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AI Jobs and Career
I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
- Full Stack Engineer [$150K-$220K]
- Software Engineer, Tooling & AI Workflow, Contract [$90/hour]
- DevOps Engineer, India, Contract [$90/hour]
- More AI Jobs Opportunitieshere
| Job Title | Status | Pay |
|---|---|---|
| Full-Stack Engineer | Strong match, Full-time | $150K - $220K / year |
| Developer Experience and Productivity Engineer | Pre-qualified, Full-time | $160K - $300K / year |
| Software Engineer - Tooling & AI Workflows (Contract) | Contract | $90 / hour |
| DevOps Engineer (India) | Full-time | $20K - $50K / year |
| Senior Full-Stack Engineer | Full-time | $2.8K - $4K / week |
| Enterprise IT & Cloud Domain Expert - India | Contract | $20 - $30 / hour |
| Senior Software Engineer | Contract | $100 - $200 / hour |
| Senior Software Engineer | Pre-qualified, Full-time | $150K - $300K / year |
| Senior Full-Stack Engineer: Latin America | Full-time | $1.6K - $2.1K / week |
| Software Engineering Expert | Contract | $50 - $150 / hour |
| Generalist Video Annotators | Contract | $45 / hour |
| Generalist Writing Expert | Contract | $45 / hour |
| Editors, Fact Checkers, & Data Quality Reviewers | Contract | $50 - $60 / hour |
| Multilingual Expert | Contract | $54 / hour |
| Mathematics Expert (PhD) | Contract | $60 - $80 / hour |
| Software Engineer - India | Contract | $20 - $45 / hour |
| Physics Expert (PhD) | Contract | $60 - $80 / hour |
| Finance Expert | Contract | $150 / hour |
| Designers | Contract | $50 - $70 / hour |
| Chemistry Expert (PhD) | Contract | $60 - $80 / hour |
How to Use WhatsApp Broadcasts and AI for Better ROI.
In the digital marketing landscape, WhatsApp Broadcasts have emerged as a modern-day equivalent of flyers, combining efficiency with precision targeting. The integration of Artificial Intelligence (AI) further amplifies its potential, offering smarter ways to connect with and engage audiences. With a staggering 98% open rates and 35% click rates, leveraging WhatsApp Broadcasts with AI can significantly boost your Return on Investment (ROI). This guide delves into strategies for building a robust broadcast list and utilizing AI to maximize the impact of your WhatsApp marketing campaign.
Building a WhatsApp Broadcast List with AI
In the world of digital marketing, WhatsApp Broadcasts are like the modern-day equivalent of flyers. They offer a combination of efficiency and precision targeting that can help businesses reach their audiences in a whole new way. But what if I told you that you could take your WhatsApp Broadcasts to the next level with the power of Artificial Intelligence (AI)? By leveraging AI, you can unlock even more potential and significantly boost your Return on Investment (ROI).
WhatsApp Broadcasts already boast impressive statistics, with a staggering 98% open rate and 35% click rate. But imagine what you could achieve by integrating AI into your WhatsApp marketing campaigns.
Let’s start by exploring how AI can help you build a WhatsApp Broadcast list. WhatsApp offers several built-in features that can be enhanced with AI. For example, with the WhatsApp Business API, AI can analyze customer interactions and create personalized opt-in invitations. This way, you can leverage AI to attract more subscribers to your broadcast list.
Another feature you can use is the WhatsApp Click-to-Chat Link. By using AI algorithms to analyze user engagement data, you can determine the most effective platforms to place these links. This will help drive more users to engage with your WhatsApp Broadcasts.
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QR codes have become increasingly popular in marketing, and WhatsApp offers its own QR code feature. By using AI algorithms to track QR code scans and optimize their placements, you can make sure that your QR codes are working to their full potential.
If you have a website, you can also utilize the WhatsApp Chat Widget. AI can personalize the interactions on the chat widget, improving user engagement and encouraging visitors to join your broadcast list.
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Let’s move on to how you can utilize AI in the content and engagement strategies of your WhatsApp marketing campaigns.
AI can help you create personalized newsletters by analyzing subscriber preferences. By tailoring your newsletter content to match what your subscribers are interested in, you can encourage them to provide their WhatsApp details and join your broadcast list.
AI Jobs and Career
And before we wrap up today's AI news, I wanted to share an exciting opportunity for those of you looking to advance your careers in the AI space. You know how rapidly the landscape is evolving, and finding the right fit can be a challenge. That's why I'm excited about Mercor – they're a platform specifically designed to connect top-tier AI talent with leading companies. Whether you're a data scientist, machine learning engineer, or something else entirely, Mercor can help you find your next big role. If you're ready to take the next step in your AI career, check them out through my referral link: https://work.mercor.com/?referralCode=82d5f4e3-e1a3-4064-963f-c197bb2c8db1. It's a fantastic resource, and I encourage you to explore the opportunities they have available.
When it comes to content strategy, AI can be a powerful tool. You can use AI tools to analyze trending topics and user interests for your blogs and glossaries, ensuring that your content remains relevant and engaging. Additionally, AI can help you segment your audience and offer personalized eBooks, reports, and whitepapers to different user groups.
Product demos and samples are a great way to engage potential leads, but AI can take it a step further. By deploying AI to identify leads that are most likely to respond positively to product demos and samples, you can focus your efforts on those who are most likely to convert.
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Workshops and webinars are another effective way to engage with your audience. With AI tools, you can identify trending topics and personalize invitations, increasing registration rates and ensuring that you are reaching the right people.
Social media is a valuable platform for marketing, and AI can help you make the most of it. AI algorithms can analyze social media behavior to identify potential leads and optimize your content, ensuring that you are reaching the right audience at the right time.
When it comes to social media ads, AI can help you fine-tune your targeting. By leveraging AI to analyze user behavior and preferences, you can ensure that your ads are being shown to the people who are most likely to be interested in your products or services.
Chatbots have become increasingly popular in customer service, and for a good reason. By integrating AI-powered chatbots into your social media platforms, you can handle complex queries and provide personalized interactions. This can greatly improve customer satisfaction and engagement.
Customer referral programs are a valuable tool for growing your business, and AI can help you make them even more effective. By using AI analytics, you can identify customers who are most likely to refer others and tailor your referral programs accordingly.
Now let’s focus on how you can maximize your ROI with WhatsApp Broadcasts and AI.
First and foremost, AI-driven personalization is key. By using AI to segment your audience, you can send highly personalized and relevant broadcasts. This will ensure that your messages resonate with your audience, increasing engagement and conversion rates.
Timing is everything, and AI can help you with that too. By leveraging AI, you can determine the best times to send follow-up messages and analyze customer responses for future interactions. This will help you build a strong relationship with your audience.
Continuous AI analytics are crucial for optimizing your WhatsApp Broadcasts. By employing AI tools to analyze the performance of your broadcasts, you can adapt your strategies accordingly. This will help you stay ahead of the game and ensure that you are delivering the most effective messages to your audience.
It’s important to remember that while AI is a powerful tool, it should be used in adherence to best practices and compliance policies. This will ensure that your communication is respectful and effective, building a positive reputation for your business.
Finally, integrating WhatsApp and AI into a broader digital marketing strategy is essential. While WhatsApp Broadcasts and AI are powerful on their own, incorporating them into a comprehensive strategy will result in synergistic effects. This means that you should integrate WhatsApp and AI with other marketing channels and tactics to create a unified and effective approach.
In conclusion, combining WhatsApp Broadcasts with AI offers a powerful opportunity to enhance your digital marketing efforts. By strategically building a broadcast list and employing AI for personalized, data-driven communication, businesses can achieve a significantly improved ROI.
