Kathryn Hodge

Meta's new magic number is 49%. First it wrote a $14B check for 49% of Scale AI, poaching CEO Alex Wang. Now the same playbook hits venture land: Meta is offering to buy up to 49% of Nat Friedman & Daniel Gross's $1.1B NFDG fund - and hiring the duo to run its shiny new AI lab - letting early LPs cash out at a 4x markup while still calling the fund "independent". Antitrust alarms? Snoozing. All thanks to that cute little missing 1%.

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If you're building with LLMs, watch out for this hidden cost... LLMs are evolving faster than your prompt stack can keep up. What breaks isn't always visible. Until it is. Every model shift carries silent regressions that chip away at velocity. The more LLM logic you ship, the more fragile your system becomes. Here's where the real cost shows up.

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Hiring in SF right now is a total bloodbath. It’s no secret the pendulum has swung back to in-person work. → Now there are hundreds of companies fighting over the same “top talent.” → Meanwhile, the AI labs (and a few big winners) are dominating by offering meaningful work and more liquidity options before an exit. So everyone’s chasing the same people… …and wondering why pipelines are tiny. But the truth is: this is a signaling problem. You’re not the only company that raised significant capital. And you can’t rely on “we raised a round” as a differentiator anymore. Differentiation at every level matters: ◆ WHAT — What are you building that’s actually unique? ◆ WHO — Who are the founders, and why are they positioned to win? ◆ MOMENTUM — What does traction look like? Who are the customers? ◆ UPSIDE: What does the outcome look like for early employees? And yes, “storytelling” has become a cliché. Every startup has a hype video. Every founder claims they’re building something “category-defining.” But the teams that win this market aren’t the loudest. They’re the ones who treat recruiting like a real priority. The founders who are willing to: • show up • pitch candidates directly • shake hands and kiss babies • and do it consistently for months …are the ones who will win the talent war.

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If you’re a recruiting firm thinking about building your own AI stack: Here’s my rule: Build if you have a real CTO + continuous engineering capacity. Buy if you don’t. “Vibe coding” with a part-time dev is how you spend: $100k waste hundreds of hours and end up with a fragile system you now have to maintain forever Vibe coding ≠ reliability. And reliability is the product. Ask yourself: Who’s on call when it breaks? How are you managing PII and permissions? Are you prepared for legal/compliance realities (including data broker registration in some cases)? What happens if LinkedIn bans a profile for unapproved tooling? If you’re building, I’ll swap war stories anytime.

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Update: TechKareer is helping 6 top orgs build their teams 🥳 - Google backed startup, data infra for robotics - SF Deep Tech Hiring app (raised $2M+) - Venture Studio ($20M+ rev in '24) - Web3 x AI app by serial founders - SF Top Debt Collection Org - Soonicorn Consulting Org

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This guy fooled everyone in Silicon Valley. He made $850k/year pretending to work for AI start-ups. This week, the CEO of Playground AI called him out on X. Here's the crazy story of Soham Parekh so far: Soham simultaneously worked for YC startups like: • Playground AI • Fleet AI • Lindy Founders said he would ace his interviews. Then he would vanish once hired. How did he trick some of the top start-ups in the world? Automation. • Mouse jigglers • Fake time trackers • Cloned lines from real engineers He made it seem like he was always online. Then this week something crazy happened: Playground AI founder Suhail Doshi exposed him. He called out Soham for submitting a resume that was “90% fake.” And let him go within a week. The post went viral. Soon, other founders piled on. Dozens came forward with the same story: “Didn’t ship a thing.” “Great interview.” “Total ghost.” Turns out Soham had a playbook—and it worked. He had built a rep as a top-tier engineer. People even vouched for him. Because he knew how to sound smart in interviews. But never stayed long enough to prove otherwise. So why did it work for so long? Because remote hiring is broken: • Resumes aren’t verified • Interviews are rushed • Outputs aren’t tracked It’s the perfect storm for fake players to sneak in. When exposed, Soham didn’t deny it. He told Suhail: “I want to come clean.” Someone claiming to be him posted online saying: “I’m focusing on one company now.” “I want to rebuild trust.” The crazy part? Some people want to forgive him. One founder even said: “Everyone deserves a second chance.” Why? Because the real villain might not be Soham... It might be us. Founders chasing “10x” devs. VCs demanding unrealistic output. Startups skipping due diligence. We created the system Soham exploited. Then acted shocked when someone gamed it. This isn’t just a Soham story; it’s a tech story. Because in a world of remote work and AI shortcuts, Perception often beats proof. And the most dangerous resume... Is the one you never check. Is Soham a scammer? Maybe. But he also held up a mirror to tech. He forced a question no one wants to ask: If a fake engineer can earn $850K a year... What does that say about how we hire real ones? The Soham story is a brutal reminder: If you don’t build a credible personal brand... Someone else can fake one and win. People trust who they see online. So be the one they remember. In today’s world, attention = leverage. A strong personal brand is insurance. • It builds trust. • Attracts talent. • Protects you from going invisible. Don’t rent your reputation. Own it. Founders - We'll build your personal/company brand on X (and beyond) while you leave it on cruise control. We've already helped 40+ Founders get 1.2 billion combined views. Interested in how we can do this for you? Book your free discovery call here: https://lnkd.in/eUKpRuRW

