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The US produces enough biogas to make over $20 billion worth of chemicals a year 60% of it is wasted or flared in low-margin applications It comes off wastewater plants, farms and landfillsGas a large-scale chemical plant can't capture So Rise Reforming goes to the gasIts modular technology fits inside a shipping container and runs on-site The result is the cheapest feedstock and no fossil fuels as raw material Out come chemicals like dimethyl ether, methanol and dimethyl carbonateThat last one has no domestic manufacturer in the US The three founders met studying molecular engineering at UChicagoThey got close over late-night problem setsEnvironmentalists turned engineers, drawn in by the chemical industry's 5% share of global emissionsThen Y Combinator, to learn how to sell chemicals 1,800+ hours of stable syngas productionFirst in the Americas to produce syngas from wastewater biogasA binding biogas supply agreement and a conditional DME offtake for its first commercial unit DME is first, targeting aerosol and propane starting in 2027 Rise Reforming sums it up in six words:"Unlocking resilience for the chemical industry" ‍ 🎙️ George Rose, Founder & CEO, Rise Reforming on Fondo START   ‍ 00:45 Supply-secure chemicals from waste gases01:45 Why centralized plants are exposed to conflicts and natural disasters02:00 Making the chemicals where the waste is actually generated02:32 Dimethyl carbonate and the lack of a single domestic manufacturer03:04 DME, methanol, and the markets Rise Reforming is targeting04:48 Environmentalists turned engineers05:32 Green premium?05:57 Price parity with the natural gas alternative06:27 A new methanol process and the cheapest feedstock07:05 Meeting cofounder at UChicago over late-night problem sets08:25 Doing YC to learn how to sell chemicals Check out www.rise-reforming.com

‍A clinical trial at Stanford enrolled a patient living in Africa. He was never physically there. He had been diagnosed with a rare genetic disorder, and Stanford was running a trial for it. The research team gathered his genetic information and medical records. They monitored him remotely and enrolled him. The patient was Melissa Bime’s father. She was working at Stanford as a clinical trial nurse at the time. She had trained as a nurse in Cameroon and moved to the US, where she worked through the COVID vaccine trials. She says that was the spark. Why not build digital versions of patients and get them into clinical trials. That’s Infiuss Health. It builds medical digital twins from real-world and historical data. Trial teams rehearse a protocol across thousands of virtual patients before a single one enrolls. The twins flag who is likely to drop out mid-study. Who has a comorbidity that affects how the drug performs. Whether two hours of travel to a research site is feasible over two years. Infiuss reports up to a 30% reduction in sample size and design cycles 10x faster. During the recent Ebola outbreak, Infiuss helped companies running vaccine trials recruit patients with a specific genetic trait. Melissa’s long-term view is approval with a hundred patients instead of thousands. Her focus is small and mid-sized life science companies. Most have one or two drug candidates the entire company hinges on. Infiuss is built to give them more tries. "AI-Powered clinical rehearsal. Run the trial before you run the trial." 🎙️Melissa Bime, Founder, Infiuss Health on Fondo START   ‍ 01:29 From nursing school in Cameroon to healthcare entrepreneurship02:29 The diagnosis that changed everything04:22 Building computational versions of real patients05:27 Why clinical R&D costs billions and fails nine times out of ten06:23 What digital patient twins can predict today07:35 Cutting roughly six to eight months from the process08:05 Approval with a hundred patients instead of thousands08:59 A clinical trial without a control group09:50 Why smaller life science companies need more chances to fail10:21 “You shouldn’t have to go out of business ’cause you failed a clinical trial”10:55 Finding the right genetic trait during the Ebola outbreak Check out infiuss.com

