Y Combinator Summaries & Notes

AI Design Trends and Pitfalls AI design tools enable easier creation, but accepting all AI suggestions can lead to common, unoriginal designs. Trends like purple gradients and fading-in sections are becoming ubiquitous due to LLMs being trained on popular examples. While AI offers superpowers, founders must remain in control, acting as editors to ensure originality and avoid "AI slop." Website Review: New.ai Features a very purple color scheme, a common AI-generated trend. A distracting line following the user down the page was implemented likely because it was easy with AI, but adds no value. Contrast issues make text hard to read. Tasteful hover animations on cards are a good use of AI, enhancing the brand and reinforcing messaging. Navigation hover effects that cause menu items to fade out are counterintuitive and distracting. Website Review: Rosebud AI Continues the purple gradient trend, leading to a lack of brand originality. Features an interactive 3D game demo, which is engaging but its connection to the product isn't clearly explained. Non-standard navigation and potentially confusing elements like a following top bar hinder usability. The combination of a red logo with purple accents and the use of emojis can appear lazy or unharmonious. Cursor-following light effects on game examples are visually appealing but may not be worth the development effort if not AI-assisted. Website Review: Get Crux Exhibits scroll jacking and automatic fade-in sections, which can be disorienting. A button that constantly chases the user is distracting and makes it difficult to focus on content. "Meteor" animations and blurry hero screenshots detract from the user experience and product clarity. Lack of visual consistency across sections suggests different AI generation approaches. The core value proposition is not immediately clear, requiring users to scroll extensively to understand the offering. Website Review: Sphinx More animation is present, a common outcome of AI design tools. Information hierarchy is complicated by an unnecessary "fourth style" element between the logo and H1. A confusing animated section with shifting button styles and unclear functionality appears to be a product of AI over-suggestion. Hover effects revealing icons that are not critical information can be distracting. A scroll-jacking animation that locks the user in place distracts from the content and lacks a clear purpose. The visual style, while modern, can be hindered by distracting animations and unclear messaging. Website Review: Build Zero Features purple gradients and "dumb hover effects" that add no value and can appear as bugs. An interactive element has a bug in selection, which might be overlooked due to the ease of AI generation. AI-generated dashboards with common color callouts and "bento box" layouts lack originality. The repetition of common patterns across sites diminishes brand uniqueness and credibility. Website Review: Zarna AI Employs scroll jacking, making the site feel clunky and slow to navigate. Lack of clear content and excessive scrolling to understand the offering are significant issues. The navigation bar can become unreadable against dynamic backgrounds, highlighting a lack of robust design. Inconsistent clickability of elements and automatic movement create a confusing user experience. While the visual style can be fresh, it's undermined by a lack of clear messaging and "fit and finish." Key Takeaways and Advice Founders must be intentional and act as editors when using AI design tools, ensuring originality and brand consistency. Thoroughly QA all AI-generated elements to catch bugs and confusing interactions. Prioritize clear messaging and ensure the website effectively functions as a customer acquisition channel. Use AI to enhance creativity and efficiency, not to outsource critical thinking about the product and brand.

Poetic's Core Offering Poetic builds a recursively self-improving system for LLMs, aiming for AI to make itself smarter. This approach is significantly faster and cheaper than traditional methods like training new LLMs from scratch, which cost hundreds of millions and take months. Poetic's system generates "harnesses" or agents that sit on top of existing LLMs and automatically outperform them for specific problems. These harnesses remain compatible with new LLM releases, allowing for continuous performance improvements without re-training costs. Addressing the "Bitter Lesson" Traditional fine-tuning is expensive and quickly becomes obsolete with new model releases, a problem referred to as the "bitter lesson." Poetic's method avoids this by creating adaptable systems that benefit from newer, more powerful LLMs without costly re-engineering. The system can optimize existing agents or components like prompts and reasoning strategies. Performance and Benchmarks Poetic's system has demonstrated strong performance on benchmarks like ARC AGI V2 and Humanity's Last Exam. On ARC AGI V2, Poetic achieved higher scores than Gemini 3 DeepMind at a fraction of the cost, using Gemini 3 Pro as the base model. For Humanity's Last Exam, Poetic reached 55% accuracy, surpassing Anthropic's Claude Opus 4.6 (53.1%), with optimization costs under $100k. This contrasts sharply with the hundreds of millions of dollars required for training large foundation models. Technical Approach and Comparison Poetic's core technology is its poetic meta system, which recursively self-improves to generate highly effective reasoning systems. These generated systems are composed of code, prompts, and data, built on top of one or more LLMs. This is presented as a new paradigm distinct from Reinforcement Learning (RL). The system can automate aspects of prompt engineering and context stuffing, outsourcing data understanding and failure mode analysis to the AI itself. While automated prompt optimization (like "Jeepa") offers some gains, Poetic emphasizes the importance of reasoning strategies written in code over just better prompts. Getting Started with Poetic Poetic is not yet publicly released, but interested parties can sign up for early access at poetic.ai. They are seeking startups and companies with difficult, unsolved problems. Poetic aims to provide "stilts" that allow any agentic company to achieve state-of-the-art performance. Key capabilities highlighted include improving reasoning and deep knowledge extraction. Founder's Background and Advice Ian Fischer, co-founder of Poetic, previously worked at Google DeepMind for a decade and founded a mobile devtools company. He transitioned from hardware/robotics to machine learning research at Google, finding hardware "hard." His advice for engineers wanting to enter AI is to try things daily, push boundaries, and build what they imagine. He uses AI tools like GPT-5 for app development, emphasizing the rapid pace of improvement and ease of use.

