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Gemini 3.7 Flash now also powers Spark! I think Spark is massively underrated for tons of work tasks, and 3.7 Flash means it now has better tool calling and really high success at multi-step, multi-skill workflows. These 10 prompts are my go-to for automating the weekly grind:

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Step 3.7 Flash is now free for 30 days via Nous Portal It is a new MoE vision-language model focused on agent efficiency, coding, search, and multimodal workflows — and Hermes Agent users have been loving it, so thank you to StepFun for hooking them up!

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Most agentic workflows die a slow death from cost creep before they ever finish the job. SenseTime's new Token Plan flips that: complex multi-step tasks now run at ~60% lower token cost, and for the first month you get 1,500 calls refreshing every 5 hours — free. That's enough room to actually stress-test long-chain workflows instead of babysitting your token meter. Try it →

Most agentic workflows die a slow death from cost creep before they ever finish the job. SenseTime's new Token Plan flips that: complex multi-step tasks now run at ~60% lower token cost, and for the first month you get 1,500 calls refreshing every 5 hours — free. That's enough room to actually stress-test long-chain workflows instead of babysitting your token meter. Try it →

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Newburgh, New York Walmart has all their lights flashing. This has been happening all over the country, it also just happened at Asheville, North Carolina airport Is this due to the “Drones/ Orbs?” Are they testing our infrastructure for some upcoming event? “The lights do not always flash. I go to Walmart all the time and they never flash like this”

Newburgh, New York Walmart has all their lights flashing. This has been happening all over the country, it also just happened at Asheville, North Carolina airport Is this due to the “Drones/ Orbs?” Are they testing our infrastructure for some upcoming event? “The lights do not always flash. I go to Walmart all the time and they never flash like this”

Wall Street Apes

5,261,810 görüntüleme • 1 yıl önce

🚨Gemini 3.6 Flash is trash I tested it on a 3D Golden Gate Bridge, and the results were awful. • I had to re-prompt it three times because it repeatedly ignored the instructions. • First attempt, instead of creating the requested .html file, it first tried to build the experience inside the Gemini app using simulations. • Then second attempt it started placing images from the web into the chat rather than actually producing the file. • Even after getting it to complete the task, the final output was dramatically worse than Gemini 3.1 Pro, which is 5 months old and now not even a top 10 model on leaderboards. This feels like a regression from Gemini 3.5 Flash and honestly, it is one of the weakest models I have tested in the past few months. Has anyone else tested Gemini 3.6 Flash yet, and are you seeing the same thing?

🚨Gemini 3.6 Flash is trash I tested it on a 3D Golden Gate Bridge, and the results were awful. • I had to re-prompt it three times because it repeatedly ignored the instructions. • First attempt, instead of creating the requested .html file, it first tried to build the experience inside the Gemini app using simulations. • Then second attempt it started placing images from the web into the chat rather than actually producing the file. • Even after getting it to complete the task, the final output was dramatically worse than Gemini 3.1 Pro, which is 5 months old and now not even a top 10 model on leaderboards. This feels like a regression from Gemini 3.5 Flash and honestly, it is one of the weakest models I have tested in the past few months. Has anyone else tested Gemini 3.6 Flash yet, and are you seeing the same thing?

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Multi-robot learning is getting a serious boost! 📚 Researchers have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

Anthropic dropped 33 pages for Claude trading bots Last night I decided to try writing one and it worked out for me In 10 hours this script made me $561 The bot has a win rate of about 71% Wallet: Copytrade: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions, scripts, and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three-layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed, reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated, the skill executes a predefined multi-step process These workflows can include data analysis, document generation, automation scripts, or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions, Claude performs tasks using consistent methodology rather than ad-hoc prompting Testing and iteration Skills are continuously refined through triggering tests, functional validation, and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time, the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity, improves output consistency, and scales productivity across thousands of tasks

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Woow Google has just rolled out the AI model Flash Thinking 2.0 This is the first reasoning model capable of accessing YouTube and it changes everything: - Search for a video on your topic - Ask Gemini to think about the video - You'll have a tailor-made result in 10 sec. And it's even connected to Google Search and Maps!

