AI Dynamics

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  • AI Monitoring Tool Claims Superiority Over Claude Access

    By

    @scobleizer

    –

    My answer: I built this with AI to watch the entire AI community and have a way better AI that you don’t have access to yet: https://
    alignednews.com/ai In other words I can do better work than anyone who uses Claude.

    → View original post on X — @scobleizer

  • Algorithmic Greenwashing: Lessons from Building an AI Agent
    Algorithmic Greenwashing: Lessons from Building an AI Agent

    By

    @gp_pulipaka

    –

    Algorithmic Greenwashing: Lessons from Building an AI Agent! #BigData #Analytics #AI #MachineLearning #DataScience #IoT #IIoT #Python #RStats #TensorFlow #JavaScript #ReactJS #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode geni.us/Algorithmic-Green

    → View original post on X — @gp_pulipaka, 2026-04-07 06:26 UTC

  • AI Social Contract Debate Undermined by Human Oversight Requirements

    By

    @aihighlight

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    The social contract conversation might eventually be necessary. It's just hard to take seriously when the machines making the case for it still need a human watching the terminal.

    → View original post on X — @aihighlight

  • CEO Tests MemPalace AI Memory System with 79 Employees
    CEO Tests MemPalace AI Memory System with 79 Employees

    By

    @ceobillionaire

    –

    We at The Zero-Human Company have been testing MemPalace by the amazing @bensig and Milla Jovovich and are absolutely blown away! It is a freaking masterpiece and we have deployed it to 79 employees at the company. Each worker will be testing and expanding on MemPalace. I will have a lot to say about how we are using it and how you should to. Ben Sigman (@bensig) My friend Milla Jovovich and I spent months creating an AI memory system with Claude. It just posted a perfect score on the standard benchmark – beating every product in the space, free or paid. It's called MemPalace, and it works nothing like anything else out there. Instead of sending your data to a background agent in the cloud, it mines your conversations locally and organizes them into a palace – a structured architecture with wings, halls, and rooms that mirrors how human memory actually works. Here is what that gets you: → Your AI knows who you are before you type a single word – family, projects, preferences, loaded in ~120 tokens → Palace architecture organizes memories by domain and type – not a flat list of facts, a navigable structure → Semantic search across months of conversations finds the answer in position 1 or 2 → AAAK compression fits your entire life context into 120 tokens – 30x lossless compression any LLM reads natively → Contradiction detection catches wrong names, wrong pronouns, wrong ages before you ever see them The benchmarks: 100% recall on LongMemEval — first perfect score ever recorded. 500/500 questions. Every question type at 100%. 92.9% on ConvoMem — more than 2x Mem0's score. 100% on LoCoMo — every multi-hop reasoning category, including temporal inference which stumps most systems. No API key. No cloud. No subscription. One dependency. Runs on your machine. Your memories never leave. MIT License. 100% Open Source. github.com/milla-jovovich/me… Community note: The claimed 100% LongMemEval score uses targeted fixes for the 3 failing questions and LLM reranking (held-out score: 98.4%). The 100% LoCoMo score uses top-k=50 exceeding session count with reranking (honest top-10 no rerank: 88.9%). github.com/milla-jovovich… — https://nitter.net/bensig/status/2041236952998171118#m

    → View original post on X — @ceobillionaire, 2026-04-07 06:08 UTC

  • Consumption-Based Pricing for AI Agents: Enterprise vs Consumer Dilemma

    By

    @aihighlight

    –

    The first company to move to consumption-based pricing for agents wins the enterprise market and loses the consumer narrative simultaneously. Neither wants to blink first.

    → View original post on X — @aihighlight

  • Model-Level Compaction Targets Configuration for AI Agents

    By

    @steipete

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    Oh, currently there's already a way to set lower compaction targets per model, you want an override per agent as well? Wouldn't setting that per-model be what you need?

    → View original post on X — @steipete

  • Hermes-Agent integrates Karpathy’s LLM-Wiki for knowledge management
    Hermes-Agent integrates Karpathy’s LLM-Wiki for knowledge management

    By

    @scobleizer

    –

    The LLM Wiki by @karpathy is now a built-in skill in Hermes-Agent, give it a try Teknium (e/λ) (@Teknium) Hermes Agent now comes packaged with Karpathy's LLM-Wiki for creating knowledgebases and research vaults with Obsidian! In just a short bit of time Hermes created a large body of research work from studying the web, code, and our papers to create this knowledge base around all of Nous' projects. Just `hermes update` and type /llm-wiki <research x> in a new message or session to begin 🙂 github.com/NousResearch/herm… — https://nitter.net/Teknium/status/2041370915012071577#m

    → View original post on X — @scobleizer, 2026-04-07 04:53 UTC

  • Milla Jovovich co-develops record-breaking AI memory system MemPalace
    Milla Jovovich co-develops record-breaking AI memory system MemPalace

    By

    @ceobillionaire

    –

    Milla Jovovich has a Github 😏 She's co-developed the highest-scoring AI memory system ever benchmarked with @bensig Totally free and OSS. What a boss. Ben Sigman (@bensig) Excited to announce a new open-source, free-to-use memory tool I have been developing with my good friend @MillaJovovich. The project is called MemPalace and it is an agentic memory tool that scored 100% on LongMemEval – the industry standard benchmark for memory… this is higher on than any other published results – free or paid – and it is available now on GitHub. You can check out Milla’s video about it on her Instagram. I’ll also put some links in the comments below – please try it out, critique it, fork it, contribute to it – and join our discord. — https://nitter.net/bensig/status/2041229266432733356#m

    → View original post on X — @ceobillionaire, 2026-04-07 04:48 UTC

  • Training AI Agent Takes Two Months Success

    By

    @scobleizer

    –

    Thanks! It took two months to train my agent to do it.

    → View original post on X — @scobleizer

  • Gemma 4 Reasoning Adapter Trained on Opus Released Open Source
    Gemma 4 Reasoning Adapter Trained on Opus Released Open Source

    By

    @clementdelangue

    –

    🤯 GEMMA 4 + OPUS 4.6 REASONING DROPPED @kaiostephens goal: produce a Gemma 4-31B reasoning adapter trained only on Opus reasoning 🧠 What the model is: 🧬 Tiny QLoRA adapter on Gemma 4 31B-it 📊 Fine-tuned on ~1,900 curated Opus Examples ⚡ Trained in ~1 hour on a single GH200 GPU 📖 Fully open Apache 2.0 What it does: ✨ Boosts overall quality, coherence, and personality 🧮 Stronger math, code, and Opus problem solving 💬 More refined, thoughtful responses 🏠 Built for local agents, workflows, and heavy daily Vs base Gemma 4 31B: 📐 Same efficient base model, no extra size or speed 📈 Noticeable step up in real-world depth and quality 💪 Base was already strong this levels It up! Grab the adapter here 👇🏻 huggingface.co/kai-os/gemma4…

    → View original post on X — @clementdelangue, 2026-04-07 03:51 UTC