Innovation @Colt_Technology. Dare mighty things. đŸ‡ȘđŸ‡ș

Milano, Lombardia
Joined January 2011
Mirko Voltolini retweeted
Right now, billions of people are watching videos. Most of them are being processed by code one man wrote alone in 2000. đŸ€Ż You’ve probably never heard his name. >Meet Fabrice Bellard. >A French programmer working solo >In 2000, built FFmpeg alone ~ the engine behind YouTube, Netflix, Spotify, WhatsApp, Instagram, TikTok, Chrome, Firefox, VLC, and Discord >NASA even runs FFmpeg on the Perseverance rover on Mars 🚀 >In 2003, built QEMU alone ~ the foundation of every Android emulator and most cloud virtualization on Earth >Wrote the Tiny C Compiler. Self-hosting. >One of the fastest C compilers ever built. >In 2009, broke the world record for calculating Pi ~ 2.7 trillion digits on a single desktop PC >Won the International Obfuscated C Code Contest 3 times >Discovered a new mathematical formula to calculate digits of Pi at age 25 >Co-founded Amarisoft in 2012 ~ runs full 4G/5G base stations entirely in software on a normal PC đŸ”„ >Won the Google-O’Reilly Open Source Award >Never worked at Google. Never worked at Meta. Never worked at any Big Tech company. One man. The hidden backbone of modern computing. Absolute Legend 🐐
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Mirko Voltolini retweeted
The reason it is so important for everyone to keep pretending that AGI is definitely right around the corner is that there is now over $1T of investment riding on this belief (either already expended, or committed) Current (and recent past) capex cannot be justified by current use cases and technology (currently spending $10-15 to make $1). To ever be in the black you'd need dramatically better tech/applications, and you'd need them fast -- before current datacenters depreciate, which is a 3-5 years timescale
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Mirko Voltolini retweeted
Deutsche Telekom is one of several companies advertising sovereignty, but its cloud infrastructure is based on Huawei and US products. Read more on Light Reading: bit.ly/3UuVZp5
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Mirko Voltolini retweeted
GenAI isn't just a technology; it's an informational pollutant—a pervasive cognitive smog that touches and corrupts every aspect of the Internet. It's not just a productivity tool; it's a kind of digital acid rain, silently eroding the value of all information. Every image is no longer a glimpse of reality, but a potential vector for synthetic deception. Every article is no longer a unique voice, but a soulless permutation of data, a hollow echo in the digital chamber. This isn't just content creation; it's the flattening of the entire vibrant ecosystem of human expression, transforming a rich tapestry of ideas into a uniform, gray slurry of derivative, algorithmically optimized outputs. This isn't just innovation; it's the systematic contamination of our data streams, a semantic sludge that clogs the channels of genuine communication and cheapens the value of human thought—leaving us to sift through a digital landfill for a single original idea.
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Mirko Voltolini retweeted
QS World University Rankings 2026: Politecnico di Milano ranks 98th: it is the first time that an Italian university enters the world's top 100 in the prestigious international ranking! #QSWUR #Ranking #Polimi @TopUnis @worlduniranking polimi.it/en/the-politecnico

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Mirko Voltolini retweeted
It’s hard to believe it’s been 20 years since Steve told Stanford graduates to stay hungry and stay foolish. His powerful advice still rings true, and I hope it helps guide this year’s new grads as they begin their journey to become tomorrow’s leaders. stevejobsarchive.com/exhibit

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Mirko Voltolini retweeted
AI teams are like football ⚜ teams. 1. Good teams buy the best local and foreign talent. 2. The UK has great players but no team. 3. Corporations own teams in foreign countries. 4. The US owns almost all teams: @GoogleDeepMind @MicrosoftAI @AnthropicAI @OpenAI @xai @Meta etc. 5. Europe only has 1 small team @MistralAI - European management and laws are rubbish for creating teams. No team means no one will care about your opinion. 6. I think Canada has 1 small team @cohere despite having had the best academies. 7. China has many good teams and an excellent academy, and focused management with winning mentality @deepseek_ai @Alibaba_Qwen @Skywork_ai etc. 8. Middle East money is starting to rule, but people: AI is not football. I play for a supportive and exceptional US team @MicrosoftAI in the UK. I’m loving it, and my team empowers me to teach AI online and travel to teach AI in Africa and LatAm. My previous team @GoogleDeepMind is also amazing in this regard. If any European politicians get to read this they should see their biggest growing problem. @EuroParlPress @EU_Commission @uksciencechief @SciTechgovuk Wake up! Only France is seeing it. Again: Africa, LatAm, India, etc are all out of the picture. It’s hard to overcome history even in the age of AI.
