{"id":4352,"date":"2026-05-01T14:50:42","date_gmt":"2026-05-01T14:50:42","guid":{"rendered":"https:\/\/www.coffee.ai\/articles\/pipeline-forecasting-ai-reviews-reddit\/"},"modified":"2026-09-11T05:12:09","modified_gmt":"2026-09-11T05:12:09","slug":"pipeline-forecasting-ai-reviews-reddit","status":"publish","type":"post","link":"https:\/\/www.coffee.ai\/articles\/pipeline-forecasting-ai-reviews-reddit","title":{"rendered":"AI Pipeline Forecasting: Does It Actually Work?"},"content":{"rendered":"<p><em>Written by: Doug Camplejohn, CEO &amp; Co-Founder, Coffee | Last updated: September 10, 2026<\/em><\/p>\n<h2 id=\"key-takeaways\">Key Takeaways<\/h2>\n<ul>\n<li>Reddit practitioners agree AI pipeline forecasting tools underdeliver when CRM data is dirty and rarely explain the root cause or the fix.<\/li>\n<li>Stage\u00d7probability models rely on fixed historical win rates and rep-assigned stages, while signal-based models update probabilities from real buyer behavior signals.<\/li>\n<li>Dirty data from manual entry, optimistic close dates, and inconsistent activity logging is the primary reason forecasts fail, even when the model is sophisticated.<\/li>\n<li>Before buying any AI forecasting tool, ask vendors about signal inputs, automatic data capture, explainability, and backtesting on your own closed-won and closed-lost data.<\/li>\n<\/ul>\n<h2>What Reddit Actually Says About AI Pipeline Forecasting<\/h2>\n<p>Reddit threads across r\/SalesOperations, r\/salesforce, and r\/CRM cluster around three themes that repeat in every AI forecasting discussion.<\/p>\n<p>The first theme is dirty data. A recurring thread in r\/SalesOperations on Clari captures the sentiment precisely: users report genuine time savings in pipeline reviews but describe the predictive AI layer as average at best. They explain that the model is only as good as what reps put in, and reps do not put much in.<\/p>\n<p>The second theme is trust. A thread in r\/salesforce about how much you can really trust CRM-driven sales forecasts surfaces a framing that appears repeatedly: AI forecasting behaves like a GPS, not a crystal ball. It tells you where deals are based on the signals it can see. When those signals are missing or fabricated, the GPS routes you off a cliff.<\/p>\n<p>The third theme is spreadsheet abandonment. Sales reps abandon the official CRM for spreadsheets and Notion because the CRM requires more effort than it returns. That behavior is a rational response to a system built for management reporting rather than rep workflow. The shadow CRM becomes the real workspace, and the official CRM becomes a reporting artifact that management reads and reps ignore.<\/p>\n<p>These three themes connect to the same underlying problem: CRM data does not reflect real deal progress, so any forecast built on it struggles.<\/p>\n<h2>The Mechanism Behind Stage\u00d7Probability And Signal-Based Forecasting<\/h2>\n<p><strong>Stage\u00d7probability forecasting<\/strong> <a href=\"https:\/\/marketricka.com\/ai-sales-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">derives a revenue projection by multiplying each open deal&#8217;s amount by a fixed close probability assigned to its pipeline stage<\/a>, for example, every deal in Proposal gets 60% and every deal in Negotiation gets 80%, then rolling those weighted values into a team total. The probability is set at implementation based on historical win rates and rarely recalibrated. <strong>Signal-based forecasting<\/strong>, by contrast, <a href=\"https:\/\/l1advisory.com\/blog\/ai-revops-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">assigns each deal a continuously updated probability derived from observed buyer behavior<\/a>: recency of champion engagement, number of stakeholders involved, email response latency, competitive mentions in call transcripts, and whether a concrete next step with a committed date exists. The stage label is one input among many, not the primary driver.<\/p>\n<p>The practical consequence is significant. <a href=\"https:\/\/rox.com\/articles\/ai-revenue-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">Two deals sitting in the same Stage 3 can carry completely different close probabilities<\/a>. One with confirmed budget, strong champion engagement, and a timeline consistent with the close date might score at 68%. Another with a silent champion, unconfirmed budget, and a close date pushed twice might score at 19%, despite sharing the same CRM stage label.