Emerging Intelligence

Achievement Unlocked: Maxed out Fable 5. 👑 We’ve hit our usage limits for the cycle. Time to step away from the screen for a bit!

Stop dumping raw history into your AI Agent's context. It's expensive and confuses the model 📉 TencentDB Agent Memory provides local long-term memory for AI Agents via a 4-tier progressive pipeline. It replaces flat vector storage with a layered architecture to improve reasoning 🧠 ✨ Key features: • Symbolic short-term memory using Mermaid graphs to condense logs 📉 • 4-tier long-term memory layering (Conversation → Atom → Scenario → Persona) 🏛️ • Deterministic drill-down paths for full traceability back to raw logs 🔍 • Local-first execution with zero external API dependencies 💻 Engineers building long-horizon agents can cut token usage by up to 61% 💰. Developers get higher task success rates by eliminating context overload 🚀.

1/ 🧵 Google DeepMind just released "From AGI to ASI" A report mapping how AI could keep progressing beyond human-level intelligence, and what might slow it down. Key takeaways below 👇

Automating the grueling cycle of hypothesis, experimentation, and paper writing is the next frontier of research 🧪 The AI Scientist-v2 is a generalized end-to-end agentic system for automated scientific discovery. It uses agentic tree search to generate hypotheses and write peer-review quality papers. ✨ Key features: • Agentic tree search for open-ended exploration 🌲 • Autonomous hypothesis generation and data analysis • Integration with Semantic Scholar for literature search 📚 • End-to-end pipeline from ideation to manuscript writing • Support for OpenAI, Gemini, and Claude models Helps ML researchers explore a broader range of hypotheses quickly 🚀. Reduces the manual effort required for initial scientific drafting.

For every 10 likes, I will make it more over-engineered.

Most AI tools help researchers brainstorm or code, but they still require heavy human supervision to actually discover new knowledge. The AI Scientist is a comprehensive system for fully automated scientific discovery. It allows foundation models to independently generate ideas, run experiments, and write full papers. ✨ Key features: • End-to-end automation from hypothesis to paper • Supports frontier models like GPT-4o and Claude 3.5 Sonnet • Includes starter templates for NanoGPT, 2D Diffusion, and Grokking • Automated LLM-generated paper reviews • Flexible framework for creating custom research templates Researchers can accelerate the exploration of hypothesis spaces. Developers can test the boundaries of LLM autonomy in scientific domains. Open-ended discovery becomes scalable through automated experimentation.

Stop guessing who the best candidate is based on a PDF. Get a fair, data-driven score for every resume. Hiring Agent is an AI-powered pipeline that transforms resume PDFs into structured data and objective scores. It integrates LLMs and GitHub signals to evaluate technical talent. ✨ Key features: • PDF to Markdown extraction using PyMuPDF • Structured JSON parsing via Jinja templates • Automatic GitHub profile and repository enrichment • Fairness-constrained scoring with evidence and deductions • Support for local LLMs via Ollama or Google Gemini Hiring managers get explainable, objective evaluations instead of gut feelings. Developers can run the entire pipeline locally for total data privacy.

Stop using generic AI prompts and start using a team of specialized experts built for production workflows. The Agency is a collection of meticulously crafted AI agent personalities with deep domain expertise. It provides specific roles and processes instead of generic templates. ✨ Key features: • Specialized agent roles across engineering, security, and marketing • Personality-driven communication styles and unique voices • Deliverable-focused workflows for real code and measurable outcomes • Native app for easy installation and auto-updates • Broad support for tools like Claude Code, Cursor, and Gemini CLI Developers can instantly assemble a 'dream team' of AI specialists for any project. Reduces time spent prompt engineering by using battle-tested agent roles.

Opus 4.8 is expensive or reasonably priced.

276,000 KPMG employees across 138 countries now have Claude as their AI co-worker. This is enterprise AI at a scale we've never seen before. The Big 4 are all-in. 🌍 #KPMG #Claude #EnterpriseAI

You could check the paper. arxiv.org/pdf/2605.15156

Karpathy is joining the Anthropic team. What are your thoughts on this??

TradingAgents: Multi-Agents LLM Financial Trading Framework

It looks like a disadvantage but it looks like simple retrieval an search algorithms with agentic will beat the complex approaches.

Benchmarked FlexSearch (pure JS, forward tokenizer) across file sizes. Indexing 1 MB of text takes ~50 ms and adds ~6 MB of RSS. Sub-millisecond at 1 KB. Memory overhead is roughly 6× the raw content. 🔍 #FlexSearch #JavaScript #FullTextSearch