Cursor has become one of those tools that quietly works its way into every coding session: autocomplete here, chat there, a quick refactor before committing. That convenience is exactly what makes pricing changes painful. When usage is tied to requests, model choices, or plan limits, the monthly bill can stop feeling like a fixed subscription and start feeling like a moving target.
After one billing cycle came in higher than expected, I wanted something more practical than checking the dashboard after the fact. I needed a lightweight way to track how often I was using AI assistance, estimate what that usage might cost, and catch runaway patterns before they turned into budget surprises.
As an Amazon Associate I earn from qualifying purchases.
This walkthrough covers the tracker I built for that purpose: the workflow, the data collection layer, the cost estimation , budget alerts, and the habits it exposed once I started watching my AI coding usage day to day.
Why Cursor’s Pricing Caught Me Off Guard
For a long time, my mental model for Cursor spending was simple: pay for the subscription, use the editor heavily, and assume the monthly bill would stay roughly the same. That worked while my usage felt comfortably inside the plan’s boundaries. I used chat for debugging, inline edits for refactors, composer-style workflows for larger changes, and the occasional expensive model when I wanted a second opinion on architecture. None of it felt like “metered infrastructure” in the way a cloud database or hosted GPU does.
#1 Best Overall
- Design: The monitor stand for the desk has a large 14.6 x 9.3 inches metal shelf that fits most flat screen displays, laptops, and printers, with a maximum support weight of up to 44 lbs (20kg). Rubber pads prevent slipping or damage to your work surface
- Ergonomic: The height-adjustable monitor riser can raise a computer monitor, notebook, or any device by 3.9 inches, 4.7 inches, or 5.5 inches off the desk to create a comfortable viewing and sitting position which helps reduce stress on the neck and back
- Ventilated: The computer stand has a large sturdy platform with vented holes, this stand will prevent overheating and keep the device running cool
- Under-stand Storage: Open space beneath the stand for storing keyboards, notebooks and other desk accessories to reduce desktop clutter
- Wide Compatibility: Works for single or dual monitor arrangements and laptop setups for home and office desks
The pricing changes disrupted that assumption because AI-assisted coding cost is not tied to one obvious action. A single work session might include dozens of small autocomplete interactions, several chat requests, a few long-context codebase questions, and one or two large edits that send substantial context to a frontier model. From the user’s side, those actions all feel like normal editor usage. From the billing side, they can have very different weights depending on model, request type, context size, and whether the request falls inside or outside the included allowance.
The part that caught me off guard was not simply that Cursor could cost more. It was that I could no longer estimate cost from time spent coding. Two hours of careful work on a small file might be cheap. Twenty minutes spent asking a large model to inspect a messy repository, generate a migration plan, and rewrite several files could consume much more. The editor made it easy to move quickly, but that convenience also blurred the line between lightweight assistance and high-cost model calls.
Where my old budgeting approach failed
- No per-session visibility: I could remember that I used Cursor a lot on a given day, but not how many requests I made or which workflows generated them.
- No model-level breakdown: Switching between cheaper and more capable models changed the economics, but I did not have a habit of tracking those choices.
- No context-size awareness: Asking questions over a large codebase felt similar to asking about a single function, even though the amount of data sent could be very different.
- No early warning system: By the time usage felt “high,” I had already made the requests. I wanted alerts before a month turned expensive, not after.
This uncertainty changed how I behaved. I found myself hesitating before using the tool for legitimate work because I did not know whether I was being efficient or wasteful. That is a bad place for a developer tool to land: either I ignore the cost and risk a surprise, or I overcorrect and stop using the assistant in places where it genuinely saves time. The problem was not that I needed perfect accounting down to the cent. I needed enough visibility to make sane decisions during the month.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
That became the motivation for building a tracker. I wanted a practical ledger of AI coding activity: when requests happened, which model or feature they used, how much they might cost, and how quickly I was moving through a monthly budget. Cursor’s pricing changes turned usage from an invisible background detail into something I needed to observe, just like test duration, cloud spend, or CI minutes. Once I framed it that way, the next step was clear: build a small system that made my AI usage visible while I was still in control of it.
