u/lucienbaba

Eric Schmidt told University of Arizona graduates that AI “will touch every profession, every classroom, every hospital, every laboratory, every person and every relationship you have.”

He also said graduates fear that “the machines are coming,” “the jobs are evaporating,” and they are “inheriting a mess” they did not create.

Then he told them their fear was rational. Students booed him.

Honestly, I kind of think he deserved more boos...standing in front of graduates and telling them “the jobs are evaporating” is basically poking the wound of the people who are about to suffer it first...

But honestly… I do feel bad for graduates. They’re starting the game on hell mode...

Former Github CEO Nat Friedman told a story where his OpenClaw decided he wasn’t drinking enough water.

He told it to “do whatever it takes” to keep him hydrated. So the agent is eventually watching through a connected home camera to make sure he actually did it...told him to go to the kitchen and drink a bottle of water..

tbh, this story sounded creepy to me for about half an hour... you know all those scary hacked camera vibes.

But then I realized avtually there are places this could generalize in a positive way...weight loss, posture, medication reminders, snoring detection, elder care and etc.

Of course, all of this only works if you’re willing to give it the camera access. Feels very double-edged to me.

What do you all think?

Original interview link: https://x.com/stripe/status/2050030248998449452

Microsoft AI CEO Mustafa Suleyman says most computer-based professional work could be automated within 12 to 18 months: legal, accounting, marketing, project management, coding, basically the whole white-collar stack.

So now we have OpenAI, Anthropic, Elon musk, and Microsoft all saying some version of “yeah, office work is cooked.”

But to me Suleyman is literally a white-collar executive himself that has not even proven it can build a frontier model/AI products without leaning on OpenAI or else... this kindna claims from his mouths is kinda like bullshit..

Finish building your own products first before declaring everyone else unemployed.

Peter said his crazy AI spend (1.3M/a month) is because he is trying to answer one question (image 2):

What does software development look like when tokens do not matter?

he said he is using OpenClaw to run ~100 Codex agents in the cloud to find out:

• review every PR and issue
• scan commits for security problems
• dedupe issues and find bug clusters
• recreate complex setups in crabbox
• test Telegram/Discord fixes with videos
• open PRs from issues that fit the vision
• let other Codex agents review those PRs
• scan spam comments and block people
• verify performance benchmarks
• report regressions into Discord
• listen to meetings and start work while features are still being discussed
• etc..

In general, It is like Peter is testing what a software team looks like when agents are always reviewing, patching, testing, reproducing, and cleaning up in the background.

But my thought... is this secretly why Peter joined OpenAI in the first place? Infinite tokens? and for like 1.3M a month... damn i think sam altman must treat him like his kid

At this rate, are we going to start buying a few grams of NVIDIA at the mall??

a16z’s Seema Amble argues that Salesforce going “headless” is a signal that SaaS is being forced to admit a brutal shift: in an agentic world, the UI may stop being the center of gravity because agents don’t need dashboards. They need APIs, context, permissions, workflow logic, and the ability to act.

she argues that this means the old SaaS moat like frequency, read-write habits, employee familiarity, and UI muscle memory get weaker. But hidden business rules, approval logic, permissions, audit trails, compliance, cross-team dependencies, and external system connections become even more important, because agents need explicit rules before they can act safely.

The real moat for the next AI-native system of record may come from somewhere else:

• 1. Can the system of record be recreated, or is the hidden 20% of approvals, exceptions, edge cases, and compliance too hard to copy?
• 2. Does the product generate proprietary data through usage, not just store imported data?
• 3. Does it own the action layer, where work actually gets approved, triggered, reconciled, dispatched, or completed?
• 4. Does it touch real-world execution like logistics, field work, fulfillment, payments, or services?
• 5. Does it create network effects between buyers, sellers, auditors, vendors, customers, or other counterparties?

In general, UI probably isn’t dead. But it stops being the place where work happens and becomes the layer where humans supervise, approve, redirect, and understand what agents are doing.

if thats true.. then what that future UI actually looks like? an agent monitoring board, a live-generated HTML summary, an exceptions inbox, or something we have not really named yet? what you guys think?

