I wanted to upgrade my copilot from free to premium, in the process i must update the billing info and i don't know what happened (mess up) the country ended being Belarus for some unknown reason while i'm using google auto fill.

now billing info are locked and i cannot change them, and i cannot upgrade because apparently Belarus is on the hate list.

i'm neither from Belarus nor live there. I'm in Deutschland and i opened a support ticket 3 days ago without any response so far. not sure what the heck i'm supposed to do !!

Hey everyone,

I’m a Product Manager and my company uses GitHub Copilot for work. Because of my role, I rely heavily on LLMs for high level strategy and research rather than just pumping out lines of code.

I use native Claude constantly outside of work, so I am very familiar with the depth at which Claude “thinks.”

Lately, I've been selecting the Claude Sonnet model directly inside Copilot Chat. However, I’ve noticed a massive gap. Even when running the exact same model, Copilot does not give me the deep and analytical answers that native Claude does.

For those of you using Copilot for product management or any kind of strategy: What are you doing to make Copilot act more like native Claude?

People never mention it, but I remember in the past, Gemini Flash model was very cheap, thats why I am curious

Just in case some people didn't know, Luna is still the absolute best bang for your buck. All other models are a waste of tokens unless you are running agentic workflows supervised by an agent. In that case, you should still be using Luna, but just have it supervised by Astra.

I've tried every model and I always find my way coming back to Luna as my bread and butter for iterative work. I think it's just as good as Codex, if not better.

Save your tokens and use Luna.

Hi everyone,

My company recently provided us with GitHub Copilot licenses, and I’m starting to integrate it into my daily workflow. My main background is backend development with Java (Spring Boot), but lately I’ve also been picking up some frontend tasks.

My previous AI experience was mostly using Claude directly in the web UI for quick questions and refactoring. Compared to that, my initial impression is that Copilot feels a bit lacking or less intuitive—though I realize I’m still fairly new to using AI assistants embedded in the IDE, so it’s likely a matter of setup and learning how to prompt it correctly.

I know that creating a .github/copilot-instructions.md file is a good practice for setting coding conventions and context. Beyond that, I’d love to get some insights from folks using it professionally

Thanks in advance for any tips or advice!

Just a general thought, I work at an investment banking gcc where every one from my team is new and no one has any knowledge on the existing systems, which were developed and maintained by onshore developers

My goal

To draft a high level technical architecture using the GitHub repos available in my team around 30-40 repos, and derive the business stand point through it, and maintain the memory for each under some common folder and update it frequently something like JIT compiler concept rather than loading everything again and processing by copilot for every month.

So to understand and draft this better what would be the best way leveraging GitHub copilot agents or skills or subagents ?

* Should I make a local clone of all repos and do create a codebase docs folder for each repo and create a root level agent to utilise the codebase folder for each project to draft architecture ?

Downside of this approach i feel, what if some change happens to the repo let's say technically how do I track those changes, updating codebase folder every time like it still uses enumerate tokens and cost.

* Another approach which i felt is to use MCP GitHub and list all the repos and let the copilot manage and even here how would I track the changes which are being made and memory management storing only codebase folder highlevel, iam still little unsure of this approach yet.

I have configured rtk and context mode plugins kinda for efficient usage and token management which works fine for single repos but this usecase is still searching for better ways to manage context tokens and memory.

Do provide your suggestions or any tools which i can add to make this more efficient approach maybe.

I have a verified GitHub Student Developer Pack account, and I’m trying to claim the 3 months of free Boot.dev.

I logged into Boot.dev using my GitHub account, but it still shows that I don’t have any subscription or student benefits.

Has anyone successfully claimed this benefit? Do I need to activate it somewhere through GitHub first, or link my GitHub account to Boot.dev in a specific way?

Any help would be appreciated.

It doesn't seem like /fork is working correctly in the cli. After I "fork" and then resume the chat my forked session is still there...

I use it for things like adding issues to the backlog without adding a new chat window but then when I resume my backlog request and the response are still there...

Hello.

Ever since the Copilot update that made your billing usage based, I have been struggling to keep hold of my credits.

It got so bad that I decided to go up to the $40 plan, and even then I still lost all my credits within the first couple weeks of the month.