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Ayron Jones

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After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

After a few more hours, I think I've figured out Opus 5. Opus 5 is trained to be more agentic than anything I've used. All Claude 5 models are like that. So what changes? The way to interact with Opus 5 or contextualize it won't work the same way as with other models. It loves exploring, so it doesn't need much guidance for it. Unique preferences, artifacts, and references compliment it well and enable cleaner and more effective exploration and execution. Now that it can explore more effectively on its own and understand intent better, the best thing to do is to get out of its way (e.g., it doesn't need examples of your preferences; a clear high-level description of it works best). It's truly agentic in that sense. A good first step to provide better context for Opus 5 is to distinguish between what's situational and what needs persistence. Regardless, persistent system prompts and CLAUDE.MD needs to stay lightweight. Remove memories and tool descriptions from these. CLAUDE.MD is also a great place to tap into progressive disclosure by linking command/skills to it. On the situational side, agent skills and auto-memory can leverage progressive disclosure and the improved ability of the model to use its external context/knowledge. Conflicting and unnecessary instructions, which are common at this layer (mainly to ensure reliability), are going to throw off this model easily. That's the biggest change I had to make. Simple, clean, and clear prompts and skills work best. I had to clean a lot of my skills and system prompts. The way I prompt remains the same (usually clear and well-scoped). MCP tool descriptions are also more descriptive and have been deduped from the system prompt. Anthropic released a guide on the new rules for context engineering, which was helpful here. I started to test the recommendations and created a little artifact with the things that worked along the way. This might feel like a lot of work. Believe me, it has been frustrating. But I think we can expect future frontier models to become more agentic and smarter at figuring out the right context/gaps. The best thing to do is to prepare for that now. Boris Cherny mentioned that Opus 5 is their least prompt-injectable model yet. I am not sure if that was something they intentionally trained for or if it emerged based on how it was trained, which is to be extremely agentic in nature and more direct in execution.

elvis

37,685 görüntüleme • 27 gün önce

Claude can make your own money printer That is exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasks

Claude can make your own money printer That is exactly what happened to me I wrote my own script It took me 6 hours On the very first night the bot made $2,705 profit Copytrade: Wallet: Here is the full strategy: The system builds automated workflows for Claude by packaging domain expertise into structured skills that activate automatically when relevant tasks appear Skill architecture Each skill is structured as a modular package containing instructions scripts and reference materials This allows Claude to apply specialized workflows without requiring the user to repeat instructions in every conversation Progressive context loading Skills follow a three layer architecture where only minimal metadata is loaded initially Full instructions and supporting files are accessed only when needed reducing token usage while maintaining specialized expertise Trigger detection Skills activate when the user request matches defined trigger phrases or workflows This ensures the correct workflow loads automatically without requiring manual prompting Workflow execution Once activated the skill executes a predefined multi step process These workflows can include data analysis document generation automation scripts or coordination across external tools Consistency and reliability Because workflows are encoded directly in the skill instructions Claude performs tasks using consistent methodology rather than ad hoc prompting Testing and iteration Skills are continuously refined through triggering tests functional validation and performance comparisons to ensure reliable execution Automation edge Instead of solving tasks from scratch each time the system repeatedly applies optimized workflows Over time this dramatically reduces prompt complexity improves output consistency and scales productivity across thousands of tasks

winkle.

53,951 görüntüleme • 5 ay önce

What does market conviction look like? A $350M raise at a $7.5B valuation. Today, we have closed that $350M round and nearly doubled our valuation, all backed by Vitruvian Partners, Accenture Ventures, J.P. Morgan Growth Equity Partners, D. E. Shaw Ventures, Pinegrove Opportunity Partners, and our returning investors CapitalG, Goldman Sachs Alternatives, and Viking Global Investors. Doubling down on their investment, we have also established a new channel partnership with Accenture to further fuel organizations embedding and scaling market intelligence into their core agentic workflows. The era of fragmented tools in market intelligence has given way to a more integrated approach with the advent of AI and that's why we are also announcing the launch of SuperAnalyst. AI prompts and queries are the workflow most analysts know today but now multi-step research and monitoring tasks can be automated on the user's behalf. These announcements are more than just momentous milestones: they are an inflection point for the future of market intelligence. We couldn't be more excited to keep building it.