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Mirko Voltolini retweeted
We’re excited to share our plans for IBM Quantum Starling, expected to be the world’s first large-scale, fault-tolerant quantum computer. This new system, to be delivered to clients by 2029, is expected to perform 20,000x more operations than today’s quantum computers. Read more here: ibm.biz/BdnLUF
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Mirko Voltolini retweeted
New Anthropic research: Tracing the thoughts of a large language model. We built a "microscope" to inspect what happens inside AI models and use it to understand Claude’s (often complex and surprising) internal mechanisms.
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Mirko Voltolini retweeted
I read the entire 340 page "AI Trends" report by legendary investor Mary Meeker which was released 48 hours ago. Mary used to publish the famous annual “Internet Trends” before most of us were born - her insights are invaluable. Sharing 10 takeaways from this report —
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Mirko Voltolini retweeted
There's a new paper circulating looking in detail at LMArena leaderboard: "The Leaderboard Illusion" arxiv.org/abs/2504.20879 I first became a bit suspicious when at one point a while back, a Gemini model scored #1 way above the second best, but when I tried to switch for a few days it was worse than what I was used to. Conversely as an example, around the same time Claude 3.5 was a top tier model in my personal use but it ranked very low on the arena. I heard similar sentiments both online and in person. And there were a number of other relatively random models, often suspiciously small, with little to no real-world knowledge as far as I know, yet they ranked quite high too. "When the data and the anecdotes disagree, the anecdotes are usually right." (Jeff Bezos on a recent pod, though I share the same experience personally). I think these teams have placed different amount of internal focus and decision making around LM Arena scores specifically. And unfortunately they are not getting better models overall but better LM Arena models, whatever that is. Possibly something with a lot of nested lists, bullet points and emoji. It's quite likely that LM Arena (and LLM providers) can continue to iterate and improve within this paradigm, but in addition I also have a new candidate in mind to potentially join the ranks of "top tier eval". It is the @openrouter LLM rankings: openrouter.ai/rankings Basically, OpenRouter allows people/companies to quickly switch APIs between LLM providers. All of them have real use cases (not toy problems or puzzles), they have their own private evals, and all of them have an incentive to get their choices right, so by choosing one LLM over another they are directly voting for some combo of capability+cost. I don't think OpenRouter is there just yet in both the quantity and diversity of use, but something of this kind I think has great potential to grow into a very nice, very difficult to game eval.
