Laksitha Ranasingha (@lakytwitt) profile picture

Love coding and building stuff. Passionate AI builder and a user. Co-founded Fund Synopsis & built Precis (intelligent fund commentary SaaS) in the past.

London UK
Joined May 2010
Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)Photo by Laksitha Ranasingha (@lakytwitt)
Laksitha Ranasingha retweeted
Demis Hassabis says in school time should go to people skills, projects, entrepreneurialism, creativity and judgment, the skills he expects to matter most Hassabis says rote learning belongs outside the classroom, where personalized AI tailors the curriculum to the exact speed and needs of each student Hassabis says world-class teachers could do the lecturing for everyone, so no teacher builds their own slide decks for the same GCSE maths course
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Laksitha Ranasingha retweeted
Turns out the future does have flying cars
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Hey @bt_uk why is your web app is so buggy? I have been trying to change my direct debit and other broadband settings without success at least for a couple of weeks. It just keeps breaking. I am on latest Chrome and nothing special. Fix your crap!
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Laksitha Ranasingha retweeted
I can’t stress enough how little an idea matters compared to the agency of the people executing the idea. I have had the privilege of knowing and sometimes even working with some of the most successful people (by various metrics). The difference between mediocre and excellent work and outcomes is predominantly one of agency. In practice this means: they dont wait for things to happen to them they go out and make things happen for them. They don’t wait for someone else to do something, for someone to teach them, for someone to give them the path, etc. They just go out and find a way to do it. I think the single biggest superpower these people have is the realization/belief that the world around them is completely mutable. Most everything that happens is because a person made it happen. I used to tell people to look around the room you’re sitting in. Look at everything. Every noun. It almost all exists because a person willed it into existence. Nothing is stopping you from doing the same. I see people online all the time dismissing someone else’s success because “I had that idea first” or whatever. I mean… yeah? If so then the difference is… you. So a bit of a self own whenever I hear that. Number one tip: act with agency.
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Laksitha Ranasingha retweeted
We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem concerns whether the description of smooth three-dimensional fluid motion modeled by the Navier-Stokes equations can break down. It has remained unresolved for roughly 90 years.
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Laksitha Ranasingha retweeted
I turn 50 today. This is a message to my kids but I offer it to you in case any of it rings useful. I was born in Sri Lanka in 1976 in a country that, at the time, was about to enter a decades long civil war. My father was able to get stationed at the Sri Lankan High Commission in Canada for four years in the early 80s - when some of the fighting was most severe - and which from our vantage point in Canada gave us some respite from an otherwise omnipresent conflict. He came as a diplomat but then my father filed for refugee status after four years when his life was threatened by militants. We stayed in Canada as refugees. My dad never really landed on his feet and we grew up on welfare. My mom stepped up. She did an incredible thing and put our family on her back and kept marching forward. Sometimes the steps were very small but she always tried to keep moving forward. I worked at Burger King starting at 14, giving my paycheck to my mom and dad to support our family in the role that I was assigned - earn enough to pay for our bus passes because we couldn’t afford a car. I was a loser growing up. Some friends, some good memories but mostly forgettable and lonely. I wasn’t very studious but did get my act together in my final year and got into a great university. I immigrated to the US soon after graduating and found an opportunity. Things clicked and I have been traveling a path to find myself ever since. Only in America is this possible… I worked hard, followed the rules, paid my taxes and stayed in California out of loyalty for the opportunities it has given me despite its dysfunction. I found my wife here. Had my kids here. Found my tribe here. There have been some very hard times as well - deaths, divorce, financial pain, mistakes of omission and mistakes of commission. You can’t dwell on these things - they are weights that will pull you down if you’re not careful. Learn from them. Respect them. BUT MOVE ON. To think 50 years ago I could have ever gotten a chance to meet the people I’ve met and have had the experiences I’ve had would have been mind boggling to my parents. It’s mind boggling to me now. We forget often and try to convince others it’s not true but America truly is the best country in the world. Everything and anything is possible here. I can testify. Be grateful to be here. This is the only place that gives EVERYONE a chance to live a great adventure. The first 50 years have been that for me - an incredible adventure. Notice there is nothing here about accomplishments or money. Because it doesn’t matter and it won’t matter. You will be dust in less than 100 years from now and the decision to be remembered won’t be yours so stop sweating it. It’s not in your control. So what is in your control? Be kind. Have fun. Work on something meaningful to you. Find your tribe. Try to find perspective. Find an incredible partner. Have a bunch of kids. Live an adventure. Forgive your parents - they were doing the best they could even if that wasn’t what you deserved. I will do what I can to make my next 50 years a wild ride. You should too. I love you and thank you. ❤️ 🙏🏽🙏🏽
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Laksitha Ranasingha retweeted
Sending lots of love to the Apple community on my last day as CEO. My title changes tomorrow, but the love I have for the Apple community never will. Thank you for being a constant source of inspiration. My gratitude is endless, and I’m excited for the next chapter!
