Follow the market, your way
There's too much AI × GTM news to read. I built a system to synthesize it for me. So should you.
Just last month: Anthropic shipped Claude Opus 4.7 and pushed Managed Agents into public beta. OpenAI reworked the Agents SDK and turned Codex into a general-purpose desktop agent. Salesforce moved Agentforce into GA. Hightouch crossed $100M ARR explicitly on AI-marketing tooling. Four foundation-model releases landed from labs in three countries. Nine GTM-relevant funding rounds closed. Clay shipped Functions. Plus the usual layer of vendor blogs, podcasts, and LinkedIn takes underneath.
No operator can’t keep up with that—just reading it all is a full-time job. The old consumption stack — a few newsletters, a curated X list, a Pocket queue with the best intentions — was built for a slower market. But now content creation has easily 10x—so how do we stay informed?
Reading isn’t the bottleneck
Reading more isn’t the answer. Picking smarter sources isn’t the answer. The real problem is real-time synthesis of a market producing primary signal faster than synthesis can keep up.
The old move was: find the right newsletter, follow the right people, trust the curators. That worked when the rate-limiting step was finding the news. The rate-limiting step now is making sense of it — connecting a foundation-model price drop on a Tuesday to a vendor’s positioning shift on Thursday to a funding round on Friday. Curators can flag the items. Synthesis is on you.
If production has outrun reading, the only honest fix is on the consumer side. Not “AI summarizes my newsletters” — that’s still the old shape, scaled up. A system that captures the whole surface, indexes it, and surfaces what connects across stories you wouldn’t have read in the first place.
What I built
I built one. It runs monthly. It captures across the source layers I care about — foundation labs, thought leaders, tool changelogs, podcasts, funding, communities — passes the corpus through a few different operator lenses, and produces a brief tuned for GTM practitioners trying to make stack decisions.
Original use case: walking into the AI Tinkerers Seattle meetup with the sharpest possible read on the prior thirty days. The output earned its own publication slot.
How it’s built is a separate post. For now, the output matters more than the recipe.
What it produces
The first month is up. April 2026 surfaced three structural shifts GTM operators should plan around in Q2/Q3: named AI VPs going into production, the agent-infrastructure layer arriving, and foundation-model pricing pressure reshaping vendor economics.
I wrote those up in a companion post: Tarka Research, April 2026
The full brief — 19 tools indexed, 9 funding rounds with operator implications, 13 reads worth your time — all lives at tarka.ai/research in an easy-to-browse and dive deeper manner.
Build your own
I’m sharing this because (a) it might save you hours each month, (b) it only took me a few hours to completely build—from ideation to publish—and you can build your unique one, today.
What mine surfaces reflects the priors I bring — GTM operators making AI-stack decisions, founders trying to read a category before it names itself. Yours will surface different things. The value of a personal consumption layer is that the priors are yours, not someone else’s. A brief I share is mine. A brief you build is yours.
How to build one is a follow-up post. For now, the deal is simple: read the April brief at tarka.ai/research. Next month’s TLDR shows up here when it ships.
Tarka builds AI-first infrastructure for GTM teams. Learn more and get our playbook at tarka.ai