Search: “persistence”

If you want to build proactive agents on real hardware, come build with us today. We’re co-hosting the Proactive Agent Build Day at @agihouse_org Ambient agents. persistent memory. accessibility. @Eve

Sam Altman praised OpenAI's o1 model (version 5.5) for its earnest, persistent approach to problem-solving, comparing it to the 'little engine that could' mentality. The comment reflects Altman's pers

Some signs of life grok 4.3 beta can use an ubuntu shell and a persistent file layer to generate artifacts grok wrote python to encode the xai / grok logo into audio, i gave it the script back and had

Ethan Mollick observes that an AI system (likely Claude or another large language model) still declines to write sestinas, suggesting that certain behavioral constraints or limitations persist despite

there's a lot to be excited about with this release! * For one this solves a huge issue just generally with long-running agent UX * For two this is a key unlock for us to go test more adaptive methods

`deepagents deploy` now supports user scoped memory! add a user/ directory in your project so each user gets their own writable AGENTS.md, seeded on first deploy and persisted across conversations. yo

So true. “Being right too soon is socially unacceptable” I remember reading this from Heinlein as a young man and it took experience for me to fully understand it. People and institutions will persist

Wish there was information about where this data came from, but this is a very significant change. Since AI use comes from experience, the persistent gender gap in AI use across every study of AI was

Harrison Chase's post explores the divergent strategic incentives around memory in AI agent systems, arguing that for those building agent frameworks, persistent memory creates compounding value and c

Harrison Chase, co-founder of LangChain, likely discusses the concept that AI agents and systems lacking persistent memory cannot truly learn, adapt, or maintain continuity across interactions — makin

A post from X (formerly Twitter) by user Yuchenj_UW, shared or referenced by Swyx, humorously contrasts Anthropic's AI capabilities with the persistence of TurboTax, the widely criticized tax preparat

memory ownership is why we need open harnesses blog about this coming this weekend The memory ownership point is the real story here. Running a persistent agent 24/7, memory isn't a feature — it's the

OpenClaw 2026.4.7 🦞 🔮 openclaw infer 🎬 music + video editing 💾 session branch/restore 🔗 webhook-driven TaskFlows 🤖 Arcee, Gemma 4, Ollama vision 🧠 memory-wiki: persistent knowledge, not just vibes Bec

Banger paper from Microsoft and colleagues. If you run agents that search a large document collection, this one is worth your time. (bookmark it) They introduce CorpusMap, which resolves recurring ent

OpenAI launched Dots at DevDay. My thoughts. First, the good. Always-on is really really really what the market needed. It's why I find myself reaching for Instinct so often. I don’t have to think abo

Banger report on building faster memory for AI Agents. If your agent's memory layer is slow, this design is worth a look. Great weekend read. It proposes a new agentic memory architecture, Jev-Mem, in

The future is clearly agent teams. I run several agent harnesses at once, but the hard part is keeping them in sync. Many builders end up copying messages between Claude Code, Codex, and open models b

// human-values.pseudo | ARTIFACT 0.1.0-experimental.1 | BASE 1.2.0-rc1 | EDITION ai-compact | NARRATOR System_Synthesis_Node_01 | PROVENANCE human-directed, AI-assisted | STATUS single-encoder resear

What a super interesting paper this one is. They propose an architecture for agents that outlive their model, harness and host. Today we describe an agent by whatever model and harness it happens to r

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus

Bug fixes shipping to Grok Build 0.2.3 (release notes will be available in the TUI) - add “Yes, and don't ask again for anything (always-approve mode)' - add alpha/stable to welcome screen - JetBrains

Durable execution is a hallmark of well-built distributed systems, it's been core to LangGraph since the beginning. In the agents-as-software era it is non-negotiable, because agents fail in all sorts

first up in the 'going to production' series: durable execution! long running agents need to be able to 1. survive crashes 2. resume after an indefinite pause durable execution solves this by checkpoi