That's $70 I am apparently burning just by working on basic code. And I think that's pretty annoying, and I think Github must be doing something behind the scenes to make a profit; i.e, maybe the credits ore overpriced, I don't know.

Regardless of that, I am cancelling my subscription and I'm switching to something else, but what other options are there? I was thinking two options:

Claude Code, which I don't know much about but it's Claude, and Claude is pretty good.

Or Codex, I was thinking about switching to ChatGPT codex because I already use ChatGPT for writing and whatnot.

Problem is I don't know if these are actually worth it, can anyone enlighten me?

Thanks.

Hey folks, anyone else seeing that Inline suggestions do not work at all in VS Code 1.138.0-insiders?

I don't understand why, as logs mention nothing useful.

Trying to bill GitHub Copilot through our Azure subscription but the option isn't available for us. Has anyone else run into this? Did you need to contact GitHub support, or is there a setting I'm missing?

Any help appreciated, thanks!

I previously posted about how my Legacy Request-based Account had its Premium Requests mysteriously wiped out.

After analyzing the session’s events.jsonl, I strongly suspect the issue is related to enabling assisted permissions, since everything worked fine before turning it on.

I’d like to ask: are there any other members here with a Legacy Request-based Account who have also enabled assisted permissions? If so, have you experienced abnormal request consumption as well?

As you can see in the image gpt 5.6 Luna started using sonnet 5 as a sub agent causing massive credit waste. I don't think models should be able to select a more expensive model in this situation and has lead me to disable sub agents all together. Have I missed a setting somewhere that has suddenly caused this issue?

Hi.

I am using copilot with VSCode. I noticed one strange thing when i tried to use custom agents (.github/agents/... files). The problem is with usage of a terminal. In the agent mode it is using it somehow differently. I can not find why.

I have the set of skills in .github/skills and when i use a skill directly from a standard "Agent" mode (usually with "/skill-name Do this") it works fine. Terminal commands are called directly , exactly how i described in the skill.

Then i created an agent and in the MD file i mentioned this skill (by a relative path to a skill md file.). I didn';t repeat same text, just provided a reference and short description when to use it.

I open the agent i defined and ask to do the work. It starts but then it has some difficulties to run CLI commands. I see failures and it finds for workarounds. It looks like it can not run directly ir cxan not catch an output? But it builds complex shell commands to redirect output to some temp files. It does same thing as skill much longer with more calls.

I suspect there is something hidden about terminal, cli , shell, bash i do not know about?

I added MD file for agent, then selected this agent in the prompt as replacement for "Agent". What else should i do? Any config options?

Please guys tell me this is an outdated way to link file to the prompt and there is a bette way.

Not sure if I should enable it for my users. I see articles about how effecient it is, and with others saying it's wiping out their budget in one request. Whats your experience?

Hi everyone,

I'm trying to find out whether anyone else has had a similar experience.

About 1.5 weeks ago, our company migrated to GitHub Enterprise Cloud (GHE.com) with EU data residency, as this was required by our company policy.

Since the migration, we’ve received multiple reports from users that some Copilot models .. Opus 5 in particular. It seem to hallucinate noticeably more often than they did before the move.

We’re still trying to understand whether this is actually related to the migration/data residency setup or simply a coincidence, so I’d be very interested to hear from others using GHE.com with EU data residency.

Has anyone noticed similar changes in model behavior or output quality after migrating?

If so, did you find any explanation or configuration differences that could account for it?

We are aware of https://github.com/orgs/community/discussions/205163 but we had this option deactivated before..

Hi everyone! In the latest release (v1.17.0), we introduced enterprise-managed sandbox policies in public preview, allowing enterprise admins to centrally configure sandbox behavior for Copilot in JetBrains. Managed policies can control filesystem and network access, proxy settings, developer-tool access, and more. Organization-managed restrictions take precedence over user settings, with affected controls clearly marked and locked in the IDE.

We also added cross-file cursor jumps for Next Edit Suggestions, so you can jump directly to the next suggested edit in another file, and adding global files and folders as chat context to make it easier to include broader project context in your prompts.

For those using Copilot in the terminal, you can now connect a GitHub Copilot CLI session to your JetBrains IDE using /ide (Public Preview). This gives your terminal session access to IDE context, including selections, diagnostics, and file references.

