Final result: Clean architecture diagram that accurately reflected the docs. No prompt engineering. No tool-switching. Just described the outcome and let the agent handle the workflow.

Here's what happened: Opened Qoder's task screen. Selected Coding task. Pointed it to a workspace folder. Prompt: "Read these docs and create an architecture diagram." Picked Qwen 3.8 Flash (running at 0.0x Credits through September 30). Hit send.

Tested Qoder this week. Dropped a docs link into the task screen. Asked it to generate an architecture diagram. The agent read the docs, planned the structure, and built the diagram without me touching a single tool.

Python Automation Cookbook: Check out this book if you are looking for automating your AI workflows. This book has 100+ recipes for scalable workflows, MCP integrations and AI powered automation. Book link: acesse.one/rd7a7qm

Can you chat with Jev? @typesafeai The simple answer is NO. If you ask this or any question to any regular LLM, it tries go generate paragraphs, but Jev just responds with structured decisions in fraction of seconds.

Just built Parked. Snap where you parked. Tap to walk back. No account. Works inside garages where GPS fails because the photo is the source of truth. Built this app in 15mins with Rork + GPT-6 Astra. @rork

3/ Two-Line Migration Works with OpenAI SDK, LangChain, Vercel AI SDK, or anything that accepts an OpenAI-compatible base URL. To switch: → Change the base URL → Swap the API key No refactoring. No redeployment. No downtime.

2/ Market Prices, Not List Prices Official APIs charge fixed rates. Dit.ai creates a real-time auction where 160+ providers compete for every request. Your call routes to the best-priced qualified provider automatically. Typical savings: 30-70% per call.

1/ One API Key for 50+ Models Stop managing separate accounts for OpenAI, Anthropic, Google, and DeepSeek. Dit.ai gives you GPT, Claude, Gemini, Grok, and dozens more through a single OpenAI-compatible endpoint. Access everything without the vendor sprawl.

I cut my AI API costs by 60% without changing a single line of application logic. Same models. Same SDK. One routing layer that makes 160+ providers compete for every call. Dit.ai is the infrastructure layer you need if you're building agents or shipping AI features. 🧵

Run frontier MoE models on hardware you already own. Colibrì is a pure C inference engine that runs massive Mixture-of-Experts models by treating VRAM, RAM, and NVMe as one memory hierarchy. MoE models have hundreds of billions or trillions of parameters, but only a small subset of experts activate per token. Colibrì moves weights across storage tiers based on which experts are needed. Hot experts stay in VRAM or RAM. Cold experts live on NVMe and stream in when the router selects them. It tracks routing patterns, keeps frequent experts closer to compute, and prefetches the next layer before it's requested. The result: limited VRAM changes speed, not which model you can run. Key capabilities: - Memory multitiering: VRAM, RAM, and NVMe as placement tiers - Expert streaming: Load routed experts from storage instead of keeping all in memory - Routing-aware caching: Per-layer LRU, hot-expert pinning, and one-layer-ahead prefetching - Heterogeneous execution: CPU, CUDA, Metal, NUMA memory, partial or full expert residency - Frontier-scale support: Qwen3.6 (35B) to Kimi K3 (2.8T), plus GLM-5.2, DeepSeek V4 Flash, and others 100% open source.

You can literally visualize your entire AI workflow using this amazing tool for FREE.

Stop guessing which models fit in your VRAM. llmfit is a CLI tool that detects your hardware and ranks 206 models by what actually runs on your system. No more downloading 50GB models that crash. 🧵

Three major releases in six months — Tencent Hunyuan is moving fast, and Hy4 preview has entered the open-source big leagues. 🚀 workbuddy.ai @TencentHunyuan @TencentAI_News @WorkBuddy_AI It brings 770B total parameters, 49B active parameters, and a 1M+ token context window, built for coding, productivity, research and real-world agentic tasks. Tencent’s “model + product” co-design approach also brings it closer to products like WorkBuddy, with training shaped around real-world workflows. In a blind test across 203 engineering tasks with 163 experts, Hy4 preview scored 2.99/4, ahead of GLM 5.3 and Kimi K3. On public benchmarks, it clearly beats GLM 5.2 and sits right alongside GLM 5.3. And the API pricing is the real surprise: $0.834/M input, $2.501/M output, and $0.042/M cache hits. Top-tier performance, open-source access, and a seriously budget-friendly price. Hy4 preview on WorkBuddy is currently free to try for two weeks for a limited time. 🚀 If you’re curious about what it can actually do, give it a try while the free access is available. 👀

Long-horizon agency requires more than a larger context window. An agent must evaluate its own progress, learn from unfamiliar environments, remember what matters, coordinate tools, and adapt when users or external conditions change. dots3-note preview brings these capabilities together in an open-weight multimodal model: • Recursive self-critique with TEMPO • Test-time learning and memory updates • Text, vision, and speech understanding • Complex reasoning and tool use • Multi-stage task execution • Adaptation across evolving real-life scenarios Read the technical write-up: studio.dots.ai/dots/dots3-en… Try dots3-note preview free on OpenRouter: openrouter.ai/dots-studio/do… Follow @dotsstudioai for more research, demos, and updates.

