CodeGraph turns any project folder into a local, queryable Knowledge Graph 1️⃣ Parses 20+ languages locally 2️⃣ Uses a local model to find hidden relationships. 3️⃣ Exposes the graph via an MCP medium.com/agentic-builders/…? #CodeGraph #AIAgents #SoftwareEngineering #MCP #Claude #Cursor
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Protecting AI Agents: How Claw Patrol Safeguards Your Systems from Accidental Damage and Attacks #ai #safety #agentic medium.com/agentic-builders/…
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The best enterprise agents in 2026 use a 3-layer stack: 1️⃣ Skills (Domain Knowledge) 2️⃣ MCP (Secure Connectivity) 3️⃣ CLI (Token-Efficient Execution) Stop overloading your LLM. Read the full architecture breakdown here: medium.com/agentic-builders/…
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Ana Bildea retweeted
Amazing. No words to describe this tune. Emotional and to the point. New Iranian LEGO movie : We Share the Same Pain 💔 😭🥹 via Brick Beat Battalion
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First systematic map of "AI Agent Traps" adversarial web content engineered to hijack autonomous agents. 6 attack classes targeting every layer of an agent's operating cycle: How to Defend Against AI Agent Traps: #ai #agents #Security medium.com/agentic-builders/…
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Agent Design Patterns for Long-Running AI Agents medium.com/agentic-builders/… #ai #agents #llm #production #google
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How to Use Graphify: Turn Any Folder Into a Knowledge Graph medium.com/agentic-builders/… #aiagents #graph #knowledgegraph #ai
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How much budget did you actually approve for your Agentic AI strategy? Not the roadmap. Not the slide deck. The actual signed check. That number tells you everything. #AIStrategy #EnterpriseAI #AgenticAI #AI #Leader
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Ana Bildea retweeted
i just published a short article on building applications w/ Claude. captures a few lessons from my own work and many discussions with others at Anthropic. claude.com/blog/harnessing-c…
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Ana Bildea retweeted
"Mixture-of-Depths Attention" This paper teaches a Transformer to attend not just across tokens, but also to depth KV from its earlier layers. That helps recover shallow-layer signals that standard residual stacking tends to dilute, improving performance with only a small extra compute cost. Similar idea to Kimi’s Attention Residuals, but MoDA modifies the attention module itself, while AttnRes changes the residual/depth aggregation path.
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