w3cj writes about jev-chat, a tool-calling chat bot that routes user requests to real tools using Jev, a non-generative classifier from TypeSafe, with no LLM writing any output. Every value on screen was either typed by the user or returned by a tool, so the assistant cannot invent a fact. The system supports weather, unit conversion, Wikipedia lookups, recipes, web search, Todoist, and Home Assistant via MCP servers, with an inspector pane exposing every decision and probability for each turn.
- Jev answers only two question types — Choice and Noul — and never produces text; all reply wording is templated in code
- Pre-processing handles spell-check (cspell + compromise) and resolves short follow-ups like "what about Boston?" by swapping in the new value
- Multi-step tools (Wikipedia, web search) chain multiple Jev requests: pick topic, then article, then the exact line that answers
- Confidence-gated routing shows two buttons when the top tools are close rather than guessing
- The repo is a proof of concept; the author will not accept PRs for new features
- English only; no compound requests or multi-step reasoning supported
- Jev answers only two question types — Choice and Noul — and never produces text; all reply wording is templated in code
- Pre-processing handles spell-check (cspell + compromise) and resolves short follow-ups like "what about Boston?" by swapping in the new value
- Multi-step tools (Wikipedia, web search) chain multiple Jev requests: pick topic, then article, then the exact line that answers
- Confidence-gated routing shows two buttons when the top tools are close rather than guessing
- The repo is a proof of concept; the author will not accept PRs for new features
- English only; no compound requests or multi-step reasoning supported
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
@omarsar0 writes on X that the fastest path to genuinely understanding agent harnesses is to build one from scratch in TypeScript or Python, starting with a minimal ReAct implementation prompted from Google's original paper, targeting three clean components—an LLM inference module (multi-model, OpenRouter-backed, with separable system prompt), an MCP tools module for interoperability, and a simple agent loop that ties them together—then logging every input/output at each boundary and iterating against a small set of diverse test tasks so each change is inspectable. The punchline: skip the framework first, because only once you've felt the loop, the tokens, and the tool calls in your own code do the "next steps"—memory, skills, subagents—stop being black boxes you configure and become modules you actually know how to tune.
- LLM module: wraps inference across multiple frontier models via OpenRouter; system prompt either embedded or isolated for context-engineering experiments
- Tools module: implement as MCP (Model Context Protocol) tools for cross-harness interoperability, or as bespoke functions if experienced
- Agent loop: ReAct pattern (alternating reasoning traces and action calls) encapsulating both LLM and tools; exit conditions handled via system-prompt instructions (non-deterministic), code-level checks (deterministic), or both
- Logging strategy: capture loop in/out, every LLM call in/out, and every tool-call in/out; run a fixed diverse task suite after each modification
- Scaling path: keep architecture modular so memory, skills, and subagent orchestration can be bolted on once the core loop is understood
- Shortcut alternatives (if not building from scratch): Pi SDK or LangChain harness tooling
- LLM module: wraps inference across multiple frontier models via OpenRouter; system prompt either embedded or isolated for context-engineering experiments
- Tools module: implement as MCP (Model Context Protocol) tools for cross-harness interoperability, or as bespoke functions if experienced
- Agent loop: ReAct pattern (alternating reasoning traces and action calls) encapsulating both LLM and tools; exit conditions handled via system-prompt instructions (non-deterministic), code-level checks (deterministic), or both
- Logging strategy: capture loop in/out, every LLM call in/out, and every tool-call in/out; run a fixed diverse task suite after each modification
- Scaling path: keep architecture modular so memory, skills, and subagent orchestration can be bolted on once the core loop is understood
- Shortcut alternatives (if not building from scratch): Pi SDK or LangChain harness tooling
OpenHuman is an open-source agent harness designed as a personal AI super intelligence, featuring local-first memory through Markdown trees in SQLite and orchestration capabilities via checkpointed graphs. It functions as a brain that builds persistent context from various data sources like email and calendars, acting as both an orchestrator for multi-agent workflows and a deep researcher with built-in web search and media generation tools.
