from the good-deals-on-cool-stuff dept

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Filed Under: daily deal

from the gaming-like-it’s-1930 dept

We’re past the halfway point in our series of spotlight posts looking at the winners of our eighth annual public domain game jam, Gaming Like It’s 1930! We’ve already covered the Best Adaptation, Best Deep Cut, and Best Visuals winners, and this week we’re looking at the winner of Best Remix: Lilac Song by Autumn Chen.

There were fewer interactive fiction submissions in this year’s jam than there often have been in past editions, but even if the field had been more crowded, Lilac Song would have undoubtedly stood out. It’s a somber, thoughtful story that casts the player as a servant to Prussian Minister-President Otto Braun during the last few years of the Weimar Republic. It revolves around and intriguing and fitting premise: the servant has been designing a simulation game about power and politics in Germany, from which she aims to draw insights that could preserve democracy and prevent the rise of Hitler and the Nazi Party.

The story is far more than a cursory look at these events: it’s clearly rooted in robust historical knowledge about this critical time and place, with myriad details about the specifics of the political situation as well as an additional exploration of gender politics and transgenderism in the era. But what’s especially notable for this jam is the way it weaves in a wide variety of artistic and musical works from 1930, which form the backdrop of its setting and the game itself. Amidst the story unfolding (and careening towards its inevitable ending) the player wanders the halls of Braun’s house and chooses paintings to admire and music to listen to. These works (by Paul Klee, Wassily Kandinsky, Felix Mendelssohn, and more) become the wallpaper and soundtrack of the game.

Though the story takes center stage, the careful selection and use of these public domain works lend verisimilitude to the story and polish to the game design, resulting in more immersion than the text alone could achieve. For employing a curated combination of newly-public-domain works that elevates the interactive fiction without overtaking it, Lilac Song is this year’s Best Remix.

Congratulations to Autumn Chen for the win! You can play Lilac Song in your browser on Itch. We’ll be back next week with another winner spotlight, and don’t forget to check out the many great entries that didn’t quite make the cut. And stay tuned for next year, when we’ll be back for Gaming Like It’s 1931!

Filed Under: copyright, game jam, games, gaming, gaming like it’s 1930, public domain, winner spotlight

Remmy Evans learned via a friend that a Tesla Model 3 was sitting in some guy’s driveway in Idaho. The owner had bought it cheaply with the intention of removing the drivetrain and installing it in an old car from the 1970s, but he abandoned the plan after realizing how much time the body work would take. Evans was able to negotiate a price of exactly $2k and walk away with a rolling chassis that was still capable of moving on its own.

The seats and steering wheel remained in place, as did the motors, battery pack, and center screen. Everything else had been stripped out. There were no body panels remaining, including the windshield. The tires were so worn down that you could see the wires through the rubber in a couple locations, and this car had been lying unregistered for at least two years. Nonetheless, the electric motors started immediately away, and the readout displayed a full battery charge.

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Evans fitted it with bright red wheels and new tires so that it could really grip the road rather than destroying the tires every time he turned the wheel. As a safety measure, he looped a heavy-duty ratchet strap across his lap, similar to a makeshift harness. He also disabled the car’s road-sensing safety measures, allowing it to run freely in track mode.

However, charging was a potential disaster. The first attempt at a fast charger was unsuccessful since the adapter just would not fit the port. Evans went to a hardware store, got a cutting tool, and fixed the problem by cutting the top of the adaptor on the spot so it could slide in. Once connected, the battery charged to full capacity and the LCD displayed 212 miles of range remaining. Home charging took roughly 7 or 8 hours on a Level 2 unit, or closer to 14 hours on a standard wall plug.

So, with the car all fixed up, Evans took it for a test drive, and unexpectedly, it passed the test without drawing any unwelcome notice from the cops. Later practices included donuts in a parking lot, burnouts, and a few of open-road runs that exceeded 60 mph. Because there was no top, the wind blew directly into the automobile, but it handled perfectly. At a friend’s home, the stripped-down Tesla ripped over dirt berms, jumped off a tabletop jump, and just kept going. One of his buddies rode along and claimed it was similar to the three-wheeled roadster that some people enjoy, but more faster.

Heavy drifting around the lot had a toll on the battery, which depleted far faster than it would on a regular drive. After a long afternoon of zooming around, the range had dropped to just 18 miles remaining, yet the car still made it home with a mile to spare. There was only one evident drawback: the onboard computer was logging an alarming 78 error codes because all of the cameras and sensors were missing.
[Source]

AI is already changing how the world works, but it’s also quietly making one of our biggest environmental problems even worse. And no, this isn’t about energy consumption this time. It’s about the hardware. Because every smarter AI model comes with a physical cost.

AI is about to supercharge the e-waste problem

According to a study published in Nature Computational Science (via Rest of World), the rapid rise of AI could add between 1.2 to 5 million metric tons of e-waste by 2030. The reason is pretty simple. AI relies on high-performance hardware like GPUs and specialized servers, and these don’t last very long. Most of this equipment gets replaced every 2 to 5 years, which means older hardware is quickly discarded as newer, faster systems take over.

