Roughly, the more tokens you throw at a coding problem, the better the result is. We call this test time compute. One way to make the result even better is to use separate context windows. This is what makes subagents work, and also why one agent can cause bugs and another
PROMPT ENGINEERING
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SIPDO: AI that auto-discovers and fixes its own instruction errors
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What if your AI could automatically hunt for its own mistakes and fix its instructions in a continuous loop? Researchers from UIUC, HKUST, USF, and http://
Starc.Institute introduce SIPDO to do exactly that. Instead of relying on a fixed set of data, SIPDO uses a feedback loop -
LangSmith Launches Multi-Modal Support for Evaluators
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We just launched multi-modal support for evaluators in LangSmith! You can now pass attachments and base64 multi-modal content directly into evaluators with flexible mapping, allowing you to measure quality, safety, and performance across the full interaction end to end. Docs:
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Use AST Dump and Spec File Before Coding Implementation
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something like: Before starting your task, dump the relevant AST branch with a tool instead of using search to map out the cleanest insertion points. Write a spec to the folder with your implementation plan for review before we continue.
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Claude AI Revolutionizes Internet Capabilities: Planning, Automation, and Task Solving
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BREAKING : Claude vient de révolutionner la moitié d'internet. Il peut maintenant planifier des projets, automatiser des workflows, rédiger des rapports et résoudre des tâches complexes comme un COO à 500€/h (gratuitement). Voici 12 prompts Claude pour accomplir des mois de
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Claude AI Revolutionizes Internet: Planning, Automation, and Complex Task Solving
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BREAKING : Claude vient de révolutionner la moitié d'internet. Il peut maintenant planifier des projets, automatiser des workflows, rédiger des rapports et résoudre des tâches complexes comme un COO à 500€/h (gratuitement). Voici 12 prompts Claude pour accomplir des mois de
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Databricks KARL: Multi-Task RL for Enterprise Search Agents
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New research from Databricks. It's about training enterprise search agents via RL. KARL introduces a multi-task RL approach where agents are trained across heterogeneous search behaviors, constraint-driven entity search, cross-document synthesis, and tabular reasoning. It
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Daily Prompt for Investment Opportunity Analysis and Predictions
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Frankly, I just put something generic. I put exactly this: "Every morning at 9 a.m., analyze investment opportunities, predict the day's ups and downs (sectors, stocks) and give me a clear summary of the global financial situation."
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Key Viral Prompt Strategies in NotebookLM
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The pattern behind all viral NotebookLM prompts: → Request specific quotes and references
→ Seek contradictions, not just summaries
→ Demand acknowledgment of gaps
→ Enforce structured output formats
NotebookLM excels when you leverage its architecture -
5 Key Questions for In-Depth Analysis
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1/ THE "5 ESSENTIAL QUESTIONS" PROMPT On Reddit, it was called a 'game changer.' It forces NotebookLM to extract a pedagogically sound structure instead of superficial summaries: "Analyze all inputs and generate 5 essential questions that, when answered,