{"id":1898,"date":"2026-09-04T11:58:44","date_gmt":"2026-09-04T11:58:44","guid":{"rendered":"https:\/\/predictleads.com\/blog\/?p=1898"},"modified":"2026-10-21T15:01:39","modified_gmt":"2026-10-21T15:01:39","slug":"industries-accelerating-hiring-momentum","status":"publish","type":"post","link":"https:\/\/predictleads.com\/blog\/industries-accelerating-hiring-momentum\/","title":{"rendered":"Hiring Momentum: How to Spot Industries Accelerating Hiring (October 2026)"},"content":{"rendered":"\n<p><em>By Robert Fon \/ October 21, 2026<\/em><br>\n<small>Originally published September 4, 2026. Updated October 21, 2026: this piece has been restructured from a single-month tracker into a reusable method, with the September findings kept as a clearly dated worked example rather than presented as current data.<\/small><\/p>\n\n<p>Most teams that track hiring activity default to one number: how many job openings a company or an industry has posted this month. That number tells you size, not direction, so it misses the accounts and sectors that are quietly speeding up before the headline totals catch up. Hiring momentum fixes this by scoring the rate of change in hiring against an industry&#8217;s or an account&#8217;s own recent baseline, not against an arbitrary volume threshold. This guide explains how PredictLeads&#8217; Hiring Momentum Score works, walks through the Top 10 industries from the most recently completed analysis, and shows how to build the same view from Job Openings data using the API, flat files, or MCP.<\/p>\n\n<p><strong>TLDR:<\/strong><\/p>\n<ul>\n<li>Hiring momentum measures how fast an industry or an account is accelerating against its own recent pace, not how many job openings it has open right now.<\/li>\n<li>In PredictLeads&#8217; most recently completed analysis (data window July 29 to August 27, 2026), the median month-over-month growth across the ten fastest-accelerating industries was 12.2%.<\/li>\n<li>A rising Hiring Momentum Score in the weeks before a company resumes active hiring is a stronger early signal than a one-time spike in open job count.<\/li>\n<li>In that same analysis window, companies whose hiring was decelerating raised financing at a higher rate (2.999%) than companies whose hiring was accelerating (1.688%), a reminder that funding is not an automatic hiring trigger.<\/li>\n<li>PredictLeads tracks hiring momentum from 279.2M+ Job Openings records collected since 2018, covering 10.2M+ active openings at any time across 710,600+ companies currently hiring.<\/li>\n<\/ul>\n\n<h2>What Hiring Momentum Is (and What a Raw Job Count Is Not)<\/h2>\n\n<p>Hiring momentum is a measure of how much an industry&#8217;s or a company&#8217;s hiring activity is accelerating relative to its own recent pace, not a count of how many jobs it has open right now. The <a href=\"https:\/\/www.bls.gov\/jlt\/jltdef.htm\" target=\"_blank\" rel=\"noopener\">U.S. Bureau of Labor Statistics defines a job opening<\/a>, in its JOLTS survey, as a specific position that is open and could start within 30 days; PredictLeads applies the same first_seen_at and last_seen_at logic to each posting&#8217;s lifecycle to track that activity continuously, rather than through employer self-reporting. A sector can carry a large volume of open roles and still be flat or slowing, while a much smaller sector can be the one actually speeding up.<\/p>\n\n<p>Hiring momentum is also not a prediction of future headcount: a job opening is evidence of active recruiting, not a confirmed hire. It is not comparable in absolute terms across industries either, because each industry is scored against its own baseline rather than against every other industry&#8217;s volume. In PredictLeads&#8217; most recent analysis, health care hospitals carried 191,117 open job listings in a 30-day window, by far the largest raw total measured, yet hospitals did not rank among the ten fastest-accelerating industries because that volume was not accelerating relative to the sector&#8217;s own recent pace. Every PredictLeads job opening record carries a first_seen_at and a last_seen_at timestamp, which is what makes a rate-of-change score possible in the first place.<\/p>\n\n<h2>How the Hiring Momentum Score Works<\/h2>\n\n<p>PredictLeads built the Hiring Momentum Score from five weighted components, each capturing a different angle on acceleration rather than size. No single component decides the ranking; an industry has to show up across more than one of these measures to land in the top tier.<\/p>\n\n<ul>\n<li><strong>Acceleration versus 90-day pace (30%):<\/strong> how much faster an industry is hiring compared with its own trailing-quarter average.<\/li>\n<li><strong>Month-over-month change (25%):<\/strong> the direct percentage change in job openings from the prior month.<\/li>\n<li><strong>Growth in companies hiring (20%):<\/strong> whether more distinct companies in the industry are posting at all, not just whether a few large employers are posting more.<\/li>\n<li><strong>Hirers beating their own baseline (15%):<\/strong> the share of hiring companies that are individually outpacing their personal recent average.<\/li>\n<li><strong>Newly active share (10%):<\/strong> the percentage of hiring companies that had posted no openings in the prior five months.