A collection of upcoming CFPs (call for papers) from across the internet and around the world.

The post Call For Papers Listings for 6/26 appeared first on Leon Adato.

Job postings that came across my desk, slack, email, discord, etc this week.

The post Job listings for week ending 6/26 appeared first on Leon Adato.

OpenAI previews GPT-5.6 Sol, a next-generation model with stronger capabilities in coding, science, and cybersecurity, paired with its most advanced safety stack.

When I joined GitHub, my legal name was Ursula—but my handle was gleeblezoid. Now, as Arthur, I’m still gleeblezoid.

Since our remote-first culture primarily uses handles, transitioning at GitHub was easier than it would have been earlier in my career. I previously worked in IT at companies that only used names for identification, which can be challenging for professionals transitioning.

I started my career doing IT support and operational work, but being interested in computers meant teaching myself how to code. A colleague from a previous role referred me to GitHub, and I started out five years ago on the IT Engineering team. After repeatedly bothering various security teams with issues and pull requests, I got adopted into the Enterprise Security team after just six months. I’ve been there ever since.

While here, I’ve been proud to work with my team on migrating our main SaaS platform to infrastructure as code, and to be a guest speaker a handful of times at Oxford University on the subject of version control.

But another great thing about working here: GitHub offers gender affirming care related benefits to all employees in terms of covering healthcare; I can expense voice training, HRT prescriptions, and therapy among other things.

There are also less obvious things that made GitHub a safe place for me to transition. Being a remote-first company means I don’t need to agonize over what to wear to the office or who will see me on the way there. Most of my work is captured in writing within Slack or GitHub itself, so when I started voice training and eventually having my voice break on HRT I wasn’t spending the whole day talking to people out loud.

We have the kind of culture where my main avatar can be a cartoon frog in a suit and nobody bats an eye, which removes the entire problem of people guessing my gender by my appearance.

I know a lot of people in the tech industry, and more trans people than the average person probably does. I know people who are closeted at work, people who go through bureaucratic nightmares on changing their name, and people for whom coming out at work is something they end up doing on a recurring basis with every new set of people they interact with.

I’ve not experienced that. Outside of the understandable bureaucratic friction of changing my name in places like payroll it’s been smooth. My team call me what I want to be called and treat me like a regular human being—as has everyone else at work I’ve interacted with. I updated my name and pronouns on our internal systems, and that was that.

Being trans isn’t easy or universally accepted. I’m not sure when I’ll next get to see my overseas teammates in person, for example. It’s also not an experience solely defined by hardship or social barriers. There is a great deal of joy in showing up as yourself and in sharing that joy with others. I nearly cried on a Zoom call when I heard someone use my name and refer to me as “him” for the first time at work, and I absolutely did cry when one of my teammates sent me a shaving kit in the mail.

Every hubber I have mentioned my transition to has been genuinely happy for me. Several of them expressed this through Arthur the aardvark, and Monty Python Holy Grail memes (we are geeks after all).

I’ve always been a man, I just needed time and support to live as one and participate as one in society. In most settings I need to explain to people how Ursula Searle became Arthur Searle, but at GitHub I’ve thankfully always been gleeblezoid.

The post Transitioning as a hubber appeared first on The GitHub Blog.

This week host and Turing Post founder Ksenia Se threaded the latest news into a single argument: AI is moving out of conversation and into the operational loops where real work happens. From SpaceX’s $60 billion acquisition in the developer tools market to the G7’s debate about frontier model access to image generation company Midjourney’s pivot to medical hardware, the stories all pointed in the same direction.

When agents own the loop, the IDE becomes infrastructure

SpaceX’s acquisition of Anysphere, the company behind Cursor, for a reported $60 billion in stock is the kind of deal that looks straightforward until you think about what Cursor actually is. On the surface, it’s a popular AI-assisted code editor. (It’s also one of many in a highly competitive market.) However, Ksenia argued that that’s thinking too small, especially for Elon Musk. SpaceX may be angling to position Cursor as the new center of software work, in the same way GitHub became the center of the previous era.

