Bolt

Bolt.new: How we scaled $0-20m ARR in 60 days, with 15 people — Eric Simons, Bolt

Jul 15, 2025 · 17:33

Eric Simons, CEO of Bolt.new, explains how his team of less than 20 people scaled the company from $0 to $20M ARR in just 60 days, achieving the second fastest product growth in history by staying lean and focusing on high-impact decisions. He describes building a remote team with high context and agency, resisting pressure to hire more during the 2021 boom. They relied on 'things that don't scale' like weekly office hours to build user love, and leveraged AI support tools like PeraHelp to handle 90% of tickets. Community initiatives such as a Guinness World Record hackathon (over 80,000 participants) further amplified growth without adding headcount. Simons emphasizes taking consistent shots on goal and making independent bets rather than following VC trends.

Don’t get one-shotted: Use AI to test, review, merge, and deploy code — Tomas Reimers, Graphite

Jun 27, 2025 · 5:45

Tomas Reimers, CPO and co-founder of Graphite, argues that AI-generated code accelerates the inner loop (development) but bottlenecks the outer loop (review, test, merge, deploy), necessitating AI-native tooling across the entire workflow. He presents Graphite's AI code reviewer, Diamond, which achieves a 52% acceptance rate on its comments—exceeding the 45–50% rate for human comments—while only 4% of its comments are dismissed. Reimers emphasizes that AI tools must deeply understand the codebase, integrate with CI/testing pipelines, and provide high-signal, low-noise feedback to handle the increasing volume of AI-assisted code. He cites that 46% of GitHub code is now written by Copilot, and predicts the outer loop will break without AI-driven prioritization, review automation, and optimized deployment. The talk advocates for self-driving code review that lets developers focus on product features rather than manual processes.

The 4 Patterns of AI Native Development — Patrick Debois

Jun 4, 2025 · 14:11

Patrick Debois presents four patterns of AI-native development that redefine software engineering roles: from producer to manager (reviewing AI-generated code, managing agents), from implementation to intent (specifying requirements in markdown files), from delivery to discovery (rapid prototyping with tools like Lovable and Bolt), and from content creation to knowledge (capturing learnings for reuse). He argues these shifts mirror senior developer activities and are supported by new tools, such as the AI Native Dev landscape featuring roughly 300 tools. The talk emphasizes that AI changes not just coding speed but the entire workflow, reducing cognitive load through smarter reviews and intent-based coding.

Why the Best AI Agents Are Built Without Frameworks (Primitives over Frameworks) — Ahmad Awais, CHAI

Jun 3, 2025 · 27:06

Ahmad Awais, founder of LangBase/CHAI, argues that production AI agents like Perplexity, Cursor, v0, Lovable, Bolt, and CHAI itself should be built using AI primitives rather than frameworks. He demonstrates live coding a 'Chat with PDF' agent using LangBase primitives (memory, parser, chunker, threads). Awais presents eight agent architectures—augmented LLM, prompt chaining, agent router, parallel agents, orchestrator-worker, evaluator-optimizer, tool-calling, and memory-based—all built with plain code and no framework. He claims frameworks are bloated and slow, while primitives offer flexibility and automatic scaling (e.g., memory storing terabytes). Awais shows how to compose multiple LLMs (Gemini, DeepSeek Llama, Claude) in a router agent, and notes that his team has built agents for OCR, image analysis, and deep research using primitives and external tools like Mistral OCR.