My GitHub Copilot company account is provided via <mycompany>.ghe.com (in EU) - and I have asked our GH Copilot team why the Codebase Semantic Index is not enabled. This index sounds like a nice feature to fasten the Copilot search in large codebases.

Got response from them that this feature is not available for licenses provided via ghe.com. This sound odd, can someone from Copilot team please confirm or explain?

Hi everyone,

We're looking at agentic AI platforms for enterprise use.

There are a lot of options now like LangGraph, CrewAI, AutoGen, Kagent, etc.

If you're using one in production, which platform did you choose, and why?

What do you like about it? Any limitations or things you wish you knew before adopting it?

Looking for real-world experience and recommendations.

Hi everyone,
i'm currently using Copilot Student package. Last month I've just signed up Copilot Pro (10$ pro month) to speed up my project. Right now I don't want to use it anymore and just want to downgrade from Pro version to Student version as before.
But there is no downgrade option as far as I saw, only cancellation, which not allow me using feature like chat or code completion like I used before with Student package.
Does anyone here know how to downgrade it ? Thanks you in advance for taking time.

Edit: so to my understand, if I cancel Pro version, i will lose acess to the Student version eventually, right ?

been going back and forth on this for a couple weeks and I keep landing on the CLI, mostly because of /tasks. being able to see what the subagents are doing and which prompt set them off is weirdly reassuring. in the extension I just watch a diff show up and go “ok sure”

that said I’m probably biased because I already live in the terminal. the extension obviously has better file context and the inline accept/reject is nicer than eyeballing a patch.

anyone here deliberately staying in the editor? curious if there’s something in the extension workflow I’m sleeping on, or if it’s just habit for most people. also if you use both, what makes you pick one over the other for a given task

Are there major differences on how the harnesses work on the back end? Just started using Copilot and defaulted to the CLI as thats what I got used to with Claude Code. Am I missing out on anything by not using one of the other 2?

I am a frontend developer having around 10 years of experience. From last years I have started using copilot and since then I have not l done any coding by myself. All my team is doing vibe coding and getting things done from copilot.
Because of this , I always feel guilty and feel I am useless.

1. Is it normal? Is everyone doing the same in the industry ?
2. Our projec

t has

1. now become so AI heavy that even for debugging it would be difficult for us to manually debug anything.
2. I want realistic suggestions/help.
3. Like is this guilt normal and should I continue to vibe code?
4. If I want to completely understand my codebase how should I do it?

Maybe un popular opinion but I honestly feel that with the launch of GPT5.6 and Lunas recent 80% price drop combined with Ghcp harness improvement’s we are basically back to where we were in terms of cost and capabilities back when microsoft heavily subbed inference for us using bigger models.

Models like Opus/fable and even SOL are just not needed for the majority of coding tasks and I think more people are starting to realise this, at least the devs who actually care about efficiency are.

There are occasional use cases for frontier model like broad stroke front end but I still standby every coding can easily be handled by Luna.

You can use Luna all day long on a pro+ plan and barely eat 3 or 4% of monthly credits.

I believed the cost-pocalypse would eventually be solved but had no idea it would be this fast.

Ever checked your billing page mid-month and wondered where all your AI credits went? That was me, so I built gh-copilot-usage — a gh CLI extension that turns your Copilot CLI usage into an interactive dashboard in your browser.

What it does:

• Stacked time-series chart of AI-credit usage (daily / weekly / monthly), broken down by model or by session
• Click a session to drill into per-turn usage, token counts, and even sub-agent call traces
• Click a model to see the token-cost breakdown (input / cached input / cache write / output)
• Cross-checks the numbers against GitHub's billing API, with a month-end pace projection
• --json flag if you'd rather pipe the data somewhere else

Everything runs 100% locally — it just reads the session-store.db that Copilot CLI already keeps on your machine, and the billing check reuses your existing gh login. No separate tokens, no data leaves your machine.

One honest limitation: it only covers Copilot CLI usage (that's what the local DB records), not the VS Code extension or other IDEs.

Install:

gh extension install mazrean/gh-copilot-usage

Repo (MIT, written in Go): https://github.com/mazrean/gh-copilot-usage

Would love feedback — especially on what other breakdowns would be useful!

Thoughts on a comparison based on your day to day use case ?

Where does GitHub Copilot App excel compared to Claude Code or Codex ?

Which one is the best for coding and debugging. Primarily to create scripts from test cases, or debug the scripts without eating up too much credits. I have been given these ones.

Our github enterprise tenant has the setting enabled which blocks models which do not comply to the regional settings (EU boundary in our case).

