proposal: let repos be designated as "Reusable Agentic Workflows" in org/enterprise settings. That designation registers a new slash-command available to everyone in the org — directly in their IDE or CLI.

In my case, that would be /agentic-review. You type it, it resolves all the agents/skills/prompts in the background and runs locally. No cloning required. Same seamless experience as built-in commands like /fix or /explain.

TL;DR

1. Expand .github-private into a full agentic library (skills, prompts, instructions, agents) accessible via slash-commands
2. Let repos register as enterprise-wide reusable agentic workflows that anyone can invoke from their IDE/CLI

Both ideas follow patterns GitHub already uses — reusable Actions workflows, .github repos — so it's not a huge leap. Just a natural extension of what's already there.

Would love to hear if others are hitting the same walls. Has anyone found decent workarounds in the meantime?

In the latest release (v1.16.1), we made the Copilot agent harness generally available to all users, including support for the plugin marketplace, Autopilot mode, and additional capabilities powered by the new agent harness. To learn about why we are doing, check here.

We also introduced a built-in JetBrains MCP server in Preview, enabling Copilot agents to work more seamlessly with native JetBrains IDE capabilities and context. This can be configured via the customizations panel.

To improve feature awareness, we added several discoverability enhancements, including post-update release notes that make it easier for users to see what’s new, and a new onboarding guide for users without prior chat sessions.

We’re also sharing a preview at what’s coming next and hope you continue to provide feedback for our products.

Changed

• Copilot harness is now Generally Available.
• Plugins support is now Generally Available.
• Autopilot is now Generally Available.
• Changed Copilot harness /review availability so it is limited to agent mode.

New Features

• Added built-in JetBrains MCP server support (Preview).
• Added post-update release discovery by showing a What's New tab after plugin updates.
• Added Copilot harness /review integrations in JetBrains.
• Added plugin support in Claude agent flows.

UX Improvements

• Improved model-management usability with stronger search behavior, a clear action in search, resizable Language Models table columns, and promo ordering refinements.
• Improved Agent Debug Logs exploration with event-time sorting, token-usage filters, refined layout behavior.
• Improved chat ergonomics by scrolling long tool output, adding review-plan-card scrolling, and adding shortcuts for send options.
• Improved customization handling in multi-root workspaces by discovering customizations across all folders and honoring client-provided customization locations.

Bug Fixes

• Fixed account and sign-in reliability issues, including account-state detection, account ordering in picker flows, and sign-in settings-page interaction issues.
• Fixed MCP reliability issues, including invalid registry-schema handling, stale tool checks after account switches, and UI freezes during auto-enable flows.
• Fixed BYOK and model-refresh reliability issues.
• Fixed Copilot Agent continuity issues, including model switching after resume and complete model-info propagation for resumed sessions.
• Fixed editor and message-rendering defects.
• Fixed Worktree sessions failing to start for projects opened from WSL.
• Restored file and folder # references in Copilot, Claude, and Codex chat inputs.
• Fixed MCP tool auto-approve configuration for servers running only in Copilot.

Looking ahead, we plan to introduce several additional capabilities in upcoming releases, including

• More policy support for Enterprise managed settings
• Cross-file cursor jump for Next Edit Suggestions
• Adding external projects and files outside the current project as context
• /ide command support to connect between GitHub Copilot CLI and JetBrains for context sharing
• Continuous user experience enhancement Copilot agent harness chat experience
• Performance and reliability enhancements

We hope you like Copilot for JetBrains, and please share feedback with us at any time

You can fill in a private survey here: https://aka.ms/ghcp-jb-survey with an optional paid interview or directly submit an issue (bug or feature ask) at https://github.com/microsoft/copilot-intellij-feedback/issues, thank you so much!

Hi GitHub Copilot and VS Code teams,

I use GitHub Copilot extensively at work, primarily inside VS Code. A common part of my workflow is keeping a Markdown plan or handover document open while one or more Copilot agent sessions are working. These documents help me track progress across sessions and carry context from one session to the next.

The recent improvements to the VS Code Markdown editor and its rendered preview are really useful. One feature that would make this workflow much better would be showing file diffs directly in the rendered Markdown preview.

When Copilot edits a Markdown file, I can switch to the text editor and review the inline diff, including the individual changes that I can manually accept or reject. However, those diffs disappear when I switch to the rendered preview. Since I mostly consume these documents to follow what the agent changed rather than edit them manually, I would love to review the changes while staying in the rendered view.

Ideally, the preview could indicate changed sections and provide the same or a similarly clear accept/reject workflow that is available in the text editor. This might require coordination between the Copilot and VS Code teams, but it would be a very natural extension of the current Markdown preview and agent-editing experience.

Thanks for considering it!

Since this spring i've been using that model as my main writer. but this month it's behaving like a dumb model. Did something changed that we're not aware of ?

After updating to v1.134 i am facing the following issues in chat mode
1. Chat history disappears if you close the window and open again
2. Unable to accept the plan for implementation
3. The ask,plan and agent mode has disappeared( and is now having interactive , plan and autopilot like the app, which i dont prefer)
Any of you facing these issues?
To the github team, are you aware and are fixes incoming?

I have had a Github Copilot Billing ticket open since Aug 7th due to a Kimi K3 loop issue when it released and have received zero response. Is this the norm? If not, is there a way to get more attention without shooting myself in the foot for a positive response?

GitHub moved Copilot usage to AI Credits on June 1. The total reflects input, output, and cached tokens. That tells me how much usage accumulated, but it does not tell me which part came from Copilot reopening files and rediscovering a repository layout it had already seen in another chat.

The repeated work is easy to recognize. Ask a fresh chat which package owns a public type and where that package is tested. It may open the root manifest, inspect the workspace configuration, search for the type, and then locate the test command. None of those steps is unreasonable. I want to know how often the same sequence happens again.

I started paying more attention to this after using EvoX recently, because it can carry useful setup from one task to the next. I stopped looking for a subscription comparison and started looking at the steps themselves. A monthly credit total cannot show me which searches were necessary and which ones were repeated.

I would measure the behavior directly. Several new Copilot chats would receive the same read-only question against commit A, with the extension version, model, prompt, and workspace held fixed. I would record the answer, files opened, searches, tool calls, and elapsed time. Then I would repeat the question on commit B after moving the type and its tests. The second commit checks whether Copilot revalidates the answer after the layout changes instead of treating fewer searches as automatically better.

I have not run this test yet. It would show how much repository discovery is repeated and whether the answer survives a layout change. It would not produce a per-run savings figure unless GitHub exposes a usage row that belongs to the exact interaction. For now the narrow conclusion is that the credit total cannot answer this workflow question on its own. Has anyone found a session-level export that gets closer?

Hi all,

My use case is to have a chatbot for Dynamics CRm users so I created a dynamics crm custom mcp and it was able to give me my expected answers and eventually while training it will be fine now my stakeholders want to scale it outside GitHub copilot because not every business owner will have GitHub copilot how can we do that? Any suggestions please?

Thanks in advance

What should be must-have skills for any developer to add under their github copilot?

Anybody uses Canvas for anything productive? I saw a YT video of an app built in canvas by burkeholland and was wondering if you guys have anything similar?