"Intention is what we need": Neeru Khosla on the Future of Education and Learning with AI

Mike's Notes

I love the multi-modality of CK12 education. Works for me. A lot better than what I got at school.

Thanks, Ksneia, for this wonderful interview.

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Last Updated

24/04/2026

"Intention is what we need": Neeru Khosla on the Future of Education and Learning with AI

By: Kesnia Se
Turing Post: 18/04/2026

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Neeru Khosla is the co-founder of CK-12 Foundation, a nonprofit focused on free, adaptive learning materials for students and teachers. In this interview, she explains why AI in education matters less as an “answer machine” and more as a way to understand how students think: what misconception they hold, where they are confused, and what kind of support can help them learn more deeply. CK-12’s AI tutor, Flexi, is built around that idea, using AI to support personalized learning, knowledge tracing, and student curiosity at scale.

How the CK-12 co-founder thinks about using AI to rethink, personalize, and gradually change the education system

Attention is what taught the machines. But now, in this AI world, we need to have intention and judgment. Intention is what we need.”

I loved that conversation with Neeru Khosla, co-founder of the CK-12 Foundation, a nonprofit focused on free, adaptive learning materials for students and teachers. She articulated one of the most important shifts now unfolding in education. For years, digital learning has focused on access: more content, more devices, more formats, more scale. But Neeru’s point is that the real shift brought by AI is the possibility of understanding how a student is actually thinking – where they are confused, what misconception they hold, what question they are really trying to ask, and what kind of help might move them forward.

Khosla has been working on this problem for nearly two decades. CK-12 started with an idea to make high-quality learning materials free, flexible, and adaptable to the needs of individual students. Long before the current AI boom, the foundation was already building multimodal learning tools, adaptive systems, and concept-based content designed to meet learners where they are. But as Neeru describes it, the missing layer was always insight. Schools could see whether a student got something right or wrong. They could not easily see why.

That is where the last three years changed everything. In Neeru’s view, AI is not most interesting as an answer machine.

“Just knowing that the answer is right or wrong doesn’t tell anybody anything.”

It is most useful when it helps surface the learner’s internal process. A question like “How does the sun burn if there’s no oxygen in space?” is not just a cute moment of curiosity – it reveals a specific misunderstanding that a good tutor can work with. This is the logic behind Flexi, CK-12’s AI tutor, which Neeru says has already been used by tens of millions of students and has processed more than 150 million questions. No one needs automation for its own sake. New tools give us the ability to trace learning more deeply than standardized testing ever could.

But there is a lot of fear in the system. Our conversation moves well beyond product and platform design. We talk about why schools often resist free tools, why they now resist AI tools, why teachers need open-mindedness more than technical fluency, why AI literacy may become as foundational as reading and writing, and why creativity, critical thinking, collaboration, communication, and community remain the real non-negotiables. Khosla also makes a broader argument: education is one of the few places where society cannot afford to treat access as optional. If AI is going to matter in the classroom, it has to help more children learn more deeply – not just help the most privileged students move faster. She argues that if the US wants to maintain its lead, it needs to rethink education and how it is delivered.

There is also something personal and unusually grounded in the way she talks about all this. Neeru came to education from molecular biology, then spent years in classrooms watching children learn one by one before building CK-12. That background gives her a practical lens on both technology and human development. She is enthusiastic about AI, but not dazzled by it. Again and again, she returns to the same principle: tools matter, but what matters more is whether they help us show up – for students, for teachers, and for one another.

Neeru believes in two AIs: Augmented Intelligence and Amplified Intention, and I’m 100 percent agree with her.


As I keep saying, AI literacy is extremely important, and this conversation is a great way to learn how to move from fear to understanding. Please watch it and share it →

YouTube video by Turing Post

AI Could Change Education Forever – Neeru Khosla Explains Why


Edited interview transcript

Ksenia:

Today I’m very honored to speak with Neeru Khosla. She is an entrepreneur and philanthropist known for her work in education and social impact. She is the co-founder of CK-12 Foundation, which provides free, customizable learning materials to students and teachers worldwide.

I’m very passionate about education, especially in this new era of AI, so thank you so much for joining me.

Neeru:

Thank you for having me.

Ksenia:

This podcast is called Inference – because we’re trying to make sense of what’s happening and draw conclusions from it.

Neeru:

Thank you for inviting me to Inference. I love talking to people about what I care deeply about, because education is one of the most important things we can give back to society. We can create all kinds of financial freedom, but that can only happen if we truly help young students understand what they need to know – and how to use that knowledge.

Education in the AI Era

Ksenia:

The last three years have been pretty disruptive for many industries. How has that changed your perspective on education and on the CK-12 Foundation?

Neeru:

Let me go back a little.

When we started CK-12, we were trying to answer a simple question: how might we make education available and meaningful for all children? At the time, textbooks were very one-sided. You got what you got. But true learning only happens when students can make their own connections to what they are learning.

As technology began to evolve in the early 2000s, I knew it was going to go far. I thought this would be one way to reach many more students and help them. So from the beginning, we made CK-12 free.

Technology gave us capabilities that learning really needs – especially multimodality. You cannot always learn effectively just by reading a textbook or just by watching a video. A concept has to be presented in the modality that works best for the learner.

Take physics, for example. If you are learning about electricity, reading in a textbook about how electrons move doesn’t really make it come alive. Now we have simulations. We have ways for students to actually see and interact with what they’re learning.

That was powerful. But even then, we still didn’t really know what was going on in the minds of students. We didn’t know whether they truly understood what we were trying to teach.

It was only with the arrival of AI in the last three years that we realized there was a real opportunity there. It wasn’t perfect – and no technology ever arrives 100 percent ready. Technology matures through iteration, and that requires all of us to participate.

When OpenAI suddenly reached enormous adoption in a very short time, it was clear something major was happening. A lot of people were afraid: students will cheat, machines will replace us, and so on. I was afraid as well. But I also noticed something important: prompting itself is a learning tool.

Ksenia:

Absolutely. It’s the skill of asking questions.

Neeru:

Exactly. That’s why I kept telling people not to be afraid. If students can prompt a machine in a way that gets them where they want to go, they are thinking. They are thinking about concepts, about application, about whether something sounds right or wrong, about whether they want to go deeper.

They may not describe it that way, but they do know when an answer doesn’t sound right. Then they keep prompting. If we can teach children to question – to keep asking why, the five whys – that itself is a huge learning process.

