GitHub will move Copilot to usage-based billing on June 1, 2026, replacing premium request units with GitHub AI Credits that are consumed according to token usage across inputs, outputs, and cached context. The uproar is not just about a higher bill. It is about the end of the comforting fiction that agentic coding could be sold like a gym membership. Microsoft and GitHub are now asking developers to confront the metered economics that were always hiding behind the magic.
For years, Copilot’s pitch was deliciously simple: pay a predictable monthly fee and get a coding assistant that seemed to grow more capable every quarter. That simplicity mattered. Developers do not love procurement theater, and small teams especially embraced Copilot because it felt like a tool rather than another cloud meter humming in the background.
The new model breaks that spell. GitHub says base subscription prices are not changing: Copilot Pro remains $10 per month, Pro+ remains $39, Business remains $19 per user, and Enterprise remains $39 per user. But those figures now describe included credit value, not the practical ceiling of what a power user might consume.
That distinction is why the backlash has been so loud. A developer who thinks they are buying “Copilot” may now discover they are really buying a monthly allowance against an inference bill. Once that allowance is gone, the experience depends on budget controls, additional usage, and the particular models and workflows being invoked.
The sticker shock examples circulating on Reddit and X may not represent typical usage. But they are rhetorically powerful because they expose the shape of the new bargain. The customer is no longer paying for access to an assistant; the customer is sharing more directly in the cost of running it.
That is the company’s strongest argument. The old premium request system abstracted away too much. A “request” could mean a lightweight prompt or a sprawling agentic workflow that reads files, proposes changes, loops through errors, and burns compute while the user watches or steps away.
The June 1 shift replaces that abstraction with AI Credits consumed according to token usage. In practical terms, the bill is tied to how much text and code is fed into the model, how much comes back, and how much cached context is used. This is familiar to anyone who has used model APIs directly, but it is a psychological change for people who experienced Copilot as a consumer-style subscription inside VS Code.
That psychological change is the real product news. GitHub is not merely updating a billing page. It is moving Copilot from the Netflix era of AI coding to the AWS era, where consumption, governance, and cost visibility become part of the workflow.
Still, dismissing those users as reckless vibe coders misses the point. GitHub spent the last two years making Copilot feel more autonomous, more persistent, and more ambitious. It promoted agentic development, repository-aware assistance, code review automation, and multi-step flows as the future of programming.
When a vendor trains users to offload more work to the assistant, then turns around and says the assistant’s long-running behavior must now be priced like raw compute, users will feel baited even if the economics are defensible. The grievance is not simply “I want free tokens forever.” It is “you taught me a new workflow and then changed the meter underneath it.”
There is a parallel here with early cloud adoption. Teams first fell in love with elastic infrastructure because it removed friction. Then they learned, sometimes painfully, that elastic also meant elastic bills. Copilot’s new pricing moment is the same lesson, compressed into the editor.
This is where Microsoft’s broader AI posture matters. The company has pushed Copilot branding across Windows, Microsoft 365, GitHub, Azure, security tools, and developer platforms. That strategy depends on making AI feel ubiquitous, but ubiquitous inference is not free. Someone eventually pays for the GPUs, model providers, routing layers, caching systems, latency budgets, and reliability engineering.
For Microsoft and GitHub, usage-based billing is an admission that developer AI is becoming infrastructure. Infrastructure can be packaged beautifully, but at scale it tends to revert to meters. The company can call them AI Credits; administrators will read them as another spend category to forecast, cap, and defend.
That does not make the move cynical by default. It may be necessary. But it does reveal the gap between AI marketing and AI operations. The marketing says the assistant is always there. The operations team says the assistant has a marginal cost every time it thinks.
Smaller teams and solo developers live in a different world. They often chose Copilot precisely because it did not require financial modeling. The $10 or $39 monthly fee could be treated like a utility subscription, not a variable production cost.
Usage billing makes those users behave more like cloud administrators. They must understand which models they are using, how agentic tasks consume tokens, whether code review burns additional resources, and whether a runaway session is worth the output. That is a lot to ask from a freelance developer, a student, or a two-person startup trying to ship.
The irony is sharp. Copilot’s original promise was to reduce cognitive overhead. The new billing model may add a new kind of cognitive overhead: watching the meter while the machine writes code.