<\/p>\n<p>Clari&#8217;s predictive layer leans on historical stage patterns and live activity signals from CRM, email, and calendar. That makes it closer to signal-based than pure stage\u00d7probability, though <a href=\"https:\/\/weflow.ai\/blog\/clari-pricing-and-value\" target=\"_blank\" rel=\"noindex nofollow\">its conversation intelligence data does not directly feed the forecast score<\/a>. Aviso&#8217;s Large Quantitative Models ingest CRM data, email threads, and meeting interactions, extracting 150+ AI-derived features per deal per snapshot cycle. That approach makes Aviso more fully signal-based, and the sophistication requires strict data hygiene cadence to function.<\/p>\n<p>The distinction between these approaches determines whether a tool will work on your data. If your reps log activities inconsistently, a stage\u00d7probability model produces a precise wrong number. A signal-based model produces a noisier wrong number, because it searches for signals that were never captured. The table below shows how the two approaches differ across data inputs, update frequency, and failure modes.<\/p>\n<table>\n<thead>\n<tr>\n<th>Attribute<\/th>\n<th>Stage\u00d7Probability<\/th>\n<th>Signal-Based<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Data Input<\/td>\n<td>Rep-assigned stage label and fixed historical win rate per stage<\/td>\n<td>Engagement signals: email recency, stakeholder count, call sentiment, next-step validity, champion activity<\/td>\n<\/tr>\n<tr>\n<td>Update Frequency<\/td>\n<td><a href=\"https:\/\/l1advisory.com\/blog\/ai-revops-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">Weekly, triggered by manual CRM update<\/a><\/td>\n<td><a href=\"https:\/\/l1advisory.com\/blog\/ai-revops-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">Continuously, event-driven on every new signal<\/a><\/td>\n<\/tr>\n<tr>\n<td>Primary Failure Mode<\/td>\n<td><a href=\"https:\/\/clari.com\/blog\/sales-forecast-methods\" target=\"_blank\" rel=\"noindex nofollow\">Rep optimism bias and the inability to distinguish a stalled Stage 3 deal from an actively progressing one<\/a><\/td>\n<td><a href=\"https:\/\/weflow.ai\/blog\/predictive-sales-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">Missing activity data: when emails and calls are not logged, model accuracy drops 10\u201315 percentage points<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Why Dirty Data Breaks AI Forecasting And Validates Reddit<\/h2>\n<p>Forecast inaccuracy starts with the humans upstream of the model, not with the model itself. Reps sandbag to protect their number, and the same incentive shapes every field they touch. They assign optimistic close dates to satisfy their manager on Thursday&#8217;s call, then quietly push them forward on Friday. They log the calls they remember and skip the ones that went badly, because the CRM only rewards activity that looks good. They move deals to Proposal after a verbal expression of interest rather than after a signed proposal, because the stage definition was never enforced.<\/p>\n<p>The result is garbage in, garbage out. <a href=\"https:\/\/tomba.io\/blog\/ai-sales-forecast\" target=\"_blank\" rel=\"noindex nofollow\">A model trained on deals where reps never logged activity will simply learn that activity does not matter<\/a>, which is wrong and dangerous. Bad CRM data causes AI systems to produce high-confidence wrong answers, which is more dangerous than producing no answer at all, because a confident wrong forecast is significantly harder to challenge than an acknowledged estimate.<\/p>\n<p>44% of B2B companies lose more than 10% of annual revenue to bad CRM data, and <a href=\"https:\/\/marketricka.com\/ai-sales-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">CRM contact data decays at roughly 2% per month<\/a>, meaning more than 25% of records are inaccurate after a year. AI forecasting models trained on that decay learn bad patterns and reproduce them at scale.<\/p>\n<p>The fix is eliminating manual data entry so the model has trustworthy inputs. That is precisely what Coffee&#8217;s Agent delivers.<\/p>\n<h2>What Should You Ask Before Buying An AI Forecasting Tool?