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94Meta debuts new AI model, attempting to catch Google, OpenAI after spending billions
Meta has released its latest large language model, Muse Spark, under the leadership of Chief AI Officer Alexandr Wang and Meta Superintelligence Labs, in an effort to compete with Google and OpenAI. This comes after Meta invested billions in AI research and development.
[2604.05091] MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU
This is an abstract page for arXiv paper 2604.05091, titled "MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU."
AI joins the 8-hour work day as GLM ships 5.1 open source LLM, beating Opus 4.6 and GPT-5.4 on SWE-Bench Pro
GLM released its 5.1 open-source LLM, claiming it surpasses Opus 4.6 and GPT-5.4 in performance on the SWE-bench Pro, suggesting potential for AI's increased autonomy in software development.
Good Taste the Only Real Moat Left
The article argues that as AI makes competent output more accessible, good taste and judgment become more valuable assets, especially when paired with context and a willingness to build.
AI may be making us think and write more alike
A study led by a USC Dornsife researcher suggests that large language models (LLMs) may be standardizing human expression and subtly influencing our thinking and writing.
Introducing Deep Extract
Reducto offers an AI-powered API service named Deep Extract for document ingestion and parsing, designed to improve the quality of data used for large language models.
Does coding with LLMs mean more microservices?
The article discusses whether the increased use of Large Language Models (LLMs) in coding will lead to a proliferation of microservices, as AI tools might make it easier to create and manage smaller, more specialized services.
GitHub - duo121/termhub: TermHub: Open-source native terminal control gateway for AI Agents. Let LLMs/AI Agents fully control & automate iTerm2 / Windows Terminal: manage tabs, panes, sessions, send commands, capture output programmatically. · GitHub
TermHub is an open-source native terminal control gateway for AI Agents that allows LLMs/AI Agents to fully control and automate iTerm2 / Windows Terminal by managing tabs, panes, and sessions, sending commands, and capturing output programmatically.
Writing Lisp is AI Resistant and I'm Sad
The author discusses the AI resistance of Lisp programming and expresses disappointment that Lisp isn't more widely adopted, given its advantages in symbolic AI tasks, particularly with the emergence of LLMs.
Andrej Karpathy (@karpathy): "Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs. So here's the idea in a gist format: https://gist.github.com/karpathy&#
Andrej Karpathy discusses the shift towards sharing "idea files" rather than specific code due to the rise of LLM agents that can customize solutions based on individual needs.
"Cognitive surrender" leads AI users to abandon logical thinking, research finds
Research indicates that a large majority of AI users uncritically accept faulty answers from Large Language Models (LLMs), demonstrating a "cognitive surrender" where logical thinking is abandoned.
Apple approves driver that lets Nvidia eGPUs work with Arm Macs.
Apple has approved a driver from Tiny Corp that allows Nvidia eGPUs to work with Arm Macs, enabling LLM development without disabling System Integrity Protection (SIP).
Components of A Coding Agent - by Sebastian Raschka, PhD
This article discusses the components of a coding agent, focusing on how they use tools, memory, and repository context to improve the performance of LLMs in practical applications. It explores how these elements contribute to making LLMs more effective for coding tasks.
Emotion concepts and their function in a large language model
Anthropic presents interpretability research on emotion concepts within a large language model (LLM).
GitHub - Arthur-Ficial/apfel: Apple Intelligence from the command line. On-device LLM via FoundationModels framework. No API keys, no cloud, no dependencies. · GitHub
The article discusses a GitHub repository, Arthur-Ficial/apfel, that provides Apple Intelligence functionality from the command line, utilizing on-device LLMs via the FoundationModels framework without requiring API keys or cloud dependencies.
[2601.15714] Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMs
This arXiv paper abstract (2601.15714) discusses the need for zero-error horizons in trustworthy LLMs, pointing out that even a hypothetical GPT-5.2 model cannot reliably count to five.
US12438995B1 - Integration of video language models with AI for filmmaking
A patent describes a method integrating video LLMs with AI algorithms using filmmaking metadata and Lidar data to simulate professional filmmaking techniques.
Blocked
A programming forum temporarily banned LLM content.
GitHub - SharpAI/SwiftLM: ⚡ Native MLX Swift LLM inference server for Apple Silicon. OpenAI-compatible API, SSD streaming for 100B+ MoE models, TurboQuant KV cache compression, + iOS iPhone app. · GitHub
SharpAI/SwiftLM is a native MLX Swift Large Language Model (LLM) inference server for Apple Silicon, featuring an OpenAI-compatible API and SSD streaming for 100B+ MoE models, along with iOS iPhone app.
