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We’ve taken one small step towards robot police officers: a drone capable of disarming a suspect: In a June 22 video posted on the Sacramento County Sheriff’s Office’s Instagram page, an officer wearing goggles can be seen operating a drone to retrieve a knife from an armed suspect hiding inside a cluttered house. “After not responding to negotiators, a drone was deployed inside the residence,” the post says. “Drone pilots located the suspect hiding in a corner of a garage” and then used a high-powered magnet attached to the drone to grab the knife out of the suspect’s hand. In the video ­ which is soundtracked by the “Mission: Impossible” theme song—the intercepted knife can be seen spinning around in the air as the drone carries it back to the deputies...

Flexion Robotics, a startup founded by ex-Nvidia engineers, has a clever way of training robots to do useful work.

Aseon Labs, which came out of Y Combinator's 2026 spring cohort, has raised $10 million from Crane Venture Partners and others.

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

General Intuition has raised $320 million to scale AI trained on millions of hours of gameplay, betting action data can help AI develop something closer to human intuition.

The Department of Transportation wants to remove the brake-pedal requirement for vehicles "designed to be driven exclusively by automated driving systems."

Farday Future hasn't quite given up on EVs, but it's now also pitching a lineup of robots, including humanoids and a quadruped with an optional canine heads.

"Operation Endgame" simultaneously disrupts two widely used crime tools.

Agility Robotics, the humanoid robotics startup that spun out of Oregon State University in 2015, expects to generate $620 million in proceeds.

The new Zoox robotaxi has more cushioning, lighter colors, and a better microphone and speaker for communicating with Zoox Support.

The first time I wired Dependabot up to a private package registry, it took three meetings and a calendar reminder set six months ahead. The reminder was for the personal access token I had to mint to do it, the one I would have to rotate by hand before it expired, living in a config that drifted somewhere between repo settings, a .github file, and an internal wiki page nobody had touched in a year. On June 23 GitHub posted a small changelog item that quietly retires that whole ritual: Dependabot can now read your private GitHub Packages and GHCR registries through its own GITHUB_TOKEN , no PAT required. If you have ever had to explain to a security review why your bot account holds a token your team owns but a person minted, you already know why this lands. The PAT we all had and nobody loved Until this week, Dependabot's path to a private GitHub-hosted package was the same path it took years ago. A human minted a personal access token, scoped wide enough to read the registry, stored it as a repo or org secret, and plugged it into Dependabot so the bot could authenticate when it tried to resolve dependencies. That token expired on a human schedule. It belonged to whoever happened to set Dependabot up, which is rarely the person who still owns the repo a year later. You can feel how that ends. Tokens that quietly expire and break a Dependabot PR the day someone needs the patch. Tokens that follow an offboarded engineer out the door. Tokens with scopes wider than the job actually requires, because nobody wanted to mint a fine-grained one per registry path. None of those failures are catastrophic on their own. All of them are the kind of papercut a platform team ends up owning. What the GITHUB_TOKEN does here The new behavior is straightforward. Dependabot's job-scoped GITHUB_TOKEN can now request a packages: read permission. When the bot pulls from a hostname under *.pkg.github.com or ghcr.io , it sends that token instead of asking for a PAT. The same token that alre

Summer is for relaxing, not cleaning. Upgrade your backyard setup with a robot that cleans your pool for you.

US autoworkers union warns of robot automation as dark factory future looms.

Shinkei makes a refrigerator-sized robot called Poseidon to kill fish quickly and humanely.

Figure says its F.02 robot "contributed to the production of 30,000+ X3 vehicles" at BMW's plant in Spartanburg, South Carolina. Loaded 90,000-plus sheet metal parts. Logged 1,250-plus hours on a live assembly line. After ten years of stage demos and treadmill walks, that is a real number from a real factory, and it deserves to be read carefully. So here is the part most coverage skipped: that robot has been retired. The headline numbers are real Two of the loudest names in the field finally stopped quoting choreography and started quoting line output. Figure's Spartanburg run hit greater than 99% placement success per shift on a 37-second load cycle, ten-hour shifts, five days a week, all on the chassis assembly line. Tesla, separately, says more than 1,000 Optimus units were already working its Fremont floor in January 2026, doing battery assembly, pack loading, cable routing and parts handling, with a dedicated line targeting 100,000 to 300,000 units this year per The Robot Report. I want to be clear that this is genuinely new. A fixed pick-and-place task, run for months on a production line at automotive takt, with a placement success number you can audit, is not a demo. It is the first time the category has produced metrics an operations lead can actually argue about. Take the capability seriously. The trouble starts the moment you treat the capability number as an availability number. The footnote that inverts the headline The single most important sentence in Figure's announcement is the one about retirement. F.02 "return[ed] to HQ from BMW as part of our fleet-wide retirement" once Figure 03 launched. So the 30,000-car figure is the lifetime output of a pilot that has ended, not the running rate of a station that still exists. As of now there are no Figure robots on the Spartanburg line. BMW's own June 2026 material reads the same way once you stop skimming. The company frames its next move as a new pilot at Plant Leipzig in Germany starting summer 2026, wit

