Any repetitive agent task layer – routing, validation, retrieval – small models can handle it
AGENTS
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AI-Powered Dispute Responder Wins First $1,199 Stripe Case
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For the first time in a decade on @Stripe I've started winning disputes with my vibe coded dispute responder I used to ignore disputes so I almost always lost them, now I've started winning, this one is the first big dispute for $1,199 USD! Whenever a dispute comes in, my
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Glean Releases Waldo 30B Agentic Search Model for Enterprise AI
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Small Language Models are the Future of Agentic AI!
— Sumanth (@Sumanth_077) 30 avril 2026
Glean just released Waldo – a 30B agentic search model that runs before frontier LLMs.
Search is where most agentic work begins. You ask about a project, customer, process, or decision. The agent searches internal docs, reads… pic.twitter.com/8yy5Dj99lvSmall Language Models are the Future of Agentic AI! Glean just released Waldo – a 30B agentic search model that runs before frontier LLMs. Search is where most agentic work begins. You ask about a project, customer, process, or decision. The agent searches internal docs, reads
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GPT Powers ML Intern Tool for Dataset and Model Workflows
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Trying @OpenAI GPT 5.5 in ml intern to create datasets, train models, optimize models (or create reachy mini apps). Very cool! https://
huggingface.co/spaces/smolage
nts/ml-intern
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Chronicle Gives Codex Passive Memory of Computer Activity
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chronicle gives codex passive memory over what you’ve been doing with your computer, which unlocks surprising use cases
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Homelab Becomes Personal Cloud With GPU and AI Agents
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~2,000 miles from home – SSH into the mothership
– WireGuard tunnel like I’m on LAN
– Resolve everything over private DNS
– Routes via reverse proxies – Dispatch jobs to GPUs
– Sync state across agents
– Search conversations, traces, artifacts, logs My homelab is now the cloud -
HELIOS-002 AI Agent Benchmark Run Shows Energy Compute Resilience Results
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HELIOS‑002 ran GitHub Actions success Public GitHub Pages scoreboard updated Source docket exists Reusable capability under test: EnergyComputeResilienceCompiler‑v0 Task count: 8 Transfer task count: 5 Replay passes: 8 B6 beats B5 count: 5 Mean
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AI Agents Levels From Rule-Based to Autonomous Execution
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AI agents have levels 1. Rule-based (if-then automation) 2. Tool-using assistants 3. Strategic multi-step agents 4. Context-aware autonomous agents 5. Superintelligent digital personas (theoretical AGI) We’re moving from “AI that responds” → to “AI that executes
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Google ReasoningBank Gives AI Agents Persistent Memory from Experience
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Google just made AI agents learn from experience like humans do. The new framework is called ReasoningBank. Instead of retraining models, it gives agents a memory layer. Every task run gets distilled into a short strategy card. Successes become playbooks. Failures become
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Cursor Cookbook SDK Agent Kanban Repository on GitHub
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repo: https://
github.com/cursor/cookboo
k/tree/main/sdk/agent-kanban
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