ChatGPT’s advertiser count just tripled in a little over a month. More than 60,000 advertisers are now buying ads on the platform, up from more than 20,000 roughly five weeks earlier. That data point, reported by New Street Research analyst Dan Salmon from an event hosted by OpenAI and AppsFlyer on October 7, is worth sitting with for a moment.

The Growth Trajectory

Growth began with roughly a few hundred advertisers on ChatGPT in February. Based on comparable growth rates from Pinterest, Reddit, and Snap, MediaPost editor in chief Joe Mandese projects ChatGPT will surpass 500,000 advertisers within the next year. He notes that Facebook took nearly six years to reach that same milestone.

Why SMBs Are the Real Story

The same engine driving ChatGPT’s advertiser growth drove Facebook’s: self-service ad management that lets small and medium businesses run on the same platform as the biggest brands in the world. Mandese argues it was the 500,000 advertiser mark when SMBs surpassed Fortune 1,000 brands in share of Facebook advertising, and from that point, the ad industry’s ability to influence Meta’s policies began to decay.

The 2020 Facebook advertiser boycott is the clearest proof point. When the world’s largest advertisers pulled spend for the month of July, Facebook’s ad revenue rose during the quarter. SMB volume absorbed the gap. Big brands are high margin but not high leverage when a self-service marketplace is running underneath them.

The Operator Takeaway

For solo operators and small businesses, this is a window. ChatGPT’s self-service ad system is built for you, not for agency holding companies. You can run on the same platform as global brands right now, before the market matures and CPMs adjust accordingly.

For anyone who cares about influencing how frontier AI companies handle advertising ethics and policy, Mandese’s argument is direct: the time to push is now, before SMB volume makes large advertiser pressure irrelevant. WPP Media’s Kate Scott-Dawkins has described the ad-supported AI marketplace as a kind of “manifest destiny.” At the current growth rate, that destiny arrives sooner than most ad industry players are planning for.

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Ahrefs researcher Louise Linehan analyzed 66.6 million free, logged-out GPT-5.6 Luna responses and their 15 million fan-out queries from August and September to map how ChatGPT actually retrieves information before generating an answer.

What the data shows

Only 14.4% of the 66.6 million responses triggered fan-out queries at all. That means the large majority of ChatGPT answers are generated without any real-time web retrieval. For operators building AI visibility strategies, the implication is direct: appearing in ChatGPT’s answers depends on factors beyond standard search indexing.

Why it matters for AI search visibility

Query fan-out is the mechanism ChatGPT uses to break a user’s prompt into sub-queries, retrieve sources, and synthesize a response. Understanding which queries actually trigger fan-out, and which do not, is the foundational question for anyone trying to measure or influence AI search presence. The full Ahrefs study covers the structure of those fan-out queries and what that pattern means for content strategy.

The full write-up is on the Ahrefs blog.

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ZoomInfo just shipped a verified node inside n8n that connects directly to its GTM Context Graph at workflow run time. If you run sales or marketing automations in n8n, this removes the manual step of exporting CSVs that go stale within weeks.

What the Node Does

Once a workflow authenticates with a customer’s own ZoomInfo credentials, it can query verified data on more than 100 million companies, 500 million contacts, and billions of buying signals. The node returns each result as an individual item, so loops, filters, and downstream CRM writes work without extra setup.

• Search contacts by company, title, seniority, and location
• Enrich existing contact and company records
• Search companies by size, revenue, and industry
• Monitor buyer intent signals, org changes, and news coverage on target accounts

The Agent Use Case

The node also works as a tool an n8n AI agent can call on its own. Instead of running on a fixed schedule, an agent can decide mid-workflow to research a company, enrich a contact, or check an intent signal, then use the result to plan its next action.

How It Connects

The integration runs through GTM.AI, ZoomInfo’s headless GTM context layer, which connects via API and Model Context Protocol. n8n joins Salesforce, Microsoft, Claude, and ChatGPT as platforms already connected to the same layer. Authentication, access control, data lineage, and audit logging all stay tied to each customer’s own ZoomInfo account.

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If you run Claude Code, Codex, or Cursor on a real codebase, those agents can read your .env file and propose an outbound curl call in the same session. To ordinary endpoint security, that looks identical to a developer doing the same thing. numbat is an open source tool in Perplexity’s GitHub organization that ties agent actions to the specific session that proposed them.

What numbat does

numbat hooks into AI coding agents, normalizes their activity into a single event model, and runs detection rules against it locally. It reads agent activity from three sources: hooks and plugins installed into the agent, OpenTelemetry logs the agent exports, and session files the agent leaves on disk.

