PhDing in Robotics and AI | creating cyber-physical commons: code, culture, capital, coordination | ex @KERNEL0x https://teapawt.org/t.co/p99leFCelp @initc3org @GoldmanSachs
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Joined September 2013
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sidcode retweeted
I wrote a quick new post on "Digital Hygiene".
Basically there are some no-brainer decisions you can make in your life to dramatically improve the privacy and security of your computing and this post goes over some of them. Blog post link in the reply, but copy pasting below too.
Every now and then I get reminded about the vast fraud apparatus of the internet, re-invigorating my pursuit of basic digital hygiene around privacy/security of day to day computing. The sketchiness starts with major tech companies who are incentivized to build comprehensive profiles of you, to monetize it directly for advertising, or sell it off to professional data broker companies who further enrich, de-anonymize, cross-reference and resell it further. Inevitable and regular data breaches eventually runoff and collect your information into dark web archives, feeding into a whole underground spammer / scammer industry of hacks, phishing, ransomware, credit card fraud, identity theft, etc. This guide is a collection of the most basic digital hygiene tips, starting with the most basic to a bit more niche.
Password manager. Your passwords are your "first factor", i.e. "something you know". Do not be a noob and mint new, unique, hard passwords for every website or service that you sign up with. Combine this with a browser extension to create and Autofill them super fast. For example, I use and like 1Password. This prevents your passwords from 1) being easy to guess or crack, and 2) leaking one single time, and opening doors to many other services. In return, we now have a central location for all your 1st factors (passwords), so we must make sure to secure it thoroughly, which brings us to...
Hardware security key. The most critical services in your life (e.g. Google, or 1Password) must be additionally secured with a "2nd factor", i.e. "something you have". An attacker would have to be in possession of both factors to gain access to these services. The most common 2nd factor implemented by many services is a phone number, the idea being that you get a text message with a pin code to enter in addition to your password. Clearly, this is much better than having no 2nd factor at all, but the use of a phone number is known to be extremely insecure due to the SIM swap attack. Basically, it turns out to be surprisingly easy for an attacker to call your phone company, pretend they are you, and get them to switch your phone number over to a new phone that they control. I know this sounds totally crazy but it is true, and I have many friends who are victims of this attack. Therefore, purchase and set up hardware security keys - the industrial strength protection standard. In particular, I like and use YubiKey. These devices generate and store a private key on the device secure element itself, so the private key is never materialized on a suspiciously general purpose computing device like your laptop. Once you set these up, an attacker will not only need to know your password, but have physical possession of your security key to log in to a service. Your risk of getting pwned has just decreased by about 1000X. Purchase and set up 2-3 keys and store them in different physical locations to prevent lockout should you physically lose one of the keys. The security keys support a few authentication methods. Look for "U2F" in the 2nd factor settings of your service as the strongest protection. E.g. Google and 1Password support it. Fallback on "TOTP" if you have to, and note that your YubiKeys can store TOTP private keys, so you can use the YubiKey Authenticator app to access them easily through NFC by touching your key to the phone to get your pin when logging in. This is significantly better than storing TOTP private keys on other (software) authenticator apps, because again you should not trust general purpose computing devices. It is beyond the scope of this post to go into full detail, but basically I strongly recommend the use of 2-3 YubiKeys to dramatically strengthen your digital security.
Biometrics. Biometrics are the third common authentication factor ("something you are"). E.g. if you're on iOS I recommend setting up FaceID basically everywhere, e.g. to access the 1Password app and such.
Security questions. Dinosaur businesses are obsessed with the idea of security questions like "what is your mother's maidan name?", and force you to set them up from time to time. Clearly, these are in the category of "something you know" so they are basically passwords, but conveniently for scammers, they are easy to research out on the open internet and you should refuse any prompts to participate in this ridiculous "security" exercise. Instead, treat security questions like passwords, generate random answers to random questions, and store them in your 1Password along with your passwords.
Disk encryption. Always ensure that your computers use disk encryption. For example, on Macs this total no-brainer feature is called "File Vault". This feature ensures that if your computer gets stolen, an attacker won't be able to get the hard disk and go to town on all your data.
