Category Focus/categories/analytics
Analytics AI Tools
Discover and compare the best analytics AI tools and software. Browse 135+ curated tools with reviews and rankings.
Projects tracked
135
Sort mode
RECENT
Page
1
Analytics
Clocknext is monetization and billing infrastructure built specifically for AI agents and AI-native applications. It lets teams track AI usage from their product in real time, turn that usage into billable metrics with flexible pricing plans, and monitor costs, margins, and profitability at every level. The platform is aimed at AI founders along with revenue and engineering teams who need to convert tokens, completed outcomes, and countable units into revenue. Its stated goal is to make AI billing simple again: every AI interaction has to be metered, priced, and billed, and Clocknext supplies the infrastructure for that so teams do not have to assemble it themselves. The problem Clocknext addresses is the gap between counting usage and earning from it. As the company puts it, every token is easy to count, but turning it into revenue isn't. Without the right infrastructure, teams risk losing revenue, momentum, and peace of mind. Cost visibility is a large part of that risk: a model switch made in June should not surface as a surprise in September, which is why Clocknext tracks cost as it happens, against the signal that caused it. Engineering dependency is the other friction point — metering pipelines, wallet logic, and invoicing all have to be built and maintained unless a platform owns them, and pricing changes routinely get queued behind engineering work. Clocknext is designed so that pricing moves at the speed of the business rather than the deployment calendar. Pricing is assembled from primitives that can be mixed and changed without touching the code that reports usage. Credits are a token-denominated allowance drawn from a wallet as agents work. Outcomes price the completed result rather than the compute behind it. Units cover seats, repos, integrations, and anything countable. Composites combine primitives for multi-action agents and bill them as one line. In the product's own example, 100,000 credits at $0.008 come to $800.00, 47 outcomes at $12.00 come to $564.00, 12 seats at $29.00 come to $348.00, and a voice agent composite spanning four meters comes to $410.00 — all of it landing on one plan and one invoice totaling $2,122.00. Teams start with one primitive, add the rest when needed, and change the mix freely. Payment methods are equally configurable, supporting prepaid, postpaid, hybrid, and top-ups. With prepaid, customers fund a wallet before they use anything and usage draws it down in real time, so there is no chasing payment after the work is done. Hybrid combines a commitment upfront that covers expected volume with anything beyond it billed after, with both landing on the same invoice under one plan. Postpaid meters through the month and settles once at the end, fitting the purchase order and procurement cycle enterprise buyers already run. Top-ups let customers buy more credits or outcomes on their own whenever they need them, with the balance landing in their wallet straight away. Margin control sits alongside billing. Clocknext bridges the gap between provider cost and usage with margins that keep a product's bottom line healthy in real time, showing revenue against provider cost with margin as the visible band between them. A customer view surfaces cash balance, credit allowance, units consumed, revenue, provider cost, gross margin, and month-over-month movement, alongside a monthly table of revenue, provider cost, margin, and gross margin. Because margin is tracked by account, by agent, and by model, an account that falls below target is flagged — for example, a customer showing 8.2% margin against peers at 74.9% and 66.9% — and can be repriced immediately. Changes to the plan, the rate, or the credit conversion take effect on save, with no engineering involvement. Operationally, the product is built around usage signals rather than a bespoke metering pipeline. Setup runs in three steps: install the SDK, send a usage signal, and map signals to pricing. From there, pricing packages are built by mixing credits, outcomes, and units and bundling them into composites when a product needs its own price, then published to all customers live with no deploy. Engineering teams get a drop-in usage SDK and API so there is no metering pipeline to own, while wallets and invoicing are built in. Product teams can run no-code pricing experiments, ship new plans in hours, and A/B test monetization without an engineering dependency. The stated outcomes are speed and financial clarity. Clocknext reports a time to live of two days from signup to the first invoice out the door, eight hours of billing operations saved every week on reconciliation and invoicing, and a 90% increase in gross margin after pricing moved onto the platform. Teams can catch losses immediately because cost is attributed to the signal that caused it, and they can reprice without a deploy whenever a rate or conversion no longer works. For revenue teams, the value is per-agent margin analytics, self-serve credit top-ups, and revenue and provider cost in a single view, so pricing decisions can be made against profit rather than top-line numbers alone. Concrete workflows follow from those building blocks. A team launches a product with a hybrid plan, covering expected volume with a commitment and billing overage after. A voice agent with several billable actions is modeled as a composite of four meters and billed as one line item. A revenue team notices an account whose gross margin has slipped below target and reprices that offering, with the change going live on save. Customers top up credits on their own instead of waiting on an account manager. An enterprise buyer procures on postpaid terms through a purchase order while prepaid customers fund a wallet before use. Product teams test a new rate with an A/B experiment and publish the winner to all customers. Clocknext is positioned for AI-native teams globally, including AI founders and the product, engineering, and revenue functions inside those companies, and it lists Boomerang, Facilio, Superb AI, Strivelabs, and Ninjahire among the teams using it. The platform is offered with a free start and a free trial — free up to $100K USD in billed revenue — alongside a demo booking path for teams that want to see it in action. It also publishes a guide to pricing economics for AI agents, aimed at teams refining their pricing strategy. In summary, Clocknext is monetization infrastructure for AI agents that unifies metering, flexible pricing primitives, prepaid and postpaid payments, and real-time margin analytics in one system. Its primary value proposition is straightforward: meter AI usage as it happens, price it in whatever mix of credits, outcomes, and units fits the product, change that pricing without a deploy, and see margin per customer, per agent, and per model so AI billing stops being the bottleneck between usage and revenue.