There is truth in that critique. Agentic coding can become wasteful quickly. A user who repeatedly asks a model to rewrite large chunks of a project, debug blindly, or iterate without reading the output can consume enormous context and generate enormous waste. Token-based billing exposes that waste in a way flat pricing concealed.
But the moralizing tone is too convenient. Modern developer tools are deliberately designed to encourage experimentation. The whole pitch of AI coding assistants is that they make iteration cheaper, faster, and less intimidating. If GitHub did not want users to lean into agentic workflows, it would not have built so much product around them.
The better critique is not that users are foolish. It is that the industry is still learning what competent AI-assisted development looks like when the meter is visible. The answer will not be “never use agents.” It will be better defaults, clearer estimates, smarter caps, and more disciplined workflows.
Under usage-based billing, the experience is governed by available credits and budget controls. That is cleaner from an accounting perspective, but harsher from a user-experience perspective. A cap is a cap; a budget is a budget. Once exhausted, the assistant may stop being available for the expensive work the user expected it to do.
For administrators, this is probably welcome. Shadowy overages are poison in enterprise IT. A hard budget can prevent a team from accidentally turning a coding tool into an uncontrolled AI spend pipeline.
For individual developers, it may feel like a downgrade. The old fallback model preserved a sense that Copilot was always around, even if diminished. The new model more clearly says: when the credits are gone, the economics decide.
This matters for WindowsForum’s sysadmin and IT pro readership because developer tooling is increasingly inseparable from platform governance. GitHub Actions already requires guardrails in serious organizations. If Copilot review becomes another Actions-adjacent cost center, administrators will need policies for when it runs, who can trigger it, and how much review automation is worth.
The risk is not just overspending. It is workflow distortion. If teams become afraid to run AI review because it consumes credits and minutes, the feature may become reserved for high-value repositories or senior-approved pull requests. If they run it indiscriminately, finance may notice before engineering does.
That tension will shape adoption. The strongest AI tools will not merely be the most capable. They will be the ones that fit into predictable team budgets without making every pull request feel like a billing event.
That may ultimately be good for serious software shops. Pooled usage can reduce waste from unused individual allowances. Cost center budgets can distinguish between teams experimenting with agents and teams doing routine maintenance. User-level controls can prevent one enthusiastic engineer from consuming a disproportionate share of the organization’s credits.
But this also means AI coding assistants are graduating from perk to managed service. Procurement will ask why one team needs premium models. Security will ask what code is being sent where. Engineering leadership will ask whether token burn correlates with merged work, reduced incidents, or faster delivery.
That is the hidden win for GitHub. Usage billing does not just improve cost recovery. It creates telemetry and governance hooks that make Copilot easier to sell upward into enterprises. The individual developer may feel squeezed, but the CIO gets a dashboard.
That is convenient until it is not. A developer working on a Windows laptop can now summon remote models from inside the editor, ask an agent to reason over a repository, trigger cloud-backed review, and integrate with CI. The experience feels local, but the economics are remote.
This is why the billing change deserves attention beyond GitHub power users. It previews how Microsoft’s AI layer may mature across products. First comes bundling. Then comes habit formation. Then comes tiering, governance, and consumption pricing for the expensive parts.
Windows enthusiasts have seen this movie in other forms: OneDrive storage, Microsoft 365 subscriptions, Azure meters, Teams add-ons, security SKUs. Copilot is following the same enterprise software gravity. The difference is that this time, the meter is attached to the act of thinking with a machine.
GitHub says it is providing preview billing and visibility into projected costs. That is essential, but it is only the starting point. The company needs to make cost legible at the moment of use, not merely after the fact. If a developer launches an agentic task, the interface should make the likely cost range understandable before the session runs wild.
The same applies to model choice. If one model is cheap and good enough for routine refactoring, and another is expensive but better for deep architectural reasoning, the product should guide users accordingly. Otherwise the bill becomes a punishment for ignorance rather than a reflection of deliberate choice.
There is also a trust issue around measurement. Per-token billing depends on users believing the meter. As AI systems introduce caching, hidden context, tool calls, and autonomous loops, GitHub will need to explain what is counted in plain English. Developers are unusually good at finding edge cases, and they will not tolerate a black-box bill for long.