Thanks for the authors’ feedback, we’re always looking to improve the platform! If a model does well on LMArena, it means that our community likes it! Yes, pre-release testing helps model providers identify which variant our community likes best. But this doesn’t mean the leaderboard is biased; see the clarification below. The leaderboard reflects millions of fresh, real human preferences. One might disagree with human preferences—they’re subjective—but that’s exactly why they matter. Understanding subjective preference is essential to evaluating real-world performance, as these models are used by people. That’s why we’re working on statistical methods—like style and sentiment control—to decompose human preference into its constituent parts. We are also strengthening our user base to include more diversity. And if pre-release testing and data helps models optimize for millions of people’s preferences, that’s a positive thing! Pre-release model testing is also a huge part of why people come to LMArena. Our community loves being the first to test the best and newest AIs! That’s why we welcome all model providers to submit their AIs to battle and win the preferences of our community. Within our capacity, we are trying to satisfy all requests for testing we get from model providers. We are committed to fair, community-driven evaluations, and invite all model providers to submit more models for testing and to improve their performance on human preference. If a model provider chooses to submit more tests than another model provider, this does not mean the second model provider is treated unfairly. Every model provider makes different choices about how to use and value human preferences. We helped Meta with pre-release testing for Llama 4, like we have helped many other model providers in the past. We support open-source development. Our own platform and analysis tools are open source, and we have released millions of open conversations as well. This benefits the whole community. We agree with a few of this writeup’s suggestions (e.g. implementing an active sampling algorithm) and are happy to consider more. Unfortunately, there are also a number of factual errors and misleading statements in this writeup. - The simulation of LMArena, e.g. in Figures 7/8, is flawed. It’s like saying: “The average 3-point percentage in the NBA is 35%. Steph Curry has the highest 3-point percentage in the NBA at 42%. This is unfair, because he comes from the distribution of NBA players, and they all have the same latent mean.” - We designed our policy to prevent model providers from just reporting the highest score they received during testing. We only publish the score for the model they release publicly. - Many of the numbers in the paper do not reflect reality: see the blog below (released a few days ago) for the actual statistics on the number of models tested from different providers. See also in thread our longstanding policy on pre-release testing. We have been doing so transparently with the support of our community for over a year.
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Mirko Voltolini retweeted
Replying to @dylan522p
How about we remove all export controls on NVIDIA and just be competitive instead of trying to slow others down.
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Sam Altman explains what it means to be product-focused In his Startup Playbook, Sam writes: “Your goal as a startup is to make something users love. If you do that, then you have to figure out how to get a lot more users. But this first part is critical—think about the really successful companies of today. They all started with a product that their early users loved so much they told other people about it. If you fail to do this, you will fail. If you deceive yourself and think your users love your product when they don’t, you will still fail.” In this interview, he explains what it means to be product-focused: “Maybe if you’re really good and really dedicated, you can work like 70 really productive hours in a week. So you have 70 hours to allocate and there’s the question of how you allocate those. When we [at Y Combinator] say ‘focus on product,’ it means that you put almost all of those hours into talking to users, creating product, and making sure that you have some users that really love your product.” Sam continues: “Most startups in the early days face a choice of either 1) getting a lot of users that like your product but don’t love it; or 2) a small number of users that really love your product. Every really successful company that I have been involved with has been in that second category. So, focus on product means: focus on love, not like; and spending your time writing code and talking to users. You need to get the product to the point where some people would be really bummed if it went away.” Video source: @GreylockVC (2015)
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Mirko Voltolini retweeted
AI OVERPRODUCTION China seeks to commoditize their complements. So, over the following months, I expect a complete blitz of Chinese open-source AI models for everything from computer vision to robotics to image generation. Why? I’m just inferring this from public statements, but their apparent goal is to take the profit out of AI software since they make money on AI-enabled hardware. Basically, they want to do to US tech (the last stronghold) what they already did to US manufacturing. Namely: copy it, optimize it, scale it, then wreck the Western original with low prices. I don’t know if they’ll succeed. But here’s the logic: (1) First, China noticed that DeepSeek’s release temporarily knocked ~$1T off US tech market caps. (2) Second, China’s core competency is exporting physical widgets, more than it is software. (3) Third, China’s other core competency is exporting things at such massive scale that all foreign producers are bankrupted and they win the market. See what they’re doing to German and Japanese cars, for example. (4) Fourth, China is well aware that it