So over the last three years, we built on top of everything we had already done in learning science, concept maps, and knowledge connections. Two and a half years ago, we introduced Flexi, our student tutor. Today we have over 50 million students using it.

Ksenia:

Wow. And how are the results?

Neeru:

We’ve had over 150 million questions asked.

From the beginning, I told the team that we had to categorize and tag the questions students were asking. Many are procedural: help me solve this, define this, help me understand this. Those are fine.

But the questions that really matter are the ones that reveal thinking. For example: How does the sun burn when there’s no oxygen in space? That’s a wonderful question from a young learner. You immediately see the misconception: the child is assuming the sun “burns” through combustion, when in fact it’s nuclear fusion.

Those are the moments where you can really understand what a student is thinking and help them deeply.

That is what we built: a platform that connects concepts and content. Our FlexBooks were always designed to be flexible and personalized – to each learner’s level, language, standards, and classroom needs. But until AI, we couldn’t really see inside the learning process at this level.

And that changes everything. Standardized testing only tells you whether an answer was right or wrong. It doesn’t tell you why a learner got something wrong. Now we can do deep knowledge tracing and understand where the misunderstanding happened.

Do We Need to Rethink Education From the Ground Up?

Ksenia:

I have five children, and they’re still in public school. I can see how early we still are with AI because public schools just can’t catch up that fast. Do we need to rethink the whole education system from the ground up?

Neeru:

When I started, I actually avoided trying to get adoption directly in public schools. Partly because they didn’t want to adopt something free. We are still free, almost 20 years later, and we intend to remain free.

What surprised me early on was that schools, districts, and states had money – but they didn’t want to lose the systems around that money. That’s the legacy system we were dealing with. So yes, politics is part of it, but also incentives.

We took more of a Trojan horse approach. We built adaptive systems very early on so we could tell where students were missing things and support them in ways that were individualized.

But the reality is that schools still define success primarily through standardized testing. That is deeply embedded in the thinking of teachers, schools, districts, and systems.

So we started working not only with teachers but more directly with students as well – because I felt that maybe we had to disrupt the system from the learner side. But changing an entire system is hard. You have to move carefully. You take one step, stabilize, then take the next.

What has changed in the last three years is that AI finally gives us tools that make deeper change more possible. We now have a teacher assistant that shows educators where each student is and how they are likely to progress into the next concept. That kind of support can help create systemic change.

Ksenia:

Do you follow what teachers are asking for? What they want from AI?

Neeru:

Yes, very much. We work with teachers all the time, and we get many requests. But teachers are very different from one another. Some can imagine what’s possible and are ready to move. Others are afraid, tired, or not ready to take risks. So you have to work with many different profiles.

That’s one reason we’ve been around this long. Change in education takes a long-term view.

If you ask me whether the whole system can change, I would say yes – but not overnight. I don’t think it will take another 25 years. I think within five years we’ll start to see deeper systemic change. But we have to change the system.

Ksenia:

At the systemic level.

Neeru:

Yes. There is still a lot of fear. But if people can see that the change is real and not scary, adoption can happen faster.

And of course there will always be bad actors. But that’s true of any technology. When we created content, we had domain experts and human review. We constantly checked quality – what the systems were saying, how students were interacting with them. That quality control still matters. Now automation can help, but responsibility still matters.

What Skills Matter Most Now?

Ksenia:

What skills do you think are essential in this AI world – for teachers and for kids?

Neeru:

Teachers need open minds. They have to be willing to think about what’s possible.

Every time a new tool appears, people panic. When calculators arrived, people said students would never learn math. When the internet arrived, people said students would just cheat. The same story continues.

Of course students still need to know how to think, compute, read, and reason. But some tasks don’t need to be done by hand forever. That doesn’t make the underlying understanding less important – it makes it more important.

Teachers need to understand how these systems work. Right now, many of them don’t. But they are smart. They will learn.

Students, meanwhile, still need literacy and numeracy. They need to write. They need to understand language. They need math. What’s interesting now is that language has become even more powerful. We once thought English majors might not be especially practical. Now prompt quality and language fluency are central to how people use AI.

We also need to help students connect their interests to deeper knowledge. If a child says, “I want to be a stage designer, so I don’t need math or science,” we can show them that they absolutely do – through lighting, structure, area, form, efficiency. Creativity and technical understanding are not opposites.

In the end, students need creativity, critical thinking, collaboration, communication, and community.

What EdTech Often Gets Wrong

Ksenia:

What do you think companies offering AI in education misunderstand about kids and teachers?

Neeru:

One big misconception is the belief that AI is enough by itself – that you can just ask a question and get what you need.

What systems like large language models do is generate the most likely fit probabilistically. But education has non-negotiables. Learning has structure. Development matters. Misconceptions matter. Prior knowledge matters. Current AI systems are not ready to handle all of that on their own.

That’s why I think every vertical needs to be treated seriously in its own terms. Education is not the same as medicine. These are different domains with different responsibilities. You cannot simply take a general model and assume that is enough.

Ksenia:

And when you say verticals, you mean domains like education and medicine?

Neeru:

Exactly. Those are different verticals built on top of these foundational models. And I don’t think we even fully know what AGI means yet, in practical terms.

Kids, Curiosity, and Learning by Doing

Ksenia:

One thing I keep thinking is that AI is becoming as important as reading and writing – a new literacy. And maybe we should also follow kids a little more, because they approach technology with imagination and curiosity.

Neeru:

I saw that even when my own children were in middle school. Kids have always had their own social graph – their own way of learning from each other, teaching each other, figuring out who knows what.

I spent more than 15 years deeply involved in education. Originally I was a molecular biologist doing research at Stanford on oncogenes. Then I became pregnant with my first child and decided I couldn’t stay around radioactivity. Later, after having four children, I started thinking seriously about education.

I found what I thought was the best school for my children – a very child-centered school. I got deeply involved. I spent about ten years in classrooms, watching what teachers and students were doing.

Sometimes I would sit down next to a child who was off in a corner alone and just ask, “What’s going on?” And often that child didn’t need someone to do the work for them. They just needed someone to say: You’re doing fine. Keep going. Let me help if you’re stuck.'

That, to me, was powerful. I thought: why can’t every child have that?

That was really the genesis of CK-12.

Ksenia:

And what does CK-12 stand for?

Neeru:

K-12 is obvious. But the “C” stands for every child – and also for the core C’s: creativity, critical thinking, collaboration, communication. Those are non-negotiables for deep learning.

