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that's pretty much what dynamic workflows is innit
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Replying to @nietras1
Yeah, but that's exactly what I would like to avoid. My repos today are using a single external shared workflow that I can modify to propagate changes. It's not like I have one or 2 repos to update. I will need to automate the update of all these files. Defeating the purpose of shared workflows.
待てよ。するってーと、Dynamic Workflowsを組み込めば、本家とニアリーイコールな状態にできるのでは? 明日やってみよう
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Peter Nuon retweeted
Infinite canvas for Claude Code workflows github.com/DeadWaveWave/open…
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🪼 HybridClaw v0.23.0 is here! 🦞 "Fire and Ice" 📹 Blink Skill — Packaged workflows for Blink camera and video-doorbell account setup, inventory, clip retrieval, guarded arm/disarm controls, and artifact-backed media. Your coworker keeps an eye on the front door. 💡 Philips Hue Skill — Local CLIP v2 Bridge setup, light/group/scene reads, guarded changes, and LAN policy diagnostics. Dim the lights, set the scene, all from chat. 🗺️ Fleet Topology Console — New /admin/fleet-topology for A2A instance identity and trust-ledger peer management. See your whole agent fleet at a glance. 🔐 Write-Only Secrets Manager — New /admin/secrets lets you manage secrets from the browser without ever returning cleartext values back to it. Set it, never leak it. 💻 Richer Chat Code Blocks — Web chat now syntax-highlights completed code blocks with language labels and copy controls. 🐞 Sentry Reporting — Optional Sentry integration captures startup/runtime failures with release naming, secret redaction, and graceful shutdown flushes. 🎛️ Plus immediate agent hatching from web chat, heartbeat_poll scheduler jobs that skip empty heartbeats, wrapped /second-opinion terminal output, chat-friendly /env and /secret skill setup, sanitized Unicode before provider prompts, and trace exports that preserve hash IDs under redaction. Full details → github.com/HybridAIOne/hybri…

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Replying to @Veltrxai
This is such a simple but clever idea. The best AI workflows aren't always bigger models, they're better feedback loops. QuickClaw all benefit from having cheap signals that tell you when context quality is degrading. A dead canary is cheaper than a wasted afternoon.
AI automation is now within reach for small businesses, but the right platform depends on your workflows, data, budget, and risk tolerance. Our new guide compares leading AI automation tools and explains how to adopt them without creating chaos. intersog.co.il/blog/comprehe… #AI
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Replying to @trikcode
There are definitely thousands of people including myself who have used AI to optimise workflows, save manual work hours etc and thus made money more efficiently than they otherwise would
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Another GPT-5.6 leak Rumors say OpenAI could drop GPT-5.6 on June 23. > Its 3× cheaper than Fable > Up to 1.5M token context > Stronger agentic coding workflows > Direct competition with Claude style systems The timing is interesting. Some think OpenAI is waiting for June 23 when many Fable users are forced onto premium plans. If true... Will GPT-5.6 cook Fable? Lets discuss in comments.
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fábio retweeted
Coding agent spawning parallel LLM workflows github.com/opendev-to/opende…
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AI Research Intern – Lexsi Labs Commitment: Full-time internship (6 months; potential extension or full-time offer) Start Date: Rolling About Lexsi Labs Lexsi Labs is one of the leading frontier labs focusing on building aligned, interpretable and safe Superintelligence. Most of the work involves on creating new methodologies for efficient alignment, interpretability lead-strategies and tabular foundational model research. Our mission is to create AI tools that empower researchers, engineers, and organizations to unlock AI's full potential while maintaining transparency and safety. Our team thrives on a shared passion for cutting-edge innovation, collaboration, and a relentless drive for excellence. At Lexsi.ai, everyone contributes hands-on to our mission in a flat organizational structure that values curiosity, initiative, and exceptional performance. As a research intern at Lexsi.ai, you will be uniquely positioned in our team to work on very large-scale industry problems and push forward the frontiers of AI technologies. You will become a part of the unique atmosphere where startup culture meets research innovation, with key outcomes of speed and reliability. What You’ll Do We work on multiple frontier research ideas and challenges. If you are selected, you would be working on one of these following areas. Collaborate closely with our research and engineering teams on one of the areas: Library Development: Architect and enhance open-source Python tooling for alignment, explainability, model alginment, uncertainty quantification, robustness, and machine unlearning Explainability & Trust: Improve and find new observations using our and other SOTA XAI techniques (DLB, LRP, SHAP, Grad-CAM, Backtrace) across text, image, and tabular modalities to understand and present new model interpretability. Mechanistic Interpretability: Probe internal model representations and circuits—using activation patching, feature visualization, and related methods—to diagnose failure modes and emergent behaviors. Uncertainty & Risk: Develop, implement, and benchmark uncertainty estimation methods (Bayesian approaches, ensembles, test-time augmentation) alongside robustness metrics for foundation models. Tabular Foundational Models (Orion): Work with our leading Tabular Foundational Model team to improve and launch new tabular foundational model architectures and work on our leading opesource library TabTune. Reinforcement Learning: Explore new ideas and algorithm around RL and our new RL fine-tuning library. Research Contributions: