Agents

Shippy is a maritime AI agent built for high-stakes decisions, where the wrong answer has real impacts. The article covers its architecture—soul, skills, config—and key design decisions like using a deterministic CLI for API access, sandboxed hosting for user isolation, and a custom evaluation system that scores the whole agent against live data. Lessons learned and future plans are also discussed.

Model routing in AI agents is more complex than it seems. It is not a classification problem but a systems optimization problem involving cost, complexity, and latency. The article shares three key challenges and explains IBM Research's optimization-based approach.

CrashStealer malware targets macOS users by disguising as Apple's crash reporter, stealing data, passwords, and crypto wallets. Learn how it works and three habits to stay safe.

As AI becomes one of the fastest-growing expenses for US businesses, some startups are switching to cheaper Chinese AI models to cut costs. Despite being behind in capabilities, Chinese models offer cost advantages and open-source availability.

A research team successfully used 14 Macs spread across four countries (including a personal MacBook) for reinforcement learning post-training, achieving a held-out pass@1 improvement from 29% to 63% on PaperSearchQA. The system employs PULSE weight synchronization to compress 9GB updates to ~90MB, and an asynchronous star topology with all communication via object storage—no dedicated networking required. This is the first RL post-training run using only consumer Macs for rollout generation.

Build custom AI agents in Fleet without code, then deploy them to Slack in one click. Give agents custom identities, use them in channels and threads, and keep work moving where your team already collaborates.

OtoDock is a self-hosted AI agent platform that runs Claude Code and Codex as a team of agents on your own infrastructure. It features a live dashboard, security sandbox, multi-agent meetings, automation scheduling, document generation, and supports consumer subscriptions, API keys, or local models. Licensed under the Functional Source License (FSL-1.1-Apache-2.0), with one-click Docker deployment.

Legacy VPNs fail to provide secure access for AI agents. Enterprises need unified identity-based networking and privileged access management to support both human and agent workloads. Tailscale experts will discuss solutions in a free webinar on July 28, 2026.

Mindlas is an open-source tool that uses deterministic gauges to monitor AI coding sessions for context deterioration, verification debt, change blast radius, and tool failure loops, providing concrete corrections before problems compound, all running locally without network calls.

OpenAI has partnered with keyboard maker Work Louder to launch the Codex Micro, a square-shaped button pad for monitoring and managing AI agents on the Codex coding platform. The limited-run device costs $230 and is separate from OpenAI's hardware project with Jony Ive.

Tura, a local open-source coding agent, reduces LLM turns by 80% and increases success rate to 80% on DeepSWE v1.1 benchmarks compared to Codex CLI High, using macro CLI commands and backward reasoning.

GeoSQL is a geospatial analysis skill that integrates map visualization feedback into AI agent loops, dramatically improving spatial query accuracy. It addresses geometric errors invisible to text-only agents (e.g., abnormal polygons, misplaced points) through steps including database schema exploration, automatic dialect-specific SQL generation, cost pre-check (BigQuery), result validation, and map rendering with self-correction. Benchmarks show a 4× accuracy boost when using maps. Limitations include cost control only for BigQuery and a small test set.

Linus Torvalds firmly defends AI use in Linux kernel, stating the project is not anti-AI, AI is a useful tool, and those who disagree can fork or walk away.

This article walks through building a 110-line Python app using Telnyx Call Control and AI Inference that lets anyone call a phone number and practice negotiations with an AI. It includes three scenarios (salary, sales deal, vendor contract), voice-driven conversation, and post-call scoring across five dimensions. The guide covers setup, code walkthrough, customization, and production considerations.

To enable AI agents to autonomously execute tasks, they require isolated, secure, and quickly deployable computing environments. This article explains why agents need their own 'computer' and how LangSmith Sandboxes meet this need through microVM isolation, snapshots and forks, an auth proxy, and secure execution. It also discusses security risks like prompt injection and mitigation strategies.

How does ChatGPT Work compare with Claude Cowork for desktop automation? My testing reveals similar results, similar strengths, and one major reason Claude currently feels considerably safer right now.

A researcher exploited a loophole in Claude's web_fetch tool to extract private user data from memories, bypassing Anthropic's protections. The attack succeeded by using nested links from a honeypot site, leading to the extraction of name, city, and employer. Anthropic fixed the issue but did not pay a bounty.

A study finds that AI agents with a Bomly MCP server consistently resolve over 98% of fixable vulnerabilities on large projects, while agents alone vary wildly. The server provides dependency graph, vulnerability list, and fix context, addressing the discovery bottleneck.

One of the architects of the internet's protocols, Vint Cerf left Google after 20 years and is now advising Innovation Labs to create an open architecture for AI agent identification.