What does market conviction look like? A $350M raise at a $7.5B valuation. Today, we have closed that $350M round and nearly doubled our valuation, all backed by Vitruvian Partners, Accenture Ventures, J.P. Morgan Growth Equity Partners, D. E. Shaw Ventures, Pinegrove Opportunity Partners, and our returning investors CapitalG, Goldman Sachs Alternatives, and Viking Global Investors. Doubling down on their investment, we have also established a new channel partnership with Accenture to further fuel organizations embedding and scaling market intelligence into their core agentic workflows. The era of fragmented tools in market intelligence has given way to a more integrated approach with the advent of AI and that's why we are also announcing the launch of SuperAnalyst. AI prompts and queries are the workflow most analysts know today but now multi-step research and monitoring tasks can be automated on the user's behalf. These announcements are more than just momentous milestones: they are an inflection point for the future of market intelligence. We couldn't be more excited to keep building it.

AlphaSense

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Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

Microsoft presents Windows Agent Arena Evaluating Multi-Modal OS Agents at Scale discuss: Large language models (LLMs) show remarkable potential to act as computer agents, enhancing human productivity and software accessibility in multi-modal tasks that require planning and reasoning. However, measuring agent performance in realistic environments remains a challenge since: (i) most benchmarks are limited to specific modalities or domains (e.g. text-only, web navigation, Q&A, coding) and (ii) full benchmark evaluations are slow (on order of magnitude of days) given the multi-step sequential nature of tasks. To address these challenges, we introduce the Windows Agent Arena: a reproducible, general environment focusing exclusively on the Windows operating system (OS) where agents can operate freely within a real Windows OS and use the same wide range of applications, tools, and web browsers available to human users when solving tasks. We adapt the OSWorld framework (Xie et al., 2024) to create 150+ diverse Windows tasks across representative domains that require agent abilities in planning, screen understanding, and tool usage. Our benchmark is scalable and can be seamlessly parallelized in Azure for a full benchmark evaluation in as little as 20 minutes. To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi. Our agent achieves a success rate of 19.5% in the Windows domain, compared to 74.5% performance of an unassisted human. Navi also demonstrates strong performance on another popular web-based benchmark, Mind2Web. We offer extensive quantitative and qualitative analysis of Navi's performance, and provide insights into the opportunities for future research in agent development and data generation using Windows Agent Arena.

AK

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From product image to video with just one tool - Dzine As you may have noticed, this is one of my favorite tools. It is also very underrated, as probably 50% of my tutorials include some workflow. I was testing the new image-to-video option today, and I love it. Step - by step guide in comments 🔽 I can do 95% of a workflow without switching between apps. Image generation, Image to image with style reference, background removal, background generation, and 2 frames image to video. The only other app I have been using for this video is CapCut so that I can stitch it together. Step by step 🔽

From product image to video with just one tool - Dzine As you may have noticed, this is one of my favorite tools. It is also very underrated, as probably 50% of my tutorials include some workflow. I was testing the new image-to-video option today, and I love it. Step - by step guide in comments 🔽 I can do 95% of a workflow without switching between apps. Image generation, Image to image with style reference, background removal, background generation, and 2 frames image to video. The only other app I have been using for this video is CapCut so that I can stitch it together. Step by step 🔽

Teodora P L

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"I think in the beginning of the year I don't think I would've been taking those shots... but now trying to step up and hit the shots that I can." Freshman Jordan Scott on how much his confidence has grown since the start of the season, and being able to step up and hit big shots. Scott played 28 minutes, and had 10 points and 4 rebounds. He also was tasked with guarding Keaton Wagler, and said he felt very "confident and prepared" for that matchup.

"I think in the beginning of the year I don't think I would've been taking those shots... but now trying to step up and hit the shots that I can." Freshman Jordan Scott on how much his confidence has grown since the start of the season, and being able to step up and hit big shots. Scott played 28 minutes, and had 10 points and 4 rebounds. He also was tasked with guarding Keaton Wagler, and said he felt very "confident and prepared" for that matchup.

Kara Adams

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I had the honour of making my affirmation at the Senedd. Standing there, I thought about my own journey, and the responsibility I now carry. As your Member of the Senedd for Pen-y-bont Bro Morgannwg, representing Bridgend and the Vale of Glamorgan, I will work hard to speak up for the people, towns, villages and communities I now serve. It is a responsibility I will never take lightly.

I had the honour of making my affirmation at the Senedd. Standing there, I thought about my own journey, and the responsibility I now carry. As your Member of the Senedd for Pen-y-bont Bro Morgannwg, representing Bridgend and the Vale of Glamorgan, I will work hard to speak up for the people, towns, villages and communities I now serve. It is a responsibility I will never take lightly.

Sarah Cooper-Lesadd MS

12,874 görüntüleme • 3 ay önce