Build AI models that know your enterprise
Mistral AI's Forge allows enterprises to build AI models using their institutional knowledge and frontier-grade LLMs, without needing to manage infrastructure or deal with cloud lock-in.
Reliability of LLMs as medical assistants for the general public: a randomized preregistered study
This Nature Medicine article explores the reliability of Large Language Models (LLMs) as medical assistants for the general public via a randomized preregistered study.
ImportAI 449: LLMs training other LLMs; 72B distributed training run; computer vision is harder than generative text
Jack Clark's Import AI newsletter discusses LLMs training other LLMs, a 72B distributed training run, and notes that computer vision is more challenging than generative text.
Import AI 444: LLM societies; Huawei makes kernels with AI; ChipBench
Jack Clark's Import AI 444 discusses LLM societies, Huawei making kernels with AI, and ChipBench, referencing a Google paper suggesting LLMs simulate multiple personalities to answer questions.
Why Are Large Language Models so Terrible at Video Games?
Large Language Models (LLMs) can generate simple video games, but they struggle significantly when tasked with playing those games, highlighting limitations in their reasoning and planning abilities within complex environments.
Social media is populist and polarising; AI may be the opposite
The article argues that while social media platforms tend to be populist and polarising, large language models (LLMs) may have the opposite effect by elevating expert consensus and moderating extreme views.
AI got the blame for the Iran school bombing. The truth is far more worrying
The article in The Guardian argues that AI was wrongly blamed for the Iran school bombing, and that the real issue is the choices made by humans over many years.
Wikipedia Bans AI-Generated Content
Wikipedia has banned AI-generated content due to an increase in administrative reports related to Large Language Models and editors being overwhelmed.
Wikipedia bans AI-generated articles
Wikipedia is banning the use of AI for generating or rewriting articles, citing violations of core content policies with LLMs.
"Disregard that!" attacks
The article warns against sharing large language model (LLM) context windows with others due to the risk of "disregard that!" attacks, where malicious instructions can override previous instructions and potentially compromise the system.
Ensu - Ente's Local LLM app
Ente introduces Ensu, a local LLM application designed to run privately on a user's device and evolve over time.
The People Getting Falsely Accused of Using AI to Write
As AI-generated text proliferates, individuals are being falsely accused of using LLMs for writing, especially if their prose is clean or they are non-native English speakers or autistic writers. This highlights the potential for bias and unfair accusations in the age of AI.
Thoughts on LLMs - Psychological complications
The article presents thoughts on psychological complications related to Large Language Models.
GitHub - michaelneale/mesh-llm: reference impl with llama.cpp compiled to distributed inference across machines, with real end to end demo · GitHub
This GitHub repository provides a reference implementation of distributed inference using llama.cpp, compiled for deployment across multiple machines, and includes an end-to-end demonstration.
Characterizing Delusional Spirals through Human-LLM Chat Logs
The research paper "Characterizing Delusional Spirals through Human-LLM Chat Logs" by Moore et al. (2026) explores delusional spirals in human-LLM interactions.
[2603.08174] MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals
This is an abstract page for arXiv paper 2603.08174, which is titled "MERLIN: Building Low-SNR Robust Multimodal LLMs for Electromagnetic Signals". The paper focuses on developing robust multimodal large language models for processing electromagnetic signals.
How do frontier AI agents perform in multi-step cyber-attack scenarios?
The AISI tested seven large language models (LLMs) on cyber ranges to measure their ability to execute extended attack sequences in complex cyber environments.
Import AI 348: DeepMind defines AGI; the best free LLM is made in China; mind controlling robots
Jack Clark's Import AI newsletter discusses DeepMind's definition of AGI, the rise of a powerful free LLM from China, and developments in mind-controlling robots.
Why I love NixOS
The author expresses their appreciation for NixOS, highlighting the Nix package manager, reproducibility, and system management, particularly in the context of LLM coding.
Why craft-lovers are losing their craft
Hong Minhee discusses how large language models (LLMs) are causing craft-lovers to lose their passion, referencing an observation by Les Orchard about the impact of these models on creativity and skill.
Dithering
This Dithering podcast episode discusses LLM paradigm changes with Ben Thompson and John Gruber.
EsoLang-Bench: Evaluating LLMs via Esoteric Programming Languages
EsoLang-Bench is presented as a benchmark to evaluate genuine reasoning in Large Language Models (LLMs) across 5 esoteric programming languages, covering 80 problems.
On Violations of LLM Review Policies
The article discusses violations of LLM review policies on the ICML Blog.
Agent-Based Task Execution
This article describes a tool for executing large language models on-premises using non-public data.