<\/li>\n<\/ul>\n\n<figure style=\"margin:20px 0;\">\n<svg viewBox=\"0 0 700 260\" width=\"100%\" height=\"auto\" role=\"img\" aria-labelledby=\"hmsTitle hmsDesc\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"max-width:640px;display:block;\">\n<title id=\"hmsTitle\">Hiring Momentum Score component weights<\/title>\n<desc id=\"hmsDesc\">Acceleration versus 90-day pace is weighted 30 percent, month-over-month change 25 percent, growth in companies hiring 20 percent, hirers beating their own baseline 15 percent, newly active share 10 percent.<\/desc>\n<rect x=\"0\" y=\"0\" width=\"700\" height=\"260\" rx=\"14\" fill=\"#f3ecf8\"><\/rect>\n<text x=\"16\" y=\"38\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" fill=\"#3a2f4d\">Acceleration vs 90-day pace<\/text>\n<rect x=\"260\" y=\"20\" width=\"380\" height=\"26\" rx=\"6\" fill=\"#efe4f6\"><\/rect>\n<rect x=\"260\" y=\"20\" width=\"380\" height=\"26\" rx=\"6\" fill=\"#76508e\"><\/rect>\n<text x=\"648\" y=\"38\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" font-weight=\"700\" fill=\"#3a2f4d\">30%<\/text>\n<text x=\"16\" y=\"84\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" fill=\"#3a2f4d\">Month-over-month change<\/text>\n<rect x=\"260\" y=\"66\" width=\"380\" height=\"26\" rx=\"6\" fill=\"#efe4f6\"><\/rect>\n<rect x=\"260\" y=\"66\" width=\"317\" height=\"26\" rx=\"6\" fill=\"#76508e\"><\/rect>\n<text x=\"585\" y=\"84\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" font-weight=\"700\" fill=\"#3a2f4d\">25%<\/text>\n<text x=\"16\" y=\"130\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" fill=\"#3a2f4d\">Growth in companies hiring<\/text>\n<rect x=\"260\" y=\"112\" width=\"380\" height=\"26\" rx=\"6\" fill=\"#efe4f6\"><\/rect>\n<rect x=\"260\" y=\"112\" width=\"253\" height=\"26\" rx=\"6\" fill=\"#76508e\"><\/rect>\n<text x=\"521\" y=\"130\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" font-weight=\"700\" fill=\"#3a2f4d\">20%<\/text>\n<text x=\"16\" y=\"176\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" fill=\"#3a2f4d\">Hirers beating their baseline<\/text>\n<rect x=\"260\" y=\"158\" width=\"380\" height=\"26\" rx=\"6\" fill=\"#efe4f6\"><\/rect>\n<rect x=\"260\" y=\"158\" width=\"190\" height=\"26\" rx=\"6\" fill=\"#76508e\"><\/rect>\n<text x=\"458\" y=\"176\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" font-weight=\"700\" fill=\"#3a2f4d\">15%<\/text>\n<text x=\"16\" y=\"222\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" fill=\"#3a2f4d\">Newly active share<\/text>\n<rect x=\"260\" y=\"204\" width=\"380\" height=\"26\" rx=\"6\" fill=\"#efe4f6\"><\/rect>\n<rect x=\"260\" y=\"204\" width=\"127\" height=\"26\" rx=\"6\" fill=\"#76508e\"><\/rect>\n<text x=\"395\" y=\"222\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"13\" font-weight=\"700\" fill=\"#3a2f4d\">10%<\/text>\n<\/svg>\n<figcaption style=\"font-size:13px;color:#3a2f4d;margin-top:6px;\">The five weighted components of PredictLeads&#8217; Hiring Momentum Score.<\/figcaption>\n<\/figure>\n\n<p>Weighting acceleration and newly active hirers this heavily is what keeps a handful of large, steady employers from dominating the ranking. It also means the score can be recalculated for any O*NET-coded job category, not only at the industry level, since PredictLeads applies the same first_seen_at and last_seen_at logic to individual job records.<\/p>\n\n<h2>Worked Example: The Industries That Were Accelerating Hiring in PredictLeads&#8217; Most Recent Analysis<\/h2>\n\n<div style=\"background:#efe4f6;border-left:4px solid #76508e;padding:14px 18px;border-radius:6px;margin:16px 0;color:#3a2f4d;\">\n<strong>Data note:<\/strong> This section reflects PredictLeads&#8217; most recently completed hiring momentum analysis, covering job postings from July 29 to August 27, 2026, scored across 1,305,998 companies and 200 qualifying industries out of 1,009 categories tracked. That remains the latest full run available at the time of this October 2026 update. Treat the list below as a worked example of how the score behaves, not as this month&#8217;s leaderboard.\n<\/div>\n\n<p>The ranking below excludes the broad Professional, Scientific, and Technical Services sector, which includes most software and technology companies, because that sector contracted 3.2% month over month in the same window and would have crowded out smaller, faster-moving categories.<\/p>\n\n<h3>The Top 10 Accelerating Industries<\/h3>\n\n<div style=\"overflow-x:auto;\">\n<table style=\"width:100%;border-collapse:collapse;font-family:Arial,Helvetica,sans-serif;font-size:14px;\">\n<thead>\n<tr style=\"background-color:#76508e;color:#ffffff;text-align:left;\">\n<th style=\"padding:10px 12px;\">Rank<\/th>\n<th style=\"padding:10px 12px;\">Industry<\/th>\n<th style=\"padding:10px 12px;\">Momentum Score<\/th>\n<th style=\"padding:10px 12px;\">MoM Change<\/th>\n<th style=\"padding:10px 12px;\">Openings (30 Days)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr style=\"background-color:#ffffff;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">1<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Tour Operators<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">94.3<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+12.1%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">2,659<\/td><\/tr>\n<tr style=\"background-color:#f3ecf8;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">2<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Exam Preparation and Tutoring<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">93.1<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+16.0%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">4,326<\/td><\/tr>\n<tr style=\"background-color:#ffffff;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">3<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Golf