In the old model, GitHub owned the pull request. But in the new model, the question of who owns the full loop where agents read a repo, write code, open pull requests, run tests, handle failures, and enforce engineering standards is still open. GitHub still owns the system of record and is moving to defend it: Chief product officer Mario Rodriguez recently told Turing Post that GitHub’s mission has shifted from human-developer collaboration to developer-and-agent collaboration, with the platform becoming agent-native across its APIs, UX, and underlying infrastructure. But as Ksenia explained, “Cursor’s advantage is that it owns the developer’s active coding surface” where the work starts.

If agents write more code than humans, software infrastructure should be redesigned around agents from the start. Cursor was built for agents. GitHub was built for humans and is now playing catch-up. That architectural choice may matter more than any individual product feature.

Frontier AI access is becoming a geopolitical question

The G7 summit this week included discussions about a “trusted partners” framework that would give select allied nations access to advanced US AI models, following a US order that restricted foreign nationals from accessing Anthropic’s frontier systems on national security grounds. AI models that can write software, find vulnerabilities, and operate across tools are capability systems, not just productivity software. The access rules are catching up to that reality, although as Ksenia noted, things haven’t yet come into complete focus.

For a long time, AI regulation sounded like: How do we label synthetic media? How do we reduce hallucinations, prevent bias, make chatbots safer? Now the question is so much bigger. Who can use these capable systems? Can allies use them? Can cybersecurity firms outside the US use them? Can non-US employees at US labs use them? Can European companies use American models if those models are also strategically sensitive? This isn’t traditional software licensing anymore. This is capability access control.

The underlying tension behind the G7 conversation is the dual-use problem: A model capable enough to find software vulnerabilities for defense can also find them for offense. The “trusted partners” framework reflects the new geopolitics of AI as countries jockey with rivals to secure strategic benefits for themselves and their allies. It represents an alliance layer for AI access that applies access structures previously reserved for physical military hardware to capabilities too strategically important to make fully open and too useful to keep entirely locked down. As Ksenia noted, the alliance is “not literally NATO, but [it is founded on] the same kind of logic.”

But access restrictions might also impact the talent that built these systems, who are increasingly not citizens of the country trying to control it. For instance, AI researcher Andrej Karpathy, recently hired by Anthropic, is publicly described as Slovak-Canadian. If access controls apply to non-US citizens, he and others like him may be denied access to the very systems they’ve been hired to work on. It’s an area we’ll continue to watch closely.

AI is entering the measurement loop

Midjourney, the company you probably associate with AI-generated images, has announced a new medical division and a full-body ultrasound scanner built around water immersion, developed in partnership with medical imaging hardware maker Butterfly Network. The device is designed to scan the entire body in 60 seconds: A person descends into a shallow pool on a motorized platform, passing through a ring of roughly half a million ultrasound sensors, each functioning as both a transmitter and receiver. The system uses over two petaflops of processing power to reconstruct a 3D body map from the returning wave data. Midjourney says the resulting images look comparable to today’s MRI output at a fraction of the cost and time, though that claim still needs serious clinical validation before it can stand.

The current prototype uses 40 Butterfly ultrasound-on-chip devices per system, according to a disclosure from Butterfly Network, which confirmed its codevelopment and licensing agreement with Midjourney. Midjourney plans to open a facility in San Francisco in 2027, embedding its device in a spa environment alongside hot tubs, saunas, and cold plunges. Diagnostic medical uses will require FDA approval; the initial focus is body composition mapping.

If Midjourney can build a library of full-body scans taken over months and years, that longitudinal record would give doctors and AI health tools a level of baseline data that doesn’t currently exist at scale outside of clinical trials. That’s the same structural logic Ksenia traced through Cursor and GitHub: The value compounds inside the loop through repeated, precise measurement over time. Midjourney is positioning itself to own that loop in the health domain.

What’s next

The competition for AI advantage is moving from model capability to infrastructure position. Who owns the coding loop? Who controls access to frontier systems? Who builds the measurement environment where health data accumulates over time? Those questions are about where intelligence meets operational reality, not which model scores highest on a benchmark.