Orphex MCP is a unified Model Context Protocol gateway that connects a digital marketing stack to the AI assistants teams already work in. It links supported advertising, analytics, commerce, subscription and app growth accounts, then brings that performance data into conversations with Claude, ChatGPT, Microsoft Copilot, Grok, Cursor, VS Code and Gemini CLI. Orphex describes itself as a connection layer between many platforms and one AI assistant, with tools to read performance and request supported changes. The product is built for data-oriented marketing teams that want to ask questions about their own account numbers and get answers from their connected sources, rather than opening yet another dashboard or export. Marketing analysis normally lives across a patchwork of systems. Ad platforms hold spend and conversions, analytics tools hold acquisition and on-site behavior, commerce platforms hold products and sales, and warehouses hold still other tables. The Orphex site frames the everyday problem as tab switching and repeated exports: teams move between multiple platforms to answer a single question, then rebuild context by hand. Orphex places the AI assistant at the start of that workflow instead of at the end of an export. The company states its aim is to move AI beyond QA and empower it to analyze integrated performance and take every action a human marketer can, such as reallocating budgets, pausing unprofitable creatives or adjusting products from a single prompt. The connection layer covers a broad set of sources grouped into Advertising, Analytics, Commerce, App growth, Data warehouse, Subscriptions and Creative. Advertising sources listed on the site include Meta, Google Ads, TikTok, LinkedIn, Pinterest, Snapchat, Reddit, Microsoft Ads, Criteo, DV360, Apple Search Ads, Amazon Ads, X Ads, OpenAI Ads, AppLovin Ads, Unity Ads, Yandex Direct, Taboola, RTB House and Akakçe. Analytics coverage includes GA4, Search Console, Tag Manager, CM360, YouTube, Facebook Page, Meta Pixel, TikTok Pixel, Microsoft Clarity, Similarweb, Context dev, Amplitude and HubSpot. Commerce lists Shopify, IKAS, Ticimax, T-Soft, Akinon, Merchant Center, Meta Catalog, TikTok Catalog, Klaviyo, Insider One, SAP Emarsys, Customer.io and D·engage, while app growth covers Adjust, AppsFlyer, Singular, Google Play, App Store Connect, AppLovin MAX, Appfigures, OneSignal, Unity LevelPlay and AdMob. RevenueCat and Adapty cover subscriptions, BigQuery supplies warehouse-backed context, and Creative includes Content Automation Creative, Higgsfield and Playablex. Orphex notes that coverage and available actions vary by platform and account permissions. Those connections bring in spend, conversions and campaign performance, and the site states that a user's own account data becomes the basis for each question. On top of the data, Orphex adds a Skill Catalog of prepared reviews, action procedures and report templates that give the assistant a method to follow. The example given is a weekly performance review: read the connected sources, compare complete periods, surface the main changes and prepare the next steps. The site also mentions creative fatigue and anomaly investigations with eligible skills, executive summaries and budget reallocation proposals with sources and guardrails. Curated guidance helps the assistant choose the next step, use the right metrics and check the evidence before drawing conclusions, and Orphex is careful to note that a report or proposal does not itself change an advertising account. Every requested change stays within the user's access. The assistant prepares the change and shows a preview, and nothing runs until the user confirms it; a workspace can also require a teammate's approval. The site illustrates this with a proposal that moves $500/day from Brand Search to Performance Max while keeping total daily budget at $3,200 unchanged, and it explicitly states that the proposal does not predict a performance improvement. Orphex also says every change is logged and protected by safety guardrails. On security, the site lists GDPR and KVKK compliance for EU and Turkish data protection law, ISO 27001 audited security processes, and SSL/TLS encryption for all platform traffic with enterprise-grade network protection. Overall, the product is presented as a seven-layer stack. Connected Platforms bring in advertising, analytics, commerce and app growth accounts. Performance Data adds spend, conversions and campaign performance from those accounts. Orphex MCP is the single connection layer that exposes tools to read performance and request supported changes. The Skill Catalog supplies prepared reviews, action procedures and report templates. Curated Guidance helps the assistant pick the next step and the right metrics. Access & Approval keeps each change inside the user's permissions with a review step. AI Assistants are where the user actually works: they ask a question, explore the answer and decide what happens next. Setup is described as taking a few minutes with a single sign-in. The stated benefits follow that structure. Users start with a question instead of another export, compare the same metric across two complete periods while keeping source, dates and numbers in view, drill into the campaign behind a change, and turn a finding into a focused review before acting. Because the assistant answers from connected account data, the conversation keeps the definition of each metric visible and flags missing data. Routine work gets a head start from curated skills, so teams keep the method and skip the blank page. Orphex also states that users do not need to replace their dashboards: they can investigate a question in conversation and follow the evidence back to existing tools for a deeper view. The site's documented use cases read as concrete workflows. For advertising performance and actions, teams compare campaign spend, conversions and efficiency across complete periods, drill into ad, keyword and creative reporting, and review supported budget or status changes before approval. For analytics and customer journeys, they put GA4 acquisition and Search Console organic performance in the same discussion while keeping each source's metric definitions visible. For commerce and product performance, they connect marketing questions to products and sales in Shopify, IKAS, Ticimax or T-Soft and prepare a product performance summary. For subscriptions and app growth, they read RevenueCat project overviews, Adapty subscription charts and attribution data from Adjust and AppsFlyer. For warehouse-backed context, they bring supported BigQuery datasets into the same marketing review. And with curated skills, they run weekly performance reports, executive summaries, creative fatigue checks and anomaly investigations. Orphex targets data-oriented marketing teams, including teams managing $30M+ in monthly ad spend, and lists brands such as Paşabahçe, Arby's, Vestel, Burger King, Bilyoner, Popeyes, Karaca, Sbarro, Beymen, Usta, Getir, DeFacto, Mavi, TurkNet, SneaksUp, TatilBudur, Avansas, Altınyıldız, QNB, Edenred and Analytica House as users. Pricing runs from a Free plan at $0 with 250 reads per month, 50 writes per month, one user, one workspace, three account connections and all data sources; a Read plan at $33.32/month billed yearly at $399.90 with 2,000 reads, ten users, three workspaces and 25 connections; and an Action plan at $124.99/month billed yearly at $1,499.90 with 2,000 reads, 500 writes, ten users, three workspaces and 100 connections. Enterprise offers custom limits, contract billing and onboarding support. Yearly billing includes two months free, and every plan works with Claude, ChatGPT, Microsoft Copilot, Grok and Cursor. In short, Orphex MCP consolidates a fragmented marketing stack into one connection layer so an AI assistant can answer questions from real account data and prepare controlled changes for human approval. The value proposition is continuity: the same conversation moves from signal to evidence to a reviewed next step, while permissions, approvals, logging and compliance keep the marketer in control.