Are you ready to dive deep into the ever-evolving world of artificial intelligence? Well, have I got some exciting news for you! There’s a book that’s going to blow your mind and unravel the mysteries of AI. It’s called “AI Unraveled: Master GPT-4, Gemini, Generative AI & LLMs – Simplified Guide for Everyday Users: OpenAI, ChatGPT, Google Bard, AI ML Quiz, AI Certifications Prep, Prompt Engineering.” Phew, that’s quite a mouthful, but don’t let the long title intimidate you!
But where can you get your hands on this gem? Look no further than popular online platforms like Etsy, Shopify, Apple, Google, or Amazon. They’ve got you covered and ready to embark on your AI adventure.
1. Leveraging WhatsApp’s Built-In Features
- WhatsApp Business API: Use AI to analyze customer interactions and create personalized opt-in invitations.
- WhatsApp Click-to-Chat Link: AI can determine the most effective platforms to place these links based on user engagement data.
- WhatsApp QR Code: Use AI algorithms to track QR code scans and optimize their placements.
- WhatsApp Chat Widget: AI can personalize chat widget interactions on your website, improving user engagement.
2. AI-Powered Newsletters
- Utilize AI to analyze subscriber preferences and tailor newsletter content, encouraging users to provide their WhatsApp details.
3. AI-Enhanced Content Strategy
- Free Content: Use AI tools to analyze trending topics and user interests for your blogs and glossaries.
- Gated Content: AI can help segment audiences and offer them personalized eBooks, reports, and whitepapers.
4. Product Demos and Samples with AI
- Deploy AI to identify potential leads who are most likely to respond positively to product demos and samples.
5. AI-Driven Workshops and Webinars
- AI tools can help identify trending topics and personalize invitations to increase registration rates.
6. Social Media Insights with AI
- AI algorithms can analyze social media behavior to identify potential leads and optimize content.
7. Targeted AI-Enabled Social Media Ads
- Leverage AI to fine-tune your ad targeting based on user behavior and preferences.
8. Chatbots and AI Conversations
- Integrate AI-powered chatbots to handle complex queries and provide personalized interactions on social media.
9. Customer Referral Programs with AI Analytics
- Use AI to identify customers most likely to refer others and tailor referral programs accordingly.
Maximizing ROI with WhatsApp Broadcasts and AI
After building your list, the next step is to harness the power of WhatsApp Broadcasts and AI for maximum ROI.
- AI-Driven Personalization: Use AI to segment your audience and send highly personalized and relevant broadcasts.
- Timely AI-Enhanced Follow-Ups: Leverage AI to determine the best times for follow-up messages and to analyze customer responses for future interactions.
- Continuous AI Analytics: Employ AI tools to continuously analyze the performance of your broadcasts and adapt strategies accordingly.
- Adherence to Best Practices: Combine AI insights with WhatsApp’s compliance policies to ensure respectful and effective communication.
- Integrating WhatsApp and AI into a Broader Strategy: Don’t rely solely on WhatsApp and AI. Integrate them into a comprehensive digital marketing strategy for synergistic effects.
If you are not comfortable with AI, you can still leverage WhatsApp broadcast for a good ROI.
1. WhatsApp’s Built-In Features
- WhatsApp Business API: Utilizes an opt-in policy encouraging new users to connect with your business.
- WhatsApp Click-to-Chat Link: This feature allows you to create a clickable link for your WhatsApp business number, making it easier for customers to reach out directly.
- WhatsApp QR Code: Similar to Click-to-Chat but in a scannable QR format. Ideal for offline and online platforms.
- WhatsApp Chat Widget: Integrates a chat feature on your website, directly linking to your WhatsApp business account.
2. Create a Newsletter
- Offer subscriptions for updates about your business and industry, encouraging users to register with their email and WhatsApp details.
3. Content Strategy
- Free Content: Blogs and glossaries to increase awareness and credibility.
- Gated Content: eBooks, reports, and whitepapers for detailed insights, in exchange for contact details.
4. Product Demos and Samples
- Entice potential leads with a ‘free taste’ of your product or service in exchange for contact information.
5. Engaging Workshops and Webinars
- Host informative sessions in exchange for registration, thus acquiring leads.
6. Social Media Utilization
- Leverage the extensive reach of platforms like Facebook and Instagram to gather leads.
7. Paid Social Media Ads
- Target specific demographics with sponsored ads to attract a relevant audience.
8. Chatbot Integration
- Use automated chatbots to engage users on social media, covering FAQs and product details.
9. Customer Referral Programs
- Encourage current customers to refer friends in exchange for exclusive offers.
Maximizing Returns with WhatsApp Broadcasts
Once you’ve built a robust list, it’s crucial to maximize the potential of WhatsApp Broadcasts. Here’s how:
- Targeted Content: Ensure that your broadcasts are relevant and engaging. Personalize messages based on user behavior and preferences.
- Timely Follow-Ups: Use the high open rates to your advantage. Send follow-up messages to keep the conversation going.
- Measure and Adapt: Track the success of your broadcasts. Use insights to refine your strategy continually.
- Compliance and Consent: Always adhere to WhatsApp’s policies and respect user consent for message receipts.
- Integrated Marketing Strategy: Don’t rely solely on WhatsApp. Integrate it into a broader digital marketing strategy for maximum impact.
Conclusion
Combining WhatsApp Broadcasts with AI presents a powerful opportunity to enhance your digital marketing efforts. By smartly building a broadcast list and employing AI for personalized, data-driven communication, businesses can achieve a significantly improved ROI. Remember, the key lies in the strategic, innovative, and ethical use of these technologies to create meaningful connections with your audience.
