ElevenLabs explains how to build meeting transcription products using its Scribe v2 and Scribe v2 Realtime models through its API. Real-time transcription suits live captions and in-meeting bots, while batch transcription suits post-meeting notes and records, with Scribe v2 Realtime reporting 150 ms latency and supporting up to 50 key terms for prompting.
The author suggests installing a coding agent such as Pi or DeepSeek Harness in a cloud computer and allocating unused Code plan quota to it. Grok Bot could then use those tokens to write code and render videos, so leftover quota is not wasted.
LangChain has added three engineering upgrades to Skills in its Deep Agents framework: tool-binding Skills, pinned Skills, and mid-thread reloading. Tool-binding lets a SKILL.md declare tools via metadata.include_tools, so tools are injected only when the Skill is read, and pinned Skills inject full instructions before the next model call, skipping a round trip. Setting skills_metadata to None rescans the Skills library mid-thread without restarting, at the cost of invalidating the cache.
How H-Company Optimizes VLM Serving for Computer Use Agents With NVIDIA Dynamo https://x.com/i/broadcasts/1dxYlandleDJX
Muse, Dots, or other agents with cloud computers can all be used.
The author set up a Grok bot to produce a daily morning AI news video on a schedule, running content collection, code writing, and video rendering entirely on Grok's cloud virtual machine without local computers. The author says the results are quite good and shares the full prompt so others can run the same workflow with their own Grok bot.
Every moved its Slack-based Every Agent from its own OpenClaw fleet to Anthropic's Claude Managed Agents, letting the team focus on building a shared coworker. The newsletter, written by engineer Paridhi Agarwal, covers the gains and trade-offs of stopping self-run agent infrastructure.
LangChain built Restock, a sample office-supply agent that runs in Slack on Managed Deep Agents and pays through Stripe's Link wallet. The agent searches products, builds a cart, and pays over the Machine Payments Protocol, with the user approving the purchase in Slack and the payment in Link. The post uses a pens order at $22.18 to show the flow from request to confirmed order.
Why it matters: The post walks through how an agent handles search, budget limits, Slack review, and Link approval, showing where each control sits outside the model.
CS 329Z: Engineering AI Agents Stanford / Fall 2026 @stanfordnlp https://cs329z.stanford.edu Lectures 4 and 5 are now public! @Diyi_Yang's "Tool Use" https://web.stanford.edu/class/cs329z/slides/lecture04.pdf @Diyi_Yang @michaelryan207 @jyangballin's "Frameworks & Orchestration" https://web.stanford.edu/class/cs329z/slides/lecture05.pdf
A Hugging Face blog author used the ML-Intern agent in HuggingChat to build six small models by writing detailed prompts that specify datasets, base models, baselines, smoke tests, and spending limits. The projects include a citrus disease vision-language model, a Huggy character LoRA, a camera-angle LoRA, a doodle-to-object LoRA, a 0.8B prompt rewriter, and a 4-step distilled Agate model, with total compute cost of about USD 103. Each project's prompts and public models are linked from the post.
Why it matters: The author shows how prompt structure, baselines, smoke tests, and budget caps shape an agent-driven training workflow, with per-project costs given.
🔖 Just used the @GeminiApp + @Google_Tasks to keep me held accountable for reading a different @NewYorker article every night. Really excited for the one about the fraudulent wine dealer (?!) tomorrow - is that even a thing?! Who knew! 👀
One of my favorite PM use cases for Claude is asking "who used <feature> the most last week? make me a artifact of the top 10 by usage, then reach out and schedule 15 min to chat." It's the fastest way to get user feedback!
ask your Grok @Bot to monitor X for user feedback on your products! send feature requests to your issue tracker, bug reports to a cursor cloud agent or project to triage and fix the loop is complete
i love the idea of identicons, giving ephemeral data some identity. now having opus come up with deterministic generative rules for chat icons based on uuid
2/ SWA bounded replay: rebuilding sliding-window KV exactly after a prefix hit means replaying 40 × 128 tokens. Bounded replay reruns only the last 128. In prefill, layers 21–39 run only on each request's last 128 tokens. With CUDA graphs, prefill compute drops 30–40%.
1/ Three weeks from day 0, DeepSeek-V4.1-Flash on vLLM runs 1.9× faster at low concurrency and delivers 5.3× the throughput at 150 TPS per user on @SemiAnalysis_ AgentX. Here is how, with interactive figures you can step through 🧵 https://vllm.ai/blog/2026-10-07-deepseek-v41-flash
Use a compatible driver from @browser_use, @browserbase, @e2b, @daytonaio, or write your own based on the example drivers in the quickstarts. Quickstart: https://github.com/anthropics/claude-quickstarts/tree/main/computer-toolset Docs: https://platform.claude.com/docs/en/agents-and-tools/tool-use/browser-use-sdk
poteto @poteto - is there a way to set up @bot experience in @cursor_ai? asking for a friend who only has access to cursor, not grok bot
Microsoft's Foundry blog guide advises keeping existing Azure Document Intelligence workloads that meet production requirements. It recommends evaluating Azure Content Understanding for high-variation, unstructured, reasoning, RAG, or multimodal document scenarios, and for new cloud OCR or layout workloads.
If you're on API billing, you can set Haiku 5.5's autocompact window to 100K so you stay in the cheaper token pricing tier! It's saved per model so this only applies to Haiku (incl. subagents) > /model haiku > /autocompact 100k
With just 2.5GB VRAM, you can train your own Decision Model using small models like Laya. Training is simply done through a UI interface. Video tutorial and analysis are in our guide. GitHub repo: https://github.com/unslothai/unsloth
@antigravity @stitchbygoogle Watch the full walkthrough: https://goo.gle/4jKK8AV
Google DevRel's James O'Reilly compares two ways Antigravity exposes local MCP tools from Agent Plugins to the model. Eager loading registers each tool as a top-level function with its full schema in every turn's system prompt, which speeds calls but consumes fixed tokens. Lazy loading, the plugin default, exposes tools through a proxy call_mcp_tool and reads schemas on demand, saving baseline context at the cost of an extra discovery step.
