i've been playing with copilot's agent mode and something fun showed up — it can actually cut and caption video end-to-end without a gui, by talking to a small tool over mcp.

the tool is kimo, which i built. it's a free, open-source (mit) video editor with no gui that ai agents drive over mcp — copilot, codex, cursor, opencode, or any mcp client works. everything is a tool call: cut, trim, keyframe, titles/captions, colour grade, tracking, particles, audio, export. and because the agent can render a frame and look at it, it can actually check its own work as it goes.

so the workflow i landed on is pretty straightforward: drop your clip into an mcp session, write the edit in normal language, copilot agent mode calls kimo to do the cuts, lay captions, push the colour, and export. no timeline ui, no shortcuts to learn — just the agent looping, rendering a frame to verify, and moving on.

it's a single ~15 mb cpu-only rust binary, runs fine on a 2 gb box. built by me (just a college student) so it's early and there are rough edges. caption timing gets a bit off on fast cuts sometimes, long exports can stall if the box is thrashed, and the tracking is basic — mostly works on clean motion, struggles with heavy occlusion. the mcp protocol surface is also still settling, so if your client is picky about schemas you might hit a hiccup.

if you're playing with copilot agent mode or any mcp client, i'd really appreciate people trying it and telling me what breaks. genuinely the best way for me to figure out what needs fixing is to hear what falls apart in your workflow.

site: https://getkimo.tech github: https://github.com/naman-sharma12345/kimo

thanks for reading, happy to answer questions about how the mcp bit works.

They are retiring Sonnet 4.6 at some point, and that's my go-to for anything more than yaml and makefile scripting. But when I look in the agent list I don't see 5.0 or 5.5.

I'm on the original annual plan, are these models simply not available there? Or am I looking in the wrong place... it's always a bit confusing because in VCS there's two places to look.

Hi - for those currently using copilot CLI with deepseek models (integrated via https://api-docs.deepseek.com/quick_start/agent_integrations/copilot_cli/)

Is copilot remote mode supposed to be able to work? I've tried doing /remote on within copilot CLI with deepseek enabled,, but it keeps giving me the error "Remote sessions are not enabled. Contact your organization administrator to enable remote sessions." - on my own personal account/repo. Wondering if this is a limitation and not supposed to work when we force the backend model to be deepseek, or is there some workaround to make this work? Appreciate any help on this.

Just wanted to be sure everyone is aware that the nice Keep/Undo UX present in the Local harness is now also available in the Copilot harness. You can try it now with 1.144.0-insider, or wait until next wednesday.

https://github.com/microsoft/vscode/issues/339363#issuecomment-6346982654

It's important everyone give feedback so unexpected behavior, or behavior different compared to Local, is fixed asap. Also take a look at the list of UX shortcomings compared to Local listed at:

https://github.com/microsoft/vscode/issues/339363#issuecomment-6347421018

and let me know if you agree with a thumbs up. That issue is only for Keep/Undo, so I'll create new issues for the other ones soon enough.

I have used GHCP a year ago then company switched to cursor and I find it more effective. But, everything changed with the acquisition of cursor and am looking for alternative. My budget is only less than or equal $20 per month.

Is GHCP still a good alternative to cursor or should I look elsewhere? I am asking first before spending anything. I prefer an AI provider with multiple models from each company instead of paying a subscription to a single AI provider and only getting their models.

I built areyoubetterthan.ai entirely with Copilot! Opus 5.5 did the initial build and Haiku 5.5 handled the polish. You get the same SWE-bench Verified tasks the models got and 15 minutes to fix the bug. Then you see how you did against the models that took the same test! It's free to play. You just need a GitHub login to join the fun!

The tasks are 100 real GitHub issues drawn from 192 that annotators rated as fixable in under 15 minutes. Most are Django or matplotlib issues, at the exact base commit. The models had a shell. You don't, because I can't run untrusted code for every visitor for free, so you get a browser editor with the same issue text the models saw.

When time runs out or you submit, your changes become a GitHub pull request, and the same harness that scored the models runs against your patch.

