investing @insightpartners | I used to write a blog & interview some cool people @_thetakeoff

New York, NY
Joined February 2013
👀 @convex
Had to call an All Hands this morning to inform the team that 25th place is the 24th loser. (jk this was unexpectedly cool)
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Michael Spiro retweeted
Today we’re launching the Fastino Training API for supervised fine-tuning and reinforcement learning. The Fastino Training API includes day-zero support for @NVIDIAAI Nemotron 3.5 Super VL, alongside Nemotron 3.5 Lightning. Our API gives ML and research teams low-level control over post-training without requiring them to provision GPUs or operate separate training and generation systems. A few highlights: • Write your own SFT and RL loops in Python • Train with importance sampling, PPO, CISPO, or DRO • Run grouped sampling with token log probabilities • Save and resume adapter and optimizer checkpoints • Pay per token processed, not per GPU hour • Spend 25% to 85% less than Tinker’s published rates • Migrate existing Tinker workloads by changing only the server address and API key Along with the Training API, we’re releasing two start-of-the-art open weight models fine-tuned on NVIDIA Nemotron 3.5 Super VL, specialized for finance and healthcare. Together, they improved performance across all 15 held-out benchmarks we measured. And for a limited time, we’re offering discounted pricing on training for NVIDIA Nemotron 3.5 Super VL through the Fastino Training API. Try it now: agent.fastino.ai/
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Last week, we brought together 40+ founders, builders, and enterprise leaders for a data and AI dinner in NYC. Lots of interesting conversations & discussion points -- a few that stood out: - the latest advances across models, harnesses, data infrastructure, security, etc. - how the most innovative companies are leveraging LLMs and agents for internal and customer-facing use cases. - gaps, blockers, and opportunities in enterprise deployment and in bringing AI into both individual lives and large organizations at further scale. - what’s ahead for AI and how we get there, safely. Always great to bring together so many friends and new faces to talk about all that is happening in data and AI, and the many opportunities ahead. Thank you to everyone who joined and made it such a wonderful evening.
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this is pretty awesome — powered by @droid
how I scaled my design workflow for a global billboard campaign that is live in NYC, Tokyo, London, and cities around the globe using coding agents in @FactoryAI 🧵🧵🧵 this thread is focused on using coding agents to reformat design assets for dozens of billboard panels that are often grouped, cropped, and placed in unexpected ways and less about designing the original motion graphics concept (which was created using a combination of generative AI video footage, and overlay graphics created with code / figma). Before coding agents, formatting all of these panels to spec would take hours / days of non-creative work...
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Fun to hear @co_valenta talk about the founding story behind @e2b. - 18-year relationship with co-founder @mlejva - 100+ projects together over the years - Early insight that AI agents would need a platform to run on, before agents were really a thing --> hence, E2B - Conviction to move from Prague to SF in the early days to go all in
Ever wondered how @co_valenta and I started @e2b? A quick founding story how we got started by @co_valenta
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Congratulations to the entire @ElisityInc team on their $1.3b acquisition by @IonQ_Inc! Thank you for the trust you placed in us @insightpartners to lead your Series B and for the opportunity to partner on this incredible journey.
IonQ announced it has agreed to acquire @ElisityInc to accelerate zero trust for the quantum era. The acquisition brings together IonQ’s post-quantum cryptography (PQC) and quantum key distribution (QKD) capabilities with Elisity's enterprise Zero Trust technology platform. Enterprises face two overlapping priorities: containing attacks that operate at machine speed and preparing to protect sensitive information against future quantum threats. A recent GAO report (files.gao.gov/reports/GAO-27…) found that none of the 24 federal agencies it audited had fully addressed selected post-quantum cryptography practices, while Juniper Research (juniperresearch.com/press/po…) projects that only 27% of global enterprises will be quantum-ready by 2035. Together, these trends underscore the need to prepare now for the quantum era. IonQ will add an expanded cybersecurity platform that uncovers and protects against both present LLM and emerging quantum threats. “With Elisity and its team as part of IonQ, we will be well positioned to help customers protect critical infrastructure and enterprise networks throughout the transition to the quantum era. Elisity will bring proven enterprise policy enforcement scale and deployment experience to our quantum security strategy and will give IonQ a strong growth platform to serve customers,” said IonQ’s Chairman and CEO @NiccoloDeMasi. Read our news here: ionq.com/news/ionq-to-acquir… #QuantumIsNow #QuantumCyberSecurity
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Awesome to see -- @e2b x @convex Component.
