Amir Kabir 🤓

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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**The Rise of Inference Startups: Unlocking the Future of AI** Inferact’s recent $150M seed round, positioning them at an impressive $800 million valuation, underscores a pivotal trend in AI development: the commercialization of vLLM technology. At ElevenX Capital, we recognize the potential of such advancements to transform industries by accelerating inference speeds and reducing costs. What are your thoughts on the long-term impact of inference technology on investment strategies in the AI sector? #investing #innovation #venturecapital #entrepreneurship

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𝗪𝗵𝘆 𝘄𝗲 𝗯𝗮𝗰𝗸𝗲𝗱 𝘁𝗵𝗲 "𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺" 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗜 𝗖𝗹𝗼𝘂𝗱. When we first met the team at hosted·ai, one stat blew us away: AI workloads often consume only 40% of the GPU capacity they are paying for. In the Gold Rush of 2026, everyone is selling shovels. But hosted.ai is building the automated machinery that makes those shovels work 24/7. They announced their Series A to scale their mission of making GPU infrastructure simple, affordable, and—most importantly—profitable for providers outside the "Big Three" hyperscalers. It’s rare to find a team with this much "scar tissue" in infrastructure (25+ years of experience) tackling the most pressing bottleneck in tech today. Onward and upward! 📈 #Startups #Founders #AI #GPUaaS #InvestmentNews z21 Ventures Raj Shekhar Singh Jyotika Gupta Sudarshan Ravi Udit Batish Carl Fritjofsson

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HARPETH VENTURES ANNOUNCES INVESTMENT IN VENTEUR Harpeth Ventures Opportunity Fund (“Harpeth Ventures”) is pleased to announce its participation in Venteur’s recent $20 million Series A funding round. The round was led by Informed Ventures and American Family Ventures, with participation from Morgan Health, Ingeborg Investments, Catalyst by Wellstar, Plug and Play, Revelry Venture Partners, Houghton Street Ventures, and Techstars. San Francisco-based Venteur is the leading healthcare benefits technology company transforming how employers offer and manage health insurance through Individual Coverage Health Reimbursement Arrangements (ICHRAs). Venteur’s platform empowers employers to offer flexible, personalized health benefits by enabling employees to select individual plans that fit their unique needs. Through a combination of technology and concierge support, Venteur helps individuals navigate their healthcare options while driving improved outcomes and greater cost efficiency for employers. More information is available at Venteur.com. “We greatly appreciate Harpeth Ventures’ involvement from both a capital and relationship perspective,” said Stacy Edgar, Co-Founder and CEO of Venteur. “The Harpeth team has already been instrumental in several business development opportunities.” Added Chuck Byrge of Harpeth Ventures, “With our relationship-driven approach, we were fortunate to meet Stacy in early 2024 and have followed their growth closely. We couldn’t be more excited about their future and the tailwinds driving ICHRA adoption.”

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This a seductive idea; that organization's context fragments, decision logic gets lost, and an organization is prone to tackle collective amnesia. BUT this comes with a lot of caveats!! - MONOCULTURE: When one system becomes the authoritative interpreter of "why things are the way they are", it will collapse pluralism. An organization actually benefits from various "contested memories". An engineer and sales guy remember different things from a discussion and that helps. Employees can stop arguing with the system not because its correct but its "coherent". - OBSERVER's EFFECT: When people know that the reasoning within systems is being captured, they will change what gets recorded. The negotiations will become "safer" and will contaminate raw signals. - CENTRALIZATION: Interpretation gets centralized upwards. It may appear leadership or executives are gaining synthesis and becoming closer to the ground. But there is no comparison to living through the frictions, not even eclectic summaries. LEADERSHIP MUSCLE NEEDS TO PRACTICE LIVING THROUGH FRICTION OTHERWISE IT WILL TURN SENILE. - BIAS AMPLIFICATION: Model's inbuilt biases can combine with company's cultural quirks resulting in misinterpretations and distortions, worse they get reinforced over time. System will learn how the company thinks and then will optimize for continuity. AMNESIA IS SOMETIME A FEATURE. - SEMANTIC TRUTHS: Unlike objective truths this system will play on subjectivity and will build "causal" relationships that are based on semantics. This over time can form mythical decision norms with no external validity. The real question isn't "CAN IT BE BUILT?". Of course it can be. But "HOW IT SHOULD BE POSITIONSED". #llms #leadership #ai

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With RL techniques like GRPO, it’s become easy to fine-tune small models to outperform foundation models on vertical tasks. We’re open sourcing Osmosis-Apply-1.7B: a small model that merges code (similar to Cursor’s instant apply) better than foundation models. Links in comments! Using foundation models for high specificity, low complexity tasks like code merging can be overkill - instead, it’s better to use a specialized model instead for higher performance and latency. We fine-tuned the model on 100K real-world code commits where the model needs to merge a code edit snippet into an original code snippet. On the test dataset (subset of CommitPackFT), foundation model performance ranged between 0.77-0.93 reward score. In comparison, Osmosis-Apply-1.7B achieved a 0.98 reward score - while also being 3X-5X more affordable than the next cheaper model AND ~10X faster! Why this matters: the best AI agents use multiple models to ensure task specialization and reliability at scale - on top of cost and latency benefits as well. Links to the model and full write-up (with reward function, hyperparameters, etc.) are in the comments - reach out if you’re interested in using reinforcement learning!

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"Build the event you wish existed" That's what we at Monadical did last week at #NYTW. We wanted an AI Engineers discussion for Engineers. There's always a lot of events out there for VCs to network, or for startups to learn about G2M, but very little on lessons learned from actual engineers in the field. So that's the event we hosted. Big thank yous to our awesome panel, Roy Pereira, Ben Cohen and Corey J. Gallon. Here's the key takeaways and the AI tools we're using. 🚀 All three panelists independently called AI Agents the most transformative LLM application they’ve used. They specifically called out Claude 3.5 Sonnet for its accuracy and reliability. 🪨 We dug into how LLMs are “jagged”, not general. They can be shockingly good at some tasks and completely fail at others. Everyone agreed: good evaluations are critical (and hard.) 🧪 Corey noted how public benchmarks and reality are two different things. Most public evals are saturated or gamed. ♊ Ben emphasized that AI projects are actually two projects: building the tool and building the evaluation process. 🧱 We explored how falling dev costs may impact startup defensibility and labor demand. Roy shared that founders are already shifting strategies in response. ⚒️ In a world of daily AI launches, the panel discussed how they decide what’s worth attention, and what’s just noise. They called out tools like Goose, Aider, Claude Code, and Monadical’s own Cubbi, which helps run agentic workflows safely in dev environments. (links in the comments). CTA: What would you want to hear in an AI Applied Engineering talk you attended?

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