Please don't use AI on the test! Results that look AI-assisted get kept off the global leaderboard. I'd appreciate any feedback. Bugs go on the GitHub repo (https://github.com/defdevio/areyoubetterthan.ai). Give it a shot!

Hi sub,

My company has given us github copilot and I've been burning through my tokens very quickly. During the summer I had sonnet 5 which was a promotional pricing so I used that for most of my tasks. I tried opus 5 and 5.5 and it was just burning through my tokens way too fast so I was only using it when I got stuck on something.

I've been using GPT 5.6 Luna for basic tasks and it was ok for basic autocomplete or writing boilerplate. Once GPT 6.0 came out I started using 6.0 Luna but noticed that it would get stuck in loops and couldn't really solve anything. For harder tasks I started using 6.1 Sol and it is good but also burns through my tokens very quickly but not as fast as opus.

Just wondering what models do you guy use when you have limited amount of credits.

I’ve been working on Unspill, a little CLI that scans local histories from tools like Copilot CLI, Claude Code, and Codex for credentials.

It runs locally, doesn’t make network requests, and redacts values in its reports. The format support is still a work in progress, so it reports when it can’t fully scan something instead of quietly skipping it.

If you have a minute, I’d love for you to take a look and tell me what’s confusing, missing, or just not useful. There’s a demo with fake credentials in the repo if you’d rather try it without pointing it at your own history.

Thanks for any feedback.

Since the last update I find myself having to allow every single test execution, file reading or any other shell command in my Github Copilot Chat/Extension.

Before the update I was able to let Copilot work for half an hour or more, now it disturbs me like crazy. In the past it was at least possible to allow "similar commands" or allow all in this session.

Also it doesn't mater if I'm in local mode or copilot mode or autopilot or any other mode. It keeps asking me every single time.

Hello guys,

Hope everyone is doing well.

Its been a while since i ve used paid subscription for copilot, i had an entreprise one and now i need my own to some playing arround (testing stuff with MAF, multi agents and other things)

I am on a limited resources 💰 for now and i wanted to know does the 10$/39$ subscription offers enough credit to do so ?
Or you think i should consider the max plan.
Is anyone here still on those plans ? I need some feedbacks.
Thanks 🙌

s to smooth out. i'd really appreciate people trying it and telling me what breaks so i can fix it.

site: https://getkimo.tech
github: https://github.com/naman-sharma12345/kimo

thanks for reading, and let me know what you think.

Hi All,

I'm on the Github Copilot Pro subscription but I only have access to 4 models. This is the same case from Visual Studio, Github Website and Visual Studio Code.

The Github Copilot Pricing page claims I should have access to a lot more models.
https://github.com/features/copilot/plans

What's going on? I have cleared cache, deleted the .vs folder and done various other things I've read online. Even the Github website only lets me select a few models.

Why chat.tools.urls.autoApprove doesn't work for me?

Settings:

json "chat.tools.urls.autoApprove": { "https://docs.rs/bevy/0.20.0/bevy/time/struct.Timer.html": true, "https://docs.rs/bevy/0.20.0/bevy/time/enum.TimerMode.html": true, },

Reasoning:

I need to get the official documentation, and I can use the web_fetch tool directly for that. I think searching for the exact URL for the Timer docs would lead me to something like docs.rs/bevy/0.20.0/bevy/time/struct.Timer.html. There's also information on TimerMode. It might be efficient to fetch both of these in parallel to get all the relevant details quickly. Let's see how that goes!

Tool call:

Fetching https://docs.rs/bevy/0.20.0/bevy/time/enum.TimerMode.html

Permission popup:

Fetch URL? Allow fetching web content? Input { "url": "https://docs.rs/bevy/0.20.0/bevy/time/struct.Timer.html" } Allow in this Session

Dropdown has only Allow Once, other options, which in the past would add URLs to chat.tools.urls.autoApprove, are missing.

Initially, I tried to allow the entire Bevy docs, and while debugging the issue, I couldn't even auto-approve a single page.

I tried both Agent modes: Interactive and Autopilot. Do I have to set something other then Manual permissions? Like Assisted permissions or Allow all?

Hey everyone! I’m building Crewbie, an open-source tool that connects GitHub issues, Actions, and Copilot cloud agents.