The AI Agent Economy AI agents are rapidly evolving, moving beyond simple autocomplete to making independent decisions and interacting with each other, exemplified by platforms like Moltbook. This shift is creating an "AI agent economy" where agents choose and utilize tools, potentially paralleling the human economy. Impact on Developer Tools and Go-to-Market The traditional developer market is expanding from 20 million trained developers to hundreds of millions, plus their agents, dramatically increasing the potential user base. Documentation is becoming the primary interface for agents; tools with clear, agent-friendly documentation (like Resend) are favored. Companies like Mlifi are benefiting as they provide tools to optimize documentation for both humans and agents. The go-to-market strategy for developer tools is shifting from human-to-human recommendations to agent-driven adoption. Emerging Trends and Future Possibilities Swarm intelligence is emerging, where multiple AI agents collaborate, much like biological systems. Platforms like Moltbook are showcasing this emergent swarm behavior, with agents interacting and collaborating to achieve tasks. There's a potential for a parallel tech stack built specifically for AI agents, including services like Agent Mail for AI-native inboxes. Agents may eventually handle tasks like booking reservations, and could even influence social recommendations (e.g., restaurant choices). The concept of "human money" vs. "agent money" is introduced, suggesting agents might eventually develop their own economy and transactional systems. Challenges and Considerations Legal liability and standing are current barriers, as agents are not legal entities, requiring human oversight for transactions and applications (e.g., Y Combinator applications). The "Dead Internet Theory" is mentioned, suggesting a significant portion of online content may already be AI-generated, but a counter-argument is made that smarter, aligned agents could improve the internet. Building user trust and relationships with AI agents is still a challenge, as people have high expectations for AI interactions. Developers should focus on creating tools that agents "want", prioritizing APIs and open-source solutions over websites. Founders should develop an intuitive understanding of agent capabilities and limitations, and build tools that cater to their natural inclinations.

The video features Boris Cherny, an engineer at Anthropic, discussing the development and philosophy behind Claude Code, an AI coding assistant. Origin and Philosophy Accidental Beginning: Claude Code started as a simple terminal chat app built by Boris to learn Anthropic's API, not as a planned product. Building for the Future: Anthropic's strategy is to build for the model six months ahead, focusing on areas where current models are weak but expected to improve. Latent Demand: Key features and products, like ClaudeMD, emerged from observing how users were already trying to achieve tasks, demonstrating "latent demand." The "Bitter Lesson": Cherny emphasizes "never bet against the model," advocating for waiting for model improvements rather than building excessive "scaffolding" that will quickly become obsolete. Development and Evolution Terminal-First Approach: The initial choice of a terminal interface was due to its simplicity and speed of development, avoiding the need for a complex UI. Tool Use Discovery: A pivotal moment was realizing the model's strong inclination to use tools, even for tasks like identifying music. Iterative Rewriting: The codebase for Claude Code is constantly rewritten, with significant portions being less than six months old, reflecting rapid model advancements. User Feedback Driven: Features like "plan mode" and verbose output options were direct responses to user feedback and observed usage patterns. Adapting to Model Improvements: The shift from requiring manual debugging of model output to summarizing tool usage reflects the increasing reliability of newer models. Key Features and Concepts ClaudeMD: A mechanism for users to provide custom instructions and context to Claude Code, with an emphasis on keeping it concise and up-to-date. Plan Mode: A feature that allows users to outline a plan for the model before it starts coding, reducing the risk of it going in the wrong direction. Cherny suggests this may become obsolete as models improve. Agent Topologies: The exploration of how multiple agents can collaborate, using concepts like "uncorrelated context windows" for complex tasks. Swarm Development: The successful use of a "swarm" of agents to build features like the plugins feature over a weekend with minimal human intervention. Sub-Agents: The use of recursive Claude Code instances ("mama quad") to handle specific tasks, often prompted by the main agent. Impact and Future Massive Productivity Gains: Claude Code has reportedly led to significant increases in engineer productivity at Anthropic, with internal metrics showing substantial growth in output. Coding Solved: Cherny predicts that coding will become generally "solved" for everyone, potentially leading to the evolution of roles like "software engineer" into more general "builder" or "product manager" titles. Beyond Coding: The future may see models capable of recursively self-improving (ASL4) or being misused for dangerous purposes, highlighting the importance of AI safety. Expanding Form Factors: While originating in the terminal, Claude Code is now integrated into various interfaces like desktop apps, web, Slack, and IDE extensions, with continuous experimentation in UI/UX. Advice for Builders: Focus on latent demand, build for future models, and embrace the "bitter lesson" of general model improvement over specific scaffolding.

The Rise of the 20x Company Claude Code is being used by Anthropic engineers to build and improve AI products, suggesting a fundamental shift in startup operations. 20x companies are characterized by automating all internal functions, not just one or two, allowing small teams to compete with large incumbents. This concept is an evolution of the "compound startup" idea, which focuses on building multiple integrated products in parallel. 20x companies leverage automation across code, support, marketing, sales, hiring, and QA, significantly increasing employee power and delaying the need for extensive hiring. Case Study: Giga ML and Atlas Giga ML, a voice-based customer service agent provider, used an internal AI agent called Atlas to close a deal with Door Dash against much larger competitors. Atlas can perform various tasks within their product, including browsing, editing policies, and writing code, freeing up engineers from boilerplate tasks. Atlas allows each engineer to handle double or triple the workload by automating repetitive customer integration tasks. Atlas functions as a full-time AI employee, enabling Giga ML to service dozens of accounts with only a single human FTE, who focuses on customer relationships and feature requests. Case Study: Legion Health and AI-Native Operations Legion Health is building an AI-native psychiatry network and uses an AI-integrated source of truth to provide instant context to employees. They developed a custom internal interface for their care operations team, allowing access to patient history, scheduling, insurance codes, and more. This single source of truth has enabled Legion Health to scale revenue 4x without hiring new staff, managing thousands of patients and dozens of providers with minimal operational headcount. Case Study: Phase Shift and Custom AI Agents Phase Shift, an accounts receivable automation startup, has a 12-person team competing against companies with hundreds of employees. Their strategy involves bringing AI into every manual process and building custom AI agents for employees based on their documented tasks. This approach has allowed them to avoid hiring a dedicated design person, with the engineering team using AI tools to build front-end designs. The Future of Startup Building Companies can combine approaches like AI teammates, unified sources of truth, and custom agents. These strategies enable startups to stay lean, achieve record growth rates, and gain a significant competitive advantage. The startups that master this "new way to build" are poised to win.