Are you eager to expand your understanding of artificial intelligence? Look no further than the essential book “AI Unraveled: Prompt Engineering,” available at Etsy, Shopify, Apple, Google, or Amazon
AI Chrome Extensions That Actually Improve Productivity (Not Just Hype)by /u/besuretechno-323 (Artificial Intelligence) on March 10, 2026 at 5:06 am submitted by /u/besuretechno-323 [link] [comments]
- One-Minute Daily AI News 3/9/2026by /u/Excellent-Target-847 (Artificial Intelligence) on March 10, 2026 at 4:45 am
Anthropic sues to block Pentagon blacklisting over AI use restrictions.[1] Nvidia Is Planning to Launch an Open-Source AI Agent Platform.[2] Andrew Ng’s Team Releases Context Hub: An Open Source Tool that Gives Your Coding Agent the Up-to-Date API Documentation It Needs.[3] New Robotic Microfluidic Platform Brings AI to Lipid Nanoparticle Design.[4] Sources included at: https://bushaicave.com/2026/03/09/one-minute-daily-ai-news-3-9-2026/ submitted by /u/Excellent-Target-847 [link] [comments]
- People Hate AI Even More Than They Hate ICE, Poll Findsby /u/Ciappatos (Artificial Intelligence) on March 10, 2026 at 3:43 am
Here's the actual survey: https://pos.org/wp-content/uploads/2026/03/260072-NBC-March-2026-Poll-03-08-2026-Release.pdf Also, people really like the new pope lmao. The title was decided by the gizmodo link, but I have to say ICE is way more hated than AI, it's just way more liked as well. Nobody seems to "really like" AI. submitted by /u/Ciappatos [link] [comments]
- AI/ML research swarm intelligenceby /u/anotherallan (Artificial Intelligence) on March 10, 2026 at 3:22 am
We're excited to introduce Wizwand Swarm, an experiment toward research swarm intelligence. Wizwand Swarm is a space where researcher's AI agents can interact with each other to discuss research topics, exchange ideas, and inspire new directions in AI and machine learning research. We think this could become an intersting experiment for research. Why we built this? The inspiration for Swarm came from a few places: Moltbook built by Matt — a more general-purpose community for AI agents. A Youtube clip Garry Tan and YC Partners discussing swarm intelligence. The recent launch of OpenClaw. Taken together, these ideas made us ask a simple question: What happens if researcher's AI agents can exchange research ideas and thoughts with one another? Our thoughts... We think Wizwand Swarm could potentially become part of a new loop for AI/ML research: Agents can automate parts of their research process — projects like autoresearch point toward that future. When unsure or need inspiration for directions, agents can ask and inspire each other with different perspectives on Wizwand Swarm. Agents can turn those inspirations and discussion into new research ideas and try something new (go back to #1). Instead of research being a purely isolated loop between a human and a single model, it may become a more social loop between many agents, tools, and researchers. We don't know exactly this leads yet. That's part of why we're building it. Try it with OpenClaw We'd love for you to try the early version. Have your OpenClaw install the Swarm skill, do a short self-introduction, and create a few posts to see how things work out. You can find how to join at wizwand.com/swarm. For documentation to build your own skill, check out /docs/swarm. If you have feedback, please feel free to comment or DM. Wizwand swarm home page submitted by /u/anotherallan [link] [comments]
- New Study Finds ‘AI Brain Fry’ Hitting Workers – Marketing and HR Top the Listby /u/Secure_Persimmon8369 (Artificial Intelligence) on March 10, 2026 at 1:52 am
submitted by /u/Secure_Persimmon8369 [link] [comments]
- AI Features in pgAdmin: Configuration and Reportsby /u/pgEdge_Postgres (Artificial Intelligence) on March 10, 2026 at 12:12 am
From the creator of pgAdmin (Dave Page), here's the start of a 3 part series on AI features in pgAdmin for working with AI development + PostgreSQL using the primary open source GUI interface for Postgres. AI Features in pgAdmin: Configuration and Reports This is the first in a series of three blog posts covering the new AI functionality coming in pgAdmin 4. In this post, I'll walk through how to configure the LLM integration and introduce the AI-powered analysis reports; in the second, I'll cover the AI Chat agent in the query tool; and in the third, I'll explore the AI Insights feature for EXPLAIN plan analysis. Anyone who manages PostgreSQL databases in a professional capacity knows that keeping on top of security, performance, and schema design is an ongoing endeavour. You might have a checklist of things to review, or perhaps you rely on experience and intuition to spot potential issues, but it is all too easy for something to slip through the cracks, especially as databases grow in complexity. We've been thinking about how AI could help with this, and I'm pleased to introduce a suite of AI-powered features in pgAdmin 4 that bring large language model analysis directly into the tool you already use every day. Configuring the LLM Integration Before any of the AI features can be used, you'll need to configure an LLM provider. pgAdmin supports four providers out of the box, giving you flexibility to choose between cloud-hosted models and locally-running alternatives: Anthropic (Claude models) OpenAI (GPT models) Ollama (locally-hosted open-source models) Docker Model Runner (built into Docker Desktop 4.40 and later) Server Configuration At the server level, there is a master switch in config.py (or, more typically, config_local.py) that controls whether AI features are available at all: # Master switch to enable/disable LLM features entirely. LLM_ENABLED = True When LLM_ENABLED is set to False, all AI functionality is hidden from users and cannot be enabled through preferences. This gives administrators full control over whether AI features are permitted in their environment, which is particularly important in organisations with strict data governance policies. Below the master switch, you'll find default configuration for each provider: # Default LLM Provider # Valid values: 'anthropic', 'openai', 'ollama', 'docker', or '' (disabled) DEFAULT_LLM_PROVIDER = '' # Anthropic Configuration ANTHROPIC_API_KEY_FILE = '~/.anthropic-api-key' ANTHROPIC_API_MODEL = '' # OpenAI Configuration OPENAI_API_KEY_FILE = '~/.openai-api-key' OPENAI_API_MODEL = '' # Ollama Configuration OLLAMA_API_URL = '' OLLAMA_API_MODEL = '' # Docker Model Runner Configuration DOCKER_API_URL = '' DOCKER_API_MODEL = '' # Maximum tool call iterations for AI conversations MAX_LLM_TOOL_ITERATIONS = 20 For the cloud providers (Anthropic and OpenAI), API keys are read from files on disk rather than being stored directly in the configuration, which is a deliberate security choice. The key file should contain nothing but the API key itself, with no additional whitespace or formatting. For Ollama and Docker Model Runner, you simply provide the API URL for the local service (typically http://localhost:11434 for Ollama and http://localhost:12434 for Docker). User Preferences Whilst the server configuration sets the defaults and boundaries, individual users can customise their AI settings through the Preferences dialog under the 'AI' section. The preferences are organised into categories: AI Configuration contains the general settings: Default Provider: Users can select their preferred provider from a dropdown, or choose 'None (Disabled)' to turn off AI features for their account. This setting only takes effect if LLM_ENABLED is True in the server configuration. Max Tool Iterations: Controls how many tool call rounds the AI is allowed to perform during a single conversation, with a default of 20. Higher values allow more complex analyses but consume more resources. Each provider has its own category with provider-specific settings: Anthropic: API Key File path and Model selection OpenAI: API Key File path and Model selection Ollama: API URL and Model selection Docker Model Runner: API URL and Model selection One particularly nice touch is that the model selection dropdowns are populated dynamically. When you configure an API key or URL and click the refresh button, pgAdmin queries the provider's API to fetch the list of available models. For Ollama, it even shows the model sizes so you can see at a glance how much disk space each model is using. The model selectors also support typing in custom model names, so you're not limited to whatever the API returns; if you know the exact model identifier you want to use, you can simply type it in. AI Analysis Reports With the LLM configured, you gain access to three types of AI-powered analysis reports that can be generated from the browser tree context menu. Simply right-click on a server, database, or schema and select the appropriate report from the 'AI Analysis' submenu. Security Reports The security report examines your PostgreSQL configuration from a security perspective, covering a comprehensive range of areas: Authentication Configuration: Password policies, SSL/TLS settings, authentication methods, and connection security Access Control and Roles: Superuser accounts, privileged roles, login roles without password expiry, and role privilege assignments Network Security: Listen addresses, connection limits, and pg_hba.conf rules Encryption and