What I Needed From a Usage Tracker
Once I realized my AI-assisted coding spend could move faster than I expected, the tracker had to answer one simple question: am I still within the budget I thought I was using? Cursor’s interface gave me enough information to understand that usage was happening, but not enough day-to-day visibility to make decisions while I was coding. I did not want a finance dashboard I would check once a month. I needed something close to the development loop: fast to update, easy to inspect, and opinionated enough to warn me before a normal refactor turned into an expensive afternoon.
The first requirement was tracking request volume. Even if pricing is not purely “one request equals one fixed cost,” request counts are still the most practical early signal. I wanted to see how many times I was asking the assistant for help, which models or modes were involved where possible, and whether certain sessions were unusually chatty. A big feature build with a few focused prompts is very different from repeatedly asking the model to rewrite the same file, explain compiler errors, and regenerate tests. The tracker needed to capture that pattern, not just the final bill.
The second requirement was cost estimation, not perfect accounting. I was not trying to reverse-engineer every internal billing detail or create a legally precise invoice replica. I wanted a conservative estimate that was useful for behavior changes. If the tracker said I was trending toward $18 on a $20 monthly ceiling, that was enough to slow down, switch models, batch prompts, or finish the task manually. Precision mattered less than timeliness, consistency, and making the hidden burn rate visible while there was still time to react.
Free tools Windows power users keep installed
One-click scans. No signup required.
Rank #2
- 【Ergonomic Design】：OPNICE newly releases the monitor stand for desk organizer! This computer stand elevates your monitor or laptop to a comfortable viewing height, relieving pressure on your neck, shoulders. Ideal for strengthening office organization and increasing comfort levels
- 【Save Space】：This 2-Tier monitor stand with drawer and 2 hanging pen holders provides ample storage space to keep your office supplies and office desk accessories neatly organized and easily accessible, keeping your workspace tidy and improving your sense of well-being
- 【Durable and Stable】：The metal computer stand is made of high quality material with sturdy construction, it can easily carry the weight of the display and computer accessories, to ensure stable and non-shaking for a long time, ideal for use in the office, dorm room or home
- 【Sleek and Aesthetic】：This desktop organizer features a modern minimalist design that blends seamlessly with any office decor. It not only enhances functionality but also adds a touch of style and aesthetic to your workspace, making it an essential piece for your office organization efforts
- 【Hassle-free Shopping】：OPNICE is committed to providing excellent after-sales service and offers a 100-day unconditional return policy for desk organizers and accessories. Comes with four non-slip pads that are height-adjustable to protect your table from scratches(U.S. Patent Pending)
Core requirements
- Daily and monthly totals: I needed both short-term and billing-cycle views so I could catch spikes without losing sight of the larger budget.
- Per-project breakdowns: Work projects, side projects, experiments, and throwaway prototypes should not all blend into one number.
- Model or request category labels: When available, the tracker should distinguish lightweight completions from heavier agent-style interactions.
- Estimated spend: Every tracked event should roll up into an approximate dollar amount using configurable rates.
- Budget alerts: The system should warn at thresholds such as 50%, 80%, and 100% of a monthly limit.
- Low-friction logging: If tracking required manual data entry after every prompt, I knew I would abandon it within a week.
I also wanted the tracker to preserve context around usage. A raw count of 120 requests is less useful than knowing those requests happened during “migration from Jest to Vitest” or “debugging OAuth callback failures.” So the design needed optional metadata: repository name, branch, task label, and timestamp. That gave me a way to compare the cost of different kinds of work. For example, generating boilerplate might be cheap and efficient, while vague debugging conversations might consume a lot of requests without moving the task forward.
Privacy shaped the requirements too. I did not want to store full prompts, source code, API keys, stack traces, or customer data just to estimate spend. The tracker only needed operational metadata: when a request happened, what category it belonged to, which project it was associated with, and the estimated cost. That kept the database small and reduced the risk of creating a second sensitive system next to my editor.
Finally, the tracker had to be adjustable. Pricing models change, my workflow changes, and Cursor’s product behavior can change as features evolve. Hard-coding assumptions would make the tool stale almost immediately. I needed rates, budgets, project names, and alert thresholds to live in configuration rather than deep in the code. The goal was not to predict the future perfectly; it was to build a practical control panel for my own usage so surprises became visible early instead of arriving with the bill.