But as usage-based pricing makes raw inference costs more visible, Copilot’s value proposition shifts. Developers will compare it not only with other coding assistants, but with model APIs, local models, open-source agents, and rival IDE-integrated tools. If GitHub charges close to underlying API economics, customers will ask what premium the wrapper earns.
The answer has to be workflow. Copilot must be better at repository context, safer at edits, clearer about diffs, more reliable in review, more governable for organizations, and less wasteful in token use. If it is merely a convenient front end to expensive models, some users will route around it.
That competition is healthy. It will push coding assistants to optimize not just for demo magic, but for cost-efficient software development. The best agent is not the one that thinks the longest. It is the one that knows when to stop.
The early Copilot bargain was emotional as much as financial. Pay a modest amount, get a futuristic assistant, and do not think too hard about the machinery behind it. That bargain helped normalize AI coding across the profession.
The new bargain is more adult and less fun. Pay a subscription, receive a credit allowance, monitor usage, choose models carefully, and accept that autonomous work has a measurable marginal cost. That is probably where the market had to go. It is also why the transition feels like the end of an era.
The phrase “golden age” is overused in tech, but it fits here in a narrow sense. The golden age was the period when users could experiment heavily while someone else absorbed the mismatch between product pricing and compute reality. GitHub is now closing that gap.
Copilot’s Flat-Rate Era Was a Subsidy Wearing a Hoodie
For years, Copilot’s pitch was deliciously simple: pay a predictable monthly fee and get a coding assistant that seemed to grow more capable every quarter. That simplicity mattered. Developers do not love procurement theater, and small teams especially embraced Copilot because it felt like a tool rather than another cloud meter humming in the background.The new model breaks that spell. GitHub says base subscription prices are not changing: Copilot Pro remains $10 per month, Pro+ remains $39, Business remains $19 per user, and Enterprise remains $39 per user. But those figures now describe included credit value, not the practical ceiling of what a power user might consume.
That distinction is why the backlash has been so loud. A developer who thinks they are buying “Copilot” may now discover they are really buying a monthly allowance against an inference bill. Once that allowance is gone, the experience depends on budget controls, additional usage, and the particular models and workflows being invoked.
The sticker shock examples circulating on Reddit and X may not represent typical usage. But they are rhetorically powerful because they expose the shape of the new bargain. The customer is no longer paying for access to an assistant; the customer is sharing more directly in the cost of running it.
The Token Meter Arrives Where Developers Actually Work
GitHub’s explanation is straightforward: Copilot is no longer just an autocomplete system. It has become a portal into chats, code review, repository-scale context, agent mode, cloud agents, third-party agents, and higher-end frontier models. A one-line question and a long-running autonomous coding session can no longer be treated as economically equivalent.That is the company’s strongest argument. The old premium request system abstracted away too much. A “request” could mean a lightweight prompt or a sprawling agentic workflow that reads files, proposes changes, loops through errors, and burns compute while the user watches or steps away.
The June 1 shift replaces that abstraction with AI Credits consumed according to token usage. In practical terms, the bill is tied to how much text and code is fed into the model, how much comes back, and how much cached context is used. This is familiar to anyone who has used model APIs directly, but it is a psychological change for people who experienced Copilot as a consumer-style subscription inside VS Code.
That psychological change is the real product news. GitHub is not merely updating a billing page. It is moving Copilot from the Netflix era of AI coding to the AWS era, where consumption, governance, and cost visibility become part of the workflow.
The Angry Screenshots Are a Symptom, Not the Whole Diagnosis
The most dramatic complaints describe projected bills jumping from tens of dollars to hundreds or thousands. Those numbers should be treated carefully. Some may reflect unusual workloads, preview estimates, heavy agent use, expensive models, or workflows that were never going to survive contact with cost accounting.Still, dismissing those users as reckless vibe coders misses the point. GitHub spent the last two years making Copilot feel more autonomous, more persistent, and more ambitious. It promoted agentic development, repository-aware assistance, code review automation, and multi-step flows as the future of programming.
When a vendor trains users to offload more work to the assistant, then turns around and says the assistant’s long-running behavior must now be priced like raw compute, users will feel baited even if the economics are defensible. The grievance is not simply “I want free tokens forever.” It is “you taught me a new workflow and then changed the meter underneath it.”