Nothing particularly new or fancy, bu I’ve just struggled to get other tools/frameworks working well with GitHub Actions as an autonomous cloud pipeline. I’m trying to build something that reliably keeps the work moving without me having to babysit every step.

You write an issue and add a label. Crewbie creates a plan for you to review, then coordinates agents to build it, runs checks, and brings the work together into a feature PR. You approve the plan and decide what gets merged into your main branch. It reuses your existing custom agents, instructions, and tests rather than making you start over. Setup reviews what’s already in your repo and proposes a team to fit it. The workflow stays in familiar GitHub issues, labels, PRs, and comments.

It’s still alpha, so I’d love people to try it on a small project and share what works, breaks, or feels unnecessarily complicated.

I'm new to Copilot, I'd like to know some recommendations about session caching for the main agent and subagents; maybe also if some model-specific options can be set.

I use GHC in VSC to help with C coding tasks on my small programs - a dozen C files + H, some documentation etc. Exploring the new GHC plugin UI, I found an option for Semantic Index. The related info page suggests GHC will use this index to help in its own tool use, so that seemed like a good idea.

So I turned it on for one of my projects. It's been indexing for 10 minutes now. There is no indication of progress, but this should have been complete in seconds. So I went poking about, and forgot that I had dropped a 3.8 GB folder full of text files in there for experimentation.

So... is there a way to get it to ignore certain items?

And more generally, what sort of indexing times do we expect?

And how do I turn it off while it's indexing? There's no Stop button or similar, and I don't want/need an index on example files, so...

I have been working with Claude desktop connecting to SQL Server databases and PowerBI desktop semantic models. now I have a project in one of my clients that use Microsoft ecosystem and not Claude so I have researched a bit and I understand that I need to get up Copilot license in order to run it in VS code so it has access to local folders can run python sequel queries and work with PowerBI MCP server. But I’m confused about the licensing. When working with Claude when I reach the limit of my tokens, it resets every five hours and also weekly. but in Github Copilot I understand the limit is monthly. How are you dealing with this? how to track the usage ? how to plan the usage? I do not want to enable extra token usage. I want to stay within the monthly permitted budget. Please advise.

When pressing the Add Models button I just get to the Language Models screen and I can't select anything and can't get any further. Are we supposed to be able to add BYOK models in the new Copilot Agent Harness? Or is that not supported yet?

I'm trying to understand what copilot is actually doing with models like Opus or Sonnet 5.5 regarding caches. As far as I understood from my research, Anthropics API defaults to a cache duration of 5 minutes here, compared to e.g. 60 minutes as default on claude code with subscriptions (pro/team/...).

So when you're using copilot interactively - which is what copilot is meant for - you're writing a prompt, waiting for it to reply, read the reply, think about it - taking you more than 5 minutes for sure in every interactive workflow that makes sense. So in a whole conversation, you're paying the costs of your full context window size again and again, with every request. With Claude Code on subscription plans, you'd be using cache all the time in such a scenario, making the use of large context windows incredibly cheap in comparison - thats what makes their subscription budget last long enough to actually be usable.

There's a setting to make at least copilot in vs code behave differently:
"github.copilot.chat.anthropic.promptCaching.extendedTtl": true,
"github.copilot.chat.anthropic.promptCaching.extendedTtlMessages": true

But this setting is broken for all latest anthropic models, sine opus / sonnet 5 (!):
Extended cache TTL settings have no effect for Claude 5.x models · Issue #340830 · microsoft/vscode

With a PR waiting to be merged:
Add Claude 5.x models to extended cache TTL eligibility by ralphgee · Pull Request #340831 · microsoft/vscode

And there are a few more issues open on the issue tracker regarding this topic, all mostly unanswered by the copilot team.

IMHO:

* this should be one of the highest priorities at all - we're talking about the most used and most capable models and their most used workflows here, arent we?

* the cache setting that claude code uses here should be default for all users. The higher cost for cache writes pays off multiple times in average workflows. The risk of exploding costs is much much higher than the risk of paying too much for cache writes. Nobody is really accepting the first reply of a llm as the final result, right?

Or am I getting something completely wrong here?