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Oct 8

TodayOct 8Thu51 items
  2. Allie K. MillerAI score14

    Perhaps my most unhinged workflow: I was talking nonstop to Instinct about everything on my list - drafting, prioritizing, strategizing. Before I went to bed, I had Instinct create a massive prompt for Codex to execute the entire 19-item list and then email that prompt to me. Then I had Instinct write the prompt I gave to Codex to search my email and find the note Instinct left for it. 1/3

    Perhaps my most unhinged workflow: I was talking nonstop to Instinct about everything on my list - drafting, prioritizing, strategizing. Before I went to bed, I had Instinct create a massive prompt for Codex to execute the entire 19-item list and then email that prompt to me. Then I had Instinct write the prompt I gave to Codex to search my email and find the note Instinct left for it. 1/3

  3. Databricks BlogAI score35

    How to build governed enterprise apps on Databricks with Replit and Lakebase

    Replit and Databricks integration, now generally available with native Lakebase support, lets enterprise teams build apps from plain-language prompts using Replit Agent and deploy them as Databricks Apps. Deployed apps inherit automatic user authentication and Unity Catalog access controls, and Replit Agent auto-provisions a managed Lakebase Postgres database for operational data. Lakebase keeps app-written data inside the Databricks perimeter instead of a separate external database.

  4. ElevenLabs BlogAI score26

    How to build a meeting transcription API with Scribe v2 and Scribe v2 Realtime

    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.

  6. meng shaoAI score49

    LangChain adds three Deep Agents Skills upgrades: tool binding, pinning, reloading

    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.

  9. Guizang (歸藏)AI score26

    Grok bot auto-generates a daily AI news video in the cloud

    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.

  11. LangChain BlogAI score67

    LangChain's Restock agent shows how to build a payment-capable AI agent

    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.

    AIWhy 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.

Oct 7

Oct 7Wed
  2. Hugging Face BlogAI score66

    How one developer built six custom models with ML-Intern for about USD 103

    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.

    AIWhy 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.

  3. Paige BaileyAI score9

    🔖 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! 👀

    🔖 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! 👀

  4. Cat WuAI score14

    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!

    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!

  7. vLLMAI score28

    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%.

    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%.

  8. vLLMAI score46

    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

    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

  9. ClaudeDevsAI score22

    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

    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

  11. Microsoft Foundry BlogAI score22

    Azure Document Intelligence vs. Content Understanding: Choosing the Right Document Service

    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.

  12. Lydia HallieAI score34

    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

    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

  13. Unsloth AIAI score23

    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

    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

  15. Google Cloud TechAI score34

    Antigravity agent plugins weigh eager versus lazy loading of MCP tools

    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.

  16. Allie K. MillerAI score3

    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).

    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).

  17. Unsloth AIAI score40

    Unsloth lets users train local decision models on 4GB VRAM

    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.

  18. NVIDIA Technical BlogAI score22

    Validate AI Factory Changes with Digital Twins and AI Agents

    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.

  19. Ai2AI score18

    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.

    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.

  20. AWS Machine Learning BlogAI score38

    Agentic Automation Business Cases Need to Count More Than Saved Hours

    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.

  21. AWS Machine Learning BlogAI score44

    Qlik Builds Grounded Enterprise AI Answers Using Amazon Bedrock

    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.

  22. AWS Machine Learning BlogAI score32

    AWS playbook: six-week program closes AI builder gap for non-engineers

    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.

  23. Rowan CheungAI score13

    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 :^)

    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 :^)

  24. Simon WillisonAI score14

    Michael Lynch lists anti-patterns in software blogging, from meandering intros to overly formal prose

    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.

  26. GoogleAI score20

    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

    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

  27. Allie K. MillerAI score22

    Three agent use cases that act like an EA with calendar access

    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.

  29. ElevenLabs BlogAI score14

    Contact center automation guide explains AI tools for faster customer support

    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.