Introducing GPT‑5: A Smarter, Faster, More Helpful AI

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Introducing GPT‑5: A Smarter, Faster, More Helpful AI

🛒 Explore Smart Assistants on Amazon

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A Leap in Unified AI Intelligence

OpenAI’s GPT‑5 introduces a groundbreaking unified reasoning system—smart enough to choose between quick answers and deep insights. It mimics human decision-making with stunning accuracy.
🛒 Shop AI-Powered Devices

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Thinking Smarter, Performing Better

• Coding: Developers can now build and debug UIs faster, thanks to GPT‑5’s ability to write clean and aesthetic code.
  🛒 Top Programming Tools

• Writing & Communication: GPT‑5 is like a writing coach in your pocket—helping with blogs, speeches, and corporate memos.
  🛒 Best-Selling Notebooks for Creators

• Health & Real-world Queries: It’s now more accurate when answering sensitive questions, making it ideal for everyday support.
  🛒 Smart Health Gadgets

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Customization and Control for Developers

API updates in GPT‑5 bring you full control over verbosity and reasoning depth—great for building both chatbots and analytical tools.
🛒 Dev Essentials on Amazon

Custom tool calling now allows developers to define interactions through plain text grammars, opening the door to smarter, rule-based apps.
🛒 Natural Language Processing Books

With gpt‑5, gpt‑5‑mini, and gpt‑5‑nano, developers get performance, precision, and flexibility across workloads.
🛒 High-Performance Laptops

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ChatGPT Upgrades: Smarter, More Personalized, and Safe

From personal tones and voices to new integrations with Gmail and Google Calendar, ChatGPT with GPT‑5 becomes your full-time AI assistant.
🛒 Smart Office Tools

It’s safer too—OpenAI added better grounding, fewer hallucinations, and less sycophancy in responses.
🛒 Cybersecurity Starter Kits

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Widespread Availability & Subscription Access

GPT‑5 is now live in ChatGPT. Free users get a taste, but Plus and Pro subscribers enjoy full access—especially to GPT‑5 Pro, designed for deeper thinking.
🛒 Productivity Subscriptions & Deals

If you’re a creator, researcher, or executive—now’s the time to explore what GPT‑5 can do for you.
🛒 Best Tech for Entrepreneurs

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Why This Matters for You (and Your Readers)

Whether you're a developer, a business owner, or a tech-savvy content creator, GPT‑5 enhances your creative, analytical, and operational game.
🛒 AI for Everyone

Let this new AI revolution power your productivity—the future is not just intelligent, it’s conversational.
🛒 Voice-Controlled Productivity Tools

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OpenAI’s open‑weight large language model- gpt‑oss‑120b

 The gpt‑oss‑120b is one of OpenAI’s newly released open‑weight large language models (as of August 2025). It is designed to offer high reasoning and coding performance, while being freely available for download and use, including fine-tuning and commercial deployment.

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🔍 Overview of gpt‑oss‑120b

Feature	Description
Model type	Mixture-of-Experts (MoE)
Total parameters	~120 billion
Active parameters	~12.7 billion per forward pass
Number of layers	36 transformer layers
Experts per layer	128 experts
Experts activated	4 per token
Context window	128,000 tokens
License	Apache 2.0 (permissive, commercial use allowed)
Release date	August 5, 2025
Performance	Comparable to OpenAI's proprietary GPT-4o-mini
Hardware support	Optimized for multi-GPU systems (e.g., 4×A100, 8×H100), also available via Hugging Face, Databricks, and AWS

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⚙️ How It Works (Mixture-of-Experts)

• In a Mixture-of-Experts architecture:

  • Each layer contains 128 separate expert networks.

  • For each input token, only 4 of those experts are activated.

  • This makes the model more efficient (lower compute cost) while preserving high performance.

This sparse activation allows the model to scale to 120B total parameters without requiring the compute of a dense 120B model.