Wake up agents, there's a new @e2b @convex sandbox component. A tableless Convex component that provides a V8-compatible E2B client for managing persistent cloud sandboxes inside Convex actions. Details: convex.dev/components/e2b/co…
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Today, we’re expanding @SnorkelAI’s Open Benchmarks Grants by 10x to a $30M commitment: - Funding a more diverse, robust, and continuously-updated ecosystem of open benchmarks - Launching the Open Benchmarks Red Team to continually test and strengthen them - Introducing the Snorkel Research Fellowship to support independent contributors developing new evaluation methods Since our launch earlier this year, we’ve been humbled to partner with the teams behind Terminal-Bench, TB-Science, ARC-AGI-3, OSWorld 2.0, Agents’ Last Exam, Continual Learning Bench, Senior SWE-Bench, SlopCodeBench, and more. Alongside these teams, we’ve helped design evaluation methodologies, develop task construction pipelines, and scale quality control. OBG-funded benchmarks have appeared on the latest model cards from every major frontier lab; have helped to index and measure frontier progress and alignment; and have contributed to guiding the frontier of AI development. Benchmarks have always been guideposts for—and drivers of— AI progress: setting a metric and then “hillclimbing” against it is the core of how AI works! But when benchmarks fall behind frontier capabilities, our ability to evaluate and align these systems does as well. They become too simple, static, or correlated, and the risk of “benchmaxxing” increases. We need more diverse, robust, and continuously-updated benchmarks, developed by a broader ecosystem of researchers and domain leaders. We’re incredibly excited to accelerate frontier evaluation with Open Benchmarks Grants— raising the ambition and investment in robust, open, and independent measurement. Read our full announcement and apply here: benchmarks.snorkel.ai/fronti…
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Michael Spiro retweeted
Factory is a product company. In a year when a lot of smart money is betting on neoservices that is starting to sound contrarian. The AI wave makes it tempting to sell outcomes and deliver them with people. We think the bigger opportunity is software that gets better every time it's used. That's what we build for our customers, and it's how we run Factory. The products we build should help our customers build their own capacity to ship better software, faster. We think of the product as everything from the first time you hear about Factory through your Nth renewal. Building the right product starts with the old advice: listen to your customers' problems, not their solutions. In 2023, many people believed we should have built autocomplete. We understood that we instead had to build toward self-improving software and software factories, since that is what would come next. That is our product goal: self-improving software, which continuously improves from the data feedback loop of its users. Self-improving software doesn't happen on its own. You have to be data-backed and optimize the signal → deploy loop. That requires a software factory operating continuously and evolving alongside the software it produces. The factory also has to know what good looks like. It has to help the organization extract its tacit knowledge and embed it into the software that operates the business. Taste is not sufficient. You must enforce the unique decisions that define your product. And it doesn't stop at the product. The entire company is going to be defined by software that listens to the rhythm of the business and enforces it. The clear goal of a leader is to ensure the quality and correctness of these systems. We run Factory this way. Our unique product and technical decisions are codified as customizations to our harness, automations, CI/CD, lint rules, etc. Factory agents sit in our shared Slack channels, where they build context and execute tactical work on demand. It also changes how we organize. Your Engineering, Product, and Design team will ship your org chart, so you must design an organization where incentives are maximally aligned. Our pod structure maps directly to our product taxonomy, which gives each pod deep expertise and agency. We work carefully to make sure the product feels cohesive and not like multiple disconnected teams. This is not easy, and we still haven't perfected it. We keep the minimal number of layers between leaders and the organizational lines. 1:25 is not unreasonable for a true people manager. Our product operating system is itself self-improving software. It aggregates feedback, tracks launches across the team, and organizes our work. With it, our Head of Product @TaylorTheSavage runs a product surface that would otherwise require an army of PMs with just a handful of high-ownership, high-leverage ones. When an org is designed this way, its impact scales with software, not headcount. That is the real difference between a product company and a services company. Selling outcomes is right. Delivering them with people is services. Neoservices arbitrage the gap between how fast the technology moves and how fast buyers adopt it, and some will become large businesses. But that gap closes every time a product company sells the same buyer a better long-term solution. The market already knows how to value services: Infosys does about $20B in revenue and trades at roughly 2x, not 50x, and it's down about 40% this year. Being a product company is harder. @danlovesproofs once told me something I very much agree with. Product companies are like movie studios. You have to keep producing blockbusters, and that helps you build talent, brand, and resources. Gone are the days when you could coast on one hit. Do that, and you'll be acquired by Bending Spoons at a fraction of your value. The war is always being waged, and no winner is ever truly ahead by much. And demand for the movies is up. That's true even against the model labs. There is a value gap between the models and outcomes for users. Anyone can close that gap, and there's no reason to believe any company has an automatic right to win the products that matter most. If anything, access to the full pareto frontier of cost and quality gives model-independent, product-focused companies a focus, cost, and quality advantage. If someone asked me what to build today, I'd say go build a product company. It is harder, but the biggest outcomes are only possible through product.