Ghostlaunch is a seven-day validation service for startup ideas that have not been built yet. A founder describes the idea in a few sentences, and Ghostlaunch asks a handful of questions to get the details right. From there the service builds a landing page written and designed to convert, complete with the founder's pricing, fast on any phone, and then puts real people in front of it. Every button on that page opens a 'coming soon' note that asks for an email. Ghostlaunch finds people on Reddit, X and LinkedIn who are already asking for a fix to the problem the idea solves, reaches out to each one about what they asked, and tracks every visit, scroll, click and email for seven days. On day 7 the test closes and a full report arrives: who came, how far they read, which plan they reached for, whether to build it, and every lead, ready to contact. The product sums the division of labour up as 'five minutes of yours, seven days of ours.' Ghostlaunch exists because, in its own argument, the usual ways to validate an idea share the same flaw: nobody has anything at stake. Asking AI means the model has read everything about your market but has never wanted to buy anything. Asking friends means they will tell you what you want to hear, because saying yes costs them nothing. Surveys produce hypothetical answers to hypothetical questions, and 'I would pay for that' is free to say. Feedback swaps are coloured by the other person's own launch. Market research tells you the market exists, not that anyone wants yours; Ghostlaunch calls it homework, not proof. Building first is expensive too. Ghostlaunch's own tally runs to 300 hours for the MVP, 40 hours designing it and writing the page, $600 for domain, hosting and tools, 20 hours of launch posts, DMs and cold emails, $500 of ads for first visitors, and 30 hours refreshing the dashboard for signups, plus endless second-guessing: 390+ hours and $1,100 to find out nobody wanted it. The only test Ghostlaunch treats as valid is putting a real page in front of people who are already looking for a fix, and watching what they actually do. The first half of the service is the page itself. Ghostlaunch writes and designs a landing page made to convert, includes the founder's pricing, and keeps it fast on any phone. Every button opens a short 'coming soon' note that asks for an email and sometimes one follow-up question, so nobody is charged and nothing is sold. The page is editable before the test starts: a founder can change any text or rewrite a section with one click. Ghostlaunch publishes examples of what these pages look like, built from the idea underneath each one, for products that do not exist yet: Paylark, a tool for freelance designers that automatically chases late invoices with polite, escalating reminders; Migalo, an app for neighbourhood bakeries that alerts locals over WhatsApp when fresh bread comes out and lets them reserve before it sells out; Nibblo, an app for parents of picky eaters that suggests tiny, realistic next foods from a photo; Strainly, a Slack bot for remote engineering managers that spots quiet overload; and Slotpup, a booking tool for independent dog groomers that fills last-minute cancellations from a waitlist. A gallery of 44 more examples is linked from the site. The second half is distribution and measurement. Ghostlaunch does the outreach itself: it finds people on Reddit, X and LinkedIn already asking about the problem the idea solves, keeps only the ones who clearly want a fix, and reaches out to each one about what they asked. Founders do not post, DM or ask anyone. Ghostlaunch addresses the obvious objection directly, stating that it only reaches people who are already asking about the problem, one conversation at a time, with no mass posting and no bought lists. That is what makes the traffic meaningful rather than a vanity number: the visitors arriving at the page are people who had already described the problem in public before the page existed. Measurement is continuous and public to the founder through a live dashboard. The site shows the feed filling up during a test on day 3 of 7: a visitor from a Reddit DM reached Pricing, clicked 'Get Team' on the Team plan, opened the waitlist and joined it; a visitor from a Reddit comment reached the FAQ and opened a specific question; a visitor from X reached the footer and scrolled 100%; a visitor from LinkedIn reached How it works. Every visit, scroll, click and email is tracked in that dashboard whenever the founder looks. At the end of the seven days, Ghostlaunch closes the test and writes the report: the verdict, the numbers behind it, what visitors told us, and what to change if you run it again. A published sample report for an idea called Shiftswap returned a 'Promising' verdict with 140 visitors, 8 emails, 6% of visitors leaving an email and 34% of button clicks going to a paid plan, plus a recommendation to build the smallest version, starting with the restaurants that left an email. If almost nobody shows up, the verdict says 'not enough data yet' below 100 visitors, together with how many more visitors it would take; Ghostlaunch states it never stretches a small sample into a conclusion. The overall method is deliberately linear, and Ghostlaunch frames it as sending the ghost first: watch the idea go out before the code does. The founder describes the idea, Ghostlaunch builds the page, Ghostlaunch finds the high-intent leads, Ghostlaunch measures everything, and on day 7 it delivers the verdict. The pitch throughout is to pay for answers rather than for code you will throw away, and the closing line is 'Don't build it yet. Ghostlaunch it.' After day 7 the page and the data stay in the founder's dashboard rather than disappearing, so the test can be revisited, relaunched or rewritten later on a subscription plan. Because the whole cycle is short and the output is a report rather than a product, the benefits are stated in terms of decisions rather than features. A founder gets a verdict grounded in what visitors did, not in what they said, along with the underlying numbers: how many came, how far they read, and which plan they reached for. The emails collected are the founder's to keep, stored with the plan each person picked and what they told Ghostlaunch, visible in the dashboard as they arrive, included in the day-7 report and exportable to CSV at any time. The report also records what visitors said and what to change on a second run, so a negative result still leaves something actionable. And the cost of being wrong drops from the estimated 390+ hours and $1,100 of building first to seven days and the price of a test. Concrete workflows follow directly from the published examples. A founder with a Slack bot idea for remote engineering managers can see whether managers click through to a paid plan before writing any bot. A maker with a booking tool for dog groomers, a reminder tool for freelance designers, a feeding app for parents of picky eaters or a WhatsApp reservation app for bakeries can put the pitch in front of people already complaining about those problems. Founders who are unsure what to build can start from Ghostlaunch's idea library, which it built by reading 1,452 posts where people asked for help and turning what they keep asking for into 50 ideas, each with the posts behind it, grouped into AI business ideas, app ideas, online business ideas, SaaS ideas and small business ideas. Founders working through a notebook of ideas can queue several tests rather than running one. Ghostlaunch is aimed at founders who have an idea but have not built it, including those testing in batches. Pricing reflects that: One test costs $49 once and includes the page built and hosted, 7 days of outreach to high-intent leads, every email collected yours to keep, the live dashboard and the day-7 report. Founder is $99 per month and is marked as recommended for a notebook full of ideas, with 3 tests a month, the ability to rewrite any section and relaunch any page, and everything in One test. Studio is $249 per month for testing in batches, with 10 tests a month, everything in Founder, and priority for its tests. Separately, the team notes that Ghostlaunch itself is for ideas you have not built yet, and points founders with a live product to ReplyHey, which finds people on Reddit, X and LinkedIn asking for what you sell, writes the reply and posts it from your account every day. In short, Ghostlaunch replaces encouragement with evidence. One described idea becomes a converting page, targeted outreach to people already asking for a fix, seven days of click-level measurement and a written verdict with every lead attached, so the decision to build rests on what strangers did rather than on what friends politely said.
