:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of LLMs 3.3 Choice of benchmarks 3.4 Evaluation procedure 3.5 Model parameters and 3.6 Source code and data Evaluation 4.1 Pass@1 … Read more
:::info Author: (1) Enkhbold Nyamsuren, School of Computer Science and IT University College Cork Cork, Ireland, T12 XF62 (enyamsuren@ucc.ie). ::: Table of Links Abstract and Introduction Related Works 2.1 Code LLMs 2.2 Quantization 2.3 Evaluation benchmarks for code LLMs and 2.4 Evaluation metrics 2.5 Low- and high-resource languages Methodology 3.1 Run-time environment 3.2 Choice of … Read more
:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more
:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more
:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more
:::info Authors: (1) Pavan L. Veluvali, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (2) Jan Heiland, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg; (3) Peter Benner, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg. ::: Table of Links … Read more
Everyone watches cool crypto influencers and bloggers talk about the altseason — all while making money from our subscriptions, ads, and trading commissions. And many of them might not even be in the market themselves. But if altseason actually comes and you earn your profits — or if you just need to cash out your … Read more
After years of building products, mentoring, and making (a lot of) mistakes, I decided to write this list. Whether you’re just starting or already on your journey, I hope this helps you avoid a few traps and move faster. 1. You don’t need to know everything before building. You’ll learn the most by doing — … Read more
In the age of Agentic AI – where autonomous systems launch campaigns, personalise content, and trigger actions without human approval – data quality is everything. These AI systems act on what’s in your CRM, ad platform, or analytics table. If that data is outdated, duplicated, or inconsistent, they don’t just make small mistakes – they … Read more
NERD RAGE! how I got a Russian oligarch’s yacht kicked out of a port in Turkey, raised hell, struck a blow for Ukraine, got seen more than a million times plus irritated a bunch of powerful people using this one weird old trick. A true story So the TL;DR is down at the bottom. But … Read more
The use of AI agents skyrocketed in 2024. This is attributed to the ease with which LLM agents can carry out activities. Popular LLM agents, such as Deep Research by OpenAI and Replit AI Agent, have been shown to possess higher capabilities than typical LLMs. Building a peer-to-peer evaluation of AI agent systems using human-centric … Read more
Listen Here → https://open.spotify.com/episode/1MmUcQ5nSYLRFBUQopeRm2?si=RZ5n9Ez_RUWVhVx2iJbcHQ&embedable=true The Hidden Business Strategy No One Talks About Your relationship is failing. Not your business relationships. Your real ones. The ones waiting for you at home while you “crush it” for another 14-hour day. While you’re optimizing conversion rates and chasing investor meetings, your actual life is quietly unraveling. Your partner … Read more
There’s a particularly cheeky episode of Brooklyn 99 about a fictitious Tri-State Police Officers Convention, an annual conference that brings police officers—including the show’s ensemble—together for presentations, professional development, and parties. “Cop-Con” is a ride: Despite their captain urging them to be on their best behavior, members of the squad spend their time at the … Read more
How are you, hacker? 🪐 What’s happening in tech today, June 1, 2025? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Marilyn Monroe Was Born in 1926, The European Central Bank Was Founded in 1998, Nissan Motor Came Into Existence in 1934, and we present you with these top … Read more
How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Decentralized Identity (DID) and KYC in Blockchain Gambling: Privacy vs. Compliance By @casinosblockchain [ 2 Min read ] Explore how Decentralized … Read more
:::info Authors: (1) Harshvardhan J. Pandit, ADAPT Centre, Dublin City University, Dublin, Ireland, and Cybersecurity and Data Protection Group, National Standards Institute, Ireland (me@harshp.com) (2) Jan Lindquist, Privacy and Security Group, Institute for Standards, Sweden (jan@linaltec.com); (3) Georg P. Krog, Signatu AS, Oslo, Norway (georg@signatu.com). ::: Table of Links Abstract and 1 Introduction 2 Overview … Read more
:::info Authors: (1) Harshvardhan J. Pandit, ADAPT Centre, Dublin City University, Dublin, Ireland, and Cybersecurity and Data Protection Group, National Standards Institute, Ireland (me@harshp.com) (2) Jan Lindquist, Privacy and Security Group, Institute for Standards, Sweden (jan@linaltec.com); (3) Georg P. Krog, Signatu AS, Oslo, Norway (georg@signatu.com). ::: Table of Links Abstract and 1 Introduction 2 Overview … Read more
Table of Contents AutoGraph transformations Conditionals Loops Limitations Executing Python side effects All outputs of a tf.function must be return values Recursive tf.fuctions are not supported. Known issues Depending on Python global and free variables Depending on Python objects Creating tf.Variables AutoGraph transformations AutoGraph is a library that is on by default in tf.function, and transforms … Read more
Table of Contents Overview What are graphs? The benefits of graphs Setup Taking advantage of graphs Converting Python functions to graphs Polymorphism: one tf.function, many graphs Using tf.function Graph execution vs eager execution Non-strict execution tf.function best practices Seeing the speed-up Performance trade offs When is a tf.function tracing? Next steps. Overview This guide goes … Read more
:::info Authors: (1) Mohamed A. Abba, Department of Statistics, North Carolina State University; (2) Brian J. Reich, Department of Statistics, North Carolina State University; (3) Reetam Majumder, Southeast Climate Adaptation Science Center, North Carolina State University; (4) Brandon Feng, Department of Statistics, North Carolina State University. ::: Table of Links Abstract and 1 Introduction 1.1 … Read more