Babe wake up, Microsoft released a 1-bit LLM under MIT that is optimized for running on CPUs: microsoft/bitnet-b1.58-2B-4T
Salvatore Sanfilippo (aka antirez) is back!
After stepping away from Redis for some time, he's returned with a major contribution: a brand-new data type called vector sets. This addition brings semantic similarity search to Redis, making it possible to query based on meaning rather than exact matches.
Check it out: https://redis.io/blog/announcing-vector-sets-a-new-redis-data-type-for-vector-similarity
After stepping away from Redis for some time, he's returned with a major contribution: a brand-new data type called vector sets. This addition brings semantic similarity search to Redis, making it possible to query based on meaning rather than exact matches.
Check it out: https://redis.io/blog/announcing-vector-sets-a-new-redis-data-type-for-vector-similarity
Redis
Announcing vector sets, a new Redis data type for vector similarity | Redis
Developers love Redis. Unlock the full potential of the Redis database with Redis Enterprise and start building blazing fast apps.
A must-watch for anyone interested in the future of #AI. In this interview for NVIDIA Developer, Yann LeCun - Turing Award winner and Chief AI Scientist at Meta - shares his take (and a very robust contrarian opinion, IMHO) on the limits of today’s language models.
In the interview, he argues that LLMs (like OpenAI's GPT or Meta's LLaMA) are not the path to true artificial general intelligence. They're impressive, yes, but fundamentally constrained by the Transformer architecture. Scaling up won’t solve this. Why? Because human intelligence isn’t just about language or token prediction - it’s about understanding, reasoning, and interacting with the physical world through systems more akin to what psychologists call System 1 and System 2.
Think about a cat: it can leap with precision without any concept of physics, nor of the languages (mathematical and linguistic) which can explicitly explain physics. Ask your local orange alley cat about it, if you don't believe me.
LeCun also shares some striking numbers to highlight just how limited language-based learning really is:
• Human language processing has a very low data rate - roughly 12 bytes per second. That’s about 4.5 words per second, assuming each word is encoded in about 2 bytes.
• Vision operates on an entirely different scale. Our two optical nerves transmit a combined stream of roughly 20 megabytes per second, based on the million fibers in each nerve sending about 10 bytes per second.
• Over just four years of being awake, a child accumulates around a petabyte of visual experience - far more than the total training data of even the largest language models.
To put it plainly:
Visual perception carries around 16 million times more data than reading or listening to language, and a preschooler has already taken in 50 times more data than what goes into the largest text-trained LLMs.
LeCun’s takeaway is that the infrastructure modeling the data and the structure of data matter just as much as the quantity of data. Self-Supervised Learning thrives on redundancy, and sensory inputs (especially vision) are packed with the kind of statistical richness that language alone can’t provide.
Most of what we know - and certainly what animals know - comes from experience, not explanation. Language is a brilliant tool, but it's the final layer, not the foundation.
I explored this in my own way in Hangman and Circles, where I reported some results of a research by the Apple AI Team, reflected on the limits of linguistic abstraction, and talked about why it may be misleading us in the pursuit of AGI:
https://t.me/bytebaibyte/19
This interview is amazing, and super-fun too - definitely worth your time:
https://youtu.be/eyrDM3A_YFc?si=oMiDKJAXUYIjfjIu
PS. I also found watching the ~2h interview on the Lex Fridman Podcast extremely interesting - it dives deeper into the topics mentioned during the Nvidia Developer interview.
In the interview, he argues that LLMs (like OpenAI's GPT or Meta's LLaMA) are not the path to true artificial general intelligence. They're impressive, yes, but fundamentally constrained by the Transformer architecture. Scaling up won’t solve this. Why? Because human intelligence isn’t just about language or token prediction - it’s about understanding, reasoning, and interacting with the physical world through systems more akin to what psychologists call System 1 and System 2.