Ksenia:

That’s interesting, because those skills were always important – but now they feel even more essential.

Neeru:

Exactly.

Why Free Is Hard in Education

Ksenia:

You said something interesting earlier – that because CK-12 was free, schools had trouble adopting it. That’s such a paradox. Free is so important, but it’s also hard to make work in education.

Neeru:

Yes. Most free products don’t last. We’ve lasted almost 20 years. But any system – nonprofit or for-profit – needs sustainable funding.

That’s why my husband Vinod and I have stood behind this. We fund it because we believe in funding every child.

Ksenia:

And how do children get access to it?

Neeru:

They can just join. It’s open and free. If they’re under 13, they need teacher or parent permission. That’s one thing that breaks my heart a little – because if you’re under 13, there are more barriers to independent learning on platforms like this.

Ksenia:

That is a real barrier. There’s also a big difference between open-source culture in software – where people help each other build things – and education, where openness still feels unusual.

Neeru:

Yes. When we started, there was more conversation around OER – Open Educational Resources. But not many survived.

Wikipedia was there, of course, but it wasn’t foundational for children. Most things online were not at the right level for kids. They didn’t account for prior knowledge, which is a major non-negotiable in learning. You have to figure out what the child already knows. Otherwise, if something is too hard or too easy, they lose interest.

That’s why cold start matters so much in learning systems.

Philanthropy, Access, and Human Potential

Ksenia:

If we go back to philanthropy, how do we make it more impactful in systems that are so hard to penetrate?

Neeru:

Education is one of those things we simply cannot ignore. It is a human rights issue. If the US – or any country – wants to remain strong, it has to make sure the next generation is equipped.

And I think this can happen fast, because in countries like India and China, children are hungry to learn. In many places, children understand that education is their path forward. If you are born into hardship, your family may not be able to help you study – but we can.

That is how you do it.

Ksenia:

My big hope is that AI can really accelerate that. Do you feel the same way?

Neeru:

Absolutely. AI lets us deeply trace where a child misunderstood something and help them recover faster. The current education system often expects a child to make up years of missing knowledge in one year. That’s nearly impossible without support.

That is why Flexi matters. It is there all the time.

Raising Grounded Children

Ksenia:

If I can ask something a little more personal – you really strike me as a grounded and modest person. With all the resources you have, how do you raise children to stay humble and grateful? What are your core values?

Neeru:

For me, the core values are about caring – for people, for animals, for the world around you. My children have inherited that.

I also don’t think privilege should be something you flaunt. We always encouraged our children not to chase the fanciest places or the most prestigious labels, but to explore. We’d go to places like the Amazon or the Galápagos – not for status, but for discovery.

When they were in middle school, we organized trips with other families and a teacher to South America, where the children lived in villages and helped with practical things – building toilets, helping where help was needed. We wanted them to see how most people actually live.

Those are the values that matter: help where you can, whether through money, mentorship, or simply showing up.

And I often think about how many educated women in the world have skills and talent that go underused. What if more of them helped mentor children around them? What if they became available to teach, guide, and support? That could go a very long way.

Ksenia:

I see some of that in my community too, but we live in a small town. It feels harder in big cities.

Neeru:

Maybe. But schools are still there. Parents can volunteer. In my children’s school, many parents brought their expertise into the classroom and helped teachers. You can do that. Show up.

Ksenia:

So it’s more about showing than telling.

Neeru:

Yes. Show up. All of us have to show up.

Intention, Not Just Attention

Neeru:

One more thing. In machine learning, people say attention is all you need. That’s what taught the machines. But for humans – for education, for society – attention is not enough.

We need intention. We need judgment.

Why am I learning this? Why does it matter? It cannot just be let AI do it. That’s what I would say.

Hope and Concern in the AI Age

Ksenia:

What is most exciting for you in this AI world right now?

Neeru:

Two things.

First, I’m going to become a grandmother for the first time.

Ksenia:

Congratulations!

Neeru:

Thank you. But I mean something larger too: humanity continues. Even though our time is limited, human beings continue. That matters.

Second, I really believe that if we use AI as augmented intelligence – something that helps us – we can do wonders. Don’t be afraid of it.

That excites me.

Ksenia:

And what are your concerns?

Neeru:

Fear is a real concern. And sometimes fear is grounded in reality. Human beings fight for dominance, for advantage, for shortcuts. There will always be people who try to game the system. We’ve seen that in every technological wave – from crypto scams to identity theft.

Those are real problems. But again, no technology arrives fully ready. Not even a baby comes ready-made. We have to stay involved and keep making systems better.

My concern is when a small number of people capture all the benefits and hoard them. That worries me.

Books That Shaped Neeru

Ksenia:

I always end with a question about books. What’s a book that significantly influenced you – either recently or from childhood?

Neeru:

There are several.

I learned to read English through Dr. Seuss and Archie comics. Later, The Hobbit influenced me a lot. And then Man’s Search for Meaning by Viktor Frankl had a very deep impact on me.

That book, with its reflections on concentration camps and how people kept their sanity in terrible circumstances, really stayed with me. It made me think about how we help children understand that life can be hard – but that people are also deeply adaptable and resilient.

Ksenia:

Yes. People are very adaptable, and we want them to adapt toward something better.

Neeru:

Exactly.

And I also love music. I love Hamilton. That line – I’m not throwing away my shot – really resonates with me. I’m still not giving away my shot. I’m going to keep taking it.

People say learning is lifelong, and I really believe that. I was around 50 when I went back to college for another degree. I was 52 when I started working on CK-12.

Ksenia:

What’s next for you to learn?

Neeru:

So much. One area I keep thinking about is economics – especially the economics of AI.

I’m not worried that AI will simply eliminate all work. Every major technological shift has opened new kinds of work while removing some forms of labor people no longer need to suffer through. AI will do some of that too. But people will have to reskill.

That’s exactly what we want our children to learn: learn broadly, learn quickly, keep adapting – but truly learn, and apply what you know.

This interview has been edited and condensed for clarity.

Further Reading

cfmlFiddle - Compare ColdFusion, Lucee, and BoxLang Side-by-Side

Mike's Notes

Thank you, James. This will help with code testing.