telemetry.dev is an LLM observability platform for AI applications and agents, built on OpenTelemetry. It shows each run of an AI agent as one trace, where each model call and tool call is a span carrying its duration, tokens, cost, and error. From that trace, developers follow model calls, tool steps, and retrievals together, find failures, inspect latency, and track token usage and cost. The product is aimed at teams building and operating AI agents and LLM-backed applications who need to see what actually happens inside a run instead of inferring it from scattered logs. An agent run is rarely a single request. The live demo on the site shows a support-agent trace containing thirteen spans: loading a customer profile, loading a session, routing intent, retrieving policy context, generating embeddings with text-embedding-3-small, calling gpt-4.1-mini, searching a help center, running a second retrieval, calling the model again, running a guardrail output step, and saving the session. When something breaks in that chain, the failure can sit in any step, and the slowest step is often not the model call itself. telemetry.dev frames the problem plainly: find the slow step, inspect the failed call, pinpoint a tool timeout, inspect a model response, and see where your budget goes. It also aims to tell you about errors, cost spikes, agent loops, and model or tool errors before your users do. The core of the product is trace detail. Each trace lists its spans with the duration of every step, so a run is readable end to end. In the example trace, a track_shipment span takes 2,418ms and accounts for 49% of the run, while the two chat steps take 845ms and 1.16s. Selecting a span opens its summary and attributes, including fields such as gen_ai.operation.name set to "execute_tool", gen_ai.tool.name set to "track_shipment", and gen_ai.tool.call.id. Those attributes identify precisely which tool executed inside the run, which is what makes it possible to separate a model problem from a tool timeout. Finding the right trace is handled through search and filtering. Traces can be searched by name, or filtered by status, environment, framework, or session. The production view lists runs such as support-agent, classify-ticket, and summarize-session with their durations, and a selected trace reports how many spans it contains and what it cost. A separate overview keeps cost and reliability in view by comparing spend, latency, and errors by model, provider, and environment. In the sample dashboard, LLM cost for the last seven days totals $2.98 across gpt-4.1-mini, claude-sonnet-4.5, gemini-2.5-flash, and text-embedding-3-small, alongside 812 requests, a p95 latency of 1.8s, and 2.6M tokens. For Model Context Protocol workloads, telemetry.dev reports spans, failures, and p95 duration by method and tool, measured from both the client and the server. The example MCP analytics table lists create_refund with 2,330 spans, a 2.4s p95, and 18.0% failed; get_invoice with 5,912 spans, 640ms, and 1.1% failed; check_inventory with 4,208 spans, 310ms, and 0.2% failed; and cancel_order with 1,077 spans, 880ms, and 0.6% failed. Email alerts complement the dashboards, covering cost, budgets, agent loops, and model or tool errors, with states such as tool errors alerting, monthly budget all clear, agent loop all clear, and missing prices needing more data. Privacy controls determine what gets stored. Prompt capture is on by default and can be turned off for an environment before storage. The settings screen lists production, staging, development, and preview with whether stored prompts and responses were captured, and in the example production shows prompts and responses are not stored. Redaction patterns, both built-in and project-specific, are applied, but the product states that pattern matching cannot catch all sensitive data, that capture controls only cover recognized prompt, response, and tool-payload fields, and that removing sensitive content in your instrumentation before export is the way to prevent transmission. Separately, prompts and evals use the same traces: a prompt can be changed without a deploy, then run on a dataset and its scores compared with production. Overall the product works by ingesting OpenTelemetry data. You create a project API key, configure a telemetry.dev integration or an OTLP/HTTP exporter, run an instrumented request, and open its trace in your project. Cost is calculated at ingest from reported token usage and a model-pricing catalog, with cache-read and cache-write tokens using separate rates when available, reasoning tokens not charged separately, and an explicit span cost taking precedence; without matching model pricing or token usage, computed cost is unavailable. Trace detail depends on what your instrumentation emits, and each integration guide describes its coverage. Ingestion is metered in ingestion units, where each normalized span, OTLP log record, or metric data point uses one unit. The benefits follow from that structure. Instead of stitching together provider dashboards and application logs, a team gets one view of a run with durations per step, so a tool timeout or a slow model call is visible in context. Cost is attributed by model, provider, and environment, which makes the source of spend changes observable. Failure rates and p95 latency for MCP tools show which tool calls are unreliable. Alerts on budgets, agent loops, and errors surface incidents before users report them, and per-environment prompt capture settings give a way to keep recognized prompt and response fields out of storage. Typical scenarios map directly to the interface. A developer debugging a failing support agent opens the trace, scans spans for the longest duration, and inspects the selected tool span and its attributes. An engineer investigating errors in production filters traces by status and environment to isolate affected sessions. A team watching spend compares the cost breakdown across models and providers over the last seven days and drills into the traces contributing to it. An MCP-based integration checks which tools have the highest failure rate or p95 duration. An on-call engineer relies on email alerts for cost spikes, budget thresholds, agent loops, and tool errors. A team preparing a prompt change tests it on a dataset and compares scores against production on the same traces. Development teams connect through a first-party TypeScript SDK or Python SDK, or through any exporter that sends OTLP/HTTP. Model provider integrations cover OpenAI, Anthropic, Claude SDK, Claude Platform, Google Gen AI, Amazon Bedrock, OpenRouter, and LiteLLM. Framework integrations cover Vercel AI SDK, TanStack AI, LangChain and LangGraph, CrewAI, Pydantic AI, Eve, and Model Context Protocol. Coding agents can be traced as well, including Claude Code, Cursor, opencode, OpenClaw, Hermes Agent, Oh My Pi, and Pi, and the platform exposes a read-only MCP server so an AI agent can investigate errors, latency, and cost through telemetry.dev itself. The Free plan costs $0 with no credit card, includes 30,000 ingestion units per month, 14-day retention, one project, and two seats. A demo call is offered for teams that want tracing, cost tracking, and privacy controls walked through for their own frameworks and providers. In short, telemetry.dev turns an AI agent run into a single inspectable trace of model calls, tool steps, and retrievals with duration, tokens, cost, and errors attached to each span, and layers search, cost and reliability overviews, MCP analytics, alerts, and prompt capture controls on top of OpenTelemetry data you already emit.
