Search: “llm”

TurboOCR is a high-performance OCR project that combines PaddleOCR with NVIDIA TensorRT, implemented in C++ and CUDA, achieving throughput of 270–1,200 images per second using FP16 half-precision infe

arXiv:2604.09442v1 Announce Type: new Abstract: UI-to-Code generation requires vision-language models (VLMs) to produce thousands of tokens of structured HTML/CSS from a single screenshot, making visu

This r/ollama Reddit post appears to be a community discussion about using Ollama in a minimal, retro, or stripped-down fashion — likely exploring terminal-based, command-line-only, or low-tech intera

arXiv:2602.17667v2 Announce Type: replace-cross Abstract: In video search systems, user historical behaviors provide rich context for identifying search intent and resolving ambiguity. However, tradit

Harrison Chase, co-founder of LangChain, shared or engaged with commentary emphasizing the importance of owning your own memory harness in AI applications rather than relying on third-party API-based

This r/MachineLearning discussion centers on Google's TurboQuant, a training-free KV cache compression algorithm released in March 2026 that compresses cache storage from 16 bits down to 3 bits with m

deepagents is a harness / planning tool, filesystem backend, subagent spawning, memory management / thats the stack that matters / models are the cpu, the harness is the os / anyways, check it out htt

Directionally correct Open memory standards will need to emerge But it’s so early right now. We’re still just figuring out what best practices are. And so a lot is at the mercy of harnesses Agents.md

Harrison Chase, co-founder of LangChain, argues that while prompt engineering is foundational and can get you 80% of the way to a functional AI system, the remaining 20% of engineering effort involves

@hwchase17 is right, memory creates lock-in. But not just conversations. Tool registries, hooks, agent conventions -> that's harness memory too. In my setup I define agent tools once in a shared regis

I just love the language of this study...it speaks of the shifting 'community language'...and that is so true...have you noticed the new 'community language' is 'harness', it was 'contextual prompting

I'm frequently using Claude Code and i love @openclaw. But i would say Hermes Agent from @NousResearch is the best open-source agent I’ve ever used, especially given that it comes from an independent

Harrison Chase, co-founder of LangChain, discusses why agent harnesses are becoming the dominant architectural pattern for building AI agents, likely referencing a thread by Howie Chase (@hwchase17).

Lorsque vous avez le besoin de lever des dizaines de milliards de dollars, l'importance de créer un contexte de stupéfaction est déterminent. Les soi-disantes failles de sécurité détectées par Mythos,

Marcus Hutchins, the cybersecurity researcher known for halting the 2017 WannaCry ransomware attack by registering a kill-switch domain, is cited as offering a notable perspective on AI systems conduc

This Reddit post examines the true total cost of running OpenClaw, an open-source AI agent framework, across different deployment models. While OpenClaw itself is free and open-source, hosting costs r

Iran's blockade of the Strait of Hormuz, following the U.S.-Israel war on Iran that began in late February 2026, has triggered a historic oil supply shock and sent global energy prices soaring. Despit

This r/MachineLearning discussion thread addresses the common and frustrating experience of receiving highly polarizing reviewer scores for ICML paper submissions — for example, one reviewer rating a

Harrison Chase, co-founder and CEO of LangChain, made a post on X affirming a point about where value lies in AI systems, specifically arguing that value is found 'in the harness' — referring to the s

This one gives a lot of perspective over the fact that memory isn't just a plugin anymore. My take is, each approach has it's tradeoffs but for me the mental model is, if the memory is more than or 80

Harrison Chase (creator of LangChain) shared a concise architectural principle regarding AI agent design: memory and harness components should remain on the application side of the API boundary rather

very memgpt / sarah wooders coded. memory isn’t a layer, it is the system. most teams think they’re choosing a model, but they’re really choosing where their memory lives and like ben thompson says, o

This Reddit post from r/ollama showcases a demonstration of running Kimi AI agents — powered locally via Ollama — within a platform called CopeAi.net, which appears to integrate AI agents with the Eth

without memory, ux with an agent is really bad memory is what makes an experience with an agent feel personal and optimized i'm doing a bunch of research re how folks are building memory into their ag

**llama.cpp release b8753** is a specific tagged build of the [llama.cpp](https://github.com/ggml-org/llama.cpp) project by ggml-org, an open-source C/C++ framework for running large language model...

The search results did not return the specific changelog details for build b8756. Based on what is available and the general context of llama.cpp's rolling release model, here is a factual summary ...

Chiasmus (reversing grammatical structures in two sentences for drama). Asyndetic tricolon (three items listed without a conjunction). Parataxis (short and somewhat disconnected dramatic sentences). S

@hwchase17 Agent harnesess will simplify multi-agent orchestration and it is finally the next big thing, albeit it looks different from what we had imagined 3 year ago (AutoGen & CrewAI), 2 year ago (

@hwchase17 @sarahwooders like a the old children's school flipbook - each one wakes up, takes in the context(memory) via harness does its thing, then rests.... next flip book page, the harness manages

A developer shared an open-source Android keyboard on r/ollama that integrates local AI capabilities by connecting to self-hosted inference servers such as Ollama, LM Studio, or any OpenAI-compatible

The specific Reddit thread could not be retrieved directly, but based on its title, source, and the broader context of r/MachineLearning discussions, here is a fitting knowledge base summary: This r/M

Most illuminating graph I have seen for definition of harness. Memory and context are deeply coupled with harness. In my humble opinion, how they are injected and managed are one of most important pie

Users running OpenClaw with `gemma4:26b` via Ollama encounter significantly slow or timed-out first turns in a session, even though the model responds quickly when queried directly through raw Olla...

The most important post I've read all week by @hwchase17 . Memory, particularly memory consistency is the biggest performance inhibitor to AI agents today. As we interact with different harnesses and

This is the right frame. We’re currently designing our agent memory platform, and the hardest part isn’t storage — it’s deciding what to remember, when to retrieve, and how to keep context clean. All

What if owning the memory layer goes far beyond escaping lock-in? What if memory becomes fully editable intelligence? You could debug bad patterns, reinforce good ones, and crucially synthesize new on

A Reddit discussion from the r/ollama community where a user seeks advice on which AI language model to run locally using Ollama. Responses likely include hardware-based recommendations (such as RAM a

arXiv:2604.06227v1 Announce Type: new Abstract: Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developi

Apple is presenting new research at the ACM CHI Conference on Human Factors in Computing Systems, held in person in Barcelona, Spain, from April 13–17, 2026, and is a sponsor of the event. Research...

arXiv:2604.07615v1 Announce Type: new Abstract: In language model interpretability research, extbf{circuit tracing} aims to identify which internal features causally contributed to a particular outp

arXiv:2604.08077v1 Announce Type: new Abstract: Processing long-form videos with Video Large Language Models (Video-LLMs) is computationally prohibitive. Current efficiency methods often compromise fi

arXiv:2604.08525v1 Announce Type: cross Abstract: Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginni

arXiv:2306.02781v4 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in moder

arXiv:2604.07755v1 Announce Type: new Abstract: Despite extensive research, Large Language Models continue to hallucinate when generating code, particularly when using libraries. On NL-to-code benchma

**deepagents** (github.com/langchain-ai/deepagents) is an open-source, MIT-licensed agent harness built on LangChain and LangGraph, equipped with built-in task planning, a filesystem backend, subag...

As agent systems scale, the control layer becomes the main source of complexity. What starts simple turns into coordination and composition problems. I built langchain-middleware-stack to better struc