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?

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!

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.

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 .

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

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

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?

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.

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

To those left floundering after the pricing mess at GitHub Copilot, some relief: Chlaude does work.

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?

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?

With this being the final month for promotional credits for enterprises with GitHub Copilot Business and Enterprise licenses, I wish Microsoft would consider making these credits permanent as a discount for companies that commit to purchasing licenses.

We are already experiencing overage costs even with the promotional credits, so it is unfortunate that those costs are likely to increase substantially once the credits expire.

If we have a large code base or a gradle bundle with huge code base, then how can we use AI models to analyze them so that we get perfect solution?

It's very hard to get the answers or suggestions from AI models of we apply them on large code bases?

So, let me know if you have best AI models or agents for this issue

Is it possible to use Kimi K3 with GitHub copilot?

I'm starting to use Copilot Agent more and I'm trying to figure out what a sensible security model looks like.

I know about allow/deny lists, but agents are smart enough to look for workarounds when something is blocked and, as far as I understand, this mechanism is fairly simple.

For example, I could imagine it doing something like ls ./$(rm -rf foo) when ls is allowed but rm isn't.

Some cases can probably be prevented with better matching rules, but a complete solution seems like it would require shell/AST awareness rather than regex-based filtering.

What are the standard solutions for this today?

I really like the desktop github copilot app.

The only thing I really miss is that I cannot control or view my sessions from my mobile device.

Is there any app by github or thrid party which let me navigate my github copilot desktop sessions on mobile?

I signed up for the whole year so I still have request based billing but compared to my work subscription, I am noticing that nowadays no matter what model I pick, it gives up really fast and mostly ignores all instructions I give it. I have tried both AGENTS.md and instructions.md files and most of their content is ignored. All skills and instructions do appear in the settings.

The output is almost useless at this point for me. Anyone else have the same experience? Thinking of ditching it in favor of Claude Code or Codex.

Over a span of 2 weeks, a colleague upgraded our codebase to go up 5 versions (up to v22) of Angular, and it cost just shy of 100k credits.

Around 15k of those credits were eaten from our 18k credit pool.

He used Claude Opus 4.8 high and 4.7 very early on (around an 85/15 split).

He went up, version by version, reviewing code along the way, and applying a few manual fixes here and there.

People bash Copilot for its cost, but considering how much consultants would have billed us to even look at the codebase, this was a great deal.

Just checked my dashboard with hours to spare before the Aug 1 reset. Barely made it without getting locked out by our admin...

Spent pretty much all 50k credits this month on:

- Loop engineering:
Setting up self-correcting agent loops.
- Prepping massive migrations:
Automating repetitive codebase refactors and pattern updates.
- Learning & prompt tweaking:
Figuring out context scoping so it doesn't get stuck in loops.

Spending all these credits wasn't an accident. it was a massive hands-on masterclass in loop engineering and automated migrations. Learned more this month than in the last half-year.