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$55 million in seed funding just hit the Radical AI books, led by RTX Ventures, with a crew stacked deeper than an ISS launch manifest: nVentures (NVIDIA's VC arm), Eni Next (of Eni S.p.A.), noa, Infinite Capital, and AlleyCorp. That's not just a round, that's a declaration. And the message is clear: #materialsscience isn't slow and academic anymore. It's autonomous, atomically precise, and scaling faster than most folks can spell "interatomic potential." Founded in 2024 and now running full throttle from Midtown Manhattan, Radical AI is rebuilding the entire scientific process from the molecular level up. Joseph F. Krause, PhD, ex-Army National Guard CBRN Specialist, Army Research Lab fellow, and AlleyCorp investor, partnered with serial builder and AlleyCorp Board Partner Jorge Colindres to construct something we don't see enough of: a deep tech company with actual technical depth. Add Gerbrand Ceder to the founding trio, Samsung Distinguished Chair at University of California, Berkeley, 550+ publications, 130,000 citations, and more patents than most folks have unread emails, and you've got a team calibrated for moonshots, not MVPs. Their product? A fully autonomous lab, an A-Lab, capable of running 50 to 100 times more materials science experiments than traditional setups, without needing a human to refill the coffee pot. It's AI, #robotics, and molecular #quantum mechanics choreographed to a single tempo. Inside is TorchSim, their PyTorch-native #simulation engine running atomistic calcs at speeds that make DFT look like dial-up. They've built their own supercomputer. Created the world's fastest #MachineLearning Interatomic Potential. Dropped the largest known #datasets in materials science. Then mapped out a nine-tool full-stack robotic lab and launched the OS to run it. And they did all this in a year. Radical AI isn't playing the game. They're changing the field. Their platform doesn't treat discovery, creation, and deployment as a sequence. It's one flow, one intelligence, merging #physics with robotics and computation into an autonomous loop. That's not disruption. That's reconstitution. And the industries paying attention? #Defense. #Energy. #Biotech. #Space. #Semiconductors. All the arenas where slow science bottlenecks fast futures. #Startups #StartupFunding #VentureCapital #SeedRound #AI #Materials #MaterialsTech #Research #Robotics #RobotTech #DeepTech #Infrastructure #Technology #Innovation #TechEcosystem #StartupEcosystem If engineering peace of mind is what you crave, Vention is your zen.

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Breaking: This YC-backed startup just raised $4.1M to turn a viral text-to-3D tool into a full-fledged AI copilot. Adam (YC W25), founded by Zach Dive and Aaron Li is building a platform where creators, engineers and product teams can go from a text prompt directly to a CAD-ready 3D model - no sculpting, no Blender expertise required. Lead investors: Y Combinator, Moxxie Ventures, & Caffeinated Capital. Why this deserves a double-tap: 1. From viral demo to funded startup Adam’s “type to 3D” prototype went viral earlier this year, and now they’ve turned that momentum into $4.1M in seed funding. 2. AI copilot for 3D creation Instead of replacing designers, Adam acts like an assistant - generating base models, refining geometry, and letting creators iterate conversationally. 3. Democratizing design No more learning Blender or Fusion 360 - just describe what you want (“a curved lamp base with cable groove”) & watch it appear. 4. Built by builders The founding trio are ex-YC builders who saw how hard it was to prototype in hardware. So they’re using AI to compress days of design into minutes. 5. The bigger picture If ChatGPT changed how we write and Mid-Journey changed how we see, Adam might just change how we build. So next time someone says “AI can’t touch real-world creation,” point them to the founders of Adam. Because the future of design isn’t just visual, it’s conversational.