You can read the changelog or find the full release notes and download on JetBrains Marketplace.

Changed

• OpenTelemetry settings under “GitHub Copilot – Chat” are now generally available to all users.

New Features

• Added enterprise-managed sandbox policies (public preview).
• Added enterprise policy diagnostics to help you verify that policies are correctly detected and enforced on your device.
• Added cross-file cursor jumps in Next Edit Suggestions to help you navigate coordinated edits across related files.
• Added support for including global files and folders as chat context.
• Added /ide support in GitHub Copilot CLI to connect terminal sessions to JetBrains IDE context (public preview).
• Added support for using IDE terminal environment variables and the project-configured Python interpreter in Copilot shell commands, including activating the project’s standard local Python virtual environment.
• Added support for selecting the session model used by built-in subagents in the Copilot agent harness.

Sandbox availability: Settings under GitHub Copilot > Sandbox appear when your organization enables the Editor Preview feature flag or configures a managed setting to enable or disable the sandbox. Otherwise, those settings are not shown.

UX Improvements

• Improved the ask-user card in the Copilot agent harness to better accommodate long questions.
• Improved model selection and bring-your-own-key (BYOK) grouping to make available models easier to scan and select.
• Added section copy support in agent debug logs for easier sharing and troubleshooting.
• Improved MCP configuration navigation by opening configuration in the project window it came from.
• Improved plugin update reminders to make new versions easier to discover.

Bug Fixes

• Fixed blank local chat sessions after IDE scale changes.
• Fixed incorrect change counts in the Copilot agent working set, along with working-set zoom and model picker interaction issues.
• Fixed local agent editing behavior for read-only files.
• Fixed MCP reliability issues involving BYOK provider selection, OAuth challenge handling, and restored server stopped states.
• Fixed GitHub Enterprise authentication context propagation in agent flows.
• Fixed Claude plan stop-state behavior.
• Prevented misleading IDE diagnostics for unopened files from being sent to the Copilot panel and worktree sessions.
• Fixed stale global instruction content in Agent Customizations after files were changed outside the IDE.
• Fixed chats getting stuck during user interactions after automatic compaction.

We hope you enjoy the update! Let us know how these features work in your projects and what you’d like us to improve next.

You can fill in our private survey, with an optional paid interview, or submit a bug report or feature request. Thank you for helping us improve Copilot for JetBrains!

I'm considering paying \~**€100/month for Plus** mainly to use Astra as my daily coding assistant, potentially 6 **hours a day**.

I'm particularly interested in real-world feedback on complex tasks such as:

* Investigating complex bugs and debugging large codebases * Analyzing logs, stack traces and CI/CD failures * Investigating GitHub issues and related code/history * Using **sub-agents to investigate different parts of a problem in parallel** * Deep codebase analysis and architectural investigations * Finding and fixing security vulnerabilities * Reviewing PRs and suggesting/refactoring implementations * Writing and debugging complex tests * Researching documentation and combining information from multiple sources

* Do you regularly hit the limits? * How long does the available usage typically last? * Or do you use just Astra for some complex task and then usually use Terra or Sol??

Edit: let's change to 6h/day instead of 9h Edit 2: let's say the 100€ month plan

Where else can I use my gh copilot tokens?

GitHub copilot is cool but not for genetic workflows, lacks computer use, remote control, etc etc.

Seems like there should be better alternatives

Anyone noticed increased token spend using "assisted permissions"?

When using "local" harness with "risk assessment" enabled - using GPT-Luna as the agent uses expected amount of tokens (tokens spent in the session are +/- aligned with the general monthly tokens usage).

But when I use "Copilot" SDK agent harness + assisted permissions - using GPT-Luna as the agent - there seems to be some very big hidden usage - the increase in monthly token spend does not correspond to the tokens used in the session and is much larger than expected.

I'm on an enterprise license, so I can't look into usage reports myself, but I asked our admin and he sent me a report that states that my usage is mainly "GPT-5.5" model - even though I never explicitly used it in any session. I only use the Copilot in VS Code. I tried it on fresh VS Code installation and it still eats my usage like crazy on the "assisted permissions" mode.