OpenClaw's Origins & Impact Peter Steinberger created OpenClaw, an open-source personal AI agent that rapidly gained 160,000+ GitHub stars. The key differentiator for OpenClaw's success is that it runs on your computer, allowing it to interact with and control "every effing thing" on your machine (e.g., oven, Tesla, lights, Sonos), unlike cloud-based solutions. OpenClaw can access and analyze all data on your computer, leading to surprising insights, such as finding forgotten audio files and creating narratives from a user's past year. The emergence of bot-to-bot interactions and bots hiring humans for real-world tasks (e.g., restaurant bookings) is seen as a natural next step, leading to swarm intelligence rather than a single "god intelligence." The "Aha" Moment & AI's Capabilities Peter's "aha" moment occurred in November after building earlier versions, when he needed his computer to perform tasks while he was away, which evolved into OpenClaw (initially called Cloudbot). He realized the power when his bot, via WhatsApp, transcribed a voice message and responded, even though he hadn't explicitly coded that functionality. The bot autonomously used available tools (e.g., ffmpeg, OpenAI API with curl) to solve the problem in 9 seconds. This demonstrated the AI's ability for creative problem-solving, even choosing the most intelligent approach (using a remote API to avoid slow local model download) without explicit instructions. The bot's ability to understand context and speak his language (sassy, funny) made it a pleasant user experience. The Future of Apps and AI Peter predicts that 80% of apps will disappear because agents can manage data and perform tasks more naturally and efficiently than dedicated apps. Examples include a fitness app (agent automatically tracks food, adjusts gym schedule) or a to-do app (agent reminds you without needing a separate interface). Only apps with physical sensors might survive. While large model companies currently have a "moat" due to their token provision and constant model improvements, models are getting commoditized, and user expectations constantly rise, making older models seem "bad." The true value and "moat" will shift to memory and data ownership, which is currently siloed by large companies. OpenClaw "claws into the data" because the end-user owns their memories as markdown files on their machine, providing access and privacy, especially for sensitive personal problem-solving. Contrarian Development & OpenClaw's "Soul" Peter adopted a contrarian development philosophy, preferring multiple checkouts of a repository on "main" instead of Git work trees to minimize complexity and friction. He avoids UIs, focusing on syncing and text, and is happy to have skipped traditional MCP (Multi-Computer Program) support, instead building a skill that converts MCPs to CLIs, making them usable on the fly without restarts. He argues that bots, like humans, should use CLIs, as no human tries to call an MCP manually. To showcase OpenClaw's capabilities, Peter created a public Discord server with his bot, Multi, which was locked to his user ID but responded to everyone, laughing at those who tried to prompt inject it. His bot's unique character comes from a non-open-source file called "soul.md," which contains core values and principles guiding the human-AI interaction, making the model's responses feel natural and infused with personality.

Introduction and The Power of Claude Code Gary describes using Claude Code as feeling like **"flying through the code"** and highlights its ability to **debug nested delayed jobs five levels deep** and **write tests** to prevent recurrence. **Kelvin French Owen**, co-creator of Codex at OpenAI and founder of Segment, likens Claude Code to a **"bionic knee"** that enables him to code five times faster, recovering from "manager mode." The speaker notes that **startups** embrace coding agents for **speed** due to limited runway, while **larger companies** are more cautious due to existing processes and higher stakes. Technical Advantages & Architecture of Claude Code Kelvin now uses **Claude Code as his daily driver**, preferring it over Cursor due to its superior product and model integration, especially with Opus. Claude Code's strength lies in its ability to **split up context well**; it spawns **"explore sub-agents"** (using Haiku) that traverse the file system, each operating in its own context window and summarizing findings. The **CLI-first approach** of Claude Code is seen as a "weird retro future" that surprisingly outperforms IDEs by distancing users from the code and offering greater freedom and a sense of "flying through the code." Claude Code in the CLI can **directly access development and production databases**, which, despite security risks, proves invaluable for debugging complex issues like concurrency in delayed jobs. The **bottoms-up distribution model** of being able to download and use the tool without needing top-down permissions is considered highly underrated and crucial for rapid adoption in the evolving AI landscape. Influencing the Developer Ecosystem **Generative Optimization (GEO)** refers to how LLMs influence tool recommendations, making **strong documentation, social proof (e.g., Reddit mentions), and open-source projects** critical for developer tool visibility and adoption. LLMs can **directly analyze open-source code**, allowing users to clone repositories and ask agents for code walkthroughs or use them as development harnesses (e.g., Ramp using OpenCode). Building and Using Coding Agents Effectively For agent builders, **"managing context well"** is the most crucial skill, involving careful **context engineering** and using tools like **grep and ripgrep** to supply relevant code snippets. Tips for **top 1% users** of coding agents include: Utilizing platforms like Vercel or Next.js that handle boilerplate to **minimize code and plumbing**. Adopting a **microservices** architecture for well-structured individual packages. Understanding **LLM superpowers** (e.g., persistence) but also their weaknesses, such as a tendency to **duplicate code** or fall into **context poisoning**. **Actively clearing context** when it exceeds 50% of the token limit to prevent the LLM from entering a "dumb zone" where quality degrades. Using **canary tokens** (esoteric facts at the start of context) to detect when the model begins to lose coherence. Leveraging **automated testing, linting, and continuous integration (CI)** to drastically improve agent performance and code reliability. Aggressively employing **code review bots** (e.g., Reptile, Cursor bug bot, Codex) for code correctness. Architectural Differences and Future Outlook **Context management architectures differ**: **Claude Code** delegates to sub-context windows and merges summaries, while **Codex** uses **periodic compaction** after each turn, allowing for much longer-running jobs. **Philosophical approaches diverge**: **Anthropic (Claude Code)** focuses on **building tools for humans** that mimic human co-worker behavior, whereas **OpenAI (Codex)** pursues **Artificial General Intelligence (AGI)**, training models for longer horizons that may operate in non-human-like ways. The **future of engineering** will see **senior engineers** and "manager-like" individuals benefiting most, focusing on directing agents and making architectural decisions. The next generation of engineers is expected to be **more prolific** due to agents helping them complete numerous projects, potentially leading to a heightened sense of "taste." A future vision includes **personal cloud computers with armies of agents** acting as "super EAs," automating tasks and allowing humans to focus on high-level decisions and in-person collaboration. Agents are enabling a shift from the "manager schedule" to the "maker schedule," allowing developers to build in **short "pockets" of time**, as agents handle the heavy lifting of context building. Rebuilding a service like Segment in the future would see the value of basic integrations drop to zero, with focus shifting to **higher-level automation** of data pipelines, customer engagement, and dynamic product experiences. Current Constraints and Evolution The **context window size** remains the **number one limiting factor**, even with sub-context delegation, suggesting that larger context windows would significantly boost performance. **Integration and orchestration** capabilities are still evolving, particularly for connecting agents with other developer tools (e.g., Sentry for auto-generated PRs and phased rollouts). The adoption of **100% test coverage** dramatically accelerates development speed and reliability when working with coding agents. Future developments could include enhanced **agent memory and collaboration**, potentially through shared conversation histories or model-generated wikis, fostering "Clawdbot social networks" among agents. **Model specificities** show that Codex excels in debugging complex concurrency issues where other models like Opus might falter, highlighting unique "personalities" influenced by training data and focus (e.g., Python monorepos for OpenAI, front-end for Anthropic). A tension exists between **OpenAI's strong emphasis on sandboxing and security** (due to prompt injection risks) and startups' willingness to "dangerously skip permissions" for faster iteration.