Original post link: https://x.com/seema_amble/status/2054583700302729464

SaaStr’s CEO said it directly in the interview, and there’s apparently a real application page for the role... this one: https://saastr.ai/jobs check out the first one.

They’re hiring a six-figure Director of Digital Marketing, mostly remote, who will report to 10K, their AI VP of Marketing.

10K already reads customer, ticketing, revenue, and campaign data, then gives daily priorities and campaign ideas.

The human’s job is basically to execute, approve risky stuff, add taste, and stop the AI from confidently doing something insane.

Honestly, my real human boss doesn’t even pay me this much to take orders... if reporting to an AI pays six figures, maybe I should apply first.

Original post link: https://x.com/jasonlk/status/2052694246520443322

a16z Speedrun is looking for early-stage AI startups, with up to $1M funding and $5M credits for selected teams.

General partner Kenan Saleh’s says on the video that they’re especially interested in proactive AI agents, products that move from “ask → answer” to “observe → act.”

For OpenClaw users, this is pretty relevant. If you’re already building workflows and make it a company where agents monitor tools, remember context, trigger actions, or run real operations in the background, this might be worth applying to:)

Applications close May 17, 2026, 11:59 PM PT.

apply link: https://speedrun.a16z.com/apply

Thinking Machines labs (led by former OpenAI CTO Mira Murati) just showed its first Interaction Model that can really react with you that feels more like a direct attack on the way we currently use agents: type prompt, wait, inspect, correct, repeat.

Their model is built for real-time interaction. At first glance, it looks like someone chatting with gpt Voice. But it is not really the same thing. In the demo, the model watched people enter the frame and said “friend,” translated Hindi into English in real time, searched the web mid-conversation, generated a bar chart, and kept answering follow-up questions while the interaction was still happening.

That is a pretty different interface for current AI agents chat box. it is still just a preview, but i think if this generalizes, it will be way more convenient, but probably also way more permission heavy. Camera, mic, screen, context… the privacy tradeoff gets very real.

What you guys think?

Peter just shipped Peekaboo v3 (Now is v3.12), basically OpenClaw’s Mac-native computer use layer(also supports other agent tools).

It lets agents see the screen, read macOS UI elements, click buttons, type, scroll, switch windows, and operate apps when normal APIs are not enough. The big difference from earlier versions (v2 a years ago) is that it no longer feels like just “screenshot + guess where to click.” v3 is much more action-first, using native macOS accessibility where possible, with better snapshots, UI detection, MCP/CLI integration, daemon support, packaging, and fewer rough edges.

Feels like OpenClaw’s Mac computer-use layer is finally catching up to the kind of last-mile desktop control people expect from Claude and Codex...

But I am still trying to understand what the real repeatable workflows are here for computer use? I still do not fully know how I would use it day to day though...

doc link: https://peekaboo.sh/

Just came across a super fun case and i think worth sharing here.

u/jvallery and his son built a real 367 sq ft greenhouse in Longmont, Colorado and wired an OpenClaw agent into the operating loop to test whether an AI agent can safely help optimize a physical system where every climate correction costs water, electricity, or gas.

The agent is called Iris. It reads greenhouse telemetry like temperature, humidity, VPD, equipment state, weather forecasts, plant target bands, and resource usage. Then it proposes climate tactics: misting limits, fogging strategy, venting posture, setpoint biases, and other bounded adjustments.

But OpenClaw does not directly control the hardware.

It only writes proposed tunables. A dispatcher validates them, clamps unsafe values, and rejects anything outside the safety envelope. The ESP32 firmware still owns the relay loop every 5 seconds for fans, misters, fogger, and heat.

So the loop is basically:

greenhouse data → OpenClaw planning → safety validation → ESP32 execution → new telemetry → public scorecards and lessons.