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Laksitha Ranasingha retweeted
Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at: discoveryloop.com
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What a legendary life you lived Nims. May you attain nibbana @nimsdai!
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AI Didn’t Make Programming Easier. It Just Made It Differently Difficult – Communications of the ACM cacm.acm.org/opinion/ai-didn…
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Laksitha Ranasingha retweeted
If you recognise this, you had a wonderful childhood
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Take a read!
Today I'm publishing a new essay, Policy on the AI Exponential. AI is progressing extremely fast—much faster than the policy process was built to handle. The essay lays out where I think the technology is now, and the action needed to close the gap: darioamodei.com/post/policy-…
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Laksitha Ranasingha retweeted
Agentic coding is a form of machine learning. Generated code is best treated as a blackbox artifact whose behavior and generalization should be managed via empirical evaluation, like with any ML model.
Sufficiently advanced agentic coding is essentially machine learning: the engineer sets up the optimization goal as well as some constraints on the search space (the spec and its tests), then an optimization process (coding agents) iterates until the goal is reached. The result is a blackbox model (the generated codebase): an artifact that performs the task, that you deploy without ever inspecting its internal logic, just as we ignore individual weights in a neural network. This implies that all classic issues encountered in ML will soon become problems for agentic coding: overfitting to the spec, Clever Hans shortcuts that don't generalize outside the tests, data leakage, concept drift, etc. I would also ask: what will be the Keras of agentic coding? What will be the optimal set of high-level abstractions that allow humans to steer codebase 'training' with minimal cognitive overhead?
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Laksitha Ranasingha retweeted
100 years old and still the coolest person alive. Happy birthday, Sir David!
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Laksitha Ranasingha retweeted
It is so funny to watch non-compiler engineers suddenly discovering that many modern compilers are actually non-deterministic. Sweet summer children! Can’t wait them discovering how many known bugs a typical compiler has.
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Laksitha Ranasingha retweeted
BREAKING: Anthropic is in talks to buy AI Chips from a UK startup! The startup, @fractile_ai, is one of the UK's most exciting companies at the moment. Its chips rely on a different kind of memory technology to Nvidia’s GPUs, which is called static random access memory or SRAM. It is meant to improve the speed and cost of AI inference. The supposed deal comes as @AnthropicAI is looking to reduce its reliance on Nvidia. Fractile is currently raising $200m at a $1bn+ valuation, and no doubt this news will help. We've seen a lot of news recently about Anthropic and OpenAI expanding their london offices which is GREAT, but it doesn't compare with seeing UK startups going toe to toe with Nvidia. LETS GO (story from @theinformation which I'll link below)
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Laksitha Ranasingha retweeted
A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work. His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing. In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen. Here's the framework that has been quoted by every serious scientist for the last 40 years. His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired. He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow. The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one. The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed. The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else. The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices. He finished the lecture with a line I have never been able to shake. He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day. The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword. Hamming died in 1998. He gave his final lecture a few weeks before. He was 82. The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.
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Laksitha Ranasingha retweeted
This paper is perfect if you want to study the math behind machine learning. It covers probabilities, linear algebra, and calculus and optimization. You can get it for free from the University of Berkeley: gwthomas.github.io/docs/math…
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