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🎬 The GPT-3.5 moment for AI video just arrived. ByteDance dropped Seedance 2.0 — and it's being called the most advanced AI video generation model available right now 🤯 This isn't another cool demo that falls apart in motion. Seedance 2.0 enters what researchers call the "Director Era": ✨ Multi-modal input: up to 12 reference files 📸 Image→Video | Text→Video | Video→Video 🎵 Native audio generation + 8-language lip-sync 🎥 2K resolution | 30% faster than v1 🧲 Physics-aware engine: hair, water, fabric finally behave naturally 🎭 Character consistency: faces, outfits, scenes stay coherent ⏱️ 20-sec coherent videos, multi-shot narratives from a single prompt CTOL testing: outperforms Sora 2 and Veo 3.1 👀 Chinese media stocks surged But controversy too ⚠️ The model can "hallucinate" hidden building angles from photos and clone voices from face photos alone — raising serious privacy concerns. The one-person film crew era is officially here. Are you ready? 💡 Save this — the most important AI video breakthrough you need to know. #AI #Seedance #ByteDance #AIVideoGeneration #AIMovie #GenerativeAI #TechNews #Tenten #ArtificialIntelligence #VideoRevolution #FutureOfFilm 📍 Follow @Tenten — Asia's Frontline AI Media

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$650M says Saudi isn’t a maybe. Not to long ago Adam Neumann just launched Flow in Riyadh. Not with a trial. Not with a waitlist. With $350M from a16z, $300M from Saudi family offices, and a 92% trial-to-tenant conversion. That’s not testing waters. That’s a full send. Flow isn’t “WeLive in a thobe.” It’s a market signal: Saudi’s Build-to-Rent (BtR) market is wide open — and the smartest investors already see the opportunity. Here’s what most of them say behind closed doors: “Your recruiter is a force multiplier. Not overhead.” – a16z They tell founders this early and often — especially at Seed to Series A & B. Why? → Bad hires bleed runway → Slow hires kill momentum → And early team misses? They ripple for years You don’t need 300 resumes. You need 3 people: Who actually fit your GTM motion. Who understand your cultural context. Who can drive you to $10M+ ARR and beyond. Because early-stage hiring isn’t about headcount. It’s about trajectory. So if your recruiter isn't acting like a co-pilot? You’re not behind. But you’re definitely not in the lead.

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On July 2nd, multiple YC founders discovered they'd all hired the same engineer. Within days, 19 companies came forward. But Soham Parekh was just following a playbook that 459,000 people on Reddit share daily. Here's how deep this goes: 1. The numbers are unreal 459,000 members on r/overemployed. Daily posts like "5 Js @ $800k TC" (that's 5 jobs, $800k total compensation). One member breaks it down: J1 pays $175k for 12 hours/week. J2 pays $140k for 1 hour/week. They call it "ROI-grading employers." 2. They have a proven system Low-cash, high-equity offers from early-stage startups. Ace the interview (Soham was described as "so sharp" by every founder). Target fully-remote roles across time zones. Never overlap meetings. Use different emails, different machines, different backgrounds for each job. 3. The trust collapse is REAL Varunram Ganesh, Head of Growth at Warp, captured the damage: "Pretty sure very few YC startups will hire remote [South Asians] now." One bad actor destroyed trust for thousands of legitimate engineers. Founders are sharing blacklists. Someone built SohamTracker to crowdsource vetting. The damage spreads far beyond 19 companies. 4. It's not JUST about the money An X user captured the mindset: "Your company makes you do 5 people's jobs on one salary - we're just pricing it in." The subreddit frames it as rational self-insurance. Companies can fire you anytime. To them, multiple income streams are just fair risk management. 5. There are detection methods that actually work Work sample timing inconsistencies. Overlapping calendar conflicts. Background check timing (Soham accepted 5 jobs in 2 months). Reference calls that hit the same people. Simple stuff - but most startups skip it because "he seemed so sharp." 6. What's already changing Founders are sharing red-flag lists on private Slack channels. Multiple startups now require overlapping reference checks. Some YC companies stopped hiring remote altogether. The trust-based hiring playbook from 2020 is finished. 7. The scale recruiters are dealing with 459,000 people actively sharing strategies. Growing from 220k to 459k members in two years. Daily posts about optimizing multiple income streams. This isn't a few bad actors - it's an ENTIRE shadow workforce that's been operating in plain sight. TAKEAWAY: Remote hiring isn't broken. It's just operating on outdated trust. That trust made sense when switching jobs meant moving cities. When a commute created natural friction. When colleagues could physically see you at your desk. None of that exists anymore. So either build verification systems that match this new reality... Or accept that your next hire might be someone on r/overemployed’s “J3.” Your call.