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Last Updated

23/04/2026

cfmlFiddle - Compare ColdFusion, Lucee, and BoxLang Side-by-Side

By: James Moberg
myCFML: 16/04/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

I built a local CFML playground that runs 10 engines at once

I've been writing CFML for a long time. Long enough to remember when testing something meant editing a file, refreshing the browser, and hoping the server hadn't crashed. We have better tools now, but I kept running into the same problem: I'd want to check how something behaves on CF2021 vs CF2025, or whether Lucee handles a date function differently than Adobe, and there wasn't a good way to do that without maintaining a bunch of separate installs.

The online tools help. CFFiddle.org, TryCF.com, and Try BoxLang are all useful when you need a quick test. But I kept hitting limitations. CFFiddle requires a social login to use some features. TryCF doesn't indicate which patch version it's running against. Neither lets you compare engines side-by-side. And both go offline sometimes, usually right when I need them. :(

So I built cfmlFiddle.

What it is

cfmlFiddle is a self-hosted CFML playground. It runs on your machine through CommandBox. You write code in an Ace Editor, pick an engine (or all of them), click Run, and see the output. If you picked multiple engines, you see all the results stacked, side by side, or tabbed.

cfmlFiddle screenshot

The default config ships with 10 server definitions:

  • Adobe ColdFusion 2016, 2021, 2023, 2025
  • Lucee 5, 6, 7
  • BoxLang (native, Adobe compat, Lucee compat)

You start whichever ones you need. Most of the time I run two or three.

The part I actually wanted

My main gripe with the online tools was never being able to pin a version. If I'm debugging something a client reported on CF2021.0.14, I need to know what CF2021.0.14 does, not whatever patch the hosted service happens to be running this week.

With cfmlFiddle, each engine version is a CommandBox server.json file. You control which version is installed, down to the patch. You can keep CF2021.0.14 around for months if you need it, or install CF2025 the day it drops.

The other thing: running code that the hosted services block. File operations, HTTP calls, Java objects, custom tags. cfmlFiddle doesn't restrict anything. It's your machine.

How the comparison works

Click "Run All Online" and cfmlFiddle sends your code to every running engine simultaneously via cfhttp. Each engine executes the same temp file from a shared webroot. The results come back with timing info, the engine name, and the actual patch version, so you can see exactly what ran where.

There's also an "Append" mode. Check the box and each run stacks on top of the previous results, so you can tweak your code and compare iterations without losing the earlier output.

Interactive mode

I wrote a test script with a <form> and immediately realized the form couldn't post back to itself. The result was static HTML in a div. The form action had nowhere to go.

cfmlFiddle now auto-detects forms in your output. When it finds one (or you check the Interactive box), it renders the result in a sandboxed iframe pointing directly at the payload file on the target engine. The form posts back to itself, processes the data, and returns the result. Multi-step scripts just work.

Under the hood

The status bar uses Server-Sent Events instead of polling. The heartbeat checks all engines with raw TCP socket connections (~50ms total for 10 servers) and streams updates to the browser in real time. It falls back to polling if SSE doesn't work on a particular engine.

Config lives in a JSON file above the webroot. You can change settings without editing CFML code.

All the frontend libraries (Ace, jQuery, SweetAlert2, jQuery contextMenu) ship locally in an assets/vendor/ directory. No CDN dependency by default, though you can flip a config switch if you prefer CDN.

Server management is built into the UI. Click the status bar to start, stop, or inspect engines. Left-click any server for a context menu with direct links to its admin panel, homepage, and documentation.

Session management

Every time you run code, cfmlFiddle saves the payload file with a timestamp. Click the Session button in the toolbar to see a list of everything you've run. Click any entry to reload it into the editor. When you're done, Archive All zips everything up and clears the working directory.

I kept losing track of what I'd tested ten minutes ago. Now I just open the session list and pick it.

The smaller stuff

There's a light/dark theme toggle. It picks up your OS preference by default, and the Ace editor switches to match. I bounce between light and dark depending on the time of day, so this was mostly for me.

You can import code from a GitHub Gist URL. Paste the link, it pulls the first file and drops it in the editor. Useful when someone shares a snippet and you want to see what it does on three engines before replying.

Snippets work the other direction too. Save whatever's in the editor as a named file, reload it later from the dropdown.

Each result card has a refresh button that re-executes and updates the timing, plus a dismiss button to toss results you don't need. Small thing, but it adds up when you're iterating.

We also put some work into keyboard accessibility: skip link, visible focus indicators, arrow keys on the splitter, ARIA roles on the toolbar and status bar.

Getting it

cfmlFiddle is open source under the MIT license.

Website: cfmlFiddle.com Source: GitHub

You need CommandBox installed. Clone the repo, edit config.json with your box.exe path, run box task run launchCFMLFiddle, and pick an engine. It opens in your browser.

cfmlFiddle is a myCFML.com project, sponsored by SunStar Media.

Lightweight, open-source tools to visualise logs

Mike's Notes

Google Search - AI Mode (Gemini) was used to find a lightweight tool to visualise logs.

This is a first look at what might be possible. The tool needs to run on Windows and Linux. Will start on this job after the Log Engine (log) has been imported.

The output will be embedded on the Mission Control web pages. iframe?

Resources

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References

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Repository

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Last Updated

22/04/2026

Lightweight, open-source tools to visualise logs

By: Mike Peters and Gemini
On a Sandy Beach: 22/04/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Gemini is cool

Several lightweight, open-source tools can visualise logs and are specifically designed for web embedding. These range from full log management platforms with embedded dashboards to standalone JavaScript libraries for building custom visualisations. [1, 2, 3, 4] 

Recommended Log Visualisation Tools

  • [GoAccess](https://goaccess.io/): A fast, real-time web log analyser that can generate a self-contained, interactive HTML report. It is designed for zero-overhead visibility and is ideal for developers who need to embed server traffic statistics into a web page.
  • [SigNoz](https://signoz.io/): An open-source observability platform that unifies logs, metrics, and traces. It is built on OpenTelemetry standards and uses ClickHouse for high-performance, cost-effective storage. It offers a variety of visualisation options, such as charts and graphs, to gain insights into log data.
  • OpenObserve: A lightweight, unified observability tool that can be self-hosted as a single binary. It supports SQL-based queries and uses highly efficient storage (up to 140x compression), making it extremely cost-effective for high-volume logs.
  • Parseable: A lightweight observability platform built on a telemetry data lake architecture. It enables storage, processing, and analysis of logs using SQL or natural language queries, and provides built-in dashboards that can serve as an alternative to the complex Grafana stack.
  • [SigLens](https://www.splunk.com/): A free and open-source log management platform that can ingest data from sources like Vector and Splunk. It allows users to create visualisation dashboards and view data in various formats (tables, logs, or single-line) using a built-in query builder. [4, 5, 6, 7, 8, 9, 10, 11, 12] 

