Jisong L.

BaseFrame (YC W26) helps hardware teams find mistakes in their project designs and explore tradeoffs early. Anshul and Vaibhav were roommates at Berkeley and faced these issues themselves when working on robotics startups in the past. Their vision is to help hardware teams bring breakthrough products to market faster, leaner, and with data-driven compromise. To make this work, BaseFrame’s specialized AI agents validate your scope against industry best practices to surface gaps and areas for improvement. They then generate a Bill of Materials by parsing datasheets, manufacturer data, and past designs made by the team and give comprehensive comparison tables and citations. No more 15 tabs of datasheets clogging your workflow. Teams can branch designs, manage versions, and test multiple architectures simultaneously to highlight tradeoffs in cost, price, and lead time. Congrats Anshul Paul and Vaibhav Agrawal on the launch! https://lnkd.in/e6xnmnm2

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While OpenAI signed $1.15 trillion in compute contracts through 2035, DeepSeek trained a frontier model for $6 million. This was 2025’s central question : are we building on bedrock or quicksand? The top 10 posts of 2025 examined some of these topics : Are we in a bubble echoing the telecom crash, or building the next internet? Do traditional exit paths still work when secondaries dominate & IPOs vanish? How do you design tools when the user is AI, not human? 2025 forced a reckoning with reality. 1. How AI Tools Differ from Human Tools (https://lnkd.in/d9qcnbyz) : I consolidated my 100+ AI tools into unified, parameter-rich interfaces based on Anthropic’s research. The counterintuitive finding : AI systems need complex tools with complete context, while humans need simple, chunked interfaces. Claude’s success rate approached 100% after the redesign. 2. Back to Text (https://lnkd.in/dWcy53fv) : How AI Might Reverse Web Design : I watched an open-source agent book flights by navigating airline websites, extracting data from visual chaos. If AI thrives on pure text, the future of the web might look exactly like it started : simple text, but for robots instead of humans. The better AI performs, the fewer websites we’ll visit. 3. Circular Financing (https://lnkd.in/d8-KMGZf) : Does Nvidia’s $110B Bet Echo the Telecom Bubble? : Nvidia’s vendor financing totals $110B in direct investments plus $15B+ in GPU-backed debt, 2.8x larger relative to revenue than Lucent’s exposure in 2000. But unlike the telecom bubble, Nvidia’s top customers generated $451B in operating cash flow in 2024. The merry-go-round has paying riders. Read the full post here : https://lnkd.in/d8Xw6tJt

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This is crazy: NONE of the GenAI unicorns that started post-transformer are YC companies. And 85% were founded in just the last 2 years: 55% in 2023, 30% in 2022 (we are early in the cycle) via @BennettSiegel

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The future of voice AI is unfolding on two fronts: 1) Speech-to-speech models that capture not just what we say, but how we feel 2) Developer-first “Voice in a Box” APIs that make conversational apps work As voice becomes a dominant interface layer, the infrastructure behind it needs to evolve fast. The demand is already here: sales agents, schedulers, care providers. Every founder building in this space is building toward a much bigger shift. “Voice AI in a box” platforms will make conversational apps and agents more personal and make developers' lives easier, like getting phone numbers, scaling calls, translation, custom-branded voices, voice cloning, and more. This area is poised to grow fast both now and well into the future as interfaces like avatars become mainstream.   If you’re building here, I’d love to meet! https://lnkd.in/dD9U3jpy

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Back in 2023, TGI has initiated a movement where optimized inference engines rely on the model architectures from transformers as the source of modeling truth. This approach is now adopted by downstream inference engines: vLLM, SGLang, as well as local engines with inter-compatibility such as llama.cpp or MLX. Given we at Hugging Face contribute to those community runtimes, we decided to switch text-generation-inference itself to maintenance mode. Thanks for all the tokens 🔥 Hat/tip Lysandre Debut

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💻 Anysphere, the company behind the AI coding assistant Cursor, has raised $900 million in a funding round led by Thrive Capital, tripling its valuation to $9 billion. Andreessen Horowitz and Accel also participated in the round. Cursor has gained popularity among developers for its AI-powered code generation capabilities, reportedly producing nearly a billion lines of code daily through natural language prompts. The tool has achieved $200 million in annual recurring revenue and is used by companies like Stripe, Spotify, and OpenAI. This significant investment reflects the growing trend of AI-driven developer tools, with "vibe coding" — a term popularized by AI researcher and OpenAI cofounder Andrej Karpathy — becoming increasingly prevalent. Vibe coding emphasizes a more intuitive and fluid coding experience, allowing developers to interact with AI assistants to generate and modify code seamlessly. The surge in funding for Anysphere underscores the broader shift in investor focus from foundational AI models to application-level tools that integrate AI into everyday workflows. In 2024 alone, AI application startups raised $8.2 billion, more than double the previous year's total. As AI continues to transform software development, tools like Cursor are at the forefront, offering developers enhanced productivity and a new approach to coding. Want to keep up with AI? 🤖 Follow Evolving AI to stay ahead of your competition (trusted by +3 million followers online) ✉️ Join 50,000+ newsletter readers and stay updated on the latest AI insights: https://lnkd.in/em9B--mb

