Joe Zhou

Northflank is partnering again with Cerebral Valley, PyTorch, CoreWeave (and more) for the OpenEnv Hackathon in San Francisco, March 7–8. Participants will be building RL environments and post-training base models across 5 themes, solving the most pressing problems in RL and agentic orchestration. We'll be supporting teams in deploying their models through the Northflank platform with compute from our wonderful partners, CoreWeave (Matthew Lu, Jacob Feldman). $100K+ prize pool and and a stacked lineup of judges and mentors from Meta, Hugging Face, University of California, Berkeley, Cursor, Scale AI, and more. You can submit an application at the link in comments.

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The TechCrunch Disrupt 2025 event, happening this October in San Francisco, will gather over 10,000 tech leaders, investors, and innovators under one roof. What really stands out is the scale of opportunity for startups to gain direct access to decision-makers who can propel their growth. Exhibitors get not only a physical presence but priority branding, press exposure, and multiple full-access passes for their teams to network and build meaningful connections. This isn’t just another conference — it’s a three-day sprint where being visible means attracting your next key partners, customers, or investors before your competitors do. When the floor is packed with the brightest minds from Netflix, Sequoia Capital, and other heavy hitters, the brands that act fast will be the ones capitalizing on the momentum. The takeaway? In today’s hyper-competitive market, strategic presence in such ecosystems isn’t a luxury but a necessity. How are you planning to maximize face time and genuine engagement at large industry gatherings like this? What tactics have helped you turn fleeting networking into lasting business impact? #TechCrunchDisrupt #StartupGrowth #TechInnovation #NetworkingStrategy

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Thrilled to announce that Together Fund is investing in Sentra, alongside a16z speedrun! You track results in Jira. Decisions in Notion. Conversations in Slack. But the reasoning, the debates, trade-offs, and context behind why you chose A over B, disappears into what we call "Dark Matter." A decision made in March looks insane by July because no one remembers the constraints that made it smart. I lived this firsthand at Twitter scaling from 800 to 8,000 employees, and at Google while launching AI Overviews to billions at planet scale. The problem isn't process. Process is compensation for something deeper: organizational amnesia. An organization’s "Systems of Record" doesn’t solve this, they encode it. They store what happened, never why. That's why we are investing in Sentra. Sentra is the always-on collective memory that eliminates organizational amnesia by maintaining accurate context for all members and agents, functioning as an operational nervous system. It connects to every channel where work happens, meetings, Slack, email, code commits, docs, calendars, and treats them not as artifacts to search, but as living signals to synthesize. The fleeting and the permanent, unified into a memory that understands. The founding team is built for this: - Jae Gwan Park (CEO): Product-first founder, memory systems research at UofT and MIT - Ashwin Gopinath (CSO): Former MIT professor, created "Reflexion" (NeurIPS 2023), agents that learn from mistakes, 2x founder - Andrey Starenky (CTO): Early Vapi engineer, ex-IBM, built to process enterprise-scale data firehose Together is an operator-led fund. We invest in problems we've lived. This is one of them. Many congrats Jae, Ashwin and Andrey, we are so excited to partner with you! Read the full thesis: https://lnkd.in/gixj9cE4 Book a demo: https://www.sentra.app/ #OrganizationalMemory #AI #Sentra #TogetherFund #a16z #ContextGraphs

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Unpopular opinion: The RL hype is a red flag for technical depth. When I hear founders or VCs casually dropping "reinforcement learning" in pitches, it's usually code for "we don't actually understand our problem space." Here's the reality check: RL only works when you have: - Perfect simulation environments - Extremely fast iteration cycles - Clear reward functions - Tolerance for massive compute costs That's why it succeeds in games (perfect simulation) and search optimization (Massive compute capacity at Deepmind AlphaGo/Anthropic/OpenAI) but fails spectacularly in the messy, slow-feedback real world. The graveyard of "RL-powered" startups tells the story. They burned through $$$$ trying to apply algorithms to problems that needed simple heuristics or supervised learning. Most of the time, a well-tuned evolutionary algorithm will outperform your fancy RL setup while using <1/10th the compute and delivering results you can actually explain to stakeholders. The next time someone pitches you an RL solution, ask them: "Have you tried solving this with a basic optimization approach first?" Watch how quickly the conversation changes. I am surprised when people are surprised that Direct Preference Optimization (DPO) can exceed PPO-based RLHF [1] or Reflective Prompt Evolution Can Outperform Reinforcement Learning [2]. [1] https://lnkd.in/gCY3bcTQ [2] https://lnkd.in/gQRwyQyM

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Y Combinator startups are proving that Claude Code is fundamentally changing how companies build, with non-technical founders shipping products and winning contracts that previously required entire engineering teams

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Terminal Use (YC W26): Vercel for background agents. Terminal Use is hosting infrastructure for background agents that makes harness engineering easy. https://lnkd.in/gv99wN2t

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Don't Sell Blood to Vampires. "Don't Build an RL Environment Startup. Reinforcement Learning environments are sandboxes for AI models to learn from. More and more startups are offering increasingly challenging environments under the assumption that machine learning is solved and the only remaining work is to provide models with simulations to learn from. The supply side is set to become increasingly crowded - it likely won't be a long-lived business." https://lnkd.in/gGZ6wTyR

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FurtherAI raised $25M Series A led by Andreessen Horowitz i joined as the first hire when we had no product, just conviction. we envisioned AI agents that could truly understand insurance - learning from every interaction and getting smarter within the platforms where real work happens. the journey hasn't been linear. we started automating submission intake - extracting key details from broker emails and PDFs, running eligibility checks 30x faster. policy comparisons with state-of-the-art accuracy. the feedback loop was intense: ship fast, learn faster, iterate relentlessly. customers loved the speed, but speed without simplicity is chaos. so we went back to first principles. every UI decision, every feature — distilled down to its essence. because complexity kills adoption. in a space moving this fast, momentum is the MOAT. while others debate AI's potential, we're shipping irreversible value. we're not just digitizing legacy systems - we're redefining how an entire industry works. this is just the beginning 🚀 cc: Nexus Venture Partners , South Park Commons, Y Combinator Converge VC, Pioneer Fund and others!

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DeepSeek’s rise has been wild to watch. From hedge fund roots to topping app store charts, all while running models cheaper and faster than the big names? That’s no small feat. Their focus on reasoning models is especially interesting and definitely shaking things up in the AI space. #AI #DeepLearning #Innovation #TechTrends #MachineLearning #Startups https://lnkd.in/dDfmM99D

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