SSL: SSL/TLS configuration, password encryption methods, and data-at-rest encryption settings Object Permissions: Schema, table, and function access control lists, default privileges, and ownership (at database scope) Row-Level Security: RLS policies, RLS-enabled tables, and policy coverage analysis Security Definer Functions: Functions running with elevated privileges and their permission settings Audit and Logging: Connection logging, statement logging, error logging, and audit trail configuration Extensions: Installed extensions and their security implications Security reports can be generated at the server level (covering server-wide configuration such as authentication and network settings), the database level (adding object permissions and RLS analysis), or the schema level (focusing on a specific schema's security posture). Performance Reports The performance report analyses your server and database configuration for potential optimisation opportunities: Memory Configuration: shared_buffers, work_mem, effective_cache_size, maintenance_work_mem, and related settings Checkpoint and WAL: Checkpoint settings, WAL configuration, and background writer statistics Autovacuum Configuration: Autovacuum settings, tables needing vacuum, and dead tuple accumulation Query Planner Settings: Cost parameters, statistics targets, JIT compilation, and planner optimisation settings Parallelism and Workers: Parallel query configuration and worker process settings Connection Management: Maximum connections, reserved connections, timeouts, and current connection status Cache Efficiency: Buffer cache hit ratios, database-level cache statistics, and table-level I/O patterns Index Analysis: Index utilisation, unused indexes, tables that might benefit from additional indexes, and index size analysis Query Performance: Slowest queries and most frequent queries (when pg_stat_statements is available) Replication Status: Replication lag, standby status, and WAL sender statistics Performance reports are available at both the server and database levels, with database-level reports including additional detail on index usage and cache efficiency for that specific database. Schema Design Reports The design review report examines your database schema for structural quality and best practices: Table Structure: Table definitions, column counts, sizes, ownership, and documentation coverage Primary Key Analysis: Primary key design and tables lacking primary keys Referential Integrity: Foreign key relationships, orphan references, and relationship coverage Index Strategy: Index definitions, duplicate indexes, index types, and coverage analysis Constraints: Check constraints, unique constraints, and data validation coverage Normalisation Analysis: Repeated column patterns, potential denormalisation issues, and data redundancy Naming Conventions: Table and column naming patterns, consistency analysis, and naming standard compliance Data Type Review: Data type usage patterns, type consistency, and type appropriateness Design reports are available at the database and schema levels, allowing you to review either an entire database's schema design or focus on a specific schema. How the Reports Work Under the hood, the report generation follows a sophisticated multi-stage pipeline that keeps each LLM interaction within manageable token limits whilst still producing comprehensive output: Planning: The LLM first reviews the available analysis sections and the database context (server version, table count, available extensions, and so on), then selects which sections are most relevant to analyse. This means the report is tailored to your specific environment rather than running every possible check regardless of applicability. Data Gathering: For each selected section, pgAdmin executes a set of SQL queries against the database to collect the relevant configuration data, statistics, and metadata. Section Analysis: Each section's data is sent to the LLM independently for analysis. The LLM classifies findings by severity (Critical, Warning, Advisory, or Good) and provides specific, actionable recommendations, including SQL commands where relevant. Synthesis: Finally, the individual section analyses are combined into a cohesive report with an executive summary, a critical issues section aggregating the most important findings, the detailed section analyses, and a prioritised list of recommendations. As the pipeline works through these stages, the UI shows real-time progress updates: the current stage name (Planning Analysis, Gathering Data, Analysing Sections, Creating Report), a description of what's being processed (for example, 'Analysing Memory Configuration...'), and a progress bar showing how many sections have been completed out of the total. Once all four stages are finished, the completed report is rendered in the panel in one go. Each report can also be downloaded as a Markdown file for archiving or sharing with colleagues. The reports are designed to be genuinely useful rather than generic. Because the LLM receives actual data from your database (configuration settings, role definitions, table statistics, and index information), its analysis is grounded in reality. A security report will flag your specific pg_hba.conf rules that might be overly permissive, a performance report will identify your specific tables that are missing useful indexes, and a design report will point out your specific naming inconsistencies. A Note on Privacy and Data It is worth noting that when using cloud-hosted LLM providers (Anthropic or OpenAI), the database metadata and configuration data gathered for reports is sent to those providers' APIs. No actual table data is sent for the reports (only metadata, configuration settings, and statistics), but administrators should be aware of this and ensure it aligns with their organisation's data handling policies. For environments where sending any data externally is not acceptable, the Ollama and Docker Model Runner options allow you to run models entirely locally. Getting Started If you'd like to try the AI features, the quickest way to get started is to configure an API key for either Anthropic or OpenAI, set the default provider in Preferences, and then right-click on a server in the browser tree to generate your first report. If you prefer to keep everything local, installing Ollama and pulling a model such as llama3.2 is straightforward, and Docker Desktop users on version 4.40 or later can enable the built-in model runner without any additional setup. In the next post, I'll cover the AI Chat agent in the query tool, which brings natural language to SQL translation directly into your workflow, along with database-aware conversational assistance. Stay tuned. submitted by /u/pgEdge_Postgres [link] [comments]
- Interested in pivoting my careerby /u/Fickle-Put9304 (Marketing & Advertising) on March 9, 2026 at 11:21 pm
I’ve worked in marketing since 2021. I’ve been a social media manager for healthcare clients, food & bev, and real estate. I’ve realized that I don’t really…want to be on social media anymore. That’s the only thing I don’t like about my work…which is ALL my work as a social media manager. It seems impossible to work in marketing and NOT use social media excessively because I have to constantly follow trends and what’s “hot” for effective posts. Are there ANY marketing roles where I don’t have to consume so much social media to stay ahead of trends? Or should I just look into a different field altogether, is using social media inevitable for this field? submitted by /u/Fickle-Put9304 [link] [comments]
- What AI tools help you the most at the moment?by /u/Rico_8 (Artificial Intelligence) on March 9, 2026 at 9:56 pm
Might be a bit late but i just discovered the capabilities of notebooklm. Like i always knew that it was a thing and apparantly really good but i never got around to trying it. After doing so i want to know about more groundbreaking tools that im missing. submitted by /u/Rico_8 [link] [comments]
- Ever wonder what it would be like to talk to an AI with a totally random system prompt? Try it here.by /u/AppropriateLeather63 (Artificial Intelligence) on March 9, 2026 at 9:46 pm
We accomplish this by chaining two api calls. The first api call generates a random system prompt, and then feeds it to the second. The second API call only has the output of the first as the system prompt, resulting in a truly randomized personality each time. Created by Dakota Rain Lock. I call this app “The Species”Try it here: https://claude.ai/public/artifacts/44cbe971-6b6e-4417-969e-7d922de5a90b submitted by /u/AppropriateLeather63 [link] [comments]
- Honest question: does "real-time AI video" actually mean anything yet or is it just marketing?by /u/Final_Employment1067 (Artificial Intelligence) on March 9, 2026 at 9:11 pm
Genuinely asking. I see it in headlines constantly but when I dig in, most of what's being called "real-time" is just faster video generation? Which is cool, don't get me wrong. But "real-time" to me implies something interactive, something that's responding to live input, not just a shorter render queue. Am I being too strict about the definition? Or is there stuff out there that's actually doing the live/interactive thing at a level worth paying attention to? submitted by /u/Final_Employment1067 [link] [comments]