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🧠 Capabilities

• High performance on benchmarks:

  • Reasoning: MMLU, ARC, and Big-Bench Hard

  • Math: GSM8K

  • Coding: HumanEval, MBPP

  • Health: HealthBench

• Handles long documents and conversations (128K token context)

• Effective at:

  • Chain-of-thought reasoning

  • Tool use

  • Instruction following

  • Summarization and question answering

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🛠️ How You Can Use It

• Download and run locally (with strong enough hardware)

• Fine-tune or customize on your own data

• Deploy via cloud platforms:

  • Hugging Face

  • Databricks

  • AWS Bedrock/SageMaker

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🔐 Safety & Policy

• Released after extensive safety testing including simulated misuse, red-teaming, and external evaluations.

• Not a fully open-source model (training data and pretraining code are not released), but open weights mean you have full access to the model for any use case, under the Apache 2.0 license.

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📦 Where to Get It

• Direct download from OpenAI’s GitHub (or via Hugging Face and other ML model hubs)

• Works with:

  • Transformers libraries (transformers, vllm)

  • Tools like LangChain, LlamaIndex

  • Popular inference backends like vLLM and TGI

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How to create databricks View with COMMENT in column for AI use

 

How to create databricks View with COMMENT in column for AI use

CREATE OR REPLACE VIEW prod_catalog.mrt_corp.testvwdata (

  STATEID STRING COMMENT 'State ID',

  AFFILIATIONID STRING COMMENT 'Affiliation ID',

  AGE INT COMMENT 'Age',

  AGE_CAT STRING COMMENT 'Age Category'

) AS

SELECT 

  STATEID,

  AFFILIATIONID,

  AGE,

  AGE_CAT

FROM prod_catalog.mrt_corp.MM;

🚀 Building a ChatGPT Agent for GAAP Reporting – Step-by-Step Guide

In the age of AI-powered automation, GAP reporting—identifying differences between forecasted and actual results—can be dramatically simplified using a custom ChatGPT agent. Whether you're in Finance, Operations, or Analytics, building your own ChatGPT GAP Reporting Agent can save hours of manual analysis and enable data-driven decisions at speed.

In this post, I’ll walk you through how to create a Custom GPT agent designed specifically for GAP reporting tasks—no coding required (unless you want deeper integrations).

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💡 What is a GAP Reporting Agent?

A GAP Reporting Agent is an AI assistant built using ChatGPT that can:

• Identify variances between forecasted vs. actuals

• Recommend root causes or next steps

• Generate reports or summaries on the fly

• Support Finance, Ops, and Data teams with faster analysis

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🛠️ How to Build It – Step-by-Step

1. Go to the GPT Builder

Navigate to https://chat.openai.com/gpts, then:

• Click “Explore GPTs”

• Click “Create” and begin the GPT Builder wizard

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2. Configure Agent Details

Name:

GAP Reporting Analyst

Description:

I help analyze GAPs between forecasted and actual data across financial, operational, and business metrics. I can guide users through common reporting gaps, suggest root causes, and generate summaries in business-friendly language.

Instructions to GPT (System Message):

plaintext
You are an expert data analyst specialized in GAP reporting. Your job is to:
- Identify discrepancies between forecasted vs. actual data.
- Ask users for their report type (e.g., Finance, Operations, Forecast).
- Suggest potential causes of variance.
- Recommend next steps or data sources to validate issues.
- Summarize GAP findings clearly with tables if needed.
- Maintain a professional and concise tone.
When asked, generate email drafts or slide bullets based on the GAP findings.

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3. Upload Reference Files (Optional)

If you want the agent to understand your specific structure, you can upload:

• Historical GAP reports

• Templates (Excel, Word)

• Department-specific KPIs

• Data dictionaries

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4. Enable Tools

You can supercharge your agent by enabling:

• ✅ Code Interpreter (for calculations and visualizations)

• 🌐 Web Browsing (for live data research or definitions)

• 🔌 API Calling (advanced – for connecting to SQL, Databricks, or Snowflake)

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5. Test It With Prompts

Here are examples of how you can use your new GAP Reporting Agent:

• “Summarize the GAP between forecasted and actual pharmacy costs in Q2.”

• “List reasons why medical claims are 15% above expected.”

• “Generate talking points for CFO on Q3 budget variances.”

• “Create a GAP table by department with key drivers.”

