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- 2 days ago12 min
START: Jonathan Li, Founder & CEO, Quippy "Helps build social skills through daily practice"
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
- 5 days ago15 min
START: Eric Chernoff, CEO & Founder, Fancysauce.ai "AI Cost Management, extra Fancy: Track usage, monitor ROI, and optimize spend across every AI workflow"
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
- 2 weeks ago6 min
START: Ryaan Aqid, Founder & CEO, Quirk “ Infrastructure for information asymmetry”
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
- 2 weeks ago9 min
START: Ilya Valmianski, CEO & Co-Founder, Signals "The AI store associate that turns buyers into regulars"
Spend enough at a luxury house and someone actually calls to ask how the bag is working out. Ilya Valmianski brought that up on the show as the piece of retail everyone else quietly gave up on, less because it stopped working than because people are expensive and most brands can't put a store associate on every order.Signals makes that level of attention cheap enough to give every customer one.Today it looks like an AI store associate that reaches customers over iMessage after they buy. It answers sizing questions, recommends exchanges when something doesn't fit, flags when a sold-out item is back, remembers preferences, and works out what someone might want next. It's aimed at the relationship rather than the single sale. Their A/B tests against a holdout put it at roughly $30 of incremental repeat revenue per conversation.The problem underneath started with a number Ilya dropped early in the episode. Every year roughly $1.2 trillion of apparel and fashion sells online, and about $300 billion of it comes back. His argument is that most of those returns aren't buyer's remorse. People bought because they wanted the product. Then the sizing was off, nobody told them what to do about it, and the only obvious path on the screen said Return.Signals tries to get there first. Rather than processing refunds more efficiently, it opens a conversation that can turn a would-be refund into an exchange, and increasingly into a customer who keeps coming back.The bigger idea reaches well past ecommerce. Ilya calls it super-staffing: one customer support rep at a $20 million company spends the day putting out fires, but give that same company a thousand AI associates and the job stops resembling support. Everybody gets someone paying attention to them.He pointed at healthcare, where he worked before this. In nursing, he argues, the ideal staffing level is closer to 100× the current one.Brands have never lacked the data to treat you like a regular, only the staff to act on it."The AI store associate that turns buyers into regulars"🎙️ Ilya Valmianski, CEO & Co-Founder, Signals, on Fondo START 01:25 The $300B ecommerce returns problem02:23 Bringing luxury concierge service to every customer02:55 Why AI enables personalized support at scale03:39 Early traction and YC growth ambitions04:18 Turning refunds into exchanges04:42 The economics of revenue retention05:31 Why proactive support beats return portals06:47 The concept of "super-staffing"07:08 Reimagining customer support with AI08:23 The real secret behind startup successCheck out returnsignals.com
- 2 weeks ago14 min
START: Aoden Teo, CEO & Co-Founder, Miso Labs: "The most emotive foundation models for voice"
Voice AI can pass a Turing test. For about a minute.That's a generated clip, though. Have a human actually talk back and the number collapses to six or seven seconds, roughly where generated voice sat three years ago.One reason, per Aoden Teo of Miso Labs: real conversation isn't turn-based. Around 20% of the time more than one person is speaking, and laughter drives a lot of that overlap, since you laugh at a joke while it's still being told. We also adjust our pacing toward whoever we're talking to without noticing we're doing it.Voice models struggle with all of this. Full-duplex voice, where a model listens and speaks at the same time, is still extremely early.So an agent can know your joke is funny and still have to wait until you've finished before it laughs, by which point the timing has killed it.Aoden describes a second consequence: agents get pushed toward almost "psychotically emotive" behavior. If they can only talk once you've stopped, they need some other way to show they were listening. You finish your sentence, and the thing goes "Hmm?" You've heard it.Underneath that sits an architecture problem. Voice models have to respond fast, which constrains how large they can be, and fast means something different here than it does in text. Working with an LLM like Claude, Aoden points out, you care how quickly it finishes your code, not how quickly it starts.Voice inverts that. Nobody needs 10 hours of audio generated in two seconds, because nobody can listen to 10 hours of audio in two seconds; what matters is reaction time. Most architectural decisions trade latency against throughput, and Aoden expects voice to keep moving away from LLM-style designs toward ones built around very low latency.Miso is already pushing on it. Miso-1 got 3,000 stars on GitHub and 5 million views on Twitter, and they record data in their own LA studio because the internet doesn't contain every kind of audio a voice model might need. Nobody has released a podcast of someone reading millions and millions of email addresses, and people still want voice models that can read email addresses aloud, so teams end up generating some very strange training data themselves.The clip isn't the hard part. The hard part starts when you talk back."The most emotive foundation models for voice"🎙️Aoden Teo, CEO & Co-Founder, Miso Labs on Fondo START 1:03 Miso-1: 3K+ GitHub stars + 5M X views1:59 Why emotiveness matters for games, UGC + interactive products3:06 Measuring progress in voice AI with longer Turing tests4:01 Why interactive conversation is harder than generating convincing clips5:08 Full-duplex voice, interruptions + why laughter matters6:04 Latency vs. throughput - and why voice differs from LLMs7:09 Miso's LA recording studio + the challenge of voice training data9:02 Talking teddy bears, UGC, anime + unexpected voice AI use cases10:19 From serious chess player to math obsession to building @MisoLabsAI12:11 The surprise YC interviewCheck out misolabs.ai
