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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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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.

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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

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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.

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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.

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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.

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