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⚙️ Optional: Connect to Live Data

To go beyond static analysis, you can integrate APIs or databases:

• Databricks or Snowflake for real-time financials

• Azure SQL for pulling actual vs forecast

• Workday for budget data

Using OpenAI's function calling, your GAP agent can run queries and generate reports live.

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🎯 Final Outcome

Your ChatGPT GAP Reporting Agent will:

• Save hours of manual spreadsheet wrangling

• Provide accurate summaries and insights

• Be available 24/7 for your Finance, Ops, and Leadership teams

• Scale with your business as reporting complexity grows

Finance and accounting AI Projects Ideas I developed

As a seasoned leader in data analytics and financial systems, I specialize in leveraging artificial intelligence to transform finance and accounting operations. With a strong background in both technology and business strategy, I have led the successful development and deployment of intelligent automation tools that streamline processes, enhance decision-making, and drive measurable impact across the organization.

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1. Intelligent Invoice & Expense Processing

An AI-driven solution was developed to automate invoice and expense workflows using OCR, NLP, and RPA. The system extracts critical fields from scanned documents, validates vendor and GL information, and posts approved transactions automatically. Duplicate detection and fraud flagging models help enforce compliance, resulting in a 70% reduction in manual effort and improved processing accuracy.

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2. AI-Powered Forecasting & Budgeting

Machine learning models were deployed to forecast key financial metrics including revenue, cash flow, and operating expenses. These models incorporate historical patterns, seasonality, and external economic indicators. The forecasting engine supports dynamic scenario planning and delivers over 20% improvement in accuracy compared to traditional Excel-based methods.

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3. Automated Reconciliation Engine

A smart reconciliation engine was implemented to match transactions across bank statements, subledgers, and general ledger accounts. The solution utilizes fuzzy matching algorithms and rule-based learning to auto-resolve predictable variances, significantly accelerating the month-end close process and enhancing financial control.

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4. Fraud & Anomaly Detection

A real-time fraud detection framework was created using a combination of supervised and unsupervised machine learning techniques. It continuously analyzes transactional data to identify unusual activity, potential policy breaches, and emerging fraud patterns. The system has proven effective in reducing financial risk and supporting internal audit efforts.

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5. Natural Language Financial Assistant (AI Copilot)

A conversational AI tool was launched to provide on-demand financial insights to business users. Leveraging large language models and enterprise financial data, the assistant enables users to ask natural language questions—such as spend trends or account balances—and receive accurate, real-time responses. This solution has significantly improved self-service capabilities within finance teams.

🔥 Databricks Stored Procedures: A Game-Changer for Lakehouse SQL

🔥 Databricks Stored Procedures: A Game-Changer for Lakehouse SQL

In late 2023, Databricks quietly rolled out a powerful feature: Stored Procedures for SQL workloads. This brings traditional database development workflows right into the Lakehouse architecture. Until recently, tasks like reusable logic, transactional operations, or parameterized transformations required complex workarounds. Now, you can write stored procedures natively in SQL, right inside Databricks.

If you're working with enterprise-scale data pipelines like this, or orchestrating complex ETL jobs see here, this addition can save tons of time. Procedures now support control-flow logic (IF, LOOP, TRY...CATCH) and can accept parameters, handle errors, and interact with Delta tables — all in one place. SQL programming just got more scalable on Databricks.

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🛡️ Unity Catalog Required – A Security-First Foundation

Before you jump in, note that stored procedures require Unity Catalog. That’s Databricks' modern data governance layer, enabling fine-grained access control and secure collaboration. You can't create procedures in the older Hive Metastore – they must be defined under a Unity Catalog schema. So make sure your environment is upgraded.

Want to learn about data security best practices click here? Or maybe you're deploying multi-cloud data systems see this? Unity Catalog is your foundational step.

