"The priority for defenders is to start building now: the scaffolds, the pipelines, the maintainer relationships, the integration into development workflows. The models are ready. The question is whether the rest of the ecosystem is." aisle.com/blog/ai-cybersecur…
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Managed Agents: Fast Development and Robust Production Deployment
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I've found Managed Agents to somehow be both the fastest way to hack together a weekend agent project and the most robust way to ship one to millions of users.
— Alex Albert (@alexalbert__) 8 avril 2026
It eliminates all the complexity of self-hosting an agent but still allows a great degree of flexibility with setting… https://t.co/DFcEVauM8TI've found Managed Agents to somehow be both the fastest way to hack together a weekend agent project and the most robust way to ship one to millions of users. It eliminates all the complexity of self-hosting an agent but still allows a great degree of flexibility with setting
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Runway AI Web App and API Now Available for Builders
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Available to try now on the web app and to start building via the Runway API. Learn more:
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Cognitive Engines Hard-Coded Pipelines Over Symbolic Reasoning
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he literallt argued that symbols were like phlogiston (once) and that using them was like using gas engines when we should just electric. the cognitive engines as you call are now hard coded with pipelines to use tools and massive examples for how. not gonna continue the
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Hermes Ecosystem Map: Comprehensive Guide to All Projects
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Introducing the Hermes Ecosystem Map I was an early user of Hermes Agent from @NousResearch and have been a power user ever since But as the ecosystem has grown, its been hard to keep up, so I did some research: > Scraped every GitHub repo related to Hermes > Filtered out repos that looked unfinished or had 0 stars > Built an ecosystem map of everything created and organized it all by category > Published a website where you can see all the projects with star ratings, and if you hover over you get a short description and link to the repo Then I had Claude run a security check on every repo to exclude anything that looked sus Link is in the replies, and also open sourced the repo so feel free to submit PRs if you see anything missing Oh, and the repo has a /research folder that includes a scrape of everything I could find that's been published on Hermes – you can clone that and add it to your personal knowledge base / wiki
→ View original post on X — @scobleizer, 2026-04-08 17:54 UTC
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OpenClaw Adds Lightweight Inferrs Provider for AI Inference
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Also check out https://
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OpenClaw Adds Inferrs Support for Efficient Local Model Inference
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Some folks try to spin a narrative that I don't like local models, meanwhile I spent a lot of time making it easy to use OpenClaw with them. Latest release adds support for inferrs, which is a new super efficient TurboQuant inference server: https://
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AIMock: Universal Mock Server for AI Agent Stack
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✨ Introducing AIMock – one mock server for your entire agentic stack!
— CopilotKit🪁 (@CopilotKit) 8 avril 2026
Your AI app calls LLMs, MCP tools, A2A agents, vector DBs, search, reranking, and moderation. If any of those are live in your tests, you've got flaky CI and burned tokens.
No tool mocked all of it. So we… pic.twitter.com/4k3fYPtQr5✨ Introducing AIMock – one mock server for your entire agentic stack! Your AI app calls LLMs, MCP tools, A2A agents, vector DBs, search, reranking, and moderation. If any of those are live in your tests, you've got flaky CI and burned tokens. No tool mocked all of it. So we built one. One package. One port. Plus drift detection and record & replay that nobody else ships. Zero dependencies. Open source. Mock with one command: `pnpm add @copilotkit/aimock`
→ View original post on X — @scobleizer, 2026-04-08 17:27 UTC
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EvoKernel: Self-Evolving AI Agent for NPU Code
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How can LLMs code for cutting-edge hardware when there's almost no training data?
— 机器之心 JIQIZHIXIN (@jiqizhixin) 8 avril 2026
Researchers from Shanghai Jiao Tong University, Shanghai AI Lab, and MemTensor present EvoKernel!
This self-evolving AI agent teaches LLMs to write code for new, data-scarce hardware. It uses a… pic.twitter.com/dHIJZlxYTdHow can LLMs code for cutting-edge hardware when there's almost no training data? Researchers from Shanghai Jiao Tong University, Shanghai AI Lab, and MemTensor present EvoKernel! This self-evolving AI agent teaches LLMs to write code for new, data-scarce hardware. It uses a clever memory system to prioritize and learn from the most valuable coding experiences, continually refining its drafts. EvoKernel boosts code correctness for NPU kernel synthesis from a mere 11% to an impressive 83% and speeds up programs by 3.6x over initial drafts! Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis Project: evokernel.zhuo.li Paper: arxiv.org/abs/2603.10846 Our report: mp.weixin.qq.com/s/0TOzZ_rZn… 📬 #PapersAccepted by Jiqizhixin
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Build AI Agents from Scratch: 9-Step Roadmap for Production
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How to build AI agents from scratch (9 steps): 1. Purpose & scope 2. I/O schemas 3. System instructions 4. Reasoning + tools 5. Multi-agent orchestration 6. Memory & context 7. Multimodal 8. Structured outputs 9. UI / API Ship agents that do work, not just talk. 🤖⚡️ Where are you on this roadmap? Credit: @getintoai #AIAgents #AgenticAI #GenAI #LLM
→ View original post on X — @ingliguori, 2026-04-08 17:25 UTC