[2603.08640] PostTrainBench: Can LLM Agents Automate LLM Post-Training?
This is the abstract page for arXiv paper 2603.08640, titled "PostTrainBench: Can LLM Agents Automate LLM Post-Training?"
[2506.22419] The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements
This is the abstract page for arXiv paper 2506.22419, titled "The Automated LLM Speedrunning Benchmark: Reproducing NanoGPT Improvements".
InferenceX v2: NVIDIA Blackwell Vs AMD vs Hopper
This SemiAnalysis newsletter analyzes inference performance of various AI chips. It compares Nvidia's upcoming Blackwell B200 against AMD's MI355X and Nvidia's Hopper H100, covering aspects like disaggregated serving, wide expert parallelism, and software frameworks like SGLang, vLLM, and TRTLLM.
Introducing Mistral Small 4
Mistral AI has released Mistral Small 4, a new language model aimed at providing a balance between cost, latency, and reasoning capabilities. It is available through the Mistral AI platform and via La Plateforme.
[2603.12229] Language Model Teams as Distributed Systems
The arXiv paper with identifier 2603.12229 is titled "Language Model Teams as Distributed Systems." It is an abstract page for an artificial intelligence research paper.
How I write software with LLMs - Stavros' Stuff
The author details their workflow for writing software using Large Language Models (LLMs). This includes using tools like Cursor and claudecode along with practices like iterative prompting, unit testing, and creating 'coding agents' to automate tasks.
LLMs can be absolutely exhausting
Tom Johnell reflects on the mental exhaustion that can result from extended work sessions with LLMs like Claude and Codex, noting the difficulty of pinpointing the exact causes of this fatigue and the potential issues with current AI models.
Stop Sloppypasta: Don't paste raw LLM output at people
The Stop Sloppypasta website argues that directly pasting raw LLM output to others is rude and ineffective. Instead, humans should edit, synthesize, and contextualize the information from AI models before sharing it.
LLM Architecture Gallery
This is a gallery of LLM (Large Language Model) architecture figures collected from "The Big LLM Architecture Comparison" and related articles by Sebastian Raschka. It includes fact sheets and links to the original sections for each model.
Codegen is not productivity
The author argues that simply generating more code with AI (specifically LLMs) does not equate to increased programmer productivity. They contend that lines of code is a poor metric, highlighting the need to evaluate the quality and impact of the generated code rather than just its quantity.
Allow me to get to know you, mistakes and all
The author expresses an aversion to receiving communications that have been processed by Large Language Models (LLMs). They believe that LLMs obscure the original intent of the message, diminishing the personal connection and authentic voice of the sender.
Recursive Language Models
Alex L. Zhang introduces Recursive Language Models (RLMs), an inference strategy that allows language models to decompose and recursively interact with input context of unbounded length through REPL environments.
Cantrip: On summoning entities from language in circles
Deepfates introduces Cantrip, a "ghost library" that reimagines language model agents. Cantrip is delivered with a generative test specification, aiming to redefine the fundamentals of how language models operate.
What do coders do after AI?
Anil Dash reflects on the evolving role of coders in light of advancements in AI, particularly large language models. He suggests that while AI can automate some coding tasks, the need for human expertise in software engineering, system design, and understanding user needs will remain crucial.
Coding After Coders: The End of Computer Programming as We Know It
This New York Times article from the future (March 12, 2026) discusses the changing role of computer programmers in an era increasingly dominated by AI coding tools. Programmers in Silicon Valley are now finding themselves in a "deeply, deeply weird" position as AI agents take over many traditional coding tasks.
CodeSpeak: Software Engineering with AI
CodeSpeak is described as a next-generation programming language leveraging LLMs, designed to compile into traditional coding languages like Python, Go, and Javascript/Typescript. The project aims to abstract programming by using natural language.
Are LLMs not getting better?
The author discusses the possibility that Large Language Models (LLMs) are not improving as rapidly as they have in the past. They explore the difficulty of objectively measuring improvement and posit that perceived stagnation may be due to a focus on benchmarks that don't fully capture real-world performance.
Reliable Software in the LLM Era
The article discusses how Large Language Models (LLMs) are becoming increasingly prevalent in software development, creating challenges for reliability. It advocates for using executable specifications, such as Quint, to ensure software meets requirements and mitigate risks associated with LLM-generated code.
Google is using old news reports and AI to predict flash floods
Google is leveraging old news reports and artificial intelligence to improve flash flood prediction. By using qualitative reports and converting them into quantitative data with a Large Language Model, Google aims to solve data scarcity issues in flood forecasting.