lacks global prestige as it’s historically been a copycat. With DeepSeek, becoming #1 in AI is now something they actually consider possibly achievable, and a matter of national pride. (5) Fifth, DeepSeek has gone viral in China and its open source nature means that everyone can rapidly integrate it, down to the level of local officials and obscure companies. And they are doing so, and posting the results for praise on WeChat. (6) Finally, while DeepSeek was obscure before recent events, it’s now a household name, and the founder (Liang Wengfeng) has met both with Xi but also the #2 in China, Li Qiang. They likely have unlimited resources now. So, if you put all that together, China thinks it has an opportunity to hit US tech companies, boost its prestige, help its internal economy, and take the margins out of AI software globally (at least at the model level). They will instead make their money by selling inexpensive AI-enabled hardware of increasing quality, from smart homes and self-driving cars to consumer drones and robot dogs. Basically, China is trying to do to AI what they always do: study, copy, optimize, and then bankrupt everyone with low prices and enormous scale. I don’t know if they’ll succeed at the app layer. But it could be hard for closed-source AI model developers to recoup the high fixed costs associated with training state-of-the-art models when great open source models are available. Last, I agree it’s surprising that the country of the Great Firewall is suddenly the country of open source AI. But it is consistent in a different way, which is that China is just focused on doing whatever it takes to win — even to the point of copying partially-abandoned Western values like open source, which seemed like the hardest thing to adopt. On that point: they did build censorship into the released DeepSeek AI models, but in a manner that’s easily circumvented outside China. So, you might conclude they don’t really care what non-Chinese people are saying outside China in other languages, so long as this doesn’t “interfere with China’s internal affairs.” Anyway —this is an area I’ve been watching, and my reluctant conclusion is that China is getting better at software faster than the West is getting better at hardware.
What's the best explanation you've heard for why China is leaning so hard into open source? It's now an official position from the foreign ministry apparently.
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Mirko Voltolini retweeted
Physics professor discloses in 1 minute the full insight of Quantum Mechanics.
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Mirko Voltolini retweeted
Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful! In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment. As coding becomes easier, more people should code, not fewer! Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step. I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively. One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that. When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result. Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do. [Original text: deeplearning.ai/the-batch/is
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Mirko Voltolini retweeted
To sum up, I'm still trying to wrap my head around this! why do recent frontier LLMs struggle on simple math if results on extremely hard math problems show some "signs of reasoning". Different hypotheses: 1⃣ CoT in data & RL are not enough to teach them proper search & backtracking 2⃣ Maybe it's not reasoning, it's just sophisticated pattern matching 3⃣Their internal computational graphs differ from those of humans, so teaching them linearized algorithms that seem simple to us might not translate effectively for them.
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Marc Andreessen explains IBM founder Thomas Watson‘s famous “Wild Ducks” program Marc believes that the organizational complexity is one reason you don’t see innovation at large companies. But that’s not the only reason: “I think there’s another deeper thing underneath that that people really don’t like to talk about, which is the sheer number of people in the world who are capable of doing new things is just a very small set of people. You’re not going to have a hundred of them in a company
 You’re going to have 3, 8, or 10, maybe.” Marc learned this early in his career at IBM, which was one of the most powerful companies in the world and had over 440,000 employees at the time. “They had a system that worked really well for 50 years. Most of the employees in the company were expected to basically follow rules
 But they had this category of people they called ‘Wild Ducks.’ This was an idea that the founder Thomas Watson came up with. They often had the formal title of an IBM Fellow and they were the people who could make new things.” He continues: “There were eight of them and they got to break all the rules and invent new products. They got to go off and work on something new, they didn’t have to report back, they got to pull people off of other projects to work with them, they got budget when they needed it, and they reported directly to the CEO.” Marc recalls one wild duck, Andy Heller, putting his cowboy boots on the conference room table “amongst an ocean of men in blue suits, white shirts, and red ties.” It was fine for Andy Heller to do that, but it was not fine for you to do that. “They very specifically identified almost like an aristocratic class within our company that gets to play by different rules
 Their job is to invent the next breakthrough product. We, IBM management, know that the 6,000 person division is not going to invent the next product. We know it’s going to be crazy Andy Heller and his cowboy boots.” Marc believes companies like IBM and HP ultimately collapsed when venture capital emerged as a parallel funding system for these wild ducks to start their own companies. Video source: @hubermanlab (2023)
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