Think about a cat: it can leap with precision without any concept of physics, nor of the languages (mathematical and linguistic) which can explicitly explain physics. Ask your local orange alley cat about it, if you don't believe me.
LeCun also shares some striking numbers to highlight just how limited language-based learning really is:
• Human language processing has a very low data rate - roughly 12 bytes per second. That’s about 4.5 words per second, assuming each word is encoded in about 2 bytes.
• Vision operates on an entirely different scale. Our two optical nerves transmit a combined stream of roughly 20 megabytes per second, based on the million fibers in each nerve sending about 10 bytes per second.
• Over just four years of being awake, a child accumulates around a petabyte of visual experience - far more than the total training data of even the largest language models.
To put it plainly:
Visual perception carries around 16 million times more data than reading or listening to language, and a preschooler has already taken in 50 times more data than what goes into the largest text-trained LLMs.
LeCun’s takeaway is that the infrastructure modeling the data and the structure of data matter just as much as the quantity of data. Self-Supervised Learning thrives on redundancy, and sensory inputs (especially vision) are packed with the kind of statistical richness that language alone can’t provide.
Most of what we know - and certainly what animals know - comes from experience, not explanation. Language is a brilliant tool, but it's the final layer, not the foundation.
I explored this in my own way in Hangman and Circles, where I reported some results of a research by the Apple AI Team, reflected on the limits of linguistic abstraction, and talked about why it may be misleading us in the pursuit of AGI:
https://t.me/bytebaibyte/19
This interview is amazing, and super-fun too - definitely worth your time:
https://youtu.be/eyrDM3A_YFc?si=oMiDKJAXUYIjfjIu
PS. I also found watching the ~2h interview on the Lex Fridman Podcast extremely interesting - it dives deeper into the topics mentioned during the Nvidia Developer interview.
YouTube
Frontiers of AI and Computing: A Conversation With Yann LeCun and Bill Dally | NVIDIA GTC 2025
As artificial intelligence continues to reshape the world, the intersection of deep learning and high performance computing becomes increasingly crucial. This talk brings together Yann LeCun, a pioneer in deep learning and the chief AI scientist at Meta,…
Byte by Byte
A must-watch for anyone interested in the future of #AI. In this interview for NVIDIA Developer, Yann LeCun - Turing Award winner and Chief AI Scientist at Meta - shares his take (and a very robust contrarian opinion, IMHO) on the limits of today’s language…
also relevant: https://arxiv.org/abs/2503.21934
arXiv.org
Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad
Recent math benchmarks for large language models (LLMs) such as MathArena indicate that state-of-the-art reasoning models achieve impressive performance on mathematical competitions like AIME,...
Who would have thought we'd see PewDiePie advocating for Linux on the desktop before GTA 6?
YouTube
I installed Linux (so should you)
#ad - Shop Gfuel sale: https://creator.gfuel.com/pewdiepie
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In the upcoming Ubuntu 25.10, Canonical plans to use an alternative to sudo that's being developed by the sudo-rs project and written in Rust. In March, a similar decision was made to replace GNU Coreutils with uutils, which is also written in Rust. There are currently initiatives under consideration to replace zlib and ntpd with zlib-rs and ntpd-rs.
https://www.phoronix.com/news/Ubuntu-25.10-sudo-rs-Default
https://www.phoronix.com/news/Ubuntu-25.10-sudo-rs-Default
Phoronix
Ubuntu 25.10 Plans To Use sudo-rs By Default For Memory-Safe, Rust-Based sudo
With Ubuntu 25.10 Canonical is planning to make use of more Rust-written system components and so far most of that talk has been about transitioning to Rust Coreutils 'uutils' in place of GNU Coreutils
via Valentina Lenarduzzi on LinkedIn:
"Our paper "Does #Microservices Adoption Impact the Velocity? A #Cohort Study" has been accepted at Empirical Software Engineering Journal
Microservices are often praised for improving development speed thanks to their modular and independent nature. But do they actually lead to faster feature delivery and bug fixing? In our latest study, we explored this question using a retrospective #Cohort design - a methodology widely used in medical research but still rare in software engineering.