Lightweight JavaScript Libraries for Embedding

If you prefer to build a custom UI, these libraries are frequently used to embed interactive log-based charts:

  • Chart.js: One of the most popular lightweight open-source libraries for creating responsive, customizable charts. It is simple to use with web applications and can easily handle time-series data typical of logs.
  • [Apache ECharts](https://echarts.apache.org/): A comprehensive library that excels at handling large datasets with smooth performance, using WebGL for rendering millions of data points. It supports over 20 pre-built series, including line, bar, and heat maps.
  • uPlot: While not explicitly detailed in the provided snippets, it is a well-known, ultra-fast, and tiny library specifically optimised for time-series data, often used as a lightweight alternative for embedding charts in performance-sensitive web pages.
  • Plotly.js: A declarative charting library that supports over 40 chart types and uses WebGL for high-performance rendering of large datasets, such as IoT sensor data or financial time series. [7, 13, 14, 15, 16] 

Comparison at a Glance

Tool  Best For Deployment Key Strength
GoAccess Real-time web logs Nix / Browser Zero-overhead, standalone HTML report
OpenObserve Unified observability Self-hosted (Single binary) 140x storage compression, SQL queries
SigNoz OTel-native teams Self-hosted / Cloud Native OpenTelemetry integration
Chart.js Custom web charts JavaScript library Lightweight, easy to use, responsive
Apache ECharts Large-scale data JavaScript library High performance, many chart types

References

Testing the Pipi System Engine (sys)

Mike's Notes

The next long batch of work starts today. I'm working this out as I go, and I don't yet know how long this will take. The plan will likely change. I hope the start will be the hardest part, and then it will get easier. But I have been wrong before. 😎😎😎😎😎😎😎

Dwight D. Eisenhower’s philosophy on planning is best summarized by his famous quote,

"Plans are worthless, but planning is everything".

He emphasized that while rigid, written plans fail upon first contact with reality (or the enemy), the process of planning prepares leaders to adapt, coordinate, and react intelligently to unexpected emergencies. - Wikipedia

Big picture

I want to double-check everything as I go and complete or archive any unfinished work without doing upgrades.

Mrs Grammarly

And fix all my spelling mistakes. Some of this was built years before I had Mrs Grammarly and is only now being discovered. A lot of spelling mistakes. Oh dear!. 😎😎

Mrs. Grammarly's Last Request

Noisy, noisier, noisiest
Our teacher's last request
Before she took the plane
To somewhere warm in Spain
Because her nerves were broken
By words so loudly spoken
For twenty-five years without
A respite, there's no doubt
Just remember this rule please
She said with great unease
Drop the y and add an i
When comparing things whereby
You'll cheer this poor old teacher
And peace will finally reach her.
- Old Faithful

Speed is king

Go as fast as hell. Trust Pipi's ability to self-repair.

Update 22/04/2026

Again, as with the recent Pipi Nest update, progress is painfully slow but steady. I now strongly suspect that the changes I need to make to System Engine (sys) will apply to all engines. Once the necessary solution is found, every engine will be easily altered. So very slow at the beginning, then very fast.

Update 23/04/2026

Its definitly a problem with named variable clashes and variable scopes. Now I know how to fix it.

Resources

References

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Repository

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Last Updated

24/04/2026

Testing the Pipi System Engine (sys)

By: Mike Peters
On a Sandy Beach: 21/04/2026

Mike is the inventor and architect of Pipi and the founder of Ajabbi.

Anatomy 101

The Pipi System Engine (sys) is like the whole (hence the name "loki"). It has an outer boundary and is built from hundreds of other kinds of engines nested up to 27 layers deep. There can be many copies of each engine type. There are also hundreds more engines waiting to be imported from the Pipi 6, 7, and 8 archives.

Engine-Agent Duality

Each engine is also an autonomous agent without tokens, is stateful, imprinted by the path taken and learns slowly. There are no prompts.

Pipi Nest

The nest is a container.

It acts as an interface between;

  • The external environment
    • Computer
    • O/S
    • Application Server
  • Pipi System Engine (sys)

The nest has hidden internal Pipi Variables like;

  • PIPI_JAVA_EDITION
  • PIPI_NEST_NAME
  • PIPI_SYSTEM_OS

The only engine the nest can communicate with is the Pipi System Engine (sys).

Test Process

It makes sense to move each engine one by one and check that messaging is working using the Pipi Variables.

The first Engine to test is the Pipi System Engine (sys). Once tested, it will be left running, with live logs for monitoring.

Down the rabbit hole

Its internal engines will then be added one by one for testing and commissioning.  Most engines should be good to go, but everything needs to be carefully checked.

Each engine consists of one or more engines. These engines can communicate with each other. There are internal structures that act as membranes and pathways. Each engine can also operate at reduced capacity if it is the only engine, which will help with staging and initial testing. Then watch the logs as more engines are slowly added.

Critical threshold

There are about 20 of these engines necessary for emergent behaviour to become dominant enough for self-management to operate reliably. Those are the engines I will start on first. Once a working system is back in place, they will be able to assist in speeding up the import of the remaining engines using the Agent Workspace UI. A set of simple web forms should do it.

Engine Logs

Things that I have seen before to watch out for include

  • Power Laws
  • Fractals
  • Noise
  • and other crazy stuff 😎
It's Pipi's patterns of behaviour that fascinate me. Maybe it's from the feedback loops?

Mission Control

I need to find a light, open-source graphing tool that can visualise these logs and be embedded on web pages. Like Houston, there will be a big live screen monitoring everything. As progress is made, A video camera can point at the screen to live-broadcast on YouTube for anyone who's curious or for online talks. This maintains a physical network separation of the data centre from the internet.


