Developer Tool GTM

Building GTM AI Agents: Lessons from Deploying to 6,000 Users — Sait Izmit, Snowflake

Aug 26, 2026 · 20:39

Sait Izmit of Snowflake says winning over 6,000 go-to-market users comes down to quality over coverage: he wrote 150 sales questions before testing, accepted 50% first accuracy, and chose 50 questions at 95% over 100 at 70%. The Snowflake Cowork agent, live since September, has answered over a million questions (~40,000 weekly); 60% of data arrived post-launch, and it now spans 15 semantic views, 85 tables, 3,000 columns, MCPs, and 20 skills. After pilot and a 600-user 10% beta with 70% retention, GA showed only 20% of the org tried it, so Izmit spends 60-70% of his time on demos and sales meetings. He warns the wow factor collapses quickly, so teams must move from data chat to workflow automation, build fast with today's stack, accept rearchitecture, and mine logs for feedback loops.

How Forward Deployed Engineering is done at Factory — Eno Reyes

Jul 28, 2026 · 21:21

Factory's forward deployed engineers serve as the tip of the spear, embedding with customers to build software factories where signals flow through validation stages and ship outcomes autonomously. Co-founder and CTO Eno Reyes stresses a model-independent harness the customer owns, enabling air-gapped operation in finance, healthcare, or government. The autonomy maturity model scores codebases on agent readiness: linters, type checkers, and verifiable tasks let agents work longer without humans—enabling migrations of 30–50 million line codebases at banks. Reyes uses Disney's Epcot analogy to warn: build achievable future cities, not theme parks, or adoption stalls. Factory runs with an autonomy ratio in the upper 80%; legal Droid is fully autonomous, and the role demands business judgment, systems thinking, and communication skills as much as engineering.

Forward Deployed Engineering 101 — Kevin Bai, Anthropic, ex Palantir & Rippling Founding FDE

Jul 28, 2026 · 17:48

Kevin Bai of Anthropic, formerly at Palantir and Rippling, argues that Forward Deployed Engineering (FDE) is the go-to-market motion for selling highly technical platforms to non-technical buyers, as Palantir did with its Foundry platform. The key is not building bespoke solutions from scratch but assembling outcomes on a reusable platform of shared primitives; otherwise it becomes a dev shop. FDE targets a specific quadrant: complex product + non-technical buyer, exemplified by Palantir's $4M average contract value vs. ServiceNow's $1.2M and Workday's $600K. Two questions determine need: is your product complex enough to require hand-holding, and do you have engineers who can carry that? AI has made building easy and nearly everything agentic, pushing FDE toward the center of software sales.

Forward Deployed Engineering at Cursor — Pauline Brunet

Jul 14, 2026 · 20:47

Pauline Brunet, VP of Forward Deployed Engineering at Cursor, explains how to build an effective FDE team by mapping customer digital maturity and product customization to the right engagement model—self-service, traditional SaaS deployment, advisory, or embedded transformation. She defines the ideal FDE as a highly technical engineer with strong customer-facing skills, capable of scoping impactful projects, co-developing with customer teams, and feeding insights back to product. Cursor’s FDE team uses a project-based approach, hires experienced unicorns (5+ year software engineers), and structures around geographies, industries, and product SMEs. Best practices include solving the right problem, defining success metrics upfront, keeping scope directional, involving customers at every step, measuring ROI (revenue, cost, risk), and leaving documentation behind. Brunet advises learning fast, pivoting, partnering with system integrators, and not being afraid to say no to mismatched use cases.

GTM Is You - Victoria Melnikova, Evil Martians

Jul 7, 2026 · 12:35

Victoria Melnikova argues that a founder's personal brand is the most underrated competitive advantage for developer tool startups in 2026, citing interviews with Sam Lambert (Planet Scale), Zeno Rocha, and David Cramer. She presents the Product-Market Fit Compass, a framework from Evil Martians that analyzed 37 successful DevTools, finding that 9 out of 10 times product signal is stronger than revenue, making distribution the bottleneck. Key tactics include moving to San Francisco for network effects, using banners and billboards to signal credibility (as TypeCents did with hundreds of billboards), hosting events like Supabase's user conference, and embracing failures (Zeno Rocha walked away from 300k ARR to start over). Melnikova emphasizes being unapologetically yourself, as AI can amplify but not replace unique personal brands, and notes that founder-led sales builds trust in early-stage startups.

A year of Gemini progress + what comes next — Logan Kilpatrick, Google DeepMind

Jul 10, 2025 · 11:58

Logan Kilpatrick, head of product for Google AI Studio at DeepMind, announces the final update to Gemini 2.5 Pro, which achieves state-of-the-art results on Aider and HLE benchmarks. He details Google's 50x increase in AI inference over the past year, driven by merging research and product teams into DeepMind. The episode outlines Gemini's evolution toward a universal assistant that unifies Google products, with upcoming features including proactivity, native audio and video capabilities (Veo), and smaller models. Kilpatrick also previews developer-focused updates: a SOTA embeddings model, a deep research API, and Veo 3 and Imagine 4 in the API, alongside repositioning AI Studio as a dedicated developer platform.

Your AI Agent Isn't an Engineer: The Art of Thoughtful Anthropomorphism

Feb 22, 2025 · 20:20

Rizel Scarlet, a staff developer advocate at Block, argues that marketing AI agents as "software engineers" creates unrealistic expectations and alienates developers. She explains that such anthropomorphism—attributing human traits to AI—stems from lazy marketing to executives, but it backfires by making developers feel threatened and discrediting products when AI fails to meet human-level performance. Citing a Wired article, she notes 52% of game companies have adopted generative AI, yet 30% of surveyed developers expressed negative sentiment. To build trust, she proposes a framework: understand how AI works (e.g., the agentic loop), use non-human names like "copilot" (pioneered by GitHub), focus on augmentation over replacement, be transparent, give developers control, show real demos (including live troubleshooting), provide clear documentation, and foster open collaboration (e.g., Cursor.Directory). The episode reframes AI as a tool to work in parallel, not a replacement.