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Valmis, an OpenClaw alternative built for work, with security in mind

Hacker News

Valmis, an OpenClaw alternative built for work, with security in mind

Hi HN, I would like to share with you Valmis, an AI agent harness built for work, with the ability to connect to 100+ business and productivity integrations. The system is designed with security in mind, and agents run in isolated containers, meaning AI never gets access to your API credentials or your host files. I know the first question you are going to ask is: Why another AI agent harness? And here is why I'm building it. OpenClaw (or many other harnesses) is a great tool for creating personal assistants, but it is not built for work. The biggest concern is security, as agents store credentials in their memory as plain text and sometimes send credentials directly to LLM providers. Valmis addresses this issue with a proxy system: the Dockerized agent runtime can only make API requests to the host machine by providing the relevant credential ID. The host then makes the actual request and returns the JSON data back to the agent runtime. Even the LLM API calls themselves are made using proxy. With this design, you can even turn off the internet access of the agent container while making it work. Agents can only work for you when they have access (safely) to your apps. Our proxy system now supports 100+ business and productivity integrations, including all Google Workspace apps, Slack, Notion, Hubspot, Salesforce, and Figma. Finally, you can automate multi-step workflows using our workflow feature. Each workflow can be triggered by cron, webhooks, app events, and it supports conditions and loops. You can create workflows using our workflow builder UI, or simply ask your agent to create one by providing a description. Other cool features: - Agents have cross-session memory: Your agents are able to automatically write memory when you tell them anything worth remembering or when it discovers something that might be useful in the future. - Browser automation: Agents can operate a headless browser, navigate, fill forms, click, read pages, and take screenshots. Browsers are also managed by the host machine, so agents interact with them using proxy. - Human in the loop: Whenever there is a critical decision to make, the agent pauses and asks the human for a set of options. - Team knowledge base and skill system. Overall, Valmis is designed to run on the cloud and collaborate with humans to get work done (Valmis means "done" and "completed" in Estonian). If you have any questions about the project, please leave a comment, and I'll reply to all questions.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, agents, agent · Missing: macos, cursor, claude
100%100% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: supports, including · Missing: reddit linkedin, podcasting, created
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, google · Missing: mobile apps, ios, entrepreneurs
53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io, including · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, builder, calls · Missing: plus, platform, intuitive
40%40% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
24%24% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: collaborate · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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