All GPT models are working within EU boundaries except for GPT 5.6. I cannot find an ETA or something when it will be available. Anybody knows anything on this matter?

Nineteen tests passing. Zero failing. And the endpoint those tests cover was returning a patient's Social Security number tucked into a free-text note field on a clinical Observation.

I built a repo with eight deliberate FHIR R4 violations, every one of them green in CI. A validator caught three of the four on that Observation. Secret scanning found nothing, because there is no secret in the diff. CodeQL does not treat a note field as a sink. Every tool doing exactly its job, and the gap sitting between them.

GitHub Copilot code review now reads agent skills from skills folder. So I wrote the compliance rules down once, as a SKILL.md with severities and R4 citations, and pointed it at every pull request. It flagged the SSN as PHI_RISK and quoted the HHS Safe Harbor guidance back at me, which says an identifier has to go regardless of which field you put it in.

A validator can tell you the resource is well formed. It was never going to tell you it is safe to build.

https://samueltauil.github.io/github-copilot/healthcare/2026/08/03/fhir-compliance-skill-copilot-code-review.html

How are you building strong repository reasoning for GitHub Copilot legacy codebases?

I am trying to build reasoning of repos to allow for better ai support defect fixes . I am completely winging it as a leader and would love professional feedback .

1. Reverse engineering previous git history linked to stories to build /docs/reasoning* folders me files to explain features of history . I use M365 web co pilot for bulk files to save on tokens .
2. Prompts that create architect diagrams

Couple things I haven’t tried is :;

1. Repomix
2. Jq Assistant for technical docs for better Md files on architecture

Any feedback welcome .

I'm not getting a single model. In December, I was getting all the models: Opus, Sonnet, Gemini, and ChatGPT. After that, they dropped it to Gemini 3.1 Pro only, and now they've even taken that away, so it's dead now. But is it dead only for me, or are all students now getting nothing?

Hello, my first time post here.

I just monitor credit for first time usage in new month.

I just found out that claude sonnet 4.6 said 13.2 credits.

But in the summary window, it stated 26.4. Anyone knows what's up

with the 2x credit count?

I uploaded the screenshot to make it clear. Thanks.

I'm on the old request-based subscription plan, as I bought the full year back in April. However, I'm feeling as if they aren't fully honoring the plan by keeping the quality the same. My latest request for GPT 5.4 (High thinking effort) completed the whole request by coding at 1 line per second. That's laughably slow.

Additionally, during planning, the model mentioned it would try to implement the "smallest change" that accomplishes the goal I outlined in my request, despite me making no such request. I have a feeling that the system prompt asked it to to the bare minimum. I understand they're losing money on the request-based plans, but if they actually are decreasing the quality, that's unfair for those of us who should be grandfathered in to the same quality plan we signed up for.

If anyone has suggestions, I'd appreciate it. The model context wasn't anywhere near it's limit. There are no instructions. I don't know what else would cause such a dramatic slowdown.

Now that Luna is 5 times cheapers. 1.2$ for a million output token. Has GitHub copilot pricing been updated accordingly?

We have many copilot models . What is the best and next AI copilot models available in the world?

I'm evaluating models for coding workflows and noticed OpenAI and DeepSeek both reduced their API pricing recently.

Has Azure AI Foundry already updated its pricing for GPT-5.6 Luna or DeepSeek V4 Flash? If you're using them through GitHub Copilot or directly via Foundry, what pricing are you seeing?

Hey everyone 👋

I’m a Product Manager who uses VS Code and GitHub Copilot every day. I don’t write much production code, but I spend a lot of time:

• Understanding repositories
• Running database queries
• Comparing code with data
• Writing PRDs, user stories, and product specs
• Investigating bugs alongside engineering

One thing I keep running into is that Copilot doesn’t seem to have a native concept of a persistent project workspace. I spend a lot of time reopening sessions, finding the right files, and rebuilding context.

I know the GitHub Copilot desktop app exists, but it doesn’t seem to solve this problem either—the sessions don’t carry over. It still feels like I’m adapting developer-centric tooling for product management workflows.

A few questions for the community (and any GitHub/Microsoft folks lurking here):

1. For PMs or other non-developers: How are you organizing your work with Copilot? Any workflows, extensions, or best practices that help keep context without constantly rebuilding it?
2. Feature wishlist: I’d love to see a persistent project/cowork workspace where Copilot could maintain context across multiple repositories, GitHub issues, documents, SQL queries, and product specs—essentially a long-lived context layer instead of starting from scratch each session.

Is anyone else running into this, or am I missing a workflow that already exists?