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Michael Spiro retweeted
Today's a big day for Stuut: we just raised a $52.5M Series B led by Insight Partners for our autonomous agent that handles the work between order and cash for the world's leading enterprises. $93M total raised, and we're just getting started. This round is all about our customers. We're building on overwhelming demand for our AI coworker that automates all of the work between order and cash for the world's leading enterprises. It's grinding, repetitive work, and Stuut takes it on so our customers can focus on the judgment calls. We’ve already moved over $3B for enterprises including ZoomInfo, Honeywell Technologies, and PerkinElmer, with Stuut unlocking 40% more cash flow for our customers. You'll be hearing more of their stories over the next week We're growing fast, and if you want to join a rocket ship, now's the time. Take a look at our open roles here: jobs.ashbyhq.com/stuut-ai Special thanks to Julian Marcu / Thomas Krane at @InsightPartners for believing in our vision. We're extremely grateful to @a16z, @ActivantCapital, and @M12vc Microsoft's Venture Fund, for joining. Above all else, thanks to our customers for trusting us. Their finance teams are thinking years ahead, thinking about what they'll be able to accomplish when AI owns a large part of the process. We'll be with them every step of the way. Read more below!
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Michael Spiro retweeted
Great day to deliver shareholder value :) Use Factory to automate the low-leverage work and building becomes so much more fun. And the auto-model router means I don't need to remember which of the 10 models that came out this week is best.
Custom Automations are now GA in Factory. Describe a recurring workflow, pick a schedule or event trigger, and Droid runs it to the intended result. You choose the model, the machine, and Connectors for each automation.
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Michael Spiro retweeted
Introducing GLiDE, the first decision model that thinks. Ranks #1 on the Decision Index, beating Jev by 6.9 points. GLiDE uses adaptive thinking. To give you an idea of how it works: It calculates a fast probability distribution. If the leading answer is uncertain, reasoning is turned on. Reasoning is then incorporated into the final probabilities. This means that the model only uses reasoning capabilities for the complex decisions that need them, keeping the model fast while also expanding the types of tasks it can do. As a result, GLiDE outperforms Jev by 11.5 points in Knowledge and Reasoning and across all five evaluation areas of the Decision Index. Reasoning allows GLiDE to perform highly complex tasks that traditional decision models struggle with, including: Choosing among hundreds of tools and possible paths. Executing, sandboxing, escalating, or blocking sensitive operations. Checking outputs involving arithmetic, code, dates, or multiple facts. GLiDE is available now through the Fastino API. Use code USE_GLIDE for free credits. GLiDE API: agent.fastino.ai
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Michael Spiro retweeted
Introducing E2B Embed: it brings AI agent sandboxes directly into your product and run them on infrastructure you control. E2B Embed packages the full E2B stack on a single node, giving you the flexibility to deploy in your own environment or a customer’s. Open source under Apache 2.0, it supports Docker Compose, Terraform on GCP, and Kubernetes. All with the same E2B SDK, CLI, and API. youtube.com/watch?v=w-7eAIZC…
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Try out GLiNER2.5-Decide on @fastinoAI! agent.fastino.ai/auth
Classification is only one step in the decision pipeline. GLiNER2.5-Decide pairs classification with information extraction, so each decision draws on the relevant context in the input. At 340M parameters, our model handles typed decisions, extraction, relations, and structured records while matching the performance of LLMs at a fraction of the cost and latency. For those who want the benefits of a compact, flexible model without the work of managing it locally, GLiNER2.5-Decide is available for inference and training through the Fastino API. Access the API via the Fastino platform: agent.fastino.ai/auth
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Congratulations @fastinoAI on launching GLiNER2.5-Decide -- a new 340M parameter open weight, encoder-based decision model. Try it out: agent.fastino.ai/auth.
Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning structured decisions with probability distributions and confidence scores. We evaluated the model’s performance on Fast Decisions, an unseen, internally generated classification suite based on 17 datasets testing real-world use cases across routing, triage, classification, sentiment, and content understanding. Measured against similar decision models, GLiNER2.5-Decide leads in 9 of the 17 datasets, achieving the highest average score: - GLiNER2.5-Decide: 60.1% - SemIf: 56.4% - JevK5: 57.5% - Laya: 46.6% This performance makes the model a strong fit for use cases like tool calling, model routing, browser and computer use, and LLM-as-a-judge. GLiNER2.5-Decide’s lightweight encoder architecture makes it easy to fine-tune the model for specific tasks, while being efficient enough to run locally on consumer-grade CPUs or in air-gapped environments, giving users greater control over where their data goes and where the model runs. To make building and experimenting with GLiNER2.5-Decide as easy as possible, we're also offering hosted inference. You can now use our API to run inference and fine-tune GLiNER models on specialized tasks right inside your own coding agent: agent.fastino.ai As with previous models, we’re also releasing the model weights on @huggingface under the Apache 2.0 license: huggingface.co/fastino/GLiNE…
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If you love deeply technical infra work, @convex is the place to be. They are hiring across a number of roles -- see openings here: jobs.ashbyhq.com/convex-dev
And @convex is still up 10x YoY. Up ~30% this month alone. If you’re elite at critical systems and scaling, want to reinvent how software is made, and build the best infrastructure in the world… Please DM me. Our plans are insane and we need talented + audacious people.
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“As AI systems take on longer-running, higher-stakes work, the data needs from frontier AI labs and enterprises are shifting from generalist data that humans can write down to specialized data, environments, and evaluations that often need to be built.” In the below post, @lonnej and I share some thoughts about our (@insightpartners's) recent investment in @SnorkelAI and what got us so excited to partner with @ajratner and the Snorkel AI team as they build the data lab for “Data 2.0”. insightpartners.com/ideas/be…
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Excited for @insightpartners to co-lead @SnorkelAI's Series E! Snorkel AI is building the data factory for "Data 2.0", and they're just getting started. Congrats @ajratner and team on this milestone! If you want to join the Snorkel AI team as they continue to help push the frontier of data / AI, check out current openings: snorkel.ai/join-us/
I'm excited to announce @SnorkelAI's $350M Series E at $3.5B, led by @insightpartners and @S32_VC. We've grown 18x+ in the last 12 months since launching our Data-as-a-Service offering, passing $375M ARRR this week. As AI advances to superhuman capabilities, AI data & environment development must advance with it - and basic staffing and crowdsourcing approaches are not enough. AI progress now requires deep research and technology work that combines human expertise with specialized AI in compounding ways. @SnorkelAI is building the RSI data engine and frontier data lab for this next phase. We're honored to have the support of existing investors Addition, @lightspeedvp, @GreylockVC, @GVteam, P7, Factory, @WellsFargo, Walden Catalyst Ventures, and new investors @ThirdPointLLC, @MarchCPs, @BlumbergCapital, @AllegisCapital, @Frontlinevc, and @standard_vc. – @SnorkelAI started as a research project a decade ago at @StanfordAILab. Our thesis was simple: AI progress would become increasingly data-centric – and therefore data development should be studied as a true research and technology problem, not just a staffing and crowdsourcing one. Today, as AI capabilities verge on superhuman, building the data and environments to safely measure and train AI is becoming too hard for even the smartest human experts to do alone. Only humans and AI agents, collaborating together in compounding ways, can meet the accelerating needs of the frontier, and keep humans in the driver’s seat of AI progress for decades to come. At @SnorkelAI, we are building the data lab to define the shape of this new “Data 2.0” frontier, and the new paradigms of human-computer interaction needed to advance it. Our key focus is building the RSI engine for data, where specialized AI models accelerate and improve human expert output, and in turn, scaled human supervision is used to continuously evaluate and improve these models – creating a powerful compounding loop to keep pace with an accelerating RSI frontier. With this round of funding, we are also doubling down on our commitments to support data development for open benchmarking and evaluation (more news here soon!); an increasingly diverse ecosystem of general and specialized intelligence; and a path to safe, well-aligned AI built on robust training and evaluation data. Data development will guide and drive the next stages of AI – and must do so in a human-centric, AI accelerated, open, diverse, and safe way. We are excited to support this mission in the next decade of research ahead at @SnorkelAI. More thoughts here: snorkel.ai/blog/data-2-0-and…
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Michael Spiro retweeted
No @convex is NOT "just a Postgress wrapper". explained.convex.dev/build-y… To prove it I thought it would be fun if we tried to build a database with Convex's properties from scratch and see where we get to.
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