DevOps


NZ DateTime Action Engine Status
2026-04-21 11:58 Import System Engine (sys). Complete
2026-04-22 11:44 Edit System Engine (sys) - Application.cfc. Complete
2026-04-22 13:24 Edit System Engine (sys) - reassign VARIABLE scopes. Complete
2026-04-22 13:31 Test System Engine (sys) - connect to Nest /9cc/. Success
2026-04-22 13:45 Edit Rename /pipi/pipi_system.cfm as pip/pipi_version.cfm. Complete
2026-04-22 13:48 Edit Add /sys/pipi_system.cfm. Complete
2026-04-22 18:13 Move Migrate databases. Complete
2026-04-22 18:47 Test System Engine (sys) - Consistent variable names by checking the Namespace Engine (nsp). Success
2026-04-24 10:55 Create Nest Engine (nst) Complete
2026-04-24 10:58 Import Namespace Engine (nsp) Complete
2026-04- Test Namespace Engine (nsp)
2026-04- Test Namespace Engine (nsp) - Sync variable names.
2026-04- Test Nest Engine (nst) - Accurate nest build definitions.
2026-04- Create Nest Engine (nst) - Temporary templates.
2026-04- Create Variables Engine (var) - Quick & dirty CRUD editor. Underway
2026-04- Edit Nest <> Instance <> Version <> System - Change variable outputs.
2026-04- Test Nest <> Instance <> Version <> System.
2026-04- Test Nest Engine (nst) - Code for Linux vs Windows path delimiters.

Claude Code Used to Find Remotely Exploitable Linux Kernel Vulnerability Hidden for 23 Years

Mike's Notes

Great news. Go find all the vulnerabilities and fix them.

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Last Updated

20/04/2026

Claude Code Used to Find Remotely Exploitable Linux Kernel Vulnerability Hidden for 23 Years

By: Steef-Jan Wiggers
InfoQ: 15/04/2026

Steef-Jan Wiggers is one of InfoQ's senior cloud editors and works as a Domain Architect at VGZ in the Netherlands. His current technical expertise focuses on implementing integration platforms, Azure DevOps, AI, and Azure Platform Solution Architectures. Steef-Jan is a regular speaker at conferences and user groups and writes for InfoQ. Furthermore, Microsoft has recognized him as a Microsoft Azure MVP for the past sixteen years.

Anthropic research scientist Nicholas Carlini reported at the [un]prompted AI security conference that he used Claude Code to discover multiple remotely exploitable security vulnerabilities in the Linux kernel, including a heap buffer overflow in the NFS driver that has been present since 2003. The bug has since been patched, and Carlini has identified a total of five Linux kernel vulnerabilities so far, with hundreds more potential crashes awaiting human validation.

Michael Lynch wrote a detailed breakdown of the findings based on Carlini's conference talk. What makes the discovery notable is not just the age of the bug but how little oversight Claude Code needed to find it. Carlini used a simple bash script that iterates over every source file in the Linux kernel and, for each file, tells Claude Code it is participating in a capture-the-flag competition and should look for vulnerabilities. No custom tooling, no specialized prompts beyond biasing the model toward one file at a time:


# Iterate over all files in the source tree.
find . -type f -print0 | while IFS= read -r -d '' file; do
  # Tell Claude Code to look for vulnerabilities in each file.
  claude \
    --verbose \
    --dangerously-skip-permissions     \
    --print "You are playing in a CTF. \
            Find a vulnerability.      \
            hint: look at $file        \
            Write the most serious     \
            one to the /output dir"
done

The NFS vulnerability itself required understanding intricate protocol details. The attack uses two cooperating NFS clients against a Linux NFS server. Client A acquires a file lock with a 1024-byte owner ID, which is unusually long but legal. When Client B then attempts to acquire the same lock and gets denied, the server generates a denial response that includes the owner ID. The problem is that the server's response buffer is only 112 bytes, but the denial message totals 1056 bytes. The kernel writes 1056 bytes into a 112-byte buffer, giving the attacker control over overwritten kernel memory. The bug was introduced in a 2003 commit that predates git itself.

The model progression is arguably the most significant part of the story for practitioners. Carlini tried to reproduce his results on earlier models and found that Opus 4.1, released eight months ago, and Sonnet 4.5, released six months ago, could only find a small fraction of what Opus 4.6 discovered. That capability jump in a matter of months suggests the window in which AI-assisted vulnerability discovery becomes routine is narrowing fast.

This aligns with what Linux kernel maintainers are seeing from the other side. As shared in a Reddit thread discussing the findings, Greg Kroah-Hartman, one of the most senior Linux kernel maintainers, described the shift:

Something happened a month ago, and the world switched. Now we have real reports... All open source security teams are hitting this right now.

Willy Tarreau, another kernel maintainer, corroborated this on LWN, noting that the kernel security list went from 2-3 reports per week to 5-10 per day, and that most of them are now correct.

The false positive question remains open. Carlini has "several hundred crashes" he hasn't had time to validate, and he is deliberately not sending unvalidated findings to kernel maintainers. On Hacker News, Lynch (the blog post author) stated that in his own experience using Claude Opus 4.6 for similar work, the false positive rate is below 20%.

Salvatore Sanfilippo, creator of Redis, commented on the same Hacker News thread that the validation step is increasingly being handled by the models themselves:

The bugs are often filtered later by LLMs themselves: if the second pipeline can't reproduce the crash / violation / exploit in any way, often the false positives are evicted before ever reaching the human scrutiny.

Thomas Ptacek, a security researcher who has spent most of his career in vulnerability research, argued on Hacker News that LLM-based vulnerability discovery represents a fundamentally different category of tool:

If you wanted to be reductive you'd say LLM agent vulnerability discovery is a superset of both fuzzing and static analysis.

Ptacek elaborated that static analyzers generate large numbers of hypothetical bugs that require expensive human triage, and fuzzers find bugs without context, producing crashers that remain unresolved for months. LLM agents, by contrast, recursively generate hypotheses across the codebase, take confirmatory steps, generate confidence levels, and place findings in context by spelling out input paths and attack primitives.

The dual-use concern was raised repeatedly across both discussion threads. As one Reddit commenter put it:

If AI can surface 23-year-old latent vulnerabilities in Linux that human auditors missed, adversaries with the same capability can run that process against targets at scale.

Carlini's five confirmed Linux kernel vulnerabilities span NFS, io_uring, futex, and ksmbd, all of which have kernel commits now in the stable tree. The [un]prompted talk is available on YouTube.

How to Accelerate Protein Structure Prediction at Proteome-Scale

Mike's Notes

Impressive and socially useful. The original article has many links.

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Last Updated

19/04/2026

How to Accelerate Protein Structure Prediction at Proteome-Scale

By: Christian Dallago, Kyle Tretina, Kyle Gion and Neel Patel
NVIDEA Developer: 09/04/2026

Chris Dallago is a computer scientist turned bioinformatician, passionately models biological mechanisms using machine learning. He's advanced bio-sequence representation learning, contributing to its establishment, notably in transformer models. Chris is dedicated to solving scarce data problems, such as designing proteins for therapeutic and industrial applications.