There is a parallel here with early cloud adoption. Teams first fell in love with elastic infrastructure because it removed friction. Then they learned, sometimes painfully, that elastic also meant elastic bills. Copilot’s new pricing moment is the same lesson, compressed into the editor.
Microsoft’s AI Strategy Meets the Accountant
The uncomfortable truth is that GitHub’s old model probably made less sense with every new agentic feature. A fixed monthly fee can subsidize autocomplete and modest chat. It struggles when users expect frontier models to trawl entire repositories, run planning loops, draft pull requests, and perform review-like work.This is where Microsoft’s broader AI posture matters. The company has pushed Copilot branding across Windows, Microsoft 365, GitHub, Azure, security tools, and developer platforms. That strategy depends on making AI feel ubiquitous, but ubiquitous inference is not free. Someone eventually pays for the GPUs, model providers, routing layers, caching systems, latency budgets, and reliability engineering.
For Microsoft and GitHub, usage-based billing is an admission that developer AI is becoming infrastructure. Infrastructure can be packaged beautifully, but at scale it tends to revert to meters. The company can call them AI Credits; administrators will read them as another spend category to forecast, cap, and defend.
That does not make the move cynical by default. It may be necessary. But it does reveal the gap between AI marketing and AI operations. The marketing says the assistant is always there. The operations team says the assistant has a marginal cost every time it thinks.
Small Teams Lose the Comfort of Predictability
Enterprise customers are not immune to the pain, but they are better equipped for it. GitHub is adding pooled included usage for businesses, budget controls at enterprise, cost center, and user levels, and promotional included usage for Business and Enterprise customers during the first three months of the transition. That is the language of corporate adoption: central governance, cost allocation, and spending caps.Smaller teams and solo developers live in a different world. They often chose Copilot precisely because it did not require financial modeling. The $10 or $39 monthly fee could be treated like a utility subscription, not a variable production cost.
Usage billing makes those users behave more like cloud administrators. They must understand which models they are using, how agentic tasks consume tokens, whether code review burns additional resources, and whether a runaway session is worth the output. That is a lot to ask from a freelance developer, a student, or a two-person startup trying to ship.
The irony is sharp. Copilot’s original promise was to reduce cognitive overhead. The new billing model may add a new kind of cognitive overhead: watching the meter while the machine writes code.
The “Vibe Coding” Defense Lets GitHub Off Too Easily
Some developers have responded to the backlash by arguing that only careless users will see giant bills. Use Copilot like a tool, they say, and the cost remains reasonable. Use it as a slot machine for entire applications, and you should expect to pay.There is truth in that critique. Agentic coding can become wasteful quickly. A user who repeatedly asks a model to rewrite large chunks of a project, debug blindly, or iterate without reading the output can consume enormous context and generate enormous waste. Token-based billing exposes that waste in a way flat pricing concealed.
But the moralizing tone is too convenient. Modern developer tools are deliberately designed to encourage experimentation. The whole pitch of AI coding assistants is that they make iteration cheaper, faster, and less intimidating. If GitHub did not want users to lean into agentic workflows, it would not have built so much product around them.
The better critique is not that users are foolish. It is that the industry is still learning what competent AI-assisted development looks like when the meter is visible. The answer will not be “never use agents.” It will be better defaults, clearer estimates, smarter caps, and more disciplined workflows.
The Loss of Fallbacks Changes the Safety Net
One underappreciated part of the transition is GitHub’s statement that fallback experiences will no longer be available under the new model. In the legacy setup, users who exhausted premium request allowances could continue with lower-cost included models, subject to availability and limits. That softened the edge of the quota.Under usage-based billing, the experience is governed by available credits and budget controls. That is cleaner from an accounting perspective, but harsher from a user-experience perspective. A cap is a cap; a budget is a budget. Once exhausted, the assistant may stop being available for the expensive work the user expected it to do.
For administrators, this is probably welcome. Shadowy overages are poison in enterprise IT. A hard budget can prevent a team from accidentally turning a coding tool into an uncontrolled AI spend pipeline.