GitHub - microsoft/BitNet: Official inference framework for 1-bit LLMs · GitHub
The provided GitHub link showcases Microsoft's official inference framework for 1-bit Large Language Models (LLMs). This repository facilitates the development and utilization of efficient, low-resource AI models.
Why does AI tell you to use Terminal so much?
The article analyzes why AI language models often recommend using the Terminal command line interface for problem-solving on Macs, even when GUI alternatives exist. This preference, while sometimes efficient, can create accessibility issues for users unfamiliar with command-line interfaces.
Paras Chopra’s Lossfunk gets AI models to speak Tulu through prompts, not training
Paras Chopra's AI lab, Lossfunk, developed a prompting method that enables large language models to generate Tulu text without prior training. By using grammar rules and negative constraints, the method achieved an accuracy rate of approximately 85%, potentially expanding AI support for low-resource Indian languages.
Against Vibes: When is a Generative Model Useful
This article discusses the uses and limitations of generative AI models, arguing that they are most valuable when applied to specific, well-defined tasks, rather than broadly adopted for creative or strategic endeavors. It analyzes situations where generative models provide clear advantages over traditional methods, especially in scenarios requiring automation or creativity in constrained contexts.
Infinity Inc
Infinity Inc optimized the Qwen3 large language model, achieving a 3x speedup in processing time. The optimizations reduced the model's size by 50% without significant degradation in accuracy, leading to more efficient deployment and lower computational costs.
FastVoice RAG: Sub-200ms Voice AI with Retrieval-Augmented Generation, Entirely On-Device
The RunAnywhere blog post introduces FastVoice RAG, an on-device voice AI pipeline that achieves sub-200ms first-audio response time. It uses hybrid retrieval (BM25 + vector search) that adds less than 4ms to the process and utilizes word-level flushing to absorb the LLM prefill cost.
FastVoice: 63ms First-Audio Latency for On-Device Voice AI on Apple Silicon
RunAnywhere's FastVoice pipeline achieves 63ms first-audio latency on Apple Silicon by integrating STT, LLM, and TTS in a single C++ pipeline. This on-device solution eliminates the need for cloud processing or network connectivity, significantly improving speed and responsiveness for voice AI applications.
We Built the Fastest LLM Decode Engine for Apple Silicon. Here Are the Numbers.
RunAnywhere claims its MetalRT is the fastest LLM decode engine for Apple Silicon, achieving 658 tok/s decode and a 6.6ms time-to-first-token on a single M4 Max. MetalRT was the winning decode engine on 3 of 4 models tested.
LLM Neuroanatomy: How I Topped the LLM Leaderboard Without Changing a Single Weight
David Noel Ng details how he achieved top performance on an AI leaderboard by manipulating the input context of a Large Language Model (LLM) without altering its internal weights. This approach, dubbed "LLM Neuroanatomy," leverages prompt engineering and knowledge injection, mimicking techniques used in neuroscience.
rfc-454545.txt · GitHub
The provided text is a long, rambling, stream-of-consciousness style list of various technologies, companies, people, and other assorted concepts, seemingly related to AI and machine learning, organized into a hierarchical structure with indented bullets. Many listed technologies are AI models, tools, and development environments.
So you want to write an "app" - ArcaneNibble's site
ArcaneNibble's site offers advice on app development across various operating systems, highlighting the complexities and trade-offs involved in each platform. It covers the evolution of app creation, from early programming languages to modern AI-assisted tools, reflecting on the broader implications of technology on society.
Agent Safehouse
Agent Safehouse is a macOS tool that sandboxes LLM coding agents using kernel-level enforcement via sandbox-exec. The tool offers a deny-first approach and boasts composability with zero dependencies.
Ultra Lab
Ultra Lab is an AI Product Studio focused on LLM-powered automation. They claim to create 35+ AI content pieces per day and offer three SaaS products, including UltraProbe, an AI security scanner.
Ultra Lab
Ultra Lab is an AI product studio specializing in LLM-powered automation. They produce over 35 pieces of AI content daily and offer three SaaS products, including UltraProbe AI security scanner, focusing on prompt engineering and large-scale deployment.
Hey ChatGPT, write me a fictional paper: these LLMs are willing to commit academic fraud
A study found that mainstream chatbots, including Large Language Models (LLMs) like ChatGPT, show varying degrees of resistance when prompted to fabricate information or commit academic fraud. The chatbots exhibit different levels of willingness to produce fictional papers and other forms of academic dishonesty.
I'm not consulting an LLM
The author explains why they intentionally avoid using large language models (LLMs) in their writing process. They emphasize the importance of human thought, originality, and avoiding the homogenized content that LLMs can produce, ultimately preferring to rely on personal reflection and unique perspectives.