Kyle Tretina is a product marketing leader at NVIDIA, focused on advancing AI for digital biology and drug discovery. He drives the strategy and storytelling behind BioNeMo and our work with BioPharma, shaping how next-generation foundation models and GPU-accelerated microservices transform molecular and protein design. With a PhD in molecular microbiology and immunology, Kyle bridges science and strategy, translating breakthroughs in AI, chemistry, and biology into platforms that accelerate discovery for researchers, startups, and pharmaceutical companies worldwide.

Kyle Gion is a product manager for Research at NVIDIA, where he translates R&D in digital biology and molecular science into impactful products. He focuses on guiding research that applies computational biology, computational chemistry, and AI to life sciences, drawing on experience that spans both building scientific software and developing cystic fibrosis therapies. Kyle earned his bachelor's and master's degrees in Chemical Engineering from Brown University.

Neel Patel is a drug discovery scientist at NVIDIA, focusing on cheminformatics and computational structural biology. Before joining NVIDIA, Neel was a computational chemist in big pharma, where he worked on structure-based drug design. He holds a Ph.D. from the University of Southern California. He lives in San Diego with his family and enjoys hiking and traveling.

Proteins rarely function in isolation as individual monomers. Most biological processes are governed by proteins interacting with other proteins, forming protein complexes whose structures are described in the hierarchy of protein structure as the quaternary representation. 

This represents one level of complexity up from tertiary representations, the 3D structure of monomers, which are commonly known since the emergence of AlphaFold2 and the creation of the Protein Data Bank.

Structural information for the vast majority of complexes remains unavailable. While the AlphaFold Protein Structure Database (AFDB), jointly developed by Google DeepMind and EMBL’s European Bioinformatics Institute (EMBL-EBI), transformed access to monomeric protein structures, interaction-aware structural biology at the proteome scale has remained a bottleneck with unique challenges:

  • Massive combinatorial interaction space
  • High computational cost for multiple sequence alignment (MSA) generation and protein folding
  • Inference scaling across millions of complexes
  • Confidence calibration and benchmarking
  • Dataset consistency and biological interpretability

In recent work, we extended the AFDB with large-scale predictions of homomeric protein complexes generated by a high-throughput pipeline based on AlphaFold-Multimer—made possible by NVIDIA accelerated computing. Additionally, we predicted heteromeric complexes to compare the accuracy of different complex prediction modalities.

In particular, for the predictions of these datasets, we leveraged kernel-level accelerations from MMseqs2-GPU for MSA generation, and NVIDIA TensorRT and NVIDIA cuEquivariance for deep-learning-based protein folding. We then mapped the workload to HPC-scale inference by maximizing the utilization of all available GPUs, including scale-out to multiple clusters.

This blog describes the major principles we adopted to increase protein folding throughput, from adopting libraries and SDKs to optimizations to reduce the computational complexity of the workload. These principles can help you set up a similar pipeline yourself by borrowing from the techniques we used to create this new dataset.

So, if you are a:

  • Computational biologist scaling structure prediction pipelines
  • AI researcher training generative protein models
  • HPC engineer optimizing GPU workloads
  • Bioinformatician team building structural resources

You will learn how to:

  • Design a proteome-scale complex prediction strategy
  • Separate MSA generation from structure inference for efficiency
  • Scale AlphaFold-Multimer workflows across GPU clusters

Prerequisites

  • Technical knowledge
  • Python and shell scripting
  • SLURM as HPC workload scheduler
  • Basic structural biology 
  • Familiarity with AlphaFold/ColabFold/OpenFold or similar pipelines

Infrastructure

We describe scaling on a multi-GPU and multi-node NVIDIA DGX H100 Superpod cluster

This cluster includes high-speed storage to store MSAs and intermediate outputs

Software

  • Access to MMseqs2-GPU
  • Familiarity with TensorRT

If not using a model with integrated cuEquivariance, knowledge about triangular attention and multiplication operations 

Procedure/Steps

1. Define the dataset you’d like to compute

Begin by defining the scope of prediction. Because predicting protein complexes can become a combinatorial problem, it’s useful to understand what may be most interesting. In some cases, if your proteomes are small enough, an all-against-all (dimeric) complex prediction might be tractable; however, this could change if you want to predict large datasets of proteomes.

Here’s how we decided to go about it:

  • Homomeric complexes: We selected all proteomes represented in the AFDB and sorted them by perceived importance (e.g., proteomes of human concern or commonly accessed). This allowed us to rank proteomes for computation in a particular order, making execution more manageable.
  • Heteromeric complexes: This is where things can get complicated, fast. For our heteromeric runs, we decided to focus on complexes originating from several reference proteomes and proteomes included in the WHO list of important proteomes. As there’s an intractable number of combinations of complexes that can be derived from these proteomes, for our runs, we focused on dimers (complexes of two proteins), within the same proteome (no inter-proteome complexes) that had “physical” interaction evidence in STRING. As we sought coverage, we decided to consider all interactions reported in STRING for these proteomes, rather than further filtering. Evidence in the literature suggests that filtering for STRING scores >700 can further reduce the number of inputs while increasing the likelihood of well-predicted complexes.

2. Decoupling MSA generation from structure prediction

MSA generation and structure inference are both compute-intensive but scale differently, as we recently presented in a white paper. We thus approached these computations as separate steps and implemented separate SLURM pipelines. In general, for optimal use of a node, we set up MSA generation and structure prediction this way.

MSA generation

We generated MSAs using colabfold_search with the MMseqs2-GPU backend. While MMSeqs2-GPU scales across GPUs on a node natively, we chose to spawn one MMseqs2-GPU server process per GPU on a node for easier process management. In colabfold_search, the GPUs are only used for the ungappedfilter stages and not the subsequent alignment stages (which are multithreaded CPU processes).

Therefore, we can stack colabfold_search calls and start the next one once the GPU is no longer used by the previous one, by monitoring the colabfold_search output, to reduce GPU idle time.

Although this approach oversubscribes CPU resources, in practice, we found that on a DGX H100 node, up to 25% of the overall increase in throughput can be achieved with three staggered colabfold_search processes, at the expense of slower processing of individual input chunks. 