Courses and Country Clubs<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">88.0<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+15.4%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">2,476<\/td><\/tr>\n<tr style=\"background-color:#f3ecf8;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">4<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Automotive Parts and Accessories Retailers<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">85.7<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+12.4%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">4,125<\/td><\/tr>\n<tr style=\"background-color:#ffffff;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">5<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Language Schools<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">85.0<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+4.3%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">1,975<\/td><\/tr>\n<tr style=\"background-color:#f3ecf8;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">6<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Jewelry Retailers<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">84.8<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+7.4%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">3,724<\/td><\/tr>\n<tr style=\"background-color:#ffffff;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">7<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Janitorial Services<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">81.7<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+9.4%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">7,378<\/td><\/tr>\n<tr style=\"background-color:#f3ecf8;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">8<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Sporting Goods Retailers<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">79.7<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+4.0%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">6,897<\/td><\/tr>\n<tr style=\"background-color:#ffffff;\"><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">9<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">Sporting and Athletic Goods Manufacturing<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">78.1<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">+20.8%<\/td><td style=\"padding:9px 12px;border-bottom:1px solid #ece0f5;\">2,134<\/td><\/tr>\n<tr style=\"background-color:#f3ecf8;\"><td style=\"padding:9px 12px;\">10<\/td><td style=\"padding:9px 12px;\">Used Car Dealers<\/td><td style=\"padding:9px 12px;\">77.6<\/td><td style=\"padding:9px 12px;\">+15.1%<\/td><td style=\"padding:9px 12px;\">3,296<\/td><\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p style=\"font-size:13px;color:#3a2f4d;\"><em>Source: PredictLeads Job Openings data, July 29 to August 27, 2026 analysis window (most recent completed run as of this October 2026 update).<\/em><\/p>\n\n<p>The gap between rank 1 and rank 10 is wide, 94.3 versus 77.6, but every industry on the list beat its own 90-day pace by a wide margin, which is the entire point of scoring acceleration instead of volume.<\/p>\n\n<h3>What Was Driving the Movement<\/h3>\n\n<ul>\n<li><strong>Exam Preparation and Tutoring<\/strong> was hiring to build the platform, not the tutor roster: postings skewed toward software development and data roles rather than instructional staff.<\/li>\n<li><strong>Used Car Dealers<\/strong> were scaling operations ahead of sales, with openings concentrated in operations and support roles before any corresponding increase in sales postings.<\/li>\n<li><strong>Jewelry Retailers<\/strong> started holiday staffing in August, earlier than the pre-holiday seasonal build the <a href=\"https:\/\/nrf.com\/media-center\/press-releases\/nrf-expects-holiday-sales-to-surpass-1-trillion-for-the-first-time-in-2025\" target=\"_blank\" rel=\"noopener\">National Retail Federation<\/a> has projected for the season.<\/li>\n<li><strong>Tour Operators<\/strong>, the top-ranked industry, were posting for AI engineers alongside traditional travel-operations roles.<\/li>\n<\/ul>\n\n<h2>Three Signals Hidden Inside the Hiring Data<\/h2>\n\n<p>Beyond the industry ranking, three patterns in the same dataset are useful for anyone building an account list from hiring data.<\/p>\n\n<h3>Financing Shows Up During the Plateau, Not the Spike<\/h3>\n\n<p>In the same analysis window, companies with decelerating hiring raised financing at a rate of 2.999%, compared with 1.688% for companies with accelerating hiring and 0.576% for companies that were newly active after a hiring gap. That pattern runs counter to the common assumption that a funding event precedes a hiring wave. It is more consistent with financing arriving after a company has already leveled off its hiring, possibly to fund the next phase rather than the current one. Treat a recent funding event as one input alongside hiring data, not as confirmation that hiring is about to increase.<\/p>\n\n<h3>A Quarter of Hirers Were Not Hiring Five Months Earlier<\/h3>\n\n<p>Across the ten fastest-accelerating industries, an average of 25.6% of currently hiring companies had posted no job openings in the prior five months, ranging from 19.3% in Golf Courses and Country Clubs to 33.0% in Language Schools. That share is a useful account-prioritization filter on its own: a company that just resumed hiring after a gap is a different kind of prospect than one that has been steadily hiring all year. PredictLeads&#8217; last_seen_at field on each job opening record is what makes a hiring gap identifiable in the first place, since it marks when a posting was last confirmed active.