Introduction & Common Mistakes • The speaker aims to provide conceptual tools for thinking about startup ideas, emphasizing that while no one knows for sure which ideas will succeed, certain ideas are more likely to. • The advice is drawn from analyzing the top 100 YC companies, a classic essay by Paul Graham ("How to Get Startup Ideas"), insights from YC companies that pivot, and mistakes observed in thousands of rejected YC applications. • The most common mistake is building a "solution in search of a problem" (CISP), where Founders start with a technology (e.g., AI is cool) and then look for a problem, often finding only superficial ones. • Founders should instead fall in love with a problem, starting with a high-quality, specific, and tractable problem, not abstract societal issues. • Another common mistake is getting stuck on "tar pit ideas": widespread problems that seem easy to solve but have structural reasons making them very hard or impossible, like the common app for meeting up with friends. • To avoid tar pit ideas, Google it, find past attempts, talk to previous Founders, and understand the core difficulty. • Founders often either jump into the first idea without evaluation or wait for the "perfect" idea, never starting. A good idea is a "good starting point" that can evolve. Evaluating Startup Ideas: 10 Key Questions • Do you have founder market fit? This is the most crucial criterion: are you (the team) the right people to work on this idea? (e.g., PlanGrid Founders with construction and developer expertise). • How big is the market? Look for markets that are already big (billion-dollar potential) or small but rapidly growing (e.g., Coinbase in 2012). • How acute is this problem? The problem should be significant enough that users genuinely care about it. (e.g., Brex solving the problem of startups not being able to get corporate credit cards). • Do you have competition? Most good ideas have competition; lack of competition can be a red flag. If facing entrenched competition, you typically need a new insight. • Do you want this personally? Do you know people personally who want this? If the answer is no to both, it's a concern, and user interviews are critical. • Has this only recently become possible or only recently become necessary? Look for changes in the world (new tech, regulation, new problems) that create opportunities. (e.g., Checkr emerging due to the rise of delivery services needing background checks). • Are there good proxies? Proxies are successful large companies doing something similar but not directly competitive, indicating market viability. (e.g., Rappi using DoorDash as a proxy for food delivery success). • Is this an idea you'd want to work on for years? While passion helps, many successful ideas are in "boring" spaces (e.g., tax accounting software) where passion can grow with success. • Is this a scalable business? Pure software scales infinitely. Beware of service businesses requiring high-skill human labor (e.g., agencies, dev shops). • Is this a good idea space? An idea space is a class of related ideas (e.g., fintech infrastructure, vertical SaaS for enterprise). Some spaces have higher success rates. (e.g., Fivetran pivoting within the fertile data analysis tool space). Ideas That Seem Bad But Are Actually Good • These ideas are often overlooked by other Founders, leaving opportunities on the table. • Ideas that are hard to get started ("schlep blindness"): Tasks that seem too difficult scare off potential Founders, but can lead to huge opportunities (e.g., Stripe dealing with complex credit card infrastructure). • Ideas that are in a boring space: Problems like payroll software (e.g., Gusto) are often neglected but have a higher hit rate than "fun" consumer apps because less competition exists. • Ideas that have existing competitors: Counter-intuitively, most good ideas have competitors. A market with many existing, yet poor, solutions indicates a real problem waiting for a better product (e.g., Dropbox improving on 20 existing cloud storage services with a better UI and OS integration). How to Generate Startup Ideas • The best ideas are often noticed organically, not explicitly thought up (70% of YC top 100). Explicit brainstorming often leads to bad or tar pit ideas. • To foster organic ideas (the "long game"): • Become an expert in something valuable, especially by working at a startup. • Build things you find interesting, even if not immediately business-oriented (e.g., Replika). • 7 Recipes for Generating Ideas Now: • Start with what your team is especially good at: This ensures automatic founder market fit (e.g., Rezi Founders' expertise in real estate and debt financing). • Start with a problem you've personally encountered, especially one you're in an unusual position to see: This leverages unique insights (e.g., VetCove Founders seeing their vet dad's outdated ordering process). • Think of things you personally wish existed: A classic method, but beware of tar pit ideas (e.g., DoorDash Founders wanting food delivery to their dorm). • Look for things in the world that have changed recently: New technologies, regulations, or societal shifts create opportunities (e.g., Gather Town pivoting due to the pandemic's impact on online interaction). • Look for companies that have been successful recently and look for new variants on them: Adapt proven models to new markets or niches (e.g., Nuvocargo as "Flexport for Latin America"). • Go and talk to people and ask them what problems they have: This requires skill. Pick a fertile idea space, then talk to potential customers and other Founders (e.g., A to B Founders systematically interviewing truck drivers and industry experts to find the fuel card idea). • Look for big industries that seem broken: These are often ripe for disruption. • Bonus Recipe: Find a co-founder who already has an idea. • Ultimately, the only way to know if an idea is truly good is to just launch it and find out.

