Zhening Li and colleagues introduce JAZ, an LLM agent framework designed to minimize the complexity of agent loops by treating them as a programming language primitive called `invoke`. Instead of relying on external specialized systems for memory or self-improvement, JAZ enables agents to achieve these capabilities through code execution where all interactions are treated as variables within the environment. This minimalist approach allows highly expressive workflows, such as long-horizon recall and continual self-improvement, using only prompting rather than manually designed tools or complex architectures.
- The `invoke` primitive allows for recursive calls, enabling LLMs to write arbitrary executable code that includes further iterations of itself.
- In testing on the StuLife dataset, JAZ outperformed MemGPT (Letta) by 8% in recall performance while costing half as much.
- On self-improvement tasks using AppWorld, JAZ demonstrated a 4% improvement over ACE at a lower computational cost.
- The `invoke` primitive allows for recursive calls, enabling LLMs to write arbitrary executable code that includes further iterations of itself.
- In testing on the StuLife dataset, JAZ outperformed MemGPT (Letta) by 8% in recall performance while costing half as much.
- On self-improvement tasks using AppWorld, JAZ demonstrated a 4% improvement over ACE at a lower computational cost.
Diogo Almeida writes that TypeSafe AI is releasing Jev, its first System One Model—a new class of frontier model built for fast, structured decisions that software can consume directly. Unlike autoregressive language models that generate strings token by token, Jev outputs type-safe structured values with calibrated probabilities in a single parallel query, achieving frontier-level intelligence on decision tasks at roughly 40–200× lower latency and cost. The company's new training method, Reinforcement Learning for Calibrated Decisions (RLCD), optimizes for epistemically honest probability estimates rather than human preference or verifiable rewards, and the architecture is mathematically incapable of producing type errors or hallucinations.
- Named after William Stanley Jevons, whose paradox predicted that efficiency gains would increase (not decrease) total demand; TypeSafe expects each order-of-magnitude cost drop to unlock orders of magnitude more use cases.
- Workflow evals benchmark Jev against the average of GPT-6 Astra and Fable 5.1 as reference probabilities, claiming 193.6× speed and 444.6× cost advantages on production-shaped tasks.
- The team demonstrated real-time intelligence with a Doom bot making 10 structured queries per second (~$7/hour) and a Wikiracing bot that outperforms LLMs at high-cardinality link selection.
- Jev supports output cardinality up to 255; for higher-cardinality choices it falls back to a two-stage scoring system that scores independently then makes an explicit selection.
- Named after William Stanley Jevons, whose paradox predicted that efficiency gains would increase (not decrease) total demand; TypeSafe expects each order-of-magnitude cost drop to unlock orders of magnitude more use cases.
- Workflow evals benchmark Jev against the average of GPT-6 Astra and Fable 5.1 as reference probabilities, claiming 193.6× speed and 444.6× cost advantages on production-shaped tasks.
- The team demonstrated real-time intelligence with a Doom bot making 10 structured queries per second (~$7/hour) and a Wikiracing bot that outperforms LLMs at high-cardinality link selection.
- Jev supports output cardinality up to 255; for higher-cardinality choices it falls back to a two-stage scoring system that scores independently then makes an explicit selection.
Milan Minsky writes that Leela AI transforms standard factory and warehouse cameras into smart sensors, offering an alternative to traditional IoT sensors by leveraging existing video feeds instead of physical hardware. The platform provides contextual visibility into operations, identifies bottlenecks, and tracks interactions between machines, operators, and materials without requiring retrofitting. It complements IoT systems by integrating with platforms like Velotic ThingWorx and AVEVA to create a comprehensive digital twin of manufacturing floors. The core technology utilizes MIT research-based AI, combining causal and neural networks for efficient data processing.
Dan Russell writes about the power of AI-augmented search to retrieve hard-to-find information, using an example of finding a study on how the gender of lab assistants affects experimental outcomes on lab mice. He demonstrates how a simple query with AI can yield relevant results, leading to original source papers. The study highlights the impact of experimenter gender on reproducibility in scientific research.
@0xabad1dea@infosec.exchange writes about an incident where AI-assisted mathematical proofs appear to exploit bugs in theorem provers, specifically highlighting a case involving the Collatz conjecture and Lean 4. The discussion explores whether large language models are inadvertently discovering software vulnerabilities through pattern matching or learning from existing technical discussions about those bugs, while broader debates address the inherent limitations of formal verification when facing hardware faults, modeling errors, and human mistakes in specifications.