Designing the Tracking Workflow
The first design decision was to treat Cursor usage as an event stream rather than a monthly total. A monthly number is useful for billing, but it is too late to change behavior by the time it arrives. I wanted each AI interaction to become a small record with enough context to answer practical questions: when did it happen, what kind of request was it, which project triggered it, and how much should I assume it cost?
I split the workflow into four stages: capture, normalize, estimate, and review. Capture is responsible for collecting raw activity as close to the source as possible. Normalize converts messy activity into consistent fields. Estimate applies pricing assumptions so requests turn into projected spend. Review turns those rows into something I can actually use during the workday, such as a daily total, a project breakdown, and a warning when I am getting close to a self-imposed limit.
Core event model
The tracker revolves around a simple usage event. I intentionally kept the schema small because a tracker that requires constant manual cleanup will not survive more than a week. Each row represents one AI-assisted coding action, whether it came from a chat message, an inline edit, or a larger agent-style task.
| Field | Purpose |
|---|---|
| timestamp | Groups usage by hour, day, week, and billing period. |
| project | Shows which repositories or clients are driving spend. |
| request_type | Separates chat, inline edit, composer, and agent-like workflows. |
| model_tier | Tracks whether the request used a cheaper or more expensive model class. |
| estimated_units | Stores the request count, token estimate, or multiplier used for costing. |
| estimated_cost | Turns the event into a budget number that can be aggregated. |
I also added two optional fields: source and confidence. Source tells me whether the event came from an automated log, a local script, or a manual entry. Confidence is a small but useful guardrail because not every estimate deserves the same trust. A request pulled from a structured local log gets high confidence. A manually entered “long agent session” gets medium confidence. A rough backfill from memory gets low confidence and is excluded from some charts.
Rank #3
- Design: The monitor stand for the desk has a large 14.6 x 9.3 inches plastic shelf that fits most flat screen displays, laptops, and printers, with a maximum support weight of up to 44 lbs (20kg). Rubber pads prevent slipping or damage to your work surface
- Ergonomic: The height-adjustable monitor riser can raise a computer monitor, notebook, or any device by 4.5 inches, 5.3 inches, or 6.1 inches off the desk to create a comfortable viewing and sitting position which helps reduce stress on the neck and back
- Ventilated: The computer stand has a large sturdy platform with vented holes, this stand will prevent overheating and keep the device running cool
- Organization: The sleek modern black design complements any desk while adding extra space underneath the stand for storage
- Easy Installation: Tools are not required for assembly of this computer accessories. All components fit together smoothly for fast setup to organize your desk quickly
Daily workflow
The day-to-day flow had to be nearly invisible. When I start working, the tracker uses the current Git repository name as the default project. As I use Cursor, events are appended to a local store. At intervals, a small aggregation job calculates spend for today, this week, and the current billing cycle. I do not need perfect real-time accounting; I need fast feedback before an ordinary coding session turns into an expensive one.
- During coding: collect request events with timestamp, project, and request type.
- Every few minutes: recalculate running totals and compare them with daily and monthly limits.
- At the end of the day: review project totals and mark suspicious rows for correction.
- At the end of the week: adjust estimates if actual billing data suggests the model is drifting.
The most useful design choice was separating raw events from pricing rules. Cursor can change plan details, mulliers, or included usage, but historical records should not have to be rewritten. Instead, the tracker stores the original activity and applies a versioned pricing profile on top. If my assumptions change, I can rerun the estimate for the month and immediately see how the forecast moves. That made the system resilient enough to handle uncertainty, which was the entire point of building it in the first place.
Building the Data Collection Layer
The tracker became useful only when it stopped depending on memory. I wanted every AI-assisted coding action to leave a small, structured record behind: when it happened, which workspace it belonged to, what kind of request it was, and any details that would help estimate cost later. Since Cursor does not expose a perfect personal billing event stream for every interaction, I treated the data collection layer as a practical approximation rather than a financial ledger.