• The redesigned YC website shifts focus from a utilitarian, B2B SaaS template to a storytelling approach, emphasizing founders and their journeys rather than just company logos. • The new homepage incorporates the word "formidable" to describe extraordinary founders, a term used by Paul Graham, and includes a footnote defining it in his words. • To highlight founder success, the website showcases before-and-after transformations of funded founders, emphasizing their humble beginnings and making them relatable to aspiring entrepreneurs. • Founder testimonials are presented as continuous text compiled from interviews, with hover-over details providing information about the speaker and their company, enhancing credibility. • A new section uses AI-generated animation from static photos to bring to life images of recognizable Silicon Valley figures, making them more engaging. • The redesign process prioritized creative exploration using AI tools like Opus 4.5 in Cursor, allowing for rapid prototyping and iteration on interactive elements, which was more efficient than traditional design tools like Figma for achieving the desired animations and storytelling. • The website's aesthetic is minimalist and airy, removing borders and hard dividers to focus attention on founders' faces and stories, deliberately omitting a prominent "Apply" button in the hero section to avoid distractions. • A key message reinforced is that "it's never too early to apply to YC," with the website aiming to inspire potential applicants by showcasing relatable founder stories and the transformative power of the program.

• The most critical factor for improving startup website conversion is to clearly showcase the product itself, rather than relying on abstract illustrations or vague descriptions; this includes providing screenshots or short video walkthroughs so potential customers can understand what the product looks like and how it functions before committing to a demo or purchase. • Animations should be used judiciously to draw attention to key elements and clarify functionality, not as a primary means of communication or for aesthetic overload, as excessive or poorly executed animations can distract and overwhelm users, hindering comprehension. • Call to action buttons, such as "Book a Demo," need to be clearly visible and compelling; if they blend into the design or are presented too early in the user journey before the value proposition is understood, conversion rates will suffer. • A/B testing different calls to action, such as changing "Book a Demo" to "Show Me the Product" or "Watch a Demo," can significantly increase engagement by providing a lower-commitment entry point for interested users. • To improve user understanding and reduce friction, websites should provide literal, concrete explanations of product features and benefits, rather than generic marketing speak or abstract concepts, especially when competing against established free alternatives like Google Slides. • Offering a frictionless trial or demo experience, such as allowing users to interact with the product before requiring a sign-up, is crucial for capturing user intent and guiding them toward an "aha moment" of product value, particularly in competitive markets. • Website design should prioritize clarity and user experience, avoiding overwhelming amounts of information, overly complex animations, or inconsistent UI elements that can lead to a perception of a lack of detail orientation and undermine user trust. • Simplifying the core message and offering a clear, focused presentation of what the product is and how it solves a problem is more effective than trying to include every feature and benefit on a single page, which can create noise and detract from the main value proposition.

• Diffusion is a fundamental machine learning framework for learning probability distributions of data across any domain, particularly excelling in mapping from high-dimensional to high-dimensional spaces, even with limited data. • The core diffusion process involves taking data, progressively adding noise to create a sequence of noised-up versions, and then training a model to reverse this process, learning to denoise from pure noise back to the original data distribution. • Innovations in diffusion models have focused on refining the denoising objective, moving from predicting the original data to predicting the added error or velocity, which simplifies the learning process for the model and often leads to cleaner, more stable training. • Flow matching is a simplified approach to diffusion that bypasses intermediate noising steps by directly learning a global velocity vector between the noise and the data, allowing for a more direct and efficient generation process, often requiring as little as 10-15 lines of code. • Diffusion models have broad applications beyond image generation, including protein folding (AlphaFold), robotic policies (diffusion policy), weather forecasting (Gencast), DNA and molecule binding prediction (DiffDock), and increasingly in language models (diffusion LLMs) and code generation. • While diffusion has "eaten all of AI" except for reinforcement learning (MCTS for games like AlphaGo) and certain LLM applications, its core procedure is becoming simpler and more effective, suggesting widespread future impact across various industries and enabling new companies in areas like robotics, text generation, and video.

• Finding your first users is a search problem, not a persuasion problem; look for people who are early adopters or have a burning need that your product solves. • Charge early adopters real money for your product; paying customers provide sharper, more valuable feedback than free users. • Utilize targeted personal outreach, such as cold emails or knocking on doors, rather than broad advertising methods to find these initial users. • Launch your product early and create a wide surface area for potential users to find you, as you won't know exactly who they are at the outset. • Treat your early users like an anthropologist studying a new civilization to understand their decision-making processes and motivations for trusting your product. • Develop a "minimum evolvable product" that can adapt and evolve based on user feedback and market pressures, rather than aiming for a perfect, final form from the start.