Becoming an AI superuser is NOT about knowing where the buttons are - it's a mindset and behavior shift. And the Mastermind is where we get it done. You've got less than 3 more hours to grab the AI Agent Mastermind at $200 off. And then it's full price until we launch on Oct 19 or until seats run out. Our first two cohorts completely sold out. Join us for the third and jump into how you should be working with AI in 2026 (and beyond).
Unsloth released an open-source method to fine-tune LLMs into decision models that run locally, lifting Qwen3.5 0.8B's aggregate accuracy from 20.7% to 74.3% across three decision benchmarks. The team used a Clef head with LoRA (r=64) for one epoch on just 4GB VRAM, with the approach applicable to models such as Qwen3.8 and Gemma 4. A guide and notebooks are available on the Unsloth documentation site and GitHub.
NVIDIA describes using digital twins and AI agents to validate changes to AI factory infrastructure, which combines GPUs, CPUs, switches, DPUs, and SuperNICs with schedulers, orchestration services, security controls, and a fast-changing software stack. The source frames the challenge as confirming that hardware, software, and policies work together for target workloads before deployment. The available excerpt does not give further detail on specific tools or results.
Most language models split text into subwords—words or fragments of words from a fixed vocabulary. This can obscure spelling details across writing systems & split meaningful units in code or math. Byte-level models work directly with the bytes computers use to represent text.
AWS Machine Learning Blog argues that the traditional hours-saved ROI model, built for rule-based RPA, misses most of the value of agentic automation. It proposes an Agentic Value Model covering time savings, exception handling, decision quality, and change resilience, with value counted only when tied to a defined P&L mechanism and owner.
Qlik built Qlik Answers, a natural-language assistant that returns sourced answers from knowledge bases, analytics apps, glossaries, and documents, using Amazon Bedrock for model access. The system routes each question through specialist agents and retrieval on Amazon OpenSearch Service, with Amazon Bedrock Guardrails applied to every request and response. Qlik serves more than 40,000 customers across regions, using Amazon SageMaker AI as an in-Region fallback when models are not yet available on Bedrock.
AWS ran a six-week program pairing non-engineering professionals with mentors and tools like Amazon Bedrock AgentCore and the Strands Agents SDK to build working AI prototypes. Four participants with no engineering background built WealthWise, a multi-agent financial advisory tool with five agents on Amazon Nova models, which won first place. The article says participants who completed the phased program retained three times more practical skills than those in two-day intensive formats.
AI is getting REALLY good at building your wardrobe. I built an app with GPT-6 Astra that picks my outfit every morning based on live weather, with me as the model. It's super simple to do: > Upload a few full-body photos and one face photo > Add your height, sizes, and fit notes > List the colors you like and the ones you refuse > Paste the master prompt and let it build for 1 to 2 hours One less decision every morning :^)
Michael Lynch warns software bloggers against meandering intros, misjudging reader knowledge, assuming readers have read earlier posts, excessive formality, and overreliance on links instead of explaining terminology. He advises that an article should still make sense even if readers click no links. Simon Willison endorses the advice and argues that writing in one's own voice matters as more developers delegate writing to AI.
Learn more about identifying AI-generated content: https://blog.google/innovation-and-ai/models-and-research/google-deepmind/synth-id-ai-content/
Want to check if an image, video, or audio file was made using AI? Here’s how: 1️⃣ Go to https://synthid.com and upload an image, video, or audio file 2️⃣ The portal scans the media to detect if the file contains a SynthID watermark from Google or our partners
Allie K. Miller outlines three agent workflows that work like an executive assistant and need only calendar access. The agent screens junk signups and sends only high-signal email recaps, routes speaking and advising inquiries with org research and a worth-your-time verdict, and builds a living CRM from forwarded emails that flags relevant contacts for follow-up.
Lauren Tan recommends new Grok Bot users first connect regularly used apps such as calendar, Slack, issue tracker, CRM, and Google Drive so the bot has work context and needed tools. She advises starting with one primary bot before adding specialized bots, which can be designed or chosen from the bot marketplace.
Contact center automation uses AI to handle customer support workflows with little or no human intervention, including voice, chat, and email. Unlike traditional IVR systems, AI contact center software understands intent, retrieves customer data, and routes complex cases to human agents. The guide cites Klarna, Rohlik, and Getmobil deployments of ElevenAgents, with Klarna offering voice support to 35 million US customers.
The final post in O'Reilly Radar's four-part post-training series walks readers through implementing the classic ChatGPT pipeline on Qwen2.5-1.5B, covering SFT, reward model training, and PPO. The walkthrough uses torchtune for SFT and verl, a Ray-based RL framework from ByteDance's team, for reinforcement learning. The author says the goal is hands-on understanding rather than reproducing InstructGPT, which took a large team and thousands of GPU-hours.
Trying to have Opus 5.5 build a website that explains Qimen Dunjia (奇门遁甲).
Anthropic added build-eval and hillclimb commands to its claude-api skill for designing evaluations and iteratively improving applications against them. The article covers eval design principles, including production-representative tasks, headroom and low variance, and guards against overfitting through train/test splits. Two examples report results: a customer support benchmark where cost fell to under half while accuracy rose, and a claude-api skill eval that rose from 66% to 88%.
Why it matters: The article gives a concrete workflow for designing evals and hillclimbing without overfitting, with two worked cost and performance examples that show the tradeoffs.