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

The post Job listings for week ending 8/7 appeared first on Leon Adato.

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

The post Call For Papers Listings for 8/7 appeared first on Leon Adato.

In the realm of software development, the persistent question lingers: Is code crafted for human eyes or for artificial intelligences that interpret and execute it? This inquiry often leads to a paradox that can paralyze progress. Rather than seeing this duality as a limitation, we should embrace the complexity and plurality of code’s audience. Acknowledging that some code is worth writing while others may never be worth even reading can serve as our leverage point as the boundaries of software evolve.

Consider this: Code has a third, oft-overlooked audience—algorithms. These algorithms are not lay consumers of code but are integral to the very tools that define good coding practices and environments. They power our compilers, editors, static analysis tools, and even coding agents. Their success and integration into our processes are so seamless that we often forget their presence.

Based on content from Software Should Work

Visualize the capability to verify an essential requirement across all possible inputs or to assure that a certain class of security vulnerabilities is entirely preempted by design. This assurance is possible with formal methods and verification techniques devoid of niche research, ready to be ushered into industry practice. Thus, we can build a foundation of certainty into an uncertain tomorrow.

Ray Myers, in a captivating talk at Software Should Work 2026, revisits an aspect of computer history most tech enthusiasts vividly recall—the infamous Blue Screen of Death from the 1990s Windows era. Despite its notoriety, Myers reminds us that the screen wasn’t officially dubbed a ‘death screen,’ but the public consensus insisted otherwise—a testament to software’s obligation to function properly.

Having a rich history in software, Myers elaborates on the duality that software should work both as originally intended and in a manner that is beneficial for its user base and society at large. The Blue Screen serves as an example of software visibly failing this primary duty.

In the current tech climate, the discussion has grown more complex with the advent and exponential growth of AI. However, the concept of a “software quality crisis,” as Myers coins, transcends the technical faults and enters public consciousness—becoming a recognized issue among those unconnected to software. This is when the crisis becomes an irrefutable business impact.

Interventions like Microsoft’s approach to combating the Blue Screen of Death through static analysis and other methods demonstrate that software crises can be tackled with strategic initiatives such as improved documentation, better architecture, dynamic error reporting, and verification engines. These ideas were far from simple fixes; they were innovative defenses against persistent problems.

Fast forward to today, and the spectrum of software’s metamorphosis is vast, with AI being a major player, and the narrative surrounding it diverging. Yet, as Myers contends, we’re not merely passengers on this technological journey. We are the builders tasked with shaping the path forward. Thus, discussions on the intertwining roles of humans and AI in coding must consider context, application, and a strategic balance rather than pursuing one exclusive direction.

Ultimately, the potential trajectory of a “dark software factory” aimed at fostering fully agentic environments may seem alluring or even practical in specific niches. However, it is not a one-size-fits-all solution for the complexities of software across industries. Positioned against indiscriminately adopting such frameworks is Myers’ “show method”—a call for judicious application of both human-driven and automated coding practices. It emphasizes that adapting to technological shifts need not abandon established, effective methodologies but should continue to evaluate their role within the new paradigms of code crafting.

Moonlight & Mayhem (Raccoon Heist by Codex + GPT-5.6 Sol Ultra)

I decided to pose the exact same prompt to Codex Desktop running GPT-5.6 Sol Ultra - the mode where Sol makes aggressive use of sub-agents - to see how it would do.

It produced a much better game! Here's Moonlight & Mayhem - GitHub repository here, including the textures and prompts it generated using gpt-image-2.

The original GPT-3 generated game description included:

In “Raccoon Heist”, you and your team of thieving raccoons are tasked with pulling off a series of daring heists. From robbing banks to stealing priceless art, no job is too big or too small for your furry crew.

Fable's version had you as a single raccoon running around a back yard collecting coins and fish. GPT-5.6 Sol has you in a museum, rescuing your two other raccoon crewmates in order to stack on top of each other and bust the golden sardine out of its case.

Much more heisty!

There was one catch though: the version produced from the one-shot prompt had a bug where each raccoon had an eyeball that was enlarged to the size of a giant sphere floating over their head!

You can play that version here.

Despite reviewing screenshots during development Codex failed to spot and correct this bug.

I fixed it by prompting:

Why do the raccoons have huge black spheres on them?

And then:

Fix it

Which resulted in this fix.

I shared the full Codex transcript in the repository - I wish Claude Code had the same "copy as Markdown" feature.

Tags: game-design, ai, openai, generative-ai, llms, coding-agents, gpt

The Tokenpocalypse Is Here: Companies Are Scrambling To Stop Spending So Much on AI

“We’re seeing from some of the data internally at least that it’s actually not our engineers that are driving the token consumption. It’s a lot of the non-engineers that are doing some of those behaviors [...] you were talking about,” Justice Kwak, Accenture’s agentic AI strategy lead, said [...]

Stuart Henderson, Accenture’s client group lead, interrupts. He jokes he hopes Kwak didn’t just convert a PDF into images and then into markdown files. “I’m learning that’s one of the big token chewers,” Henderson says. “Turning PDFs into markdown: is that right?”

That’s when Kwak says that’s what Accenture’s own data shows.

Maybe if Accenture figure out that PDFs are a terrible medium for communicating information they'll be able to push that message out to the rest of the business world too!

Via @404.media on TikTok

Tags: pdf, markdown, ai, generative-ai, llms, ai-misuse