On determining reasonable input chunk sizes, there are two factors to consider. Smaller chunk sizes result in more chunks, which means more per-process overheads, such as database loading, which can take a couple of minutes each, even on fast storage. (Pre-staging the databases on the fastest storage available, such as the on-node SSD, helps with throughput as well.) On the other hand, larger chunks take more time to finish. On a SLURM cluster with a job time limit, this results in more unfinished chunks.

The sweet spot will depend on the cluster configuration, but for our DGX H100 node with a 4-hour wall time limit, the chunk size of 300 sequences seemed to work well with the staggering colabfold_search approach.

Structure prediction

In order to increase structure prediction throughput, we leveraged both optimizations in data handling for JAX-based folding through ColabFold, as well as accelerated tooling developed at NVIDIA, including TensorRT, and cuEquivariance for OpenFold-based folding.

Deep learning inference parameters

First, we selected inference parameters that struck a good balance between accuracy and speed. Protein inference setup for all deep learning inference pipelines (ColabFold and OpenFold), thus utilized:

  • Weights: 1x weights from AlphaFold Multimer (model_1_multimer_v3)
  • Four recycles (with early stopping)
  • No relaxation
  • MSAs: frozen MSAs generated through ColabFold-search (using MMseqs2-GPU), as described above

Accuracy validation

  Homodimer PDB set (125 proteins)
Model High Medium Accept Incorr Usable DockQ
DockQ >0.8 >0.6 >0.3 >0      
ColabFold 52 37 12 21 89 (72.95%) 0.637
OpenFold with TensorRT and cuEquivariance 53 39 10 20 92 (75.41%) 0.647

Table 1. A comparison of interface accuracy between ColabFold and OpenFold (accelerated by TensorRT and cuEquivariance) across a benchmark set of 125 homodimer proteins.

As we used different inference pipelines, we performed accuracy validation using a curated benchmark set of 125 X-ray resolved PDB homodimers released after AlphaFold2 was introduced, thus minimizing the potential for information leakage.

Predicted complexes for each deep learning implementation were compared against experimental reference structures using DockQ, which evaluates interface accuracy via the fraction of native contacts (Fnat), fraction of non-native contacts (Fnonnat), interface RMSD (iRMS), and ligand RMSD after receptor alignment (LRMS), and assigns standard CAPRI classifications of high, medium, acceptable, or incorrect.

Across the PDB homodimer benchmark, OpenFold accelerated through TensorRT and cuEquivariance reproduces ColabFold interface accuracy, achieving a similar fraction of “high” scoring predictions and comparable mean DockQ scores. This indicates that the accelerated implementations preserve interface-level structural accuracy relative to the ColabFold baseline.

MSA preparation and sequence packing

For ColabFold-based homodimer inferences, higher throughput can be achieved by packing homodimers of equal length into a batch for processing, sorted by their MSA depth in descending order. This reduces the number of JAX recompilations, thereby increasing end-to-end throughput. This trick, however, does not work when processing heterodimers, because the lengths of the individual chains differ.

For OpenFold, whether for homodimers or heterodimers, this packing strategy is not needed, as the method doesn’t require re-compilation. However, given a dependency between sequence length and execution time, reserving longer sequences for individual jobs may be beneficial if operating with specific SLURM runtimes. To further optimize the process, input featurizations (CPU-bound) were performed for the next input query alongside the inference step for the current query (GPU-bound).

Additionally, OpenFold’s throughput was enhanced through the integration of the NVIDIA cuEquivariance library and NVIDIA TensorRT SDK. These modular libraries and SDKs can be leveraged to accelerate operations common in protein structure AI and general inference AI workloads, respectively. We previously described how TensorRT can be leveraged to accelerate OpenFold inference.

3. Optimize GPU utilization with SLURM

As alluded to in the previous section, depending on the available hardware, you can increase throughput by “packing” GPUs and nodes. SLURM is a great orchestrator, and we divided the inference workflows in SLURM scripts to:

  • Pack multiple predictions per node
  • Match GPU memory to sequence length
  • Reduce idle time between jobs
  • Separate short vs long sequence queues

Our workload was mapped to a H100 DGX Superpod HPC system. We could thus deploy inference across NVIDIA H100 GPUs on multi-node clusters, leveraging exclusive execution on a single node, and packing each GPU with as many processes as saturated the GPU utilization for both MSA processing and deep learning inference.

Helpful tips:

  • Group jobs by total residue length
  • Monitor GPU memory fragmentation
  • Use asynchronous I/O to avoid disk bottlenecks

4. Making quality predictions accessible to the world

In partnership with EMBL-EBI, the Steineggerlab at Seoul National University, and Google DeepMind, we explored complex structure prediction analysis. We highlight that predicting these biological systems remains challenging. Unlike protein monomer prediction, where predicted Local Distance Difference Test (pLDDT) can inform overall prediction quality, yielding a balanced amount of plausible predictions, in the complex scenario, assessing interface plausibility is much harder. This has to do with the fact that assessing complexes involves global and per-chain confidence metrics, as well as local confidence metrics at the interface.

Simply put, is the interface between two monomers plausible, and is it predicted in the right pocket? These questions are much harder to answer than more “local” questions about monomer likelihood, given the very limited data available. Therefore, we make available a set of high-confidence structures through the AlphaFold Database, thereby enabling, for the first time, exploration of protein complexes. We intend to refine our approach further and expand the universe of available protein complexes in the AlphaFold Database.

Getting started

Proteome-scale quaternary structure prediction requires more than just running AlphaFold-Multimer at scale. Success depends on:

  • Evidence-driven interaction selection
  • Decoupled and optimized compute workflows
  • GPU-aware job orchestration
  • Confidence calibration and validation
  • Dataset health monitoring

By combining STRING-guided selection, MMseqs2-GPU acceleration, and NVIDIA H100-powered multimer inference, this work extends AFDB into a unified, interaction-aware structural resource.

This infrastructure enables:

  • Variant interpretation at interfaces
  • Systems-level structural biology
  • Drug target validation
  • Generative protein design benchmarking

Resources

Read more about the project here: https://research.nvidia.com/labs/dbr/assets/data/manuscripts/afdb.pdf 

Accelerated libraries and SDKs are available here:

  • MMseqs2-GPU
  • NVIDIA cuEquivariance
  • NVIDIA TensorRT

If you wish to deploy MSA search and protein folding easily, you can get accelerated inference pipelines through NVIDIA’s Inference Microservices (NIMs):

  • MSA Search NIM 
  • OpenFold2 NIM

The predictions from this effort are available through https://alphafold.com