Perplexity Computer is massively multi-model. Computer orchestrates models to... run agents in parallel, leveraging Opus to match each task to the model best suited for it. In total, Computer can route work across 19 different models.show more

Perplexity Computer orchestrates 20 different AI models - and... now you can embed them directly inside apps you create. To prove it, we built CEO Chat. Text Elon, Jensen, Zuck, and other tech CEOs. They text back.show more

🚨 Perplexity dropped "Computer" and nobody is talking about... it. An AI system that runs 19 models at once and builds entire projects by itself. It researches, designs, codes, and deploys live websites in minutes. No subscriptions to 5 different tools. No switching tabs. Here's how to use it:show more

You don’t have to use one model (or one... provider!) for everything. With Workshop, you can combine frontier and local models in the same workflow. For example: Opus can be the main agent, and delegate specific tasks to Gemma 4 via subagents. Better quality where it matters. Better privacy, speed, and cost where it counts. One workflow, best model for each task.show more

ami is now a desktop app run parallel coding... agents from your computer without breaking your flow claude opus 4.5 is unreasonably good but the interfaces around it haven't caught up go try it out for free :)show more

The AI Super Bowl… ChatGPT vs Claude 🏈 Run... your prompt across multiple models at once with /multi-agent then an AI judge picks the best answer so you don't have to. Need to quickly switch between models? Use /agent to switch to a different model in seconds but retain your context.show more

Thank you Perplexity and Aravind Srinivas Bhai for the... Mac Mini 🤟 Perplexity Computer + Mac Mini. Excited to test it out and see what it can do.show more

today we're launching a dataset of computer-use recordings to... build the next generation of computer-use agents. 300+ tasks across all kinds of software. open-source. link in the comments. Markovshow more

Introducing screengrasp - The new SOTA agent click position... model - beating Anthropic Computer Use, OmniParser and Molmo by a wide margin. - Simplified API access to the best click models on the market. 🔁Retweet and DM for free API credits. More details 👇show more

subagents are just recursive agents where you can apply... different prompts + models depending on the task. since they’re just a primitive, Cursor cli can actually spawn subagents by calling cursor-agent in headless mode via shell commands. that’s what makes the cli so nice. you can extend it, experiment, and have a lot of fun exploring orchestration patterns. here’s one way to do it w. dynamic model selection: 1. create a subagents.mdc rule 2. drop in: ``` --- alwaysApply: true --- ALWAYS spawn subagents by running `cursor-agent -p [task] --output-format=text --force --model [model]` in the terminal. Each subagent should return a summary of the changes it made. Subagents should be used for ALL tasks You can adopt a fan-out pattern where you spawn subagents to perform parallel isolated tasks, and then fan-in the results. Use the following models: - `--model gpt-5` for reasoning, researching, and planning - `--model sonnet-4` for implementation ``` 3. start cursor cli and try it out you can also adjust the rule to be more explicit when it should use subagents, when not to, which models when etc.show more

Open weight models are now available in Kiro. Three... open weight models are available in the IDE and CLI, so you don’t have to default to the largest model for every task. Choose the right model based on speed, cost, or long-running agentic workflows. What model would you like to see supported next? #Kiro #BuildwithKiro #AICodingshow more

Today, we’re launching computer-use CAD environments to train AI... models for engineering. Task prompts, reference files, gold outputs, rubrics & human walkthrough screen-recordings. We're open-sourcing 50 tasks across AutoCAD, Solidworks & Siemens-NX. Link below @markov__aishow more

DDR4 ram can actually be modified to work in... DDR5 motherboards - this is a safe and effective way to use the cheaper, inexpensive previous gen memory in your computer. 💯show more

We made it easy to run local models on... your computer and use them from your phone. A secure, end-to-end encrypted connection in just a few clicks. Available now with LM Studio and Locally AI - Local AI Chat.show more

Devin for Terminal is a local agent that works... with all frontier models, including Opus 4.7, GPT 5.5, and SWE-1.6. You can switch model mid-session, or handoff to Devin in the cloud.show more

Introducing Matrix I crawled 100,000+ agents, skills and tools... to train a new model which can answer what capabilities are the best match for a task. Think Google, but for agents. A living model that learns from the gossiping network, and gets smarter with every interaction.show more

Perplexity Computer just opened a PR on my repo,... starting with a simple task... improving SEO What stands out is how it executes. Exploring with Gemini, then switching to Opus when it’s time to actually write code (I haven’t seen it use Codex or Grok) I’m going to ramp up the complexity from here. So far, it’s delivered on every task. I have a few more in progress that I’ll be sharing over the next few days.show more

Working with multiple models in Chat? The model picker... in VS Code is now organized by provider, making it easier to browse, search, and switch between models. You'll also see provider names next to your recent models for quicker recognition. 💡 Tip: Use /models for quick access.show more

New open-source agent harness just landed! I got early... access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.show more

Computer model of the distribution of space objects in... Earth's immediate vicinity According to NASA, 95% of them are space debris, while the rest are active satellites, small asteroids, etc.show more