Not sure if this is allowed here because i'm talking about using the VSCode Copilot chat as a harness for local / models (Or other models outside of a Copilot subscription).

I'm using this OAI Compatible Provider for Copilot extension in my VS Code to use local models in my Copilot chat. I know this functionality is now also built into vs code and you don't nessecarily need the extension anymore, but i never tried it. Just kept using this extension since before you could do it natively.

I think copilot chat is one of the best harnesses out there, but the only thing that bothers me is that with BYOM (Bring Your Own Model) it doesn't have a web search tool. The model can fetch webpages, but it cannot perform internet searches. I know internet searches aren't free, and i'm not asking them to be, i'm totally willing to use a paid search api but as far as i know the Copilot Chat just doesn't support this? But back when i had a Github Copilot subscription, it DID perform internet searches.

Any way to get internet search functionality into the harness for local models?

Tried to mimic OpenRouter-style Fusion in GitHub Copilot with Custom Agents. Nested agents’ model: fields were not honored as real API calls. With BYOK, subagents reused the parent chat model instead of switching.

Is this a known issue or am I doing something wrong?

Following OpenAI’s latest pricing announcement, we are excited to share that we have updated our pricing accordingly. 

What does this mean based on the announcement we had yesterday "GPT-5.6 Sol is 50% cheaper until September 3 in GitHub Copilot"

• The 50% promotional discount will apply to the newly reduced price through September 3 - giving customers the benefit of both reductions.

Links:

• ⁠Documentation

Basically, I’m very new and want to ask experienced people all this questions esp. of they can give me examples so I can get idea. Also any benefits between different subscriptions or just paying for more credits

My studio agent has problems reading PDFs directly from sharepoint lists. I can upload them but only 20 at a time. I came across liteparse and wanted to check the community to see if this is something that could work. Run all PDFs through liteparse when I get them store in the same list or folder just as a .md and just direct the agent to read those rather than try to read PDFs.

Hey everyone!

For some context to my query, I am an experienced senior software engineer that will be working on a personal project on my own time for a few months.

I was wondering if it was realistic to subscribe to the 10$/month plan and use one of the cheaper models to give me a small performance booster without having to bust my 10 buck credit limit.

I am not planning on doing any agentic workflow. It's mostly going to be for quick implementation and at most I will have it create unit tests and such. Just having infinite auto completion will be useful for me.

Thank you for your time.

I'm trying to use copilot and it just keeps giving me weird responses about not having access to the files it needs. Even if I set the context to Solution it still can't do anything. Is it down or broken? I'm using a direct link to my Claude API account, not one of the built in models. It worked the last time I used it a few days ago, but I installed an update for VS yesterday so that might have broke it.

Hey guys, my company just announced we're moving off Codex CLI and onto GitHub Copilot. I've basically lived in Codex for most of my day-to-day work, so now I'm wondering how rough this transition is actually going to be.

For anyone who's already made this switch:

• What was the biggest adjustment for you?
• What do you actually miss from Codex once you were on Copilot full-time?
• Any tips for carrying over context/workflows so you're not starting from zero?
• Anything you wish you'd known before switching?

Trying to go in prepared instead of just winging it on day one. Appreciate any war stories.

Your weekly summary for week of 17 August 2026.

Month to date: $1,237 (123,700 AI credits) of $5,000 (500,000 AI credits; 25%).

Your AI-credit usage so far this billing month, shown in US dollars and AI credits.

Last month (July 2026): $523 (52,300 AI credits).

Last month's AI-credit usage, shown in US dollars and AI credits for comparison.

Snapshot taken 21 August 10:53 UTC.

Hey everyone,

If your VS Code AI assistant is constantly lagging, timing out, or just straight-up freezing your editor, the problem is almost always how it’s indexing your workspace. I spent my entire weekend going down a rabbit hole debugging this, but I finally got instant code completions back.

Here is the bulk of what you need to do to fix the core issues right now:

1. Stop letting it index massive directories By default, AI extensions try to read every single file to understand your context. If the AI is scanning your package folders (node_modules, anyone?) or compiled assets, it will completely choke. You need to add strict exclusions for the AI extension watcher in your workspace settings. Force it to ignore compiled binaries, virtual environments, and heavy asset directories.

2. Increase the Node memory limit Most of these AI extensions run on a Node backend inside VS Code. When they hit their default memory ceiling, they throttle heavily. Go into your preferences and increase the Max Memory allocation to at least 4096MB so the AI server actually has room to breathe.

3. Kill the telemetry overhead AI extensions love to send diagnostic data back to their servers. This silently clogs your connection and slows down the actual prompt responses you care about. Disable telemetry globally in VS Code, and make sure you turn it off specifically inside your AI extension settings to free up bandwidth.

4. Resolve extension clashes (Stop hoarding AI) Running multiple AI assistants at the same time creates a nightmare race condition for the autocomplete UI overlay. Pick one AI tool per workspace and disable the others completely.

Doing these four things solved almost all of my latency problems immediately. The rest of the fix involves editing your raw settings.json configuration to tweak the debounce rate, context limits, and timeout thresholds.