Internet of Things. More like @internetofshit. Whenever possible, avoid "smart" devices, which are essentially incredibly insecure, internet-connected computers that gather tons of data, get hacked all the time, and that people willingly place into their homes. These things have microphones, and they routinely send data back to the mothership for analytics and to "improve customer experience" lol ok. As an example, in my younger and naive years I once purchased a CO2 monitor from China that demanded to know everything about me and my precise physical location before it would tell me the amount of CO2 in my room. These devices are a huge and very common attack surface on your privacy and security and should be avoided.
Messaging. I recommend Signal instead of text messages because it end-to-end encrypts all your communications. In addition, it does not store metadata like many other apps do (e.g. iMessage, WhatsApp). Turn on disappearing messages (e.g. 90 days default is good). In my experience they are an information vulnerability with no significant upside.
Browser. I recommend Brave browser, which is a privacy-first browser based on Chromium. That means that basically all Chrome extensions work out of the box and the browser feels like Chrome, but without Google having front row seats to your entire digital life.
Search engine. I recommend Brave search, which you can set up as your default in the browser settings. Brave Search is a privacy-first search engine with its own index, unlike e.g. Duck Duck Go which basically a nice skin for Bing, and is forced into weird partnerships with Microsoft that compromise user privacy. As with all services on this list, I pay $3/mo for Brave Premium because I prefer to be the customer, not the product in my digital life. I find that empirically, about 95% of my search engine queries are super simple website lookups, with the search engine basically acting as a tiny DNS. And if you're not finding what you're looking for, fallback to Google by just prepending "!g" to your search query, which will redirect it to Google.
Credit cards. Mint new, unique credit cards per merchant. There is no need to use one credit card on many services. This allows them to "link up" your purchasing across different services, and additionally it opens you up to credit card fraud because the services might leak your credit card number. I like and use privacy dot com to mint new credit cards for every single transaction or merchant. You get a nice interface for all your spending and notifications for each swipe. You can also set limits on each credit card (e.g. $50/month etc.), which dramatically decreases the risk of being charged more than you expect. Additionally, with a privacy dot com card you get to enter totally random information for your name and address when filling out billing information. This is huge, because there is simply no need and totally crazy that random internet merchants should be given your physical address. Which brings me to...
Address. There is no need to give out your physical address to the majority of random services and merchants on the internet. Use a virtual mail service. I currently use Earth Class Mail but tbh I'm a bit embarrassed by that and I'm looking to switch to Virtual Post Mail due to its much strong commitments to privacy, security, and its ownership structure and reputation. In any case, you get an address you can give out, they receive your mail, they scan it and digitize it, they have an app for you to quickly see it, and you can decide what to do with it (e.g. shred, forward, etc.). Not only do you gain security and privacy but also quite a bit of convenience.
Email. I still use gmail just due to sheer convenience, but I've started to partially use Proton Mail as well. And while we're on email, a few more thoughts. Never click on any link inside any email you receive. Email addresses are extremely easy to spoof and you can never be guaranteed that the email you got is a phishing email from a scammer. Instead, I manually navigate to any service of interest and log in from there. In addition, disable image loading by default in your email's settings. If you get an email that requires you to see images, you can click on "show images" to see them and it's not a big deal at all. This is important because many services use embedded images to track you - they hide information inside the image URL you get, so when your email client loads the image, they can see that you opened the email. There's just no need for that. Additionally, confusing images are one way scammers hide information to avoid being filtered by email servers as scam / spam.
VPN. If you wish to hide your IP/location to services, you can do so via VPN indirection. I recommend Mullvad VPN. I keep VPN off by default, but enable it selectively when I'm dealing with services I trust less and want more protection from.
DNS-based blocker. You can block ads by blocking entire domains at the DNS level. I like and use NextDNS, which blocks all kinds of ads and trackers. For more advanced users who like to tinker, pi-hole is the physical alternative.
Network monitor. I like and use The Little Snitch, which I have installed and running on my MacBook. This lets you see which apps are communicating, how much data and when, so you can keep track of what apps on your computer "call home" and how often. Any app that communicates too much is sus, and should potentially be uninstalled if you don't expect the traffic.
I just want to live a secure digital life and establish harmonious relationships with products and services that leak only the necessary information. And I wish to pay for the software I use so that incentives are aligned and so that I am the customer. This is not trivial, but it is possible to approach with some determination and discipline.