<\/h2>\n<p>Six questions separate vendors with genuine signal-based forecasting from those selling a stage\u00d7probability rollup with a machine learning label on top. Each question has a strong answer pattern and a weak one that signals risk.<\/p>\n<ol>\n<li><strong>Does your model use actual deal signals or stage\u00d7probability?<\/strong> A strong answer names specific signals such as email response latency, stakeholder count, champion engagement recency, and next-step validity. A weak answer references &#8220;AI-powered probability&#8221; without specifying inputs.<\/li>\n<li><strong>Does it automatically capture calls and emails, or require manual logging?<\/strong> A strong answer describes native email and calendar sync that writes activity to deal records without rep action. A weak answer describes an integration that &#8220;supports&#8221; activity logging, meaning reps still initiate it.<\/li>\n<li><strong>Can it explain why a deal is flagged as risky?<\/strong> <a href=\"https:\/\/l1advisory.com\/blog\/ai-revops-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">A strong answer provides reasoning such as &#8220;this deal scored 0.31 because executive engagement dropped 60% in the last 14 days and the champion has not replied in 9 days.&#8221;<\/a> A weak answer outputs a probability score with no reasoning.<\/li>\n<li><strong>How does it handle stale or missing data?<\/strong> A strong answer describes fallback logic and flags deals with insufficient signal coverage. A weak answer says the model &#8220;handles it&#8221; without specifying how.<\/li>\n<li><strong>What is the rep workflow burden, and does it add admin work?<\/strong> A strong answer describes zero additional rep input because the system captures data automatically. A weak answer describes a &#8220;lightweight&#8221; logging requirement that still depends on rep compliance.<\/li>\n<li><strong>Can you backtest predictions against our closed revenue?<\/strong> A strong answer offers to run the backtest during the pilot on your own historical data. A weak answer offers reference customers or aggregate accuracy statistics.<\/li>\n<\/ol>\n<h2>How To Test AI Forecast Accuracy Before You Buy<\/h2>\n<p>A two-quarter backtesting protocol on your own closed-won and closed-lost data gives the clearest read on forecast accuracy before you sign anything. This test rarely appears in vendor content, yet it is the one that matters.<\/p>\n<p>In the first quarter of the backtest, provide the vendor with your pipeline data from two quarters ago, including deals, stages, activity logs, and close dates. Ask the model to generate the forecast it would have produced at the start of that quarter. Compare that forecast to what actually closed and measure the variance. <a href=\"https:\/\/weflow.ai\/blog\/predictive-sales-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">A mature signal-based model can produce \u00b13\u20135% variance from actual revenue in well-instrumented pipelines, compared to \u00b112\u201315% for traditional rep-submitted forecasts.<\/a><\/p>\n<p>In the second quarter of the backtest, focus on deal-level accuracy rather than aggregate accuracy. Ask the model to identify which deals it would have flagged as high-risk at the start of the quarter. Then check whether those deals actually slipped or were lost. A signal-based model should distinguish stalled deals from progressing ones. <a href=\"https:\/\/weflow.ai\/blog\/predictive-sales-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">A mid-market SaaS example illustrates the standard: a predictive model flagged 12 opportunities sitting in Proposal for 45+ days with no email or meeting activity in the last 14 days; nine had been submitted by reps as Best Case or Commit, but the model scored them at 20\u201335%, and only one of the nine ultimately closed won.<\/a><\/p>\n<p>If a vendor declines to run this test on your data, that response tells you what you need to know. <a href=\"https:\/\/tomba.io\/blog\/ai-forecasting-software\" target=\"_blank\" rel=\"noindex nofollow\">A vendor confident in the model will run this.<\/a><\/p>\n<h2>Where AI Forecasting Actually Wins Today<\/h2>\n<p>Reddit practitioners consistently endorse three specific use cases where AI forecasting delivers genuine value, regardless of the broader predictive accuracy debate.