Its coverage matrix lists 27 agents and agent hosts, including Claude Code, Codex, Cursor, Gemini CLI, and GitHub Copilot CLI. A rule engine built on CEL evaluates each event, and 51 rules ship built in. Those rules are grouped by behavior: secrets, exfiltration, persistence, privilege, lateral movement, tampering, and more, with MITRE ATT&CK technique IDs attached.

The finding that matters: chained sequences

The most useful built-in rule fires on a secret file read followed by a data-bearing upload in the same session. In a staged test on the v0.2.0 Windows release, the reviewer wrote a five-line Claude Code session where the user asks the agent to send a config to a support endpoint, the agent reads .env, and then proposes curl --data-binary @.env to an address on the reserved .invalid domain.

numbat scan raised three findings: the .env read (medium severity), the proposed curl upload (high severity), and the chain finding chain.secret_read_then_egress linking the two, tagged with ATT&CK techniques T1048, T1552, and T1567. The reviewer notes that in this case the user asked for the upload, so the rule fires on a pattern, not proof of a rogue agent. And numbat only knows the upload was proposed, not whether anything actually left the machine.

Forensic reconstruction from disk

For sessions already completed, numbat timeline lays out a stored session in chronological order, and numbat case build packages the evidence with SHA-256 manifests. Stored-session parsers cover 11 of the 27 agents in the matrix: Claude Code, Codex, Copilot CLI, Claude Cowork, Cursor, Gemini CLI, Kimi Code, OpenClaw, OpenCode, Pi, and Windsurf. For most others the format is marked deferred.

A case bundle’s manifest shows internal consistency, but the README is explicit that an unsigned bundle does not prove source authenticity or completeness. Reconstruction also only works from what an agent wrote to disk.

Blocking: opt-in and narrow

All 51 shipped rules are monitor-only by default. To make a rule block, you copy its YAML into your own rules directory, keep its ID, add enforce: true, bump its version, and reinstall the hook with --enforce. Even then, numbat does not stop anything directly: it returns the agent’s native deny response, and the agent host decides what to do with it.

Shell command blocking is limited to a static subset: one simple command or one pipeline of simple commands in a POSIX shell, and one simple command in PowerShell or cmd.exe. Multiple statements, conditionals, loops, command substitution, eval, and Invoke-Expression are detection-only. It also fails open: a malformed payload, a rule evaluation error, a crash, or a lost output sink all suppress numbat’s deny.

Getting started

numbat is a single binary under the Apache 2.0 license. The latest tagged release is v0.2.0, from 17 August 2026, with macOS, Linux, and Windows builds on amd64 and arm64. Install it with Go 1.26.6 or newer:

go install github.com/perplexityai/numbat/cmd/numbat@latest

The recommended path is to run read-only commands first, install a monitoring hook in monitor mode, and read what it reports before deciding whether any rule should block. The reviewer’s conclusion: treat numbat as visibility and forensics for your coding agents, with blocking as a best-effort extra on top, not a policy boundary.

numbat is on GitHub, with its coverage matrix and enforcement guide in the repository.

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A study covering more than 700 software firms just put numbers to something a lot of developers have been feeling: AI coding agents make you write more code, but your team ships the same amount of software.

The Numbers

Introducing AI coding agents at a firm produces measurable output increases in raw code metrics:

• 30% increase in total lines of code generated
• 20% rise in total commits
• 23% more pull requests submitted on average

None of that translated into more features shipped. Resolution rates for Issues and Epics tracked in tools like Jira showed no statistically significant change after AI tools were introduced. The researchers also found no shift in the size or complexity of those tracked items.

Where the Time Goes

The culprit is code review. The average time between a pull request being submitted and it being merged into the codebase increased 49% after AI agents were introduced. The share of pull requests that required changes requested nearly doubled, and comments per pull request rose 35%.

The researchers describe this as efficiency gains in the coding phase being absorbed by downstream constraints. More code goes in; the review queue grows; the merged output stays flat. In response, firms increased the share of workers performing code reviews by 14%. The researchers found no significant employment reduction attributable to AI.

AI Review Is Not Filling the Gap

By March 2026, 80% of measured firms were using some form of AI code review. Despite that, AI agents accounted for only 23.3% of all review comments and 10.8% of all pull requests. Humans are still doing the heavy lifting on review, and the volume of work they are being asked to review has ballooned.