PSYONIC's prosthetic touch data is now training ABB robots. Gatik signed the first Fortune 50 commercial autonomous freight contract with PepsiCo. Burro drove Physical AI onto the construction site. Experts set $20k as the humanoid price target. And someone just called Edge AI the Windows of robotics. This week, Physical AI crossed three invisible lines at once. A company that makes prosthetic hands figured out that the touch data from amputees is exactly what industrial robots need to learn how to grip. A Fortune 50 company signed not a pilot but a commercial contract for autonomous freight. A 44-horsepower robot drove off the warehouse floor and onto the construction site. And two separate conversations about software and pricing suggest that the next wave of robotics adoption will be driven by access, not capability. Here is what happened, and why it matters beyond the headlines. Value Description Fortune 50 PepsiCo becomes first to sign a commercial contract for autonomous freight with Gatik $20k Target price point for humanoid robots, Robotics Summit consensus: achievable by 2028–2030 1M hours Burro's field experience backing the Grande 44 autonomous outdoor platform 100+ Pressure sensors per fingertip in PSYONIC's Ability Hand, now training ABB GoFa A Prosthetic Hand Is Now Teaching an Industrial Robot How to Grip The standard approach to teaching a robot how to handle objects has been simulation, teleoperation, or labor-intensive physical demonstrations. PSYONIC and ABB just introduced a different source of data : the hands of people who have already learned to feel again. PSYONIC's Ability Hand is a prosthetic with more than 100 pressure sensors per fingertip . The company has been collecting kinesthetic data from users with upper-limb amputations. That data, which captures how a human hand adjusts grip pressure, contact area, and force across thousands of everyday tasks, is now being fed as training data into ABB GoFa robot arm models. The implication is no

Karamo Brown, famous for his pep talks on Netflix’s “Queer Eye,” has jumped into the wellness and AI space with his new app, Kē. After spending a year and a half focusing on his own journey—from fitness and nutrition to meditation, sobriety, relationships, and personal growth—Brown wants to help others do the same. Kē offers […]

The company has identified at least 13 instances where its robotaxis drove into highway sections closed for construction.

A trader I was talking to recently said something that stuck with me: "I've blown accounts just from slow fills or missed order cancellations." He was talking about CEX perpetuals. But the problem is identical on Polymarket's CLOB - just measured in seconds instead of milliseconds. My TypeScript bot was averaging 340ms from signal detection to order placement on Polymarket's Central Limit Order Book. On a 5-minute market with a ~2.7-second mispricing window, that's 12% of the entire opportunity window consumed before a single byte hits Polymarket's servers. I was consistently entering at 74¢ when I'd detected the signal at 70¢. The market had already repriced against me. So I rewrote it in Rust. This article documents exactly what I found, what changed, and - critically - what didn't. Background: What My Bot Was Doing If you've read my earlier posts in this series ( architecture , Kelly Criterion sizing , last-60-seconds capture ), you know the context. But the short version: The bot targets Polymarket's 5-minute and 15-minute crypto up/down binary markets (BTC, ETH, XRP, SOL, DOGE, BNB). The strategy is simple: find markets that are briefly mispriced relative to real-time spot momentum, enter at a discount to fair value, hold to resolution. A 5-minute "XRP Up" market priced at 70¢ when spot momentum suggests 82% probability = +12¢ edge per dollar wagered. Do that 50 times a day with disciplined sizing and the math works - if you can actually get filled at the price you detected. The problem: by the time my TypeScript code detected the signal, formatted the order, opened an HTTP connection to Polymarket's CLOB API, waited for TLS handshake, serialized the payload, and received confirmation, the market had often moved to 74-76¢. I was paying for an edge I wasn't capturing. Profiling the TypeScript Bot: Where Was the 340ms Going? Before rewriting anything, I instrumented every stage of the order path. Here's what I found across 500 sampled trades: Stage Average time %

The former Sequoia Capital leader is filling an "existing vacancy" on SpaceX's board, days after the company went public in the largest IPO ever.