Finally, what's not on the list. I mostly still use Gmail + Gsuite because it's just too convenient and pervasive. I also use 𝕏 instead of something exotic (e.g. Mastodon), trading off sovereignty for convenience. I don't use a VoIP burner phone service (e.g. MySudo) but I am interested in it. I don't really mint new/unique email addresses but I want to. The journey continues. Let me know if there are other digital hygiene tips and tricks that should be on this list.
Link to blog post version in the reply, on my brand new Bear ʕ•ᴥ•ʔ blog cute 👇
sidcode retweeted
MORE IMPORTANTLY,
I’ve been thinking deeply about what a PhD looks like in this day and age.
I write about what I’m looking forward to: rishflections.substack.com/p…
sidcode retweeted
Earlier, quite a few people who attended CVPR told me they came away pretty disappointed — including some extremely senior people in the field. At the time, I didn’t fully understand why. But after I attended ECCV, I finally did.
There was a surprising amount of frustration at the conference, especially since it happened right after the GPT-6 release. People were openly wondering what traditional CV research is going to look like from here, and how much of it will eventually just be absorbed by large foundation models.
Actually, when I gave talks about GenCeption to a few academic groups, I had already started to see that kind of frustration.
GenCeption essentially leverages a large-scale pretrained diffusion model to build a single unified model that can match — and often outperform — specialized SOTA models across a surprisingly broad range of vision tasks, including DepthAnything3, D4RT, VGGT Omega, SAM3, Genmo, Lotus-2, and others. (The code is now open-sourced! genception.github.io/)
Quite a few PhD students told me the work made them question the direction of their own research.
Part of me was happy, because it meant GenCeption had genuinely changed how some people thought about the problem. But that feeling was quickly outweighed by how much I felt bad and empathized with the junior PhD students. Some of them seemed genuinely unsure where to go next, especially when years of work on specialized architectures or training recipes could potentially be absorbed by a sufficiently capable general pretrained model. GPT-6 seems to be just creating that same feeling on a much broader scale.
It reminds me a lot of what happened with GPT-1 and BERT — tons of NLP PhDs' years of work and entire research directions perish overnight.
There were even some pretty spicy takes floating around: “CV conferences are dead,” “academia is becoming disconnected from where some of the most important progress is actually happening,” “with increasingly noisy review systems and declining review quality, rejection sometimes is almost a signal of truly fundamental work,” and “conferences increasingly reward work delicately engineered around established topics or methodologies, rather than work truly at the frontier.” Maybe it's too harsh and too absolute, but the frustration behind them was definitely real.
Conferences used to feel so fruitful, but this time it was hard to get genuinely excited. A lot of papers seemed to solve problems that no longer felt that important, or make progress that felt increasingly incremental. In the era of paper inflation, most people are busy trying to survive the research game, rather than thinking about how to go big after one or two years of diving deep into something. Most valuable skills such as critical thinking and taste never get trained well enough, from the beginning, all the way to the end.
On the other hand, most people simply don't have enough context or a big enough picture about what problem actually matters. They get stuck optimizing for local problems that their immediate environment tells them are important. I suffered from this for a long time too, but luckily I gradually started to see a much bigger picture.
My advice to junior PhD students is: when you start a project, try to collect as much context as possible before you even begin — by all means.
One way is to explore the boundaries of existing models as aggressively as you can. Play with the SOTA models extensively. Keep throwing harder and harder problems at them until they fundamentally break. Understand not just what they can do today, but where their capabilities end and why.
Another way is to put yourself wherever the strongest signals are. That could be a visionary academic lab, a team at an industry frontier lab, a genuinely insightful seminar, or simply a senior student or researcher who has seen a much broader landscape than you have. Expand your local context as much as possible.
In an era where things are moving this fast, the information gap alone can be large enough to make years of carefully built work suddenly meaningless. Looking up and making sure you're walking in the right direction matters much more than keeping your head down and perfecting every step along the way — though, of course, being able to do both is the best :)
We stand on the shoulders of giants not just to go further, but to see further — to understand which problems will actually matter in the future.
If a problem is likely to be easily swallowed by large models once they have sufficient data, attention, or business interest, either avoid spending years engineering a specialized solution for it, or contribute the data and benchmarks that will help move the frontier forward faster. The problems that deserve the deepest thinking and genuinely new methods are the ones where existing models fundamentally cannot do the thing you need them to do.
