Dan Abelon

I’m thrilled to officially announce our $3M seed investment in Propel People, a company we built inside the 1848 Ventures studio to tackle one of the biggest challenges facing the #construction industry today: the skilled #labor shortage. For small and medium-sized contractors, hiring isn't just a challenge. It's the number one threat to their growth, profitability, and safety. With 94% of contractors struggling to find qualified workers, it’s clear that traditional hiring methods aren't built for the trades. That’s why we built Propel People. It’s a mobile-first, AI-powered hiring platform designed for how construction actually works: in the field. By leveraging smart candidate ranking, instant #SMS-based screening, and a fully #bilingual interface, Propel helps contractors build great crews faster and more efficiently. I’m also thrilled to formally announce that industry veteran Dexter Bachelder is at the helm as CEO. Having worked with Dexter and the team over the last few months, we've already seen the impact of his leadership. His 25 years of experience scaling construction tech companies will be instrumental as Propel People enters this initial stage of growth. This investment reinforces our core thesis at 1848 Ventures: building AI-native companies that solve fundamental pain points for the #SMBs that form the backbone of our economy. A huge congratulations to Dexter and the entire Propel People team on this milestone. We are incredibly proud to partner with you to support the people who build our world. Read the full announcement below. #constructiontech #venturecapital #seedfunding #ai #skilledtrades #smb

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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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I’m thrilled to announce defy.vc’s investment in Birches Health as part of their $20M combined Seed and Series A funding round, alongside incredible partners like AlleyCorp, General Catalyst, Will Ventures, and others. As someone deeply passionate about backing companies that tackle important problems for our society, I couldn’t be more excited about what Birches is building—a national virtual clinic dedicated to treating gambling addiction and related behavioral disorders like gaming, sex, porn, and internet addictions. What excites me most is Birches’ innovative health assurance model: proactive, accessible, and affordable care that’s evidence-based and delivered right at home. With the explosion of legal sports betting—think nearly $1.5 billion wagered on this year’s Super Bowl alone—we’re staring down a looming public health emergency. Traditional options like hotlines or generic therapy fall short for this high-acuity issue, but Birches changes that by offering specialized therapy from trained clinicians, multimodal treatment, and peer support, all covered by major insurers like United Healthcare, Cigna, Aetna, and Blue Cross Blue Shield. It’s available in all 50 states, breaking down geographical and financial barriers that have left so many without help. The impact is already profound: 96% of patients report an improved quality of life, and 94% give Birches a 5/5 rating. By partnering with state governments, payors, and policymakers, Birches is pushing gambling addiction into the spotlight of behavioral health conversations, where it’s been neglected for too long—often leading to shame, worsened outcomes, and higher suicidality risks. This funding will supercharge their growth: expanding the provider network, boosting clinical excellence, scaling infrastructure, and deepening insurance and state partnerships to meet surging demand. Very excited to partner with Elliott Rapaport and the entire Birches team! See more about our POV in the blog post linked in the comments. #GamblingAddiction #BehavioralHealth #HealthTech #VentureCapital #Innovation

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Every #VentureStudio GP knows some portfolio bets won’t hit. That's expected - and it's one of the reasons studios are such a powerful vehicle for innovation. And while a venture's failure might be what's visible to the outsider, what keeps them up at night isn’t failure at the venture level — it’s failure at the systemic level within the studio. One bad entity design. One IP misstep. One governance gap. That’s how studio operations fall apart. We break down how to spot (and fix) these cracks before they turn into fault lines in our latest paper, "The GP Dilemma" - DM me and I'll send you a copy!

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30+ VCs Backing Marketplace Startups Below is a cross-stage, cross-sector mix of firms that back marketplace and platform companies early and support them as they grow. Consumer and platform investors Upfront Ventures 1752vc (Formerly Pegasus) BAM Ventures Forerunner Brand Foundry Ventures Maveron BoxGroup Greycroft Alumni Ventures Marketplace natives and network effects specialists FJ Labs NFX Great Oaks Venture Capital RiverPark Ventures Left Lane Capital First Round Capital Lerer Hippeau Precursor Ventures Seed partners building two-sided platforms early Founder Collective NextView Ventures Uncork Capital Slow Ventures Bling Capital Gaingels Initialized Capital Felicis Multi-stage firms backing marketplace winners at scale Accel Bessemer Venture Partners Lightspeed General Catalyst New Enterprise Associates (NEA) Kleiner Perkins Bain Capital Ventures (BCV) Founders Fund

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While there has never been a more exciting time to be a founder building dev tooling or next-gen infra, it has also never been less investable at seed/pre-seed. I'm either really missing something or a lot of my peers are lost. As someone who has not just written, but also SHIPPED, about 75k lines of code in the past 6 months I can tell you that the evolution of how to build products has changed as much in the past year as it did in the entirety of 2007-2017. The complete rise and fail of frameworks, platforms, methodologies, etc... paved over and forgotten... that is of course except for the 1 company that gets a 1000x return from a wildly overvalued hyper-scaler or drunken growth stage investor obsessed with compounding at scale. Imagine a world where any seed investor in trends like Openstack, Hadoop, PaaS, etc all took a full loss on their investment. That's what we're looking at right now. I personally know of over a dozen well-funded seed-stage companies building in these spaces, with years of runway, scrambling to get acquired for a return of capital + several million personally while they're still relevant. If you're not seeing this unfold in front of you, you either aren't paying attention or you're satisfied playing the lottery instead of investing.

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founders that have only ever worked within a large company tend to be empty vessels. the most valuable skill at a big company is identifying who has (or could soon have) power and then aligning with and advocating for their ideas as if they were your own. take a spin through the recent YC class and you see a pattern of big company mids all founding the same AI for xyz... its not really their idea, it just sounds good. the board meetings will be especially painful. founders acting like they work for the investors, seeking approval, lost in the woods. there are exceptions. but they tend to be founded before zirp. when founding a company was hard and low prestige and there wasn't capital at every turn. an empty vessel doesn't jump into the abyss without first raising a $5m round from the 9 figure 'seed firms'. 

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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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