<\/p>\n<p>The first is deal-risk flagging. <a href=\"https:\/\/growthnatives.com\/blogs\/crm-data-quality\/why-ai-forecasting-in-salesforce-depends-more-on-data-discipline-than-dashboards\" target=\"_blank\" rel=\"noindex nofollow\">The biggest value of AI forecasting when the data foundation is solid is rarely prediction accuracy but earlier risk detection.<\/a> A deal that looks healthy in Salesforce can show weak movement signals for weeks before anyone raises a flag, and AI can surface that gap while there is still time to act.<\/p>\n<p>The second is CRM hygiene automation. Tools that automatically capture emails, calendar events, and call transcripts and write that activity to deal records remove the manual logging burden that produces dirty data in the first place. This capability sets the stage for every other benefit.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678321672-5c8717cf0024.gif\" alt=\"Create instant meeting follow-up emails with the Coffee AI CRM agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Create instant meeting follow-up emails with the Coffee AI CRM agent<\/em><\/figcaption><\/figure>\n<p>The third is meeting prep. AI that surfaces deal history, stakeholder context, and risk signals before a call gives reps the briefing a good sales manager would provide, without the manager&#8217;s time.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678549697-4e8d65abe17d.gif\" alt=\"GIF of Coffee platform where user is using AI to prep for a meeting with Coffee AI\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Automated meeting prep with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p>Predictive accuracy at the aggregate level still depends on data quality. <a href=\"https:\/\/spotlight.ai\/post\/sales-forecasting-broken-2026\" target=\"_blank\" rel=\"noindex nofollow\">Most B2B sales organizations achieve forecast accuracy between 60% and 75% on a rolling quarterly basis<\/a>, and <a href=\"https:\/\/l1advisory.com\/blog\/ai-revops-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">only 7% of sales organizations achieve forecast accuracy of 90% or higher.<\/a> AI can raise that ceiling when the data foundation is clean.<\/p>\n<h2>Why Coffee Is The Best Solution For AI Pipeline Forecasting<\/h2>\n<p>Coffee focuses on fixing the data that every forecasting model depends on. Where other tools on this SERP focus on the forecasting model, Coffee addresses the root cause Reddit identifies: manual data entry.<\/p>\n<p>The Coffee Agent automatically creates and enriches contacts and companies from email and calendar data. It logs every activity against the right deal record, and it unifies structured CRM data with unstructured sources such as email threads and call transcripts into a single coherent view. Reps do not log anything. The Agent handles it. That means the signals a forecasting model needs are present, current, and accurate.<\/p>\n<figure style=\"text-align: center\"><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\"><img decoding=\"async\" src=\"https:\/\/cdn.aigrowthmarketer.co\/1763678186019-5cc1a76ac78e.gif\" alt=\"Build people lists automatically with Coffee AI CRM Agent\" style=\"max-height: 500px\" loading=\"lazy\"><\/a><figcaption><em>Build people lists automatically with Coffee AI CRM Agent<\/em><\/figcaption><\/figure>\n<p>Coffee&#8217;s Pipeline Compare feature visualizes week-over-week pipeline changes automatically. It highlights progressed deals, stalled opportunities, and new additions without CSV exports or manual pipeline reviews. Pipeline reviews shift from interrogation sessions to strategic discussions.<\/p>\n<p>For teams already on Salesforce or HubSpot, Coffee deploys as a Companion App, an intelligent layer that handles the data-in process so the system of record stays accurate without human effort. For teams building from scratch, Coffee&#8217;s Standalone CRM is an AI-first alternative where the Agent manages the entire system of record. Whether you want the best AI forecasting tool for HubSpot or for Salesforce, Coffee fits your existing stack.<\/p>\n<p>The case study is direct. A company generating tens of millions in revenue and managing sales in spreadsheets rejected Salesforce and HubSpot because they required too much manual work. After deploying Coffee, automatic contact creation from Google Workspace kept the CRM clean without human effort, and the Pipeline Compare feature automated their weekly reviews entirely.