The universe is a combinatorial dance.
AI
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Hermes with Discord on a local 3070 VM Qwen 3.5 not on bingo card
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Hermes w/ Discord on a local Qwen 3.5 9B (Unsloth UD-IQ3_XXS) running on an unused 3070 VM I used for game streaming via Moonlight back in the day was not on my bingo card for 2026
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OpenClaw costs scaling from thousands to affordable monthly pricing
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Magical OpenClaw experiences that use frontier models cost $300-1,000/day today, heading to $10,000/day and more. The future shape of the entire technology industry will be how to drive that to $20/month.
→ View original post on X — @ceobillionaire, 2026-04-07 06:09 UTC
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CEO Tests MemPalace AI Memory System with 79 Employees
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We at The Zero-Human Company have been testing MemPalace by the amazing @bensig and Milla Jovovich and are absolutely blown away!
— Brian Roemmele (@BrianRoemmele) 7 avril 2026
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… https://t.co/Y53xJb0ucG pic.twitter.com/Tp6tQSZPNOWe 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
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Grok’s Unique Value: Native Twitter Data Access at 2%
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Grok at 2% purely for Twitter context is exactly the right use. Native platform data access is the one thing it does that nothing else can replicate.
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Consumption-Based Pricing for AI Agents: Enterprise vs Consumer Dilemma
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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.
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AI study analyzes 35000 small accounts for aligned news
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It actually helps small accounts more than big ones. But my AI studies 35,000 small accounts here on X to build this: https://
alignednews.com/ai There is always a way -
Model-Level Compaction Targets Configuration for AI Agents
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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?
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$125B Training Costs: Capital Structure Depends on Perfect Timing
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$125B in training costs by 2029 means the capital structure only works if the timeline is right. No soft landing if it's wrong.
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Python Data Analyst Project Ideas for Data Science and Big Data
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Python #DataAnalyst Project Ideas by @Python_Dv #DataScience #BigData
→ View original post on X — @ronald_vanloon, 2026-04-07 05:50 UTC