Most sales teams are competing for the same companies. Not necessarily because they're the best customers, but because they're the easiest to find. They have LinkedIn profiles, public hiring activity, and enough online signals to show up in every prospecting database. But plenty of good businesses don't leave that kind of trail. Ludo Granger kept noticing this when visiting customers. He'd look at their prospect databases and find that he could sometimes identify roughly three times as much additional market opportunity. The companies were there. The sales tools just weren't finding them. A hotel, a restaurant, or an early-stage startup might be a perfect customer. But if it's not generating the right online signals, it can be almost invisible to traditional prospecting software. That became the problem Ludo wanted to solve with Leadbay. Instead of relying only on the signals everyone else is tracking, Leadbay tries to learn how a good salesperson evaluates a company. It looks at your business, identifies what a promising customer might look like, and uses feedback from the leads you pursue to get better over time. But Ludo isn't trying to build another database with more companies in it. He thinks the way salespeople use databases is changing altogether. During SF Tech Week, he used Claude from his car to work with five highly qualified prospects rather than sorting through thousands of accounts. Then he told the agent how to handle outreach and follow-ups. That's the distinction he's betting on. Finding more companies matters, but knowing which ones are worth pursuing matters more. Especially when the person making that first decision isn't a person anymore. It's an agent. 🎙️ Ludovic Granger, CEO & Co-Founder, Leadbay on Fondo START with Guest Host: Kamran Karic, COO & Co-founder, Ploid (YC F26)01:42 Why everyone is chasing the same companies 02:08 The untapped market traditional sales databases overlook 02:34 How Leadbay can expand a company's addressable market 02:51 Why startups should build something agents want 03:17 The shift from human interfaces to AI agents 03:48 How AI identifies promising businesses with limited online data 04:19 Teaching AI to recognize and improve lead quality 05:01 Finding five highly qualified prospects instead of 5,000 05:29 Trusting AI agents to coordinate outreach and follow-ups 07:04 Why qualified data is the fuel for AI-powered sales ‍ Check out leadbay.ai

When everyone has access to the same AI models, what actually makes one AI product better than another? It's no longer just about the intelligence. It's about how reliably you put it to work. An agent that responds faster. Makes fewer mistakes. Delivers better outcomes for customers. That's the problem Niyaz Puzhikkunnath and Faiz Vadakkumpadath are tackling with Papaya (YC F26): continuously improving AI agents already running in production. After spending more than a decade developing their expertise at Amazon, the founders set out to build a company of their own. Their first product focused on data infrastructure. But as they began working with customers, they discovered something interesting. Customers liked the data product, but were especially interested in the AI agent harness the founders had built to make their system more reliable. Rather than asking customers to migrate their existing data infrastructure, they saw an opportunity to make that technology useful across a much broader range of applications. So they developed the harness into a standalone product, laying the foundation for Papaya. Today, Papaya analyzes how customers use production AI agents, identifies issues across prompts, context, caching, and tool calls, and opens pull requests with proposed fixes. It then tracks whether those changes actually improve quality, latency, and cost. Because knowing why your agent failed is only half the problem. Actually improving it is what matters. That's what Papaya is building toward: AI agents that get better over time, without engineers having to manually investigate and fix every issue. If you're running AI agents in production, check out what the team is building at papaya.fyi. 🎙️ Faiz Vadakkumpadath & Niyaz Puzhikkunnath, Co-Founders, Papaya on Fondo START with Guest Host: Maanav Agrawal, Founder & CEO, Memoir  ‍ 01:43 How Papaya identifies problems in production AI agents and proposes fixes 02:49 Why fixing agents matters more than monitoring them 03:02 Why companies prioritize agent quality over cost 03:34 Papaya's early traction and first paying customers 04:58 Automating the traditional software debugging process 05:47 Why AI could eliminate the traditional maintenance phase 06:18 Why identifying agent failures isn't enough 06:39 Optimizing agents that power critical business workflows 07:46 How Niyaz's experience debugging agents at Amazon inspired Papaya 08:33 How customer feedback transformed a data platform into Papaya ‍ Check out papaya.fyi

How many companies die as an idea because the founder can't build the first version? That's who Simon Shu and Justin Sato have in mind with Restart, which they call the AI company builder. You describe the idea in plain language and Restart sharpens it, then builds a working web product while you watch the pages, copy, and design fill in on a live preview. It publishes to a real URL when you say so, and changes work the same way: say what should be different and watch it happen. Most people building on it are starting their first company, and most of them aren't engineers. They're making SaaS, consumer apps, marketplaces, e-commerce, service businesses, and local businesses; even a coffee shop and a restaurant right alongside apps. The whole thing started from one question: what will companies look like five years from now? Advanced AI will make companies, the economy, and society work in fundamentally different ways. In that world, what changes for workers? They should get to be creatives and visionaries, and Simon and Justin are building Restart so anyone can. Simon brings experience from Tesla and Scale AI. Justin studied CS at Stanford after doing physics and AI safety research, and he's worked in AI engineering at several startups, including Tomo. They met over poker in SF. Restart has already grown to around 50K users, largely through social, and the team keeps shipping and improving the product. There's a free plan with daily build credits, so you can start building today. 🎙️ Simon Shu & Justin Sato, Co-Founders, Restart on Fondo START with Guest Host: @peggy_wang, Founder & CEO, @sparkchat_so @digipals_app  01:40 Meeting at a founders-and-investors poker game in SF02:15 The ride home where they started trading startup ideas02:42 Building a startup always felt inevitable to both of them03:27 Moving into Justin's Stanford dorm room04:03 Grinding it out and raising a pre-seed round04:20 The starting question: what will companies look like in five years?04:55 Shipping before it's ready, and getting to ~50K users05:18 Justin's LinkedIn post gets picked up by Indian news05:35 The 20-iPhone content operation06:21 "Restart is the AI company builder."06:30 The early wedge: incorporation, payments, software07:33 Who it's for: coffee shops, restaurants, and apps07:58 What they love about SF09:24 The guests flip it: what does the host think about AGI?10:30 Simon on turning workers into creators in the AGI era11:01 Justin on Restart's responsibility to make companies AI-native Try it at restart.new

