Alternatives
Products that do what AXSDK does
Kill the menus. Let AI execute user intent instantly.
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I’ve spent the last two months building AgenticSeek, a privacy-focused alternative to cloud-based AI tools like ManusAI. It runs entirely on your machine—no API calls, no data leaks. Why AgenticSeek? Optimized for local LLMs (developed mostly on an RTX 3060 running deepseek r1 14b). Truly private: All components (TTS, STT, planner) run locally. More responsive than alternatives (we respond fast to issues + active Discord). Designed to be fun—think JARVIS-like voice control, multi-agent workflows, and a slick web UI. Current Features: Web browsing (research + form filling), code…
2025 · github.com
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com
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Hi HN! Erik here from Pig.dev, and today I'd like to share a new project we've just open sourced: Muscle Mem is an SDK that records your agent's tool-calling patterns as it solves tasks, and will deterministically replay those learned trajectories whenever the task is encountered again, falling back to agent mode if edge cases are detected. Like a JIT compiler, for behaviors. At Pig, we built computer-use agents for automating legacy Windows applications (healthcare, lending, manufacturing, etc). A recurring theme we ran into was that businesses already had RPA (pure-software scripts), and…
2025 · github.com
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I run a building design consultancy. I got tired of paying Wix $40/month for a brochure that couldn’t answer simple service questions, and me wasting hours on the same FAQs. So I killed it all and spent 4 months building a 'talker': https://axoworks.com The stack is completely duct-taped: Netlify’s 10s serverless timeout forced me to split the agent into three pieces: Brain (Edge), Hands (Browser), and Voice (Edge). I haven’t coded in 30 years. This was 3 steps forward, 2 steps back, heavily guided by AI. The fight that proved it worked: 2 weeks ago, a licensed architect…
Mar 2026
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Hi, I built Axiomeer, an open-source marketplace protocol for AI agents. The idea: instead of hardcoding tool integrations into every agent, agents shop a catalog at runtime, and the marketplace ranks, executes, validates, and audits everything. How it works: - Providers publish products (APIs, datasets, model endpoints) via 10-line JSON manifests - Agents describe what they need in natural language or structured tags - The router scores all options by capability match (70%), latency (20%), cost (10%) with hard constraint filters - The top pick is executed, output is validated (citations…
Feb 2026 · github.com
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Hi HN, I’m the creator of Cordum. I’ve been working in DevOps and infrastructure for years (currently in the fintech/security space), and as I started playing with AI agents, I noticed a scary pattern. Most "safety" mechanisms rely on system prompts ("Please don't do X") or flimsy Python logic inside the agent itself. If we treat agents as autonomous employees, giving them root access and hoping they listen to instructions felt insane to me. I wanted a way to enforce hard constraints that the LLM cannot override, no matter how "jailbroken" it gets. So I built Cordum. It’s an open-source…
Jan 2026 · github.com
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A self-hosted ReAct agent with 12 tools and real memory
Jul 2026 · phinn.github.io
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Hi HN, Over Thanksgiving weekend I wanted to build an AI agent. As a design exercise, I wrote it as a set of React components. The component model made it easier to reason about the moving parts, composability was straightforward (e.g., reusing agents/tools), and hooks/state felt like a reasonable way to model behavior that changes over time. As I kept experimenting, I realized I was more interested in the framework (and it was a good excuse to explore React’s internals) than in the agent I originally planned, so I built this project instead. In this framework: - Agents are React…
Dec 2025 · github.com
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Hi HN, we’re building AxonFlow for teams running LLMs or agents in real production systems. Once agent workflows move past demos, failures are rarely model issues. They tend to show up as execution problems during real runs. Short 2-minute technical demo showing execution control and auditability in practice: https://youtu.be/FNgnESo9RtI AxonFlow is a self-hosted, source-available (BSL 1.1) control plane that sits inline in the execution path and governs LLM calls, tool calls, retries, approvals, and policy enforcement step by step. It does not replace your orchestrator and…
Jan 2026
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Cheers HN, We're Oli and Pascal, two friends from ETH Zürich. We built a network of AI Agents for large organisations, that finally gets rid of all admin work for employees. Current features are: - Schedule, move or cancel meetings (via Google Calendar or locally) - Dynamically adapt meetings according to stakeholders’ availabilities (internal communication of the agents) - Summarize incoming mails (via Gmail) - Create a project plan (command: plan XXX = [project description]) including stakeholders, timeline and cost estimate - Plan, assign and view tasks - Do all of the above via audio We…
2025 · github.com
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