Jonny Hsu
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on the atlas · 56
- Man-Computer Symbiosis6 savers
- Why is progress in biology so slow?10 savers
- Webinar Information - Zoom1 savers
- RNA-based therapeutics: an overview and prospectus | Cell Death & Disease1 savers
- Reader Mode - All-in-one reading, bookmarking and research tool1 savers
- Sci-Hub | | 10.5465/amj.2012.05221 savers
- Fundraising for Founder-led Biotech – Matt Krisiloff1 savers
- Managing Lockheed’s Skunk Works - by Eric Gilliam1 savers
- Bonus: More details on how Bell Labs operated1 savers
- bell labs14 savers
- Guest Post: Things I learned talking to the new breed of scientific institution1 savers
- Engineering Plastic-Degrading Enzymes and PCSK9 Binders with Protein AI Tools1 savers
- The Psychology of The Trickster - Eternalised1 savers
- (Spoilers All) What is the meaning behind Tywin's "fool" quote? : r/asoiaf1 savers
- It's often said that the jester is the only one that can speak truth to the king. Historically speaking though, has that ever been the case, using comedy or satire to persuade or inform an absolute monarchy or similar form of authority? : r/AskHistorians1 savers
- Fooling Around the World: The History of the Jester1 savers
- What Clayton Christensen Got Wrong – Stratechery by Ben Thompson1 savers
- The Relentless Jeff Bezos – Stratechery by Ben Thompson1 savers
- Polaris Q3/Q4 2024 Post-Launch Plan (WIP) - Google Docs1 savers
- Remains of the Day7 savers
- Status as a Service (StaaS) — Remains of the Day28 savers
- Surviving the traffic cop role - by Karl Yang1 savers
- 99% Derisible | Yoni Rechtman | Substack2 savers
- Opinion | A Likely Story . . . And That's Precisely the Problem - The Washington Post1 savers
- Anatomy of a Biotech Business Development Deal | Andreessen Horowitz1 savers
- The Residency4 savers
- A primer on ML in antibody engineering2 savers
- Inbox (1) - ellenjxu@gmail.com - Gmail11 savers
- whoyoucallingajesse1 savers
- 01 N/O ch 1.rev1 savers
- Home Page34 savers
- Introductions - jxnl.co3 savers
- The Most Intolerant Wins: The Dictatorship of the Small Minority | by Nassim Nicholas Taleb | INCERTO | Medium3 savers
- Availability Cascades Run The World4 savers
- Defensibility in the Age of AI2 savers
- The Rise of HuggingFace 🤗 - by Mark Saroufim2 savers
- Curius / Onboarding2704 savers
- Bookshelf · Patrick Collison21 savers
- Finding Person-Problem Fit - Compound Manual20 savers
- Advice to Young People, The Lies I Tell Myself - jxnl.co19 savers
- resistance & regret - by Isabel - Mind Mine18 savers
- Goodhart's law15 savers
- The maze is in the mouse. What ails Google. And how it can turn… | by Praveen Seshadri | Feb, 2023 | Medium14 savers
- First Principles: The Building Blocks of True Knowledge - Farnam Street14 savers
- Are You the Same Person You Used to Be? | The New Yorker13 savers
- The Michael Scott Theory of Social Class – Welcome to Dancoland8 savers
- Hallucinations in AI - by John Luttig - luttig's learnings8 savers
- The Roots of Progress7 savers
- Invisible asymptotes — Remains of the Day7 savers
- How People Get Rich Now6 savers
- Product Management Mental Models for Everyone | by Brandon Chu | The Black Box of Product Management5 savers
- The Universe Is Not Locally Real, and the Physics Nobel Prize Winners Proved It - Scientific American4 savers
- Real-time machine learning: challenges and solutions3 savers
- TechCrunch2 savers
- Announcing the next Betaworks Camp program — AI Camp: Agents | by Jordan Crook | Nov, 2023 | Betaworks2 savers
- Seven Powers in Life Sciences - Axial2 savers
highlights · 271
I was told to do research on vacuum tubes.
bell labsensured that the integration of its best researchers and most pressing problems
bell labsregular interactions between the basic researchers and Bell’s fundamental development researchers, engineers, manufacturing facilities, and implementation staff.
bell labslong leash, but a narrow fence
bell labsto maintaining small teams and fiercely anti-bureaucratic processes proved difficult for competitors to replicate. The success of Johnson’s methods and the failures of competitors to replicate them carries key lessons for R&D organizations and funders
Managing Lockheed’s Skunk Works - by Eric GilliamIt would be completely unreasonable to expect the best problems to find the best people without a little more planning.
Bonus: More details on how Bell Labs operatedThis group was charged with testing and proving out a functional performance of the ideas originating in the Research and Fundamental Development portion of the org
Bonus: More details on how Bell Labs operatedpoints: it helps people self-select into where they best belong.