What does it take to build a world-class startup ecosystem? Rodney Fong's answer starts with connection. As President & CEO of the San Francisco Chamber of Commerce, he brings together founders, established businesses, policymakers, finance, technology, and the broader community - helping turn San Francisco’s density of talent into opportunity. Through YES SF, the Chamber hosts 300+ events a year, creating a physical hub where ideas, industries, and people can collide. Rodney’s philosophy is simple: Put ambitious people in the same room. Make it easier to build. Give new ideas room to grow. 🎙️ Rodney A. Fong, President & CEO, San Francisco Chamber of Commerc on Fondo START with Guest Host: Grace Gong, Founder @ Smart Venture Media 01:01 SF Tech Week 01:48 the Chamber’s role and history 02:10 How YES SF connects founders and businesses 03:31 San Francisco’s comeback 04:50 Why SF’s talent density matters 06:10 YES SF’s 300+ annual events 06:53 Why this startup cycle feels different 07:35 A sea urchin startup idea 08:33 Robotics and everyday automation 09:13 The human side of technology

Most hiring software finds the candidates you ask for. But what if you're asking for the wrong person? Lantern AI, founded by Thomas Chan and Justin Tang, is building an AI hiring manager for the question that comes before the search: who should you actually hire? It learns what strong talent looks like at your company, then searches 50+ data sources for people a keyword match on the job description would miss. As hiring managers react to its picks, Lantern sharpens its sense of what they want. And when their requirements don't match the talent market, it pushes back instead of just following the brief. That's the interesting part. Sometimes the best candidate misses a box or two; sometimes the person a company thinks it needs isn't the one who'd help it most. A great headhunter knows when to challenge the brief, and Thomas and Justin are building that judgment into the software. "Turn your taste into hires."  ‍ 🎙️ Thomas Chan & Justin Tang, Co-Founders, Lantern AI on Fondo START   ‍ 01:27 Thomas introduces Lantern's AI hiring manager and personalized talent discovery.01:53 Explains why static job descriptions can overlook exceptional candidates.02:39 Justin describes Lantern's interactive approach to learning hiring preferences.03:26 Thomas explains how Lantern challenges unrealistic hiring requirements.04:17 Discusses Lantern's sourcing strategy using 50+ data providers.05:19 Shares how AI is influencing demand for engineering talent.06:43 Explains why companies need to rethink hiring as technology evolves.08:25 Describes how Lantern evaluates candidates beyond their resumes.09:48 Explains how AI can assess supporting evidence of candidates' work.10:24 Discusses how Lantern helps hiring managers streamline recruiting. ‍ Check out www.lantern.md

