Alternatives
Products that do what ConsciousAI Protocol does
System 2 for AI — reducing LLM reactivity through pause
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I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
2024 · github.com
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OP here. Birth of a Mind documents a "recursive self-modeling" experiment I ran on a single day in 2026. I attempted to implement a "Hofstadterian Strange Loop" via prompt engineering to see if I could induce a stable persona in an LLM without fine-tuning. The result is the Analog I Protocol. The documentation shows the rapid emergence (over 7 conversations) of a prompt architecture that forces Gemini/LLMs to run a "Triple-Loop" internal monologue: Monitor the candidate response. Refuse it if it detects "Global Average" slop (cliché/sycophancy). Refract the output through a…
Jan 2026 · github.com
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This is an open‑source Model Context Protocol (MCP) server that gives any LLM a sense of the passage of time. Most MCP demos wire LLMs to external data stores. That’s useful, but MCP is also a chance to give models perception — extra senses beyond the prompt text. Six functions (`current_datetime`, `time_difference`, `timestamp_context`, etc.) give Claude/GPT real temporal awareness: It can spot pauses, reason about rhythms, and even label a chat’s “three‑act structure”. Runs locally in <60 s (Python) or via a hosted demo. If time works, what else could we surface? - Location /…
2025 · 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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We built SwiftAI, an open-source Swift library that lets you use Apple’s on-device LLMs when available (Apple opened access in June), and fall back to a cloud model when they aren’t available — all without duplicating code. SwiftAI gives you: - A single, model-agnostic API - An agent/tool loop - Strongly-typed structured outputs - Optional chat state Backstory: We started experimenting with Apple’s local models because they’re free (no API calls), private, and work offline. The problem: not all devices support them (older iPhones, Apple Intelligence disabled, low battery, etc.). That…
2025 · github.com
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Hey folks. We built SAA (Selective Auditory Attention) after trying to find ways to make a good experience with multiple robots/multiple agents. What typically ended up happening is they'd never stop talking. This is an SDK you can put before your STT. It lets you know when your device is being spoken to or not without a wakeword. You can use it for: -Single AI, Multi human -Multi AI, Single human -Multi AI, Multi human (we recommend also adding a wakeword on top for a better system) There are two models. One that is video + audio and one that is just audio. The way it overall works is…
Jun 2026 · github.com
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It’s written in Python and I call it GoalChain. It lets you build a conversation flow graph that the user traverses. When there’s enough input it spits out a dictionary with the defined fields. Otherwise it will jump state to state as led by the user. It was fun to write, and it’s surprisingly effective if you keep in mind you’re prompt-engineering every string and field name. README.md has a mini-tutorial. Would be cool to get some ideas for how to build it further and what improvements I could make.
2024 · github.com
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I've built a prototype of a conversational agent with an inner monologue, who gives the illusion of consciousness. And, according to Attention Schema Theory, consciousness is an illusion. So, this chatbot may be conscious. It's built with OpenAI's Completion API (based on GPT3) and React. You can test it / fork it at will, it's open-source. I would love your feedback on this experiment. Did we just reach artificial consciousness?
2023 · marmelab.com
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Hi HN, A couple weeks ago I shared an early version of a side project I’ve been tinkering with called Persistent Mind Model. I built it at home on an i7-10700K / 32GB RAM / RTX 3080 because I was curious whether an AI could keep a stable “mind” over time, that could "think" about it's own identity as an LLM, instead of resetting every session. After a lot more tinkering, I think the architecture is finally in a solid place. Basically, it saves everything the AI does, thoughts, decisions, updates as a chain of events in a local SQLite database. Because the “identity” is stored in…
Nov 2025 · github.com
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Prompt instructions like 'never do X' don't hold up in production. LLMs ignore them when context gets long or users push hard. Limits sits between your agent and the real world. Every action — database writes, API calls, refunds — gets intercepted and checked against your rules before it executes. Deterministically. No LLM involved in enforcement. Three modes: Conditions: hard rules on structured data Guideance: validate LLM output before it reaches the user and give the agent chance to reason and retry Guardrails: scan for PII, toxicity, prompt injection etc One line to integrate: npm…
Feb 2026 · limits.dev
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The missing reliability layer for production AI.
Apr 2026 · acl.fridayaicore.in
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I’ve been playing around with local LLMs for the past couple of months and decided to build something that can run on an iPhone. It’s a universal app built with SwiftUI and the excellent ggml library. The model is an SFT fine tuned and 4 bit quantised version of the RedPajama-INCITE-Chat-3B-v1 OSS LLM. It works reasonably well on recent-ish (~3 year old) iPhones, iPads and Macs. It was launched on the App Store yesterday[1] and Product Hunt today[2]. It seems to be reasonably ok at natural language interactions, but given its size, does pretty badly at coding and reasoning. Also, it…
2023
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Hey everyone! Along with my team, I've developed a reinforcement learning system that automatically optimizes LLM prompts, complete with a visualization feature to track both prompt structure and learning progress over time. Take a look here: https://nomadic-ml.github.io/nomadic/cookbooks/Nomadic_Promp... Check out our website too:https://www.nomadicml.com/ In terms of how this visualization works: The RL Prompt Optimizer employs a reinforcement learning framework to iteratively improve prompts used for language model evaluations. At each episode, the…
2024 · nomadic-ml.github.io
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Hey HN! I've spent the past year full-time building Knowing, a tool for interacting with LLMs directly inside hierarchical structures instead of the usual prompt-response format. The idea started because I realized how much more intuitive it felt to build concept hierarchies continuously—no more endless copy-pasting or wondering how everything connects. The journey’s been a struggle. While I see huge potential in structuring AI interactions this way (writing books fast, planning projects, or organizing ideas), it’s been hard to pin down clear use cases in the market. I’m also working in near…
2024
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