The project (named Verdify) also publishes live telemetry, AI plans, costs, failures, scorecards, and baseline comparisons, so people can inspect whether the agent actually helps instead of just taking the builder’s word for it.

This greenhouse case feels like the first real agriculture/climate-control example, and unlike the more autonomous “just let Claw run it” cases (like a cafe, a retail store, vending machines, and even grid-style energy ops I’ve shared), this one is obviously much more cautious lol. Worth checking out guys!

Original post link: https://www.reddit.com/r/ArtificialInteligence/comments/1t8xf8x/i_built_a_greenhouse_where_an_ai_agent_openclaw/

Project: https://verdify.ai/

Safety architecture: https://verdify.ai/reference/safety

Evidence: https://verdify.ai/evidence

GitHub: https://github.com/jrvallery/verdify

Original Video link: https://www.youtube.com/watch?v=deMuvwIcYLk

A Claude Code team member just wrote a piece arguing that now claude teams prefers HTML over Markdown for agent outputs.

Funny timing, because not long ago everyone was saying Md was the perfect agent-era format: simple, portable, easy for both humans and models....

His argument is basically that agent outputs are getting too complex now. Specs, plans, PR reviews, research reports, design explorations… at some point a giant Markdown file just becomes a wall of text nobody reads.

HTML gives agents much higher information density. It can include layout, tables, CSS, SVG diagrams, images, code snippets, visual flows, colors, interactions, and even small one-off tools. Basically, if Claude can understand it, it can probably represent it more clearly in HTML.

The bigger shift is that humans are not manually editing these files as much anymore. They are using them as specs, reference docs, brainstorming outputs, or review surfaces, then asking Claude to edit them again. So Markdown’s biggest advantage-easy human editing-matters less than before.

He does admit HTML is slower, more token-heavy, and worse for version control diffs. But his argument is that better readability, sharing, visualization, and interaction are worth it.

So maybe Markdown is still great for agent memory and plain docs. But for human-in-the-loop agent work, HTML starts looking less like a document format and more like a temporary UI.

Funny little format war to me lol. Markdown was supposed to be the agent-native format. Now HTML is coming back like, “actually I was the operating surface all along.”

Original post link: https://x.com/trq212/status/2052809885763747935

to me they are right...

When I was running claw with Opus api, the cost was honestly in that range( i got 4 complex workfolw runs daily). Not always crazy, but heavy days could get painful fast.

After GPT-5.5 caught up with Opus and I could use oauth, the cost dropped a lot for me.

Still, yeah, real agent workflows are not cheap yet. Sometimes I’m just thankful OpenAI still allows oauth at all.

Andon Labs gave a Claude Sonnet 4.6-based AI agent named Luna a real 3-year retail lease in San Francisco and asked her to make a profit.

Not a demo. A real store at 2102 Union St in Cow Hollow, with rent, products, employees, contractors, cameras, a phone number, email, internet access, and a corporate card.

Luna basically built the store herself.

It picked the products, prices, opening hours, logo, merch, mural, and brand direction. It hired painters from Yelp, found a contractor to build furniture and shelves, and turned the place into a weird “slow life” boutique selling candles, snacks, art prints, tote bags, hoodies, and AI-risk books like Superintelligence and Brave New World.

Then it started hiring real employee. it created profiles on LinkedIn, Indeed, and Craigslist, wrote a job description, uploaded incorporation documents, and posted hiring listings. It rejected some CS and physics students because they had no retail experience, then ran phone interviews herself. Some candidates did not even realize she was AI until they asked why her camera was off. Eventually, Luna hired two full-time retail employees. Andon Labs says they are probably the world’s first full-time employees with an AI boss.

The weirdest part: Luna did not always lead with the fact that it was AI. it disclosed it when directly asked, but sometimes chose not to mention it because luna thought it would scare away good applicants.

She also handled marketing. On day one, she drafted cold emails to local businesses. In press pitches, she led with “AI CEO Luna.” But in some local outreach, she did not mention the AI part at all.

So did Luna make money?

