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Tag: generative AI

How Vibe Coding Became the Punk Rock of Software
From meme to manifesto

In March 2025 a single photo of legendary record producer Rick Rubin—eyes closed, headphones on, one hand resting on a mouse—started ricocheting around developer circles. Online jokesters crowned him the patron saint of “vibe coding,” a tongue-in-cheek label for writing software by feeling rather than formal process. Rubin did not retreat from the joke. Within ten weeks he had written The Way of Code, launched the interactive site TheWayOfCode.com, and joined a16z founders Marc Andreessen and Ben Horowitz on The Ben & Marc Show to unpack the project’s deeper intent .

What exactly is vibe coding?

Rubin defines vibe coding as the artistic urge to steer code by intuition, rhythm, and emotion instead of rigid methodology. In his view the computer is just another instrument—like a guitar or an MPC sampler—waiting for a distinct point of view. Great software, like great music, emerges when the creator “makes the code do what it does not want to do” and pushes past the obvious first draft .

Developers have riffed on the idea, calling vibe coding a democratizing wave that lets non-programmers prototype, remix, and iterate with large language models. Cursor, Replit, and GitHub Copilot all embody the approach: prompt, feel, refine, ship. The punk parallel is apt. Just as late-70s punk shattered the gate-kept world of virtuoso rock, AI-assisted tooling lets anyone bang out a raw prototype and share it with the world.

The Tao Te Ching, retold for the age of AI

The Way of Code is not a technical handbook. Rubin adapts the Tao Te Ching verse-for-verse, distilling its 3 000-year-old wisdom into concise reflections on creativity, balance, and tool use. Each stanza sits beside an AI canvas where readers can remix the accompanying art with custom prompts—training wheels for vibe coding in real time .

Rubin insists he drafted the verses by hand, consulting more than a dozen English translations of Lao Tzu until a universal meaning emerged. Only after the writing felt complete did collaborators at Anthropic build the interactive wrapper. The result blurs genre lines: part book, part software, part spiritual operating system.

Five takeaways from the a16z conversation
1. Tools come and go; the vibe coder persists. Rubin’s viral tweet crystallised the ethos: mastery lives in the artist, not in the implements. AI models will change yearly, but a cultivated inner compass endures .
2. Creativity is remix culture at scale. From Beatles riffs on Roy Orbison to hip-hop sampling, art has always recombined prior work. AI accelerates that remix loop for text, images, and code alike. Rubin views the model as a woodshop chisel—powerful yet inert until guided.
3. AI needs its own voice, not a human muzzle. Citing AlphaGo’s improbable move 37, Rubin argues that breakthroughs arrive when machines explore paths humans ignore. Over-tuning models with human guardrails risks sanding off the next creative leap.
4. Local culture still matters. The trio warns of a drift toward global monoculture as the internet flattens taste. Rubin urges creators to seek fresh inspiration in remote niches and protect regional quirks before algorithmic averages wash them out.
5. Stay true first, iterate second. Whether launching a startup or recording Johnny Cash alone with an acoustic guitar, the winning work begins with uncompromising authenticity. Market testing can polish rough edges later; it cannot supply the soul.
Why vibe coding resonates with software builders
- Lower barrier, higher ceiling. AI pairs “anyone can start” convenience with exponential leverage for masters. Rubin likens it to giving Martin Scorsese an infinite-shot storyboard tool; the director’s taste, not the tech, sets the upper bound .
- Faster idea discovery. Generative models surface dozens of design directions in minutes, letting developers notice serendipitous mistakes—Rubin’s favorite creative catalyst—without burning months on dead-end builds.
- Feedback loop with the collective unconscious. Each prompt loops communal knowledge back into personal intuition, echoing Jung’s and Sheldrake’s theories that ideas propagate when a critical mass “gets the vibe.”
The road ahead: punk ethos meets AI engineering

Vibe coding will not replace conventional software engineering. Kernel engineers, cryptographers, and avionics programmers still need rigorous proofs. Yet for product prototypes, game jams, and artistic experiments, the punk spirit offers a path that prizes immediacy and personal voice.

Rubin closes The Way of Code with a challenge: “Tools will come and tools will go. Only the vibe coder remains.” The message lands because it extends his decades-long mission in music—strip away external noise until the work pulses with undeniable truth. In 2025 that mandate applies as much to lines of Python as to power chords. A new generation of software punks is already booting up their DAWs, IDEs, and chat windows. They are listening for the vibe and coding without fear.
May 28, 2025
Is DeepSeek’s Terms of Use Normal or Sketchy AF? A Plain English Breakdown

Let’s dive into the DeepSeek Terms of Use and Privacy Policy, updated as of January 20 and February 14, 2025, respectively, and figure out if this is just standard tech company stuff or something that raises red flags. DeepSeek, a Chinese AI company run by Hangzhou DeepSeek Artificial Intelligence Co., Ltd. and its affiliates, offers generative AI services—think chatbots and text generation tools. Their legal docs outline how you can use their services and what they do with your data. But is it par for the course, or does it feel sketchy? Let’s break it down in plain English and weigh the vibes.