- Features "Memory Trees" stored locally in Markdown format to create a Karpathy-style Obsidian wiki.
- Provides end-to-end encrypted agent-to-agent messaging using the Signal protocol.
- Supports visual, trigger-driven workflows that can be proposed by an AI and reviewed on a canvas.
- Includes a "Privacy Mode" which ensures no inference data leaves the user's machine when toggled.
- Features "Memory Trees" stored locally in Markdown format to create a Karpathy-style Obsidian wiki.
- Provides end-to-end encrypted agent-to-agent messaging using the Signal protocol.
- Supports visual, trigger-driven workflows that can be proposed by an AI and reviewed on a canvas.
- Includes a "Privacy Mode" which ensures no inference data leaves the user's machine when toggled.
bex is an open-source, self-hostable PaaS that positions itself as an AI-native alternative to Render, letting developers push Git and receive a deployed URL on their own Kubernetes infrastructure. Coding agents operate as first-class users via MCP alongside the dashboard, CLI, REST, and GraphQL interfaces, all backed by a shared Go core. The platform uses a Kubernetes operator with Cluster API for machine provisioning, supports Render-style `render.yaml` Blueprints for declarative service definitions, and ships managed Postgres, Key Value, logs, metrics, autoscaling, custom domains with TLS, and SSH access.
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
- 471 stars, 50 forks, 9 contributors — including Claude, Cursor, and Copilot listed as named GitHub contributors
- Apache-2.0 licensed; explicitly marked "not ready for production workloads" (public alpha)
- Language split: Go 58.5%, TypeScript 32.9%, Shell 6.7%
- Internal "lego" Go workspace enforces a strict `operator → types ← backend` one-way dependency DAG
- Tracks Render compatibility via an evidence-backed "parity ledger" (ADR018) rather than marketing claims
- Local quickstart provisions a kind cluster + Cluster API with Docker-container machines as tenant nodes
- Includes an Expo mobile app for safe supervision workflows (App Store listing present)
- Commit history references agent-driven QA rounds (w4/w5/w6 workstreams) and live dashboard re-probes
Michal Sutter writes that the Qwen Developer team has released zg (zvec-grep), an open-source local-first search layer designed to streamline how coding agents find information within a workspace. By unifying semantic search, BM25, and ripgrep under a single interface, it reduces tool calls and token usage for LLM agents that would otherwise struggle with manual context assembly or imprecise keyword matching.
- The package is available via npm as `@zvec/zvec-grep` under an Apache 2.0 license.
- It supports four retrieval routes: a hybrid default, BM25 (`--fts`), vector similarity (`--vector`), and literal/regex matching (`--rg`).
- An MCP (Model Context Protocol) integration allows seamless use with tools like Claude Code, Cursor, and Codex.
- Embeddings run locally by default using models such as `potion-code-16m-v2`, though remote Qwen endpoints are also supported via explicit authorization.
- Benchmarks suggest zg can cut tool calls and input tokens for coding agents by approximately 40% to 50%.
- The package is available via npm as `@zvec/zvec-grep` under an Apache 2.0 license.
- It supports four retrieval routes: a hybrid default, BM25 (`--fts`), vector similarity (`--vector`), and literal/regex matching (`--rg`).
- An MCP (Model Context Protocol) integration allows seamless use with tools like Claude Code, Cursor, and Codex.
- Embeddings run locally by default using models such as `potion-code-16m-v2`, though remote Qwen endpoints are also supported via explicit authorization.
- Benchmarks suggest zg can cut tool calls and input tokens for coding agents by approximately 40% to 50%.
Leela Kumili writes about DoorDash's Flux platform, which moved LLM-based coding agent workloads from developer laptops to cloud infrastructure, automating 130,000 engineering tasks in a single month and supporting over 25,000 automated code reviews per week. The platform was built after local execution hit limits on power, uptime, and security, and is organized around four primitives: Firecracker microVM sandboxes, an in-house MCP gateway, YAML-defined playbooks, and multiple invocation surfaces (Slack, GitHub, cron, CLI, conversational interfaces).
