Luke Slevin

Hoosier 🏀 | AI + people + process | Scaling PE-backed companies

[Brian Armstrong @brian_armstrong]

How to keep AI spend flat while token usage grows exponentially: Not with friction and spend alerts. With better defaults, routing, and caching.

Better Defaults (not Usage Caps) – Engineers can choose any model they want, but defaults matter. We’re experimenting with defaulting to open weight models like GLM 5.2 and Kimi 2.7 through our LLM gateway, while still encouraging engineers to choose the right model for the task. 91% of our employees were never hitting their usage caps, so instead of lowering caps and driving up alerts, we're moving to cheaper defaults. Note that code reviews use a diversity of models, so they can check each other's work.

Better Routing – In our custom harnesses, we preprocess prompts and route to the best model for the job, considering cache hits and model pricing. For instance, you may want a frontier model for planning, but not for execution where they can be overkill. Ultimately, humans shouldn't be choosing models - AI can automate this task.

Better Caching – Cache misses are the easiest way to drive your cost up. All of our requests are cache aware, so we’re reusing a warm cache wherever possible. For example, our cache hit rate went from 5% → 60% in LibreChat once properly implemented.

Keep Context Lean – Start fresh sessions when switching tasks. Scope file context narrowly. Disconnect unused tools. Don't just compact. The goal isn't fewer tokens used, it's fewer tokens wasted.

Better Visibility – Our engineers can use as many tokens as they want, from whatever model they want, but we’ve made usage visible – and the more you spend on AI, the more impact we expect.

The goal isn't to suppress usage. It's to build the infrastructure that makes exponential growth sustainable.

Putting this into practice has cut our AI spend nearly in half, while our token usage continues to grow.

[Satya Nadella @satyanadella]

https://t.co/vLmiBKTtX3

[nascarcasm @nascarcasm]

We're not stopping with the Bears. We're heading to Cincinnati tomorrow to see how the Batesville Bengals sounds to them. We are Indiana. We are a football state. We are unstoppable.

[BuBBliK @k1rallik]

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS

Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal.

The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code.

- Every API resource is a subcommand: messages, models, files, agents, sessions
- Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command
- Spin up a session, send it an event, then pull every event and tool call back from the same CLI
- Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code

Agents just stopped being prompts you babysit and became infrastructure you deploy.

[Marlene Mhangami @marlene_zw]

I genuinely thought no one was recording😂 Shocked to see this online, but here's the workshop from yesterday's Code with Claude event!

[vas @vasuman]

https://t.co/PetMluobip

[Aaron Levie @levie]

Right now there’s a temporary mismatch between the jobs that used to be sought after in some fields and the new jobs that are becoming in demand in those fields.

For instance, if you studied CS, for years the general direction of travel was often to join a tech company and build customer-facing software in some form. A significant portion of the CS pipeline from college to hire was built for this.

When you realize that AI is going to make coding abundant, you realize everyone will need technical talent to implement agentic systems. This means the types of roles engineers should be thinking about radically expands.

I was talking to a Fortune 500 pharma CEO a week ago that commented on how much more technical talent they need right now. The job may be different from what it was 5 years ago when thinking about tech, but the demand for the skills are still there. And this is what I’m hearing from every CIO and CEO across nearly every industry right now.

We definitely need colleges to wake up to this; but we equally need companies think about how they craft pipelines into these jobs.

[Matt Braynard @MattBraynard]

We're so back.

An entrepreneur is restoring Pizza Huts to their former glory.

[0xMarioNawfal @RoundtableSpace]

GITHUB JUST CERTIFIED THE AI JOB OF THE FUTURE

* GitHub launched GH-600 for “Agentic AI Developers” managing autonomous AI workflows
* Focuses on supervising agents across coding, CI/CD, automation, and production systems
* Signals that AI agent operations are becoming a real engineering discipline with formal credentials

Link: https://t.co/BIZ3cnuL8B

[Aaron Levie @levie]

Whether it’s existing consulting firms, new ones that emerge, FDEs from agent vendors, or new internal agent engineering roles, the amount of work that is going to be created to implement agents in enterprises will exceed anything we imagine today.

The complexity of implementing agents in any existing organizations is very real. When I talk to large enterprises, as you move from a chat paradigm to agents that participate in meaningful workflows, there are a number of things they need to do.

First, you have to get agents to be able to talk to your data securely across your systems. In many cases, enterprises have decades of legacy infrastructure that contain the valuable context for AI agents. That’s going to take a ton of work to go modernize and move to systems that work well with agents.