And this is happening at scale. As companies race to build bigger data centers and train more powerful models, the demand for hardware keeps rising, along with the pile of obsolete machines left behind.

This isn’t just a tech problem but a global one

E-waste is already one of the fastest-growing waste streams in the world, with tens of millions of tonnes generated every year. And the worst part? A large chunk of it isn’t properly recycled. Improper handling can release toxic materials like lead and mercury into the environment, posing serious risks to both ecosystems and human health. And here’s the uncomfortable truth: most of this waste ends up in lower-income countries, where recycling often happens under unsafe conditions. That means that while the benefits of AI are global, the environmental cost is not equally shared.

At the end of the day, AI might feel like a purely digital revolution. But behind the scenes, it’s building a very real, very physical footprint. And if things don’t change, that footprint is only going to keep growing.

For smaller releases, including LPs from midsize indie labels (Drag City, Kranky, Superior Viaduct), check out Midheaven Mailorder. This site is run by the San Francisco distributor Revolver USA, which sells to record stores around the US but also sells direct to consumers. Here you’ll find a ton of independent music, including choice reissues from bigger names like Devo, Sonic Youth, and The Fall.

Another great independent record store is Amoeba Music, made famous for its amazing selection at numerous California stores, and its What’s in Your Bag YouTube series. If you want to support a good brick-and-mortar record store and know what you want, we always recommend buying from Amoeba instead of Amazon.

Independent Labels

Screenshot courtesy of Parker Hall

Record labels like Stones Throw, Sub Pop, Secretly Group, Fat Possum, Rough Trade, Daptone, Blue Note, International Anthem, and hundreds more all have websites and sell records directly. If you notice that there is an artist you like signed to a given smaller label, there is a good chance that other artists on the label have a similar vibe.

Start by checking out the liner notes on the back of all your albums to see who made them, then chase down those labels on the web. You’ll likely find some cool new tunes on vinyl along the way.

Directly From the Artists

We’ve saved the best for last. Pay the artists for their work by putting cash—or a Venmo transaction—directly in their hands. Buying a record at the merch table when an artist plays in your town is not only convenient for you (there’s no shipping, and you typically won’t be charged tax), but it’s one of the biggest income streams for bands that tour. You can often find special editions, special colored vinyl variants, and even records that haven’t yet been released just by stopping at the merch table. And if you’re lucky enough to meet the artist at the venue, they will usually sign the record jacket for you.

If you don’t get out much (we know, concerts are expensive), just find their website, social media page, or record label and place an order. I have at least a few records with personal thank-yous and signatures from small artists, which feels really special every time I listen to them!

Online dating is already a trust minefield, and now Tinder wants to add an eyeball scan to the mix. The popular dating app has announced a global partnership with World, the biometric identity company founded by OpenAI’s Sam Altman. To prove you are a real human on Tinder, you will soon have the option to get your eyes scanned by a physical orb device.

What is World ID and how does Tinder’s human verification work?

World is a company built around the idea that proving your humanity online will become increasingly important as AI bots multiply and outnumber real people on the internet. Its solution is a proprietary scanning device called the Orb, which scans your irises at its physical outlets to verify that you are a real person.

Once verified, you receive a World ID linked to that scan. World already ran a pilot of this verification process with Tinder in Japan earlier this year, and that trial was apparently successful enough to warrant a worldwide rollout.

On Tinder, users who go through the World ID verification process will receive a badge on their profile indicating they are a verified human. To sweeten the deal, Tinder is also offering five free Boosts to anyone who completes the verification. The company hopes that this incentive is meaningful enough for people to hand over their biometric data.

Is this just about dating apps, or is the World orb coming for everything?

Tinder is just the beginning. Zoom is now integrating World ID so that meeting hosts can verify participants’ identity before joining a call. DocuSign is also adopting the technology, letting users require World verification on contracts. Meanwhile, Reddit might adopt World ID as a bot detection tool.

On top of that, World has launched Concert Kit, a tool that lets artists reserve concert tickets for verified humans only, taking direct aim at scalper bots. Concert Kit will soon be tested at a Bruno Mars World Tour show in San Francisco.

Even though World is pushing hard for mainstream adoption, governments in Brazil and several other countries have banned it over privacy concerns. Whether handing your biometric data to a third party becomes the new normal is a question that is only going to get louder from here.

The journey from a laboratory hypothesis to a pharmacy shelf is one of the most grueling marathons in modern industry, typically spanning 10 to 15 years and billions of dollars in investment.

Progress is often stymied not just by the inherent mysteries of biology, but by the “fragmented and difficult to scale” workflows that force researchers to manually pivot between the actual experimental design equipment, software, and databases.

But OpenAI is releasing a new specialized model GPT-Rosalind specifically to speed up this process and make it more efficient, easier, and ideally, more productive. Named after the pioneering chemist Rosalind Franklin, whose work was vital to the discovery of DNA’s structure (and was often overlooked for her male colleagues James Watson and Francis Crick), this new frontier reasoning model is purpose-built to act as a specialized intelligence layer for life sciences research.

