boldstart ventures

🔥 up to announce @boldstartvc Fund VII $250M to back bold technical founders building the autonomous enterprise. From Inception. Before the world believes. It always starts with an idea that feels insane… until it isn’t. 🎥👇🧵

Today, I'm excited to announce the industry's first proactive AI SRE agent with @grepr_ai. It works by finding novel behaviors in your environment and only asking an LLM to investigate those. By focusing on novel behaviors, we make applying LLMs on an entire stream of observability data possible. Read more: grepr.ai/blog/proactive…

39 startups are building alongside Vercel for six weeks. Backed by $8M in credits from AWS, Anthropic, OpenAI, ElevenLabs, and more, they have the resources to scale their infrastructure. Meet the 2026 Vercel AI Accelerator cohort. vercel.com/blog/2026-verc…

When @bling0 says the party goes on, rain or shine, he means it. Great time last night at @blingcapital @JoinAtomic @GVteam @Boldstartvc @stripe Miami Open Cocktail Mixer with so many of my favorite people. @chest @edsim

Happening now at #NVIDIAGTC: Generalist’s GEN-0 model autonomously packing phones on @Universal_Robot arms in our first public demo. To move robotics beyond the lab, systems need to operate in real time on industrial hardware. See the demo below, and stop by booth #1840 👇🤖

36.8 % of AI agent skills have security issues. That’s why we partnered with Snyk. Every skill in the Tessl Registry now has a Snyk security score and is scanned at publish, browse & install. Skills aren’t just code; they’re instructions that need a different security model. More in our blog. bit.ly/47PGQFH

Your agent is only as trustworthy as the environment it runs in. So today we launch something new with @NVIDIA. AI agents have gone from prompt-and-response tools to autonomous systems that run for hours, write their own code, build their own tools, and learn as they go. The OpenClaw project earlier this year made this concrete, self-evolving agents that plan complex tasks, generate their own tools, and run continuous workflows. We built CrewAI for exactly this. Long-running multi-agent systems. Persistent memory. A dual-layer architecture where Flows handle deterministic control, and Crews handle reasoning. Developers get precise control over how much autonomy each part of the system gets. But here's what keeps coming up with enterprise teams. When an agent can install packages, write files, and generate its own tools, it can also do things you didn't plan for. Most agents inherit the full permissions of whoever launched them. Security checks are usually built inside the agent — so a self-evolving agent could, in theory, work around its own guardrails. This is the trust gap. The real reason most enterprise agent projects don't make it to production. CrewAI addresses a lot of this at the orchestration layer: guardrails, human-in-the-loop, and hierarchical task scoping. But orchestration alone can't close the full gap. You also need enforcement at the infrastructure level, below the agent, where the agent can't reach. That's why we're working with NVIDIA on NemoClaw. NVIDIA NemoClaw is an open-source stack that simplifies running OpenClaw always-on assistants safely, with a single command. It includes the NVIDIA OpenShell Runtime with three core capabilities: A sandbox for isolated execution — agents operate freely without affecting the host. A policy engine that evaluates every action at the binary, destination, and network level. A privacy router that directs inference to local or external models based on your enterprise policies. The critical design choice: enforcement happens at the infrastructure layer, not inside the agent's code. Even if an agent's logic changes unexpectedly, the runtime blocks anything that violates policy. Agents start with zero permissions. Every escalation requires human approval. Every decision gets logged. CrewAI handles orchestration. NemoClaw handles the secure runtime. Together, organizations can run powerful autonomous agents while maintaining real control over their infrastructure and data. We've powered roughly 2 billion agentic executions over the past year and work with more than 60% of the Fortune 500. NemoClaw's infrastructure layer closes the gap between what these agents can do and what enterprises need to trust them in production.

Our new product, @Clay Ads, is officially live! This might be the most electric product-market fit we've ever seen. I met with 8 prospects last week and they couldn't get enough. In just two months, our team used Clay Ads to cut LinkedIn CPL from $250 to $25. Here's how it works: Exclusion lists that auto-sync to Salesforce so ads only reach people who can actually buy. If you’re an existing customer, open opp, or partner, you won’t see our ads. This is by design! Meta became a real B2B channel for us too. Work emails rarely match personal profiles, so most B2B teams skip it entirely. Clay enriches contacts with personal emails before audiences ever hit the platform. 60%+ match rates. One recent campaign generated 200 leads at $10 each within 24 hours. And the audiences stay fresh on their own. When someone becomes a customer, they stop seeing your ads the next day. When sales shifts priorities, targeting shifts with them. This all happens automatically -- across both platforms. And no CSV exports or manual rebuilds required! Customers like Slack, Anthropic and Rippling are already hitting 90%+ match rates on LinkedIn and 60%+ on Meta. That is 2-4x what they were getting before. This is a massive win for marketing teams. Learn more here: clay.com/ads

We're excited about the results from @baz_scm. Because they're newer, their sample size is smaller (i.e. more noise in measurement). Taking their results at face value, they are #1 in precision, #2 in F_1 score, and #1 in F_0.5 score. It's an impressive David & Goliath story for the Israeli company -- enough that our @joshgreaves_ml made it the default in our ARES repo