- 3 weeks ago7 min
START: Zach Nieman, Founder, Snap Studio: “Unleash Your Studio - The Ultimate Portable Vocal Booth"
It started with a dream. “Guys, let’s make it to the GRAMMYs.” 18 months later, he did it. Zach Nieman’s GRAMMY-Nominated production “Cali Coast (Psionics Remix)” by Soul Pacific led him to walk the red carpet and planted the seeds for his first startup. It all began when he was recording music at home. The gear was right. The room wasn't.Zach had the interface, the cables, the mics, and the chops to lay down a real take. He hit record and realized: “It sounded like garbage.”So he built a workaround: a frame with sound absorption blankets over it, enough to kill the reverb and get a usable signal he could mix in post. He told the guys “Let’s make this album so good that we get to the GRAMMYs.” The songs they tracked inside it made that dream come true.Then he put the booth away for over a year. It took fellow GRAMMY Nominated producer Josh Williams asking “whatever happened with that booth?” to get Zach to pull it back out and think product instead of prototype.Snap Studio is the commercialized product of that vision: a 360-degree acoustic isolation shield trusted by thousands of artists, singers, and voice actors worldwide. The popular Standard and XL models break down in minutes and fit into a duffel bag, making them perfect for storage or travel.Get Rolling Stone's #1 recommended portable recording booth here: www.snapstudio.com🎙️ Zach Nieman, CEO & Founder, Snap Studio on Fondo START01:08 Why San Francisco became the Hollywood of startups02:08 The thing that decides whether a take is usable, and it isn't the gear02:50 The recording that came back unusable03:28 From a blanket-and-frame prototype to the Grammy red carpet03:38 The question from a friend that restarted the whole project03:58 Launching during COVID, when nobody could get to a studioCheck it out at www.snapstudio.com
- Aug 21, 20268 min
START: Varun Agarwal, Founder, Envariant "Interpretability and reasoning infra for foundation models."
Fine-tune a frontier LLM on addition and it handles one through five digits without breaking a sweat. Six digits, a friend of Varun's found performance collapsing to zero.The model had learned addition up to the length it saw in training and nothing underneath it, no rule it could extend. Varun calls it a fatal flaw, one he expects to surface in deep tech and safety-critical applications, and rather than argue about it he'd rather hand builders a way to look inside and check.You hand Envariant 50 examples where your model tells the truth and 50 where it hallucinates. It finds the surface inside the model where the difference lives, and from there you can amplify that behavior, suppress it, or trace what caused it. The same approach pulls out the principles a model has learned in a form a human can actually read, and generates the edge cases most likely to break them.Which is the part that traces straight back to biology. Varun was building foundation models to design synthetic viral genomes: DNA in, DNA out, and no way to tell what the thing had worked out about virology along the way. Figuring out how to read that back out became the company.🎙️ Varun Agarwal, Founder, Envariant on Fondo START02:02 An interpretability SDK for foundation model builders02:34 Finding the right surface inside the model02:47 Detecting hallucinations by finding internal model representations04:00 Why scale and compute still dominate but are hitting walls04:42 The 6-digit addition collapse and what it means for AI reasoning06:02 Closing the gap from 95% demo to 99.99% production07:37 From designing synthetic viral genomes to founding Envariant08:15 thoughts on AGICheck out envariant.ai
- Aug 20, 20265 min
START: Abhinav Gopal & Darren Hsu , Co-Founders, Rubbrband "AI creative company"
Hollywood-grade storytelling, startup-speed execution.Most AI ads feel disposable. Rubbrband is betting the future looks cinematic instead.Jeremy Lee, Abhinav Gopal, and Darren Hsu: 3 CS researchers out of Berkeley who loved film built video models for Hollywood.They got good enough at cinematic content that they now do it for some of the world's top brands, and they do it without the hundreds of thousands of dollars companies have been handing traditional ad studios.Discovery call, creative brief, script, production. Start to finish in weeks, not months. With real narrative and real pacing, the kind of thing that's actually worth watching.Everyone's figured out by now that cheap AI content makes taste worth more. The brands that win won't explain their product; they'll make you feel something.Smaller teams, faster launches, higher production value than traditional headcount & budget should allow for. That's the shift rubbrband is building for.🎙️ Abhinav Gopal & Darren Hsu, Co-Founders, Rubbrband on Fondo START pod00:45 What Rubbrband does01:53 From Berkeley engineers to building AI models for Hollywood02:30 The creative process behind every launch campaign03:14 Hollywood-quality production for startups at a fraction of the cost03:35 Why they're determined to avoid AI slop04:17 Why AI video adoption is still in the early innings04:55 Hollywood's growing adoption of AI05:20 Where to find RubbrbandCheck out www.rubbrband.com
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