6/ Travel planning doesn’t stop when the itinerary is created User preferences can emerge gradually. Weather changes. Prices move. Inventory disappears. Flights and schedules may be updated without warning. In this simulated End-to-End Travel Concierge scenario, dots3-note preview maintains task state across stages, coordinates services through tools, and adapts the itinerary as new constraints appear. The challenge is not producing one polished travel plan. It is staying useful throughout the journey—remembering earlier decisions, detecting when assumptions are no longer valid, and revising the plan without losing sight of the traveler’s broader goals. This is what long-horizon agency looks like when the environment refuses to stand still.

5/ Now move from a controlled environment into business operations Running an e-commerce business is not a one-step task. E-commerce operations unfold across multiple stages, where user needs, service updates, and external conditions can change over time. Each new event may affect decisions that were made earlier. In this simulated Coffee E-commerce Operations scenario, dots3-note preview works across multiple stages, uses tools to handle different parts of the workflow, and maintains the context needed to keep its actions coordinated. When new information changes the situation, it can revisit the existing plan instead of starting over. That continuity is essential for agents designed to support real, evolving operations.

4/ Perception is only the beginning dots3-note preview jointly understands text, vision, and speech, then connects that perception with complex reasoning, coding, tool calling, and result delivery. In this demo, the model must interpret information across different modalities, decide which tools are needed, coordinate their use, inspect the results, and continue working toward the broader objective. The key is orchestration: individual capabilities are not used in isolation. They become part of one continuous workflow. Perceive → reason → choose tools → act → evaluate → deliver

3/ Watch that learning loop unfold in ARC-AGI-3 The model enters an unfamiliar interactive environment with no complete explanation of its controls, objectives, or hidden mechanics. Across thousands of turns, it must experiment, observe the results, build a working theory, and update its memory whenever that theory fails. A wrong prediction is not simply discarded. The model can identify the source of the mistake, preserve the correction, and apply the revised rule to later levels. This is autonomous exploration at test time: **Hypothesize → test → detect errors → update memory → try again** The environment keeps changing—and the model keeps learning.

2/ Test-Time Learning in Novel Environments How does an agent improve when it enters an environment it has never seen before? dots3-note preview explores, forms hypotheses, tests them against the environment, and updates its memory when those assumptions fail. Explore → hypothesize → test → evaluate → update memory On ARC-AGI-3, this learning loop continues across long interactions, allowing the model to refine its understanding as it gathers more evidence. The chart compares average ARC-AGI-3 score against inference cost, showing the performance–cost trade-offs across the evaluated models. Through RL, dots3-note preview learns not only how to act, but also what is worth remembering and what should be forgotten.

1/ Recursive Self-Critique via TEMPO Long-horizon reinforcement learning has a fundamental problem: a rollout may continue for hours, while the final reward arrives too late to explain which of thousands of earlier decisions actually mattered. TEMPO lets the same model alternate between actor and critic. At each macro-step, the model pauses, examines its current state, reasons about the remaining task, and calls tools to estimate whether its trajectory is moving toward success. Instead of waiting until the end to discover that a plan failed, it can generate earlier and richer signals about genuine progress. The goal is not simply to act for longer—it is to recognize when the current direction needs to change.

What happens when an AI task doesn’t end after one prompt—but continues for hours, days, or even weeks? The agent must remember what happened, judge whether it is making progress, use tools, and revise its plans as the world changes. Meet dots3-note preview, an open-weight multimodal model developed by rednote’s dots studio @dotsstudioai for long-horizon agency in real life. 280B total parameters. 16B active. 512K context. Text, vision, and speech. Here’s what it can do. 🧵

Mac Mini with M6 is here! but it starts at $899 and $799 for students. Will you considering buying it??

Promotional video creation is very smooth. Revid is able to get the core idea of the web app that I provided. However, it could get the exact screenshot of the website, I was also not expecting that. I could have shared few screenshots. Otherwise nice video with voice over and text.

Top spot on outbid is $14,205 We can't afford it, so we built one. Here is it: claimthespots.com

Claim The Spots just launched. claimthespots.com It's a pay-to-rank leaderboard. You submit your product or profile, bid to claim a position, and anyone can outbid you for that spot. The mechanic is simple. Higher bids get higher visibility. The leaderboard is public. Every bid drives more traffic to the top positions, which makes those positions worth more. It's the same concept that made outbid blow up this week. If you're launching something and want immediate eyeballs, this is another channel to test.

Here is my version of outbid! claimthespots.com No ads. No API keys. No revenue sharing. Claim your spot for $2. Top spot is up for $3.

Elon is coming after Microsoft, and Cursor is actively targeting GitHub. Here is what went down yesterday: The Blackout: GitHub was down for 7.5 hours across Actions, Copilot, and core code workflows. The Counter-Strike: Cursor dropped their new product, Origin, right in the middle of the outage.