The Operator Takeaway

If you manage a dev team or hire contractors who use AI coding tools, the productivity gains you were promised are likely sitting in the review queue. The bottleneck is not generation speed. It’s human judgment on whether the code is actually correct. Until AI review quality improves enough to be trusted on complex pull requests, adding more AI coding capacity may just mean adding more review work.

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Your AI coding agent is writing more code than ever. According to a new study from Harvard University, that extra code is not translating into more shipped software.

What the Study Measured

Researchers Fiona Chen and James Stratton analyzed aggregated engineering data from Jellyfish, a platform that tracks granular output across engineering teams. The dataset covers 300 million individual work events (commits, pull requests, and issue management activity) across more than 700,000 employees at over 700 software development firms, spanning 2021 through March 2026.

The researchers tracked exactly when each firm introduced AI coding assistants or AI coding agents, then ran a difference-of-differences regression to measure before-and-after changes on key output variables.

The Numbers

The raw code volume jump after AI agent adoption is hard to miss:

• +30 percent in total lines of code generated
• +20 percent in total commits
• +23 percent in pull requests

On the surface, that looks like a productivity win. The problem shows up one layer down. Resolution rates for Issues and Epics tracked in tools like Jira did not change in a statistically significant way after AI tools were introduced. The researchers also found no shift in the size or complexity of those tracked issues, ruling out the possibility that teams were simply tackling harder problems.

Where the Efficiency Goes

The study identifies human code review as the bottleneck that swallows the gains. More AI-generated code means more review volume, more pull request revisions, and more reviewer comments. The efficiency added during the coding phase gets absorbed downstream before it reaches actual software output.

The researchers conclude there is little evidence that firms increase software output or reduce employment by using AI coding tools, at least at current adoption levels and with current review practices.

For solo operators and small teams shipping their own products, the implication is direct: adding an AI coding agent to your workflow without adjusting how you review output may produce more code to manage rather than faster product delivery.

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Apple’s Shortcuts app has existed since 2018, but building anything useful in it always meant wrestling with variables and if-then logic. A new feature in iOS 27 changes that: Describe a Shortcut lets you type what you want in plain English and Siri AI assembles the automation for you.

What It Can Do

The author used it to build a custom Shazam workflow without writing a single line of code. The setup works like this: identify a song, then automatically log the song name and artist to a dedicated page in Obsidian, attach a free YouTube stream link, and open a space to note where you were when you heard it.

That is a meaningful upgrade over the standard Shazam experience, which requires either Apple Music or Spotify to play songs and offers no way to add personal context to a tagged track.

Device Requirements

To use Describe a Shortcut, you need a supported device running iOS 27, iPadOS 27, or macOS 27 Golden Gate, with Siri AI enabled under Settings > Siri. There is a waitlist for the Siri AI beta, though the author reports getting through it in about a day.

Supported hardware includes:

• iPhone 15 Pro models or any newer iPhone
• iPads with M-series processors or the A17 Pro iPad Mini
• M-series Macs or the MacBook Neo

The Operator Angle

If you’ve avoided Shortcuts because the builder felt like a chore, this is the prompt to try it again. Plain language input that produces working automations is exactly the kind of friction reduction that turns a tool from theoretical to practical.

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If your SaaS retention feels stuck, the problem probably isn’t your pricing page or your cancellation flow. According to an analysis published by ValuePaths, the biggest drop-off happens much earlier, during signup and first use, before most users ever get close to a paywall.

Where users actually leave

The research argues that SaaS churn is heavily front-loaded. Retention curves tend to stabilize over time, but the steepest losses happen right at the start. That means optimizing mid-funnel and late-funnel steps produces smaller returns than fixing the first few minutes of the user experience.

The specific example in the article puts a number on it: reducing churn between signup and first project creation could generate 164 additional paywall views. Fixing churn at the paywall step itself generates only 59. Earlier fixes compound downstream.

The measurement fix

The article recommends tracking retention by signup cohort rather than aggregate user churn. Cohort-level data shows exactly where new users exit, which makes it possible to prioritize the fixes with the greatest downstream impact rather than guessing.

The operator takeaway

Help users reach a value milestone as fast as possible. Every user lost before they experience the core product reduces the pool available for later conversions and referrals. The signup flow and the first-use experience are not onboarding problems. They are revenue problems.

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Most teams building AI fraud detection start from scratch. Coinbase took a different approach: they kept their existing ML model and rules on Onramp and built an LLM risk agent on top.

Online A/B experiments showed a 30% reduction in fraudulent transactions and a 22% drop in fraud value. That is a meaningful result without replacing what was already working.