- AI allows hackers to identify anonymous social media accounts, study findsby /u/SweetFrauline (Artificial Intelligence) on March 9, 2026 at 8:05 pm
This really sucks for anyone trying to provide sensitive information anonymously. If there is any evidence to support that so many countries are becoming police states, this is probably good proof of it. submitted by /u/SweetFrauline [link] [comments]
- News article: Can AI Replace Humans for Market Research?by /u/XIFAQ (Artificial Intelligence) on March 9, 2026 at 7:31 pm
What are your thoughts on this: Can AI Replace Humans for Market Research? Yes or No or Maybe ? submitted by /u/XIFAQ [link] [comments]
- CodeGraphContext (MCP server to index code into a graph) now has a website playground for experimentby /u/Desperate-Ad-9679 (Artificial Intelligence) on March 9, 2026 at 7:11 pm
Hey everyone! I have been developing CodeGraphContext, an open-source MCP server transforming code into a symbol-level code graph, as opposed to text-based code analysis. This means that AI agents won’t be sending entire code blocks to the model, but can retrieve context via: function calls, imported modules, class inheritance, file dependencies etc. This allows AI agents (and humans!) to better grasp how code is internally connected. What it does CodeGraphContext analyzes a code repository, generating a code graph of: files, functions, classes, modules and their relationships, etc. AI agents can then query this graph to retrieve only the relevant context, reducing hallucinations. Playground Demo on website I've also added a playground demo that lets you play with small repos directly. You can load a project from: a local code folder, a GitHub repo, a GitLab repo Everything runs on the local client browser. For larger repos, it’s recommended to get the full version from pip or Docker. Additionally, the playground lets you visually explore code links and relationships. I’m also adding support for architecture diagrams and chatting with the codebase. Status so far- ⭐ ~1.5k GitHub stars 🍴 350+ forks 📦 100k+ downloads combined If you’re building AI dev tooling, MCP servers, or code intelligence systems, I’d love your feedback. Repo: https://github.com/CodeGraphContext/CodeGraphContext submitted by /u/Desperate-Ad-9679 [link] [comments]
- New Job Listingsby /u/AutoModerator (Marketing & Advertising) on March 9, 2026 at 7:10 pm
Are you looking to hire? Share your opening to the marketing professionals here on r/marketing. Please include title, description, full-time or part-time, location (on-site location or remote), and a link to apply. Don't forget to add to our community job board for more exposure. If you are looking to be hired, this is not the place to post that and your post will be removed. submitted by /u/AutoModerator [link] [comments]
- What’s the most useful giveaway you’ve come across?by /u/Mumster-Love (Marketing & Advertising) on March 9, 2026 at 7:09 pm
If you had to choose one swag item for field events that people would actually keep, what would it be? submitted by /u/Mumster-Love [link] [comments]
- With little effort, autonomous AI agents can be manipulated into leaking private information, sharing documents and even erasing entire email servers, researchers find.by /u/NGNResearch (Artificial Intelligence) on March 9, 2026 at 6:43 pm
A team of researchers at Northeastern University began toying with a new kind of autonomous AI “agent.” The more they tested the capabilities and limits of these AI models, which have persistent memory and can take some actions on their own, the more troubling behavior they witnessed. The “agents of chaos” struggled to keep secrets and were easily guilt tripped into divulging information. submitted by /u/NGNResearch [link] [comments]
- We heard you - r/ArtificialInteligence is getting sharperby /u/NeuralNomad87 (Artificial Intelligence) on March 9, 2026 at 6:25 pm
Alright r/ArtificialInteligence, let's talk. Over the past few months, we heard you — too much noise, not enough signal. Low-effort hot takes drowning out real discussion. But we've been listening. Behind the scenes, we've been working hard to reshape this sub into what it should be: a place where quality rises and noise gets filtered out. Today we're rolling out the changes. What changed We sharpened the mission. This sub exists to be the high-signal hub for artificial intelligence — where serious discussion, quality content, and verified expertise drive the conversation. Open to everyone, but with a higher bar for what stays up. Please check out the new rules & wiki. Clearer rules, fewer gray areas We rewrote the rules from scratch. The vague stuff is gone. Every rule now has specific criteria so you know exactly what flies and what doesn't. The big ones: High-Signal Content Only — Every post should teach something, share something new, or spark real discussion. Low-effort takes and "thoughts on X?" with no context get removed. Builders are welcome — with substance. If you built something, we want to hear about it. But give us the real story: what you built, how, what you learned, and link the repo or demo. No marketing fluff, no waitlists. Doom AND hype get equal treatment. "AI will take all jobs" and "AGI by next Tuesday" are both removed unless you bring new data or first-person experience. News posts need context. Link dumps are out. If you post a news article, add a comment summarizing it and explaining why it matters. New post flairs (required) Every post now needs a flair. This helps you filter what you care about and helps us moderate more consistently: 📰 News · 💬 Discussion · 🔬 Research · 🛠 Project/Build · 📚 Tutorial/Guide · ❓ Question · 🤖 New Model/Tool · 😂 Fun/Meme · 💼 Industry/Career · 📊 Analysis/Opinion Expert verification flairs Working in AI professionally? You can now get a verified flair that shows on every post and comment: 🔬 Verified Engineer/Researcher — engineers and researchers at AI companies or labs 🚀 Verified Founder — founders of AI companies 🎓 Verified Academic — professors, PhD researchers, published academics 🛠 Verified AI Builder — independent devs with public, demonstrable AI projects We verify through company email, LinkedIn, or GitHub — no screenshots, no exceptions. Request verification via modmail.:%0A-%20%F0%9F%94%AC%20Verified%20Engineer/Researcher%0A-%20%F0%9F%9A%80%20Verified%20Founder%0A-%20%F0%9F%8E%93%20Verified%20Academic%0A-%20%F0%9F%9B%A0%20Verified%20AI%20Builder%0A%0ACurrent%20role%20%26%20company/org:%0A%0AVerification%20method%20(pick%20one):%0A-%20Company%20email%20(we%27ll%20send%20a%20verification%20code)%0A-%20LinkedIn%20(add%20%23rai-verify-2026%20to%20your%20headline%20or%20about%20section)%0A-%20GitHub%20(add%20%23rai-verify-2026%20to%20your%20bio)%0A%0ALink%20to%20your%20LinkedIn/GitHub/project:**%0A) Tool recommendations → dedicated space "What's the best AI for X?" posts now live at r/AIToolBench — subscribe and help the community find the right tools. Tool request posts here will be redirected there. What stays the same Open to everyone. You don't need credentials to post. We just ask that you bring substance. Memes are welcome. 😂 Fun/Meme flair exists for a reason. Humor is part of the culture. Debate is encouraged. Disagree hard, just don't make it personal. What we need from you Flair your posts — unflaired posts get a reminder and may be removed after 30 minutes. Report low-quality content — the report button helps us find the noise faster. Tell us if we got something wrong — this is v1 of the new system. We'll adjust based on what works and what doesn't. Questions, feedback, or appeals? Modmail us. We read everything. submitted by /u/NeuralNomad87 [link] [comments]
- Chatbots are "constantly validating everything" even when you're suicidal. New research measures how dangerous AI psychosis really isby /u/fortune (Artificial Intelligence) on March 9, 2026 at 5:13 pm
submitted by /u/fortune [link] [comments]
- Does anyone else feel like they're faking it in marketingby /u/Mindless_Cook7821 (Marketing & Advertising) on March 9, 2026 at 4:28 pm
I've been working in marketing for like 5 years, and I still feel like I don't know what I'm doing half the time I can execute campaigns and hit KPIs but I don't feel like a "real" marketer everyone else seems so confident in their strategy and I'm just hoping mine works. is this imposter syndrome or am I actually just not that good at this submitted by /u/Mindless_Cook7821 [link] [comments]
- AI legal research tools and accuracyby /u/IWorkOnlineCom (Artificial Intelligence) on March 9, 2026 at 4:02 pm
I've been testing various AI legal research tools for my firm trying to separate hype from reality. We're looking specifically at tools that can: Find district court cases based on procedural posture like "cases where motion to dismiss was denied" Summarize complex filings Pull deadlines and key dates So far we've looked at Westlaw's AI features, Lexis+ AI, and a newer tool called AskLexi. The first two are obviously big names, but they're expensive and the Stanford/Yale study showed they still hallucinate roughly 17 to 33 percent of the time. AskLexi is cheaper and seems more targeted, but I'm worried about reliability for client work. Curious if anyone has hard data or experience on these tools. I'm less interested in marketing claims and more in actual testing results. What AI tools have you actually deployed in practice? submitted by /u/IWorkOnlineCom [link] [comments]
