elvis

🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4

Must-read paper integrating decision models like Jev into harnesses. They distilled an open 4B decision model, mostly from Jev's answers on the agent's own states, that beats Jev inside that agent and is much cheaper to run. Their harness, JevAgentHarness, sends each routine choice to the decision model and uses its answer only when the confidence clears a threshold fitted on the agent's own trajectories. Below the threshold, the LLM decides as usual. With the fitted threshold, tool and argument cascades cut cost by 14.2% without changing the outcome of any of 500 tasks. A fixed threshold of 0.5 lost 21.5 points. They also train AgentDecision-4B on states the agent reaches after its own mistakes, with Jev's answers as labels. On tasks unlike its training data, the agent solved 91.5% with it and 82.0% with Jev. On familiar tasks, 4B models trained this way made agents 81.1 to 89.0% cheaper and up to 2.5 times faster while staying within 3 points of success. Paper: academy.dair.ai/papers/jevagentharness-benchmarking-deploying-and-training-decision-models-that-share-c-2610.21412

Why are you still using pull requests? Is it for code review? If so, do you really think your or your colleagues catch bugs and mistakes that an Opus 5.5 or Fable or Astra won't find? You can literally spawn all three of them and have them check four times each. (Also, don't tell me you don't have a "stamp plz" Slack channel or group chat somewhere.) Is it for knowledge sharing? Surely there's better ways for that now than the PR, no? We have models that can generate explainer videos, presentations, podcasts, games, quizzes, walkthroughs, ... for any piece of code it can reach. Is CI the reason? Well ackshually, CI means continuous integration and its original meaning is the *opposite* of having long-lived branches and PRs. But, leaving that aside, your agents are hopefully already running the tests before they push something, and hopefully they can run as long as they want and don't block you so they can run the whole test suite and generate "irrefutable proof" and end-to-end demo videos and so on... And so they run the tests and confirm and then you push to a branch and everything runs again and then you merge and then it runs again? That doesn't make sense, does it? Maybe you work in open source and you accept contributions, in which case I can't speak to the value of pull requests. It's been 2 years since my last OSS contribution. But from what I heard, the time of the PR is coming to an end in OSS too? I honestly don't get why people still stick to PRs. With agents you can now move 10x faster, you can finally do real continuous integration, and get more confidence in the changes the agent made. Don't slow yourself down by having all the changes pile up in a PR.

Spent years watching models ace a single image and completely whiff on anything that moves or changes angle. Today we’re fixing that. Try Glimpse now, our first visual thinking model with competitive frontier performance compared to Astra on key public visual reasoning benchmarks.

Introducing Hugo: the first human companion robot. At Boston Dynamics, I was Chief Strategy Officer, bringing robots into the real world. My co-founder, Farbod Farshidian, led research at the RAI Institute. Together, 15+ years teaching robots to run, jump, and work. So we asked: what if robots could connect with people? Hugo runs on the first Vision-Language-Expression (VLE) model. He links what he sees and hears to movement, expression, and gesture. He notices you, reads the room, and reacts in real time. And he's only the first. Dynamic Creatures is @BostonDynamics' official entertainment and hospitality partner. We're building a cast of characters with film studios, resorts, and theme parks. dynamiccreatures.com

Today AiSDR is disrupting pricing for outbound sales. $1.5 per person prospected, unlimited touchpoints, EVERYTHING included. No minimum. We will take care of email infra, warmup, leads (2B+ contacts, including off-LinkedIn), enrichment (waterfall of 10+ providers), AI-researched, unique multi-channel sequence to each prospect, AI reply-classification and reply handling, everything syncs back to your CRM and tracks influenced and closed won pipeline. Ami AI, trained on 20k+ qualified meetings booked across 27 industries to help you ideate, monitor, improve and run your campaigns. All the latest and greatest in GTM tech on one platform, starting at $1.5. There is literally no alternative to this offer. - Have an offer and an audience to test for 100 leads - AiSDR is your place to go - Want to test outbound, but buying 5 tools and managing them feels like a pain - AiSDR - Planning to go to a conference and need to book few meetings - AiSDR - Just came back from a conference and have a 200 leads to follow-up with - AiSDR Any kind of outreach via email and LinkedIn - we've got you. Fully autonomous with the best GTM AI agent you can buy in the market. Outbound on demand, priced like ads. Try it today at aisdr.com.

We built context graphs to help agents work through long-horizon legal tasks. Starting with Legal Agent Bench tasks in 11 categories like M&A diligence and contract negotiation, we had agents build context graphs of the underlying documents (without access to the eval tasks or grading rubrics). Context graphs dramatically improved agent performance on LAB tasks: - Firm Knowledge tasks: 65% → 80% - Precedent lists tasks: 67% → 80% - Matter counting tasks: 53% → 83% They also boost agent efficiency: we found a 48% cost reduction for agents given access to a context graph to assist their work. Full video below - more to come soon.

Pure AI: six fingers and a vibe. Pure stock: the same photo your competitor used last week. So why choose between? Start with something real. Riff until it's right. 30M+ artist-created assets. Now editable with AI on Envato.

Today we’re releasing Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software engineering, and multi-step reasoning. Key Model Specs: → 816B total / 42B active parameters → 1M token context window → Natively multimodal across text, image, video, audio

interpretability might be the most important scientific problem in the world right now, as I don't think we can solve alignment if we don't know what we're building. I think I have good news about it. I co-authored early work in mechanistic interpretability at OpenAI and came away pessimistic, because neural networks appeared to fold into arbitrary tangled knots. years later, I started openai's first large-scale automated interpretability project, finding about 2% of neurons legible with the primitive tools we had with modern agentic tools and a much closer look, the picture looks totally different. the vast majority in models up to trillions of parameters are legible, and their circuits beautiful. many famous examples of tangled neurons turned out to be misunderstood. The roadblocks I expected mostly aren't there. I now think ambitious interpretability will be solved by the time we need it most, and possibly quite soon, as automated scientists keep improving. it may be a bitter lesson for interpretability: the simple thing, at absurd scale, works already-clean.goodfire.com/

iMessage agents for your business in 30s. Send a link and Bland learns your business, plugs into your APIs, and creates a branded agent for you automatically. The agent can do things like Tapbacks (❤️,👍,❗), send embedded components, and even accept payments within a chat. It's live now. Text +1 (415) 481-7537.

For 25 years, Salesforce, SAP and enterprise software were built around one assumption: humans operate the software. We’re changing that. Introducing ERP•AI and Proto: business software built for agents, and an agent that operates business software. Describe the system you need. ERP•AI builds it. Then Proto takes over. It manages records, runs workflows and executes actions inside the software you just created. This is Headless SaaS. 1: get ERP•AI 2: build your SaaS 3: post below Best one wins a MacBook. First 100 decent entries get $100. (a message from Steve Papa / founding team)