Guest Post: Things I learned talking to the new breed of scientific institutionScientists and engineers alike are ultimately human, and the outward appearance of Big And Important things happening at a research institution goes a long way in piquing their interest.
Guest Post: Things I learned talking to the new breed of scientific institutionApple’s focus on user experience as a differentiator has significant strategic implications as well, particularly in the context of the Innovator’s Dilemma: namely, it is impossible for a user experience to be too good.
What Clayton Christensen Got Wrong – Stratechery by Ben Thompsonbusiness buyers are usually extremely rational. A CIO, for example, must justify a software purchase, and said justification usually comes down to balancing lists of features versus prices.
What Clayton Christensen Got Wrong – Stratechery by Ben Thompsonconsumers are not rational. They have widely varying motivations, are susceptible to advertising, lack product knowledge, fall prey to the need for instant gratification, etc.
What Clayton Christensen Got Wrong – Stratechery by Ben Thompsonintegrated backwards into the world of atoms. Real moats are built with real dollars, and Bezos has been relentless in pushing the company to continually invest in solving problems with real world costs, from delivery trucks to data centers and everything in-between. This application of tech economics to the real world is what sets Bezos apart.
The Relentless Jeff Bezos – Stratechery by Ben Thompsonthat tech is governed by a world of zero marginal costs, is what makes tech economics fundamentally different from most businesses.
The Relentless Jeff Bezos – Stratechery by Ben ThompsonOUNT PAID $4,999.00 DATE PAID A
Inbox (1) - ellenjxu@gmail.com - GmailSequencing Technologies and Applications”.
Inbox (1) - ellenjxu@gmail.com - GmailFor me, I noticed people were always coming for me to edit their writing. And, a key piece of editing well was predicting how the information would be received by its audience. Often this was blog posts or marketing page copy, but the stuff that would really light me up was the stuff getting shared with other Stripes (All Hands presentations, shipped emails, product requirement docs). As I got deeper into the world of editing internal docs, I increasingly found myself looped into matters of internal communication. Noted.
Finding Person-Problem Fit - Compound Manualunderstanding ourselves is that the way we see ourselves is all tangled up in what we admire in others, and what our colleagues deem the “cool team,” and what our company leaders tell us is the highest priority thing to work on today, what our parents wanted us to be when we grew up, and all sorts of other stuff
Finding Person-Problem Fit - Compound Manual. For example, businesses can track changes in public sentiment on their brands and products by continuously analyzing social media streams, and respond in a timely fashion as the necessity arises.
What Is Streaming Data? | Amazon Web Services (AWS)The first question solving the puzzle is whether your model needs to provide answers instantly using the latest data in real-time (online) or with some accepted latency (batch).
What is Model DeploymentIf you had gone the traditional route and batch trained your recommendation engine once a day, it would still be stuck offering the same type of content, even though the underlying world changed dramatically¹. You should be serving up domestic news right now, but aren’t because your system is too slow.
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | MediumTo make the best possible decisions right now, we can’t afford to have a model that only knows about things that happened yesterday.
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | MediumIdeally, what you want is a model that can learn from new examples in something close to real time. Not only predict in real time, but learn in real time, too.
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | MediumIn order to react to new data and make an AI that learns over time, ML practitioners typically do one of two things: They manually train on newer data, and deploy the resulting model once they are happy with its performance They schedule training on new data to take place, say, once a week and automatically deploy the resulting model
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | MediumAnd if we’re being honest, horizontally scalable is the best type of scalable.
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | MediumML models, save for some exceptions, are static things. They are essentially collections of parameters. After you’ve trained a model, its parameters don’t change. From a technical perspective, that’s good news: if you want to serve predictions over an API, you can instantiate several instances of a model, place a load balancer on top of them,
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | Mediumyou need two things for machine learning: data and a suitable learning algorithm. The learning algorithm learns from/trains on your data and produces a (hopefully) accurate model, typically used for prediction on new data. I’m oversimplifying things, but that’s the core idea.
What is Online Machine Learning?. Making machines learn in real time | by Max Pagels | The Hands-on Advisors | MediumContinual learning can also help with the cold start problem. A user just joined your app and you have no information on them yet. If you don’t have the capacity for any form of continual learning, you’ll have to serve your users generic recommendations until the next time your model is trained offline.
Machine learning is going real-timeUser preferences for items like houses, cars, flights, hotels are unlikely to change from a minute to the next, so it would make little sense for systems to continually learn. However, user preferences for online content – videos, articles, news, tweets, posts, memes – can change very quickly (“I just read that octopi sometimes punch fish for no reason and now I want to see a video of it”). As preferences for online content change in real-time, ads systems also need to be updated in real-time to show relevant ads.