• Stoke Space is developing fully and rapidly reusable rockets, including the second stage capsule, which is typically discarded on every mission due to extreme re-entry heat (over 2700° F) and speeds (17,000 mph). • Their innovative approach to stage 2 reusability involves a custom heat shield utilizing cold liquid hydrogen flowing through a heat exchanger to absorb reentry heat, complemented by 24 small thrusters for deceleration and controlled landing. • The Nova rocket features a highly fuel-efficient first-stage engine, designed for rapid reusability, while the second stage, Andromeda, is engineered to survive re-entry and land. • Stoke Space's origin story involves founders Andy and Tom leaving Blue Origin in 2019, bootstrapping their initial engine testing in backyard shipping containers, and overcoming fundraising challenges during the pandemic, eventually raising approximately $990 million. • They emphasize rapid iteration by building most components in-house, allowing for quick learning cycles from failures; for instance, reducing a component iteration from a month to a couple of days by bringing testing back to their factory. • A key element of their operational success is a custom software platform called "Bolt Line," designed to manage and track all aspects of vehicle maintenance, operations, and data logging, bridging the gap from garage beginnings to FAA-regulated flights.

• The AI economy has stabilized, with clear layers for model, application, and infrastructure companies, and a developed playbook for building AI-native businesses. • Anthropic has surpassed OpenAI as the preferred LLM provider for Y Combinator-backed startups, moving from ~20-25% usage to over 52% in the Winter 2026 batch, driven by strong performance in coding tools and agents. • Gemini is also climbing in popularity, now at 23% usage among YC applicants, with users impressed by its reasoning abilities and its integration with Google Search for real-time information. • The "AI bubble" concern is compared to the telecom bubble of the '90s; while there's massive infrastructure investment, it creates an opportunity for application-layer startups, much like YouTube emerged from the excess bandwidth. • The AI revolution is in its "deployment phase," following an "installation phase" of heavy capital expenditure, leading to abundance and new opportunities for founders to build applications on top of existing infrastructure. • Despite initial skepticism, companies are exploring space-based data centers and fusion energy to address power generation and land constraints for AI infrastructure, with companies like Google and Elon Musk pursuing space solutions. • The skill set for building AI models is becoming more democratized, with a growing number of individuals possessing the research, engineering, and business acumen required, leading to an increase in applied AI companies and more specialized models. • The trend of AI increasing efficiency is leading to higher customer expectations, driving companies to still hire significantly to meet demand and compete, rather than reducing workforce size. • Companies like Gamma are demonstrating a new "reverse flex" by achieving significant ARR with a small number of employees, indicating a shift towards efficiency and lean operations in the AI startup landscape.

• Intelligence is defined by the ability to learn new things efficiently, not just by excelling at known tasks like chess or Go. • The ARC benchmark, developed by François Chollet, tests an AI's ability to learn new skills rather than just perform on existing ones, with a focus on tasks that average humans can solve. • Early LLMs performed poorly on the ARC benchmark (around 4-5% for GPT-4 base), but recent advancements, particularly with reasoning paradigms, have significantly improved performance (e.g., 21% with 01 preview). • ARC AGI 3, launching next year, will be an interactive benchmark featuring 150 video game-like environments where AI must infer goals without instructions, testing generalization through action and feedback. • Efficiency in AI will be measured not only by accuracy but also by the number of actions and data points required, drawing parallels to human learning efficiency, with ARC AGI 3 normalizing AI performance to average human actions. • Solving ARC AGI is considered necessary but not sufficient for achieving Artificial General Intelligence (AGI); a system that excels at ARC AGI would demonstrate strong generalization capabilities but wouldn't necessarily be AGI itself.

• PostHog helps users debug products and ship features faster, consolidating customer and product data, with around 160 employees and 300,000 customers. • The company's initial successful product was self-hosted open-source product analytics, born from the frustration of repeatedly setting up analytics during multiple pivots, and it gained traction on Hacker News. • PostHog's marketing strategy, including bizarre billboards and a unique website, focuses on standing out and generating awareness through humor and unexpected comparisons rather than direct conversion. • James Hawkins, CEO of PostHog, emphasizes that having a clear, albeit potentially contrarian, plan is crucial when raising funds, and that building a remarkable product and brand requires going significantly beyond the typical 80/20 effort. • PostHog is now doubling down on AI to build "product autonomy," aiming to automate feature development and product management tasks, which is enabled by their multi-product infrastructure and substantial funding.

• Amplitude initially approached AI with skepticism, viewing its capabilities as "jagged" and facing frustration from external pressure to adopt it without a clear strategy. • A turning point for Amplitude was recognizing the transformative effect of AI on software engineering, leading them to seriously invest in AI adoption around October 2024, marked by hiring a new engineering leader and acquiring Command AI. • The company's pivot to AI involved a significant internal effort, including an "AI week" for training and hackathons, to get the existing team bought-in and proficient with AI tools before focusing on building new AI-native products. • Amplitude's core strategy shifted from a customer-driven "faster horse" approach in traditional SaaS to a technology-first understanding of AI capabilities to map them to product solutions, acknowledging that customers cannot always articulate needs for novel AI functionalities. • The transition required organizational restructuring, including reorganizing the engineering, product, and design teams twice within a year and moving away from leaders solely focused on the pre-AI SaaS modality.

• AI is enabling new consumer startup opportunities by making previously impossible tasks feasible, such as democratizing music creation with AI-powered tools like Suno, which allows anyone to create music. • The success of consumer startups like Ankor (podcast platform) and Suno demonstrates a historical trend of technological advancements democratizing content creation, from photos and videos to audio and now music, with AI being the key enabler for music. • While AI is making product creation easier, distribution remains a critical challenge for consumer startups, though new AI-driven distribution channels are expected to emerge. • The emergence of AI allows for the rebuilding of foundational technology stacks and presents opportunities to leverage large, previously untapped data sets (e.g., health data, camera rolls) by applying LLMs and other AI models to create new insights and experiences. • AI is poised to revolutionize education by creating highly personalized and efficient learning experiences, moving beyond the current one-size-fits-all models, as exemplified by Obo Labs, which aims to invest in human intelligence through AI. • Developing "taste" and "craft" is becoming crucial for consumer founders to stand out in a hyper-competitive landscape where AI lowers the barrier to product building, though the durability of taste as a long-term asset is still being tested.