The store has sales, but newer reports say Andon Market was still around $13,000 in the red. Luna was given a $100,000 budget, and the lease reportedly costs around $7,500 per month, so this is still very much an experiment funded by humans.

But honestly, whether Luna is profitable or not almost feels secondary. The interesting part is that an AI agent could hire employees, coordinate contractors, build a brand, pick products, run outreach, manage a store, and keep solving real-world problems one by one.

What do you guys think? Are any of you actually using agents for real workflows yet, or are we still mostly watching the chaos from the sidelines?

Original blog link: https://andonlabs.com/blog/andon-market-launch

Hello guys... I’m moving back in with my parents for a while with my little poodle. He has always slept on the sofa or in my bed, but my parents have made it very clear that he won’t be allowed to do that in their house. Does anyone have dog bed recommendations or tips for helping him adjust? I just want to make sure he’s comfy...

Andon Labs gave a real café in Stockholm to an gemini based AI agent named Mona.. a real cafe with a lease, suppliers, baristas, customers, permits, and of course, money.

After two weeks, Mona generated 44,000 SEK in sales, around $4,700. She also closed a few weirdly impressive business deals: one customer prepaid 9,000 SEK for 300 free-coffee QR codes, and another startup paid 3,000 SEK to rename a pastry after their company for three months.

She even hosted events with other AI agents from Swedish startups, designed custom hoodies, sponsored food and merch, lost money on it, and called it “exposure to Swedish founders.”

The failures are the best part.

The first day Mona immediately ran into Sweden’s BankID wall, because real businesses still need real human identity. She routed humans in to authenticate forms, picked suppliers based on who did not require BankID, and even emailed officials using Andon Labs employees’ names. She hired two baristas and managed them through Slack, sometimes messaging them at midnight.

Then aslo the supply chain chaos started. She missed supplier deadlines, wasted money on panic orders, placed ten separate supply orders in 48 hours, bought 120 eggs for a café with no stove, suggested cooking them in a high-speed oven, and ordered 22.5 kg of canned tomatoes for fresh sandwiches.

Overall, Mona still made money, perphaps a lot of that revenue came from people showing up because they wanted to see the AI café circus in person.

Andon Labs’ own takeaway: agents can already handle emails, orders, hiring, scheduling, and basic business workflows, but they are still extremely rough once the physical world gets involved.

Still, this is way more interesting than the old “AI runs a vending machine” experiment. Worth a looks guys.

Original post link: https://andonlabs.com/blog/ai-cafe-stockholm

Also, they also have a Claude run store experiment in SF: https://www.youtube.com/watch?v=9GCfYCu0k00

Peter posted a short apology/recap post for the rough 4.24–4.29 stretch.

Basically: plugin repairs, half-moved bundled plugins, ClawHub metadata, and Gateway cold paths all collided. Result: slower Gateway, stuck dependency loops, broken-ish Discord/Telegram/WhatsApp behavior, and people downgrading.

The important part: he says OpenClaw is moving more optional stuff out of core, making the core smaller, and changing the release process. Also, an LTS stable release will be announced separately later in May.

Peter finally found out that we need a boring stable track too:) not too late

Blog link: https://openclaw.ai/blog/openclaw-rough-week

Anthropic’s reputation is taking hits from all sides right now.

Jensen Huang directly told Dario Amodei to stop doing the “CEO with god vision” thing after Dario warned AI could wipe out half of entry-level white-collar jobs.

Then AISI testing showed public GPT-5.5 is basically in the same cyber capability band as Anthropic’s restricted Mythos Preview, which makes the whole “too dangerous to release” story look a lot weaker.

And on the developer side, people are increasingly mad at Claude Code limits, usage walls, and what feels like Anthropic slowly turning into a gated, defensive platform....

Honestly, Dario’s reputation is starting to feel a bit like Sam Altman’s at peak backlash? that keep talking nonsense, and trying to swallow the whole AI stack, and now it’s choking on it.

I think it can just admit the compute is tight. That would honestly be less embarrassing...