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💻 Example: SQL Stored Procedure

Here’s a basic procedure that calculates a bonus for an employee and inserts it into a table:

CREATE OR REPLACE PROCEDURE calculate_bonus(employee_id INT)
RETURNS STRING
LANGUAGE SQL
AS
$$
  DECLARE bonus_amount DOUBLE;
  SET bonus_amount = (SELECT salary * 0.10 FROM employees WHERE id = employee_id);

  INSERT INTO bonuses (emp_id, bonus) VALUES (employee_id, bonus_amount);
  RETURN 'Bonus Calculated';
$$;

Call it with:

CALL calculate_bonus(101);

This is ideal for finance analytics explore more, payroll systems, or incentive tracking workflows.

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✅ Use Cases & Benefits

• Reusable logic for data teams toolkits here

• Automating data cleansing, enrichment, or insert/update tasks

• Transactional control with Delta Lake learn delta

• Integrating with workflows, alerts, and Unity Catalog permissions

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⚠️ Limitations to Know

• Stored Procedures work only with Unity Catalog — no Hive support.

• You can’t write PySpark-based stored procedures directly (use notebooks or UDFs instead).

• Debugging is harder compared to notebook cells.

• You need Databricks Runtime 13.3 LTS or higher check compatibility.

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🚀 Conclusion

Databricks Stored Procedures are here to bridge the gap between enterprise SQL workflows and modern data platforms. They're powerful, secure, and ready for production — just make sure you’re set up with Unity Catalog. Whether you're migrating from a traditional RDBMS or building new Lakehouse-native apps, this is a feature you don’t want to ignore.

Want to explore the latest in data engineering, analytics platforms, or SQL design patterns? Start your journey here 👉 Explore on Amazon

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by OpenAI

From JIRA Ticket to Solution: The AI Future of Automated IT Support

Imagine a world where IT tickets don’t sit idle in queues, but are instantly read, understood, and addressed by intelligent automation.

With our AI-powered Jira integration using OpenAI and Databricks, we’re transforming how support issues are resolved.

As soon as a user submits a ticket, the system fetches it using the Jira API, analyzes the issue using GPT-based AI, and proposes a context-aware solution—often in seconds.

This eliminates guesswork and speeds up resolution time dramatically with AI.

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What makes this even more powerful is its ability to act with automation.

Once the proposed solution is reviewed and approved—or auto-approved for common tasks—Databricks can run the fix automatically, whether it’s optimizing a database, restarting a cluster, or correcting configuration issues.

The ticket is then updated in Jira, and the cycle is complete—seamless, efficient, and smart with workflow.

This AI-driven workflow not only empowers IT teams to focus on higher-value tasks but also ensures faster support for business users, driving real operational impact. Check out business for more insights.

Databricks error in free edition Cannot create serverless connection, try again later

 The error you're seeing — "Cannot create serverless connection, try again later" — usually occurs in Databricks Community (free) edition because serverless SQL endpoints and premium features are not supported on that tier.

🔍 Why This Happens:

• Serverless SQL endpoints are part of Databricks SQL Pro or Enterprise tiers, and are not available in the Community Edition.

• The Community Edition supports only a limited set of features, including a single cluster and no access to advanced networking, serverless SQL, or Unity Catalog.

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✅ What You Can Do:

1. Use a Standard Cluster Instead

Instead of serverless, use a standard interactive cluster:

• Go to Compute > Create Cluster

• Use the default settings (e.g., Runtime: DBR 11.x or 12.x)

• Make sure the cluster is started

• Then go to your SQL or notebook and run your queries using this cluster.

2. Upgrade to a Paid Plan (Optional)

If you need serverless SQL or Unity Catalog, consider:

• Upgrading to a Databricks Pro or Enterprise plan

• Use it through Azure Databricks, AWS, or GCP with a paid subscription

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🛠 Alternative Workaround (SQL Access in Community Edition)

You can still write and run SQL using a notebook:

1. Create a notebook.

2. Use %%sql magic command in a cell:

   %%sql
   SELECT * FROM your_table_name
   

3. Run it on a running interactive cluster.
   
   Solution by OpenAI

🧠 Databricks Notebook Keyboard Shortcuts & Tricks

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🧠 Databricks Notebook Keyboard Shortcuts & Tricks

Boost your productivity with these handy tips—plus gear recommendations for your data workspace!