What I actually worry about is not that conferences will suddenly “die,” but that if they start becoming genuinely irrelevant to where the frontier is moving, they will simply fade away.
And maybe one early sign of that is that a lot of people I used to want to meet aren’t really coming to conferences anymore — they’re busy with the big things happening at frontier labs, or, well, busy making money.
It reminds me a little of what happened to control theory as the field matured. As many of the core problems were gradually solved, parts of the community started inventing increasingly artificial use cases, making increasingly unrealistic assumptions, and playing increasingly elaborate mathematical games within them. That possibility genuinely scares me — not because the work becomes unnecessarily sophisticated, but because it can become disconnected from problems that actually matter.
I still believe that conferences will remain places people genuinely want to come to: to exchange ideas, reconnect with old friends, meet new people, and simply have fun together. We just need to keep doing the right things to make that happen :)
What is the role of academic computer vision research in the age of increasingly powerful large models? Is GPT-6 Astra a step change? How can a researcher have an impact today in academia?
These are the questions I ask myself as I head off to ECCV 2026, a conference I’ve attended since 1992. One of my papers this year is VIGA, a method that takes an image as input and outputs a 3D Blender scene that represents that image. This is a classical inverse-graphics task and VIGA was the first method to solve it using an agentic approach.
The idea is now several years old and the first version of the paper was rejected. This delayed publication significantly. After it was accepted at ECCV, it was quickly surpassed by people using Claude Code for the same purpose. Today GPT-6 Astra blows away all previous results. But we still head off to ECCV to tell the community about our invention that is now fully out of date.
The way academic work often progresses is that one reads recent papers, notices that they have limitations, comes up with a new idea, explores this, publishes it, etc. Any published paper I read today is based on ideas that are at least a year old. And those ideas were based on the literature of the time, which was also a year old. That means that any paper I see at ECCV is likely two years out of date. In AI today, two years means your work is likely irrelevant.
At CVPR this summer I noticed that many authors have not gotten the message. They continue to work on “old” problems that have a long history. This history is based on assumptions about how the “vision problem” will be “solved”. The truth is that it is being solved in a very different way and many of these problems are no longer relevant. Another group of papers focuses on very niche problems where large models likely fail because of insufficient data or lack of business interest. The impactful papers were largely from industry and had long author lists and massive data+compute behind them. These papers were also out of data, describing systems that had been released months before, but at least they served to provide the community with more complete documentation and analysis of commercial systems.
So what should academics do? First, we need to put aside the tools we’ve used for years and start from scratch. Every project should start by trying really hard to solve the problem with existing tools. I would like to see every paper begin with a detailed experimental analysis of how existing models perform and why they fail (if they do). This gives the kind of insight we need today. Then, assuming current models fail, the solution should provide some fundamental insight that will outlive the next release of such models.
Reviewers today still focus on technical novelty. This pushes people to focus on tweaking architectures rather than clearly moving the field forward. Papers need to be judged based on their novel insight and not their novel technical contribution. This is a real shift in thinking but it focuses us on what matters - progress of the field.
If we want there to be a “field” of computer vision, then it can’t become a marginal backwater, focusing on esoteric problems. If you haven’t tried using Astra (or whatever comes next) to solve your problem, then you have not done your homework. This omission should be seen as negatively as not having a previous work section.
Concretely, I think papers should include a new section analogous to “Related Work” where that related work is current models and how they perform on the task. Reviewers should start asking for this and expecting authors to be able to articulate their insights about the limitations of existing large models.
I'm interested in your thoughts.
sidcode retweeted
People making grand claims about #robotics really need to spend time with actual robots. Debug the controller yourself. Deal with contact, latency, calibration, failures. And before declaring old problems obsolete or rebranding long-standing robotics ideas around foundation models, engage seriously with the field you are making claims about. #ECCV2026
sidcode retweeted
We are now poised to:
Formalize all known Human Mathematics.
This is the modern equivalent of the Human Genome Project. or AlphaFold more recently, whereby all data of its kind is fully classified.
With sufficient funding and compute/tokens, a coordinated effort among academia, frontier labs, philanthropic organizations, and the federal government can formalize all Human Mathematics in the next year.
sidcode retweeted
ETH Zurich just open-sourced their entire 2026 robot learning course.