I started with a local-first design. Each coding machine writes usage events to a lightweight SQLite database, then a small sync job can export daily summaries to a shared location. That avoided sending prompts or source code to another service just to track spending. The event rows intentionally store metadata, not content. A chat request might be recorded as “composer request in project-api at 14:03,” but not the actual prompt, file contents, or generated code.
Event schema
The first version used a single table called usage_events. Keeping the schema boring made it easier to inspect with standard tools and change without migrations becoming a side project.
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →| Field | Purpose |
|---|---|
| timestamp | Records when the event happened, in UTC. |
| workspace | Groups usage by repo, client project, or personal project. |
| action_type | Labels the request as chat, edit, agent, autocomplete, or manual adjustment. |
| model | Stores the selected model or plan tier when detectable. |
| estimated_units | Holds the request count, token estimate, or weighted unit used for cost modeling. |
| source | Identifies whether the event came from a shortcut, log parser, CLI command, or import. |
For collection, I used three inputs. The most reliable one was a tiny command-line helper that I could trigger from shell aliases and editor tasks. For example, before starting a long agent-style refactor, I logged an agent_start event, and after it finished I logged the result size as small, medium, or large. That sounds manual, but it quickly became muscle memory for expensive actions. The second input was a log parser that scanned local application and workspace files for timestamps and interaction markers where available. It did not need to understand everything; it only needed to catch enough repeated patterns to reduce manual entry. The third input was a daily correction command for reconciling the tracker against Cursor’s visible usage or billing screen.
The parser was deliberately conservative. If it could not classify an entry with confidence, it skipped it rather than inflating totals with bad data. I also added a manual_adjustment action type for cases where the estimate was clearly low, such as a long session that involved repeated agent retries. This mattered because the goal was not forensic accuracy; it was avoiding budget surprises. A tracker that is consistently within 10 to 20 percent is far more useful than a complicated system I would stop maintaining after a week.
Rank #4
- 【Ample Storage Space】The dual monitor stand features two magnetic pen holders and a drawer, allowing you to easily organize your desk accessories and office supplies, keeping your workspace clear and tidy for easier access.
- 【Work with ease】The Gianotter monitor stand for desk can adjust the monitor height to eye level, reducing neck and eye strain, improving posture, and enhancing focus and work efficiency.
- 【Maximize desktop space】By raising the monitor height, the space underneath the computer stand can be utilized for storing your mouse, keyboard, or other office supplies, maximizing your desktop area.
- 【No Assembly Required】This monitor riser allows you to skip the hassle of assembly—just unbox it and effortlessly transform cluttered desktop areas, decorating your desktop to enhance your workspace aesthetics!
- 【Quality Assurance】This desk shelf for monitor is meticulously crafted with a perfect design ratio and high-strength metal materials, ensuring exceptional support performance to easily meet your needs. Whether you're raising your monitor or optimizing your workspace, it's the ideal choice to revitalize your desktop! (USPTO patented product)
To make the data collection layer durable, I added two small safeguards. First, every insert is idempotent when possible, using a hash of timestamp, workspace, action type, and source so repeated imports do not double-count the same request. Second, the daily export writes both raw events and rollups, so I can rebuild the dashboard if the pricing model changes. That turned out to be valuable because the pricing assumptions were the least stable part of the whole project. The raw history stayed the same, while the cost estimator could be updated independently.
Estimating Cost and Setting Budget Alerts
Once the tracker was collecting each AI coding request, the next problem was turning raw activity into something financially useful. A request count by itself was not enough. One tiny inline edit and one long agent-style task could both appear as a single interaction, yet they have very different cost implications. I treated the cost model as an estimate rather than an invoice replica: close enough to guide behavior, simple enough to maintain when Cursor changes plan details again.
Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCrashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteThe cost estimator runs as a small calculation layer on top of the request log. Each event gets normalized into a few fields: timestamp, project, feature type, model tier when available, prompt size bucket, response size bucket, and whether the request was manual chat, inline completion, composer, or a longer background task. For anything the tracker cannot measure directly, it uses conservative defaults. That bias is intentional. I would rather be warned too early than discover at the end of the month that my “normal” coding week quietly turned into a larger bill.
Cost fields I track for each request
- Request category: chat, inline edit, agent task, autocomplete, or refactor flow.