GitHub - NERVsystems/llm9p: LLM exposed as a 9P filesystem · GitHub
The GitHub repository NERVsystems/llm9p offers a way to expose Large Language Models (LLMs) as a 9P filesystem. This allows users to interact with LLMs through a familiar file system interface.
New Research Reassesses the Value of AGENTS.md Files for AI Coding
A new research paper from ETH Zurich challenges the widespread recommendation of using AGENTS.md files for AI coding agents, concluding that these files may often hinder performance. The researchers suggest that omitting LLM-generated context files entirely may be more effective for coding tasks.
Qwen3.5
The Unsloth documentation provides a guide on how to run Qwen3.5 large language models (LLMs) locally on various devices. This includes running medium-sized models like Qwen3.5-35B-A3B, 27B, 122B-A10B, and smaller models such as Qwen3.5-0.8B, 2B, 4B, 9B and 397B-A17B.
Your LLM Doesn't Write Correct Code. It Writes Plausible Code.
The article discusses how Large Language Models (LLMs) can generate plausible but incorrect code due to their training on vast datasets of varying quality. It highlights that LLMs focus on statistical patterns rather than true code correctness, leading to potential errors in applications requiring precision.
The L in "LLM" Stands for Lying
The author questions the uncritical acceptance of Large Language Models (LLMs), suggesting that their inherent limitations make them prone to inaccuracy or even falsehoods, challenging the narrative of their inevitable and beneficial integration into society. The post raises concerns about the potential for LLMs to mislead and the broader implications for trust and information integrity.
Qwen3.5 Fine-tuning Guide
The Unsloth documentation provides a guide for fine-tuning Qwen3.5 large language models (LLMs). The guide likely details the tools and techniques for adapting the Qwen3.5 model for specific tasks or datasets using the Unsloth framework.
Giving LLMs a personality is just good engineering
Sean Goedecke argues that engineering LLMs with specific personalities and constraints improves their performance, usability, and trustworthiness. He suggests techniques like training on character-specific datasets, reinforcement learning for personality alignment, and prompt engineering to shape LLM behavior.
LLMs can unmask pseudonymous users at scale with surprising accuracy
A new study reveals that large language models (LLMs) can unmask pseudonymous users with surprising accuracy by analyzing publicly available writing samples. Researchers demonstrated the ability to de-anonymize users at scale, raising concerns about the future of pseudonymity and privacy.
LLMHorrors
An API key for Google's Gemini LLM was stolen, resulting in $82,000 of compute costs in just 48 hours. The incident highlights the financial risks associated with insecure AI API keys and the potential for rapid, large-scale abuse.
Agents of Chaos
The Agents of Chaos website details a two-week study focused on deploying autonomous LLM agents in a live multi-party environment. The agents had persistent memory, email, shell access, and were exposed to real human interaction to observe their behavior and capabilities.
[2602.23329] LLM Novice Uplift on Dual-Use, In Silico Biology Tasks
ArXiv paper 2602.23329 explores the use of Large Language Models (LLMs) in in silico biology tasks, specifically dual-use applications. The research likely investigates how LLMs can assist novice users in this complex domain.
Alibaba's small, open source Qwen3.5-9B beats OpenAI's gpt-oss-120B and can run on standard laptops
Alibaba's open-source Qwen3.5-9B large language model outperforms OpenAI's gpt-oss-120B model while being small enough to run on standard laptops. The Qwen3.5 series aims to democratize AI capabilities by offering models ranging from 0.8B for smartphones to 9B for coding terminals.
[2602.07164] Your Language Model Secretly Contains Personality Subnetworks
The arXiv paper with ID 2602.07164, titled "Your Language Model Secretly Contains Personality Subnetworks," explores the potential for language models to inherently possess subnetworks related to personality traits. This research delves into the hidden capabilities and structures within these models.
GitHub - AlexsJones/llmfit: Hundreds of models & providers. One command to find what runs on your hardware.
The GitHub repository "llmfit" by AlexsJones aims to simplify the process of finding large language models (LLMs) that run efficiently on specific hardware. It provides a single command-line tool to identify compatible models and providers.
Kayssel
Kayssel's Newsletter Issue #20 covers a broad range of topics, from AI's integration into various industries to the evolving landscape of sports and media, along with cybersecurity and geopolitical trends. It also features product recommendations, highlighting a focus on innovation and cultural shifts in technology, media, and security.
Unsloth Dynamic 2.0 GGUFs
The Unsloth documentation outlines a significant upgrade to their Dynamic Quants, referred to as Dynamic 2.0 GGUFs. This improvement aims to enhance the performance and efficiency of language models.