Not a MOOC. The actual course. Slides, lecture recordings, coding assignments, GitHub repo.
The curriculum goes from imitation learning and RL all the way to Vision-Language-Action models and foundation models for robotics.
Guest lectures from the co-founder of Physical Intelligence. The creator of Diffusion Policy. Pieter Abbeel. Dieter Fox.
12 weeks. Free. No signup.
Taught by Oier Mees and the team at ETH Zurich.
If you want to understand where robot intelligence is actually heading… this is the reading list the field is using right now.
📍[cvg.ethz.ch/lectures/Robot-L…]
——
Weekly robotics and AI insights.
Subscribe free: 22astronauts.com
sidcode retweeted
A few reflections on my "LLMs Can’t Jump" paper:
My position paper recently got some traction here, so I wanted to share a few thoughts and clarify a few things.
First things first: some people are framing this as "DeepMind is throwing cold water on AI for science" or claiming the paper argues LLMs can never make real scientific discoveries. This is NOT the case.
This is a personal position paper, not the company's view on AI for science. This is also not my position. As a core contributor to AlphaProof (the first AI system to win an IMO medal), I know firsthand that my colleagues at DeepMind, other frontier labs, and academia have made amazing discoveries with LLMs and will continue to do so. This paper is NOT an "LLMs are a dead end" kind of thing.
Rather, the paper is the result of a deep dive I took to study the invention of General Relativity. I wanted to explore what it would take for a modern AI system to make that exact kind of jump. Specifically, I focused on the equivalence principle—a key axiom that Einstein formulated through thought experiments grounded in his physical intuition. I was trying to figure out what it would take to give modern AI systems that sort of thinking.
Giving AI this specific capability isn't necessarily the most urgent thing to do next. It is very likely that improving our current recipes will lead to many exciting discoveries in the near future. In fact, that is what I am personally working on these days (sorry to disappoint you!). It is also quite possible that I am wrong, and that simply scaling our current systems will lead to new inventions in physics and elsewhere.
Nevertheless, this was my position last winter when I wrote the paper, and I'm sticking to it. I think that there are a few interesting ideas to explore in this space which could influence the next generation of AI systems. I was very lucky to receive a lot of interesting feedback about this position—thank you for all the messages!
sidcode retweeted
Unpopular fact: 90% of data companies collecting robotics data are doing it wrong. And most don’t even know it.
The inbound from new robotics data vendors has been nonstop since last November. Half the companies that messaged us back then have since pivoted to something else. Collecting high quality robotics data is just brutally hard, and the market keeps proving it
The data ops team @sundayrobotics is genuinely world-class, led by the one and only @perryzjia. Come join us, we’re hiring :)
sidcode retweeted
"Even more bugs are inevitable, software is all going to become probabilistic now" is cope. "AI bug-finding means we have to embrace closed-source now" is a psyop.
Writing buggy code has moved from hard to trivial. Writing secure code has moved from impossible to hard.
sidcode retweeted
the real lesson: DNSSEC saved this. the attacker had DNS control but couldn't forge the signing keys — so the auth chain broke and browsers rejected it.
web3 infra sitting on web2 DNS registrars is the actual attack surface. this keeps happening (cowswap, now eth.limo). the fix isn't trust — it's making the fallback cryptographically sound.
sidcode retweeted
🚨 Two major supply chain attacks today, hitting both PyPI and npm simultaneously.
Socket detected and confirmed malicious code in lightning versions 2.6.2 and 2.6.3 on PyPI, and intercom-client version 7.0.4 on npm. Both attacks use nearly identical tooling. Both are live right now.
lightning is one of the most popular deep learning frameworks in the Python ecosystem, with millions of downloads per month. intercom-client is Intercom's official Node.js SDK, with roughly 360K weekly downloads. These are not typosquats. These are the real packages.
The payloads are almost identical across both attacks:
• Both inject an ~11MB obfuscated JavaScript file (router_runtime.js) and a setup script that downloads and executes the Bun runtime from GitHub
• Both harvest GitHub tokens, npm tokens, AWS/Azure/GCP credentials, Kubernetes secrets, Vault tokens, and CI/CD environment variables
• Both exfiltrate stolen credentials through the GitHub API
• Both execute automatically, lightning on import, intercom-client on install
The lightning attack goes further. It uses stolen GitHub tokens to commit poisoned files to every branch of every repository the token can write to, impersonating Anthropic's Claude Code as the committer ([email protected]). It also infects local npm tarballs by injecting a postinstall hook and bumping the patch version, so the next publish silently ships malware to downstream users.
