Pica – Rust-based agentic AI infrastructure (open-source)

Hacker News

Hi HN, Founder here. We built Pica, an open-source platform in Rust to enable agentic AI with three main focus areas: - Access to APIs and tools: Universal SDKs that let AI agents use thousands of external actions without blowing up your context window. - Visibility and traceability: Full audit logs of every decision/action to ensure transparency and accountability. - Alignment with human intentions: Seamless guardrails for autonomous tasks; e.g., restricting certain email actions to human approval. Why this matters: As autonomy in AI grows, we need robust solutions for trust and oversight. Pica aims to empower developers with the building blocks for safe and capable agentic systems. We’d love your feedback—check it out: https://hellopica.com/ If you find our project useful, consider giving us a on GitHub! It means the world to us and helps others discover our work. https://github.com/picahq/pica

Visit product View on Hacker NewsJan 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, agentic · Missing: mac, macos, cursor

91%91% 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 · Missing: supports, reddit linkedin, podcasting

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

46%46% 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: platform · Missing: plus, intuitive, reviews

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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