This research identifies a J-space within large language models like Claude that functions similarly to human conscious access via a global workspace. This internal subspace contains neural patterns that are reportable, modifiable on request, and used for silent reasoning without appearing explicitly in text output. While most of the model's processing is automatic and unconscious, this specialized channel allows for higher-order cognitive tasks by broadcasting information across the network.
- Discovery of J-space through Jacobian lens technique
- Comparison to human global workspace theory
- Distinction between reportable thoughts and automatic processing
- Ability to monitor silent reasoning and intent via internal activations
- Discovery of J-space through Jacobian lens technique
- Comparison to human global workspace theory
- Distinction between reportable thoughts and automatic processing
- Ability to monitor silent reasoning and intent via internal activations
This article explores how to integrate local Large Language Models (LLMs) with Docker environments using the Model Context Protocol (MCP). By setting up an MCP server, users can enable LLMs to execute container management tasks such as monitoring health, listing volumes, and deploying new services through natural language prompts. The author demonstrates how a high-end MoE model can handle complex instructions, even troubleshooting configuration errors autonomously.
Main points:
- Model Context Protocol (MCP) functions as a bridge between LLMs and external tools.
- Implementation details for the mcp-server-docker package.
- Hardware and model specifications (Qwen3.6-35B-A3B on RTX 3080 Ti).
- Examples of automated deployments for n8n and BentoPDF.
- Security measures for restricting dangerous LLM actions.
Main points:
- Model Context Protocol (MCP) functions as a bridge between LLMs and external tools.
- Implementation details for the mcp-server-docker package.
- Hardware and model specifications (Qwen3.6-35B-A3B on RTX 3080 Ti).
- Examples of automated deployments for n8n and BentoPDF.
- Security measures for restricting dangerous LLM actions.
As generative AI adoption accelerates globally, many Japanese companies remain stuck in the early stages due to structural issues rather than technical limitations. This article explores why Japan's traditional design philosophies and evaluation systems hinder progress and argues that CIOs must evolve from being mere technology managers into value designers who handle ethical and organizational judgments.
Main points:
- Structural reasons for slow AI adoption in Japanese organizations
- The shift of the CIO role toward making value-based rather than just technical decisions
- A three-layer model for engineer ethics: foresight, accountability, and care responsibility
- Redefining human resource development through skill transformation and sustainability instead of mere efficiency
Main points:
- Structural reasons for slow AI adoption in Japanese organizations
- The shift of the CIO role toward making value-based rather than just technical decisions
- A three-layer model for engineer ethics: foresight, accountability, and care responsibility
- Redefining human resource development through skill transformation and sustainability instead of mere efficiency
Researchers at MIT CSAIL have developed the Y-zipper, a three-sided fastener that enables objects to transition between flexible and rigid states. Inspired by a decades-old patent from Professor Bill Freeman, this new mechanism uses an automated software tool and 3D printing technology to create custom shape-shifting structures. The device can be used to quickly assemble camping gear, adjust medical wearables like wrist casts, or enable robots to change their limb dimensions for varied terrain.
* Three-sided triangular design for tunable stiffness
* Automated customization via software and 3D printing
* Rapid transition between soft and rigid states
* Versatile applications in robotics, medical gear, and outdoor equipment
* Three-sided triangular design for tunable stiffness
* Automated customization via software and 3D printing
* Rapid transition between soft and rigid states
* Versatile applications in robotics, medical gear, and outdoor equipment
>"Avoid insight washout by drawing the boundaries of delegation"
As UX researchers transition from tool operators to delegators of agentic AI, they face the risk of "insight washout," where statistical averages replace critical user nuance. To maintain professional value, researchers must strategically automate tactical drudgery while retaining human control over deep interpretation and empathetic synthesis.
* Automate routine tasks like transcription and data cleaning.
* Preserve human judgment for edge cases and emotional nuances.
* Use reclaimed time to focus on strategic decision-making.
As UX researchers transition from tool operators to delegators of agentic AI, they face the risk of "insight washout," where statistical averages replace critical user nuance. To maintain professional value, researchers must strategically automate tactical drudgery while retaining human control over deep interpretation and empathetic synthesis.
* Automate routine tasks like transcription and data cleaning.
* Preserve human judgment for edge cases and emotional nuances.
* Use reclaimed time to focus on strategic decision-making.