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YC used to invest across multiple rounds. Seed, Series A, later stages through their Continuity Fund... But they shut it down. As accelerators scale, the logic shifts from founder advocacy to capital deployment optimization. YC's Continuity Fund created two problems: 1) Negative signalling : • YC follow-on \= validation signal • No YC follow-on \= implied rejection from your own accelerator • Market interpreted this as meaningful information asymmetry 2) Incentive misalignment : • Group partners operated on 3-month cycles (batch start to Demo Day) • Continuity team operated on multi-year investment cycles • Result: handoff friction and compartmentalization Garry Tan restructured YC around Group partners being an equal investing partnership like Benchmark but with 15 people. That's the core of what YC is, supported by world-class media and software teams. Now YC does what they uniquely do best… the initial batch. The new approach = • Partners commit to founders for company lifetime • YC invests only at inception (seed stage) • Later-stage VCs become partners, not competitors This resolves the trust problem. Trust matters more than capital for founders. You don't really know who to trust when you're starting. Having one person at YC committed to you for the life of the company is better than 90% of founder situations. The returns evidence supports this… YC portfolio value grew from $300B to $800B after shutting down Continuity, not despite it. Why?... ... Small funds (YC at batch level) generate alpha through idiosyncratic bets on nascent companies. ... Large funds (Continuity-style) generate beta through momentum exposure to validated companies. It is impossible to optimize for both simultaneously. The incentive structures are fundamentally opposed. YC’s comparative advantage is in company formation, not capital deployment. Do what you uniquely do best. Partner with others for the rest.

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A lot of VCs use founder age as a proxy for how well they’ll leverage LLMs and the latest AI. In my experience, the stronger signal is how technical they are. Technical founders understand how LLMs work and can exploit them fully; non-technical founders see magic, not mechanisms.

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Many YC founders go to Demo Day expecting to fill their round and get back to work. But then reality hits: Lots of them don't raise a dollar. You just don't hear about these stories. You only hear about the successes. YC partners are clear about this: "Don't think that just because you're YC, you'll lift a finger and raise your round." The badge gets you in the room. But you're still competing against 50-100 other startups for maybe 5-10 investment spots per fund. If I were to do YC again, here’s how I’d approach it: Use the $500K to create a 3-year runway and build something people want with a team of 2 in total. I won’t show up to demo day. Just focus on building. I’d forget about: Growth targets. Fundraising decks. Creating a one-liner. 100% focus on profitability. That's how you become default alive and don’t need to beg for money.

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🚀 New partnership: Mento VC × Modal We teamed up with Modal so founders can spend less time on infra and more time shipping! Serverless compute for AI inference, fine-tuning, and batch - when you need it, at the scale you need. Founders: what’s the toughest part of your AI infra today - GPU availability, cold starts, or cost control? Comment below; we’ll share what’s working across teams we back. Transparency note: Mento portfolio companies can redeem $5,000 in Modal credits.

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I met a YC founder in SF that just raised $44M. this is how 👇 HappyRobot (Pablo Palafox, Javi Palafox & Luis Paarup) just raised a $44m Series B from Base10 Partners, Andreessen Horowitz and Y Combinator. They’re building the AI workforce for supply chains, a digital twin of your business and a team of AI agents that automate repetitive work for global logistics companies. 8 of the top 10 freight brokers already use them. 2 of the 3 biggest ocean carriers too. When I asked them what made the difference, here’s what they told me: 1. 𝐖𝐨𝐫𝐤 𝐥𝐢𝐤𝐞 𝐢𝐭’𝐬 24/7 “Everybody says you have to work hard, but almost nobody really does. You’ll think it’s obvious—until you realize how much work it actually takes.” 2. 𝐆𝐞𝐭 𝐜𝐥𝐨𝐬𝐞 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 “They became insiders by spending every day inside client operations, watching what was manual, and asking ‘how can AI do this?’” 3. 𝐁𝐞 𝐫𝐞𝐚𝐝𝐲 𝐭𝐨 𝐩𝐢𝐯𝐨𝐭 “They changed their idea during YC Demo Day.* If they hadn’t been open-minded, HappyRobot wouldn’t exist.” 4. 𝐁𝐮𝐢𝐥𝐝 𝐰𝐡𝐞𝐫𝐞 𝐭𝐡𝐢𝐧𝐠𝐬 𝐡𝐚𝐩𝐩𝐞𝐧 “Being in SF keeps you surrounded by people who are just as obsessed. When you’re tired, you talk to another founder who’s also suffering—and you keep going.” 5. 𝐂𝐨𝐦𝐩𝐨𝐮𝐧𝐝𝐢𝐧𝐠 𝐞𝐟𝐟𝐨𝐫𝐭 𝐛𝐞𝐚𝐭𝐬 𝐥𝐮𝐜𝐤 “Things take time to mature. Most people quit too early. The ones who stay in the game long enough are the ones who win.” Two years after YC, they’ve gone from a pivot to a $44M round. From outsiders to redefining how global supply chains work. Hard work, compounding, and being in the right place. That’s the real playbook Shoutout to Rubén Domínguez Ibar for the help with the on the street interview ⚡

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The big labs are expansive in their product ambition, especially since foundation models have largely improved in lockstep - in order to compete with them you have to do things they won’t which are: - building a very rich software ecosystem around a primitive - orchestration across multiple models - going insanely deep on product and growth for a narrow vertical domain

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