Scope already had a live product with revenue when its customers started pulling founder Anand Pajaniradjane somewhere else. It started in GEO and AI search optimization.In customer conversations Anand kept showing people a testing suite the team had built around its own MCP, and they got more excited about that than the product he was actually selling. Then one Wednesday a unicorn told him the GEO product was well thought out and well done but they weren't sure they had budget for it.Anand brought up AI agents in the same conversation and got forwarded to the right person on the spot. He built a small MVP that night.The next morning he talked to about 15 companies and roughly 12 said they'd pay for it if he could make it work.That became Scope's new direction. AI agents now discover software, evaluate it and use it to complete tasks, but they don't use it exactly the way people do.An agent might click through a UI, dig into a GitHub repo or go looking for examples, and its behavior can shift a lot depending on the product and context. Scope has now logged millions of agent runs studying that behavior.The company is building an Agent Experience Platform that shows software companies how agents find and use their products and where that experience breaks down. 🎙️ Anand Pajaniradjane, CEO & Founder, Scope on Fondo START   ‍ 01:04 Scope’s mission: making software discoverable and usable by AI agents01:58 How customer demand pulled Scope away from its original GEO product02:32 The week Anand realized the new product had stronger customer pull02:57 The unicorn conversation that strengthened the signal03:27 Building the first MVP overnight and validating demand in person03:53 Roughly 12 of 15 companies indicate they would pay for a working product04:39 How AI agents discover and evaluate software differently from AI search05:12 What agents actually inspect: interfaces, repositories, and examples06:03 Why context changes agent behavior and one-size-fits-all doesn’t work yet06:51 Anand’s experience in Founders Inc.’s Artifact program08:18 Applying to YC as a solo founder08:47 Anand’s first YC interview experience09:58 Why AI agents are becoming a new class of software user10:31 Scope’s focus on sharing what it has learned from customers and agent behavior11:43 Where to follow Anand and Scope Learn more at www.tryscope.com

Wait two weeks to find out a part doesn’t work.Then wait two more weeks to try again. Sam Mathew knows what that does to a builder. He started in hardware. During COVID, he switched to software because he couldn’t get parts. Slow iteration wasn’t just costing him time. It was discouraging him from building hardware. Now he’s back, making parts for other builders. twentyfour26 manufactures metal parts in San Francisco for robotics and consumer hardware startups.But it didn’t start as a machine shop. Sam and his co-founder, Hemant Mahendru, were building software for manufacturers. Selling it was hard. So on the first day of Founders Inc., they changed the business: instead of selling software to manufacturers, they became one and started taking orders themselves. Their approach is deliberately focused. Rather than trying to manufacture everything, they standardize the machine, materials, tools and workholding around a narrower set of parts. Their software handles quoting and programming, and they run production themselves. Upload a STEP file. The software checks whether the design fits their setup, flags anything that needs changing, and turns accepted designs into a quote and machining plan. When the part fits and capacity allows, they can cut it the same day. For a hardware team, that’s more than a shorter delivery time. It changes how often they can test a design, learn what’s wrong and try again. The obvious cost of waiting is a delayed prototype. The less obvious cost is a builder deciding to work on something else. Sam’s building for the people who want to stay in hardware. "Send CAD. Get metal." 🎙️ Sam Mathew, CTO & Co-Founder, twentyfour26 on Fondo START  01:57 Making new toys from old ones 02:22 Dropping out in 10th grade to build an edtech startup 04:05 The outdated spreadsheet that sparked the company 04:43 Lessons from their first YC interview 05:18 Pivoting from manufacturing software to making parts 06:00 Turning CAD files into metal parts 06:28 Why faster access to parts matters for hardware builders 06:52 The cost of waiting between prototypes 07:35 The practical reason Sam shaved his head 07:48 How a seed-round joke became a viral video ‍ Check out twentyfour26.com

Shipping code got faster. Security has to keep up. Before starting Trident, Dekai Li’s co-founder saw a workflow that concerned him: A product manager supplied a project description. An engineer pasted it into a coding agent. The output became a pull request. Dekai’s concern: manual pentesting couldn’t keep pace with how quickly developers were shipping. That’s the problem they built Trident to address. Trident uses AI to continuously pentest applications and APIs, review code, and identify security issues across cloud infrastructure. But finding a possible vulnerability isn’t enough. You have to prove it’s real. Dekai says validation is one of the hardest parts of AI pentesting. That’s why Trident’s findings come with reproducible evidence, and fixes can be proposed as draft pull requests for engineers to review and merge. The goal isn’t to give back the speed AI makes possible. It’s to build security that can keep up with it. Check out Trident "Continuous Security. AI agents test your apps, APIs and cloud, review every pull request, and answer your team in Slack." ‍ 🎙️Dekai Li Co-Founder & CTO, Trident on Fondo START  w/ Guest Host, Grace Gong, Founder, Smart Venture Media ‍ 01:05 Why manual pentesting struggles to keep pace with development02:12 Earning trust as young cybersecurity founders03:25 Validating vulnerabilities and navigating AI guardrails04:02 Building credibility through experienced hires and product results05:18 A customer case: five critical vulnerabilities in 20 minutes06:10 The founders’ cybersecurity backgrounds and path to Trident07:02 How AI coding workflows helped inspire the company08:13 Lessons from YC and building around customer feedback09:32 Scanning software packages before they enter the codebase10:21 Focusing on the security problems customers prioritize‍ Check out tridentsecurity.io