<\/p>\n\n<h3>Momentum Is Usually Broad, Not Three Large Employers<\/h3>\n\n<p>PredictLeads&#8217; methodology checks whether an industry&#8217;s score is being driven by a small number of large employers or by broad participation across many companies, because a volume spike concentrated in two or three large accounts is a different signal than a sector-wide acceleration. Five candidate industries were excluded from the most recent ranking after this check, including one case where a single company&#8217;s posting pattern accounted for most of the apparent movement. That is also why growth in companies hiring exists as its own 20% component rather than being folded into the overall month-over-month change.<\/p>\n\n<h2>How to Build Your Own Hiring Momentum View<\/h2>\n\n<p>The same method behind this ranking works at the account level, which is the more useful version for a <a href=\"https:\/\/predictleads.com\/blog\/hiring-signals-b2b-sales-account-prioritization\/\">GTM or RevOps team prioritizing accounts by hiring signals<\/a> instead of reading an industry report.<\/p>\n\n<h3>Steps 1 to 4<\/h3>\n<ol>\n<li>Pull job opening history for your target accounts or industries. Use <code>\/discover\/job_openings<\/code> filtered by <a href=\"https:\/\/www.onetcenter.org\/\" target=\"_blank\" rel=\"noopener\">O*NET occupation code<\/a> and location for an industry view, or <code>\/companies\/{domain}\/job_openings<\/code> for named accounts.<\/li>\n<li>Establish each account&#8217;s own 90-day baseline hiring pace instead of comparing it to an industry average. An account&#8217;s baseline is just its own trailing count of active openings over the prior quarter.<\/li>\n<li>Calculate the month-over-month change in both open job count and in the share of previously quiet accounts that are newly posting.<\/li>\n<li>Weight the components. Start with the 30\/25\/20\/15\/10 split described above, then adjust it for your own sales motion. For example, a team selling into seasonal retail might weight newly active share higher.<\/li>\n<\/ol>\n\n<figure style=\"margin:20px 0;\">\n<svg viewBox=\"0 0 900 180\" width=\"100%\" height=\"auto\" role=\"img\" aria-labelledby=\"hmWorkflowTitle hmWorkflowDesc\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" style=\"display:block;\">\n<title id=\"hmWorkflowTitle\">Four-step hiring momentum workflow<\/title>\n<desc id=\"hmWorkflowDesc\">Step 1, pull job opening history. Step 2, set each account&#8217;s 90-day baseline. Step 3, calculate month-over-month change. Step 4, weight the components and score.<\/desc>\n<rect x=\"0\" y=\"0\" width=\"900\" height=\"180\" fill=\"#ffffff\"><\/rect>\n<rect x=\"20\" y=\"40\" width=\"190\" height=\"100\" rx=\"14\" fill=\"#efe4f6\" stroke=\"#76508e\" stroke-width=\"2\"><\/rect>\n<text x=\"115\" y=\"72\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-weight=\"800\" font-size=\"16\" fill=\"#76508e\">Step 1<\/text>\n<text x=\"115\" y=\"96\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">Pull job opening<\/text>\n<text x=\"115\" y=\"113\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">history per account<\/text>\n<line x1=\"210\" y1=\"90\" x2=\"238\" y2=\"90\" stroke=\"#76508e\" stroke-width=\"3\"><\/line>\n<polygon points=\"238,84 250,90 238,96\" fill=\"#76508e\"><\/polygon>\n<rect x=\"250\" y=\"40\" width=\"190\" height=\"100\" rx=\"14\" fill=\"#efe4f6\" stroke=\"#76508e\" stroke-width=\"2\"><\/rect>\n<text x=\"345\" y=\"72\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-weight=\"800\" font-size=\"16\" fill=\"#76508e\">Step 2<\/text>\n<text x=\"345\" y=\"96\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">Set each account&#8217;s<\/text>\n<text x=\"345\" y=\"113\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">90-day baseline<\/text>\n<line x1=\"440\" y1=\"90\" x2=\"468\" y2=\"90\" stroke=\"#76508e\" stroke-width=\"3\"><\/line>\n<polygon points=\"468,84 480,90 468,96\" fill=\"#76508e\"><\/polygon>\n<rect x=\"480\" y=\"40\" width=\"190\" height=\"100\" rx=\"14\" fill=\"#efe4f6\" stroke=\"#76508e\" stroke-width=\"2\"><\/rect>\n<text x=\"575\" y=\"72\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-weight=\"800\" font-size=\"16\" fill=\"#76508e\">Step 3<\/text>\n<text x=\"575\" y=\"96\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">Calculate month-<\/text>\n<text x=\"575\" y=\"113\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">over-month change<\/text>\n<line x1=\"670\" y1=\"90\" x2=\"698\" y2=\"90\" stroke=\"#76508e\" stroke-width=\"3\"><\/line>\n<polygon points=\"698,84 710,90 698,96\" fill=\"#76508e\"><\/polygon>\n<rect x=\"710\" y=\"40\" width=\"190\" height=\"100\" rx=\"14\" fill=\"#efe4f6\" stroke=\"#76508e\" stroke-width=\"2\"><\/rect>\n<text x=\"805\" y=\"72\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-weight=\"800\" font-size=\"16\" fill=\"#76508e\">Step 4<\/text>\n<text x=\"805\" y=\"96\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">Weight the<\/text>\n<text x=\"805\" y=\"113\" text-anchor=\"middle\" font-family=\"Poppins, Segoe UI, sans-serif\" font-size=\"12\" fill=\"#3a2f4d\">components and score<\/text>\n<\/svg>\n<figcaption style=\"font-size:13px;color:#3a2f4d;margin-top:6px;\">Steps 1 to 4 of building a hiring momentum view from PredictLeads Job Openings data.<\/figcaption>\n<\/figure>\n\n<h3>Steps 5 to 6<\/h3>\n<ol start=\"5\">\n<li>Rank the resulting list and drop any account you cannot verify with a second signal, such as a News Events entry categorized <code>increases_headcount_by<\/code> or a recent Financing Event.