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 Media‍01:05 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 prioritize‍Check 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 problem‍Check 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 LinkedInCheck 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 ownSilen 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 & speedCheck 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 BenLearn more at freestyle.sh

For years designers made pictures of a product and engineers turned them into codeAI changed that. Designers can now build with code themselvesBut 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 reviewThe AI coding tools also work one step at a time. Ask for something, get something back, ask againDesign doesn't work that way. You try a few ideas side by side, keep the best and mix in parts of anotherPutri built Lunagraph for this. It's a design canvas where everything you place on it is real codeYou 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 linkEngineers 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 Media‍01:42 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 pricing‍Check out www.lunagraph.com

There are over a billion cameras in the world. They can all see. Almost none of them can tell you what they saw.Andrey Gizdov has been in computer vision since 2016, when CNNs were all the rage, researching real-time vision models.He met his co-founder Vishal Urlam at a hackathon. His co-founder had deployed cameras and IoT devices across India's power grid to monitor it.Then they went to conferences on cameras and intelligence and found the state of affairs grim.From that point, they knew this was a company that was going to exist.OpenVector connects what cameras see to what businesses do.Connect an existing camera and describe a workflow in plain English:Track a misplaced item.Check that an SOP was followed.Detect an unscanned item.Turn an empty shelf into a restocking task.When something needs attention, OpenVector can take the next step inside the software a business already uses - creating records, sending requests, updating tasks, or notifying someone to act.Vision → Language → Action.Doing that without replacing the existing camera infrastructure is the hard part.Historically, bandwidth and compute pushed vision systems onto on-prem hardware. And ripping out a customer's existing setup doesn't scale.Andrey says OpenVector has significantly reduced those requirements with almost no loss in accuracy. Today, the company describes its underlying technology as the world's fastest and lowest-bandwidth VLM engine.During YC, they sold to major companies, including some of the biggest gas-station operators in the country.The larger bet is that AI is moving out of the chat window and into the physical world.OpenVector is building the layer between what a camera sees and what a business does next.🎙️Andrey Gizdov, CEO & Co-Founder, OpenVector on Fondo START01:39 Harvard and the path into computer vision02:42 Vision, language, action systems03:08 Typed commands into camera workflows03:17 Warehouse and subway use cases05:15 Why build it now05:35 Meeting his co-founder06:02 The camera conference06:19 Getting into YC, second try06:38 How they got the domain07:45 Biggest YC lesson: sales08:05 Client ROI and talking price09:23 Why nobody solved this sooner10:25 Cutting bandwidth and compute11:22 Foundation models for vision11:42 AI moving to the physical worldCheck out openvector.com

Quippy lets you rehearse the conversation before you have it.Pushing back on a boss. A first date. Short practice scenarios, line-by-line feedback on what landed and what backfired, and the app turns your weak spots into drills. Then you do it again tomorrow, and it gets sharper each round because it's learning what you specifically keep getting wrong.Jonathan Li built the whole thing solo, and he's in the current Fall YC batch.His bet is on the habit, more so than the content. You can build a genuinely good curriculum for almost any skill and watch it go unused, because the bottleneck was never whether the lessons work. It's whether anyone opens the app on a Tuesday when nothing is forcing them to. Small consistent practice beats heroic bursts, and the heroic burst is what most products accidentally optimize for.So Quippy is built like a game, not a course.Consumer is a thin slice of his incoming batch. He thinks value has to trickle down to the consumer eventually, and he'd rather be early.🎙️ Jonathan Li, Founder & CEO, Quippy on Fondo START w/ Guest Host, @gracegongGG, Founder, Smart Venture Media‍01:15 Two and a half years as a PM at Duolingo01:50 Leaving, a seven-month detour, and starting Quippy02:45 Duolingo-inspired gamification applied to social skills05:40 Why building a skill is a habit problem05:55 How Quippy builds a personalized curriculum06:50 Dating and work: where people actually use it08:30 Two months heads down as a solo founder10:15 Why consumer distribution comes down to volume of experiments‍Check out quippyapp.com

A CFO at a publicly traded company on variable AI pricing:"This is introducing my worst nightmare, which is a blank check" Claude, Codex, Cursor, Devin, Gemini... Every workflow creates usage and every token creates cost, and unlike something like a Gong license, the price isn't fixed. That's the problem Eric Chernoff is building Fancysauce to solve: AI Cost Management, extra Fancy. Track usage, monitor ROI and optimize spend across every AI workflow. Fancysauce shows every token and ties it to the team, product, model and business value behind it.Spend lands in the right bucket, COGS or OPEX.Then you decide what to do about it.Eric sees a second shift in how companies organize work.He calls it "death of the org chart, birth of the work chart"A human can own a task while AI assists, or AI can own it while a human approves; some tasks go entirely to one side.Fancysauce maps that ownership across people and AI, with spend by team and project and waste, ROI and per-unit efficiency in real time.🎙️ Eric Chernoff, CEO & Founder, Fancysauce.ai on Fondo START 01:57 Every token: tracking usage across Claude, Codex, Cursor, Devin, Gemini + more03:22 Three lanes of AI spend: in-product AI, internal automation and individual usage05:34 AI budgets by person, and understanding spend across every tool06:30 Why the work chart replaces the org chart07:13 Human-owned, AI-assisted vs. AI-owned, human-approved work08:42 Why reporting to AI may happen at the task level, not the job level10:49 The CFO problem: variable AI pricing with no fixed cost13:14 Retain AI measured human work; Fancysauce measures work flowing through agents15:01 Why big markets carry companies, and why Eric believes AI is an even bigger opportunity‍fancysauce.ai

Somewhere, someone already has the thing you're looking for. A dataset, a patent, a capability, a customer, an answer. Most of the time neither of you ever finds out.That's the problem Ryaan Aqid built Quirk around, and he first recognized it in Bangladesh, watching workers earning $50–100 a month while Upwork listed work paying ten times more. Nothing separated them except information. The opportunity already existed; the connection never happened.It runs through everything. Institutions sit on datasets they'll never open, companies shelve technology somebody else is desperate for, and entire markets fail to form because two people who'd have built something never met. Ryaan calls it the largest economy nobody can see: the things that were never made.Quirk builds infrastructure to dissolve that asymmetry, with agents that do the looking so neither side has to move first. Holding something you won't use? It works out who it's worth something to. Stuck? It finds whoever got past this a year ago, without you having to describe the problem."Agents that find deals neither side knew existed."🎙️ Ryaan Aqid, Founder & CEO, Quirk on Fondo START pod‍01:05 Getting the buyers to say exactly what they needed‍01:30 Building distribution through government relationships in Bangladesh‍02:00 Why he thinks the average person's information gets sold through intermediaries‍02:40 The high school nonprofit in Bangladesh‍03:08 Realizing freelance platforms could 10x a worker's income‍03:25 Moving people up Maslow's hierarchy of needs‍04:05 Leaving Cornell two days after classes started‍05:05 What's next and where to follow along‍Check out www.quirklabs.ai