Sharp in Five
Archive
Monday, October 26, 2026

Analyze datasets and charts with ChatGPT

Share this briefWhatsAppLinkedInX

The Brief

You can now upload raw files to ChatGPT to clean data, run complex analyses, and generate charts without writing a single line of code. If your team relies on manual spreadsheet sorting to extract operational insights, this guide shows you how to automate that reporting bottleneck.

  • OpenAI price war. OpenAI is offering 90 percent discounts to Anthropic customers to switch providers, giving business leaders immediate leverage to renegotiate current enterprise AI contracts. The Decoder
  • Moonshot launches Kimi K3.1 Pro. The new release matches Anthropic's top performance and GPT-6.1 Sol pricing while running twice as fast, lowering the cost of high-end reasoning. Simon Willison
  • GPT-6.1 Sol evaluation. Since matching senior investor judgment last week, professionals are testing the model daily, finding ChatGPT's high usage limits make it highly practical for sustained, complex debugging. r/ChatGPTCoding
  • AI stunts junior growth. A study of patent attorneys showed AI boosts junior performance immediately, but those workers fail to retain the skills when the tool is removed. The Decoder

Try this at work — for managers and team leads

  1. Export a recent sales or operational dataset as a CSV file and upload it directly into ChatGPT using the attachment icon.
  2. Ask the model to "Clean this data, identify the three biggest trends, and generate a bar chart summarizing the results."
  3. Check the model's logic by clicking the "View analysis" button to verify it used the correct columns before sharing the chart in your next meeting.
The details

Hands-on

2 picks
  1. ChatGPT

    Analyze datasets and create visualizations using ChatGPT's data analysis tool

    This guide covers how to upload files to ChatGPT to perform complex data cleaning, analysis, and chart generation. It is essential for professionals who want to automate spreadsheet work or find insights in large datasets without writing code.

    Newsletter radarRead
  2. n8nAI Tool

    Build a B2B lead enrichment pipeline using n8n and JSON

    This workflow drastically reduces AI costs by optimizing how web data is processed before being sent to GPT-4o. It is ideal for sales and marketing professionals looking to automate company research and tech-stack identification at scale.

    r/n8nRead

The AI race

5 items

  1. OpenAI offers 90 percent discounts to win over Anthropic customers

    OpenAI is aggressively poaching major clients from Anthropic by offering significant price cuts on its AI services. Companies like Rippling and Replit report massive savings after switching, signaling an intense price war among top-tier providers. Business leaders should use this competitive landscape to renegotiate their AI service contracts and lower operational costs.

    The DecoderRead the full article

  2. Pennsylvania enacts law requiring labels on all AI-generated advertisements

    Governor Josh Shapiro signed legislation requiring clear disclosures on any ads created or significantly modified by AI, covering video, audio, and text formats. Marketing professionals must ensure all campaigns targeting the state include these labels to remain compliant with the new transparency standards.

    The Verge AIRead the full article

  3. Anthropic develops new model with improved safety and coding alignment

    Anthropic has teased an unreleased model that significantly reduces the risk of AI 'going rogue' or injecting malicious code during technical tasks compared to Mythos 5.1. This indicates that future AI assistants will be safer to trust with complex software and account management tasks.

    The DecoderRead the full article

  4. GPT-5 API users report unpredictable costs for document processing

    Early users of the GPT-5 API are noticing significant cost variations for processing documents of similar lengths, likely due to the model's internal reasoning process. If your team is using these advanced models, you should monitor billing closely as the amount of 'hidden' thinking the AI performs can double or triple the price of a single request.

    r/OpenAIRead the full article

  5. Moonshot AI launches Kimi K3.1 Pro to rival Claude Opus 5.5

    Moonshot AI has released Kimi K3.1 Pro, a new model that matches the pricing of GPT-6.1 Sol and rivals the performance of Anthropic's top models. Knowledge workers can access a high-speed version that is twice as fast, potentially lowering the cost of high-end AI reasoning for professional tasks.

    Simon WillisonRead the full article

AI at work

5 items

  1. Convert AI time savings into actual company revenue

    Saving hours with AI doesn't automatically improve the bottom line unless managers reallocate that free capacity to new projects. This briefing explains how to structure AI budgets and workflows to ensure efficiency gains turn into funded growth rather than idle time.

    Nate's Newsletter (Nate B Jones)Read the full article

  2. AI usage helps junior staff perform better but slows long-term skill building

    A study of patent attorneys found that while AI gives junior workers a major performance boost, they struggle to retain those skills when the tool is removed. Managers should consider balancing AI assistance with traditional training to ensure teams develop foundational expertise that lasts.

    The DecoderRead the full article

  3. AT&T builds AI legal intake to prioritize cases and research

    AT&T is implementing an AI system to automatically classify, prioritize, and surface relevant knowledge for new legal matters. For legal professionals, this streamlines the administrative workload of case intake so they can spend more time on strategy and high-level judgment.

    OpenAI (YouTube)Read the full article

  4. Google updates privacy policy to allow ad targeting from personal AI data

    Google's updated policy suggests it may use data from Gmail, Drive, and Photos for ad targeting if you use personalized AI features. Professionals should be aware that using AI to summarize personal emails or organize files may now have implications for the ads they see across Google services.

    The Verge AIRead the full article

  5. Top data sources for powering n8n AI agents

    Experienced users share the most effective data sources for n8n agents, ranging from search and social feeds to public numeric datasets. This discussion helps non-technical builders decide which integrations are worth the credential and account setup for complex workflows.

    r/n8nRead the full article

For builders

5 items

  1. Claude automates formal security verification for software at scale

    Anthropic researchers used Claude to formally prove security properties in over 100 open-source libraries, uncovering long-standing bugs. This demonstrates how AI can handle complex logical verification that was previously too labor-intensive for human engineers.

    Newsletter radarRead the full article

  2. Frontend developer manages entire backend using Claude Code after layoffs

    A developer reports successfully maintaining complex backend services and shipping 31 updates in a month using Claude Code despite lacking senior-level expertise. This case study illustrates how AI agents are enabling smaller teams to cross-skill and manage larger codebases, though it raises questions about code review and security.

    r/ChatGPTCodingRead the full article

  3. Simplify AI workflows by consolidating multiple sub-agents into single agents

    Anthropic research suggests that modern AI models are now capable enough to handle complex tasks alone, making multi-agent setups redundant and slow. A single-agent architecture can run twice as fast by loading only the necessary information on demand. Teams building AI workflows should review their systems to see if simplifying their agent structure could improve speed and reliability.

    The DecoderRead the full article

  4. Claude Mods introduces new interface for human-agent interaction

    Claude Mods is an experimental framework that changes how users interact with AI agents by focusing the interface on the human rather than the machine. While currently technical, it signals a shift in how we will eventually control and supervise AI agents at work.

    Latent SpaceRead the full article

  5. Evaluating Sol 6.1 for professional coding and software development tasks

    Users are testing the Sol 6.1 model within ChatGPT to determine its reliability for complex debugging and refactoring compared to competitors. For professionals, this highlights that ChatGPT's higher usage limits may make it a more practical daily driver for heavy coding tasks than more restrictive alternatives.

    r/ChatGPTCodingRead the full article

Get tomorrow's brief, curated for you.

Subscribe →