<\/li>\n<li>Re-run the score on a fixed cadence. PredictLeads refreshes Job Openings data approximately every 36 hours, so a weekly re-score is realistic without waiting on a monthly batch.<\/li>\n<\/ol>\n\n<h2>Reading These Numbers Responsibly<\/h2>\n\n<p>A few guardrails matter when acting on hiring momentum data. A job opening is evidence that a company is actively recruiting, not confirmation that the role will be filled on any particular timeline, so hedge any outreach messaging that assumes a hire has already happened. The financing-and-hiring relationship described above is a correlation observed in one data window, not a causal rule, so do not message a prospect as though a recent raise guarantees a hiring wave. Finally, the industries excluded from this analysis, a small group whose apparent movement traced back to one company&#8217;s posting pattern rather than sector-wide activity, are a reminder to check company-level concentration before building a list off any single aggregate number.<\/p>\n\n<h2>How PredictLeads Powers Hiring Momentum Analysis<\/h2>\n\n<p>Hiring momentum, as described in this guide, is built entirely from PredictLeads&#8217; Job Openings dataset: 279.2M+ historical job records collected since 2018, with 10.2M+ openings active at any given time across 710,600+ companies currently hiring. Every record carries first_seen_at, last_seen_at, an O*NET occupation code, seniority, and one of 26 job categories, which is what allows the same baseline-versus-current-pace calculation used above to run at the company, industry, or job-category level.<\/p>\n\n<div style=\"display:flex;flex-wrap:wrap;gap:12px;margin:16px 0;\">\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:10px;padding:16px;text-align:center;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;\">279.2M+<\/div>\n<div style=\"font-size:13px;color:#3a2f4d;\">Job Openings records since 2018<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:10px;padding:16px;text-align:center;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;\">10.2M+<\/div>\n<div style=\"font-size:13px;color:#3a2f4d;\">Active openings at any time<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:10px;padding:16px;text-align:center;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;\">710,600+<\/div>\n<div style=\"font-size:13px;color:#3a2f4d;\">Companies currently hiring<\/div>\n<\/div>\n<div style=\"flex:1 1 200px;background:#ffffff;border:1px solid #ece0f5;border-radius:10px;padding:16px;text-align:center;\">\n<div style=\"font-size:24px;font-weight:800;color:#76508e;\">210,800+<\/div>\n<div style=\"font-size:13px;color:#3a2f4d;\">Financing Events since 2016<\/div>\n<\/div>\n<\/div>\n\n<p>Job Openings data pairs with two adjacent datasets for a fuller account view. News Events (10M+ signals since 2016, across 37 categories including <code>increases_headcount_by<\/code> and <code>expands_offices_in<\/code>) can confirm a hiring signal with a public statement, a practice covered in more depth in our guide to <a href=\"https:\/\/predictleads.com\/blog\/company-signals-hiring-news-funding-technology\/\">combining hiring, news, funding, and technology signals<\/a>. Financing Events (210,800+ events since 2016) can be checked against hiring momentum using the pattern described above, rather than assumed to predict it. For accounts where the hiring shift concerns engineering or IT roles, see our guide on <a href=\"https:\/\/predictleads.com\/blog\/companies-hiring-data-engineers-adopting-data-tools\/\">what hiring data signals about tooling adoption<\/a>, since Technology Detections provide evidence of which technologies a company uses or has recently used across 99M+ companies.<\/p>\n\n<p>All of this is available through the API, flat files, webhooks, or MCP, so a hiring momentum view can run as a live scoring pipeline instead of a monthly report. PredictLeads is SOC 2 Type II compliant and built on public sources only, consistent with GDPR and CCPA. For a broader comparison of providers in this space, see our roundup of <a href=\"https:\/\/predictleads.com\/blog\/top-5-job-data-providers-2026\/\">job posting and hiring intent data providers<\/a>, and for a prospecting-focused walkthrough, see <a href=\"https:\/\/predictleads.com\/blog\/how-to-use-job-openings-data-for-sales-prospecting\/\">using Job Openings data for sales prospecting<\/a>.<\/p>\n\n<h2>Final Thoughts on Hiring Momentum<\/h2>\n\n<p>A single month&#8217;s open job count tells you which industries are big. Hiring momentum tells you which ones are moving, and moving is the more useful signal for timing an outreach push, a market analysis, or a deal thesis. If you are building outreach cadences around this kind of data, our guide to <a href=\"https:\/\/predictleads.com\/blog\/sales-trigger-data-b2b-outreach-workflows\/\">building outreach workflows around trigger data<\/a> and our guide to <a href=\"https:\/\/predictleads.com\/blog\/competitor-hiring-spikes-guide\/\">tracking competitor hiring spikes<\/a> both extend this method. For accounts in sectors like the ones in the worked example above, our note on <a href=\"https:\/\/predictleads.com\/blog\/auto-tariffs-automotive-hiring-data\/\">automotive hiring data<\/a> and our guide to <a href=\"https:\/\/predictleads.com\/blog\/how-to-find-companies-similar-to-your-best-customers\/\">finding companies similar to your best customers<\/a> are natural next reads. The worked example in this guide will look dated again in a few months, which is exactly why the method matters more than any single month&#8217;s leaderboard.