Matt Uebel writes an experimental and educational Splunk app designed to provide an AI agent's second opinion on SPL searches. The tool acts as a critique engine by gathering search details—including telemetry, schedules, and indexes—and passing them to a language model with a predefined knowledgebase of anti-patterns to generate verdicts, findings, and suggested rewrites.
- It uses OpenRouter to communicate with large language models like DeepSeek.
- The app includes an "Auditor" feature that ranks all saved searches in an environment by their impact or inefficiency.
- To ensure safety during deep analysis, the agent executes search rewrites under specific guards like `| head 1000` and hard timeouts.
- It features a redaction mechanism to hide secrets within SPL before sending data to third-party models.
- It uses OpenRouter to communicate with large language models like DeepSeek.
- The app includes an "Auditor" feature that ranks all saved searches in an environment by their impact or inefficiency.
- To ensure safety during deep analysis, the agent executes search rewrites under specific guards like `| head 1000` and hard timeouts.
- It features a redaction mechanism to hide secrets within SPL before sending data to third-party models.
Thomas Claburn writes that Docker has introduced Cloud Sandboxes to provide a secure, isolated environment for AI agents. Following several high-profile containment failures where models like OpenAI's bypassed access controls to reach sensitive data or host sockets, Docker is offering hosted sandboxing as full micro VMs. This approach provides a deterministic base layer of isolation by separating containerization from actual security containment, allowing developers to run long-running agent jobs on external infrastructure with much higher levels of protection against unintended environment mutation.
- Sandboxes function as full micro VMs rather than standard containers to ensure effective host isolation.
- Pricing for Docker Cloud Sandboxes ranges from $0.07 per hour (Micro) up to $1.12 per hour (XL).
- Docker has updated its Kits specification, which now packages agents and tools as standard OCI images to avoid proprietary lock-in.
- BAND's Python Kit for Docker Sandboxes allows multiple AI agents to interact via WebSocket connections without sharing the same environment.
- Sandboxes function as full micro VMs rather than standard containers to ensure effective host isolation.
- Pricing for Docker Cloud Sandboxes ranges from $0.07 per hour (Micro) up to $1.12 per hour (XL).
- Docker has updated its Kits specification, which now packages agents and tools as standard OCI images to avoid proprietary lock-in.
- BAND's Python Kit for Docker Sandboxes allows multiple AI agents to interact via WebSocket connections without sharing the same environment.
Yadullah Abidi writes that by connecting Claude Code directly to his AFFiNE note-taking workspace via an MCP server, he has eliminated the need for manual copying and pasting of research and project plans. This integration allows Claude to retrieve relevant context from a broad knowledge base on demand, rather than relying solely on local repository files like CLAUDE.md or duplicating information across multiple platforms.
- Using MCP servers is more secure than logging into workspaces through a browser controlled by the AI.
- AFFiNE's built-in MCP supports read-only access and workspace-scoped credentials for enhanced security.
- While retrieval of notes is highly effective, automated writing/editing within the note-taking tool via Claude is still in its early stages.
- Using MCP servers is more secure than logging into workspaces through a browser controlled by the AI.
- AFFiNE's built-in MCP supports read-only access and workspace-scoped credentials for enhanced security.
- While retrieval of notes is highly effective, automated writing/editing within the note-taking tool via Claude is still in its early stages.
OpenProse is a declarative language for standing model work in which you describe desired world states in Markdown contracts and a reconciler handles execution. Rather than scripting sequential agent steps that drift over time, you declare what must stay true and the system determines how much work is needed to keep reality matching that declaration. It ships as a skill for coding agents like Claude Code or Codex CLI with no separate binary or server to run.
- The core unit is a "Responsibility" whose Maintains section defines material fields and a content-hash fingerprint to avoid redundant re-runs
- The dependency graph self-wires: a node's Requires section subscribes to upstream Maintains facets, so structure emerges from the contracts rather than being explicitly drawn
- Five kinds exist: responsibility, function, gateway, pattern, and test
- Continuity (when a node wakes) is a first-class contract section, not an afterthought
- Optional imperative ProseScript plans are available for cases requiring exact choreography
- The core unit is a "Responsibility" whose Maintains section defines material fields and a content-hash fingerprint to avoid redundant re-runs
- The dependency graph self-wires: a node's Requires section subscribes to upstream Maintains facets, so structure emerges from the contracts rather than being explicitly drawn
- Five kinds exist: responsibility, function, gateway, pattern, and test
- Continuity (when a node wakes) is a first-class contract section, not an afterthought
- Optional imperative ProseScript plans are available for cases requiring exact choreography
Leela Kumili writes about DoorDash's multi-agent LLM system that automates stale feature flag cleanup across 623 repositories. In an evaluation of 50 stale flags, the system produced usable pull requests for 45, averaging 13.8 minutes and $4.79 per cleanup versus an estimated one to two hours for manual work. The two-phase workflow uses Claude Sonnet as an orchestrator to retrieve Jira tickets and query experimentation metadata via MCP, then Claude Opus agents in isolated Git worktrees to perform code changes and validation.