Author and maintain experiment code, run systematic studies, and co-author whitepapers or conference submissions. General Required Qualifications Strong Python expertise: writing clean, modular, and testable code. Theoretical foundations: deep understanding of machine learning and deep learning principles with hands-on experience with PyTorch. Transformer architectures & fundamentals: comprehensive knowledge of attention mechanisms, positional encodings, tokenization and training objectives in BERT, GPT, LLaMA, T5, MOE, Mamba, etc. Version control & CI/CD: Git workflows, packaging, documentation, and collaborative development practices. Collaborative mindset: excellent communication, peer code reviews, and agile teamwork. Preferred Domain Expertise (Any one of these is good) : Explainability: applied experience with XAI methods such as DLB, SHAP, LIME, IG, LRP, DL-Bactrace or Grad-CAM. Mechanistic interpretability: familiarity with circuit analysis, activation patching, and feature visualization for neural network introspection. Uncertainty estimation: hands-on with Bayesian techniques, ensembles, or test-time augmentation. Quantization & pruning: applying model compression to optimize size, latency, and memory footprint. LLM Alignment techniques: crafting and evaluating few-shot, zero-shot, and chain-of-thought prompts; experience with RLHF workflows, reward modeling, and human-in-the-loop fine-tuning. Tabular Foundational Models: Should have used or improved TFMs like Orion, TabPFN, TabICL etc Post-training adaptation & fine-tuning: practical work with full-model fine-tuning and parameter-efficient methods (LoRA, adapters), instruction tuning, knowledge distillation, and domain-specialization. Additional Experience (Nice-to-Have) Publications: contributions to CVPR, ICLR, ICML, KDD, WWW, WACV, NeurIPS, ACL, NAACL, EMNLP, IJCAI or equivalent research experience. Open-source contributions: prior work on AI/ML libraries or tooling. Domain exposure: risk-sensitive applications in finance, healthcare, or similar fields. Performance optimization: familiarity with large-scale training infrastructures. What We Offer Real-world impact: address high-stakes AI challenges in regulated industries. Compute resources: access to GPUs, cloud credits, and proprietary models. Competitive stipend: with potential for full-time conversion. Authorship opportunities: co-authorship on papers, technical reports, and conference submissions. apply:app.screenloop.com/careers/a…
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Replying to @alexkaybuilds
That feeling of losing track of time while building is hard to replace. 😄 Tools changed, workflows changed, but the obsession stays the same.
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あああああ、ここでDynamic Workflowsを使ってサブエージェントのモデルをタスクに応じてsonnetやHaikuに変えるのかぁぁぁ ってことは、タスクによって推論モデルを使わずにモデルを切り替えることで速度を出してたのか 理解したっ よく出来てるなぁ。すげーシステムだ
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Oh I definitely don’t want to handle deliberately myself. For the system, I basically need an API that exposes create and edit for workflows/rules etc so we can do everything without using the UI and keep everything in sync easily. Mostly because besides lifecycle marketing and broadcasts, we use email for all learner progress notifications etc. and it also needs to be in sync with push notifications and other delivery methods. But right now, I’m leaning more towards using a full Resend setup than handling branching and workflow logic on our side.
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Fiverr reports a 938% surge in demand for Claude Code specialists. n8n automation searches are up 125%, while AI voice agent searches rose 49%. To be honest the shift is clear. Businesses aren’t buying prompts anymore, they’re buying delegation. They want systems, workflows, and automation that keep working even when nobody is watching. The real challenge isn’t building the automation. It’s making sure it actually runs, scales, and delivers results. That’s where most automation projects succeed or fail.
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Changlin Guo retweeted
MATLAB R2026a has new features for Economists 👇 🤝 Integrate MATLAB into Agentic AI Workflows (as shown) to review macroeconomic model code, debug state-space and forecasting workflows, and generate econometric analysis. ...and More! 🔗 spr.ly/6015B87ADh
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This is not going to age well. I'm seeing tangible productivity gains in everything from bespoke fabrication workflows to contracts management and logistics. The ability to roll custom software per employee is, in my estimation, revolutionary.
Replying to @emergenteffects
It isn’t ‘just 20-30pc’, outside software development, it’s zero or negative. Two banks just spent a billion with nothing to show for it. The public is far smarter than you give them credit for.
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Lei Li retweeted
Check out Awesome Vibe Research by @ModelScope2022 : a curated repo for AI-assisted scientific research workflows, covering agents, tools, skills, and best practices across the full research lifecycle. Glad to see PaperBanana included in the visualization section! github.com/modelscope/Awesom…
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Yash Singhal Story Teller | Brand Identity Creator retweeted
🚀 AI & No-Code are making innovation accessible to everyone. Learn to build apps, automate workflows, and create opportunities—without coding. Designed for students, professionals, job seekers & business owners. Learn • Build • Automate • Grow #AI #NoCode #Automation
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Builders, where are you? 👋 Whether you're building: 🧠 AI Agents ⚡ SaaS Products 🚀 Tech Startups 📲 Automation Workflows 🌐 Web Applications 💻 Developer Tools I want to hear your story. 👇 Share: ✅ What you're building ✅ Your target audience ✅ A link if it's live Let's discover cool products and connect with fellow builders. 🚀🔥 #StartupCommunity #BuildInPublic #AI #SaaS #WebApps #Entrepreneurship
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