Hiring news from the week reinforces how seriously the labs are treating this phase. John Jumper, the Nobel laureate who shared the prize with Demis Hassabis for AlphaFold, left Google DeepMind for Anthropic. Noam Shazeer, one of the coauthors of “Attention Is All You Need,” reportedly left Google for OpenAI after Google paid approximately $2.7 billion to bring him back in 2024. The labs are betting on scientific talent at the same time they’re betting on infrastructure.

Next week, host Andreas Welsch will be back to discuss multi-vendor strategy with Conductor’s Matt Palmer. They’ll cover Sakana’s launch of Fugu, Qualcomm’s ~$4B move for Modular, Anthropic’s Claude Tag stepping into Slack as a virtual coworker, Samsung putting ChatGPT and Codex in front of its entire workforce, and more. Register here to attend live.

Starting in July, the live event will move inside the O’Reilly learning platform, but we’ll continue to publish our takeaways here on Radar each Friday and share full episodes on YouTube, Spotify, and Apple.

Olaitan Fashanu: The PO Who Doesn't Care vs the PO Who Always Has the Answer

Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.

 

In this episode, we refer to a recurring theme in past podcast episodes—the proxy product owner who can't make decisions because they're not theirs to make.

The Great Product Owner: Always Available, Always Decisive, Always Has the Context

Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.

 

"There was nothing you tell, any questions you have about a particular feature that this guy doesn't have an answer to. And that really moved the team so fast." - Olaitan Fashanu

 

The best PO Olaitan ever worked with was the mirror opposite of every anti-pattern he'd seen. Deeply involved in refinement. Took backlog management seriously. Always brought the context. Always available to the team. And—maybe most importantly—always ready to make a decision when devs surfaced trade-offs. The team could ask any question about any feature, and the answer was right there. Not "let me check," not "I'll get back to you," not "what do you think?"—a decision. That single quality, Olaitan says, was what moved the team faster than anything else. As a Scrum Master, when you see a great PO at work, you also see the amplifying waves of impact: motivation rises, quality rises, ownership grows. Olaitan's takeaway is sharp: the success of our job depends on how well the product owner does theirs.

 

Self-reflection Question: When was the last time your PO made a real-time decision that unblocked the team in a single conversation—and what's preventing that from being the norm?

The Bad Product Owner: Doesn't Care About Impact, Can't Make Decisions

Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.

 

"The product owner cares about delivery. We just need to release to the customer. That is something I don't like." - Olaitan Fashanu

 

Olaitan describes two anti-patterns wrapped into one bad-PO type. The first: the PO who doesn't care about the impact of their work on the team. Tickets dropped without context. No refinement. No problem framing. Just "ship by end of month." The data shows up in Jira if you're paying attention—patterns of churn, quality issues, customer complaints, slow market response. Beyond the numbers, the team loses motivation, frustration creeps in, and eventually you lose the team entirely. The second anti-pattern, layered on top: the PO who can't make decisions. Developers come back with two options and the trade-offs—and the PO can't pick one. Vasco connects it to the proxy PO pattern explored in past episodes—a PO whose decisions aren't actually theirs to make. The cost is the same either way: the team stalls, ownership erodes, and stakeholder conflict grows.

 

In this segment, we refer to the proxy PO anti-pattern explored in earlier episodes of the podcast.

 

Self-reflection Question: Is your PO unable to decide—or unable to be allowed to decide? The difference changes which conversation you need to have, and with whom.

 

[The Scrum Master Toolbox Podcast Recommends]

🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥

Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people.

 

🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.

 

Buy Now on Amazon

 

[The Scrum Master Toolbox Podcast Recommends]

 

About Olaitan Fashanu

 

Olaitan Fashanu is a customer-focused professional with expertise in product management, technology, and coaching. He drives digital and agile transformation, builds collaborative cross-functional teams, and delivers high-quality products across markets. Curious and strategic, he explores AI and data intelligence while balancing technical depth, business goals, culture, structure, and long-term vision.

 

You can link with Olaitan Fashanu on LinkedIn.

Download audio: https://traffic.libsyn.com/secure/scrummastertoolbox/20260626_Olaitan_Fashanu_F.mp3?dest-id=246429