Machine learning is going real-timeIt’s possible because ByteDance, the company behind TikTok, has set up a mature infrastructure that allows their recommendation systems to learn their user preferences (“user profiles” in their lingo) in real-time.
Machine learning is going real-timeMost companies do stateless retraining – the model is trained from scratch each time. Continual learning means allowing stateful training – the model continues training on new data (fine-tuning).
Machine learning is going real-timetheir iteration cycle from learning to deploying model updates is 10 minutes.
Machine learning is going real-timemethod suffers from catastrophic forgetting
Machine learning is going real-timeThis seems shaky. Much of the infra today is built to complement the limitations of LLMs: complementary modalities, increased monitoring, chained commands, increased memory. But foundation models are built via API to abstract away infrastructure from developers. Incremental model updates cannibalize the complementary infra stack that has been built around them. If only a handful of LLMs dominate, then a robust infrastructure ecosystem matters less.
Hallucinations in AI - by John Luttig - luttig's learningsIt suffices for an intransigent minority –a certain type of intransigent minorities –to reach a minutely small level, say three or four percent of the total population, for the entire population to have to submit to their preferences.
The Most Intolerant Wins: The Dictatorship of the Small Minority | by Nassim Nicholas Taleb | INCERTO | MediumVCs have a strong predisposition to hallucinate market structures in the quest for AI returns.
Hallucinations in AI - by John Luttig - luttig's learningsBut VCs aren’t the primary shareholders in the current leading AI companies. How many VCs hold equity in NVIDIA or Microsoft? Even among the startups with scale, Midjourney was bootstrapped, and OpenAI has only raised a small fraction of its capital from VCs.
Hallucinations in AI - by John Luttig - luttig's learningsThe counterfactual seems preposterous: there will be a 20-year platform shift and no new startups win? Unlikely.
Hallucinations in AI - by John Luttig - luttig's learningsEven with GPT-4 capabilities (not to mention subsequent models), there will be entirely new product form factors that won’t have the formula of incumbent software workflow + AI. They will require radically different user experiences to win customers. This is white space for new companies with embedded counter-positioning against incumbents, who can’t re-architect their entire product UX.
Hallucinations in AI - by John Luttig - luttig's learningsLLMOps isn't anything new (except as a term) but rather a sub-category of MLOps.
LLMOps: MLOps for Large Language ModelsIt needs the development of specialized tools to support the operations of large language models in production.
Rise of Large Language Model Operations - by Shabaz PatelTelling TechCrunch that his company has long been an engineering-led organization, he told us that it’s not enough to make something cool. If the company doesn’t market a new feature, sell it, and get it used, then it hasn’t actually innovated. Innovation, he said, is an experience, not a product. The final result of innovation, he added, is a customer testimonial of a new feature – when someone will go on record and say that a new thing really is good. Which requires people to, well, know that something exists so that they can give it a whirl.
When to pivot your product, and a tale of two earnings calls | TechCrunchI led with curiosity on each of my onboarding calls,” he says. “‘What’s your favorite feature, what would you cut out? How would you rate us today on a scale of 1-10? What would take us to a 10? What’s the one feature that would get you back in this community every single day? Let’s imagine in one year, this network ends up failing, why do you think it failed?’”
A Founder’s Step-by-Step Guide to Getting Your First 1,000 Community Members | First Round ReviewIn production systems, it is critical that we can observe, evaluate, optimize, and debug the code. With LLMs (or AI in general), the issue of observability gets exacerbated due to their blackbox nature
LLMOps: My Thesis & Market Map. Table of Contents | by Rachit Kansal | MediumHowever, on a high-level, we know that data quality, generation method, and the input context affects hallucinations.
LLMOps: My Thesis & Market Map. Table of Contents | by Rachit Kansal | MediumLLMs for all their goodness can hence become an architectural nightmare in production — you cannot just train once and deploy-forever.
LLMOps: My Thesis & Market Map. Table of Contents | by Rachit Kansal | Mediumxcessive metrics and dashboard can also be counterproductive, a phenomenon known as dashboard rot. It’s important to pick the right metrics or abstract out lower-level metrics to compute higher-level signals that make better sense for your specific tasks.
Data Distribution Shifts and Monitoringmost important characteristics of a software system in production is availability — how often the system is available to offer reasonable performance to users. This characteristic is measured by uptime, the percentage of time a system is up. The conditions to determine whether a system is up are defined in the service level objectives (SLOs) or service level agreements (SLAs).
Data Distribution Shifts and Monitoringcross-functional approach of building and shipping applications in a faster and more iterative manner. In adopting a DevOps development process, you are making a decision to improve the flow and value delivery of your application by encouraging a more collaborative environment at all stages of the development cycle.
What is DevOps? | GitLabBut now investors need founders more than founders need investors, and that, combined with the increasing amount of venture capital available, has driven up valuations.
How People Get Rich Now