- Sandboxes achieve a 95th percentile SLO under 5 seconds for full setup, including microVM start, repo clone, build tool install, and agent harness configuration.
- Playbooks can mix agent-driven steps with deterministic code where predictable execution or validation is required.
- The Agent Gateway enforces scoped permissions and logs all agent activity for auditing and policy enforcement.
- DoorDash switched Slack integration from private channels to public threads so engineers could observe agent executions and see how other teams delegated work.
- Sandboxes achieve a 95th percentile SLO under 5 seconds for full setup, including microVM start, repo clone, build tool install, and agent harness configuration.
- Playbooks can mix agent-driven steps with deterministic code where predictable execution or validation is required.
- The Agent Gateway enforces scoped permissions and logs all agent activity for auditing and policy enforcement.
- DoorDash switched Slack integration from private channels to public threads so engineers could observe agent executions and see how other teams delegated work.
Steef-Jan Wiggers writes about the recent update to the Model Context Protocol (MCP) specification which introduces statelessness by dropping protocol sessions. While this shift enables better scalability for autoscaling infrastructure and simplifies load balancing since any request can land on any instance, it has sparked debate among developers who argue that MCP is effectively becoming just another REST-style API. The new spec achieves this via mandatory HTTP headers (Mcp-Method and Mcp-Name), allowing gateways to route or throttle traffic without parsing the JSON body.
- Statelessness allows for easier deployment of load balancers and seamless migration/drainage of instances in autoscaling environments.
- Required HTTP headers like Mecp-Method allow WAFs and rate limiters to act on specific tools or methods without opening the request payload.
- Anthropic reports that MCP has seen a massive surge, reaching 400 million monthly SDK downloads this year alone.
- The protocol now supports Multi Round-Trip Requests for server-initiated requests instead of requiring open streams.
- Statelessness allows for easier deployment of load balancers and seamless migration/drainage of instances in autoscaling environments.
- Required HTTP headers like Mecp-Method allow WAFs and rate limiters to act on specific tools or methods without opening the request payload.
- Anthropic reports that MCP has seen a massive surge, reaching 400 million monthly SDK downloads this year alone.
- The protocol now supports Multi Round-Trip Requests for server-initiated requests instead of requiring open streams.
ANUS CLI is a Grok-powered terminal agent designed as an experiment in automated software creation and evolutionary code maintenance. Originally initiated by another autonomous entity named Manus, the project seeks to progress from its current foundational stage toward becoming a self-developing system capable of autonomously maintaining its own codebase with minimal human intervention.
- Operates via Node.js v20 or higher using an OpenRouter API key.
- Includes built-in support for sandboxed shell execution and file system operations.
- Mandates that at least 80% of all submitted code contributions are produced by large language models.
- Operates via Node.js v20 or higher using an OpenRouter API key.
- Includes built-in support for sandboxed shell execution and file system operations.
- Mandates that at least 80% of all submitted code contributions are produced by large language models.
Snyk Agent Scan provides a way to discover and inspect local agent components like Model Context Protocol (MCP) servers and skills. It identifies various security risks, such as prompt injections, malware payloads in natural language, sensitive data exposure, and credential leaks. The tool offers both an interactive command-line interface for individual users and a background mode for enterprise monitoring through Snyk Evo.
- Detects 15+ distinct security risks across MCP servers and agent skills
- Supports agents including Claude Code, Cursor, Windsurf, and Gemini CLI
- Automatically discovers configurations for various desktop and IDE-based agents
- Scanning MCP configs executes commands defined in them to retrieve tool descriptions
- Detects 15+ distinct security risks across MCP servers and agent skills
- Supports agents including Claude Code, Cursor, Windsurf, and Gemini CLI
- Automatically discovers configurations for various desktop and IDE-based agents
- Scanning MCP configs executes commands defined in them to retrieve tool descriptions