For individual developers, it may feel like a downgrade. The old fallback model preserved a sense that Copilot was always around, even if diminished. The new model more clearly says: when the credits are gone, the economics decide.
Code Review Is Becoming a Metered Workflow Too
Copilot code review is especially important because it sits at the boundary between personal productivity and team process. GitHub says code completions and Next Edit suggestions remain included, but Copilot code review will consume AI Credits and also GitHub Actions minutes. That means a workflow that feels like part of the development platform may now draw from two separate meters.This matters for WindowsForum’s sysadmin and IT pro readership because developer tooling is increasingly inseparable from platform governance. GitHub Actions already requires guardrails in serious organizations. If Copilot review becomes another Actions-adjacent cost center, administrators will need policies for when it runs, who can trigger it, and how much review automation is worth.
The risk is not just overspending. It is workflow distortion. If teams become afraid to run AI review because it consumes credits and minutes, the feature may become reserved for high-value repositories or senior-approved pull requests. If they run it indiscriminately, finance may notice before engineering does.
That tension will shape adoption. The strongest AI tools will not merely be the most capable. They will be the ones that fit into predictable team budgets without making every pull request feel like a billing event.
The Enterprise Version of This Story Is FinOps for Prompts
Large organizations will probably adapt faster, because they already have a vocabulary for this problem. Cloud cost management taught them to tag resources, allocate spending, cap runaway jobs, and make teams accountable for usage. Copilot’s new billing model invites the same discipline into the developer environment.That may ultimately be good for serious software shops. Pooled usage can reduce waste from unused individual allowances. Cost center budgets can distinguish between teams experimenting with agents and teams doing routine maintenance. User-level controls can prevent one enthusiastic engineer from consuming a disproportionate share of the organization’s credits.
But this also means AI coding assistants are graduating from perk to managed service. Procurement will ask why one team needs premium models. Security will ask what code is being sent where. Engineering leadership will ask whether token burn correlates with merged work, reduced incidents, or faster delivery.
That is the hidden win for GitHub. Usage billing does not just improve cost recovery. It creates telemetry and governance hooks that make Copilot easier to sell upward into enterprises. The individual developer may feel squeezed, but the CIO gets a dashboard.
The Windows Developer Angle Is Bigger Than GitHub
For Windows developers, the Copilot shift lands in an ecosystem where Microsoft has been steadily embedding AI into the tools of daily work. Visual Studio, VS Code, Windows Terminal, GitHub, Azure, and Microsoft 365 all now orbit some version of Copilot or AI-assisted workflow. The boundary between local developer environment and cloud inference keeps getting thinner.That is convenient until it is not. A developer working on a Windows laptop can now summon remote models from inside the editor, ask an agent to reason over a repository, trigger cloud-backed review, and integrate with CI. The experience feels local, but the economics are remote.
This is why the billing change deserves attention beyond GitHub power users. It previews how Microsoft’s AI layer may mature across products. First comes bundling. Then comes habit formation. Then comes tiering, governance, and consumption pricing for the expensive parts.
Windows enthusiasts have seen this movie in other forms: OneDrive storage, Microsoft 365 subscriptions, Azure meters, Teams add-ons, security SKUs. Copilot is following the same enterprise software gravity. The difference is that this time, the meter is attached to the act of thinking with a machine.
Transparency Will Decide Whether This Feels Fair
Token billing is not inherently abusive. In fact, it can be more honest than request billing if users can see what they are consuming and why. The problem is that token economics are opaque to most people, even many developers. A “request” is intuitive. A blend of input tokens, output tokens, cached tokens, model rates, and agentic tool behavior is not.GitHub says it is providing preview billing and visibility into projected costs. That is essential, but it is only the starting point. The company needs to make cost legible at the moment of use, not merely after the fact. If a developer launches an agentic task, the interface should make the likely cost range understandable before the session runs wild.
The same applies to model choice. If one model is cheap and good enough for routine refactoring, and another is expensive but better for deep architectural reasoning, the product should guide users accordingly. Otherwise the bill becomes a punishment for ignorance rather than a reflection of deliberate choice.
There is also a trust issue around measurement. Per-token billing depends on users believing the meter. As AI systems introduce caching, hidden context, tool calls, and autonomous loops, GitHub will need to explain what is counted in plain English. Developers are unusually good at finding edge cases, and they will not tolerate a black-box bill for long.