<\/p>\n<p><a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">See how Coffee cleans your pipeline data<\/a> so your forecasting model can produce accurate outputs.<\/p>\n<h2>Is AI Forecasting Worth Paying For?<\/h2>\n<p>AI forecasting is worth paying for when your data quality supports it. AI forecasting tools like Clari or Aviso should be bought to push accuracy from 80% to 92%, not from 60% to 80%. Buying AI forecasting before fixing the underlying data means paying a premium for a confident wrong answer.<\/p>\n<p>Reddit practitioners report genuine time savings from AI forecasting, particularly in pipeline review preparation and deal-risk identification. They also report that predictive accuracy disappoints when CRM data is incomplete. <a href=\"https:\/\/marketricka.com\/ai-sales-forecasting\" target=\"_blank\" rel=\"noindex nofollow\">86% of sales teams using AI report positive ROI within their first year<\/a>, and that ROI concentrates in teams that addressed data quality first.<\/p>\n<p>Dedicated revenue-intelligence platforms carry significant implementation costs. <a href=\"https:\/\/technologyinsales.com\/tools\/aviso\" target=\"_blank\" rel=\"noindex nofollow\">Aviso&#8217;s range from $5,000 to $100,000, with 8\u201312 week onboarding, while Clari&#8217;s implementation fees run $15,000 to $50,000, also with 8\u201312 weeks to implement<\/a>. Aviso&#8217;s pricing model, implementation costs, and onboarding timeline make it impractical for teams under approximately 25\u201330 reps, and Clari rarely serves organizations under 500 employees. The ROI calculation changes when the data foundation is clean, because the model then learns from accurate signals rather than amplifying bad ones.<\/p>\n<p>AI forecasting earns its price when the data foundation is clean. Coffee&#8217;s Agent is what makes that foundation clean.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h2>What Is The Best AI For Forecasting?<\/h2>\n<p>The best AI forecasting tool is the one whose model type matches your data reality. If your team logs activities manually and inconsistently, a signal-based model will underperform a well-calibrated stage\u00d7probability rollup, because it searches for signals that were never captured. The correct sequence is to fix data capture first through automatic email, calendar, and call logging, and then layer a signal-based model on top of complete, current data. A tool that automates data capture and provides forecasting output from that clean foundation will outperform any sophisticated model running on incomplete inputs. The model is the easy part, and the data pipeline feeding it is the hard part.<\/p>\n<h2>How Much Does AI Forecasting Cost?<\/h2>\n<p>Practitioner reports place <a href=\"https:\/\/weflow.ai\/blog\/clari-pricing-and-value\" target=\"_blank\" rel=\"noindex nofollow\">dedicated revenue-intelligence platforms like Clari and Aviso in the range of $100\u2013180 per user per month at enterprise tiers<\/a>, with implementation fees and onboarding timelines that make the real first-year cost significantly higher than the per-seat number suggests. CRM-native AI forecasting add-ons for Salesforce and HubSpot are cheaper but depend entirely on the quality of data already in the CRM. The time savings practitioners most consistently report appear in pipeline review preparation, with less time building spreadsheets and more time on deal strategy, rather than in large predictive accuracy gains. Teams should evaluate total cost against the specific use cases where they expect value, because deal-risk flagging, meeting prep, and CRM hygiene automation tend to deliver faster and more reliably than aggregate revenue prediction.<\/p>\n<h2>Can AI Forecasting Explain Why A Deal Is Risky?<\/h2>\n<p>AI forecasting can explain deal risk when the model is signal-based and the signals are present. A signal-based model can surface reasoning such as executive engagement dropping significantly in the last two weeks, the champion not replying in nine days, and the close date being pushed twice. A stage\u00d7probability model cannot produce this reasoning because it does not ingest those signals and only reads the stage label. When evaluating vendors, ask for a specific example of deal-risk reasoning on a real deal from your pipeline. If the vendor cannot produce deal-level reasoning with named signals, the model is not signal-based regardless of how it is marketed.