The earnings call gets the headlines. The shareholder vote can change the company. CEO compensation. Board control. Major corporate transactions. Some of the biggest decisions at public companies are shaped through shareholder votes - but most people never see the work behind them. Ryan Nowicki Stewart spent years inside that process. At State Street, he met with boards and C-suites and helped the firm arrive at voting decisions as a major institutional shareholder. Later, at PJT, he sat on the other side of the table, advising companies navigating those same investors. And behind a single decision could be thousands of pages of SEC filings, third-party research and years of internal notes. Ryan describes the workflow as “a total patchwork.” So he and his co-founder Tommy - who he’s known since undergrad and started alongside at BlackRock -  built Pennant. Pennant is building the corporate governance OS for institutional investors. The platform brings meetings, filings, proposals, voting policies and decision records into one place - helping teams analyze and coordinate proxy voting, apply their own principles at scale and maintain a clear record of how decisions were made. And because Ryan and Tommy spent years working with the exact people they’re now building for, their product bar is simple: Would our former bosses and teammates use this day in and day out? That mindset has shaped how they build. Instead of guessing what customers need, the Pennant team spends time on the desk with institutional investors, starts with their biggest problem and builds from there. Know the workflow deeply enough, and your former job can become your startup’s unfair advantage. ‍ 🎙️Ryan Nowicki Stewart, CEO & Co-Founder, Pennant on Fondo START  ‍ 00:57 From BlackRock and State Street to building Pennant01:27 The hidden governance layer behind public companies02:05 Meeting Tim Cook as Nike’s compensation committee chair03:47 How boards think about CEO pay and incentives05:42 Why shareholder voting matters to institutional investors06:08 The thousands-of-pages “patchwork” Pennant is replacing07:18 Building software their former teammates would actually use08:17 Why Pennant starts with each client’s biggest problem ‍Check out getpennant.ai

Most companies ask what they should post on LinkedIn. Sky Yang is focused on a more valuable question: How does all that activity actually translate into growth? At Imagine AI, his team is treating LinkedIn less like a content calendar and more like a measurable growth system. They study how content performs, who it reaches, and how those interactions connect back to prospects and the deal cycle—then use that data to help companies make better decisions about what to say and who should say it. It starts before a post is ever written. Imagine AI works with companies to define their strategy, positioning, audience, differentiation, and KPIs. That becomes context the agent can use to create content grounded in what the company is actually trying to accomplish. Then Sky looks at distribution differently. A founder, company account, and growth leader each have their own network. Imagine AI uses that to help companies reach more of the people they actually want to reach instead of relying on a single account to carry the entire strategy. And it doesn’t stop at engagement. Imagine AI integrates with CRM data so teams can map content interactions to prospects and follow those touchpoints through different stages of the deal cycle. With enough data, the goal is to build a clearer picture of which content decisions are contributing to revenue. His team has studied how LinkedIn engagement is distributed and is building around the parts companies can make more intelligent. Imagine AI is taking LinkedIn from “we should probably post more” to a data-driven growth system companies can actually learn from. ‍ 🎙️ Sky Yang, CEO & Co-Founder, Imagine AI on Fondo START   ‍ 01:12 Imagine AI’s data-driven approach to LinkedIn01:43 The data behind why some LinkedIn posts take off02:37 Building content from strategy and positioning04:08 Reaching more of the right audience through your team06:18 Connecting content engagement to CRM and revenue08:47 Why Imagine AI is going deep on LinkedIn10:42 Using AI to surface the DMs that actually matter11:54 Relationship data, repeated touchpoints, and smarter outreach14:03 Sky’s advice for growing on LinkedIn Check out at imagineai.me