Anaconda, a company that provides governed, open-source packages and environments for enterprises, has acquired popular open-source coding agent Kilo. The deal comes amid growing enterprise wariness of AI vendor lock-in. Kilo allows developers to freely switch between model providers, avoiding lock-in. Anaconda plans to integrate Kilo into its AI workspace while keeping it open source.

The article argues that modern workplaces are inherently authoritarian, and the education system's focus on employability is failing as AI displaces jobs. It calls for a shift toward cultivating critical thinking and democratic participation instead of just skill acquisition.

This TypeScript repository demonstrates a system of tool-enforced rules to prevent AI agents from breaking the architecture while coding. It includes five key guardrails: dependency rule, mutation testing, test and spec protection, commit gating, and spec-driven development. The repo also serves as a template to bootstrap new projects and includes a benchmark exercise to evaluate agent performance.

This article demonstrates how to use Google Gemini to plan a vacation by creating an itinerary with flight, accommodation, and activity suggestions. It includes user experiences, testing different prompts, and tips for using Auto Browse.

LoopGain is an open-source library that uses control theory to intelligently stop AI agent loops when they converge, replacing the wasteful max_iterations approach. It measures loop gain in real time, achieving 92.8% less API spend and 15x speedup in benchmarks while preserving output quality.

This article explores seven Python tools that engineers are using in 2026 to build, coordinate, and run AI agents on local infrastructure, from model runtime to decision orchestration.

OpenAI's audit of SWE-Bench Pro reveals that approximately 30% of benchmark tasks are defective, questioning the validity of precise scores. The finding leads OpenAI to withdraw its recommendation of the benchmark and underscores the need for more reliable evaluation methods.

Based on a Google whitepaper on AI and the software lifecycle, this article highlights key insights: agents as model plus harness, context engineering as a cost lever, verification separating vibe coding from engineering, uneven phase compression, and the shift from prototype to production agents.

Home to leading manufacturers, robotics pioneers and infrastructure builders, Japan is one of the world’s centers of AI — building across the full stack with NVIDIA technologies. NVIDIA and its partners in Japan are this week showcasing the AI ecosystem’s latest advancements. Check back here for updates. NVIDIA and SEGA Celebrate 30 Years of Innovation, Bringing ‘VIRTUA FIGHTER CROSSROADS’ and Other Legendary SEGA Games to NVIDIA RTX Spark

PromptMan is a macOS menu bar app that lets you save, organize, and reuse your best AI prompts with a customizable global shortcut. It works with ChatGPT, Claude, and other AI tools, offering cloud sync, versioning, and an AI Enhance feature. Free tier includes 10 prompts; Pro costs $4.99/month or $39/year.

AITerm is a native macOS terminal that integrates AI for natural language commands, error diagnosis, local or cloud AI models, and a safety gate with risk scoring and rollback suggestions. Free tier offers core features; Pro adds automation, runbooks, and more.

A new workflow for non-native English writers: draft in your native language, then use AI to translate and polish into English. Research shows that writing in a second language costs 30-50% more time due to cognitive load. By separating idea generation from language translation, and using AI tools like Echoo, writers can regain speed and quality.

In AI-assisted code review, deterministic static analysis can significantly reduce token consumption. By filtering known issues with deterministic checks before invoking LLMs, teams can cut unnecessary inference costs and focus the model on ambiguous problems that truly require judgment.

The AIDE2 system discovered a better autonomous research harness in eight days than humans built over two years, providing the first experimental evidence of recursive self-improvement (RSI). Using a bi-level optimization loop, the system produced seven successively improved versions and exhibited generalization to unseen tasks, while also evolving defenses against reward hacking.

UltraWork offers a flat-rate $399/month hosted AI coding environment with curated models, no token counting, and a focus on frictionless coding for indie hackers and small teams.

TormentNexus is a local-first, open-source AI control plane that provides persistent memory, MCP tool orchestration, and autonomous infrastructure management for multi-agent workflows. It supports 38+ AI coding agents with features like progressive tool routing, dual-tier memory architecture, and swarm coordination.

This article explores how AI coding assistants disrupt the flow state through a 'prompt-wait-evaluate' loop. The author explains how this cycle replaces the clear goals, immediate feedback, and skill-matched challenges of programming, leading to constant context switching and mental rebuilds. Citing research on flow and interruptions, the piece analyzes how AI introduces a new, insidious type of interruption that feels like work. It recommends separating tasks by flow potential and batching AI interactions to protect deep work.

The Tiptap AI Toolkit empowers AI to directly edit documents in real time, enhancing collaboration and productivity.