By shifting AI’s role from a general-purpose assistant to a domain-specific “reasoning” partner, OpenAI is signaling a long-term commitment to biological and chemical discovery.

What GPT-Rosalind offers

GPT-Rosalind isn’t just about faster text generation; it is designed to synthesize evidence, generate biological hypotheses, and plan experiments—tasks that have traditionally required years of expert human synthesis.

At its core, GPT-Rosalind is the first in a new series of models optimized for scientific workflows. While previous iterations of GPT excelled at general language tasks, this model is fine-tuned for deeper understanding across genomics, protein engineering, and chemistry.

To validate its capabilities, OpenAI tested the model against several industry benchmarks. On BixBench, a metric for real-world bioinformatics and data analysis, GPT-Rosalind achieved leading performance among models with published scores.

In more granular testing via LABBench2, the model outperformed GPT-5.4 on six out of eleven tasks, with the most significant gains appearing in CloningQA—a task requiring the end-to-end design of reagents for molecular cloning protocols.

The model’s most striking performance signal came from a partnership with Dyno Therapeutics. In an evaluation using unpublished, “uncontaminated” RNA sequences, GPT-Rosalind was tasked with sequence-to-function prediction and generation.

When evaluated directly in the Codex environment, the model’s submissions ranked above the 95th percentile of human experts on prediction tasks and reached the 84th percentile for sequence generation.

This level of expertise suggests the model can serve as a high-level collaborator capable of identifying “expert-relevant patterns” that generalist models often overlook.

The new lab workflow

OpenAI is not just releasing a model; it is launching an ecosystem designed to integrate with the tools scientists already use. Central to this is a new Life Sciences research plugin for Codex, available on GitHub.

Scientific research is famously siloed. A single project might require a researcher to consult a protein structure database, search through 20 years of clinical literature, and then use a separate tool for sequence manipulation. The new plugin acts as an “orchestration layer,” providing a unified starting point for these multi-step questions.

• Skill Set: The package includes modular skills for biochemistry, human genetics, functional genomics, and clinical evidence.

• Connectivity: It connects models to over 50 public multi-omics databases and literature sources.

• Efficiency: This approach targets “long-horizon, tool-heavy scientific workflows,” allowing researchers to automate repeatable tasks like protein structure lookups and sequence searches.

Limited and gated access

Given the potential power of a model capable of redesigning biological structures, OpenAI is eschewing a broad “open-source” or general public release in favor of a Trusted Access program.

The model is launching as a research preview specifically for qualified Enterprise customers in the United States. This restricted deployment is built on three core principles: beneficial use, strong governance, and controlled access.

Organizations requesting access must undergo a qualification and safety review to ensure they are conducting legitimate research with a clear public benefit.

Unlike general-use models, GPT-Rosalind was developed with heightened enterprise-grade security controls. For the end-user, this means:

• Restricted Access: Usage is limited to approved users within secure, well-managed environments.

• Governance: Participating organizations must maintain strict misuse-prevention controls and agree to specific life sciences research preview terms.

• Cost: During the preview phase, the model will not consume existing credits or tokens, allowing researchers to experiment without immediate budgetary constraints (subject to abuse guardrails).

Warm reception from initial industry partners

The announcement garnered significant buy-in from OpenAI parnters across the pharmaceutical and technology sectors.

Sean Bruich, SVP of AI and Data at Amgen, noted that the collaboration allows the company to apply advanced tools in ways that could “accelerate how we deliver medicines to patients”.The impact is also being felt in the specialized tech infrastructure that supports labs:

• NVIDIA: Kimberly Powell, VP of Healthcare and Life Sciences, described the convergence of domain reasoning and accelerated computing as a way to “compress years of traditional R&D into immediate, actionable scientific insights”.

• Moderna: CEO Stéphane Bancel highlighted the model’s ability to “reason across complex biological evidence” to help teams translate insights into experimental workflows.

• The Allen Institute: CTO Andy Hickl emphasized that GPT-Rosalind stands out for making manual steps—like finding and aligning data—more “consistent and repeatable in an agentic workflow”.

This builds on tangible results OpenAI has already seen in the field, such as its collaboration with Ginkgo Bioworks, where AI models helped achieve a 40% reduction in protein production costs.

What’s next for Rosalind and OpenAI in life sciences?

OpenAI’s mission with GPT-Rosalind is to narrow the gap between a “promising scientific idea” and the actual “evidence, experiments, and decisions” required for medical progress.

By partnering with institutions like Los Alamos National Laboratory to explore AI-guided catalyst design and biological structure modification, the company is positioning GPT-Rosalind as more than a tool—it is meant to be a “capable partner in discovery”.

As the life sciences field becomes increasingly data-dense, the move toward specialized “reasoning” models like Rosalind may become the standard for navigating the “vast search spaces” of biology and chemistry.