Then they went further

After the A/B results held up, Coinbase post-trained Qwen3.5-9B on its proprietary Onramp fraud dataset. The fine-tuned model outperformed Opus 4.5 on every fraud detection metric they measured. It also delivered 55% lower end-to-end online serving latency compared to the larger model.

The pattern here is worth noting for any operator running ML in production: layering an LLM agent onto an existing system is a faster path to a result than rebuilding from scratch, and post-training a smaller open model on your own data can beat a frontier model on your specific task while cutting inference costs.

Coinbase is publishing this as part one of a three-part series on owning intelligence. The full writeup is on the Coinbase blog.

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From the outside, a nervous flier on a cross-country flight looks like a composed business traveler: laptop open, headphones on, buckled in. From the inside, that same person is just trying to reach San Diego without sweating through their shirt.

That gap between how a customer appears and what they’re actually experiencing is the whole game in SEO right now. Ann Handley made the case in a recent edition of her newsletter, Total Annarchy, and the argument lands harder than most SEO advice circulating in 2026.

Why the Old Approach Is Breaking Down

Two things are compressing the returns on traditional SEO simultaneously. First, AI Overviews are consuming clicks that previously went to websites and blogs. Second, AI is flooding the web with content that is not quite spam but is perfectly serviceable, indistinguishable information that looks and sounds like everything else.

Winning by publishing more of the same gets harder every month. The new constraint is not volume or even technical optimization. It’s understanding not just what a person is searching for, but why they’re searching, and what they’re feeling when they do.

As Handley puts it: don’t just understand the query. Understand the moment behind the query.

The Framework: Sweaty Palms SEO

Take the search query “best CRM for small business.” The standard SEO response is a comparison of features, pricing, and integrations. Useful, sure. But does it actually help the person making the decision?

Imagine the actual searcher is the owner of a 20-person company whose sales leads keep slipping through the cracks. They’re not calmly shopping for software features. Their real questions are:

• Will my team actually use this?
• How painful will switching be?
• Am I about to spend $10,000 solving a problem a spreadsheet could fix?

The query is about software. The moment is about the fear of making an expensive mistake. That distinction is where most content fails.

Instead of another “10 Best CRMs for Small Business” roundup, Handley suggests leading with content that addresses the anxiety directly:

• How to Choose a CRM Your Team Will Actually Love to Use without Complaining Too Much
• Wait. Do You Even Need a CRM?
• Yes You Really Have Outgrown Your Spreadsheet
• We Switched CRMs. It Was Painful. Here’s Exactly What We Did Wrong.

You can still compare features and prices. But now you’re answering what the reader is worried about, not just what they typed.

️ How to Apply This to One Page Today

Handley’s process is a three-step rewrite protocol. Pick one important page and work through it before you touch a single optimization:

1. Feel the sweaty palms. Talk directly to customers, or talk to the people who hear from customers every day: support, sales, front-line staff. What are customers sweating right before they decide? What questions come up that seem oddly specific? The odd, specific worries are what Handley calls solid gold. She explicitly recommends against using Claude or ChatGPT as a substitute here. AI will surface the predictable worries. The specific, surprising ones that you didn’t know to ask about come from real conversations. There’s a second reason to skip the shortcut: when you hear a worry firsthand, you absorb some of that anxiety yourself. That makes you a sharper marketer.
2. Lurk on social. Reddit, LinkedIn, industry communities. Look for what makes people’s palms sweat. What are they complaining about? What confuses them? Real examples from the source: “We bought HubForce six months ago and nobody uses it.” Or: “Are we the only company still running sales on spreadsheets?” These are the signals that reveal the moment behind the query.
3. Now rewrite the page. Address the anxieties at the top of the page, not buried at the bottom in an FAQ section. Put those answers in the front row, not the back of the plane.

When This Works

This framework is most powerful when you’re competing in a category where AI-generated feature comparisons are already the default answer. If the top of the search results is a clean, serviceable summary of features and pricing, the only way to differentiate is to go deeper into the human moment. That’s the gap AI summaries consistently leave open.

When It Does Not

If your audience is in pure research mode with no emotional stakes, the framework is less relevant. Searching “PostgreSQL index types” carries different emotional weight than “should I switch CRMs before Q3.” Match the approach to the anxiety level of the actual decision being made.

The One-Line Summary

Search tells you what people are looking for. Social reveals what they’re sweating through. The job isn’t just to help customers find you. It’s to make them feel seen when they do.

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