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🚨 BREAKING: Meta just dropped its biggest AI update yet. A standalone Meta AI app. Voice-first, context-aware, and powered by Llama 4. Here’s everything you need to know: 1. The Meta AI App is here. A new way to interact with your assistant. It remembers your preferences. It talks like a human. And it works across Meta’s entire ecosystem. Download it here: IOS: https://lnkd.in/g3TAMnYh Android: https://lnkd.in/g56N8cuH 2. It’s voice-first. You can speak to Meta AI naturally and it responds instantly. Thanks to new full-duplex speech tech, the app talks like it’s in a live convo. No more pause-and-wait. No more robotic tone. Just real dialogue, in real time. 3. Deep personalization is now live. Meta AI learns what matters to you. - It remembers facts you share - It uses context from Meta apps - It tailors responses to your interests It even improves if you link Facebook and Instagram in your Accounts Center. 4. The app is your AI control center. - Discover feed shows how others use AI - Chat history lets you pick up conversations anywhere - Device tab manages your Ray-Ban Meta glasses - Web sync keeps it all connected It's one assistant. Everywhere. 5. Speaking of glasses… The Meta AI app now replaces the Meta View app. You can now: - Start chatting with AI on your glasses - Pick up the convo later in the app - Manage devices in one place Wearable AI just leveled up. 6. Big upgrades to the web interface too. Meta AI on desktop now includes: - Voice interaction - A Discover feed - Better image generation - A rich doc editor (in testing) - PDF export and doc analysis features Designed for creators and productivity. 7. Privacy + Control = Non-negotiable - Voice controls are transparent - Nothing is shared unless you choose to post - You decide what Meta AI remembers about you - “Ready to talk” toggle gives you control over voice features Meta says: You’re in charge. 📲 The Meta AI app is available today on iOS + Android. US, Canada, Australia, and New Zealand get voice features first. Try it. Break it. Give feedback. This is just version one but it’s the start of something big. __ I put together a free community for people building real AI systems. It’s got agent templates, tutorials, and answers to questions I wish I had earlier. If that sounds useful, I’d love to have you in there. No pressure at all just here if it helps: https://lnkd.in/g93Kt6iU Follow me Brendan for more and repost this to spread the word.

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Threw up a quick tutorial on how to create a slack bot that can summarize threads and your workspace using Pydantic AI and OpenAI and Logfire.ai. huge shout out to Médéric HURIER for all the help on this one. Let me know if you like it and we will make more! This is just a minute exert, whole video up on youtube dot come slash mlopscommunty

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ByteDance just dropped BAGEL a new open-source AI model that understands and creates text, images, and even videos. It can generate images, edit them, and add motion.

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🚨 YouTube monetization is about to change in a big way 🚨 Here’s what we know… 1️⃣ YouTube will demonetize content with synthetic (auto-narrated) or AI-generated voices unless videos are clearly transformative and add real human value. 2️⃣ Low-effort and reused content like repackaged clips or TV footage with zero commentary and storytelling will be demonetized. 3️⃣ Videos entirely generated using AI won’t see monetization. Channels like AI Yeti and Stormtrooper Vlogs wouldn’t receive ad revenue. 4️⃣ YouTube’s review process is getting smarter about spotting repetitive formats, mass uploads, and content farms. Channels producing this kind of content also won’t be eligible for monetization. 5️⃣ YouTube is drawing a line to protect advertisers. If I’m spending money to advertise on the platform, I want my ads to run on channels with real humans who’ve built organic communities. I think this is a step in the right direction for YouTube, protecting its creators and advertisers. Expect to see a lot of people talking about this on Twitter, and I’d bet a lot of channels will be affected when these new guidelines go into effect today.