Notch Metrics is a native macOS app that puts your revenue, MRR and subscriber numbers in your MacBook's notch. Instead of opening a dashboard, a tab or a spreadsheet, you look up at the camera housing and the number is already there. The product is aimed at founders, builders and marketing people who check the same handful of figures every day and want them closer than a browser tab. It pulls numbers from the payment, analytics and code platforms you already use and turns them into widgets that live in the notch: the strip expands over whatever you are doing when you rest the pointer on the camera housing, and folds back to the shape it had when you leave. The tagline on the site sums up the intent directly — every number you check, in the notch. The problem it addresses is the daily routine behind those numbers. MRR lives inside Stripe, today's takings inside Polar, trials inside RevenueCat, stars and open issues inside GitHub, traffic inside Plausible or Google Analytics. Checking them means opening a dashboard, finding the right tab and repeating that several times a day, which turns a five-second question into a context switch. Notch Metrics takes the opposite approach. Rather than building another destination to visit, it puts the figures in the one place a laptop user already looks at constantly — the notch at the top of the screen — so a glance replaces a visit. The site describes the payoff in plain terms: look up, and the number is already there, and the recent-payments feed means you see who just paid without opening a dashboard. The core of the product is the widget strip in the notch. Revenue, subscribers and today's takings sit side by side, and the app keeps nine widgets open at a glance while, in the site's words, the island still reads in a second. Each widget is bound to a source and a metric: the examples shown include a Stripe MRR tile reading $12,480 with +4.2% vs last month, a Stripe subscribers tile at 318, a Polar "Today" tile at $640, a GitHub stars tile at 2,104, a RevenueCat trials tile at 27, a RevenueCat active subscribers tile at 1,208 with +38 this week, a Dodo Payments revenue tile at $3,920 with +11% vs last week, a Dodo Payments orders tile at 146 and a GitHub open issues tile at 14. Widgets come in four tile sizes, from a single square up to the full width of the notch, and you drag to arrange them and resize them in place, inside the notch itself. Two details make the notch format practical rather than gimmicky. First, the strip opens on hover: rest the pointer on the camera housing and the notch expands over whatever you are doing, and when you leave it folds back to the shape it had. There is nothing to launch, click or dismiss. Second, the widgets are not only numbers. A recent-payments feed sits beside the headline figures, listing entries such as "Pro · yearly $79" and "Pro · monthly $19", so you can see who just paid without opening a dashboard. Between the tiles and the lists, the notch covers both the trend and the individual event — the monthly figure and the payment that just landed. Notch Metrics connects to 16 live sources. The connectors named across the site include Stripe, Polar, Paddle, Dodo Payments, Lemon Squeezy, RevenueCat, Shopify, Plausible, Google Analytics, Google Search Console, PostHog, Amplitude, Mixpanel, GitHub, YouTube, Claude and OpenAI. They are deliberately mixed in type: payments beside analytics beside code, on the same strip of glass, so a Stripe tile can sit next to a Plausible tile next to a GitHub tile. Dimmed marks on the site are sources on the way, and the site states that a new one arrives without an app update. Connecting happens on the web: connect a source once, and every Mac you own picks it up. Underneath, Notch Metrics is a small native app written in Swift, built for Apple silicon and macOS 14 or later. It has no dock icon, no window and, in the site's phrasing, no menu bar clutter while you work, and it is described as light on memory and on your battery. Metrics are fetched and normalised by the company's servers on a schedule, which is why a subscription is per account rather than per machine, and why your connections and layout are already there when you sign in on another Mac. Provider keys are handled carefully: a key is encrypted the moment you paste it, with a key of its own, and your account, the integration and the provider are bound into that encryption so a row copied anywhere else decrypts to nothing. The key never leaves the server — Notch Metrics calls Stripe for you and hands your Mac a number, which is all any app on it could find — and disconnecting an integration deletes its encrypted key with no second copy anywhere to clean up. Every plan also includes custom metrics authored by your own coding agent over MCP, which the Product Hunt listing frames as using Claude Code to build analytics widgets for your notch. The benefits follow from that design. You stop paying the cost of a context switch for a question that takes a second to answer, because the figure is already in the notch rather than behind a login. The app stays quiet while you work — no dock icon, no window, no menu bar clutter — and only takes visual space when you deliberately hover. Because every connector is included with no per-integration charge, you are not choosing between tracking revenue or tracking product analytics; the same strip can carry both. Because the layout syncs between Macs, the setup follows you rather than being rebuilt machine by machine, and updates are signed and notarised for as long as you subscribe. Concrete use cases come straight from the widgets and sources described. A founder starts the day by hovering the notch to read MRR and its +4.2% vs last month against the subscriber count, then leaves the strip folded away. After a launch, a recent-payments feed of Pro monthly and yearly entries shows who just paid without opening a payments dashboard. A marketer keeps Dodo Payments revenue for the last seven days next to Plausible traffic and Google Search Console. A developer keeps GitHub stars and open issues beside the same revenue tiles, since code activity and business activity share one strip. Someone running an app business watches RevenueCat trials and active subscribers tick up week over week. And anyone with a second Mac signs in and finds the same connections and the same layout waiting. Target users are named on the site as builders, founders and marketing. The integrations list covers payment platforms (Stripe, Polar, Paddle, Dodo Payments, Lemon Squeezy, RevenueCat, Shopify), analytics and search tools (Plausible, Google Analytics, Google Search Console, PostHog, Amplitude, Mixpanel), code and content (GitHub, YouTube) and AI providers (Claude, OpenAI). Pricing is a paid subscription with no free tier: $9 per month, or $49 per year billed once at $108, working out at $4.08 a month and a saving of 55%. Every plan includes every connector, as many widgets as fit in the notch and in list view, custom metrics via MCP, every Mac you sign in to with your layout synced, and signed notarised updates while you subscribe. There is a seven-day free trial with no card on file. When the trial ends your widgets stop updating but nothing is deleted; cancelling keeps access until the end of the paid period, and a failed payment gets a grace period while the card is retried. Checkout and subscriptions are handled by Dodo Payments, so the company never sees your card number. Requirements are Apple silicon and macOS 14 or later, with the current build listed as v0.4.2. The takeaway is simple. Notch Metrics does not try to be another analytics suite; it takes the numbers you already check obsessively and moves them into the MacBook notch, where a glance is enough. Sixteen live sources, encrypted server-side keys, four tile sizes, a recent-payments feed and custom metrics over MCP all serve one idea: never miss a number, and never open a dashboard to find it.
