
DecisionMesh
Every AI decision — governed, audited, compliant
What it does
DecisionMesh is the AI Intent Control Plane for enterprises. Unlike observability tools that explain what happened after an AI request, DecisionMesh governs what is allowed before it reaches an LLM. Enforce intent-based policies, mask PII before transmission, maintain immutable audit trails, control budgets, and route across OpenAI, Anthropic, Gemini, DeepSeek, and self-hosted models. Built for regulated industries where AI governance, compliance, and accountability are critical.
Does the same job
all alternatives →
DecisionBox EnterpriseApr 2026 · decisionbox.io · ▲67Agent that writes SQL for you to validate database insights

- TATraceMem – A trace-native memory layer for AI agent decisionsJan 2026 · tracemem.com · ▲15
Hi HN, There’s been a lot of discussion lately around context graphs, decision traces, and how AI systems reason. One thing we kept running into: when AI agents make real decisions, the why behind those decisions often disappears. The context is scattered across prompts, tools, policies, and approvals. Logs show what happened, but not why it was allowed. TraceMem is an attempt to make decision context durable. It records the reasoning, authority, and context behind AI actions as a system of record, not as monitoring data, but as memory. Happy to share more details or answer questions. - Tommi

Conduct, open-source guardrails for LLM and MCP tool calls9d ago · github.com · ▲22Runtime governance for AI agents. Allow, warn, or block every model and tool call before it commits. Hash-chained audit for every decision. Compliance packs for SOC 2, HIPAA, PCI DSS, EU AI Act, SR 11-7, and FDA CSA. Apache 2.0. - sseshachala/conductai
- DEDecision engine – AI-assisted decision making2022 · decision-engine.app · ▲17
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com


Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com


Launched alongside, June 2026
the whole month →
Fundraisly▲1,544AI fundraising agent that finds investors and books meetings
AI · Jun 2026 · fundraisly.com
- H6Homebrew 6.0.0▲1,481
Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
Dev tools · Jun 2026 · brew.sh
- PU
hope you enjoy
Life & fun · Jun 2026 · vorpus.github.io


- IM
Life & fun · Jun 2026 · hackernewstrends.com