Search: “models”

Xiaomi's MiMo-V2.5 and MiMo-V2.5-Pro are both now available in Hermes Agent through Nous Portal and OpenRouter! Just `hermes update`! Xiaomi MiMo-V2.5 Series: Pushing Open-Source Agents Forward 🔸 MiMo

Having a 20-agent system is a million times more powerful than 100 agents working in silos. Last night, I stayed up way too late fixing and improving my digital workforce for my AI Agent Mastermind pr

I agree, and I will add to this. Companies should own their memories, skills and other resources agents need. Use Claude code, codex, copilot, etc for intelligence but your execution layer should plug

K2.6 + hermes = 4 hr session setting up qwen 3.6 training regime on dgx spark with the current autnomous session currently lasting 70+ min without any prompting. Kimi with hermes is next level. @NousR

Kimi K2.6 demonstrates strong long-horizon coding in complex engineering tasks: Kimi K2.6 successfully downloaded and deployed the Qwen3.5-0.8B model locally on a Mac. By implementing and optimizing m

LLMs are still not consistent judges of qualitative work, and small changes to how that work is presented affect outcomes. Better harnessing and methods (multiple judging runs with randomized orders,

The very first quadcopter prototype was the Breguet-Richet Gyroplane No. 1, created in 1907 by Breguet Aviation in France. The first consumer quadcopter drone was the Parrot AR.Drone, released in 2010

We're open-sourcing FlashKDA — our high-performance CUTLASS-based implementation of Kimi Delta Attention kernels. Achieves 1.72×–2.22× prefill speedup over the flash-linear-attention baseline on H20,

Beyond generating high-fidelity visuals, we wanted to test the limits of what Nano Banana Pro can do. We worked with design partners Porto Rocha to build out a hypothetical brand called YOYOYO to see

Getting LLMs to simulate “true” randomness or generate diverse outputs is surprisingly difficult. We found a simple prompting trick that solves this by having the model generate and manipulate a rando

Philosophy (among other things) grad here. I could write a whole essay about this video, and mostly the reactions to it. People are dunking on her because of what she symbolises more than what she say

You should watch this. It just shows how disconnected we are from the small group of people making decisions that will impact our future heavily. These people have so much ai psychosis. If you listen

One of the premier journals in my field... I think there are very valid reasons to set rules on AI in peer review (including disclosure), but the idea that all AI models steal your data is very 2023.

We push Prefill/Decode disaggregation beyond a single cluster: cross-datacenter + heterogeneous hardware, unlocking the potential for significantly lower cost per token. This was previously blocked by

LLM agents loop, drift, and get stuck on hard reasoning tasks up to 30% of the time. Current fixes are either too blunt (hard step limits) or too expensive (LLM-as-judge adding 10-15% overhead per ste

Coding agents learn from experience, but that knowledge stays locked in silos. Solve a thousand SWE tasks, and none of that wisdom helps with competitive coding. What if memories could transfer across

I edited the intro because I realized I buried the lede originally- The 1M context window is a double-edged sword. It allows Claude to do more complex tasks but it can also leads to more context pollu

LiteParse hit 4.3K+ GitHub stars in a few weeks. Today it officially joins the LlamaIndex ecosystem, with its own page at http://www.llamaindex.ai/liteparse?utm_medium=socials&utm_source=twitter&utm_c

LiteParse should be the default document parser you use with any AI agent (Claude Code, Claude Cowork, OpenClaw, Codex, and more) The core is extremely fast text and accurate parsing from any document

Nous Research's Hermes is a series of fine-tuned large language models known for strong instruction-following, reasoning, and conversational capabilities. The post appears to be enthusiastic user feed

Nous Research shared an announcement or update on their X (formerly Twitter) account, likely relating to their ongoing work in AI model development, fine-tuning, or research releases. Nous Research is

Most AI assistants wait for you to ask. But a truly useful agent should notice you need help before you say anything. New research takes a serious shot at building proactive agents that work in real t