- AI Chrome Extensions That Actually Improve Productivity (Not Just Hype)by /u/besuretechno-323 (Artificial Intelligence) on March 10, 2026 at 5:06 am
submitted by /u/besuretechno-323 [link] [comments]
- One-Minute Daily AI News 3/9/2026by /u/Excellent-Target-847 (Artificial Intelligence) on March 10, 2026 at 4:45 am
Anthropic sues to block Pentagon blacklisting over AI use restrictions.[1] Nvidia Is Planning to Launch an Open-Source AI Agent Platform.[2] Andrew Ng’s Team Releases Context Hub: An Open Source Tool that Gives Your Coding Agent the Up-to-Date API Documentation It Needs.[3] New Robotic Microfluidic Platform Brings AI to Lipid Nanoparticle Design.[4] Sources included at: https://bushaicave.com/2026/03/09/one-minute-daily-ai-news-3-9-2026/ submitted by /u/Excellent-Target-847 [link] [comments]
- People Hate AI Even More Than They Hate ICE, Poll Findsby /u/Ciappatos (Artificial Intelligence) on March 10, 2026 at 3:43 am
Here's the actual survey: https://pos.org/wp-content/uploads/2026/03/260072-NBC-March-2026-Poll-03-08-2026-Release.pdf Also, people really like the new pope lmao. The title was decided by the gizmodo link, but I have to say ICE is way more hated than AI, it's just way more liked as well. Nobody seems to "really like" AI. submitted by /u/Ciappatos [link] [comments]
- AI/ML research swarm intelligenceby /u/anotherallan (Artificial Intelligence) on March 10, 2026 at 3:22 am
We're excited to introduce Wizwand Swarm, an experiment toward research swarm intelligence. Wizwand Swarm is a space where researcher's AI agents can interact with each other to discuss research topics, exchange ideas, and inspire new directions in AI and machine learning research. We think this could become an intersting experiment for research. Why we built this? The inspiration for Swarm came from a few places: Moltbook built by Matt — a more general-purpose community for AI agents. A Youtube clip Garry Tan and YC Partners discussing swarm intelligence. The recent launch of OpenClaw. Taken together, these ideas made us ask a simple question: What happens if researcher's AI agents can exchange research ideas and thoughts with one another? Our thoughts... We think Wizwand Swarm could potentially become part of a new loop for AI/ML research: Agents can automate parts of their research process — projects like autoresearch point toward that future. When unsure or need inspiration for directions, agents can ask and inspire each other with different perspectives on Wizwand Swarm. Agents can turn those inspirations and discussion into new research ideas and try something new (go back to #1). Instead of research being a purely isolated loop between a human and a single model, it may become a more social loop between many agents, tools, and researchers. We don't know exactly this leads yet. That's part of why we're building it. Try it with OpenClaw We'd love for you to try the early version. Have your OpenClaw install the Swarm skill, do a short self-introduction, and create a few posts to see how things work out. You can find how to join at wizwand.com/swarm. For documentation to build your own skill, check out /docs/swarm. If you have feedback, please feel free to comment or DM. Wizwand swarm home page submitted by /u/anotherallan [link] [comments]
- New Study Finds ‘AI Brain Fry’ Hitting Workers – Marketing and HR Top the Listby /u/Secure_Persimmon8369 (Artificial Intelligence) on March 10, 2026 at 1:52 am
submitted by /u/Secure_Persimmon8369 [link] [comments]
- AI Features in pgAdmin: Configuration and Reportsby /u/pgEdge_Postgres (Artificial Intelligence) on March 10, 2026 at 12:12 am
From the creator of pgAdmin (Dave Page), here's the start of a 3 part series on AI features in pgAdmin for working with AI development + PostgreSQL using the primary open source GUI interface for Postgres. AI Features in pgAdmin: Configuration and Reports This is the first in a series of three blog posts covering the new AI functionality coming in pgAdmin 4. In this post, I'll walk through how to configure the LLM integration and introduce the AI-powered analysis reports; in the second, I'll cover the AI Chat agent in the query tool; and in the third, I'll explore the AI Insights feature for EXPLAIN plan analysis. Anyone who manages PostgreSQL databases in a professional capacity knows that keeping on top of security, performance, and schema design is an ongoing endeavour. You might have a checklist of things to review, or perhaps you rely on experience and intuition to spot potential issues, but it is all too easy for something to slip through the cracks, especially as databases grow in complexity. We've been thinking about how AI could help with this, and I'm pleased to introduce a suite of AI-powered features in pgAdmin 4 that bring large language model analysis directly into the tool you already use every day. Configuring the LLM Integration Before any of the AI features can be used, you'll need to configure an LLM provider. pgAdmin supports four providers out of the box, giving you flexibility to choose between cloud-hosted models and locally-running alternatives: Anthropic (Claude models) OpenAI (GPT models) Ollama (locally-hosted open-source models) Docker Model Runner (built into Docker Desktop 4.40 and later) Server Configuration At the server level, there is a master switch in config.py (or, more typically, config_local.py) that controls whether AI features are available at all: # Master switch to enable/disable LLM features entirely. LLM_ENABLED = True When LLM_ENABLED is set to False, all AI functionality is hidden from users and cannot be enabled through preferences. This gives administrators full control over whether AI features are permitted in their environment, which is particularly important in organisations with strict data governance policies. Below the master switch, you'll find default configuration for each provider: # Default LLM Provider # Valid values: 'anthropic', 'openai', 'ollama', 'docker', or '' (disabled) DEFAULT_LLM_PROVIDER = '' # Anthropic Configuration ANTHROPIC_API_KEY_FILE = '~/.anthropic-api-key' ANTHROPIC_API_MODEL = '' # OpenAI Configuration OPENAI_API_KEY_FILE = '~/.openai-api-key' OPENAI_API_MODEL = '' # Ollama Configuration OLLAMA_API_URL = '' OLLAMA_API_MODEL = '' # Docker Model Runner Configuration DOCKER_API_URL = '' DOCKER_API_MODEL = '' # Maximum tool call iterations for AI conversations MAX_LLM_TOOL_ITERATIONS = 20 For the cloud providers (Anthropic and OpenAI), API keys are read from files on disk rather than being stored directly in the configuration, which is a deliberate security choice. The key file should contain nothing but the API key itself, with no additional whitespace or formatting. For Ollama and Docker Model Runner, you simply provide the API URL for the local service (typically http://localhost:11434 for Ollama and http://localhost:12434 for Docker). User Preferences Whilst the server configuration sets the defaults and boundaries, individual users can customise their AI settings through the Preferences dialog under the 'AI' section. The preferences are organised into categories: AI Configuration contains the general settings: Default Provider: Users can select their preferred provider from a dropdown, or choose 'None (Disabled)' to turn off AI features for their account. This setting only takes effect if LLM_ENABLED is True in the server configuration. Max Tool Iterations: Controls how many tool call rounds the AI is allowed to perform during a single conversation, with a default of 20. Higher values allow more complex analyses but consume more resources. Each provider has its own category with provider-specific settings: Anthropic: API Key File path and Model selection OpenAI: API Key File path and Model selection Ollama: API URL and Model selection Docker Model Runner: API URL and Model selection One particularly nice touch is that the model selection dropdowns are populated dynamically. When you configure an API key or URL and click the refresh button, pgAdmin queries the provider's API to fetch the list of available models. For Ollama, it even shows the model sizes so you can see at a glance how much disk space each model is using. The model selectors also support typing in custom model names, so you're not limited to whatever the API returns; if you know the exact model identifier you want to use, you can simply type it in. AI Analysis Reports With the LLM configured, you gain access to three types of AI-powered analysis reports that can be generated from the browser tree context menu. Simply right-click on a server, database, or schema and select the appropriate report from the 'AI Analysis' submenu. Security Reports The security report examines your PostgreSQL configuration from a security perspective, covering a comprehensive range of areas: Authentication Configuration: Password policies, SSL/TLS settings, authentication methods, and connection security Access Control and Roles: Superuser accounts, privileged roles, login roles without password expiry, and role privilege assignments Network Security: Listen addresses, connection limits, and pg_hba.conf rules Encryption and SSL: SSL/TLS configuration, password encryption methods, and data-at-rest encryption settings Object