Then, you need to ensure that you’ve implemented agents with the right access controls and entitlements, the right scopes to be safely used, and have ways of monitoring, logging, and securing the work that they do.

Next, you need to actually document the processes in the organization in a way that agents can utilize for doing the work. You also need to figure out what the new workflow looks like when agents and people are working together on a process, and who steps in where. Just replicating the old workflow will mute the gains. Oh and you likely need to create evals for your top new end-state processes.

Finally, you have to keep up with a rapidly changing set of best practices and architectural shifts happening in the agent space. While it’s fun for people to change their personal productivity tools on a dime, it’s 100X harder to do this in a business process. The speed of change is a blessing and a curse right now for anyone trying to keep a stable system design.

All of this means that individuals and companies that develop expertise on the above set of components (and more) are going to be needed to help organizations actually implement agents at scale. This is also the rationale for vertical AI agents right now that can go in deep on a business domain and help bring automation to it.

This is a huge opportunity right now whether you’re doing this internally or as an external business provider.

[George Kurdin @GeorgeKurdin]

Get paid fast

On average, customers see 40% lower DSO, 24% higher collections response rate and 25+ hours/month saved.

[rvivek @rvivek]

The hottest job for the next five years is going to be the agent operator.

They don't need to be an engineer. They can walk into marketing, legal, or life sciences research and actually make agents work for that function.

Required skills:
> MCPs
> CLIs
> Writing skills (the file kind)
> agents.md fluency
> Business acumen

None of this is in any CS curriculum today.

Soon, enterprises will be pressured to redesign their workflows for agents, not for people. And when that happens, agent operators will be in massive demand.

[Aaron Levie @levie]

If you read this and don’t understand why it’s happening it’s an opportunity to reset your understanding of how the real world works.

The real world will need a ton of help actually getting agents going in the enterprise. Companies have legacy tech stacks they need to modernize, data in tons of fragmented tools, knowledge that isn’t captured or digitized, and change management needed to actually utilize agents effectively. And they have to do all this while still running their business day-to-day, unlike startups.

This is why there is so much opportunity for companies (software or services) to actually deploy agents in specific domains and workflows. This remains a big opportunity for both existing services providers but also tons of new startups as well. Every new technology wave produces a new era of consulting firms that can deliver on that technology.

It’s also why the FDE model is going to be alive and well for a long time because companies will want to have their vendor actually help drive the change management and implementation for their new workflows.

The people aren’t going away. Far from it.

[ClaudeDevs @ClaudeDevs]

New blog: Building agents that reach production systems with MCP.

When should agents use direct APIs vs CLIs vs MCP? Plus patterns for building MCP servers, context-efficient clients and pairing MCP with skills.

https://t.co/Q4UrUVgVYB

[Luke Slevin @Luke_Slevin]

Very impressed with this so far and the design systems it can create quickly

[Claude @claudeai]

Introducing Claude Design by Anthropic Labs: make prototypes, slides, and one-pagers by talking to Claude.

Powered by Claude Opus 4.7, our most capable vision model. Available in research preview on the Pro, Max, Team, and Enterprise plans, rolling out throughout the day.

[Aaron Levie @levie]

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise.

Some quick takeaways:

* Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow.

* Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated.

* Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs).

* Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these.

* Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs.

* Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy.

* Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems.

* Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been.

One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise.

This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

[Anthropic @AnthropicAI]

Mythos Preview has already found thousands of high-severity vulnerabilities—including some in every major operating system and web browser.

[Collin Rugg @CollinRugg]

NEW: The CIA used a secret tool called "Ghost Murmur" that uses AI to find heartbeats to rescue the U.S. airman who was stranded in Iran, according to the New York Post.

The secret technology was allegedly used for the first time in the field, according to the Post.

"The secret technology uses long-range quantum magnetometry to find the electromagnetic fingerprint of a human heartbeat and pairs the data with artificial intelligence software to isolate the signature from background noise," the Post reported.

"It’s like hearing a voice in a stadium, except the stadium is a thousand square miles of desert," the source said.

"In the right conditions, if your heart is beating, we will find you."

"The name is deliberate. ‘Murmur’ is a clinical term for a heart rhythm. ‘Ghost’ refers to finding someone who, for all practical purposes, has disappeared..."

"Advances in a field known as quantum magnetometry, specifically sensors built around microscopic defects in synthetic diamonds, have apparently made it possible to detect these signals at dramatically greater distances."

CIA Director John Ratcliffe appeared to hint at this technology on Monday, saying the CIA possessed "unique capabilities" but said he couldn't "tell you everything that you want to know."

President Trump also revealed during the press conference that the CIA spotted the officer from about "40 miles away."

Insane.