<\/p>\n\n<div style=\"background:#f3ecf8;border:1px solid #ece0f5;border-radius:10px;padding:20px;margin:24px 0;text-align:center;\">\n<p style=\"font-size:18px;font-weight:800;color:#76508e;margin:0 0 8px 0;\">Ready to see this in your own data?<\/p>\n<p style=\"margin:0 0 12px 0;color:#3a2f4d;\">Get 100 free API requests per month &#8211; no credit card, no sales call.<\/p>\n<a href=\"https:\/\/predictleads.com\/sign_up?utm_source=blog&#038;utm_medium=cta&#038;utm_campaign=hiring-momentum-refresh-oct2026\" style=\"display:inline-block;background:#76508e;color:#ffffff;padding:10px 22px;border-radius:6px;text-decoration:none;font-weight:700;\">Start free<\/a>\n<\/div>\n\n<h2>Frequently Asked Questions<\/h2>\n\n<p><strong>What is hiring momentum, and how is it different from a company&#8217;s total headcount growth?<\/strong><br>\nHiring momentum measures the rate of change in a company&#8217;s or an industry&#8217;s hiring activity relative to its own recent baseline, while headcount growth measures the net change in total employees over time. A company can show strong hiring momentum in its job postings well before that activity shows up as confirmed headcount growth in a later report. PredictLeads scores momentum directly from Job Openings records, each timestamped with first_seen_at and last_seen_at, rather than waiting on lagging headcount estimates.<\/p>\n\n<p><strong>How is the Hiring Momentum Score calculated?<\/strong><br>\nThe score weights five components: acceleration versus a 90-day pace (30%), month-over-month change (25%), growth in the number of companies hiring (20%), the share of hirers beating their own baseline (15%), and the share of newly active hirers (10%). Weighting acceleration and new activity this heavily keeps a few large, steady employers from dominating the ranking. PredictLeads calculates this from 279.2M+ Job Openings records collected since 2018.<\/p>\n\n<p><strong>Is PredictLeads&#8217; September 2026 hiring momentum ranking still current in October 2026?<\/strong><br>\nNo. The Top 10 ranking in this guide reflects PredictLeads&#8217; most recently completed analysis, covering the July 29 to August 27, 2026 data window, and it is presented here as a worked example of how the scoring method behaves rather than as a live October 2026 leaderboard. Industry rankings shift with every data cycle because the score is relative to each industry&#8217;s own recent pace. Readers who need a current ranking should run the method described in this guide against live Job Openings data rather than relying on a single historical snapshot.<\/p>\n\n<p><strong>How can I build a hiring momentum view for my own target accounts?<\/strong><br>\nPull job opening history per account through the PredictLeads API, using \/companies\/{domain}\/job_openings for named accounts or \/discover\/job_openings filtered by O*NET code and location for an industry view, then set each account&#8217;s own 90-day baseline and weight the components to match your sales motion. PredictLeads refreshes Job Openings data approximately every 36 hours, so a weekly re-score is realistic for an active account list. Flat files and webhooks are also available for teams running this at scale.<\/p>\n\n<p><strong>Does a company raising funding mean it is about to increase hiring?<\/strong><br>\nNot reliably. In PredictLeads&#8217; most recent hiring momentum analysis, companies with decelerating hiring actually raised financing at a higher rate (2.999%) than companies with accelerating hiring (1.688%), suggesting funding often arrives after a hiring plateau rather than before a hiring wave. Treat a Financing Event as one input to check against hiring data, not as a standalone predictor. PredictLeads tracks 210,800+ Financing Events since 2016 alongside its Job Openings dataset for exactly this kind of cross-checking.<\/p>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"BlogPosting\",\n  \"headline\": \"Hiring Momentum: How to Spot Industries Accelerating Hiring (October 2026)\",\n  \"description\": \"Hiring momentum shows which industries are accelerating hiring now, not just which have the most openings. See how PredictLeads scores it, updated October 2026.\",\n  \"datePublished\": \"2026-09-04\",\n  \"dateModified\": \"2026-10-21\",\n  \"author\": {\n    \"@type\": \"Organization\",\n    \"name\": \"PredictLeads\",\n    \"url\": \"https:\/\/predictleads.com\"\n  },\n  \"mainEntityOfPage\": {\n    \"@type\": \"WebPage\",\n    \"@id\": \"https:\/\/predictleads.com\/blog\/industries-accelerating-hiring-momentum\/\"\n  },\n  \"url\": \"https:\/\/predictleads.com\/blog\/industries-accelerating-hiring-momentum\/\",\n  \"wordCount\": 2450,\n  \"keywords\": [\n    \"hiring momentum\",\n    \"industries accelerating hiring\",\n    \"Job Openings data\",\n    \"hiring signals\",\n    \"B2B sales triggers\",\n    \"hiring intent data\",\n    \"account prioritization\",\n    \"PredictLeads\"\n  ]\n}\n<\/script>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is hiring momentum, and how is it different from a company's total headcount growth?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Hiring momentum measures the rate of change in a company's or an industry's hiring activity relative to its own recent baseline, while headcount growth measures the net change in total employees over time. A company can show strong hiring momentum in its job postings well before that activity shows up as confirmed headcount growth in a later report. PredictLeads scores momentum directly from Job Openings records, each timestamped with first_seen_at and last_seen_at, rather than waiting on lagging headcount estimates.