Cursor vs LangChain

SpaceX's AI business beats expectations with $4.4B profit

SpaceX reported Q3 2026 revenue of $14.6 billion, with net income of $4.4 billion, driven by its AI-focused 'Super Intelligence' segment. This segment, which includes the Grok bot, the acquired Cursor coding tool, and neocloud services (1.7 GW compute), contributed $7.5 billion in revenue and $431 million in profit, excluding $1.2 billion in one-time stock-based compensation. AI now accounts for 52% of company income. The company continues heavy investment in data centers (Colossus II) and Starship, with capex of $19.6 billion in Q3 and plans for $35-40 billion in Q4. Starlink subscribers grew to 13.6 million. CEO Elon Musk projects annual recurring revenue of $500 billion to $1 trillion by end of 2027.

• SpaceX reported Q3 2026 revenue of $14.6 billion and net income of $4.4 billion, a turnaround from a $1 billion loss a year earlier.
• The 'Super Intelligence' business line generated $7.5 billion in Q3 revenue, including $431 million in profit (excluding $1.2 billion in one-time stock-based compensation from the Cursor acquisition).
• AI now accounts for 52% of SpaceX's income.

Clay's Kareem Amin Discusses Rise of GTM Engineer

Clay co-founder and CEO Kareem Amin will speak at TechCrunch Disrupt 2026 about the emergence of the 'GTM engineer' role. This new discipline uses AI, data, and automation to build repeatable revenue systems, replacing manual go-to-market processes. Clay coined the role in 2023 and reports about 100 GTM engineering job listings each month. The company has seen rapid growth, with ARR tripling to $100 million in a year, a $115 million Series D round at a $7.1 billion valuation, and a $1 million scholarship fund to train GTM engineers. Amin's session will explore how AI is transforming go-to-market strategies and what this means for company growth.

• Clay coined the GTM engineer role in 2023.
• Approximately 100 GTM engineering job listings appear each month.
• Clay tripled annual recurring revenue to $100 million in a year.

Advanced Evals Guide: Finding Hidden AI Failures

This article is a comprehensive guide on advanced evaluation (evals) practices for AI products, authored by Hamel Husain and Shreya Shankar. It emphasizes the importance of error discovery before writing metrics, contrasting it with product discovery. The authors outline a three-step process for effective error discovery using coding agents like Codex or Claude, highlighting the pitfalls of automation bias and criteria drift. They introduce an open-source 'evals skills' plugin that assists in trace review and clustering. The article cites real-world examples from companies like Shopify, Cursor, Ramp, and Harvey, demonstrating how evals have led to significant product improvements. The piece also discusses synthetic data generation and the importance of human-in-the-loop annotation, recommending a target of 100 traces for meaningful analysis.

• Shopify used evals to build an AI workflow builder that was 2.2x faster and 68% cheaper than the frontier-model system it replaced.
• Cursor developed its Auto Balance routing performance with evals, reducing costs by 41%.
• Ramp increased its automatic receipt collection product's accuracy from 35% to 83% after investing in evals.

New LangChain Blog Posts: Snyk Case Study, Stripe Link Integration, Deep Agents Updates

New blog posts added: 'How Snyk turned an internal support agent into a customer feature' (Oct 8, 2026), 'Agents that can pay: building Restock with Stripe's Link and Managed Deep Agents' (Oct 8, 2026), 'Revamping Skills in Deep Agents' (Oct 7, 2026), 'Managed Deep Agents v0.9: schedules, per-run configuration, and Slack reactions' (Oct 7, 2026), and 'How to Build a Model Router in the Harness' (Oct 1, 2026).

New in LangSmith: Engine v2, Managed Deep Agents, Fine-Tuning, and More

LangChain announces LangSmith Engine v2 with red teaming and automated testing, Managed Deep Agents v0.8 with new auth, memory, and channels, Trajectories for readable agent sessions, and LangSmith Fine-Tuning, among other updates.

Versos AI Launches NVIDIA NeMo Agents for Video Training Data Curation

Versos AI, a provider of video training data infrastructure for library owners and AI labs, announced a new capability built with NVIDIA NeMo. This allows AI teams to describe their dataset needs in natural language, which the platform then uses to search, evaluate, and assemble rights-cleared video footage into structured training datasets. The workflow accelerates model development by reducing manual search and review time. It also enhances discoverability of premium video libraries for AI partnerships. The technology leverages NVIDIA CUDA for accelerated inference, NVIDIA Nemotron Ultra for agent reasoning, and LangChain for agent orchestration. Versos AI will demonstrate this at IBC2026 in Amsterdam.

• Versos AI launched a new capability built with NVIDIA NeMo.
• The system interprets natural language requests to find and assemble video training data.
• Built on NVIDIA CUDA Toolkit, NVIDIA Nemotron Ultra, and LangChain.