What we did: We conducted the first large-scale empirical study comparing GitHub projects built with #Microservices from the start against similar monolithic projects, using a #Cohort study to assess causality-not just correlation.
What we found: Surprisingly, no statistically significant difference in development velocity was observed. Even after controlling for confounding variables, #Microservices adoption didn't show a measurable impact on how quickly projects deliver features or fix bugs.
Why it matters: This study not only challenges assumptions about #Microservices and velocity, but also introduces a powerful empirical methodology to our field. We're excited to contribute one of the first works applying cohort studies in software engineering research.
https://www.researchgate.net/publication/391482952_Does_Microservice_Adoption_Impact_the_Velocity_A_Cohort_Study
"Our paper "Does #Microservices Adoption Impact the Velocity? A #Cohort Study" has been accepted at Empirical Software Engineering Journal
Microservices are often praised for improving development speed thanks to their modular and independent nature. But do they actually lead to faster feature delivery and bug fixing? In our latest study, we explored this question using a retrospective #Cohort design - a methodology widely used in medical research but still rare in software engineering.
What we did: We conducted the first large-scale empirical study comparing GitHub projects built with #Microservices from the start against similar monolithic projects, using a #Cohort study to assess causality-not just correlation.
What we found: Surprisingly, no statistically significant difference in development velocity was observed. Even after controlling for confounding variables, #Microservices adoption didn't show a measurable impact on how quickly projects deliver features or fix bugs.
Why it matters: This study not only challenges assumptions about #Microservices and velocity, but also introduces a powerful empirical methodology to our field. We're excited to contribute one of the first works applying cohort studies in software engineering research.
https://www.researchgate.net/publication/391482952_Does_Microservice_Adoption_Impact_the_Velocity_A_Cohort_Study
ResearchGate
(PDF) Does microservice adoption impact the velocity? A cohort study
PDF | Context] Microservices enable the decomposition of applications into small, independent, and connected services. The independence between services... | Find, read and cite all the research you need on ResearchGate
WOAH
[...] we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data. [...]
https://arxiv.org/abs/2505.03335
[...] we propose a new RLVR paradigm called Absolute Zero, in which a single model learns to propose tasks that maximize its own learning progress and improves reasoning by solving them, without relying on any external data. [...]
https://arxiv.org/abs/2505.03335
arXiv.org
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning capabilities of large language models by learning directly from outcome-based rewards. Recent...
My fellow UX/UI designers, please welcome: Google's Material Design 3 Expressive
Material Design
Build beautiful, usable products faster. Material Design is an adaptable system—backed by open-source code—that helps teams build high quality digital experiences.
Man, nobody told me Gemini Advanced on 2.5 Pro was this good. First drafts always look great, no need for revision. Its answers are just straight to the point, no introductory bootlicking like "oh yours is a very good question". It gulps down whatever context file and doesn't need any iteration or prompt fragmentation.
I love it. Definitely not going back to chat gippity after this.
I love it. Definitely not going back to chat gippity after this.
Yesterday Microsoft killed the paid AI code editor market (in a good way):
"We will open source the code in the GitHub Copilot Chat extension under the MIT license"
https://code.visualstudio.com/blogs/2025/05/19/openSourceAIEditor
"We will open source the code in the GitHub Copilot Chat extension under the MIT license"
https://code.visualstudio.com/blogs/2025/05/19/openSourceAIEditor
Visualstudio
VS Code: Open Source AI Editor
We will open source the GitHub Copilot Chat extension. It’s the next step towards making VS Code an open source AI editor.
Okay, this is a bit Cicero pro domo sua, but hear me out, okay? ;)
Read my latest article: In Defense of LLMs
Read my latest article: In Defense of LLMs