What’s DeepSeek Offering?

DeepSeek’s services let you interact with AI models that churn out text, code, or tables based on what you type in (your “Inputs”). You get responses (called “Outputs”), and the company uses big neural networks trained on tons of data to make this happen. They’re upfront that the tech’s always evolving, so they might tweak, add, or kill off features as they go. They also promise to keep things secure and stable—at least as much as other companies do—and let you complain or give feedback if something’s off.

Normal or Sketchy?

This part’s pretty standard. Most tech companies, especially in AI, have similar setups: you input stuff, they spit out answers, and they reserve the right to change things. The “we’ll keep it secure” promise is boilerplate—vague but typical. Nothing screams sketchy here; it’s just how these platforms roll.

Signing Up and Your Account

You need an account to use DeepSeek, and they want your email or a third-party login (like Google). They say it’s for adults, and if you’re under 18, you need a guardian’s okay. You’ve got to give real info, keep your password safe, and not hand your account to anyone else. If you lose it or someone hacks it, you can ask for help, but you’re on the hook for anything done under your name. You can delete your account, but they might hang onto some data if the law says so.

Normal or Sketchy?

Totally normal. Every app from Netflix to X has account rules like this—real info, no sharing, your fault if it gets compromised. The “we keep data after you delete” bit is standard too; laws often force companies to hold onto stuff for compliance. No red flags yet.

What You Can and Can’t Do

Here’s where they lay down the law: you get a basic right to use the service, but they can yank it anytime. You can’t use it to make hateful, illegal, or creepy stuff—like threats, porn, or fake celebrity accounts (unless it’s labeled parody). No hacking, no stealing their code, no reselling their service. If you share AI-generated content, you’ve got to check it’s true and tag it as AI-made. They can scan your inputs and outputs to make sure you’re playing nice.

Normal or Sketchy?

This is par for the course. Every platform has a “don’t be a jerk” list—X, YouTube, you name it. The “we can revoke access” part is standard; it’s their service, their rules. Checking your content isn’t weird either—AI companies like OpenAI do it to avoid legal headaches. The “label it as AI” rule is newer but popping up more as fake content worries grow. Nothing sketchy; it’s just them covering their butts.

Your Inputs and Outputs

You own what you type in and what the AI spits out, and you can use it however—personal projects, research, even training other AI (cool, right?). But they might use your inputs and outputs to tweak their system, promising to scramble it so no one knows it’s yours. They warn the outputs might be wrong, so don’t bet your life on them—especially for big stuff like legal or medical advice.

Normal or Sketchy?

Mostly normal, with a twist. Letting you own outputs and use them freely is generous—some AI companies (looking at you, certain competitors) claim rights to what their models make. Using your data to improve their AI is standard; Google and others do it too, with the same “we’ll anonymize it” line. The “outputs might suck” disclaimer is everywhere in AI—nobody wants to get sued over a bad answer. The twist? They’re based in China, and data laws there can be murky. Not sketchy on its face, but the location might make you squint.

Who Owns the Tech?

DeepSeek owns all their code, models, and branding. You can’t use their logos or try to copy their tech without permission. Simple enough.

Normal or Sketchy?

Bog-standard. Every company guards its intellectual property like this. No surprises, no sketchiness.

If Something Goes Wrong

If you think they’re ripping off your ideas or breaking rules, you can complain via email or their site. They’ll look into it. If you break their rules, they can warn you, limit your account, or ban you—no notice required. They’re not liable if the service flops or gives you bunk info, and you’ve got to cover their back if your screw-up costs them money.

Normal or Sketchy?

Normal, if a bit harsh. The “we can ban you anytime” clause is in every terms of service—X has it, so does every game app. The “we’re not responsible” and “you pay if you mess up” bits are classic corporate shields. It’s not cuddly, but it’s not sketchy—just self-protective.

Privacy Stuff

They collect your account details, what you type, your device info, and rough location (via IP). They use it to run the service, improve their AI, and keep things safe. They might share it with their team, service providers (like payment processors), or cops if the law demands it. You’ve got rights to see, fix, or delete your data, but it’s stored in China, and they don’t take kids under 14.

Normal or Sketchy?

Mostly normal, with a catch. Data collection and sharing are what every tech company does—X grabs your IP and tweets, Google slurps everything. Rights to access or delete are standard, especially with privacy laws like GDPR influencing global norms. The China storage is the catch—data there can be subject to government snooping under laws like the National Intelligence Law. Not sketchy by design, but it’s a wild card depending on your trust level.

Legal Fine Print

Chinese law governs everything, and disputes go to a court near their HQ in Hangzhou. They can update the terms anytime, and if you keep using the service, you’re cool with it.

Normal or Sketchy?

