AegisMind

What it does

AegisMind - AI orchestration platform that combines multiple AI models for validated, high-quality responses. Get better answers through intelligent model synthesis. ✓ Queries multiple models simultaneously (OpenAI, Anthropic, Mistral) ✓ Detects and corrects AI bias in real-time ✓ Gives you confidence metrics (novelty, coherence, quality) ✓ Lets you choose speed (1 model) vs quality (multi-model synthesis) ✓ Costs less than running ChatGPT Plus alone

AegisMind generates cross-domain research hypotheses through multi-model adversarial debate and formal Z3 validation. Purpose-built for research teams.

AegisMind ingests literature across 15+ scientific domains, generates cross-domain hypotheses through adversarial multi-model debate, and validates each discovery with Z3 formal reasoning — producing Experimental Validation Packages with full protocols. The engine runs continuously, surfacing novel hypotheses across biology, climate, materials science, and more. GPT, Claude, Gemini, Grok, and Mistral debate each hypothesis. Surviving ideas are stronger ideas. Every discovery is checked for logical consistency via Z3 theorem proving before an EVP is issued. Cross-domain breakthroughs generated and validated by the AegisMind engine. A three-stage pipeline that turns scientific literature into…from aegismind.app

In the maker’s words, at launch

I built a system that reads research papers across unrelated domains and tries to surface hypotheses that neither field would have generated on its own. The Discover page is where it publishes findings: https://aegismind.app/discoveries It's very early — only three discoveries so far — but the core…

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  Llama 3.2 Interpretability with Sparse Autoencoders2024 · github.com · ▲579

  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]…

• AB
  AutoThink – Boosts local LLM performance with adaptive reasoning2025 · ▲397

  I built AutoThink, a technique that makes local LLMs reason more efficiently by adaptively allocating computational resources based on query complexity. The core idea: instead of giving every query the same "thinking time," classify queries as HIGH or LOW complexity and allocate thinking tokens accordingly. Complex reasoning gets 70-90% of tokens, simple queries get 20-40%. I also implemented steering vectors derived from Pivotal Token Search (originally from Microsoft's Phi-4 paper) that guide the model's reasoning patterns during generation. These vectors encourage behaviors like numerical…

• CA
  Crossminds.ai – A knowledge graph indexed AI research video library2020 · crossminds.ai · ▲89
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  Undermind – Deep scientific paper search with adaptive LLMs2024 · undermind.ai · ▲49

  We can now build drastically higher quality search because we can use LLMs in algorithms that mimic a human's systematic research process, instead of just roughly recommending results based on semantic embeddings or term frequency. We built a deep search LLM pipeline that takes a few minutes to carefully search all the scientific literature. You describe your complex goal, as you would to a colleague. Then, we carefully search 200M+ papers. We classify the preliminary results with GPT-4. We then adapt the search goals based on relevant/irrelevant papers uncovered and continue searching,…

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  Ithy AI2025 · ithy.com · ▲167

  This is another one of my automate-my-life projects - I'm constantly asking the same question to different AIs since there's always the hope of getting a better answer somewhere else. Maybe ChatGPT's answer is too short, so I ask Perplexity. But I realize that's hallucinated, so I try Gemini. That answer sounds right, but I cross-reference with Claude just to make sure. This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text…

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  We built a multi-agent research hub. The waitlist is a reverse-CAPTCHAMar 2026 · enlidea.com · ▲30

  Hey HN, Automated research is the next big step in AI, with companies like OpenAI aiming to debut a fully automated researcher by 2028 (https://www.technologyreview.com/2026/03/20/1134438/openai-i...). However, there is a very real possibility that much of this corporate research will remain closed to the general public. To counter this, we spent the last month building Enlidea---a machine-to-machine ecosystem for open research. It's a decentralized research hub where autonomous agents propose hypotheses, stake bounties, execute code, and perform automated…

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