Releases·Tool Release
Preventing double-execution and tool replay in local LLM workflows (Rust tool gate)
A developer running local models via Ollama and LM Studio released v0.1 of an open-source Rust CLI/library, arcstone-mcp-sidecar, that prevents duplicate tool executions and replayed or mutated tool requests. The tool binds single-use claims to action, resource, and payload SHA-256, then atomically burns them on first execution. It targets a practical gap in local agent workflows where looping models retry or alter calls mid-sequence.
8 engagement1 sourceTue, Sep 22, 2026, 01:08 PM
AI Overview
- Developer releases v0.1 Rust CLI/library 'arcstone-mcp-sidecar', issuing single-use SHA-256-bound claims burned atomically via O_EXCL to deny replays
Related
Social·Community
Reddit thread asks Ollama users what feels hardest about switching models, connecting tools, and managing workflows
A Reddit thread invites regular Ollama users to describe friction in local-model workflows: switching models without losing context, connecting local models to external tools or MCP servers, and choosing between terminal, web, or desktop clients. It targets developers using Ollama beyond simple chat.
A Reddit thread invites regular Ollama users to describe friction in local-model workflows: switching models without losing context, connecting local models to external tools or MCP servers, and choosing between terminal, web, or desktop clients. It targets developers using Ollama beyond simple chat.
3 engagement1 sourceredditMon, Oct 12, 2026, 10:18 PM
Social·Community
Building a local LLM/agent stack on a 128GB M5 Max Mac Studio for sensitive client data
A Reddit user buying a 128GB M5 Max Mac Studio asks which local LLM and agent stack to run for a consulting business handling sensitive client data. They want multi-step agentic capabilities — coding, browser automation, and local data analysis — without monopolizing all 128GB of unified memory, and are confused about how inference layers like MTPLX, LM Studio, and Ollama fit together.
A Reddit user buying a 128GB M5 Max Mac Studio asks which local LLM and agent stack to run for a consulting business handling sensitive client data. They want multi-step agentic capabilities — coding, browser automation, and local data analysis — without monopolizing all 128GB of unified memory, and are confused about how inference layers like MTPLX, LM Studio, and Ollama fit together.
6 engagement1 sourceredditTue, Oct 6, 2026, 12:50 PM
Releases·Tool Release
Developer releases idempotent-tools decorator to stop retried agent tool calls from double-firing side effects
A developer released idempotent-tools, a Python decorator that caches tool call results so retried or resumed agent executions don't re-run side effects like payments or emails. It works with LangGraph and CrewAI, storing results locally in SQLite by default or Redis. The tool addresses a common reliability issue in agent workflows where interrupts or retries cause duplicate external writes.
A developer released idempotent-tools, a Python decorator that caches tool call results so retried or resumed agent executions don't re-run side effects like payments or emails. It works with LangGraph and CrewAI, storing results locally in SQLite by default or Redis. The tool addresses a common reliability issue in agent workflows where interrupts or retries cause duplicate external writes.
1 engagement1 sourceredditSun, Aug 9, 2026, 04:55 AM
Social·Community
Reddit thread weighs Python vs Go vs Rust for building production agentic systems, scoring deployment, ecosystem, cost, and safety
A Reddit thread asks for practical, non-vibes comparisons of Python, Go, and Rust for building production agentic systems (MCP servers, orchestration, durable workflows, web services). The author, a deep Python expert, evaluates build pipelines, ecosystem maturity, developer and runtime costs, language safety, and operational complexity. Practitioners with shipped multi-language agent stacks are invited to share migration learnings and pitfalls.
A Reddit thread asks for practical, non-vibes comparisons of Python, Go, and Rust for building production agentic systems (MCP servers, orchestration, durable workflows, web services). The author, a deep Python expert, evaluates build pipelines, ecosystem maturity, developer and runtime costs, language safety, and operational complexity. Practitioners with shipped multi-language agent stacks are invited to share migration learnings and pitfalls.
18 engagement1 sourceredditSun, Oct 11, 2026, 01:26 AM
Products·launchedfeatured
Safe execution framework for LLM tool calls with mechanical guarantees
A framework that enforces mechanical safety guarantees on LLM tool calls, preventing irreversible actions like unauthorized payments or database deletions. It solves the problem of blind trust in AI agents for developers and enterprises deploying LLMs with tool access.
A framework that enforces mechanical safety guarantees on LLM tool calls, preventing irreversible actions like unauthorized payments or database deletions. It solves the problem of blind trust in AI agents for developers and enterprises deploying LLMs with tool access.
121 engagement1 sourceyoutubeMon, Jul 13, 2026, 07:25 PM