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Datalab has released lift, a 9B open-weights vision model for structured extraction. You pass it a JSON schema, and it returns a JSON object that matches. The model reads PDFs and images directly, then decodes against your schema. This is Datalab’s first model built purely for extraction. The team already ships open-source OCR tools: chandra, […]
Prime Intellect has released prime-rl version 0.6.0. The framework targets reinforcement learning on trillion-parameter Mixture-of-Experts (MoE) models. It focuses on heavy agentic workloads, like long-horizon software-engineering tasks. The research team trained GLM-5 on SWE tasks at up to 131k sequence length. Step times stayed under five minutes. The batch size was 256 rollouts. The run
xAI shipped a new mode called /goal inside Grok Build, its terminal coding agent. The feature targets long-running, autonomous task execution. You hand the agent a larger implementation task, then step back. Most coding sessions require back-and-forth execution and verification. You prompt, the agent acts, and you verify each step. /goal changes that loop. The
Today, Sakana AI launched Sakana Fugu. It is a multi-agent orchestration system that behaves like one model. You send a request to a single endpoint. Fugu decides how to handle it internally. It solves a task directly when that is enough. It also assembles and coordinates a team of expert models when needed. The complexity
Sakana AI launched Fugu to orchestrate multi-agent operations and mitigate single-vendor dependency risks in enterprise deployments. Enterprises face operational vulnerabilities when relying entirely on monolithic AI APIs. Japanese AI firm Sakana AI designed Fugu as a response to these concentration risks by creating an orchestration language model that calls upon a pool of varied models
Mitigating vendor lock-in with Sakana AI Fugu multi-agent models Read More »
Large language models are stateless by default. Each API call starts fresh. The model forgets your last message once the response returns. That is fine for a single question. It breaks the moment you build an agent. Agents plan, call tools, and run across many steps. They need to remember. Memory is the infrastructure that
The 7 Types of Agent Memory: A Technical Guide for AI Engineers Read More »
In this tutorial, we build a full Crawlee-for-Python workflow that covers environment setup, local website generation, static crawling, dynamic crawling, structured extraction, and downstream data processing. We begin by configuring a compatible Crawlee runtime with pinned Pydantic support, Playwright browser installation, persistent storage directories, and Colab-safe execution handling. We then generate a realistic local demo
Getting prompts right is still the hardest part of shipping reliable LLM applications. Small wording changes can swing accuracy by 20 percent. What works on a few examples often breaks at scale. When a multi-step pipeline returns a wrong answer, finding the failing step means inspecting intermediate outputs by hand. Cisco AI introduced FAPO to
Nous Research has added a Blank Slate setup mode to its open-source Hermes Agent. It inverts the usual onboarding. Instead of a fully loaded default, you start with almost nothing. Hermes Agent is the self-improving agent framework from Nous Research. It runs on your own machine. The team announced the new mode on X. Blank
SAP and Google Cloud are deploying agentic commerce architecture to automate multi-agent marketing and retail operations at enterprise scale. SAP research indicates 78 percent of businesses consider AI essential for retaining customers in 2026. However, the same data reveals fewer than two in five companies share customer data across customer experience (37%) or CRM (39%)
SAP and Google Cloud deploy agentic commerce architecture Read More »