Every AI model. One key. Zero markup. That’s the starting point. The bigger ambition: help companies turn their AI traffic into better models they own Silen Naihin helped grow AutoGPT to 160,000 GitHub stars. Now, at Experiential Labs, he’s working to bring model access, evaluation and training into one unified experience.  The open-source gateway gives companies control over model access. From there, the team is building simulations around their traffic to understand different workloads and recommend models based on three things: cost, quality and speed. The goal goes beyond choosing a model. It’s helping companies evaluate performance and train specialized models around their own work when they need them. Silen expects agents to do most of the work. So the product has to serve both: humans who need to understand what’s happening and agents that need to use it.   Start with access. Build toward ownership. "The open source AI gateway. Hosted providers, your own keys, and your own GPUs behind one endpoint, at the provider's price." ‍ 🎙️Silen Naihin, CTO & Co-Founder, Experiential Labs on Fondo START   02:51 Going off the beaten path: TKS, Minerva & dropping out03:37 Getting into YC & the research behind Experiential Labs05:00 From 8 H100s and continual learning to building a company05:58 Why every company will become an AI company06:42 Cost, quality & speed: the “holy trinity” of AI models07:00 When fine-tuning beats prompting08:14 What happens when powerful AI gets incredibly cheap09:00 Why the future could be a model—or LoRA—per customer10:00 Open-source models get the tokens; closed models get the spend10:27 Jevons paradox, DeepSeek & exploding AI usage11:06 Why OpenAI and Anthropic aren’t the next Yahoo13:05 Experiential Labs’ bigger vision beyond the AI gateway13:45 Building AI infrastructure for humans and agents14:01 Optimizing models across cost, quality & speed Check out experientiallabs.ai

“I don't believe Claude Code will exist in its current form in six months” Ben Swerdlow, founder of Freestyle, thinks coding agents are moving from local machines to the cloud, where a single task could get the attention of 20 agents at once. Each gets a complete copy of your stack, production environment included. Each can spend a week testing and refining its approach. You compare the results and take the best one forward. The economics aren’t there yet. Ben expects cost per task to fall another 99% over the next four years, making that level of parallel work practical. Freestyle builds the computers for it: full Linux VMs for tasks that run for hours, days, or weeks. Clone a running machine, memory included, and let agents pursue different approaches from the same starting point. Pause and resume with their state intact. Inside Freestyle, Ben already gives agents a week to find improvements to its VM technology. Roughly 700 tests and 90 metrics help the team judge whether the work made things better. He calls this goal engineering: define the outcome clearly, give agents time to work toward it, and measure whether they’re making progress. "Full Linux VMs for AI Agents" Built for complex tasks that run for hours, days, or weeks 🎙️ Benjamin Swerdlow, CEO & Co-Founder, Freestyle on Fondo START  ‍00:57 Why coding agents could move from local to the cloud02:22 From harness engineering to goal engineering03:08 Giving agents a week to improve measurable results04:13 Why defining the problem becomes the bottleneck04:48 How early access to o1 changed Freestyle’s direction05:51 Why frustrated sandbox users revealed a bigger opportunity07:28 Giving agents a computer instead of building custom tools08:12 Getting into YC 11:17 Snapshotting VMs and running agents in parallel12:24 The economics of letting multiple agents attempt every task13:26 How GPU supply could drive down agent costs15:09 Try Freestyle and follow Ben Learn more at freestyle.sh

For years designers made pictures of a product and engineers turned them into code AI changed that. Designers can now build with code themselves But their work is scattered. One of Putri Karunia's users told her he now spends only about 10% of his time in Figma. The rest is split across code, prototypes and code changes for engineers to review The AI coding tools also work one step at a time. Ask for something, get something back, ask again Design doesn't work that way. You try a few ideas side by side, keep the best and mix in parts of another Putri built Lunagraph for this.  It's a design canvas where everything you place on it is real code You explore the way designers always have. Then click through the result to see how it actually works and share it with your team as a link Engineers get working code instead of a picture. They take the component they need and add it to the product "Design with code, on your familiar design canvas" Design, explore, and hand off the whole experience. States, interactions, and flows,all as React code, ready for your engineers or your coding agents. ‍ 🎙️ Putri Karunia, CEO & Founder, Lunagraph on Fondo START w/ Guest Host, Grace Gong, Founder, Smart Venture Media ‍ 01:42 Why the design canvas itself is made of code03:12 Where designers work now that Figma isn't home03:46 Why design exploration isn't linear and coding agents are05:19 How Lunagraph differs from Lovable and Claude Design06:21 Why a chat box can't describe a shadow08:21 Why designers don't always need to open production PRs09:16 Handing engineers working code instead of static designs10:26 Building for the one-designer startup10:55 Using Lunagraph daily on paying client work11:25 Bring your own agent and pricing ‍ Check out www.lunagraph.com