Permissions: Schema, table, and function access control lists, default privileges, and ownership (at database scope) Row-Level Security: RLS policies, RLS-enabled tables, and policy coverage analysis Security Definer Functions: Functions running with elevated privileges and their permission settings Audit and Logging: Connection logging, statement logging, error logging, and audit trail configuration Extensions: Installed extensions and their security implications Security reports can be generated at the server level (covering server-wide configuration such as authentication and network settings), the database level (adding object permissions and RLS analysis), or the schema level (focusing on a specific schema's security posture). Performance Reports The performance report analyses your server and database configuration for potential optimisation opportunities: Memory Configuration: shared_buffers, work_mem, effective_cache_size, maintenance_work_mem, and related settings Checkpoint and WAL: Checkpoint settings, WAL configuration, and background writer statistics Autovacuum Configuration: Autovacuum settings, tables needing vacuum, and dead tuple accumulation Query Planner Settings: Cost parameters, statistics targets, JIT compilation, and planner optimisation settings Parallelism and Workers: Parallel query configuration and worker process settings Connection Management: Maximum connections, reserved connections, timeouts, and current connection status Cache Efficiency: Buffer cache hit ratios, database-level cache statistics, and table-level I/O patterns Index Analysis: Index utilisation, unused indexes, tables that might benefit from additional indexes, and index size analysis Query Performance: Slowest queries and most frequent queries (when pg_stat_statements is available) Replication Status: Replication lag, standby status, and WAL sender statistics Performance reports are available at both the server and database levels, with database-level reports including additional detail on index usage and cache efficiency for that specific database. Schema Design Reports The design review report examines your database schema for structural quality and best practices: Table Structure: Table definitions, column counts, sizes, ownership, and documentation coverage Primary Key Analysis: Primary key design and tables lacking primary keys Referential Integrity: Foreign key relationships, orphan references, and relationship coverage Index Strategy: Index definitions, duplicate indexes, index types, and coverage analysis Constraints: Check constraints, unique constraints, and data validation coverage Normalisation Analysis: Repeated column patterns, potential denormalisation issues, and data redundancy Naming Conventions: Table and column naming patterns, consistency analysis, and naming standard compliance Data Type Review: Data type usage patterns, type consistency, and type appropriateness Design reports are available at the database and schema levels, allowing you to review either an entire database's schema design or focus on a specific schema. How the Reports Work Under the hood, the report generation follows a sophisticated multi-stage pipeline that keeps each LLM interaction within manageable token limits whilst still producing comprehensive output: Planning: The LLM first reviews the available analysis sections and the database context (server version, table count, available extensions, and so on), then selects which sections are most relevant to analyse. This means the report is tailored to your specific environment rather than running every possible check regardless of applicability. Data Gathering: For each selected section, pgAdmin executes a set of SQL queries against the database to collect the relevant configuration data, statistics, and metadata. Section Analysis: Each section's data is sent to the LLM independently for analysis. The LLM classifies findings by severity (Critical, Warning, Advisory, or Good) and provides specific, actionable recommendations, including SQL commands where relevant. Synthesis: Finally, the individual section analyses are combined into a cohesive report with an executive summary, a critical issues section aggregating the most important findings, the detailed section analyses, and a prioritised list of recommendations. As the pipeline works through these stages, the UI shows real-time progress updates: the current stage name (Planning Analysis, Gathering Data, Analysing Sections, Creating Report), a description of what's being processed (for example, 'Analysing Memory Configuration...'), and a progress bar showing how many sections have been completed out of the total. Once all four stages are finished, the completed report is rendered in the panel in one go. Each report can also be downloaded as a Markdown file for archiving or sharing with colleagues. The reports are designed to be genuinely useful rather than generic. Because the LLM receives actual data from your database (configuration settings, role definitions, table statistics, and index information), its analysis is grounded in reality. A security report will flag your specific pg_hba.conf rules that might be overly permissive, a performance report will identify your specific tables that are missing useful indexes, and a design report will point out your specific naming inconsistencies. A Note on Privacy and Data It is worth noting that when using cloud-hosted LLM providers (Anthropic or OpenAI), the database metadata and configuration data gathered for reports is sent to those providers' APIs. No actual table data is sent for the reports (only metadata, configuration settings, and statistics), but administrators should be aware of this and ensure it aligns with their organisation's data handling policies. For environments where sending any data externally is not acceptable, the Ollama and Docker Model Runner options allow you to run models entirely locally. Getting Started If you'd like to try the AI features, the quickest way to get started is to configure an API key for either Anthropic or OpenAI, set the default provider in Preferences, and then right-click on a server in the browser tree to generate your first report. If you prefer to keep everything local, installing Ollama and pulling a model such as llama3.2 is straightforward, and Docker Desktop users on version 4.40 or later can enable the built-in model runner without any additional setup. In the next post, I'll cover the AI Chat agent in the query tool, which brings natural language to SQL translation directly into your workflow, along with database-aware conversational assistance. Stay tuned. submitted by /u/pgEdge_Postgres [link] [comments]
- Interested in pivoting my careerby /u/Fickle-Put9304 (Marketing & Advertising) on March 9, 2026 at 11:21 pm
I’ve worked in marketing since 2021. I’ve been a social media manager for healthcare clients, food & bev, and real estate. I’ve realized that I don’t really…want to be on social media anymore. That’s the only thing I don’t like about my work…which is ALL my work as a social media manager. It seems impossible to work in marketing and NOT use social media excessively because I have to constantly follow trends and what’s “hot” for effective posts. Are there ANY marketing roles where I don’t have to consume so much social media to stay ahead of trends? Or should I just look into a different field altogether, is using social media inevitable for this field? submitted by /u/Fickle-Put9304 [link] [comments]
- What AI tools help you the most at the moment?by /u/Rico_8 (Artificial Intelligence) on March 9, 2026 at 9:56 pm
Might be a bit late but i just discovered the capabilities of notebooklm. Like i always knew that it was a thing and apparantly really good but i never got around to trying it. After doing so i want to know about more groundbreaking tools that im missing. submitted by /u/Rico_8 [link] [comments]
- Ever wonder what it would be like to talk to an AI with a totally random system prompt? Try it here.by /u/AppropriateLeather63 (Artificial Intelligence) on March 9, 2026 at 9:46 pm
We accomplish this by chaining two api calls. The first api call generates a random system prompt, and then feeds it to the second. The second API call only has the output of the first as the system prompt, resulting in a truly randomized personality each time. Created by Dakota Rain Lock. I call this app “The Species”Try it here: https://claude.ai/public/artifacts/44cbe971-6b6e-4417-969e-7d922de5a90b submitted by /u/AppropriateLeather63 [link] [comments]
- Honest question: does "real-time AI video" actually mean anything yet or is it just marketing?by /u/Final_Employment1067 (Artificial Intelligence) on March 9, 2026 at 9:11 pm
Genuinely asking. I see it in headlines constantly but when I dig in, most of what's being called "real-time" is just faster video generation? Which is cool, don't get me wrong. But "real-time" to me implies something interactive, something that's responding to live input, not just a shorter render queue. Am I being too strict about the definition? Or is there stuff out there that's actually doing the live/interactive thing at a level worth paying attention to? submitted by /u/Final_Employment1067 [link] [comments]
- AI allows hackers to identify anonymous social media accounts, study findsby /u/SweetFrauline (Artificial Intelligence) on March 9, 2026 at 8:05 pm
