Michał Januszewski and Viren Jain write that researchers have released a complete wiring diagram of the male fruit fly's brain and central nervous system, marking it as the largest brain map to date. In collaboration with HHMI Janelia, this project utilized computing and advanced techniques to create an annotated cellular-scale map containing over 166,000 neurons and 125 million synaptic connections. This foundational resource aims to help scientists understand how nervous systems function and may eventually assist in understanding complex human brain processes like memory or disease.
- The connectome includes the ventral nerve cord, which functions similarly to a spinal cord.
- Researchers are using similar mapping techniques on vertebrates, including fish and mice.
- This map complements previously released female fruit fly connectomes, allowing for studies of sexual dimorphism in behavior like courtship or aggression.
- The connectome includes the ventral nerve cord, which functions similarly to a spinal cord.
- Researchers are using similar mapping techniques on vertebrates, including fish and mice.
- This map complements previously released female fruit fly connectomes, allowing for studies of sexual dimorphism in behavior like courtship or aggression.
Mantis is a modular, stack-agnostic toolkit of security review skills designed for AI coding agents to autonomously discover, reproduce, and patch vulnerabilities. It provides a sequential pipeline—ranging from architecture synthesis and threat modeling to automated vulnerability research and remediation—to enable continuous and iterative defensive security reviews in an isolated environment.
- Designed for use with various coding agent frameworks like Gemini CLI or Antigravity SDK.
- Emphasizes the need for execution within sandboxed environments (e.g., gVisor, Docker) due to the risk of AI-generated code.
- Includes a "Snapshot Model" that allows for pinned, authoritative security passes on living codebases via non-destructive synchronization.
- Features an extensive set of specialized skills including `mantis-researcher` for flaw sweeping and `mantis-patch` for automated remediation.
- Designed for use with various coding agent frameworks like Gemini CLI or Antigravity SDK.
- Emphasizes the need for execution within sandboxed environments (e.g., gVisor, Docker) due to the risk of AI-generated code.
- Includes a "Snapshot Model" that allows for pinned, authoritative security passes on living codebases via non-destructive synchronization.
- Features an extensive set of specialized skills including `mantis-researcher` for flaw sweeping and `mantis-patch` for automated remediation.
Chao Zhang and colleagues study the design space of proactive AI agents that provide higher-level cognitive support during writing, moving beyond simple textual assistance like autocomplete. Through a technology probe deployed with 16 participants, researchers found that users prefer to configure custom partners by setting specific roles and proactivity levels in advance. The findings indicate that such tools can be used for both idea generation and self-monitoring when interventions are presented through lightweight visual representations and non-directive framing to minimize intrusiveness.
- Participants planned their AI support prospectively rather than reacting to real-time interruptions.
- Suggestions served a dual purpose of sparking new ideas and assisting in the self-monitoring process.
- The study emphasizes that rhetorical framing is as critical to user experience as the timing of an intervention.
- Participants planned their AI support prospectively rather than reacting to real-time interruptions.
- Suggestions served a dual purpose of sparking new ideas and assisting in the self-monitoring process.
- The study emphasizes that rhetorical framing is as critical to user experience as the timing of an intervention.
Google's research shows that LLMs experience difficulty recalling facts when questions reverse the usual subject/object entity order.
Roger Montti writes that Google has released version 0.2 of the Open Knowledge Format, incorporating five trust signals to facilitate data verification for consumers. These updates cover provenance, generation and verification status, freshness via expiration dates, lifecycle stages, and computational attestation to address questions regarding a concept's origin, accuracy, and timeliness. By providing raw metadata rather than subjective credibility scores, Google enables users or agents to dynamically evaluate information based on objective signals such as authorship and usage counts.
- Attested Computation is a new type used to confirm that values are calculated using sanctioned methods instead of improvised logic.
- The "sources" field provides objective data points like author and last modified date rather than an inherent score.
- Verification can be performed by humans or machine agents, allowing users to filter content based on verification level.
- Attested Computation is a new type used to confirm that values are calculated using sanctioned methods instead of improvised logic.
- The "sources" field provides objective data points like author and last modified date rather than an inherent score.