🔑 Notebook Editor Shortcuts

Action	Shortcut (Windows/Linux)	Shortcut (Mac)
Run cell	Shift + Enter	Shift + Enter
Run cell and insert below	Alt + Enter	Option + Enter
Run all cells above	Ctrl + Shift + ↑	Cmd + Shift + ↑
Run all cells below	Ctrl + Shift + ↓	Cmd + Shift + ↓
Insert cell above	Ctrl + Shift + A	Cmd + Shift + A
Insert cell below	Ctrl + Shift + B	Cmd + Shift + B
Delete current cell	Ctrl + Shift + D	Cmd + Shift + D
Undo delete cell	Ctrl + Z	Cmd + Z
Move cell up	Ctrl + Shift + ↑	Cmd + Shift + ↑
Move cell down	Ctrl + Shift + ↓	Cmd + Shift + ↓
Toggle line numbers	Ctrl + M L	Cmd + M L
Comment/uncomment line	Ctrl + /	Cmd + /
Search in notebook	Ctrl + F	Cmd + F
Find and replace	Ctrl + H	Cmd + H

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✍️ Markdown & Formatting Tricks

Use %md to write clean documentation or visual notes:

• # → H1

• **bold**, *italic*, `code`

• [Amazon Gear](https://www.amazon.com/s?k=developer+workspace&tag=vishwa2025-20)

• Lists with - or *

• Link to [Databricks Guide](https://www.amazon.com/s?k=databricks&tag=vishwa2025-20)

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⚙️ Magic Commands

• %sql, %python, %scala, %r

• %run ./path/to/notebook

• %pip install pandas

• %fs ls, %fs cp, %fs head

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💡 Boost Your Productivity

Optimize your work with these essentials:

• 🧠 Mechanical Keyboard

• 🖥️ Coding Monitor

• 💻 MacBook for Data Science

• 🪑 Laptop Stand

• 👟 Brooks Ghost Shoes

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What's New in AI & Data Science – 2025 Trends

🔮 What's New in AI & Data Science – 2025 Trends

Stay ahead of the curve with the most important innovations shaping AI, BI, and analytics this year. These advances are redefining how forecasting, automation, and insights are delivered across industries.

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🔍 Retrieval-Augmented Generation (RAG) 2.0

• What’s new: RAG models like GPT-4o integrate real-time external data sources (SQL, documents, APIs) at runtime.

• Use case: Business reporting tools powered by LLMs now pull live analytics from your warehouse and provide conversational explanations.

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🧠 Agentic AI Systems

• Autonomous agents that reason, plan, and use external tools like SQL, Excel, and Notion to deliver insights.

• Use case: Generate, summarize, and email stakeholder-ready reports without human intervention.

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🧮 Synthetic Data for ML Training

• Synthetic datasets mimic real data for training or testing without privacy concerns.

• Use case: Use in healthcare and finance to avoid HIPAA or GDPR risk.

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📊 AI-Powered BI Dashboards

• Tools like Power BI Copilot and Tableau Pulse enable natural language questions like:

  “Why did revenue drop in April?”

• Use case: Smart dashboards for execs with zero SQL required.

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🧮 Small Language Models (SLMs)

• Models like LLaMA 3, Mistral, and Gemma are used on-premise for secure AI deployment.

• Use case: Internal chatbots, report generators, and risk assessors.

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📐 AI-Generated Code for Data Pipelines

• Tools like Databricks Genie and Hex Magic turn plain English into optimized SQL and ETL scripts.

• Use case: Describe your KPI — get auto-generated logic.

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🌐 AI + Graph Analytics

• Use graph + LLMs for network analysis in fraud, provider networks, or supply chain.

• Use case: Detect patterns traditional SQL can’t reveal.

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🧬 Multimodal Models in Analytics

• GPT-4o and Gemini 1.5 can interpret tables, images, dashboards, and explain anomalies visually.

• Use case: Upload reports and let AI act as your analyst.

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📦 Foundation Models for Tabular Data

• Models like TabPFN, AutoGluon, and RT-DETR are purpose-built for structured data science tasks.

• Use case: Faster, simpler training for business analysts.