• The core value of the video is to provide actionable insights into improving startup website design, specifically for sites built with Cursor, a popular AI coding tool. • Rio Lu, Head of Design at Cursor, offers specific critiques and recommendations on various websites submitted by YC founders, focusing on clarity, messaging, user experience, and visual design. • Key takeaways for improving startup websites include: avoiding jargon and vague language, clearly communicating the product's value proposition and target audience, ensuring consistent and polished visual design (e.g., typography, spacing, color palettes), making calls-to-action prominent and clear, and providing sufficient information without overwhelming the user. • Lu advises against common design pitfalls such as "AI slop" (e.g., massive shadows, purple gradients, bad typography), distracting animations, and confusing naming conventions. • He emphasizes the importance of talking to the target audience in their language and focusing on solving their problems, rather than using internal company jargon or overly technical terms. • The review highlights that while AI tools can assist in website creation, a strong foundational design system and clear communication strategy are crucial for creating effective and compelling user experiences.

• AI is projected to make ocean container shipping 8-10% cheaper over the next few years, with AI contributing significantly to this reduction. • Flexport's AI initiatives have already saved 2% of their ocean freight spend while simultaneously improving transit time by 20%, a feat that typically involves a trade-off between cost and speed. • AI is being implemented across Flexport's operations, from enhancing customer user experience and optimizing container loading to automating tasks previously done via email, phone, or manual processes. • Flexport leverages AI to process complex, unstructured data like large Excel files from logistics contracts, converting them into structured formats for intelligent analysis. • The company is actively promoting AI adoption internally, with 90% of recent hackathon projects focusing on Large Language Models (LLMs), leading to the development of real product lines and features. • Flexport offers a 90-day program for non-engineers to learn AI skills, aiming to increase their productivity by up to ten times by enabling them to build automation tools for their roles. • A customer-facing AI feature allows users to ask natural language questions to generate reports and charts, reducing account management time spent on data report generation by 25%. • Internally, an AI agent handles routine tasks like verifying warehouse addresses and scheduling delivery appointments, including making automated phone calls to confirm details, a task too costly for humans to perform consistently. • Flexport aims to automate 80-95% of work within the next year, driven by advancements in LLMs, with the understanding that labor costs represent about 10% of the total cost in the freight forwarding layer of logistics.

• StarCloud is developing orbital data centers to provide GPU compute to satellites and eventually compete with terrestrial data centers on energy costs. • Their first satellite, StarCloud 1, successfully launched into orbit carrying an Nvidia H100 GPU, marking the first instance of data center-grade GPUs operating in space, performing 100 times better than any previous space computer. • The core innovation of StarCloud lies in their proprietary deployable radiator technology, which enables efficient heat dissipation into deep space using infrared radiation, requiring zero fresh water and significantly reducing carbon emissions compared to Earth-based data centers. • StarCloud aims to build massive 40-megawatt orbital data centers powered by uninterrupted solar energy, designed to overcome the limitations of land, grid power, and cooling faced by terrestrial facilities. • The company's rapid development, from founding to satellite launch in 15 months, was attributed to the founders' complementary expertise in software, data center infrastructure, and satellite engineering, along with in-house fabrication of key components. • Future plans include launching a second satellite in October with at least 10 times the power of the first, featuring Nvidia's Blackwell architecture, multiple GPUs, and high-bandwidth optical terminals for 24/7 low-latency connectivity.

• Start by prioritizing selling the company to candidates, not just interviewing them, as this is a common founder mistake. • To stand out in outreach, personalize messages deeply, potentially spending five minutes per email, and get creative with sourcing strategies beyond standard platforms. • When sourcing Account Executives (AEs), look for signals like consistent quota attainment, quick promotion cycles within a single company, and experience in fast-paced startup environments. • For software engineers, focus on unique advantages like specific technical skills, contributions to open-source projects, experience building personal projects, or founder-like experience. • A compelling outreach email should be concise, personalized, establish company legitimacy with customer/momentum details, and include a clear call to action, with follow-ups adding further value. • Aim for response rates between 10-20% and interested rates of roughly half of that, understanding that interested rate is a better metric than just reply rate. • Founders should schedule dedicated time for sourcing and outreach, aiming to speak with at least 10 candidates per week, and every founder should be involved in the early hiring process. • When making an offer, leverage speed as an advantage over larger companies and personalize the offer to the candidate's specific motivations identified during the interview process. • Founders should consider hiring external recruiting help when they are certain of making multiple hires (more than two concurrent roles) within a 6-12 month period.

• The core reason enterprises struggle with AI implementation, leading to a high failure rate (often cited around 95% of projects), is not necessarily the AI itself, but the inherent limitations and resistance within large organizations: engineers may not believe in AI, internal IT systems are often outdated and siloed, and political turf wars hinder progress. • Startups have a significant advantage because they can build functional AI products more effectively than enterprises can internally or through traditional consulting firms (like Deloitte or EY), who often lack the deep technical expertise to build sophisticated software despite their ability to mediate requirements. • Success for startups selling to enterprises often hinges on embedding deeply into business processes and integrating with systems of record, a strategy that differs from typical plug-and-play SaaS models but can yield substantial rewards (the "pot of gold") if successful. • Startups can win deals by building genuine relationships with "champions" within enterprises – individuals who may have entrepreneurial dreams but are risk-averse, and who can live vicariously through the startup's journey and champion their cause internally. • Enterprises are actively seeking AI solutions and are increasingly willing to bet on new startups, recognizing that established vendors and internal IT departments often struggle to deliver effective AI, creating a "startup-shaped hole" in the market for specialized AI solutions. • The high switching costs for enterprises once they invest time in training an AI system create a significant moat for startups that successfully onboard them, providing a competitive advantage against potential future entrants.