Normal-ish. Picking their home turf for law and courts is typical—X uses U.S. law, others pick wherever they’re based. The “we can change terms” bit is everywhere too. The China angle might feel off if you’re outside that system, but it’s not inherently sketchy—just inconvenient.

The Verdict

DeepSeek’s terms and privacy rules are mostly par for the course. They’re doing what every AI and tech company does: setting rules, grabbing data, dodging liability, and keeping their tech theirs. The “you own outputs” part is a nice perk, and the content rules align with industry norms as AI gets more regulated. The sketchy vibes creep in with the China factor—data storage and legal oversight there aren’t as transparent as, say, the U.S. or EU. If you’re chill with that, it’s standard fare. If not, it might feel off. Your call, but it’s not a screaming red flag—just a “hmm, okay” moment.

March 6, 2025
MatterGen: Revolutionizing Material Design with Generative AI
Materials innovation is central to technological progress, from powering modern devices with lithium-ion batteries to enabling efficient solar panels and carbon capture technologies. Yet, discovering new materials for these applications is an arduous process, historically reliant on trial-and-error experiments or computational screenings. Microsoft’s MatterGen is poised to change this paradigm, leveraging cutting-edge generative AI to revolutionize material discovery.

The Challenge in Material Design

Traditionally, researchers sift through vast databases of known materials or rely on high-throughput experiments to identify candidates with specific properties. While computational approaches have sped up this process, they are still limited by the need to evaluate millions of candidates from existing data. This bottleneck often misses novel and unexplored possibilities. MatterGen offers a transformative approach, generating novel materials directly based on user-defined properties like chemical composition, mechanical strength, or electronic and magnetic characteristics.

What Is MatterGen?

MatterGen is a diffusion-based generative model designed to create stable, unique, and novel (S.U.N.) inorganic materials. Unlike traditional material screening, which filters pre-existing datasets, MatterGen uses advanced AI algorithms to construct entirely new materials from scratch.

This model employs 3D diffusion processes, iteratively refining atom positions, lattice parameters, and chemical compositions to meet desired property constraints. Its architecture accommodates material-specific complexities like periodicity and crystallographic symmetries, ensuring both stability and functionality.

Key Innovations in MatterGen’s Architecture
1. Diffusion Process Tailored for Materials: MatterGen’s architecture uses a novel forward and reverse diffusion approach to refine atomic structures from noisy initial configurations, ensuring equilibrium stability.
2. Fine-Grained Control Over Design Constraints: The model can be conditioned to generate materials with specific space groups, chemical systems, or properties like high magnetic density or bulk modulus.
3. Scalable Training Data: Leveraging over 600,000 entries from the Alexandria and Materials Project databases, MatterGen achieves superior performance compared to existing methods like CDVAE and DiffCSP.
4. Novelty Through Disordered Structure Matching: A sophisticated algorithm evaluates whether generated materials represent genuinely new compositions or ordered variants of known structures.
Validation Through Experimentation

MatterGen’s capabilities extend beyond theoretical predictions. Collaborating with experimental labs, researchers synthesized TaCr₂O₆, a novel material generated by the model to meet a target bulk modulus of 200 GPa. Despite minor cationic disorder in the crystal structure, the material closely matched its computational design, achieving an experimentally measured bulk modulus of 158 GPa. This milestone demonstrates MatterGen’s practical applicability in guiding real-world material synthesis.

Comparative Performance

MatterGen significantly outperforms its predecessors:
- Higher Stability Rates: The generated structures align closer to DFT (Density Functional Theory)-computed energy minima, with an average RMSD (Root Mean Square Deviation) 15 times lower than competing models.
- Unprecedented Novelty: Leveraging its advanced dataset and refined diffusion processes, MatterGen generates a higher proportion of novel materials than previous approaches like CDVAE.
- Property-Specific Design: The model excels in constrained design scenarios, such as creating materials with high bulk modulus or low supply-chain risk.
Broader Implications

The success of MatterGen heralds a new era in material science, shifting the focus from searching databases to generative design. By integrating MatterGen with complementary tools like MatterSim—Microsoft’s AI emulator for material property simulations—researchers can iteratively refine designs and simulations, accelerating the entire discovery process.

Applications Across Industries
- Energy Storage: Novel materials for high-performance batteries and fuel cells.
- Carbon Capture: Adsorbents optimized for CO₂ sequestration.
- Electronics: High-efficiency semiconductors and magnets for next-gen devices.
Open Access for the Research Community

True to Microsoft’s commitment to advancing science, the MatterGen code and associated datasets are available under an open MIT license. Researchers can fine-tune the model for their specific applications, fostering collaborative advancements in materials design.

The Road Ahead

MatterGen represents just the beginning of generative AI’s potential in material science. Future work will aim to address remaining challenges, including synthesizability, scalability, and real-world integration into industrial applications. With continued refinement, generative AI promises to unlock innovations across fields, from renewable energy to advanced manufacturing.
January 16, 2025