How to get progress bar in Python? Here is the code snippet to include progress bar for your Python loops. Let me know what you think about it in the comments.

Intro to Deep Agents Course for FREE! @LangChain academy is offering this foundation course for FREE. You will learn how to build long running agents for complex workflows with Deep Agents. Find the course link in the comments!

The weekend is here and if you want to dive into AI Agents, check out this book. You can read this book for FREE. Find the link 🧵👇

Dreamina Seedance 2.5 has officially launched on Dreamina, the dedicated AI creation platform by ByteDance. If you are planning to build with Seedance, this is the exact platform I highly recommend using. There are two massive upgrades that really stand out to me: 🎬 Up to 50 multimodal reference assets (images, videos, and audio) processed in a single generation. ⏱️ Up to 30-second continuous video generation, which unlocks longer and incredibly coherent storytelling. When you fuse these capabilities with Dreamina's built-in editing workflow, multilingual support, and the expansive Seedance ecosystem, it genuinely feels like a major leap toward production-ready AI video. This hub consolidates the entire Seedance lineup in one place—spanning from Seedance 2.0 Mini straight through to Seedance 2.5. It features creator-friendly pricing, making it one of the most cost-effective official routes to experiment, rapidly iterate, and produce content at scale. Less time juggling different apps. More time actually creating. #Dreamina #Seedance25 #AIVideo #DreaminaPartner

🚨 Dreamina Seedance 2.5 is coming soon to the Dreamina platform. ByteDance’s next AI video upgrade is bringing a more complete creation workflow. What’s coming: - Up to 50 multimodal references in one generation - Up to 30 seconds of continuous video - White-model and green-screen reference control - Multilingual creation for global content - Precise local editing for targeted revisions This is a move from short AI clips toward longer, more controllable visual storytelling. 👉 aisecret.me/seedance25 #dreamina #dreaminapartner #seedance #dreaminaseedance25

Go annual on Pro or above → bonus AI credits on your usage: → $100 on Pro → $150 on Max → $200 on Ultra

Launch offer: Subscribe to one agent → get the other's Max plan free for 30 days. Run OpenClaw and Hermes side by side on real work before you commit to either one.

MyClaw started with OpenClaw. Now Hermes is live. Claude Code and Codex are coming next. Pick the agent runtime that fits your workflow. No Docker, no local setup, no server maintenance. Just managed agents that finish the job.

ChatGPT gives you an answer. MyClaw gives you the finished thing. Give an agent a goal and the completed work lands in your WhatsApp, Telegram, or inbox. A sourced market report. Triaged emails. A competitor alert the moment something changes. Not a chat window you forget to check. Actual deliverables.

This literally change the way we write docs for Agents. OpenWiki is a CLI that writes and maintains agent documentation for your codebase. Open source

Speech-to-text models keep guessing wrong on the words that actually matter. Brand names become common words. Medical terms get butchered. Project codes vanish. AssemblyAI Universal-3.5 Pro fixed this with contextual prompting. Feed it your domain context and it stops guessing. Here's how it works 🧵👇

Not gonna lie — I was skeptical when I saw this robot in my feed. Another humanoid? Another hype cycle? But the more I looked into it, the more I realized I'd never seen anything quite like Q1. It's at the AI for Good Summit in Geneva. It's 80cm tall. It's the first personal humanoid robot ever built — and it actually looks like something you'd want in your home, not just in a museum. I've been burned by too many overpromised tech products. But this one feels different. Like they actually thought about the person on the other end, not just the investors. Anyone else feel like personal robots are finally becoming real? Or am I just getting my hopes up?

I tested Tencent Hunyuan Hy3 with a small agent-building prompt on OpenRouter. Prompt I used: “Build a simple AI agent that turns a user request into a step-by-step action plan.” I wanted to see if Hy3 could turn a simple instruction into something closer to real product logic. The result was clean. Hy3 created an agent flow that started from the user request, understood the intent, broke the task into steps, and prepared a final action plan. That’s useful because most AI agents need more than just answers. They need structure. My takeaway: Hy3 feels strong for agent-style workflows where the model needs to reason step by step and keep the output organized. For builders working on AI assistants, automation tools, or workflow agents, this is worth testing. Try Hy3 on OpenRouter: openrouter.ai/tencent/hy3:fr… @TencentHunyuan #Hy3 #Hunyuan #TencentAI #AIAgents

How to become an AI Engineer in 2026? Andrew Ng just dropped this course on DeepLearning Platform. - 9+ hours - 31 video lessons - 7 Code Examples Get started for Free. Find the course link in 👇🧵

Python and ML Books for FREE! - Intro to ML - ML Projects - Think Python - Python for Data Analysis Like, RT, and comment “Books,” and I’ll DM you the links.

Never ever do this mistake if you want to have a good weekend. what do you say?

The new CEO of WhatsApp. How of you know him?