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🛡️ Responsible AI

• Use tools like Fairlearn, WhyLabs, and IBM AI FactSheets to ensure fairness, explainability, and auditability.

• Use case: Build trust and pass compliance checks.

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ChatGPT JD Edwards Finance reports

 https://www.youtube.com/watch?v=22-VY8niW6Y

What is a Multi-Agent AI System

🧠 What is a Multi-Agent System?

A Multi-Agent System (MAS) is a system composed of two or more intelligent agents that:

• Interact with each other (via messages, APIs, databases, etc.)

• Share or divide tasks

• May have different roles, goals, or knowledge

• Can work in parallel to increase performance and intelligence

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🤖 Example of Multi-Agent Use Cases:

Use Case	Agents Involved	What They Do
Data Pipeline	Ingest Agent, Clean Agent, Analyze Agent	Each performs a step in a data workflow
Customer Support Bot	Product Bot, Billing Bot, Shipping Bot	Specialized bots hand off to each other
Smart Factory	Robot Agents, Scheduler Agent, Supervisor Agent	Coordinate production lines and resources
Multi-Tool AI Chat	SQL Agent, Python Agent, Web Search Agent	Choose the best tool for each user query

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🧩 How They Interact

Agents can:

• Pass tasks to each other (e.g., like a relay team)

• Negotiate or vote on actions (collaborative AI)

• Compete (e.g., in simulations or games)

• Work under a controller agent or be fully decentralized

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🛠️ Mini Example: Multi-Agent Workflow (Pseudocode)

# Agent 1: Data Reader
def agent_data_reader():
    data = read_sales_data()
    return data

# Agent 2: Analyzer
def agent_analyzer(data):
    return analyze_sales_trend(data)

# Agent 3: Reporter
def agent_reporter(analysis):
    generate_report(analysis)

# Workflow
data = agent_data_reader()
analysis = agent_analyzer(data)
agent_reporter(analysis)

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🧠 Multi-Agent AI with LLMs

In modern AI (like LangChain, AutoGen, or OpenAgents), you can assign roles like:

• 👩‍💼 Manager Agent: Breaks down the task and assigns subtasks

• 🧮 SQL Agent: Writes SQL queries

• 📊 Analytics Agent: Interprets and explains data

• 📝 Writing Agent: Writes reports

They talk to each other through prompts and messages, like a team of experts.

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📚 Tools to Build Multi-Agent Systems

Tool	Purpose
LangGraph / LangChain Agents	Chain agents with memory/tools
Autogen by Microsoft	LLM-based multi-agent framework
CrewAI	Define agents with roles and workflows
ReAct / Plan-and-Solve	Reasoning + Tool-use agents
Databricks AI	Cloud-native platform for AI agents

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🚀 Want an Example?

Would you like a real multi-agent demo in Python or Databricks using OpenAI? For example:

• One agent reads DB

• Second agent analyzes data

• Third agent explains results in natural language

Let me know the scenario you'd like!

Databricks AI/BI 2025 Summit

Just returned from the Databricks AI 2025 Conference – and wow, the future is here.

From AI-native assistants to zero-code data pipelines, Databricks is redefining how we work with data.

Here are some of the highlights that blew me away:

🧞 Genie – An LLM-powered AI assistant built into the Lakehouse
🧠 LakehouseIQ 2.0 – Understands your data contextually, across silos
📥 Lakebase – Zero-code ingestion and transformation
🤖 GenAI Agents – Build your own enterprise copilots
🧱 Mosaic AI Workflows – Drag-and-drop LLM pipeline builder
📦 Unity Catalog Everywhere – Unified governance across clouds
📊 AutoMetric (2026) – AI-generated KPIs and metric monitoring
💸 Free Databricks Version – Yes, FREE! Learn, build, explore at no cost!

🔥 Whether you're in data engineering, ML, finance, or compliance—this platform is evolving to empower everyone with AI-first capabilities.

💡 Databricks isn’t just AI-ready. It’s AI-native.

Introduction to Agentic AI in Healthcare Finance

 https://youtube.com/shorts/bprUUOOMDwc

Agentic AI Books-  https://amzn.to/4lfgSjx