- Verification can be performed by humans or machine agents, allowing users to filter content based on verification level.
Google is rolling out its latest model, Gemini 3.5 Flash-Lite, within Google Search to support agentic search experiences and potentially enhance features like AI Overviews and AI Mode. This new model focuses on providing low latency and high throughput for workflows such as document processing and multi-step subagent tasks.
* Improved instruction following and better understanding of user intent
* Significant performance gains in coding (Terminal-Bench) and real-world task execution
* Optimized for agentic systems through built-in computer use tools and high-volume scalability
* Improved instruction following and better understanding of user intent
* Significant performance gains in coding (Terminal-Bench) and real-world task execution
* Optimized for agentic systems through built-in computer use tools and high-volume scalability
This guide provides a comprehensive walkthrough on using Google's Gemma 4 model to build autonomous AI agents through tool calling. It explores how this feature enables models to move beyond simple text generation by interacting with external APIs and systems via structured function calls.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
Key topics covered in the article include:
- The mechanics of the tool calling loop, from reasoning and selection to execution and final response.
- Setting up a Python development environment using Hugging Face and necessary libraries like transformers and torch.
- Defining JSON schemas for tools to ensure precise model understanding.
- Implementing a full agent workflow by parsing function call responses and executing Python functions.
- A practical end-to-end demonstration of building a weather lookup agent.
- Managing multi-turn conversations through state management and conversation history.
- Best practices for production deployment, including argument validation, execution timeouts, and logging.
The article discusses the benefits of running Google’s Gemma 4 models locally on personal hardware to ensure data privacy and independence from cloud services. By executing these multimodal models on a laptop, users can process images and audio without exposing sensitive information to third-party servers. The text highlights how efficient architecture allows for vision capabilities and speech recognition even with limited VRAM.
* Localized processing avoids the risks of uploading private or regulated data
* Native audio support in specific model variants like E2B and E4B
* Large context windows allow for deep analysis of lengthy documents and codebases
* Reduced reliance on internet connectivity during mobile workflows
* Localized processing avoids the risks of uploading private or regulated data
* Native audio support in specific model variants like E2B and E4B
* Large context windows allow for deep analysis of lengthy documents and codebases
* Reduced reliance on internet connectivity during mobile workflows
Andrej Karpathy, Google, and Garry Tan are all adopting Markdown as the primary format for agent memory and organizational context. While Karpathy focuses on personal knowledge, Google targets enterprise runbooks, and Tan defines agent roles, they all share a reliance on versioned Markdown files. This shift suggests that the strategic advantage is moving from the specific large language model used to the curated knowledge base a team accumulates.
- Karpathy's LLM Wiki for personal knowledge bases
- Google's Open Knowledge Format for enterprise context
- Garry Tan's gstack for defining agent roles
- The migration of the competitive moat from models to portable data files
- Karpathy's LLM Wiki for personal knowledge bases
- Google's Open Knowledge Format for enterprise context
- Garry Tan's gstack for defining agent roles
- The migration of the competitive moat from models to portable data files
Google's release of Gemma 4 marks a major turning point for open-source AI, offering a versatile family of multimodal models under a permissive Apache 2.0 license. Built using Gemini 3 technology, these models demonstrate massive leaps in math and coding performance, rivaling much larger proprietary systems while remaining efficient enough to run on local hardware ranging from smartphones to high-end GPUs. This release positions Google as a formidable competitor in the open-weights ecosystem, prioritizing user ownership and deployment efficiency.
* Apache 2.0 license
* Multimodal intelligence
* Local hardware deployment
* Massive benchmark leaps
* Efficient MoE architecture
**Models**
* E2B: Mobile efficiency
* E4B: Edge specialist
* 26B MoE: Speed meets intelligence
* 31B Dense: Top-tier performance
* Apache 2.0 license
* Multimodal intelligence
* Local hardware deployment
* Massive benchmark leaps
* Efficient MoE architecture
**Models**
* E2B: Mobile efficiency
* E4B: Edge specialist
* 26B MoE: Speed meets intelligence
* 31B Dense: Top-tier performance
