Brian

Product Security Leader | Investor | BJJ Black Belt 🥋

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[Brian @BrianLevinePM]

1600 findings reported to maintainers. 97 patched. The backlog continues to grow and clearly human capacity to resolve the issues cannot match the pace of discovery with the current approaches. We need mythos-class tools for all defenders in all layers of the stack and threat space, not just application code but in active detection and defense.

[Mitchell Hashimoto @mitchellh]

Fork your dependencies, trim them to only your use case, never update unless it breaks for your users. I’ve been vocal about this for 10+ years. I’ve always said that updating is way riskier than latent bugs (which can be tracked and CVEs monitored).

If you are updating a dependency, it’s on you to analyze every single commit in the full transitive set of dependencies. If you dont see anything compelling, dont update!

I remember at HashiCorp once in awhile an engineer would try to update a dep or replace a DIY lib with an external one and id always ask “show me the commit we need.” Dont update for the sake of it.

Feeling pretty swell about this mentality with all the supply chain attacks happening.

[Boris Cherny @bcherny]

Dogfooding Opus 4.7 the last few weeks, I've been feeling incredibly productive. Sharing a few tips to get more out of 4.7 🧵

[Ananay @ananayarora]

Marcus Hutchins, the guy famous for stopping the WannaCry Ransomware, probably has the best take on Mythos doing vulnerability research

[Brian @BrianLevinePM]

@DanielMiessler Looking forward to Insane mode!

[Jamieson O'Reilly @theonejvo]

https://t.co/Ng5pZytOgv

[Boris Cherny @bcherny]

I wanted to share a bunch of my favorite hidden and under-utilized features in Claude Code. I'll focus on the ones I use the most.

Here goes.

[Alfred Lin @Alfred_Lin]

A CEO from one of our portfolio companies shared this with their team. I’m re-sharing it with their permission, because it resonated and reflects what all founders and CEOs should be communicating.

--
We are living through a period of compounding change. And in moments like this, the biggest risk is no longer making the wrong decision. It is moving too slowly while the world moves around you.

There are two paths. We can play defense:
- Protect what we have
- Optimize what works
- Wait for clarity

It feels safe. It isn’t.

Or we can play offense:
- Learn faster than the environment changes
- Use new tools to solve old problems in better ways
- And create entirely new strategies and businesses

That’s where the opportunity is.

Challenge yourself to do things faster and better than you have ever attempted. Stay uncomfortable. Stay on the front foot.

[Brian @BrianLevinePM]

@dotta I need to try this.

[Codie Sanchez @Codie_Sanchez]

You eventually start to realize, no job is safe.

[Brian @BrianLevinePM]

Exactly the reason I've been scaffolding an Agent OS and an Agentic Enterprise OS.

BRIEF.md — defines a sprint or project goals, constraints, and "Done When" criteria. The system keeps iterating until Done is achieved.
Dynamic team planning — Orchestrator Archie reads BRIEF.md, scans available agent personas, and proposes the agent team for the project. Agents run headless with optionally segregated environment
Agent-to-agent messaging — agents coordinate via a local MCP message bus
Configurable — arch.yaml defines agent settings and optional pre-configured agent pool
Isolated git worktrees — agents work in parallel without filesystem conflicts
Human User or COO Agent Supervises - watch progress, answer escalations, and review results as work completes.

[Brian @BrianLevinePM]

"You can't hill-climb if you don't have a thing to hill-climb against." — @karpathy

Building AEOS around this — a living OS for an AI-native business. Ideal state + agent stack, closing the gap.

via @danielmiessler https://t.co/YkZKOC7atB

https://t.co/8yb3hQ5v98 + https://t.co/bHGFld2FIT

[Brian @BrianLevinePM]

@kchonyc >Only apply to tool-triggering instructions. Leave other uses of emphasis alone.

[Boris Cherny @bcherny]

🎁 Happy Friday - Opus 4.6 1M is now the default Opus model for Claude Code users on Max, Team, and Enterprise plans.

Pro and Sonnet users can opt in with /extra-usage.

[Brian @BrianLevinePM]

@CodeAkram @bcherny +1. very flaky so far testing in desktop browser and Claude mobile

[Brian @BrianLevinePM]

Everyone ships now.

[signüll @signulll]

the most underrated hire right now is a great product person.

when i say product person i'm def not talking about a product manager. perhaps i think there has to be somewhat of a new role. i don't have a good name for it yet but maybe something like "product thinker".. someone with an intuitive grasp of the product as it exists, where it's soft, where it sings, & how to iterate it toward something even sharper. in some sense, this person has to cohesively hold in their head where this product should be 2 years from now & work backwards from that.

i say this cuz when building was hard, engineering was the bottleneck & the status hierarchy often reflected that. building is no longer hard. which means the variance in outcomes has shifted almost entirely to judgment on what to build, how to sequence it, & how to talk about it.

& the story matters as much as the thing. internally, it organizes the team around a shared model of why. externally, it shapes the interpretive frame users bring to their first experience. you can't retrofit narrative onto a product & expect it to land, it has to be load bearing from the start.

the rarest version of this person sits at the intersection of culture & deep technology. someone genuinely bilingual. they know what's technically possible & they know which cultural currents are real vs. ephemeral. that combo is what separates products that feel inevitable from products that feel assembled.

before ppl clap back with this person has always been valuable, i know.. i am just saying now they might be the most *important* person in the room. their value compounds like never before.

[Brian @BrianLevinePM]

@heynavtoor "breaking"

[ᴅᴀɴɪᴇʟ ᴍɪᴇssʟᴇʀ 🛡️ @DanielMiessler]

https://t.co/YgsGdPRvAI