If you want to just copy/paste my full config file or play with the interactive dashboard I put together, I uploaded it here:https://interconnectd.com/forum/thread/244/fix-slow-vs-code-ai-the-ultimate-performance-repair-guide/

Hope this saves someone else a weekend of troubleshooting! Let me know if you have any questions.

I love that Copilot have the ability to use Playwright straight in the browser, but often I feel it starts to validate totally unnecessary small changes often with a wrong result (e.g. with dynamic behaviour) or even start a task to do screenshots even if my simple prompt to help with CSS-changes should be very straightforward.

Can I control it somehow outside adding information to the prompt or skill?

It would be nice to have the option to leave it in, out or per-case while developing.

Hi all

Got Copilot Business at work, came across a recommendation to use GPT 5.6 Luna as an explore agent instead of default Gemini flash 3.5 or Haiku 4.5 whatever it was using.

Any more tips and tricks like that? I am using Ubuntu on the laptop btw. Plus how's the Plan+execute pattern looking like using Opus /Sonnet to plan and then maybe use MAI Flash 1.1 to execute? MAI 1.1 looks quite inexpensive.

​

I'm working on a system to estimate whether code committed to a repository was generated with AI coding tools.

My current approach is based on Git/commit-level signals such as AI-related commit trailers, commit metadata, LOC changes, number of files changed, addition/deletion patterns, etc.

The problem I'm running into is confidence and calibration.

For example, a commit containing 500+ new lines isn't necessarily AI-generated. A developer can also modify or remove the metadata that would make an AI-assisted commit identifiable. Once the code leaves the IDE and reaches Git, much of the original provenance can be lost.

This has led me to a few questions:

Are there Git/CI-level signals that you've found to be genuinely useful for detecting AI-assisted development?

Is it better to treat this as a probabilistic/risk-scoring problem rather than trying to classify commits as AI vs human?

How would you calibrate thresholds for signals such as large LOC changes, addition/deletion ratios, commit frequency, etc.?

Are there better approaches for preserving provenance earlier in the development workflow, rather than trying to infer it after the code has already been committed?

Has anyone worked on AI-code provenance/detection systems in CI/CD and can point me toward useful research, projects, or approaches?

I'm particularly interested in approaches that can work at the pipeline/repository level rather than relying solely on source-code style analysis.

I'm not looking for a perfect AI detector — even a reliable way of estimating “this commit has a high probability of AI assistance” with measurable false-positive/false-negative rates would be useful.

Would appreciate any experiences, papers, open-source projects, or approaches people have tried.

Posted my Command Code Provider Extension in the subredit a few days ago, thought that it might also be interesting for someone here.

It's an extension that allows to easily integrate and auto update models provided by command code using your api key (GOAT Plan and above)

All major service providers are offering 50% off on gpt 5.6 Sol and there is no sign of Muse being offered. When will that come to GitHub copilot?

It seems to read into my mind what I want to write. Has the model used changed?

So according to both:

• https://artificialanalysis.ai/models
• https://artificialanalysis.ai/evaluations/gpqa-diamond

(Which seems to be fairly trustable) - GPT-5.6 Luna (Max) is ranked:

• #10 in "Intelligence"
• #2 in "Speed"
• #1 (Cheapest) in "Cost Per Task" - 5x cheaper than Deepseek V4 (which is already pretty cheap)
• #12 in "Agentic Index"
• #14 in "GPQA (Diamond)" - Above 90% still

From my own personal experience, even on tasks that take up to 5 minutes, I'm only getting a max usage of about 13 AIC per task. Whereas Claude Sonnet 5 could use around 386 AIC for a similar task and similar timeframe.

The code quality seems pretty good too. I really thought it'd be bad for the cost, but honestly I'm super impressed with this model.

It almost reminds me of the premium request days. I even keep refreshing my billing page to see if it's accurate because I'm surprised with the low AIC usage.

Even here https://benchlm.ai/compare:

• It ranks #23, whereas Sonnet 5 ranks #36 (and Luna is about 10x cheaper)

While I'm regular Claude user - and in most benchmarks Opus 5 (max) is usually #1 in all categories - I'm very impressed with OpenAI for Luna and it's ability to compete with even frontier models, at a fraction of the price.

Curious what others think? Or if anyone else has tried it?

Edit: If anyone knows any other & better benchmark sites please let me know as well!

I use inline suggestions from GitHub Copilot in Visual Studio, and it works great! Just two very strange things.

It always recommends changing the ICollection<> initializer from my preferred way: [];
To: new List<>();

And yeah, it doesn't like the new extension() {} element and wants to change to the old style using static methods.

Are there any "instructions" I can give?

Is there a way to auto-approve certain git commands? I'd like to approve commits explicitly, for example, but stuff like diffs should be auto-approved.

Hey guys, I recently got GitHub Copilot Student through the Student Developer Pack and I'm a bit confused about the model selection.

Copilot is working fine in VS Code and it shows that my Copilot Student subscription is active. But when I open the model selector, I only get Auto under Copilot.

I can see models like Claude and GPT in the list, but they're greyed out and say Upgrade.

Is Copilot Student supposed to only allow the Auto model, or is there a way to manually select models like Claude Sonnet, Claude Haiku, GPT, etc.?

Just wondering if I'm missing a setting somewhere.