Paste a TikTok link and download the video in seconds. Free, no login, no app needed. Works on iPhone, Android, and PC. Tiktok Video Downloader
ilha is a native Mac app that lives in the notch of a Mac's display — and appears as a small pill on other displays — to show how much of the usage limits on your AI coding accounts you have already spent. It watches the usage limits of every Claude Code, Codex and Grok Build account you have, reporting the 5-hour and weekly windows for each one, when those windows reset, and whether you are on pace to run out. Its stated purpose is short: "Hit a limit? Keep coding." Rather than letting a session stop mid-task when a limit lands, ilha moves the CLI to an account that still has room — offering the switch at 90% and making it automatically at 98%. It is built for developers who run these coding agents daily, often across more than one paid account. The problem ilha addresses is described directly on its site: the limit hits mid-task, your session stops, and the reset is hours away. Every account carries a 5-hour window and a weekly window, and when either one fills, the work simply stops until it refills — even though another account you already pay for may have capacity sitting unused. Tracking that by hand means guessing at percentages, losing track of reset times, and discovering too late that a work account has run dry while a personal one is barely touched. ilha puts that information in constant view and then acts on it, so the boundary between accounts stops being a stopping point for the work itself. The core of the app is the live view in the notch. For each account it shows how much of its 5-hour and weekly limits has been used, along with the time remaining until each window resets — for example a work account at 78% of its 5-hour window with 1h 48m left, and 41% of its weekly window with 2d 19h left. You pick any two accounts to sit beside the notch, so a work account and a personal account can be compared side by side, and the app keeps every tool you use balanced whether it is beside the notch or not. A fuller Usage view lists each account with both windows and their percentages, and one click switches to the account you choose. Switching is where the notch display becomes an action. At 90%, ilha offers a way out: before the limit, one click moves you to an account with room. At 98%, it switches for you, so your session keeps running on an account with room, with undo right there if you want to go back. The site stresses that nothing is logged out in the process, and that Claude Code, Codex and Grok Build are each switched the way it expects. There is also a separate control for banked resets: if you are out of room but have a reset in the bank, one press on the ticket uses it, framed as "banked resets, never wasted." Insights answers a different question: what the week cost. Every hour and every project are priced at API rates, read from the logs your CLIs already keep on your Mac, and nothing leaves it. The view shows a weekly total in dollars at API prices, the share of usage across your accounts, a day-by-day chart for the week, a projection such as usage rising to a given percentage by a particular day, the share taken by subagents, and how many 5-hour limits were hit. That turns an abstract limit percentage into a concrete cost picture, project by project and day by day. Alongside the band and the Usage view, ilha provides alerts, a forecast and sounds. The hourglass forecast says when a window fills, so you know how long you have before a limit lands rather than finding out when the session stops. Sounds accompany each moment — or none, if you prefer silence. These items are listed among the free features, together with the band, the peek and the Usage view covering both windows and their resets at a glance. Overall, the product's approach is to sit locally on the Mac and watch rather than to intercept or proxy your work. It reads the logs the CLIs already write, keeps your logins in the Keychain, requires no account to create, and states that it never sees your usage. The single piece of behaviour that changes your workflow is the handoff: at a threshold the CLI is pointed at another account you already own, in the manner that CLI expects, without a logout. The notch is simply where that state lives, always visible, one click from being acted on. The outcomes ilha promises follow from that: sessions that keep running when a limit would otherwise have stopped them, no wasted capacity on accounts you already pay for, visibility into both the 5-hour and weekly windows and their reset times, a forecast of when a window will fill, and a weekly cost picture priced at API rates and attributed to projects. Because undo is offered after an automatic switch, the change is reversible, and because banked resets can be spent with a press, a saved reset does not have to sit unused. Concrete scenarios come straight from the site. A developer deep in a task on a work account at 90% clicks once and continues on the personal account instead of waiting hours for a reset. Another, at 98%, is switched automatically and keeps the session running, with undo available if the switch was not wanted. Someone who wants to know where the week went opens Insights to see the weekly cost at API prices broken down by project, the daily chart and the projection for the rest of the week. A team member juggling a work and a personal account keeps both beside the notch, comparing their 5-hour and weekly percentages at a glance, and switches with one click. And when a limit is reached but a reset is banked, one press on the ticket spends it. The product is a Mac app requiring macOS 15 or later, aimed at people running Claude Code, Codex or Grok Build who may hold several paid accounts across those tools. Pricing is free to watch, Pro to switch: every Mac starts with 14 days of Pro with no account and no card. The Free tier covers the island for the account each CLI is using, including the band, peek and Usage view, alerts and the forecast, and sounds, at $0 forever with no license needed. Pro is $20 once for one, two or three Macs, paid once with a year of updates, and covers every account of every CLI, switching and auto-switch, and Insights. After the year of updates, every version from that year keeps Pro for good, and you can renew any time; moving to a new Mac means pasting your key, with the Mac seen longest ago giving up its seat. Prices are in US dollars plus tax where it applies, sold by Paddle.com as reseller. The site is explicit about data. There is no account to create, no trackers or ads, and logins stay in the Keychain. Once a day the app sends an update check and an anonymous count under a random id — app version, plan, macOS, Mac type, how many accounts you keep per CLI, and where auto-switch is on — plus a crash or hang report of where and why, never your data. No IP address is kept, and Settings › General › Anonymous counts and crashes turns both off. With Pro, the key and a hash of your Mac's ID are involved, and a download keeps only its time. Usage, logins and logs are never sent. In short, ilha's value proposition is to keep coding past the limit: it keeps every account's Claude Code, Codex and Grok Build usage in the notch, warns before a window fills, moves the CLI to an account with room when you are close, spends banked resets on request, and shows what the week actually cost at API prices — all from logs that stay on your Mac.