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How is the Hiring Momentum Score calculated?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The score weights five components: acceleration versus a 90-day pace (30%), month-over-month change (25%), growth in the number of companies hiring (20%), the share of hirers beating their own baseline (15%), and the share of newly active hirers (10%). Weighting acceleration and new activity this heavily keeps a few large, steady employers from dominating the ranking. PredictLeads calculates this from 279.2M+ Job Openings records collected since 2018.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Is PredictLeads' September 2026 hiring momentum ranking still current in October 2026?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"No. The Top 10 ranking in this guide reflects PredictLeads' most recently completed analysis, covering the July 29 to August 27, 2026 data window, and it is presented as a worked example of how the scoring method behaves rather than as a live October 2026 leaderboard. Industry rankings shift with every data cycle because the score is relative to each industry's own recent pace. Readers who need a current ranking should run the method described in this guide against live Job Openings data rather than relying on a single historical snapshot.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How can I build a hiring momentum view for my own target accounts?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Pull job opening history per account through the PredictLeads API, using \/companies\/{domain}\/job_openings for named accounts or \/discover\/job_openings filtered by O*NET code and location for an industry view, then set each account's own 90-day baseline and weight the components to match your sales motion. PredictLeads refreshes Job Openings data approximately every 36 hours, so a weekly re-score is realistic for an active account list. Flat files and webhooks are also available for teams running this at scale.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does a company raising funding mean it is about to increase hiring?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Not reliably. In PredictLeads' most recent hiring momentum analysis, companies with decelerating hiring actually raised financing at a higher rate (2.999%) than companies with accelerating hiring (1.688%), suggesting funding often arrives after a hiring plateau rather than before a hiring wave. Treat a Financing Event as one input to check against hiring data, not as a standalone predictor. PredictLeads tracks 210,800+ Financing Events since 2016 alongside its Job Openings dataset for exactly this kind of cross-checking.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What industries were accelerating hiring fastest in the most recent PredictLeads analysis?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"In the July 29 to August 27, 2026 data window, the ten fastest-accelerating industries were led by Tour Operators (score 94.3), Exam Preparation and Tutoring (93.1), and Golf Courses and Country Clubs (88.0), with a median month-over-month growth of 12.2% across the group. The ranking excluded the broad Professional, Scientific, and Technical Services sector, which contracted 3.2% in the same window. These figures are from PredictLeads' most recently completed run and are presented as a worked example rather than a live ranking.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Why might a company stop posting new jobs for several months and then start again?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"A hiring gap can reflect a hiring freeze, a completed hiring round, a budget cycle, or simply a pause between projects, and PredictLeads' data cannot confirm the specific cause from the posting pattern alone. What it can confirm is the gap itself: the last_seen_at field on a prior job opening and the first_seen_at field on a new one together show exactly when hiring resumed. In PredictLeads' most recent analysis, an average of 25.6% of hirers across the ten fastest-accelerating industries had been quiet for the prior five months before resuming.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can hiring momentum be a reliable B2B sales trigger?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Hiring momentum can be a useful trigger when paired with a second confirming signal, since a job opening by itself is evidence of active recruiting, not a guarantee of budget or urgency. A company that is newly active after a hiring gap, or that is beating its own 90-day pace, is generally a stronger prospect than one simply carrying a large static count of open roles. PredictLeads supports this kind of layering by combining Job Openings with News Events and Financing Events in a single API.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How do you avoid job-board noise when scoring industries by hiring activity?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads' methodology checks whether an industry's apparent movement is driven by broad participation across many companies or by a small number of large employers, and excludes categories where a single company's posting pattern accounts for most of the measured change. In the most recent analysis, this check removed five candidate industries before publishing the final Top 10 ranking. The growth in companies hiring component, worth 20% of the overall score, exists specifically to catch this kind of concentration.