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I’m not scaling users. I’m scaling logic. > Here’s why that matters more. Most “tech companies” aren’t tech. They’re logos on top of Stripe, GPT, Firebase, and a Notion doc. A front-end stitched together by SaaS tools, API calls, and borrowed infrastructure. The founder has a pitch. The team has Figma files. And the infrastructure? Outsourced. Invisible. Untouched. They say they’re building a company. But what they’re really building is a branded interface—wrapped around someone else’s logic. I’ve stopped calling them startups. They’re skin. Thin wrappers over code they don’t control. Nothing wrong with that—unless you claim you’re building technology. Because if you don’t own the logic layer, you don’t own the execution. And if you don’t own execution, you’re a tenant in someone else’s system. You’re not building software. You’re assembling dependencies. And eventually, those dependencies will determine your ceiling—and your failure mode. This is why I started OddX Group. If you care about depth, you can’t start with interface. You start with logic. Execution logic. The layer that defines how systems operate, scale, and recover. We don’t build apps. We build infrastructure for infrastructure. Not to be contrarian. Not to be theoretical. But because execution is too important to leave to abstraction. Execution stacks outlast interfaces. They define the constraints others have to live with. If you get that layer right, you don't just build companies— you shape how other companies are built. Everyone says: > “We’re an AI company now.” But they still schedule retros in Notion. Everyone says: > “We want to scale.” But they’re scaling assumptions, not execution. A real tech company doesn’t raise the most money. It rewrites the most logic. It encodes fewer opinions and more primitives. If you’re serious about deep tech: Don’t just build features. Build structure. Don’t just integrate tools. Replace them. Don’t just talk autonomy. Engineer it into the core. This morning I woke up to a boiling plate of eba, not tea and bread. And for some reason, that made me think deeply about all this. Comfort isn’t what builds systems. Constraints do. At OddX, we’re not trying to be visible. We’re trying to be foundational. And foundations don’t shout. They support everything else that will. That’s the work.

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From a New York startup to a $1.9B facial recognition powerhouse in under 8 years. Proven accuracy, 9,500+ clients, and climbing fast. Clearview AI isn’t your typical startup trajectory. Founded in 2017 by Hoan Ton-That, Charles C. Johnson, and Richard Schwartz in New York, the company set out to solve crime, fraud, and risk with the most ambitious use of facial recognition ever attempted. What began with a small group of engineers and a big idea has turned into a team of 30 building the world’s largest facial image database, now powering over 13 million searches every day. By 2025, Clearview’s relentless business growth stands out: a $1.9 billion valuation, 47% YoY revenue growth, and a client roster that includes 7,200 police departments, 78 Fortune 500 companies, plus major government and security agencies worldwide. A big part of that growth? Clearview’s web-based SaaS platform, which makes use of 60+ billion public images. Its NIST-tested algorithms hit 99.7% identification accuracy and less than 0.2% false positives, outperforming industry peers for clients that need answers in seconds, not hours. The business runs with 87% gross margins and a 94% renewal rate. Over half of revenue comes from law enforcement subscriptions. Major investors (from Peter Thiel to Kirenaga Partners) bet early on Clearview’s trajectory, and the company’s recently expanded exec team features veterans from LexisNexis and Motorola Solutions. Clearview AI’s story is one of fast, relentless business execution. From a bold founding vision to high-stakes, fast-scaling SaaS for modern public safety and security. For ambitious engineers and operators, it’s one of the highest-impact seats in the space.

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While OpenAI burned $100M on training, this startup built enterprise AI for $700K and landed on Forbes' list. Storytime: Back in 2015, May Habib and Waseem Alshikh launched Writer as a simple translation software. Little did they know their creation would evolve into a $1.9 billion AI powerhouse. They started this to solve the ongoing problems enterprises faced: - Complex AI models requiring dedicated dev teams - Months-long implementation cycles - Uncertain ROI on AI investments Their tools automate tedious corporate tasks, from writing product descriptions to drafting emails. By using this tool, companies saw immediate cost savings, not future promises. Unlike OpenAI or Anthropic, Writer doesn't sell raw AI models. This $1.9 billion company built plug-and-play tools that work immediately. Today, multi-billion dollar giants like L'Oréal, Uber, Salesforce, Victoria's Secret, and Intuit are among 300+ companies that have adopted Writer into their operations. Here's how they made it happen: → Built smarter, not harder: Created enterprise-ready AI for $700K while competitors burned millions on general models → Plain English commands: Users describe workflows in simple language - no need for complex prompt engineering like with ChatGPT or Claude. The AI agents understand and execute steps automatically. → Enterprise-grade security: Unlike OpenAI or Anthropic where data trains shared models, Writer's dedicated servers keep client information completely private → Ready from day one: While tools like Copy.ai and Jasper offer customizable workflows that may require initial setup and tuning, Writer provides over 70 ready-to-use applications designed to work out of the box. The results speak for themselves: ↳ Writer’s health retailer client generated $5M in value in year one.  ↳ Victoria's Secret translated 2,900 products in 10 days.  ↳ Their customers expand contracts by 60% on average. I believe Writer cracked what most AI companies miss. They don't sell technology. They sell time saved and money earned. Which repetitive task drains your team's productivity the most? #ai #enterprise #automation #innovation

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