CoolDock is a native Mac app that adds a second, fully customizable Dock beside your original macOS Dock and fills it with live widgets, quick actions, apps, folders and useful everyday information. As the website puts it, CoolDock brings music, todos, events, weather, search, stats, quick actions, and more useful widgets into one beautiful live dock beside your original Mac Dock. It is built for Mac users who want their Dock to do more than launch apps, and its stated purpose is simple: make your Mac Dock more useful. Importantly, CoolDock does not replace the original Dock — it works beside it, so you can keep your existing Dock exactly as it is, or combine CoolDock with macOS Dock auto-hide if you prefer a cleaner setup. The default Mac Dock is a launcher: it shows app icons and little else. The information people check throughout the day — what is next on the calendar, which reminders are still open, the current temperature, how much memory the machine is using, what is playing — normally lives inside separate apps or in desktop widgets that sit away from where the pointer already is. CoolDock's answer is to move that information into a second Dock that stays in the same predictable place. One testimonial quoted on the site captures the tension the product is aimed at: “Usually you have to choose between functional, fast, or sexy. With CoolDock, it's all three — AND customizable.” Another early user writes that they have replaced their default dock entirely, calling it “the kind of dock Apple should have built.” The Apps, Files & Quick Access group is about reaching things fast. It includes Apps & Folders, so you can pin favorite apps and organize apps into folders; Bookmarks for saving useful links; File Shelf for holding temporary files while you work; Files & Downloads for getting to downloads quickly; Search for finding files; an App Switcher for moving between open apps; and a Dropbox widget. Together these cover the bread-and-butter of dock usage — launching, switching and finding — but extended with folders, saved links and a temporary holding area. Because they sit in the same second Dock as everything else, the workflow stays in one place instead of spreading across Finder windows, browser tabs and separate utilities. The Productivity & Daily Focus set is the day-planning half of CoolDock. It includes a Pomodoro timer, a Drink Water hydration reminder, Calendar & Meetings, Reminders & Todos, a Progress widget, Time Widgets, Notes and Mail. The intent, as described on the site, is to help you stay on top of your day with todos, reminders, Pomodoro timers, hydration reminders, calendar events, meetings, progress trackers, clocks and quick notes. Each of these is an at-a-glance widget rather than a full application: a Pomodoro timer visible without opening a timer app, a calendar event sitting next to the apps you are launching, a todo list you can glance at between tasks. For anyone who works from a Mac all day, this group turns the Dock into a lightweight daily dashboard. The Mac Controls & System Tools group covers the machine itself. It includes System Stats, Quick Toggles, Audio Controls, Clipboard, Screenshots, Voice Memo, Emoji, Weather, Photos and Now Playing. The site summarizes the benefit as controlling your Mac faster with live system stats, quick toggles, audio controls, clipboard access, screenshots, voice memos, emoji, weather, photos and media controls. In practice that means checking resource usage, flipping a toggle, looking at what is on the clipboard, taking a screenshot or capturing a quick voice memo without hunting through menus or opening separate apps. Weather and Now Playing round the group out with ambient information — temperature and what is currently playing — that many people check repeatedly during the day. Creator & Utility Widgets collect small, frequently needed tools: a Color Picker, a Converter, Bookmarks, Notes, Clipboard and Screenshots. The stated aim is to let you pick colors, convert currencies and units, calculate values, save useful links, capture ideas, and reuse everyday tools without opening extra apps. These are exactly the utilities that normally interrupt a workflow — grabbing a color value, converting a currency or a unit, jotting down an idea — because they require leaving whatever you were doing. Placing them in the second Dock means the interruption costs a glance and a click instead of an app switch. For designers the Color Picker is the obvious anchor; for everyone else the Converter and Notes widgets cover the small calculations and quick captures that come up constantly. Two groups are aimed specifically at people who run or build products. Founder & Business Widgets let you track revenue, sales, followers, website analytics, link performance, email activity, payments, and ecommerce metrics from one beautiful Dock, and include Revenue, Social Followers, Website Analytics, Shopify, Resend, Dub, Dodo Payments, Polar, Plausible and DataFast. Developer & Startup Widgets are for monitoring tools, projects, databases, issues, deployments and backend services, and include GitHub, Linear, Supabase, Dropbox, Resend, Dub and Website Analytics. The distinction is worth noting: the founder set is oriented around commercial metrics and payments, while the developer set is oriented around project and infrastructure status. Both turn the second Dock into a small live operations panel that is visible while you work, rather than another dashboard tab you have to remember to open. CoolDock is built as a native Mac app, and the site describes it as local first and privacy first. Local widgets work on your Mac, while connected widgets only use the access needed for that specific feature — a calendar widget needs Calendar access, a reminders widget needs Reminders access, and so on. CoolDock asks for permissions only when a feature needs them; the permissions named on the site include Calendar, Reminders, Contacts, Photos, Microphone, Accessibility and Automation. The second Dock itself is customizable: you can change widgets, apps, folders, layout, sizes and appearance. A customization overview video also covers profiles, wallpapers, floating layouts and appearance settings. CoolDock is designed for modern Mac setups including external displays, and it requires macOS Sonoma 14.0 or later. The outcomes CoolDock promises are practical rather than abstract. Information you would otherwise go looking for — the next meeting, an open reminder, system stats, the weather, what is playing — is already visible in the second Dock. Actions you would otherwise take through menus, such as quick toggles, screenshots or clipboard access, are a click away. The site reinforces this with user testimonials: one early user says they have replaced their default dock entirely, and another calls it beautiful and functional and says it gives them superpowers on their Mac. CoolDock is also positioned as a one-time purchase with no subscription and no automatic renewal, which the pricing section states explicitly, and every plan carries a 14-day money-back guarantee. Concrete workflows follow directly from the widget groups. A designer can keep the Color Picker, Screenshots, Clipboard and File Shelf in the second Dock while working in a design tool. Someone managing their day can line up the Pomodoro timer, Reminders & Todos, Calendar & Meetings and a Progress widget. A founder can watch Revenue, Website Analytics, Shopify and payment widgets while working, and a developer can keep GitHub, Linear and Supabase status visible beside the code editor. General Mac use is covered too: music and Now Playing controls, weather, quick toggles and Search. Because CoolDock works alongside the original Dock and supports external displays, these layouts can move with a multi-monitor setup. CoolDock is aimed at Mac users — designers, makers, founders, developers and anyone who works from a Mac desktop all day — who want more information and faster actions from their Dock without giving up the Dock they already know. Platform support is macOS Sonoma 14.0 or later, and it is a native Mac app designed for modern setups including external displays. Pricing is one-time only: a one-year license is $14.99 for one Mac, including one year of access and one year of updates; lifetime access is $34.99 for one device, $57.99 for two devices or $69.99 for three devices, each including lifetime access and one year of updates; and the Forever Edition is $115.99 for up to five Macs with all future updates for life. A 14-day money-back guarantee applies, taxes may apply, and no plan renews automatically. CoolDock's core proposition is that the Mac Dock should show you something, not just launch things. By adding a second, customizable Dock filled with live widgets — apps and folders, files and downloads, reminders and todos, calendar and Pomodoro, system stats and quick toggles, color picking and conversion, plus founder and developer metrics — it turns a static launcher into a live surface for the information and actions you use every day. It sits beside the original Dock rather than replacing it, runs as a native, local-first Mac app, and is sold as a one-time purchase. For Mac users who want their Dock to be genuinely useful, that is the pitch.