The Real Product Is No Longer the Model
One reason Copilot became dominant is that it was not just a model. It was distribution. It lived where developers already worked, integrated with GitHub identity, plugged into editors, and benefited from Microsoft’s platform reach. That remains a formidable advantage even if billing becomes less friendly.But as usage-based pricing makes raw inference costs more visible, Copilot’s value proposition shifts. Developers will compare it not only with other coding assistants, but with model APIs, local models, open-source agents, and rival IDE-integrated tools. If GitHub charges close to underlying API economics, customers will ask what premium the wrapper earns.
The answer has to be workflow. Copilot must be better at repository context, safer at edits, clearer about diffs, more reliable in review, more governable for organizations, and less wasteful in token use. If it is merely a convenient front end to expensive models, some users will route around it.
That competition is healthy. It will push coding assistants to optimize not just for demo magic, but for cost-efficient software development. The best agent is not the one that thinks the longest. It is the one that knows when to stop.
The Backlash Is Really About a Broken Social Contract
GitHub can argue, correctly, that the old premium request model was no longer sustainable. Developers can argue, correctly, that they adopted Copilot under a different set of expectations. Both sides can be right because the social contract changed faster than the product label.The early Copilot bargain was emotional as much as financial. Pay a modest amount, get a futuristic assistant, and do not think too hard about the machinery behind it. That bargain helped normalize AI coding across the profession.
The new bargain is more adult and less fun. Pay a subscription, receive a credit allowance, monitor usage, choose models carefully, and accept that autonomous work has a measurable marginal cost. That is probably where the market had to go. It is also why the transition feels like the end of an era.
The phrase “golden age” is overused in tech, but it fits here in a narrow sense. The golden age was the period when users could experiment heavily while someone else absorbed the mismatch between product pricing and compute reality. GitHub is now closing that gap.
The June 1 Bill Shock Is a Warning Label for Agentic Development
The practical lesson is not that developers should abandon Copilot. It is that AI-assisted development now needs the same operational discipline teams already apply to cloud infrastructure, CI pipelines, and SaaS sprawl.- GitHub Copilot’s base plan prices are staying the same, but the meaning of those prices changes because they now correspond to included AI Credits rather than a broad flat-rate experience.
- The largest cost swings are likely to come from agentic workflows, large context windows, premium models, repeated iterations, and review automation rather than ordinary inline completions.
- Individual developers and small teams should set budgets early, review projected usage, and treat long-running autonomous sessions as billable work rather than harmless experimentation.
- Enterprise administrators should prepare policies for pooled credits, cost centers, user caps, model access, and Copilot code review before the new billing model becomes a help-desk surprise.
- GitHub’s biggest challenge is not proving that inference costs money; it is making the meter transparent enough that developers believe the bill and can change behavior before they exceed it.
References
- Primary source: TechCrunch
Published: Sat, 30 May 2026 16:30:00 GMT
'What a joke': Github Copilot's new token-based billing spurs consternation among devs | TechCrunch
The golden age of Microsoft's Github Copilot appears to be at an end.
techcrunch.com
- Official source: docs.github.com
Requests in GitHub Copilot (legacy) - GitHub Docs
Learn about requests in Copilot, including premium requests, how they work, and how to manage your usage effectively.
docs.github.com
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www.agent-wars.com
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24-ai.news
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- Official source: github.com
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github.com
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GitHub Copilot Drops Its Model Fallback on June 1: What the Token-Based Billing Switch Means for AI Engineering Teams — TheRouter.ai
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GitHub Copilot Usage-Based Billing | AI Credits & Cost Optimization
Prepare your enterprise for GitHub Copilot Usage-Based Billing with readiness assessment, AI governance, cost visibility, usage optimization, and adoption strategies with Canarys.
ecanarys.com
- Related coverage: itpro.com
Everything you need to know about the GitHub Copilot pricing changes
GitHub Copilot pricing changes mean users will be charged based on consumption, rather than a set number of credits
www.itpro.com
- Related coverage: windowscentral.com
Copilot is now injecting ads into GitHub pull requests. It's a disaster.
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www.windowscentral.com