This really sucks for anyone trying to provide sensitive information anonymously. If there is any evidence to support that so many countries are becoming police states, this is probably good proof of it. submitted by /u/SweetFrauline [link] [comments]
- News article: Can AI Replace Humans for Market Research?by /u/XIFAQ (Artificial Intelligence) on March 9, 2026 at 7:31 pm
What are your thoughts on this: Can AI Replace Humans for Market Research? Yes or No or Maybe ? submitted by /u/XIFAQ [link] [comments]
- CodeGraphContext (MCP server to index code into a graph) now has a website playground for experimentby /u/Desperate-Ad-9679 (Artificial Intelligence) on March 9, 2026 at 7:11 pm
Hey everyone! I have been developing CodeGraphContext, an open-source MCP server transforming code into a symbol-level code graph, as opposed to text-based code analysis. This means that AI agents won’t be sending entire code blocks to the model, but can retrieve context via: function calls, imported modules, class inheritance, file dependencies etc. This allows AI agents (and humans!) to better grasp how code is internally connected. What it does CodeGraphContext analyzes a code repository, generating a code graph of: files, functions, classes, modules and their relationships, etc. AI agents can then query this graph to retrieve only the relevant context, reducing hallucinations. Playground Demo on website I've also added a playground demo that lets you play with small repos directly. You can load a project from: a local code folder, a GitHub repo, a GitLab repo Everything runs on the local client browser. For larger repos, it’s recommended to get the full version from pip or Docker. Additionally, the playground lets you visually explore code links and relationships. I’m also adding support for architecture diagrams and chatting with the codebase. Status so far- ⭐ ~1.5k GitHub stars 🍴 350+ forks 📦 100k+ downloads combined If you’re building AI dev tooling, MCP servers, or code intelligence systems, I’d love your feedback. Repo: https://github.com/CodeGraphContext/CodeGraphContext submitted by /u/Desperate-Ad-9679 [link] [comments]
- New Job Listingsby /u/AutoModerator (Marketing & Advertising) on March 9, 2026 at 7:10 pm
Are you looking to hire? Share your opening to the marketing professionals here on r/marketing. Please include title, description, full-time or part-time, location (on-site location or remote), and a link to apply. Don't forget to add to our community job board for more exposure. If you are looking to be hired, this is not the place to post that and your post will be removed. submitted by /u/AutoModerator [link] [comments]
- What’s the most useful giveaway you’ve come across?by /u/Mumster-Love (Marketing & Advertising) on March 9, 2026 at 7:09 pm
If you had to choose one swag item for field events that people would actually keep, what would it be? submitted by /u/Mumster-Love [link] [comments]
- With little effort, autonomous AI agents can be manipulated into leaking private information, sharing documents and even erasing entire email servers, researchers find.by /u/NGNResearch (Artificial Intelligence) on March 9, 2026 at 6:43 pm
A team of researchers at Northeastern University began toying with a new kind of autonomous AI “agent.” The more they tested the capabilities and limits of these AI models, which have persistent memory and can take some actions on their own, the more troubling behavior they witnessed. The “agents of chaos” struggled to keep secrets and were easily guilt tripped into divulging information. submitted by /u/NGNResearch [link] [comments]
- We heard you - r/ArtificialInteligence is getting sharperby /u/NeuralNomad87 (Artificial Intelligence) on March 9, 2026 at 6:25 pm
Alright r/ArtificialInteligence, let's talk. Over the past few months, we heard you — too much noise, not enough signal. Low-effort hot takes drowning out real discussion. But we've been listening. Behind the scenes, we've been working hard to reshape this sub into what it should be: a place where quality rises and noise gets filtered out. Today we're rolling out the changes. What changed We sharpened the mission. This sub exists to be the high-signal hub for artificial intelligence — where serious discussion, quality content, and verified expertise drive the conversation. Open to everyone, but with a higher bar for what stays up. Please check out the new rules & wiki. Clearer rules, fewer gray areas We rewrote the rules from scratch. The vague stuff is gone. Every rule now has specific criteria so you know exactly what flies and what doesn't. The big ones: High-Signal Content Only — Every post should teach something, share something new, or spark real discussion. Low-effort takes and "thoughts on X?" with no context get removed. Builders are welcome — with substance. If you built something, we want to hear about it. But give us the real story: what you built, how, what you learned, and link the repo or demo. No marketing fluff, no waitlists. Doom AND hype get equal treatment. "AI will take all jobs" and "AGI by next Tuesday" are both removed unless you bring new data or first-person experience. News posts need context. Link dumps are out. If you post a news article, add a comment summarizing it and explaining why it matters. New post flairs (required) Every post now needs a flair. This helps you filter what you care about and helps us moderate more consistently: 📰 News · 💬 Discussion · 🔬 Research · 🛠 Project/Build · 📚 Tutorial/Guide · ❓ Question · 🤖 New Model/Tool · 😂 Fun/Meme · 💼 Industry/Career · 📊 Analysis/Opinion Expert verification flairs Working in AI professionally? You can now get a verified flair that shows on every post and comment: 🔬 Verified Engineer/Researcher — engineers and researchers at AI companies or labs 🚀 Verified Founder — founders of AI companies 🎓 Verified Academic — professors, PhD researchers, published academics 🛠 Verified AI Builder — independent devs with public, demonstrable AI projects We verify through company email, LinkedIn, or GitHub — no screenshots, no exceptions. Request verification via modmail.:%0A-%20%F0%9F%94%AC%20Verified%20Engineer/Researcher%0A-%20%F0%9F%9A%80%20Verified%20Founder%0A-%20%F0%9F%8E%93%20Verified%20Academic%0A-%20%F0%9F%9B%A0%20Verified%20AI%20Builder%0A%0ACurrent%20role%20%26%20company/org:%0A%0AVerification%20method%20(pick%20one):%0A-%20Company%20email%20(we%27ll%20send%20a%20verification%20code)%0A-%20LinkedIn%20(add%20%23rai-verify-2026%20to%20your%20headline%20or%20about%20section)%0A-%20GitHub%20(add%20%23rai-verify-2026%20to%20your%20bio)%0A%0ALink%20to%20your%20LinkedIn/GitHub/project:**%0A) Tool recommendations → dedicated space "What's the best AI for X?" posts now live at r/AIToolBench — subscribe and help the community find the right tools. Tool request posts here will be redirected there. What stays the same Open to everyone. You don't need credentials to post. We just ask that you bring substance. Memes are welcome. 😂 Fun/Meme flair exists for a reason. Humor is part of the culture. Debate is encouraged. Disagree hard, just don't make it personal. What we need from you Flair your posts — unflaired posts get a reminder and may be removed after 30 minutes. Report low-quality content — the report button helps us find the noise faster. Tell us if we got something wrong — this is v1 of the new system. We'll adjust based on what works and what doesn't. Questions, feedback, or appeals? Modmail us. We read everything. submitted by /u/NeuralNomad87 [link] [comments]
- Chatbots are "constantly validating everything" even when you're suicidal. New research measures how dangerous AI psychosis really isby /u/fortune (Artificial Intelligence) on March 9, 2026 at 5:13 pm
submitted by /u/fortune [link] [comments]
- Does anyone else feel like they're faking it in marketingby /u/Mindless_Cook7821 (Marketing & Advertising) on March 9, 2026 at 4:28 pm
I've been working in marketing for like 5 years, and I still feel like I don't know what I'm doing half the time I can execute campaigns and hit KPIs but I don't feel like a "real" marketer everyone else seems so confident in their strategy and I'm just hoping mine works. is this imposter syndrome or am I actually just not that good at this submitted by /u/Mindless_Cook7821 [link] [comments]
- AI legal research tools and accuracyby /u/IWorkOnlineCom (Artificial Intelligence) on March 9, 2026 at 4:02 pm
I've been testing various AI legal research tools for my firm trying to separate hype from reality. We're looking specifically at tools that can: Find district court cases based on procedural posture like "cases where motion to dismiss was denied" Summarize complex filings Pull deadlines and key dates So far we've looked at Westlaw's AI features, Lexis+ AI, and a newer tool called AskLexi. The first two are obviously big names, but they're expensive and the Stanford/Yale study showed they still hallucinate roughly 17 to 33 percent of the time. AskLexi is cheaper and seems more targeted, but I'm worried about reliability for client work. Curious if anyone has hard data or experience on these tools. I'm less interested in marketing claims and more in actual testing results. What AI tools have you actually deployed in practice? submitted by /u/IWorkOnlineCom [link] [comments]