Cited answers, a compliance matrix, and a deterministic verifier that refuses to export what it cannot prove.
shim-cli is an open source command line tool that runs on your own machine next to Claude Code, Codex, Copilot CLI and VS Code. Its purpose is stated plainly on the website: see what your coding agent sends, and mask what you can. The CLI finds secrets and personal data in what an agent sends, masks those values where the client allows it, and shows what each Claude Code session sent and cost. It is built for developers who use AI coding agents every day and want visibility into the files, tool results and prompts that reach the model, without adding an account, an API key or a network call to shim. Claude Code keeps every session on your disk, and an agent at work reads files, runs commands and pulls in tool results. Some of those files hold API keys, database URIs, customer email addresses, IBANs and other personal values. Once a value is in a request, it has reached the model. shim-cli starts from that reality: before you install anything, it can show what already went out, and after installation it can place a placeholder between the file and the model. The project describes itself as narrowing exposure rather than acting as a data-loss prevention boundary, so it is positioned as a practical layer of containment and measurement for everyday agent work rather than a compliance guarantee. Masking is handled by shim install , which installs a native hook in your agent. When the agent reads a file or a tool result holding a key, an IBAN or an email, the model receives a placeholder such as instead of the value. The detector covers email addresses, phone numbers, credit cards, IBANs, Turkish national IDs, Turkish tax IDs, API keys and secrets, passwords in config and commands, database URIs, IP addresses, MAC addresses and US SSNs, plus Turkish licence plates as an opt-in. IBANs and Turkish IDs are verified by checksum, so a mistyped number is not reported. You can add your own patterns, including your own project names or host formats. Where the client allows it, masking happens before the model reads the content; where it does not, shim reports the finding or, in enforce mode, stops it instead. Two commands answer two different questions. shim install claude masks sensitive data in every session. shim watch -- claude runs a local proxy for one command and reports tokens, spend and every secret or personal value that went past; it changes nothing, writes no request to disk and leaves nothing behind. For history, shim audit reads the Claude Code sessions already stored on your disk and counts what reached the model, broken down by kind, by project and by the way it came in, for example @.env, a failed Bash command or your own prompt. The sample output shows counts for SECRET, EMAIL and DB_URI, a per-project breakdown, values already masked by shim, and model output that repeated an email. shim audit prints no value, keeps nothing and sends nothing, and it reads Claude Code history only, only when you run it. shim watch currently supports Claude Code only. Supporting commands make the setup auditable. shim demo runs the detector on a made-up sentence and shows the before and after lines without installing anything. shim install --dry-run previews the hook and shows the target file and the fragment before you commit, and --json returns machine-readable output that branches on a code and a fix. shim doctor checks the installation and reports every check that is not PASS along with its code and its fix. shim keys .env --json lists the names in a .env file without printing values. At the end of a turn, Claude Code users see a summary: how many values were masked and from which read, which items were flagged, and the overhead. The website shows a real session on a customer file with 60 emails, 60 IBANs, 60 phone numbers and 60 Turkish national IDs masked from Read customers.csv, one INSTRUCTION_OVERRIDE and one HIDDEN_TEXT flagged from Read runbook.md, and 62 ms median overhead. Support for each client differs, and the documentation is explicit about it. In Claude Code, a native hook masks secrets and personal data in the files and tool results the agent reads before the model sees them, and the tool can also be installed from the Claude Code marketplace as a plugin, using one method and not both. In Codex, shim reports what it found in your prompt and lets it through, and you switch to enforce mode to block it instead; Codex also skips a hook until you open /hooks, review the shim entry and trust it. In Copilot CLI, shim replaces the prompt the model receives with the redacted text, so the original values never reach it, and there is no trust step. In VS Code there is no shim install vscode; the plugin is the whole route, added through the chat.pluginLocations user setting with an absolute path, and it reports findings, stops a prompt or denies a tool call under enforcement, and never masks a result that has already reached the model. The outcome the project promises is narrower exposure with measurable cost. Values that would otherwise travel in plain text become placeholders, prompts can be blocked under enforce mode, and the end-of-turn summary shows how much overhead the hook adds. The website reports that these numbers are measured on the released build: 2,300+ tests run on every release, 70 ms median hook cost including interpreter start, 727 detector cases graded on exact output, and zero accounts, API keys or telemetry added by shim. The same page is candid about the boundary: a prompt typed into Claude Code or Codex is not masked because neither client lets a hook rewrite it, Codex and Copilot CLI tool traffic is not inspected at all, VS Code masks nothing, in Claude Code shell commands and file writes are reported or denied rather than masked, the output of a failed command and a file attached with @ are reported rather than masked, Windows is not supported and WSL is not yet verified, and detection is best-effort and can miss a value. Concrete use cases follow from those mechanics. A developer reading a customer file with an agent sees 60 email addresses, 60 IBANs, 60 phone numbers and 60 Turkish national IDs replaced with placeholders before the model reads the CSV. A runbook containing text that tries to give the agent orders is flagged as an instruction override, alongside hidden text in the same document. A team that wants to know what already leaked runs shim audit over past Claude Code sessions to count secrets by project before changing any settings. A developer who wants to understand where tokens and money went runs shim watch -- claude for a single command and gets a report without anything written to disk. Someone who only wants to list the names in a .env file uses shim keys .env --json so no value is printed, and someone wiring the tool into a new machine runs shim demo, shim install --dry-run and shim doctor in sequence. shim-cli is distributed as an open source CLI under Apache-2.0, currently at v1.2.1, and runs on macOS and Linux with Python 3.10 or newer. Installation is a single command through uv or pipx. It integrates with Claude Code, Codex, Copilot CLI and VS Code through native hooks or a plugin, and requires no account, no API key and no network call to shim. The whole CLI is on GitHub, so you can read the detector, run the tests, open an issue or send a pull request. For teams that need the same detection in front of the AI calls their own products make, the site points to the AI Gateway, which adds audit, budgets and roles on Enterprise. The takeaway is straightforward: shim-cli makes the traffic between your coding agent and the model visible, masks what the client allows it to mask, measures what a Claude Code session sent and cost, and states its limits openly. It is a practical, local-first way to reduce what your agent exposes without adding an account or telemetry.