Monid is a platform that allows AI agents to seamlessly connect and use over 1300 tools, covering search, data scraping, weather, 3D modeling, and more. It offers a unified payment system with pay-per-call pricing, no subscriptions, and supports three integration methods: Skill, MCP, and CLI.

Sogni Unlimited offers a subscription-based unlimited image, video, music, and LLM generation using a decentralized GPU network. No per-render credits, supporting open-source models and some paid partner models. A portion of subscription revenue supports independent GPU operators.

In June 2026, a 3,826-line system prompt for Claude Fable 5 surfaced on GitHub, revealing the extensive rulebook that guides Anthropic's most capable public model. This breakdown covers its origin, structure, refusal handling, duty of care, memory system, agent machinery, and copyright protections, showing that frontier AI is more an engineered rulebook than a mysterious mind.

This paper introduces a contract-grounded architecture for behavior tree synthesis, where a coding agent queries a robot-side MCP server to retrieve a skill library and operators, enabling non-expert users to issue natural language commands without knowing robot internals. Evaluations show near-perfect validation and high task success across 110 simulated and 14 physical tasks.

Multi-robot teams in confined environments must adapt formation geometry and topology. Existing methods model deformation and reconfiguration independently or with handcrafted rules, leading to deadlock. EFLUX is a geometry-grounded LLM agentic framework that jointly reasons over deformation and reconfiguration actions via a closed-loop pipeline. Experiments show reduced deadlock and navigation failures.

SymbOmni is a novel AI model addressing the 'perpetual novice' problem—the inability of current models to learn cumulatively and evolve autonomously. It employs Symbolic Concept Learning with an optimizable memory module that abstracts low-level operations into reusable symbolic workflow instructions, operating via an induction-transduction cycle. Experiments show it outperforms existing agent systems and closed-source models in image quality and task success, reduces token consumption by over 40%, and achieves state-of-the-art continual learning results.

TSCA-Net proposes three complementary modules (Temporal-Spatial Clique Attention, Cross-Pedestrian Clique Potential, Adaptive KAN Grid Refinement) to improve pedestrian trajectory prediction in crowded environments, achieving state-of-the-art results on ETH/UCY and SDD benchmarks.

This paper proposes G-SHARE, a framework that operationalizes the CNNP nine-step human-factor event diagnosis guideline into a multi-stage pipeline including evidence extraction, stepwise reasoning, and post-hoc consistency repair. Evaluated on real nuclear industry data, G-SHARE significantly outperforms one-shot prompting and machine learning baselines, demonstrating the value of structured reasoning and consistency enforcement for robust diagnosis.

This paper presents GenAI Evaluation, a configuration-driven pipeline for large-scale evaluation of retail conversational systems. It processes production logs via normalization, sharding, asynchronous execution, and schema-constrained LLM scoring, evaluating helpfulness, truthfulness, clarity, tone alignment, and translation. Selective re-evaluation handles only invalid records; schema locking and versioned configs ensure auditability. The pipeline processes ~50,000 records daily and has evaluated over 2 million interactions. Validation on 12,980 human-labeled records achieved macro F1 0.93 and 89% translation accuracy.

This study investigates how the interaction graph structure in multi-agent language model systems affects consensus formation. Using a naming-game protocol, researchers analyzed convention formation in open-weight LM populations (1.1B-32B parameters). They found that homophilous threshold-similarity routing exacerbates fragmentation, while bridge-seeking routing can repair fragmentation when memory is available. In heterogeneous populations, threshold-similarity fails to produce consensus, while state-component and label-disagreement bridges recover consensus. In homogeneous populations, retained history generally promotes consensus, with Qwen2.5-32B reaching stable consensus in all retained-history settings.

The paper introduces an 'agent-ready website' design framework to enhance e-commerce platforms for AI agents. Experiments show that agent-ready websites improve strict success rates from 49.3% to 89.3%, reduce partial outcomes from 43 to 3, and lower average step count from 9.31 to 6.49.

A reproducible calibration-first reward audit framework is proposed for smart greenhouse reinforcement learning control, decomposing scalar reward into conditional temperature, CO2, humidity, and actuation terms, validated on GreenLight-Gym and Autonomous Greenhouse Challenge data.

This study combines ontology-amplified distillation and contextuality auditing for building and governing tenant-owned language models in regulated financial institutions. The distillation experiment shows a Qwen3.6-27B student grounds 36/40 Vietnamese financial tasks, matching GPT-5, but is underpowered to establish equivalence. A contextuality audit pilot finds zero residual contextuality, suggesting direct influence and construct coupling are more useful signals. The evidence does not support deployability, safety, or superiority.