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
I may be late to the game but this research paper, “Simulating Time in Square-Root Space” by Ryan Williams seems very interesting.
Abstract:
Abstract:
We show that for all functions t(n)n, every multitape Turing machine running in time t can be simulated in space only O(tlogt) . This is a substantial improvement over Hopcroft, Paul, and Valiant's simulation of time t in O(tlogt) space from 50 years ago [FOCS 1975, JACM 1977]. Among other results, our simulation implies that bounded fan-in circuits of size s can be evaluated on any input in only spoly(logs) space, and that there are explicit problems solvable in O(n) space which require n2− time on a multitape Turing machine for all 0, thereby making a little progress on the P versus PSPACE problem.
Our simulation reduces the problem of simulating time-bounded multitape Turing machines to a series of implicitly-defined Tree Evaluation instances with nice parameters, leveraging the remarkable space-efficient algorithm for Tree Evaluation recently found by Cook and Mertz [STOC 2024].
eccc.weizmann.ac.il
ECCC - TR25-017
Homepage of the Electronic Colloquium on Computational Complexity located at the Weizmann Institute of Science, Israel
yoooooo we’ve got V-JEPA 2 before GTA6
arXiv.org
V-JEPA 2: Self-Supervised Video Models Enable Understanding,...
A major challenge for modern AI is to learn to understand the world and learn to act largely by observation. This paper explores a self-supervised approach that combines internet-scale video data...
V-JEPA 2 is the new iteration of the V-JEPA architecture, which according to Yann LeCun will replace Transformer-based LLMs for the so-called “World” models - models that understand the real world rather than mere words.