- Estimated weight: a multiplier that separates lightweight completions from expensive multi-file operations.
- Project label: client work, personal project, open source, or experiments.
- Billing period: grouped by calendar month, with a reset date matching my subscription cycle.
- Running estimate: current projected spend based on observed usage pace.
I store pricing assumptions in a plain configuration file rather than hard-coding them into the collector. That file includes the monthly plan amount, included usage allowance if applicable, an estimated overage rate, and mulliers for different request types. When pricing changes, I edit one file and rerun the daily rollup. This also lets me test scenarios, such as “what if this week’s usage continues for the rest of the month?” or “what if agent tasks cost twice as much as I estimated?” Those projections are more useful than a static total because budget trouble usually comes from a trend, not a single request.
| Alert | Trigger | Action |
|---|---|---|
| Daily spike | Today’s estimated usage is 2x my 14-day average | Send a local notification and tag the top project |
| Monthly threshold | Estimated spend passes 50%, 75%, or 90% of budget | Post a summary to my terminal dashboard |
| Projection warning | Current pace projects above the monthly cap | Suggest switching heavy AI work to review-only mode |
The most useful alert turned out to be the projection warning. A monthly total only tells me what already happened, but a projection catches the pattern while I can still change course. If I spend three intense days asking Cursor to rewrite tests, generate migrations, and chase TypeScript errors across a large codebase, the tracker shows the month-end estimate immediately. That gives me a chance to batch future prompts, use smaller context windows, or move some exploration back to local search and manual editing.
I also added a small “budget mode” flag to the dashboard. When it is off, the tracker simply records and reports. When it is on, the daily becomes more direct: which project consumed the most estimated cost, which workflow produced the largest jump, and how much room is left before the next threshold. I did not build anything that blocks Cursor or interferes with coding. The goal is visibility, not punishment. The best version of the tracker is quiet most of the time, then loud exactly when my habits start drifting beyond the budget I meant to keep.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →What the Tracker Revealed About My AI Coding Habits
After a few weeks of collecting request counts, model choices, estimated cost, editor context, and short task labels, the tracker became more useful than the budget guardrail I originally wanted. It showed patterns I had missed while working inside the editor. My usage did not rise evenly through the month. It spiked around specific kinds of work: unfamiliar codebases, test repair, large refactors, and late-day debugging sessions where I kept asking variations of the same question instead of stepping back and reading the code myself.
Best Value
- DUAL MONITOR STAND RISER FOR CUSTOMIZED DESK SETUPS: Create an optimized dual-screen workspace with a 31.5"-42.5" expandable design. The swivel side shelves support straight, angled or corner layouts, giving you flexible options for different monitor arrangements in home office, work from home and gaming setups.
- ERGONOMIC DUAL SCREEN WORKSPACE: Elevate two monitors with this ergonomic monitor riser to a more comfortable viewing height, helping reduce neck, back and shoulder strain during long work, study or gaming sessions. Create a cleaner and more productive desk setup.
- 3 COMPARTMENT DESK ORGANIZER WITH LARGE STORAGE: Maximize your desktop space with 3 open storage compartments for keyboards, mice, files, notebooks, docking stations, office supplies and gaming devices. It works as a desktop organizer and desk shelf to keep your workspace tidy and easy to use.
- PHONE HOLDER & CABLE MANAGEMENT: Keep your phone, devices and cables organized with the built-in phone holder slot and cable management opening. This dual monitor stand for desk helps create a cleaner workspace with frequently used items always within reach.
- STABLE WOODEN MONITOR STAND & EASY ASSEMBLY: Built with a sturdy wooden structure and non-slip pads, this white monitor stand provides reliable support while protecting your desk surface. Assemble in about 2 minutes with included screws and create a modern, organized workspace setup.
The biggest surprise was how many requests were not tied to hard engineering problems. A large share came from small convenience prompts: “rename this cleanly,” “write a quick helper,” “explain this function,” or “make this error message better.” None of those were wasteful by themselves, but grouped together they made up a meaningful part of my estimated spend. The tracker helped separate high-value AI usage from background friction. When I used the assistant to map an unfamiliar service, draft a migration plan, or generate test cases for edge conditions, the cost felt justified. When I sent twelve prompts to tweak a small UI component, it usually meant I had not defined the target clearly enough before involving the model.