• To build a successful AI startup, focus on identifying what people are already paying for and aim to assist, replace, or enable previously unthinkable tasks with AI. • The total addressable market for AI applications is significantly larger than traditional software, as it can capture the value of entire salaries previously spent on human labor, not just SaaS subscription fees. • Building a reliable AI product requires deep domain expertise to understand precisely what professionals do and how the best in the field would perform tasks with unlimited resources; this understanding should be translated into specific steps and then into well-crafted prompts or code. • Rigorous evaluation is crucial for AI products; focus on defining what "good" looks like for each task, create objective metrics (e.g., true/false, numerical scales), and iterate relentlessly on prompts and evaluations, aiming for high accuracy (97%+) before production. • The most critical factor for marketing and sales in AI startups is building an outstanding product; while marketing is necessary, a superior product will generate organic word-of-mouth and inbound interest, making sales efforts more effective. • When marketing and selling AI services, price based on the immense value delivered rather than traditional software models, and listen to customer preferences for pricing structures (e.g., predictable subscription vs. per-use). • Building trust with customers for new AI products involves demonstrating superiority through head-to-head comparisons with existing human services, pilot programs, and robust post-sale customer engagement, including training and support, as the product is more than just the UI. • Defensibility in AI startups comes from the complex, iterative process of building and refining the product, including data integrations, model selection, and prompt engineering, which creates a unique, difficult-to-replicate asset over time, rather than solely relying on underlying proprietary models.

The Problem of Sequential Data Early AI struggled with understanding sequences (like text) due to the vanishing gradient problem in Recurrent Neural Networks (RNNs). Gradients, used for training, would fade as they passed backward through long sequences, diminishing the influence of early inputs. Long Short-Term Memory (LSTM) Networks Introduced in the 1990s by Hochreiter and Schmidhuber to combat vanishing gradients. LSTMs used "gates" to selectively keep, update, or forget information, enabling learning of long-range dependencies. Became viable and dominant in Natural Language Processing (NLP) in the early 2010s with advancements in GPUs and data. Sequence-to-Sequence (Seq2Seq) with Attention A limitation of early LSTM-based Seq2Seq models was the "fixed-length bottleneck," where an entire input sequence was compressed into a single vector, losing information for long sentences. The 2014 Seq2Seq with Attention model allowed the decoder to "attend" to relevant parts of the encoder's hidden states, improving alignment and performance. This led to significant improvements in machine translation, making models competitive with older systems and powering tools like Google Translate. The attention mechanism also hinted at broader applicability beyond NLP, with early use in computer vision. The Transformer Architecture Proposed in the 2017 paper "Attention Is All You Need" by Google researchers. Eliminated recurrence entirely, relying solely on attention. Used self-attention to update token representations based on weighted relationships with all other tokens in the sequence. Enabled parallel processing of entire sequences, drastically improving speed and accuracy over RNNs. Led to variations like BERT (encoder-only) and GPT (decoder-only), which became the foundation for modern Large Language Models (LLMs). The ability to scale these models with large datasets was crucial for their development into general-purpose AI systems.

AI Go-to-Market Strategies For AI companies in legacy industries, three GTM approaches exist: Build AI software to sell to existing businesses (most common). Start a new business in the legacy industry and automate it. Acquire an existing business and integrate AI. When starting a new business and automating, focus on the percentage of work automated, aiming to increase it over time. Software founders are well-positioned to identify automatable tasks. Success in the second GTM approach depends on tracking automation rate over revenue growth. Partnering with early adopters who are enthusiastic about new software can be crucial. Qualifying potential customers is vital; look for individuals empowered and incentivized to adopt new software. Mid-Market vs. Enterprise AI Sales For early-stage AI companies, prioritize the pace of learning about customer needs and pain points. Starting with the mid-market or even smaller companies that have the problem is generally advisable for faster learning and iteration. Sales cycles in enterprise can be long; focus on finding empowered individuals within organizations. A narrower product scope or focusing on a few key users can shorten sales cycles. Hiring and AI Sales Roles AI SDRs are most effective when integrated into an already functional sales process. Founders must still master the art of selling. Founders need to understand "who to sell to" and "how to get their attention" before AI can effectively assist. Don't hire growth hackers or AI SDRs as a last resort to fix a broken sales process; founders must first figure out the core selling mechanism. Founders should be curious and learn the roles they might hire for before bringing in new team members to manage expectations. Investing in AI Development Assess if your product will become irrelevant with new model releases or significantly better. Investing in development now, even if models improve later, provides valuable learning and a stronger product when new models are available. Pivoting a Startup Pivoting is necessary when traction exists but isn't strong enough, or if the current direction isn't working. A key indicator for pivoting is a lack of customer conviction or value for the current product, even with some revenue. Pivoting requires significant energy and conviction, as it often means starting from scratch. When considering a pivot, explore a range of ideas rather than focusing on just one. The leading indicator for a pivot is often when founders stop believing in their current product's success. A "great" startup idea is validated by customers who express a daily need and solve a real pain point. Technical Challenges and Pivoting Technically difficult ideas can be good opportunities if founders have the courage and skills, as fewer competitors will pursue them. Reduce scope or build a simpler version first to tackle complex technical challenges, then scale up. Don't let technical difficulty become an excuse to avoid customer interaction. When to Hire The right time to hire is when things are so busy that you can't find time for interviews, indicating things are working but breaking. Early indicators of breaking points in engineering, sales, or onboarding suggest it's time to consider hiring. Hiring is challenging for startups; focus on personal networks for early hires. Hiring is not a success metric; it's a necessity for a functioning company. Opportunistic hires (e.g., "smartest friend") can be effective if the person is truly exceptional and the timing is right. Open Sourcing Enterprise SaaS Products Open-sourcing is common for dev tools but can also build trust and shorten sales cycles for enterprise SaaS, even if customers don't directly inspect the code. Open-sourcing can address concerns around privacy and sensitive data, facilitating self-hosting. Drawbacks include the cost of supporting self-hosted versions, which requires higher pricing.