Cognition AI has published a technical report detailing the design methodology behind their reward system, likely related to their Devin AI software engineering agent. The report covers the performanc

Benchmarked @DJLougen ’s Ornstein-27B-v2 Q6_K on my RTX 3090 using hermes-bench, my new open-source benchmarking UI for local LLMs and Hermes agents. Ornstein is a Qwen 3.5 27B fine-tune trained on re

Deepagents is an open-source project by LangChain that implements deep research-style agentic workflows, enabling AI agents to perform iterative, multi-step research and reasoning tasks. The repositor

From this perspective, Gemini is also worse than the original Bard. Sydney was the original sin of LLMs anthropormism, but also got the idea that AIs can sometimes be better with personalities right.

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,

memory is core to how you get enhanced output. it is key to defensibility across AI products: 1. increase personalization and therefore lock-in 2. benefit from distribution through multiplayer-AI inte

Harrison Chase discusses the concept of 'memory lock-in' in AI agent frameworks, arguing that the switching cost doesn't occur at the point of adoption but rather accumulates over time as agents build

Memory features in AI platforms represent a strategic mechanism for increasing user retention and platform lock-in, as personalized context and learned preferences become increasingly difficult to mig

A lot of our education on writing well focuses on logic, clarity, and argument. AI will force us to think more about style. The boredom that comes from everything on the internet reading Claude-y now,

If you don't own the memory, you don't own the agent: - memory is what makes your agent get smarter over time - without it, anyone with the same tools can copy your agent overnight - with it, you buil

time to help builders own their intelligence, open everything 🤝 the data produced and Experiential Memory gained from every agent interaction is the one of the most valuable things you can own to impr

Nous Research shared a tip or reminder encouraging users to run 'hermes update' when experiencing issues or uncertainty with their Hermes model or related tooling, suggesting this command resolves com

All is not lost. Duckerton is still possible. Here is Seedance 2.0 with the same prompt. Media My most popular Sora video was “an Elaborate regency romance where everyone is wearing a live duck for a

@IeloEmanuele is on a roll! you can now use claude code's advisor strategy with @LangChain agents because langchain/deepagents are provider agnostic, you can use different providers for your advisor a

im excited about agent harnesses because i think are the first stable agent abstractions we can build on top (which is why we're investing so much in deepagents) we always wanted to run llms in a loop

LiteParse is the best document parsing library for coding agents. It's free, fast, integrates natively with the LLM's native visual understanding capabilities, and comes with support for 50+ formats a

The reason symmetry is so important in physics is because symmetry is a highly effective compression operator. If a system is invariant under some symmetry, you only need to explain one axis of it. Sc

There were some exceptionally cool demos from @ollama and omlx using MLX to run Qwen 3.5 and Gemma 4 on Apple silicon. The capabilities of local LLMs and the surrounding ecosystem have come a long way

Things that make the jagged intelligence of AI harder to deal with than the jaggedness of humans: 1) Weaknesses are not always intuitive or identifiable in advanced 2) All LLMs have similar weaknesses

This week @HumanXCo was a reminder that the AI conversation has moved on. No more benchmarks vs. bubble debate. The questions were practical: → How big do you need to be to train your own model? → How

AI is jagged, but I think sometimes it is easy to overly focus on that. The generalness is a surprise too! LLMs may be optimized for verifiable fields like coding, but AI is also not bad at corporate

Gotta love @LangChain! 🥹 Providing high quality open source agent infra DAILY Single handedly advancing the trajectory of AI Open Harness, Model Choice, Open Memory (take it wherever you need), Open P

great q! deepagents has more 'batteries included', which langchain v1 is a very minimalistic agent harness if you are doing more complex workflows (eg claude code for X) -> deepagents if you want some

Note: The specific X/Twitter post (status ID 2042295692324991329) was not accessible via search. The summary below is based on current data from the official ChatGPT pricing page and related source...