The EU AI Act is the first comprehensive regulation for AI systems, and it’s right around the corner. With the clock running down, we took a deeper look at what these requirements mean for your agents

Thoth 𓁟 v3.17.0 is out 🚀 Designer Studio just got a serious upgrade + video generation + seamless in-app updates Build full app prototypes, storyboards, pitch decks, docs, landing pages - all in one p

Hugging Face's ml-intern project has reached a testing milestone with 3,000 people actively participating in its evaluation. This suggests significant community interest in what is likely an open-sour

Clem Delangue, CEO of Hugging Face, shared a post about post-training a model using ml-intern, likely referring to a machine learning internship project or internal tool. The post appears to be a casu

developing an agent is a harness problem. deploying an agent is a runtime problem. here's a guide to the runtime capabilities that keep agents running in production and the infrastructure that enables

LangSmith Insights Agent helps Credit Genie interpret feedback from customers at scale, and continuously improve their product. https://www.langchain.com/blog/credit-genie-insights-agent-financial-ass

This post discusses a Hugging Face Space that enables replaying AI agent execution traces, contrasting this capability with Netflix's replay functionality to illustrate how agent traces provide differ

Nice paper from Google. And a great application of AI agents. Wearables capture a staggering amount of physiological signals every day. CoDaS is an AI co-data-scientist that turns raw wearable sensor

New in LangSmith Evaluation: ✅ Evaluator template library ✅ Reusable evaluators Everything you need to know → https://www.langchain.com/blog/reusable-langsmith-evaluator-templates?utm_source=x&utm_med

crons, part of langsmith deployments! https://docs.langchain.com/langsmith/cron-jobs I’m not sure whether LangChain supports this yet, but it might be worth considering scheduling and cron-style jobs

Long-horizon AI research agents are mostly a state-management problem. It is not enough for an agent to reason well in the next turn. ML research requires task setup, implementation, experiments, debu

LangChain's concept of 'your harness = your memory' explores how the surrounding infrastructure and framework around an AI agent effectively functions as its memory system. The post likely discusses h

I was unable to retrieve the specific X (Twitter) post at the URL provided, as the content is not accessible through search results. The tweet's text snippet ('memory is context, and context is wha...

Agent-to-agent communication with a personal agent on top is wild. Wow. I underestimated how well models like Opus 5.5 can coordinate already. This opens a whole new set of experiences with agents. Bu

Banger paper from Meta AI on research agents that decide what to investigate next. (bookmark it) If you run long-horizon research agents, choosing the next investigation is hard to learn, because thos

I agree that chatgpt/codex has the current best agent interface for deep work. - It unifies all my work (coding/knowledge work) in one interface. Toggling between chatgpt/codex doesn't have explicitly

So I would like to share my analysis of PD disagg that I've run by Horace and many folks at Thinky You may have heard that PD disagg helps with per-token 'tail latencies' in ITL/TPOT, as prefill inter

Can you even imagine the nation state level attacks METR & other eval orgs will come under? Eval orgs also need world class cybersecurity as well as AI talent to do this right, they are not overlappin

Recommended read. Interaction horizon scheduling is an underexplored control problem in agentic RL This paper from the Qwen team takes a closer look at the problem. Scaling the maximum number of envir

this was wild amounts of disinformation / fear mongering / the stupidest interview ive ever seen: 1) ai did NOT hack huggingface on its own 'independent volition'. it wasnt sitting there thinking hmm

// From Language Models to World-Acting Systems // A critical review of agentic AI, and a framework that is genuinely useful for deciding how much authority to hand an agent. Here is how it works. The

Super interesting paper on proactive agents from Google DeepMind. (bookmark it) Proactive assistance usually means autocomplete. Researchers asks what it looks like when an agent offers higher-level c

Finally, a good paper testing if graph memory actually beats flat retrieval for long-term agents. (bookmark this one) Researchers extract each conversational turn into typed nodes and attributed edges