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What is the difference between hiring momentum and raw job opening volume?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Raw job opening volume counts how many roles are currently open, while hiring momentum measures how much that activity is accelerating relative to an industry's or account's own recent pace. In PredictLeads' most recent analysis, health care hospitals carried the largest raw volume, 191,117 openings in 30 days, but did not rank in the Top 10 for momentum, because that volume was not accelerating relative to the sector's own baseline. The two numbers answer different questions: volume answers how big, momentum answers how fast.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How often is PredictLeads' Job Openings data refreshed?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"PredictLeads refreshes Job Openings data approximately every 36 hours, which is frequent enough to support a weekly or more frequent hiring momentum re-score. Each record also carries first_seen_at and last_seen_at timestamps so changes in status can be tracked between refreshes. The dataset covers 279.2M+ historical records since 2018 and 10.2M+ openings active at any given time.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Can I track hiring momentum through an API instead of reading a blog ranking?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Yes. The same scoring method described in this guide can be run directly against PredictLeads' Job Openings API, flat files, or MCP server, rather than relying on a published snapshot. Pulling data through \/discover\/job_openings by O*NET code and location, or through \/companies\/{domain}\/job_openings for named accounts, gives you the raw inputs to calculate the same five weighted components. This approach also lets you re-weight the components for your own sales motion instead of using the general-purpose weighting shown here.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Why do retail-adjacent industries often accelerate hiring together?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"In PredictLeads' most recent analysis, four retail-adjacent categories, including Jewelry Retailers and Sporting Goods Retailers, accelerated hiring together, consistent with an early start to pre-holiday seasonal staffing. The National Retail Federation has projected seasonal hiring in a broadly similar range to the prior year, which provides outside context for why several retail categories can move at once rather than independently. PredictLeads' Job Openings data can confirm the timing of that staffing build at the company level through posted_at and category fields.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"What does it mean when a company's job postings shift toward quality assurance or support roles instead of its core function?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"A shift in posting mix toward quality assurance, support, or operations roles can indicate a company is scaling existing output rather than expanding its core offering, though PredictLeads' data shows the posting pattern, not the company's internal rationale. In the most recent hiring momentum analysis, Used Car Dealers showed this pattern, with operations and support postings increasing ahead of any corresponding rise in sales-role postings. PredictLeads categorizes every job opening into one of 26 job categories, which makes this kind of mix shift visible.\"\n      }\n    },\n    {\n      \"@type\": \"Question\",\n      \"name\": \"How is O*NET used to categorize hiring momentum by job function?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"Every job opening in PredictLeads' dataset is tagged with an O*NET occupation code, which allows hiring momentum to be calculated for a specific function, such as software development or sales, rather than only at the company or industry level. This is also how the \/discover\/job_openings endpoint supports filtering by onet_codes alongside location. Because O*NET is a standardized federal taxonomy, the codes stay consistent for cross-company and cross-industry comparison.\"\n      }\n    }\n  ]\n}\n<\/script>\n\n","protected":false},"excerpt":{"rendered":"<p>By Robert Fon \/ October 21, 2026 Originally published September 4, 2026. Updated October 21, 2026: this piece has been [&hellip;]<\/p>\n","protected":false},"author":8,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center 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center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[157,127],"tags":[36,11,34],"class_list":["post-1898","post","type-post","status-publish","format-standard","hentry","category-data-reports","category-job-posting-data","tag-data","tag-hiring-intent","tag-predictleads"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v28.4 (Yoast SEO v28.8) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Hiring Momentum, September 2026: 10 Accelerating Industries<\/title>\n<meta name=\"description\" content=\"Hiring momentum, September 2026: PredictLeads ranked 10 industries where hiring is accelerating fastest across 1,305,998 companies, and how to prospect them.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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