In both cases, the attackers appear to have compromised maintainer accounts and used them to suppress disclosure. On the Lightning-AI GitHub, the pl-ghost account closed Socket's disclosure issue within one minute and posted a meme. On the Intercom GitHub, the nhur account closed, redacted, and retitled security reports to "N/A." Both accounts show bursts of suspicious branch-creation activity consistent with the Shai-Hulud worm's credential-probing pattern, including misspelled Dependabot impersonation branches.
The attackers posted an onion link in the Lightning-AI issue thread claiming affiliation with "Team PCP" and referencing LAPSUS$. Socket has not verified these claims. The intercom-client attack also shows direct Shai-Hulud hallmarks, including repos created with descriptions reading "A Mini Shai-Hulud has Appeared."
Socket's AI scanner flagged the malicious lightning versions 18 minutes after publication.
If you use either package:
• Remove lightning 2.6.2/2.6.3 and intercom-client 7.0.4 immediately
• Downgrade to lightning 2.6.1 / intercom-client 7.0.3
• Rotate all credentials in affected environments
• Audit repos for unauthorized commits from [email protected] and unexpected files in .claude/ or .vscode/
• More advice in our full research posts...
This is the same attacker campaign operating across two language ecosystems simultaneously. The playbook is credential theft, repo poisoning, and worm-style propagation. The scope is still being determined.
Developing story...
sidcode retweeted
crypto has created a substrate for funding "long tail public goods" on an internet-wide scale
short tail public goods are the things we all recognize as valuable. they deserve and often get the lion's share of funding.
but every short tail public good started out on the long tail once upon a time
crypto-native mechanisms like quadratic funding provide money + signal to promising efforts on the long tail, accelerating their ascent to the left side of this curve
we are only a few years in, but there are already dozens of projects that have crossed over
even more exciting is that many of the projects that are now short tail public goods are becoming long tail allocators in their own right
@gitcoin @buidlguidl @hatsprotocol @Giveth are all examples of this
if you are a long tail public good, wear it as a badge of honor: 2024 will be the year of the long tail public good 🫡
sidcode retweeted
I’m sharing my RetroPGF voting strategy to provide transparency to projects and other badgeholders regarding the impact I care about and how I value it
Ballot is ready to go, will cast it tomorrow. Feedback on the strategy is welcome; shilling is not 🔴
cerv1.notion.site/cerv1/Carl…
sidcode retweeted
Kernel appears in 15 ballots in Optimism's RetroPGF 3.
Here is an excerpt on Ether’s Phoenix, an inspiration for RetroPGF, from the Kernel (hyper)textbook.
🔴 Feel your heartbeat and lift your head, friend. Look out to the far horizon, for the phoenix awaits. 🔴
It is fire which does not consume, but gives flight to what is true; to what cannot die; to what was not born; to what gives you life in return for how you choose to recycle.
This essay’s purpose is to start your regen journey. We’ll do so by turning to the trees, and asking a man who cuts them for a living what they truly have to teach. 'Sprout Lands: Tending the Endless Gift of Trees' by William Bryant Logan is where we hope to catch a glimpse of Ether’s playful phoenix.
This is not a series of instructions. It is a web of co-relations. Kernel cannot tell you exactly how to regenerate your environment, but we can discover together why it is a virtuous goal.
‘Regen’ is neither regressive nor progressive. It does not make the twin mistakes of thinking we can conquer our environment, nor of imagining that we are separate from our environment and can return it to some pristine state by retracting our participation.
There is no escape. There is no ‘right’ way. There is no ‘good’ solution. There are no righteous few. There is no ‘them’. There are none who are not complicit.
There is attention. There is awareness. There is thanksgiving. There is reciprocity. There is awe. There is humility. There is listening, learning, and love.
“(Trees are) a living cathedral dedicated to the power of sprouting. As often as you cut it, all by itself it grows its pillars again.”
So, we begin with what may seem like heresy: trees are for cutting. However, this does not give us a free licence, and it does not mean trees are for cutting in some existential sense. It means that our relationship with trees revolves around cutting, its right rhythm, and the sort of respect and reverence required for life to be continually revived in generation after generation.