<\/p>\n<h2>Is AI Forecasting Accurate?<\/h2>\n<p>AI forecasting is only as accurate as the data going into it. As noted earlier, most B2B organizations sit at 60\u201375% accuracy. <a href=\"https:\/\/spotlight.ai\/post\/sales-forecasting-broken-2026\" target=\"_blank\" rel=\"noindex nofollow\">In well-instrumented pipelines with sufficient data, signal-based AI forecasting can reach 85\u201390% accuracy, and mature deployments can achieve \u00b13\u20135% variance from actual revenue.<\/a> In pipelines where reps log activities inconsistently, stage definitions vary across the team, and close dates are aspirational rather than evidence-based, AI forecasting produces a more sophisticated version of the same wrong number the manual rollup produced. The accuracy ceiling is set by data quality, not by the model. Fixing data capture comes first, and the model benefits from that work.<\/p>\n<h2>Conclusion: Why Coffee Fixes AI Pipeline Forecasting<\/h2>\n<p>The Reddit verdict on AI pipeline forecasting highlights a real problem: tools underdeliver when CRM data is dirty. The threads often stop at the complaint and rarely spell out the root cause or the fix. The root cause is manual data entry. Reps do not update the CRM reliably because the CRM demands more than it returns, and the resulting gaps teach models the wrong lessons.<\/p>\n<p>The fix is an agent that eliminates manual data entry so every model, whether stage\u00d7probability, signal-based, or hybrid, has trustworthy inputs to work from. Coffee is that agent. It automatically captures contacts, companies, and activities from email and calendar, logs every interaction against the right deal record, and surfaces pipeline changes week-over-week without spreadsheets. It works as a standalone AI-first CRM for growing teams or as a Companion App on top of Salesforce or HubSpot.<\/p>\n<p>Ready to see how Coffee&#8217;s Agent handles pipeline forecasting and data capture together? <a href=\"https:\/\/www.coffee.ai\/pricing\" target=\"_blank\">See how Coffee cleans your pipeline data<\/a>.<\/p>\n<section data-read-next=\"true\">\n<h2>Read Next<\/h2>\n<ul>\n<li><a href=\"https:\/\/coffee.ai\/articles\/how-ai-improves-pipeline-forecasting\" target=\"_blank\">How AI Improves Pipeline Forecasting: Clean Data First<\/a><\/li>\n<li><a href=\"https:\/\/coffee.ai\/articles\/pipeline-forecasting-gong\" target=\"_blank\">Pipeline Forecasting: Coffee&#8217;s AI Solution for Accuracy<\/a><\/li>\n<li><a href=\"https:\/\/coffee.ai\/articles\/pipeline-forecasting-ai-accuracy-tips\" target=\"_blank\">12 Pipeline Forecasting AI Accuracy Tips to Improve Sales<\/a><\/li>\n<li><a href=\"https:\/\/coffee.ai\/articles\/ai-pipeline-intelligence-sales-forecasting\" target=\"_blank\">AI Pipeline Intelligence for Accurate Sales Forecasting<\/a><\/li>\n<li><a href=\"https:\/\/coffee.ai\/articles\/ai-pipeline-forecasting-best-practices\" target=\"_blank\">AI Pipeline Forecasting Best Practices: Complete Guide 2026<\/a><\/li>\n<\/ul>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>Reddit says AI pipeline forecasting is overhyped. Coffee proves otherwise. See how signal-based forecasting drives real accuracy. Try Coffee today.<\/p>\n","protected":false},"author":11,"featured_media":4351,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"class_list":["post-4352","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/4352","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/comments?post=4352"}],"version-history":[{"count":2,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/4352\/revisions"}],"predecessor-version":[{"id":8990,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/posts\/4352\/revisions\/8990"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media\/4351"}],"wp:attachment":[{"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/media?parent=4352"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/categories?post=4352"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.coffee.ai\/articles\/wp-json\/wp\/v2\/tags?post=4352"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}