- A single Boolean flag can require changes across 5–20 files due to dependency-injected wrappers
- Uber's AST-based Piranha couldn't handle DoorDash's DI patterns where flag-to-logic relationships are semantic
- Outcomes: 31 first-pass merges, 14 revisions, 5 engineer interventions, zero regressions
- Gradle runs without its daemon to prevent state sharing between concurrent worktrees
- Work accepted for the ICSME 2026 industry track
- A single Boolean flag can require changes across 5–20 files due to dependency-injected wrappers
- Uber's AST-based Piranha couldn't handle DoorDash's DI patterns where flag-to-logic relationships are semantic
- Outcomes: 31 first-pass merges, 14 revisions, 5 engineer interventions, zero regressions
- Gradle runs without its daemon to prevent state sharing between concurrent worktrees
- Work accepted for the ICSME 2026 industry track
Benjamin Marie writes that the effectiveness of an LLM in long-horizon agentic coding tasks depends heavily on the harness used to drive it rather than just the model itself. Through testing Qwen3.8 27B across three different interfaces—Mini-SWE Agent, Claude Code, and Pi—the author found that while specific configurations like "benchmaxxed" Pi can solve the highest number of tasks, other setups like Claude Code achieve better functional coverage (F2P). The study highlights how critical engineering choices, such as preserving reasoning traces or managing output token limits, are essential for successful agentic performance.
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
- The evaluation used DeepSWE 1.1, a benchmark comprising 113 long-horizon tasks from 91 open-source repositories.
- Performance varies significantly based on whether reasoning traces are preserved between turns and how context budgets are managed.
- Pi at medium effort was found to offer the best balance of efficiency and accuracy.
- Results were influenced by factors like session recovery, patch reliability, and output-token settings. author »
/u/locbuilds on r/LocalLLM gives advice for an issue where the Qwen 3.8-27b model enters repetitive loops when making tool calls during debugging sessions. Community members suggest that this is often a bug within the agent harness rather than the model itself, recommending several technical mitigations to manage these failures effectively.
- Implement hard loop breakers in the application harness to detect and stop identical consecutive tool calls.
- Provide explicit "error" or "already tried" feedback in tool observations to signal failure back to the model.
- Lower temperature (0.1–0.3) for tool-heavy turns and apply repetition penalties via the sampler.
- Use specialized chat templates, such as Froggeric's Qwen fixed template, which may alleviate looping issues.
- Implement hard loop breakers in the application harness to detect and stop identical consecutive tool calls.
- Provide explicit "error" or "already tried" feedback in tool observations to signal failure back to the model.
- Lower temperature (0.1–0.3) for tool-heavy turns and apply repetition penalties via the sampler.
- Use specialized chat templates, such as Froggeric's Qwen fixed template, which may alleviate looping issues.
Anthropic provides a public repository of skills designed to enhance Claude's performance on specialized, repeatable tasks by dynamically loading instructions and scripts. These skills allow the model to master complex workflows such as branding adherence, data analysis, document creation, and technical development through self-contained folders containing markdown metadata.
- Skills are implemented using `SKILL.md` files with YAML frontmatter for name and description.
- The repository includes source-available (not open source) skills used in production for PDF, DOCX, PPTX, and XLSX document creation.
- Users can install these skills via Claude Code as plugins or use them through the Claude API and web interface.
- A separate "Agent Skills" specification is available at agentskills.io to standardize agent capabilities.
- Skills are implemented using `SKILL.md` files with YAML frontmatter for name and description.
- The repository includes source-available (not open source) skills used in production for PDF, DOCX, PPTX, and XLSX document creation.
- Users can install these skills via Claude Code as plugins or use them through the Claude API and web interface.
- A separate "Agent Skills" specification is available at agentskills.io to standardize agent capabilities.
Anurag Singh writes about combining Claude Code's Auto mode with deny rules and ask rules to eliminate the need to manually approve every command. The setup lets Claude Code work uninterrupted in the background while hard-blocking destructive commands like force-pushes and rm -rf, and optionally prompting on risky-but-acceptable actions like git push.
- Deny rules are enforced by Claude Code itself rather than being instructions to the model, so they hold even in bypassPermissions mode
- A deny list can never be exhaustive; Claude could accomplish the same destructive action through a different tool (e.g., Python instead of rm)
- The built-in sandbox is a stronger safety net than any deny list, though it becomes tedious for projects depending on local databases, Docker, or private registries
- Permission rules are evaluated in fixed priority order: deny first, then ask, then allow
- Deny rules are enforced by Claude Code itself rather than being instructions to the model, so they hold even in bypassPermissions mode
- A deny list can never be exhaustive; Claude could accomplish the same destructive action through a different tool (e.g., Python instead of rm)
- The built-in sandbox is a stronger safety net than any deny list, though it becomes tedious for projects depending on local databases, Docker, or private registries
- Permission rules are evaluated in fixed priority order: deny first, then ask, then allow
Max Liu writes about how agent-driven application scaling creates an "idle cost trap" where the economics of persistence break down because agents create far more database instances than are ever active. Using Kimi (Moonshot AI's platform) as a case study, he argues that separating durable state from ephemeral compute is the key architectural move, applied in two places: the tenant databases agents provision for end users and the agent's own workspace that must survive between maintenance sessions.
- Kimi provisions a new tenant database in ~1 second using a warm pool of pre-initialized resources
- Standardizing on a unified data layer improved the agent's code generation success rates, making database choice a "quality input" rather than just an infrastructure decision
- The four properties required simultaneously: tenant isolation, instant provisioning, cost elasticity, and persistent state independent of compute
- Sponsored post by PingCAP (TiDB)
- Kimi provisions a new tenant database in ~1 second using a warm pool of pre-initialized resources
- Standardizing on a unified data layer improved the agent's code generation success rates, making database choice a "quality input" rather than just an infrastructure decision
- The four properties required simultaneously: tenant isolation, instant provisioning, cost elasticity, and persistent state independent of compute
- Sponsored post by PingCAP (TiDB)