Patterns that changed how I worked
- Context-heavy prompts were the cost drivers. Requests that pulled in multiple files or long chat history were much more expensive than short, targeted edits.
- Debugging loops were easy to miss. A single bug could create a long chain of requests because each failed fix felt like a small continuation rather than a new expense.
- Morning sessions were more efficient. Prompts written with a fresh understanding of the task had fewer follow-ups and lower estimated cost per completed change.
- Test generation was consistently worth it. Even when it produced imperfect first drafts, it reduced the time I spent thinking through boundary cases.
- Autopilot mode was the budget leak. The tracker made it obvious when I was using AI as a reflex instead of as a tool for a defined job.
I added a simple “task outcome” field after noticing that raw request totals did not tell the full story. At the end of a session, I marked the result as shipped, discarded, research only, or needs manual rewrite. That small label made the dashboard much more honest. Some days with higher spend were perfectly reasonable because they produced merged code, better tests, or a clear implementation plan. Other days looked cheap by request count but expensive by value because the generated code never made it past review.
| Habit | What the data showed | Change I made |
|---|---|---|
| Repeated fix attempts | Long chains of similar prompts on the same error | Stop after three attempts and inspect the runtime state manually |
| Broad file context | Higher estimated cost with no consistent quality gain | Attach only the files needed for the current question |
| Vague edit requests | More follow-up prompts and more discarded output | Write acceptance criteria before asking for code |
| AI-assisted tests | High merge rate and fewer missed edge cases | Keep using AI for test scaffolding and scenario coverage |
The most practical result was not that I used the assistant less. I used it more deliberately. I started batching related questions, trimming unnecessary context, and choosing cheaper paths for low-risk tasks. I also became more comfortable spending on the sessions that genuinely accelerated the work. The tracker turned the pricing change from a source of anxiety into a feedback loop: each request had a visible cost, but each productive session also had visible value.
Frequently Asked Questions
Can I track Cursor usage accurately if Cursor does not expose a detailed billing API?
Yes, but you may need to combine several signals instead of relying on one perfect source. A practical tracker can log your own AI requests locally, record timestamps, model names when available, estimated token counts, and reconcile those numbers against Cursor’s account or billing page. The result will usually be an estimate, but it is often accurate enough to spot runaway usage before the bill surprises you.
What should I track for each AI coding request?
At minimum, track the time, workspace or project, request type, model used, and an estimated input and output size. If possible, also record whether the request came from chat, inline edit, agent mode, or background context gathering. Those categories make it much easier to see which workflows are consuming the most budget.
How can I estimate cost when token counts are not visible?
You can estimate tokens from character counts, file sizes, pasted context, and response length, then apply a conservative mullier to avoid undercounting. For day-to-day budgeting, it is usually better to overestimate by 10–25% than to aim for perfect precision. You can refine the estimate later by comparing your tracked totals with actual billing data.
What kind of budget alerts are useful for Cursor usage?
The most useful alerts are threshold-based warnings, such as 50%, 75%, and 90% of your monthly budget. Daily spend-rate alerts are also helpful because they catch sudden changes in behavior, such as leaving agentic workflows running across a large codebase. If you work across mulle projects, per-project limits can show which client, repo, or experiment is driving the increase.
Recommended Free Tools
Did tracking usage actually change how you used AI coding tools?
Yes, because the tracker made expensive habits visible. Large context prompts, repeated retries, and agent runs over broad parts of a repo often cost more than short, targeted questions. After tracking usage, it becomes easier to reserve heavy AI workflows for high-value tasks and use cheaper, narrower prompts for routine edits.
Bottom Line
Cursor can still be a great AI coding companion, but pricing changes make it much easier to lose track of how much you are really using it. A simple usage tracker gives you visibility into request volume, estimated spend, and usage patterns before they turn into an unpleasant billing surprise.
If your AI coding workflow is becoming part of your daily development routine, treat it like any other metered infrastructure: monitor it, set thresholds, and review the data regularly. Start with a lightweight tracker, refine it as your habits change, and let the numbers guide how you use Cursor without blowing your budget.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute
