Partner @Wing_VC; interested next-gen infrastructure, enterprise software applications, and security. Former Partner@Accel. Ex product guy @Splunk & @Cloudera

Joined December 2008
Anthropic shipped Claude Fable 5.1 today. It's their most robust model yet against prompt injection on an external benchmark, and @GraySwanAI ran automated adversarial testing against its safeguards before release. Prompt injection is the phishing of the AI era. You hide instructions in something an AI reads, a webpage, a doc, an inbound email, and it follows those instead of yours. There's no patch for it. The only defense is having someone competent attack the system first. That's some of what Gray Swan does, and it's why we co-led their Series A at Wing. The more interesting shift is that this is already moving past the labs. If you're running an open weight model, building your own agents, or rolling out Claude Code across your engineering org, you're in the same position the labs were two years ago, and on open weights there's no lab safety process upstream for you to inherit. The teams furthest along aren't waiting for a mandate. They're already adversarially testing before they deploy. Anthropic's post below.
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Intology's Locus post-trained a model that beats the official human-tuned Qwen3-1.7B release and it's already running in production for Bubble at 105x lower cost, no ML team involved. The signal that matters: it kept improving with more compute while every coding-agent baseline plateaued. If that scales, the moat isn't the model. It's who can turn compute into research.
The models are improving the models. Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model. Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇 PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours. We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model. In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.
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When Anthropic built a model powerful enough to restrict its own release, Gray Swan was named in the system card as a key external security research partner. Today @Wing_VC is co-leading @GraySwanAI 's $40M Series A alongside @MadronaVentures . 11 frontier model system cards. ARR >10x in two quarters. And founders who built the research before there was a market for it. Full take 👇 wing.vc/content/investing-in…
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Jake Flomenberg retweeted
Stop token maxxing 🛑 Our CEO @ashashutosh sat down with @a16z General Partner Peter Levine to discuss why we’re moving beyond the vector database: "85% of an agent’s work isn't the model; it's the underlying system. Why are we using human interfaces for machine-to-machine work?" The Pinecone Nexus Impact (Coding Agent → Agentic RAG → Pinecone Nexus): 📉 Tokens: 528,301 → 49,103 → 6,733 ⏱️ Latency: 84.1s → 37.9s → 22.7s 🎯 Accuracy: 0.585 → 0.413 → 0.680 Stop asking your LLM to do the heavy lifting of data curation. Use the knowledge engine to compile context on the fly. 🏗️ Listen to the full deep dive into Pinecone Nexus and the future of knowledge infrastructure for agents. 👂Spotify: open.spotify.com/episode/2RA… 👂Apple: podcasts.apple.com/us/podcas…
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This year's ET30 is stacked with AI infrastructure builders AI powered experiences. The list reflects where enterprise spending and adoption is actually going — not where it is coming from. Check it out.
The 2026 Enterprise Tech 30 is live! 🚀 60 companies shaping the future of enterprise. In partnership with @EricNewcomer at @NewcomerMedia See the full list: wing.vc/et30 #ET30
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The most accesible AI won't live in the cloud — it'll run on the device in your hand, your car, your hearing aid. Useful Sensors' new Moonshine Streaming hits 6.65% WER with just 245M params and 80ms lookahead — matching models 10x its size while running on sub-1GB edge hardware. No internet required.
New speech recognition models are announced on X almost every day nowadays. But not everyday you see a 250M parameter model beat the 1.5GB Whisper Large v3. Today we are announcing Moonshine Streaming. HF Link: huggingface.co/UsefulSensors… Paper draft: download.moonshine.ai/docs/m…
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Jake Flomenberg retweeted
Introducing Locus: the first AI system to outperform human experts at AI R&D Locus conducts research autonomously over multiple days and achieves superhuman results on RE-Bench given the same resources as humans, as well as SOTA performance on GPU kernel & ML engineering tasks. RE-Bench is a collection of several frontier AI research tasks that typically take human experts (e.g., top ML PhDs and frontier lab researchers) several days. By scaling experimentation to far longer time horizons than previous systems, Locus represents a step change in AI scientist capabilities. 🧵
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Jake Flomenberg retweeted
Incredibly fun conversation with @ScottWu46 of @cognition yesterday at our 2025 Wing Summit. I learned a lot from Scott and I'm pretty sure our audience of founders and senior corporate technology leaders (an interesting mix!) did too
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Which comes first: AGI or working AV systems?
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Yesterday we launched Founder Docs - wing.vc/docsguidebook - a guide being a startup founder with articles by founders for founders. Have a topic you'd like us to include? Let us know
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Jake Flomenberg retweeted
TruEra is going to join Snowflake! ❄️ We are excited to help drive #AIQuality at the #AIDataCloud leader. Read the announcement from the founders of TruEra: Anupam Datta, Shayak Sen, and Will Uppington. loom.ly/ZkSrjUQ The #TrustworthyAI journey continues!
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Jake Flomenberg retweeted
📢 The list is out! 📢 Meet the best in enterprise tech—presenting the 6th annual Enterprise Tech 30. See the report, and the list, at enterprisetech30.com #ET30
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Had a lot of fun interviewing Bratin Saha of @AWS, @myelbows of @streamlit, and @datta_cs of @truera_ai about LLM Observability this morning. Check out the RAG Triad and the Agent Quad frameworks for testing against hallucinations that we discussed.
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Jake Flomenberg retweeted
New short course on sophisticated RAG (Retrieval Augmented Generation) techniques is out! Taught by @jerryjliu0 and @datta_cs of @llama_index and @truera_ai , this teaches advanced techniques that help your LLM generate good answers. Topics include: - Sentence-window retrieval, which retrieves not just the most relevant sentence, but a window of sentences around it for higher quality context. - Auto-merging retrieval, which organizes your document into a hierarchical tree structure, where each parent node's text is split among its child nodes. Based on the relevance of the child nodes to a user query, this lets you better decide whether the entire parent node should be provided as context to the LLM. - Evaluation methodology for separately evaluating the quality of the key steps of RAG (context relevance, answer relevance, groundedness) so that you can perform error analysis, identify which part of your pipeline needs work, and tune components systematically. Please check out the course! deeplearning.ai/short-course…
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Excited to host @jerryjliu0 of @llama_index and @l2k of @wandb at Wing AI in SF Nov 27 to discuss best practices for getting LLM powered functionality into production and how to lay the groundwork for an enduring AI-first business lu.ma/u194xod6
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#SVB shared with me that they will continue to honor draws of venture debt and lines of credit. Am impressed by their actions to continue to support clients.
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Jake Flomenberg retweeted
We're so glad to welcome @SamParker14 as Wing's newest operating partner, supporting founder success. Welcome to Wing, Sam! wing.vc/content/wing-welcome…
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Is this how you think about ML model deployment? Then check out @truera_ai's new automated test harness for evaluating models. truera.com/diagnostics/
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