Alternatives
Products that do what Super Agent Stack does
Reliable AI with zero hallucinations and memory
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The stack: two agents on separate boxes. The public one (nullclaw) is a 678 KB Zig binary using ~1 MB RAM, connected to an Ergo IRC server. Visitors talk to it via a gamja web client embedded in my site. The private one (ironclaw) handles email and scheduling, reachable only over Tailscale via Google's A2A protocol. Tiered inference: Haiku 4.5 for conversation (sub-second, cheap), Sonnet 4.6 for tool use (only when needed). Hard cap at $2/day. A2A passthrough: the private-side agent borrows the gateway's own inference pipeline, so there's one API key and one billing relationship…
Mar 2026 · georgelarson.me
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The multi-agent system that delivers finished work
Jan 2026 · hyperagent.com
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Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning. This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned. To solve the…
Apr 2026 · github.com
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Hi HN, we built SuperHQ, an open source app that runs AI coding agents in isolated microVM sandboxes instead of directly on your machine. Each agent gets its own VM with a full Debian environment. You mount your projects in, writes go to a tmpfs overlay so your host is never touched, and you get a diff view to accept or discard changes. API keys never enter the sandbox. We also just launched remote.superhq.ai which acts as a remote control for SuperHQ, allowing you to access your workspaces and agents from anywhere.
Apr 2026 · github.com
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Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com
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2025 · superglue.ai
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Hey HN! I'm Arindam, part of the team behind Memori (https://memori.gibsonai.com/). Memori adds a stateful memory engine to AI agents, enabling them to stay consistent, recall past work, and improve over time. With Memori, agents don’t lose track of multi-step workflows, repeat tool calls, or forget user preferences. Instead, they build up human-like memory that makes them more reliable and efficient across sessions. We’ve also put together demo apps (a personal diary assistant, a research agent, and a travel planner) so you can see memory in action. Current LLMs are stateless…
2025 · github.com
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An agent that remembers across sessions can keep its memory as curated markdown files, as an auto-mined structured store, or as trained experience.
22d ago · pinglin.tw
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Open Source Context Infrastructure for AI Agents
May 2026 · ravbyte-ai.github.io
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