Ether’s Phoenix is the idea that an abundant future of public goods will indefinitely and retroactively reward the contributors who helped create it.
Moreover, as the Optimism team puts it in their blog: “It is also a mindset: that optimism prevails, that better systems are possible, and that humankind will be rewarded for its cooperative revolution.”
However, even well-intentioned religions and traditions, who create potentially good future attractors can fail to move towards them over time. Something more than an 🔴optimistic 🔴mindset is required.
Logan and the trees offer us a further consideration: “The work in which head and heart and hand participate, at once, yields the only objective knowledge. It employs the ratiocinative capacity, the ability to discriminate and choose.”
It is not only mind. It is not only heart. It is not only hands.
It is all three in concert, with reverence for this world as it is, and how it resists even the faintest glimmer of our own projections.
Moreover, there is a tekne (both technique and art) to understanding how our present actions may affect the future. Such craft combines scientific knowledge, lived experience, and tender care.
In attuning to the consequences of our actions, we are more capable of understanding what the right decision is, at the right time, in the right place.
sidcode retweeted
T-4 days until KB8 apps close on December 1st.
We will accept late apps through 12/15, but the block may fill at any time.
The spirited, the creative, the curious, the builders are encouraged to please apply.
KB8 begins on January 12th.
Apply now 👇
apply.kernel.community/
sidcode retweeted
Kernel is building a custom internet age institution, one peer-learning environment at a time.
♾KB8♾ is our current time capsule, with applications for the block closing Dec 1st. Why should you apply?
1) Each Kernel Fellow joins as an individual, first.
Kernel Fellows represent 50+ countries, top institutions, and vocations like technologist, (circus/DAO) director, (product/industrial) designer, (bestselling/internet) author, (hardware/software) engineer, and ecologist. 40% of us belong to underrepresented groups.
Kernel primarily attempts to welcome KB8 fellows as individuals first, encouraging one another as peers.
2) Personalized building/creating support
Each fellow has something to build or create. Kernel fellows have created projects like @sherlockdefi, @pushprotocol, @halofi_me, @Showtime_xyz, @llama, @jokerace_xyz, and @verses_xyz. Nearly 100 Kernel Fellows have raised significant funding ($1mm-$10mm).
Fellows have also started DAOs, written PhD theses, launched art collections, and planted gardens. Each has meaningful space within a Kernel block.
3) Learn a wider lens of technology
Crypto is a part of the greater evolution of technology, economics, and society. Kernel draws from library science and time designers as much as from cryptographers and technologists.
The wider lens of technology (Gr: 'techne' - art, skill, craft) is key. We explore eight @Kernel0x modules in KB8 with technical and personal inquiries, in efforts for our personal growth to match our technical excellence.
4) Kernel Firesides x Office Hours
Each week, we host two capstone events.
Kernel Office Hours on Wednesday to support *you* & your work, via deep-time with peer mentors: @jack__sanford, @0xzak, @Sim_Pop, @jenilt, @_juliettech, @johnx25bd, + 20 more.
Kernel Fireside on Thursday to learn *collectively*, on topics like internet age institutions, the evolution of trust, gift-giving, and local financial systems. Guests include @owocki, @ljin18, @xochitlcazador, @brewster_kahle, @maira, @keikreutler, @samecwilliams, and @VitalikButerin.
5) Host or join a ‘Convo’
Kernel Fellows propose convos on any topic. If it’s over an IRL meal or a class, Kernel funds it. We never underestimate the magic of a meal (or a learning session) shared amongst friends.
6) Engage in peer-learning
Subjects as wide-ranging as solidity, smart contract auditing, token primitives, mindful movement, death, poetry, indigenous protocols, open data, de-school, and gratitude filter through Kernel via the 1,800 fellows who come before you.
7) Building a better web starts with you
Kernel provides a reflective and supportive environment for honing your unique strengths and aligning your work to your gifts. We hope it is a place where you pursue your life's [current] work and find it supported by the environment.
8) Take a step back from the highs and lows.
Relationships are an antidote to both FUD and FOMO. We offer an environment where the priority is the way in which we interact with each other. This extends well beyond Kernel.
We'd love to hear from you in KB8. Please apply if interested ♾️