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The weekly top 10 for B2B tech operators · Every Friday

Top 10 in Tech - What to know for Week ending October 17 2025

Friday 09:00 NZT Curated by Jon Davies
Top 10 in Tech - What to know for Week ending October 17, 2025

SaaS METRIC OF THE WEEK: Forecasting

Forecasting MRR is more art than science - but this post gives you both. A smart walk-through of how to project MRR using cohort-based logic, with a FREEEEE Excel template that looks like it might actually work well.

GTM

Tomasz Tunguz just dropped the GTM guide every startup needs. Learn when to hire your first AE (after 10 founder-led deals is a great tech-ism), how to pick your sales model (field vs inside vs self-serve or a hybrid), and why the best GTM teams layer focus before they layer headcount. Includes pipeline targets, comp models, and team structures from $1M to $100M ARR.

MARKET

Check out the GTMfund's take on the State of the Market: 75% of seed-stage startups have \<12 months runway, 60% of Series A+ aren't hitting growth targets, and 25% of sales teams are shrinking. Shheeeeeeeeet. Buyers are more risk-averse, and PLG is on the rise again - not for price, but for speed.

SALES 1

Early-stage selling is heavy on Founder-Led sales, but founders often get sales wrong because they confuse product belief with buyer readiness. This playbook is great and covers how to build a sales process from scratch, including market mapping, email scripts, handling objections, founder-led tactics, and why closing isn't the hard part - it's getting to the first meeting.

SALES 2

What do you do when growth stalls? Jason Lemkin lays it out for us: double down on sales efficiency, revisit churn (see 10 below), and stop assuming marketing will save you. TL;DR: The answer is almost always "more pipeline."

MULTI-LLM

I often run multiple LLM agents on one task just to verify output - I have a lot of hallucination trust issues emerging. Simon Willison explores the rise of using agents in parallel for coding - as it's not just faster, but safer. Think multi-threaded prompts, error-checking via ensemble output, and LLM diversity as a helpful hedge against those friggin hallucinations.

CASE STUDY

A two-year mission to reduce churn. This post breaks down what actually worked with onboarding tweaks, activation metrics, internal alignment (and what didn't). The big takeaway is that there are no silver bullets, just sustained efforts to figure it out.

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